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A novel spatial outlier detection technique

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  • Alok Kumar Singh
  • S. Lalitha

Abstract

Spatial outliers are spatially referenced objects whose non spatial attribute values are significantly different from the corresponding values in their spatial neighborhoods. In other words, a spatial outlier is a local instability or an extreme observation that deviates significantly in its spatial neighborhood, but possibly not be in the entire dataset. In this article, we have proposed a novel spatial outlier detection algorithm, location quotient (LQ) for multiple attributes spatial datasets, and compared its performance with the well-known mean and median algorithms for multiple attributes spatial datasets, in the literature. In particular, we have applied the mean, median, and LQ algorithms on a real dataset and on simulated spatial datasets of 13 different sizes to compare their performances. In addition, we have calculated area under the curve values in all the cases, which shows that our proposed algorithm is more powerful than the mean and median algorithms in almost all the considered cases and also plotted receiver operating characteristic curves in some cases.

Suggested Citation

  • Alok Kumar Singh & S. Lalitha, 2018. "A novel spatial outlier detection technique," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(1), pages 247-257, January.
  • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:1:p:247-257
    DOI: 10.1080/03610926.2017.1301477
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    Cited by:

    1. Moreno Bevilacqua & Christian Caamaño-Carrillo & Reinaldo B. Arellano-Valle & Camilo Gómez, 2022. "A class of random fields with two-piece marginal distributions for modeling point-referenced data with spatial outliers," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 644-674, September.

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