Initial Setup

##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

Pakistan Shape Files

## 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_LengShape_AreaADM2_ENADM2_PCODEADM2_REFADM2ALT1ENADM2ALT2ENADM1_ENADM1_PCODEADM0_ENADM0_PCODEdatevalidOnvalidTogeometry
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33.890581937, 33.8930028570001, 33.894790239, 33.895034993, 33.895438045, 33.8961708850001, 33.897237766, 33.8974003590001, 33.8981170810001, 33.899208254, 33.900088789, 33.9010604220001, 33.9012610890001, 33.901980678, 33.9007074470001, 33.900451666, 33.899512529, 33.899173042, 33.8981975460001, 33.8970540170001, 33.896666796, 33.896582632, 33.896764182, 33.896922429, 33.8977117360001, 33.897855431, 33.898009076, 33.898043428, 33.8989315430001, 33.8993084320001, 33.899856051, 33.900246301, 33.900667814,
33.902429539, 33.901725921, 33.902378411, 33.9034851710001, 33.903174634, 33.902859263, 33.9030991720001, 33.9033929280001, 33.903976206, 33.904326172, 33.905248296, 33.9040560230001, 33.9019599160001, 33.901161201, 33.8973359240001, 33.8964285760001, 33.8943199280001, 33.8933890440001, 33.892947367, 33.8907935330001, 33.889924164, 33.8877826730001, 33.886046434, 33.8852269170001, 33.88435967, 33.883390832, 33.8820955420001, 33.880328298, 33.879878341, 33.8798558760001, 33.8798456750001, 33.879857696,
33.879893248, 33.8800053790001, 33.879957103, 33.879948792, 33.8782579200001, 33.876771551, 33.8776808870001, 33.8780572910001, 33.8781311610001, 33.878390059, 33.880609172, 33.8809056970001, 33.881129548, 33.8827914230001, 33.883302734, 33.8843508730001, 33.885051693, 33.885778825, 33.8868280510001, 33.890149551, 33.8906952180001, 33.8904075620001, 33.889592527, 33.889287424, 33.8889901800001, 33.8889381170001, 33.8888831410001, 33.888829602, 33.8885295320001, 33.888261538, 33.887904732, 33.8877350540001,
33.887499544, 33.887807948, 33.8878499260001, 33.890191637, 33.8920480940001, 33.893234421, 33.8946010860001, 33.894718674, 33.8953547180001, 33.895805302, 33.8979776190001, 33.898682423, 33.8995532540001, 33.903180874, 33.903723551, 33.90701155, 33.9099054280001, 33.910282587, 33.913373925, 33.914587914, 33.9146229720001, 33.914667288, 33.9147337140001, 33.913883298, 33.9137140280001, 33.9127432400001, 33.9124808290001, 33.912145045, 33.913088744, 33.914955666, 33.9157966720001, 33.916659774, 33.9190332090001,
33.920476965, 33.920985166, 33.924148496, 33.9277549240001, 33.928984164, 33.9301385590001, 33.930523157, 33.9316324050001, 33.933628082, 33.9366121890001, 33.937371109, 33.937404046, 33.9376940820001, 33.938354777, 33.9387514540001, 33.9391952880001, 33.941195798, 33.9441405480001, 33.9461267570001, 33.9479144810001, 33.9504188060001, 33.952585126, 33.9536544760001, 33.957405802, 33.9587543740001, 33.961899551, 33.966239548, 33.967396697, 33.9680736820001, 33.9687045950001, 33.9692957980001, 33.9693679960001,
33.9694928910001, 33.969919056, 33.9699530840001, 33.970236287, 33.97155317, 33.9742185420001, 33.977192643, 33.97804941, 33.9784398300001, 33.978270728, 33.9775589970001, 33.9773857980001, 33.977238195, 33.976656185, 33.9764183280001, 33.9761876740001, 33.9754234800001, 33.97399254, 33.972000494, 33.97046226, 33.9684246190001, 33.966650945, 33.9657478330001, 33.965576055, 33.963929669, 33.961400132, 33.9598588860001, 33.957262922, 33.955992769, 33.9535400560001, 33.949272681, 33.9471508880001, 33.94588055,
33.9444364920001, 33.9419434170001, 33.939589046, 33.9396555600001, 33.9401574290001, 33.9376796830001, 33.9362091630001, 33.9342569620001, 33.933909252, 33.933057658, 33.9313449270001, 33.9300817930001, 33.926223303, 33.924999686, 33.924400459, 33.9233334190001, 33.9200951120001, 33.919501671, 33.9167375430001, 33.9169900700001, 33.9190502820001, 33.918448918, 33.9171564110001, 33.916181307, 33.914930231, 33.9142758030001, 33.9137575020001, 33.912209751, 33.912334737, 33.9134501130001, 33.9139242750001,
33.9148002530001, 33.9144014620001, 33.914100794, 33.911822069, 33.9096084990001, 33.9105635810001, 33.9147120160001, 33.920596656, 33.9261001020001, 33.9397053100001, 33.946582624, 33.950105475, 33.9512596050001, 33.9678773850001, 33.975890598, 33.9780417100001, 33.991383483, 33.9946282490001, 34.0011050410001, 34.011238631, 34.0176730630001, 34.026100139, 34.0330090370001, 34.0388858160001, 34.047129999, 34.0495200240001, 34.061661019, 34.0861060550001, 34.096651367, 34.099435245, 34.1070147340001,
34.1072396810001, 34.1076708200001, 34.1079523020001, 34.1082300030001, 34.1081400030001, 34.1081400030001, 34.1084199960001, 34.109459998, 34.1106800000001, 34.110960005, 34.110960005, 34.1106800000001, 34.1098300030001, 34.111149998, 34.1142599980001, 34.1153, 34.1159600030001, 34.1166199950001, 34.1181200010001, 34.11841, 34.1185, 34.1180300020001, 34.11769, 34.1193000060001, 34.12116, 34.1217013350001, 34.122270003, 34.1234000050001, 34.1240599970001, 34.12418, 34.1243506000001, 34.1244300020001,
34.1249, 34.1256599970001, 34.126879998, 34.128299998, 34.128670003, 34.1282000050001, 34.127553318, 34.1268999970001, 34.1261417310001, 34.1263200000001, 34.1269587090001, 34.127540002, 34.128580003, 34.1292418300001, 34.1296100000001, 34.1303699970001, 34.130929995, 34.131220005, 34.131499998, 34.1320599970001, 34.1310793170001, 34.1305200040001, 34.129239995, 34.127450002, 34.1261700040001)))

Test Dataframe

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

Conflict Data

##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 Conflict Intersect Method

## 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

Protests Intersect

## 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_idisoevent_id_cntyevent_id_no_cntyevent_dateyeartime_precisionevent_typesub_event_typeactor1assoc_actor_1inter1actor2assoc_actor_2inter2interactionregioncountryadmin1admin2admin3locationgeo_precisionsourcesource_scalenotesfatalitiestimestampiso3geometry
9185669586PAK1697169704 January 201020101ProtestsPeaceful protestProtesters (Pakistan)ASWJ: Ahle Sunnat Wal Jamaat; Women (Pakistan)6060South AsiaPakistanSindhKarachi CityKarachi SouthKarachi - Saddar1Right Vision NewsNationalOn 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.01651182380PAKc(67.016, 24.8542)
8995733586PAK131303 January 201020101ProtestsPeaceful protestProtesters (Pakistan)Health Workers (Pakistan); Labour Group (Pakistan); Lawyers (Pakistan)6060South AsiaPakistanFederal Capital TerritoryIslamabadIslamabadIslamabad1Right Vision NewsNationalOn 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.01649284578PAKc(73.0432, 33.7214)
7832074586PAK101002 January 201020101ProtestsPeaceful protestProtesters (Pakistan)YDA: Young Doctors Association; Health Workers (Pakistan)6060South AsiaPakistanPunjabLahoreLahore1Daily Times (Pakistan)NationalThe Young Doctors Association staged a rally to condemn the registration of criminal cases against their colleagues.01618442078PAKc(74.3436, 31.5497)
4966092586PAK9901 January 201020101ProtestsPeaceful protestProtesters (Pakistan)6060South AsiaPakistanSindhHyderabadHyderabadHyderabad1Right Vision NewsNationalProtests occurred in Hyderabad in solidarity with those in Karachi against the bombing of a religious procession earlier in the week.01552577624PAKc(68.3736, 25.3942)
7832076586PAK2201 January 201020101ProtestsPeaceful protestProtesters (Pakistan)Students (Pakistan)6060South AsiaPakistanPunjabLahoreLahore1Daily Times (Pakistan)NationalA 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.01618442078PAKc(74.3436, 31.5497)
7832077586PAK1101 January 201020101ProtestsPeaceful protestProtesters (Pakistan)6060South AsiaPakistanSindhKarachi CityKarachi1Daily Regional Times (Pakistan); Right Vision NewsSubnational-NationalReligious 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.01618442078PAKc(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

Battles Intersect

## 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_idisoevent_id_cntyevent_id_no_cntyevent_dateyeartime_precisionevent_typesub_event_typeactor1assoc_actor_1inter1actor2assoc_actor_2inter2interactionregioncountryadmin1admin2admin3locationgeo_precisionsourcesource_scalenotesfatalitiestimestampiso3geometry
4965984586PAK202004 January 201020101BattlesArmed clashTTP: Tehreek-i-Taliban Pakistan2Police Forces of Pakistan (2008-2013)112South AsiaPakistanFATABajaur AgencySalarzai TehsilWanda2Right Vision NewsNational2 suspected Taliban militants were killed and 2 were injured at a security forces check post near Shuza Algad.21552577624PAKc(71.5867, 34.8341)
4965985586PAK212104 January 201020101BattlesArmed clashTTP: Tehreek-i-Taliban Pakistan2Police Forces of Pakistan (2008-2013)112South AsiaPakistanKhyber PakhtunkhwaBannuBannuJanikhel1Right Vision NewsNational2 suspected Taliban militants were killed and 2 arrested at Maddi near Jani Khel.21552577624PAKc(70.5083, 32.8011)
7729045586PAK151504 January 201020101BattlesArmed clashTTP: Tehreek-i-Taliban Pakistan2Aman Lashkar424South AsiaPakistanFATAMohmand AgencyHalimzaiGhalanai1Right Vision NewsNational2 members of a peace committee were gunned down by suspected Taliban militants in Mohmand agency.21614792735PAKc(71.3911, 34.3233)
7743762586PAK191904 January 201020101BattlesArmed clashTTP: Tehreek-i-Taliban Pakistan2Police Forces of Pakistan (2008-2013)112South AsiaPakistanFATANorth Waziristan AgencyRazmakRazmak1Right Vision NewsNationalSuspected Taliban militants fired mortars at security forces, injuring 2. At least 1 militant was also killed during operations by security forces in the area.11615334312PAKc(69.8343, 32.6893)
7743777586PAK161604 January 201020101BattlesArmed clashTTP: Tehreek-i-Taliban Pakistan2Police Forces of Pakistan (2008-2013)112South AsiaPakistanFATASouth Waziristan AgencyLadhaLadha1Right Vision NewsNationalSuspected Taliban militants fired on Pakistani security forces with small arms and fire was returned; no casualties reported.01615334312PAKc(69.8291, 32.568)
7743873586PAK171704 January 201020101BattlesArmed clashTTP: Tehreek-i-Taliban Pakistan2Police Forces of Pakistan (2008-2013)112South AsiaPakistanFATABajaur AgencyKharMamond2Right Vision NewsNational4 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.41615334313PAKc(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

Explosions/Remote Violence

## 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_idisoevent_id_cntyevent_id_no_cntyevent_dateyeartime_precisionevent_typesub_event_typeactor1assoc_actor_1inter1actor2assoc_actor_2inter2interactionregioncountryadmin1admin2admin3locationgeo_precisionsourcesource_scalenotesfatalitiestimestampiso3geometry
4966012586PAK181804 January 201020101Explosions/Remote violenceShelling/artillery/missile attackTTP: Tehreek-i-Taliban Pakistan2Police Forces of Pakistan (2008-2013)112South AsiaPakistanFATASouth Waziristan AgencyMakinMetari Narai1Right Vision NewsNationalSuspected Taliban militants fired a rocket at security forces, killing 1 and wounding 5.11552577624PAKc(69.894, 32.6329)
7728072586PAK121203 January 201020101Explosions/Remote violenceRemote explosive/landmine/IEDMilitary Forces of the United States (2009-2017)8TTP: Tehreek-i-Taliban Pakistan228South AsiaPakistanFATANorth Waziristan AgencyMiran ShahMiranshah2Dawn (Pakistan)National5 people including 3 Arabs were killed in an attack on Mosakki village in North Waziristan.51614792733PAKc(70.0698, 33.0001)
7729040586PAK111102 January 201020101Explosions/Remote violenceRemote explosive/landmine/IEDUnidentified Armed Group (Pakistan)3030South AsiaPakistanFATAKhyber AgencyBaraBara2Right Vision NewsNationalMilitants blew up 2 schools and a basic health centre in the Malangi area of Tehsil Mamund near Bara. No casualties were reported.01614792735PAKc(71.4622, 33.9217)
4966091586PAK8801 January 201020101Explosions/Remote violenceSuicide bombTTP: Tehreek-i-Taliban Pakistan2Civilians (Pakistan)727South AsiaPakistanKhyber PakhtunkhwaLakki MarwatLakki MarwatLakki Marwat1Right Vision NewsNationalA 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.931552577624PAKc(70.8511, 32.5992)
7028462586PAK6601 January 201020101Explosions/Remote violenceAir/drone strikeMilitary Forces of the United States (2009-2017)8TTP: Tehreek-i-Taliban Pakistan228South AsiaPakistanFATANorth Waziristan AgencySpinwamGundi Khala1Right Vision NewsNationalAt 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.31585692285PAKc(70.4325, 33.15)
7743890586PAK7701 January 201020101Explosions/Remote violenceRemote explosive/landmine/IEDTTP: Tehreek-i-Taliban Pakistan2Civilians (Pakistan)727South AsiaPakistanFATABajaur AgencySalarzaiSalarzai1Right Vision NewsNationalAt 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.51615334313PAKc(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

Riots

## 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_idisoevent_id_cntyevent_id_no_cntyevent_dateyeartime_precisionevent_typesub_event_typeactor1assoc_actor_1inter1actor2assoc_actor_2inter2interactionregioncountryadmin1admin2admin3locationgeo_precisionsourcesource_scalenotesfatalitiestimestampiso3geometry
5950612586PAK11911915 January 201020102RiotsViolent demonstrationRioters (Pakistan)5Police Forces of Pakistan (2008-2013)115South AsiaPakistanSindhKhairpurKhairpurKhairpur1Daily Regional Times (Pakistan)SubnationalPrisoners 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.01567539398PAKc(68.7452, 27.5271)
7547397586PAK12012015 January 201020101RiotsViolent demonstrationRioters (Pakistan)5Police Forces of Pakistan (2008-2013)115South AsiaPakistanSindhKarachi CityKarachi SouthKarachi - Lyari1Daily 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.01611096243PAKc(66.9946, 24.8687)
7548312586PAK909013 January 201020101RiotsViolent demonstrationRioters (Pakistan)Students (Pakistan)5Police Forces of Pakistan (2008-2013)115South AsiaPakistanSindhKarachi CityKarachi EastKarachi - Gulshan1Daily Times (Pakistan)NationalScores 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.01611096246PAKc(67.1279, 24.9293)
7546113586PAK717111 January 201020101RiotsViolent demonstrationRioters (Pakistan)PPP: Pakistan Peoples Party5Police Forces of Pakistan (2008-2013)115South AsiaPakistanSindhKarachi CityKarachi SouthKarachi - Lyari1Daily Times (Pakistan); Daily Regional Times (Pakistan)Subnational-NationalThousands 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.01611096240PAKc(66.9946, 24.8687)
5943605586PAK484809 January 201020101RiotsViolent demonstrationRioters (Pakistan)5050South AsiaPakistanSindhKhairpurKhairpurKhairpur1Daily Regional Times (Pakistan)SubnationalPrisoners in Khairpur Central Prison have put 5 barracks under siege, demanding that conditions in the prisons be improved, particularly overcrowding.01567539390PAKc(68.7452, 27.5271)
4966022586PAK141403 January 201020102RiotsViolent demonstrationRioters (Pakistan)5050South AsiaPakistanPunjabRawalpindiRawalpindiRawalpindi1Right Vision NewsNationalMembers 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.01552577624PAKc(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

Violence Against Civilians

## 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_idisoevent_id_cntyevent_id_no_cntyevent_dateyeartime_precisionevent_typesub_event_typeactor1assoc_actor_1inter1actor2assoc_actor_2inter2interactionregioncountryadmin1admin2admin3locationgeo_precisionsourcesource_scalenotesfatalitiestimestampiso3geometry
7832060586PAK707011 January 201020101Violence against civiliansAttackUnidentified Armed Group (Pakistan)3Civilians (Pakistan)Ahmadiyya Muslim Group (Pakistan)737South AsiaPakistanPunjabLahoreLahore1Daily Times (Pakistan)National2 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.'11618442078PAKc(74.3436, 31.5497)
7832063586PAK606010 January 201020101Violence against civiliansAttackUnidentified Armed Group (Pakistan)3Civilians (Pakistan)MQM: Muttahida Qaumi Movement737South AsiaPakistanSindhKarachi CityKarachi1Daily Times (Pakistan); Right Vision NewsNational5 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.51618442078PAKc(67.0822, 24.9056)
7546019586PAK444408 January 201020101Violence against civiliansAttackUnidentified Armed Group (Pakistan)3Civilians (Pakistan)MQM: Muttahida Qaumi Movement737South AsiaPakistanSindhKarachi CityKarachi SouthKarachi - Lyari1Daily Times (Pakistan); Right Vision NewsNationalAt least 2 MQM and 2 ANP activists were killed in a bout of targeted attacks in the Lyari area of Karachi.41611096240PAKc(66.9946, 24.8687)
7552783586PAK373707 January 201020101Violence against civiliansAttackUnidentified Armed Group (Pakistan)3Civilians (Pakistan)GBBT: Gutter Baghicha Bachao Tehreek737South AsiaPakistanSindhKarachi CityKarachi SouthKarachi - Saddar1Right Vision NewsNationalThe 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 13.81611096256PAKc(67.016, 24.8542)
7552806586PAK363607 January 201020101Violence against civiliansAttackUnidentified Armed Group (Pakistan)3Civilians (Pakistan)MQM: Muttahida Qaumi Movement737South AsiaPakistanSindhKarachi CityMalirKarachi - Orangi1Right Vision NewsNationalAn MQM activist in Orange Town was killed in a target killing.11618566845PAKc(67.1317, 24.9507)
7832075586PAK3301 January 201020101Violence against civiliansAttackUnidentified Armed Group (Pakistan)3Civilians (Pakistan)MQM-H: Mohajir Qaumi Movement-Haqiqi737South AsiaPakistanSindhKarachi CityKarachi1Daily Times (Pakistan)National2 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.31618442078PAKc(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

Strategic Developments

## 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_idisoevent_id_cntyevent_id_no_cntyevent_dateyeartime_precisionevent_typesub_event_typeactor1assoc_actor_1inter1actor2assoc_actor_2inter2interactionregioncountryadmin1admin2admin3locationgeo_precisionsourcesource_scalenotesfatalitiestimestampiso3geometry
7831969586PAK67567518 February 201020102Strategic developmentsArrestsPolice Forces of Pakistan (2008-2013)1Unidentified Armed Group (Pakistan)TTP: Tehreek-i-Taliban Pakistan; Al Qaeda313South AsiaPakistanSindhKarachi CityKarachi1Daily Regional Times (Pakistan); Right Vision NewsSubnational-NationalArrests: 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.01618442077PAKc(67.0822, 24.9056)
7264255586PAK42942904 February 201020101Strategic developmentsAgreementShiite Muslim Militia (Pakistan)4Sunni Muslim Militia (Pakistan)444South AsiaPakistanKhyber PakhtunkhwaKohatKohatKohat1Right Vision NewsNationalAgreement: 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).01603755485PAKc(71.5439, 33.4491)
7832010586PAK43543504 February 201020101Strategic developmentsArrestsPolice Forces of Pakistan (2008-2013) Pakistan Rangers1Civilians (Pakistan)717South AsiaPakistanSindhKarachi CityKarachi1Right Vision NewsNationalArrests: 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.01618442077PAKc(67.0822, 24.9056)
9279022586PAK27727724 January 201020101Strategic developmentsDisrupted weapons useTTP: Tehreek-i-Taliban Pakistan2Military Forces of the United States (2009-2017)828South AsiaPakistanFATANorth Waziristan AgencyMiran ShahMiranshah2Right Vision NewsNationalOn 24 January 2010, a suspected U.S. Drone was shot down by Taliban militants near Hamzoni in the Miran Shah area (NWA, FATA).01652909182PAKc(70.0698, 33.0001)
4966090586PAK4401 January 201020101Strategic developmentsLooting/property destructionTTP: Tehreek-i-Taliban Pakistan2NATO: North Atlantic Treaty Organization828South AsiaPakistanBalochistanKilla AbdullahChamanChaman2Right Vision NewsNationalLooting: 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.01552577624PAKc(66.4681, 30.8944)
7137741586PAK5501 January 201020102Strategic developmentsArrestsPolice Forces of Pakistan (2008-2013) Pakistan Rangers1Police Forces of India (2004-2014)818South AsiaPakistanBalochistanQuettaQuetta CityQuetta2Right Vision NewsNationalArrests: 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).01593548001PAKc(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

Counts by Table Method

##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
admin22010201120122013201420152016201720182019202020212022
101       1       000102000
Abbottabad241012       12       6119192033564156
Astore001       1       020023555
Attock1756       11       58492417131836
Awaran356       12       6177842184033
Badin13170121       103       66917610293245375209129
Bagh441       3       61094916202
Bahawalnagar738       10       47928232582624
Bahawalpur31714       34       3046272934701541841
Bajaur100       0       0000381276490104
Bajaur Agency931934       39       56494930240000
Baltistan012       5       1324664915
Bannu171225       51       343636253538215973
Barkhan035       1       112002676
Batagram122       1       2211312129187
Bhakkar113       3       152312014
Bhimber332       2       610111719292211
Buner415       6       8611131516162311
Chagai0310       4       7102121318171838
Chakwal24116       12       10610633327
Charsadda132643       38       373541314032265076
Chiniot212       4       5427861512
Chitral5104       1       81413102435194642
Dadu135102202       159       6398147222298258491292188
Dera Bugti84863       21       213033201314121224
Dera Ghazi Khan101011       3       626192817226921
Dera Ismail Khan18925       23       222515214560256489
Diamir523       3       06521099614
Faisalabad433556       59       5475484781113557585
FR Bannu000       0       010000000
FR Dera Ismail Khan000       0       011000001
FR Kohat18126       9       133010000
FR Peshawar100       0       000000000
FR Tank211       2       310100000
Ghanche000       0       000045263
Ghizer001       0       010249775
Ghotki6642100       55       151823124011410416965
Gilgit5317       6       413871717212135
Gujranwala201230       22       3040434910757293343
Gujrat8111       15       112513292030321426
Gwadar996       10       718815122184353
Hafizabad6514       18       17221111332621412
Hangu312219       33       422098626263838
Haripur614       3       16951432174469
Harnai050       1       2412211161325
Hattian Bala321       1       887810211010
Haveli121       1       812597313700
Hunza101       0       0110569915
Hyderabad514219612       374       175199241158283236496590545
Islamabad13294177       112       12415691129226267160225188
Jacobabad493077       81       556843555715335116788
Jaffarabad11617       9       111365614131622
Jamshoro612054       47       3910578166134114185195115
Jhal Magsi012       6       230306176
Jhang449       6       51216314179821
Jhelum544       2       4414718310
Kachhi122320       22       1121310511182641
Kalat20910       10       7231061114121545
Karachi City6145401.07e+031.04e+03629511421354521519656483706
Karak11715       27       222948285257264634
Kashmore472683       77       574922294955478257
Kasur1517       15       71719285247142217
Kech112530       25       40362933222163130155
Khairpur228133277       292       132146184254266191207189114
Khanewal1429       2       5641520144712
Kharan030       5       25325581922
Kharmang000       0       000023245
Khushab215       0       433157227
Khuzdar272217       33       23107151322152042
Khyber000       0       0000571064357108
Khyber Agency116108241       142       2151519187250000
Killa Abdullah121913       27       10813182133751627
Killa Saifullah214       3       2602782610
Kohat361538       24       193320282368305988
Kohistan335       1       3135662271417
Kohlu674       2       17111058232430
Kotli353       25       1417193229463338
Kurram000       0       0000821121920
Kurram Agency717077       47       1453253660000
Lahore251149279       237       161295305190392408384234315
Lakki Marwat16728       20       131216143064264476
Larkana193112227       200       96159310339597196269361318
Las Bela1299       3       49108623163044
Layyah514       1       2461369415
Lehri113       2       255200889
Lodhran402       0       021518226
Loralai3137       7       41115824111321
Lower Dir261323       21       102729276791276664
Malakand PA435       5       37126611111322
Mandi Bahauddin215       2       8766712144
Mansehra301318       23       2219192435119666173
Mardan271847       36       163528215348516365
Mastung191422       24       101613111516232025
Matiari642031       44       1722319194016311042
Mianwali412       6       7548355210
Mirpur11410       11       15117131625520
Mirpur Khas7365150       94       4779509498163342147108
Mohmand000       0       00002369201814
Mohmand Agency1046860       29       45476537200000
Multan293063       42       4862425170732163762
Musakhel000       0       000011220
Muzaffarabad141519       25       261828243974192528
Muzaffargarh944       8       9148101520161719
Nagar000       0       000014255
Nankana Sahib136       9       42296311817
Narowal510       11       617810211771518
Nasirabad83410       28       201428191833141318
Naushahro Feroze20364160       158       68101501391699013813367
Neelum132       2       561065242041
North Waziristan000       0       000057924789113
North Waziristan Agency16783133       93       171832614210000
Nowshera212037       32       102535103139384149
Nushki324       9       465549131326
Okara8413       17       111824262426101733
Orakzai000       0       0000722311411
Orakzai Agency1464956       18       14164820000
Pakpattan517       9       17573622868
Panjgur467       17       1718817616193940
Peshawar165167277       315       254247245161213286325259350
Pishin066       10       2245516201315
Poonch1026       13       1415122218361217
Qambar Shahdadkot321551       51       202210221732647748
Quetta147158301       179       117127947811316997220367
Rahim Yar Khan12217       25       71715262935111836
Rajanpur114       4       42687124313
Rawalpindi774287       83       6159686718617121677122
Sahiwal614       8       5121315233172026
Sanghar463954       80       272213365812413417988
Sargodha6315       11       1321313253414515
Shaheed Benazirabad12872147       111       55774011811668148154113
Shangla121       5       2416203869164935
Sheerani511       0       120000034
Sheikhupura12212       13       511212426385523
Shigar000       0       000022245
Shikarpur311738       22       28485850638511910246
Sialkot331423       24       83445372114102381919
Sibi598       10       96614715101832
Skardu000       0       100000223
Sohbatpur137       2       472115131310
South Waziristan100       0       00001837223946
South Waziristan Agency525665       28       2424171760000
Sudhanoti222       1       9945715010
Sujawal261610       41       41384166557814310782
Sukkur235179289       102       7513065806084278137111
Swabi16860       30       142131214060475861
Swat742344       36       283766254854316387
Tando Allah Yar171426       16       15661285346475
Tando Muhammad Khan242827       37       151019193672224
Tank15911       17       11141591025273846
Tharparkar12312       28       8164242627413013396
Thatta633468       63       325133388212317117092
Toba Tek Singh6112       26       2032465173631471630
Tor Ghar000       0       000015222
Umerkot172935       21       121414171152798066
Upper Dir2716       4       9132281828122221
Vehari1225       4       1216111341913412
Washuk103       4       4500141489
Zhob2410       3       310571010121117
Ziarat013       2       145239131411
##change admin2 to ADM2_EN to match
pak.district.con = dplyr::rename(pak.district.con, ADM2_EN = admin2)

##Check/view
pak.district.con
ADM2_EN2010201120122013201420152016201720182019202020212022
101       1       000102000
Abbottabad241012       12       6119192033564156
Astore001       1       020023555
Attock1756       11       58492417131836
Awaran356       12       6177842184033
Badin13170121       103       66917610293245375209129
Bagh441       3       61094916202
Bahawalnagar738       10       47928232582624
Bahawalpur31714       34       3046272934701541841
Bajaur100       0       0000381276490104
Bajaur Agency931934       39       56494930240000
Baltistan012       5       1324664915
Bannu171225       51       343636253538215973
Barkhan035       1       112002676
Batagram122       1       2211312129187
Bhakkar113       3       152312014
Bhimber332       2       610111719292211
Buner415       6       8611131516162311
Chagai0310       4       7102121318171838
Chakwal24116       12       10610633327
Charsadda132643       38       373541314032265076
Chiniot212       4       5427861512
Chitral5104       1       81413102435194642
Dadu135102202       159       6398147222298258491292188
Dera Bugti84863       21       213033201314121224
Dera Ghazi Khan101011       3       626192817226921
Dera Ismail Khan18925       23       222515214560256489
Diamir523       3       06521099614
Faisalabad433556       59       5475484781113557585
FR Bannu000       0       010000000
FR Dera Ismail Khan000       0       011000001
FR Kohat18126       9       133010000
FR Peshawar100       0       000000000
FR Tank211       2       310100000
Ghanche000       0       000045263
Ghizer001       0       010249775
Ghotki6642100       55       151823124011410416965
Gilgit5317       6       413871717212135
Gujranwala201230       22       3040434910757293343
Gujrat8111       15       112513292030321426
Gwadar996       10       718815122184353
Hafizabad6514       18       17221111332621412
Hangu312219       33       422098626263838
Haripur614       3       16951432174469
Harnai050       1       2412211161325
Hattian Bala321       1       887810211010
Haveli121       1       812597313700
Hunza101       0       0110569915
Hyderabad514219612       374       175199241158283236496590545
Islamabad13294177       112       12415691129226267160225188
Jacobabad493077       81       556843555715335116788
Jaffarabad11617       9       111365614131622
Jamshoro612054       47       3910578166134114185195115
Jhal Magsi012       6       230306176
Jhang449       6       51216314179821
Jhelum544       2       4414718310
Kachhi122320       22       1121310511182641
Kalat20910       10       7231061114121545
Karachi City6145401.07e+031.04e+03629511421354521519656483706
Karak11715       27       222948285257264634
Kashmore472683       77       574922294955478257
Kasur1517       15       71719285247142217
Kech112530       25       40362933222163130155
Khairpur228133277       292       132146184254266191207189114
Khanewal1429       2       5641520144712
Kharan030       5       25325581922
Kharmang000       0       000023245
Khushab215       0       433157227
Khuzdar272217       33       23107151322152042
Khyber000       0       0000571064357108
Khyber Agency116108241       142       2151519187250000
Killa Abdullah121913       27       10813182133751627
Killa Saifullah214       3       2602782610
Kohat361538       24       193320282368305988
Kohistan335       1       3135662271417
Kohlu674       2       17111058232430
Kotli353       25       1417193229463338
Kurram000       0       0000821121920
Kurram Agency717077       47       1453253660000
Lahore251149279       237       161295305190392408384234315
Lakki Marwat16728       20       131216143064264476
Larkana193112227       200       96159310339597196269361318
Las Bela1299       3       49108623163044
Layyah514       1       2461369415
Lehri113       2       255200889
Lodhran402       0       021518226
Loralai3137       7       41115824111321
Lower Dir261323       21       102729276791276664
Malakand PA435       5       37126611111322
Mandi Bahauddin215       2       8766712144
Mansehra301318       23       2219192435119666173
Mardan271847       36       163528215348516365
Mastung191422       24       101613111516232025
Matiari642031       44       1722319194016311042
Mianwali412       6       7548355210
Mirpur11410       11       15117131625520
Mirpur Khas7365150       94       4779509498163342147108
Mohmand000       0       00002369201814
Mohmand Agency1046860       29       45476537200000
Multan293063       42       4862425170732163762
Musakhel000       0       000011220
Muzaffarabad141519       25       261828243974192528
Muzaffargarh944       8       9148101520161719
Nagar000       0       000014255
Nankana Sahib136       9       42296311817
Narowal510       11       617810211771518
Nasirabad83410       28       201428191833141318
Naushahro Feroze20364160       158       68101501391699013813367
Neelum132       2       561065242041
North Waziristan000       0       000057924789113
North Waziristan Agency16783133       93       171832614210000
Nowshera212037       32       102535103139384149
Nushki324       9       465549131326
Okara8413       17       111824262426101733
Orakzai000       0       0000722311411
Orakzai Agency1464956       18       14164820000
Pakpattan517       9       17573622868
Panjgur467       17       1718817616193940
Peshawar165167277       315       254247245161213286325259350
Pishin066       10       2245516201315
Poonch1026       13       1415122218361217
Qambar Shahdadkot321551       51       202210221732647748
Quetta147158301       179       117127947811316997220367
Rahim Yar Khan12217       25       71715262935111836
Rajanpur114       4       42687124313
Rawalpindi774287       83       6159686718617121677122
Sahiwal614       8       5121315233172026
Sanghar463954       80       272213365812413417988
Sargodha6315       11       1321313253414515
Shaheed Benazirabad12872147       111       55774011811668148154113
Shangla121       5       2416203869164935
Sheerani511       0       120000034
Sheikhupura12212       13       511212426385523
Shigar000       0       000022245
Shikarpur311738       22       28485850638511910246
Sialkot331423       24       83445372114102381919
Sibi598       10       96614715101832
Skardu000       0       100000223
Sohbatpur137       2       472115131310
South Waziristan100       0       00001837223946
South Waziristan Agency525665       28       2424171760000
Sudhanoti222       1       9945715010
Sujawal261610       41       41384166557814310782
Sukkur235179289       102       7513065806084278137111
Swabi16860       30       142131214060475861
Swat742344       36       283766254854316387
Tando Allah Yar171426       16       15661285346475
Tando Muhammad Khan242827       37       151019193672224
Tank15911       17       11141591025273846
Tharparkar12312       28       8164242627413013396
Thatta633468       63       325133388212317117092
Toba Tek Singh6112       26       2032465173631471630
Tor Ghar000       0       000015222
Umerkot172935       21       121414171152798066
Upper Dir2716       4       9132281828122221
Vehari1225       4       1216111341913412
Washuk103       4       4500141489
Zhob2410       3       310571010121117
Ziarat013       2       145239131411
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"))

City Conflict Data from ArcPro

##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))

Population Data

# 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 Population by city/district

## 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

SPEI Data

## 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_idlongitude_loclatitude_loclonlatyearmonth../data/spei/spei12
Karachi67  24.967.2524.75201010.396
Quetta67  30.266.7530.2520101-0.798
Lahore74.431.574.2531.7520101-1.67 
Peshawar71.534  71.7534.2520101-0.12 
Karachi67  24.967.2524.75201020.501
Quetta67  30.266.7530.2520102-0.844
##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_idyearspei
Karachi20100.575 
Lahore2010-0.603 
Peshawar20100.591 
Quetta2010-1.27  
Karachi20110.186 
Lahore20110.931 
Peshawar20111.02  
Quetta2011-0.083 
Karachi2012-0.911 
Lahore20120.365 
Peshawar20120.265 
Quetta2012-0.677 
Karachi2013-0.277 
Lahore20130.831 
Peshawar20131.33  
Quetta2013-0.0417
Karachi2014-1.06  
Lahore20140.739 
Peshawar20140.524 
Quetta2014-1.25  
Karachi2015-1.41  
Lahore20151.41  
Peshawar20151.85  
Quetta2015-1.02  
Karachi2016-1.35  
Lahore20161.21  
Peshawar20161.52  
Quetta2016-1.14  
Karachi2017-1.09  
Lahore20170.611 
Peshawar20170.259 
Quetta2017-1.5   
Karachi2018-1.65  
Lahore2018-0.391 
Peshawar2018-1.09  
Quetta2018-1.91  
Karachi2019-0.881 
Lahore20190.627 
Peshawar20190.497 
Quetta2019-0.0126
Karachi20200.997 
Lahore20201.46  
Peshawar20201.4   
Quetta20201.02  
Karachi20210.318 
Lahore20210.622 
Peshawar2021-0.599 
Quetta2021-1.13  
##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_idlongitude_loclatitude_loclonlatyearmonth../data/spei/spei12
Sujawal68.224.268.2524.25201010.537 
South Karachi66.924.966.7524.7520101-0.0194
Central Karachi67.124.967.2524.75201010.396 
East Karachi67.124.967.2524.75201010.396 
Korangi Karachi67.124.867.2524.75201010.396 
West Karachi67  25  67.2524.75201010.396 
##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_idyearspei
Abbottabad20100.7    
Astore20100.913  
Attock20100.42   
Awaran2010-1.43   
Badin20100.579  
Bagh20100.273  
Bahawalnagar2010-0.619  
Bahawalpur2010-0.317  
Bajaur20100.807  
Bannu20100.558  
Barkhan20100.125  
Batagram20100.819  
Bhakkar20100.3    
Bhimber2010-0.15   
Buner20100.744  
Central Karachi20100.575  
Chagai2010-1.33   
Chakwal20100.245  
Chaman2010-1.09   
Charsadda20100.591  
Chiniot20100.208  
Chitral Lower20101.17   
Chitral Upper20101.17   
D. I. Khan20100.325  
Dadu2010-0.774  
Darel20101.09   
Dera Bugti2010-0.528  
Dera Ghazi Khan20100.249  
Diamir20100.826  
Duki2010-0.15   
East Karachi20100.575  
Faisalabad2010-0.0357 
Ghanche20101.06   
Ghizer20101.12   
Ghotki2010-0.397  
Gilgit20101.01   
Gujranwala2010-0.585  
Gujrat2010-0.337  
Gupis-Yasin20101.15   
Gwadar2010-1.41   
Hafizabad2010-0.25   
Hangu20100.519  
Haripur20100.744  
Harnai2010-0.954  
Haveli20100.18   
Hunza20101.4    
Hyderabad20100.271  
Islamabad20100.351  
Jacobabad2010-1.11   
Jaffarabad2010-1.11   
Jamshoro2010-0.0632 
Jhal Magsi2010-1.15   
Jhang20100.147  
Jhelum20100.0728 
Jhelum Valley20100.484  
Kachhi2010-1.27   
Kalat2010-1.19   
Kambar Shahdad Kot2010-1.11   
Karak20100.592  
Kashmore2010-0.855  
Kasur2010-0.449  
Kech2010-1.46   
Khairpur2010-0.0412 
Khanewal2010-0.227  
Kharan2010-1.52   
Kharmang20100.764  
Khushab20100.229  
Khuzdar2010-0.917  
Khyber20100.406  
Killa Abdullah2010-1.09   
Killa Saifullah2010-0.917  
Kohat20100.553  
Kohistan Lower20100.986  
Kohistan Upper20100.948  
Kohlu2010-0.497  
Kolai Palas Kohistan20100.948  
Korangi Karachi20100.575  
Kotli20100.0429 
Kurram20100.614  
Lahore2010-0.449  
Lakki Marwat20100.558  
Larkana2010-0.99   
Lasbela2010-0.634  
Lehri2010-1.03   
Leiah20100.209  
Lodhran2010-0.157  
Loralai2010-0.558  
Lower Dir20100.807  
Malakand20100.807  
Malir Karachi20100.0665 
Mandi Bahauddin20100.0433 
Mansehra20100.819  
Mardan20100.665  
Mastung2010-1.26   
Matiari20100.0328 
Mianwali20100.502  
Mirpur20100.0429 
Mirpur Khas20100.501  
Mohmand20100.394  
Multan20100.0568 
Musakhel20100.202  
Muzaffarabad20100.484  
Muzaffargarh20100.202  
Nagar20101.15   
Nankana Sahib2010-0.175  
Narowal2010-0.885  
Nasirabad2010-1.09   
Naushahro Feroze2010-0.386  
Neelum20100.659  
North Waziristan20100.501  
Nowshera20100.622  
Nushki2010-1.51   
Okara2010-0.312  
Orakzai20100.566  
Pakpattan2010-0.411  
Panjgur2010-1.41   
Peshawar20100.622  
Pishin2010-1.09   
Poonch20100.273  
Quetta2010-1.27   
Rahim Yar Khan2010-0.609  
Rajanpur2010-0.0938 
Rawalpindi20100.174  
Rondu20101.02   
Sahiwal2010-0.12   
Sanghar20100.522  
Sargodha20100.239  
Shaheed Benazir Abad2010-0.249  
Shaheed Sikandarabad2010-1.3    
Shangla20100.836  
Sheikhupura2010-0.603  
Sherani20100.327  
Shigar20101.06   
Shikarpur2010-0.849  
Sialkot2010-0.885  
Sibi2010-1.27   
Skardu20100.882  
Sohbatpur2010-0.707  
South Karachi20100.0994 
South Waziristan20100.332  
Sudhnoti20100.273  
Sujawal20100.813  
Sukkur2010-0.491  
Swabi20100.665  
Swat20101.04   
Tando Allahyar20100.448  
Tando Muhammad Khan20100.448  
Tangir20101.1    
Tank20100.423  
Tharparkar20100.601  
Thatta20100.567  
Toba Tek Singh2010-0.12   
Tor Ghar20100.836  
Umer Kot20100.661  
Upper Dir20101.04   
Vehari2010-0.227  
Washuk2010-1.3    
West Karachi20100.575  
Zhob2010-0.183  
Ziarat2010-0.954  
Abbottabad20111.33   
Astore20110.739  
Attock20111.23   
Awaran2011-0.641  
Badin20110.578  
Bagh20111.23   
Bahawalnagar20111.12   
Bahawalpur20111.24   
Bajaur20111.09   
Bannu20111.11   
Barkhan20111.36   
Batagram20111.22   
Bhakkar20111.08   
Bhimber20111.06   
Buner20111.39   
Central Karachi20110.186  
Chagai2011-0.321  
Chakwal20111.27   
Chaman2011-0.0737 
Charsadda20111.02   
Chiniot20111.2    
Chitral Lower20110.771  
Chitral Upper20110.502  
D. I. Khan20111      
Dadu2011-0.0131 
Darel20110.737  
Dera Bugti20110.75   
Dera Ghazi Khan20111.59   
Diamir20110.742  
Duki20111.04   
East Karachi20110.186  
Faisalabad20111.25   
Ghanche20110.65   
Ghizer20110.517  
Ghotki20110.712  
Gilgit20110.642  
Gujranwala20110.882  
Gujrat20110.977  
Gupis-Yasin20110.566  
Gwadar2011-1.23   
Hafizabad20111.07   
Hangu20110.855  
Haripur20111.39   
Harnai20110.136  
Haveli20111.04   
Hunza20110.63   
Hyderabad20110.359  
Islamabad20111.26   
Jacobabad20110.077  
Jaffarabad20110.077  
Jamshoro20110.244  
Jhal Magsi20110.0745 
Jhang20111.16   
Jhelum20111.23   
Jhelum Valley20111.1    
Kachhi20110.035  
Kalat20110.276  
Kambar Shahdad Kot2011-0.0529 
Karak20111.01   
Kashmore20110.373  
Kasur20111.22   
Kech2011-1.27   
Khairpur20110.689  
Khanewal20111.19   
Kharan2011-0.167  
Kharmang20110.843  
Khushab20111.17   
Khuzdar20110.165  
Khyber20110.7    
Killa Abdullah2011-0.0737 
Killa Saifullah20110.176  
Kohat20111.14   
Kohistan Lower20111.03   
Kohistan Upper20111.05   
Kohlu20110.703  
Kolai Palas Kohistan20111.05   
Korangi Karachi20110.186  
Kotli20111.22   
Kurram20110.756  
Lahore20111.22   
Lakki Marwat20111.11   
Larkana2011-0.116  
Lasbela2011-0.142  
Lehri20110.29   
Leiah20111.14   
Lodhran20111.34   
Loralai20110.581  
Lower Dir20111.09   
Malakand20111.09   
Malir Karachi2011-0.0192 
Mandi Bahauddin20111.18   
Mansehra20111.22   
Mardan20111.35   
Mastung2011-0.0209 
Matiari20110.254  
Mianwali20111.32   
Mirpur20111.22   
Mirpur Khas20110.595  
Mohmand20110.543  
Multan20111.51   
Musakhel20111.29   
Muzaffarabad20111.1    
Muzaffargarh20111.33   
Nagar20110.455  
Nankana Sahib20111.29   
Narowal20110.759  
Nasirabad20110.268  
Naushahro Feroze20110.203  
Neelum20110.817  
North Waziristan20110.648  
Nowshera20111.13   
Nushki2011-0.13   
Okara20111.39   
Orakzai20110.742  
Pakpattan20111.15   
Panjgur2011-0.416  
Peshawar20111.13   
Pishin2011-0.0747 
Poonch20111.23   
Quetta2011-0.083  
Rahim Yar Khan20110.878  
Rajanpur20111.32   
Rawalpindi20111.18   
Rondu20110.62   
Sahiwal20111.08   
Sanghar20110.691  
Sargodha20111.23   
Shaheed Benazir Abad20110.264  
Shaheed Sikandarabad20110.208  
Shangla20111.37   
Sheikhupura20110.931  
Sherani20110.91   
Shigar20110.621  
Shikarpur2011-0.00394
Sialkot20110.759  
Sibi20110.0831 
Skardu20110.647  
Sohbatpur20110.517  
South Karachi2011-0.12   
South Waziristan20110.859  
Sudhnoti20111.23   
Sujawal20110.535  
Sukkur20110.425  
Swabi20111.35   
Swat20110.986  
Tando Allahyar20110.515  
Tando Muhammad Khan20110.515  
Tangir20110.688  
Tank20111.05   
Tharparkar20110.861  
Thatta20110.306  
Toba Tek Singh20111.08   
Tor Ghar20111.37   
Umer Kot20110.709  
Upper Dir20110.986  
Vehari20111.19   
Washuk2011-0.273  
West Karachi20110.186  
Zhob20110.926  
Ziarat20110.136  
Abbottabad2012-0.197  
Astore2012-0.533  
Attock20120.628  
Awaran2012-0.887  
Badin2012-0.548  
Bagh2012-0.174  
Bahawalnagar20120.0758 
Bahawalpur2012-0.0335 
Bajaur20120.369  
Bannu20120.769  
Barkhan20120.136  
Batagram2012-0.235  
Bhakkar20120.592  
Bhimber20120.0299 
Buner2012-0.0677 
Central Karachi2012-0.911  
Chagai2012-0.0356 
Chakwal20120.612  
Chaman2012-0.508  
Charsadda20120.265  
Chiniot20120.404  
Chitral Lower20120.131  
Chitral Upper2012-0.414  
D. I. Khan20120.491  
Dadu2012-0.948  
Darel2012-0.511  
Dera Bugti2012-0.552  
Dera Ghazi Khan20120.0219 
Diamir2012-0.549  
Duki20120.0468 
East Karachi2012-0.911  
Faisalabad20120.401  
Ghanche20120.145  
Ghizer2012-0.47   
Ghotki2012-0.823  
Gilgit2012-0.515  
Gujranwala20120.409  
Gujrat20120.446  
Gupis-Yasin2012-0.482  
Gwadar2012-0.793  
Hafizabad20120.436  
Hangu20120.708  
Haripur2012-0.0677 
Harnai2012-0.666  
Haveli2012-0.159  
Hunza20120.627  
Hyderabad2012-0.781  
Islamabad2012-0.292  
Jacobabad2012-0.872  
Jaffarabad2012-0.872  
Jamshoro2012-0.99   
Jhal Magsi2012-0.883  
Jhang20120.323  
Jhelum20120.589  
Jhelum Valley2012-0.364  
Kachhi2012-0.803  
Kalat2012-0.748  
Kambar Shahdad Kot2012-0.957  
Karak20120.581  
Kashmore2012-0.787  
Kasur20120.392  
Kech2012-0.771  
Khairpur2012-0.708  
Khanewal20120.00972
Kharan2012-0.609  
Kharmang2012-0.543  
Khushab20120.564  
Khuzdar2012-0.943  
Khyber20120.556  
Killa Abdullah2012-0.508  
Killa Saifullah2012-0.505  
Kohat20120.628  
Kohistan Lower2012-0.117  
Kohistan Upper2012-0.256  
Kohlu2012-0.232  
Kolai Palas Kohistan2012-0.256  
Korangi Karachi2012-0.911  
Kotli2012-0.0121 
Kurram20120.692  
Lahore20120.392  
Lakki Marwat20120.769  
Larkana2012-1.07   
Lasbela2012-0.965  
Lehri2012-0.653  
Leiah20120.348  
Lodhran20120.0792 
Loralai2012-0.309  
Lower Dir20120.369  
Malakand20120.369  
Malir Karachi2012-1.05   
Mandi Bahauddin20120.531  
Mansehra2012-0.235  
Mardan20120.158  
Mastung2012-0.772  
Matiari2012-0.893  
Mianwali20120.752  
Mirpur2012-0.0121 
Mirpur Khas2012-0.628  
Mohmand20120.494  
Multan20120.0358 
Musakhel2012-0.0493 
Muzaffarabad2012-0.364  
Muzaffargarh20120.153  
Nagar20120.0806 
Nankana Sahib20120.372  
Narowal20120.406  
Nasirabad2012-0.675  
Naushahro Feroze2012-0.91   
Neelum2012-0.411  
North Waziristan20120.676  
Nowshera20120.508  
Nushki2012-0.526  
Okara20120.622  
Orakzai20120.585  
Pakpattan20120.253  
Panjgur2012-0.545  
Peshawar20120.508  
Pishin2012-0.571  
Poonch2012-0.174  
Quetta2012-0.677  
Rahim Yar Khan2012-0.36   
Rajanpur2012-0.383  
Rawalpindi20120.0708 
Rondu2012-0.0852 
Sahiwal20120.183  
Sanghar2012-0.706  
Sargodha20120.532  
Shaheed Benazir Abad2012-0.827  
Shaheed Sikandarabad2012-0.702  
Shangla2012-0.0806 
Sheikhupura20120.365  
Sherani20120.573  
Shigar20120.0667 
Shikarpur2012-1.05   
Sialkot20120.406  
Sibi2012-0.639  
Skardu2012-0.242  
Sohbatpur2012-0.618  
South Karachi2012-1.11   
South Waziristan20120.801  
Sudhnoti2012-0.174  
Sujawal2012-0.534  
Sukkur2012-0.924  
Swabi20120.158  
Swat2012-0.0257 
Tando Allahyar2012-0.686  
Tando Muhammad Khan2012-0.686  
Tangir2012-0.36   
Tank20120.793  
Tharparkar2012-0.669  
Thatta2012-0.811  
Toba Tek Singh20120.183  
Tor Ghar2012-0.0806 
Umer Kot2012-0.689  
Upper Dir2012-0.0257 
Vehari20120.00972
Washuk2012-0.147  
West Karachi2012-0.911  
Zhob2012-0.013  
Ziarat2012-0.666  
Abbottabad20130.965  
Astore20130.186  
Attock20131.31   
Awaran2013-0.683  
Badin20130.168  
Bagh20130.802  
Bahawalnagar20130.769  
Bahawalpur20130.472  
Bajaur20131.48   
Bannu20131.15   
Barkhan20130.283  
Batagram20130.852  
Bhakkar20130.901  
Bhimber20130.767  
Buner20130.996  
Central Karachi2013-0.277  
Chagai2013-0.107  
Chakwal20131.14   
Chaman2013-0.0901 
Charsadda20131.33   
Chiniot20130.854  
Chitral Lower20131.23   
Chitral Upper20130.528  
D. I. Khan20130.906  
Dadu2013-0.701  
Darel20130.295  
Dera Bugti2013-0.628  
Dera Ghazi Khan20130.533  
Diamir20130.273  
Duki20130.205  
East Karachi2013-0.277  
Faisalabad20130.748  
Ghanche2013-0.0776 
Ghizer20130.118  
Ghotki2013-0.676  
Gilgit20130.0758 
Gujranwala20130.909  
Gujrat20131.01   
Gupis-Yasin20130.333  
Gwadar2013-0.368  
Hafizabad20130.916  
Hangu20131.23   
Haripur20130.996  
Harnai2013-0.111  
Haveli20130.737  
Hunza20130.0402 
Hyderabad20130.006  
Islamabad20130.873  
Jacobabad2013-1.15   
Jaffarabad2013-1.15   
Jamshoro2013-0.224  
Jhal Magsi2013-0.9    
Jhang20130.778  
Jhelum20131.12   
Jhelum Valley20130.74   
Kachhi2013-0.797  
Kalat2013-0.164  
Kambar Shahdad Kot2013-1.03   
Karak20131.24   
Kashmore2013-0.789  
Kasur20130.744  
Kech2013-0.683  
Khairpur2013-0.416  
Khanewal20130.773  
Kharan2013-0.429  
Kharmang20130.0921 
Khushab20131      
Khuzdar2013-0.69   
Khyber20131.33   
Killa Abdullah2013-0.0901 
Killa Saifullah2013-0.0377 
Kohat20131.24   
Kohistan Lower20130.969  
Kohistan Upper20130.748  
Kohlu2013-0.0138 
Kolai Palas Kohistan20130.748  
Korangi Karachi2013-0.277  
Kotli20130.775  
Kurram20131.54   
Lahore20130.744  
Lakki Marwat20131.15   
Larkana2013-1.07   
Lasbela2013-0.507  
Lehri2013-0.559  
Leiah20130.767  
Lodhran20130.726  
Loralai20130.00875
Lower Dir20131.48   
Malakand20131.48   
Malir Karachi2013-0.293  
Mandi Bahauddin20130.993  
Mansehra20130.852  
Mardan20131.18   
Mastung2013-0.132  
Matiari2013-0.226  
Mianwali20131.13   
Mirpur20130.775  
Mirpur Khas20130.0471 
Mohmand20131.42   
Multan20130.413  
Musakhel20130.629  
Muzaffarabad20130.74   
Muzaffargarh20130.608  
Nagar2013-0.00883
Nankana Sahib20130.757  
Narowal20130.902  
Nasirabad2013-0.898  
Naushahro Feroze2013-0.704  
Neelum20130.491  
North Waziristan20131.44   
Nowshera20131.33   
Nushki2013-0.346  
Okara20130.811  
Orakzai20131.46   
Pakpattan20130.742  
Panjgur2013-0.331  
Peshawar20131.33   
Pishin2013-0.0988 
Poonch20130.802  
Quetta2013-0.0417 
Rahim Yar Khan2013-0.22   
Rajanpur2013-0.223  
Rawalpindi20131      
Rondu2013-0.113  
Sahiwal20130.819  
Sanghar2013-0.00941
Sargodha20130.932  
Shaheed Benazir Abad2013-0.528  
Shaheed Sikandarabad2013-0.572  
Shangla20131.07   
Sheikhupura20130.831  
Sherani20131.25   
Shigar2013-0.163  
Shikarpur2013-1.13   
Sialkot20130.902  
Sibi2013-0.429  
Skardu2013-0.194  
Sohbatpur2013-0.568  
South Karachi2013-0.439  
South Waziristan20131.37   
Sudhnoti20130.802  
Sujawal20130.0638 
Sukkur2013-0.809  
Swabi20131.18   
Swat20131.14   
Tando Allahyar20130.133  
Tando Muhammad Khan20130.133  
Tangir20130.534  
Tank20131.16   
Tharparkar2013-0.144  
Thatta2013-0.0288 
Toba Tek Singh20130.819  
Tor Ghar20131.07   
Umer Kot2013-0.0614 
Upper Dir20131.14   
Vehari20130.773  
Washuk2013-0.349  
West Karachi2013-0.277  
Zhob20130.72   
Ziarat2013-0.111  
Abbottabad20140.726  
Astore2014-0.262  
Attock20140.852  
Awaran2014-1.2    
Badin2014-0.604  
Bagh20140.885  
Bahawalnagar20140.5    
Bahawalpur2014-0.0975 
Bajaur20140.302  
Bannu20140.595  
Barkhan2014-0.893  
Batagram20140.34   
Bhakkar20140.591  
Bhimber20140.963  
Buner20140.671  
Central Karachi2014-1.06   
Chagai2014-0.776  
Chakwal20140.889  
Chaman2014-1.3    
Charsadda20140.524  
Chiniot20140.63   
Chitral Lower2014-1.02   
Chitral Upper2014-1.07   
D. I. Khan20140.358  
Dadu2014-1.12   
Darel2014-0.571  
Dera Bugti2014-1.25   
Dera Ghazi Khan2014-0.758  
Diamir2014-0.282  
Duki2014-0.959  
East Karachi2014-1.06   
Faisalabad20140.506  
Ghanche2014-0.464  
Ghizer2014-0.889  
Ghotki2014-1.15   
Gilgit2014-0.599  
Gujranwala20140.887  
Gujrat20140.946  
Gupis-Yasin2014-0.972  
Gwadar2014-0.896  
Hafizabad20140.828  
Hangu20140.714  
Haripur20140.671  
Harnai2014-1.28   
Haveli20140.855  
Hunza2014-0.415  
Hyderabad2014-0.774  
Islamabad20140.923  
Jacobabad2014-1.54   
Jaffarabad2014-1.54   
Jamshoro2014-0.911  
Jhal Magsi2014-1.49   
Jhang20140.571  
Jhelum20140.886  
Jhelum Valley20140.603  
Kachhi2014-1.59   
Kalat2014-1.37   
Kambar Shahdad Kot2014-1.52   
Karak20140.794  
Kashmore2014-1.23   
Kasur20140.65   
Kech2014-0.961  
Khairpur2014-0.862  
Khanewal20140.434  
Kharan2014-1.12   
Kharmang20140.332  
Khushab20140.694  
Khuzdar2014-1.19   
Khyber20140.792  
Killa Abdullah2014-1.3    
Killa Saifullah2014-1.12   
Kohat20140.73   
Kohistan Lower2014-0.142  
Kohistan Upper2014-0.125  
Kohlu2014-1.08   
Kolai Palas Kohistan2014-0.125  
Korangi Karachi2014-1.06   
Kotli20140.905  
Kurram20140.746  
Lahore20140.65   
Lakki Marwat20140.595  
Larkana2014-1.53   
Lasbela2014-1.06   
Lehri2014-1.36   
Leiah20140.143  
Lodhran20140.0615 
Loralai2014-1.08   
Lower Dir20140.302  
Malakand20140.302  
Malir Karachi2014-0.978  
Mandi Bahauddin20140.762  
Mansehra20140.34   
Mardan20140.665  
Mastung2014-1.42   
Matiari2014-0.934  
Mianwali20140.768  
Mirpur20140.905  
Mirpur Khas2014-0.627  
Mohmand20140.473  
Multan2014-0.311  
Musakhel2014-0.585  
Muzaffarabad20140.603  
Muzaffargarh2014-0.216  
Nagar2014-0.645  
Nankana Sahib20140.546  
Narowal20140.951  
Nasirabad2014-1.45   
Naushahro Feroze2014-1.23   
Neelum20140.126  
North Waziristan20140.673  
Nowshera20140.813  
Nushki2014-1.13   
Okara20140.534  
Orakzai20140.763  
Pakpattan20140.438  
Panjgur2014-0.9    
Peshawar20140.813  
Pishin2014-1.29   
Poonch20140.885  
Quetta2014-1.25   
Rahim Yar Khan2014-0.838  
Rajanpur2014-1.12   
Rawalpindi20140.933  
Rondu2014-0.559  
Sahiwal20140.523  
Sanghar2014-0.604  
Sargodha20140.683  
Shaheed Benazir Abad2014-1.1    
Shaheed Sikandarabad2014-1.07   
Shangla20140.443  
Sheikhupura20140.739  
Sherani20140.37   
Shigar2014-0.576  
Shikarpur2014-1.59   
Sialkot20140.951  
Sibi2014-1.45   
Skardu2014-0.31   
Sohbatpur2014-1.28   
South Karachi2014-1.07   
South Waziristan20140.528  
Sudhnoti20140.885  
Sujawal2014-0.814  
Sukkur2014-1.22   
Swabi20140.665  
Swat2014-0.134  
Tando Allahyar2014-0.633  
Tando Muhammad Khan2014-0.633  
Tangir2014-0.64   
Tank20140.539  
Tharparkar2014-0.0621 
Thatta2014-0.915  
Toba Tek Singh20140.523  
Tor Ghar20140.443  
Umer Kot2014-0.513  
Upper Dir2014-0.134  
Vehari20140.434  
Washuk2014-0.891  
West Karachi2014-1.06   
Zhob2014-0.402  
Ziarat2014-1.28   
Abbottabad20151.69   
Astore20151.2    
Attock20151.63   
Awaran2015-2.03   
Badin2015-0.913  
Bagh20151.78   
Bahawalnagar20151.06   
Bahawalpur20150.594  
Bajaur20151.89   
Bannu20151.65   
Barkhan20150.259  
Batagram20151.65   
Bhakkar20151.49   
Bhimber20151.79   
Buner20151.61   
Central Karachi2015-1.41   
Chagai2015-1.22   
Chakwal20151.57   
Chaman2015-0.927  
Charsadda20151.85   
Chiniot20151.35   
Chitral Lower20151.21   
Chitral Upper20150.809  
D. I. Khan20151.4    
Dadu2015-1.49   
Darel20151.02   
Dera Bugti2015-0.361  
Dera Ghazi Khan20150.611  
Diamir20151.28   
Duki20150.00813
East Karachi2015-1.41   
Faisalabad20151.27   
Ghanche20150.663  
Ghizer20150.636  
Ghotki2015-0.473  
Gilgit20150.937  
Gujranwala20151.54   
Gujrat20151.57   
Gupis-Yasin20150.742  
Gwadar2015-1.76   
Hafizabad20151.47   
Hangu20151.75   
Haripur20151.61   
Harnai2015-0.673  
Haveli20151.9    
Hunza20150.29   
Hyderabad2015-1.29   
Islamabad20151.67   
Jacobabad2015-1.2    
Jaffarabad2015-1.2    
Jamshoro2015-1.44   
Jhal Magsi2015-1.33   
Jhang20151.37   
Jhelum20151.52   
Jhelum Valley20151.8    
Kachhi2015-1.03   
Kalat2015-1.22   
Kambar Shahdad Kot2015-1.44   
Karak20151.81   
Kashmore2015-0.5    
Kasur20151.42   
Kech2015-1.77   
Khairpur2015-0.804  
Khanewal20151.17   
Kharan2015-1.32   
Kharmang20151.5    
Khushab20151.53   
Khuzdar2015-1.35   
Khyber20151.82   
Killa Abdullah2015-0.927  
Killa Saifullah2015-0.34   
Kohat20151.7    
Kohistan Lower20151.55   
Kohistan Upper20151.49   
Kohlu2015-0.274  
Kolai Palas Kohistan20151.49   
Korangi Karachi2015-1.41   
Kotli20151.63   
Kurram20151.72   
Lahore20151.42   
Lakki Marwat20151.65   
Larkana2015-1.23   
Lasbela2015-1.81   
Lehri2015-0.617  
Leiah20151.22   
Lodhran20150.949  
Loralai2015-0.252  
Lower Dir20151.89   
Malakand20151.89   
Malir Karachi2015-1.57   
Mandi Bahauddin20151.42   
Mansehra20151.65   
Mardan20151.73   
Mastung2015-1.12   
Matiari2015-1.34   
Mianwali20151.66   
Mirpur20151.63   
Mirpur Khas2015-0.867  
Mohmand20151.88   
Multan20150.802  
Musakhel20150.663  
Muzaffarabad20151.8    
Muzaffargarh20151.05   
Nagar20150.495  
Nankana Sahib20151.35   
Narowal20151.62   
Nasirabad2015-0.818  
Naushahro Feroze2015-1.29   
Neelum20151.59   
North Waziristan20151.71   
Nowshera20151.8    
Nushki2015-1.27   
Okara20151.39   
Orakzai20151.77   
Pakpattan20151.14   
Panjgur2015-1.78   
Peshawar20151.8    
Pishin2015-0.692  
Poonch20151.78   
Quetta2015-1.02   
Rahim Yar Khan2015-0.0897 
Rajanpur20150.0893 
Rawalpindi20151.58   
Rondu20150.924  
Sahiwal20151.26   
Sanghar2015-0.812  
Sargodha20151.39   
Shaheed Benazir Abad2015-1.37   
Shaheed Sikandarabad2015-1.21   
Shangla20151.74   
Sheikhupura20151.41   
Sherani20151.39   
Shigar20150.782  
Shikarpur2015-1.15   
Sialkot20151.62   
Sibi2015-0.83   
Skardu20151.28   
Sohbatpur2015-0.493  
South Karachi2015-1.86   
South Waziristan20151.59   
Sudhnoti20151.78   
Sujawal2015-0.86   
Sukkur2015-0.689  
Swabi20151.73   
Swat20151.62   
Tando Allahyar2015-1.03   
Tando Muhammad Khan2015-1.03   
Tangir20151.11   
Tank20151.55   
Tharparkar20150.0236 
Thatta2015-1.23   
Toba Tek Singh20151.26   
Tor Ghar20151.74   
Umer Kot2015-0.524  
Upper Dir20151.62   
Vehari20151.17   
Washuk2015-1.47   
West Karachi2015-1.41   
Zhob20150.693  
Ziarat2015-0.673  
Abbottabad20161.18   
Astore20160.941  
Attock20161.48   
Awaran2016-2.1    
Badin2016-0.874  
Bagh20161.03   
Bahawalnagar20160.84   
Bahawalpur20160.474  
Bajaur20161.63   
Bannu20161.7    
Barkhan20160.5    
Batagram20161.18   
Bhakkar20161.71   
Bhimber20160.858  
Buner20161.24   
Central Karachi2016-1.35   
Chagai2016-1.47   
Chakwal20161.57   
Chaman2016-0.994  
Charsadda20161.52   
Chiniot20161.5    
Chitral Lower20161.82   
Chitral Upper20161.36   
D. I. Khan20161.7    
Dadu2016-1.43   
Darel20161.12   
Dera Bugti2016-0.44   
Dera Ghazi Khan20161.04   
Diamir20160.94   
Duki20160.291  
East Karachi2016-1.35   
Faisalabad20161.49   
Ghanche20160.249  
Ghizer20161.07   
Ghotki2016-0.666  
Gilgit20160.938  
Gujranwala20161.04   
Gujrat20161.09   
Gupis-Yasin20161.23   
Gwadar2016-1.96   
Hafizabad20161.28   
Hangu20161.68   
Haripur20161.24   
Harnai2016-0.752  
Haveli20160.908  
Hunza20160.769  
Hyderabad2016-1.21   
Islamabad20161.22   
Jacobabad2016-1.11   
Jaffarabad2016-1.11   
Jamshoro2016-1.28   
Jhal Magsi2016-1.37   
Jhang20161.51   
Jhelum20161.41   
Jhelum Valley20160.993  
Kachhi2016-1.17   
Kalat2016-1.32   
Kambar Shahdad Kot2016-1.46   
Karak20161.66   
Kashmore2016-0.561  
Kasur20161.28   
Kech2016-2      
Khairpur2016-0.904  
Khanewal20161.18   
Kharan2016-1.43   
Kharmang20160.746  
Khushab20161.56   
Khuzdar2016-1.33   
Khyber20161.53   
Killa Abdullah2016-0.994  
Killa Saifullah2016-0.436  
Kohat20161.58   
Kohistan Lower20161.38   
Kohistan Upper20161.27   
Kohlu2016-0.184  
Kolai Palas Kohistan20161.27   
Korangi Karachi2016-1.35   
Kotli20161.05   
Kurram20161.61   
Lahore20161.28   
Lakki Marwat20161.7    
Larkana2016-1.27   
Lasbela2016-1.69   
Lehri2016-0.844  
Leiah20161.58   
Lodhran20160.984  
Loralai2016-0.179  
Lower Dir20161.63   
Malakand20161.63   
Malir Karachi2016-1.36   
Mandi Bahauddin20161.45   
Mansehra20161.18   
Mardan20161.41   
Mastung2016-1.2    
Matiari2016-1.25   
Mianwali20161.65   
Mirpur20161.05   
Mirpur Khas2016-0.809  
Mohmand20161.55   
Multan20160.847  
Musakhel20161.06   
Muzaffarabad20160.993  
Muzaffargarh20161.3    
Nagar20160.828  
Nankana Sahib20161.44   
Narowal20160.823  
Nasirabad2016-0.905  
Naushahro Feroze2016-1.3    
Neelum20160.858  
North Waziristan20161.57   
Nowshera20161.49   
Nushki2016-1.29   
Okara20161.23   
Orakzai20161.56   
Pakpattan20160.935  
Panjgur2016-1.87   
Peshawar20161.49   
Pishin2016-0.896  
Poonch20161.03   
Quetta2016-1.14   
Rahim Yar Khan2016-0.458  
Rajanpur2016-0.0709 
Rawalpindi20161.3    
Rondu20160.824  
Sahiwal20161.43   
Sanghar2016-0.7    
Sargodha20161.5    
Shaheed Benazir Abad2016-1.25   
Shaheed Sikandarabad2016-1.34   
Shangla20161.37   
Sheikhupura20161.21   
Sherani20161.52   
Shigar20160.711  
Shikarpur2016-1.24   
Sialkot20160.823  
Sibi2016-0.997  
Skardu20160.751  
Sohbatpur2016-0.559  
South Karachi2016-1.7    
South Waziristan20161.59   
Sudhnoti20161.03   
Sujawal2016-1.07   
Sukkur2016-0.833  
Swabi20161.41   
Swat20161.51   
Tando Allahyar2016-0.936  
Tando Muhammad Khan2016-0.936  
Tangir20161.28   
Tank20161.71   
Tharparkar2016-0.239  
Thatta2016-1.17   
Toba Tek Singh20161.43   
Tor Ghar20161.37   
Umer Kot2016-0.548  
Upper Dir20161.51   
Vehari20161.18   
Washuk2016-1.6    
West Karachi2016-1.35   
Zhob20160.81   
Ziarat2016-0.752  
Abbottabad20170.0885 
Astore20170.155  
Attock20170.427  
Awaran2017-2.15   
Badin2017-0.785  
Bagh20170.25   
Bahawalnagar20170.252  
Bahawalpur20170.0674 
Bajaur20170.351  
Bannu20170.201  
Barkhan2017-0.642  
Batagram20170.0323 
Bhakkar20170.409  
Bhimber20170.368  
Buner20170.17   
Central Karachi2017-1.09   
Chagai2017-1.83   
Chakwal20170.698  
Chaman2017-1.46   
Charsadda20170.259  
Chiniot20170.706  
Chitral Lower20170.811  
Chitral Upper20170.705  
D. I. Khan20170.111  
Dadu2017-1.51   
Darel20170.276  
Dera Bugti2017-1.41   
Dera Ghazi Khan2017-0.386  
Diamir20170.164  
Duki2017-0.824  
East Karachi2017-1.09   
Faisalabad20170.749  
Ghanche20170.798  
Ghizer20170.452  
Ghotki2017-1.28   
Gilgit20170.195  
Gujranwala20170.479  
Gujrat20170.671  
Gupis-Yasin20170.51   
Gwadar2017-2.01   
Hafizabad20170.712  
Hangu20170.16   
Haripur20170.17   
Harnai2017-1.33   
Haveli20170.312  
Hunza20171.02   
Hyderabad2017-1.05   
Islamabad20170.304  
Jacobabad2017-1.81   
Jaffarabad2017-1.81   
Jamshoro2017-1.17   
Jhal Magsi2017-1.82   
Jhang20170.58   
Jhelum20170.737  
Jhelum Valley20170.0441 
Kachhi2017-1.83   
Kalat2017-1.5    
Kambar Shahdad Kot2017-1.88   
Karak20170.238  
Kashmore2017-1.55   
Kasur20170.549  
Kech2017-1.99   
Khairpur2017-1.15   
Khanewal20170.437  
Kharan2017-1.72   
Kharmang20170.276  
Khushab20170.595  
Khuzdar2017-1.39   
Khyber20170.203  
Killa Abdullah2017-1.46   
Killa Saifullah2017-1.11   
Kohat20170.346  
Kohistan Lower20170.221  
Kohistan Upper20170.154  
Kohlu2017-1.11   
Kolai Palas Kohistan20170.154  
Korangi Karachi2017-1.09   
Kotli20170.46   
Kurram20170.212  
Lahore20170.549  
Lakki Marwat20170.201  
Larkana2017-1.72   
Lasbela2017-1.65   
Lehri2017-1.78   
Leiah20170.365  
Lodhran20170.136  
Loralai2017-1.11   
Lower Dir20170.351  
Malakand20170.351  
Malir Karachi2017-1.33   
Mandi Bahauddin20170.799  
Mansehra20170.0323 
Mardan20170.238  
Mastung2017-1.48   
Matiari2017-1.1    
Mianwali20170.429  
Mirpur20170.46   
Mirpur Khas2017-0.875  
Mohmand20170.271  
Multan2017-0.125  
Musakhel2017-0.55   
Muzaffarabad20170.0441 
Muzaffargarh20170.0878 
Nagar20170.569  
Nankana Sahib20170.698  
Narowal20170.24   
Nasirabad2017-1.73   
Naushahro Feroze2017-1.38   
Neelum20170.099  
North Waziristan20170.078  
Nowshera20170.229  
Nushki2017-1.56   
Okara20170.512  
Orakzai20170.22   
Pakpattan20170.369  
Panjgur2017-2.05   
Peshawar20170.229  
Pishin2017-1.4    
Poonch20170.25   
Quetta2017-1.5    
Rahim Yar Khan2017-0.928  
Rajanpur2017-1.11   
Rawalpindi20170.513  
Rondu20170.208  
Sahiwal20170.653  
Sanghar2017-0.782  
Sargodha20170.754  
Shaheed Benazir Abad2017-1.32   
Shaheed Sikandarabad2017-1.57   
Shangla20170.162  
Sheikhupura20170.611  
Sherani2017-0.166  
Shigar20170.509  
Shikarpur2017-1.61   
Sialkot20170.24   
Sibi2017-1.56   
Skardu20170.286  
Sohbatpur2017-1.54   
South Karachi2017-1.51   
South Waziristan2017-0.0414 
Sudhnoti20170.25   
Sujawal2017-0.895  
Sukkur2017-1.45   
Swabi20170.238  
Swat20170.337  
Tando Allahyar2017-0.854  
Tando Muhammad Khan2017-0.854  
Tangir20170.337  
Tank20170.0306 
Tharparkar2017-0.346  
Thatta2017-0.946  
Toba Tek Singh20170.653  
Tor Ghar20170.162  
Umer Kot2017-0.638  
Upper Dir20170.337  
Vehari20170.437  
Washuk2017-1.93   
West Karachi2017-1.09   
Zhob2017-0.651  
Ziarat2017-1.33   
Abbottabad2018-1.04   
Astore2018-1.24   
Attock2018-0.69   
Awaran2018-2.5    
Badin2018-1.36   
Bagh2018-0.881  
Bahawalnagar2018-0.222  
Bahawalpur2018-0.513  
Bajaur2018-1.2    
Bannu2018-0.85   
Barkhan2018-1.35   
Batagram2018-1.32   
Bhakkar2018-0.711  
Bhimber2018-0.704  
Buner2018-0.937  
Central Karachi2018-1.65   
Chagai2018-2.25   
Chakwal2018-0.531  
Chaman2018-1.94   
Charsadda2018-1.09   
Chiniot2018-0.323  
Chitral Lower2018-1.46   
Chitral Upper2018-1.62   
D. I. Khan2018-1.06   
Dadu2018-1.97   
Darel2018-1.35   
Dera Bugti2018-1.87   
Dera Ghazi Khan2018-1.36   
Diamir2018-1.33   
Duki2018-1.59   
East Karachi2018-1.65   
Faisalabad2018-0.283  
Ghanche20180.268  
Ghizer2018-1.18   
Ghotki2018-1.67   
Gilgit2018-1.2    
Gujranwala2018-0.445  
Gujrat2018-0.44   
Gupis-Yasin2018-1.5    
Gwadar2018-2.31   
Hafizabad2018-0.378  
Hangu2018-0.801  
Haripur2018-0.937  
Harnai2018-1.75   
Haveli2018-0.902  
Hunza20180.267  
Hyderabad2018-1.53   
Islamabad2018-0.795  
Jacobabad2018-2.24   
Jaffarabad2018-2.24   
Jamshoro2018-1.66   
Jhal Magsi2018-2.15   
Jhang2018-0.378  
Jhelum2018-0.464  
Jhelum Valley2018-1.2    
Kachhi2018-2.1    
Kalat2018-1.85   
Kambar Shahdad Kot2018-2.12   
Karak2018-0.722  
Kashmore2018-1.85   
Kasur2018-0.275  
Kech2018-2.37   
Khairpur2018-1.61   
Khanewal2018-0.392  
Kharan2018-2.1    
Kharmang2018-0.679  
Khushab2018-0.551  
Khuzdar2018-1.87   
Khyber2018-0.904  
Killa Abdullah2018-1.94   
Killa Saifullah2018-1.78   
Kohat2018-0.681  
Kohistan Lower2018-1.49   
Kohistan Upper2018-1.47   
Kohlu2018-1.65   
Kolai Palas Kohistan2018-1.47   
Korangi Karachi2018-1.65   
Kotli2018-0.677  
Kurram2018-0.764  
Lahore2018-0.275  
Lakki Marwat2018-0.85   
Larkana2018-2      
Lasbela2018-2.21   
Lehri2018-2.14   
Leiah2018-0.897  
Lodhran2018-0.441  
Loralai2018-1.73   
Lower Dir2018-1.2    
Malakand2018-1.2    
Malir Karachi2018-1.9    
Mandi Bahauddin2018-0.348  
Mansehra2018-1.32   
Mardan2018-1.05   
Mastung2018-1.82   
Matiari2018-1.62   
Mianwali2018-0.639  
Mirpur2018-0.677  
Mirpur Khas2018-1.38   
Mohmand2018-1.05   
Multan2018-0.799  
Musakhel2018-1.55   
Muzaffarabad2018-1.2    
Muzaffargarh2018-0.918  
Nagar2018-0.539  
Nankana Sahib2018-0.303  
Narowal2018-0.488  
Nasirabad2018-2.16   
Naushahro Feroze2018-1.79   
Neelum2018-1.28   
North Waziristan2018-0.858  
Nowshera2018-0.879  
Nushki2018-2.01   
Okara2018-0.228  
Orakzai2018-0.851  
Pakpattan2018-0.252  
Panjgur2018-2.46   
Peshawar2018-0.879  
Pishin2018-1.87   
Poonch2018-0.881  
Quetta2018-1.91   
Rahim Yar Khan2018-1.39   
Rajanpur2018-1.76   
Rawalpindi2018-0.645  
Rondu2018-0.925  
Sahiwal2018-0.274  
Sanghar2018-1.29   
Sargodha2018-0.339  
Shaheed Benazir Abad2018-1.78   
Shaheed Sikandarabad2018-1.93   
Shangla2018-1.27   
Sheikhupura2018-0.391  
Sherani2018-1.3    
Shigar2018-0.474  
Shikarpur2018-1.99   
Sialkot2018-0.488  
Sibi2018-2.01   
Skardu2018-0.942  
Sohbatpur2018-1.95   
South Karachi2018-2.01   
South Waziristan2018-1.1    
Sudhnoti2018-0.881  
Sujawal2018-1.37   
Sukkur2018-1.76   
Swabi2018-1.05   
Swat2018-1.43   
Tando Allahyar2018-1.41   
Tando Muhammad Khan2018-1.41   
Tangir2018-1.49   
Tank2018-1.01   
Tharparkar2018-0.822  
Thatta2018-1.51   
Toba Tek Singh2018-0.274  
Tor Ghar2018-1.27   
Umer Kot2018-1.05   
Upper Dir2018-1.43   
Vehari2018-0.392  
Washuk2018-2.37   
West Karachi2018-1.65   
Zhob2018-1.7    
Ziarat2018-1.75   
Abbottabad20190.526  
Astore20190.296  
Attock20190.446  
Awaran2019-1.42   
Badin2019-0.758  
Bagh20190.729  
Bahawalnagar20190.235  
Bahawalpur2019-0.132  
Bajaur20190.323  
Bannu20190.488  
Barkhan2019-0.00912
Batagram20190.381  
Bhakkar20190.547  
Bhimber20190.836  
Buner20190.418  
Central Karachi2019-0.881  
Chagai2019-0.361  
Chakwal20190.507  
Chaman20190.0252 
Charsadda20190.497  
Chiniot20190.632  
Chitral Lower2019-0.285  
Chitral Upper2019-0.188  
D. I. Khan20190.384  
Dadu2019-0.817  
Darel2019-0.0275 
Dera Bugti2019-0.637  
Dera Ghazi Khan20190.17   
Diamir20190.204  
Duki20190.0212 
East Karachi2019-0.881  
Faisalabad20190.522  
Ghanche20191.23   
Ghizer2019-0.0506 
Ghotki2019-1.09   
Gilgit20190.0583 
Gujranwala20190.683  
Gujrat20190.726  
Gupis-Yasin2019-0.137  
Gwadar2019-1.52   
Hafizabad20190.631  
Hangu20190.586  
Haripur20190.418  
Harnai20190.192  
Haveli20190.814  
Hunza20190.563  
Hyderabad2019-0.885  
Islamabad20190.618  
Jacobabad2019-0.718  
Jaffarabad2019-0.718  
Jamshoro2019-0.917  
Jhal Magsi2019-0.604  
Jhang20190.622  
Jhelum20190.6    
Jhelum Valley20190.576  
Kachhi2019-0.33   
Kalat20190.465  
Kambar Shahdad Kot2019-0.696  
Karak20190.544  
Kashmore2019-0.852  
Kasur20190.427  
Kech2019-1.44   
Khairpur2019-0.909  
Khanewal20190.457  
Kharan20190.128  
Kharmang20190.768  
Khushab20190.56   
Khuzdar2019-0.454  
Khyber20190.611  
Killa Abdullah20190.0252 
Killa Saifullah20190.285  
Kohat20190.464  
Kohistan Lower20190.13   
Kohistan Upper20190.119  
Kohlu2019-0.0567 
Kolai Palas Kohistan20190.119  
Korangi Karachi2019-0.881  
Kotli20190.728  
Kurram20190.597  
Lahore20190.427  
Lakki Marwat20190.488  
Larkana2019-0.732  
Lasbela2019-1.36   
Lehri2019-0.496  
Leiah20190.381  
Lodhran20190.259  
Loralai20190.159  
Lower Dir20190.323  
Malakand20190.323  
Malir Karachi2019-1.12   
Mandi Bahauddin20190.583  
Mansehra20190.381  
Mardan20190.391  
Mastung20190.243  
Matiari2019-1.05   
Mianwali20190.486  
Mirpur20190.728  
Mirpur Khas2019-0.822  
Mohmand20190.516  
Multan20190.112  
Musakhel20190.36   
Muzaffarabad20190.576  
Muzaffargarh20190.28   
Nagar20190.24   
Nankana Sahib20190.421  
Narowal20190.701  
Nasirabad2019-0.632  
Naushahro Feroze2019-1      
Neelum20190.436  
North Waziristan20190.648  
Nowshera20190.5    
Nushki20190.413  
Okara20190.384  
Orakzai20190.604  
Pakpattan20190.372  
Panjgur2019-0.98   
Peshawar20190.5    
Pishin20190.19   
Poonch20190.729  
Quetta2019-0.0126 
Rahim Yar Khan2019-0.784  
Rajanpur2019-0.517  
Rawalpindi20190.604  
Rondu20190.306  
Sahiwal20190.519  
Sanghar2019-0.844  
Sargodha20190.618  
Shaheed Benazir Abad2019-1.04   
Shaheed Sikandarabad2019-0.143  
Shangla20190.327  
Sheikhupura20190.627  
Sherani20190.531  
Shigar20190.562  
Shikarpur2019-1.02   
Sialkot20190.701  
Sibi2019-0.121  
Skardu20190.477  
Sohbatpur2019-0.559  
South Karachi2019-1.11   
South Waziristan20190.588  
Sudhnoti20190.729  
Sujawal2019-0.75   
Sukkur2019-0.952  
Swabi20190.391  
Swat20190.135  
Tando Allahyar2019-0.783  
Tando Muhammad Khan2019-0.783  
Tangir2019-0.0184 
Tank20190.539  
Tharparkar2019-0.617  
Thatta2019-0.806  
Toba Tek Singh20190.519  
Tor Ghar20190.327  
Umer Kot2019-0.674  
Upper Dir20190.135  
Vehari20190.457  
Washuk2019-0.583  
West Karachi2019-0.881  
Zhob20190.428  
Ziarat20190.192  
Abbottabad20200.998  
Astore20200.83   
Attock20201.31   
Awaran2020-0.301  
Badin20201.21   
Bagh20201.25   
Bahawalnagar20201.38   
Bahawalpur20200.891  
Bajaur20201.16   
Bannu20201.65   
Barkhan20201.17   
Batagram20201      
Bhakkar20202      
Bhimber20201.32   
Buner20200.946  
Central Karachi20200.997  
Chagai20200.351  
Chakwal20201.48   
Chaman20200.996  
Charsadda20201.4    
Chiniot20201.77   
Chitral Lower20200.734  
Chitral Upper20200.91   
D. I. Khan20201.95   
Dadu20200.256  
Darel20200.665  
Dera Bugti20200.21   
Dera Ghazi Khan20201.9    
Diamir20200.794  
Duki20201.16   
East Karachi20200.997  
Faisalabad20201.76   
Ghanche20200.522  
Ghizer20200.645  
Ghotki2020-0.456  
Gilgit20200.609  
Gujranwala20201.35   
Gujrat20201.47   
Gupis-Yasin20200.826  
Gwadar2020-0.828  
Hafizabad20201.49   
Hangu20201.74   
Haripur20200.946  
Harnai20201.06   
Haveli20201.43   
Hunza20200.462  
Hyderabad20200.998  
Islamabad20201.12   
Jacobabad2020-0.368  
Jaffarabad2020-0.368  
Jamshoro20200.687  
Jhal Magsi2020-0.1    
Jhang20201.93   
Jhelum20201.44   
Jhelum Valley20201.2    
Kachhi20200.283  
Kalat20200.998  
Kambar Shahdad Kot2020-0.206  
Karak20201.68   
Kashmore2020-0.342  
Kasur20201.52   
Kech2020-0.585  
Khairpur2020-0.207  
Khanewal20201.71   
Kharan20200.811  
Kharmang20201.13   
Khushab20201.74   
Khuzdar20200.385  
Khyber20201.73   
Killa Abdullah20200.996  
Killa Saifullah20201.24   
Kohat20201.43   
Kohistan Lower20200.812  
Kohistan Upper20200.77   
Kohlu20200.744  
Kolai Palas Kohistan20200.77   
Korangi Karachi20200.997  
Kotli20201.26   
Kurram20201.72   
Lahore20201.52   
Lakki Marwat20201.65   
Larkana2020-0.41   
Lasbela20200.209  
Lehri20200.24   
Leiah20202.11   
Lodhran20201.5    
Loralai20201.1    
Lower Dir20201.16   
Malakand20201.16   
Malir Karachi20200.707  
Mandi Bahauddin20201.56   
Mansehra20201      
Mardan20201.05   
Mastung20200.944  
Matiari20200.651  
Mianwali20201.62   
Mirpur20201.26   
Mirpur Khas20200.9    
Mohmand20201.75   
Multan20201.51   
Musakhel20202.07   
Muzaffarabad20201.2    
Muzaffargarh20201.94   
Nagar20200.517  
Nankana Sahib20201.66   
Narowal20201.16   
Nasirabad2020-0.0313 
Naushahro Feroze20200.043  
Neelum20201.15   
North Waziristan20201.89   
Nowshera20201.42   
Nushki20200.808  
Okara20201.65   
Orakzai20201.77   
Pakpattan20201.41   
Panjgur20200.219  
Peshawar20201.42   
Pishin20201.05   
Poonch20201.25   
Quetta20201.02   
Rahim Yar Khan20200.212  
Rajanpur20200.856  
Rawalpindi20201.23   
Rondu20200.629  
Sahiwal20201.78   
Sanghar20200.619  
Sargodha20201.66   
Shaheed Benazir Abad20200.353  
Shaheed Sikandarabad20200.648  
Shangla20200.832  
Sheikhupura20201.46   
Sherani20202.15   
Shigar20200.518  
Shikarpur2020-0.5    
Sialkot20201.16   
Sibi20200.743  
Skardu20200.734  
Sohbatpur20200.0803 
South Karachi20200.705  
South Waziristan20201.95   
Sudhnoti20201.25   
Sujawal20201.3    
Sukkur2020-0.481  
Swabi20201.05   
Swat20200.844  
Tando Allahyar20201.04   
Tando Muhammad Khan20201.04   
Tangir20200.82   
Tank20201.81   
Tharparkar20200.706  
Thatta20201.16   
Toba Tek Singh20201.78   
Tor Ghar20200.832  
Umer Kot20200.709  
Upper Dir20200.844  
Vehari20201.71   
Washuk20200.302  
West Karachi20200.997  
Zhob20202.14   
Ziarat20201.06   
Abbottabad2021-0.459  
Astore2021-0.366  
Attock2021-0.143  
Awaran2021-1.39   
Badin20210.737  
Bagh2021-0.0179 
Bahawalnagar20210.231  
Bahawalpur2021-0.111  
Bajaur2021-0.869  
Bannu2021-0.282  
Barkhan20210.126  
Batagram2021-0.611  
Bhakkar20210.0583 
Bhimber20210.361  
Buner2021-0.49   
Central Karachi20210.318  
Chagai2021-2.1    
Chakwal20210.0766 
Chaman2021-1.33   
Charsadda2021-0.599  
Chiniot20210.51   
Chitral Lower2021-1.29   
Chitral Upper2021-0.959  
D. I. Khan2021-0.156  
Dadu2021-0.178  
Darel2021-0.635  
Dera Bugti2021-0.575  
Dera Ghazi Khan20210.228  
Diamir2021-0.484  
Duki2021-0.17   
East Karachi20210.318  
Faisalabad20210.687  
Ghanche20210.784  
Ghizer2021-0.634  
Ghotki2021-1.15   
Gilgit2021-0.462  
Gujranwala20210.546  
Gujrat20210.449  
Gupis-Yasin2021-0.803  
Gwadar2021-2.04   
Hafizabad20210.492  
Hangu2021-0.41   
Haripur2021-0.49   
Harnai2021-0.637  
Haveli20210.287  
Hunza20210.111  
Hyderabad20210.486  
Islamabad2021-0.179  
Jacobabad2021-0.948  
Jaffarabad2021-0.948  
Jamshoro20210.287  
Jhal Magsi2021-0.665  
Jhang20210.441  
Jhelum20210.247  
Jhelum Valley2021-0.284  
Kachhi2021-0.784  
Kalat2021-0.863  
Kambar Shahdad Kot2021-0.833  
Karak2021-0.238  
Kashmore2021-0.944  
Kasur20210.816  
Kech2021-1.99   
Khairpur2021-0.543  
Khanewal20210.117  
Kharan2021-1.33   
Kharmang20210.119  
Khushab20210.184  
Khuzdar2021-0.168  
Khyber2021-0.511  
Killa Abdullah2021-1.33   
Killa Saifullah2021-0.894  
Kohat2021-0.163  
Kohistan Lower2021-0.826  
Kohistan Upper2021-0.716  
Kohlu2021-0.357  
Kolai Palas Kohistan2021-0.716  
Korangi Karachi20210.318  
Kotli20210.16   
Kurram2021-0.758  
Lahore20210.816  
Lakki Marwat2021-0.282  
Larkana2021-1.03   
Lasbela2021-0.305  
Lehri2021-0.798  
Leiah20210.077  
Lodhran20210.0743 
Loralai2021-0.45   
Lower Dir2021-0.869  
Malakand2021-0.869  
Malir Karachi20210.131  
Mandi Bahauddin20210.414  
Mansehra2021-0.611  
Mardan2021-0.552  
Mastung2021-0.976  
Matiari20210.244  
Mianwali20210.0199 
Mirpur20210.16   
Mirpur Khas20210.327  
Mohmand2021-0.596  
Multan20210.155  
Musakhel2021-0.151  
Muzaffarabad2021-0.284  
Muzaffargarh20210.06   
Nagar2021-0.242  
Nankana Sahib20210.807  
Narowal20210.52   
Nasirabad2021-0.833  
Naushahro Feroze2021-0.208  
Neelum2021-0.236  
North Waziristan2021-0.815  
Nowshera2021-0.468  
Nushki2021-1.59   
Okara20211.04   
Orakzai2021-0.628  
Pakpattan20210.359  
Panjgur2021-1.59   
Peshawar2021-0.468  
Pishin2021-1.15   
Poonch2021-0.0179 
Quetta2021-1.13   
Rahim Yar Khan2021-0.718  
Rajanpur2021-0.172  
Rawalpindi20210.0194 
Rondu2021-0.274  
Sahiwal20210.401  
Sanghar20210.0942 
Sargodha20210.29   
Shaheed Benazir Abad20210.0749 
Shaheed Sikandarabad2021-0.321  
Shangla2021-0.757  
Sheikhupura20210.622  
Sherani2021-0.712  
Shigar20210.271  
Shikarpur2021-1.13   
Sialkot20210.52   
Sibi2021-0.732  
Skardu20210.156  
Sohbatpur2021-0.725  
South Karachi20210.0411 
South Waziristan2021-0.791  
Sudhnoti2021-0.0179 
Sujawal20210.875  
Sukkur2021-1.18   
Swabi2021-0.552  
Swat2021-0.931  
Tando Allahyar20210.49   
Tando Muhammad Khan20210.49   
Tangir2021-0.753  
Tank2021-0.403  
Tharparkar2021-0.11   
Thatta20210.582  
Toba Tek Singh20210.401  
Tor Ghar2021-0.757  
Umer Kot2021-0.00649
Upper Dir2021-0.931  
Vehari20210.117  
Washuk2021-1.95   
West Karachi20210.318  
Zhob2021-0.761  
Ziarat2021-0.637  
## 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_idyearspei
Abbottabad20100.7  
Astore20100.913
Attock20100.42 
Awaran2010-1.43 
Badin20100.579
Bagh20100.273
## 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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179
## Imputing the SPEI values for 2022??



spei_2022 = spei2021

## Check
spei_2022
location_idyearspei
Abbottabad2021-0.459  
Astore2021-0.366  
Attock2021-0.143  
Awaran2021-1.39   
Badin20210.737  
Bagh2021-0.0179 
Bahawalnagar20210.231  
Bahawalpur2021-0.111  
Bajaur2021-0.869  
Bannu2021-0.282  
Barkhan20210.126  
Batagram2021-0.611  
Bhakkar20210.0583 
Bhimber20210.361  
Buner2021-0.49   
Central Karachi20210.318  
Chagai2021-2.1    
Chakwal20210.0766 
Chaman2021-1.33   
Charsadda2021-0.599  
Chiniot20210.51   
Chitral Lower2021-1.29   
Chitral Upper2021-0.959  
D. I. Khan2021-0.156  
Dadu2021-0.178  
Darel2021-0.635  
Dera Bugti2021-0.575  
Dera Ghazi Khan20210.228  
Diamir2021-0.484  
Duki2021-0.17   
East Karachi20210.318  
Faisalabad20210.687  
Ghanche20210.784  
Ghizer2021-0.634  
Ghotki2021-1.15   
Gilgit2021-0.462  
Gujranwala20210.546  
Gujrat20210.449  
Gupis-Yasin2021-0.803  
Gwadar2021-2.04   
Hafizabad20210.492  
Hangu2021-0.41   
Haripur2021-0.49   
Harnai2021-0.637  
Haveli20210.287  
Hunza20210.111  
Hyderabad20210.486  
Islamabad2021-0.179  
Jacobabad2021-0.948  
Jaffarabad2021-0.948  
Jamshoro20210.287  
Jhal Magsi2021-0.665  
Jhang20210.441  
Jhelum20210.247  
Jhelum Valley2021-0.284  
Kachhi2021-0.784  
Kalat2021-0.863  
Kambar Shahdad Kot2021-0.833  
Karak2021-0.238  
Kashmore2021-0.944  
Kasur20210.816  
Kech2021-1.99   
Khairpur2021-0.543  
Khanewal20210.117  
Kharan2021-1.33   
Kharmang20210.119  
Khushab20210.184  
Khuzdar2021-0.168  
Khyber2021-0.511  
Killa Abdullah2021-1.33   
Killa Saifullah2021-0.894  
Kohat2021-0.163  
Kohistan Lower2021-0.826  
Kohistan Upper2021-0.716  
Kohlu2021-0.357  
Kolai Palas Kohistan2021-0.716  
Korangi Karachi20210.318  
Kotli20210.16   
Kurram2021-0.758  
Lahore20210.816  
Lakki Marwat2021-0.282  
Larkana2021-1.03   
Lasbela2021-0.305  
Lehri2021-0.798  
Leiah20210.077  
Lodhran20210.0743 
Loralai2021-0.45   
Lower Dir2021-0.869  
Malakand2021-0.869  
Malir Karachi20210.131  
Mandi Bahauddin20210.414  
Mansehra2021-0.611  
Mardan2021-0.552  
Mastung2021-0.976  
Matiari20210.244  
Mianwali20210.0199 
Mirpur20210.16   
Mirpur Khas20210.327  
Mohmand2021-0.596  
Multan20210.155  
Musakhel2021-0.151  
Muzaffarabad2021-0.284  
Muzaffargarh20210.06   
Nagar2021-0.242  
Nankana Sahib20210.807  
Narowal20210.52   
Nasirabad2021-0.833  
Naushahro Feroze2021-0.208  
Neelum2021-0.236  
North Waziristan2021-0.815  
Nowshera2021-0.468  
Nushki2021-1.59   
Okara20211.04   
Orakzai2021-0.628  
Pakpattan20210.359  
Panjgur2021-1.59   
Peshawar2021-0.468  
Pishin2021-1.15   
Poonch2021-0.0179 
Quetta2021-1.13   
Rahim Yar Khan2021-0.718  
Rajanpur2021-0.172  
Rawalpindi20210.0194 
Rondu2021-0.274  
Sahiwal20210.401  
Sanghar20210.0942 
Sargodha20210.29   
Shaheed Benazir Abad20210.0749 
Shaheed Sikandarabad2021-0.321  
Shangla2021-0.757  
Sheikhupura20210.622  
Sherani2021-0.712  
Shigar20210.271  
Shikarpur2021-1.13   
Sialkot20210.52   
Sibi2021-0.732  
Skardu20210.156  
Sohbatpur2021-0.725  
South Karachi20210.0411 
South Waziristan2021-0.791  
Sudhnoti2021-0.0179 
Sujawal20210.875  
Sukkur2021-1.18   
Swabi2021-0.552  
Swat2021-0.931  
Tando Allahyar20210.49   
Tando Muhammad Khan20210.49   
Tangir2021-0.753  
Tank2021-0.403  
Tharparkar2021-0.11   
Thatta20210.582  
Toba Tek Singh20210.401  
Tor Ghar2021-0.757  
Umer Kot2021-0.00649
Upper Dir2021-0.931  
Vehari20210.117  
Washuk2021-1.95   
West Karachi20210.318  
Zhob2021-0.761  
Ziarat2021-0.637  
## Change year value to 2022
spei_2022 = spei_2022 %>% 
  mutate(year = 2022)

## Check
spei_2022
location_idyearspei
Abbottabad2.02e+03-0.459  
Astore2.02e+03-0.366  
Attock2.02e+03-0.143  
Awaran2.02e+03-1.39   
Badin2.02e+030.737  
Bagh2.02e+03-0.0179 
Bahawalnagar2.02e+030.231  
Bahawalpur2.02e+03-0.111  
Bajaur2.02e+03-0.869  
Bannu2.02e+03-0.282  
Barkhan2.02e+030.126  
Batagram2.02e+03-0.611  
Bhakkar2.02e+030.0583 
Bhimber2.02e+030.361  
Buner2.02e+03-0.49   
Central Karachi2.02e+030.318  
Chagai2.02e+03-2.1    
Chakwal2.02e+030.0766 
Chaman2.02e+03-1.33   
Charsadda2.02e+03-0.599  
Chiniot2.02e+030.51   
Chitral Lower2.02e+03-1.29   
Chitral Upper2.02e+03-0.959  
D. I. Khan2.02e+03-0.156  
Dadu2.02e+03-0.178  
Darel2.02e+03-0.635  
Dera Bugti2.02e+03-0.575  
Dera Ghazi Khan2.02e+030.228  
Diamir2.02e+03-0.484  
Duki2.02e+03-0.17   
East Karachi2.02e+030.318  
Faisalabad2.02e+030.687  
Ghanche2.02e+030.784  
Ghizer2.02e+03-0.634  
Ghotki2.02e+03-1.15   
Gilgit2.02e+03-0.462  
Gujranwala2.02e+030.546  
Gujrat2.02e+030.449  
Gupis-Yasin2.02e+03-0.803  
Gwadar2.02e+03-2.04   
Hafizabad2.02e+030.492  
Hangu2.02e+03-0.41   
Haripur2.02e+03-0.49   
Harnai2.02e+03-0.637  
Haveli2.02e+030.287  
Hunza2.02e+030.111  
Hyderabad2.02e+030.486  
Islamabad2.02e+03-0.179  
Jacobabad2.02e+03-0.948  
Jaffarabad2.02e+03-0.948  
Jamshoro2.02e+030.287  
Jhal Magsi2.02e+03-0.665  
Jhang2.02e+030.441  
Jhelum2.02e+030.247  
Jhelum Valley2.02e+03-0.284  
Kachhi2.02e+03-0.784  
Kalat2.02e+03-0.863  
Kambar Shahdad Kot2.02e+03-0.833  
Karak2.02e+03-0.238  
Kashmore2.02e+03-0.944  
Kasur2.02e+030.816  
Kech2.02e+03-1.99   
Khairpur2.02e+03-0.543  
Khanewal2.02e+030.117  
Kharan2.02e+03-1.33   
Kharmang2.02e+030.119  
Khushab2.02e+030.184  
Khuzdar2.02e+03-0.168  
Khyber2.02e+03-0.511  
Killa Abdullah2.02e+03-1.33   
Killa Saifullah2.02e+03-0.894  
Kohat2.02e+03-0.163  
Kohistan Lower2.02e+03-0.826  
Kohistan Upper2.02e+03-0.716  
Kohlu2.02e+03-0.357  
Kolai Palas Kohistan2.02e+03-0.716  
Korangi Karachi2.02e+030.318  
Kotli2.02e+030.16   
Kurram2.02e+03-0.758  
Lahore2.02e+030.816  
Lakki Marwat2.02e+03-0.282  
Larkana2.02e+03-1.03   
Lasbela2.02e+03-0.305  
Lehri2.02e+03-0.798  
Leiah2.02e+030.077  
Lodhran2.02e+030.0743 
Loralai2.02e+03-0.45   
Lower Dir2.02e+03-0.869  
Malakand2.02e+03-0.869  
Malir Karachi2.02e+030.131  
Mandi Bahauddin2.02e+030.414  
Mansehra2.02e+03-0.611  
Mardan2.02e+03-0.552  
Mastung2.02e+03-0.976  
Matiari2.02e+030.244  
Mianwali2.02e+030.0199 
Mirpur2.02e+030.16   
Mirpur Khas2.02e+030.327  
Mohmand2.02e+03-0.596  
Multan2.02e+030.155  
Musakhel2.02e+03-0.151  
Muzaffarabad2.02e+03-0.284  
Muzaffargarh2.02e+030.06   
Nagar2.02e+03-0.242  
Nankana Sahib2.02e+030.807  
Narowal2.02e+030.52   
Nasirabad2.02e+03-0.833  
Naushahro Feroze2.02e+03-0.208  
Neelum2.02e+03-0.236  
North Waziristan2.02e+03-0.815  
Nowshera2.02e+03-0.468  
Nushki2.02e+03-1.59   
Okara2.02e+031.04   
Orakzai2.02e+03-0.628  
Pakpattan2.02e+030.359  
Panjgur2.02e+03-1.59   
Peshawar2.02e+03-0.468  
Pishin2.02e+03-1.15   
Poonch2.02e+03-0.0179 
Quetta2.02e+03-1.13   
Rahim Yar Khan2.02e+03-0.718  
Rajanpur2.02e+03-0.172  
Rawalpindi2.02e+030.0194 
Rondu2.02e+03-0.274  
Sahiwal2.02e+030.401  
Sanghar2.02e+030.0942 
Sargodha2.02e+030.29   
Shaheed Benazir Abad2.02e+030.0749 
Shaheed Sikandarabad2.02e+03-0.321  
Shangla2.02e+03-0.757  
Sheikhupura2.02e+030.622  
Sherani2.02e+03-0.712  
Shigar2.02e+030.271  
Shikarpur2.02e+03-1.13   
Sialkot2.02e+030.52   
Sibi2.02e+03-0.732  
Skardu2.02e+030.156  
Sohbatpur2.02e+03-0.725  
South Karachi2.02e+030.0411 
South Waziristan2.02e+03-0.791  
Sudhnoti2.02e+03-0.0179 
Sujawal2.02e+030.875  
Sukkur2.02e+03-1.18   
Swabi2.02e+03-0.552  
Swat2.02e+03-0.931  
Tando Allahyar2.02e+030.49   
Tando Muhammad Khan2.02e+030.49   
Tangir2.02e+03-0.753  
Tank2.02e+03-0.403  
Tharparkar2.02e+03-0.11   
Thatta2.02e+030.582  
Toba Tek Singh2.02e+030.401  
Tor Ghar2.02e+03-0.757  
Umer Kot2.02e+03-0.00649
Upper Dir2.02e+03-0.931  
Vehari2.02e+030.117  
Washuk2.02e+03-1.95   
West Karachi2.02e+030.318  
Zhob2.02e+03-0.761  
Ziarat2.02e+03-0.637  
## Add to dataframe
test.df$spei_2022 = spei_2022$spei

Deprivation Index

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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -0.459 73  73  73  73  73  73  73  73  73  73  73  73  73  
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.2
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.3
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.2
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.2
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.2

Urbanization

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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.000221
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    

Climate Data

Temperature Data

## 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)
districtsX2010.01.01.12.00.00X2010.04.01.12.00.00X2010.07.01.12.00.00X2010.10.01.12.00.00X2011.01.01.12.00.00X2011.04.01.12.00.00X2011.07.01.12.00.00X2011.10.01.12.00.00X2012.01.01.12.00.00X2012.04.01.12.00.00X2012.07.01.12.00.00X2012.10.01.12.00.00X2013.01.01.12.00.00X2013.04.01.12.00.00X2013.07.01.12.00.00X2013.10.01.12.00.00X2014.01.01.12.00.00X2014.04.01.12.00.00X2014.07.01.12.00.00X2014.10.01.12.00.00X2015.01.01.12.00.00X2015.04.01.12.00.00X2015.07.01.12.00.00X2015.10.01.12.00.00X2016.01.01.12.00.00X2016.04.01.12.00.00X2016.07.01.12.00.00X2016.10.01.12.00.00X2017.01.01.12.00.00X2017.04.01.12.00.00X2017.07.01.12.00.00X2017.10.01.12.00.00X2018.01.01.12.00.00X2018.04.01.12.00.00X2018.07.01.12.00.00X2018.10.01.12.00.00X2019.01.01.12.00.00X2019.04.01.12.00.00X2019.07.01.12.00.00X2019.10.01.12.00.00X2020.01.01.12.00.00X2020.04.01.12.00.00X2020.07.01.12.00.00X2020.10.01.12.00.00X2021.01.01.12.00.00X2021.04.01.12.00.00X2021.07.01.12.00.00X2021.10.01.12.00.00X2022.01.01.12.00.00X2022.04.01.12.00.00X2022.07.01.12.00.00X2022.10.01.12.00.00
Abbottabad285295299294282292299293280292301292282292300294283291299292283293298293283293299295280294299295284294299292279294299293280292300295284292299293281298298294
Astore260271284274257269285274256270285270256270286277258269286272258270286273258270286275259270286276258271286269257272287273257270285275257268286273259272287273
Attock290305307301288300307300286301311299288300307301289299308299289300305300289300306303287303307303290302307299286302308301286299308301289300308300287307305302
Awaran296310308307296306309305293307310305295305309306294307309306295309309306297307309307294310308308297309310306295308309305292308310306295308309305293310303306
Badin299312307309298310308305296310308307298309307308296310309308298311307308299310308308297311305309299311308309298311309307296311309308297311308306296313304307
Bagh283293297292278289296291275289298290278289297292279288296290281290296291280291296293276292296293282292296289275291296290275289297293279289296291276295296291
## 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), ]
districtsX2010.01.01.12.00.00X2010.04.01.12.00.00X2010.07.01.12.00.00X2010.10.01.12.00.00X2011.01.01.12.00.00X2011.04.01.12.00.00X2011.07.01.12.00.00X2011.10.01.12.00.00X2012.01.01.12.00.00X2012.04.01.12.00.00X2012.07.01.12.00.00X2012.10.01.12.00.00X2013.01.01.12.00.00X2013.04.01.12.00.00X2013.07.01.12.00.00X2013.10.01.12.00.00X2014.01.01.12.00.00X2014.04.01.12.00.00X2014.07.01.12.00.00X2014.10.01.12.00.00X2015.01.01.12.00.00X2015.04.01.12.00.00X2015.07.01.12.00.00X2015.10.01.12.00.00X2016.01.01.12.00.00X2016.04.01.12.00.00X2016.07.01.12.00.00X2016.10.01.12.00.00X2017.01.01.12.00.00X2017.04.01.12.00.00X2017.07.01.12.00.00X2017.10.01.12.00.00X2018.01.01.12.00.00X2018.04.01.12.00.00X2018.07.01.12.00.00X2018.10.01.12.00.00X2019.01.01.12.00.00X2019.04.01.12.00.00X2019.07.01.12.00.00X2019.10.01.12.00.00X2020.01.01.12.00.00X2020.04.01.12.00.00X2020.07.01.12.00.00X2020.10.01.12.00.00X2021.01.01.12.00.00X2021.04.01.12.00.00X2021.07.01.12.00.00X2021.10.01.12.00.00X2022.01.01.12.00.00X2022.04.01.12.00.00X2022.07.01.12.00.00X2022.10.01.12.00.00
Korangi KarachiNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
## 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,]
districtsX2010.01.01.12.00.00X2010.04.01.12.00.00X2010.07.01.12.00.00X2010.10.01.12.00.00X2011.01.01.12.00.00X2011.04.01.12.00.00X2011.07.01.12.00.00X2011.10.01.12.00.00X2012.01.01.12.00.00X2012.04.01.12.00.00X2012.07.01.12.00.00X2012.10.01.12.00.00X2013.01.01.12.00.00X2013.04.01.12.00.00X2013.07.01.12.00.00X2013.10.01.12.00.00X2014.01.01.12.00.00X2014.04.01.12.00.00X2014.07.01.12.00.00X2014.10.01.12.00.00X2015.01.01.12.00.00X2015.04.01.12.00.00X2015.07.01.12.00.00X2015.10.01.12.00.00X2016.01.01.12.00.00X2016.04.01.12.00.00X2016.07.01.12.00.00X2016.10.01.12.00.00X2017.01.01.12.00.00X2017.04.01.12.00.00X2017.07.01.12.00.00X2017.10.01.12.00.00X2018.01.01.12.00.00X2018.04.01.12.00.00X2018.07.01.12.00.00X2018.10.01.12.00.00X2019.01.01.12.00.00X2019.04.01.12.00.00X2019.07.01.12.00.00X2019.10.01.12.00.00X2020.01.01.12.00.00X2020.04.01.12.00.00X2020.07.01.12.00.00X2020.10.01.12.00.00X2021.01.01.12.00.00X2021.04.01.12.00.00X2021.07.01.12.00.00X2021.10.01.12.00.00X2022.01.01.12.00.00X2022.04.01.12.00.00X2022.07.01.12.00.00X2022.10.01.12.00.00
Korangi Karachi298306304305297306304304296305304305297304304305296305304306297306304305298305304304296306303305298306304306297306305305295306307306297307304304295306303304
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_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022
12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  
-12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.455
17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    
23  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  
25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    
10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  
#create a copy

## Merge to the test dataframe
test.df = cbind(test.df, temp.extract.df2)


## Check 
head(test.df)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022temp.jan_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 -12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.455
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.00022123  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  
dim(test.df)
## [1] 160 107

Precipitation Data

## 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)
districtsX2010.01.01.12.00.00X2010.04.01.12.00.00X2010.07.01.12.00.00X2010.10.01.12.00.00X2011.01.01.12.00.00X2011.04.01.12.00.00X2011.07.01.12.00.00X2011.10.01.12.00.00X2012.01.01.12.00.00X2012.04.01.12.00.00X2012.07.01.12.00.00X2012.10.01.12.00.00X2013.01.01.12.00.00X2013.04.01.12.00.00X2013.07.01.12.00.00X2013.10.01.12.00.00X2014.01.01.12.00.00X2014.04.01.12.00.00X2014.07.01.12.00.00X2014.10.01.12.00.00X2015.01.01.12.00.00X2015.04.01.12.00.00X2015.07.01.12.00.00X2015.10.01.12.00.00X2016.01.01.12.00.00X2016.04.01.12.00.00X2016.07.01.12.00.00X2016.10.01.12.00.00X2017.01.01.12.00.00X2017.04.01.12.00.00X2017.07.01.12.00.00X2017.10.01.12.00.00X2018.01.01.12.00.00X2018.04.01.12.00.00X2018.07.01.12.00.00X2018.10.01.12.00.00X2019.01.01.12.00.00X2019.04.01.12.00.00X2019.07.01.12.00.00X2019.10.01.12.00.00X2020.01.01.12.00.00X2020.04.01.12.00.00X2020.07.01.12.00.00X2020.10.01.12.00.00X2021.01.01.12.00.00X2021.04.01.12.00.00X2021.07.01.12.00.00X2021.10.01.12.00.00X2022.01.01.12.00.00X2022.04.01.12.00.00X2022.07.01.12.00.00X2022.10.01.12.00.00
Abbottabad0.0008980.00195 0.007320.0004140.0007040.00355 0.00302 0.00159 0.00171 0.00292 0.00239 0.00071 0.00101 0.00275 0.00318 0.0008920.0006760.00193 0.00376 0.00128 0.00132 0.00329 0.00628 0.00166 0.00126 0.00316 0.00569 0.0002360.00456 0.0039  0.0055  0.0002360.0002860.00464 0.00695 0.00146 0.00319 0.00248 0.00415 0.0009480.00323 0.00308 0.00331 9.88e-050.00124 0.00215 0.0063  0.00157 0.00355 0.0009210.006160.000613
Astore0.00108 0.00236 0.004020.0006650.0009460.00137 0.0027  0.00136 0.00143 0.00172 0.00161 0.00111 0.001   0.00159 0.00194 0.0009350.0009730.00218 0.00194 0.00133 0.00111 0.00237 0.00337 0.00148 0.00123 0.00305 0.00306 0.0007110.00361 0.00311 0.00258 0.0008410.0008380.00231 0.00346 0.00142 0.00239 0.00185 0.00149 0.00122 0.00271 0.00184 0.00289 0.0005060.00134 0.00206 0.00385 0.0017  0.00196 0.00101 0.003540.0012  
Attock0.0002490.0002950.005910.0001810.0003240.00114 0.00275 0.0007650.0011  0.0007430.0008640.0002550.0001870.0004980.00461 0.0004097.12e-050.00126 0.00219 0.00101 0.0004580.00195 0.00459 0.0008750.0006650.0009830.00182 0.0002050.00166 0.0009690.00294 0.0001469.32e-050.00116 0.00278 0.0005530.00135 0.00143 0.00222 0.0003660.00129 0.0007460.00169 3.66e-050.0003160.00082 0.00423 0.00109 0.00175 0.0002810.004560.000257
Awaran0.0001391.89e-050.001135.81e-061.73e-050.0001290.0007583.73e-050.0002760.0001684.52e-058.69e-056.79e-060.0006780.0002320.0001490.00011 0.0002080.00018 9.11e-050.0001872.82e-050.0002263.72e-053.76e-053.42e-059.18e-057.45e-060.0005292.47e-050.0003921.05e-066.86e-070.0001167.91e-056.41e-050.0003520.0001190.0002470.0002180.0005883.64e-050.0004246.18e-071.54e-060.0001950.0004820.0005650.0006050.0001740.004842.96e-06
Badin3.75e-063.2e-06 0.001958.15e-051.95e-058.04e-060.0005712.42e-063.56e-050.0001598.82e-051.48e-051.13e-050.0002010.00103 0.00023 2.22e-077.52e-050.0003044.6e-05 1e-05       2.12e-060.00291 1.52e-051.2e-05 3.26e-060.0001430.0001721.77e-052.33e-050.00197 1.94e-062.5e-07 3.86e-050.0001125.84e-063.68e-051.62e-050.0015  0.0003722.73e-063.39e-060.0006630.0002313.39e-071.53e-050.0006654.19e-050.0003247.6e-05 0.005422.36e-06
Bagh0.00103 0.00199 0.007540.0005410.0006920.00292 0.00398 0.00126 0.00227 0.00286 0.00243 0.0008610.0009540.00218 0.00379 0.00088 0.0008530.00242 0.00367 0.0011  0.0009850.00409 0.00827 0.00142 0.00143 0.00315 0.00589 0.0003510.00482 0.00396 0.00623 0.0003710.0003880.0042  0.00646 0.00132 0.00347 0.00216 0.00477 0.00086 0.00381 0.00317 0.00286 7.37e-050.0016  0.00281 0.00545 0.00116 0.00428 0.00114 0.005870.000631
## 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), ]
districtsX2010.01.01.12.00.00X2010.04.01.12.00.00X2010.07.01.12.00.00X2010.10.01.12.00.00X2011.01.01.12.00.00X2011.04.01.12.00.00X2011.07.01.12.00.00X2011.10.01.12.00.00X2012.01.01.12.00.00X2012.04.01.12.00.00X2012.07.01.12.00.00X2012.10.01.12.00.00X2013.01.01.12.00.00X2013.04.01.12.00.00X2013.07.01.12.00.00X2013.10.01.12.00.00X2014.01.01.12.00.00X2014.04.01.12.00.00X2014.07.01.12.00.00X2014.10.01.12.00.00X2015.01.01.12.00.00X2015.04.01.12.00.00X2015.07.01.12.00.00X2015.10.01.12.00.00X2016.01.01.12.00.00X2016.04.01.12.00.00X2016.07.01.12.00.00X2016.10.01.12.00.00X2017.01.01.12.00.00X2017.04.01.12.00.00X2017.07.01.12.00.00X2017.10.01.12.00.00X2018.01.01.12.00.00X2018.04.01.12.00.00X2018.07.01.12.00.00X2018.10.01.12.00.00X2019.01.01.12.00.00X2019.04.01.12.00.00X2019.07.01.12.00.00X2019.10.01.12.00.00X2020.01.01.12.00.00X2020.04.01.12.00.00X2020.07.01.12.00.00X2020.10.01.12.00.00X2021.01.01.12.00.00X2021.04.01.12.00.00X2021.07.01.12.00.00X2021.10.01.12.00.00X2022.01.01.12.00.00X2022.04.01.12.00.00X2022.07.01.12.00.00X2022.10.01.12.00.00
Korangi KarachiNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
## 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,]
districtsX2010.01.01.12.00.00X2010.04.01.12.00.00X2010.07.01.12.00.00X2010.10.01.12.00.00X2011.01.01.12.00.00X2011.04.01.12.00.00X2011.07.01.12.00.00X2011.10.01.12.00.00X2012.01.01.12.00.00X2012.04.01.12.00.00X2012.07.01.12.00.00X2012.10.01.12.00.00X2013.01.01.12.00.00X2013.04.01.12.00.00X2013.07.01.12.00.00X2013.10.01.12.00.00X2014.01.01.12.00.00X2014.04.01.12.00.00X2014.07.01.12.00.00X2014.10.01.12.00.00X2015.01.01.12.00.00X2015.04.01.12.00.00X2015.07.01.12.00.00X2015.10.01.12.00.00X2016.01.01.12.00.00X2016.04.01.12.00.00X2016.07.01.12.00.00X2016.10.01.12.00.00X2017.01.01.12.00.00X2017.04.01.12.00.00X2017.07.01.12.00.00X2017.10.01.12.00.00X2018.01.01.12.00.00X2018.04.01.12.00.00X2018.07.01.12.00.00X2018.10.01.12.00.00X2019.01.01.12.00.00X2019.04.01.12.00.00X2019.07.01.12.00.00X2019.10.01.12.00.00X2020.01.01.12.00.00X2020.04.01.12.00.00X2020.07.01.12.00.00X2020.10.01.12.00.00X2021.01.01.12.00.00X2021.04.01.12.00.00X2021.07.01.12.00.00X2021.10.01.12.00.00X2022.01.01.12.00.00X2022.04.01.12.00.00X2022.07.01.12.00.00X2022.10.01.12.00.00
Korangi Karachi7.64e-075.5e-060.001963.08e-051.02e-056.55e-050.0009256.93e-059.05e-058.37e-050.000255.37e-051.61e-050.0002480.0006910.0001882.22e-078.87e-050.0004637.29e-054.18e-052.97e-050.001292.66e-052.96e-057.67e-060.0004131.04e-056.59e-051.88e-050.001244.69e-062.22e-076.13e-050.0001569.7e-066.05e-056.05e-050.001540.0003474.01e-066.28e-070.0006251.98e-064.93e-072.81e-050.0009350.0003350.0001223.2e-060.006751.24e-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_2010precip.apr_2010precip.jul_2010precip.oct_2010precip.jan_2011precip.apr_2011precip.jul_2011precip.oct_2011precip.jan_2012precip.apr_2012precip.jul_2012precip.oct_2012precip.jan_2013precip.apr_2013precip.jul_2013precip.oct_2013precip.jan_2014precip.apr_2014precip.jul_2014precip.oct_2014precip.jan_2015precip.apr_2015precip.jul_2015precip.oct_2015precip.jan_2016precip.apr_2016precip.jul_2016precip.oct_2016precip.jan_2017precip.apr_2017precip.jul_2017precip.oct_2017precip.jan_2018precip.apr_2018precip.jul_2018precip.oct_2018precip.jan_2019precip.apr_2019precip.jul_2019precip.oct_2019precip.jan_2020precip.apr_2020precip.jul_2020precip.oct_2020precip.jan_2021precip.apr_2021precip.jul_2021precip.oct_2021precip.jan_2022precip.apr_2022precip.jul_2022precip.oct_2022
0.898  1.95  7.320.414  0.704 3.55   3.02 1.59   1.71  2.92 2.39  0.71  1.01   2.75 3.18 0.8920.676   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 0.9483.23   3.08   3.31 0.0988  1.24    2.15  6.3  1.57  3.55 0.9216.160.613  
1.08   2.36  4.020.665  0.946 1.37   2.7  1.36   1.43  1.72 1.61  1.11  1      1.59 1.94 0.9350.973   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 3.541.2    
0.249  0.295 5.910.181  0.324 1.14   2.75 0.765  1.1   0.7430.864 0.255 0.187  0.4984.61 0.4090.0712  1.26  2.19 1.01  0.4581.95   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 0.3661.29   0.746  1.69 0.0366  0.316   0.82  4.23 1.09  1.75 0.2814.560.257  
0.139  0.01891.130.005810.01730.129  0.7580.0373 0.276 0.1680.04520.08690.006790.6780.2320.1490.11    0.208 0.18 0.09110.1870.0282 0.2260.03720.03760.0342 0.09180.007450.529 0.02470.3920.001050.0006860.116 0.07910.0641 0.352 0.119 0.2470.2180.588  0.0364 0.4240.0006180.00154 0.195 0.4820.565 0.6050.1744.840.00296
0.003750.00321.950.0815 0.01950.008040.5710.002420.03560.1590.08820.01480.0113 0.2011.03 0.23 0.0002220.07520.3040.046 0.01 0.002122.91 0.01520.012 0.003260.143 0.172  0.01770.02331.97 0.001940.00025 0.03860.112 0.005840.03680.01621.5  0.3720.002730.003390.6630.231   0.0003390.01530.6650.04190.3240.0765.420.00236
1.03   1.99  7.540.541  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   0.9854.09   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 5.870.631  
#create a copy

## Merge to the test dataframe
test.df = cbind(test.df, precip.extract.df2)


## Check 
head(test.df)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022temp.jan_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022precip.jan_2010precip.apr_2010precip.jul_2010precip.oct_2010precip.jan_2011precip.apr_2011precip.jul_2011precip.oct_2011precip.jan_2012precip.apr_2012precip.jul_2012precip.oct_2012precip.jan_2013precip.apr_2013precip.jul_2013precip.oct_2013precip.jan_2014precip.apr_2014precip.jul_2014precip.oct_2014precip.jan_2015precip.apr_2015precip.jul_2015precip.oct_2015precip.jan_2016precip.apr_2016precip.jul_2016precip.oct_2016precip.jan_2017precip.apr_2017precip.jul_2017precip.oct_2017precip.jan_2018precip.apr_2018precip.jul_2018precip.oct_2018precip.jan_2019precip.apr_2019precip.jul_2019precip.oct_2019precip.jan_2020precip.apr_2020precip.jul_2020precip.oct_2020precip.jan_2021precip.apr_2021precip.jul_2021precip.oct_2021precip.jan_2022precip.apr_2022precip.jul_2022precip.oct_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  0.898  1.95  7.320.414  0.704 3.55   3.02 1.59   1.71  2.92 2.39  0.71  1.01   2.75 3.18 0.8920.676   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 0.9483.23   3.08   3.31 0.0988  1.24    2.15  6.3  1.57  3.55 0.9216.160.613  
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 -12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.4551.08   2.36  4.020.665  0.946 1.37   2.7  1.36   1.43  1.72 1.61  1.11  1      1.59 1.94 0.9350.973   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 3.541.2    
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    0.249  0.295 5.910.181  0.324 1.14   2.75 0.765  1.1   0.7430.864 0.255 0.187  0.4984.61 0.4090.0712  1.26  2.19 1.01  0.4581.95   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 0.3661.29   0.746  1.69 0.0366  0.316   0.82  4.23 1.09  1.75 0.2814.560.257  
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.00022123  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  0.139  0.01891.130.005810.01730.129  0.7580.0373 0.276 0.1680.04520.08690.006790.6780.2320.1490.11    0.208 0.18 0.09110.1870.0282 0.2260.03720.03760.0342 0.09180.007450.529 0.02470.3920.001050.0006860.116 0.07910.0641 0.352 0.119 0.2470.2180.588  0.0364 0.4240.0006180.00154 0.195 0.4820.565 0.6050.1744.840.00296
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    0.003750.00321.950.0815 0.01950.008040.5710.002420.03560.1590.08820.01480.0113 0.2011.03 0.23 0.0002220.07520.3040.046 0.01 0.002122.91 0.01520.012 0.003260.143 0.172  0.01770.02331.97 0.001940.00025 0.03860.112 0.005840.03680.01621.5  0.3720.002730.003390.6630.231   0.0003390.01530.6650.04190.3240.0765.420.00236
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  1.03   1.99  7.540.541  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   0.9854.09   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 5.870.631  
dim(test.df)
## [1] 160 159

Build the Overall Dataframe

## 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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022temp.jan_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022precip.jan_2010precip.apr_2010precip.jul_2010precip.oct_2010precip.jan_2011precip.apr_2011precip.jul_2011precip.oct_2011precip.jan_2012precip.apr_2012precip.jul_2012precip.oct_2012precip.jan_2013precip.apr_2013precip.jul_2013precip.oct_2013precip.jan_2014precip.apr_2014precip.jul_2014precip.oct_2014precip.jan_2015precip.apr_2015precip.jul_2015precip.oct_2015precip.jan_2016precip.apr_2016precip.jul_2016precip.oct_2016precip.jan_2017precip.apr_2017precip.jul_2017precip.oct_2017precip.jan_2018precip.apr_2018precip.jul_2018precip.oct_2018precip.jan_2019precip.apr_2019precip.jul_2019precip.oct_2019precip.jan_2020precip.apr_2020precip.jul_2020precip.oct_2020precip.jan_2021precip.apr_2021precip.jul_2021precip.oct_2021precip.jan_2022precip.apr_2022precip.jul_2022precip.oct_2022total.con_2010total.con_2011total.con_2012total.con_2013total.con_2014total.con_2015total.con_2016total.con_2017total.con_2018total.con_2019total.con_2020total.con_2021total.con_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  0.898  1.95  7.320.414  0.704 3.55   3.02 1.59   1.71  2.92 2.39  0.71  1.01   2.75 3.18 0.8920.676   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 0.9483.23   3.08   3.31 0.0988  1.24    2.15  6.3  1.57  3.55 0.9216.160.613  241012126109192033564156
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 -12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.4551.08   2.36  4.020.665  0.946 1.37   2.7  1.36   1.43  1.72 1.61  1.11  1      1.59 1.94 0.9350.973   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 3.541.2    0011020023555
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    0.249  0.295 5.910.181  0.324 1.14   2.75 0.765  1.1   0.7430.864 0.255 0.187  0.4984.61 0.4090.0712  1.26  2.19 1.01  0.4581.95   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 0.3661.29   0.746  1.69 0.0366  0.316   0.82  4.23 1.09  1.75 0.2814.560.257  13461057482216131633
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.00022123  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  0.139  0.01891.130.005810.01730.129  0.7580.0373 0.276 0.1680.04520.08690.006790.6780.2320.1490.11    0.208 0.18 0.09110.1870.0282 0.2260.03720.03760.0342 0.09180.007450.529 0.02470.3920.001050.0006860.116 0.07910.0641 0.352 0.119 0.2470.2180.588  0.0364 0.4240.0006180.00154 0.195 0.4820.565 0.6050.1744.840.00296356126177842184033
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    0.003750.00321.950.0815 0.01950.008040.5710.002420.03560.1590.08820.01480.0113 0.2011.03 0.23 0.0002220.07520.3040.046 0.01 0.002122.91 0.01520.012 0.003260.143 0.172  0.01770.02331.97 0.001940.00025 0.03860.112 0.005840.03680.01621.5  0.3720.002730.003390.6630.231   0.0003390.01530.6650.04190.3240.0765.420.002361084510096658875989122534217685
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  1.03   1.99  7.540.541  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   0.9854.09   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 5.870.631  441361094916302
## 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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022temp.jan_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022precip.jan_2010precip.apr_2010precip.jul_2010precip.oct_2010precip.jan_2011precip.apr_2011precip.jul_2011precip.oct_2011precip.jan_2012precip.apr_2012precip.jul_2012precip.oct_2012precip.jan_2013precip.apr_2013precip.jul_2013precip.oct_2013precip.jan_2014precip.apr_2014precip.jul_2014precip.oct_2014precip.jan_2015precip.apr_2015precip.jul_2015precip.oct_2015precip.jan_2016precip.apr_2016precip.jul_2016precip.oct_2016precip.jan_2017precip.apr_2017precip.jul_2017precip.oct_2017precip.jan_2018precip.apr_2018precip.jul_2018precip.oct_2018precip.jan_2019precip.apr_2019precip.jul_2019precip.oct_2019precip.jan_2020precip.apr_2020precip.jul_2020precip.oct_2020precip.jan_2021precip.apr_2021precip.jul_2021precip.oct_2021precip.jan_2022precip.apr_2022precip.jul_2022precip.oct_2022total.con_2010total.con_2011total.con_2012total.con_2013total.con_2014total.con_2015total.con_2016total.con_2017total.con_2018total.con_2019total.con_2020total.con_2021total.con_2022protests_2010protests_2011protests_2012protests_2013protests_2014protests_2015protests_2016protests_2017protests_2018protests_2019protests_2020protests_2021protests_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  0.898  1.95  7.320.414  0.704 3.55   3.02 1.59   1.71  2.92 2.39  0.71  1.01   2.75 3.18 0.8920.676   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 0.9483.23   3.08   3.31 0.0988  1.24    2.15  6.3  1.57  3.55 0.9216.160.613  24101212610919203356415617101516141113171519232020
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 -12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.4551.08   2.36  4.020.665  0.946 1.37   2.7  1.36   1.43  1.72 1.61  1.11  1      1.59 1.94 0.9350.973   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 3.541.2    00110200235550000000000000
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    0.249  0.295 5.910.181  0.324 1.14   2.75 0.765  1.1   0.7430.864 0.255 0.187  0.4984.61 0.4090.0712  1.26  2.19 1.01  0.4581.95   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 0.3661.29   0.746  1.69 0.0366  0.316   0.82  4.23 1.09  1.75 0.2814.560.257  134610574822161316337781168121011913910
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.00022123  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  0.139  0.01891.130.005810.01730.129  0.7580.0373 0.276 0.1680.04520.08690.006790.6780.2320.1490.11    0.208 0.18 0.09110.1870.0282 0.2260.03720.03760.0342 0.09180.007450.529 0.02470.3920.001050.0006860.116 0.07910.0641 0.352 0.119 0.2470.2180.588  0.0364 0.4240.0006180.00154 0.195 0.4820.565 0.6050.1744.840.002963561261778421840330122323222332
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    0.003750.00321.950.0815 0.01950.008040.5710.002420.03560.1590.08820.01480.0113 0.2011.03 0.23 0.0002220.07520.3040.046 0.01 0.002122.91 0.01520.012 0.003260.143 0.172  0.01770.02331.97 0.001940.00025 0.03860.112 0.005840.03680.01621.5  0.3720.002730.003390.6630.231   0.0003390.01530.6650.04190.3240.0765.420.002361084510096658875989122534217685104716767647386848793999985
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  1.03   1.99  7.540.541  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   0.9854.09   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 5.870.631  4413610949163024423134433443
## 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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022temp.jan_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022precip.jan_2010precip.apr_2010precip.jul_2010precip.oct_2010precip.jan_2011precip.apr_2011precip.jul_2011precip.oct_2011precip.jan_2012precip.apr_2012precip.jul_2012precip.oct_2012precip.jan_2013precip.apr_2013precip.jul_2013precip.oct_2013precip.jan_2014precip.apr_2014precip.jul_2014precip.oct_2014precip.jan_2015precip.apr_2015precip.jul_2015precip.oct_2015precip.jan_2016precip.apr_2016precip.jul_2016precip.oct_2016precip.jan_2017precip.apr_2017precip.jul_2017precip.oct_2017precip.jan_2018precip.apr_2018precip.jul_2018precip.oct_2018precip.jan_2019precip.apr_2019precip.jul_2019precip.oct_2019precip.jan_2020precip.apr_2020precip.jul_2020precip.oct_2020precip.jan_2021precip.apr_2021precip.jul_2021precip.oct_2021precip.jan_2022precip.apr_2022precip.jul_2022precip.oct_2022total.con_2010total.con_2011total.con_2012total.con_2013total.con_2014total.con_2015total.con_2016total.con_2017total.con_2018total.con_2019total.con_2020total.con_2021total.con_2022protests_2010protests_2011protests_2012protests_2013protests_2014protests_2015protests_2016protests_2017protests_2018protests_2019protests_2020protests_2021protests_2022battles_2010battles_2011battles_2012battles_2013battles_2014battles_2015battles_2016battles_2017battles_2018battles_2019battles_2020battles_2021battles_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  0.898  1.95  7.320.414  0.704 3.55   3.02 1.59   1.71  2.92 2.39  0.71  1.01   2.75 3.18 0.8920.676   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 0.9483.23   3.08   3.31 0.0988  1.24    2.15  6.3  1.57  3.55 0.9216.160.613  241012126109192033564156171015161411131715192320200311473110110
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 -12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.4551.08   2.36  4.020.665  0.946 1.37   2.7  1.36   1.43  1.72 1.61  1.11  1      1.59 1.94 0.9350.973   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 3.541.2    001102002355500000000000000000000000000
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    0.249  0.295 5.910.181  0.324 1.14   2.75 0.765  1.1   0.7430.864 0.255 0.187  0.4984.61 0.4090.0712  1.26  2.19 1.01  0.4581.95   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 0.3661.29   0.746  1.69 0.0366  0.316   0.82  4.23 1.09  1.75 0.2814.560.257  1346105748221613163377811681210119139102021120220021
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.00022123  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  0.139  0.01891.130.005810.01730.129  0.7580.0373 0.276 0.1680.04520.08690.006790.6780.2320.1490.11    0.208 0.18 0.09110.1870.0282 0.2260.03720.03760.0342 0.09180.007450.529 0.02470.3920.001050.0006860.116 0.07910.0641 0.352 0.119 0.2470.2180.588  0.0364 0.4240.0006180.00154 0.195 0.4820.565 0.6050.1744.840.0029635612617784218403301223232223320000000100000
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    0.003750.00321.950.0815 0.01950.008040.5710.002420.03560.1590.08820.01480.0113 0.2011.03 0.23 0.0002220.07520.3040.046 0.01 0.002122.91 0.01520.012 0.003260.143 0.172  0.01770.02331.97 0.001940.00025 0.03860.112 0.005840.03680.01621.5  0.3720.002730.003390.6630.231   0.0003390.01530.6650.04190.3240.0765.420.00236108451009665887598912253421768510471676764738684879399998519146201471253348
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  1.03   1.99  7.540.541  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   0.9854.09   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 5.870.631  44136109491630244231344334430010000000000
## 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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022temp.jan_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022precip.jan_2010precip.apr_2010precip.jul_2010precip.oct_2010precip.jan_2011precip.apr_2011precip.jul_2011precip.oct_2011precip.jan_2012precip.apr_2012precip.jul_2012precip.oct_2012precip.jan_2013precip.apr_2013precip.jul_2013precip.oct_2013precip.jan_2014precip.apr_2014precip.jul_2014precip.oct_2014precip.jan_2015precip.apr_2015precip.jul_2015precip.oct_2015precip.jan_2016precip.apr_2016precip.jul_2016precip.oct_2016precip.jan_2017precip.apr_2017precip.jul_2017precip.oct_2017precip.jan_2018precip.apr_2018precip.jul_2018precip.oct_2018precip.jan_2019precip.apr_2019precip.jul_2019precip.oct_2019precip.jan_2020precip.apr_2020precip.jul_2020precip.oct_2020precip.jan_2021precip.apr_2021precip.jul_2021precip.oct_2021precip.jan_2022precip.apr_2022precip.jul_2022precip.oct_2022total.con_2010total.con_2011total.con_2012total.con_2013total.con_2014total.con_2015total.con_2016total.con_2017total.con_2018total.con_2019total.con_2020total.con_2021total.con_2022protests_2010protests_2011protests_2012protests_2013protests_2014protests_2015protests_2016protests_2017protests_2018protests_2019protests_2020protests_2021protests_2022battles_2010battles_2011battles_2012battles_2013battles_2014battles_2015battles_2016battles_2017battles_2018battles_2019battles_2020battles_2021battles_2022expl_2010expl_2011expl_2012expl_2013expl_2014expl_2015expl_2016expl_2017expl_2018expl_2019expl_2020expl_2021expl_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  0.898  1.95  7.320.414  0.704 3.55   3.02 1.59   1.71  2.92 2.39  0.71  1.01   2.75 3.18 0.8920.676   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 0.9483.23   3.08   3.31 0.0988  1.24    2.15  6.3  1.57  3.55 0.9216.160.613  2410121261091920335641561710151614111317151923202003114731101100753424112002
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 -12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.4551.08   2.36  4.020.665  0.946 1.37   2.7  1.36   1.43  1.72 1.61  1.11  1      1.59 1.94 0.9350.973   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 3.541.2    0011020023555000000000000000000000000000000000000000
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    0.249  0.295 5.910.181  0.324 1.14   2.75 0.765  1.1   0.7430.864 0.255 0.187  0.4984.61 0.4090.0712  1.26  2.19 1.01  0.4581.95   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 0.3661.29   0.746  1.69 0.0366  0.316   0.82  4.23 1.09  1.75 0.2814.560.257  13461057482216131633778116812101191391020211202200210221411001011
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.00022123  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  0.139  0.01891.130.005810.01730.129  0.7580.0373 0.276 0.1680.04520.08690.006790.6780.2320.1490.11    0.208 0.18 0.09110.1870.0282 0.2260.03720.03760.0342 0.09180.007450.529 0.02470.3920.001050.0006860.116 0.07910.0641 0.352 0.119 0.2470.2180.588  0.0364 0.4240.0006180.00154 0.195 0.4820.565 0.6050.1744.840.00296356126177842184033012232322233200000001000003201000001000
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    0.003750.00321.950.0815 0.01950.008040.5710.002420.03560.1590.08820.01480.0113 0.2011.03 0.23 0.0002220.07520.3040.046 0.01 0.002122.91 0.01520.012 0.003260.143 0.172  0.01770.02331.97 0.001940.00025 0.03860.112 0.005840.03680.01621.5  0.3720.002730.003390.6630.231   0.0003390.01530.6650.04190.3240.0765.420.0023610845100966588759891225342176851047167676473868487939999851914620147125334801610171465742124
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  1.03   1.99  7.540.541  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   0.9854.09   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 5.870.631  441361094916302442313443344300100000000000011310000000
## 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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022temp.jan_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022precip.jan_2010precip.apr_2010precip.jul_2010precip.oct_2010precip.jan_2011precip.apr_2011precip.jul_2011precip.oct_2011precip.jan_2012precip.apr_2012precip.jul_2012precip.oct_2012precip.jan_2013precip.apr_2013precip.jul_2013precip.oct_2013precip.jan_2014precip.apr_2014precip.jul_2014precip.oct_2014precip.jan_2015precip.apr_2015precip.jul_2015precip.oct_2015precip.jan_2016precip.apr_2016precip.jul_2016precip.oct_2016precip.jan_2017precip.apr_2017precip.jul_2017precip.oct_2017precip.jan_2018precip.apr_2018precip.jul_2018precip.oct_2018precip.jan_2019precip.apr_2019precip.jul_2019precip.oct_2019precip.jan_2020precip.apr_2020precip.jul_2020precip.oct_2020precip.jan_2021precip.apr_2021precip.jul_2021precip.oct_2021precip.jan_2022precip.apr_2022precip.jul_2022precip.oct_2022total.con_2010total.con_2011total.con_2012total.con_2013total.con_2014total.con_2015total.con_2016total.con_2017total.con_2018total.con_2019total.con_2020total.con_2021total.con_2022protests_2010protests_2011protests_2012protests_2013protests_2014protests_2015protests_2016protests_2017protests_2018protests_2019protests_2020protests_2021protests_2022battles_2010battles_2011battles_2012battles_2013battles_2014battles_2015battles_2016battles_2017battles_2018battles_2019battles_2020battles_2021battles_2022expl_2010expl_2011expl_2012expl_2013expl_2014expl_2015expl_2016expl_2017expl_2018expl_2019expl_2020expl_2021expl_2022riots_2010riots_2011riots_2012riots_2013riots_2014riots_2015riots_2016riots_2017riots_2018riots_2019riots_2020riots_2021riots_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  0.898  1.95  7.320.414  0.704 3.55   3.02 1.59   1.71  2.92 2.39  0.71  1.01   2.75 3.18 0.8920.676   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 0.9483.23   3.08   3.31 0.0988  1.24    2.15  6.3  1.57  3.55 0.9216.160.613  24101212610919203356415617101516141113171519232020031147311011007534241120027202112350022
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 -12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.4551.08   2.36  4.020.665  0.946 1.37   2.7  1.36   1.43  1.72 1.61  1.11  1      1.59 1.94 0.9350.973   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 3.541.2    00110200235550000000000000000000000000000000000000000000000000000
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    0.249  0.295 5.910.181  0.324 1.14   2.75 0.765  1.1   0.7430.864 0.255 0.187  0.4984.61 0.4090.0712  1.26  2.19 1.01  0.4581.95   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 0.3661.29   0.746  1.69 0.0366  0.316   0.82  4.23 1.09  1.75 0.2814.560.257  134610574822161316337781168121011913910202112022002102214110010114110100001010
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.00022123  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  0.139  0.01891.130.005810.01730.129  0.7580.0373 0.276 0.1680.04520.08690.006790.6780.2320.1490.11    0.208 0.18 0.09110.1870.0282 0.2260.03720.03760.0342 0.09180.007450.529 0.02470.3920.001050.0006860.116 0.07910.0641 0.352 0.119 0.2470.2180.588  0.0364 0.4240.0006180.00154 0.195 0.4820.565 0.6050.1744.840.002963561261778421840330122323222332000000010000032010000010000010000000000
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    0.003750.00321.950.0815 0.01950.008040.5710.002420.03560.1590.08820.01480.0113 0.2011.03 0.23 0.0002220.07520.3040.046 0.01 0.002122.91 0.01520.012 0.003260.143 0.172  0.01770.02331.97 0.001940.00025 0.03860.112 0.005840.03680.01621.5  0.3720.002730.003390.6630.231   0.0003390.01530.6650.04190.3240.0765.420.00236108451009665887598912253421768510471676764738684879399998519146201471253348016101714657421241389631054425
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  1.03   1.99  7.540.541  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   0.9854.09   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 5.870.631  4413610949163024423134433443001000000000000113100000000000000011001
## 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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022temp.jan_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022precip.jan_2010precip.apr_2010precip.jul_2010precip.oct_2010precip.jan_2011precip.apr_2011precip.jul_2011precip.oct_2011precip.jan_2012precip.apr_2012precip.jul_2012precip.oct_2012precip.jan_2013precip.apr_2013precip.jul_2013precip.oct_2013precip.jan_2014precip.apr_2014precip.jul_2014precip.oct_2014precip.jan_2015precip.apr_2015precip.jul_2015precip.oct_2015precip.jan_2016precip.apr_2016precip.jul_2016precip.oct_2016precip.jan_2017precip.apr_2017precip.jul_2017precip.oct_2017precip.jan_2018precip.apr_2018precip.jul_2018precip.oct_2018precip.jan_2019precip.apr_2019precip.jul_2019precip.oct_2019precip.jan_2020precip.apr_2020precip.jul_2020precip.oct_2020precip.jan_2021precip.apr_2021precip.jul_2021precip.oct_2021precip.jan_2022precip.apr_2022precip.jul_2022precip.oct_2022total.con_2010total.con_2011total.con_2012total.con_2013total.con_2014total.con_2015total.con_2016total.con_2017total.con_2018total.con_2019total.con_2020total.con_2021total.con_2022protests_2010protests_2011protests_2012protests_2013protests_2014protests_2015protests_2016protests_2017protests_2018protests_2019protests_2020protests_2021protests_2022battles_2010battles_2011battles_2012battles_2013battles_2014battles_2015battles_2016battles_2017battles_2018battles_2019battles_2020battles_2021battles_2022expl_2010expl_2011expl_2012expl_2013expl_2014expl_2015expl_2016expl_2017expl_2018expl_2019expl_2020expl_2021expl_2022riots_2010riots_2011riots_2012riots_2013riots_2014riots_2015riots_2016riots_2017riots_2018riots_2019riots_2020riots_2021riots_2022vac_2010vac_2011vac_2012vac_2013vac_2014vac_2015vac_2016vac_2017vac_2018vac_2019vac_2020vac_2021vac_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  0.898  1.95  7.320.414  0.704 3.55   3.02 1.59   1.71  2.92 2.39  0.71  1.01   2.75 3.18 0.8920.676   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 0.9483.23   3.08   3.31 0.0988  1.24    2.15  6.3  1.57  3.55 0.9216.160.613  241012126109192033564156171015161411131715192320200311473110110075342411200272021123500220232131123010
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 -12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.4551.08   2.36  4.020.665  0.946 1.37   2.7  1.36   1.43  1.72 1.61  1.11  1      1.59 1.94 0.9350.973   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 3.541.2    001102002355500000000000000000000000000000000000000000000000000000000000000000
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    0.249  0.295 5.910.181  0.324 1.14   2.75 0.765  1.1   0.7430.864 0.255 0.187  0.4984.61 0.4090.0712  1.26  2.19 1.01  0.4581.95   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 0.3661.29   0.746  1.69 0.0366  0.316   0.82  4.23 1.09  1.75 0.2814.560.257  1346105748221613163377811681210119139102021120220021022141100101141101000010100200120102001
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.00022123  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  0.139  0.01891.130.005810.01730.129  0.7580.0373 0.276 0.1680.04520.08690.006790.6780.2320.1490.11    0.208 0.18 0.09110.1870.0282 0.2260.03720.03760.0342 0.09180.007450.529 0.02470.3920.001050.0006860.116 0.07910.0641 0.352 0.119 0.2470.2180.588  0.0364 0.4240.0006180.00154 0.195 0.4820.565 0.6050.1744.840.0029635612617784218403301223232223320000000100000320100000100000100000000000000010010001
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    0.003750.00321.950.0815 0.01950.008040.5710.002420.03560.1590.08820.01480.0113 0.2011.03 0.23 0.0002220.07520.3040.046 0.01 0.002122.91 0.01520.012 0.003260.143 0.172  0.01770.02331.97 0.001940.00025 0.03860.112 0.005840.03680.01621.5  0.3720.002730.003390.6630.231   0.0003390.01530.6650.04190.3240.0765.420.002361084510096658875989122534217685104716767647386848793999985191462014712533480161017146574212413896310544252888494464114
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  1.03   1.99  7.540.541  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   0.9854.09   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 5.870.631  44136109491630244231344334430010000000000001131000000000000000110010000000000000
## 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)
districtspop_2010pop_2011pop_2012pop_2013pop_2014pop_2015pop_2016pop_2017pop_2018pop_2019pop_2020pop_2021pop_2022longitudelatitudespei_2010spei_2011spei_2012spei_2013spei_2014spei_2015spei_2016spei_2017spei_2018spei_2019spei_2020spei_2021spei_2022dep.index_2010dep.index_2011dep.index_2012dep.index_2013dep.index_2014dep.index_2015dep.index_2016dep.index_2017dep.index_2018dep.index_2019dep.index_2020dep.index_2021dep.index_2022built_2010built_2011built_2012built_2013built_2014built_2015built_2016built_2017built_2018built_2019built_2020built_2021built_2022temp.jan_2010temp.apr_2010temp.jul_2010temp.oct_2010temp.jan_2011temp.apr_2011temp.jul_2011temp.oct_2011temp.jan_2012temp.apr_2012temp.jul_2012temp.oct_2012temp.jan_2013temp.apr_2013temp.jul_2013temp.oct_2013temp.jan_2014temp.apr_2014temp.jul_2014temp.oct_2014temp.jan_2015temp.apr_2015temp.jul_2015temp.oct_2015temp.jan_2016temp.apr_2016temp.jul_2016temp.oct_2016temp.jan_2017temp.apr_2017temp.jul_2017temp.oct_2017temp.jan_2018temp.apr_2018temp.jul_2018temp.oct_2018temp.jan_2019temp.apr_2019temp.jul_2019temp.oct_2019temp.jan_2020temp.apr_2020temp.jul_2020temp.oct_2020temp.jan_2021temp.apr_2021temp.jul_2021temp.oct_2021temp.jan_2022temp.apr_2022temp.jul_2022temp.oct_2022precip.jan_2010precip.apr_2010precip.jul_2010precip.oct_2010precip.jan_2011precip.apr_2011precip.jul_2011precip.oct_2011precip.jan_2012precip.apr_2012precip.jul_2012precip.oct_2012precip.jan_2013precip.apr_2013precip.jul_2013precip.oct_2013precip.jan_2014precip.apr_2014precip.jul_2014precip.oct_2014precip.jan_2015precip.apr_2015precip.jul_2015precip.oct_2015precip.jan_2016precip.apr_2016precip.jul_2016precip.oct_2016precip.jan_2017precip.apr_2017precip.jul_2017precip.oct_2017precip.jan_2018precip.apr_2018precip.jul_2018precip.oct_2018precip.jan_2019precip.apr_2019precip.jul_2019precip.oct_2019precip.jan_2020precip.apr_2020precip.jul_2020precip.oct_2020precip.jan_2021precip.apr_2021precip.jul_2021precip.oct_2021precip.jan_2022precip.apr_2022precip.jul_2022precip.oct_2022total.con_2010total.con_2011total.con_2012total.con_2013total.con_2014total.con_2015total.con_2016total.con_2017total.con_2018total.con_2019total.con_2020total.con_2021total.con_2022protests_2010protests_2011protests_2012protests_2013protests_2014protests_2015protests_2016protests_2017protests_2018protests_2019protests_2020protests_2021protests_2022battles_2010battles_2011battles_2012battles_2013battles_2014battles_2015battles_2016battles_2017battles_2018battles_2019battles_2020battles_2021battles_2022expl_2010expl_2011expl_2012expl_2013expl_2014expl_2015expl_2016expl_2017expl_2018expl_2019expl_2020expl_2021expl_2022riots_2010riots_2011riots_2012riots_2013riots_2014riots_2015riots_2016riots_2017riots_2018riots_2019riots_2020riots_2021riots_2022vac_2010vac_2011vac_2012vac_2013vac_2014vac_2015vac_2016vac_2017vac_2018vac_2019vac_2020vac_2021vac_2022sd_2010sd_2011sd_2012sd_2013sd_2014sd_2015sd_2016sd_2017sd_2018sd_2019sd_2020sd_2021sd_2022
Abbottabad1.2e+06 1.23e+061.24e+061.26e+061.28e+061.3e+06 1.45e+061.58e+061.6e+06 1.76e+061.8e+06 1.83e+061.87e+0673.334.10.7  1.33 -0.1970.9650.7261.69 1.18 0.0885-1.04 0.5260.998-0.459 -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    12.222.3 26.221    8.9418.5 25.520    6.4218.5 28.118.9 8.7718.8 26.520.8 9.7318.1 26.219.2  9.8419.4 25  19.7  10   19.8 25.721.8 7.2521.1 25.722.1 11.1 20.8 25.618.5 6.3520.9 26.119.7   6.6 18.8 27.221.7 10.4 19   26.320.2 7.7724.9  25.120.5  0.898  1.95  7.320.414  0.704 3.55   3.02 1.59   1.71  2.92 2.39  0.71  1.01   2.75 3.18 0.8920.676   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 0.9483.23   3.08   3.31 0.0988  1.24    2.15  6.3  1.57  3.55 0.9216.160.613  2410121261091920335641561710151614111317151923202003114731101100753424112002720211235002202321311230100000001100000
Astore1.06e+051.08e+051.1e+05 1.11e+051.09e+051.1e+05 7.58e+048.22e+048.35e+049.18e+049.37e+049.56e+049.75e+0474.935.10.9130.739-0.5330.186-0.2621.2  0.9410.155 -1.24 0.2960.83 -0.366 -0.366 83.283.283.283.283.283.283.283.283.283.283.283.283.20.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 0.00413 -12.9-2.0610.50.952-16   -4.3611.80.387-17   -3.4711.7-2.91-17.1 -3.3113.33.59-15.6 -4.3712.9-0.702-15.4 -3.4412.9-0.161-14.9 -2.8212.62.28-14.3 -2.8313.32.47-14.9 -2.5912.5-4.17-16   -1.1613.6-0.0882-15.7 -3.1611.62.32-16.4 -5.5112.8-0.55-14.4 -0.76513.5-0.4551.08   2.36  4.020.665  0.946 1.37   2.7  1.36   1.43  1.72 1.61  1.11  1      1.59 1.94 0.9350.973   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 3.541.2    0011020023555000000000000000000000000000000000000000000000000000000000000000000000000000000
Attock1.78e+061.81e+061.84e+061.87e+061.9e+06 1.93e+062.4e+06 2.61e+062.64e+062.91e+062.97e+063.03e+063.09e+0672.333.50.42 1.23 0.6281.31 0.8521.63 1.48 0.427 -0.69 0.4461.31 -0.143 -0.143 78.378.378.378.378.378.378.378.378.378.378.378.378.30.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   0.938   17.231.6 34.127.9  14.8 27   33.527.2  13.2 27.9 37.426.3 14.6 26.9 33.428.2 15.7 26.1 35  25.6  15.6 26.5 32.327    15.4 27.3 33.329.8 13.7 30.1 33.629.6 16.7 29.3 34  26.3 13.2 28.4 34.627.4   13   26   35  28.3 15.5 27.3 34.827.2 14.1 33.4  31.929    0.249  0.295 5.910.181  0.324 1.14   2.75 0.765  1.1   0.7430.864 0.255 0.187  0.4984.61 0.4090.0712  1.26  2.19 1.01  0.4581.95   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 0.3661.29   0.746  1.69 0.0366  0.316   0.82  4.23 1.09  1.75 0.2814.560.257  13461057482216131633778116812101191391020211202200210221411001011411010000101002001201020010100000000000
Awaran1.38e+051.38e+051.4e+05 1.42e+051.44e+051.46e+051.4e+05 1.52e+051.54e+051.7e+05 1.73e+051.77e+051.8e+05 65.426.2-1.43 -0.641-0.887-0.683-1.2  -2.03 -2.1  -2.15  -2.5  -1.42 -0.301-1.39  -1.39  88.288.288.288.288.288.288.288.288.288.288.288.288.20.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.0002210.00022123  36.8 34.633.6  22.6 33.3 35.531.7  19.9 33.4 36.932.3 22.3 31.8 35.533.3 20.5 34.1 36  32.5  22   36.2 36  33.3  23.7 34.3 36.133.9 20.6 36.9 35.334.7 23.7 35.9 36.633.2 22   34.8 36  31.9   18.8 34.6 36.632.8 22.1 35.2 35.531.5 19.8 37    29.532.7  0.139  0.01891.130.005810.01730.129  0.7580.0373 0.276 0.1680.04520.08690.006790.6780.2320.1490.11    0.208 0.18 0.09110.1870.0282 0.2260.03720.03760.0342 0.09180.007450.529 0.02470.3920.001050.0006860.116 0.07910.0641 0.352 0.119 0.2470.2180.588  0.0364 0.4240.0006180.00154 0.195 0.4820.565 0.6050.1744.840.00296356126177842184033012232322233200000001000003201000001000001000000000000000100100010000000000000
Badin1.47e+061.49e+061.52e+061.54e+061.57e+061.59e+061.15e+061.25e+061.27e+061.39e+061.42e+061.45e+061.48e+0668.824.70.5790.578-0.5480.168-0.604-0.913-0.874-0.785 -1.36 -0.7581.21 0.737 0.737 85.285.285.285.285.285.285.285.285.285.285.285.285.20.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 0.00589 25.438.8 34.135.4  24.7 37   34.932.3  23.2 36.7 35.333.9 24.6 35.6 34  34.6 23.2 37.1 35.634.9  24.6 37.9 34  35.2  26   36.7 34.834.5 24   37.7 32.235.9 26.2 38.3 35.135.8 24.7 38.1 35.633.8   22.8 37.9 36  34.6 23.7 38.2 34.433.1 23.1 39.4  31.334    0.003750.00321.950.0815 0.01950.008040.5710.002420.03560.1590.08820.01480.0113 0.2011.03 0.23 0.0002220.07520.3040.046 0.01 0.002122.91 0.01520.012 0.003260.143 0.172  0.01770.02331.97 0.001940.00025 0.03860.112 0.005840.03680.01621.5  0.3720.002730.003390.6630.231   0.0003390.01530.6650.04190.3240.0765.420.0023610845100966588759891225342176851047167676473868487939999851914620147125334801610171465742124138963105442528884944641140111035112002
Bagh3e+05       3.05e+053.1e+05 3.15e+053.29e+053.34e+055.34e+055.79e+055.88e+056.47e+056.6e+05 6.73e+056.87e+0573.734  0.2731.23 -0.1740.8020.8851.78 1.03 0.25  -0.8810.7291.25 -0.0179-0.017973.273.273.273.273.273.273.273.273.273.273.273.273.20.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    0.63    10.219.7 23.418.9  4.8616   22.817.8  1.5616.1 25  16.7 4.6516.3 23.718.4 6.0915.3 23.317.1  7.4116.8 22.517.5  7.2517.4 23.219.7 3.0618.8 22.919.9 8.8818.5 23.116   1.8218.2 23.117.3   1.6615.8 23.819.6 5.4715.9 23.318.1 2.8421.7  22.417.8  1.03   1.99  7.540.541  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   0.9854.09   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 5.870.631  441361094916302442313443344300100000000000011310000000000000001100100000000000000000000000000
## 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)
districtslongitudelatitudegeometryyearpopspeidep.indexbuilttemp.jantemp.aprtemp.jultemp.octprecip.janprecip.aprprecip.julprecip.octtotal.conprotestsbattlesexplriotsvacsd
Abbottabad73.334.1list(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, 73.3775329590001, 73.3795013430001, 73.3821868900001, 73.3840484620001, 73.385498047, 73.3857421870001,
73.3858184810001, 73.385482788, 73.3851013180001, 73.3846664430001, 73.382156372, 73.3809661870001, 73.3793563840001, 73.37840271, 73.377159119, 73.3750686650001, 73.3736419680001, 73.371490479, 73.3695068360001, 73.368392944, 73.3678588870001, 73.3666498160001, 73.36613235, 73.3641967770001, 73.3629531860001, 73.3599929810001, 73.358177185, 73.356773376, 73.355937365, 73.3530197140001, 73.352058411, 73.3491516110001, 73.3476715090001, 73.346626282, 73.346626282, 73.347640991, 73.3482894900001, 73.34828186,
73.3480834960001, 73.3480072020001, 73.3475418090001, 73.346817017, 73.344787598, 73.3424530030001, 73.3412704470001, 73.3402862550001, 73.3392410280001, 73.3382034300001, 73.337356567, 73.33618927, 73.335327148, 73.334716797, 73.333830541, 73.3330230710001, 73.3319931030001, 73.330291748, 73.3279724120001, 73.327529907, 73.3283081050001, 73.329559326, 73.330680847, 73.3311614990001, 73.331016541, 73.330538038, 73.3297576900001, 73.328842163, 73.3278732300001, 73.3266372680001, 73.3230667110001,
73.3208312990001, 73.318908691, 73.3173599240001, 73.31778717, 73.319206238, 73.319587708, 73.3189773560001, 73.3182373050001, 73.317352295, 73.3167190550001, 73.3156661990001, 73.31477356, 73.311431885, 73.309951782, 73.3080291750001, 73.3069000240001, 73.3041610720001, 73.3020935060001, 73.2999725340001, 73.298210144, 73.296539307, 73.2958883030001, 73.2950897220001, 73.293426514, 73.291297913, 73.289627075, 73.2877426150001, 73.2855834960001, 73.2823562620001, 73.2754355710001, 73.2673110960001,
73.265968323, 73.2651290890001, 73.2635879520001, 73.262718201, 73.2612228390001, 73.2593994060001, 73.2573928830001, 73.2568664550001, 73.2552337650001, 73.2538833620001, 73.252296448, 73.2508621220001, 73.2473526, 73.2458114620001, 73.244613647, 73.2433624270001, 73.242607117, 73.241577148, 73.241027832, 73.2388534550001, 73.2380523680001, 73.237039767, 73.2367019650001, 73.2359466550001, 73.232688904, 73.231042008, 73.230079651, 73.2280197140001, 73.2259597780001, 73.22403717, 73.2211837770001,
73.219787598, 73.2184066770001, 73.2164916990001, 73.213172913, 73.211616516, 73.2102966310001, 73.2093370000001, 73.2088700040001, 73.2072000030001, 73.204579999, 73.2039012700001, 73.203300002, 73.202909998, 73.203010003, 73.2035300040001, 73.204050005, 73.2046967410001, 73.2056700040001, 73.2061438580001, 73.206500002, 73.206903191, 73.2090640590001, 73.2100800000001, 73.210870001, 73.213599431, 73.215029996, 73.2147199990001, 73.2140128070001, 73.213192365, 73.212740001, 73.212571166, 73.212439997,
73.211800004, 73.210929997, 73.2106346740001, 73.210060002, 73.2097700030001, 73.2097700030001, 73.210135, 73.211179998, 73.211870005, 73.2121816090001, 73.2129099950001, 73.213319998, 73.212734915, 73.2122099950001, 73.211750002, 73.211980004, 73.211980004, 73.211690005, 73.211030673, 73.2102299960001, 73.209009995, 73.208238098, 73.206580869, 73.2043705070001, 73.203129995, 73.201320003, 73.200525574, 73.1994599970001, 73.1975399950001, 73.195730003, 73.1952699980001, 73.195591505, 73.195730003,
73.195568651, 73.1955, 73.1955540530001, 73.1956037150001, 73.195730003, 73.1956107560001, 73.195559996, 73.1955, 73.1945100010001, 73.193990967, 73.1932007240001, 73.1923500030001, 73.1914131610001, 73.1906600030001, 73.190079994, 73.190079994, 73.1903250710001, 73.1901999980001, 73.1906940270001, 73.191418516, 73.1917699940001, 73.192261833, 73.1925300030001, 73.19363, 73.194884088, 73.195609999, 73.197249996, 73.198536689, 73.199417268, 73.201300863, 73.201975787, 73.204189107, 73.204759999, 73.2055130140001,
73.207422201, 73.208957581, 73.209880002, 73.210639998, 73.211629998, 73.2124272660001, 73.2129700030001, 73.213582729, 73.214193336, 73.2148299980001, 73.2152599990001, 73.2154099950001, 73.2150617840001, 73.2144800030001, 73.214190005, 73.213670004, 73.213670004, 73.2136099960001, 73.213295003, 73.212439997, 73.2115626080001, 73.211133799, 73.2095900030001, 73.208000003, 73.2068863250001, 73.205809995, 73.2052200040001, 73.203845798, 73.203246279, 73.2027899940001, 73.202310003, 73.2015400010001,
73.200688928, 73.2000885620001, 73.199169999, 73.198440006, 73.198, 73.196950004, 73.196429927, 73.1960200010001, 73.1953110630001, 73.194679996, 73.193330191, 73.19164872, 73.1900199980001, 73.189290005, 73.188509998, 73.1879249230001, 73.1867399980001, 73.185419997, 73.1848317550001, 73.1843199990001, 73.1837299970001, 73.1831500000001, 73.182800005, 73.182919997, 73.1837350000001, 73.1843082660001, 73.185006651, 73.185790002, 73.1862924970001, 73.185336589, 73.184670005, 73.1843199990001, 73.1842596050001,
73.1842149970001, 73.1842400040001, 73.1843199990001, 73.1843199990001, 73.184730001, 73.185418424, 73.1863579320001, 73.1874600030001, 73.1882245040001, 73.1885955770001, 73.1889849990001, 73.1894468250001, 73.1903764890001, 73.191322598, 73.191610005, 73.1917187440001, 73.1918399950001, 73.1927399950001, 73.1949600010001, 73.197029999, 73.197957865, 73.198170791, 73.1982300020001, 73.1990099970001, 73.1996399970001, 73.2000699980001, 73.200379995, 73.20102, 73.2009600040001, 73.2003000000001, 73.199962836,
73.199434141, 73.1991836880001, 73.199209996, 73.1999353200001, 73.200100002, 73.200919995, 73.2026009470001, 73.203730003, 73.2043100000001, 73.2040799970001, 73.203460003, 73.202679996, 73.201860003, 73.2009082420001, 73.2003399980001, 73.199749996, 73.199682559, 73.199479996, 73.1991982750001, 73.1990099970001, 73.1979600020001, 73.197251834, 73.1965600010001, 73.1951394210001, 73.1940600010001, 73.1935599990001, 73.1934192150001, 73.1934399950001, 73.192921734, 73.1921099950001, 73.1908699950001,
73.190489997, 73.190010005, 73.1894299960001, 73.188529997, 73.188310711, 73.1882963400001, 73.1873200000001, 73.1857999950001, 73.1848700040001, 73.184217297, 73.1840499990001, 73.183499994, 73.182959995, 73.1821535720001, 73.181089995, 73.1799800040001, 73.179449998, 73.177341878, 73.176559995, 73.176019995, 73.175658694, 73.175079999, 73.174509286, 73.173815442, 73.172430003, 73.1710300020001, 73.1702499950001, 73.170020004, 73.1697641490001, 73.16963, 73.168960003, 73.1684821100001, 73.167639997,
73.1665099950001, 73.165907782, 73.165125387, 73.1647200020001, 73.1640099960001, 73.1635500030001, 73.1634699960001, 73.163899997, 73.1648353090001, 73.165650005, 73.165890001, 73.165340288, 73.164091156, 73.163189243, 73.1628800060001, 73.162339994, 73.161676287, 73.160820001, 73.159650002, 73.158599995, 73.1578599970001, 73.157269995, 73.156419998, 73.1562579120001, 73.155910002, 73.1556378880001, 73.1553599970001, 73.153919999, 73.151711027, 73.1504100010001, 73.1495899970001, 73.1486600060001,
73.1479978610001, 73.1473300050001, 73.146682785, 73.146049996, 73.144879997, 73.143630003, 73.1431777500001, 73.1418399980001, 73.140439996, 73.1393460180001, 73.138720004, 73.137806229, 73.1374399950001, 73.136620002, 73.1358800040001, 73.1348152840001, 73.1335800030001, 73.133110005, 73.132329998, 73.1313599970001, 73.1306500030001, 73.130069994, 73.1298984590001, 73.1295999960001, 73.128899995, 73.127380002, 73.1265874040001, 73.1245700050001, 73.1223058570001, 73.1209100010001, 73.1197400010001,
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34.236570005, 34.2367999960001, 34.2367999960001, 34.236120005, 34.235370002, 34.2356700060001, 34.2367172410001, 34.238080005, 34.238680001, 34.2387003110001, 34.2387500020001, 34.239729996, 34.2406014900001, 34.2413799980001, 34.242119997, 34.242729997, 34.2425800010001, 34.242356348, 34.2421300020001, 34.241848245, 34.2413799980001, 34.2409299990001, 34.2413307250001, 34.2416800020001, 34.242659996, 34.2431143580001, 34.244160003, 34.244667705, 34.2452099980001, 34.2463132010001, 34.2471842150001,
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34.251095024, 34.251670006, 34.2535525650001, 34.254300002, 34.258083608, 34.2586599960001, 34.2612200030001, 34.2630200020001, 34.263573992, 34.26482, 34.266020003, 34.267475667, 34.268349996, 34.2708299970001, 34.272300805, 34.272889288, 34.2738200050001, 34.2751500050001, 34.276237855, 34.2771000000001, 34.278800005, 34.2799080620001, 34.281219998, 34.282259616, 34.2836499960001, 34.2841823300001, 34.285110005, 34.285229997, 34.284990001, 34.2844940690001, 34.284020001, 34.2832899960001, 34.2832899960001,
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20151.3e+06 1.69 731.099.8419.425  19.71.32 3.296.281.66 101172130
## 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")