Scaling Polygon Adjacency Algorithms to Big Data Geospatial Analysis

Adjacency and neighbor structures play an essential role in many spatial analytical tasks. The computation of adjacenecy structures is non-trivial and can form a significant processing bottleneck as the total number of observations increases. We quantify the performance of synthetic and real world binary, first-order, adjacency algorithms and offer a solution that leverages Python’s high performance containers. A comparison of this algorithm with a traditional spatial decomposition shows that the former outperforms the latter as a function of the geometric complexity, i.e the number of vertices and edges.