Discovering Spatial Patterns in Origin-Destination Mobility Data

Diansheng Guo, Xi Zhu, Hai Jin, Peng Gao, Clio Andris

Research output: Contribution to journalArticle

88 Scopus citations

Abstract

Mobility and spatial interaction data have become increasingly available due to the wide adoption of location-aware technologies. Examples of mobility data include human daily activities, vehicle trajectories, and animal movements, among others. In this article we focus on a special type of mobility data, i.e. origin-destination pairs, and present a new approach to the discovery and understanding of spatio-temporal patterns in the movements. Specifically, to extract information from complex connections among a large number of point locations, the approach involves two steps: (1) spatial clustering of massive GPS points to recognize potentially meaningful places; and (2) extraction and mapping of the flow measures of clusters to understand the spatial distribution and temporal trends of movements. We present a case study with a large dataset of taxi trajectories in Shenzhen, China to demonstrate and evaluate the methodology. The contribution of the research is two-fold. First, it presents a new methodology for detecting location patterns and spatial structures embedded in origin-destination movements. Second, the approach is scalable to large data sets and can summarize massive data to facilitate pattern extraction and understanding.

Original languageEnglish (US)
Pages (from-to)411-429
Number of pages19
JournalTransactions in GIS
Volume16
Issue number3
DOIs
StatePublished - Jun 1 2012

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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