Synonyms
Co-locations; Hotspots; K-primary-route summarization; Location prediction; Spatial autocorrelation; Spatial data analysis; Spatial decision trees; Spatial outliers; Spatial statistics; Ring shaped hotspots
Definition
Spatial data mining [19831,19832,3] 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...
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Shekhar S, Chawla S. A tour of spatial databases. Englewood-Cliffs: Prentice Hall; 2003.
Miller HJ, Han J. Geographic data mining and knowledge discovery. 2nd ed. Boca Raton: CRC Press; 2009.
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.
Kulldorff M. A spatial scan statistic. Commun Stat-Theory Methods. 1997;26(6):1481–96.
Cressie NA. Statistics for spatial data. Rev ed. New York: Wiley; 1993.
Kou Y, Lu CT, Chen D. Algorithms for spatial outlier detection. In: Proceedings of the 3rd IEEE International Conference on Data Mining; 2003. p. 597–600.
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.
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.
Jiang Z, Shekhar S, Zhou X, Knight J, Corcoran J. Focal-test-based spatial decision tree learning: a summary of results. In: Proceedings of the 13th IEEE International Conference on Data Mining; 2013. p. 320–9.
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.
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.
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. In: Proceedings of the 8th International Conference on Geographic Information Science; 2014. p. 284–300.
Eftelioglu E, Shekhar S, Kang JM, Farah CC. Ring-shaped hotspot detection. IEEE Trans Knowl Data Eng. 2016;28(12):3367–81.
Longley PA, Goodchild M, Maquire DJ, Rhind DW. Geographic information systems and science. Chichester: Wiley; 2005.
Mamoulis N, Cao H, Cheung DW. Mining frequent spatio-temporal sequential patterns. In: Proceedings of the 5th IEEE International Conference on Data Mining; 2005. p. 82–9.
Shekhar S, Lu CT, Zhang P. A unified approach to detecting spatial outliers. GeoInformatica. 2003;7(2):139–66.
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.
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.
Shekhar S, Evans M, Kang J, Mohan P. Identifying patterns in spatial information: a survey of methods. Wiley Interdiscip Rev: Data Min Knowl Disc. 2011;1(3):193–214.
Shekhar S, Gunturi V, Evans MR, Yang K. Spatial big-data challenges intersecting mobility and cloud computing. In: Proceedings of the 11th ACM International Workshop on Data Engineering for Wireless and Mobile Access; 2012. p. 1–6.
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Shekhar, S., Jiang, Z., Kang, J., Gandhi, V. (2018). Spatial Data Mining. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_357
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