How can you use R to harmonize spatial data for Geographic Information Systems (GIS)?

2 Read and write spatial data

One of the first steps in harmonizing spatial data is to read and write them in different formats. R can read and write various spatial data formats, such as shapefiles, GeoJSON, GeoTIFF, and NetCDF. To read spatial data, you can use the read_sf function for vector data and the raster function for raster data. These functions will return objects of class sf or raster, respectively, which contain the spatial information and attributes of the data. For example, to read a shapefile of US states, you can run this code: states <- read_sf("states.shp") To write spatial data, you can use the write_sf function for vector data and the writeRaster function for raster data. These functions will save the objects as files in the specified format and location. For example, to write the states object as a GeoJSON file, you can run this code: write_sf(states, "states.geojson")

3 Transform coordinate reference systems

Another step in harmonizing spatial data is to transform them to the same coordinate reference system (CRS). A CRS defines how the spatial coordinates of the data are related to the real world. Different data sources may use different CRSs, which can cause problems when you try to overlay or compare them. To transform spatial data to a different CRS, you can use the st_transform function for vector data and the projectRaster function for raster data. These functions will take an object and a target CRS as arguments, and return a new object with the transformed coordinates. For example, to transform the states object to the WGS 84 CRS, you can run this code: states_wgs84 <- st_transform(states, crs = 4326)

4 Resample and crop raster data

If you work with raster data, you may also need to resample and crop them to match the resolution and extent of other data. Resampling raster data means changing the size and number of the cells that make up the raster. This can affect the quality and accuracy of the data, so you need to choose an appropriate method for resampling, such as nearest neighbor, bilinear, or cubic. Cropping raster data means cutting off the parts of the raster that are outside a given extent. This can reduce the size and complexity of the data, and make it easier to overlay or compare with other data. To resample and crop raster data, you can use the resample and crop functions from the raster package. These functions will take a raster object and another object that defines the target resolution or extent, and return a new raster object with the resampled or cropped data. For example, to resample and crop a raster object called elevation to match the states object, you can run this code:

elevation_resampled <- resample(elevation, states, method = "bilinear")
elevation_cropped <- crop(elevation_resampled, states)        

6 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?