H3 allows us to help make sense of large amounts of data. For this blog series, we will use the NYC Taxi Data set and add in the NYC Taxi Zones, New York Counties, NYC Boroughs and NYC Buildings. I also added in Open Street Map POI data using the Python notebook from my colleague […]
After about four years (where did that time go?), I have circled back to Neo4j and H3 geospatial data processing. Since version 3.4, Neo4j has a native geospatial datatype. Neo4j uses the WGS-84 and WGS-84 3D coordinate reference system. Within Neo4j, we can index these point properties and query using our distance function or you query within a bounding […]
We are going to take a slight detour with regards to the healthcare blog series and talk about Uber H3. H3 is a hexagonal hierarchical geospatial indexing system. It comes with an API for indexing coordinates into a global grid. The grid is fully global and you can choose your resolution. The advantages and disadvantages […]