Using a discrete global grid system for a scalable, interoperable, and reproducible system of land-use mapping

An accurate and timely land-use map of an area is a valuable geospatial dataset. Making one requires assembling heterogeneous geospatial input data. This becomes computationally expensive as the number of inputs grow or the combinational classification logic becomes more convoluted. We present as a solution an analytical framework based on a DGGS. This provides a spatial data structure that: facilitates horizontal scaling; improves geospatial data interoperability; and makes classified geospatial data easier to reproduce. We demonstrate a benchmark of land-use assignment with a DGGS against the vector and raster geospatial data models. Significant performance benefits are apparent over the vector case, while performance is equivalent to raster. Yet a DGGS has other compelling benefits that makes it more convenient to use in the domain of land-use assignment than the raster data model.