Background Place / Point of Interest (POI) recommendations provide locations that are of interest to a person searching for an interesting place to see within a geographic area. Recent papers have been published to identify different approaches to address this problem. One paper looks at Graph Neural Networks. A second paper looks at using Large […]
Tag: Geospatial
Neo4j – H3 Datasets
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 […]
Neo4j – H3 Library – Update
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 […]
Neo4j – Uber H3 – Geospatial
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 […]