How can R be used for geospatial analysis?

1 Importing spatial data

One of the first steps in geospatial analysis is to import the spatial data that you want to work with. Spatial data can come in various formats, such as shapefiles, raster files, geoJSON, or CSV. R has several packages that can help you read and write different types of spatial data, such as sf, sp, raster, rgdal, and geojsonio. For example, to import a shapefile of the world's countries, you can use the sf package:

library(sf)
world <- st_read("world.shp")        

This will create a spatial data frame called world, which contains the attributes and geometry of each country.

2 Manipulating spatial data

Once you have imported your spatial data, you may want to manipulate it to suit your analysis needs. For example, you may want to filter, join, aggregate, or transform your data. R has many functions and operators that can help you do these operations, either with base R or with packages like dplyr, tidyr, and magrittr. For example, to filter the world data frame to only include African countries, you can use the dplyr package:

library(dplyr)
africa <- world %>%
  filter(continent == "Africa")        

This will create a new spatial data frame called africa, which contains only the rows that match the condition.

3 Visualizing spatial data

Another important step in geospatial analysis is to visualize your spatial data, either to explore it or to communicate your findings. R has many packages that can help you create beautiful and interactive maps, such as ggplot2, leaflet, mapview, and tmap. For example, to create a choropleth map of the population density of African countries, you can use the tmap package:

library(tmap)
tm_shape(africa) +
  tm_fill("pop_est", style = "quantile", title = "Population") +
  tm_layout(title = "Population density of Africa")        

This will create a map that shows the population density of each country using different colors, based on the pop_est column.

4 Modeling spatial data

The final step in geospatial analysis is to model your spatial data, either to test hypotheses, make predictions, or find patterns. R has many packages that can help you perform various types of spatial modeling, such as spdep, gstat, spBayes, and spatstat. For example, to fit a spatial autoregressive model to the population density of African countries, you can use the spdep package:

library(spdep)
nb <- poly2nb(africa) # create a neighborhood object
W <- nb2listw(nb, style = "W") # create a spatial weights matrix
sar <- lagsarlm(pop_est ~ 1, data = africa, listw = W) # fit a spatial autoregressive model
summary(sar) # display the model summary        

This will create a model that estimates the effect of the population density of neighboring countries on the population density of each country, and display the coefficients and diagnostics of the model.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?