# call requisite libraries
library(rgdal)
## Loading required package: sp
## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
##
## rgdal: version: 1.5-28, (SVN revision 1158)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/User/Documents/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/User/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-6
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/User/Documents/R/win-library/4.1/rgdal/proj
library(rgeos)
## rgeos version: 0.5-9, (SVN revision 684)
## GEOS runtime version: 3.9.1-CAPI-1.14.2
## Please note that rgeos will be retired by the end of 2023,
## plan transition to sf functions using GEOS at your earliest convenience.
## GEOS using OverlayNG
## Linking to sp version: 1.4-6
## Polygon checking: TRUE
library(sf)
## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE
library(sp)
library(tmap)
library(tmaptools)
# create a new shapefile
knbs_select <- readxl::read_xlsx("D:/Documents/climate and energy advisory/2022/August/Alternativie fuel resource assessment/inception report/data_and_excel sheets/county_households_new.xlsx"
)
print(length(knbs_select$name))
## [1] 15
# join the selected counties with shapefile
counties_shape <- readOGR(
dsn = "D:/DUKTO/ken_adm_iebc_20191031_shp/ken_admbnda_adm1_iebc_20191031.shp"
)
## OGR data source with driver: ESRI Shapefile
## Source: "D:\DUKTO\ken_adm_iebc_20191031_shp\ken_admbnda_adm1_iebc_20191031.shp", layer: "ken_admbnda_adm1_iebc_20191031"
## with 47 features
## It has 12 fields
households_select <- merge(counties_shape, knbs_select, by.x = "ADM1_EN", by.y = "name", all.x = F)
households_select@data
## ADM1_EN Shape_Leng Shape_Area ADM1_PCODE ADM1_REF ADM1ALT1EN
## 1 Elgeyo-Marakwet 3.888933 0.2444318 KE028 <NA> <NA>
## 2 Homa Bay 3.373781 0.3844443 KE043 <NA> <NA>
## 3 Isiolo 10.219754 2.0638503 KE011 <NA> <NA>
## 4 Kajiado 7.412835 1.7791830 KE034 <NA> <NA>
## 5 Kakamega 4.125304 0.2443814 KE037 <NA> <NA>
## 6 Kericho 3.541334 0.2041787 KE035 <NA> <NA>
## 7 Kilifi 5.744543 1.0251272 KE003 <NA> <NA>
## 8 Kirinyaga 1.947362 0.1198584 KE020 <NA> <NA>
## 9 Kitui 9.074169 2.4753375 KE015 <NA> <NA>
## 10 Kwale 5.426097 0.6732554 KE002 <NA> <NA>
## 11 Meru 4.400599 0.5680862 KE012 <NA> <NA>
## 12 Nairobi 1.658061 0.0574956 KE047 <NA> <NA>
## 13 Nyandarua 3.571838 0.2653980 KE018 <NA> <NA>
## 14 Taita Taveta 5.677845 1.3941980 KE006 <NA> <NA>
## 15 Uasin Gishu 3.926051 0.2759761 KE027 <NA> <NA>
## ADM1ALT2EN ADM0_EN ADM0_PCODE date validOn validTo Population
## 1 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 453403
## 2 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 1125823
## 3 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 267997
## 4 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 1107296
## 5 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 1861332
## 6 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 896863
## 7 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 1440958
## 8 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 605630
## 9 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 1130134
## 10 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 858748
## 11 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 1535635
## 12 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 4337080
## 13 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 636002
## 14 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 335747
## 15 <NA> Kenya KE 2017/11/03 2019/10/31 <NA> 1152671
## No.of Households Avg Household size
## 1 99861 45
## 2 262036 43
## 3 58072 46
## 4 316179 35
## 5 433207 43
## 6 206036 44
## 7 298472 48
## 8 204188 30
## 9 262942 43
## 10 173176 50
## 11 426360 36
## 12 1506888 29
## 13 179686 35
## 14 96429 35
## 15 304943 38
# view the knbs households 2019 dataset
knbs_households <- read.csv("D:/Documents/climate and energy advisory/2022/August/Alternativie fuel resource assessment/inception report/data_and_excel sheets/county_households.csv",
header = T)
# join the counties shapefile with the knbs dataset through the county names
households_shape <- merge(
counties_shape, knbs_households, by.x = "ADM1_EN", by.y = "name", all.x = T)
# draw a the selected counties overlying the previous tmap
tm_shape(
households_shape,
bbox = households_select
) + tm_polygons(
col = "No.of.Households",
palette = "-inferno",
style = "quantile",
n =7,
legend.show = F,
alpha = 0.1
) +
tm_shape(households_select) +
tm_polygons(
col = "No.of Households",
palette = "-inferno",
style = "cat",
n = 15,
title = "No. of households for selected counties"
) +
tm_text(
text = "ADM1_EN",
size = "AREA",
col = "firebrick4"
) +
tm_grid(
projection = "+init=epsg:4326",
lines = F
) + tm_legend(
legend.outside = T,
legend.outside.position = "right",
bg.color = "grey85"
) +
tm_layout(
main.title = "Distribution of households for target counties",
bg.color = "gray90",
frame = T
) +
tm_scale_bar() +
tm_compass(
position = c("right", "top")
)
## Warning in CPL_crs_from_input(x): GDAL Message 1: +init=epsg:XXXX syntax is
## deprecated. It might return a CRS with a non-EPSG compliant axis order.
# install the grid package
# install.packages("grid")
library(grid)
# the inset map
zone <- bb_poly(households_select, projection = 4326)
inset <- tm_shape(households_shape) +
tm_polygons(
col = "No.of.Households",
palette = "-inferno",
style = "quantile",
n =7,
legend.show = F
) +
tm_shape(zone) +
tm_borders(
lwd = 2,
col = "red"
)
# the overview map
overview <- tm_shape(
households_shape,
bbox = households_select
) + tm_polygons(
col = "No.of.Households",
palette = "-inferno",
style = "quantile",
n =7,
legend.show = F,
alpha = 0.1
) +
tm_shape(households_select) +
tm_polygons(
col = "No.of Households",
palette = "-inferno",
style = "cat",
n = 15,
title = "No. of households for selected counties"
) +
tm_text(
text = "ADM1_EN",
size = "AREA",
col = "firebrick4"
) +
tm_grid(
projection = "+init=epsg:4326",
lines = F
) +
tm_legend(
legend.outside = T,
legend.outside.position = "right",
bg.color = "grey85"
) +
tm_layout(
main.title = "Distribution of households for target counties",
bg.color = "gray90",
frame = T
) +
tm_scale_bar() +
tm_compass(
position = c("right", "top")
)
# draw tmap with inset map
library(tmaptools)
tmap_save(
tm = overview,
insets_tm = inset,
insets_vp = viewport(x = 0.2,
y = 0.3,
width = unit(1, "inches"),
height = unit(1, "inches")
),
filename = "D:/Documents/climate and energy advisory/2022/August/Alternativie fuel resource assessment/population_maps/final_three.jpg",
dpi = 400
)
## Map saved to D:\Documents\climate and energy advisory\2022\August\Alternativie fuel resource assessment\population_maps\final_three.jpg
## Resolution: 2704.366 by 2899.016 pixels
## Size: 6.760914 by 7.24754 inches (400 dpi)