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Comparison of local outlier detection techniques in spatial multivariate data

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Abstract

Outlier detection techniques in spatial data should allow to identify two types of outliers: global and local ones. Local outliers typically have non-spatial attributes that strongly differ from those observed on their neighbors. Detecting local outliers requires to be able to work locally, on neighborhoods, in order to take into account the spatial dependence between the statistical units under consideration, even though the outlyingness is usually measured on the non-spatial variables. Many procedures have been outlined in the literature, but their number reduces when one wants to deal with multivariate non-spatial attributes. In this paper, focus is on the multivariate context. A review of existing procedures is done. A new approach, based on a two-step improvement of an existing one, is also designed and compared with the benchmarked methods by means of examples and simulations.

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Acknowledgments

This work was partially supported by the IAP Research Network P7/06 of the Belgian State.

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Authors and Affiliations

  1. Université de Liège, Allée de la Découverte 12, Liège, Belgium

    Marie Ernst & Gentiane Haesbroeck

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  1. Marie Ernst
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  2. Gentiane Haesbroeck
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Correspondence to Marie Ernst.

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Responsible editor: Charu Aggarwal.

Appendix: Implementation: algorithm of the regularized spatial detection technique

Appendix: Implementation: algorithm of the regularized spatial detection technique

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Ernst, M., Haesbroeck, G. Comparison of local outlier detection techniques in spatial multivariate data. Data Min Knowl Disc 31, 371–399 (2017). https://doi.org/10.1007/s10618-016-0471-0

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  • DOI: https://doi.org/10.1007/s10618-016-0471-0

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