A Density-Based Spatial Flow Cluster Detection Method
Document Type
Conference Proceeding
Publication Date
2016
Digital Object Identifier (DOI)
https://doi.org/10.21433/B3118mf4r9rw
Abstract
Understanding the patterns and dynamics of spatial origin-destination flow data has been a long-standing goal of spatial scientists. 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 issues of Modifiable Areal Unit Problem (MAUP) of flows endpoints, loss of spatial information, and false positive errors on short flows.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
International Conference on GIScience Short Paper Proceedings, v. 1, issue 1, p. 288-291
Scholar Commons Citation
Tao, Ran and Thill, Jean-Claude, "A Density-Based Spatial Flow Cluster Detection Method" (2016). School of Geosciences Faculty and Staff Publications. 1277.
https://digitalcommons.usf.edu/geo_facpub/1277
