Take-Home Exercise 01

Exploratory Spatial Data Analysis (ESDA)

Getting packages

pacman::p_load(sf, tidyverse, funModeling, ggplot2)

Importing data - Geospatial

geoNGA <- st_read("data/geoBoundaries/",
                  layer = "geoBoundaries-NGA-ADM2") %>%
  st_transform(crs = 26392)

Reading layer `geoBoundaries-NGA-ADM2' from data source 
  `C:\xinyeehow\IS415-GAA\Take-Home_Ex\Take-Home_Ex01\data\geoBoundaries' 
  using driver `ESRI Shapefile'
Simple feature collection with 774 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84

NGA <- st_read("data/",
               layer = "nga_admbnda_adm2_osgof_20190417") %>%
  st_transform(crs = 26392)

Reading layer `nga_admbnda_adm2_osgof_20190417' from data source 
  `C:\xinyeehow\IS415-GAA\Take-Home_Ex\Take-Home_Ex01\data' 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 84

Extract Osun state from Nigeria

osun <- st_read(dsn = "data/", 
               layer = "nga_admbnda_adm2_osgof_20190417") %>%
      filter(`ADM1_EN` == "Osun") %>%
      st_transform(crs = 26392)

Reading layer `nga_admbnda_adm2_osgof_20190417' from data source 
  `C:\xinyeehow\IS415-GAA\Take-Home_Ex\Take-Home_Ex01\data' 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 84

Examining Osun layer

osun

Simple feature collection with 30 features and 16 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 176503.2 ymin: 331434.7 xmax: 291043.8 ymax: 454520.1
Projected CRS: Minna / Nigeria Mid Belt
First 10 features:
   Shape_Leng  Shape_Area        ADM2_EN ADM2_PCODE       ADM2_REF ADM2ALT1EN
1   1.7951405 0.062436080       Aiyedade   NG030001       Aiyedade       <NA>
2   0.7101503 0.024818478       Aiyedire   NG030002       Aiyedire       <NA>
3   0.9199564 0.038002894 Atakumosa East   NG030003 Atakumosa East       <NA>
4   0.8502782 0.030445804 Atakumosa West   NG030004 Atakumosa West       <NA>
5   0.5212768 0.012213340     Boluwaduro   NG030005     Boluwaduro       <NA>
6   0.6088930 0.011827501         Boripe   NG030006         Boripe       <NA>
7   0.4714403 0.008343638      Ede North   NG030007      Ede North       <NA>
8   0.5660235 0.017623677      Ede South   NG030008      Ede South       <NA>
9   0.8273123 0.022026327       Egbedore   NG030009       Egbedore       <NA>
10  1.1304849 0.029791275         Ejigbo   NG030010         Ejigbo       <NA>
   ADM2ALT2EN ADM1_EN ADM1_PCODE ADM0_EN ADM0_PCODE       date    validOn
1        <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
2        <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
3        <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
4        <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
5        <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
6        <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
7        <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
8        <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
9        <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
10       <NA>    Osun      NG030 Nigeria         NG 2016-11-29 2019-04-17
   validTo        SD_EN SD_PCODE                       geometry
1     <NA>    Osun West  NG03003 MULTIPOLYGON (((213526.6 34...
2     <NA>    Osun West  NG03003 MULTIPOLYGON (((212542.6 40...
3     <NA>    Osun East  NG03002 MULTIPOLYGON (((265746.8 37...
4     <NA>    Osun East  NG03002 MULTIPOLYGON (((248871.4 40...
5     <NA> Osun Central  NG03001 MULTIPOLYGON (((266092.2 43...
6     <NA> Osun Central  NG03001 MULTIPOLYGON (((255072.5 43...
7     <NA>    Osun West  NG03003 MULTIPOLYGON (((236386.9 41...
8     <NA>    Osun West  NG03003 MULTIPOLYGON (((236386.9 41...
9     <NA>    Osun West  NG03003 MULTIPOLYGON (((220756 4317...
10    <NA>    Osun West  NG03003 MULTIPOLYGON (((214422.1 42...

Importing data - Aspatial

wp_nga <- read_csv("data/WPdx.csv") %>%
  filter(`#clean_country_name` == "Nigeria", `#clean_adm1` == "Osun")

Examining the filtered waterpoint layer

glimpse(wp_nga)

Rows: 5,557
Columns: 70
$ row_id                        <dbl> 429123, 70566, 70578, 66401, 422190, 422…
$ `#source`                     <chr> "GRID3", "Federal Ministry of Water Reso…
$ `#lat_deg`                    <dbl> 8.020000, 7.317741, 7.759448, 8.031187, …
$ `#lon_deg`                    <dbl> 5.060000, 4.785507, 4.563998, 4.637400, …
$ `#report_date`                <chr> "08/29/2018 12:00:00 AM", "05/11/2015 12…
$ `#status_id`                  <chr> "Unknown", "No", "No", "No", "Unknown", …
$ `#water_source_clean`         <chr> NA, "Protected Shallow Well", "Borehole"…
$ `#water_source_category`      <chr> NA, "Well", "Well", "Well", NA, NA, "Wel…
$ `#water_tech_clean`           <chr> "Tapstand", "Mechanized Pump", "Mechaniz…
$ `#water_tech_category`        <chr> "Tapstand", "Mechanized Pump", "Mechaniz…
$ `#facility_type`              <chr> "Improved", "Improved", "Improved", "Imp…
$ `#clean_country_name`         <chr> "Nigeria", "Nigeria", "Nigeria", "Nigeri…
$ `#clean_adm1`                 <chr> "Osun", "Osun", "Osun", "Osun", "Osun", …
$ `#clean_adm2`                 <chr> "Ifedayo", "Atakumosa East", "Osogbo", "…
$ `#clean_adm3`                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#clean_adm4`                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#install_year`               <dbl> NA, NA, NA, 2004, NA, NA, 2006, 2014, 20…
$ `#installer`                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#rehab_year`                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#rehabilitator`              <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#management_clean`           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Com…
$ `#status_clean`               <chr> NA, "Abandoned/Decommissioned", "Abandon…
$ `#pay`                        <chr> NA, "No", "No", "No", NA, NA, "No", "No"…
$ `#fecal_coliform_presence`    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#fecal_coliform_value`       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#subjective_quality`         <chr> NA, "Acceptable quality", "Acceptable qu…
$ `#activity_id`                <chr> "6054e946-2573-45bf-ab7c-0ddaa68a61b4", …
$ `#scheme_id`                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#wpdx_id`                    <chr> "6FW723C6+222", "6FV68Q9P+36R", "6FV6QH5…
$ `#notes`                      <chr> "Rt Hon Kola Oluwawole Water Tap", "Temi…
$ `#orig_lnk`                   <chr> "https://nigeria.africageoportal.com/dat…
$ `#photo_lnk`                  <chr> NA, "https://akvoflow-55.s3.amazonaws.co…
$ `#country_id`                 <chr> "NG", "NG", "NG", "NG", "NG", "NG", "NG"…
$ `#data_lnk`                   <chr> "https://catalog.waterpointdata.org/data…
$ `#distance_to_primary_road`   <dbl> 4474.22234, 10130.42742, 167.82235, 4133…
$ `#distance_to_secondary_road` <dbl> 1883.98780, 17466.35720, 838.91849, 1162…
$ `#distance_to_tertiary_road`  <dbl> 1885.874322, 2376.832183, 1181.107236, 9…
$ `#distance_to_city`           <dbl> 40100.268, 31549.222, 2449.293, 16704.19…
$ `#distance_to_town`           <dbl> 8120.871, 24652.907, 9463.295, 5176.899,…
$ water_point_history           <chr> "{\"2018-08-29\": {\"source\": \"GRID3\"…
$ rehab_priority                <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ water_point_population        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, 508, …
$ local_population_1km          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 70, 647,…
$ crucialness_score             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.78…
$ pressure_score                <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.69…
$ usage_capacity                <dbl> 250, 1000, 1000, 1000, 250, 250, 1000, 3…
$ is_urban                      <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE,…
$ days_since_report             <dbl> 1483, 2689, 2689, 2700, 1483, 1483, 2688…
$ staleness_score               <dbl> 62.65911, 42.84384, 42.84384, 42.69554, …
$ latest_record                 <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
$ location_id                   <dbl> 358783, 239555, 239556, 230405, 358062, …
$ cluster_size                  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ `#clean_country_id`           <chr> "NGA", "NGA", "NGA", "NGA", "NGA", "NGA"…
$ `#country_name`               <chr> "Nigeria", "Nigeria", "Nigeria", "Nigeri…
$ `#water_source`               <chr> "Tap", "Improved Protected dug well", "I…
$ `#water_tech`                 <chr> NA, "Motorised", "Motorised", "Motorised…
$ `#status`                     <chr> NA, "Non-functional Abandoned", "Non-fun…
$ `#adm2`                       <chr> NA, "Atakumosa East", "Osogbo", "Odo-Oti…
$ `#adm3`                       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#management`                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Com…
$ `#adm1`                       <chr> NA, "Osun", "Osun", "Osun", NA, NA, "Osu…
$ `New Georeferenced Column`    <chr> "POINT (5.06 8.02)", "POINT (4.7855068 7…
$ lat_deg_original              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ lat_lon_deg                   <chr> "(8.02°, 5.06°)", "(7.3177411°, 4.785506…
$ lon_deg_original              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ public_data_source            <chr> "https://catalog.waterpointdata.org/data…
$ converted                     <chr> NA, "#status_id, #water_source, #pay, #s…
$ count                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ created_timestamp             <chr> "12/06/2021 09:12:57 PM", "06/30/2020 12…
$ updated_timestamp             <chr> "12/06/2021 09:12:57 PM", "06/30/2020 12…

Converting Aspatial to Geospatial data

wp_nga$Geometry = st_as_sfc(wp_nga$`New Georeferenced Column`)

#wp_nga = st_as_sf(wp_nga, coords = c("lat_deg", "lon_deg"),crs=4326)
#wp_nga

wp_sf <- st_sf(wp_nga, crs=4326)
st_geometry(wp_sf)

Geometry set for 5557 features 
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 4.032004 ymin: 7.060309 xmax: 5.06 ymax: 8.061898
Geodetic CRS:  WGS 84
First 5 geometries:

Extracting waterpoints in Osun area

Transforming project

wp_sf <- wp_sf %>%
  st_transform(crs = 26392)

Data Cleaning

Checking for duplicate name

NGA$ADM2_EN[duplicated(NGA$ADM2_EN)==TRUE]

[1] "Bassa"    "Ifelodun" "Irepodun" "Nasarawa" "Obi"      "Surulere"

Correcting errors

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$ADM2_EN[519] <- "Nasarawa, Kano"
NGA$ADM2_EN[520] <- "Nasarawa, Nasarawa"
NGA$ADM2_EN[546] <- "Obi, Benue"
NGA$ADM2_EN[547] <- "Obi, Nasarawa"
NGA$ADM2_EN[693] <- "Surulere, Lagos"
NGA$ADM2_EN[694] <- "Surulere, Oyo"

NGA$ADM2_EN[duplicated(NGA$ADM2_EN)==TRUE]

character(0)

Data Wrangling (water point data)

Bar graph of water status according to cleanliness of water

ggplot(wp_sf) + geom_bar(aes(y = fct_infreq(`#status_clean`), fill=`#status_clean`, stat="identity"))+  theme(legend.position = "right")

Categorising functional and non-functional water points

Functional

wp_functional <- wp_sf %>%
  filter(`#status_clean` %in%
           c("Functional",
             "Functional but not in use",
             "Functional but needs repair"))

ggplot(wp_functional) + geom_bar(aes(y = fct_infreq(`#status_clean`), fill=`#status_clean`, stat="identity"))+  theme(legend.position = "right")

Non-functional

wp_nonfunctional <- wp_sf %>%
  filter(`#status_clean` %in%
           c("Abandoned/Decommissioned",
             "Abandoned",
             "Non-Functional due to dry season",
             "Non-Functional",
             "Non functional due to dry season"))

ggplot(wp_nonfunctional) + geom_bar(aes(y = fct_infreq(`#status_clean`), fill=`#status_clean`, stat="identity"))+  theme(legend.position = "right")

Saving data

write_rds(wp_sf, "data/rds/wp_sf.rds")

Kernel density maps of functional and non-functional water points using appropriate tmap functions

Loading packages

pacman::p_load(maptools, sf, raster, spatstat, tmap)

Converting sf data frames to sp’s Spatial* class

wp_functional_sf <- as_Spatial(wp_functional)
wp_nonfunctional_sf <- as_Spatial(wp_nonfunctional)
NGA_sf <- as_Spatial(NGA)
osun_sf <- as_Spatial(osun)

wp_functional_sf

class       : SpatialPointsDataFrame 
features    : 2630 
extent      : 177285.9, 290751, 343128.1, 450859.7  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 
variables   : 70
names       : row_id,                                     X.source, X.lat_deg, X.lon_deg,          X.report_date, X.status_id, X.water_source_clean, X.water_source_category,      X.water_tech_clean, X.water_tech_category, X.facility_type, X.clean_country_name, X.clean_adm1, X.clean_adm2, X.clean_adm3, ... 
min values  :  36914, Federal Ministry of Water Resources, Nigeria, 7.0890301, 4.0320038, 01/01/2010 12:00:00 AM,     Unknown,             Borehole,                  Spring,               Hand Pump,             Hand Pump,        Improved,              Nigeria,         Osun,     Aiyedade,           NA, ... 
max values  : 471319,                                        GRID3, 8.0618983, 5.0550034, 09/16/2015 12:00:00 AM,         Yes,     Protected Spring,                    Well, Mechanized Pump - Solar,       Mechanized Pump,        Improved,              Nigeria,         Osun,       Osogbo,           NA, ... 

wp_nonfunctional_sf

class       : SpatialPointsDataFrame 
features    : 2179 
extent      : 180539, 290616, 340054.1, 450780.1  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 
variables   : 70
names       : row_id,                                     X.source, X.lat_deg, X.lon_deg,          X.report_date, X.status_id, X.water_source_clean, X.water_source_category,      X.water_tech_clean, X.water_tech_category, X.facility_type, X.clean_country_name, X.clean_adm1, X.clean_adm2, X.clean_adm3, ... 
min values  :  34829, Federal Ministry of Water Resources, Nigeria,  7.060309, 4.0618927, 01/10/2015 12:00:00 AM,          No,             Borehole,                  Spring,               Hand Pump,             Hand Pump,        Improved,              Nigeria,         Osun,     Aiyedade,           NA, ... 
max values  : 421239,                                        GRID3, 8.0611733, 5.0537867, 09/16/2015 12:00:00 AM,         Yes,     Protected Spring,                    Well, Mechanized Pump - Solar,       Mechanized Pump,        Improved,              Nigeria,         Osun,       Osogbo,           NA, ... 

NGA_sf

class       : SpatialPolygonsDataFrame 
features    : 774 
extent      : 26662.71, 1344157, 30523.38, 1096029  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 
variables   : 16
names       :    Shape_Leng,        Shape_Area,   ADM2_EN, ADM2_PCODE,  ADM2_REF, ADM2ALT1EN, ADM2ALT2EN, ADM1_EN, ADM1_PCODE, ADM0_EN, ADM0_PCODE,  date, validOn, validTo,        SD_EN, ... 
min values  :    0.10410787, 0.000342186810984, Aba North,   NG001001, Aba North,         NA,         NA,    Abia,      NG001, Nigeria,         NG, 17134,   18003,      NA, Abia Central, ... 
max values  : 6.76815542887,    0.929069095283,      Zuru,   NG037014,      Zuru,         NA,         NA, Zamfara,      NG037, Nigeria,         NG, 17134,   18003,      NA, Zamfara West, ... 

osun_sf

class       : SpatialPolygonsDataFrame 
features    : 30 
extent      : 176503.2, 291043.8, 331434.7, 454520.1  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 
variables   : 16
names       :    Shape_Leng,       Shape_Area,  ADM2_EN, ADM2_PCODE, ADM2_REF, ADM2ALT1EN, ADM2ALT2EN, ADM1_EN, ADM1_PCODE, ADM0_EN, ADM0_PCODE,  date, validOn, validTo,        SD_EN, ... 
min values  : 0.26445678806, 0.00248649736648, Aiyedade,   NG030001, Aiyedade,         NA,         NA,    Osun,      NG030, Nigeria,         NG, 17134,   18003,      NA, Osun Central, ... 
max values  :  1.8470166597,  0.0737271661922,   Osogbo,   NG030030,   Osogbo,         NA,         NA,    Osun,      NG030, Nigeria,         NG, 17134,   18003,      NA,    Osun West, ... 

Converting spatial class into generic sp format

wp_functional_sp <- as(wp_functional_sf, "SpatialPoints")
wp_nonfunctional_sp <- as(wp_nonfunctional_sf, "SpatialPoints")
NGA_sp <- as(NGA_sf, "SpatialPolygons")
osun_sp <- as(osun_sf, "SpatialPolygons")

wp_functional_sp

class       : SpatialPoints 
features    : 2630 
extent      : 177285.9, 290751, 343128.1, 450859.7  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 

wp_nonfunctional_sp

class       : SpatialPoints 
features    : 2179 
extent      : 180539, 290616, 340054.1, 450780.1  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 

NGA_sp

class       : SpatialPolygons 
features    : 774 
extent      : 26662.71, 1344157, 30523.38, 1096029  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 

osun_sp

class       : SpatialPolygons 
features    : 30 
extent      : 176503.2, 291043.8, 331434.7, 454520.1  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 

Converting generic sp format into spatstat’s ppp format

wp_functional_ppp <- as(wp_functional_sp, "ppp")
wp_functional_ppp

Planar point pattern: 2630 points
window: rectangle = [177285.9, 290750.96] x [343128.1, 450859.7] units

wp_nonfunctional_ppp <- as(wp_nonfunctional_sp, "ppp")
wp_nonfunctional_ppp

Planar point pattern: 2179 points
window: rectangle = [180538.96, 290616] x [340054.1, 450780.1] units

Plotting ppp points

plot(wp_functional_ppp)

plot(wp_nonfunctional_ppp)

Summary statistics

summary(wp_functional_ppp)

Planar point pattern:  2630 points
Average intensity 2.151545e-07 points per square unit

Coordinates are given to 2 decimal places
i.e. rounded to the nearest multiple of 0.01 units

Window: rectangle = [177285.9, 290750.96] x [343128.1, 450859.7] units
                    (113500 x 107700 units)
Window area = 12223800000 square units

summary(wp_nonfunctional_ppp)

Planar point pattern:  2179 points
Average intensity 1.787766e-07 points per square unit

Coordinates are given to 2 decimal places
i.e. rounded to the nearest multiple of 0.01 units

Window: rectangle = [180538.96, 290616] x [340054.1, 450780.1] units
                    (110100 x 110700 units)
Window area = 12188400000 square units

Checking for duplicates

any(duplicated(wp_functional_ppp))

[1] FALSE

any(duplicated(wp_nonfunctional_ppp))

[1] FALSE

Creating owin object

NGA_owin <- as(NGA_sp, "owin")
osun_owin <- as(osun_sp, "owin")

plot(NGA_owin)

plot(osun_owin)

Checking on owin summary statistics

summary(NGA_owin)

Window: polygonal boundary
794 separate polygons (3 holes)
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polygon 660 (hole)       58   -94679600     -1.04e-04
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polygon 737              88   166099000      1.82e-04
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polygon 768             245  4102640000      4.50e-03
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polygon 773             179  1813520000      1.99e-03
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polygon 775             165  1475280000      1.62e-03
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polygon 778             371  1782980000      1.96e-03
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polygon 788             251  1904040000      2.09e-03
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polygon 794             175   699163000      7.67e-04
enclosing rectangle: [26662.7, 1344157] x [30523.4, 1096029.4] units
                     (1317000 x 1066000 units)
Window area = 9.11598e+11 square units
Fraction of frame area: 0.649

summary(osun_owin)

Window: polygonal boundary
30 separate polygons (no holes)
            vertices      area relative.area
polygon 1        204 766084000       0.08870
polygon 2         81 304399000       0.03520
polygon 3         97 465688000       0.05390
polygon 4        124 373051000       0.04320
polygon 5         60 149473000       0.01730
polygon 6         84 144820000       0.01680
polygon 7         50 102243000       0.01180
polygon 8         72 216002000       0.02500
polygon 9        112 269897000       0.03130
polygon 10       125 365142000       0.04230
polygon 11        83 111191000       0.01290
polygon 12       126 192557000       0.02230
polygon 13       219 904397000       0.10500
polygon 14       174 741131000       0.08580
polygon 15        81 138742000       0.01610
polygon 16        65 119452000       0.01380
polygon 17        90 280205000       0.03240
polygon 18        69  69814600       0.00808
polygon 19        69  42727500       0.00495
polygon 20        49  30458800       0.00353
polygon 21        62 263505000       0.03050
polygon 22        93 438930000       0.05080
polygon 23        87 274127000       0.03170
polygon 24       105 509979000       0.05910
polygon 25        98 292058000       0.03380
polygon 26        64 327765000       0.03800
polygon 27       133 108945000       0.01260
polygon 28       122 462169000       0.05350
polygon 29        94 109715000       0.01270
polygon 30        95  61239800       0.00709
enclosing rectangle: [176503.22, 291043.82] x [331434.7, 454520.1] units
                     (114500 x 123100 units)
Window area = 8635910000 square units
Fraction of frame area: 0.613

Combining waterpoints with Osun map

Functional waterpoints

wp_functional_ppp_osun = wp_functional_ppp[osun_owin]
plot(wp_functional_ppp_osun)

Loading packages

pacman::p_load(maptools, sf, raster, spatstat, tmap)

Prepping KDE plot

kde_wp_functional_bw <- density(wp_functional_ppp_osun,
                              sigma=bw.diggle,
                              edge=TRUE,
                              kernel="gaussian")

Plotting KDE for functional waterpoints

plot(kde_wp_functional_bw)

Bandwidth used

bw <- bw.diggle(wp_functional_ppp_osun)
bw

   sigma 
252.1687 

Re-scaling data

wp_functional_ppp_osun.km <- rescale(wp_functional_ppp_osun, 1000, "km")

kde_wp_functional_bw <- density(wp_functional_ppp_osun.km, sigma=bw.diggle, edge=TRUE, kernel="gaussian")
plot(kde_wp_functional_bw)

Working with different kernel methods: Gaussian, Epanechnikov, Quartic and Discs

par(mfrow=c(2,2))
plot(density(wp_functional_ppp_osun.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="gaussian"), 
     main="Gaussian")
plot(density(wp_functional_ppp_osun.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="epanechnikov"), 
     main="Epanechnikov")
plot(density(wp_functional_ppp_osun.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="quartic"), 
     main="Quartic")
plot(density(wp_functional_ppp_osun.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="disc"), 
     main="Disc")

Non-functional waterpoints

wp_nonfunctional_ppp_osun = wp_nonfunctional_ppp[osun_owin]
plot(wp_nonfunctional_ppp_osun)

kde_wp_nonfunctional_bw <- density(wp_nonfunctional_ppp_osun,
                              sigma=bw.diggle,
                              edge=TRUE,
                            kernel="gaussian") 

plot(kde_wp_nonfunctional_bw)

Computing sigma

bw <- bw.diggle(wp_nonfunctional_ppp_osun)
bw

   sigma 
308.2061 

Re-scaling data

wp_nonfunctional_ppp_osun.km <- rescale(wp_nonfunctional_ppp_osun, 1000, "km")

Plotting re-scaled data

kde_wp_nonfunctional_bw <- density(wp_nonfunctional_ppp_osun.km, sigma=bw.diggle, edge=TRUE, kernel="gaussian")
plot(kde_wp_nonfunctional_bw)

Working with different kernel methods: Gaussian, Epanechnikov, Quartic and Discs

par(mfrow=c(2,2))
plot(density(wp_nonfunctional_ppp_osun.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="gaussian"), 
     main="Gaussian")
plot(density(wp_nonfunctional_ppp_osun.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="epanechnikov"), 
     main="Epanechnikov")
plot(density(wp_nonfunctional_ppp_osun.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="quartic"), 
     main="Quartic")
plot(density(wp_nonfunctional_ppp_osun.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="disc"), 
     main="Disc")

Kernel density maps on openstreetmap of Osun State, Nigeria

tmap_mode('view')
tm_shape(wp_functional) +
    tm_dots(alpha=0.4, 
            size=0.05)

Converting functional waterpoints gridded output into raster

gridded_kde_wp_functional_bw <- as.SpatialGridDataFrame.im(kde_wp_functional_bw)
spplot(gridded_kde_wp_functional_bw)

kde_wp_functional_bw_raster <- raster(gridded_kde_wp_functional_bw)

kde_wp_functional_bw_raster

class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.8948485, 0.9616045  (x, y)
extent     : 176.5032, 291.0438, 331.4347, 454.5201  (xmin, xmax, ymin, ymax)
crs        : NA 
source     : memory
names      : v 
values     : -5.092436e-15, 25.49435  (min, max)

projection(kde_wp_functional_bw_raster) <- CRS("+init=EPSG:26392")
kde_wp_functional_bw_raster

class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.8948485, 0.9616045  (x, y)
extent     : 176.5032, 291.0438, 331.4347, 454.5201  (xmin, xmax, ymin, ymax)
crs        : +init=EPSG:26392 
source     : memory
names      : v 
values     : -5.092436e-15, 25.49435  (min, max)

Plotting raster map

tm_shape(kde_wp_functional_bw_raster) + 
tm_basemap("OpenStreetMap") +
  tm_raster("v") +
  tm_layout(legend.position = c("right", "bottom"), frame = FALSE)

Converting nonfunctional waterpoints gridded output into raster

gridded_kde_wp_nonfunctional_bw <- as.SpatialGridDataFrame.im(kde_wp_nonfunctional_bw)
spplot(gridded_kde_wp_nonfunctional_bw)

kde_wp_nonfunctional_bw_raster <- raster(gridded_kde_wp_nonfunctional_bw)

kde_wp_nonfunctional_bw_raster

class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.8948485, 0.9616045  (x, y)
extent     : 176.5032, 291.0438, 331.4347, 454.5201  (xmin, xmax, ymin, ymax)
crs        : NA 
source     : memory
names      : v 
values     : -3.925434e-15, 20.49412  (min, max)

Changing projection

projection(kde_wp_nonfunctional_bw_raster) <- CRS("+init=EPSG:26392")
kde_wp_nonfunctional_bw_raster

class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.8948485, 0.9616045  (x, y)
extent     : 176.5032, 291.0438, 331.4347, 454.5201  (xmin, xmax, ymin, ymax)
crs        : +init=EPSG:26392 
source     : memory
names      : v 
values     : -3.925434e-15, 20.49412  (min, max)

tm_shape(kde_wp_nonfunctional_bw_raster) + 
  tm_basemap("OpenStreetMap") +
  tm_raster("v") +
  tm_layout(legend.position = c("right", "bottom"), frame = FALSE)

Analysis:

From the plots above, we can observe that both the functional and nonfunctional waterpoints seem to scatter unevenly through the whole of Osun state in Nigeria. For the functional waterpoints, they seem to gather around the top of Osun state in Nigeria (north of Osun), leaving the other areas of Osun to have reduced accessibility to clean water points for consumption. For the nonfunctional waterpoints, they seem to be scattered around the whole Osun state unevenly as well, with some concentration of nonfunctional waterpoints at the top (north of Osun) and centre (Osun central).

Advantages of Kernel Density Map over point maps, is that it increases the visibility of the areas with higher densities of, in this case functional/nonfunctional waterpoints. Areas with higher densities of functional/nonfunctional waterpoints can be easily pin-pointed out with the darker colour intensities on the Kernel Density Map instead of having multiple points stacked together on the point maps and having to zoom in to see the actual points.

Nearest Neighbour Analysis: Clark and Evans Test

Functional waterpoints

clarkevans.test(wp_functional_ppp,
                correction="none",
                clipregion="osun_owin",
                alternative=c("clustered"),
                nsim=99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  wp_functional_ppp
R = 0.38922, p-value = 0.01
alternative hypothesis: clustered (R < 1)

Nonfunctional waterpoints

clarkevans.test(wp_nonfunctional_ppp,
                correction="none",
                clipregion="osun_owin",
                alternative=c("clustered"),
                nsim=99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  wp_nonfunctional_ppp
R = 0.37112, p-value = 0.01
alternative hypothesis: clustered (R < 1)

Second-order Spatial Point Patterns Analysis

Analysing Spatial Point Process Using L-Function

Computing L Function estimation of Functional Waterpoints

L_wp_functional = Lest(wp_functional_ppp, correction = "Ripley")
plot(L_wp_functional, . -r ~ r, 
     ylab= "L(d)-r", xlab = "d(m)")

Performing Complete Spatial Randomness Test of Functional Waterpoints

Hypothesis:

Ho = The distribution of functional waterpoints in Osun, Nigeria are randomly distributed.

H1= The distribution of functional waterpoints in Osun, Nigeria are not randomly distributed.

Confidence Level:

The null hypothesis will be rejected if p-value if smaller than alpha value of 0.001.

L_wp_functional.csr <- envelope(wp_functional_ppp, Lest, nsim = 99, rank = 1, glocal=TRUE)

Generating 99 simulations of CSR  ...
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,  99.

Done.

plot(L_wp_functional.csr, . - r ~ r, xlab="d", ylab="L(d)-r")

Computing L Function estimation of Nonfunctional Waterpoints

L_wp_nonfunctional = Lest(wp_nonfunctional_ppp, correction = "Ripley")
plot(L_wp_nonfunctional, . -r ~ r, 
     ylab= "L(d)-r", xlab = "d(m)")

Performing Complete Spatial Randomness Test of Nonfunctional Waterpoints

Hypothesis:

Ho = The distribution of non-functional waterpoints in Osun, Nigeria are randomly distributed.

H1= The distribution of non-functional waterpoints in Osun, Nigeria are not randomly distributed.

Confidence Level:

The null hypothesis will be rejected if p-value if smaller than alpha value of 0.001.

L_wp_nonfunctional.csr <- envelope(wp_nonfunctional_ppp, Lest, nsim = 99, rank = 1, glocal=TRUE)

Generating 99 simulations of CSR  ...
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,  99.

Done.

plot(L_wp_nonfunctional.csr, . - r ~ r, xlab="d", ylab="L(d)-r")

Analysis:

The L function for both functional and nonfunctional waterpoints have the same shape of an exponential curve. Since the L function is a form of descriptive statistics generally used to determine whether points have a random, dispersed or clustered distribution pattern at certain scale, we can conclude that there are some forms of clustering for both the functional and nonfunctional waterpoints in Osun, Nigeria, at 99% confidence interval.

Spatial Correlation Analysis

I will be analysis the co-location between the functional and non-functional water points’ distribution, using the LCLQ method.

Hypothesis:

Ho = Functional waterpoints are not co-located with nonfunctional water points.

H1= Functional waterpoints are co-located with nonfunctional water points.

Confidence Level:

The null hypothesis will be rejected if p-value if smaller than alpha value of 0.05

Installing package

pacman::p_load(tidyverse, tmap, sf, sfdep) 

Preparing nearest neighbours list

Determine the k (i.e. 6) nearest neighbours for given point geometry

nb <- include_self(
  st_knn(st_geometry(wp_sf), 6))

Computing kernel weights

Derive a weights list by using a kernel function

wt <- st_kernel_weights(nb, 
                        wp_sf, 
                        "gaussian", 
                        adaptive = TRUE)

Converting functional and nonfunctional waterpoints into vectors

A <- wp_functional$`#status_clean`
B <- wp_nonfunctional$`#status_clean`

Computing LCLQ values

LCLQ <- local_colocation(A,
                         B,
                         nb,
                         wt,
                         49)

Joining output table

LCLQ_wp <- cbind(wp_sf, LCLQ)

Plotting LCLQ values

tmap_mode("view")
tm_shape(LCLQ_wp)+ 
  tm_dots(col = "Non.Functional",
             size = 0.01,
             border.col = "black",
          scale=0.15,
             border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(12, 16))

Analysis:

As we can observe from the map plotted above, waterpoints with their LCLQ value of 0.7 to 1.1 (in yellow to brown) are displayed. We can conclude that our null hypothesis is true; that functional waterpoints are not co-located with nonfunctional water points in Osun state (Nigeria).