Jason Laura
School of Geographical Sciences and Urban Planning, Arizona State University

Sergio J. Rey
School of Geographical Sciences and Urban Planning, Arizona State University

Abstract

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.

Keywords

adjacency, spatial analysis, spatial weights

DOI

10.25080/Majora-14bd3278-008

Bibtex entry

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Copyright © 2014 Jason Laura et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.