Spatio-temporal data mining: A survey of problems and methods

Gowtham Atluri, Anuj Karpatne, Vipin Kumar

Research output: Contribution to journalReview articlepeer-review

273 Scopus citations


Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains, including climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differ from relational data for which computational approaches are developed in the data-mining community for multiple decades in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data-mining community. In this article, we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data-mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data-mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data-mining problems in each of these categories.

Original languageEnglish (US)
Article number3161602
JournalACM Computing Surveys
Issue number4
StatePublished - Jul 2018

Bibliographical note

Publisher Copyright:
© 2018 ACM.


  • Data mining
  • Geographic data
  • Rasters
  • Spatial data
  • Spatiotemporal data
  • Timeseries


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