Discovering Spatial Co-Clustering Patterns in Traffic Collision Data

Authors:
Dapeng Li;Joerg Sander;Mario A. Nascimento;Dae-Won Kwon
Affiliations:
Dept. of Computing Science, University of Alberta, Canada;Dept. of Computing Science, University of Alberta, Canada;Dept. of Computing Science, University of Alberta, Canada;Office of Traffic Safety, City of Edmonton, Canada
Venue:
Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
Year:
2013

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Abstract

Identifying spatial patterns of traffic collisions is critical for improving the efficiency and effectiveness of the deployment of traffic enforcement resources as well as road safety. In recent years, many studies have focused on finding locations with high collision concentration, so-called hotspots, without integrating the likely available non-spatial attributes into analysis. In this paper we propose a method for identifying the sets of non-spatial attribute-value pairs (AVPs) that together contribute significantly to the spatial clustering of the corresponding collisions. We call such a set of AVPs a Spatial Co-Clustering Pattern (SCCP). By applying our method on the city of Edmonton's historical collision data, we discovered a larger number of meaningful hotspot patterns than traditional hotspot analysis methods did, and revealed the relevant non-spatial indicators for explaining those hotspots.