Synonyms
Neighborhood; Scan statistic; Outlier; Spatial data mining; Anomaly detection
Definition
Spatial data mining deals with the discovery of non trivial patterns in spatial datasets. It includes the discovery of regularly occurring patterns such as associations between objects and the abnormal patterns such as outlying objects in the data. This discovery differs from traditional data mining due to the complex nature of spatial data due to two important properties of spatial autocorrelation such that nearby objects behave in a similar manner to each other and spatial heterogeneitywhere nearby objects may exhibit different properties. This entry outlines the discovery of spatial anomalies which are abnormal patterns in the data. This is pertinent to the discovery for homeland security where peculiar objects and associations in the spatial context may be highly relevant. An overview is presented of spatial data mining techniques which play a key role in homeland security...
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Besag J., Newell, J.: The detection of clusters in rare diseases. J. R. Stat. Soc. Series A 154, 143–155 (1991)
Cai, G., Bolelli, L., MacEachren, A.M., Sharma, R., Fuhrmann, S., McNeese, M.: Geocollaborative crisis management: using maps to mediate eoc–mobile team collaboration. In: dg.o '04: Proceedings of the 2004 annual national conference on Digital government research, pp. 1–2. Digital Government Research Center (2004)
Duczmal L., Renato, A.: AÂ simulated annealing strategy for the detection of arbitrarily shaped spatial clusters. Comput. Statist. Data Anal. 45(2), (2004)
Ester, M., Kriegel, H., Sander, J.: Spatial data mining: A database approach. In: 5th International Symposium on Advances in Spatial Databases, pp 47–66. Springer-Verlag, London, UK (1997)
Janeja V.P., Atluri, V.: FS3: A random walk based free-form spatial scan statistic for anomalous window detection. In: ICDM, pp. 661–664 (2005)
Janeja V.P., Atluri, V.: ls3: AÂ linear semantic scan statistic technique for detecting anomalous windows. In: ACM Symposium on Applied Computing (2005)
Kang, I., Kim, T., Li, K.: A spatial data mining method by delaunay triangulation. In: 5th ACM int. workshop on Advances in GIS, pp. 35–39 (1997)
Kou, Y., Lu, C., D. Chen, D.: Spatial weighted outlier detection. In: Proceedings of the 6th SIAM Int. Conference on Data Mining 2006, Bethesda, MD, USA, 20–22 April (2006)
Kulldorff, M.: A spatial scan statistic. Commun. Stat. – Theory Meth. 26(6),1481–1496 (1997)
Lu, C., Chen, D., Kou, Y.: Detecting spatial outliers with multiple attributes. In: 15th IEEE International Conference on Tools with Artifcial Intelligence, p. 122 (2003)
MacEachren, A.M., Cai, G., McNeese, M., Sharma, R., Fuhrmann, S.: Geocollaborative crisis management: Designing technologies to meet real-world needs. In: Proceedings of the 7th annual national conference on Digital government research. Digital Government Research Center (2006)
Adam, N., Janeja, V.P., Atluri, V.: Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets. In: 19th ACM Symp. on Applied Computing (2004)
Naus, J.: The distribution of the size of the maximum cluster of points on the line. J. Am. Stat. Assoc. 60, 532–538 (1965)
Openshaw, S.: A mark 1 geographical analysis machine for the automated analysis of point data sets. Int. J. GIS 1(4):335–358 (1987)
Port Authority of NY and NJ. http://www.panynj.gov. Accessed May 2005
Kenyon, S.H.: Regional Effort Forges Emergency Coordination System, Signal Magazine (2004). http://www.afcea.org/signal/articles/anmviewer.asp?a=13z=2. Accessed Aug 2007
Shekhar, S., Lu, C., Zhang, P.: Detecting graph-based spatial outliers: algorithms and applications (a summary of results). In: 7th ACM international conference on Knowledge discovery and data mining, pp. 371–376 (2001)
Sun P., Chawla, S.: On local spatial outliers. In: 4th IEEE International Conference on Data Mining, pp. 209–216 (2004)
Tango T., Takahashi, K.: flexscan: Software for the flexible scan statistics, version 1.1.2. http://www.niph.go.jp/soshiki/gijutsu/download/ exscan/index.html (2005)
Wang, G., Chen, H., Atabakhsh, H.: Automatically detecting deceptive criminal identities. Commun. ACM 47(3):70–76 (2004)
Xu J., Chen, H.: Untangling criminal networks: A case study. In: ISI, pp. 232–248 (2003)
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Janeja, V., Adam, N. (2008). Homeland Security and Spatial Data Mining. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_568
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DOI: https://doi.org/10.1007/978-0-387-35973-1_568
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30858-6
Online ISBN: 978-0-387-35973-1
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