setwd("C:/Users/devmbeya/Documents/giswithrtutorials")
getwd() #check the file path of the current directory[1] "C:/Users/devmbeya/Documents/giswithrtutorials"Attribute and spatial queries
Overview
In this tutorial, you will learn how to select geographic features based on their association with non-spatial and also based on their location.
Data Exploration
Set working directory
Load required packages
library(sf)
library(dplyr)Read data
africa <- st_read("../assets/data/vector/africa.shp")Reading layer `africa' from data source
`C:\Users\devmbeya\Downloads\gis-remote-sensing-r\assets\data\vector\africa.shp'
using driver `ESRI Shapefile'
Simple feature collection with 55 features and 13 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -25.36042 ymin: -46.96575 xmax: 51.41704 ymax: 37.3452
Geodetic CRS: WGS 84africa_project <- st_read("../assets/data/vector/africa_project.shp")Reading layer `africa_project' from data source
`C:\Users\devmbeya\Downloads\gis-remote-sensing-r\assets\data\vector\africa_project.shp'
using driver `ESRI Shapefile'
Simple feature collection with 55 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -4296552 ymin: -3854841 xmax: 3987157 ymax: 4134822
Projected CRS: Africa_Sinusoidalafrica_continent <- st_read("../assets/data/vector/africa_continent.shp")Reading layer `africa_continent' from data source
`C:\Users\devmbeya\Downloads\gis-remote-sensing-r\assets\data\vector\africa_continent.shp'
using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -25.36055 ymin: -34.822 xmax: 63.49576 ymax: 37.34041
Geodetic CRS: WGS 84cities <- st_read("../assets/data/vector/africa_capital_cities.shp")Reading layer `africa_capital_cities' from data source
`C:\Users\devmbeya\Downloads\gis-remote-sensing-r\assets\data\vector\africa_capital_cities.shp'
using driver `ESRI Shapefile'
Simple feature collection with 51 features and 14 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: -23.521 ymin: -29.308 xmax: 47.528 ymax: 36.819
Geodetic CRS: WGS 84roads <- st_read("../assets/data/vector/africa_roads.shp")Reading layer `africa_roads' from data source
`C:\Users\devmbeya\Downloads\gis-remote-sensing-r\assets\data\vector\africa_roads.shp'
using driver `ESRI Shapefile'
Simple feature collection with 2581 features and 30 fields
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: -17.45546 ymin: -34.2343 xmax: 49.41792 ymax: 37.10583
Geodetic CRS: WGS 84Attribute queries
GIS data comes in two forms: spatial and attribute data. Spatial data represents the aspects of geography inform of map layers. Attribute data in GIS describe the properties or characteristics of the spatial features. A school would have its spatial data represented as geographic coordinates(latitudes and longitudes) and its attribute data would include name of the school, student enrollment, number of teachers, name of district it is located in etc.
Attribute queries select geographic features based on their association with attributes (non-spatial data). This is achieved by using conditions such as field names(e.g., ADIMN, NAME), operators (e.g., <, >, =, ==) and values (e.g., Malawi, TRUE, 20000).
Simple feature collection with 55 features and 13 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -25.36042 ymin: -46.96575 xmax: 51.41704 ymax: 37.3452
Geodetic CRS: WGS 84
First 10 features:
ADMIN ADM0_A3 POP_EST POP_RANK POP_YEAR GDP_MD
1 Ethiopia ETH 112078730 17 2019 95912
2 South Sudan SDS 11062113 14 2019 11998
3 Somalia SOM 10192317 14 2019 4719
4 Kenya KEN 52573973 16 2019 95503
5 Malawi MWI 18628747 14 2019 7666
6 United Republic of Tanzania TZA 58005463 16 2019 63177
7 Somaliland SOL 5096159 13 2014 17836
8 Morocco MAR 36471769 15 2019 119700
9 Western Sahara SAH 603253 11 2017 907
10 Republic of the Congo COG 5380508 13 2019 12267
GDP_YEAR ECONOMY INCOME_GRP CONTINENT
1 2019 7. Least developed region 5. Low income Africa
2 2015 7. Least developed region 5. Low income Africa
3 2016 7. Least developed region 5. Low income Africa
4 2019 5. Emerging region: G20 5. Low income Africa
5 2019 7. Least developed region 5. Low income Africa
6 2019 7. Least developed region 5. Low income Africa
7 2013 6. Developing region 4. Lower middle income Africa
8 2019 6. Developing region 4. Lower middle income Africa
9 2007 7. Least developed region 5. Low income Africa
10 2019 6. Developing region 4. Lower middle income Africa
SUBREGION REGION_WB NAME_EN
1 Eastern Africa Sub-Saharan Africa Ethiopia
2 Eastern Africa Sub-Saharan Africa South Sudan
3 Eastern Africa Sub-Saharan Africa Somalia
4 Eastern Africa Sub-Saharan Africa Kenya
5 Eastern Africa Sub-Saharan Africa Malawi
6 Eastern Africa Sub-Saharan Africa Tanzania
7 Eastern Africa Sub-Saharan Africa Somaliland
8 Northern Africa Middle East & North Africa Morocco
9 Northern Africa Middle East & North Africa Western Sahara
10 Middle Africa Sub-Saharan Africa Republic of the Congo
geometry
1 MULTIPOLYGON (((34.0707 9.4...
2 MULTIPOLYGON (((35.92084 4....
3 MULTIPOLYGON (((46.46696 6....
4 MULTIPOLYGON (((35.70585 4....
5 MULTIPOLYGON (((34.96461 -1...
6 MULTIPOLYGON (((32.92086 -9...
7 MULTIPOLYGON (((48.93911 11...
8 MULTIPOLYGON (((-8.817035 2...
9 MULTIPOLYGON (((-8.817035 2...
10 MULTIPOLYGON (((18.62639 3.... [1] "ADMIN" "ADM0_A3" "POP_EST" "POP_RANK" "POP_YEAR"
[6] "GDP_MD" "GDP_YEAR" "ECONOMY" "INCOME_GRP" "CONTINENT"
[11] "SUBREGION" "REGION_WB" "NAME_EN" "geometry" Geometry set for 55 features
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -25.36042 ymin: -46.96575 xmax: 51.41704 ymax: 37.3452
Geodetic CRS: WGS 84
First 5 geometries: [1] "Ethiopia" "South Sudan"
[3] "Somalia" "Kenya"
[5] "Malawi" "United Republic of Tanzania"
[7] "Somaliland" "Morocco"
[9] "Western Sahara" "Republic of the Congo"
[11] "Democratic Republic of the Congo" "Namibia"
[13] "South Africa" "Libya"
[15] "Tunisia" "Zambia"
[17] "Sierra Leone" "Guinea"
[19] "Liberia" "Central African Republic"
[21] "Sudan" "Djibouti"
[23] "Eritrea" "Ivory Coast"
[25] "Mali" "Senegal"
[27] "Nigeria" "Benin"
[29] "Angola" "Botswana"
[31] "Zimbabwe" "Chad"
[33] "Algeria" "Mozambique"
[35] "eSwatini" "Burundi"
[37] "Rwanda" "Uganda"
[39] "Lesotho" "Cameroon"
[41] "Gabon" "Niger"
[43] "Burkina Faso" "Togo"
[45] "Ghana" "Guinea-Bissau"
[47] "Egypt" "Mauritania"
[49] "Equatorial Guinea" "Gambia"
[51] "Bir Tawil" "Madagascar"
[53] "Comoros" "São Tomé and Principe"
[55] "Cabo Verde" Subsetting vector data
In R, you van use dplyr subsetting functions to select a subset of the data: rows or columns. You can use the filter() and slice functions to select rows and select() to subset columns.
Select countries with population greater than 80 million. Use the dplyr’s filter() function.
# Once again explore the data
head(africa)Simple feature collection with 6 features and 13 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 24.12156 ymin: -17.13534 xmax: 51.41704 ymax: 14.87953
Geodetic CRS: WGS 84
ADMIN ADM0_A3 POP_EST POP_RANK POP_YEAR GDP_MD
1 Ethiopia ETH 112078730 17 2019 95912
2 South Sudan SDS 11062113 14 2019 11998
3 Somalia SOM 10192317 14 2019 4719
4 Kenya KEN 52573973 16 2019 95503
5 Malawi MWI 18628747 14 2019 7666
6 United Republic of Tanzania TZA 58005463 16 2019 63177
GDP_YEAR ECONOMY INCOME_GRP CONTINENT SUBREGION
1 2019 7. Least developed region 5. Low income Africa Eastern Africa
2 2015 7. Least developed region 5. Low income Africa Eastern Africa
3 2016 7. Least developed region 5. Low income Africa Eastern Africa
4 2019 5. Emerging region: G20 5. Low income Africa Eastern Africa
5 2019 7. Least developed region 5. Low income Africa Eastern Africa
6 2019 7. Least developed region 5. Low income Africa Eastern Africa
REGION_WB NAME_EN geometry
1 Sub-Saharan Africa Ethiopia MULTIPOLYGON (((34.0707 9.4...
2 Sub-Saharan Africa South Sudan MULTIPOLYGON (((35.92084 4....
3 Sub-Saharan Africa Somalia MULTIPOLYGON (((46.46696 6....
4 Sub-Saharan Africa Kenya MULTIPOLYGON (((35.70585 4....
5 Sub-Saharan Africa Malawi MULTIPOLYGON (((34.96461 -1...
6 Sub-Saharan Africa Tanzania MULTIPOLYGON (((32.92086 -9...# Select countries with population greater than 80 million
pop <- filter(africa, POP_EST > 80000000)
popSimple feature collection with 4 features and 13 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2.671082 ymin: -13.45835 xmax: 47.97917 ymax: 31.65648
Geodetic CRS: WGS 84
ADMIN ADM0_A3 POP_EST POP_RANK POP_YEAR GDP_MD
1 Ethiopia ETH 112078730 17 2019 95912
2 Democratic Republic of the Congo COD 86790567 16 2019 50400
3 Nigeria NGA 200963599 17 2019 448120
4 Egypt EGY 100388073 17 2019 303092
GDP_YEAR ECONOMY INCOME_GRP CONTINENT
1 2019 7. Least developed region 5. Low income Africa
2 2019 7. Least developed region 5. Low income Africa
3 2019 5. Emerging region: G20 4. Lower middle income Africa
4 2019 5. Emerging region: G20 4. Lower middle income Africa
SUBREGION REGION_WB NAME_EN
1 Eastern Africa Sub-Saharan Africa Ethiopia
2 Middle Africa Sub-Saharan Africa Democratic Republic of the Congo
3 Western Africa Sub-Saharan Africa Nigeria
4 Northern Africa Middle East & North Africa Egypt
geometry
1 MULTIPOLYGON (((34.0707 9.4...
2 MULTIPOLYGON (((18.62639 3....
3 MULTIPOLYGON (((3.5964 11.6...
4 MULTIPOLYGON (((34.24835 31...# Plot the selected polygons
plot(pop$geometry)
Select South Africa and Malawi polygons
south_africa <- filter(africa, NAME_EN == 'South Africa' | NAME_EN=="Malawi")
plot(south_africa$geometry)
Select Malawi’s neighbors
Spatial queries
To select features by their location, we can use topological relations between vector geometries.
To select features by their location, we can use topological relations between vector geometries in sf. Common functions include:
st_intersects(x, y)– returns features inxthat intersect features iny.st_touches(x, y)– returns features inxthat touch features iny(share a boundary but do not overlap).st_overlaps(x, y)– returns features inxthat overlap features inypartially.st_contains(x, y)– returns features inxthat contain features iny.st_contains_properly(x, y)– stricter version ofst_contains.st_covers(x, y)– returns features inxthat cover features iny.st_within(x, y)– returns features inxthat are within features iny.st_covered_by(x, y)– returns features inxthat are covered by features iny.st_disjoint(x, y)– returns features inxthat do not intersect features iny.
Select cities that are located within East African countries.
# Select countries within Eastern Africa
east_africa <- filter(africa, SUBREGION == 'Eastern Africa')
# Plot East African countries
plot(east_africa$geometry)
# Select and plot all eastern African cities
east_cities <- st_intersection(x = cities, y=east_africa)
plot(east_cities$geometry, add=TRUE, col='blue', pch=16)
plot(cities$geometry, add=TRUE)