A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid

TL;DR

This paper introduces a framework that transforms global OpenStreetMap geospatial data into a comprehensive spatial-temporal knowledge graph using H3 grid alignment and Apache Sedona, enabling advanced spatial analysis.

Contribution

The paper presents a novel method for constructing a planet-scale spatial-temporal knowledge graph from OSM data with H3 grid alignment, enhancing spatial data interconnectivity.

Findings

Successfully constructed a global spatial-temporal knowledge graph

Compared and contrasted with existing spatial knowledge graphs

Demonstrated scalability and effectiveness of the approach

Abstract

Geospatial data plays a central role in modeling our world, for which OpenStreetMap (OSM) provides a rich source of such data. While often spatial data is represented in a tabular format, a graph based representation provides the possibility to interconnect entities which would have been separated in a tabular representation. We propose in our paper a framework which supports a planet scale transformation of OpenStreetMap data into a Spatial Temporal Knowledge Graph. In addition to OpenStreetMap data, we align the different OpenStreetMap geometries on individual h3 grid cells. We compare our constructed spatial knowledge graph to other spatial knowledge graphs and outline our contribution in this paper. As a basis for our computation, we use Apache Sedona as a computational framework for our Spatial Temporal Knowledge Graph construction