Predicting Density-Based Spatial Clusters Over Time
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Detection of emerging space-time clusters
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
Discovering Moving Clusters from Spatial-Temporal Databases
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
Convoy Queries in Spatio-Temporal Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A dissimilarity function for clustering geospatial polygons
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Accurate Discovery of Valid Convoys from Moving Object Trajectories
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Detecting spatio-temporal clusters, i.e. clusters of objects similar to each other occurring together across space and time, has important real-world applications such as climate change, drought analysis, detection of outbreak of epidemics (e.g. bird flu), bioterrorist attacks (e.g. anthrax release), and detection of increased military activity. Research in spatio-temporal clustering has focused on grouping individual objects with similar trajectories, detecting moving clusters, or discovering convoys of objects. However, most of these solutions are based on using a piece-meal approach where snapshot clusters are formed at each time stamp and then the series of snapshot clusters are analyzed to discover moving clusters. This approach has two fundamental limitations. First, it is point-based and is not readily applicable to polygonal datasets. Second, its static analysis approach at each time slice is susceptible to inaccurate tracking of dynamic cluster especially when clusters change over both time and space. In this paper we present a spatio-temporal polygonal clustering algorithm known as the Spatio-Temporal Polygonal Clustering (STPC) algorithm. STPC clusters spatial polygons taking into account their spatial and topological properties, treating time as a first-class citizen, and integrating density-based clustering with moving cluster analysis. Our experiments on the drought analysis application, flu spread analysis and crime cluster detection show the validity and robustness of our algorithm in an important geospatial application.