Spatio-temporal polygonal clustering with space and time as first-class citizens

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)387-412
Number of pages26
JournalGeoInformatica
Volume17
Issue number2
DOIs
StatePublished - Jan 1 2013

Fingerprint

Drought
citizen
Clustering algorithms
drought
Crime
Cluster analysis
Birds
Static analysis
anthrax
avian influenza
Climate change
meals
crime
polygon
cluster analysis
Trajectories
grouping
climate change
trajectory
Military

Keywords

  • Drought analysis
  • Polygonal clustering
  • Spatio-temporal data mining
  • Temporal data analysis
  • Trend analysis

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development

Cite this

Spatio-temporal polygonal clustering with space and time as first-class citizens. / Joshi, Deepti; Samal, Ashok K; Soh, Leen-Kiat.

In: GeoInformatica, Vol. 17, No. 2, 01.01.2013, p. 387-412.

Research output: Contribution to journalArticle

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