##Set working directory
##r code is the same level as data
##data has the following subfolders - conflict, IPUMS, NASA HiMAT, population, Suicide, and Water Basins
setwd("~/Desktop/University of Utah PhD /Research/r_code")
library(mapview) #mapping package
library(raster) #raster data manipulation (Climate Data)
library(RColorBrewer) #color palettes for visualization
library(sf) #simple features for spatial data
library(tmap) #mapping package
library(viridis) #color palette for visualization
library(ncdf4) #working with netCDF files (Climate Data)
library(leaflet) #basemaps for mapview
library(ggplot2) #better figures
library(ggcorrplot) #Load the correlation plot package
library(plotly) #interactive figures
library(maps) #mapping
library(kableExtra) #creating better tables and outputs
library(dplyr) #count and data functions
library(reshape2) ## Package used to reformat data - wide to long
library(tidyverse) ##Formatting dataframes, merge, and join
library(stargazer) ##Formatting model outputs to tables
library(pscl) ##Used to calculate pseudo r^2 values for log regression models (poisson)
library(janitor) ##Used to count/provide summaries for dataframes
library(jtools) ##Used to produce aesthetically pleasing model output tables
library(huxtable) ##Used in conjunction with jtools to export model outputs
library(flextable) ##Needed to knit. linked to the janitor library
library(geomerge) ##Merges conducts a series of spatial joins to combine geospatial data ##Andrew Linke recommendation!!
library(tidyr) ##reshaping data formats long/wide
library(lubridate) ##Helps dealing with date/time ##Needed for geomerge
library(caret) ##min/max scaling pre-process
## Read in country shape file
countries = st_read("../data/conflict/ne_50m_admin_0_countries/ne_50m_admin_0_countries.shp", quiet = TRUE)
## Read in administrative boundary (level 0) shape file
pak.bound = st_read("../data/conflict/pak_adm_wfp_20220909_shp/pak_admbnda_adm0_wfp_20220909.shp", quiet = TRUE)
## Read in the districts (level 2) shape file
pak.bound.adm2 = st_read("../data/conflict/pak_adm_wfp_20220909_shp/pak_admbnda_adm2_wfp_20220909.shp", quiet = TRUE)
## Read in the city shapefile
pak.bound.city = st_read("../data/shapefiles/Study Districts Test.shp", quiet = TRUE)
## Subset the admin2 shapefile by city
## Create a vector concatenating all of the districts of karachi
karachi = c("East Karachi", "South Karachi", "West Karachi", "Central Karachi", "Korangi Karachi")
##Create a shapefile for each karachi
ek = pak.bound.adm2[pak.bound.adm2$ADM2_EN == "East Karachi", ]
sk = pak.bound.adm2[pak.bound.adm2$ADM2_EN == "South Karachi", ]
wk = pak.bound.adm2[pak.bound.adm2$ADM2_EN == "West Karachi", ]
ck = pak.bound.adm2[pak.bound.adm2$ADM2_EN == "Central Karachi", ]
kk = pak.bound.adm2[pak.bound.adm2$ADM2_EN == "Korangi Karachi", ]
## Merge them
##karachi = st_union(ek, sk)
##karachi = st_union(karachi, wk)
##karachi = st_union(karachi, ck)
##karachi = st_union(karachi, kk)
## Drop unnecessary columns
##karachi = karachi[ , c(1:14, 71)]
## Check
tm_shape(pak.bound.city) +tm_fill(col = "blue") + tm_shape(pak.bound) + tm_borders()
##cities = pak.bound.adm2[pak.bound.adm2$ADM2_EN %in% c("Quetta", "Lahore", "Peshawar"),]
## Check
##cities
## Bind karachi together
#cities = rbind(cities, karachi)
## **NOTE** The shapefile is originally ordered by the object ID, not the ADM2_EN name***
## Order the pak.bound.adm2 boundaries alphabetically by their English name
pak.bound.adm2 = pak.bound.adm2[order(pak.bound.adm2$ADM2_EN) , ]
## Check
head(pak.bound.adm2)
Shape_Leng | Shape_Area | ADM2_EN | ADM2_PCODE | ADM2_REF | ADM2ALT1EN | ADM2ALT2EN | ADM1_EN | ADM1_PCODE | ADM0_EN | ADM0_PCODE | date | validOn | validTo | geometry |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2.52 | 0.16 | Abbottabad | PK501 | Khyber Pakhtunkhwa | PK5 | Pakistan | PK | 2022-09-02 | 2022-09-09 | list(list(c(73.3980410830001, 73.3985407750001, 73.399154464, 73.3994059830001, 73.400542557, 73.4019308100001, 73.402948063, 73.404160527, 73.4046113730001, 73.4060988420001, 73.4097135380001, 73.410326786, 73.414153328, 73.430540604, 73.435373997, 73.444976468, 73.4533223550001, 73.4563076540001, 73.464706683, 73.464117651, 73.4639413200001, 73.4622645850001, 73.464994897, 73.4702664380001, 73.4794306750001, 73.4800004530001, 73.483323676, 73.4877634650001, 73.487975856, 73.488310254, 73.488058143, 73.487197032, 73.487057189, 73.48664697, 73.487312526, 73.4880384960001, 73.4935915070001, 73.493765376, 73.4943085570001, 73.4944054950001, 73.495817951, 73.496981148, 73.501387332, 73.506102153, 73.5066973340001, 73.506814499, 73.507218649, 73.508234481, 73.5094287210001, 73.5120075290001, 73.511291504, 73.506706238, 73.504302979, 73.503219604, 73.502296448, 73.500892639, 73.500190735, 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4.4 | 0.508 | Astore | PK301 | Gilgit Baltistan | PK3 | Pakistan | PK | 2022-09-02 | 2022-09-09 | list(list(c(74.7381817670001, 74.7381817670001, 74.7384107710001, 74.738869015, 74.7400146460001, 74.7395563910001, 74.7386398930001, 74.7377235120001, 74.737265398, 74.734057518, 74.731308152, 74.729246021, 74.729017029, 74.729017029, 74.730620775, 74.7331410190001, 74.735890644, 74.738869015, 74.7413894000001, 74.744597151, 74.747804772, 74.750554138, 74.753991022, 74.7562822740001, 74.7578860200001, 74.7588025190001, 74.7585733970001, 74.757198643, 74.755824019, 74.7535327790001, 74.7519290210001, 74.7489502750001, 74.745742642, 74.7434516480001, 74.7416185220001, 74.740931145, 74.741160267, 74.7416185220001, 74.7432223850001, 74.7434516480001, 74.742764271, 74.7416185220001, 74.7397855130001, 74.7381817670001, 74.7379527740001, 74.739098136, 74.7395922680001, 74.7407718860001, 74.7416185220001, 74.7452843980001, 74.7482630270001, 74.750783401, 74.7562822740001, 74.7601771430001, 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5.2 | 0.67 | Attock | PK601 | Punjab | PK6 | Pakistan | PK | 2022-09-02 | 2022-09-09 | list(list(c(72.43119812, 72.4326705930001, 72.434066772, 72.436790466, 72.439048767, 72.441612244, 72.443138123, 72.444023132, 72.4459075930001, 72.4472198490001, 72.4478225710001, 72.448768616, 72.4533081050001, 72.4576797490001, 72.4645919800001, 72.4709124600001, 72.4726715090001, 72.47844696, 72.483192444, 72.485488892, 72.48966217, 72.4976272580001, 72.501617432, 72.503120422, 72.5048828120001, 72.507118225, 72.5084686280001, 72.5101776120001, 72.512283325, 72.5143203740001, 72.516586304, 72.5196380620001, 72.5211563110001, 72.522956848, 72.52456665, 72.525772095, 72.5265884400001, 72.527931213, 72.5289535520001, 72.53011322, 72.531578064, 72.5328216550001, 72.534072876, 72.5355682370001, 72.5394897460001, 72.540626526, 72.541748047, 72.542953491, 72.544342041, 72.546241761, 72.548080444, 72.5488763210001, 72.549568176, 72.5495376590001, 72.549110413, 72.549178462, 72.549453735, 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8.78 | 2.26 | Awaran | PK201 | Balochistan | PK2 | Pakistan | PK | 2022-09-02 | 2022-09-09 | list(list(c(65.9333502310001, 65.9351033130001, 65.937146774, 65.945404437, 65.963319131, 65.9775401910001, 65.98524008, 65.990995018, 65.997570656, 65.9992138420001, 66.0003326030001, 66.0039686850001, 66.0054004780001, 66.0111303510001, 66.0115713850001, 66.0128490810001, 66.0139046720001, 66.0142483060001, 66.016915726, 66.0182145640001, 66.021776634, 66.02296356, 66.0242979450001, 66.0286722210001, 66.032879611, 66.034079632, 66.035090166, 66.0387722360001, 66.04011468, 66.04138771, 66.04551719, 66.048388559, 66.049102336, 66.049546413, 66.051545005, 66.053640973, 66.0540988370001, 66.054153408, 66.0544638060001, 66.054502899, 66.0545420240001, 66.0544695230001, 66.0543728900001, 66.054179639, 66.0535046780001, 66.053382438, 66.053352106, 66.0518869530001, 66.051106002, 66.050981065, 66.0508873480001, 66.0507176840001, 66.050491515, 66.0488518270001, 66.0447148040001, 66.0445424480001, 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5.01 | 0.587 | Badin | PK701 | Sindh | PK7 | Pakistan | PK | 2022-09-02 | 2022-09-09 | list(list(c(68.968534204, 68.9690305130001, 68.969208205, 68.969268222, 68.969548124, 68.970027518, 68.9700677270001, 68.970080787, 68.9701155320001, 68.9703355890001, 68.970381912, 68.970500857, 68.970619748, 68.9712143190001, 68.9714124650001, 68.9714657170001, 68.9717932440001, 68.9718868460001, 68.9720837180001, 68.973002491, 68.973325484, 68.973359024, 68.973545216, 68.9741449320001, 68.9745650080001, 68.9746977600001, 68.97465222, 68.9746195910001, 68.9744672830001, 68.9744128930001, 68.9744952030001, 68.9745774740001, 68.97501637, 68.975106977, 68.975530232, 68.977101507, 68.977371345, 68.978017772, 68.9792694020001, 68.980521723, 68.9810724910001, 68.981815209, 68.9830506010001, 68.9840492580001, 68.984781282, 68.98575406, 68.991169821, 68.996907085, 68.997812863, 68.9987191380001, 68.9996932290001, 69.000667642, 69.00716507, 69.0132612, 69.0141670940001, 69.015050864, 69.017190508, 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1.59 | 0.0678 | Bagh | PK101 | Azad Kashmir | PK1 | Pakistan | PK | 2022-09-02 | 2022-09-09 | list(list(c(73.5389199940001, 73.5395200020001, 73.53884, 73.53884, 73.5393999980001, 73.540060001, 73.539869996, 73.539210004, 73.5385599950001, 73.5380800030001, 73.539243108, 73.5398600030001, 73.540719713, 73.541620004, 73.539970001, 73.538179996, 73.536199998, 73.538059369, 73.5397099950001, 73.5418499950001, 73.544362247, 73.5463084, 73.5486599970001, 73.5510800010001, 73.553439998, 73.555700002, 73.557109997, 73.5578105100001, 73.5590899950001, 73.559801647, 73.5607799950001, 73.5627699980001, 73.564859265, 73.5660500050001, 73.5665522520001, 73.5705800050001, 73.5708757670001, 73.5715200020001, 73.572591112, 73.5735000000001, 73.574540002, 73.576198057, 73.5793138020001, 73.5799000000001, 73.5825683620001, 73.583551524, 73.5845671390001, 73.5866407720001, 73.589246071, 73.591346241, 73.5920500020001, 73.593076416, 73.595219587, 73.5991532300001, 73.602695183, 73.6042821830001, 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34.127450002, 34.1261700040001))) |
Note: This is the dataframe that we will collate all of the separate data/observations to. This dataframe consists of 160 rows for each district in Pakistan. The variables/features are extracted by district, then added as a feature to the test.df. Finally, the test.df is pivoted to a long format data frame for analysis. See the “Build the Overall Dataframe section of this document.
## Build a new dataframe to put values in
test.df = data.frame(matrix(nrow = 160, ncol = 0))
## Add names to the dataframe
test.df$districts = pak.bound.adm2$ADM2_EN
## Check
str(test.df)
## 'data.frame': 160 obs. of 1 variable:
## $ districts: chr "Abbottabad" "Astore" "Attock" "Awaran" ...
head(test.df)
districts |
---|
Abbottabad |
Astore |
Attock |
Awaran |
Badin |
Bagh |
##Read in the ACLED Data
pak.con = read.csv("../data/conflict/run_2/pak_2010_2022.csv")
##Factor conversion for districts
##Is this needed?
#pak.con.v2 = pak.con
#pak.con.v2$admin2 = as.factor(pak.con.v2$admin2)
##Check
#str(pak.con.v2$admin2)
## Note - Consider a pipeline approach to data cleaning/aggregation**
pak.con.sf = st_as_sf(pak.con,
coords = c("longitude", "latitude"),
crs = 4326) #WGS84
## Separate by years
pak.con.2010 = pak.con.sf[pak.con.sf$year == "2010", ]
pak.con.2011 = pak.con.sf[pak.con.sf$year == "2011", ]
pak.con.2012 = pak.con.sf[pak.con.sf$year == "2012", ]
pak.con.2013 = pak.con.sf[pak.con.sf$year == "2013", ]
pak.con.2014 = pak.con.sf[pak.con.sf$year == "2014", ]
pak.con.2015 = pak.con.sf[pak.con.sf$year == "2015", ]
pak.con.2016 = pak.con.sf[pak.con.sf$year == "2016", ]
pak.con.2017 = pak.con.sf[pak.con.sf$year == "2017", ]
pak.con.2018 = pak.con.sf[pak.con.sf$year == "2018", ]
pak.con.2019 = pak.con.sf[pak.con.sf$year == "2019", ]
pak.con.2020 = pak.con.sf[pak.con.sf$year == "2020", ]
pak.con.2021 = pak.con.sf[pak.con.sf$year == "2021", ]
pak.con.2022 = pak.con.sf[pak.con.sf$year == "2022", ]
## Total Counts By District By Year
dist.con.2010 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2010))
dist.con.2011 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2011))
dist.con.2012 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2012))
dist.con.2013 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2013))
dist.con.2014 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2014))
dist.con.2015 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2015))
dist.con.2016 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2016))
dist.con.2017 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2017))
dist.con.2018 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2018))
dist.con.2019 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2019))
dist.con.2020 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2020))
dist.con.2021 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2021))
dist.con.2022 = lengths(st_intersects(x = pak.bound.adm2, pak.con.2022))
## Check
length(dist.con.2019)
## [1] 160
##160 observations
## This is merged into the total.df in the "overall df build" section
## Break down protests by year
protests.2010 = pak.con.2010[pak.con.2010$event_type == "Protests", ]
protests.2011 = pak.con.2010[pak.con.2011$event_type == "Protests", ]
protests.2012 = pak.con.2010[pak.con.2012$event_type == "Protests", ]
protests.2013 = pak.con.2010[pak.con.2013$event_type == "Protests", ]
protests.2014 = pak.con.2010[pak.con.2014$event_type == "Protests", ]
protests.2015 = pak.con.2010[pak.con.2015$event_type == "Protests", ]
protests.2016 = pak.con.2010[pak.con.2016$event_type == "Protests", ]
protests.2017 = pak.con.2010[pak.con.2017$event_type == "Protests", ]
protests.2018 = pak.con.2010[pak.con.2018$event_type == "Protests", ]
protests.2019 = pak.con.2010[pak.con.2019$event_type == "Protests", ]
protests.2020 = pak.con.2010[pak.con.2020$event_type == "Protests", ]
protests.2021 = pak.con.2010[pak.con.2021$event_type == "Protests", ]
protests.2022 = pak.con.2010[pak.con.2022$event_type == "Protests", ]
## Check
#head(protests.2010)
tail(protests.2010)
data_id | iso | event_id_cnty | event_id_no_cnty | event_date | year | time_precision | event_type | sub_event_type | actor1 | assoc_actor_1 | inter1 | actor2 | assoc_actor_2 | inter2 | interaction | region | country | admin1 | admin2 | admin3 | location | geo_precision | source | source_scale | notes | fatalities | timestamp | iso3 | geometry |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9185669 | 586 | PAK1697 | 1697 | 04 January 2010 | 2010 | 1 | Protests | Peaceful protest | Protesters (Pakistan) | ASWJ: Ahle Sunnat Wal Jamaat; Women (Pakistan) | 6 | 0 | 60 | South Asia | Pakistan | Sindh | Karachi City | Karachi South | Karachi - Saddar | 1 | Right Vision News | National | On 4 January 2010, women's wing of the ASWJ protested outside Karachi Press Club in Saddar town (Karachi, Sindh) to demand the arrest of the killers of Abdul Ghafoor Nadeem and his son. | 0 | 1651182380 | PAK | c(67.016, 24.8542) | ||
8995733 | 586 | PAK13 | 13 | 03 January 2010 | 2010 | 1 | Protests | Peaceful protest | Protesters (Pakistan) | Health Workers (Pakistan); Labour Group (Pakistan); Lawyers (Pakistan) | 6 | 0 | 60 | South Asia | Pakistan | Federal Capital Territory | Islamabad | Islamabad | Islamabad | 1 | Right Vision News | National | On 3 January 2010, dozens of civil society members, traders, lawyers and doctors staged a demonstration outside the National Press Club against Pakistan's Institute of Medical Sciences (PIMS) doctors who were found negligent in the death of the MNA's Faiz Muhammad Khan. | 0 | 1649284578 | PAK | c(73.0432, 33.7214) | ||
7832074 | 586 | PAK10 | 10 | 02 January 2010 | 2010 | 1 | Protests | Peaceful protest | Protesters (Pakistan) | YDA: Young Doctors Association; Health Workers (Pakistan) | 6 | 0 | 60 | South Asia | Pakistan | Punjab | Lahore | Lahore | 1 | Daily Times (Pakistan) | National | The Young Doctors Association staged a rally to condemn the registration of criminal cases against their colleagues. | 0 | 1618442078 | PAK | c(74.3436, 31.5497) | |||
4966092 | 586 | PAK9 | 9 | 01 January 2010 | 2010 | 1 | Protests | Peaceful protest | Protesters (Pakistan) | 6 | 0 | 60 | South Asia | Pakistan | Sindh | Hyderabad | Hyderabad | Hyderabad | 1 | Right Vision News | National | Protests occurred in Hyderabad in solidarity with those in Karachi against the bombing of a religious procession earlier in the week. | 0 | 1552577624 | PAK | c(68.3736, 25.3942) | |||
7832076 | 586 | PAK2 | 2 | 01 January 2010 | 2010 | 1 | Protests | Peaceful protest | Protesters (Pakistan) | Students (Pakistan) | 6 | 0 | 60 | South Asia | Pakistan | Punjab | Lahore | Lahore | 1 | Daily Times (Pakistan) | National | A large number of people including members of civil society and students, held a peace rally in solidarity with the citizens of Karachi following a deadly bombing in that city. | 0 | 1618442078 | PAK | c(74.3436, 31.5497) | |||
7832077 | 586 | PAK1 | 1 | 01 January 2010 | 2010 | 1 | Protests | Peaceful protest | Protesters (Pakistan) | 6 | 0 | 60 | South Asia | Pakistan | Sindh | Karachi City | Karachi | 1 | Daily Regional Times (Pakistan); Right Vision News | Subnational-National | Religious and political leaders, including the Sunni Rehbar Council, called for a peaceful strike to express solidarity with 'the affectees of 10th Muharram incident', nearly shutting down the city. | 0 | 1618442078 | PAK | c(67.0822, 24.9056) |
## Intersect with the adm2 shapefile
dist.protests.2010 = lengths(st_intersects(x = pak.bound.adm2, protests.2010))
dist.protests.2011 = lengths(st_intersects(x = pak.bound.adm2, protests.2011))
dist.protests.2012 = lengths(st_intersects(x = pak.bound.adm2, protests.2012))
dist.protests.2013 = lengths(st_intersects(x = pak.bound.adm2, protests.2013))
dist.protests.2014 = lengths(st_intersects(x = pak.bound.adm2, protests.2014))
dist.protests.2015 = lengths(st_intersects(x = pak.bound.adm2, protests.2015))
dist.protests.2016 = lengths(st_intersects(x = pak.bound.adm2, protests.2016))
dist.protests.2017 = lengths(st_intersects(x = pak.bound.adm2, protests.2017))
dist.protests.2018 = lengths(st_intersects(x = pak.bound.adm2, protests.2018))
dist.protests.2019 = lengths(st_intersects(x = pak.bound.adm2, protests.2019))
dist.protests.2020 = lengths(st_intersects(x = pak.bound.adm2, protests.2020))
dist.protests.2021 = lengths(st_intersects(x = pak.bound.adm2, protests.2021))
dist.protests.2022 = lengths(st_intersects(x = pak.bound.adm2, protests.2022))
## Check
length(dist.protests.2015)
## [1] 160
##160 observations
## This is merged into the total.df in the "overall df build" section
## Break down battles by year
battles.2010 = pak.con.2010[pak.con.2010$event_type == "Battles", ]
battles.2011 = pak.con.2010[pak.con.2011$event_type == "Battles", ]
battles.2012 = pak.con.2010[pak.con.2012$event_type == "Battles", ]
battles.2013 = pak.con.2010[pak.con.2013$event_type == "Battles", ]
battles.2014 = pak.con.2010[pak.con.2014$event_type == "Battles", ]
battles.2015 = pak.con.2010[pak.con.2015$event_type == "Battles", ]
battles.2016 = pak.con.2010[pak.con.2016$event_type == "Battles", ]
battles.2017 = pak.con.2010[pak.con.2017$event_type == "Battles", ]
battles.2018 = pak.con.2010[pak.con.2018$event_type == "Battles", ]
battles.2019 = pak.con.2010[pak.con.2019$event_type == "Battles", ]
battles.2020 = pak.con.2010[pak.con.2020$event_type == "Battles", ]
battles.2021 = pak.con.2010[pak.con.2021$event_type == "Battles", ]
battles.2022 = pak.con.2010[pak.con.2022$event_type == "Battles", ]
## Check
#head(protests.2010)
tail(battles.2010)
data_id | iso | event_id_cnty | event_id_no_cnty | event_date | year | time_precision | event_type | sub_event_type | actor1 | assoc_actor_1 | inter1 | actor2 | assoc_actor_2 | inter2 | interaction | region | country | admin1 | admin2 | admin3 | location | geo_precision | source | source_scale | notes | fatalities | timestamp | iso3 | geometry |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4965984 | 586 | PAK20 | 20 | 04 January 2010 | 2010 | 1 | Battles | Armed clash | TTP: Tehreek-i-Taliban Pakistan | 2 | Police Forces of Pakistan (2008-2013) | 1 | 12 | South Asia | Pakistan | FATA | Bajaur Agency | Salarzai Tehsil | Wanda | 2 | Right Vision News | National | 2 suspected Taliban militants were killed and 2 were injured at a security forces check post near Shuza Algad. | 2 | 1552577624 | PAK | c(71.5867, 34.8341) | ||
4965985 | 586 | PAK21 | 21 | 04 January 2010 | 2010 | 1 | Battles | Armed clash | TTP: Tehreek-i-Taliban Pakistan | 2 | Police Forces of Pakistan (2008-2013) | 1 | 12 | South Asia | Pakistan | Khyber Pakhtunkhwa | Bannu | Bannu | Janikhel | 1 | Right Vision News | National | 2 suspected Taliban militants were killed and 2 arrested at Maddi near Jani Khel. | 2 | 1552577624 | PAK | c(70.5083, 32.8011) | ||
7729045 | 586 | PAK15 | 15 | 04 January 2010 | 2010 | 1 | Battles | Armed clash | TTP: Tehreek-i-Taliban Pakistan | 2 | Aman Lashkar | 4 | 24 | South Asia | Pakistan | FATA | Mohmand Agency | Halimzai | Ghalanai | 1 | Right Vision News | National | 2 members of a peace committee were gunned down by suspected Taliban militants in Mohmand agency. | 2 | 1614792735 | PAK | c(71.3911, 34.3233) | ||
7743762 | 586 | PAK19 | 19 | 04 January 2010 | 2010 | 1 | Battles | Armed clash | TTP: Tehreek-i-Taliban Pakistan | 2 | Police Forces of Pakistan (2008-2013) | 1 | 12 | South Asia | Pakistan | FATA | North Waziristan Agency | Razmak | Razmak | 1 | Right Vision News | National | Suspected Taliban militants fired mortars at security forces, injuring 2. At least 1 militant was also killed during operations by security forces in the area. | 1 | 1615334312 | PAK | c(69.8343, 32.6893) | ||
7743777 | 586 | PAK16 | 16 | 04 January 2010 | 2010 | 1 | Battles | Armed clash | TTP: Tehreek-i-Taliban Pakistan | 2 | Police Forces of Pakistan (2008-2013) | 1 | 12 | South Asia | Pakistan | FATA | South Waziristan Agency | Ladha | Ladha | 1 | Right Vision News | National | Suspected Taliban militants fired on Pakistani security forces with small arms and fire was returned; no casualties reported. | 0 | 1615334312 | PAK | c(69.8291, 32.568) | ||
7743873 | 586 | PAK17 | 17 | 04 January 2010 | 2010 | 1 | Battles | Armed clash | TTP: Tehreek-i-Taliban Pakistan | 2 | Police Forces of Pakistan (2008-2013) | 1 | 12 | South Asia | Pakistan | FATA | Bajaur Agency | Khar | Mamond | 2 | Right Vision News | National | 4 suspected Taliban militants were killed by security forces in the Jani Shah area of Tehsil Momoond in Bajaur agency. A powerful roadside remote-controlled bomb was also defused in the area. | 4 | 1615334313 | PAK | c(71.5249, 34.7863) |
## Intersect with the adm2 shapefile
dist.battles.2010 = lengths(st_intersects(x = pak.bound.adm2, battles.2010))
dist.battles.2011 = lengths(st_intersects(x = pak.bound.adm2, battles.2011))
dist.battles.2012 = lengths(st_intersects(x = pak.bound.adm2, battles.2012))
dist.battles.2013 = lengths(st_intersects(x = pak.bound.adm2, battles.2013))
dist.battles.2014 = lengths(st_intersects(x = pak.bound.adm2, battles.2014))
dist.battles.2015 = lengths(st_intersects(x = pak.bound.adm2, battles.2015))
dist.battles.2016 = lengths(st_intersects(x = pak.bound.adm2, battles.2016))
dist.battles.2017 = lengths(st_intersects(x = pak.bound.adm2, battles.2017))
dist.battles.2018 = lengths(st_intersects(x = pak.bound.adm2, battles.2018))
dist.battles.2019 = lengths(st_intersects(x = pak.bound.adm2, battles.2019))
dist.battles.2020 = lengths(st_intersects(x = pak.bound.adm2, battles.2020))
dist.battles.2021 = lengths(st_intersects(x = pak.bound.adm2, battles.2021))
dist.battles.2022 = lengths(st_intersects(x = pak.bound.adm2, battles.2022))
## Check
length(dist.battles.2010)
## [1] 160
##160 observations
## This is merged into the total.df in the "overall df build" section
## Break down explosions by year
expl.2010 = pak.con.2010[pak.con.2010$event_type == "Explosions/Remote violence", ]
expl.2011 = pak.con.2010[pak.con.2011$event_type == "Explosions/Remote violence", ]
expl.2012 = pak.con.2010[pak.con.2012$event_type == "Explosions/Remote violence", ]
expl.2013 = pak.con.2010[pak.con.2013$event_type == "Explosions/Remote violence", ]
expl.2014 = pak.con.2010[pak.con.2014$event_type == "Explosions/Remote violence", ]
expl.2015 = pak.con.2010[pak.con.2015$event_type == "Explosions/Remote violence", ]
expl.2016 = pak.con.2010[pak.con.2016$event_type == "Explosions/Remote violence", ]
expl.2017 = pak.con.2010[pak.con.2017$event_type == "Explosions/Remote violence", ]
expl.2018 = pak.con.2010[pak.con.2018$event_type == "Explosions/Remote violence", ]
expl.2019 = pak.con.2010[pak.con.2019$event_type == "Explosions/Remote violence", ]
expl.2020 = pak.con.2010[pak.con.2020$event_type == "Explosions/Remote violence", ]
expl.2021 = pak.con.2010[pak.con.2021$event_type == "Explosions/Remote violence", ]
expl.2022 = pak.con.2010[pak.con.2022$event_type == "Explosions/Remote violence", ]
## Check
#head(protests.2010)
tail(expl.2010)
data_id | iso | event_id_cnty | event_id_no_cnty | event_date | year | time_precision | event_type | sub_event_type | actor1 | assoc_actor_1 | inter1 | actor2 | assoc_actor_2 | inter2 | interaction | region | country | admin1 | admin2 | admin3 | location | geo_precision | source | source_scale | notes | fatalities | timestamp | iso3 | geometry |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4966012 | 586 | PAK18 | 18 | 04 January 2010 | 2010 | 1 | Explosions/Remote violence | Shelling/artillery/missile attack | TTP: Tehreek-i-Taliban Pakistan | 2 | Police Forces of Pakistan (2008-2013) | 1 | 12 | South Asia | Pakistan | FATA | South Waziristan Agency | Makin | Metari Narai | 1 | Right Vision News | National | Suspected Taliban militants fired a rocket at security forces, killing 1 and wounding 5. | 1 | 1552577624 | PAK | c(69.894, 32.6329) | ||
7728072 | 586 | PAK12 | 12 | 03 January 2010 | 2010 | 1 | Explosions/Remote violence | Remote explosive/landmine/IED | Military Forces of the United States (2009-2017) | 8 | TTP: Tehreek-i-Taliban Pakistan | 2 | 28 | South Asia | Pakistan | FATA | North Waziristan Agency | Miran Shah | Miranshah | 2 | Dawn (Pakistan) | National | 5 people including 3 Arabs were killed in an attack on Mosakki village in North Waziristan. | 5 | 1614792733 | PAK | c(70.0698, 33.0001) | ||
7729040 | 586 | PAK11 | 11 | 02 January 2010 | 2010 | 1 | Explosions/Remote violence | Remote explosive/landmine/IED | Unidentified Armed Group (Pakistan) | 3 | 0 | 30 | South Asia | Pakistan | FATA | Khyber Agency | Bara | Bara | 2 | Right Vision News | National | Militants blew up 2 schools and a basic health centre in the Malangi area of Tehsil Mamund near Bara. No casualties were reported. | 0 | 1614792735 | PAK | c(71.4622, 33.9217) | |||
4966091 | 586 | PAK8 | 8 | 01 January 2010 | 2010 | 1 | Explosions/Remote violence | Suicide bomb | TTP: Tehreek-i-Taliban Pakistan | 2 | Civilians (Pakistan) | 7 | 27 | South Asia | Pakistan | Khyber Pakhtunkhwa | Lakki Marwat | Lakki Marwat | Lakki Marwat | 1 | Right Vision News | National | A police spokesman confirmed at least 93 were killed and as many as 100 others were injured in a vehicular suicide bombing by 3 suspected Taliban militants on a volleyball match in Shah Hassankhel town in Lakki Marwat. | 93 | 1552577624 | PAK | c(70.8511, 32.5992) | ||
7028462 | 586 | PAK6 | 6 | 01 January 2010 | 2010 | 1 | Explosions/Remote violence | Air/drone strike | Military Forces of the United States (2009-2017) | 8 | TTP: Tehreek-i-Taliban Pakistan | 2 | 28 | South Asia | Pakistan | FATA | North Waziristan Agency | Spinwam | Gundi Khala | 1 | Right Vision News | National | At least 3 Taliban militants were killed and 2 injured by a US drone strike in Ghundikala village, near Miramshah. It was confirmed that a key Taliban commander, Haji Omar, was killed in the attack. | 3 | 1585692285 | PAK | c(70.4325, 33.15) | ||
7743890 | 586 | PAK7 | 7 | 01 January 2010 | 2010 | 1 | Explosions/Remote violence | Remote explosive/landmine/IED | TTP: Tehreek-i-Taliban Pakistan | 2 | Civilians (Pakistan) | 7 | 27 | South Asia | Pakistan | FATA | Bajaur Agency | Salarzai | Salarzai | 1 | Right Vision News | National | At least 5 people, including a pro-government tribal elder, were killed by a remote detonation of a bomb near their vehicle just outside the village of Salarzai, near Khar. Local officials believe Taliban rebels planted the explosive. | 5 | 1615334313 | PAK | c(71.5141, 34.9256) |
## Intersect with the adm2 shapefile
dist.expl.2010 = lengths(st_intersects(x = pak.bound.adm2, expl.2010))
dist.expl.2011 = lengths(st_intersects(x = pak.bound.adm2, expl.2011))
dist.expl.2012 = lengths(st_intersects(x = pak.bound.adm2, expl.2012))
dist.expl.2013 = lengths(st_intersects(x = pak.bound.adm2, expl.2013))
dist.expl.2014 = lengths(st_intersects(x = pak.bound.adm2, expl.2014))
dist.expl.2015 = lengths(st_intersects(x = pak.bound.adm2, expl.2015))
dist.expl.2016 = lengths(st_intersects(x = pak.bound.adm2, expl.2016))
dist.expl.2017 = lengths(st_intersects(x = pak.bound.adm2, expl.2017))
dist.expl.2018 = lengths(st_intersects(x = pak.bound.adm2, expl.2018))
dist.expl.2019 = lengths(st_intersects(x = pak.bound.adm2, expl.2019))
dist.expl.2020 = lengths(st_intersects(x = pak.bound.adm2, expl.2020))
dist.expl.2021 = lengths(st_intersects(x = pak.bound.adm2, expl.2021))
dist.expl.2022 = lengths(st_intersects(x = pak.bound.adm2, expl.2022))
## Check
length(dist.expl.2010)
## [1] 160
##160 observations
## This is merged into the total.df in the "overall df build" section
## Break down riots by year
riots.2010 = pak.con.2010[pak.con.2010$event_type == "Riots", ]
riots.2011 = pak.con.2010[pak.con.2011$event_type == "Riots", ]
riots.2012 = pak.con.2010[pak.con.2012$event_type == "Riots", ]
riots.2013 = pak.con.2010[pak.con.2013$event_type == "Riots", ]
riots.2014 = pak.con.2010[pak.con.2014$event_type == "Riots", ]
riots.2015 = pak.con.2010[pak.con.2015$event_type == "Riots", ]
riots.2016 = pak.con.2010[pak.con.2016$event_type == "Riots", ]
riots.2017 = pak.con.2010[pak.con.2017$event_type == "Riots", ]
riots.2018 = pak.con.2010[pak.con.2018$event_type == "Riots", ]
riots.2019 = pak.con.2010[pak.con.2019$event_type == "Riots", ]
riots.2020 = pak.con.2010[pak.con.2020$event_type == "Riots", ]
riots.2021 = pak.con.2010[pak.con.2021$event_type == "Riots", ]
riots.2022 = pak.con.2010[pak.con.2022$event_type == "Riots", ]
## Check
#head(protests.2010)
tail(riots.2010)
data_id | iso | event_id_cnty | event_id_no_cnty | event_date | year | time_precision | event_type | sub_event_type | actor1 | assoc_actor_1 | inter1 | actor2 | assoc_actor_2 | inter2 | interaction | region | country | admin1 | admin2 | admin3 | location | geo_precision | source | source_scale | notes | fatalities | timestamp | iso3 | geometry |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5950612 | 586 | PAK119 | 119 | 15 January 2010 | 2010 | 2 | Riots | Violent demonstration | Rioters (Pakistan) | 5 | Police Forces of Pakistan (2008-2013) | 1 | 15 | South Asia | Pakistan | Sindh | Khairpur | Khairpur | Khairpur | 1 | Daily Regional Times (Pakistan) | Subnational | Prisoners at the Khairpur Central Jail staged a protest against the registration of cases against three of their colleagues, during which they took 7 policemen hostage. | 0 | 1567539398 | PAK | c(68.7452, 27.5271) | ||
7547397 | 586 | PAK120 | 120 | 15 January 2010 | 2010 | 1 | Riots | Violent demonstration | Rioters (Pakistan) | 5 | Police Forces of Pakistan (2008-2013) | 1 | 15 | South Asia | Pakistan | Sindh | Karachi City | Karachi South | Karachi - Lyari | 1 | Daily Regional Times (Pakistan) | Subnational | 'A shutter-down strike was observed in Lyari, where enraged people also set tyres on fire at different roads and resorted to the aerial fire, suspending business and social activities.' A large sit in was also staged against the targeted killing of Baloch people. | 0 | 1611096243 | PAK | c(66.9946, 24.8687) | ||
7548312 | 586 | PAK90 | 90 | 13 January 2010 | 2010 | 1 | Riots | Violent demonstration | Rioters (Pakistan) | Students (Pakistan) | 5 | Police Forces of Pakistan (2008-2013) | 1 | 15 | South Asia | Pakistan | Sindh | Karachi City | Karachi East | Karachi - Gulshan | 1 | Daily Times (Pakistan) | National | Scores of students from two different student organizations clashed near Safari Park and threw rocks at each other and passing vehicles. Police arrived to disperse the protesters peacefully. It was not certain what they clashed about. | 0 | 1611096246 | PAK | c(67.1279, 24.9293) | |
7546113 | 586 | PAK71 | 71 | 11 January 2010 | 2010 | 1 | Riots | Violent demonstration | Rioters (Pakistan) | PPP: Pakistan Peoples Party | 5 | Police Forces of Pakistan (2008-2013) | 1 | 15 | South Asia | Pakistan | Sindh | Karachi City | Karachi South | Karachi - Lyari | 1 | Daily Times (Pakistan); Daily Regional Times (Pakistan) | Subnational-National | Thousands of residents of Lyari march in protest against a 'biased and illegal' security crackdown in the area, ending outside the Karachi Press Club. 'Some vehicles carrying female protesters were fired at' and police resorted to shelling and aerial firing when motorcyclists tried to move towards the chief minister's house. PPP activists and members were also present during the protests and met with the chief minister to discuss the issues. | 0 | 1611096240 | PAK | c(66.9946, 24.8687) | |
5943605 | 586 | PAK48 | 48 | 09 January 2010 | 2010 | 1 | Riots | Violent demonstration | Rioters (Pakistan) | 5 | 0 | 50 | South Asia | Pakistan | Sindh | Khairpur | Khairpur | Khairpur | 1 | Daily Regional Times (Pakistan) | Subnational | Prisoners in Khairpur Central Prison have put 5 barracks under siege, demanding that conditions in the prisons be improved, particularly overcrowding. | 0 | 1567539390 | PAK | c(68.7452, 27.5271) | |||
4966022 | 586 | PAK14 | 14 | 03 January 2010 | 2010 | 2 | Riots | Violent demonstration | Rioters (Pakistan) | 5 | 0 | 50 | South Asia | Pakistan | Punjab | Rawalpindi | Rawalpindi | Rawalpindi | 1 | Right Vision News | National | Members of MJ staged a protest rally against the suicidal attacks in Karachi earlier this week. The protesters rallied, then marched from Imam Bargah Yad-I-Hussain to Rehmanabad Chowk. They also burned tires and blocked traffic for a short while on Murree Road. | 0 | 1552577624 | PAK | c(73.0679, 33.6007) |
## Intersect with the adm2 shapefile
dist.riots.2010 = lengths(st_intersects(x = pak.bound.adm2, riots.2010))
dist.riots.2011 = lengths(st_intersects(x = pak.bound.adm2, riots.2011))
dist.riots.2012 = lengths(st_intersects(x = pak.bound.adm2, riots.2012))
dist.riots.2013 = lengths(st_intersects(x = pak.bound.adm2, riots.2013))
dist.riots.2014 = lengths(st_intersects(x = pak.bound.adm2, riots.2014))
dist.riots.2015 = lengths(st_intersects(x = pak.bound.adm2, riots.2015))
dist.riots.2016 = lengths(st_intersects(x = pak.bound.adm2, riots.2016))
dist.riots.2017 = lengths(st_intersects(x = pak.bound.adm2, riots.2017))
dist.riots.2018 = lengths(st_intersects(x = pak.bound.adm2, riots.2018))
dist.riots.2019 = lengths(st_intersects(x = pak.bound.adm2, riots.2019))
dist.riots.2020 = lengths(st_intersects(x = pak.bound.adm2, riots.2020))
dist.riots.2021 = lengths(st_intersects(x = pak.bound.adm2, riots.2021))
dist.riots.2022 = lengths(st_intersects(x = pak.bound.adm2, riots.2022))
## Check
length(dist.riots.2010)
## [1] 160
##160 observations
## This is merged into the total.df in the "overall df build" section
## Break down VAC by year
vac.2010 = pak.con.2010[pak.con.2010$event_type == "Violence against civilians", ]
vac.2011 = pak.con.2010[pak.con.2011$event_type == "Violence against civilians", ]
vac.2012 = pak.con.2010[pak.con.2012$event_type == "Violence against civilians", ]
vac.2013 = pak.con.2010[pak.con.2013$event_type == "Violence against civilians", ]
vac.2014 = pak.con.2010[pak.con.2014$event_type == "Violence against civilians", ]
vac.2015 = pak.con.2010[pak.con.2015$event_type == "Violence against civilians", ]
vac.2016 = pak.con.2010[pak.con.2016$event_type == "Violence against civilians", ]
vac.2017 = pak.con.2010[pak.con.2017$event_type == "Violence against civilians", ]
vac.2018 = pak.con.2010[pak.con.2018$event_type == "Violence against civilians", ]
vac.2019 = pak.con.2010[pak.con.2019$event_type == "Violence against civilians", ]
vac.2020 = pak.con.2010[pak.con.2020$event_type == "Violence against civilians", ]
vac.2021 = pak.con.2010[pak.con.2021$event_type == "Violence against civilians", ]
vac.2022 = pak.con.2010[pak.con.2022$event_type == "Violence against civilians", ]
## Check
#head(protests.2010)
tail(vac.2010)
data_id | iso | event_id_cnty | event_id_no_cnty | event_date | year | time_precision | event_type | sub_event_type | actor1 | assoc_actor_1 | inter1 | actor2 | assoc_actor_2 | inter2 | interaction | region | country | admin1 | admin2 | admin3 | location | geo_precision | source | source_scale | notes | fatalities | timestamp | iso3 | geometry |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7832060 | 586 | PAK70 | 70 | 11 January 2010 | 2010 | 1 | Violence against civilians | Attack | Unidentified Armed Group (Pakistan) | 3 | Civilians (Pakistan) | Ahmadiyya Muslim Group (Pakistan) | 7 | 37 | South Asia | Pakistan | Punjab | Lahore | Lahore | 1 | Daily Times (Pakistan) | National | 2 unidentified assailants shot a man to death for reporting their involvement in spreading 'religious hatred against the Ahmadi sect and provoking people to kill Ahmadis.' | 1 | 1618442078 | PAK | c(74.3436, 31.5497) | ||
7832063 | 586 | PAK60 | 60 | 10 January 2010 | 2010 | 1 | Violence against civilians | Attack | Unidentified Armed Group (Pakistan) | 3 | Civilians (Pakistan) | MQM: Muttahida Qaumi Movement | 7 | 37 | South Asia | Pakistan | Sindh | Karachi City | Karachi | 1 | Daily Times (Pakistan); Right Vision News | National | 5 people, including 3 MQM activists, a policeman, and at least one MQM-H activist, in targeted killings for the fourth consecutive day. Another policeman was injured, along with at least 4 others. | 5 | 1618442078 | PAK | c(67.0822, 24.9056) | ||
7546019 | 586 | PAK44 | 44 | 08 January 2010 | 2010 | 1 | Violence against civilians | Attack | Unidentified Armed Group (Pakistan) | 3 | Civilians (Pakistan) | MQM: Muttahida Qaumi Movement | 7 | 37 | South Asia | Pakistan | Sindh | Karachi City | Karachi South | Karachi - Lyari | 1 | Daily Times (Pakistan); Right Vision News | National | At least 2 MQM and 2 ANP activists were killed in a bout of targeted attacks in the Lyari area of Karachi. | 4 | 1611096240 | PAK | c(66.9946, 24.8687) | |
7552783 | 586 | PAK37 | 37 | 07 January 2010 | 2010 | 1 | Violence against civilians | Attack | Unidentified Armed Group (Pakistan) | 3 | Civilians (Pakistan) | GBBT: Gutter Baghicha Bachao Tehreek | 7 | 37 | South Asia | Pakistan | Sindh | Karachi City | Karachi South | Karachi - Saddar | 1 | Right Vision News | National | The president of Gutter Baghicha Bachao Tehreek (GBBT) and as many as 7 others were killed in Garden Town in Saddar during targeted killings in Karachi. Nine others were wounded. Other reports have the number as high as 13. | 8 | 1611096256 | PAK | c(67.016, 24.8542) | |
7552806 | 586 | PAK36 | 36 | 07 January 2010 | 2010 | 1 | Violence against civilians | Attack | Unidentified Armed Group (Pakistan) | 3 | Civilians (Pakistan) | MQM: Muttahida Qaumi Movement | 7 | 37 | South Asia | Pakistan | Sindh | Karachi City | Malir | Karachi - Orangi | 1 | Right Vision News | National | An MQM activist in Orange Town was killed in a target killing. | 1 | 1618566845 | PAK | c(67.1317, 24.9507) | |
7832075 | 586 | PAK3 | 3 | 01 January 2010 | 2010 | 1 | Violence against civilians | Attack | Unidentified Armed Group (Pakistan) | 3 | Civilians (Pakistan) | MQM-H: Mohajir Qaumi Movement-Haqiqi | 7 | 37 | South Asia | Pakistan | Sindh | Karachi City | Karachi | 1 | Daily Times (Pakistan) | National | 2 activists of the MQM-H and 1 of the MQM were killed in separate incidents. 2 other MQM-H activists were wounded in an attack at their home. | 3 | 1618442078 | PAK | c(67.0822, 24.9056) |
## Intersect with the adm2 shapefile
dist.vac.2010 = lengths(st_intersects(x = pak.bound.adm2, vac.2010))
dist.vac.2011 = lengths(st_intersects(x = pak.bound.adm2, vac.2011))
dist.vac.2012 = lengths(st_intersects(x = pak.bound.adm2, vac.2012))
dist.vac.2013 = lengths(st_intersects(x = pak.bound.adm2, vac.2013))
dist.vac.2014 = lengths(st_intersects(x = pak.bound.adm2, vac.2014))
dist.vac.2015 = lengths(st_intersects(x = pak.bound.adm2, vac.2015))
dist.vac.2016 = lengths(st_intersects(x = pak.bound.adm2, vac.2016))
dist.vac.2017 = lengths(st_intersects(x = pak.bound.adm2, vac.2017))
dist.vac.2018 = lengths(st_intersects(x = pak.bound.adm2, vac.2018))
dist.vac.2019 = lengths(st_intersects(x = pak.bound.adm2, vac.2019))
dist.vac.2020 = lengths(st_intersects(x = pak.bound.adm2, vac.2020))
dist.vac.2021 = lengths(st_intersects(x = pak.bound.adm2, vac.2021))
dist.vac.2022 = lengths(st_intersects(x = pak.bound.adm2, vac.2022))
## Check
length(dist.vac.2010)
## [1] 160
##160 observations
## This is merged into the total.df in the "overall df build" section
## Break down VAC by year
sd.2010 = pak.con.2010[pak.con.2010$event_type == "Strategic developments", ]
sd.2011 = pak.con.2010[pak.con.2011$event_type == "Strategic developments", ]
sd.2012 = pak.con.2010[pak.con.2012$event_type == "Strategic developments", ]
sd.2013 = pak.con.2010[pak.con.2013$event_type == "Strategic developments", ]
sd.2014 = pak.con.2010[pak.con.2014$event_type == "Strategic developments", ]
sd.2015 = pak.con.2010[pak.con.2015$event_type == "Strategic developments", ]
sd.2016 = pak.con.2010[pak.con.2016$event_type == "Strategic developments", ]
sd.2017 = pak.con.2010[pak.con.2017$event_type == "Strategic developments", ]
sd.2018 = pak.con.2010[pak.con.2018$event_type == "Strategic developments", ]
sd.2019 = pak.con.2010[pak.con.2019$event_type == "Strategic developments", ]
sd.2020 = pak.con.2010[pak.con.2020$event_type == "Strategic developments", ]
sd.2021 = pak.con.2010[pak.con.2021$event_type == "Strategic developments", ]
sd.2022 = pak.con.2010[pak.con.2022$event_type == "Strategic developments", ]
## Check
#head(protests.2010)
tail(sd.2010)
data_id | iso | event_id_cnty | event_id_no_cnty | event_date | year | time_precision | event_type | sub_event_type | actor1 | assoc_actor_1 | inter1 | actor2 | assoc_actor_2 | inter2 | interaction | region | country | admin1 | admin2 | admin3 | location | geo_precision | source | source_scale | notes | fatalities | timestamp | iso3 | geometry |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7831969 | 586 | PAK675 | 675 | 18 February 2010 | 2010 | 2 | Strategic developments | Arrests | Police Forces of Pakistan (2008-2013) | 1 | Unidentified Armed Group (Pakistan) | TTP: Tehreek-i-Taliban Pakistan; Al Qaeda | 3 | 13 | South Asia | Pakistan | Sindh | Karachi City | Karachi | 1 | Daily Regional Times (Pakistan); Right Vision News | Subnational-National | Arrests: Around 18 Feb, the Pakistan Rangers arrested 59 people, including 3 people connected to al-Qaeda and the TTP, and recovered various arms during raids in different areas of Karachi city. | 0 | 1618442077 | PAK | c(67.0822, 24.9056) | ||
7264255 | 586 | PAK429 | 429 | 04 February 2010 | 2010 | 1 | Strategic developments | Agreement | Shiite Muslim Militia (Pakistan) | 4 | Sunni Muslim Militia (Pakistan) | 4 | 44 | South Asia | Pakistan | Khyber Pakhtunkhwa | Kohat | Kohat | Kohat | 1 | Right Vision News | National | Agreement: On 4 Feb, a peace agreement was reached at a government-sponsored jirga between two religious sects (Shia and Sunni) which had been engaged in hostilities for 14 years in Kohat (KP). | 0 | 1603755485 | PAK | c(71.5439, 33.4491) | ||
7832010 | 586 | PAK435 | 435 | 04 February 2010 | 2010 | 1 | Strategic developments | Arrests | Police Forces of Pakistan (2008-2013) Pakistan Rangers | 1 | Civilians (Pakistan) | 7 | 17 | South Asia | Pakistan | Sindh | Karachi City | Karachi | 1 | Right Vision News | National | Arrests: More than 50 people, including members of various political parties, were arrested by the Pakistan Rangers (in association with Karachi police forces) after they took over responsibility for security in Karachi following prolonged political violence. | 0 | 1618442077 | PAK | c(67.0822, 24.9056) | |||
9279022 | 586 | PAK277 | 277 | 24 January 2010 | 2010 | 1 | Strategic developments | Disrupted weapons use | TTP: Tehreek-i-Taliban Pakistan | 2 | Military Forces of the United States (2009-2017) | 8 | 28 | South Asia | Pakistan | FATA | North Waziristan Agency | Miran Shah | Miranshah | 2 | Right Vision News | National | On 24 January 2010, a suspected U.S. Drone was shot down by Taliban militants near Hamzoni in the Miran Shah area (NWA, FATA). | 0 | 1652909182 | PAK | c(70.0698, 33.0001) | ||
4966090 | 586 | PAK4 | 4 | 01 January 2010 | 2010 | 1 | Strategic developments | Looting/property destruction | TTP: Tehreek-i-Taliban Pakistan | 2 | NATO: North Atlantic Treaty Organization | 8 | 28 | South Asia | Pakistan | Balochistan | Killa Abdullah | Chaman | Chaman | 2 | Right Vision News | National | Looting: Suspected Taliban gunmen attacked a convoy of trucks carrying supplies for NATO/US forces and torched two tankers in the village of Puti while they were en route to the border town of Chaman. | 0 | 1552577624 | PAK | c(66.4681, 30.8944) | ||
7137741 | 586 | PAK5 | 5 | 01 January 2010 | 2010 | 2 | Strategic developments | Arrests | Police Forces of Pakistan (2008-2013) Pakistan Rangers | 1 | Police Forces of India (2004-2014) | 8 | 18 | South Asia | Pakistan | Balochistan | Quetta | Quetta City | Quetta | 2 | Right Vision News | National | Arrests: Around 1 January 2010, agents of a Pakistani law enforcement agency raided an Afghan refugee camp in the Muslim Bagh area in Quetta (Balochistan) and arrested an alleged Indian intelligence agent (from the RAW agency). | 0 | 1593548001 | PAK | c(67.0097, 30.1999) |
## Intersect with the adm2 shapefile
dist.sd.2010 = lengths(st_intersects(x = pak.bound.adm2, sd.2010))
dist.sd.2011 = lengths(st_intersects(x = pak.bound.adm2, sd.2011))
dist.sd.2012 = lengths(st_intersects(x = pak.bound.adm2, sd.2012))
dist.sd.2013 = lengths(st_intersects(x = pak.bound.adm2, sd.2013))
dist.sd.2014 = lengths(st_intersects(x = pak.bound.adm2, sd.2014))
dist.sd.2015 = lengths(st_intersects(x = pak.bound.adm2, sd.2015))
dist.sd.2016 = lengths(st_intersects(x = pak.bound.adm2, sd.2016))
dist.sd.2017 = lengths(st_intersects(x = pak.bound.adm2, sd.2017))
dist.sd.2018 = lengths(st_intersects(x = pak.bound.adm2, sd.2018))
dist.sd.2019 = lengths(st_intersects(x = pak.bound.adm2, sd.2019))
dist.sd.2020 = lengths(st_intersects(x = pak.bound.adm2, sd.2020))
dist.sd.2021 = lengths(st_intersects(x = pak.bound.adm2, sd.2021))
dist.sd.2022 = lengths(st_intersects(x = pak.bound.adm2, sd.2022))
## Check
length(dist.sd.2010)
## [1] 160
##160 observations
## This is merged into the total.df in the "overall df build" section
##Filter the conflict data by district using tabyl
##Total events by district
##Note** This uses the districts as laid out by the conflict data (Karachi City instead of E,W,S,K,C Karachi)
pak.district.con = janitor::tabyl(dat = pak.con,
admin2,
year)
##Check/View
pak.district.con
admin2 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | |
Abbottabad | 24 | 10 | 12 | 12 | 6 | 11 | 9 | 19 | 20 | 33 | 56 | 41 | 56 |
Astore | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 3 | 5 | 5 | 5 |
Attock | 17 | 5 | 6 | 11 | 5 | 8 | 4 | 9 | 24 | 17 | 13 | 18 | 36 |
Awaran | 3 | 5 | 6 | 12 | 6 | 17 | 7 | 8 | 4 | 2 | 18 | 40 | 33 |
Badin | 131 | 70 | 121 | 103 | 66 | 91 | 76 | 102 | 93 | 245 | 375 | 209 | 129 |
Bagh | 4 | 4 | 1 | 3 | 6 | 10 | 9 | 4 | 9 | 16 | 2 | 0 | 2 |
Bahawalnagar | 7 | 3 | 8 | 10 | 4 | 7 | 9 | 28 | 23 | 25 | 8 | 26 | 24 |
Bahawalpur | 31 | 7 | 14 | 34 | 30 | 46 | 27 | 29 | 34 | 70 | 154 | 18 | 41 |
Bajaur | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 127 | 64 | 90 | 104 |
Bajaur Agency | 93 | 19 | 34 | 39 | 56 | 49 | 49 | 30 | 24 | 0 | 0 | 0 | 0 |
Baltistan | 0 | 1 | 2 | 5 | 1 | 3 | 2 | 4 | 6 | 6 | 4 | 9 | 15 |
Bannu | 17 | 12 | 25 | 51 | 34 | 36 | 36 | 25 | 35 | 38 | 21 | 59 | 73 |
Barkhan | 0 | 3 | 5 | 1 | 1 | 1 | 2 | 0 | 0 | 2 | 6 | 7 | 6 |
Batagram | 1 | 2 | 2 | 1 | 2 | 2 | 11 | 3 | 12 | 12 | 9 | 18 | 7 |
Bhakkar | 1 | 1 | 3 | 3 | 1 | 5 | 2 | 3 | 1 | 2 | 0 | 1 | 4 |
Bhimber | 3 | 3 | 2 | 2 | 6 | 10 | 11 | 17 | 19 | 29 | 22 | 1 | 1 |
Buner | 4 | 1 | 5 | 6 | 8 | 6 | 11 | 13 | 15 | 16 | 16 | 23 | 11 |
Chagai | 0 | 3 | 10 | 4 | 7 | 10 | 2 | 12 | 13 | 18 | 17 | 18 | 38 |
Chakwal | 24 | 11 | 6 | 12 | 10 | 6 | 10 | 6 | 3 | 3 | 3 | 2 | 7 |
Charsadda | 13 | 26 | 43 | 38 | 37 | 35 | 41 | 31 | 40 | 32 | 26 | 50 | 76 |
Chiniot | 2 | 1 | 2 | 4 | 5 | 4 | 2 | 7 | 8 | 6 | 1 | 5 | 12 |
Chitral | 5 | 10 | 4 | 1 | 8 | 14 | 13 | 10 | 24 | 35 | 19 | 46 | 42 |
Dadu | 135 | 102 | 202 | 159 | 63 | 98 | 147 | 222 | 298 | 258 | 491 | 292 | 188 |
Dera Bugti | 8 | 48 | 63 | 21 | 21 | 30 | 33 | 20 | 13 | 14 | 12 | 12 | 24 |
Dera Ghazi Khan | 10 | 10 | 11 | 3 | 6 | 26 | 19 | 28 | 17 | 22 | 6 | 9 | 21 |
Dera Ismail Khan | 18 | 9 | 25 | 23 | 22 | 25 | 15 | 21 | 45 | 60 | 25 | 64 | 89 |
Diamir | 5 | 2 | 3 | 3 | 0 | 6 | 5 | 2 | 10 | 9 | 9 | 6 | 14 |
Faisalabad | 43 | 35 | 56 | 59 | 54 | 75 | 48 | 47 | 81 | 113 | 55 | 75 | 85 |
FR Bannu | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
FR Dera Ismail Khan | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
FR Kohat | 18 | 12 | 6 | 9 | 1 | 3 | 3 | 0 | 1 | 0 | 0 | 0 | 0 |
FR Peshawar | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
FR Tank | 2 | 1 | 1 | 2 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Ghanche | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 2 | 6 | 3 |
Ghizer | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 4 | 9 | 7 | 7 | 5 |
Ghotki | 66 | 42 | 100 | 55 | 15 | 18 | 23 | 12 | 40 | 114 | 104 | 169 | 65 |
Gilgit | 5 | 3 | 17 | 6 | 4 | 13 | 8 | 7 | 17 | 17 | 21 | 21 | 35 |
Gujranwala | 20 | 12 | 30 | 22 | 30 | 40 | 43 | 49 | 107 | 57 | 29 | 33 | 43 |
Gujrat | 8 | 1 | 11 | 15 | 11 | 25 | 13 | 29 | 20 | 30 | 32 | 14 | 26 |
Gwadar | 9 | 9 | 6 | 10 | 7 | 18 | 8 | 15 | 12 | 21 | 8 | 43 | 53 |
Hafizabad | 6 | 5 | 14 | 18 | 17 | 22 | 11 | 11 | 33 | 26 | 21 | 4 | 12 |
Hangu | 31 | 22 | 19 | 33 | 42 | 20 | 9 | 8 | 6 | 26 | 26 | 38 | 38 |
Haripur | 6 | 1 | 4 | 3 | 1 | 6 | 9 | 5 | 14 | 32 | 17 | 44 | 69 |
Harnai | 0 | 5 | 0 | 1 | 2 | 4 | 1 | 2 | 2 | 11 | 16 | 13 | 25 |
Hattian Bala | 3 | 2 | 1 | 1 | 8 | 8 | 7 | 8 | 10 | 21 | 10 | 1 | 0 |
Haveli | 1 | 2 | 1 | 1 | 8 | 12 | 5 | 9 | 7 | 31 | 37 | 0 | 0 |
Hunza | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 5 | 6 | 9 | 9 | 15 |
Hyderabad | 514 | 219 | 612 | 374 | 175 | 199 | 241 | 158 | 283 | 236 | 496 | 590 | 545 |
Islamabad | 132 | 94 | 177 | 112 | 124 | 156 | 91 | 129 | 226 | 267 | 160 | 225 | 188 |
Jacobabad | 49 | 30 | 77 | 81 | 55 | 68 | 43 | 55 | 57 | 153 | 351 | 167 | 88 |
Jaffarabad | 1 | 16 | 17 | 9 | 11 | 13 | 6 | 5 | 6 | 14 | 13 | 16 | 22 |
Jamshoro | 61 | 20 | 54 | 47 | 39 | 105 | 78 | 166 | 134 | 114 | 185 | 195 | 115 |
Jhal Magsi | 0 | 1 | 2 | 6 | 2 | 3 | 0 | 3 | 0 | 6 | 1 | 7 | 6 |
Jhang | 4 | 4 | 9 | 6 | 5 | 12 | 16 | 3 | 14 | 17 | 9 | 8 | 21 |
Jhelum | 5 | 4 | 4 | 2 | 4 | 4 | 1 | 4 | 7 | 1 | 8 | 3 | 10 |
Kachhi | 12 | 23 | 20 | 22 | 11 | 2 | 13 | 10 | 5 | 11 | 18 | 26 | 41 |
Kalat | 20 | 9 | 10 | 10 | 7 | 23 | 10 | 6 | 11 | 14 | 12 | 15 | 45 |
Karachi City | 614 | 540 | 1.07e+03 | 1.04e+03 | 629 | 511 | 421 | 354 | 521 | 519 | 656 | 483 | 706 |
Karak | 11 | 7 | 15 | 27 | 22 | 29 | 48 | 28 | 52 | 57 | 26 | 46 | 34 |
Kashmore | 47 | 26 | 83 | 77 | 57 | 49 | 22 | 29 | 49 | 55 | 47 | 82 | 57 |
Kasur | 15 | 1 | 7 | 15 | 7 | 17 | 19 | 28 | 52 | 47 | 14 | 22 | 17 |
Kech | 11 | 25 | 30 | 25 | 40 | 36 | 29 | 33 | 22 | 21 | 63 | 130 | 155 |
Khairpur | 228 | 133 | 277 | 292 | 132 | 146 | 184 | 254 | 266 | 191 | 207 | 189 | 114 |
Khanewal | 14 | 2 | 9 | 2 | 5 | 6 | 4 | 15 | 20 | 14 | 4 | 7 | 12 |
Kharan | 0 | 3 | 0 | 5 | 2 | 5 | 3 | 2 | 5 | 5 | 8 | 19 | 22 |
Kharmang | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 2 | 4 | 5 |
Khushab | 2 | 1 | 5 | 0 | 4 | 3 | 3 | 1 | 5 | 7 | 2 | 2 | 7 |
Khuzdar | 27 | 22 | 17 | 33 | 23 | 10 | 7 | 15 | 13 | 22 | 15 | 20 | 42 |
Khyber | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 106 | 43 | 57 | 108 |
Khyber Agency | 116 | 108 | 241 | 142 | 215 | 151 | 91 | 87 | 25 | 0 | 0 | 0 | 0 |
Killa Abdullah | 12 | 19 | 13 | 27 | 10 | 8 | 13 | 18 | 21 | 33 | 75 | 16 | 27 |
Killa Saifullah | 2 | 1 | 4 | 3 | 2 | 6 | 0 | 2 | 7 | 8 | 2 | 6 | 10 |
Kohat | 36 | 15 | 38 | 24 | 19 | 33 | 20 | 28 | 23 | 68 | 30 | 59 | 88 |
Kohistan | 3 | 3 | 5 | 1 | 3 | 13 | 5 | 6 | 6 | 22 | 7 | 14 | 17 |
Kohlu | 6 | 7 | 4 | 2 | 1 | 7 | 11 | 10 | 5 | 8 | 23 | 24 | 30 |
Kotli | 3 | 5 | 3 | 25 | 14 | 17 | 19 | 32 | 29 | 46 | 33 | 3 | 8 |
Kurram | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 21 | 12 | 19 | 20 |
Kurram Agency | 71 | 70 | 77 | 47 | 14 | 53 | 25 | 36 | 6 | 0 | 0 | 0 | 0 |
Lahore | 251 | 149 | 279 | 237 | 161 | 295 | 305 | 190 | 392 | 408 | 384 | 234 | 315 |
Lakki Marwat | 16 | 7 | 28 | 20 | 13 | 12 | 16 | 14 | 30 | 64 | 26 | 44 | 76 |
Larkana | 193 | 112 | 227 | 200 | 96 | 159 | 310 | 339 | 597 | 196 | 269 | 361 | 318 |
Las Bela | 12 | 9 | 9 | 3 | 4 | 9 | 10 | 8 | 6 | 23 | 16 | 30 | 44 |
Layyah | 5 | 1 | 4 | 1 | 2 | 4 | 6 | 13 | 6 | 9 | 4 | 1 | 5 |
Lehri | 1 | 1 | 3 | 2 | 2 | 5 | 5 | 2 | 0 | 0 | 8 | 8 | 9 |
Lodhran | 4 | 0 | 2 | 0 | 0 | 2 | 1 | 5 | 1 | 8 | 2 | 2 | 6 |
Loralai | 3 | 13 | 7 | 7 | 4 | 11 | 1 | 5 | 8 | 24 | 11 | 13 | 21 |
Lower Dir | 26 | 13 | 23 | 21 | 10 | 27 | 29 | 27 | 67 | 91 | 27 | 66 | 64 |
Malakand PA | 4 | 3 | 5 | 5 | 3 | 7 | 12 | 6 | 6 | 11 | 11 | 13 | 22 |
Mandi Bahauddin | 2 | 1 | 5 | 2 | 8 | 7 | 6 | 6 | 7 | 12 | 1 | 4 | 4 |
Mansehra | 30 | 13 | 18 | 23 | 22 | 19 | 19 | 24 | 35 | 119 | 66 | 61 | 73 |
Mardan | 27 | 18 | 47 | 36 | 16 | 35 | 28 | 21 | 53 | 48 | 51 | 63 | 65 |
Mastung | 19 | 14 | 22 | 24 | 10 | 16 | 13 | 11 | 15 | 16 | 23 | 20 | 25 |
Matiari | 64 | 20 | 31 | 44 | 17 | 22 | 3 | 19 | 19 | 40 | 163 | 110 | 42 |
Mianwali | 4 | 1 | 2 | 6 | 7 | 5 | 4 | 8 | 3 | 5 | 5 | 2 | 10 |
Mirpur | 11 | 4 | 10 | 11 | 15 | 11 | 7 | 13 | 16 | 25 | 5 | 2 | 0 |
Mirpur Khas | 73 | 65 | 150 | 94 | 47 | 79 | 50 | 94 | 98 | 163 | 342 | 147 | 108 |
Mohmand | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 | 69 | 20 | 18 | 14 |
Mohmand Agency | 104 | 68 | 60 | 29 | 45 | 47 | 65 | 37 | 20 | 0 | 0 | 0 | 0 |
Multan | 29 | 30 | 63 | 42 | 48 | 62 | 42 | 51 | 70 | 73 | 216 | 37 | 62 |
Musakhel | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 2 | 0 |
Muzaffarabad | 14 | 15 | 19 | 25 | 26 | 18 | 28 | 24 | 39 | 74 | 19 | 25 | 28 |
Muzaffargarh | 9 | 4 | 4 | 8 | 9 | 14 | 8 | 10 | 15 | 20 | 16 | 17 | 19 |
Nagar | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 2 | 5 | 5 |
Nankana Sahib | 1 | 3 | 6 | 9 | 4 | 22 | 9 | 6 | 3 | 11 | 8 | 1 | 7 |
Narowal | 5 | 1 | 0 | 11 | 6 | 17 | 8 | 10 | 21 | 17 | 7 | 15 | 18 |
Nasirabad | 8 | 34 | 10 | 28 | 20 | 14 | 28 | 19 | 18 | 33 | 14 | 13 | 18 |
Naushahro Feroze | 203 | 64 | 160 | 158 | 68 | 101 | 50 | 139 | 169 | 90 | 138 | 133 | 67 |
Neelum | 1 | 3 | 2 | 2 | 5 | 6 | 10 | 6 | 5 | 24 | 20 | 4 | 1 |
North Waziristan | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 92 | 47 | 89 | 113 |
North Waziristan Agency | 167 | 83 | 133 | 93 | 171 | 83 | 26 | 14 | 21 | 0 | 0 | 0 | 0 |
Nowshera | 21 | 20 | 37 | 32 | 10 | 25 | 35 | 10 | 31 | 39 | 38 | 41 | 49 |
Nushki | 3 | 2 | 4 | 9 | 4 | 6 | 5 | 5 | 4 | 9 | 13 | 13 | 26 |
Okara | 8 | 4 | 13 | 17 | 11 | 18 | 24 | 26 | 24 | 26 | 10 | 17 | 33 |
Orakzai | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 22 | 31 | 14 | 11 |
Orakzai Agency | 146 | 49 | 56 | 18 | 14 | 16 | 4 | 8 | 2 | 0 | 0 | 0 | 0 |
Pakpattan | 5 | 1 | 7 | 9 | 17 | 5 | 7 | 3 | 6 | 22 | 8 | 6 | 8 |
Panjgur | 4 | 6 | 7 | 17 | 17 | 18 | 8 | 17 | 6 | 16 | 19 | 39 | 40 |
Peshawar | 165 | 167 | 277 | 315 | 254 | 247 | 245 | 161 | 213 | 286 | 325 | 259 | 350 |
Pishin | 0 | 6 | 6 | 10 | 2 | 2 | 4 | 5 | 5 | 16 | 20 | 13 | 15 |
Poonch | 10 | 2 | 6 | 13 | 14 | 15 | 12 | 22 | 18 | 36 | 12 | 1 | 7 |
Qambar Shahdadkot | 32 | 15 | 51 | 51 | 20 | 22 | 10 | 22 | 17 | 32 | 64 | 77 | 48 |
Quetta | 147 | 158 | 301 | 179 | 117 | 127 | 94 | 78 | 113 | 169 | 97 | 220 | 367 |
Rahim Yar Khan | 12 | 2 | 17 | 25 | 7 | 17 | 15 | 26 | 29 | 35 | 11 | 18 | 36 |
Rajanpur | 1 | 1 | 4 | 4 | 4 | 2 | 6 | 8 | 7 | 12 | 4 | 3 | 13 |
Rawalpindi | 77 | 42 | 87 | 83 | 61 | 59 | 68 | 67 | 186 | 171 | 216 | 77 | 122 |
Sahiwal | 6 | 1 | 4 | 8 | 5 | 12 | 13 | 15 | 23 | 31 | 7 | 20 | 26 |
Sanghar | 46 | 39 | 54 | 80 | 27 | 22 | 13 | 36 | 58 | 124 | 134 | 179 | 88 |
Sargodha | 6 | 3 | 15 | 11 | 13 | 21 | 3 | 13 | 25 | 34 | 14 | 5 | 15 |
Shaheed Benazirabad | 128 | 72 | 147 | 111 | 55 | 77 | 40 | 118 | 116 | 68 | 148 | 154 | 113 |
Shangla | 1 | 2 | 1 | 5 | 2 | 4 | 16 | 20 | 38 | 69 | 16 | 49 | 35 |
Sheerani | 5 | 1 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 4 |
Sheikhupura | 12 | 2 | 12 | 13 | 5 | 11 | 21 | 24 | 26 | 38 | 5 | 5 | 23 |
Shigar | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 4 | 5 |
Shikarpur | 31 | 17 | 38 | 22 | 28 | 48 | 58 | 50 | 63 | 85 | 119 | 102 | 46 |
Sialkot | 33 | 14 | 23 | 24 | 83 | 44 | 53 | 72 | 114 | 102 | 38 | 19 | 19 |
Sibi | 5 | 9 | 8 | 10 | 9 | 6 | 6 | 14 | 7 | 15 | 10 | 18 | 32 |
Skardu | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 3 |
Sohbatpur | 1 | 3 | 7 | 2 | 4 | 7 | 2 | 1 | 1 | 5 | 13 | 13 | 10 |
South Waziristan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 37 | 22 | 39 | 46 |
South Waziristan Agency | 52 | 56 | 65 | 28 | 24 | 24 | 17 | 17 | 6 | 0 | 0 | 0 | 0 |
Sudhanoti | 2 | 2 | 2 | 1 | 9 | 9 | 4 | 5 | 7 | 15 | 0 | 1 | 0 |
Sujawal | 26 | 16 | 10 | 41 | 41 | 38 | 41 | 66 | 55 | 78 | 143 | 107 | 82 |
Sukkur | 235 | 179 | 289 | 102 | 75 | 130 | 65 | 80 | 60 | 84 | 278 | 137 | 111 |
Swabi | 16 | 8 | 60 | 30 | 14 | 21 | 31 | 21 | 40 | 60 | 47 | 58 | 61 |
Swat | 74 | 23 | 44 | 36 | 28 | 37 | 66 | 25 | 48 | 54 | 31 | 63 | 87 |
Tando Allah Yar | 17 | 14 | 26 | 16 | 15 | 6 | 6 | 12 | 8 | 5 | 34 | 64 | 75 |
Tando Muhammad Khan | 24 | 28 | 27 | 37 | 15 | 10 | 19 | 19 | 3 | 6 | 7 | 22 | 24 |
Tank | 15 | 9 | 11 | 17 | 11 | 14 | 15 | 9 | 10 | 25 | 27 | 38 | 46 |
Tharparkar | 12 | 3 | 12 | 28 | 8 | 16 | 42 | 42 | 62 | 74 | 130 | 133 | 96 |
Thatta | 63 | 34 | 68 | 63 | 32 | 51 | 33 | 38 | 82 | 123 | 171 | 170 | 92 |
Toba Tek Singh | 6 | 1 | 12 | 26 | 20 | 32 | 46 | 51 | 73 | 63 | 147 | 16 | 30 |
Tor Ghar | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 2 | 2 | 2 |
Umerkot | 17 | 29 | 35 | 21 | 12 | 14 | 14 | 17 | 11 | 52 | 79 | 80 | 66 |
Upper Dir | 2 | 7 | 16 | 4 | 9 | 13 | 22 | 8 | 18 | 28 | 12 | 22 | 21 |
Vehari | 12 | 2 | 5 | 4 | 12 | 16 | 11 | 13 | 4 | 19 | 13 | 4 | 12 |
Washuk | 1 | 0 | 3 | 4 | 4 | 5 | 0 | 0 | 1 | 4 | 14 | 8 | 9 |
Zhob | 2 | 4 | 10 | 3 | 3 | 10 | 5 | 7 | 10 | 10 | 12 | 11 | 17 |
Ziarat | 0 | 1 | 3 | 2 | 1 | 4 | 5 | 2 | 3 | 9 | 13 | 14 | 11 |
##change admin2 to ADM2_EN to match
pak.district.con = dplyr::rename(pak.district.con, ADM2_EN = admin2)
##Check/view
pak.district.con
ADM2_EN | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | |
Abbottabad | 24 | 10 | 12 | 12 | 6 | 11 | 9 | 19 | 20 | 33 | 56 | 41 | 56 |
Astore | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 3 | 5 | 5 | 5 |
Attock | 17 | 5 | 6 | 11 | 5 | 8 | 4 | 9 | 24 | 17 | 13 | 18 | 36 |
Awaran | 3 | 5 | 6 | 12 | 6 | 17 | 7 | 8 | 4 | 2 | 18 | 40 | 33 |
Badin | 131 | 70 | 121 | 103 | 66 | 91 | 76 | 102 | 93 | 245 | 375 | 209 | 129 |
Bagh | 4 | 4 | 1 | 3 | 6 | 10 | 9 | 4 | 9 | 16 | 2 | 0 | 2 |
Bahawalnagar | 7 | 3 | 8 | 10 | 4 | 7 | 9 | 28 | 23 | 25 | 8 | 26 | 24 |
Bahawalpur | 31 | 7 | 14 | 34 | 30 | 46 | 27 | 29 | 34 | 70 | 154 | 18 | 41 |
Bajaur | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 127 | 64 | 90 | 104 |
Bajaur Agency | 93 | 19 | 34 | 39 | 56 | 49 | 49 | 30 | 24 | 0 | 0 | 0 | 0 |
Baltistan | 0 | 1 | 2 | 5 | 1 | 3 | 2 | 4 | 6 | 6 | 4 | 9 | 15 |
Bannu | 17 | 12 | 25 | 51 | 34 | 36 | 36 | 25 | 35 | 38 | 21 | 59 | 73 |
Barkhan | 0 | 3 | 5 | 1 | 1 | 1 | 2 | 0 | 0 | 2 | 6 | 7 | 6 |
Batagram | 1 | 2 | 2 | 1 | 2 | 2 | 11 | 3 | 12 | 12 | 9 | 18 | 7 |
Bhakkar | 1 | 1 | 3 | 3 | 1 | 5 | 2 | 3 | 1 | 2 | 0 | 1 | 4 |
Bhimber | 3 | 3 | 2 | 2 | 6 | 10 | 11 | 17 | 19 | 29 | 22 | 1 | 1 |
Buner | 4 | 1 | 5 | 6 | 8 | 6 | 11 | 13 | 15 | 16 | 16 | 23 | 11 |
Chagai | 0 | 3 | 10 | 4 | 7 | 10 | 2 | 12 | 13 | 18 | 17 | 18 | 38 |
Chakwal | 24 | 11 | 6 | 12 | 10 | 6 | 10 | 6 | 3 | 3 | 3 | 2 | 7 |
Charsadda | 13 | 26 | 43 | 38 | 37 | 35 | 41 | 31 | 40 | 32 | 26 | 50 | 76 |
Chiniot | 2 | 1 | 2 | 4 | 5 | 4 | 2 | 7 | 8 | 6 | 1 | 5 | 12 |
Chitral | 5 | 10 | 4 | 1 | 8 | 14 | 13 | 10 | 24 | 35 | 19 | 46 | 42 |
Dadu | 135 | 102 | 202 | 159 | 63 | 98 | 147 | 222 | 298 | 258 | 491 | 292 | 188 |
Dera Bugti | 8 | 48 | 63 | 21 | 21 | 30 | 33 | 20 | 13 | 14 | 12 | 12 | 24 |
Dera Ghazi Khan | 10 | 10 | 11 | 3 | 6 | 26 | 19 | 28 | 17 | 22 | 6 | 9 | 21 |
Dera Ismail Khan | 18 | 9 | 25 | 23 | 22 | 25 | 15 | 21 | 45 | 60 | 25 | 64 | 89 |
Diamir | 5 | 2 | 3 | 3 | 0 | 6 | 5 | 2 | 10 | 9 | 9 | 6 | 14 |
Faisalabad | 43 | 35 | 56 | 59 | 54 | 75 | 48 | 47 | 81 | 113 | 55 | 75 | 85 |
FR Bannu | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
FR Dera Ismail Khan | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
FR Kohat | 18 | 12 | 6 | 9 | 1 | 3 | 3 | 0 | 1 | 0 | 0 | 0 | 0 |
FR Peshawar | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
FR Tank | 2 | 1 | 1 | 2 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Ghanche | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 2 | 6 | 3 |
Ghizer | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 4 | 9 | 7 | 7 | 5 |
Ghotki | 66 | 42 | 100 | 55 | 15 | 18 | 23 | 12 | 40 | 114 | 104 | 169 | 65 |
Gilgit | 5 | 3 | 17 | 6 | 4 | 13 | 8 | 7 | 17 | 17 | 21 | 21 | 35 |
Gujranwala | 20 | 12 | 30 | 22 | 30 | 40 | 43 | 49 | 107 | 57 | 29 | 33 | 43 |
Gujrat | 8 | 1 | 11 | 15 | 11 | 25 | 13 | 29 | 20 | 30 | 32 | 14 | 26 |
Gwadar | 9 | 9 | 6 | 10 | 7 | 18 | 8 | 15 | 12 | 21 | 8 | 43 | 53 |
Hafizabad | 6 | 5 | 14 | 18 | 17 | 22 | 11 | 11 | 33 | 26 | 21 | 4 | 12 |
Hangu | 31 | 22 | 19 | 33 | 42 | 20 | 9 | 8 | 6 | 26 | 26 | 38 | 38 |
Haripur | 6 | 1 | 4 | 3 | 1 | 6 | 9 | 5 | 14 | 32 | 17 | 44 | 69 |
Harnai | 0 | 5 | 0 | 1 | 2 | 4 | 1 | 2 | 2 | 11 | 16 | 13 | 25 |
Hattian Bala | 3 | 2 | 1 | 1 | 8 | 8 | 7 | 8 | 10 | 21 | 10 | 1 | 0 |
Haveli | 1 | 2 | 1 | 1 | 8 | 12 | 5 | 9 | 7 | 31 | 37 | 0 | 0 |
Hunza | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 5 | 6 | 9 | 9 | 15 |
Hyderabad | 514 | 219 | 612 | 374 | 175 | 199 | 241 | 158 | 283 | 236 | 496 | 590 | 545 |
Islamabad | 132 | 94 | 177 | 112 | 124 | 156 | 91 | 129 | 226 | 267 | 160 | 225 | 188 |
Jacobabad | 49 | 30 | 77 | 81 | 55 | 68 | 43 | 55 | 57 | 153 | 351 | 167 | 88 |
Jaffarabad | 1 | 16 | 17 | 9 | 11 | 13 | 6 | 5 | 6 | 14 | 13 | 16 | 22 |
Jamshoro | 61 | 20 | 54 | 47 | 39 | 105 | 78 | 166 | 134 | 114 | 185 | 195 | 115 |
Jhal Magsi | 0 | 1 | 2 | 6 | 2 | 3 | 0 | 3 | 0 | 6 | 1 | 7 | 6 |
Jhang | 4 | 4 | 9 | 6 | 5 | 12 | 16 | 3 | 14 | 17 | 9 | 8 | 21 |
Jhelum | 5 | 4 | 4 | 2 | 4 | 4 | 1 | 4 | 7 | 1 | 8 | 3 | 10 |
Kachhi | 12 | 23 | 20 | 22 | 11 | 2 | 13 | 10 | 5 | 11 | 18 | 26 | 41 |
Kalat | 20 | 9 | 10 | 10 | 7 | 23 | 10 | 6 | 11 | 14 | 12 | 15 | 45 |
Karachi City | 614 | 540 | 1.07e+03 | 1.04e+03 | 629 | 511 | 421 | 354 | 521 | 519 | 656 | 483 | 706 |
Karak | 11 | 7 | 15 | 27 | 22 | 29 | 48 | 28 | 52 | 57 | 26 | 46 | 34 |
Kashmore | 47 | 26 | 83 | 77 | 57 | 49 | 22 | 29 | 49 | 55 | 47 | 82 | 57 |
Kasur | 15 | 1 | 7 | 15 | 7 | 17 | 19 | 28 | 52 | 47 | 14 | 22 | 17 |
Kech | 11 | 25 | 30 | 25 | 40 | 36 | 29 | 33 | 22 | 21 | 63 | 130 | 155 |
Khairpur | 228 | 133 | 277 | 292 | 132 | 146 | 184 | 254 | 266 | 191 | 207 | 189 | 114 |
Khanewal | 14 | 2 | 9 | 2 | 5 | 6 | 4 | 15 | 20 | 14 | 4 | 7 | 12 |
Kharan | 0 | 3 | 0 | 5 | 2 | 5 | 3 | 2 | 5 | 5 | 8 | 19 | 22 |
Kharmang | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 2 | 4 | 5 |
Khushab | 2 | 1 | 5 | 0 | 4 | 3 | 3 | 1 | 5 | 7 | 2 | 2 | 7 |
Khuzdar | 27 | 22 | 17 | 33 | 23 | 10 | 7 | 15 | 13 | 22 | 15 | 20 | 42 |
Khyber | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 106 | 43 | 57 | 108 |
Khyber Agency | 116 | 108 | 241 | 142 | 215 | 151 | 91 | 87 | 25 | 0 | 0 | 0 | 0 |
Killa Abdullah | 12 | 19 | 13 | 27 | 10 | 8 | 13 | 18 | 21 | 33 | 75 | 16 | 27 |
Killa Saifullah | 2 | 1 | 4 | 3 | 2 | 6 | 0 | 2 | 7 | 8 | 2 | 6 | 10 |
Kohat | 36 | 15 | 38 | 24 | 19 | 33 | 20 | 28 | 23 | 68 | 30 | 59 | 88 |
Kohistan | 3 | 3 | 5 | 1 | 3 | 13 | 5 | 6 | 6 | 22 | 7 | 14 | 17 |
Kohlu | 6 | 7 | 4 | 2 | 1 | 7 | 11 | 10 | 5 | 8 | 23 | 24 | 30 |
Kotli | 3 | 5 | 3 | 25 | 14 | 17 | 19 | 32 | 29 | 46 | 33 | 3 | 8 |
Kurram | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 21 | 12 | 19 | 20 |
Kurram Agency | 71 | 70 | 77 | 47 | 14 | 53 | 25 | 36 | 6 | 0 | 0 | 0 | 0 |
Lahore | 251 | 149 | 279 | 237 | 161 | 295 | 305 | 190 | 392 | 408 | 384 | 234 | 315 |
Lakki Marwat | 16 | 7 | 28 | 20 | 13 | 12 | 16 | 14 | 30 | 64 | 26 | 44 | 76 |
Larkana | 193 | 112 | 227 | 200 | 96 | 159 | 310 | 339 | 597 | 196 | 269 | 361 | 318 |
Las Bela | 12 | 9 | 9 | 3 | 4 | 9 | 10 | 8 | 6 | 23 | 16 | 30 | 44 |
Layyah | 5 | 1 | 4 | 1 | 2 | 4 | 6 | 13 | 6 | 9 | 4 | 1 | 5 |
Lehri | 1 | 1 | 3 | 2 | 2 | 5 | 5 | 2 | 0 | 0 | 8 | 8 | 9 |
Lodhran | 4 | 0 | 2 | 0 | 0 | 2 | 1 | 5 | 1 | 8 | 2 | 2 | 6 |
Loralai | 3 | 13 | 7 | 7 | 4 | 11 | 1 | 5 | 8 | 24 | 11 | 13 | 21 |
Lower Dir | 26 | 13 | 23 | 21 | 10 | 27 | 29 | 27 | 67 | 91 | 27 | 66 | 64 |
Malakand PA | 4 | 3 | 5 | 5 | 3 | 7 | 12 | 6 | 6 | 11 | 11 | 13 | 22 |
Mandi Bahauddin | 2 | 1 | 5 | 2 | 8 | 7 | 6 | 6 | 7 | 12 | 1 | 4 | 4 |
Mansehra | 30 | 13 | 18 | 23 | 22 | 19 | 19 | 24 | 35 | 119 | 66 | 61 | 73 |
Mardan | 27 | 18 | 47 | 36 | 16 | 35 | 28 | 21 | 53 | 48 | 51 | 63 | 65 |
Mastung | 19 | 14 | 22 | 24 | 10 | 16 | 13 | 11 | 15 | 16 | 23 | 20 | 25 |
Matiari | 64 | 20 | 31 | 44 | 17 | 22 | 3 | 19 | 19 | 40 | 163 | 110 | 42 |
Mianwali | 4 | 1 | 2 | 6 | 7 | 5 | 4 | 8 | 3 | 5 | 5 | 2 | 10 |
Mirpur | 11 | 4 | 10 | 11 | 15 | 11 | 7 | 13 | 16 | 25 | 5 | 2 | 0 |
Mirpur Khas | 73 | 65 | 150 | 94 | 47 | 79 | 50 | 94 | 98 | 163 | 342 | 147 | 108 |
Mohmand | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 | 69 | 20 | 18 | 14 |
Mohmand Agency | 104 | 68 | 60 | 29 | 45 | 47 | 65 | 37 | 20 | 0 | 0 | 0 | 0 |
Multan | 29 | 30 | 63 | 42 | 48 | 62 | 42 | 51 | 70 | 73 | 216 | 37 | 62 |
Musakhel | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 2 | 0 |
Muzaffarabad | 14 | 15 | 19 | 25 | 26 | 18 | 28 | 24 | 39 | 74 | 19 | 25 | 28 |
Muzaffargarh | 9 | 4 | 4 | 8 | 9 | 14 | 8 | 10 | 15 | 20 | 16 | 17 | 19 |
Nagar | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 2 | 5 | 5 |
Nankana Sahib | 1 | 3 | 6 | 9 | 4 | 22 | 9 | 6 | 3 | 11 | 8 | 1 | 7 |
Narowal | 5 | 1 | 0 | 11 | 6 | 17 | 8 | 10 | 21 | 17 | 7 | 15 | 18 |
Nasirabad | 8 | 34 | 10 | 28 | 20 | 14 | 28 | 19 | 18 | 33 | 14 | 13 | 18 |
Naushahro Feroze | 203 | 64 | 160 | 158 | 68 | 101 | 50 | 139 | 169 | 90 | 138 | 133 | 67 |
Neelum | 1 | 3 | 2 | 2 | 5 | 6 | 10 | 6 | 5 | 24 | 20 | 4 | 1 |
North Waziristan | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 92 | 47 | 89 | 113 |
North Waziristan Agency | 167 | 83 | 133 | 93 | 171 | 83 | 26 | 14 | 21 | 0 | 0 | 0 | 0 |
Nowshera | 21 | 20 | 37 | 32 | 10 | 25 | 35 | 10 | 31 | 39 | 38 | 41 | 49 |
Nushki | 3 | 2 | 4 | 9 | 4 | 6 | 5 | 5 | 4 | 9 | 13 | 13 | 26 |
Okara | 8 | 4 | 13 | 17 | 11 | 18 | 24 | 26 | 24 | 26 | 10 | 17 | 33 |
Orakzai | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 22 | 31 | 14 | 11 |
Orakzai Agency | 146 | 49 | 56 | 18 | 14 | 16 | 4 | 8 | 2 | 0 | 0 | 0 | 0 |
Pakpattan | 5 | 1 | 7 | 9 | 17 | 5 | 7 | 3 | 6 | 22 | 8 | 6 | 8 |
Panjgur | 4 | 6 | 7 | 17 | 17 | 18 | 8 | 17 | 6 | 16 | 19 | 39 | 40 |
Peshawar | 165 | 167 | 277 | 315 | 254 | 247 | 245 | 161 | 213 | 286 | 325 | 259 | 350 |
Pishin | 0 | 6 | 6 | 10 | 2 | 2 | 4 | 5 | 5 | 16 | 20 | 13 | 15 |
Poonch | 10 | 2 | 6 | 13 | 14 | 15 | 12 | 22 | 18 | 36 | 12 | 1 | 7 |
Qambar Shahdadkot | 32 | 15 | 51 | 51 | 20 | 22 | 10 | 22 | 17 | 32 | 64 | 77 | 48 |
Quetta | 147 | 158 | 301 | 179 | 117 | 127 | 94 | 78 | 113 | 169 | 97 | 220 | 367 |
Rahim Yar Khan | 12 | 2 | 17 | 25 | 7 | 17 | 15 | 26 | 29 | 35 | 11 | 18 | 36 |
Rajanpur | 1 | 1 | 4 | 4 | 4 | 2 | 6 | 8 | 7 | 12 | 4 | 3 | 13 |
Rawalpindi | 77 | 42 | 87 | 83 | 61 | 59 | 68 | 67 | 186 | 171 | 216 | 77 | 122 |
Sahiwal | 6 | 1 | 4 | 8 | 5 | 12 | 13 | 15 | 23 | 31 | 7 | 20 | 26 |
Sanghar | 46 | 39 | 54 | 80 | 27 | 22 | 13 | 36 | 58 | 124 | 134 | 179 | 88 |
Sargodha | 6 | 3 | 15 | 11 | 13 | 21 | 3 | 13 | 25 | 34 | 14 | 5 | 15 |
Shaheed Benazirabad | 128 | 72 | 147 | 111 | 55 | 77 | 40 | 118 | 116 | 68 | 148 | 154 | 113 |
Shangla | 1 | 2 | 1 | 5 | 2 | 4 | 16 | 20 | 38 | 69 | 16 | 49 | 35 |
Sheerani | 5 | 1 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 4 |
Sheikhupura | 12 | 2 | 12 | 13 | 5 | 11 | 21 | 24 | 26 | 38 | 5 | 5 | 23 |
Shigar | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 4 | 5 |
Shikarpur | 31 | 17 | 38 | 22 | 28 | 48 | 58 | 50 | 63 | 85 | 119 | 102 | 46 |
Sialkot | 33 | 14 | 23 | 24 | 83 | 44 | 53 | 72 | 114 | 102 | 38 | 19 | 19 |
Sibi | 5 | 9 | 8 | 10 | 9 | 6 | 6 | 14 | 7 | 15 | 10 | 18 | 32 |
Skardu | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 3 |
Sohbatpur | 1 | 3 | 7 | 2 | 4 | 7 | 2 | 1 | 1 | 5 | 13 | 13 | 10 |
South Waziristan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 37 | 22 | 39 | 46 |
South Waziristan Agency | 52 | 56 | 65 | 28 | 24 | 24 | 17 | 17 | 6 | 0 | 0 | 0 | 0 |
Sudhanoti | 2 | 2 | 2 | 1 | 9 | 9 | 4 | 5 | 7 | 15 | 0 | 1 | 0 |
Sujawal | 26 | 16 | 10 | 41 | 41 | 38 | 41 | 66 | 55 | 78 | 143 | 107 | 82 |
Sukkur | 235 | 179 | 289 | 102 | 75 | 130 | 65 | 80 | 60 | 84 | 278 | 137 | 111 |
Swabi | 16 | 8 | 60 | 30 | 14 | 21 | 31 | 21 | 40 | 60 | 47 | 58 | 61 |
Swat | 74 | 23 | 44 | 36 | 28 | 37 | 66 | 25 | 48 | 54 | 31 | 63 | 87 |
Tando Allah Yar | 17 | 14 | 26 | 16 | 15 | 6 | 6 | 12 | 8 | 5 | 34 | 64 | 75 |
Tando Muhammad Khan | 24 | 28 | 27 | 37 | 15 | 10 | 19 | 19 | 3 | 6 | 7 | 22 | 24 |
Tank | 15 | 9 | 11 | 17 | 11 | 14 | 15 | 9 | 10 | 25 | 27 | 38 | 46 |
Tharparkar | 12 | 3 | 12 | 28 | 8 | 16 | 42 | 42 | 62 | 74 | 130 | 133 | 96 |
Thatta | 63 | 34 | 68 | 63 | 32 | 51 | 33 | 38 | 82 | 123 | 171 | 170 | 92 |
Toba Tek Singh | 6 | 1 | 12 | 26 | 20 | 32 | 46 | 51 | 73 | 63 | 147 | 16 | 30 |
Tor Ghar | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 2 | 2 | 2 |
Umerkot | 17 | 29 | 35 | 21 | 12 | 14 | 14 | 17 | 11 | 52 | 79 | 80 | 66 |
Upper Dir | 2 | 7 | 16 | 4 | 9 | 13 | 22 | 8 | 18 | 28 | 12 | 22 | 21 |
Vehari | 12 | 2 | 5 | 4 | 12 | 16 | 11 | 13 | 4 | 19 | 13 | 4 | 12 |
Washuk | 1 | 0 | 3 | 4 | 4 | 5 | 0 | 0 | 1 | 4 | 14 | 8 | 9 |
Zhob | 2 | 4 | 10 | 3 | 3 | 10 | 5 | 7 | 10 | 10 | 12 | 11 | 17 |
Ziarat | 0 | 1 | 3 | 2 | 1 | 4 | 5 | 2 | 3 | 9 | 13 | 14 | 11 |
table(is.na(pak.district.con$`2010`))
##
## FALSE
## 159
## Mapping the data by year -----Example Code-------
## Create a year character vector
#years = as.character(seq(from = 2010, to = 2022, by = 1))
# Check
# years
## Create a facet map by district and year
#my.pal = brewer.pal(n = 9, name = "Reds")
#pak.map +
#tm_fill(years,
#palette = my.pal) +
#tm_layout(legend.title.size = 1,
#legend.text.size = 0.4,
#legend.position = c("right", "bottom")) +
#tm_compass(position = c("left", "top"))
##Note: this data was processed in ArcPro. It was subsetted to include only the study area cities
pak.con.cities = read.csv("../data/conflict/run_2/city_conflict_export_table.csv")
## Convert to table
pak.con.cities.tbl = janitor::tabyl(dat = pak.con.cities,
ADM2_EN,
year_1,
event_type_1)
## Class check
class(pak.con.cities.tbl)
## [1] "list"
##Coerce as a dataframe
## Check
pak.con.cities.tbl
## $Battles
## ADM2_EN 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
## Central Karachi 32 20 51 64 83 101 53 31 15 21 8 2
## Lahore 5 1 5 1 3 13 11 8 4 6 2 1
## Peshawar 37 34 74 44 60 24 19 10 10 14 5 17
## Quetta 20 9 35 12 16 15 24 16 23 10 1 12
## 2022
## 6
## 7
## 32
## 14
##
## $`Explosions/Remote violence`
## ADM2_EN 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
## Central Karachi 11 29 47 87 68 19 15 4 2 0 7 4
## Lahore 14 2 4 5 1 5 2 5 1 2 0 1
## Peshawar 64 54 136 111 87 22 34 11 6 6 4 3
## Quetta 32 33 62 57 42 25 26 15 7 13 8 29
## 2022
## 7
## 1
## 18
## 30
##
## $Protests
## ADM2_EN 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
## Central Karachi 337 301 492 628 295 268 266 264 407 383 574 407
## Lahore 196 130 228 196 138 238 268 155 290 314 368 172
## Peshawar 88 90 120 154 126 158 164 128 167 221 301 227
## Quetta 64 81 116 83 34 50 33 27 52 116 83 170
## 2022
## 599
## 250
## 275
## 296
##
## $Riots
## ADM2_EN 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
## Central Karachi 74 41 110 58 29 27 21 22 47 42 16 19
## Lahore 24 13 33 22 15 19 13 11 65 55 11 34
## Peshawar 10 5 15 10 5 20 4 3 13 19 5 6
## Quetta 10 11 13 8 1 4 1 0 5 13 1 2
## 2022
## 44
## 32
## 13
## 14
##
## $`Strategic developments`
## ADM2_EN 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
## Central Karachi 5 3 16 4 3 7 10 4 10 11 3 0
## Lahore 1 0 2 1 0 6 4 3 10 9 1 9
## Peshawar 1 0 12 0 6 4 22 6 5 8 1 0
## Quetta 1 4 5 2 1 9 4 9 7 4 0 1
## 2022
## 2
## 10
## 2
## 3
##
## $`Violence against civilians`
## ADM2_EN 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
## Central Karachi 131 137 316 183 137 76 41 11 26 34 11 6
## Lahore 11 3 7 12 4 14 7 8 22 22 2 17
## Peshawar 15 8 24 21 23 23 12 4 14 18 9 6
## Quetta 20 20 70 17 23 26 6 11 20 13 4 8
## 2022
## 8
## 15
## 10
## 10
## Convert to long format
#cities.long = pak.con.cities.tbl %>%
#pivot_longer(cols = starts_with("20"),
#names_to = "year",
#names_transform = list(year = as.numeric))
# Timing
start = Sys.time()
## Read in the data
pop.2010 = raster("../data/population/landscan_2010/landscan-global-2010.tif")
pop.2011 = raster("../data/population/landscan_2011/landscan-global-2011.tif")
pop.2012 = raster("../data/population/landscan_2012/landscan-global-2012.tif")
pop.2013 = raster("../data/population/landscan_2013/landscan-global-2013.tif")
pop.2014 = raster("../data/population/landscan_2014/landscan-global-2014.tif")
pop.2015 = raster("../data/population/landscan_2015/landscan-global-2015.tif")
pop.2016 = raster("../data/population/landscan_2016/landscan-global-2016.tif")
pop.2017 = raster("../data/population/landscan_2017/landscan-global-2017.tif")
pop.2018 = raster("../data/population/landscan_2018/landscan-global-2018.tif")
pop.2019 = raster("../data/population/landscan_2019/landscan-global-2019.tif")
pop.2020 = raster("../data/population/landscan_2020/landscan-global-2020.tif")
pop.2021 = raster("../data/population/landscan_2021/landscan-global-2021.tif")
## Crop it
pop.2010 = crop(pop.2010, pak.bound)
pop.2011 = crop(pop.2011, pak.bound)
pop.2012 = crop(pop.2012, pak.bound)
pop.2013 = crop(pop.2013, pak.bound)
pop.2014 = crop(pop.2014, pak.bound)
pop.2015 = crop(pop.2015, pak.bound)
pop.2016 = crop(pop.2016, pak.bound)
pop.2017 = crop(pop.2017, pak.bound)
pop.2018 = crop(pop.2018, pak.bound)
pop.2019 = crop(pop.2019, pak.bound)
pop.2020 = crop(pop.2020, pak.bound)
pop.2021 = crop(pop.2021, pak.bound)
# Mask it
pop.2010 = mask(pop.2010, mask = pak.bound)
pop.2011 = mask(pop.2011, mask = pak.bound)
pop.2012 = mask(pop.2012, mask = pak.bound)
pop.2013 = mask(pop.2013, mask = pak.bound)
pop.2014 = mask(pop.2014, mask = pak.bound)
pop.2015 = mask(pop.2015, mask = pak.bound)
pop.2016 = mask(pop.2016, mask = pak.bound)
pop.2017 = mask(pop.2017, mask = pak.bound)
pop.2018 = mask(pop.2018, mask = pak.bound)
pop.2019 = mask(pop.2019, mask = pak.bound)
pop.2020 = mask(pop.2020, mask = pak.bound)
pop.2021 = mask(pop.2021, mask = pak.bound)
## Timing
end = Sys.time()
print(end - start)
## Time difference of 5.321865 secs
Note: The total Pakistan population in 2010 is 184,321,426
##Start time
st1 = Sys.time()
pop.2010.sum = cellStats(pop.2010, sum)
pop.2011.sum = cellStats(pop.2011, sum)
pop.2012.sum = cellStats(pop.2012, sum)
pop.2013.sum = cellStats(pop.2013, sum)
pop.2014.sum = cellStats(pop.2014, sum)
pop.2015.sum = cellStats(pop.2015, sum)
pop.2016.sum = cellStats(pop.2016, sum)
pop.2017.sum = cellStats(pop.2017, sum)
pop.2018.sum = cellStats(pop.2018, sum)
pop.2019.sum = cellStats(pop.2019, sum)
pop.2020.sum = cellStats(pop.2020, sum)
pop.2021.sum = cellStats(pop.2021, sum)
##Coerce into a data frame
pop.df = data.frame(year = seq(from = 2010, to = 2021, by = 1), pop = c(pop.2010.sum, pop.2011.sum, pop.2012.sum, pop.2013.sum, pop.2014.sum, pop.2015.sum, pop.2016.sum, pop.2017.sum, pop.2018.sum, pop.2019.sum, pop.2020.sum, pop.2021.sum))
#End time
et1 = Sys.time()
print(et1 - st1)
## Time difference of 0.09632397 secs
##Scale the population (by millions)
pop.df$pop_scaled = pop.df$pop / 1000000
pop.year.plot = ggplot(data = pop.df, aes(x = year, y = pop_scaled)) +
geom_point(color = "steelblue") +
geom_line(color = "darkgray", linetype = 2 ) +
theme_bw() +
xlab("Year") +
ylab("Population (in millions)")
ggplotly(pop.year.plot)
## Extract Sums by Cities/Districts
#kp.2010 = raster::extract(pop.2010, karachi, fun = sum)
#cp.2010 = raster::extract(pop.2010, pak.bound.city, fun = sum)
## 2010 Extract test
pop.extract.2010 = raster::extract(pop.2010, pak.bound.adm2, fun = sum, na.rm = TRUE)
st2 = Sys.time()
## 2011-2022 extract
pop.extract.2011 = raster::extract(pop.2011, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2012 = raster::extract(pop.2012, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2013 = raster::extract(pop.2013, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2014 = raster::extract(pop.2014, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2015 = raster::extract(pop.2015, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2016 = raster::extract(pop.2016, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2017 = raster::extract(pop.2017, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2018 = raster::extract(pop.2018, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2019 = raster::extract(pop.2019, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2020 = raster::extract(pop.2020, pak.bound.adm2, fun = sum, na.rm = TRUE)
pop.extract.2021 = raster::extract(pop.2021, pak.bound.adm2, fun = sum, na.rm = TRUE)
#pop.extract.2022 = raster::extract(pop.2022, pak.bound.adm2, fun = sum)
et2 = Sys.time()
print(et2-st2)
## Time difference of 3.679308 mins
## Check
#pop.extract.2010
## Combine to the dataframe
test.df$pop_2010 = pop.extract.2010[,1]
test.df$pop_2011 = pop.extract.2011[,1]
test.df$pop_2012 = pop.extract.2012[,1]
test.df$pop_2013 = pop.extract.2013[,1]
test.df$pop_2014 = pop.extract.2014[,1]
test.df$pop_2015 = pop.extract.2015[,1]
test.df$pop_2016 = pop.extract.2016[,1]
test.df$pop_2017 = pop.extract.2017[,1]
test.df$pop_2018 = pop.extract.2018[,1]
test.df$pop_2019 = pop.extract.2019[,1]
test.df$pop_2020 = pop.extract.2020[,1]
test.df$pop_2021 = pop.extract.2021[,1]
## Check
str(test.df)
## 'data.frame': 160 obs. of 13 variables:
## $ districts: chr "Abbottabad" "Astore" "Attock" "Awaran" ...
## $ pop_2010 : num 1204572 106390 1781469 137795 1472283 ...
## $ pop_2011 : num 1225491 108057 1811864 137740 1494783 ...
## $ pop_2012 : num 1244756 109754 1840346 139970 1518306 ...
## $ pop_2013 : num 1264224 111470 1868954 142399 1543506 ...
## $ pop_2014 : num 1282908 108685 1897417 143566 1566479 ...
## $ pop_2015 : num 1302026 109602 1925426 145611 1588666 ...
## $ pop_2016 : num 1454985 75774 2402113 140200 1149979 ...
## $ pop_2017 : num 1578204 82190 2605544 152078 1247402 ...
## $ pop_2018 : num 1600833 83452 2642899 154254 1265268 ...
## $ pop_2019 : num 1761528 91826 2908193 169795 1392270 ...
## $ pop_2020 : num 1798278 93742 2968885 173352 1421316 ...
## $ pop_2021 : num 1834322 95617 3028390 176853 1449807 ...
summary(test.df)
## districts pop_2010 pop_2011 pop_2012
## Length:160 Min. : 21416 Min. : 21752 Min. : 22366
## Class :character 1st Qu.: 191753 1st Qu.: 193270 1st Qu.: 196296
## Mode :character Median : 702557 Median : 711501 Median : 722497
## Mean :1152009 Mean :1170342 Mean :1188758
## 3rd Qu.:1477097 3rd Qu.:1500323 3rd Qu.:1523941
## Max. :8691114 Max. :8839156 Max. :8978041
## pop_2013 pop_2014 pop_2015 pop_2016
## Min. : 22892 Min. : 21980 Min. : 21820 Min. : 19632
## 1st Qu.: 199378 1st Qu.: 204137 1st Qu.: 207485 1st Qu.: 193112
## Median : 733379 Median : 743087 Median : 754125 Median : 671942
## Mean :1207218 Mean :1225697 Mean :1295210 Mean :1180216
## 3rd Qu.:1546072 3rd Qu.:1569107 3rd Qu.:1625974 3rd Qu.:1721908
## Max. :9117028 Max. :9257132 Max. :9394584 Max. :7393665
## pop_2017 pop_2018 pop_2019 pop_2020
## Min. : 21296 Min. : 21627 Min. : 24129 Min. : 24634
## 1st Qu.: 209404 1st Qu.: 212420 1st Qu.: 233672 1st Qu.: 238520
## Median : 728792 Median : 738946 Median : 812971 Median : 829956
## Mean :1280104 Mean :1298453 Mean :1428711 Mean :1458515
## 3rd Qu.:1867736 3rd Qu.:1894514 3rd Qu.:2084667 3rd Qu.:2128166
## Max. :8018491 Max. :8132722 Max. :8947619 Max. :9134190
## pop_2021
## Min. : 25138
## 1st Qu.: 243300
## Median : 846644
## Mean :1487749
## 3rd Qu.:2170823
## Max. :9317108
##Why do we have 8 NAs?
## Calculate the rate of growth
## Assumes similar growth from the year prior
pop.g.rate = ((test.df$pop_2021 - test.df$pop_2020)/test.df$pop_2020)
## Check
pop.g.rate
## [1] 0.02004362 0.02000171 0.02004288 0.02019590 0.02004551 0.02004535
## [7] 0.02004361 0.02004379 0.02004577 0.02004608 0.02003223 0.02000214
## [13] 0.02004770 0.01994984 0.02004534 0.02004391 0.02084859 0.02004524
## [19] 0.01973762 0.02004395 0.02004471 0.02004262 0.02004118 0.02004468
## [25] 0.02004343 0.01981318 0.02008022 0.02004287 0.02010251 0.02004220
## [31] 0.02003399 0.02004443 0.02004690 0.01967759 0.02004372 0.01932208
## [37] 0.02004432 0.02004367 0.02045509 0.02005512 0.02004333 0.02004636
## [43] 0.02004572 0.02004871 0.01944592 0.02416314 0.02004464 0.02005476
## [49] 0.02004271 0.02009230 0.02004139 0.02003443 0.02004461 0.02004382
## [55] 0.01952283 0.02004389 0.02004078 0.02004424 0.02004269 0.02005461
## [61] 0.01998576 0.01994647 0.02004294 0.02004481 0.02000620 0.01980244
## [67] 0.02004478 0.02002490 0.02010170 0.02070711 0.02017311 0.02004444
## [73] 0.02012212 0.02010844 0.02006302 0.02000180 0.01980593 0.01998085
## [79] 0.02017535 0.02002564 0.02004521 0.02004300 0.02005938 0.02005503
## [85] 0.02004483 0.02004394 0.02004435 0.02004751 0.02004421 0.02022275
## [91] 0.02004414 0.02005264 0.02004510 0.02005681 0.02004721 0.02004288
## [97] 0.02005243 0.02004443 0.02017022 0.02004446 0.02005029 0.02000693
## [103] 0.02004456 0.01952932 0.02004437 0.01999420 0.02004293 0.02004515
## [109] 0.01994210 0.02004496 0.02004202 0.02011693 0.02004071 0.02002260
## [115] 0.02004453 0.02001362 0.02004625 0.02006527 0.02003832 0.02004379
## [121] 0.02005342 0.02004073 0.02003792 0.02045953 0.02004461 0.02004744
## [127] 0.02004492 0.02004243 0.02002814 0.02004435 0.02003887 0.02004225
## [133] 0.02023576 0.02004709 0.02003776 0.02004375 0.02002747 0.01989420
## [139] 0.02004959 0.02005576 0.02003963 0.02004332 0.02004414 0.02004476
## [145] 0.02004423 0.02004497 0.02004329 0.01996331 0.02003709 0.02004539
## [151] 0.02004066 0.02004455 0.02004515 0.02004271 0.02004698 0.02004448
## [157] 0.02004651 0.02002828 0.02005673 0.02004618
pop_2022 = round(test.df$pop_2021 + (test.df$pop_2021*pop.g.rate))
## Check
pop_2022
## [1] 1871088 97530 3089088 180425 1478869 686988 3973547 5355326 1103104
## [10] 1229321 177529 456116 3377099 736245 764122 4393914 433926 2887183
## [19] 687690 1953077 1560319 293935 134933 2259425 1206428 42308 128411
## [28] 2762971 129930 187880 6025262 7257167 100277 95004 1509208 264566
## [37] 4571565 3515472 86493 274459 1301952 676003 1590037 95701 58521
## [46] 50355 1475972 2989581 746931 301006 1065332 102154 3558035 1905280
## [55] 255081 174939 222003 865594 964605 825836 3361492 587725 2087938
## [64] 3356856 231576 45271 2331930 564054 1274559 311942 384766 1606278
## [73] 47890 44143 68355 46346 2881734 879114 508067 9503689 1351095
## [82] 1047340 558973 76008 2919111 2310637 266970 1584243 835592 4424058
## [91] 2127892 2331934 2462961 359362 518348 2855115 1009818 1406999 861742
## [100] 5880321 94344 681495 4947332 33744 1292543 1778747 238955 1377571
## [109] 150914 1042363 1853737 245819 2968128 333346 2015677 248961 4891965
## [118] 733990 758616 2199305 5145362 1877007 6023607 25652 2742859 1807737
## [127] 4392246 1322618 162707 609468 3012783 76834 47683 737121 3630705
## [136] 171721 131801 106768 5518714 780395 432140 539521 1196991 1618244
## [145] 1659450 677969 548918 28922 631179 2369547 1068588 2472318 69141
## [154] 1215035 721502 3307854 118134 5590850 319157 138794
## Add to dataframe
test.df$pop_2022 = pop_2022
## Find the NAs
#test.df.nas = test.df %>%
#filter_all(any_vars(. %in% c(NA)))
## Check
#test.df.nas
## Set up centroids coordinates for each district
dist.ctrd.crds = st_coordinates(st_centroid(pak.bound.adm2))
## Check
##dist.ctrd.crds
## Join to the test dataframe
test.df$longitude = dist.ctrd.crds[,1]
test.df$latitude = dist.ctrd.crds[,2]
## Check
str(test.df)
## 'data.frame': 160 obs. of 16 variables:
## $ districts: chr "Abbottabad" "Astore" "Attock" "Awaran" ...
## $ pop_2010 : num 1204572 106390 1781469 137795 1472283 ...
## $ pop_2011 : num 1225491 108057 1811864 137740 1494783 ...
## $ pop_2012 : num 1244756 109754 1840346 139970 1518306 ...
## $ pop_2013 : num 1264224 111470 1868954 142399 1543506 ...
## $ pop_2014 : num 1282908 108685 1897417 143566 1566479 ...
## $ pop_2015 : num 1302026 109602 1925426 145611 1588666 ...
## $ pop_2016 : num 1454985 75774 2402113 140200 1149979 ...
## $ pop_2017 : num 1578204 82190 2605544 152078 1247402 ...
## $ pop_2018 : num 1600833 83452 2642899 154254 1265268 ...
## $ pop_2019 : num 1761528 91826 2908193 169795 1392270 ...
## $ pop_2020 : num 1798278 93742 2968885 173352 1421316 ...
## $ pop_2021 : num 1834322 95617 3028390 176853 1449807 ...
## $ pop_2022 : num 1871088 97530 3089088 180425 1478869 ...
## $ longitude: num 73.3 74.9 72.3 65.4 68.8 ...
## $ latitude : num 34.1 35.1 33.5 26.2 24.7 ...
## Create a vector of names
names = test.df$districts
## Create a vector of longitude
long = test.df$longitude
## Create a vector of latitude
lat = test.df$latitude
## Check
## No issues
## SPEI Script for four of Pakistan's cities
##Load get spei
library(getSpei)
##Run script
location_id = c("Karachi", "Lahore", "Peshawar", "Quetta")
longitude = c(67.0011, 74.3587, 71.5249, 66.9750)
latitude = c(24.8607, 31.5204, 34.0151, 30.1798)
locations_df = data.frame(location_id, longitude, latitude)
# specify arguments and run function:
d1 <- spec_spei(spei_files = "../data/spei/spei12", start_y = 2010, end_y = 2021,
locations = locations_df)
## Loading required package: chron
##
## Attaching package: 'chron'
## The following objects are masked from 'package:lubridate':
##
## days, hours, minutes, seconds, years
## The following objects are masked from 'package:raster':
##
## origin, origin<-
## Check
head(d1)
location_id | longitude_loc | latitude_loc | lon | lat | year | month | ../data/spei/spei12 |
---|---|---|---|---|---|---|---|
Karachi | 67 | 24.9 | 67.25 | 24.75 | 2010 | 1 | 0.396 |
Quetta | 67 | 30.2 | 66.75 | 30.25 | 2010 | 1 | -0.798 |
Lahore | 74.4 | 31.5 | 74.25 | 31.75 | 2010 | 1 | -1.67 |
Peshawar | 71.5 | 34 | 71.75 | 34.25 | 2010 | 1 | -0.12 |
Karachi | 67 | 24.9 | 67.25 | 24.75 | 2010 | 2 | 0.501 |
Quetta | 67 | 30.2 | 66.75 | 30.25 | 2010 | 2 | -0.844 |
##Change spei column name
colnames(d1)[which(names(d1) == "../data/spei/spei12")] = "spei"
# Check
colnames(d1)
## [1] "location_id" "longitude_loc" "latitude_loc" "lon"
## [5] "lat" "year" "month" "spei"
## Aggregate SPEI by location and year
spei = aggregate(spei ~ location_id + year,
data = d1,
FUN = mean)
## Check
spei
location_id | year | spei |
---|---|---|
Karachi | 2010 | 0.575 |
Lahore | 2010 | -0.603 |
Peshawar | 2010 | 0.591 |
Quetta | 2010 | -1.27 |
Karachi | 2011 | 0.186 |
Lahore | 2011 | 0.931 |
Peshawar | 2011 | 1.02 |
Quetta | 2011 | -0.083 |
Karachi | 2012 | -0.911 |
Lahore | 2012 | 0.365 |
Peshawar | 2012 | 0.265 |
Quetta | 2012 | -0.677 |
Karachi | 2013 | -0.277 |
Lahore | 2013 | 0.831 |
Peshawar | 2013 | 1.33 |
Quetta | 2013 | -0.0417 |
Karachi | 2014 | -1.06 |
Lahore | 2014 | 0.739 |
Peshawar | 2014 | 0.524 |
Quetta | 2014 | -1.25 |
Karachi | 2015 | -1.41 |
Lahore | 2015 | 1.41 |
Peshawar | 2015 | 1.85 |
Quetta | 2015 | -1.02 |
Karachi | 2016 | -1.35 |
Lahore | 2016 | 1.21 |
Peshawar | 2016 | 1.52 |
Quetta | 2016 | -1.14 |
Karachi | 2017 | -1.09 |
Lahore | 2017 | 0.611 |
Peshawar | 2017 | 0.259 |
Quetta | 2017 | -1.5 |
Karachi | 2018 | -1.65 |
Lahore | 2018 | -0.391 |
Peshawar | 2018 | -1.09 |
Quetta | 2018 | -1.91 |
Karachi | 2019 | -0.881 |
Lahore | 2019 | 0.627 |
Peshawar | 2019 | 0.497 |
Quetta | 2019 | -0.0126 |
Karachi | 2020 | 0.997 |
Lahore | 2020 | 1.46 |
Peshawar | 2020 | 1.4 |
Quetta | 2020 | 1.02 |
Karachi | 2021 | 0.318 |
Lahore | 2021 | 0.622 |
Peshawar | 2021 | -0.599 |
Quetta | 2021 | -1.13 |
##save file
##write.csv(spei, file = "../data/spei/city_spei_20230401.csv")
## SPEI Extraction for the centroid of each district
##Run script
location_id2 = names
longitude2 = long
latitude2 = lat
locations_df2 = data.frame(location_id2, longitude2, latitude2)
# specify arguments and run function:
d2 <- spec_spei(spei_files = "../data/spei/spei12", start_y = 2010, end_y = 2021,
locations = locations_df2)
## Check
head(d2)
location_id | longitude_loc | latitude_loc | lon | lat | year | month | ../data/spei/spei12 |
---|---|---|---|---|---|---|---|
Sujawal | 68.2 | 24.2 | 68.25 | 24.25 | 2010 | 1 | 0.537 |
South Karachi | 66.9 | 24.9 | 66.75 | 24.75 | 2010 | 1 | -0.0194 |
Central Karachi | 67.1 | 24.9 | 67.25 | 24.75 | 2010 | 1 | 0.396 |
East Karachi | 67.1 | 24.9 | 67.25 | 24.75 | 2010 | 1 | 0.396 |
Korangi Karachi | 67.1 | 24.8 | 67.25 | 24.75 | 2010 | 1 | 0.396 |
West Karachi | 67 | 25 | 67.25 | 24.75 | 2010 | 1 | 0.396 |
##Change spei column name
colnames(d2)[which(names(d2) == "../data/spei/spei12")] = "spei"
# Check
colnames(d2)
## [1] "location_id" "longitude_loc" "latitude_loc" "lon"
## [5] "lat" "year" "month" "spei"
## Aggregate SPEI by location and year
spei2 = aggregate(spei ~ location_id + year,
data = d2,
FUN = mean)
## Check
spei2
location_id | year | spei |
---|---|---|
Abbottabad | 2010 | 0.7 |
Astore | 2010 | 0.913 |
Attock | 2010 | 0.42 |
Awaran | 2010 | -1.43 |
Badin | 2010 | 0.579 |
Bagh | 2010 | 0.273 |
Bahawalnagar | 2010 | -0.619 |
Bahawalpur | 2010 | -0.317 |
Bajaur | 2010 | 0.807 |
Bannu | 2010 | 0.558 |
Barkhan | 2010 | 0.125 |
Batagram | 2010 | 0.819 |
Bhakkar | 2010 | 0.3 |
Bhimber | 2010 | -0.15 |
Buner | 2010 | 0.744 |
Central Karachi | 2010 | 0.575 |
Chagai | 2010 | -1.33 |
Chakwal | 2010 | 0.245 |
Chaman | 2010 | -1.09 |
Charsadda | 2010 | 0.591 |
Chiniot | 2010 | 0.208 |
Chitral Lower | 2010 | 1.17 |
Chitral Upper | 2010 | 1.17 |
D. I. Khan | 2010 | 0.325 |
Dadu | 2010 | -0.774 |
Darel | 2010 | 1.09 |
Dera Bugti | 2010 | -0.528 |
Dera Ghazi Khan | 2010 | 0.249 |
Diamir | 2010 | 0.826 |
Duki | 2010 | -0.15 |
East Karachi | 2010 | 0.575 |
Faisalabad | 2010 | -0.0357 |
Ghanche | 2010 | 1.06 |
Ghizer | 2010 | 1.12 |
Ghotki | 2010 | -0.397 |
Gilgit | 2010 | 1.01 |
Gujranwala | 2010 | -0.585 |
Gujrat | 2010 | -0.337 |
Gupis-Yasin | 2010 | 1.15 |
Gwadar | 2010 | -1.41 |
Hafizabad | 2010 | -0.25 |
Hangu | 2010 | 0.519 |
Haripur | 2010 | 0.744 |
Harnai | 2010 | -0.954 |
Haveli | 2010 | 0.18 |
Hunza | 2010 | 1.4 |
Hyderabad | 2010 | 0.271 |
Islamabad | 2010 | 0.351 |
Jacobabad | 2010 | -1.11 |
Jaffarabad | 2010 | -1.11 |
Jamshoro | 2010 | -0.0632 |
Jhal Magsi | 2010 | -1.15 |
Jhang | 2010 | 0.147 |
Jhelum | 2010 | 0.0728 |
Jhelum Valley | 2010 | 0.484 |
Kachhi | 2010 | -1.27 |
Kalat | 2010 | -1.19 |
Kambar Shahdad Kot | 2010 | -1.11 |
Karak | 2010 | 0.592 |
Kashmore | 2010 | -0.855 |
Kasur | 2010 | -0.449 |
Kech | 2010 | -1.46 |
Khairpur | 2010 | -0.0412 |
Khanewal | 2010 | -0.227 |
Kharan | 2010 | -1.52 |
Kharmang | 2010 | 0.764 |
Khushab | 2010 | 0.229 |
Khuzdar | 2010 | -0.917 |
Khyber | 2010 | 0.406 |
Killa Abdullah | 2010 | -1.09 |
Killa Saifullah | 2010 | -0.917 |
Kohat | 2010 | 0.553 |
Kohistan Lower | 2010 | 0.986 |
Kohistan Upper | 2010 | 0.948 |
Kohlu | 2010 | -0.497 |
Kolai Palas Kohistan | 2010 | 0.948 |
Korangi Karachi | 2010 | 0.575 |
Kotli | 2010 | 0.0429 |
Kurram | 2010 | 0.614 |
Lahore | 2010 | -0.449 |
Lakki Marwat | 2010 | 0.558 |
Larkana | 2010 | -0.99 |
Lasbela | 2010 | -0.634 |
Lehri | 2010 | -1.03 |
Leiah | 2010 | 0.209 |
Lodhran | 2010 | -0.157 |
Loralai | 2010 | -0.558 |
Lower Dir | 2010 | 0.807 |
Malakand | 2010 | 0.807 |
Malir Karachi | 2010 | 0.0665 |
Mandi Bahauddin | 2010 | 0.0433 |
Mansehra | 2010 | 0.819 |
Mardan | 2010 | 0.665 |
Mastung | 2010 | -1.26 |
Matiari | 2010 | 0.0328 |
Mianwali | 2010 | 0.502 |
Mirpur | 2010 | 0.0429 |
Mirpur Khas | 2010 | 0.501 |
Mohmand | 2010 | 0.394 |
Multan | 2010 | 0.0568 |
Musakhel | 2010 | 0.202 |
Muzaffarabad | 2010 | 0.484 |
Muzaffargarh | 2010 | 0.202 |
Nagar | 2010 | 1.15 |
Nankana Sahib | 2010 | -0.175 |
Narowal | 2010 | -0.885 |
Nasirabad | 2010 | -1.09 |
Naushahro Feroze | 2010 | -0.386 |
Neelum | 2010 | 0.659 |
North Waziristan | 2010 | 0.501 |
Nowshera | 2010 | 0.622 |
Nushki | 2010 | -1.51 |
Okara | 2010 | -0.312 |
Orakzai | 2010 | 0.566 |
Pakpattan | 2010 | -0.411 |
Panjgur | 2010 | -1.41 |
Peshawar | 2010 | 0.622 |
Pishin | 2010 | -1.09 |
Poonch | 2010 | 0.273 |
Quetta | 2010 | -1.27 |
Rahim Yar Khan | 2010 | -0.609 |
Rajanpur | 2010 | -0.0938 |
Rawalpindi | 2010 | 0.174 |
Rondu | 2010 | 1.02 |
Sahiwal | 2010 | -0.12 |
Sanghar | 2010 | 0.522 |
Sargodha | 2010 | 0.239 |
Shaheed Benazir Abad | 2010 | -0.249 |
Shaheed Sikandarabad | 2010 | -1.3 |
Shangla | 2010 | 0.836 |
Sheikhupura | 2010 | -0.603 |
Sherani | 2010 | 0.327 |
Shigar | 2010 | 1.06 |
Shikarpur | 2010 | -0.849 |
Sialkot | 2010 | -0.885 |
Sibi | 2010 | -1.27 |
Skardu | 2010 | 0.882 |
Sohbatpur | 2010 | -0.707 |
South Karachi | 2010 | 0.0994 |
South Waziristan | 2010 | 0.332 |
Sudhnoti | 2010 | 0.273 |
Sujawal | 2010 | 0.813 |
Sukkur | 2010 | -0.491 |
Swabi | 2010 | 0.665 |
Swat | 2010 | 1.04 |
Tando Allahyar | 2010 | 0.448 |
Tando Muhammad Khan | 2010 | 0.448 |
Tangir | 2010 | 1.1 |
Tank | 2010 | 0.423 |
Tharparkar | 2010 | 0.601 |
Thatta | 2010 | 0.567 |
Toba Tek Singh | 2010 | -0.12 |
Tor Ghar | 2010 | 0.836 |
Umer Kot | 2010 | 0.661 |
Upper Dir | 2010 | 1.04 |
Vehari | 2010 | -0.227 |
Washuk | 2010 | -1.3 |
West Karachi | 2010 | 0.575 |
Zhob | 2010 | -0.183 |
Ziarat | 2010 | -0.954 |
Abbottabad | 2011 | 1.33 |
Astore | 2011 | 0.739 |
Attock | 2011 | 1.23 |
Awaran | 2011 | -0.641 |
Badin | 2011 | 0.578 |
Bagh | 2011 | 1.23 |
Bahawalnagar | 2011 | 1.12 |
Bahawalpur | 2011 | 1.24 |
Bajaur | 2011 | 1.09 |
Bannu | 2011 | 1.11 |
Barkhan | 2011 | 1.36 |
Batagram | 2011 | 1.22 |
Bhakkar | 2011 | 1.08 |
Bhimber | 2011 | 1.06 |
Buner | 2011 | 1.39 |
Central Karachi | 2011 | 0.186 |
Chagai | 2011 | -0.321 |
Chakwal | 2011 | 1.27 |
Chaman | 2011 | -0.0737 |
Charsadda | 2011 | 1.02 |
Chiniot | 2011 | 1.2 |
Chitral Lower | 2011 | 0.771 |
Chitral Upper | 2011 | 0.502 |
D. I. Khan | 2011 | 1 |
Dadu | 2011 | -0.0131 |
Darel | 2011 | 0.737 |
Dera Bugti | 2011 | 0.75 |
Dera Ghazi Khan | 2011 | 1.59 |
Diamir | 2011 | 0.742 |
Duki | 2011 | 1.04 |
East Karachi | 2011 | 0.186 |
Faisalabad | 2011 | 1.25 |
Ghanche | 2011 | 0.65 |
Ghizer | 2011 | 0.517 |
Ghotki | 2011 | 0.712 |
Gilgit | 2011 | 0.642 |
Gujranwala | 2011 | 0.882 |
Gujrat | 2011 | 0.977 |
Gupis-Yasin | 2011 | 0.566 |
Gwadar | 2011 | -1.23 |
Hafizabad | 2011 | 1.07 |
Hangu | 2011 | 0.855 |
Haripur | 2011 | 1.39 |
Harnai | 2011 | 0.136 |
Haveli | 2011 | 1.04 |
Hunza | 2011 | 0.63 |
Hyderabad | 2011 | 0.359 |
Islamabad | 2011 | 1.26 |
Jacobabad | 2011 | 0.077 |
Jaffarabad | 2011 | 0.077 |
Jamshoro | 2011 | 0.244 |
Jhal Magsi | 2011 | 0.0745 |
Jhang | 2011 | 1.16 |
Jhelum | 2011 | 1.23 |
Jhelum Valley | 2011 | 1.1 |
Kachhi | 2011 | 0.035 |
Kalat | 2011 | 0.276 |
Kambar Shahdad Kot | 2011 | -0.0529 |
Karak | 2011 | 1.01 |
Kashmore | 2011 | 0.373 |
Kasur | 2011 | 1.22 |
Kech | 2011 | -1.27 |
Khairpur | 2011 | 0.689 |
Khanewal | 2011 | 1.19 |
Kharan | 2011 | -0.167 |
Kharmang | 2011 | 0.843 |
Khushab | 2011 | 1.17 |
Khuzdar | 2011 | 0.165 |
Khyber | 2011 | 0.7 |
Killa Abdullah | 2011 | -0.0737 |
Killa Saifullah | 2011 | 0.176 |
Kohat | 2011 | 1.14 |
Kohistan Lower | 2011 | 1.03 |
Kohistan Upper | 2011 | 1.05 |
Kohlu | 2011 | 0.703 |
Kolai Palas Kohistan | 2011 | 1.05 |
Korangi Karachi | 2011 | 0.186 |
Kotli | 2011 | 1.22 |
Kurram | 2011 | 0.756 |
Lahore | 2011 | 1.22 |
Lakki Marwat | 2011 | 1.11 |
Larkana | 2011 | -0.116 |
Lasbela | 2011 | -0.142 |
Lehri | 2011 | 0.29 |
Leiah | 2011 | 1.14 |
Lodhran | 2011 | 1.34 |
Loralai | 2011 | 0.581 |
Lower Dir | 2011 | 1.09 |
Malakand | 2011 | 1.09 |
Malir Karachi | 2011 | -0.0192 |
Mandi Bahauddin | 2011 | 1.18 |
Mansehra | 2011 | 1.22 |
Mardan | 2011 | 1.35 |
Mastung | 2011 | -0.0209 |
Matiari | 2011 | 0.254 |
Mianwali | 2011 | 1.32 |
Mirpur | 2011 | 1.22 |
Mirpur Khas | 2011 | 0.595 |
Mohmand | 2011 | 0.543 |
Multan | 2011 | 1.51 |
Musakhel | 2011 | 1.29 |
Muzaffarabad | 2011 | 1.1 |
Muzaffargarh | 2011 | 1.33 |
Nagar | 2011 | 0.455 |
Nankana Sahib | 2011 | 1.29 |
Narowal | 2011 | 0.759 |
Nasirabad | 2011 | 0.268 |
Naushahro Feroze | 2011 | 0.203 |
Neelum | 2011 | 0.817 |
North Waziristan | 2011 | 0.648 |
Nowshera | 2011 | 1.13 |
Nushki | 2011 | -0.13 |
Okara | 2011 | 1.39 |
Orakzai | 2011 | 0.742 |
Pakpattan | 2011 | 1.15 |
Panjgur | 2011 | -0.416 |
Peshawar | 2011 | 1.13 |
Pishin | 2011 | -0.0747 |
Poonch | 2011 | 1.23 |
Quetta | 2011 | -0.083 |
Rahim Yar Khan | 2011 | 0.878 |
Rajanpur | 2011 | 1.32 |
Rawalpindi | 2011 | 1.18 |
Rondu | 2011 | 0.62 |
Sahiwal | 2011 | 1.08 |
Sanghar | 2011 | 0.691 |
Sargodha | 2011 | 1.23 |
Shaheed Benazir Abad | 2011 | 0.264 |
Shaheed Sikandarabad | 2011 | 0.208 |
Shangla | 2011 | 1.37 |
Sheikhupura | 2011 | 0.931 |
Sherani | 2011 | 0.91 |
Shigar | 2011 | 0.621 |
Shikarpur | 2011 | -0.00394 |
Sialkot | 2011 | 0.759 |
Sibi | 2011 | 0.0831 |
Skardu | 2011 | 0.647 |
Sohbatpur | 2011 | 0.517 |
South Karachi | 2011 | -0.12 |
South Waziristan | 2011 | 0.859 |
Sudhnoti | 2011 | 1.23 |
Sujawal | 2011 | 0.535 |
Sukkur | 2011 | 0.425 |
Swabi | 2011 | 1.35 |
Swat | 2011 | 0.986 |
Tando Allahyar | 2011 | 0.515 |
Tando Muhammad Khan | 2011 | 0.515 |
Tangir | 2011 | 0.688 |
Tank | 2011 | 1.05 |
Tharparkar | 2011 | 0.861 |
Thatta | 2011 | 0.306 |
Toba Tek Singh | 2011 | 1.08 |
Tor Ghar | 2011 | 1.37 |
Umer Kot | 2011 | 0.709 |
Upper Dir | 2011 | 0.986 |
Vehari | 2011 | 1.19 |
Washuk | 2011 | -0.273 |
West Karachi | 2011 | 0.186 |
Zhob | 2011 | 0.926 |
Ziarat | 2011 | 0.136 |
Abbottabad | 2012 | -0.197 |
Astore | 2012 | -0.533 |
Attock | 2012 | 0.628 |
Awaran | 2012 | -0.887 |
Badin | 2012 | -0.548 |
Bagh | 2012 | -0.174 |
Bahawalnagar | 2012 | 0.0758 |
Bahawalpur | 2012 | -0.0335 |
Bajaur | 2012 | 0.369 |
Bannu | 2012 | 0.769 |
Barkhan | 2012 | 0.136 |
Batagram | 2012 | -0.235 |
Bhakkar | 2012 | 0.592 |
Bhimber | 2012 | 0.0299 |
Buner | 2012 | -0.0677 |
Central Karachi | 2012 | -0.911 |
Chagai | 2012 | -0.0356 |
Chakwal | 2012 | 0.612 |
Chaman | 2012 | -0.508 |
Charsadda | 2012 | 0.265 |
Chiniot | 2012 | 0.404 |
Chitral Lower | 2012 | 0.131 |
Chitral Upper | 2012 | -0.414 |
D. I. Khan | 2012 | 0.491 |
Dadu | 2012 | -0.948 |
Darel | 2012 | -0.511 |
Dera Bugti | 2012 | -0.552 |
Dera Ghazi Khan | 2012 | 0.0219 |
Diamir | 2012 | -0.549 |
Duki | 2012 | 0.0468 |
East Karachi | 2012 | -0.911 |
Faisalabad | 2012 | 0.401 |
Ghanche | 2012 | 0.145 |
Ghizer | 2012 | -0.47 |
Ghotki | 2012 | -0.823 |
Gilgit | 2012 | -0.515 |
Gujranwala | 2012 | 0.409 |
Gujrat | 2012 | 0.446 |
Gupis-Yasin | 2012 | -0.482 |
Gwadar | 2012 | -0.793 |
Hafizabad | 2012 | 0.436 |
Hangu | 2012 | 0.708 |
Haripur | 2012 | -0.0677 |
Harnai | 2012 | -0.666 |
Haveli | 2012 | -0.159 |
Hunza | 2012 | 0.627 |
Hyderabad | 2012 | -0.781 |
Islamabad | 2012 | -0.292 |
Jacobabad | 2012 | -0.872 |
Jaffarabad | 2012 | -0.872 |
Jamshoro | 2012 | -0.99 |
Jhal Magsi | 2012 | -0.883 |
Jhang | 2012 | 0.323 |
Jhelum | 2012 | 0.589 |
Jhelum Valley | 2012 | -0.364 |
Kachhi | 2012 | -0.803 |
Kalat | 2012 | -0.748 |
Kambar Shahdad Kot | 2012 | -0.957 |
Karak | 2012 | 0.581 |
Kashmore | 2012 | -0.787 |
Kasur | 2012 | 0.392 |
Kech | 2012 | -0.771 |
Khairpur | 2012 | -0.708 |
Khanewal | 2012 | 0.00972 |
Kharan | 2012 | -0.609 |
Kharmang | 2012 | -0.543 |
Khushab | 2012 | 0.564 |
Khuzdar | 2012 | -0.943 |
Khyber | 2012 | 0.556 |
Killa Abdullah | 2012 | -0.508 |
Killa Saifullah | 2012 | -0.505 |
Kohat | 2012 | 0.628 |
Kohistan Lower | 2012 | -0.117 |
Kohistan Upper | 2012 | -0.256 |
Kohlu | 2012 | -0.232 |
Kolai Palas Kohistan | 2012 | -0.256 |
Korangi Karachi | 2012 | -0.911 |
Kotli | 2012 | -0.0121 |
Kurram | 2012 | 0.692 |
Lahore | 2012 | 0.392 |
Lakki Marwat | 2012 | 0.769 |
Larkana | 2012 | -1.07 |
Lasbela | 2012 | -0.965 |
Lehri | 2012 | -0.653 |
Leiah | 2012 | 0.348 |
Lodhran | 2012 | 0.0792 |
Loralai | 2012 | -0.309 |
Lower Dir | 2012 | 0.369 |
Malakand | 2012 | 0.369 |
Malir Karachi | 2012 | -1.05 |
Mandi Bahauddin | 2012 | 0.531 |
Mansehra | 2012 | -0.235 |
Mardan | 2012 | 0.158 |
Mastung | 2012 | -0.772 |
Matiari | 2012 | -0.893 |
Mianwali | 2012 | 0.752 |
Mirpur | 2012 | -0.0121 |
Mirpur Khas | 2012 | -0.628 |
Mohmand | 2012 | 0.494 |
Multan | 2012 | 0.0358 |
Musakhel | 2012 | -0.0493 |
Muzaffarabad | 2012 | -0.364 |
Muzaffargarh | 2012 | 0.153 |
Nagar | 2012 | 0.0806 |
Nankana Sahib | 2012 | 0.372 |
Narowal | 2012 | 0.406 |
Nasirabad | 2012 | -0.675 |
Naushahro Feroze | 2012 | -0.91 |
Neelum | 2012 | -0.411 |
North Waziristan | 2012 | 0.676 |
Nowshera | 2012 | 0.508 |
Nushki | 2012 | -0.526 |
Okara | 2012 | 0.622 |
Orakzai | 2012 | 0.585 |
Pakpattan | 2012 | 0.253 |
Panjgur | 2012 | -0.545 |
Peshawar | 2012 | 0.508 |
Pishin | 2012 | -0.571 |
Poonch | 2012 | -0.174 |
Quetta | 2012 | -0.677 |
Rahim Yar Khan | 2012 | -0.36 |
Rajanpur | 2012 | -0.383 |
Rawalpindi | 2012 | 0.0708 |
Rondu | 2012 | -0.0852 |
Sahiwal | 2012 | 0.183 |
Sanghar | 2012 | -0.706 |
Sargodha | 2012 | 0.532 |
Shaheed Benazir Abad | 2012 | -0.827 |
Shaheed Sikandarabad | 2012 | -0.702 |
Shangla | 2012 | -0.0806 |
Sheikhupura | 2012 | 0.365 |
Sherani | 2012 | 0.573 |
Shigar | 2012 | 0.0667 |
Shikarpur | 2012 | -1.05 |
Sialkot | 2012 | 0.406 |
Sibi | 2012 | -0.639 |
Skardu | 2012 | -0.242 |
Sohbatpur | 2012 | -0.618 |
South Karachi | 2012 | -1.11 |
South Waziristan | 2012 | 0.801 |
Sudhnoti | 2012 | -0.174 |
Sujawal | 2012 | -0.534 |
Sukkur | 2012 | -0.924 |
Swabi | 2012 | 0.158 |
Swat | 2012 | -0.0257 |
Tando Allahyar | 2012 | -0.686 |
Tando Muhammad Khan | 2012 | -0.686 |
Tangir | 2012 | -0.36 |
Tank | 2012 | 0.793 |
Tharparkar | 2012 | -0.669 |
Thatta | 2012 | -0.811 |
Toba Tek Singh | 2012 | 0.183 |
Tor Ghar | 2012 | -0.0806 |
Umer Kot | 2012 | -0.689 |
Upper Dir | 2012 | -0.0257 |
Vehari | 2012 | 0.00972 |
Washuk | 2012 | -0.147 |
West Karachi | 2012 | -0.911 |
Zhob | 2012 | -0.013 |
Ziarat | 2012 | -0.666 |
Abbottabad | 2013 | 0.965 |
Astore | 2013 | 0.186 |
Attock | 2013 | 1.31 |
Awaran | 2013 | -0.683 |
Badin | 2013 | 0.168 |
Bagh | 2013 | 0.802 |
Bahawalnagar | 2013 | 0.769 |
Bahawalpur | 2013 | 0.472 |
Bajaur | 2013 | 1.48 |
Bannu | 2013 | 1.15 |
Barkhan | 2013 | 0.283 |
Batagram | 2013 | 0.852 |
Bhakkar | 2013 | 0.901 |
Bhimber | 2013 | 0.767 |
Buner | 2013 | 0.996 |
Central Karachi | 2013 | -0.277 |
Chagai | 2013 | -0.107 |
Chakwal | 2013 | 1.14 |
Chaman | 2013 | -0.0901 |
Charsadda | 2013 | 1.33 |
Chiniot | 2013 | 0.854 |
Chitral Lower | 2013 | 1.23 |
Chitral Upper | 2013 | 0.528 |
D. I. Khan | 2013 | 0.906 |
Dadu | 2013 | -0.701 |
Darel | 2013 | 0.295 |
Dera Bugti | 2013 | -0.628 |
Dera Ghazi Khan | 2013 | 0.533 |
Diamir | 2013 | 0.273 |
Duki | 2013 | 0.205 |
East Karachi | 2013 | -0.277 |
Faisalabad | 2013 | 0.748 |
Ghanche | 2013 | -0.0776 |
Ghizer | 2013 | 0.118 |
Ghotki | 2013 | -0.676 |
Gilgit | 2013 | 0.0758 |
Gujranwala | 2013 | 0.909 |
Gujrat | 2013 | 1.01 |
Gupis-Yasin | 2013 | 0.333 |
Gwadar | 2013 | -0.368 |
Hafizabad | 2013 | 0.916 |
Hangu | 2013 | 1.23 |
Haripur | 2013 | 0.996 |
Harnai | 2013 | -0.111 |
Haveli | 2013 | 0.737 |
Hunza | 2013 | 0.0402 |
Hyderabad | 2013 | 0.006 |
Islamabad | 2013 | 0.873 |
Jacobabad | 2013 | -1.15 |
Jaffarabad | 2013 | -1.15 |
Jamshoro | 2013 | -0.224 |
Jhal Magsi | 2013 | -0.9 |
Jhang | 2013 | 0.778 |
Jhelum | 2013 | 1.12 |
Jhelum Valley | 2013 | 0.74 |
Kachhi | 2013 | -0.797 |
Kalat | 2013 | -0.164 |
Kambar Shahdad Kot | 2013 | -1.03 |
Karak | 2013 | 1.24 |
Kashmore | 2013 | -0.789 |
Kasur | 2013 | 0.744 |
Kech | 2013 | -0.683 |
Khairpur | 2013 | -0.416 |
Khanewal | 2013 | 0.773 |
Kharan | 2013 | -0.429 |
Kharmang | 2013 | 0.0921 |
Khushab | 2013 | 1 |
Khuzdar | 2013 | -0.69 |
Khyber | 2013 | 1.33 |
Killa Abdullah | 2013 | -0.0901 |
Killa Saifullah | 2013 | -0.0377 |
Kohat | 2013 | 1.24 |
Kohistan Lower | 2013 | 0.969 |
Kohistan Upper | 2013 | 0.748 |
Kohlu | 2013 | -0.0138 |
Kolai Palas Kohistan | 2013 | 0.748 |
Korangi Karachi | 2013 | -0.277 |
Kotli | 2013 | 0.775 |
Kurram | 2013 | 1.54 |
Lahore | 2013 | 0.744 |
Lakki Marwat | 2013 | 1.15 |
Larkana | 2013 | -1.07 |
Lasbela | 2013 | -0.507 |
Lehri | 2013 | -0.559 |
Leiah | 2013 | 0.767 |
Lodhran | 2013 | 0.726 |
Loralai | 2013 | 0.00875 |
Lower Dir | 2013 | 1.48 |
Malakand | 2013 | 1.48 |
Malir Karachi | 2013 | -0.293 |
Mandi Bahauddin | 2013 | 0.993 |
Mansehra | 2013 | 0.852 |
Mardan | 2013 | 1.18 |
Mastung | 2013 | -0.132 |
Matiari | 2013 | -0.226 |
Mianwali | 2013 | 1.13 |
Mirpur | 2013 | 0.775 |
Mirpur Khas | 2013 | 0.0471 |
Mohmand | 2013 | 1.42 |
Multan | 2013 | 0.413 |
Musakhel | 2013 | 0.629 |
Muzaffarabad | 2013 | 0.74 |
Muzaffargarh | 2013 | 0.608 |
Nagar | 2013 | -0.00883 |
Nankana Sahib | 2013 | 0.757 |
Narowal | 2013 | 0.902 |
Nasirabad | 2013 | -0.898 |
Naushahro Feroze | 2013 | -0.704 |
Neelum | 2013 | 0.491 |
North Waziristan | 2013 | 1.44 |
Nowshera | 2013 | 1.33 |
Nushki | 2013 | -0.346 |
Okara | 2013 | 0.811 |
Orakzai | 2013 | 1.46 |
Pakpattan | 2013 | 0.742 |
Panjgur | 2013 | -0.331 |
Peshawar | 2013 | 1.33 |
Pishin | 2013 | -0.0988 |
Poonch | 2013 | 0.802 |
Quetta | 2013 | -0.0417 |
Rahim Yar Khan | 2013 | -0.22 |
Rajanpur | 2013 | -0.223 |
Rawalpindi | 2013 | 1 |
Rondu | 2013 | -0.113 |
Sahiwal | 2013 | 0.819 |
Sanghar | 2013 | -0.00941 |
Sargodha | 2013 | 0.932 |
Shaheed Benazir Abad | 2013 | -0.528 |
Shaheed Sikandarabad | 2013 | -0.572 |
Shangla | 2013 | 1.07 |
Sheikhupura | 2013 | 0.831 |
Sherani | 2013 | 1.25 |
Shigar | 2013 | -0.163 |
Shikarpur | 2013 | -1.13 |
Sialkot | 2013 | 0.902 |
Sibi | 2013 | -0.429 |
Skardu | 2013 | -0.194 |
Sohbatpur | 2013 | -0.568 |
South Karachi | 2013 | -0.439 |
South Waziristan | 2013 | 1.37 |
Sudhnoti | 2013 | 0.802 |
Sujawal | 2013 | 0.0638 |
Sukkur | 2013 | -0.809 |
Swabi | 2013 | 1.18 |
Swat | 2013 | 1.14 |
Tando Allahyar | 2013 | 0.133 |
Tando Muhammad Khan | 2013 | 0.133 |
Tangir | 2013 | 0.534 |
Tank | 2013 | 1.16 |
Tharparkar | 2013 | -0.144 |
Thatta | 2013 | -0.0288 |
Toba Tek Singh | 2013 | 0.819 |
Tor Ghar | 2013 | 1.07 |
Umer Kot | 2013 | -0.0614 |
Upper Dir | 2013 | 1.14 |
Vehari | 2013 | 0.773 |
Washuk | 2013 | -0.349 |
West Karachi | 2013 | -0.277 |
Zhob | 2013 | 0.72 |
Ziarat | 2013 | -0.111 |
Abbottabad | 2014 | 0.726 |
Astore | 2014 | -0.262 |
Attock | 2014 | 0.852 |
Awaran | 2014 | -1.2 |
Badin | 2014 | -0.604 |
Bagh | 2014 | 0.885 |
Bahawalnagar | 2014 | 0.5 |
Bahawalpur | 2014 | -0.0975 |
Bajaur | 2014 | 0.302 |
Bannu | 2014 | 0.595 |
Barkhan | 2014 | -0.893 |
Batagram | 2014 | 0.34 |
Bhakkar | 2014 | 0.591 |
Bhimber | 2014 | 0.963 |
Buner | 2014 | 0.671 |
Central Karachi | 2014 | -1.06 |
Chagai | 2014 | -0.776 |
Chakwal | 2014 | 0.889 |
Chaman | 2014 | -1.3 |
Charsadda | 2014 | 0.524 |
Chiniot | 2014 | 0.63 |
Chitral Lower | 2014 | -1.02 |
Chitral Upper | 2014 | -1.07 |
D. I. Khan | 2014 | 0.358 |
Dadu | 2014 | -1.12 |
Darel | 2014 | -0.571 |
Dera Bugti | 2014 | -1.25 |
Dera Ghazi Khan | 2014 | -0.758 |
Diamir | 2014 | -0.282 |
Duki | 2014 | -0.959 |
East Karachi | 2014 | -1.06 |
Faisalabad | 2014 | 0.506 |
Ghanche | 2014 | -0.464 |
Ghizer | 2014 | -0.889 |
Ghotki | 2014 | -1.15 |
Gilgit | 2014 | -0.599 |
Gujranwala | 2014 | 0.887 |
Gujrat | 2014 | 0.946 |
Gupis-Yasin | 2014 | -0.972 |
Gwadar | 2014 | -0.896 |
Hafizabad | 2014 | 0.828 |
Hangu | 2014 | 0.714 |
Haripur | 2014 | 0.671 |
Harnai | 2014 | -1.28 |
Haveli | 2014 | 0.855 |
Hunza | 2014 | -0.415 |
Hyderabad | 2014 | -0.774 |
Islamabad | 2014 | 0.923 |
Jacobabad | 2014 | -1.54 |
Jaffarabad | 2014 | -1.54 |
Jamshoro | 2014 | -0.911 |
Jhal Magsi | 2014 | -1.49 |
Jhang | 2014 | 0.571 |
Jhelum | 2014 | 0.886 |
Jhelum Valley | 2014 | 0.603 |
Kachhi | 2014 | -1.59 |
Kalat | 2014 | -1.37 |
Kambar Shahdad Kot | 2014 | -1.52 |
Karak | 2014 | 0.794 |
Kashmore | 2014 | -1.23 |
Kasur | 2014 | 0.65 |
Kech | 2014 | -0.961 |
Khairpur | 2014 | -0.862 |
Khanewal | 2014 | 0.434 |
Kharan | 2014 | -1.12 |
Kharmang | 2014 | 0.332 |
Khushab | 2014 | 0.694 |
Khuzdar | 2014 | -1.19 |
Khyber | 2014 | 0.792 |
Killa Abdullah | 2014 | -1.3 |
Killa Saifullah | 2014 | -1.12 |
Kohat | 2014 | 0.73 |
Kohistan Lower | 2014 | -0.142 |
Kohistan Upper | 2014 | -0.125 |
Kohlu | 2014 | -1.08 |
Kolai Palas Kohistan | 2014 | -0.125 |
Korangi Karachi | 2014 | -1.06 |
Kotli | 2014 | 0.905 |
Kurram | 2014 | 0.746 |
Lahore | 2014 | 0.65 |
Lakki Marwat | 2014 | 0.595 |
Larkana | 2014 | -1.53 |
Lasbela | 2014 | -1.06 |
Lehri | 2014 | -1.36 |
Leiah | 2014 | 0.143 |
Lodhran | 2014 | 0.0615 |
Loralai | 2014 | -1.08 |
Lower Dir | 2014 | 0.302 |
Malakand | 2014 | 0.302 |
Malir Karachi | 2014 | -0.978 |
Mandi Bahauddin | 2014 | 0.762 |
Mansehra | 2014 | 0.34 |
Mardan | 2014 | 0.665 |
Mastung | 2014 | -1.42 |
Matiari | 2014 | -0.934 |
Mianwali | 2014 | 0.768 |
Mirpur | 2014 | 0.905 |
Mirpur Khas | 2014 | -0.627 |
Mohmand | 2014 | 0.473 |
Multan | 2014 | -0.311 |
Musakhel | 2014 | -0.585 |
Muzaffarabad | 2014 | 0.603 |
Muzaffargarh | 2014 | -0.216 |
Nagar | 2014 | -0.645 |
Nankana Sahib | 2014 | 0.546 |
Narowal | 2014 | 0.951 |
Nasirabad | 2014 | -1.45 |
Naushahro Feroze | 2014 | -1.23 |
Neelum | 2014 | 0.126 |
North Waziristan | 2014 | 0.673 |
Nowshera | 2014 | 0.813 |
Nushki | 2014 | -1.13 |
Okara | 2014 | 0.534 |
Orakzai | 2014 | 0.763 |
Pakpattan | 2014 | 0.438 |
Panjgur | 2014 | -0.9 |
Peshawar | 2014 | 0.813 |
Pishin | 2014 | -1.29 |
Poonch | 2014 | 0.885 |
Quetta | 2014 | -1.25 |
Rahim Yar Khan | 2014 | -0.838 |
Rajanpur | 2014 | -1.12 |
Rawalpindi | 2014 | 0.933 |
Rondu | 2014 | -0.559 |
Sahiwal | 2014 | 0.523 |
Sanghar | 2014 | -0.604 |
Sargodha | 2014 | 0.683 |
Shaheed Benazir Abad | 2014 | -1.1 |
Shaheed Sikandarabad | 2014 | -1.07 |
Shangla | 2014 | 0.443 |
Sheikhupura | 2014 | 0.739 |
Sherani | 2014 | 0.37 |
Shigar | 2014 | -0.576 |
Shikarpur | 2014 | -1.59 |
Sialkot | 2014 | 0.951 |
Sibi | 2014 | -1.45 |
Skardu | 2014 | -0.31 |
Sohbatpur | 2014 | -1.28 |
South Karachi | 2014 | -1.07 |
South Waziristan | 2014 | 0.528 |
Sudhnoti | 2014 | 0.885 |
Sujawal | 2014 | -0.814 |
Sukkur | 2014 | -1.22 |
Swabi | 2014 | 0.665 |
Swat | 2014 | -0.134 |
Tando Allahyar | 2014 | -0.633 |
Tando Muhammad Khan | 2014 | -0.633 |
Tangir | 2014 | -0.64 |
Tank | 2014 | 0.539 |
Tharparkar | 2014 | -0.0621 |
Thatta | 2014 | -0.915 |
Toba Tek Singh | 2014 | 0.523 |
Tor Ghar | 2014 | 0.443 |
Umer Kot | 2014 | -0.513 |
Upper Dir | 2014 | -0.134 |
Vehari | 2014 | 0.434 |
Washuk | 2014 | -0.891 |
West Karachi | 2014 | -1.06 |
Zhob | 2014 | -0.402 |
Ziarat | 2014 | -1.28 |
Abbottabad | 2015 | 1.69 |
Astore | 2015 | 1.2 |
Attock | 2015 | 1.63 |
Awaran | 2015 | -2.03 |
Badin | 2015 | -0.913 |
Bagh | 2015 | 1.78 |
Bahawalnagar | 2015 | 1.06 |
Bahawalpur | 2015 | 0.594 |
Bajaur | 2015 | 1.89 |
Bannu | 2015 | 1.65 |
Barkhan | 2015 | 0.259 |
Batagram | 2015 | 1.65 |
Bhakkar | 2015 | 1.49 |
Bhimber | 2015 | 1.79 |
Buner | 2015 | 1.61 |
Central Karachi | 2015 | -1.41 |
Chagai | 2015 | -1.22 |
Chakwal | 2015 | 1.57 |
Chaman | 2015 | -0.927 |
Charsadda | 2015 | 1.85 |
Chiniot | 2015 | 1.35 |
Chitral Lower | 2015 | 1.21 |
Chitral Upper | 2015 | 0.809 |
D. I. Khan | 2015 | 1.4 |
Dadu | 2015 | -1.49 |
Darel | 2015 | 1.02 |
Dera Bugti | 2015 | -0.361 |
Dera Ghazi Khan | 2015 | 0.611 |
Diamir | 2015 | 1.28 |
Duki | 2015 | 0.00813 |
East Karachi | 2015 | -1.41 |
Faisalabad | 2015 | 1.27 |
Ghanche | 2015 | 0.663 |
Ghizer | 2015 | 0.636 |
Ghotki | 2015 | -0.473 |
Gilgit | 2015 | 0.937 |
Gujranwala | 2015 | 1.54 |
Gujrat | 2015 | 1.57 |
Gupis-Yasin | 2015 | 0.742 |
Gwadar | 2015 | -1.76 |
Hafizabad | 2015 | 1.47 |
Hangu | 2015 | 1.75 |
Haripur | 2015 | 1.61 |
Harnai | 2015 | -0.673 |
Haveli | 2015 | 1.9 |
Hunza | 2015 | 0.29 |
Hyderabad | 2015 | -1.29 |
Islamabad | 2015 | 1.67 |
Jacobabad | 2015 | -1.2 |
Jaffarabad | 2015 | -1.2 |
Jamshoro | 2015 | -1.44 |
Jhal Magsi | 2015 | -1.33 |
Jhang | 2015 | 1.37 |
Jhelum | 2015 | 1.52 |
Jhelum Valley | 2015 | 1.8 |
Kachhi | 2015 | -1.03 |
Kalat | 2015 | -1.22 |
Kambar Shahdad Kot | 2015 | -1.44 |
Karak | 2015 | 1.81 |
Kashmore | 2015 | -0.5 |
Kasur | 2015 | 1.42 |
Kech | 2015 | -1.77 |
Khairpur | 2015 | -0.804 |
Khanewal | 2015 | 1.17 |
Kharan | 2015 | -1.32 |
Kharmang | 2015 | 1.5 |
Khushab | 2015 | 1.53 |
Khuzdar | 2015 | -1.35 |
Khyber | 2015 | 1.82 |
Killa Abdullah | 2015 | -0.927 |
Killa Saifullah | 2015 | -0.34 |
Kohat | 2015 | 1.7 |
Kohistan Lower | 2015 | 1.55 |
Kohistan Upper | 2015 | 1.49 |
Kohlu | 2015 | -0.274 |
Kolai Palas Kohistan | 2015 | 1.49 |
Korangi Karachi | 2015 | -1.41 |
Kotli | 2015 | 1.63 |
Kurram | 2015 | 1.72 |
Lahore | 2015 | 1.42 |
Lakki Marwat | 2015 | 1.65 |
Larkana | 2015 | -1.23 |
Lasbela | 2015 | -1.81 |
Lehri | 2015 | -0.617 |
Leiah | 2015 | 1.22 |
Lodhran | 2015 | 0.949 |
Loralai | 2015 | -0.252 |
Lower Dir | 2015 | 1.89 |
Malakand | 2015 | 1.89 |
Malir Karachi | 2015 | -1.57 |
Mandi Bahauddin | 2015 | 1.42 |
Mansehra | 2015 | 1.65 |
Mardan | 2015 | 1.73 |
Mastung | 2015 | -1.12 |
Matiari | 2015 | -1.34 |
Mianwali | 2015 | 1.66 |
Mirpur | 2015 | 1.63 |
Mirpur Khas | 2015 | -0.867 |
Mohmand | 2015 | 1.88 |
Multan | 2015 | 0.802 |
Musakhel | 2015 | 0.663 |
Muzaffarabad | 2015 | 1.8 |
Muzaffargarh | 2015 | 1.05 |
Nagar | 2015 | 0.495 |
Nankana Sahib | 2015 | 1.35 |
Narowal | 2015 | 1.62 |
Nasirabad | 2015 | -0.818 |
Naushahro Feroze | 2015 | -1.29 |
Neelum | 2015 | 1.59 |
North Waziristan | 2015 | 1.71 |
Nowshera | 2015 | 1.8 |
Nushki | 2015 | -1.27 |
Okara | 2015 | 1.39 |
Orakzai | 2015 | 1.77 |
Pakpattan | 2015 | 1.14 |
Panjgur | 2015 | -1.78 |
Peshawar | 2015 | 1.8 |
Pishin | 2015 | -0.692 |
Poonch | 2015 | 1.78 |
Quetta | 2015 | -1.02 |
Rahim Yar Khan | 2015 | -0.0897 |
Rajanpur | 2015 | 0.0893 |
Rawalpindi | 2015 | 1.58 |
Rondu | 2015 | 0.924 |
Sahiwal | 2015 | 1.26 |
Sanghar | 2015 | -0.812 |
Sargodha | 2015 | 1.39 |
Shaheed Benazir Abad | 2015 | -1.37 |
Shaheed Sikandarabad | 2015 | -1.21 |
Shangla | 2015 | 1.74 |
Sheikhupura | 2015 | 1.41 |
Sherani | 2015 | 1.39 |
Shigar | 2015 | 0.782 |
Shikarpur | 2015 | -1.15 |
Sialkot | 2015 | 1.62 |
Sibi | 2015 | -0.83 |
Skardu | 2015 | 1.28 |
Sohbatpur | 2015 | -0.493 |
South Karachi | 2015 | -1.86 |
South Waziristan | 2015 | 1.59 |
Sudhnoti | 2015 | 1.78 |
Sujawal | 2015 | -0.86 |
Sukkur | 2015 | -0.689 |
Swabi | 2015 | 1.73 |
Swat | 2015 | 1.62 |
Tando Allahyar | 2015 | -1.03 |
Tando Muhammad Khan | 2015 | -1.03 |
Tangir | 2015 | 1.11 |
Tank | 2015 | 1.55 |
Tharparkar | 2015 | 0.0236 |
Thatta | 2015 | -1.23 |
Toba Tek Singh | 2015 | 1.26 |
Tor Ghar | 2015 | 1.74 |
Umer Kot | 2015 | -0.524 |
Upper Dir | 2015 | 1.62 |
Vehari | 2015 | 1.17 |
Washuk | 2015 | -1.47 |
West Karachi | 2015 | -1.41 |
Zhob | 2015 | 0.693 |
Ziarat | 2015 | -0.673 |
Abbottabad | 2016 | 1.18 |
Astore | 2016 | 0.941 |
Attock | 2016 | 1.48 |
Awaran | 2016 | -2.1 |
Badin | 2016 | -0.874 |
Bagh | 2016 | 1.03 |
Bahawalnagar | 2016 | 0.84 |
Bahawalpur | 2016 | 0.474 |
Bajaur | 2016 | 1.63 |
Bannu | 2016 | 1.7 |
Barkhan | 2016 | 0.5 |
Batagram | 2016 | 1.18 |
Bhakkar | 2016 | 1.71 |
Bhimber | 2016 | 0.858 |
Buner | 2016 | 1.24 |
Central Karachi | 2016 | -1.35 |
Chagai | 2016 | -1.47 |
Chakwal | 2016 | 1.57 |
Chaman | 2016 | -0.994 |
Charsadda | 2016 | 1.52 |
Chiniot | 2016 | 1.5 |
Chitral Lower | 2016 | 1.82 |
Chitral Upper | 2016 | 1.36 |
D. I. Khan | 2016 | 1.7 |
Dadu | 2016 | -1.43 |
Darel | 2016 | 1.12 |
Dera Bugti | 2016 | -0.44 |
Dera Ghazi Khan | 2016 | 1.04 |
Diamir | 2016 | 0.94 |
Duki | 2016 | 0.291 |
East Karachi | 2016 | -1.35 |
Faisalabad | 2016 | 1.49 |
Ghanche | 2016 | 0.249 |
Ghizer | 2016 | 1.07 |
Ghotki | 2016 | -0.666 |
Gilgit | 2016 | 0.938 |
Gujranwala | 2016 | 1.04 |
Gujrat | 2016 | 1.09 |
Gupis-Yasin | 2016 | 1.23 |
Gwadar | 2016 | -1.96 |
Hafizabad | 2016 | 1.28 |
Hangu | 2016 | 1.68 |
Haripur | 2016 | 1.24 |
Harnai | 2016 | -0.752 |
Haveli | 2016 | 0.908 |
Hunza | 2016 | 0.769 |
Hyderabad | 2016 | -1.21 |
Islamabad | 2016 | 1.22 |
Jacobabad | 2016 | -1.11 |
Jaffarabad | 2016 | -1.11 |
Jamshoro | 2016 | -1.28 |
Jhal Magsi | 2016 | -1.37 |
Jhang | 2016 | 1.51 |
Jhelum | 2016 | 1.41 |
Jhelum Valley | 2016 | 0.993 |
Kachhi | 2016 | -1.17 |
Kalat | 2016 | -1.32 |
Kambar Shahdad Kot | 2016 | -1.46 |
Karak | 2016 | 1.66 |
Kashmore | 2016 | -0.561 |
Kasur | 2016 | 1.28 |
Kech | 2016 | -2 |
Khairpur | 2016 | -0.904 |
Khanewal | 2016 | 1.18 |
Kharan | 2016 | -1.43 |
Kharmang | 2016 | 0.746 |
Khushab | 2016 | 1.56 |
Khuzdar | 2016 | -1.33 |
Khyber | 2016 | 1.53 |
Killa Abdullah | 2016 | -0.994 |
Killa Saifullah | 2016 | -0.436 |
Kohat | 2016 | 1.58 |
Kohistan Lower | 2016 | 1.38 |
Kohistan Upper | 2016 | 1.27 |
Kohlu | 2016 | -0.184 |
Kolai Palas Kohistan | 2016 | 1.27 |
Korangi Karachi | 2016 | -1.35 |
Kotli | 2016 | 1.05 |
Kurram | 2016 | 1.61 |
Lahore | 2016 | 1.28 |
Lakki Marwat | 2016 | 1.7 |
Larkana | 2016 | -1.27 |
Lasbela | 2016 | -1.69 |
Lehri | 2016 | -0.844 |
Leiah | 2016 | 1.58 |
Lodhran | 2016 | 0.984 |
Loralai | 2016 | -0.179 |
Lower Dir | 2016 | 1.63 |
Malakand | 2016 | 1.63 |
Malir Karachi | 2016 | -1.36 |
Mandi Bahauddin | 2016 | 1.45 |
Mansehra | 2016 | 1.18 |
Mardan | 2016 | 1.41 |
Mastung | 2016 | -1.2 |
Matiari | 2016 | -1.25 |
Mianwali | 2016 | 1.65 |
Mirpur | 2016 | 1.05 |
Mirpur Khas | 2016 | -0.809 |
Mohmand | 2016 | 1.55 |
Multan | 2016 | 0.847 |
Musakhel | 2016 | 1.06 |
Muzaffarabad | 2016 | 0.993 |
Muzaffargarh | 2016 | 1.3 |
Nagar | 2016 | 0.828 |
Nankana Sahib | 2016 | 1.44 |
Narowal | 2016 | 0.823 |
Nasirabad | 2016 | -0.905 |
Naushahro Feroze | 2016 | -1.3 |
Neelum | 2016 | 0.858 |
North Waziristan | 2016 | 1.57 |
Nowshera | 2016 | 1.49 |
Nushki | 2016 | -1.29 |
Okara | 2016 | 1.23 |
Orakzai | 2016 | 1.56 |
Pakpattan | 2016 | 0.935 |
Panjgur | 2016 | -1.87 |
Peshawar | 2016 | 1.49 |
Pishin | 2016 | -0.896 |
Poonch | 2016 | 1.03 |
Quetta | 2016 | -1.14 |
Rahim Yar Khan | 2016 | -0.458 |
Rajanpur | 2016 | -0.0709 |
Rawalpindi | 2016 | 1.3 |
Rondu | 2016 | 0.824 |
Sahiwal | 2016 | 1.43 |
Sanghar | 2016 | -0.7 |
Sargodha | 2016 | 1.5 |
Shaheed Benazir Abad | 2016 | -1.25 |
Shaheed Sikandarabad | 2016 | -1.34 |
Shangla | 2016 | 1.37 |
Sheikhupura | 2016 | 1.21 |
Sherani | 2016 | 1.52 |
Shigar | 2016 | 0.711 |
Shikarpur | 2016 | -1.24 |
Sialkot | 2016 | 0.823 |
Sibi | 2016 | -0.997 |
Skardu | 2016 | 0.751 |
Sohbatpur | 2016 | -0.559 |
South Karachi | 2016 | -1.7 |
South Waziristan | 2016 | 1.59 |
Sudhnoti | 2016 | 1.03 |
Sujawal | 2016 | -1.07 |
Sukkur | 2016 | -0.833 |
Swabi | 2016 | 1.41 |
Swat | 2016 | 1.51 |
Tando Allahyar | 2016 | -0.936 |
Tando Muhammad Khan | 2016 | -0.936 |
Tangir | 2016 | 1.28 |
Tank | 2016 | 1.71 |
Tharparkar | 2016 | -0.239 |
Thatta | 2016 | -1.17 |
Toba Tek Singh | 2016 | 1.43 |
Tor Ghar | 2016 | 1.37 |
Umer Kot | 2016 | -0.548 |
Upper Dir | 2016 | 1.51 |
Vehari | 2016 | 1.18 |
Washuk | 2016 | -1.6 |
West Karachi | 2016 | -1.35 |
Zhob | 2016 | 0.81 |
Ziarat | 2016 | -0.752 |
Abbottabad | 2017 | 0.0885 |
Astore | 2017 | 0.155 |
Attock | 2017 | 0.427 |
Awaran | 2017 | -2.15 |
Badin | 2017 | -0.785 |
Bagh | 2017 | 0.25 |
Bahawalnagar | 2017 | 0.252 |
Bahawalpur | 2017 | 0.0674 |
Bajaur | 2017 | 0.351 |
Bannu | 2017 | 0.201 |
Barkhan | 2017 | -0.642 |
Batagram | 2017 | 0.0323 |
Bhakkar | 2017 | 0.409 |
Bhimber | 2017 | 0.368 |
Buner | 2017 | 0.17 |
Central Karachi | 2017 | -1.09 |
Chagai | 2017 | -1.83 |
Chakwal | 2017 | 0.698 |
Chaman | 2017 | -1.46 |
Charsadda | 2017 | 0.259 |
Chiniot | 2017 | 0.706 |
Chitral Lower | 2017 | 0.811 |
Chitral Upper | 2017 | 0.705 |
D. I. Khan | 2017 | 0.111 |
Dadu | 2017 | -1.51 |
Darel | 2017 | 0.276 |
Dera Bugti | 2017 | -1.41 |
Dera Ghazi Khan | 2017 | -0.386 |
Diamir | 2017 | 0.164 |
Duki | 2017 | -0.824 |
East Karachi | 2017 | -1.09 |
Faisalabad | 2017 | 0.749 |
Ghanche | 2017 | 0.798 |
Ghizer | 2017 | 0.452 |
Ghotki | 2017 | -1.28 |
Gilgit | 2017 | 0.195 |
Gujranwala | 2017 | 0.479 |
Gujrat | 2017 | 0.671 |
Gupis-Yasin | 2017 | 0.51 |
Gwadar | 2017 | -2.01 |
Hafizabad | 2017 | 0.712 |
Hangu | 2017 | 0.16 |
Haripur | 2017 | 0.17 |
Harnai | 2017 | -1.33 |
Haveli | 2017 | 0.312 |
Hunza | 2017 | 1.02 |
Hyderabad | 2017 | -1.05 |
Islamabad | 2017 | 0.304 |
Jacobabad | 2017 | -1.81 |
Jaffarabad | 2017 | -1.81 |
Jamshoro | 2017 | -1.17 |
Jhal Magsi | 2017 | -1.82 |
Jhang | 2017 | 0.58 |
Jhelum | 2017 | 0.737 |
Jhelum Valley | 2017 | 0.0441 |
Kachhi | 2017 | -1.83 |
Kalat | 2017 | -1.5 |
Kambar Shahdad Kot | 2017 | -1.88 |
Karak | 2017 | 0.238 |
Kashmore | 2017 | -1.55 |
Kasur | 2017 | 0.549 |
Kech | 2017 | -1.99 |
Khairpur | 2017 | -1.15 |
Khanewal | 2017 | 0.437 |
Kharan | 2017 | -1.72 |
Kharmang | 2017 | 0.276 |
Khushab | 2017 | 0.595 |
Khuzdar | 2017 | -1.39 |
Khyber | 2017 | 0.203 |
Killa Abdullah | 2017 | -1.46 |
Killa Saifullah | 2017 | -1.11 |
Kohat | 2017 | 0.346 |
Kohistan Lower | 2017 | 0.221 |
Kohistan Upper | 2017 | 0.154 |
Kohlu | 2017 | -1.11 |
Kolai Palas Kohistan | 2017 | 0.154 |
Korangi Karachi | 2017 | -1.09 |
Kotli | 2017 | 0.46 |
Kurram | 2017 | 0.212 |
Lahore | 2017 | 0.549 |
Lakki Marwat | 2017 | 0.201 |
Larkana | 2017 | -1.72 |
Lasbela | 2017 | -1.65 |
Lehri | 2017 | -1.78 |
Leiah | 2017 | 0.365 |
Lodhran | 2017 | 0.136 |
Loralai | 2017 | -1.11 |
Lower Dir | 2017 | 0.351 |
Malakand | 2017 | 0.351 |
Malir Karachi | 2017 | -1.33 |
Mandi Bahauddin | 2017 | 0.799 |
Mansehra | 2017 | 0.0323 |
Mardan | 2017 | 0.238 |
Mastung | 2017 | -1.48 |
Matiari | 2017 | -1.1 |
Mianwali | 2017 | 0.429 |
Mirpur | 2017 | 0.46 |
Mirpur Khas | 2017 | -0.875 |
Mohmand | 2017 | 0.271 |
Multan | 2017 | -0.125 |
Musakhel | 2017 | -0.55 |
Muzaffarabad | 2017 | 0.0441 |
Muzaffargarh | 2017 | 0.0878 |
Nagar | 2017 | 0.569 |
Nankana Sahib | 2017 | 0.698 |
Narowal | 2017 | 0.24 |
Nasirabad | 2017 | -1.73 |
Naushahro Feroze | 2017 | -1.38 |
Neelum | 2017 | 0.099 |
North Waziristan | 2017 | 0.078 |
Nowshera | 2017 | 0.229 |
Nushki | 2017 | -1.56 |
Okara | 2017 | 0.512 |
Orakzai | 2017 | 0.22 |
Pakpattan | 2017 | 0.369 |
Panjgur | 2017 | -2.05 |
Peshawar | 2017 | 0.229 |
Pishin | 2017 | -1.4 |
Poonch | 2017 | 0.25 |
Quetta | 2017 | -1.5 |
Rahim Yar Khan | 2017 | -0.928 |
Rajanpur | 2017 | -1.11 |
Rawalpindi | 2017 | 0.513 |
Rondu | 2017 | 0.208 |
Sahiwal | 2017 | 0.653 |
Sanghar | 2017 | -0.782 |
Sargodha | 2017 | 0.754 |
Shaheed Benazir Abad | 2017 | -1.32 |
Shaheed Sikandarabad | 2017 | -1.57 |
Shangla | 2017 | 0.162 |
Sheikhupura | 2017 | 0.611 |
Sherani | 2017 | -0.166 |
Shigar | 2017 | 0.509 |
Shikarpur | 2017 | -1.61 |
Sialkot | 2017 | 0.24 |
Sibi | 2017 | -1.56 |
Skardu | 2017 | 0.286 |
Sohbatpur | 2017 | -1.54 |
South Karachi | 2017 | -1.51 |
South Waziristan | 2017 | -0.0414 |
Sudhnoti | 2017 | 0.25 |
Sujawal | 2017 | -0.895 |
Sukkur | 2017 | -1.45 |
Swabi | 2017 | 0.238 |
Swat | 2017 | 0.337 |
Tando Allahyar | 2017 | -0.854 |
Tando Muhammad Khan | 2017 | -0.854 |
Tangir | 2017 | 0.337 |
Tank | 2017 | 0.0306 |
Tharparkar | 2017 | -0.346 |
Thatta | 2017 | -0.946 |
Toba Tek Singh | 2017 | 0.653 |
Tor Ghar | 2017 | 0.162 |
Umer Kot | 2017 | -0.638 |
Upper Dir | 2017 | 0.337 |
Vehari | 2017 | 0.437 |
Washuk | 2017 | -1.93 |
West Karachi | 2017 | -1.09 |
Zhob | 2017 | -0.651 |
Ziarat | 2017 | -1.33 |
Abbottabad | 2018 | -1.04 |
Astore | 2018 | -1.24 |
Attock | 2018 | -0.69 |
Awaran | 2018 | -2.5 |
Badin | 2018 | -1.36 |
Bagh | 2018 | -0.881 |
Bahawalnagar | 2018 | -0.222 |
Bahawalpur | 2018 | -0.513 |
Bajaur | 2018 | -1.2 |
Bannu | 2018 | -0.85 |
Barkhan | 2018 | -1.35 |
Batagram | 2018 | -1.32 |
Bhakkar | 2018 | -0.711 |
Bhimber | 2018 | -0.704 |
Buner | 2018 | -0.937 |
Central Karachi | 2018 | -1.65 |
Chagai | 2018 | -2.25 |
Chakwal | 2018 | -0.531 |
Chaman | 2018 | -1.94 |
Charsadda | 2018 | -1.09 |
Chiniot | 2018 | -0.323 |
Chitral Lower | 2018 | -1.46 |
Chitral Upper | 2018 | -1.62 |
D. I. Khan | 2018 | -1.06 |
Dadu | 2018 | -1.97 |
Darel | 2018 | -1.35 |
Dera Bugti | 2018 | -1.87 |
Dera Ghazi Khan | 2018 | -1.36 |
Diamir | 2018 | -1.33 |
Duki | 2018 | -1.59 |
East Karachi | 2018 | -1.65 |
Faisalabad | 2018 | -0.283 |
Ghanche | 2018 | 0.268 |
Ghizer | 2018 | -1.18 |
Ghotki | 2018 | -1.67 |
Gilgit | 2018 | -1.2 |
Gujranwala | 2018 | -0.445 |
Gujrat | 2018 | -0.44 |
Gupis-Yasin | 2018 | -1.5 |
Gwadar | 2018 | -2.31 |
Hafizabad | 2018 | -0.378 |
Hangu | 2018 | -0.801 |
Haripur | 2018 | -0.937 |
Harnai | 2018 | -1.75 |
Haveli | 2018 | -0.902 |
Hunza | 2018 | 0.267 |
Hyderabad | 2018 | -1.53 |
Islamabad | 2018 | -0.795 |
Jacobabad | 2018 | -2.24 |
Jaffarabad | 2018 | -2.24 |
Jamshoro | 2018 | -1.66 |
Jhal Magsi | 2018 | -2.15 |
Jhang | 2018 | -0.378 |
Jhelum | 2018 | -0.464 |
Jhelum Valley | 2018 | -1.2 |
Kachhi | 2018 | -2.1 |
Kalat | 2018 | -1.85 |
Kambar Shahdad Kot | 2018 | -2.12 |
Karak | 2018 | -0.722 |
Kashmore | 2018 | -1.85 |
Kasur | 2018 | -0.275 |
Kech | 2018 | -2.37 |
Khairpur | 2018 | -1.61 |
Khanewal | 2018 | -0.392 |
Kharan | 2018 | -2.1 |
Kharmang | 2018 | -0.679 |
Khushab | 2018 | -0.551 |
Khuzdar | 2018 | -1.87 |
Khyber | 2018 | -0.904 |
Killa Abdullah | 2018 | -1.94 |
Killa Saifullah | 2018 | -1.78 |
Kohat | 2018 | -0.681 |
Kohistan Lower | 2018 | -1.49 |
Kohistan Upper | 2018 | -1.47 |
Kohlu | 2018 | -1.65 |
Kolai Palas Kohistan | 2018 | -1.47 |
Korangi Karachi | 2018 | -1.65 |
Kotli | 2018 | -0.677 |
Kurram | 2018 | -0.764 |
Lahore | 2018 | -0.275 |
Lakki Marwat | 2018 | -0.85 |
Larkana | 2018 | -2 |
Lasbela | 2018 | -2.21 |
Lehri | 2018 | -2.14 |
Leiah | 2018 | -0.897 |
Lodhran | 2018 | -0.441 |
Loralai | 2018 | -1.73 |
Lower Dir | 2018 | -1.2 |
Malakand | 2018 | -1.2 |
Malir Karachi | 2018 | -1.9 |
Mandi Bahauddin | 2018 | -0.348 |
Mansehra | 2018 | -1.32 |
Mardan | 2018 | -1.05 |
Mastung | 2018 | -1.82 |
Matiari | 2018 | -1.62 |
Mianwali | 2018 | -0.639 |
Mirpur | 2018 | -0.677 |
Mirpur Khas | 2018 | -1.38 |
Mohmand | 2018 | -1.05 |
Multan | 2018 | -0.799 |
Musakhel | 2018 | -1.55 |
Muzaffarabad | 2018 | -1.2 |
Muzaffargarh | 2018 | -0.918 |
Nagar | 2018 | -0.539 |
Nankana Sahib | 2018 | -0.303 |
Narowal | 2018 | -0.488 |
Nasirabad | 2018 | -2.16 |
Naushahro Feroze | 2018 | -1.79 |
Neelum | 2018 | -1.28 |
North Waziristan | 2018 | -0.858 |
Nowshera | 2018 | -0.879 |
Nushki | 2018 | -2.01 |
Okara | 2018 | -0.228 |
Orakzai | 2018 | -0.851 |
Pakpattan | 2018 | -0.252 |
Panjgur | 2018 | -2.46 |
Peshawar | 2018 | -0.879 |
Pishin | 2018 | -1.87 |
Poonch | 2018 | -0.881 |
Quetta | 2018 | -1.91 |
Rahim Yar Khan | 2018 | -1.39 |
Rajanpur | 2018 | -1.76 |
Rawalpindi | 2018 | -0.645 |
Rondu | 2018 | -0.925 |
Sahiwal | 2018 | -0.274 |
Sanghar | 2018 | -1.29 |
Sargodha | 2018 | -0.339 |
Shaheed Benazir Abad | 2018 | -1.78 |
Shaheed Sikandarabad | 2018 | -1.93 |
Shangla | 2018 | -1.27 |
Sheikhupura | 2018 | -0.391 |
Sherani | 2018 | -1.3 |
Shigar | 2018 | -0.474 |
Shikarpur | 2018 | -1.99 |
Sialkot | 2018 | -0.488 |
Sibi | 2018 | -2.01 |
Skardu | 2018 | -0.942 |
Sohbatpur | 2018 | -1.95 |
South Karachi | 2018 | -2.01 |
South Waziristan | 2018 | -1.1 |
Sudhnoti | 2018 | -0.881 |
Sujawal | 2018 | -1.37 |
Sukkur | 2018 | -1.76 |
Swabi | 2018 | -1.05 |
Swat | 2018 | -1.43 |
Tando Allahyar | 2018 | -1.41 |
Tando Muhammad Khan | 2018 | -1.41 |
Tangir | 2018 | -1.49 |
Tank | 2018 | -1.01 |
Tharparkar | 2018 | -0.822 |
Thatta | 2018 | -1.51 |
Toba Tek Singh | 2018 | -0.274 |
Tor Ghar | 2018 | -1.27 |
Umer Kot | 2018 | -1.05 |
Upper Dir | 2018 | -1.43 |
Vehari | 2018 | -0.392 |
Washuk | 2018 | -2.37 |
West Karachi | 2018 | -1.65 |
Zhob | 2018 | -1.7 |
Ziarat | 2018 | -1.75 |
Abbottabad | 2019 | 0.526 |
Astore | 2019 | 0.296 |
Attock | 2019 | 0.446 |
Awaran | 2019 | -1.42 |
Badin | 2019 | -0.758 |
Bagh | 2019 | 0.729 |
Bahawalnagar | 2019 | 0.235 |
Bahawalpur | 2019 | -0.132 |
Bajaur | 2019 | 0.323 |
Bannu | 2019 | 0.488 |
Barkhan | 2019 | -0.00912 |
Batagram | 2019 | 0.381 |
Bhakkar | 2019 | 0.547 |
Bhimber | 2019 | 0.836 |
Buner | 2019 | 0.418 |
Central Karachi | 2019 | -0.881 |
Chagai | 2019 | -0.361 |
Chakwal | 2019 | 0.507 |
Chaman | 2019 | 0.0252 |
Charsadda | 2019 | 0.497 |
Chiniot | 2019 | 0.632 |
Chitral Lower | 2019 | -0.285 |
Chitral Upper | 2019 | -0.188 |
D. I. Khan | 2019 | 0.384 |
Dadu | 2019 | -0.817 |
Darel | 2019 | -0.0275 |
Dera Bugti | 2019 | -0.637 |
Dera Ghazi Khan | 2019 | 0.17 |
Diamir | 2019 | 0.204 |
Duki | 2019 | 0.0212 |
East Karachi | 2019 | -0.881 |
Faisalabad | 2019 | 0.522 |
Ghanche | 2019 | 1.23 |
Ghizer | 2019 | -0.0506 |
Ghotki | 2019 | -1.09 |
Gilgit | 2019 | 0.0583 |
Gujranwala | 2019 | 0.683 |
Gujrat | 2019 | 0.726 |
Gupis-Yasin | 2019 | -0.137 |
Gwadar | 2019 | -1.52 |
Hafizabad | 2019 | 0.631 |
Hangu | 2019 | 0.586 |
Haripur | 2019 | 0.418 |
Harnai | 2019 | 0.192 |
Haveli | 2019 | 0.814 |
Hunza | 2019 | 0.563 |
Hyderabad | 2019 | -0.885 |
Islamabad | 2019 | 0.618 |
Jacobabad | 2019 | -0.718 |
Jaffarabad | 2019 | -0.718 |
Jamshoro | 2019 | -0.917 |
Jhal Magsi | 2019 | -0.604 |
Jhang | 2019 | 0.622 |
Jhelum | 2019 | 0.6 |
Jhelum Valley | 2019 | 0.576 |
Kachhi | 2019 | -0.33 |
Kalat | 2019 | 0.465 |
Kambar Shahdad Kot | 2019 | -0.696 |
Karak | 2019 | 0.544 |
Kashmore | 2019 | -0.852 |
Kasur | 2019 | 0.427 |
Kech | 2019 | -1.44 |
Khairpur | 2019 | -0.909 |
Khanewal | 2019 | 0.457 |
Kharan | 2019 | 0.128 |
Kharmang | 2019 | 0.768 |
Khushab | 2019 | 0.56 |
Khuzdar | 2019 | -0.454 |
Khyber | 2019 | 0.611 |
Killa Abdullah | 2019 | 0.0252 |
Killa Saifullah | 2019 | 0.285 |
Kohat | 2019 | 0.464 |
Kohistan Lower | 2019 | 0.13 |
Kohistan Upper | 2019 | 0.119 |
Kohlu | 2019 | -0.0567 |
Kolai Palas Kohistan | 2019 | 0.119 |
Korangi Karachi | 2019 | -0.881 |
Kotli | 2019 | 0.728 |
Kurram | 2019 | 0.597 |
Lahore | 2019 | 0.427 |
Lakki Marwat | 2019 | 0.488 |
Larkana | 2019 | -0.732 |
Lasbela | 2019 | -1.36 |
Lehri | 2019 | -0.496 |
Leiah | 2019 | 0.381 |
Lodhran | 2019 | 0.259 |
Loralai | 2019 | 0.159 |
Lower Dir | 2019 | 0.323 |
Malakand | 2019 | 0.323 |
Malir Karachi | 2019 | -1.12 |
Mandi Bahauddin | 2019 | 0.583 |
Mansehra | 2019 | 0.381 |
Mardan | 2019 | 0.391 |
Mastung | 2019 | 0.243 |
Matiari | 2019 | -1.05 |
Mianwali | 2019 | 0.486 |
Mirpur | 2019 | 0.728 |
Mirpur Khas | 2019 | -0.822 |
Mohmand | 2019 | 0.516 |
Multan | 2019 | 0.112 |
Musakhel | 2019 | 0.36 |
Muzaffarabad | 2019 | 0.576 |
Muzaffargarh | 2019 | 0.28 |
Nagar | 2019 | 0.24 |
Nankana Sahib | 2019 | 0.421 |
Narowal | 2019 | 0.701 |
Nasirabad | 2019 | -0.632 |
Naushahro Feroze | 2019 | -1 |
Neelum | 2019 | 0.436 |
North Waziristan | 2019 | 0.648 |
Nowshera | 2019 | 0.5 |
Nushki | 2019 | 0.413 |
Okara | 2019 | 0.384 |
Orakzai | 2019 | 0.604 |
Pakpattan | 2019 | 0.372 |
Panjgur | 2019 | -0.98 |
Peshawar | 2019 | 0.5 |
Pishin | 2019 | 0.19 |
Poonch | 2019 | 0.729 |
Quetta | 2019 | -0.0126 |
Rahim Yar Khan | 2019 | -0.784 |
Rajanpur | 2019 | -0.517 |
Rawalpindi | 2019 | 0.604 |
Rondu | 2019 | 0.306 |
Sahiwal | 2019 | 0.519 |
Sanghar | 2019 | -0.844 |
Sargodha | 2019 | 0.618 |
Shaheed Benazir Abad | 2019 | -1.04 |
Shaheed Sikandarabad | 2019 | -0.143 |
Shangla | 2019 | 0.327 |
Sheikhupura | 2019 | 0.627 |
Sherani | 2019 | 0.531 |
Shigar | 2019 | 0.562 |
Shikarpur | 2019 | -1.02 |
Sialkot | 2019 | 0.701 |
Sibi | 2019 | -0.121 |
Skardu | 2019 | 0.477 |
Sohbatpur | 2019 | -0.559 |
South Karachi | 2019 | -1.11 |
South Waziristan | 2019 | 0.588 |
Sudhnoti | 2019 | 0.729 |
Sujawal | 2019 | -0.75 |
Sukkur | 2019 | -0.952 |
Swabi | 2019 | 0.391 |
Swat | 2019 | 0.135 |
Tando Allahyar | 2019 | -0.783 |
Tando Muhammad Khan | 2019 | -0.783 |
Tangir | 2019 | -0.0184 |
Tank | 2019 | 0.539 |
Tharparkar | 2019 | -0.617 |
Thatta | 2019 | -0.806 |
Toba Tek Singh | 2019 | 0.519 |
Tor Ghar | 2019 | 0.327 |
Umer Kot | 2019 | -0.674 |
Upper Dir | 2019 | 0.135 |
Vehari | 2019 | 0.457 |
Washuk | 2019 | -0.583 |
West Karachi | 2019 | -0.881 |
Zhob | 2019 | 0.428 |
Ziarat | 2019 | 0.192 |
Abbottabad | 2020 | 0.998 |
Astore | 2020 | 0.83 |
Attock | 2020 | 1.31 |
Awaran | 2020 | -0.301 |
Badin | 2020 | 1.21 |
Bagh | 2020 | 1.25 |
Bahawalnagar | 2020 | 1.38 |
Bahawalpur | 2020 | 0.891 |
Bajaur | 2020 | 1.16 |
Bannu | 2020 | 1.65 |
Barkhan | 2020 | 1.17 |
Batagram | 2020 | 1 |
Bhakkar | 2020 | 2 |
Bhimber | 2020 | 1.32 |
Buner | 2020 | 0.946 |
Central Karachi | 2020 | 0.997 |
Chagai | 2020 | 0.351 |
Chakwal | 2020 | 1.48 |
Chaman | 2020 | 0.996 |
Charsadda | 2020 | 1.4 |
Chiniot | 2020 | 1.77 |
Chitral Lower | 2020 | 0.734 |
Chitral Upper | 2020 | 0.91 |
D. I. Khan | 2020 | 1.95 |
Dadu | 2020 | 0.256 |
Darel | 2020 | 0.665 |
Dera Bugti | 2020 | 0.21 |
Dera Ghazi Khan | 2020 | 1.9 |
Diamir | 2020 | 0.794 |
Duki | 2020 | 1.16 |
East Karachi | 2020 | 0.997 |
Faisalabad | 2020 | 1.76 |
Ghanche | 2020 | 0.522 |
Ghizer | 2020 | 0.645 |
Ghotki | 2020 | -0.456 |
Gilgit | 2020 | 0.609 |
Gujranwala | 2020 | 1.35 |
Gujrat | 2020 | 1.47 |
Gupis-Yasin | 2020 | 0.826 |
Gwadar | 2020 | -0.828 |
Hafizabad | 2020 | 1.49 |
Hangu | 2020 | 1.74 |
Haripur | 2020 | 0.946 |
Harnai | 2020 | 1.06 |
Haveli | 2020 | 1.43 |
Hunza | 2020 | 0.462 |
Hyderabad | 2020 | 0.998 |
Islamabad | 2020 | 1.12 |
Jacobabad | 2020 | -0.368 |
Jaffarabad | 2020 | -0.368 |
Jamshoro | 2020 | 0.687 |
Jhal Magsi | 2020 | -0.1 |
Jhang | 2020 | 1.93 |
Jhelum | 2020 | 1.44 |
Jhelum Valley | 2020 | 1.2 |
Kachhi | 2020 | 0.283 |
Kalat | 2020 | 0.998 |
Kambar Shahdad Kot | 2020 | -0.206 |
Karak | 2020 | 1.68 |
Kashmore | 2020 | -0.342 |
Kasur | 2020 | 1.52 |
Kech | 2020 | -0.585 |
Khairpur | 2020 | -0.207 |
Khanewal | 2020 | 1.71 |
Kharan | 2020 | 0.811 |
Kharmang | 2020 | 1.13 |
Khushab | 2020 | 1.74 |
Khuzdar | 2020 | 0.385 |
Khyber | 2020 | 1.73 |
Killa Abdullah | 2020 | 0.996 |
Killa Saifullah | 2020 | 1.24 |
Kohat | 2020 | 1.43 |
Kohistan Lower | 2020 | 0.812 |
Kohistan Upper | 2020 | 0.77 |
Kohlu | 2020 | 0.744 |
Kolai Palas Kohistan | 2020 | 0.77 |
Korangi Karachi | 2020 | 0.997 |
Kotli | 2020 | 1.26 |
Kurram | 2020 | 1.72 |
Lahore | 2020 | 1.52 |
Lakki Marwat | 2020 | 1.65 |
Larkana | 2020 | -0.41 |
Lasbela | 2020 | 0.209 |
Lehri | 2020 | 0.24 |
Leiah | 2020 | 2.11 |
Lodhran | 2020 | 1.5 |
Loralai | 2020 | 1.1 |
Lower Dir | 2020 | 1.16 |
Malakand | 2020 | 1.16 |
Malir Karachi | 2020 | 0.707 |
Mandi Bahauddin | 2020 | 1.56 |
Mansehra | 2020 | 1 |
Mardan | 2020 | 1.05 |
Mastung | 2020 | 0.944 |
Matiari | 2020 | 0.651 |
Mianwali | 2020 | 1.62 |
Mirpur | 2020 | 1.26 |
Mirpur Khas | 2020 | 0.9 |
Mohmand | 2020 | 1.75 |
Multan | 2020 | 1.51 |
Musakhel | 2020 | 2.07 |
Muzaffarabad | 2020 | 1.2 |
Muzaffargarh | 2020 | 1.94 |
Nagar | 2020 | 0.517 |
Nankana Sahib | 2020 | 1.66 |
Narowal | 2020 | 1.16 |
Nasirabad | 2020 | -0.0313 |
Naushahro Feroze | 2020 | 0.043 |
Neelum | 2020 | 1.15 |
North Waziristan | 2020 | 1.89 |
Nowshera | 2020 | 1.42 |
Nushki | 2020 | 0.808 |
Okara | 2020 | 1.65 |
Orakzai | 2020 | 1.77 |
Pakpattan | 2020 | 1.41 |
Panjgur | 2020 | 0.219 |
Peshawar | 2020 | 1.42 |
Pishin | 2020 | 1.05 |
Poonch | 2020 | 1.25 |
Quetta | 2020 | 1.02 |
Rahim Yar Khan | 2020 | 0.212 |
Rajanpur | 2020 | 0.856 |
Rawalpindi | 2020 | 1.23 |
Rondu | 2020 | 0.629 |
Sahiwal | 2020 | 1.78 |
Sanghar | 2020 | 0.619 |
Sargodha | 2020 | 1.66 |
Shaheed Benazir Abad | 2020 | 0.353 |
Shaheed Sikandarabad | 2020 | 0.648 |
Shangla | 2020 | 0.832 |
Sheikhupura | 2020 | 1.46 |
Sherani | 2020 | 2.15 |
Shigar | 2020 | 0.518 |
Shikarpur | 2020 | -0.5 |
Sialkot | 2020 | 1.16 |
Sibi | 2020 | 0.743 |
Skardu | 2020 | 0.734 |
Sohbatpur | 2020 | 0.0803 |
South Karachi | 2020 | 0.705 |
South Waziristan | 2020 | 1.95 |
Sudhnoti | 2020 | 1.25 |
Sujawal | 2020 | 1.3 |
Sukkur | 2020 | -0.481 |
Swabi | 2020 | 1.05 |
Swat | 2020 | 0.844 |
Tando Allahyar | 2020 | 1.04 |
Tando Muhammad Khan | 2020 | 1.04 |
Tangir | 2020 | 0.82 |
Tank | 2020 | 1.81 |
Tharparkar | 2020 | 0.706 |
Thatta | 2020 | 1.16 |
Toba Tek Singh | 2020 | 1.78 |
Tor Ghar | 2020 | 0.832 |
Umer Kot | 2020 | 0.709 |
Upper Dir | 2020 | 0.844 |
Vehari | 2020 | 1.71 |
Washuk | 2020 | 0.302 |
West Karachi | 2020 | 0.997 |
Zhob | 2020 | 2.14 |
Ziarat | 2020 | 1.06 |
Abbottabad | 2021 | -0.459 |
Astore | 2021 | -0.366 |
Attock | 2021 | -0.143 |
Awaran | 2021 | -1.39 |
Badin | 2021 | 0.737 |
Bagh | 2021 | -0.0179 |
Bahawalnagar | 2021 | 0.231 |
Bahawalpur | 2021 | -0.111 |
Bajaur | 2021 | -0.869 |
Bannu | 2021 | -0.282 |
Barkhan | 2021 | 0.126 |
Batagram | 2021 | -0.611 |
Bhakkar | 2021 | 0.0583 |
Bhimber | 2021 | 0.361 |
Buner | 2021 | -0.49 |
Central Karachi | 2021 | 0.318 |
Chagai | 2021 | -2.1 |
Chakwal | 2021 | 0.0766 |
Chaman | 2021 | -1.33 |
Charsadda | 2021 | -0.599 |
Chiniot | 2021 | 0.51 |
Chitral Lower | 2021 | -1.29 |
Chitral Upper | 2021 | -0.959 |
D. I. Khan | 2021 | -0.156 |
Dadu | 2021 | -0.178 |
Darel | 2021 | -0.635 |
Dera Bugti | 2021 | -0.575 |
Dera Ghazi Khan | 2021 | 0.228 |
Diamir | 2021 | -0.484 |
Duki | 2021 | -0.17 |
East Karachi | 2021 | 0.318 |
Faisalabad | 2021 | 0.687 |
Ghanche | 2021 | 0.784 |
Ghizer | 2021 | -0.634 |
Ghotki | 2021 | -1.15 |
Gilgit | 2021 | -0.462 |
Gujranwala | 2021 | 0.546 |
Gujrat | 2021 | 0.449 |
Gupis-Yasin | 2021 | -0.803 |
Gwadar | 2021 | -2.04 |
Hafizabad | 2021 | 0.492 |
Hangu | 2021 | -0.41 |
Haripur | 2021 | -0.49 |
Harnai | 2021 | -0.637 |
Haveli | 2021 | 0.287 |
Hunza | 2021 | 0.111 |
Hyderabad | 2021 | 0.486 |
Islamabad | 2021 | -0.179 |
Jacobabad | 2021 | -0.948 |
Jaffarabad | 2021 | -0.948 |
Jamshoro | 2021 | 0.287 |
Jhal Magsi | 2021 | -0.665 |
Jhang | 2021 | 0.441 |
Jhelum | 2021 | 0.247 |
Jhelum Valley | 2021 | -0.284 |
Kachhi | 2021 | -0.784 |
Kalat | 2021 | -0.863 |
Kambar Shahdad Kot | 2021 | -0.833 |
Karak | 2021 | -0.238 |
Kashmore | 2021 | -0.944 |
Kasur | 2021 | 0.816 |
Kech | 2021 | -1.99 |
Khairpur | 2021 | -0.543 |
Khanewal | 2021 | 0.117 |
Kharan | 2021 | -1.33 |
Kharmang | 2021 | 0.119 |
Khushab | 2021 | 0.184 |
Khuzdar | 2021 | -0.168 |
Khyber | 2021 | -0.511 |
Killa Abdullah | 2021 | -1.33 |
Killa Saifullah | 2021 | -0.894 |
Kohat | 2021 | -0.163 |
Kohistan Lower | 2021 | -0.826 |
Kohistan Upper | 2021 | -0.716 |
Kohlu | 2021 | -0.357 |
Kolai Palas Kohistan | 2021 | -0.716 |
Korangi Karachi | 2021 | 0.318 |
Kotli | 2021 | 0.16 |
Kurram | 2021 | -0.758 |
Lahore | 2021 | 0.816 |
Lakki Marwat | 2021 | -0.282 |
Larkana | 2021 | -1.03 |
Lasbela | 2021 | -0.305 |
Lehri | 2021 | -0.798 |
Leiah | 2021 | 0.077 |
Lodhran | 2021 | 0.0743 |
Loralai | 2021 | -0.45 |
Lower Dir | 2021 | -0.869 |
Malakand | 2021 | -0.869 |
Malir Karachi | 2021 | 0.131 |
Mandi Bahauddin | 2021 | 0.414 |
Mansehra | 2021 | -0.611 |
Mardan | 2021 | -0.552 |
Mastung | 2021 | -0.976 |
Matiari | 2021 | 0.244 |
Mianwali | 2021 | 0.0199 |
Mirpur | 2021 | 0.16 |
Mirpur Khas | 2021 | 0.327 |
Mohmand | 2021 | -0.596 |
Multan | 2021 | 0.155 |
Musakhel | 2021 | -0.151 |
Muzaffarabad | 2021 | -0.284 |
Muzaffargarh | 2021 | 0.06 |
Nagar | 2021 | -0.242 |
Nankana Sahib | 2021 | 0.807 |
Narowal | 2021 | 0.52 |
Nasirabad | 2021 | -0.833 |
Naushahro Feroze | 2021 | -0.208 |
Neelum | 2021 | -0.236 |
North Waziristan | 2021 | -0.815 |
Nowshera | 2021 | -0.468 |
Nushki | 2021 | -1.59 |
Okara | 2021 | 1.04 |
Orakzai | 2021 | -0.628 |
Pakpattan | 2021 | 0.359 |
Panjgur | 2021 | -1.59 |
Peshawar | 2021 | -0.468 |
Pishin | 2021 | -1.15 |
Poonch | 2021 | -0.0179 |
Quetta | 2021 | -1.13 |
Rahim Yar Khan | 2021 | -0.718 |
Rajanpur | 2021 | -0.172 |
Rawalpindi | 2021 | 0.0194 |
Rondu | 2021 | -0.274 |
Sahiwal | 2021 | 0.401 |
Sanghar | 2021 | 0.0942 |
Sargodha | 2021 | 0.29 |
Shaheed Benazir Abad | 2021 | 0.0749 |
Shaheed Sikandarabad | 2021 | -0.321 |
Shangla | 2021 | -0.757 |
Sheikhupura | 2021 | 0.622 |
Sherani | 2021 | -0.712 |
Shigar | 2021 | 0.271 |
Shikarpur | 2021 | -1.13 |
Sialkot | 2021 | 0.52 |
Sibi | 2021 | -0.732 |
Skardu | 2021 | 0.156 |
Sohbatpur | 2021 | -0.725 |
South Karachi | 2021 | 0.0411 |
South Waziristan | 2021 | -0.791 |
Sudhnoti | 2021 | -0.0179 |
Sujawal | 2021 | 0.875 |
Sukkur | 2021 | -1.18 |
Swabi | 2021 | -0.552 |
Swat | 2021 | -0.931 |
Tando Allahyar | 2021 | 0.49 |
Tando Muhammad Khan | 2021 | 0.49 |
Tangir | 2021 | -0.753 |
Tank | 2021 | -0.403 |
Tharparkar | 2021 | -0.11 |
Thatta | 2021 | 0.582 |
Toba Tek Singh | 2021 | 0.401 |
Tor Ghar | 2021 | -0.757 |
Umer Kot | 2021 | -0.00649 |
Upper Dir | 2021 | -0.931 |
Vehari | 2021 | 0.117 |
Washuk | 2021 | -1.95 |
West Karachi | 2021 | 0.318 |
Zhob | 2021 | -0.761 |
Ziarat | 2021 | -0.637 |
## Subset the dataframe by year
spei2010 = spei2 %>%
filter(year == 2010)
spei2011 = spei2 %>%
filter(year == 2011)
spei2012 = spei2 %>%
filter(year == 2012)
spei2013 = spei2 %>%
filter(year == 2013)
spei2014 = spei2 %>%
filter(year == 2014)
spei2015 = spei2 %>%
filter(year == 2015)
spei2016 = spei2 %>%
filter(year == 2016)
spei2017 = spei2 %>%
filter(year == 2017)
spei2018 = spei2 %>%
filter(year == 2018)
spei2019 = spei2 %>%
filter(year == 2019)
spei2020 = spei2 %>%
filter(year == 2020)
spei2021 = spei2 %>%
filter(year == 2021)
## Check
head(spei2010)
location_id | year | spei |
---|---|---|
Abbottabad | 2010 | 0.7 |
Astore | 2010 | 0.913 |
Attock | 2010 | 0.42 |
Awaran | 2010 | -1.43 |
Badin | 2010 | 0.579 |
Bagh | 2010 | 0.273 |
## Join the spei values to the test.df
test.df$spei_2010 = spei2010$spei
test.df$spei_2011 = spei2011$spei
test.df$spei_2012 = spei2012$spei
test.df$spei_2013 = spei2013$spei
test.df$spei_2014 = spei2014$spei
test.df$spei_2015 = spei2015$spei
test.df$spei_2016 = spei2016$spei
test.df$spei_2017 = spei2017$spei
test.df$spei_2018 = spei2018$spei
test.df$spei_2019 = spei2019$spei
test.df$spei_2020 = spei2020$spei
test.df$spei_2021 = spei2021$spei
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 |
## Imputing the SPEI values for 2022??
spei_2022 = spei2021
## Check
spei_2022
location_id | year | spei |
---|---|---|
Abbottabad | 2021 | -0.459 |
Astore | 2021 | -0.366 |
Attock | 2021 | -0.143 |
Awaran | 2021 | -1.39 |
Badin | 2021 | 0.737 |
Bagh | 2021 | -0.0179 |
Bahawalnagar | 2021 | 0.231 |
Bahawalpur | 2021 | -0.111 |
Bajaur | 2021 | -0.869 |
Bannu | 2021 | -0.282 |
Barkhan | 2021 | 0.126 |
Batagram | 2021 | -0.611 |
Bhakkar | 2021 | 0.0583 |
Bhimber | 2021 | 0.361 |
Buner | 2021 | -0.49 |
Central Karachi | 2021 | 0.318 |
Chagai | 2021 | -2.1 |
Chakwal | 2021 | 0.0766 |
Chaman | 2021 | -1.33 |
Charsadda | 2021 | -0.599 |
Chiniot | 2021 | 0.51 |
Chitral Lower | 2021 | -1.29 |
Chitral Upper | 2021 | -0.959 |
D. I. Khan | 2021 | -0.156 |
Dadu | 2021 | -0.178 |
Darel | 2021 | -0.635 |
Dera Bugti | 2021 | -0.575 |
Dera Ghazi Khan | 2021 | 0.228 |
Diamir | 2021 | -0.484 |
Duki | 2021 | -0.17 |
East Karachi | 2021 | 0.318 |
Faisalabad | 2021 | 0.687 |
Ghanche | 2021 | 0.784 |
Ghizer | 2021 | -0.634 |
Ghotki | 2021 | -1.15 |
Gilgit | 2021 | -0.462 |
Gujranwala | 2021 | 0.546 |
Gujrat | 2021 | 0.449 |
Gupis-Yasin | 2021 | -0.803 |
Gwadar | 2021 | -2.04 |
Hafizabad | 2021 | 0.492 |
Hangu | 2021 | -0.41 |
Haripur | 2021 | -0.49 |
Harnai | 2021 | -0.637 |
Haveli | 2021 | 0.287 |
Hunza | 2021 | 0.111 |
Hyderabad | 2021 | 0.486 |
Islamabad | 2021 | -0.179 |
Jacobabad | 2021 | -0.948 |
Jaffarabad | 2021 | -0.948 |
Jamshoro | 2021 | 0.287 |
Jhal Magsi | 2021 | -0.665 |
Jhang | 2021 | 0.441 |
Jhelum | 2021 | 0.247 |
Jhelum Valley | 2021 | -0.284 |
Kachhi | 2021 | -0.784 |
Kalat | 2021 | -0.863 |
Kambar Shahdad Kot | 2021 | -0.833 |
Karak | 2021 | -0.238 |
Kashmore | 2021 | -0.944 |
Kasur | 2021 | 0.816 |
Kech | 2021 | -1.99 |
Khairpur | 2021 | -0.543 |
Khanewal | 2021 | 0.117 |
Kharan | 2021 | -1.33 |
Kharmang | 2021 | 0.119 |
Khushab | 2021 | 0.184 |
Khuzdar | 2021 | -0.168 |
Khyber | 2021 | -0.511 |
Killa Abdullah | 2021 | -1.33 |
Killa Saifullah | 2021 | -0.894 |
Kohat | 2021 | -0.163 |
Kohistan Lower | 2021 | -0.826 |
Kohistan Upper | 2021 | -0.716 |
Kohlu | 2021 | -0.357 |
Kolai Palas Kohistan | 2021 | -0.716 |
Korangi Karachi | 2021 | 0.318 |
Kotli | 2021 | 0.16 |
Kurram | 2021 | -0.758 |
Lahore | 2021 | 0.816 |
Lakki Marwat | 2021 | -0.282 |
Larkana | 2021 | -1.03 |
Lasbela | 2021 | -0.305 |
Lehri | 2021 | -0.798 |
Leiah | 2021 | 0.077 |
Lodhran | 2021 | 0.0743 |
Loralai | 2021 | -0.45 |
Lower Dir | 2021 | -0.869 |
Malakand | 2021 | -0.869 |
Malir Karachi | 2021 | 0.131 |
Mandi Bahauddin | 2021 | 0.414 |
Mansehra | 2021 | -0.611 |
Mardan | 2021 | -0.552 |
Mastung | 2021 | -0.976 |
Matiari | 2021 | 0.244 |
Mianwali | 2021 | 0.0199 |
Mirpur | 2021 | 0.16 |
Mirpur Khas | 2021 | 0.327 |
Mohmand | 2021 | -0.596 |
Multan | 2021 | 0.155 |
Musakhel | 2021 | -0.151 |
Muzaffarabad | 2021 | -0.284 |
Muzaffargarh | 2021 | 0.06 |
Nagar | 2021 | -0.242 |
Nankana Sahib | 2021 | 0.807 |
Narowal | 2021 | 0.52 |
Nasirabad | 2021 | -0.833 |
Naushahro Feroze | 2021 | -0.208 |
Neelum | 2021 | -0.236 |
North Waziristan | 2021 | -0.815 |
Nowshera | 2021 | -0.468 |
Nushki | 2021 | -1.59 |
Okara | 2021 | 1.04 |
Orakzai | 2021 | -0.628 |
Pakpattan | 2021 | 0.359 |
Panjgur | 2021 | -1.59 |
Peshawar | 2021 | -0.468 |
Pishin | 2021 | -1.15 |
Poonch | 2021 | -0.0179 |
Quetta | 2021 | -1.13 |
Rahim Yar Khan | 2021 | -0.718 |
Rajanpur | 2021 | -0.172 |
Rawalpindi | 2021 | 0.0194 |
Rondu | 2021 | -0.274 |
Sahiwal | 2021 | 0.401 |
Sanghar | 2021 | 0.0942 |
Sargodha | 2021 | 0.29 |
Shaheed Benazir Abad | 2021 | 0.0749 |
Shaheed Sikandarabad | 2021 | -0.321 |
Shangla | 2021 | -0.757 |
Sheikhupura | 2021 | 0.622 |
Sherani | 2021 | -0.712 |
Shigar | 2021 | 0.271 |
Shikarpur | 2021 | -1.13 |
Sialkot | 2021 | 0.52 |
Sibi | 2021 | -0.732 |
Skardu | 2021 | 0.156 |
Sohbatpur | 2021 | -0.725 |
South Karachi | 2021 | 0.0411 |
South Waziristan | 2021 | -0.791 |
Sudhnoti | 2021 | -0.0179 |
Sujawal | 2021 | 0.875 |
Sukkur | 2021 | -1.18 |
Swabi | 2021 | -0.552 |
Swat | 2021 | -0.931 |
Tando Allahyar | 2021 | 0.49 |
Tando Muhammad Khan | 2021 | 0.49 |
Tangir | 2021 | -0.753 |
Tank | 2021 | -0.403 |
Tharparkar | 2021 | -0.11 |
Thatta | 2021 | 0.582 |
Toba Tek Singh | 2021 | 0.401 |
Tor Ghar | 2021 | -0.757 |
Umer Kot | 2021 | -0.00649 |
Upper Dir | 2021 | -0.931 |
Vehari | 2021 | 0.117 |
Washuk | 2021 | -1.95 |
West Karachi | 2021 | 0.318 |
Zhob | 2021 | -0.761 |
Ziarat | 2021 | -0.637 |
## Change year value to 2022
spei_2022 = spei_2022 %>%
mutate(year = 2022)
## Check
spei_2022
location_id | year | spei |
---|---|---|
Abbottabad | 2.02e+03 | -0.459 |
Astore | 2.02e+03 | -0.366 |
Attock | 2.02e+03 | -0.143 |
Awaran | 2.02e+03 | -1.39 |
Badin | 2.02e+03 | 0.737 |
Bagh | 2.02e+03 | -0.0179 |
Bahawalnagar | 2.02e+03 | 0.231 |
Bahawalpur | 2.02e+03 | -0.111 |
Bajaur | 2.02e+03 | -0.869 |
Bannu | 2.02e+03 | -0.282 |
Barkhan | 2.02e+03 | 0.126 |
Batagram | 2.02e+03 | -0.611 |
Bhakkar | 2.02e+03 | 0.0583 |
Bhimber | 2.02e+03 | 0.361 |
Buner | 2.02e+03 | -0.49 |
Central Karachi | 2.02e+03 | 0.318 |
Chagai | 2.02e+03 | -2.1 |
Chakwal | 2.02e+03 | 0.0766 |
Chaman | 2.02e+03 | -1.33 |
Charsadda | 2.02e+03 | -0.599 |
Chiniot | 2.02e+03 | 0.51 |
Chitral Lower | 2.02e+03 | -1.29 |
Chitral Upper | 2.02e+03 | -0.959 |
D. I. Khan | 2.02e+03 | -0.156 |
Dadu | 2.02e+03 | -0.178 |
Darel | 2.02e+03 | -0.635 |
Dera Bugti | 2.02e+03 | -0.575 |
Dera Ghazi Khan | 2.02e+03 | 0.228 |
Diamir | 2.02e+03 | -0.484 |
Duki | 2.02e+03 | -0.17 |
East Karachi | 2.02e+03 | 0.318 |
Faisalabad | 2.02e+03 | 0.687 |
Ghanche | 2.02e+03 | 0.784 |
Ghizer | 2.02e+03 | -0.634 |
Ghotki | 2.02e+03 | -1.15 |
Gilgit | 2.02e+03 | -0.462 |
Gujranwala | 2.02e+03 | 0.546 |
Gujrat | 2.02e+03 | 0.449 |
Gupis-Yasin | 2.02e+03 | -0.803 |
Gwadar | 2.02e+03 | -2.04 |
Hafizabad | 2.02e+03 | 0.492 |
Hangu | 2.02e+03 | -0.41 |
Haripur | 2.02e+03 | -0.49 |
Harnai | 2.02e+03 | -0.637 |
Haveli | 2.02e+03 | 0.287 |
Hunza | 2.02e+03 | 0.111 |
Hyderabad | 2.02e+03 | 0.486 |
Islamabad | 2.02e+03 | -0.179 |
Jacobabad | 2.02e+03 | -0.948 |
Jaffarabad | 2.02e+03 | -0.948 |
Jamshoro | 2.02e+03 | 0.287 |
Jhal Magsi | 2.02e+03 | -0.665 |
Jhang | 2.02e+03 | 0.441 |
Jhelum | 2.02e+03 | 0.247 |
Jhelum Valley | 2.02e+03 | -0.284 |
Kachhi | 2.02e+03 | -0.784 |
Kalat | 2.02e+03 | -0.863 |
Kambar Shahdad Kot | 2.02e+03 | -0.833 |
Karak | 2.02e+03 | -0.238 |
Kashmore | 2.02e+03 | -0.944 |
Kasur | 2.02e+03 | 0.816 |
Kech | 2.02e+03 | -1.99 |
Khairpur | 2.02e+03 | -0.543 |
Khanewal | 2.02e+03 | 0.117 |
Kharan | 2.02e+03 | -1.33 |
Kharmang | 2.02e+03 | 0.119 |
Khushab | 2.02e+03 | 0.184 |
Khuzdar | 2.02e+03 | -0.168 |
Khyber | 2.02e+03 | -0.511 |
Killa Abdullah | 2.02e+03 | -1.33 |
Killa Saifullah | 2.02e+03 | -0.894 |
Kohat | 2.02e+03 | -0.163 |
Kohistan Lower | 2.02e+03 | -0.826 |
Kohistan Upper | 2.02e+03 | -0.716 |
Kohlu | 2.02e+03 | -0.357 |
Kolai Palas Kohistan | 2.02e+03 | -0.716 |
Korangi Karachi | 2.02e+03 | 0.318 |
Kotli | 2.02e+03 | 0.16 |
Kurram | 2.02e+03 | -0.758 |
Lahore | 2.02e+03 | 0.816 |
Lakki Marwat | 2.02e+03 | -0.282 |
Larkana | 2.02e+03 | -1.03 |
Lasbela | 2.02e+03 | -0.305 |
Lehri | 2.02e+03 | -0.798 |
Leiah | 2.02e+03 | 0.077 |
Lodhran | 2.02e+03 | 0.0743 |
Loralai | 2.02e+03 | -0.45 |
Lower Dir | 2.02e+03 | -0.869 |
Malakand | 2.02e+03 | -0.869 |
Malir Karachi | 2.02e+03 | 0.131 |
Mandi Bahauddin | 2.02e+03 | 0.414 |
Mansehra | 2.02e+03 | -0.611 |
Mardan | 2.02e+03 | -0.552 |
Mastung | 2.02e+03 | -0.976 |
Matiari | 2.02e+03 | 0.244 |
Mianwali | 2.02e+03 | 0.0199 |
Mirpur | 2.02e+03 | 0.16 |
Mirpur Khas | 2.02e+03 | 0.327 |
Mohmand | 2.02e+03 | -0.596 |
Multan | 2.02e+03 | 0.155 |
Musakhel | 2.02e+03 | -0.151 |
Muzaffarabad | 2.02e+03 | -0.284 |
Muzaffargarh | 2.02e+03 | 0.06 |
Nagar | 2.02e+03 | -0.242 |
Nankana Sahib | 2.02e+03 | 0.807 |
Narowal | 2.02e+03 | 0.52 |
Nasirabad | 2.02e+03 | -0.833 |
Naushahro Feroze | 2.02e+03 | -0.208 |
Neelum | 2.02e+03 | -0.236 |
North Waziristan | 2.02e+03 | -0.815 |
Nowshera | 2.02e+03 | -0.468 |
Nushki | 2.02e+03 | -1.59 |
Okara | 2.02e+03 | 1.04 |
Orakzai | 2.02e+03 | -0.628 |
Pakpattan | 2.02e+03 | 0.359 |
Panjgur | 2.02e+03 | -1.59 |
Peshawar | 2.02e+03 | -0.468 |
Pishin | 2.02e+03 | -1.15 |
Poonch | 2.02e+03 | -0.0179 |
Quetta | 2.02e+03 | -1.13 |
Rahim Yar Khan | 2.02e+03 | -0.718 |
Rajanpur | 2.02e+03 | -0.172 |
Rawalpindi | 2.02e+03 | 0.0194 |
Rondu | 2.02e+03 | -0.274 |
Sahiwal | 2.02e+03 | 0.401 |
Sanghar | 2.02e+03 | 0.0942 |
Sargodha | 2.02e+03 | 0.29 |
Shaheed Benazir Abad | 2.02e+03 | 0.0749 |
Shaheed Sikandarabad | 2.02e+03 | -0.321 |
Shangla | 2.02e+03 | -0.757 |
Sheikhupura | 2.02e+03 | 0.622 |
Sherani | 2.02e+03 | -0.712 |
Shigar | 2.02e+03 | 0.271 |
Shikarpur | 2.02e+03 | -1.13 |
Sialkot | 2.02e+03 | 0.52 |
Sibi | 2.02e+03 | -0.732 |
Skardu | 2.02e+03 | 0.156 |
Sohbatpur | 2.02e+03 | -0.725 |
South Karachi | 2.02e+03 | 0.0411 |
South Waziristan | 2.02e+03 | -0.791 |
Sudhnoti | 2.02e+03 | -0.0179 |
Sujawal | 2.02e+03 | 0.875 |
Sukkur | 2.02e+03 | -1.18 |
Swabi | 2.02e+03 | -0.552 |
Swat | 2.02e+03 | -0.931 |
Tando Allahyar | 2.02e+03 | 0.49 |
Tando Muhammad Khan | 2.02e+03 | 0.49 |
Tangir | 2.02e+03 | -0.753 |
Tank | 2.02e+03 | -0.403 |
Tharparkar | 2.02e+03 | -0.11 |
Thatta | 2.02e+03 | 0.582 |
Toba Tek Singh | 2.02e+03 | 0.401 |
Tor Ghar | 2.02e+03 | -0.757 |
Umer Kot | 2.02e+03 | -0.00649 |
Upper Dir | 2.02e+03 | -0.931 |
Vehari | 2.02e+03 | 0.117 |
Washuk | 2.02e+03 | -1.95 |
West Karachi | 2.02e+03 | 0.318 |
Zhob | 2.02e+03 | -0.761 |
Ziarat | 2.02e+03 | -0.637 |
## Add to dataframe
test.df$spei_2022 = spei_2022$spei
Note:
The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) data set characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest.
Temp Resolution 2010-2020
Uses six input components: 1. Child Dependency Ration (CDR) 2. Infant Mortality Rates (IMR) 3. Subnational Human Development Index (SHDI) 4. Ratio of Built/Non-built up areas (BUILT) 5. Intensity of Nighttime Lights 6. Linear Regression of VNL (Lights - temporal requirement?)
## Read in the index
dep.ind = raster("../data/deprivation_index/dep_ind_tif/povmap-grdi-v1.tif")
## Crop and mask pipeline
dep.ind = dep.ind %>%
crop(pak.bound) %>%
mask(mask = pak.bound)
## Crop it
#dep.ind = crop(dep.ind, pak.bound)
# Mask it
#dep.ind = mask(dep.ind, mask = pak.bound)
## Check
plot(dep.ind)
plot(st_geometry(pak.bound.adm2),
add = TRUE)
## Extract the mean value by district
dep.ind.extract = raster::extract(dep.ind, pak.bound.adm2, fun = mean,
na.rm = TRUE)
## Check
summary(dep.ind.extract)
## V1
## Min. :32.88
## 1st Qu.:76.79
## Median :82.27
## Mean :79.82
## 3rd Qu.:85.37
## Max. :93.93
## Add the deprivation index to the dataframe
## Reminder - this data set covers 2010-2020
test.df$dep.index_2010 = dep.ind.extract[,1]
test.df$dep.index_2011 = dep.ind.extract[,1]
test.df$dep.index_2012 = dep.ind.extract[,1]
test.df$dep.index_2013 = dep.ind.extract[,1]
test.df$dep.index_2014 = dep.ind.extract[,1]
test.df$dep.index_2015 = dep.ind.extract[,1]
test.df$dep.index_2016 = dep.ind.extract[,1]
test.df$dep.index_2017 = dep.ind.extract[,1]
test.df$dep.index_2018 = dep.ind.extract[,1]
test.df$dep.index_2019 = dep.ind.extract[,1]
test.df$dep.index_2020 = dep.ind.extract[,1]
test.df$dep.index_2021 = dep.ind.extract[,1]
test.df$dep.index_2022 = dep.ind.extract[,1]
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 |
Note:
The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid.
GHS-SMOD layers are GeoTIFF raster products that contain grid cells with integers, which represent the assigned settlement classification per grid cell.
## Read in the data
## SMOD - Classification Variable
urban.2015 = raster("../data/urbanization/ghsl-population-built-up-estimates-degree-urban-smod-ghsl-2014-2015-30ss-v1-geotiff/ghsl-population-built-up-estimates-degree-urban-smod-ghsl-2014-2015-30ss-v1/ghsl-population-built-up-estimates-degree-urban-smod_smod-30ss-2015.tif")
## Crop and mask pipeline
urban.2015 = urban.2015 %>%
crop(pak.bound) %>%
mask(mask = pak.bound)
## Crop it
#dep.ind = crop(dep.ind, pak.bound)
# Mask it
#dep.ind = mask(dep.ind, mask = pak.bound)
## Check
plot(urban.2015)
plot(st_geometry(pak.bound.adm2),
add = TRUE)
## Extract the mean value by district
urban.2015.extract = raster::extract(urban.2015, pak.bound.adm2, fun = mean)
## Check
summary(urban.2015.extract)
## V1
## Min. :11.00
## 1st Qu.:11.11
## Median :11.58
## Mean :12.59
## 3rd Qu.:12.76
## Max. :30.00
Note:
GHS-BUILT layers are GeoTIFF raster products that contain grid cells with continuous variables, which represent the proportion of the building footprint area within the total size of the grid cell.
## Read in built area
built.2014 = raster("../data/urbanization/ghsl-population-built-up-estimates-degree-urban-smod-ghsl-2014-2015-30ss-v1-geotiff/ghsl-population-built-up-estimates-degree-urban-smod-ghsl-2014-2015-30ss-v1/ghsl-population-built-up-estimates-degree-urban-smod_built-30ss-2014.tif")
## Crop and mask pipeline
built.2014 = built.2014 %>%
crop(pak.bound) %>%
mask(mask = pak.bound)
## Crop it
#dep.ind = crop(dep.ind, pak.bound)
# Mask it
#dep.ind = mask(dep.ind, mask = pak.bound)
## Check
plot(built.2014)
plot(st_geometry(pak.bound.adm2),
add = TRUE)
## Extract the mean value by district
built.2014.extract = raster::extract(built.2014, pak.bound.adm2, fun = mean)
## Check
summary(built.2014.extract)
## V1
## Min. : 0.0000
## 1st Qu.: 0.0151
## Median : 0.2054
## Mean : 2.3783
## 3rd Qu.: 1.9565
## Max. :88.2998
## Add BUILT to test.df
## Reminder - this data is only from 2014**
test.df$built_2010 = built.2014.extract[,1]
test.df$built_2011 = built.2014.extract[,1]
test.df$built_2012 = built.2014.extract[,1]
test.df$built_2013 = built.2014.extract[,1]
test.df$built_2014 = built.2014.extract[,1]
test.df$built_2015 = built.2014.extract[,1]
test.df$built_2016 = built.2014.extract[,1]
test.df$built_2017 = built.2014.extract[,1]
test.df$built_2018 = built.2014.extract[,1]
test.df$built_2019 = built.2014.extract[,1]
test.df$built_2020 = built.2014.extract[,1]
test.df$built_2021 = built.2014.extract[,1]
test.df$built_2022 = built.2014.extract[,1]
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 |
## Read in the temperature data
pak.temp = raster("../data/temperature/seasonal_temp_data.nc", varname = "t2m")
## Look at the data
pak.temp
## class : RasterLayer
## band : 1 (of 52 bands)
## dimensions : 201, 181, 36381 (nrow, ncol, ncell)
## resolution : 0.1, 0.1 (x, y)
## extent : 59.95, 78.05, 19.95, 40.05 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : seasonal_temp_data.nc
## names : X2.metre.temperature
## z-value : 2010-01-01 12:00:00
## zvar : t2m
## 52 bands = 13 years * 4 months (Jan, Apr, July, Oct) worth of observations
## **Reminder the temperature data is in Kelvin**
## Bring in the temp data as a stack in order to extract the bands (year/month that you need)
temp.stack = stack("../data/temperature/seasonal_temp_data.nc", varname = "t2m")
## View the stack
temp.stack
## class : RasterStack
## dimensions : 201, 181, 36381, 52 (nrow, ncol, ncell, nlayers)
## resolution : 0.1, 0.1 (x, y)
## extent : 59.95, 78.05, 19.95, 40.05 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## names : X2010.01.01.12.00.00, X2010.04.01.12.00.00, X2010.07.01.12.00.00, X2010.10.01.12.00.00, X2011.01.01.12.00.00, X2011.04.01.12.00.00, X2011.07.01.12.00.00, X2011.10.01.12.00.00, X2012.01.01.12.00.00, X2012.04.01.12.00.00, X2012.07.01.12.00.00, X2012.10.01.12.00.00, X2013.01.01.12.00.00, X2013.04.01.12.00.00, X2013.07.01.12.00.00, ...
## Date/time : 2010-01-01 12:00:00 - 2022-10-01 12:00:00 (range)
## Crop and Mask Pipeline
temp.stack.mask = temp.stack %>%
crop(pak.bound) %>%
mask(mask = pak.bound)
## Check
temp.pal = brewer.pal(n = 9,
name = "OrRd")
plot(temp.stack.mask,
col = temp.pal)
plot(st_geometry(pak.bound),
border = "black",
add = TRUE)
##----TEST----##
## Works Well!
## Extract the mean value by district
test.temp.extract = raster::extract(temp.stack.mask, pak.bound.adm2, fun = mean, na.rm = TRUE)
## Check
summary(test.temp.extract)
## X2010.01.01.12.00.00 X2010.04.01.12.00.00 X2010.07.01.12.00.00
## Min. :251.6 Min. :263.6 Min. :273.4
## 1st Qu.:284.9 1st Qu.:297.3 1st Qu.:301.1
## Median :290.9 Median :305.8 Median :306.1
## Mean :287.0 Mean :300.9 Mean :303.3
## 3rd Qu.:294.4 3rd Qu.:310.1 3rd Qu.:309.0
## Max. :299.2 Max. :313.9 Max. :313.6
## NA's :1 NA's :1 NA's :1
## X2010.10.01.12.00.00 X2011.01.01.12.00.00 X2011.04.01.12.00.00
## Min. :260.1 Min. :249.0 Min. :261.4
## 1st Qu.:295.7 1st Qu.:282.1 1st Qu.:293.0
## Median :301.6 Median :288.7 Median :301.2
## Mean :298.1 Mean :284.8 Mean :297.0
## 3rd Qu.:305.8 3rd Qu.:291.9 3rd Qu.:306.0
## Max. :309.5 Max. :298.4 Max. :311.1
## NA's :1 NA's :1 NA's :1
## X2011.07.01.12.00.00 X2011.10.01.12.00.00 X2012.01.01.12.00.00
## Min. :276.9 Min. :262.4 Min. :247.3
## 1st Qu.:300.8 1st Qu.:294.0 1st Qu.:278.7
## Median :306.1 Median :300.7 Median :287.1
## Mean :303.5 Mean :296.9 Mean :282.9
## 3rd Qu.:309.3 3rd Qu.:304.7 3rd Qu.:291.4
## Max. :313.9 Max. :307.6 Max. :297.1
## NA's :1 NA's :1 NA's :1
## X2012.04.01.12.00.00 X2012.07.01.12.00.00 X2012.10.01.12.00.00
## Min. :262.1 Min. :275.2 Min. :260.7
## 1st Qu.:292.2 1st Qu.:303.3 1st Qu.:293.1
## Median :301.3 Median :308.5 Median :299.8
## Mean :296.8 Mean :305.2 Mean :295.9
## 3rd Qu.:305.2 3rd Qu.:311.5 3rd Qu.:303.6
## Max. :310.6 Max. :315.0 Max. :308.1
## NA's :1 NA's :1 NA's :1
## X2013.01.01.12.00.00 X2013.04.01.12.00.00 X2013.07.01.12.00.00
## Min. :248.2 Min. :262.1 Min. :275.4
## 1st Qu.:281.7 1st Qu.:292.4 1st Qu.:301.2
## Median :288.6 Median :301.0 Median :306.2
## Mean :284.5 Mean :296.6 Mean :304.0
## 3rd Qu.:292.9 3rd Qu.:305.7 3rd Qu.:309.7
## Max. :298.2 Max. :309.5 Max. :315.0
## NA's :1 NA's :1 NA's :1
## X2013.10.01.12.00.00 X2014.01.01.12.00.00 X2014.04.01.12.00.00
## Min. :264.0 Min. :249.8 Min. :261.5
## 1st Qu.:295.3 1st Qu.:282.4 1st Qu.:292.5
## Median :301.7 Median :289.2 Median :300.5
## Mean :298.3 Mean :284.8 Mean :296.8
## 3rd Qu.:305.5 3rd Qu.:292.6 3rd Qu.:305.9
## Max. :308.9 Max. :297.1 Max. :311.1
## NA's :1 NA's :1 NA's :1
## X2014.07.01.12.00.00 X2014.10.01.12.00.00 X2015.01.01.12.00.00
## Min. :275.2 Min. :262.2 Min. :250.1
## 1st Qu.:301.8 1st Qu.:293.7 1st Qu.:283.2
## Median :306.9 Median :300.2 Median :289.1
## Mean :304.3 Mean :296.7 Mean :285.2
## 3rd Qu.:310.1 3rd Qu.:304.3 3rd Qu.:292.3
## Max. :315.2 Max. :309.2 Max. :298.1
## NA's :1 NA's :1 NA's :1
## X2015.04.01.12.00.00 X2015.07.01.12.00.00 X2015.10.01.12.00.00
## Min. :262.0 Min. :277.1 Min. :260.4
## 1st Qu.:294.4 1st Qu.:300.1 1st Qu.:294.3
## Median :300.7 Median :305.1 Median :300.3
## Mean :297.9 Mean :302.7 Mean :297.1
## 3rd Qu.:307.2 3rd Qu.:307.6 3rd Qu.:304.7
## Max. :312.0 Max. :313.9 Max. :309.2
## NA's :1 NA's :1 NA's :1
## X2016.01.01.12.00.00 X2016.04.01.12.00.00 X2016.07.01.12.00.00
## Min. :250.2 Min. :263.3 Min. :277.1
## 1st Qu.:283.8 1st Qu.:293.8 1st Qu.:301.1
## Median :289.7 Median :302.4 Median :306.3
## Mean :286.0 Mean :298.1 Mean :303.9
## 3rd Qu.:293.5 3rd Qu.:307.2 3rd Qu.:309.5
## Max. :299.5 Max. :311.2 Max. :315.1
## NA's :1 NA's :1 NA's :1
## X2016.10.01.12.00.00 X2017.01.01.12.00.00 X2017.04.01.12.00.00
## Min. :262.2 Min. :250.7 Min. :262.9
## 1st Qu.:295.4 1st Qu.:280.1 1st Qu.:296.7
## Median :302.7 Median :287.7 Median :304.4
## Mean :298.6 Mean :283.9 Mean :300.1
## 3rd Qu.:305.9 3rd Qu.:291.5 3rd Qu.:309.6
## Max. :309.3 Max. :297.8 Max. :313.0
## NA's :1 NA's :1 NA's :1
## X2017.07.01.12.00.00 X2017.10.01.12.00.00 X2018.01.01.12.00.00
## Min. :278.2 Min. :262.3 Min. :249.7
## 1st Qu.:300.6 1st Qu.:296.3 1st Qu.:284.3
## Median :305.7 Median :303.4 Median :290.7
## Mean :303.5 Mean :299.1 Mean :286.7
## 3rd Qu.:309.3 3rd Qu.:306.5 3rd Qu.:294.3
## Max. :314.1 Max. :310.4 Max. :300.1
## NA's :1 NA's :1 NA's :1
## X2018.04.01.12.00.00 X2018.07.01.12.00.00 X2018.10.01.12.00.00
## Min. :262.8 Min. :276.5 Min. :260.4
## 1st Qu.:295.4 1st Qu.:301.5 1st Qu.:293.1
## Median :303.8 Median :306.2 Median :300.5
## Mean :299.3 Mean :304.1 Mean :296.5
## 3rd Qu.:308.3 3rd Qu.:309.8 3rd Qu.:304.9
## Max. :312.5 Max. :315.3 Max. :310.1
## NA's :1 NA's :1 NA's :1
## X2019.01.01.12.00.00 X2019.04.01.12.00.00 X2019.07.01.12.00.00
## Min. :250.1 Min. :264.3 Min. :274.2
## 1st Qu.:281.0 1st Qu.:294.1 1st Qu.:302.2
## Median :288.2 Median :302.1 Median :306.8
## Mean :284.1 Mean :298.4 Mean :304.5
## 3rd Qu.:292.4 3rd Qu.:307.1 3rd Qu.:310.0
## Max. :298.5 Max. :312.3 Max. :315.9
## NA's :1 NA's :1 NA's :1
## X2019.10.01.12.00.00 X2020.01.01.12.00.00 X2020.04.01.12.00.00
## Min. :261.2 Min. :249.3 Min. :262.4
## 1st Qu.:293.6 1st Qu.:278.6 1st Qu.:292.6
## Median :300.5 Median :286.5 Median :300.8
## Mean :296.7 Mean :282.5 Mean :297.0
## 3rd Qu.:304.1 3rd Qu.:289.9 3rd Qu.:306.0
## Max. :307.6 Max. :296.4 Max. :311.9
## NA's :1 NA's :1 NA's :1
## X2020.07.01.12.00.00 X2020.10.01.12.00.00 X2021.01.01.12.00.00
## Min. :275.7 Min. :262.0 Min. :248.9
## 1st Qu.:302.3 1st Qu.:294.8 1st Qu.:283.8
## Median :307.4 Median :302.1 Median :289.4
## Mean :304.7 Mean :297.9 Mean :285.1
## 3rd Qu.:310.8 3rd Qu.:305.5 3rd Qu.:292.7
## Max. :315.3 Max. :308.2 Max. :297.9
## NA's :1 NA's :1 NA's :1
## X2021.04.01.12.00.00 X2021.07.01.12.00.00 X2021.10.01.12.00.00
## Min. :260.2 Min. :277.9 Min. :261.9
## 1st Qu.:294.4 1st Qu.:302.2 1st Qu.:294.4
## Median :302.1 Median :307.0 Median :300.7
## Mean :297.8 Mean :304.3 Mean :297.2
## 3rd Qu.:306.6 3rd Qu.:309.9 3rd Qu.:304.7
## Max. :312.1 Max. :314.6 Max. :307.6
## NA's :1 NA's :1 NA's :1
## X2022.01.01.12.00.00 X2022.04.01.12.00.00 X2022.07.01.12.00.00
## Min. :250.6 Min. :263.2 Min. :279.3
## 1st Qu.:280.4 1st Qu.:299.4 1st Qu.:299.0
## Median :287.7 Median :307.0 Median :303.7
## Mean :283.7 Mean :302.2 Mean :301.3
## 3rd Qu.:291.3 3rd Qu.:311.1 3rd Qu.:305.8
## Max. :296.4 Max. :314.2 Max. :310.9
## NA's :1 NA's :1 NA's :1
## X2022.10.01.12.00.00
## Min. :261.4
## 1st Qu.:295.0
## Median :301.8
## Mean :297.5
## 3rd Qu.:305.1
## Max. :308.1
## NA's :1
## Add the temperature data to the test.df
#add district names to the temperature data frame
dist.names = test.df$districts
## Check the class of the temp extract
class(test.temp.extract)
## [1] "matrix" "array"
## Convert the extract to a dataframe
temp.extract.df = as.data.frame(test.temp.extract)
## Check
class(temp.extract.df)
## [1] "data.frame"
dim(temp.extract.df)
## [1] 160 52
## Good - 160 rows(districts) by 52 time steps
## Change the rownames of the temp.extract df
rownames(temp.extract.df) = dist.names
## Check
rownames(temp.extract.df)
## [1] "Abbottabad" "Astore" "Attock"
## [4] "Awaran" "Badin" "Bagh"
## [7] "Bahawalnagar" "Bahawalpur" "Bajaur"
## [10] "Bannu" "Barkhan" "Batagram"
## [13] "Bhakkar" "Bhimber" "Buner"
## [16] "Central Karachi" "Chagai" "Chakwal"
## [19] "Chaman" "Charsadda" "Chiniot"
## [22] "Chitral Lower" "Chitral Upper" "D. I. Khan"
## [25] "Dadu" "Darel" "Dera Bugti"
## [28] "Dera Ghazi Khan" "Diamir" "Duki"
## [31] "East Karachi" "Faisalabad" "Ghanche"
## [34] "Ghizer" "Ghotki" "Gilgit"
## [37] "Gujranwala" "Gujrat" "Gupis-Yasin"
## [40] "Gwadar" "Hafizabad" "Hangu"
## [43] "Haripur" "Harnai" "Haveli"
## [46] "Hunza" "Hyderabad" "Islamabad"
## [49] "Jacobabad" "Jaffarabad" "Jamshoro"
## [52] "Jhal Magsi" "Jhang" "Jhelum"
## [55] "Jhelum Valley" "Kachhi" "Kalat"
## [58] "Kambar Shahdad Kot" "Karak" "Kashmore"
## [61] "Kasur" "Kech" "Khairpur"
## [64] "Khanewal" "Kharan" "Kharmang"
## [67] "Khushab" "Khuzdar" "Khyber"
## [70] "Killa Abdullah" "Killa Saifullah" "Kohat"
## [73] "Kohistan Lower" "Kohistan Upper" "Kohlu"
## [76] "Kolai Palas Kohistan" "Korangi Karachi" "Kotli"
## [79] "Kurram" "Lahore" "Lakki Marwat"
## [82] "Larkana" "Lasbela" "Lehri"
## [85] "Leiah" "Lodhran" "Loralai"
## [88] "Lower Dir" "Malakand" "Malir Karachi"
## [91] "Mandi Bahauddin" "Mansehra" "Mardan"
## [94] "Mastung" "Matiari" "Mianwali"
## [97] "Mirpur" "Mirpur Khas" "Mohmand"
## [100] "Multan" "Musakhel" "Muzaffarabad"
## [103] "Muzaffargarh" "Nagar" "Nankana Sahib"
## [106] "Narowal" "Nasirabad" "Naushahro Feroze"
## [109] "Neelum" "North Waziristan" "Nowshera"
## [112] "Nushki" "Okara" "Orakzai"
## [115] "Pakpattan" "Panjgur" "Peshawar"
## [118] "Pishin" "Poonch" "Quetta"
## [121] "Rahim Yar Khan" "Rajanpur" "Rawalpindi"
## [124] "Rondu" "Sahiwal" "Sanghar"
## [127] "Sargodha" "Shaheed Benazir Abad" "Shaheed Sikandarabad"
## [130] "Shangla" "Sheikhupura" "Sherani"
## [133] "Shigar" "Shikarpur" "Sialkot"
## [136] "Sibi" "Skardu" "Sohbatpur"
## [139] "South Karachi" "South Waziristan" "Sudhnoti"
## [142] "Sujawal" "Sukkur" "Swabi"
## [145] "Swat" "Tando Allahyar" "Tando Muhammad Khan"
## [148] "Tangir" "Tank" "Tharparkar"
## [151] "Thatta" "Toba Tek Singh" "Tor Ghar"
## [154] "Umer Kot" "Upper Dir" "Vehari"
## [157] "Washuk" "West Karachi" "Zhob"
## [160] "Ziarat"
## Add rownames as a column
temp.extract.df = tibble::rownames_to_column(temp.extract.df, "districts")
## Check
head(temp.extract.df)
districts | X2010.01.01.12.00.00 | X2010.04.01.12.00.00 | X2010.07.01.12.00.00 | X2010.10.01.12.00.00 | X2011.01.01.12.00.00 | X2011.04.01.12.00.00 | X2011.07.01.12.00.00 | X2011.10.01.12.00.00 | X2012.01.01.12.00.00 | X2012.04.01.12.00.00 | X2012.07.01.12.00.00 | X2012.10.01.12.00.00 | X2013.01.01.12.00.00 | X2013.04.01.12.00.00 | X2013.07.01.12.00.00 | X2013.10.01.12.00.00 | X2014.01.01.12.00.00 | X2014.04.01.12.00.00 | X2014.07.01.12.00.00 | X2014.10.01.12.00.00 | X2015.01.01.12.00.00 | X2015.04.01.12.00.00 | X2015.07.01.12.00.00 | X2015.10.01.12.00.00 | X2016.01.01.12.00.00 | X2016.04.01.12.00.00 | X2016.07.01.12.00.00 | X2016.10.01.12.00.00 | X2017.01.01.12.00.00 | X2017.04.01.12.00.00 | X2017.07.01.12.00.00 | X2017.10.01.12.00.00 | X2018.01.01.12.00.00 | X2018.04.01.12.00.00 | X2018.07.01.12.00.00 | X2018.10.01.12.00.00 | X2019.01.01.12.00.00 | X2019.04.01.12.00.00 | X2019.07.01.12.00.00 | X2019.10.01.12.00.00 | X2020.01.01.12.00.00 | X2020.04.01.12.00.00 | X2020.07.01.12.00.00 | X2020.10.01.12.00.00 | X2021.01.01.12.00.00 | X2021.04.01.12.00.00 | X2021.07.01.12.00.00 | X2021.10.01.12.00.00 | X2022.01.01.12.00.00 | X2022.04.01.12.00.00 | X2022.07.01.12.00.00 | X2022.10.01.12.00.00 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 285 | 295 | 299 | 294 | 282 | 292 | 299 | 293 | 280 | 292 | 301 | 292 | 282 | 292 | 300 | 294 | 283 | 291 | 299 | 292 | 283 | 293 | 298 | 293 | 283 | 293 | 299 | 295 | 280 | 294 | 299 | 295 | 284 | 294 | 299 | 292 | 279 | 294 | 299 | 293 | 280 | 292 | 300 | 295 | 284 | 292 | 299 | 293 | 281 | 298 | 298 | 294 |
Astore | 260 | 271 | 284 | 274 | 257 | 269 | 285 | 274 | 256 | 270 | 285 | 270 | 256 | 270 | 286 | 277 | 258 | 269 | 286 | 272 | 258 | 270 | 286 | 273 | 258 | 270 | 286 | 275 | 259 | 270 | 286 | 276 | 258 | 271 | 286 | 269 | 257 | 272 | 287 | 273 | 257 | 270 | 285 | 275 | 257 | 268 | 286 | 273 | 259 | 272 | 287 | 273 |
Attock | 290 | 305 | 307 | 301 | 288 | 300 | 307 | 300 | 286 | 301 | 311 | 299 | 288 | 300 | 307 | 301 | 289 | 299 | 308 | 299 | 289 | 300 | 305 | 300 | 289 | 300 | 306 | 303 | 287 | 303 | 307 | 303 | 290 | 302 | 307 | 299 | 286 | 302 | 308 | 301 | 286 | 299 | 308 | 301 | 289 | 300 | 308 | 300 | 287 | 307 | 305 | 302 |
Awaran | 296 | 310 | 308 | 307 | 296 | 306 | 309 | 305 | 293 | 307 | 310 | 305 | 295 | 305 | 309 | 306 | 294 | 307 | 309 | 306 | 295 | 309 | 309 | 306 | 297 | 307 | 309 | 307 | 294 | 310 | 308 | 308 | 297 | 309 | 310 | 306 | 295 | 308 | 309 | 305 | 292 | 308 | 310 | 306 | 295 | 308 | 309 | 305 | 293 | 310 | 303 | 306 |
Badin | 299 | 312 | 307 | 309 | 298 | 310 | 308 | 305 | 296 | 310 | 308 | 307 | 298 | 309 | 307 | 308 | 296 | 310 | 309 | 308 | 298 | 311 | 307 | 308 | 299 | 310 | 308 | 308 | 297 | 311 | 305 | 309 | 299 | 311 | 308 | 309 | 298 | 311 | 309 | 307 | 296 | 311 | 309 | 308 | 297 | 311 | 308 | 306 | 296 | 313 | 304 | 307 |
Bagh | 283 | 293 | 297 | 292 | 278 | 289 | 296 | 291 | 275 | 289 | 298 | 290 | 278 | 289 | 297 | 292 | 279 | 288 | 296 | 290 | 281 | 290 | 296 | 291 | 280 | 291 | 296 | 293 | 276 | 292 | 296 | 293 | 282 | 292 | 296 | 289 | 275 | 291 | 296 | 290 | 275 | 289 | 297 | 293 | 279 | 289 | 296 | 291 | 276 | 295 | 296 | 291 |
## Check the NAs
## Find the NAs
#temp.df.nas = temp.extract.df %>%
#filter_all(any_vars(. %in% c(NA)))
## Check
#temp.df.nas
temp.extract.df[!complete.cases(temp.extract.df), ]
districts | X2010.01.01.12.00.00 | X2010.04.01.12.00.00 | X2010.07.01.12.00.00 | X2010.10.01.12.00.00 | X2011.01.01.12.00.00 | X2011.04.01.12.00.00 | X2011.07.01.12.00.00 | X2011.10.01.12.00.00 | X2012.01.01.12.00.00 | X2012.04.01.12.00.00 | X2012.07.01.12.00.00 | X2012.10.01.12.00.00 | X2013.01.01.12.00.00 | X2013.04.01.12.00.00 | X2013.07.01.12.00.00 | X2013.10.01.12.00.00 | X2014.01.01.12.00.00 | X2014.04.01.12.00.00 | X2014.07.01.12.00.00 | X2014.10.01.12.00.00 | X2015.01.01.12.00.00 | X2015.04.01.12.00.00 | X2015.07.01.12.00.00 | X2015.10.01.12.00.00 | X2016.01.01.12.00.00 | X2016.04.01.12.00.00 | X2016.07.01.12.00.00 | X2016.10.01.12.00.00 | X2017.01.01.12.00.00 | X2017.04.01.12.00.00 | X2017.07.01.12.00.00 | X2017.10.01.12.00.00 | X2018.01.01.12.00.00 | X2018.04.01.12.00.00 | X2018.07.01.12.00.00 | X2018.10.01.12.00.00 | X2019.01.01.12.00.00 | X2019.04.01.12.00.00 | X2019.07.01.12.00.00 | X2019.10.01.12.00.00 | X2020.01.01.12.00.00 | X2020.04.01.12.00.00 | X2020.07.01.12.00.00 | X2020.10.01.12.00.00 | X2021.01.01.12.00.00 | X2021.04.01.12.00.00 | X2021.07.01.12.00.00 | X2021.10.01.12.00.00 | X2022.01.01.12.00.00 | X2022.04.01.12.00.00 | X2022.07.01.12.00.00 | X2022.10.01.12.00.00 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Korangi Karachi | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
## Korangi Karachi - Row 77 has NaN - this is likely because of its small size ~108 km2
## Duplicate East Karachi's observations (Nearest neighbor)
kk.temp.estimate = temp.extract.df[temp.extract.df$districts == "East Karachi", 1:53]
kk.temp.estimate[1,1] = "Korangi Karachi"
temp.extract.df[77,] = kk.temp.estimate
## Check
temp.extract.df[77,]
districts | X2010.01.01.12.00.00 | X2010.04.01.12.00.00 | X2010.07.01.12.00.00 | X2010.10.01.12.00.00 | X2011.01.01.12.00.00 | X2011.04.01.12.00.00 | X2011.07.01.12.00.00 | X2011.10.01.12.00.00 | X2012.01.01.12.00.00 | X2012.04.01.12.00.00 | X2012.07.01.12.00.00 | X2012.10.01.12.00.00 | X2013.01.01.12.00.00 | X2013.04.01.12.00.00 | X2013.07.01.12.00.00 | X2013.10.01.12.00.00 | X2014.01.01.12.00.00 | X2014.04.01.12.00.00 | X2014.07.01.12.00.00 | X2014.10.01.12.00.00 | X2015.01.01.12.00.00 | X2015.04.01.12.00.00 | X2015.07.01.12.00.00 | X2015.10.01.12.00.00 | X2016.01.01.12.00.00 | X2016.04.01.12.00.00 | X2016.07.01.12.00.00 | X2016.10.01.12.00.00 | X2017.01.01.12.00.00 | X2017.04.01.12.00.00 | X2017.07.01.12.00.00 | X2017.10.01.12.00.00 | X2018.01.01.12.00.00 | X2018.04.01.12.00.00 | X2018.07.01.12.00.00 | X2018.10.01.12.00.00 | X2019.01.01.12.00.00 | X2019.04.01.12.00.00 | X2019.07.01.12.00.00 | X2019.10.01.12.00.00 | X2020.01.01.12.00.00 | X2020.04.01.12.00.00 | X2020.07.01.12.00.00 | X2020.10.01.12.00.00 | X2021.01.01.12.00.00 | X2021.04.01.12.00.00 | X2021.07.01.12.00.00 | X2021.10.01.12.00.00 | X2022.01.01.12.00.00 | X2022.04.01.12.00.00 | X2022.07.01.12.00.00 | X2022.10.01.12.00.00 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Korangi Karachi | 298 | 306 | 304 | 305 | 297 | 306 | 304 | 304 | 296 | 305 | 304 | 305 | 297 | 304 | 304 | 305 | 296 | 305 | 304 | 306 | 297 | 306 | 304 | 305 | 298 | 305 | 304 | 304 | 296 | 306 | 303 | 305 | 298 | 306 | 304 | 306 | 297 | 306 | 305 | 305 | 295 | 306 | 307 | 306 | 297 | 307 | 304 | 304 | 295 | 306 | 303 | 304 |
summary(temp.extract.df)
## districts X2010.01.01.12.00.00 X2010.04.01.12.00.00
## Length:160 Min. :251.6 Min. :263.6
## Class :character 1st Qu.:284.9 1st Qu.:297.4
## Mode :character Median :291.0 Median :305.8
## Mean :287.1 Mean :300.9
## 3rd Qu.:294.5 3rd Qu.:310.1
## Max. :299.2 Max. :313.9
## X2010.07.01.12.00.00 X2010.10.01.12.00.00 X2011.01.01.12.00.00
## Min. :273.4 Min. :260.1 Min. :249.0
## 1st Qu.:301.3 1st Qu.:295.7 1st Qu.:282.1
## Median :306.0 Median :301.7 Median :288.7
## Mean :303.3 Mean :298.2 Mean :284.8
## 3rd Qu.:309.0 3rd Qu.:305.8 3rd Qu.:292.0
## Max. :313.6 Max. :309.5 Max. :298.4
## X2011.04.01.12.00.00 X2011.07.01.12.00.00 X2011.10.01.12.00.00
## Min. :261.4 Min. :276.9 Min. :262.4
## 1st Qu.:293.1 1st Qu.:300.9 1st Qu.:294.1
## Median :301.2 Median :306.0 Median :300.8
## Mean :297.1 Mean :303.5 Mean :296.9
## 3rd Qu.:306.0 3rd Qu.:309.3 3rd Qu.:304.7
## Max. :311.1 Max. :313.9 Max. :307.6
## X2012.01.01.12.00.00 X2012.04.01.12.00.00 X2012.07.01.12.00.00
## Min. :247.3 Min. :262.1 Min. :275.2
## 1st Qu.:278.8 1st Qu.:292.3 1st Qu.:303.4
## Median :287.2 Median :301.3 Median :308.5
## Mean :283.0 Mean :296.9 Mean :305.2
## 3rd Qu.:291.5 3rd Qu.:305.2 3rd Qu.:311.5
## Max. :297.1 Max. :310.6 Max. :315.0
## X2012.10.01.12.00.00 X2013.01.01.12.00.00 X2013.04.01.12.00.00
## Min. :260.7 Min. :248.2 Min. :262.1
## 1st Qu.:293.1 1st Qu.:281.7 1st Qu.:292.4
## Median :299.9 Median :288.6 Median :301.0
## Mean :295.9 Mean :284.5 Mean :296.7
## 3rd Qu.:303.7 3rd Qu.:293.0 3rd Qu.:305.6
## Max. :308.1 Max. :298.2 Max. :309.5
## X2013.07.01.12.00.00 X2013.10.01.12.00.00 X2014.01.01.12.00.00
## Min. :275.4 Min. :264.0 Min. :249.8
## 1st Qu.:301.2 1st Qu.:295.4 1st Qu.:282.4
## Median :306.1 Median :301.7 Median :289.2
## Mean :304.0 Mean :298.3 Mean :284.9
## 3rd Qu.:309.7 3rd Qu.:305.5 3rd Qu.:292.7
## Max. :315.0 Max. :308.9 Max. :297.1
## X2014.04.01.12.00.00 X2014.07.01.12.00.00 X2014.10.01.12.00.00
## Min. :261.5 Min. :275.2 Min. :262.2
## 1st Qu.:292.7 1st Qu.:301.8 1st Qu.:293.8
## Median :300.6 Median :306.9 Median :300.2
## Mean :296.8 Mean :304.3 Mean :296.7
## 3rd Qu.:305.9 3rd Qu.:310.1 3rd Qu.:304.4
## Max. :311.1 Max. :315.2 Max. :309.2
## X2015.01.01.12.00.00 X2015.04.01.12.00.00 X2015.07.01.12.00.00
## Min. :250.1 Min. :262.0 Min. :277.1
## 1st Qu.:283.2 1st Qu.:294.5 1st Qu.:300.1
## Median :289.2 Median :300.9 Median :305.0
## Mean :285.3 Mean :297.9 Mean :302.7
## 3rd Qu.:292.4 3rd Qu.:307.1 3rd Qu.:307.6
## Max. :298.1 Max. :312.0 Max. :313.9
## X2015.10.01.12.00.00 X2016.01.01.12.00.00 X2016.04.01.12.00.00
## Min. :260.4 Min. :250.2 Min. :263.3
## 1st Qu.:294.3 1st Qu.:283.8 1st Qu.:293.8
## Median :300.4 Median :289.8 Median :302.4
## Mean :297.2 Mean :286.1 Mean :298.1
## 3rd Qu.:304.7 3rd Qu.:293.6 3rd Qu.:307.2
## Max. :309.2 Max. :299.5 Max. :311.2
## X2016.07.01.12.00.00 X2016.10.01.12.00.00 X2017.01.01.12.00.00
## Min. :277.1 Min. :262.2 Min. :250.7
## 1st Qu.:301.1 1st Qu.:295.5 1st Qu.:280.1
## Median :306.1 Median :302.7 Median :287.8
## Mean :303.9 Mean :298.6 Mean :284.0
## 3rd Qu.:309.5 3rd Qu.:305.8 3rd Qu.:291.6
## Max. :315.1 Max. :309.3 Max. :297.8
## X2017.04.01.12.00.00 X2017.07.01.12.00.00 X2017.10.01.12.00.00
## Min. :262.9 Min. :278.2 Min. :262.3
## 1st Qu.:296.8 1st Qu.:300.7 1st Qu.:296.3
## Median :304.5 Median :305.7 Median :303.5
## Mean :300.1 Mean :303.5 Mean :299.2
## 3rd Qu.:309.6 3rd Qu.:309.2 3rd Qu.:306.5
## Max. :313.0 Max. :314.1 Max. :310.4
## X2018.01.01.12.00.00 X2018.04.01.12.00.00 X2018.07.01.12.00.00
## Min. :249.7 Min. :262.8 Min. :276.5
## 1st Qu.:284.4 1st Qu.:295.5 1st Qu.:301.6
## Median :290.8 Median :303.9 Median :306.2
## Mean :286.8 Mean :299.3 Mean :304.1
## 3rd Qu.:294.4 3rd Qu.:308.2 3rd Qu.:309.8
## Max. :300.1 Max. :312.5 Max. :315.3
## X2018.10.01.12.00.00 X2019.01.01.12.00.00 X2019.04.01.12.00.00
## Min. :260.4 Min. :250.1 Min. :264.3
## 1st Qu.:293.1 1st Qu.:281.0 1st Qu.:294.1
## Median :300.6 Median :288.2 Median :302.5
## Mean :296.6 Mean :284.2 Mean :298.4
## 3rd Qu.:305.1 3rd Qu.:292.5 3rd Qu.:307.1
## Max. :310.1 Max. :298.5 Max. :312.3
## X2019.07.01.12.00.00 X2019.10.01.12.00.00 X2020.01.01.12.00.00
## Min. :274.2 Min. :261.2 Min. :249.3
## 1st Qu.:302.2 1st Qu.:293.6 1st Qu.:278.6
## Median :306.7 Median :300.5 Median :286.5
## Mean :304.5 Mean :296.7 Mean :282.5
## 3rd Qu.:310.0 3rd Qu.:304.2 3rd Qu.:290.0
## Max. :315.9 Max. :307.6 Max. :296.4
## X2020.04.01.12.00.00 X2020.07.01.12.00.00 X2020.10.01.12.00.00
## Min. :262.4 Min. :275.7 Min. :262.0
## 1st Qu.:292.6 1st Qu.:302.3 1st Qu.:294.8
## Median :300.8 Median :307.3 Median :302.1
## Mean :297.1 Mean :304.7 Mean :298.0
## 3rd Qu.:306.0 3rd Qu.:310.7 3rd Qu.:305.5
## Max. :311.9 Max. :315.3 Max. :308.2
## X2021.01.01.12.00.00 X2021.04.01.12.00.00 X2021.07.01.12.00.00
## Min. :248.9 Min. :260.2 Min. :277.9
## 1st Qu.:283.8 1st Qu.:294.7 1st Qu.:302.2
## Median :289.4 Median :302.1 Median :306.9
## Mean :285.2 Mean :297.8 Mean :304.3
## 3rd Qu.:292.9 3rd Qu.:306.6 3rd Qu.:309.8
## Max. :297.9 Max. :312.1 Max. :314.6
## X2021.10.01.12.00.00 X2022.01.01.12.00.00 X2022.04.01.12.00.00
## Min. :261.9 Min. :250.6 Min. :263.2
## 1st Qu.:294.4 1st Qu.:280.4 1st Qu.:299.5
## Median :300.7 Median :287.8 Median :307.0
## Mean :297.2 Mean :283.8 Mean :302.2
## 3rd Qu.:304.7 3rd Qu.:291.3 3rd Qu.:311.0
## Max. :307.6 Max. :296.4 Max. :314.2
## X2022.07.01.12.00.00 X2022.10.01.12.00.00
## Min. :279.3 Min. :261.4
## 1st Qu.:299.1 1st Qu.:295.0
## Median :303.7 Median :301.9
## Mean :301.3 Mean :297.6
## 3rd Qu.:305.8 3rd Qu.:305.1
## Max. :310.9 Max. :308.1
## Create a vector for temp_
#temp_ = rep(c("temp.jan_", "temp.apr_", "temp.jul_", "temp.oct_"), times = 13)
## Duplicate the dataframe
temp.extract.df2 = temp.extract.df
## Create a vector for years
year = c("temp.jan_2010", "temp.apr_2010", "temp.jul_2010", "temp.oct_2010", "temp.jan_2011", "temp.apr_2011", "temp.jul_2011", "temp.oct_2011", "temp.jan_2012", "temp.apr_2012", "temp.jul_2012", "temp.oct_2012", "temp.jan_2013", "temp.apr_2013", "temp.jul_2013", "temp.oct_2013", "temp.jan_2014", "temp.apr_2014", "temp.jul_2014", "temp.oct_2014", "temp.jan_2015", "temp.apr_2015", "temp.jul_2015", "temp.oct_2015", "temp.jan_2016", "temp.apr_2016", "temp.jul_2016", "temp.oct_2016", "temp.jan_2017", "temp.apr_2017", "temp.jul_2017", "temp.oct_2017", "temp.jan_2018", "temp.apr_2018", "temp.jul_2018", "temp.oct_2018", "temp.jan_2019", "temp.apr_2019", "temp.jul_2019", "temp.oct_2019", "temp.jan_2020", "temp.apr_2020", "temp.jul_2020", "temp.oct_2020", "temp.jan_2021", "temp.apr_2021", "temp.jul_2021", "temp.oct_2021", "temp.jan_2022", "temp.apr_2022", "temp.jul_2022", "temp.oct_2022")
## Change the names!!
names(temp.extract.df2)[2:53] = year
## Check
names(temp.extract.df2)
## [1] "districts" "temp.jan_2010" "temp.apr_2010" "temp.jul_2010"
## [5] "temp.oct_2010" "temp.jan_2011" "temp.apr_2011" "temp.jul_2011"
## [9] "temp.oct_2011" "temp.jan_2012" "temp.apr_2012" "temp.jul_2012"
## [13] "temp.oct_2012" "temp.jan_2013" "temp.apr_2013" "temp.jul_2013"
## [17] "temp.oct_2013" "temp.jan_2014" "temp.apr_2014" "temp.jul_2014"
## [21] "temp.oct_2014" "temp.jan_2015" "temp.apr_2015" "temp.jul_2015"
## [25] "temp.oct_2015" "temp.jan_2016" "temp.apr_2016" "temp.jul_2016"
## [29] "temp.oct_2016" "temp.jan_2017" "temp.apr_2017" "temp.jul_2017"
## [33] "temp.oct_2017" "temp.jan_2018" "temp.apr_2018" "temp.jul_2018"
## [37] "temp.oct_2018" "temp.jan_2019" "temp.apr_2019" "temp.jul_2019"
## [41] "temp.oct_2019" "temp.jan_2020" "temp.apr_2020" "temp.jul_2020"
## [45] "temp.oct_2020" "temp.jan_2021" "temp.apr_2021" "temp.jul_2021"
## [49] "temp.oct_2021" "temp.jan_2022" "temp.apr_2022" "temp.jul_2022"
## [53] "temp.oct_2022"
## Conversion to celsius
## kelvin - 273.15 = celsius
k_to_c = function(x) {
(x - 273.15)
}
## Test
#x = c(287.0471, 306.7410, 303.9115, 301.3161)
#k_to_c(x)
## Convert to Celsius
temp.extract.df2 = k_to_c(temp.extract.df2[,2:53])
## Check
head(temp.extract.df2)
temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 |
-12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 |
17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 |
23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 |
25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 |
10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 |
#create a copy
## Merge to the test dataframe
test.df = cbind(test.df, temp.extract.df2)
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 | temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | -12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 |
dim(test.df)
## [1] 160 107
## Read in the temperature data
pak.precip = raster("../data/precipitation/seasonal_precip_data.nc", varname = "tp")
## Look at the data
pak.precip
## class : RasterLayer
## band : 1 (of 52 bands)
## dimensions : 201, 181, 36381 (nrow, ncol, ncell)
## resolution : 0.1, 0.1 (x, y)
## extent : 59.95, 78.05, 19.95, 40.05 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : seasonal_precip_data.nc
## names : Total.precipitation
## z-value : 2010-01-01 12:00:00
## zvar : tp
## 52 bands = 13 years * 4 months (Jan, Apr, July, Oct) worth of observations
## **Reminder the precipitation is in meters*
## Bring in the temp data as a stack in order to extract the bands (year/month that you need)
precip.stack = stack("../data/precipitation/seasonal_precip_data.nc", varname = "tp")
## View the stack
precip.stack
## class : RasterStack
## dimensions : 201, 181, 36381, 52 (nrow, ncol, ncell, nlayers)
## resolution : 0.1, 0.1 (x, y)
## extent : 59.95, 78.05, 19.95, 40.05 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## names : X2010.01.01.12.00.00, X2010.04.01.12.00.00, X2010.07.01.12.00.00, X2010.10.01.12.00.00, X2011.01.01.12.00.00, X2011.04.01.12.00.00, X2011.07.01.12.00.00, X2011.10.01.12.00.00, X2012.01.01.12.00.00, X2012.04.01.12.00.00, X2012.07.01.12.00.00, X2012.10.01.12.00.00, X2013.01.01.12.00.00, X2013.04.01.12.00.00, X2013.07.01.12.00.00, ...
## Date/time : 2010-01-01 12:00:00 - 2022-10-01 12:00:00 (range)
## Crop and Mask Pipeline
precip.stack.mask = precip.stack %>%
crop(pak.bound) %>%
mask(mask = pak.bound)
## Check
precip.pal = brewer.pal(n = 9,
name = "Blues")
plot(precip.stack.mask,
col = precip.pal)
plot(st_geometry(pak.bound),
border = "black",
add = TRUE)
##----TEST----##
## Works Well!
## Extract the mean value by district
test.precip.extract = raster::extract(precip.stack.mask, pak.bound.adm2, fun = mean, na.rm = TRUE)
## Check
summary(test.precip.extract)
## X2010.01.01.12.00.00 X2010.04.01.12.00.00 X2010.07.01.12.00.00
## Min. :0.0000008 Min. :0.0000008 Min. :0.0000009
## 1st Qu.:0.0000601 1st Qu.:0.0001130 1st Qu.:0.0016671
## Median :0.0001957 Median :0.0003290 Median :0.0022929
## Mean :0.0004016 Mean :0.0007072 Mean :0.0034333
## 3rd Qu.:0.0006948 3rd Qu.:0.0009532 3rd Qu.:0.0051513
## Max. :0.0019477 Max. :0.0044754 Max. :0.0107855
## NA's :1 NA's :1 NA's :1
## X2010.10.01.12.00.00 X2011.01.01.12.00.00 X2011.04.01.12.00.00
## Min. :0.0000009 Min. :0.0000008 Min. :0.0000047
## 1st Qu.:0.0000396 1st Qu.:0.0000357 1st Qu.:0.0002609
## Median :0.0001283 Median :0.0001379 Median :0.0009015
## Mean :0.0002052 Mean :0.0002892 Mean :0.0009536
## 3rd Qu.:0.0003241 3rd Qu.:0.0005546 3rd Qu.:0.0013354
## Max. :0.0013761 Max. :0.0011260 Max. :0.0035496
## NA's :1 NA's :1 NA's :1
## X2011.07.01.12.00.00 X2011.10.01.12.00.00 X2012.01.01.12.00.00
## Min. :0.0000004 Min. :0.0000003 Min. :0.0000011
## 1st Qu.:0.0006060 1st Qu.:0.0000460 1st Qu.:0.0001172
## Median :0.0013493 Median :0.0002543 Median :0.0005603
## Mean :0.0017266 Mean :0.0005118 Mean :0.0007173
## 3rd Qu.:0.0025921 3rd Qu.:0.0008946 3rd Qu.:0.0011069
## Max. :0.0068876 Max. :0.0026864 Max. :0.0025603
## NA's :1 NA's :1 NA's :1
## X2012.04.01.12.00.00 X2012.07.01.12.00.00 X2012.10.01.12.00.00
## Min. :0.0000803 Min. :0.0000006 Min. :0.0000033
## 1st Qu.:0.0003990 1st Qu.:0.0001530 1st Qu.:0.0000764
## Median :0.0008312 Median :0.0007149 Median :0.0002726
## Mean :0.0010334 Mean :0.0008379 Mean :0.0003981
## 3rd Qu.:0.0014018 3rd Qu.:0.0013154 3rd Qu.:0.0005845
## Max. :0.0037093 Max. :0.0028573 Max. :0.0020580
## NA's :1 NA's :1 NA's :1
## X2013.01.01.12.00.00 X2013.04.01.12.00.00 X2013.07.01.12.00.00
## Min. :0.0000027 Min. :0.0000650 Min. :0.0000025
## 1st Qu.:0.0000289 1st Qu.:0.0003347 1st Qu.:0.0004086
## Median :0.0001237 Median :0.0005795 Median :0.0011750
## Mean :0.0002884 Mean :0.0009026 Mean :0.0015216
## 3rd Qu.:0.0005127 3rd Qu.:0.0012991 3rd Qu.:0.0023050
## Max. :0.0011741 Max. :0.0039829 Max. :0.0056144
## NA's :1 NA's :1 NA's :1
## X2013.10.01.12.00.00 X2014.01.01.12.00.00 X2014.04.01.12.00.00
## Min. :0.0000002 Min. :0.0000002 Min. :0.0000174
## 1st Qu.:0.0000916 1st Qu.:0.0000293 1st Qu.:0.0001764
## Median :0.0002542 Median :0.0001335 Median :0.0007146
## Mean :0.0003839 Mean :0.0002327 Mean :0.0008910
## 3rd Qu.:0.0006147 3rd Qu.:0.0003802 3rd Qu.:0.0013936
## Max. :0.0016052 Max. :0.0009734 Max. :0.0033757
## NA's :1 NA's :1 NA's :1
## X2014.07.01.12.00.00 X2014.10.01.12.00.00 X2015.01.01.12.00.00
## Min. :0.0000002 Min. :0.0000009 Min. :0.0000015
## 1st Qu.:0.0003608 1st Qu.:0.0000982 1st Qu.:0.0001060
## Median :0.0010408 Median :0.0005462 Median :0.0004566
## Mean :0.0014506 Mean :0.0006309 Mean :0.0004658
## 3rd Qu.:0.0021202 3rd Qu.:0.0010157 3rd Qu.:0.0007630
## Max. :0.0064551 Max. :0.0028523 Max. :0.0016857
## NA's :1 NA's :1 NA's :1
## X2015.04.01.12.00.00 X2015.07.01.12.00.00 X2015.10.01.12.00.00
## Min. :0.0000021 Min. :0.0000022 Min. :0.0000024
## 1st Qu.:0.0001682 1st Qu.:0.0015604 1st Qu.:0.0000508
## Median :0.0009682 Median :0.0024138 Median :0.0005884
## Mean :0.0011269 Mean :0.0027414 Mean :0.0006975
## 3rd Qu.:0.0017430 3rd Qu.:0.0035513 3rd Qu.:0.0012442
## Max. :0.0041256 Max. :0.0092792 Max. :0.0024737
## NA's :1 NA's :1 NA's :1
## X2016.01.01.12.00.00 X2016.04.01.12.00.00 X2016.07.01.12.00.00
## Min. :0.0000009 Min. :0.0000020 Min. :0.0000048
## 1st Qu.:0.0000839 1st Qu.:0.0000875 1st Qu.:0.0002653
## Median :0.0003277 Median :0.0004970 Median :0.0014042
## Mean :0.0004906 Mean :0.0009780 Mean :0.0017218
## 3rd Qu.:0.0007515 3rd Qu.:0.0017356 3rd Qu.:0.0027332
## Max. :0.0025292 Max. :0.0041632 Max. :0.0060888
## NA's :1 NA's :1 NA's :1
## X2016.10.01.12.00.00 X2017.01.01.12.00.00 X2017.04.01.12.00.00
## Min. :0.0000002 Min. :0.0000121 Min. :0.0000016
## 1st Qu.:0.0000103 1st Qu.:0.0003159 1st Qu.:0.0000448
## Median :0.0001232 Median :0.0009645 Median :0.0002743
## Mean :0.0002263 Mean :0.0014258 Mean :0.0008797
## 3rd Qu.:0.0002721 3rd Qu.:0.0019575 3rd Qu.:0.0014712
## Max. :0.0013246 Max. :0.0054022 Max. :0.0045751
## NA's :1 NA's :1 NA's :1
## X2017.07.01.12.00.00 X2017.10.01.12.00.00 X2018.01.01.12.00.00
## Min. :0.0000003 Min. :0.0000002 Min. :0.0000002
## 1st Qu.:0.0010642 1st Qu.:0.0000003 1st Qu.:0.0000010
## Median :0.0016356 Median :0.0000242 Median :0.0000682
## Mean :0.0021045 Mean :0.0001820 Mean :0.0002010
## 3rd Qu.:0.0029899 3rd Qu.:0.0002014 3rd Qu.:0.0002861
## Max. :0.0078421 Max. :0.0012759 Max. :0.0012789
## NA's :1 NA's :1 NA's :1
## X2018.04.01.12.00.00 X2018.07.01.12.00.00 X2018.10.01.12.00.00
## Min. :0.0000071 Min. :0.0000006 Min. :0.0000002
## 1st Qu.:0.0001715 1st Qu.:0.0001754 1st Qu.:0.0000191
## Median :0.0005100 Median :0.0012961 Median :0.0001537
## Mean :0.0011070 Mean :0.0018685 Mean :0.0004043
## 3rd Qu.:0.0017080 3rd Qu.:0.0028965 3rd Qu.:0.0007497
## Max. :0.0053184 Max. :0.0075518 Max. :0.0021630
## NA's :1 NA's :1 NA's :1
## X2019.01.01.12.00.00 X2019.04.01.12.00.00 X2019.07.01.12.00.00
## Min. :0.0000093 Min. :0.0000109 Min. :0.0000026
## 1st Qu.:0.0002009 1st Qu.:0.0004896 1st Qu.:0.0006972
## Median :0.0007448 Median :0.0011681 Median :0.0012961
## Mean :0.0010039 Mean :0.0012433 Mean :0.0017202
## 3rd Qu.:0.0013535 3rd Qu.:0.0017962 3rd Qu.:0.0023878
## Max. :0.0039088 Max. :0.0041843 Max. :0.0059645
## NA's :1 NA's :1 NA's :1
## X2019.10.01.12.00.00 X2020.01.01.12.00.00 X2020.04.01.12.00.00
## Min. :0.0000565 Min. :0.0000007 Min. :0.0000004
## 1st Qu.:0.0003315 1st Qu.:0.0006182 1st Qu.:0.0000842
## Median :0.0005545 Median :0.0010772 Median :0.0005542
## Mean :0.0006398 Mean :0.0012961 Mean :0.0008013
## 3rd Qu.:0.0009728 3rd Qu.:0.0018864 3rd Qu.:0.0012232
## Max. :0.0021143 Max. :0.0039313 Max. :0.0033619
## NA's :1 NA's :1 NA's :1
## X2020.07.01.12.00.00 X2020.10.01.12.00.00 X2021.01.01.12.00.00
## Min. :0.0000019 Min. :0.0000002 Min. :0.0000002
## 1st Qu.:0.0004193 1st Qu.:0.0000003 1st Qu.:0.0000005
## Median :0.0011833 Median :0.0000168 Median :0.0000579
## Mean :0.0013063 Mean :0.0001188 Mean :0.0003486
## 3rd Qu.:0.0019237 3rd Qu.:0.0000996 3rd Qu.:0.0005808
## Max. :0.0051720 Max. :0.0009457 Max. :0.0022900
## NA's :1 NA's :1 NA's :1
## X2021.04.01.12.00.00 X2021.07.01.12.00.00 X2021.10.01.12.00.00
## Min. :0.0000006 Min. :0.0000060 Min. :0.0000020
## 1st Qu.:0.0001261 1st Qu.:0.0003493 1st Qu.:0.0000734
## Median :0.0004081 Median :0.0010142 Median :0.0003461
## Mean :0.0007171 Mean :0.0019638 Mean :0.0006142
## 3rd Qu.:0.0010928 3rd Qu.:0.0033949 3rd Qu.:0.0010896
## Max. :0.0033070 Max. :0.0094433 Max. :0.0029658
## NA's :1 NA's :1 NA's :1
## X2022.01.01.12.00.00 X2022.04.01.12.00.00 X2022.07.01.12.00.00
## Min. :0.0001209 Min. :0.0000010 Min. :0.0005713
## 1st Qu.:0.0004296 1st Qu.:0.0000408 1st Qu.:0.0033251
## Median :0.0008349 Median :0.0001121 Median :0.0043912
## Mean :0.0012103 Mean :0.0003211 Mean :0.0043734
## 3rd Qu.:0.0015770 3rd Qu.:0.0004942 3rd Qu.:0.0053260
## Max. :0.0047234 Max. :0.0015722 Max. :0.0094819
## NA's :1 NA's :1 NA's :1
## X2022.10.01.12.00.00
## Min. :0.0000002
## 1st Qu.:0.0000008
## Median :0.0000159
## Mean :0.0002416
## 3rd Qu.:0.0003436
## Max. :0.0017988
## NA's :1
## Note!
## **Korangi Karchi NAs**
## Add the precipitation data to the test.df
#add district names to the temperature data frame
dist.names = test.df$districts
## Check the class of the temp extract
class(test.precip.extract)
## [1] "matrix" "array"
## Convert the extract to a dataframe
precip.extract.df = as.data.frame(test.precip.extract)
## Check
class(precip.extract.df)
## [1] "data.frame"
dim(precip.extract.df)
## [1] 160 52
## Good - 160 rows(districts) by 52 time steps
## Change the rownames of the precip.extract df
rownames(precip.extract.df) = dist.names
## Check
rownames(precip.extract.df)
## [1] "Abbottabad" "Astore" "Attock"
## [4] "Awaran" "Badin" "Bagh"
## [7] "Bahawalnagar" "Bahawalpur" "Bajaur"
## [10] "Bannu" "Barkhan" "Batagram"
## [13] "Bhakkar" "Bhimber" "Buner"
## [16] "Central Karachi" "Chagai" "Chakwal"
## [19] "Chaman" "Charsadda" "Chiniot"
## [22] "Chitral Lower" "Chitral Upper" "D. I. Khan"
## [25] "Dadu" "Darel" "Dera Bugti"
## [28] "Dera Ghazi Khan" "Diamir" "Duki"
## [31] "East Karachi" "Faisalabad" "Ghanche"
## [34] "Ghizer" "Ghotki" "Gilgit"
## [37] "Gujranwala" "Gujrat" "Gupis-Yasin"
## [40] "Gwadar" "Hafizabad" "Hangu"
## [43] "Haripur" "Harnai" "Haveli"
## [46] "Hunza" "Hyderabad" "Islamabad"
## [49] "Jacobabad" "Jaffarabad" "Jamshoro"
## [52] "Jhal Magsi" "Jhang" "Jhelum"
## [55] "Jhelum Valley" "Kachhi" "Kalat"
## [58] "Kambar Shahdad Kot" "Karak" "Kashmore"
## [61] "Kasur" "Kech" "Khairpur"
## [64] "Khanewal" "Kharan" "Kharmang"
## [67] "Khushab" "Khuzdar" "Khyber"
## [70] "Killa Abdullah" "Killa Saifullah" "Kohat"
## [73] "Kohistan Lower" "Kohistan Upper" "Kohlu"
## [76] "Kolai Palas Kohistan" "Korangi Karachi" "Kotli"
## [79] "Kurram" "Lahore" "Lakki Marwat"
## [82] "Larkana" "Lasbela" "Lehri"
## [85] "Leiah" "Lodhran" "Loralai"
## [88] "Lower Dir" "Malakand" "Malir Karachi"
## [91] "Mandi Bahauddin" "Mansehra" "Mardan"
## [94] "Mastung" "Matiari" "Mianwali"
## [97] "Mirpur" "Mirpur Khas" "Mohmand"
## [100] "Multan" "Musakhel" "Muzaffarabad"
## [103] "Muzaffargarh" "Nagar" "Nankana Sahib"
## [106] "Narowal" "Nasirabad" "Naushahro Feroze"
## [109] "Neelum" "North Waziristan" "Nowshera"
## [112] "Nushki" "Okara" "Orakzai"
## [115] "Pakpattan" "Panjgur" "Peshawar"
## [118] "Pishin" "Poonch" "Quetta"
## [121] "Rahim Yar Khan" "Rajanpur" "Rawalpindi"
## [124] "Rondu" "Sahiwal" "Sanghar"
## [127] "Sargodha" "Shaheed Benazir Abad" "Shaheed Sikandarabad"
## [130] "Shangla" "Sheikhupura" "Sherani"
## [133] "Shigar" "Shikarpur" "Sialkot"
## [136] "Sibi" "Skardu" "Sohbatpur"
## [139] "South Karachi" "South Waziristan" "Sudhnoti"
## [142] "Sujawal" "Sukkur" "Swabi"
## [145] "Swat" "Tando Allahyar" "Tando Muhammad Khan"
## [148] "Tangir" "Tank" "Tharparkar"
## [151] "Thatta" "Toba Tek Singh" "Tor Ghar"
## [154] "Umer Kot" "Upper Dir" "Vehari"
## [157] "Washuk" "West Karachi" "Zhob"
## [160] "Ziarat"
## Add rownames as a column
precip.extract.df = tibble::rownames_to_column(precip.extract.df, "districts")
## Check
head(precip.extract.df)
districts | X2010.01.01.12.00.00 | X2010.04.01.12.00.00 | X2010.07.01.12.00.00 | X2010.10.01.12.00.00 | X2011.01.01.12.00.00 | X2011.04.01.12.00.00 | X2011.07.01.12.00.00 | X2011.10.01.12.00.00 | X2012.01.01.12.00.00 | X2012.04.01.12.00.00 | X2012.07.01.12.00.00 | X2012.10.01.12.00.00 | X2013.01.01.12.00.00 | X2013.04.01.12.00.00 | X2013.07.01.12.00.00 | X2013.10.01.12.00.00 | X2014.01.01.12.00.00 | X2014.04.01.12.00.00 | X2014.07.01.12.00.00 | X2014.10.01.12.00.00 | X2015.01.01.12.00.00 | X2015.04.01.12.00.00 | X2015.07.01.12.00.00 | X2015.10.01.12.00.00 | X2016.01.01.12.00.00 | X2016.04.01.12.00.00 | X2016.07.01.12.00.00 | X2016.10.01.12.00.00 | X2017.01.01.12.00.00 | X2017.04.01.12.00.00 | X2017.07.01.12.00.00 | X2017.10.01.12.00.00 | X2018.01.01.12.00.00 | X2018.04.01.12.00.00 | X2018.07.01.12.00.00 | X2018.10.01.12.00.00 | X2019.01.01.12.00.00 | X2019.04.01.12.00.00 | X2019.07.01.12.00.00 | X2019.10.01.12.00.00 | X2020.01.01.12.00.00 | X2020.04.01.12.00.00 | X2020.07.01.12.00.00 | X2020.10.01.12.00.00 | X2021.01.01.12.00.00 | X2021.04.01.12.00.00 | X2021.07.01.12.00.00 | X2021.10.01.12.00.00 | X2022.01.01.12.00.00 | X2022.04.01.12.00.00 | X2022.07.01.12.00.00 | X2022.10.01.12.00.00 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 0.000898 | 0.00195 | 0.00732 | 0.000414 | 0.000704 | 0.00355 | 0.00302 | 0.00159 | 0.00171 | 0.00292 | 0.00239 | 0.00071 | 0.00101 | 0.00275 | 0.00318 | 0.000892 | 0.000676 | 0.00193 | 0.00376 | 0.00128 | 0.00132 | 0.00329 | 0.00628 | 0.00166 | 0.00126 | 0.00316 | 0.00569 | 0.000236 | 0.00456 | 0.0039 | 0.0055 | 0.000236 | 0.000286 | 0.00464 | 0.00695 | 0.00146 | 0.00319 | 0.00248 | 0.00415 | 0.000948 | 0.00323 | 0.00308 | 0.00331 | 9.88e-05 | 0.00124 | 0.00215 | 0.0063 | 0.00157 | 0.00355 | 0.000921 | 0.00616 | 0.000613 |
Astore | 0.00108 | 0.00236 | 0.00402 | 0.000665 | 0.000946 | 0.00137 | 0.0027 | 0.00136 | 0.00143 | 0.00172 | 0.00161 | 0.00111 | 0.001 | 0.00159 | 0.00194 | 0.000935 | 0.000973 | 0.00218 | 0.00194 | 0.00133 | 0.00111 | 0.00237 | 0.00337 | 0.00148 | 0.00123 | 0.00305 | 0.00306 | 0.000711 | 0.00361 | 0.00311 | 0.00258 | 0.000841 | 0.000838 | 0.00231 | 0.00346 | 0.00142 | 0.00239 | 0.00185 | 0.00149 | 0.00122 | 0.00271 | 0.00184 | 0.00289 | 0.000506 | 0.00134 | 0.00206 | 0.00385 | 0.0017 | 0.00196 | 0.00101 | 0.00354 | 0.0012 |
Attock | 0.000249 | 0.000295 | 0.00591 | 0.000181 | 0.000324 | 0.00114 | 0.00275 | 0.000765 | 0.0011 | 0.000743 | 0.000864 | 0.000255 | 0.000187 | 0.000498 | 0.00461 | 0.000409 | 7.12e-05 | 0.00126 | 0.00219 | 0.00101 | 0.000458 | 0.00195 | 0.00459 | 0.000875 | 0.000665 | 0.000983 | 0.00182 | 0.000205 | 0.00166 | 0.000969 | 0.00294 | 0.000146 | 9.32e-05 | 0.00116 | 0.00278 | 0.000553 | 0.00135 | 0.00143 | 0.00222 | 0.000366 | 0.00129 | 0.000746 | 0.00169 | 3.66e-05 | 0.000316 | 0.00082 | 0.00423 | 0.00109 | 0.00175 | 0.000281 | 0.00456 | 0.000257 |
Awaran | 0.000139 | 1.89e-05 | 0.00113 | 5.81e-06 | 1.73e-05 | 0.000129 | 0.000758 | 3.73e-05 | 0.000276 | 0.000168 | 4.52e-05 | 8.69e-05 | 6.79e-06 | 0.000678 | 0.000232 | 0.000149 | 0.00011 | 0.000208 | 0.00018 | 9.11e-05 | 0.000187 | 2.82e-05 | 0.000226 | 3.72e-05 | 3.76e-05 | 3.42e-05 | 9.18e-05 | 7.45e-06 | 0.000529 | 2.47e-05 | 0.000392 | 1.05e-06 | 6.86e-07 | 0.000116 | 7.91e-05 | 6.41e-05 | 0.000352 | 0.000119 | 0.000247 | 0.000218 | 0.000588 | 3.64e-05 | 0.000424 | 6.18e-07 | 1.54e-06 | 0.000195 | 0.000482 | 0.000565 | 0.000605 | 0.000174 | 0.00484 | 2.96e-06 |
Badin | 3.75e-06 | 3.2e-06 | 0.00195 | 8.15e-05 | 1.95e-05 | 8.04e-06 | 0.000571 | 2.42e-06 | 3.56e-05 | 0.000159 | 8.82e-05 | 1.48e-05 | 1.13e-05 | 0.000201 | 0.00103 | 0.00023 | 2.22e-07 | 7.52e-05 | 0.000304 | 4.6e-05 | 1e-05 | 2.12e-06 | 0.00291 | 1.52e-05 | 1.2e-05 | 3.26e-06 | 0.000143 | 0.000172 | 1.77e-05 | 2.33e-05 | 0.00197 | 1.94e-06 | 2.5e-07 | 3.86e-05 | 0.000112 | 5.84e-06 | 3.68e-05 | 1.62e-05 | 0.0015 | 0.000372 | 2.73e-06 | 3.39e-06 | 0.000663 | 0.000231 | 3.39e-07 | 1.53e-05 | 0.000665 | 4.19e-05 | 0.000324 | 7.6e-05 | 0.00542 | 2.36e-06 |
Bagh | 0.00103 | 0.00199 | 0.00754 | 0.000541 | 0.000692 | 0.00292 | 0.00398 | 0.00126 | 0.00227 | 0.00286 | 0.00243 | 0.000861 | 0.000954 | 0.00218 | 0.00379 | 0.00088 | 0.000853 | 0.00242 | 0.00367 | 0.0011 | 0.000985 | 0.00409 | 0.00827 | 0.00142 | 0.00143 | 0.00315 | 0.00589 | 0.000351 | 0.00482 | 0.00396 | 0.00623 | 0.000371 | 0.000388 | 0.0042 | 0.00646 | 0.00132 | 0.00347 | 0.00216 | 0.00477 | 0.00086 | 0.00381 | 0.00317 | 0.00286 | 7.37e-05 | 0.0016 | 0.00281 | 0.00545 | 0.00116 | 0.00428 | 0.00114 | 0.00587 | 0.000631 |
## Check the NAs
## Find the NAs
#precip.df.nas = precip.extract.df %>%
#filter_all(any_vars(. %in% c(NA)))
## Check
#precip.df.nas
precip.extract.df[!complete.cases(precip.extract.df), ]
districts | X2010.01.01.12.00.00 | X2010.04.01.12.00.00 | X2010.07.01.12.00.00 | X2010.10.01.12.00.00 | X2011.01.01.12.00.00 | X2011.04.01.12.00.00 | X2011.07.01.12.00.00 | X2011.10.01.12.00.00 | X2012.01.01.12.00.00 | X2012.04.01.12.00.00 | X2012.07.01.12.00.00 | X2012.10.01.12.00.00 | X2013.01.01.12.00.00 | X2013.04.01.12.00.00 | X2013.07.01.12.00.00 | X2013.10.01.12.00.00 | X2014.01.01.12.00.00 | X2014.04.01.12.00.00 | X2014.07.01.12.00.00 | X2014.10.01.12.00.00 | X2015.01.01.12.00.00 | X2015.04.01.12.00.00 | X2015.07.01.12.00.00 | X2015.10.01.12.00.00 | X2016.01.01.12.00.00 | X2016.04.01.12.00.00 | X2016.07.01.12.00.00 | X2016.10.01.12.00.00 | X2017.01.01.12.00.00 | X2017.04.01.12.00.00 | X2017.07.01.12.00.00 | X2017.10.01.12.00.00 | X2018.01.01.12.00.00 | X2018.04.01.12.00.00 | X2018.07.01.12.00.00 | X2018.10.01.12.00.00 | X2019.01.01.12.00.00 | X2019.04.01.12.00.00 | X2019.07.01.12.00.00 | X2019.10.01.12.00.00 | X2020.01.01.12.00.00 | X2020.04.01.12.00.00 | X2020.07.01.12.00.00 | X2020.10.01.12.00.00 | X2021.01.01.12.00.00 | X2021.04.01.12.00.00 | X2021.07.01.12.00.00 | X2021.10.01.12.00.00 | X2022.01.01.12.00.00 | X2022.04.01.12.00.00 | X2022.07.01.12.00.00 | X2022.10.01.12.00.00 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Korangi Karachi | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
## Korangi Karachi - Row 77 has NaN - this is likely because of its small size ~108 km2
## Duplicate East Karachi's observations (Nearest neighbor)
kk.precip.estimate = precip.extract.df[precip.extract.df$districts == "East Karachi", 1:53]
kk.precip.estimate[1,1] = "Korangi Karachi"
precip.extract.df[77,] = kk.precip.estimate
## Check
precip.extract.df[77,]
districts | X2010.01.01.12.00.00 | X2010.04.01.12.00.00 | X2010.07.01.12.00.00 | X2010.10.01.12.00.00 | X2011.01.01.12.00.00 | X2011.04.01.12.00.00 | X2011.07.01.12.00.00 | X2011.10.01.12.00.00 | X2012.01.01.12.00.00 | X2012.04.01.12.00.00 | X2012.07.01.12.00.00 | X2012.10.01.12.00.00 | X2013.01.01.12.00.00 | X2013.04.01.12.00.00 | X2013.07.01.12.00.00 | X2013.10.01.12.00.00 | X2014.01.01.12.00.00 | X2014.04.01.12.00.00 | X2014.07.01.12.00.00 | X2014.10.01.12.00.00 | X2015.01.01.12.00.00 | X2015.04.01.12.00.00 | X2015.07.01.12.00.00 | X2015.10.01.12.00.00 | X2016.01.01.12.00.00 | X2016.04.01.12.00.00 | X2016.07.01.12.00.00 | X2016.10.01.12.00.00 | X2017.01.01.12.00.00 | X2017.04.01.12.00.00 | X2017.07.01.12.00.00 | X2017.10.01.12.00.00 | X2018.01.01.12.00.00 | X2018.04.01.12.00.00 | X2018.07.01.12.00.00 | X2018.10.01.12.00.00 | X2019.01.01.12.00.00 | X2019.04.01.12.00.00 | X2019.07.01.12.00.00 | X2019.10.01.12.00.00 | X2020.01.01.12.00.00 | X2020.04.01.12.00.00 | X2020.07.01.12.00.00 | X2020.10.01.12.00.00 | X2021.01.01.12.00.00 | X2021.04.01.12.00.00 | X2021.07.01.12.00.00 | X2021.10.01.12.00.00 | X2022.01.01.12.00.00 | X2022.04.01.12.00.00 | X2022.07.01.12.00.00 | X2022.10.01.12.00.00 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Korangi Karachi | 7.64e-07 | 5.5e-06 | 0.00196 | 3.08e-05 | 1.02e-05 | 6.55e-05 | 0.000925 | 6.93e-05 | 9.05e-05 | 8.37e-05 | 0.00025 | 5.37e-05 | 1.61e-05 | 0.000248 | 0.000691 | 0.000188 | 2.22e-07 | 8.87e-05 | 0.000463 | 7.29e-05 | 4.18e-05 | 2.97e-05 | 0.00129 | 2.66e-05 | 2.96e-05 | 7.67e-06 | 0.000413 | 1.04e-05 | 6.59e-05 | 1.88e-05 | 0.00124 | 4.69e-06 | 2.22e-07 | 6.13e-05 | 0.000156 | 9.7e-06 | 6.05e-05 | 6.05e-05 | 0.00154 | 0.000347 | 4.01e-06 | 6.28e-07 | 0.000625 | 1.98e-06 | 4.93e-07 | 2.81e-05 | 0.000935 | 0.000335 | 0.000122 | 3.2e-06 | 0.00675 | 1.24e-05 |
summary(precip.extract.df)
## districts X2010.01.01.12.00.00 X2010.04.01.12.00.00
## Length:160 Min. :7.636e-07 Min. :7.640e-07
## Class :character 1st Qu.:5.957e-05 1st Qu.:1.103e-04
## Mode :character Median :1.903e-04 Median :3.267e-04
## Mean :3.991e-04 Mean :7.028e-04
## 3rd Qu.:6.933e-04 3rd Qu.:9.432e-04
## Max. :1.948e-03 Max. :4.475e-03
## X2010.07.01.12.00.00 X2010.10.01.12.00.00 X2011.01.01.12.00.00
## Min. :9.460e-07 Min. :8.903e-07 Min. :7.636e-07
## 1st Qu.:1.671e-03 1st Qu.:3.928e-05 1st Qu.:3.345e-05
## Median :2.251e-03 Median :1.259e-04 Median :1.367e-04
## Mean :3.424e-03 Mean :2.041e-04 Mean :2.874e-04
## 3rd Qu.:5.099e-03 3rd Qu.:3.210e-04 3rd Qu.:5.536e-04
## Max. :1.079e-02 Max. :1.376e-03 Max. :1.126e-03
## X2011.04.01.12.00.00 X2011.07.01.12.00.00 X2011.10.01.12.00.00
## Min. :4.689e-06 Min. :3.610e-07 Min. :2.618e-07
## 1st Qu.:2.528e-04 1st Qu.:6.104e-04 1st Qu.:4.656e-05
## Median :8.822e-04 Median :1.331e-03 Median :2.543e-04
## Mean :9.480e-04 Mean :1.722e-03 Mean :5.090e-04
## 3rd Qu.:1.334e-03 3rd Qu.:2.577e-03 3rd Qu.:8.930e-04
## Max. :3.550e-03 Max. :6.888e-03 Max. :2.686e-03
## X2012.01.01.12.00.00 X2012.04.01.12.00.00 X2012.07.01.12.00.00
## Min. :1.102e-06 Min. :8.032e-05 Min. :6.283e-07
## 1st Qu.:1.097e-04 1st Qu.:3.973e-04 1st Qu.:1.560e-04
## Median :5.590e-04 Median :8.269e-04 Median :7.041e-04
## Mean :7.133e-04 Mean :1.027e-03 Mean :8.342e-04
## 3rd Qu.:1.101e-03 3rd Qu.:1.401e-03 3rd Qu.:1.315e-03
## Max. :2.560e-03 Max. :3.709e-03 Max. :2.857e-03
## X2012.10.01.12.00.00 X2013.01.01.12.00.00 X2013.04.01.12.00.00
## Min. :3.265e-06 Min. :2.731e-06 Min. :6.497e-05
## 1st Qu.:7.529e-05 1st Qu.:2.706e-05 1st Qu.:3.330e-04
## Median :2.640e-04 Median :1.232e-04 Median :5.752e-04
## Mean :3.959e-04 Mean :2.867e-04 Mean :8.985e-04
## 3rd Qu.:5.809e-04 3rd Qu.:5.048e-04 3rd Qu.:1.293e-03
## Max. :2.058e-03 Max. :1.174e-03 Max. :3.983e-03
## X2013.07.01.12.00.00 X2013.10.01.12.00.00 X2014.01.01.12.00.00
## Min. :2.486e-06 Min. :2.222e-07 Min. :2.222e-07
## 1st Qu.:4.115e-04 1st Qu.:9.197e-05 1st Qu.:2.901e-05
## Median :1.152e-03 Median :2.533e-04 Median :1.216e-04
## Mean :1.516e-03 Mean :3.827e-04 Mean :2.312e-04
## 3rd Qu.:2.301e-03 3rd Qu.:6.122e-04 3rd Qu.:3.791e-04
## Max. :5.614e-03 Max. :1.605e-03 Max. :9.734e-04
## X2014.04.01.12.00.00 X2014.07.01.12.00.00 X2014.10.01.12.00.00
## Min. :1.744e-05 Min. :2.320e-07 Min. :8.761e-07
## 1st Qu.:1.543e-04 1st Qu.:3.634e-04 1st Qu.:9.672e-05
## Median :6.916e-04 Median :1.008e-03 Median :5.359e-04
## Mean :8.860e-04 Mean :1.444e-03 Mean :6.274e-04
## 3rd Qu.:1.393e-03 3rd Qu.:2.119e-03 3rd Qu.:1.012e-03
## Max. :3.376e-03 Max. :6.455e-03 Max. :2.852e-03
## X2015.01.01.12.00.00 X2015.04.01.12.00.00 X2015.07.01.12.00.00
## Min. :1.508e-06 Min. :2.122e-06 Min. :0.0000022
## 1st Qu.:1.040e-04 1st Qu.:1.671e-04 1st Qu.:0.0015347
## Median :4.513e-04 Median :9.588e-04 Median :0.0024132
## Mean :4.631e-04 Mean :1.120e-03 Mean :0.0027324
## 3rd Qu.:7.623e-04 3rd Qu.:1.736e-03 3rd Qu.:0.0035289
## Max. :1.686e-03 Max. :4.126e-03 Max. :0.0092792
## X2015.10.01.12.00.00 X2016.01.01.12.00.00 X2016.04.01.12.00.00
## Min. :2.376e-06 Min. :8.651e-07 Min. :2.005e-06
## 1st Qu.:4.884e-05 1st Qu.:6.691e-05 1st Qu.:8.367e-05
## Median :5.652e-04 Median :3.273e-04 Median :4.739e-04
## Mean :6.933e-04 Mean :4.877e-04 Mean :9.720e-04
## 3rd Qu.:1.242e-03 3rd Qu.:7.488e-04 3rd Qu.:1.726e-03
## Max. :2.474e-03 Max. :2.529e-03 Max. :4.163e-03
## X2016.07.01.12.00.00 X2016.10.01.12.00.00 X2017.01.01.12.00.00
## Min. :4.767e-06 Min. :2.222e-07 Min. :1.214e-05
## 1st Qu.:2.682e-04 1st Qu.:1.034e-05 1st Qu.:3.048e-04
## Median :1.387e-03 Median :1.230e-04 Median :9.628e-04
## Mean :1.714e-03 Mean :2.249e-04 Mean :1.417e-03
## 3rd Qu.:2.733e-03 3rd Qu.:2.684e-04 3rd Qu.:1.957e-03
## Max. :6.089e-03 Max. :1.325e-03 Max. :5.402e-03
## X2017.04.01.12.00.00 X2017.07.01.12.00.00 X2017.10.01.12.00.00
## Min. :1.583e-06 Min. :3.260e-07 Min. :2.222e-07
## 1st Qu.:4.389e-05 1st Qu.:1.069e-03 1st Qu.:2.778e-07
## Median :2.669e-04 Median :1.581e-03 Median :2.037e-05
## Mean :8.743e-04 Mean :2.099e-03 Mean :1.809e-04
## 3rd Qu.:1.446e-03 3rd Qu.:2.982e-03 3rd Qu.:1.973e-04
## Max. :4.575e-03 Max. :7.842e-03 Max. :1.276e-03
## X2018.01.01.12.00.00 X2018.04.01.12.00.00 X2018.07.01.12.00.00
## Min. :2.222e-07 Min. :7.063e-06 Min. :6.280e-07
## 1st Qu.:9.613e-07 1st Qu.:1.673e-04 1st Qu.:1.740e-04
## Median :6.787e-05 Median :5.045e-04 Median :1.176e-03
## Mean :1.998e-04 Mean :1.100e-03 Mean :1.858e-03
## 3rd Qu.:2.860e-04 3rd Qu.:1.700e-03 3rd Qu.:2.892e-03
## Max. :1.279e-03 Max. :5.318e-03 Max. :7.552e-03
## X2018.10.01.12.00.00 X2019.01.01.12.00.00 X2019.04.01.12.00.00
## Min. :2.222e-07 Min. :9.318e-06 Min. :1.093e-05
## 1st Qu.:1.812e-05 1st Qu.:1.854e-04 1st Qu.:4.651e-04
## Median :1.533e-04 Median :7.272e-04 Median :1.127e-03
## Mean :4.019e-04 Mean :9.980e-04 Mean :1.236e-03
## 3rd Qu.:7.496e-04 3rd Qu.:1.353e-03 3rd Qu.:1.774e-03
## Max. :2.163e-03 Max. :3.909e-03 Max. :4.184e-03
## X2019.07.01.12.00.00 X2019.10.01.12.00.00 X2020.01.01.12.00.00
## Min. :2.567e-06 Min. :5.651e-05 Min. :6.600e-07
## 1st Qu.:6.994e-04 1st Qu.:3.319e-04 1st Qu.:6.097e-04
## Median :1.300e-03 Median :5.388e-04 Median :1.074e-03
## Mean :1.719e-03 Mean :6.379e-04 Mean :1.288e-03
## 3rd Qu.:2.374e-03 3rd Qu.:9.708e-04 3rd Qu.:1.879e-03
## Max. :5.965e-03 Max. :2.114e-03 Max. :3.931e-03
## X2020.04.01.12.00.00 X2020.07.01.12.00.00 X2020.10.01.12.00.00
## Min. :3.580e-07 Min. :1.869e-06 Min. :2.222e-07
## 1st Qu.:7.892e-05 1st Qu.:4.217e-04 1st Qu.:2.751e-07
## Median :5.385e-04 Median :1.180e-03 Median :1.533e-05
## Mean :7.963e-04 Mean :1.302e-03 Mean :1.180e-04
## 3rd Qu.:1.222e-03 3rd Qu.:1.923e-03 3rd Qu.:9.921e-05
## Max. :3.362e-03 Max. :5.172e-03 Max. :9.457e-04
## X2021.01.01.12.00.00 X2021.04.01.12.00.00 X2021.07.01.12.00.00
## Min. :2.222e-07 Min. :6.280e-07 Min. :5.978e-06
## 1st Qu.:4.786e-07 1st Qu.:1.161e-04 1st Qu.:3.548e-04
## Median :5.624e-05 Median :4.062e-04 Median :9.771e-04
## Mean :3.464e-04 Mean :7.128e-04 Mean :1.957e-03
## 3rd Qu.:5.769e-04 3rd Qu.:1.078e-03 3rd Qu.:3.373e-03
## Max. :2.290e-03 Max. :3.307e-03 Max. :9.443e-03
## X2021.10.01.12.00.00 X2022.01.01.12.00.00 X2022.04.01.12.00.00
## Min. :1.996e-06 Min. :0.0001209 Min. :1.034e-06
## 1st Qu.:7.425e-05 1st Qu.:0.0004217 1st Qu.:3.973e-05
## Median :3.407e-04 Median :0.0008342 Median :1.107e-04
## Mean :6.124e-04 Mean :0.0012035 Mean :3.191e-04
## 3rd Qu.:1.088e-03 3rd Qu.:0.0015538 3rd Qu.:4.903e-04
## Max. :2.966e-03 Max. :0.0047234 Max. :1.572e-03
## X2022.07.01.12.00.00 X2022.10.01.12.00.00
## Min. :0.0005713 Min. :2.222e-07
## 1st Qu.:0.0033260 1st Qu.:8.477e-07
## Median :0.0043958 Median :1.534e-05
## Mean :0.0043883 Mean :2.402e-04
## 3rd Qu.:0.0053293 3rd Qu.:3.349e-04
## Max. :0.0094819 Max. :1.799e-03
## Duplicate the dataframe
precip.extract.df2 = precip.extract.df
## Create a vector for years
year = c("precip.jan_2010", "precip.apr_2010", "precip.jul_2010", "precip.oct_2010", "precip.jan_2011", "precip.apr_2011", "precip.jul_2011", "precip.oct_2011", "precip.jan_2012", "precip.apr_2012", "precip.jul_2012", "precip.oct_2012", "precip.jan_2013", "precip.apr_2013", "precip.jul_2013", "precip.oct_2013", "precip.jan_2014", "precip.apr_2014", "precip.jul_2014", "precip.oct_2014", "precip.jan_2015", "precip.apr_2015", "precip.jul_2015", "precip.oct_2015", "precip.jan_2016", "precip.apr_2016", "precip.jul_2016", "precip.oct_2016", "precip.jan_2017", "precip.apr_2017", "precip.jul_2017", "precip.oct_2017", "precip.jan_2018", "precip.apr_2018", "precip.jul_2018", "precip.oct_2018", "precip.jan_2019", "precip.apr_2019", "precip.jul_2019", "precip.oct_2019", "precip.jan_2020", "precip.apr_2020", "precip.jul_2020", "precip.oct_2020", "precip.jan_2021", "precip.apr_2021", "precip.jul_2021", "precip.oct_2021", "precip.jan_2022", "precip.apr_2022", "precip.jul_2022", "precip.oct_2022")
## Change the names!!
names(precip.extract.df2)[2:53] = year
## Check
names(precip.extract.df2)
## [1] "districts" "precip.jan_2010" "precip.apr_2010" "precip.jul_2010"
## [5] "precip.oct_2010" "precip.jan_2011" "precip.apr_2011" "precip.jul_2011"
## [9] "precip.oct_2011" "precip.jan_2012" "precip.apr_2012" "precip.jul_2012"
## [13] "precip.oct_2012" "precip.jan_2013" "precip.apr_2013" "precip.jul_2013"
## [17] "precip.oct_2013" "precip.jan_2014" "precip.apr_2014" "precip.jul_2014"
## [21] "precip.oct_2014" "precip.jan_2015" "precip.apr_2015" "precip.jul_2015"
## [25] "precip.oct_2015" "precip.jan_2016" "precip.apr_2016" "precip.jul_2016"
## [29] "precip.oct_2016" "precip.jan_2017" "precip.apr_2017" "precip.jul_2017"
## [33] "precip.oct_2017" "precip.jan_2018" "precip.apr_2018" "precip.jul_2018"
## [37] "precip.oct_2018" "precip.jan_2019" "precip.apr_2019" "precip.jul_2019"
## [41] "precip.oct_2019" "precip.jan_2020" "precip.apr_2020" "precip.jul_2020"
## [45] "precip.oct_2020" "precip.jan_2021" "precip.apr_2021" "precip.jul_2021"
## [49] "precip.oct_2021" "precip.jan_2022" "precip.apr_2022" "precip.jul_2022"
## [53] "precip.oct_2022"
## Conversion to millimeters
## 1m = 1000 mill
m_to_mm = function(x) {
(x*1000)
}
## Convert to Celsius
precip.extract.df2 = m_to_mm(precip.extract.df2[,2:53])
## Check
head(precip.extract.df2)
precip.jan_2010 | precip.apr_2010 | precip.jul_2010 | precip.oct_2010 | precip.jan_2011 | precip.apr_2011 | precip.jul_2011 | precip.oct_2011 | precip.jan_2012 | precip.apr_2012 | precip.jul_2012 | precip.oct_2012 | precip.jan_2013 | precip.apr_2013 | precip.jul_2013 | precip.oct_2013 | precip.jan_2014 | precip.apr_2014 | precip.jul_2014 | precip.oct_2014 | precip.jan_2015 | precip.apr_2015 | precip.jul_2015 | precip.oct_2015 | precip.jan_2016 | precip.apr_2016 | precip.jul_2016 | precip.oct_2016 | precip.jan_2017 | precip.apr_2017 | precip.jul_2017 | precip.oct_2017 | precip.jan_2018 | precip.apr_2018 | precip.jul_2018 | precip.oct_2018 | precip.jan_2019 | precip.apr_2019 | precip.jul_2019 | precip.oct_2019 | precip.jan_2020 | precip.apr_2020 | precip.jul_2020 | precip.oct_2020 | precip.jan_2021 | precip.apr_2021 | precip.jul_2021 | precip.oct_2021 | precip.jan_2022 | precip.apr_2022 | precip.jul_2022 | precip.oct_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.898 | 1.95 | 7.32 | 0.414 | 0.704 | 3.55 | 3.02 | 1.59 | 1.71 | 2.92 | 2.39 | 0.71 | 1.01 | 2.75 | 3.18 | 0.892 | 0.676 | 1.93 | 3.76 | 1.28 | 1.32 | 3.29 | 6.28 | 1.66 | 1.26 | 3.16 | 5.69 | 0.236 | 4.56 | 3.9 | 5.5 | 0.236 | 0.286 | 4.64 | 6.95 | 1.46 | 3.19 | 2.48 | 4.15 | 0.948 | 3.23 | 3.08 | 3.31 | 0.0988 | 1.24 | 2.15 | 6.3 | 1.57 | 3.55 | 0.921 | 6.16 | 0.613 |
1.08 | 2.36 | 4.02 | 0.665 | 0.946 | 1.37 | 2.7 | 1.36 | 1.43 | 1.72 | 1.61 | 1.11 | 1 | 1.59 | 1.94 | 0.935 | 0.973 | 2.18 | 1.94 | 1.33 | 1.11 | 2.37 | 3.37 | 1.48 | 1.23 | 3.05 | 3.06 | 0.711 | 3.61 | 3.11 | 2.58 | 0.841 | 0.838 | 2.31 | 3.46 | 1.42 | 2.39 | 1.85 | 1.49 | 1.22 | 2.71 | 1.84 | 2.89 | 0.506 | 1.34 | 2.06 | 3.85 | 1.7 | 1.96 | 1.01 | 3.54 | 1.2 |
0.249 | 0.295 | 5.91 | 0.181 | 0.324 | 1.14 | 2.75 | 0.765 | 1.1 | 0.743 | 0.864 | 0.255 | 0.187 | 0.498 | 4.61 | 0.409 | 0.0712 | 1.26 | 2.19 | 1.01 | 0.458 | 1.95 | 4.59 | 0.875 | 0.665 | 0.983 | 1.82 | 0.205 | 1.66 | 0.969 | 2.94 | 0.146 | 0.0932 | 1.16 | 2.78 | 0.553 | 1.35 | 1.43 | 2.22 | 0.366 | 1.29 | 0.746 | 1.69 | 0.0366 | 0.316 | 0.82 | 4.23 | 1.09 | 1.75 | 0.281 | 4.56 | 0.257 |
0.139 | 0.0189 | 1.13 | 0.00581 | 0.0173 | 0.129 | 0.758 | 0.0373 | 0.276 | 0.168 | 0.0452 | 0.0869 | 0.00679 | 0.678 | 0.232 | 0.149 | 0.11 | 0.208 | 0.18 | 0.0911 | 0.187 | 0.0282 | 0.226 | 0.0372 | 0.0376 | 0.0342 | 0.0918 | 0.00745 | 0.529 | 0.0247 | 0.392 | 0.00105 | 0.000686 | 0.116 | 0.0791 | 0.0641 | 0.352 | 0.119 | 0.247 | 0.218 | 0.588 | 0.0364 | 0.424 | 0.000618 | 0.00154 | 0.195 | 0.482 | 0.565 | 0.605 | 0.174 | 4.84 | 0.00296 |
0.00375 | 0.0032 | 1.95 | 0.0815 | 0.0195 | 0.00804 | 0.571 | 0.00242 | 0.0356 | 0.159 | 0.0882 | 0.0148 | 0.0113 | 0.201 | 1.03 | 0.23 | 0.000222 | 0.0752 | 0.304 | 0.046 | 0.01 | 0.00212 | 2.91 | 0.0152 | 0.012 | 0.00326 | 0.143 | 0.172 | 0.0177 | 0.0233 | 1.97 | 0.00194 | 0.00025 | 0.0386 | 0.112 | 0.00584 | 0.0368 | 0.0162 | 1.5 | 0.372 | 0.00273 | 0.00339 | 0.663 | 0.231 | 0.000339 | 0.0153 | 0.665 | 0.0419 | 0.324 | 0.076 | 5.42 | 0.00236 |
1.03 | 1.99 | 7.54 | 0.541 | 0.692 | 2.92 | 3.98 | 1.26 | 2.27 | 2.86 | 2.43 | 0.861 | 0.954 | 2.18 | 3.79 | 0.88 | 0.853 | 2.42 | 3.67 | 1.1 | 0.985 | 4.09 | 8.27 | 1.42 | 1.43 | 3.15 | 5.89 | 0.351 | 4.82 | 3.96 | 6.23 | 0.371 | 0.388 | 4.2 | 6.46 | 1.32 | 3.47 | 2.16 | 4.77 | 0.86 | 3.81 | 3.17 | 2.86 | 0.0737 | 1.6 | 2.81 | 5.45 | 1.16 | 4.28 | 1.14 | 5.87 | 0.631 |
#create a copy
## Merge to the test dataframe
test.df = cbind(test.df, precip.extract.df2)
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 | temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 | precip.jan_2010 | precip.apr_2010 | precip.jul_2010 | precip.oct_2010 | precip.jan_2011 | precip.apr_2011 | precip.jul_2011 | precip.oct_2011 | precip.jan_2012 | precip.apr_2012 | precip.jul_2012 | precip.oct_2012 | precip.jan_2013 | precip.apr_2013 | precip.jul_2013 | precip.oct_2013 | precip.jan_2014 | precip.apr_2014 | precip.jul_2014 | precip.oct_2014 | precip.jan_2015 | precip.apr_2015 | precip.jul_2015 | precip.oct_2015 | precip.jan_2016 | precip.apr_2016 | precip.jul_2016 | precip.oct_2016 | precip.jan_2017 | precip.apr_2017 | precip.jul_2017 | precip.oct_2017 | precip.jan_2018 | precip.apr_2018 | precip.jul_2018 | precip.oct_2018 | precip.jan_2019 | precip.apr_2019 | precip.jul_2019 | precip.oct_2019 | precip.jan_2020 | precip.apr_2020 | precip.jul_2020 | precip.oct_2020 | precip.jan_2021 | precip.apr_2021 | precip.jul_2021 | precip.oct_2021 | precip.jan_2022 | precip.apr_2022 | precip.jul_2022 | precip.oct_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 | 0.898 | 1.95 | 7.32 | 0.414 | 0.704 | 3.55 | 3.02 | 1.59 | 1.71 | 2.92 | 2.39 | 0.71 | 1.01 | 2.75 | 3.18 | 0.892 | 0.676 | 1.93 | 3.76 | 1.28 | 1.32 | 3.29 | 6.28 | 1.66 | 1.26 | 3.16 | 5.69 | 0.236 | 4.56 | 3.9 | 5.5 | 0.236 | 0.286 | 4.64 | 6.95 | 1.46 | 3.19 | 2.48 | 4.15 | 0.948 | 3.23 | 3.08 | 3.31 | 0.0988 | 1.24 | 2.15 | 6.3 | 1.57 | 3.55 | 0.921 | 6.16 | 0.613 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | -12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 | 1.08 | 2.36 | 4.02 | 0.665 | 0.946 | 1.37 | 2.7 | 1.36 | 1.43 | 1.72 | 1.61 | 1.11 | 1 | 1.59 | 1.94 | 0.935 | 0.973 | 2.18 | 1.94 | 1.33 | 1.11 | 2.37 | 3.37 | 1.48 | 1.23 | 3.05 | 3.06 | 0.711 | 3.61 | 3.11 | 2.58 | 0.841 | 0.838 | 2.31 | 3.46 | 1.42 | 2.39 | 1.85 | 1.49 | 1.22 | 2.71 | 1.84 | 2.89 | 0.506 | 1.34 | 2.06 | 3.85 | 1.7 | 1.96 | 1.01 | 3.54 | 1.2 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 | 0.249 | 0.295 | 5.91 | 0.181 | 0.324 | 1.14 | 2.75 | 0.765 | 1.1 | 0.743 | 0.864 | 0.255 | 0.187 | 0.498 | 4.61 | 0.409 | 0.0712 | 1.26 | 2.19 | 1.01 | 0.458 | 1.95 | 4.59 | 0.875 | 0.665 | 0.983 | 1.82 | 0.205 | 1.66 | 0.969 | 2.94 | 0.146 | 0.0932 | 1.16 | 2.78 | 0.553 | 1.35 | 1.43 | 2.22 | 0.366 | 1.29 | 0.746 | 1.69 | 0.0366 | 0.316 | 0.82 | 4.23 | 1.09 | 1.75 | 0.281 | 4.56 | 0.257 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 | 0.139 | 0.0189 | 1.13 | 0.00581 | 0.0173 | 0.129 | 0.758 | 0.0373 | 0.276 | 0.168 | 0.0452 | 0.0869 | 0.00679 | 0.678 | 0.232 | 0.149 | 0.11 | 0.208 | 0.18 | 0.0911 | 0.187 | 0.0282 | 0.226 | 0.0372 | 0.0376 | 0.0342 | 0.0918 | 0.00745 | 0.529 | 0.0247 | 0.392 | 0.00105 | 0.000686 | 0.116 | 0.0791 | 0.0641 | 0.352 | 0.119 | 0.247 | 0.218 | 0.588 | 0.0364 | 0.424 | 0.000618 | 0.00154 | 0.195 | 0.482 | 0.565 | 0.605 | 0.174 | 4.84 | 0.00296 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 | 0.00375 | 0.0032 | 1.95 | 0.0815 | 0.0195 | 0.00804 | 0.571 | 0.00242 | 0.0356 | 0.159 | 0.0882 | 0.0148 | 0.0113 | 0.201 | 1.03 | 0.23 | 0.000222 | 0.0752 | 0.304 | 0.046 | 0.01 | 0.00212 | 2.91 | 0.0152 | 0.012 | 0.00326 | 0.143 | 0.172 | 0.0177 | 0.0233 | 1.97 | 0.00194 | 0.00025 | 0.0386 | 0.112 | 0.00584 | 0.0368 | 0.0162 | 1.5 | 0.372 | 0.00273 | 0.00339 | 0.663 | 0.231 | 0.000339 | 0.0153 | 0.665 | 0.0419 | 0.324 | 0.076 | 5.42 | 0.00236 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 | 1.03 | 1.99 | 7.54 | 0.541 | 0.692 | 2.92 | 3.98 | 1.26 | 2.27 | 2.86 | 2.43 | 0.861 | 0.954 | 2.18 | 3.79 | 0.88 | 0.853 | 2.42 | 3.67 | 1.1 | 0.985 | 4.09 | 8.27 | 1.42 | 1.43 | 3.15 | 5.89 | 0.351 | 4.82 | 3.96 | 6.23 | 0.371 | 0.388 | 4.2 | 6.46 | 1.32 | 3.47 | 2.16 | 4.77 | 0.86 | 3.81 | 3.17 | 2.86 | 0.0737 | 1.6 | 2.81 | 5.45 | 1.16 | 4.28 | 1.14 | 5.87 | 0.631 |
dim(test.df)
## [1] 160 159
## Add the rest of the total counts
test.df$total.con_2010 = dist.con.2010
test.df$total.con_2011 = dist.con.2011
test.df$total.con_2012 = dist.con.2012
test.df$total.con_2013 = dist.con.2013
test.df$total.con_2014 = dist.con.2014
test.df$total.con_2015 = dist.con.2015
test.df$total.con_2016 = dist.con.2016
test.df$total.con_2017 = dist.con.2017
test.df$total.con_2018 = dist.con.2018
test.df$total.con_2019 = dist.con.2019
test.df$total.con_2020 = dist.con.2020
test.df$total.con_2021 = dist.con.2021
test.df$total.con_2022 = dist.con.2022
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 | temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 | precip.jan_2010 | precip.apr_2010 | precip.jul_2010 | precip.oct_2010 | precip.jan_2011 | precip.apr_2011 | precip.jul_2011 | precip.oct_2011 | precip.jan_2012 | precip.apr_2012 | precip.jul_2012 | precip.oct_2012 | precip.jan_2013 | precip.apr_2013 | precip.jul_2013 | precip.oct_2013 | precip.jan_2014 | precip.apr_2014 | precip.jul_2014 | precip.oct_2014 | precip.jan_2015 | precip.apr_2015 | precip.jul_2015 | precip.oct_2015 | precip.jan_2016 | precip.apr_2016 | precip.jul_2016 | precip.oct_2016 | precip.jan_2017 | precip.apr_2017 | precip.jul_2017 | precip.oct_2017 | precip.jan_2018 | precip.apr_2018 | precip.jul_2018 | precip.oct_2018 | precip.jan_2019 | precip.apr_2019 | precip.jul_2019 | precip.oct_2019 | precip.jan_2020 | precip.apr_2020 | precip.jul_2020 | precip.oct_2020 | precip.jan_2021 | precip.apr_2021 | precip.jul_2021 | precip.oct_2021 | precip.jan_2022 | precip.apr_2022 | precip.jul_2022 | precip.oct_2022 | total.con_2010 | total.con_2011 | total.con_2012 | total.con_2013 | total.con_2014 | total.con_2015 | total.con_2016 | total.con_2017 | total.con_2018 | total.con_2019 | total.con_2020 | total.con_2021 | total.con_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 | 0.898 | 1.95 | 7.32 | 0.414 | 0.704 | 3.55 | 3.02 | 1.59 | 1.71 | 2.92 | 2.39 | 0.71 | 1.01 | 2.75 | 3.18 | 0.892 | 0.676 | 1.93 | 3.76 | 1.28 | 1.32 | 3.29 | 6.28 | 1.66 | 1.26 | 3.16 | 5.69 | 0.236 | 4.56 | 3.9 | 5.5 | 0.236 | 0.286 | 4.64 | 6.95 | 1.46 | 3.19 | 2.48 | 4.15 | 0.948 | 3.23 | 3.08 | 3.31 | 0.0988 | 1.24 | 2.15 | 6.3 | 1.57 | 3.55 | 0.921 | 6.16 | 0.613 | 24 | 10 | 12 | 12 | 6 | 10 | 9 | 19 | 20 | 33 | 56 | 41 | 56 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | -12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 | 1.08 | 2.36 | 4.02 | 0.665 | 0.946 | 1.37 | 2.7 | 1.36 | 1.43 | 1.72 | 1.61 | 1.11 | 1 | 1.59 | 1.94 | 0.935 | 0.973 | 2.18 | 1.94 | 1.33 | 1.11 | 2.37 | 3.37 | 1.48 | 1.23 | 3.05 | 3.06 | 0.711 | 3.61 | 3.11 | 2.58 | 0.841 | 0.838 | 2.31 | 3.46 | 1.42 | 2.39 | 1.85 | 1.49 | 1.22 | 2.71 | 1.84 | 2.89 | 0.506 | 1.34 | 2.06 | 3.85 | 1.7 | 1.96 | 1.01 | 3.54 | 1.2 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 3 | 5 | 5 | 5 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 | 0.249 | 0.295 | 5.91 | 0.181 | 0.324 | 1.14 | 2.75 | 0.765 | 1.1 | 0.743 | 0.864 | 0.255 | 0.187 | 0.498 | 4.61 | 0.409 | 0.0712 | 1.26 | 2.19 | 1.01 | 0.458 | 1.95 | 4.59 | 0.875 | 0.665 | 0.983 | 1.82 | 0.205 | 1.66 | 0.969 | 2.94 | 0.146 | 0.0932 | 1.16 | 2.78 | 0.553 | 1.35 | 1.43 | 2.22 | 0.366 | 1.29 | 0.746 | 1.69 | 0.0366 | 0.316 | 0.82 | 4.23 | 1.09 | 1.75 | 0.281 | 4.56 | 0.257 | 13 | 4 | 6 | 10 | 5 | 7 | 4 | 8 | 22 | 16 | 13 | 16 | 33 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 | 0.139 | 0.0189 | 1.13 | 0.00581 | 0.0173 | 0.129 | 0.758 | 0.0373 | 0.276 | 0.168 | 0.0452 | 0.0869 | 0.00679 | 0.678 | 0.232 | 0.149 | 0.11 | 0.208 | 0.18 | 0.0911 | 0.187 | 0.0282 | 0.226 | 0.0372 | 0.0376 | 0.0342 | 0.0918 | 0.00745 | 0.529 | 0.0247 | 0.392 | 0.00105 | 0.000686 | 0.116 | 0.0791 | 0.0641 | 0.352 | 0.119 | 0.247 | 0.218 | 0.588 | 0.0364 | 0.424 | 0.000618 | 0.00154 | 0.195 | 0.482 | 0.565 | 0.605 | 0.174 | 4.84 | 0.00296 | 3 | 5 | 6 | 12 | 6 | 17 | 7 | 8 | 4 | 2 | 18 | 40 | 33 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 | 0.00375 | 0.0032 | 1.95 | 0.0815 | 0.0195 | 0.00804 | 0.571 | 0.00242 | 0.0356 | 0.159 | 0.0882 | 0.0148 | 0.0113 | 0.201 | 1.03 | 0.23 | 0.000222 | 0.0752 | 0.304 | 0.046 | 0.01 | 0.00212 | 2.91 | 0.0152 | 0.012 | 0.00326 | 0.143 | 0.172 | 0.0177 | 0.0233 | 1.97 | 0.00194 | 0.00025 | 0.0386 | 0.112 | 0.00584 | 0.0368 | 0.0162 | 1.5 | 0.372 | 0.00273 | 0.00339 | 0.663 | 0.231 | 0.000339 | 0.0153 | 0.665 | 0.0419 | 0.324 | 0.076 | 5.42 | 0.00236 | 108 | 45 | 100 | 96 | 65 | 88 | 75 | 98 | 91 | 225 | 342 | 176 | 85 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 | 1.03 | 1.99 | 7.54 | 0.541 | 0.692 | 2.92 | 3.98 | 1.26 | 2.27 | 2.86 | 2.43 | 0.861 | 0.954 | 2.18 | 3.79 | 0.88 | 0.853 | 2.42 | 3.67 | 1.1 | 0.985 | 4.09 | 8.27 | 1.42 | 1.43 | 3.15 | 5.89 | 0.351 | 4.82 | 3.96 | 6.23 | 0.371 | 0.388 | 4.2 | 6.46 | 1.32 | 3.47 | 2.16 | 4.77 | 0.86 | 3.81 | 3.17 | 2.86 | 0.0737 | 1.6 | 2.81 | 5.45 | 1.16 | 4.28 | 1.14 | 5.87 | 0.631 | 4 | 4 | 1 | 3 | 6 | 10 | 9 | 4 | 9 | 16 | 3 | 0 | 2 |
## Counts don't match e.g. Attock 17 vs 13
## Badin 131 to 108
## Spatial mismatch with points on the border?
## **NOTE**
## We use this count to build our table to stay spatially consistent with the pak.bound.adm2 shapefile
## Add protest columns
test.df$protests_2010 = dist.protests.2010
test.df$protests_2011 = dist.protests.2011
test.df$protests_2012 = dist.protests.2012
test.df$protests_2013 = dist.protests.2013
test.df$protests_2014 = dist.protests.2014
test.df$protests_2015 = dist.protests.2015
test.df$protests_2016 = dist.protests.2016
test.df$protests_2017 = dist.protests.2017
test.df$protests_2018 = dist.protests.2018
test.df$protests_2019 = dist.protests.2019
test.df$protests_2020 = dist.protests.2020
test.df$protests_2021 = dist.protests.2021
test.df$protests_2022 = dist.protests.2022
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 | temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 | precip.jan_2010 | precip.apr_2010 | precip.jul_2010 | precip.oct_2010 | precip.jan_2011 | precip.apr_2011 | precip.jul_2011 | precip.oct_2011 | precip.jan_2012 | precip.apr_2012 | precip.jul_2012 | precip.oct_2012 | precip.jan_2013 | precip.apr_2013 | precip.jul_2013 | precip.oct_2013 | precip.jan_2014 | precip.apr_2014 | precip.jul_2014 | precip.oct_2014 | precip.jan_2015 | precip.apr_2015 | precip.jul_2015 | precip.oct_2015 | precip.jan_2016 | precip.apr_2016 | precip.jul_2016 | precip.oct_2016 | precip.jan_2017 | precip.apr_2017 | precip.jul_2017 | precip.oct_2017 | precip.jan_2018 | precip.apr_2018 | precip.jul_2018 | precip.oct_2018 | precip.jan_2019 | precip.apr_2019 | precip.jul_2019 | precip.oct_2019 | precip.jan_2020 | precip.apr_2020 | precip.jul_2020 | precip.oct_2020 | precip.jan_2021 | precip.apr_2021 | precip.jul_2021 | precip.oct_2021 | precip.jan_2022 | precip.apr_2022 | precip.jul_2022 | precip.oct_2022 | total.con_2010 | total.con_2011 | total.con_2012 | total.con_2013 | total.con_2014 | total.con_2015 | total.con_2016 | total.con_2017 | total.con_2018 | total.con_2019 | total.con_2020 | total.con_2021 | total.con_2022 | protests_2010 | protests_2011 | protests_2012 | protests_2013 | protests_2014 | protests_2015 | protests_2016 | protests_2017 | protests_2018 | protests_2019 | protests_2020 | protests_2021 | protests_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 | 0.898 | 1.95 | 7.32 | 0.414 | 0.704 | 3.55 | 3.02 | 1.59 | 1.71 | 2.92 | 2.39 | 0.71 | 1.01 | 2.75 | 3.18 | 0.892 | 0.676 | 1.93 | 3.76 | 1.28 | 1.32 | 3.29 | 6.28 | 1.66 | 1.26 | 3.16 | 5.69 | 0.236 | 4.56 | 3.9 | 5.5 | 0.236 | 0.286 | 4.64 | 6.95 | 1.46 | 3.19 | 2.48 | 4.15 | 0.948 | 3.23 | 3.08 | 3.31 | 0.0988 | 1.24 | 2.15 | 6.3 | 1.57 | 3.55 | 0.921 | 6.16 | 0.613 | 24 | 10 | 12 | 12 | 6 | 10 | 9 | 19 | 20 | 33 | 56 | 41 | 56 | 17 | 10 | 15 | 16 | 14 | 11 | 13 | 17 | 15 | 19 | 23 | 20 | 20 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | -12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 | 1.08 | 2.36 | 4.02 | 0.665 | 0.946 | 1.37 | 2.7 | 1.36 | 1.43 | 1.72 | 1.61 | 1.11 | 1 | 1.59 | 1.94 | 0.935 | 0.973 | 2.18 | 1.94 | 1.33 | 1.11 | 2.37 | 3.37 | 1.48 | 1.23 | 3.05 | 3.06 | 0.711 | 3.61 | 3.11 | 2.58 | 0.841 | 0.838 | 2.31 | 3.46 | 1.42 | 2.39 | 1.85 | 1.49 | 1.22 | 2.71 | 1.84 | 2.89 | 0.506 | 1.34 | 2.06 | 3.85 | 1.7 | 1.96 | 1.01 | 3.54 | 1.2 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 3 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 | 0.249 | 0.295 | 5.91 | 0.181 | 0.324 | 1.14 | 2.75 | 0.765 | 1.1 | 0.743 | 0.864 | 0.255 | 0.187 | 0.498 | 4.61 | 0.409 | 0.0712 | 1.26 | 2.19 | 1.01 | 0.458 | 1.95 | 4.59 | 0.875 | 0.665 | 0.983 | 1.82 | 0.205 | 1.66 | 0.969 | 2.94 | 0.146 | 0.0932 | 1.16 | 2.78 | 0.553 | 1.35 | 1.43 | 2.22 | 0.366 | 1.29 | 0.746 | 1.69 | 0.0366 | 0.316 | 0.82 | 4.23 | 1.09 | 1.75 | 0.281 | 4.56 | 0.257 | 13 | 4 | 6 | 10 | 5 | 7 | 4 | 8 | 22 | 16 | 13 | 16 | 33 | 7 | 7 | 8 | 11 | 6 | 8 | 12 | 10 | 11 | 9 | 13 | 9 | 10 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 | 0.139 | 0.0189 | 1.13 | 0.00581 | 0.0173 | 0.129 | 0.758 | 0.0373 | 0.276 | 0.168 | 0.0452 | 0.0869 | 0.00679 | 0.678 | 0.232 | 0.149 | 0.11 | 0.208 | 0.18 | 0.0911 | 0.187 | 0.0282 | 0.226 | 0.0372 | 0.0376 | 0.0342 | 0.0918 | 0.00745 | 0.529 | 0.0247 | 0.392 | 0.00105 | 0.000686 | 0.116 | 0.0791 | 0.0641 | 0.352 | 0.119 | 0.247 | 0.218 | 0.588 | 0.0364 | 0.424 | 0.000618 | 0.00154 | 0.195 | 0.482 | 0.565 | 0.605 | 0.174 | 4.84 | 0.00296 | 3 | 5 | 6 | 12 | 6 | 17 | 7 | 8 | 4 | 2 | 18 | 40 | 33 | 0 | 1 | 2 | 2 | 3 | 2 | 3 | 2 | 2 | 2 | 3 | 3 | 2 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 | 0.00375 | 0.0032 | 1.95 | 0.0815 | 0.0195 | 0.00804 | 0.571 | 0.00242 | 0.0356 | 0.159 | 0.0882 | 0.0148 | 0.0113 | 0.201 | 1.03 | 0.23 | 0.000222 | 0.0752 | 0.304 | 0.046 | 0.01 | 0.00212 | 2.91 | 0.0152 | 0.012 | 0.00326 | 0.143 | 0.172 | 0.0177 | 0.0233 | 1.97 | 0.00194 | 0.00025 | 0.0386 | 0.112 | 0.00584 | 0.0368 | 0.0162 | 1.5 | 0.372 | 0.00273 | 0.00339 | 0.663 | 0.231 | 0.000339 | 0.0153 | 0.665 | 0.0419 | 0.324 | 0.076 | 5.42 | 0.00236 | 108 | 45 | 100 | 96 | 65 | 88 | 75 | 98 | 91 | 225 | 342 | 176 | 85 | 104 | 71 | 67 | 67 | 64 | 73 | 86 | 84 | 87 | 93 | 99 | 99 | 85 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 | 1.03 | 1.99 | 7.54 | 0.541 | 0.692 | 2.92 | 3.98 | 1.26 | 2.27 | 2.86 | 2.43 | 0.861 | 0.954 | 2.18 | 3.79 | 0.88 | 0.853 | 2.42 | 3.67 | 1.1 | 0.985 | 4.09 | 8.27 | 1.42 | 1.43 | 3.15 | 5.89 | 0.351 | 4.82 | 3.96 | 6.23 | 0.371 | 0.388 | 4.2 | 6.46 | 1.32 | 3.47 | 2.16 | 4.77 | 0.86 | 3.81 | 3.17 | 2.86 | 0.0737 | 1.6 | 2.81 | 5.45 | 1.16 | 4.28 | 1.14 | 5.87 | 0.631 | 4 | 4 | 1 | 3 | 6 | 10 | 9 | 4 | 9 | 16 | 3 | 0 | 2 | 4 | 4 | 2 | 3 | 1 | 3 | 4 | 4 | 3 | 3 | 4 | 4 | 3 |
## Add battles columns
test.df$battles_2010 = dist.battles.2010
test.df$battles_2011 = dist.battles.2011
test.df$battles_2012 = dist.battles.2012
test.df$battles_2013 = dist.battles.2013
test.df$battles_2014 = dist.battles.2014
test.df$battles_2015 = dist.battles.2015
test.df$battles_2016 = dist.battles.2016
test.df$battles_2017 = dist.battles.2017
test.df$battles_2018 = dist.battles.2018
test.df$battles_2019 = dist.battles.2019
test.df$battles_2020 = dist.battles.2020
test.df$battles_2021 = dist.battles.2021
test.df$battles_2022 = dist.battles.2022
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 | temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 | precip.jan_2010 | precip.apr_2010 | precip.jul_2010 | precip.oct_2010 | precip.jan_2011 | precip.apr_2011 | precip.jul_2011 | precip.oct_2011 | precip.jan_2012 | precip.apr_2012 | precip.jul_2012 | precip.oct_2012 | precip.jan_2013 | precip.apr_2013 | precip.jul_2013 | precip.oct_2013 | precip.jan_2014 | precip.apr_2014 | precip.jul_2014 | precip.oct_2014 | precip.jan_2015 | precip.apr_2015 | precip.jul_2015 | precip.oct_2015 | precip.jan_2016 | precip.apr_2016 | precip.jul_2016 | precip.oct_2016 | precip.jan_2017 | precip.apr_2017 | precip.jul_2017 | precip.oct_2017 | precip.jan_2018 | precip.apr_2018 | precip.jul_2018 | precip.oct_2018 | precip.jan_2019 | precip.apr_2019 | precip.jul_2019 | precip.oct_2019 | precip.jan_2020 | precip.apr_2020 | precip.jul_2020 | precip.oct_2020 | precip.jan_2021 | precip.apr_2021 | precip.jul_2021 | precip.oct_2021 | precip.jan_2022 | precip.apr_2022 | precip.jul_2022 | precip.oct_2022 | total.con_2010 | total.con_2011 | total.con_2012 | total.con_2013 | total.con_2014 | total.con_2015 | total.con_2016 | total.con_2017 | total.con_2018 | total.con_2019 | total.con_2020 | total.con_2021 | total.con_2022 | protests_2010 | protests_2011 | protests_2012 | protests_2013 | protests_2014 | protests_2015 | protests_2016 | protests_2017 | protests_2018 | protests_2019 | protests_2020 | protests_2021 | protests_2022 | battles_2010 | battles_2011 | battles_2012 | battles_2013 | battles_2014 | battles_2015 | battles_2016 | battles_2017 | battles_2018 | battles_2019 | battles_2020 | battles_2021 | battles_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 | 0.898 | 1.95 | 7.32 | 0.414 | 0.704 | 3.55 | 3.02 | 1.59 | 1.71 | 2.92 | 2.39 | 0.71 | 1.01 | 2.75 | 3.18 | 0.892 | 0.676 | 1.93 | 3.76 | 1.28 | 1.32 | 3.29 | 6.28 | 1.66 | 1.26 | 3.16 | 5.69 | 0.236 | 4.56 | 3.9 | 5.5 | 0.236 | 0.286 | 4.64 | 6.95 | 1.46 | 3.19 | 2.48 | 4.15 | 0.948 | 3.23 | 3.08 | 3.31 | 0.0988 | 1.24 | 2.15 | 6.3 | 1.57 | 3.55 | 0.921 | 6.16 | 0.613 | 24 | 10 | 12 | 12 | 6 | 10 | 9 | 19 | 20 | 33 | 56 | 41 | 56 | 17 | 10 | 15 | 16 | 14 | 11 | 13 | 17 | 15 | 19 | 23 | 20 | 20 | 0 | 3 | 1 | 1 | 4 | 7 | 3 | 1 | 1 | 0 | 1 | 1 | 0 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | -12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 | 1.08 | 2.36 | 4.02 | 0.665 | 0.946 | 1.37 | 2.7 | 1.36 | 1.43 | 1.72 | 1.61 | 1.11 | 1 | 1.59 | 1.94 | 0.935 | 0.973 | 2.18 | 1.94 | 1.33 | 1.11 | 2.37 | 3.37 | 1.48 | 1.23 | 3.05 | 3.06 | 0.711 | 3.61 | 3.11 | 2.58 | 0.841 | 0.838 | 2.31 | 3.46 | 1.42 | 2.39 | 1.85 | 1.49 | 1.22 | 2.71 | 1.84 | 2.89 | 0.506 | 1.34 | 2.06 | 3.85 | 1.7 | 1.96 | 1.01 | 3.54 | 1.2 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 3 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 | 0.249 | 0.295 | 5.91 | 0.181 | 0.324 | 1.14 | 2.75 | 0.765 | 1.1 | 0.743 | 0.864 | 0.255 | 0.187 | 0.498 | 4.61 | 0.409 | 0.0712 | 1.26 | 2.19 | 1.01 | 0.458 | 1.95 | 4.59 | 0.875 | 0.665 | 0.983 | 1.82 | 0.205 | 1.66 | 0.969 | 2.94 | 0.146 | 0.0932 | 1.16 | 2.78 | 0.553 | 1.35 | 1.43 | 2.22 | 0.366 | 1.29 | 0.746 | 1.69 | 0.0366 | 0.316 | 0.82 | 4.23 | 1.09 | 1.75 | 0.281 | 4.56 | 0.257 | 13 | 4 | 6 | 10 | 5 | 7 | 4 | 8 | 22 | 16 | 13 | 16 | 33 | 7 | 7 | 8 | 11 | 6 | 8 | 12 | 10 | 11 | 9 | 13 | 9 | 10 | 2 | 0 | 2 | 1 | 1 | 2 | 0 | 2 | 2 | 0 | 0 | 2 | 1 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 | 0.139 | 0.0189 | 1.13 | 0.00581 | 0.0173 | 0.129 | 0.758 | 0.0373 | 0.276 | 0.168 | 0.0452 | 0.0869 | 0.00679 | 0.678 | 0.232 | 0.149 | 0.11 | 0.208 | 0.18 | 0.0911 | 0.187 | 0.0282 | 0.226 | 0.0372 | 0.0376 | 0.0342 | 0.0918 | 0.00745 | 0.529 | 0.0247 | 0.392 | 0.00105 | 0.000686 | 0.116 | 0.0791 | 0.0641 | 0.352 | 0.119 | 0.247 | 0.218 | 0.588 | 0.0364 | 0.424 | 0.000618 | 0.00154 | 0.195 | 0.482 | 0.565 | 0.605 | 0.174 | 4.84 | 0.00296 | 3 | 5 | 6 | 12 | 6 | 17 | 7 | 8 | 4 | 2 | 18 | 40 | 33 | 0 | 1 | 2 | 2 | 3 | 2 | 3 | 2 | 2 | 2 | 3 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 | 0.00375 | 0.0032 | 1.95 | 0.0815 | 0.0195 | 0.00804 | 0.571 | 0.00242 | 0.0356 | 0.159 | 0.0882 | 0.0148 | 0.0113 | 0.201 | 1.03 | 0.23 | 0.000222 | 0.0752 | 0.304 | 0.046 | 0.01 | 0.00212 | 2.91 | 0.0152 | 0.012 | 0.00326 | 0.143 | 0.172 | 0.0177 | 0.0233 | 1.97 | 0.00194 | 0.00025 | 0.0386 | 0.112 | 0.00584 | 0.0368 | 0.0162 | 1.5 | 0.372 | 0.00273 | 0.00339 | 0.663 | 0.231 | 0.000339 | 0.0153 | 0.665 | 0.0419 | 0.324 | 0.076 | 5.42 | 0.00236 | 108 | 45 | 100 | 96 | 65 | 88 | 75 | 98 | 91 | 225 | 342 | 176 | 85 | 104 | 71 | 67 | 67 | 64 | 73 | 86 | 84 | 87 | 93 | 99 | 99 | 85 | 1 | 9 | 14 | 6 | 20 | 14 | 7 | 12 | 5 | 3 | 3 | 4 | 8 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 | 1.03 | 1.99 | 7.54 | 0.541 | 0.692 | 2.92 | 3.98 | 1.26 | 2.27 | 2.86 | 2.43 | 0.861 | 0.954 | 2.18 | 3.79 | 0.88 | 0.853 | 2.42 | 3.67 | 1.1 | 0.985 | 4.09 | 8.27 | 1.42 | 1.43 | 3.15 | 5.89 | 0.351 | 4.82 | 3.96 | 6.23 | 0.371 | 0.388 | 4.2 | 6.46 | 1.32 | 3.47 | 2.16 | 4.77 | 0.86 | 3.81 | 3.17 | 2.86 | 0.0737 | 1.6 | 2.81 | 5.45 | 1.16 | 4.28 | 1.14 | 5.87 | 0.631 | 4 | 4 | 1 | 3 | 6 | 10 | 9 | 4 | 9 | 16 | 3 | 0 | 2 | 4 | 4 | 2 | 3 | 1 | 3 | 4 | 4 | 3 | 3 | 4 | 4 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
## Add expl columns
test.df$expl_2010 = dist.expl.2010
test.df$expl_2011 = dist.expl.2011
test.df$expl_2012 = dist.expl.2012
test.df$expl_2013 = dist.expl.2013
test.df$expl_2014 = dist.expl.2014
test.df$expl_2015 = dist.expl.2015
test.df$expl_2016 = dist.expl.2016
test.df$expl_2017 = dist.expl.2017
test.df$expl_2018 = dist.expl.2018
test.df$expl_2019 = dist.expl.2019
test.df$expl_2020 = dist.expl.2020
test.df$expl_2021 = dist.expl.2021
test.df$expl_2022 = dist.expl.2022
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 | temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 | precip.jan_2010 | precip.apr_2010 | precip.jul_2010 | precip.oct_2010 | precip.jan_2011 | precip.apr_2011 | precip.jul_2011 | precip.oct_2011 | precip.jan_2012 | precip.apr_2012 | precip.jul_2012 | precip.oct_2012 | precip.jan_2013 | precip.apr_2013 | precip.jul_2013 | precip.oct_2013 | precip.jan_2014 | precip.apr_2014 | precip.jul_2014 | precip.oct_2014 | precip.jan_2015 | precip.apr_2015 | precip.jul_2015 | precip.oct_2015 | precip.jan_2016 | precip.apr_2016 | precip.jul_2016 | precip.oct_2016 | precip.jan_2017 | precip.apr_2017 | precip.jul_2017 | precip.oct_2017 | precip.jan_2018 | precip.apr_2018 | precip.jul_2018 | precip.oct_2018 | precip.jan_2019 | precip.apr_2019 | precip.jul_2019 | precip.oct_2019 | precip.jan_2020 | precip.apr_2020 | precip.jul_2020 | precip.oct_2020 | precip.jan_2021 | precip.apr_2021 | precip.jul_2021 | precip.oct_2021 | precip.jan_2022 | precip.apr_2022 | precip.jul_2022 | precip.oct_2022 | total.con_2010 | total.con_2011 | total.con_2012 | total.con_2013 | total.con_2014 | total.con_2015 | total.con_2016 | total.con_2017 | total.con_2018 | total.con_2019 | total.con_2020 | total.con_2021 | total.con_2022 | protests_2010 | protests_2011 | protests_2012 | protests_2013 | protests_2014 | protests_2015 | protests_2016 | protests_2017 | protests_2018 | protests_2019 | protests_2020 | protests_2021 | protests_2022 | battles_2010 | battles_2011 | battles_2012 | battles_2013 | battles_2014 | battles_2015 | battles_2016 | battles_2017 | battles_2018 | battles_2019 | battles_2020 | battles_2021 | battles_2022 | expl_2010 | expl_2011 | expl_2012 | expl_2013 | expl_2014 | expl_2015 | expl_2016 | expl_2017 | expl_2018 | expl_2019 | expl_2020 | expl_2021 | expl_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 | 0.898 | 1.95 | 7.32 | 0.414 | 0.704 | 3.55 | 3.02 | 1.59 | 1.71 | 2.92 | 2.39 | 0.71 | 1.01 | 2.75 | 3.18 | 0.892 | 0.676 | 1.93 | 3.76 | 1.28 | 1.32 | 3.29 | 6.28 | 1.66 | 1.26 | 3.16 | 5.69 | 0.236 | 4.56 | 3.9 | 5.5 | 0.236 | 0.286 | 4.64 | 6.95 | 1.46 | 3.19 | 2.48 | 4.15 | 0.948 | 3.23 | 3.08 | 3.31 | 0.0988 | 1.24 | 2.15 | 6.3 | 1.57 | 3.55 | 0.921 | 6.16 | 0.613 | 24 | 10 | 12 | 12 | 6 | 10 | 9 | 19 | 20 | 33 | 56 | 41 | 56 | 17 | 10 | 15 | 16 | 14 | 11 | 13 | 17 | 15 | 19 | 23 | 20 | 20 | 0 | 3 | 1 | 1 | 4 | 7 | 3 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 7 | 5 | 3 | 4 | 2 | 4 | 1 | 1 | 2 | 0 | 0 | 2 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | -12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 | 1.08 | 2.36 | 4.02 | 0.665 | 0.946 | 1.37 | 2.7 | 1.36 | 1.43 | 1.72 | 1.61 | 1.11 | 1 | 1.59 | 1.94 | 0.935 | 0.973 | 2.18 | 1.94 | 1.33 | 1.11 | 2.37 | 3.37 | 1.48 | 1.23 | 3.05 | 3.06 | 0.711 | 3.61 | 3.11 | 2.58 | 0.841 | 0.838 | 2.31 | 3.46 | 1.42 | 2.39 | 1.85 | 1.49 | 1.22 | 2.71 | 1.84 | 2.89 | 0.506 | 1.34 | 2.06 | 3.85 | 1.7 | 1.96 | 1.01 | 3.54 | 1.2 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 3 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 | 0.249 | 0.295 | 5.91 | 0.181 | 0.324 | 1.14 | 2.75 | 0.765 | 1.1 | 0.743 | 0.864 | 0.255 | 0.187 | 0.498 | 4.61 | 0.409 | 0.0712 | 1.26 | 2.19 | 1.01 | 0.458 | 1.95 | 4.59 | 0.875 | 0.665 | 0.983 | 1.82 | 0.205 | 1.66 | 0.969 | 2.94 | 0.146 | 0.0932 | 1.16 | 2.78 | 0.553 | 1.35 | 1.43 | 2.22 | 0.366 | 1.29 | 0.746 | 1.69 | 0.0366 | 0.316 | 0.82 | 4.23 | 1.09 | 1.75 | 0.281 | 4.56 | 0.257 | 13 | 4 | 6 | 10 | 5 | 7 | 4 | 8 | 22 | 16 | 13 | 16 | 33 | 7 | 7 | 8 | 11 | 6 | 8 | 12 | 10 | 11 | 9 | 13 | 9 | 10 | 2 | 0 | 2 | 1 | 1 | 2 | 0 | 2 | 2 | 0 | 0 | 2 | 1 | 0 | 2 | 2 | 1 | 4 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 | 0.139 | 0.0189 | 1.13 | 0.00581 | 0.0173 | 0.129 | 0.758 | 0.0373 | 0.276 | 0.168 | 0.0452 | 0.0869 | 0.00679 | 0.678 | 0.232 | 0.149 | 0.11 | 0.208 | 0.18 | 0.0911 | 0.187 | 0.0282 | 0.226 | 0.0372 | 0.0376 | 0.0342 | 0.0918 | 0.00745 | 0.529 | 0.0247 | 0.392 | 0.00105 | 0.000686 | 0.116 | 0.0791 | 0.0641 | 0.352 | 0.119 | 0.247 | 0.218 | 0.588 | 0.0364 | 0.424 | 0.000618 | 0.00154 | 0.195 | 0.482 | 0.565 | 0.605 | 0.174 | 4.84 | 0.00296 | 3 | 5 | 6 | 12 | 6 | 17 | 7 | 8 | 4 | 2 | 18 | 40 | 33 | 0 | 1 | 2 | 2 | 3 | 2 | 3 | 2 | 2 | 2 | 3 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 | 0.00375 | 0.0032 | 1.95 | 0.0815 | 0.0195 | 0.00804 | 0.571 | 0.00242 | 0.0356 | 0.159 | 0.0882 | 0.0148 | 0.0113 | 0.201 | 1.03 | 0.23 | 0.000222 | 0.0752 | 0.304 | 0.046 | 0.01 | 0.00212 | 2.91 | 0.0152 | 0.012 | 0.00326 | 0.143 | 0.172 | 0.0177 | 0.0233 | 1.97 | 0.00194 | 0.00025 | 0.0386 | 0.112 | 0.00584 | 0.0368 | 0.0162 | 1.5 | 0.372 | 0.00273 | 0.00339 | 0.663 | 0.231 | 0.000339 | 0.0153 | 0.665 | 0.0419 | 0.324 | 0.076 | 5.42 | 0.00236 | 108 | 45 | 100 | 96 | 65 | 88 | 75 | 98 | 91 | 225 | 342 | 176 | 85 | 104 | 71 | 67 | 67 | 64 | 73 | 86 | 84 | 87 | 93 | 99 | 99 | 85 | 1 | 9 | 14 | 6 | 20 | 14 | 7 | 12 | 5 | 3 | 3 | 4 | 8 | 0 | 16 | 10 | 17 | 14 | 6 | 5 | 7 | 4 | 2 | 1 | 2 | 4 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 | 1.03 | 1.99 | 7.54 | 0.541 | 0.692 | 2.92 | 3.98 | 1.26 | 2.27 | 2.86 | 2.43 | 0.861 | 0.954 | 2.18 | 3.79 | 0.88 | 0.853 | 2.42 | 3.67 | 1.1 | 0.985 | 4.09 | 8.27 | 1.42 | 1.43 | 3.15 | 5.89 | 0.351 | 4.82 | 3.96 | 6.23 | 0.371 | 0.388 | 4.2 | 6.46 | 1.32 | 3.47 | 2.16 | 4.77 | 0.86 | 3.81 | 3.17 | 2.86 | 0.0737 | 1.6 | 2.81 | 5.45 | 1.16 | 4.28 | 1.14 | 5.87 | 0.631 | 4 | 4 | 1 | 3 | 6 | 10 | 9 | 4 | 9 | 16 | 3 | 0 | 2 | 4 | 4 | 2 | 3 | 1 | 3 | 4 | 4 | 3 | 3 | 4 | 4 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
## Add riots columns
test.df$riots_2010 = dist.riots.2010
test.df$riots_2011 = dist.riots.2011
test.df$riots_2012 = dist.riots.2012
test.df$riots_2013 = dist.riots.2013
test.df$riots_2014 = dist.riots.2014
test.df$riots_2015 = dist.riots.2015
test.df$riots_2016 = dist.riots.2016
test.df$riots_2017 = dist.riots.2017
test.df$riots_2018 = dist.riots.2018
test.df$riots_2019 = dist.riots.2019
test.df$riots_2020 = dist.riots.2020
test.df$riots_2021 = dist.riots.2021
test.df$riots_2022 = dist.riots.2022
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 | temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 | precip.jan_2010 | precip.apr_2010 | precip.jul_2010 | precip.oct_2010 | precip.jan_2011 | precip.apr_2011 | precip.jul_2011 | precip.oct_2011 | precip.jan_2012 | precip.apr_2012 | precip.jul_2012 | precip.oct_2012 | precip.jan_2013 | precip.apr_2013 | precip.jul_2013 | precip.oct_2013 | precip.jan_2014 | precip.apr_2014 | precip.jul_2014 | precip.oct_2014 | precip.jan_2015 | precip.apr_2015 | precip.jul_2015 | precip.oct_2015 | precip.jan_2016 | precip.apr_2016 | precip.jul_2016 | precip.oct_2016 | precip.jan_2017 | precip.apr_2017 | precip.jul_2017 | precip.oct_2017 | precip.jan_2018 | precip.apr_2018 | precip.jul_2018 | precip.oct_2018 | precip.jan_2019 | precip.apr_2019 | precip.jul_2019 | precip.oct_2019 | precip.jan_2020 | precip.apr_2020 | precip.jul_2020 | precip.oct_2020 | precip.jan_2021 | precip.apr_2021 | precip.jul_2021 | precip.oct_2021 | precip.jan_2022 | precip.apr_2022 | precip.jul_2022 | precip.oct_2022 | total.con_2010 | total.con_2011 | total.con_2012 | total.con_2013 | total.con_2014 | total.con_2015 | total.con_2016 | total.con_2017 | total.con_2018 | total.con_2019 | total.con_2020 | total.con_2021 | total.con_2022 | protests_2010 | protests_2011 | protests_2012 | protests_2013 | protests_2014 | protests_2015 | protests_2016 | protests_2017 | protests_2018 | protests_2019 | protests_2020 | protests_2021 | protests_2022 | battles_2010 | battles_2011 | battles_2012 | battles_2013 | battles_2014 | battles_2015 | battles_2016 | battles_2017 | battles_2018 | battles_2019 | battles_2020 | battles_2021 | battles_2022 | expl_2010 | expl_2011 | expl_2012 | expl_2013 | expl_2014 | expl_2015 | expl_2016 | expl_2017 | expl_2018 | expl_2019 | expl_2020 | expl_2021 | expl_2022 | riots_2010 | riots_2011 | riots_2012 | riots_2013 | riots_2014 | riots_2015 | riots_2016 | riots_2017 | riots_2018 | riots_2019 | riots_2020 | riots_2021 | riots_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 | 0.898 | 1.95 | 7.32 | 0.414 | 0.704 | 3.55 | 3.02 | 1.59 | 1.71 | 2.92 | 2.39 | 0.71 | 1.01 | 2.75 | 3.18 | 0.892 | 0.676 | 1.93 | 3.76 | 1.28 | 1.32 | 3.29 | 6.28 | 1.66 | 1.26 | 3.16 | 5.69 | 0.236 | 4.56 | 3.9 | 5.5 | 0.236 | 0.286 | 4.64 | 6.95 | 1.46 | 3.19 | 2.48 | 4.15 | 0.948 | 3.23 | 3.08 | 3.31 | 0.0988 | 1.24 | 2.15 | 6.3 | 1.57 | 3.55 | 0.921 | 6.16 | 0.613 | 24 | 10 | 12 | 12 | 6 | 10 | 9 | 19 | 20 | 33 | 56 | 41 | 56 | 17 | 10 | 15 | 16 | 14 | 11 | 13 | 17 | 15 | 19 | 23 | 20 | 20 | 0 | 3 | 1 | 1 | 4 | 7 | 3 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 7 | 5 | 3 | 4 | 2 | 4 | 1 | 1 | 2 | 0 | 0 | 2 | 7 | 2 | 0 | 2 | 1 | 1 | 2 | 3 | 5 | 0 | 0 | 2 | 2 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | -12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 | 1.08 | 2.36 | 4.02 | 0.665 | 0.946 | 1.37 | 2.7 | 1.36 | 1.43 | 1.72 | 1.61 | 1.11 | 1 | 1.59 | 1.94 | 0.935 | 0.973 | 2.18 | 1.94 | 1.33 | 1.11 | 2.37 | 3.37 | 1.48 | 1.23 | 3.05 | 3.06 | 0.711 | 3.61 | 3.11 | 2.58 | 0.841 | 0.838 | 2.31 | 3.46 | 1.42 | 2.39 | 1.85 | 1.49 | 1.22 | 2.71 | 1.84 | 2.89 | 0.506 | 1.34 | 2.06 | 3.85 | 1.7 | 1.96 | 1.01 | 3.54 | 1.2 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 3 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 | 0.249 | 0.295 | 5.91 | 0.181 | 0.324 | 1.14 | 2.75 | 0.765 | 1.1 | 0.743 | 0.864 | 0.255 | 0.187 | 0.498 | 4.61 | 0.409 | 0.0712 | 1.26 | 2.19 | 1.01 | 0.458 | 1.95 | 4.59 | 0.875 | 0.665 | 0.983 | 1.82 | 0.205 | 1.66 | 0.969 | 2.94 | 0.146 | 0.0932 | 1.16 | 2.78 | 0.553 | 1.35 | 1.43 | 2.22 | 0.366 | 1.29 | 0.746 | 1.69 | 0.0366 | 0.316 | 0.82 | 4.23 | 1.09 | 1.75 | 0.281 | 4.56 | 0.257 | 13 | 4 | 6 | 10 | 5 | 7 | 4 | 8 | 22 | 16 | 13 | 16 | 33 | 7 | 7 | 8 | 11 | 6 | 8 | 12 | 10 | 11 | 9 | 13 | 9 | 10 | 2 | 0 | 2 | 1 | 1 | 2 | 0 | 2 | 2 | 0 | 0 | 2 | 1 | 0 | 2 | 2 | 1 | 4 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 | 0.139 | 0.0189 | 1.13 | 0.00581 | 0.0173 | 0.129 | 0.758 | 0.0373 | 0.276 | 0.168 | 0.0452 | 0.0869 | 0.00679 | 0.678 | 0.232 | 0.149 | 0.11 | 0.208 | 0.18 | 0.0911 | 0.187 | 0.0282 | 0.226 | 0.0372 | 0.0376 | 0.0342 | 0.0918 | 0.00745 | 0.529 | 0.0247 | 0.392 | 0.00105 | 0.000686 | 0.116 | 0.0791 | 0.0641 | 0.352 | 0.119 | 0.247 | 0.218 | 0.588 | 0.0364 | 0.424 | 0.000618 | 0.00154 | 0.195 | 0.482 | 0.565 | 0.605 | 0.174 | 4.84 | 0.00296 | 3 | 5 | 6 | 12 | 6 | 17 | 7 | 8 | 4 | 2 | 18 | 40 | 33 | 0 | 1 | 2 | 2 | 3 | 2 | 3 | 2 | 2 | 2 | 3 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 | 0.00375 | 0.0032 | 1.95 | 0.0815 | 0.0195 | 0.00804 | 0.571 | 0.00242 | 0.0356 | 0.159 | 0.0882 | 0.0148 | 0.0113 | 0.201 | 1.03 | 0.23 | 0.000222 | 0.0752 | 0.304 | 0.046 | 0.01 | 0.00212 | 2.91 | 0.0152 | 0.012 | 0.00326 | 0.143 | 0.172 | 0.0177 | 0.0233 | 1.97 | 0.00194 | 0.00025 | 0.0386 | 0.112 | 0.00584 | 0.0368 | 0.0162 | 1.5 | 0.372 | 0.00273 | 0.00339 | 0.663 | 0.231 | 0.000339 | 0.0153 | 0.665 | 0.0419 | 0.324 | 0.076 | 5.42 | 0.00236 | 108 | 45 | 100 | 96 | 65 | 88 | 75 | 98 | 91 | 225 | 342 | 176 | 85 | 104 | 71 | 67 | 67 | 64 | 73 | 86 | 84 | 87 | 93 | 99 | 99 | 85 | 1 | 9 | 14 | 6 | 20 | 14 | 7 | 12 | 5 | 3 | 3 | 4 | 8 | 0 | 16 | 10 | 17 | 14 | 6 | 5 | 7 | 4 | 2 | 1 | 2 | 4 | 1 | 3 | 8 | 9 | 6 | 3 | 1 | 0 | 5 | 4 | 4 | 2 | 5 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 | 1.03 | 1.99 | 7.54 | 0.541 | 0.692 | 2.92 | 3.98 | 1.26 | 2.27 | 2.86 | 2.43 | 0.861 | 0.954 | 2.18 | 3.79 | 0.88 | 0.853 | 2.42 | 3.67 | 1.1 | 0.985 | 4.09 | 8.27 | 1.42 | 1.43 | 3.15 | 5.89 | 0.351 | 4.82 | 3.96 | 6.23 | 0.371 | 0.388 | 4.2 | 6.46 | 1.32 | 3.47 | 2.16 | 4.77 | 0.86 | 3.81 | 3.17 | 2.86 | 0.0737 | 1.6 | 2.81 | 5.45 | 1.16 | 4.28 | 1.14 | 5.87 | 0.631 | 4 | 4 | 1 | 3 | 6 | 10 | 9 | 4 | 9 | 16 | 3 | 0 | 2 | 4 | 4 | 2 | 3 | 1 | 3 | 4 | 4 | 3 | 3 | 4 | 4 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
## Add VAC columns
test.df$vac_2010 = dist.vac.2010
test.df$vac_2011 = dist.vac.2011
test.df$vac_2012 = dist.vac.2012
test.df$vac_2013 = dist.vac.2013
test.df$vac_2014 = dist.vac.2014
test.df$vac_2015 = dist.vac.2015
test.df$vac_2016 = dist.vac.2016
test.df$vac_2017 = dist.vac.2017
test.df$vac_2018 = dist.vac.2018
test.df$vac_2019 = dist.vac.2019
test.df$vac_2020 = dist.vac.2020
test.df$vac_2021 = dist.vac.2021
test.df$vac_2022 = dist.vac.2022
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 | temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 | precip.jan_2010 | precip.apr_2010 | precip.jul_2010 | precip.oct_2010 | precip.jan_2011 | precip.apr_2011 | precip.jul_2011 | precip.oct_2011 | precip.jan_2012 | precip.apr_2012 | precip.jul_2012 | precip.oct_2012 | precip.jan_2013 | precip.apr_2013 | precip.jul_2013 | precip.oct_2013 | precip.jan_2014 | precip.apr_2014 | precip.jul_2014 | precip.oct_2014 | precip.jan_2015 | precip.apr_2015 | precip.jul_2015 | precip.oct_2015 | precip.jan_2016 | precip.apr_2016 | precip.jul_2016 | precip.oct_2016 | precip.jan_2017 | precip.apr_2017 | precip.jul_2017 | precip.oct_2017 | precip.jan_2018 | precip.apr_2018 | precip.jul_2018 | precip.oct_2018 | precip.jan_2019 | precip.apr_2019 | precip.jul_2019 | precip.oct_2019 | precip.jan_2020 | precip.apr_2020 | precip.jul_2020 | precip.oct_2020 | precip.jan_2021 | precip.apr_2021 | precip.jul_2021 | precip.oct_2021 | precip.jan_2022 | precip.apr_2022 | precip.jul_2022 | precip.oct_2022 | total.con_2010 | total.con_2011 | total.con_2012 | total.con_2013 | total.con_2014 | total.con_2015 | total.con_2016 | total.con_2017 | total.con_2018 | total.con_2019 | total.con_2020 | total.con_2021 | total.con_2022 | protests_2010 | protests_2011 | protests_2012 | protests_2013 | protests_2014 | protests_2015 | protests_2016 | protests_2017 | protests_2018 | protests_2019 | protests_2020 | protests_2021 | protests_2022 | battles_2010 | battles_2011 | battles_2012 | battles_2013 | battles_2014 | battles_2015 | battles_2016 | battles_2017 | battles_2018 | battles_2019 | battles_2020 | battles_2021 | battles_2022 | expl_2010 | expl_2011 | expl_2012 | expl_2013 | expl_2014 | expl_2015 | expl_2016 | expl_2017 | expl_2018 | expl_2019 | expl_2020 | expl_2021 | expl_2022 | riots_2010 | riots_2011 | riots_2012 | riots_2013 | riots_2014 | riots_2015 | riots_2016 | riots_2017 | riots_2018 | riots_2019 | riots_2020 | riots_2021 | riots_2022 | vac_2010 | vac_2011 | vac_2012 | vac_2013 | vac_2014 | vac_2015 | vac_2016 | vac_2017 | vac_2018 | vac_2019 | vac_2020 | vac_2021 | vac_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 | 0.898 | 1.95 | 7.32 | 0.414 | 0.704 | 3.55 | 3.02 | 1.59 | 1.71 | 2.92 | 2.39 | 0.71 | 1.01 | 2.75 | 3.18 | 0.892 | 0.676 | 1.93 | 3.76 | 1.28 | 1.32 | 3.29 | 6.28 | 1.66 | 1.26 | 3.16 | 5.69 | 0.236 | 4.56 | 3.9 | 5.5 | 0.236 | 0.286 | 4.64 | 6.95 | 1.46 | 3.19 | 2.48 | 4.15 | 0.948 | 3.23 | 3.08 | 3.31 | 0.0988 | 1.24 | 2.15 | 6.3 | 1.57 | 3.55 | 0.921 | 6.16 | 0.613 | 24 | 10 | 12 | 12 | 6 | 10 | 9 | 19 | 20 | 33 | 56 | 41 | 56 | 17 | 10 | 15 | 16 | 14 | 11 | 13 | 17 | 15 | 19 | 23 | 20 | 20 | 0 | 3 | 1 | 1 | 4 | 7 | 3 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 7 | 5 | 3 | 4 | 2 | 4 | 1 | 1 | 2 | 0 | 0 | 2 | 7 | 2 | 0 | 2 | 1 | 1 | 2 | 3 | 5 | 0 | 0 | 2 | 2 | 0 | 2 | 3 | 2 | 1 | 3 | 1 | 1 | 2 | 3 | 0 | 1 | 0 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | -12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 | 1.08 | 2.36 | 4.02 | 0.665 | 0.946 | 1.37 | 2.7 | 1.36 | 1.43 | 1.72 | 1.61 | 1.11 | 1 | 1.59 | 1.94 | 0.935 | 0.973 | 2.18 | 1.94 | 1.33 | 1.11 | 2.37 | 3.37 | 1.48 | 1.23 | 3.05 | 3.06 | 0.711 | 3.61 | 3.11 | 2.58 | 0.841 | 0.838 | 2.31 | 3.46 | 1.42 | 2.39 | 1.85 | 1.49 | 1.22 | 2.71 | 1.84 | 2.89 | 0.506 | 1.34 | 2.06 | 3.85 | 1.7 | 1.96 | 1.01 | 3.54 | 1.2 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 3 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 | 0.249 | 0.295 | 5.91 | 0.181 | 0.324 | 1.14 | 2.75 | 0.765 | 1.1 | 0.743 | 0.864 | 0.255 | 0.187 | 0.498 | 4.61 | 0.409 | 0.0712 | 1.26 | 2.19 | 1.01 | 0.458 | 1.95 | 4.59 | 0.875 | 0.665 | 0.983 | 1.82 | 0.205 | 1.66 | 0.969 | 2.94 | 0.146 | 0.0932 | 1.16 | 2.78 | 0.553 | 1.35 | 1.43 | 2.22 | 0.366 | 1.29 | 0.746 | 1.69 | 0.0366 | 0.316 | 0.82 | 4.23 | 1.09 | 1.75 | 0.281 | 4.56 | 0.257 | 13 | 4 | 6 | 10 | 5 | 7 | 4 | 8 | 22 | 16 | 13 | 16 | 33 | 7 | 7 | 8 | 11 | 6 | 8 | 12 | 10 | 11 | 9 | 13 | 9 | 10 | 2 | 0 | 2 | 1 | 1 | 2 | 0 | 2 | 2 | 0 | 0 | 2 | 1 | 0 | 2 | 2 | 1 | 4 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 2 | 0 | 0 | 1 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 | 0.139 | 0.0189 | 1.13 | 0.00581 | 0.0173 | 0.129 | 0.758 | 0.0373 | 0.276 | 0.168 | 0.0452 | 0.0869 | 0.00679 | 0.678 | 0.232 | 0.149 | 0.11 | 0.208 | 0.18 | 0.0911 | 0.187 | 0.0282 | 0.226 | 0.0372 | 0.0376 | 0.0342 | 0.0918 | 0.00745 | 0.529 | 0.0247 | 0.392 | 0.00105 | 0.000686 | 0.116 | 0.0791 | 0.0641 | 0.352 | 0.119 | 0.247 | 0.218 | 0.588 | 0.0364 | 0.424 | 0.000618 | 0.00154 | 0.195 | 0.482 | 0.565 | 0.605 | 0.174 | 4.84 | 0.00296 | 3 | 5 | 6 | 12 | 6 | 17 | 7 | 8 | 4 | 2 | 18 | 40 | 33 | 0 | 1 | 2 | 2 | 3 | 2 | 3 | 2 | 2 | 2 | 3 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 | 0.00375 | 0.0032 | 1.95 | 0.0815 | 0.0195 | 0.00804 | 0.571 | 0.00242 | 0.0356 | 0.159 | 0.0882 | 0.0148 | 0.0113 | 0.201 | 1.03 | 0.23 | 0.000222 | 0.0752 | 0.304 | 0.046 | 0.01 | 0.00212 | 2.91 | 0.0152 | 0.012 | 0.00326 | 0.143 | 0.172 | 0.0177 | 0.0233 | 1.97 | 0.00194 | 0.00025 | 0.0386 | 0.112 | 0.00584 | 0.0368 | 0.0162 | 1.5 | 0.372 | 0.00273 | 0.00339 | 0.663 | 0.231 | 0.000339 | 0.0153 | 0.665 | 0.0419 | 0.324 | 0.076 | 5.42 | 0.00236 | 108 | 45 | 100 | 96 | 65 | 88 | 75 | 98 | 91 | 225 | 342 | 176 | 85 | 104 | 71 | 67 | 67 | 64 | 73 | 86 | 84 | 87 | 93 | 99 | 99 | 85 | 1 | 9 | 14 | 6 | 20 | 14 | 7 | 12 | 5 | 3 | 3 | 4 | 8 | 0 | 16 | 10 | 17 | 14 | 6 | 5 | 7 | 4 | 2 | 1 | 2 | 4 | 1 | 3 | 8 | 9 | 6 | 3 | 1 | 0 | 5 | 4 | 4 | 2 | 5 | 2 | 8 | 8 | 8 | 4 | 9 | 4 | 4 | 6 | 4 | 1 | 1 | 4 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 | 1.03 | 1.99 | 7.54 | 0.541 | 0.692 | 2.92 | 3.98 | 1.26 | 2.27 | 2.86 | 2.43 | 0.861 | 0.954 | 2.18 | 3.79 | 0.88 | 0.853 | 2.42 | 3.67 | 1.1 | 0.985 | 4.09 | 8.27 | 1.42 | 1.43 | 3.15 | 5.89 | 0.351 | 4.82 | 3.96 | 6.23 | 0.371 | 0.388 | 4.2 | 6.46 | 1.32 | 3.47 | 2.16 | 4.77 | 0.86 | 3.81 | 3.17 | 2.86 | 0.0737 | 1.6 | 2.81 | 5.45 | 1.16 | 4.28 | 1.14 | 5.87 | 0.631 | 4 | 4 | 1 | 3 | 6 | 10 | 9 | 4 | 9 | 16 | 3 | 0 | 2 | 4 | 4 | 2 | 3 | 1 | 3 | 4 | 4 | 3 | 3 | 4 | 4 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
## Add SD columns
test.df$sd_2010 = dist.sd.2010
test.df$sd_2011 = dist.sd.2011
test.df$sd_2012 = dist.sd.2012
test.df$sd_2013 = dist.sd.2013
test.df$sd_2014 = dist.sd.2014
test.df$sd_2015 = dist.sd.2015
test.df$sd_2016 = dist.sd.2016
test.df$sd_2017 = dist.sd.2017
test.df$sd_2018 = dist.sd.2018
test.df$sd_2019 = dist.sd.2019
test.df$sd_2020 = dist.sd.2020
test.df$sd_2021 = dist.sd.2021
test.df$sd_2022 = dist.sd.2022
## Check
head(test.df)
districts | pop_2010 | pop_2011 | pop_2012 | pop_2013 | pop_2014 | pop_2015 | pop_2016 | pop_2017 | pop_2018 | pop_2019 | pop_2020 | pop_2021 | pop_2022 | longitude | latitude | spei_2010 | spei_2011 | spei_2012 | spei_2013 | spei_2014 | spei_2015 | spei_2016 | spei_2017 | spei_2018 | spei_2019 | spei_2020 | spei_2021 | spei_2022 | dep.index_2010 | dep.index_2011 | dep.index_2012 | dep.index_2013 | dep.index_2014 | dep.index_2015 | dep.index_2016 | dep.index_2017 | dep.index_2018 | dep.index_2019 | dep.index_2020 | dep.index_2021 | dep.index_2022 | built_2010 | built_2011 | built_2012 | built_2013 | built_2014 | built_2015 | built_2016 | built_2017 | built_2018 | built_2019 | built_2020 | built_2021 | built_2022 | temp.jan_2010 | temp.apr_2010 | temp.jul_2010 | temp.oct_2010 | temp.jan_2011 | temp.apr_2011 | temp.jul_2011 | temp.oct_2011 | temp.jan_2012 | temp.apr_2012 | temp.jul_2012 | temp.oct_2012 | temp.jan_2013 | temp.apr_2013 | temp.jul_2013 | temp.oct_2013 | temp.jan_2014 | temp.apr_2014 | temp.jul_2014 | temp.oct_2014 | temp.jan_2015 | temp.apr_2015 | temp.jul_2015 | temp.oct_2015 | temp.jan_2016 | temp.apr_2016 | temp.jul_2016 | temp.oct_2016 | temp.jan_2017 | temp.apr_2017 | temp.jul_2017 | temp.oct_2017 | temp.jan_2018 | temp.apr_2018 | temp.jul_2018 | temp.oct_2018 | temp.jan_2019 | temp.apr_2019 | temp.jul_2019 | temp.oct_2019 | temp.jan_2020 | temp.apr_2020 | temp.jul_2020 | temp.oct_2020 | temp.jan_2021 | temp.apr_2021 | temp.jul_2021 | temp.oct_2021 | temp.jan_2022 | temp.apr_2022 | temp.jul_2022 | temp.oct_2022 | precip.jan_2010 | precip.apr_2010 | precip.jul_2010 | precip.oct_2010 | precip.jan_2011 | precip.apr_2011 | precip.jul_2011 | precip.oct_2011 | precip.jan_2012 | precip.apr_2012 | precip.jul_2012 | precip.oct_2012 | precip.jan_2013 | precip.apr_2013 | precip.jul_2013 | precip.oct_2013 | precip.jan_2014 | precip.apr_2014 | precip.jul_2014 | precip.oct_2014 | precip.jan_2015 | precip.apr_2015 | precip.jul_2015 | precip.oct_2015 | precip.jan_2016 | precip.apr_2016 | precip.jul_2016 | precip.oct_2016 | precip.jan_2017 | precip.apr_2017 | precip.jul_2017 | precip.oct_2017 | precip.jan_2018 | precip.apr_2018 | precip.jul_2018 | precip.oct_2018 | precip.jan_2019 | precip.apr_2019 | precip.jul_2019 | precip.oct_2019 | precip.jan_2020 | precip.apr_2020 | precip.jul_2020 | precip.oct_2020 | precip.jan_2021 | precip.apr_2021 | precip.jul_2021 | precip.oct_2021 | precip.jan_2022 | precip.apr_2022 | precip.jul_2022 | precip.oct_2022 | total.con_2010 | total.con_2011 | total.con_2012 | total.con_2013 | total.con_2014 | total.con_2015 | total.con_2016 | total.con_2017 | total.con_2018 | total.con_2019 | total.con_2020 | total.con_2021 | total.con_2022 | protests_2010 | protests_2011 | protests_2012 | protests_2013 | protests_2014 | protests_2015 | protests_2016 | protests_2017 | protests_2018 | protests_2019 | protests_2020 | protests_2021 | protests_2022 | battles_2010 | battles_2011 | battles_2012 | battles_2013 | battles_2014 | battles_2015 | battles_2016 | battles_2017 | battles_2018 | battles_2019 | battles_2020 | battles_2021 | battles_2022 | expl_2010 | expl_2011 | expl_2012 | expl_2013 | expl_2014 | expl_2015 | expl_2016 | expl_2017 | expl_2018 | expl_2019 | expl_2020 | expl_2021 | expl_2022 | riots_2010 | riots_2011 | riots_2012 | riots_2013 | riots_2014 | riots_2015 | riots_2016 | riots_2017 | riots_2018 | riots_2019 | riots_2020 | riots_2021 | riots_2022 | vac_2010 | vac_2011 | vac_2012 | vac_2013 | vac_2014 | vac_2015 | vac_2016 | vac_2017 | vac_2018 | vac_2019 | vac_2020 | vac_2021 | vac_2022 | sd_2010 | sd_2011 | sd_2012 | sd_2013 | sd_2014 | sd_2015 | sd_2016 | sd_2017 | sd_2018 | sd_2019 | sd_2020 | sd_2021 | sd_2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 1.2e+06 | 1.23e+06 | 1.24e+06 | 1.26e+06 | 1.28e+06 | 1.3e+06 | 1.45e+06 | 1.58e+06 | 1.6e+06 | 1.76e+06 | 1.8e+06 | 1.83e+06 | 1.87e+06 | 73.3 | 34.1 | 0.7 | 1.33 | -0.197 | 0.965 | 0.726 | 1.69 | 1.18 | 0.0885 | -1.04 | 0.526 | 0.998 | -0.459 | -0.459 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 73 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 1.09 | 12.2 | 22.3 | 26.2 | 21 | 8.94 | 18.5 | 25.5 | 20 | 6.42 | 18.5 | 28.1 | 18.9 | 8.77 | 18.8 | 26.5 | 20.8 | 9.73 | 18.1 | 26.2 | 19.2 | 9.84 | 19.4 | 25 | 19.7 | 10 | 19.8 | 25.7 | 21.8 | 7.25 | 21.1 | 25.7 | 22.1 | 11.1 | 20.8 | 25.6 | 18.5 | 6.35 | 20.9 | 26.1 | 19.7 | 6.6 | 18.8 | 27.2 | 21.7 | 10.4 | 19 | 26.3 | 20.2 | 7.77 | 24.9 | 25.1 | 20.5 | 0.898 | 1.95 | 7.32 | 0.414 | 0.704 | 3.55 | 3.02 | 1.59 | 1.71 | 2.92 | 2.39 | 0.71 | 1.01 | 2.75 | 3.18 | 0.892 | 0.676 | 1.93 | 3.76 | 1.28 | 1.32 | 3.29 | 6.28 | 1.66 | 1.26 | 3.16 | 5.69 | 0.236 | 4.56 | 3.9 | 5.5 | 0.236 | 0.286 | 4.64 | 6.95 | 1.46 | 3.19 | 2.48 | 4.15 | 0.948 | 3.23 | 3.08 | 3.31 | 0.0988 | 1.24 | 2.15 | 6.3 | 1.57 | 3.55 | 0.921 | 6.16 | 0.613 | 24 | 10 | 12 | 12 | 6 | 10 | 9 | 19 | 20 | 33 | 56 | 41 | 56 | 17 | 10 | 15 | 16 | 14 | 11 | 13 | 17 | 15 | 19 | 23 | 20 | 20 | 0 | 3 | 1 | 1 | 4 | 7 | 3 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 7 | 5 | 3 | 4 | 2 | 4 | 1 | 1 | 2 | 0 | 0 | 2 | 7 | 2 | 0 | 2 | 1 | 1 | 2 | 3 | 5 | 0 | 0 | 2 | 2 | 0 | 2 | 3 | 2 | 1 | 3 | 1 | 1 | 2 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
Astore | 1.06e+05 | 1.08e+05 | 1.1e+05 | 1.11e+05 | 1.09e+05 | 1.1e+05 | 7.58e+04 | 8.22e+04 | 8.35e+04 | 9.18e+04 | 9.37e+04 | 9.56e+04 | 9.75e+04 | 74.9 | 35.1 | 0.913 | 0.739 | -0.533 | 0.186 | -0.262 | 1.2 | 0.941 | 0.155 | -1.24 | 0.296 | 0.83 | -0.366 | -0.366 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 83.2 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | 0.00413 | -12.9 | -2.06 | 10.5 | 0.952 | -16 | -4.36 | 11.8 | 0.387 | -17 | -3.47 | 11.7 | -2.91 | -17.1 | -3.31 | 13.3 | 3.59 | -15.6 | -4.37 | 12.9 | -0.702 | -15.4 | -3.44 | 12.9 | -0.161 | -14.9 | -2.82 | 12.6 | 2.28 | -14.3 | -2.83 | 13.3 | 2.47 | -14.9 | -2.59 | 12.5 | -4.17 | -16 | -1.16 | 13.6 | -0.0882 | -15.7 | -3.16 | 11.6 | 2.32 | -16.4 | -5.51 | 12.8 | -0.55 | -14.4 | -0.765 | 13.5 | -0.455 | 1.08 | 2.36 | 4.02 | 0.665 | 0.946 | 1.37 | 2.7 | 1.36 | 1.43 | 1.72 | 1.61 | 1.11 | 1 | 1.59 | 1.94 | 0.935 | 0.973 | 2.18 | 1.94 | 1.33 | 1.11 | 2.37 | 3.37 | 1.48 | 1.23 | 3.05 | 3.06 | 0.711 | 3.61 | 3.11 | 2.58 | 0.841 | 0.838 | 2.31 | 3.46 | 1.42 | 2.39 | 1.85 | 1.49 | 1.22 | 2.71 | 1.84 | 2.89 | 0.506 | 1.34 | 2.06 | 3.85 | 1.7 | 1.96 | 1.01 | 3.54 | 1.2 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 3 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Attock | 1.78e+06 | 1.81e+06 | 1.84e+06 | 1.87e+06 | 1.9e+06 | 1.93e+06 | 2.4e+06 | 2.61e+06 | 2.64e+06 | 2.91e+06 | 2.97e+06 | 3.03e+06 | 3.09e+06 | 72.3 | 33.5 | 0.42 | 1.23 | 0.628 | 1.31 | 0.852 | 1.63 | 1.48 | 0.427 | -0.69 | 0.446 | 1.31 | -0.143 | -0.143 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 78.3 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 0.938 | 17.2 | 31.6 | 34.1 | 27.9 | 14.8 | 27 | 33.5 | 27.2 | 13.2 | 27.9 | 37.4 | 26.3 | 14.6 | 26.9 | 33.4 | 28.2 | 15.7 | 26.1 | 35 | 25.6 | 15.6 | 26.5 | 32.3 | 27 | 15.4 | 27.3 | 33.3 | 29.8 | 13.7 | 30.1 | 33.6 | 29.6 | 16.7 | 29.3 | 34 | 26.3 | 13.2 | 28.4 | 34.6 | 27.4 | 13 | 26 | 35 | 28.3 | 15.5 | 27.3 | 34.8 | 27.2 | 14.1 | 33.4 | 31.9 | 29 | 0.249 | 0.295 | 5.91 | 0.181 | 0.324 | 1.14 | 2.75 | 0.765 | 1.1 | 0.743 | 0.864 | 0.255 | 0.187 | 0.498 | 4.61 | 0.409 | 0.0712 | 1.26 | 2.19 | 1.01 | 0.458 | 1.95 | 4.59 | 0.875 | 0.665 | 0.983 | 1.82 | 0.205 | 1.66 | 0.969 | 2.94 | 0.146 | 0.0932 | 1.16 | 2.78 | 0.553 | 1.35 | 1.43 | 2.22 | 0.366 | 1.29 | 0.746 | 1.69 | 0.0366 | 0.316 | 0.82 | 4.23 | 1.09 | 1.75 | 0.281 | 4.56 | 0.257 | 13 | 4 | 6 | 10 | 5 | 7 | 4 | 8 | 22 | 16 | 13 | 16 | 33 | 7 | 7 | 8 | 11 | 6 | 8 | 12 | 10 | 11 | 9 | 13 | 9 | 10 | 2 | 0 | 2 | 1 | 1 | 2 | 0 | 2 | 2 | 0 | 0 | 2 | 1 | 0 | 2 | 2 | 1 | 4 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Awaran | 1.38e+05 | 1.38e+05 | 1.4e+05 | 1.42e+05 | 1.44e+05 | 1.46e+05 | 1.4e+05 | 1.52e+05 | 1.54e+05 | 1.7e+05 | 1.73e+05 | 1.77e+05 | 1.8e+05 | 65.4 | 26.2 | -1.43 | -0.641 | -0.887 | -0.683 | -1.2 | -2.03 | -2.1 | -2.15 | -2.5 | -1.42 | -0.301 | -1.39 | -1.39 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 88.2 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 0.000221 | 23 | 36.8 | 34.6 | 33.6 | 22.6 | 33.3 | 35.5 | 31.7 | 19.9 | 33.4 | 36.9 | 32.3 | 22.3 | 31.8 | 35.5 | 33.3 | 20.5 | 34.1 | 36 | 32.5 | 22 | 36.2 | 36 | 33.3 | 23.7 | 34.3 | 36.1 | 33.9 | 20.6 | 36.9 | 35.3 | 34.7 | 23.7 | 35.9 | 36.6 | 33.2 | 22 | 34.8 | 36 | 31.9 | 18.8 | 34.6 | 36.6 | 32.8 | 22.1 | 35.2 | 35.5 | 31.5 | 19.8 | 37 | 29.5 | 32.7 | 0.139 | 0.0189 | 1.13 | 0.00581 | 0.0173 | 0.129 | 0.758 | 0.0373 | 0.276 | 0.168 | 0.0452 | 0.0869 | 0.00679 | 0.678 | 0.232 | 0.149 | 0.11 | 0.208 | 0.18 | 0.0911 | 0.187 | 0.0282 | 0.226 | 0.0372 | 0.0376 | 0.0342 | 0.0918 | 0.00745 | 0.529 | 0.0247 | 0.392 | 0.00105 | 0.000686 | 0.116 | 0.0791 | 0.0641 | 0.352 | 0.119 | 0.247 | 0.218 | 0.588 | 0.0364 | 0.424 | 0.000618 | 0.00154 | 0.195 | 0.482 | 0.565 | 0.605 | 0.174 | 4.84 | 0.00296 | 3 | 5 | 6 | 12 | 6 | 17 | 7 | 8 | 4 | 2 | 18 | 40 | 33 | 0 | 1 | 2 | 2 | 3 | 2 | 3 | 2 | 2 | 2 | 3 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Badin | 1.47e+06 | 1.49e+06 | 1.52e+06 | 1.54e+06 | 1.57e+06 | 1.59e+06 | 1.15e+06 | 1.25e+06 | 1.27e+06 | 1.39e+06 | 1.42e+06 | 1.45e+06 | 1.48e+06 | 68.8 | 24.7 | 0.579 | 0.578 | -0.548 | 0.168 | -0.604 | -0.913 | -0.874 | -0.785 | -1.36 | -0.758 | 1.21 | 0.737 | 0.737 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 85.2 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 0.00589 | 25.4 | 38.8 | 34.1 | 35.4 | 24.7 | 37 | 34.9 | 32.3 | 23.2 | 36.7 | 35.3 | 33.9 | 24.6 | 35.6 | 34 | 34.6 | 23.2 | 37.1 | 35.6 | 34.9 | 24.6 | 37.9 | 34 | 35.2 | 26 | 36.7 | 34.8 | 34.5 | 24 | 37.7 | 32.2 | 35.9 | 26.2 | 38.3 | 35.1 | 35.8 | 24.7 | 38.1 | 35.6 | 33.8 | 22.8 | 37.9 | 36 | 34.6 | 23.7 | 38.2 | 34.4 | 33.1 | 23.1 | 39.4 | 31.3 | 34 | 0.00375 | 0.0032 | 1.95 | 0.0815 | 0.0195 | 0.00804 | 0.571 | 0.00242 | 0.0356 | 0.159 | 0.0882 | 0.0148 | 0.0113 | 0.201 | 1.03 | 0.23 | 0.000222 | 0.0752 | 0.304 | 0.046 | 0.01 | 0.00212 | 2.91 | 0.0152 | 0.012 | 0.00326 | 0.143 | 0.172 | 0.0177 | 0.0233 | 1.97 | 0.00194 | 0.00025 | 0.0386 | 0.112 | 0.00584 | 0.0368 | 0.0162 | 1.5 | 0.372 | 0.00273 | 0.00339 | 0.663 | 0.231 | 0.000339 | 0.0153 | 0.665 | 0.0419 | 0.324 | 0.076 | 5.42 | 0.00236 | 108 | 45 | 100 | 96 | 65 | 88 | 75 | 98 | 91 | 225 | 342 | 176 | 85 | 104 | 71 | 67 | 67 | 64 | 73 | 86 | 84 | 87 | 93 | 99 | 99 | 85 | 1 | 9 | 14 | 6 | 20 | 14 | 7 | 12 | 5 | 3 | 3 | 4 | 8 | 0 | 16 | 10 | 17 | 14 | 6 | 5 | 7 | 4 | 2 | 1 | 2 | 4 | 1 | 3 | 8 | 9 | 6 | 3 | 1 | 0 | 5 | 4 | 4 | 2 | 5 | 2 | 8 | 8 | 8 | 4 | 9 | 4 | 4 | 6 | 4 | 1 | 1 | 4 | 0 | 1 | 1 | 1 | 0 | 3 | 5 | 1 | 1 | 2 | 0 | 0 | 2 |
Bagh | 3e+05 | 3.05e+05 | 3.1e+05 | 3.15e+05 | 3.29e+05 | 3.34e+05 | 5.34e+05 | 5.79e+05 | 5.88e+05 | 6.47e+05 | 6.6e+05 | 6.73e+05 | 6.87e+05 | 73.7 | 34 | 0.273 | 1.23 | -0.174 | 0.802 | 0.885 | 1.78 | 1.03 | 0.25 | -0.881 | 0.729 | 1.25 | -0.0179 | -0.0179 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 73.2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 0.63 | 10.2 | 19.7 | 23.4 | 18.9 | 4.86 | 16 | 22.8 | 17.8 | 1.56 | 16.1 | 25 | 16.7 | 4.65 | 16.3 | 23.7 | 18.4 | 6.09 | 15.3 | 23.3 | 17.1 | 7.41 | 16.8 | 22.5 | 17.5 | 7.25 | 17.4 | 23.2 | 19.7 | 3.06 | 18.8 | 22.9 | 19.9 | 8.88 | 18.5 | 23.1 | 16 | 1.82 | 18.2 | 23.1 | 17.3 | 1.66 | 15.8 | 23.8 | 19.6 | 5.47 | 15.9 | 23.3 | 18.1 | 2.84 | 21.7 | 22.4 | 17.8 | 1.03 | 1.99 | 7.54 | 0.541 | 0.692 | 2.92 | 3.98 | 1.26 | 2.27 | 2.86 | 2.43 | 0.861 | 0.954 | 2.18 | 3.79 | 0.88 | 0.853 | 2.42 | 3.67 | 1.1 | 0.985 | 4.09 | 8.27 | 1.42 | 1.43 | 3.15 | 5.89 | 0.351 | 4.82 | 3.96 | 6.23 | 0.371 | 0.388 | 4.2 | 6.46 | 1.32 | 3.47 | 2.16 | 4.77 | 0.86 | 3.81 | 3.17 | 2.86 | 0.0737 | 1.6 | 2.81 | 5.45 | 1.16 | 4.28 | 1.14 | 5.87 | 0.631 | 4 | 4 | 1 | 3 | 6 | 10 | 9 | 4 | 9 | 16 | 3 | 0 | 2 | 4 | 4 | 2 | 3 | 1 | 3 | 4 | 4 | 3 | 3 | 4 | 4 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
## Extract the shapefile geometry and add it to the dataframe
geometry = st_geometry(pak.bound.adm2)
## Add to test.df
test.df$geometry = geometry
## Quick script to drop columns
#drop <- c("dep_index")
#test.df = test.df[,!(names(test.df) %in% drop)]
## Check
#head(test.df)
## Tidy up the dataframe
## pivot from wide to long
## Create a vector of variable names
#var.names = c("pop", "spei", "dep.index", "built", "total.con", "protests", "battles", "expl", "riots", "vac", "sd")
## Pivot Longer
complete.df = test.df %>%
pivot_longer(
cols = -c(districts, longitude, latitude, geometry),
names_sep = "_",
names_to = c(".value", "year")
)
## Check
head(complete.df)
districts | longitude | latitude | geometry | year | pop | spei | dep.index | built | temp.jan | temp.apr | temp.jul | temp.oct | precip.jan | precip.apr | precip.jul | precip.oct | total.con | protests | battles | expl | riots | vac | sd |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Abbottabad | 73.3 | 34.1 | list(list(c(73.3980410830001, 73.3985407750001, 73.399154464, 73.3994059830001, 73.400542557, 73.4019308100001, 73.402948063, 73.404160527, 73.4046113730001, 73.4060988420001, 73.4097135380001, 73.410326786, 73.414153328, 73.430540604, 73.435373997, 73.444976468, 73.4533223550001, 73.4563076540001, 73.464706683, 73.464117651, 73.4639413200001, 73.4622645850001, 73.464994897, 73.4702664380001, 73.4794306750001, 73.4800004530001, 73.483323676, 73.4877634650001, 73.487975856, 73.488310254, 73.488058143, 73.487197032, 73.487057189, 73.48664697, 73.487312526, 73.4880384960001, 73.4935915070001, 73.493765376, 73.4943085570001, 73.4944054950001, 73.495817951, 73.496981148, 73.501387332, 73.506102153, 73.5066973340001, 73.506814499, 73.507218649, 73.508234481, 73.5094287210001, 73.5120075290001, 73.511291504, 73.506706238, 73.504302979, 73.503219604, 73.502296448, 73.500892639, 73.500190735, 73.498779297, 73.4975891110001, 73.4965591430001, 73.495536804, 73.494293213, 73.492683411, 73.4905700680001, 73.488380432, 73.4869613650001, 73.485603333, 73.4828872680001, 73.480026245, 73.4786071780001, 73.476722717, 73.475418091, 73.4745635990001, 73.4731979370001, 73.4719031650001, 73.471763611, 73.471443176, 73.471473694, 73.4715423580001, 73.470069885, 73.469566345, 73.468696594, 73.4683685300001, 73.4650802610001, 73.4639968870001, 73.4652099610001, 73.4647064210001, 73.463912964, 73.46321106, 73.4619712830001, 73.4607696530001, 73.460327148, 73.4598236080001, 73.4587783810001, 73.45765686, 73.4566726680001, 73.455886841, 73.4548721310001, 73.454334303, 73.453849792, 73.4530029300001, 73.45211792, 73.4509429930001, 73.45010376, 73.4490127560001, 73.447853088, 73.446800232, 73.4452362060001, 73.4437332150001, 73.4433364870001, 73.4423980710001, 73.4420928960001, 73.4415893550001, 73.440696716, 73.44052887, 73.4406661990001, 73.440299988, 73.4394836430001, 73.4385528560001, 73.4374389650001, 73.4365768430001, 73.4353866580001, 73.4339675900001, 73.4324798580001, 73.4318237300001, 73.4312973020001, 73.43031311, 73.428848267, 73.427200317, 73.4254989620001, 73.424430847, 73.423629761, 73.423080444, 73.422767639, 73.4235992430001, 73.4241409300001, 73.4235763550001, 73.422599792, 73.420883179, 73.419403076, 73.417671204, 73.4161834720001, 73.41620636, 73.4174728390001, 73.4181671140001, 73.417823792, 73.41481781, 73.4144668580001, 73.4147491460001, 73.4148864750001, 73.414222717, 73.4131774900001, 73.4112396240001, 73.409370422, 73.408363342, 73.406967163, 73.406013489, 73.4054183960001, 73.4046630860001, 73.40411377, 73.402633667, 73.401351929, 73.398033142, 73.3967971800001, 73.3934190680001, 73.392562866, 73.3921966550001, 73.391777039, 73.3908233640001, 73.389709473, 73.38848877, 73.3873367310001, 73.3854370120001, 73.383712769, 73.3819274900001, 73.3799896240001, 73.3787002560001, 73.378196716, 73.3781661990001, 73.378158569, 73.3764038090001, 73.3742828370001, 73.3743769330001, 73.376266479, 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## Save the wide data frame
#write.csv(test.df,
#file = "../data/conflict/complete_dataframe_20230404.csv",
#append = TRUE)
## Save the long dataframe
## Orignal Version is dated 20230405
## Version with geometry is dated 20230411
#write.csv(complete.df,
#file = "../data/conflict/complete_dataframe_long_20230411.csv",
#append = FALSE)
## If writing the spatial data, may need to write it as a shape file?
#st_write(complete.df,
#dsn = "../data/conflict/complete_data_20230411.shp")