CoChat
Sign Up
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

Open access
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
Open this paper in CoChat — free accountOpen PDF

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.

Overview

Integrity signals

Registry

Crossref · 10.3390/ijgi4042306

Cites

182 of 182 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 185 references

3 of the 185 references couldn’t be matched to a known paper.

Research works printed but not linked: 23

  1. Shekhar, S., and Chawla, S. (2003). Spatial Databases: A Tour, Prentice Hall.
  2. Issaks, E.H., and Svivastava, RM. (1989). Applied Geostatistics, Oxford University Press.
  3. Scally, R. (2006). GIS for Environmental Management, ESRI Press.
  4. Lang, L. (1999). Transportation GIS, ESRI Press.
  5. Ruß, G., and Brenning, A. (2010). Computational Intelligence for Knowledge-Based Systems Design, Springer.
  6. Marcus, 2014, Eight (no, nine!) problems with big data, N. Y. Times, 6, 2014
  7. Shekhar, S., Zhang, P., Huang, Y., and Vatsavai, R.R. (2003). Data Mining: Next Generation Challenges and Future Directions, AAAI Press.
  8. Koperski, K., Adhikary, J., and Han, J. (1996, January 4–6). Spatial data mining: Progress and challenges survey paper. Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, QC, Canada.
  9. Cheng, T., Haworth, J., Anbaroglu, B., Tanaksaranond, G., and Wang, J. (2014). Handbook of Regional Science, Springer.
  10. Yuan, M. (1996, January 21–26). Temporal GIS and spatio-temporal modeling. Proceedings of the Third International Conference Workshop on Integrating GIS and Environment Modeling, Santa Fe, NM, USA.
  11. George, B., and Shekhar, S. (2006, January 6–9). Time Aggregated Graphs: A model for spatio-temporal network. Proceedings of the Workshops (CoMoGIS) at the 25th International Conference on Conceptual Modeling (ER2006), Tucson, AZ, USA.
  12. Bolstad, P. (2002). GIS Fundamentals: A First Text on GIS, Eider Press.
  13. Quinlan, J. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers.
  14. Barnett, V., and Lewis, T. (1994). Outliers in Statistical Data, John Wiley. [3rd ed.].
  15. Shekhar, S., Lu, C., and Zhang, P. (2001, January 26–29). Graph-based outlier detection: Algorithms and applications (a summary of results). Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.
  16. Luc, 1995, Local indicators of spatial association: LISA, Geogr. Anal., 27, 93, 10.1111/j.1538-4632.1995.tb00338.x · DOI
  17. Luc, A. (1994). New Tools for Spatial Analysis, Wiley.
  18. Li, X., Han, J., and Kim, S. (2006). Intelligence and Security Informatics, Springer.
  19. Chen, C., Zhang, D., Castro, P.S., Li, N., Sun, L., and Li, S. (2012). Mobile and Ubiquitous Systems: Computing, Networking, and Services, Springer.
  20. Guting, R., and Schneider, M. (2005). Moving Object Databases, Morgan Kaufmann.
  21. Wang, M., Wang, A., and Li, A. (2006). Advanced Data Mining and Applications, Springer.
  22. Avery, C. (2011, January 29). Giraph: Large-scale graph processing infrastructure on hadoop. Proceedings of the Hadoop Summit, Santa Clara, CA, USA.
  23. Low, Y., Gonzalez, J.E., Kyrola, A., Bickson, D., Guestrin, C.E., and Hellerstein, J. Graphlab: A New Framework for Parallel Machine Learning. Available online: http://arxiv.org/pdf/1408.2041. · arXiv

Cited by

Sorted by citations

Items 201–206 of 206

Related

See all

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review