Global cities have experienced a disparate set of urban growth patterns over the last forty years. As populations have grown, some cities have densified while others have sprawled, expanding further out into the periphery. Over time, cities capable of minimizing the expansion of urban land cover relative to population growth have been more "efficient" with their land use.
We analyzed land use efficiency using raster surfaces of remotely sensed urban land cover and grid cell level population data from the Global Human Settlement Layer. We measured "land use efficiency" by calculating the change in population density over time. The 250m resolution raster data enabled us to visualize small-scale land use trends within each city. We then developed a "land use efficiency index" for each metropolitan area. This metric shows exactly where each city falls on the continuum from least to most sprawling. We developed a catalog of data visualizations for eight different world cities (see below). This type of analysis can help improve our understanding of how land use patterns vary across the globe.
I created these visualizations using exclusively open-source mapping and layout tools in R. This scalable mapping and data visualization process made it easy to re-create a complex data visualization page for eight different cities (or even beyond).