And while hexagons tile a plane beautifully, you can’t tesselate them to create a perfect larger hexagon, which means it’s difficult to create a larger or smaller grid that relates to your original grid. However, these custom hex grids are usually project-specific and don’t conform to any sort of common, well-known grid, making it difficult to pull in other hexed data. They don’t “tile the plane” in a standard, repeating pattern that allows you to see how a dataset changes over space.īecause hexagons are the bestagons, you may have created hex grids that cover their area of interest and then aggregate their point data to these hexes. However, administrative boundaries like tracts, cities, or political districts are inherently irregular. Census tracts or blocks are often popular targets, allowing you to compare your data to census data.
You’ve probably aggregated and mapped point data before by using the Summarize Within tool to spatially join your points to other boundary datasets, like cities or counties. This is a great tool for aggregating points into regularly-sized polygon geometries so that you can map and evaluate the spatial trends of a point-based phenomenon, but it needs a bit of an introduction to understand why it’s great and how to use it with traditional GIS data.
I recently discovered Uber’s open-source H3 geospatial indexing system while working on a project with a lot of point data. By Jake Adams on Aggregating and Analyzing Point Data with H3 Hexes and Pandas
0 kommentar(er)
