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- W1559632079 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." @default.
- W1559632079 created "2016-06-24" @default.
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- W1559632079 date "2012-05-28" @default.
- W1559632079 modified "2023-10-01" @default.
- W1559632079 title "Discovering Spatial Patterns in Origin-Destination Mobility Data" @default.
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- W1559632079 doi "https://doi.org/10.1111/j.1467-9671.2012.01344.x" @default.
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