Encyclopedia of Database Systems

Living Edition
| Editors: Ling Liu, M. Tamer Özsu

Spatial Data Mining

  • Shashi Shekhar
  • Zhe Jiang
  • James Kang
  • Vijay Gandhi
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7993-3_357-2

Synonyms

Definition

Spatial data mining [1, 2, 3] is the process of discovering nontrivial, interesting, and useful patterns in large spatial datasets. The most common spatial pattern families are co-locations, spatial hotspots, spatial outliers, and location predictions.

Figure 1 gives an example of a spatial hotspot pattern (in the green circle) detected by SaTScan [ 4] from 250 cholera cases (shown by red points) that occurred near Broad Street in London, 1854. Notice that discovering spatial hotspots here is a nontrivial process due to the irregular size and special shape of the pattern. In addition, not all incidents contribute to the hotspot (e.g., red points outside the circles). Discovery of this pattern is very useful and interesting to detect outbreak of disease for public...
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Recommended Reading

  1. 1.
    Shekhar S, Chawla S. A tour of spatial databases. Englewood-Cliffs: Prentice Hall; 2003.Google Scholar
  2. 2.
    Miller HJ, Han J. Geographic data mining and knowledge discovery. 2nd ed. Boca Raton: CRC Press; 2009.Google Scholar
  3. 3.
    Zhou X, Shekhar S, Ali R. Spatiotemporal change footprint pattern discovery: an inter-disciplinary survey. WIREs Interdiscip Rev: Data Min Knowl Disc(DMKD), 4, 1, 1–23, 2014.Google Scholar
  4. 4.
    Kulldorff M. A spatial scan statistic. Commun Stat-Theory Methods. 1997;26(6):1481–96.MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Cressie NA. Statistics for spatial data. Rev ed. New York: Wiley; 1993.Google Scholar
  6. 6.
    Kou Y, Lu CT, Chen D. Algorithms for spatial outlier detection. In: Proceedings of 2003 IEEE international conference on data mining; 2003. p. 597–600.Google Scholar
  7. 7.
    Huang Y, Shekhar S, Xiong H. Discovering co-location patterns from spatial datasets: a general approach. IEEE Trans Knowl Data Eng. 2004;16(12):1472–85.CrossRefGoogle Scholar
  8. 8.
    Shekhar S, Schrater P, Vatsavai R, Wu W, Chawla S. Spatial contextual classification and prediction models for mining geospatial data. IEEE Trans Multimed. (special issue on Multimedia Databases). 2002;4(2):174–88.CrossRefGoogle Scholar
  9. 9.
    Jiang Z, Shekhar S, Zhou X, Knight J, Corcoran J. Focal-test-based spatial decision tree learning: a summary of results. In: Data mining (ICDM), 2013 IEEE 13th international conference on. IEEE. p. 320–9.Google Scholar
  10. 10.
    Oliver D, Shekhar S, Kang J, Laubscher R, Carlan V, Bannur A. A K-main routes approach to spatial network activity summarization. IEEE Trans Trans Knowl Data Eng. 2014;26(6):1464–78.CrossRefGoogle Scholar
  11. 11.
    US Department of Justice – Mapping and Analysis for Public Safety report. Mapping crime: understanding hot spots. 2005. http://www.ncjrs.gov/pdffiles1/nij/209393.pdf.
  12. 12.
    Oliver D, Shekhar S, Zhou X, Eftelioglu E, Evans MR, Zhuang Q, Kang JM, Laubscher R, Farah C. Significant route discovery: a summary of results. GIScience. 2014:284–300.Google Scholar
  13. 13.
    Eftelioglu E, Shekhar S, Kang JM, Farah CC. Ring-shaped hotspot detection. IEEE Trans Knowl Data Eng. 2016;28(12):3367–81.Google Scholar
  14. 14.
    Longley PA, Goodchild M, Maquire DJ, Rhind DW. Geographic information systems and science. Chichester: Wiley; 2005.Google Scholar
  15. 15.
    Mamoulis N, Cao H, Cheung DW. Mining frequent spatio-temporal sequential patterns. In: Proceedings of 2003 IEEE international conference on data mining; 2005. p. 82–9.Google Scholar
  16. 16.
    Shekhar S, Lu CT, Zhang P. A unified approach to detecting spatial outliers. GeoInformatica. 2003;7(2):139–66.CrossRefGoogle Scholar
  17. 17.
    Shekhar S, Zhang P, Huang Y, Vatsavai R, Kargupta H, Joshi A, Sivakumar K, Yesha Y. Trend in spatial data mining. In: Data mining: next generation challenges and future directions. AAAI/MIT Press; 2003.Google Scholar
  18. 18.
    Solberg AH, Taxt T, Jain AK. A Markov random field model for classification of multisource satellite imagery. IEEE Trans Geosci Remote Sens. 1996;34(1):100–13.CrossRefGoogle Scholar
  19. 19.
    Shekhar S, Evans M, Kang J, Mohan P. Identifying patterns in spatial information: a survey of methods Wiley Interdiscip Rev: Data Min Knowl Disc 1, 3 (2011): 193–214.Google Scholar
  20. 20.
    Shekhar S, Gunturi V, Evans MR, Yang K. Spatial big-data challenges intersecting mobility and cloud computing. In: Proceedings of the eleventh ACM international workshop on data engineering for wireless and mobile access. ACM; 2012. p. 1–6.Google Scholar

Copyright information

© Springer Science+Business Media LLC (outside the USA) 2017

Authors and Affiliations

  • Shashi Shekhar
    • 1
  • Zhe Jiang
    • 2
  • James Kang
    • 3
  • Vijay Gandhi
    • 3
  1. 1.Department of Computer ScienceUniversity of MinnesotaMinneapolisUSA
  2. 2.University of AlabamaTuscaloosaUSA
  3. 3.University of MinnesotaMinneapolisUSA

Section editors and affiliations

  • Dimitris Papadias
    • 1
  1. 1.Department of Computer Science and EngineeringHong Kong University of Science and TechnologyKowloonChina