Spatiotemporal Data Mining: A Computational Perspective
Open accessAbstract
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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High-resolution urban heat modeling involves large spatiotemporal datasets and repeated model training, which may require substantial computational time.
“High-resolution urban heat modeling involves large spatiotemporal datasets and repeated model training, which may require substantial computational time [17, 18].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [17]
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Self-citation
- 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
- 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)
- 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
- gold
- doi.org
- 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 › Signal Processing › 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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