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pacman::p_load(sf, tidyverse, funModeling, tmap)In Class Exercise 2
Install Packages
Tip
Packages are important. Try to use SF, not SP
Loading the file (Convert to the projection)
geoNGA <- st_read("data/geospatial/nigeria_nga_l2/", layer="geoBoundaries-NGA-ADM2") %>%
st_transform(crs=26392)Reading layer `geoBoundaries-NGA-ADM2' from data source
`D:\Documents\IS415-GAA-WY\lessons\lesson2\data\geospatial\nigeria_nga_l2'
using driver `ESRI Shapefile'
Simple feature collection with 774 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84The below file is the same as the above.
Except this has more information
NGA <- st_read("data/geospatial/nigeria_nga_l2/", layer="nga_admbnda_adm2_osgof_20190417") %>%
st_transform(crs=26392)Reading layer `nga_admbnda_adm2_osgof_20190417' from data source
`D:\Documents\IS415-GAA-WY\lessons\lesson2\data\geospatial\nigeria_nga_l2'
using driver `ESRI Shapefile'
Simple feature collection with 774 features and 16 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84We pick the second one as it gives the state and LGA (local government area) boundary
Water point data for Nigeria
wp_nga <- read_csv("data/aspatial/water_point_data_exchange/wpdx.csv") %>% filter(`#clean_country_name`=="Nigeria")Convert water point data into sf point features
Note
You can take the latitude degree and longitude degress
wp_nga$Geometry <- st_as_sfc(wp_nga$`New Georeferenced Column`)
wp_nga# A tibble: 97,478 × 75
row_id `#source` #lat_…¹ #lon_…² #repo…³ #stat…⁴ #wate…⁵ #wate…⁶ #wate…⁷
<dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 158721 Federal Minis… 5.07 6.62 02/19/… Yes Boreho… Well Mechan…
2 158892 Federal Minis… 5.09 7.09 02/06/… Yes Boreho… Well Hand P…
3 323117 Federal Minis… 5.91 8.77 08/31/… Yes Boreho… Well Hand P…
4 300176 Federal Minis… 5.23 7.32 05/17/… Yes Boreho… Well Mechan…
5 324346 Federal Minis… 6.88 3.36 08/17/… Yes Boreho… Well Mechan…
6 297273 Federal Minis… 6.59 3.29 05/26/… Yes Boreho… Well Mechan…
7 296853 Federal Minis… 6.60 3.26 06/02/… Yes Boreho… Well Mechan…
8 323866 Federal Minis… 6.20 6.73 09/18/… Yes Boreho… Well Mechan…
9 297044 Federal Minis… 6.61 3.30 05/26/… Yes Boreho… Well Mechan…
10 324321 Federal Minis… 6.96 3.60 08/16/… Yes Boreho… Well Mechan…
# … with 97,468 more rows, 66 more variables: `#water_tech_category` <chr>,
# `#facility_type` <chr>, `#clean_country_name` <chr>, `#clean_adm1` <chr>,
# `#clean_adm2` <chr>, `#clean_adm3` <chr>, `#clean_adm4` <chr>,
# `#install_year` <dbl>, `#installer` <chr>, `#rehab_year` <lgl>,
# `#rehabilitator` <lgl>, `#management_clean` <chr>, `#status_clean` <chr>,
# `#pay` <chr>, `#fecal_coliform_presence` <chr>,
# `#fecal_coliform_value` <dbl>, `#subjective_quality` <chr>, …Convert tibble dataframe into SF object
Note
Need to convert Aspatial into Geospatial, but they do not have projection.
So need to tell R what is the projection in Aspatial (if it is wgs84, reconvert to that) -> then transform from 26392
wp_sf <- st_sf(wp_nga, crs=4326)
wp_sfSimple feature collection with 97478 features and 74 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 2.707441 ymin: 4.301812 xmax: 14.21828 ymax: 13.86568
Geodetic CRS: WGS 84
# A tibble: 97,478 × 75
row_id `#source` #lat_…¹ #lon_…² #repo…³ #stat…⁴ #wate…⁵ #wate…⁶ #wate…⁷
* <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 158721 Federal Minis… 5.07 6.62 02/19/… Yes Boreho… Well Mechan…
2 158892 Federal Minis… 5.09 7.09 02/06/… Yes Boreho… Well Hand P…
3 323117 Federal Minis… 5.91 8.77 08/31/… Yes Boreho… Well Hand P…
4 300176 Federal Minis… 5.23 7.32 05/17/… Yes Boreho… Well Mechan…
5 324346 Federal Minis… 6.88 3.36 08/17/… Yes Boreho… Well Mechan…
6 297273 Federal Minis… 6.59 3.29 05/26/… Yes Boreho… Well Mechan…
7 296853 Federal Minis… 6.60 3.26 06/02/… Yes Boreho… Well Mechan…
8 323866 Federal Minis… 6.20 6.73 09/18/… Yes Boreho… Well Mechan…
9 297044 Federal Minis… 6.61 3.30 05/26/… Yes Boreho… Well Mechan…
10 324321 Federal Minis… 6.96 3.60 08/16/… Yes Boreho… Well Mechan…
# … with 97,468 more rows, 66 more variables: `#water_tech_category` <chr>,
# `#facility_type` <chr>, `#clean_country_name` <chr>, `#clean_adm1` <chr>,
# `#clean_adm2` <chr>, `#clean_adm3` <chr>, `#clean_adm4` <chr>,
# `#install_year` <dbl>, `#installer` <chr>, `#rehab_year` <lgl>,
# `#rehabilitator` <lgl>, `#management_clean` <chr>, `#status_clean` <chr>,
# `#pay` <chr>, `#fecal_coliform_presence` <chr>,
# `#fecal_coliform_value` <dbl>, `#subjective_quality` <chr>, …Convert to the Nigeria projection system
wp_sf <- wp_sf %>% st_transform(crs=26392)
wp_sfSimple feature collection with 97478 features and 74 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 28907.91 ymin: 33736.93 xmax: 1293293 ymax: 1092883
Projected CRS: Minna / Nigeria Mid Belt
# A tibble: 97,478 × 75
row_id `#source` #lat_…¹ #lon_…² #repo…³ #stat…⁴ #wate…⁵ #wate…⁶ #wate…⁷
* <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 158721 Federal Minis… 5.07 6.62 02/19/… Yes Boreho… Well Mechan…
2 158892 Federal Minis… 5.09 7.09 02/06/… Yes Boreho… Well Hand P…
3 323117 Federal Minis… 5.91 8.77 08/31/… Yes Boreho… Well Hand P…
4 300176 Federal Minis… 5.23 7.32 05/17/… Yes Boreho… Well Mechan…
5 324346 Federal Minis… 6.88 3.36 08/17/… Yes Boreho… Well Mechan…
6 297273 Federal Minis… 6.59 3.29 05/26/… Yes Boreho… Well Mechan…
7 296853 Federal Minis… 6.60 3.26 06/02/… Yes Boreho… Well Mechan…
8 323866 Federal Minis… 6.20 6.73 09/18/… Yes Boreho… Well Mechan…
9 297044 Federal Minis… 6.61 3.30 05/26/… Yes Boreho… Well Mechan…
10 324321 Federal Minis… 6.96 3.60 08/16/… Yes Boreho… Well Mechan…
# … with 97,468 more rows, 66 more variables: `#water_tech_category` <chr>,
# `#facility_type` <chr>, `#clean_country_name` <chr>, `#clean_adm1` <chr>,
# `#clean_adm2` <chr>, `#clean_adm3` <chr>, `#clean_adm4` <chr>,
# `#install_year` <dbl>, `#installer` <chr>, `#rehab_year` <lgl>,
# `#rehabilitator` <lgl>, `#management_clean` <chr>, `#status_clean` <chr>,
# `#pay` <chr>, `#fecal_coliform_presence` <chr>,
# `#fecal_coliform_value` <dbl>, `#subjective_quality` <chr>, …Excluding Redundant fields
The code below takes only the relevant column (column 3,4,8,9). As we only need them
NGA <- NGA %>%
select(c(3:4, 8:9))Checking for duplicate name
Check for the quality of the data (data duplication, spatial data we want to check for missing value)
In this case, we have a lot of duplicated fields
NGA$ADM2_EN[duplicated(NGA$ADM2_EN)==TRUE][1] "Bassa" "Ifelodun" "Irepodun" "Nasarawa" "Obi" "Surulere"The reason why is because there are 6 LGAs with the same name, but are in different states.
Append the state to ensure no duplicatation
NGA$ADM2_EN[94] <- "Bassa, Kogi"
NGA$ADM2_EN[95] <- "Bassa, Plateau"
NGA$ADM2_EN[304] <- "Ifelodun, Kwara"
NGA$ADM2_EN[305] <- "Ifelodun, Osun"
NGA$ADM2_EN[355] <- "Irepodun, Kwara"
NGA$ADM2_EN[356] <- "Irepodun, Osun"
NGA$ADM2_EN[519] <- "Nasawara, Kano"
NGA$ADM2_EN[520] <- "Nasawara, Nasawara"
NGA$ADM2_EN[546] <- "Obi, Benue"
NGA$ADM2_EN[547] <- "Obi, Nasawara"
NGA$ADM2_EN[693] <- "Surulere, Lagos"
NGA$ADM2_EN[694] <- "Surulere, Oyo"freq(data = wp_sf, input = "#status_clean")
#status_clean frequency percentage cumulative_perc
1 Functional 47228 48.45 48.45
2 Non-Functional 30638 31.43 79.88
3 <NA> 10154 10.42 90.30
4 Functional, needs repair 4792 4.92 95.22
5 Non-Functional, dry 2473 2.54 97.76
6 Functional, not in use 1775 1.82 99.58
7 Abandoned/Decommissioned 321 0.33 99.91
8 Functional but not in use 86 0.09 100.00
9 Non-Functional due to dry season 7 0.01 100.01
10 Functional but needs repair 4 0.00 100.00Mutate allows us to do data processing, replacing #status_clean to remove the ‘#’
We replace all na fields with ‘unknown’
wp_sf_nga <- wp_sf %>%
rename(status_clean = "#status_clean") %>%
select(status_clean) %>%
mutate(status_clean = replace_na(status_clean, "unknown"))SF is a simple feature object, it will always have a geometric field, even though we only select one column which is ‘status_clean’
Group them by:
Functional
wp_functional <- wp_sf_nga %>%
filter(status_clean %in% c("Functional",
"Functional but not in use",
"Functional but needs repair"))Non-functional
wp_nonfunctional <- wp_sf_nga %>%
filter(status_clean %in% c("Abandoned/Decommissioned",
"Abandoned",
"Non-Functional due to dry season",
"Non-Functional",
"Non functional due to dry season"))Unknown
wp_unknown <- wp_sf_nga %>%
filter(status_clean %in% c("unknown"))Append the water point count into the new dataframe
Note
The code below tells us how many water point intersects each LGA. (functional, nonfunctional, unknown) then append that information into the original NGA dataframe by creating a new dataframe NGA_wp
NGA_wp <- NGA %>%
mutate(`total_wp` = lengths(
st_intersects(NGA, wp_sf_nga))) %>%
mutate(`wp_functional` = lengths(
st_intersects(NGA, wp_functional))) %>%
mutate(`wp_nonfunctional` = lengths(
st_intersects(NGA, wp_nonfunctional))) %>%
mutate(`wp_unknown` = lengths(
st_intersects(NGA, wp_unknown)))Plot the distribution of water point
ggplot(data = NGA_wp, aes(x = total_wp)) +
geom_histogram(bins=20, color="black", fill="light blue") +
geom_vline(aes(xintercept=mean(total_wp,na.rm=T)), color="red",linetype="dashed", size=0.8) +
ggtitle("Distribution of total water points by LGA") +
xlab("No. of water points") +
ylab("No. of\nLGAs") +
theme(axis.title.y=element_text(angle=0))
tmap_mode('view') +
tm_shape(wp_sf_nga) +
tm_dots(col="status_clean",
size=0.01,
border.lwd=0.5) +
tm_view(set.zoom.limits = c(9,16))