Patterns in Spatio-temporal Data
Evolving spatial patterns; Spatio-temporal association patterns; Spatio-temporal object association
Spatio‐temporal data refer to data that are both spatial and time-varying in nature, for instance, the data concerning traffic flows on a highway during rush hours. Spatio‐temporal data are also being abundantly produced in many scientific domains. Examples include the datasets in computational fluid dynamics that describe the evolutionary behavior of vortices in fluid flows, and the datasets in bioinformatics that study the folding pathways of proteins from an initially string-like 3D structure to their respective native 3D structure.
One important issue in analyzing spatio‐temporal data is to characterize the spatial relationship among spatial entities and, more importantly, to define how such a relationship evolves or changes over time. In the traffic flow example, one might be interested in identifying and monitoring the automobiles that are following one another...
KeywordsSpatial Cluster Geometric Object Geographic Information System Scientific Domain Association Pattern
- 1.Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM. 26(11), 832–843 (1983)Google Scholar
- 2.Ester, M., Kriegel, H.P., Sander, J.: Algorithms and applications for spatial data mining. Geographic Data Mining and Knowledge Discovery, Research Monographs. In: GIS Chapter 7 (2001)Google Scholar
- 4.Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases. In SSD 95: Proceedings of the 4th International Symposium on Advances in Spatial Databases, pp. 47–66. Springer-Verlag (1995)Google Scholar
- 5.Mokbel, M.F., Ghanem, T.M., Aref, W.G.: Spatio-temporal access methods. Technical report, Department of Computer Sciences, Purdue UniversityGoogle Scholar
- 6.Morimoto, Y.: Mining frequent neighboring class sets in spatial databases. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 353–358. ACM Press (2001)Google Scholar
- 7.Neill, D.B., Moore, A.W., Sabhnani, M., Daniel, K.: Detection of emerging space-time clusters. In: Proceedings of SIGKDD 2005, pp. 218–227 (2005)Google Scholar
- 9.Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoo, J.S.: A framework for discovering co-location patterns in data sets with extended spatial objects. SIAM Intl. Conf. on Data Mining (SDM), April 2004Google Scholar
- 10.Yang, H., Parthasarathy, S., Mehta, S.: A generalized framework for mining spatio-temporal patterns in scientific data. In KDD 2005: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 716–721. ACM Press, New York, NY, USA (2005)CrossRefGoogle Scholar
- 11.Yang, H., Parthasarathy, S., Ucar, D.: A spatio-temporal mining approach towards summarizing and analyzing protein folding trajectories. Algorithms Mol. Biol. 2(3) (2007)Google Scholar