A Density-Based Spatial Flow Cluster Detection Method

20 minutes
CommutingCommuter ShedCluster Identification
Understanding the patterns and dynamics of spatial origin-destination flow data has been a long standing goal of spatial scientists. However, extracting useful information from massive flow events is usually challenging due to the occlusion and cluttered nature of spatial flow data. Cluster analysis methods have proved useful, yet existing studies suffer problems like the Modifiable Areal Unit Problem (MAUP) of flows endpoints, loss of spatial information, and false positive errors on short flows. In this paper we introduce a density-based cluster detection method tailored for disaggregated spatial flow data. The basic idea is to first measure flow density considering both endpoint coordinates and flow lengths, and combine it with state-of-art density-based clustering methods. We experiment with a carefully designed synthetic dataset. The results prove that our method can effectively extract flow clusters from various situations encompassing varied flow densities, lengths, hierarchies and, at the same time, avoid the above-mentioned problems.
University of North Carolina, Charlotte
Ph.D. Candidate (ABD)

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