Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1111/j.1467-9671.2012.01344.x | TRANSACTIONS IN GIS |
Keywords | DocType | Volume |
null | Journal | 16 |
Issue | ISSN | Citations |
3 | 1361-1682 | 35 |
PageRank | References | Authors |
1.11 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diansheng Guo | 1 | 517 | 40.40 |
| Xi Zhu | 2 | 92 | 3.53 |
| Hai Jin | 3 | 55 | 2.73 |
| Peng Gao | 4 | 35 | 1.11 |
| Clio Andris | 5 | 117 | 9.67 |