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Cited by 206Data Management and AlgorithmsData Mining Algorithms and ApplicationsSpatial and Panel Data Analysis

The DOI matches its Crossref record; 182 of the 185 references it cites were found in a registry or an index.

Spatiotemporal Data Mining: A Computational Perspective

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Shekhar, Shashi, Jiang, Zhe, Ali, Reem, Eftelioglu, Emre, Tang, Xun, Gunturi, Venkata, Zhou, Xun·2015·ISPRS International Journal of Geo-Information
DOI 10.3390/ijgi4042306
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Abstract

Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

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Research works printed but not linked: 23

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Metadata

Authors
Shekhar, S., Jiang, Z., Ali, R., Eftelioglu, E., Tang, X., Gunturi, V., & Zhou, X.
Affiliations
  • University of Minnesota (US)
  • Indraprastha Institute of Information Technology Delhi (IN)
  • University of Iowa (US)
Corresponding author
Jiang, Zhe
ORCID
Venue
ISPRS International Journal of Geo-Information
Publisher
MDPI AG
Published
2015-10-28
Volume
4
Issue
4
Pages
2306-2338
ISSN
2220-9964 (ISSN-L)
DOI
10.3390/ijgi4042306
OpenAlex
W1850843018
Cited by
206
References
185
Citations by year
2026: 7 · 2025: 22 · 2024: 16 · 2023: 13 · 2022: 21 · 2021: 21 · 2020: 32 · 2019: 31 · 2018: 18 · 2017: 18 · 2016: 7
FWCI
17.45
Type
journal article
Language
en
Open access
Licence
Locations
  • ISPRS International Journal of Geo-Information (publishedVersion, cc-by)
  • Repository@Hull (Worktribe) (University of Hull) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation (0940818, 1218168, 1320580, 1029711)
  • U.S. Department of Defense (HM02101310005, HM15820810017)
  • University of Minnesota
Topics
  • Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Keywords
spatio-temporal data mining, spatiotemporal patterns, telecoupling, spatiotemporal prediction, change detection, spatio-temporal databases, statistical foundations

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