A Space-Time GIS for Visualizing and Analyzing Clusters in Large Tracking Datasets
Emerging large individual-based tracking datasets have challenged the GIS community to develop effective research tools for analyzing such datasets and uncovering hidden information. Rooted in the time-geographic framework, a space-time GIS approach is proposed to facilitate the representation of the trajectories generated by the tracked moving objects and exploration of the spatiotemporal patterns of stations where the objects cluster. In particular, the space-time path concept is used to model trajectories, and the station concept is used to guide the aggregation of space-time paths to define the locations where the paths cluster in space and time. Since the spatial and temporal extent of a station may vary in different applications, several spatial and temporal methods are introduced and discussed in this study to aggregate the paths. A 3D (2D space + 1D time) space-time GIS environment is developed to support the implementation of these concepts and methods. Stations derived from aggregating space-time paths are represented and visualized as spatiotemporal cylinders in the space-time GIS environment. The space-time GIS, together with the aggregation methods supported by the station concept, offers a useful exploratory analysis environment to support the investigation of hidden spatiotemporal patterns in large trajectory datasets of moving objects.
KeywordsSpace-time GIS Tracking dataset Aggregation Space-time paths Station Spatiotemporal cluster
- Golledge, R., & Stimson, R. (1997). Spatial Behavior: A Geographic Perspective. New York: The Guilford Press.Google Scholar
- Kwan, M.-P., & Hong, X. (1998). Network-based constraints-oriented choice set formation using GIS. Geographical Systems, 5, 139–162.Google Scholar
- Parkes, D., & Thrift, N. (1980). Times, spaces, and places: A chronogeographic perspective. New York: Wiley.Google Scholar
- Porkaew, K., Lazaridis, I., & Mehrotra, S. (2001). Querying mobile objects in spatio-temporal databases. SSTD, 2001, 59–78.Google Scholar
- Rinner, C. (2004). Three-dimensional visualization of activity-travel patterns. In M. Raubal, A. Sliwinski & K. Kuhn (Eds.), Geoinformation und Mobilität [Geoinformation and Mobility], Proc. of the Münster GI Days, 1–2 July 2004, Münster, Germany, IfGIprints series No. 22. Verlag Natur and Wissenschaft, Solingen, Germany, pp. 231–237. http://www.ryerson.ca/~crinner/pubs/rinner-3d-vis_full.pdf. Accessed on Jan 8, 2008.
- Vazirgiannis, M., & Wolfson, O. (2001). A spatiotemporal model and language for moving objects on road networks. SSTD, 2001, 20–35.Google Scholar
- Yuan, M., Mark, D., Egenhofer, M., & Peuquet, D. (2004). Extensions to geographic representations. In R. McMaster & E. Usery (Eds.), A research agenda for geographic information science (pp. 129–156). Boca Raton, FL: CRC Press.Google Scholar