Workflows for combining raster and vector data

Now that you’ve gotten some experience building databases using tabular and vector data, we’ll take a look at combining data models (i.e., using vectors and rasters together). We’ll rely largely on terra::rasterize, terra::zonal, and terra::extract to summarize data in ways that allow them to be combined with other tabular and vector data. We’ll also take a look at the speed of different approaches and think about scaling each approach to larger geographic extents.

Resources

• The Spatial Data Operations Chapter in (Lovelace et al. 2019) provides a nice overview of raster processing in R.

• This post on Processing Large Rasters in R by Matt Strimas-Mackey has a lot of useful background on what’s happening under the hood when processing large rasters in R along with examples of benchmarking to characterize speedups.

• The Chapters 9 and 10 on raster processing in Michael Wimberly’s “Geospatial Data Science With R: Applications in Environmental Geography” bookdown project has a lot of useful examples using gridded climate data.

Slides

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• Workflows for combining raster and vector data
• Revisiting raster operations
• Zonal statistics
• Extractions
• Scaling