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On detecting spatial categorical outliers

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Abstract

Spatial outlier detection is an important research problem that has received much attentions in recent years. Most existing approaches are designed for numerical attributes, but are not applicable to categorical ones (e.g., binary, ordinal, and nominal) that are popular in many applications. The main challenges are the modeling of spatial categorical dependency as well as the computational efficiency. This paper presents the first outlier detection framework for spatial categorical data. Specifically, a new metric, named as Pair Correlation Ratio (PCR), is measured for each pair of category sets based on their co-occurrence frequencies at specific spatial distance ranges. The relevances among spatial objects are then calculated using PCR values with regard to their spatial distances. The outlierness for each object is defined as the inverse of the average relevance between an object and its spatial neighbors. Those objects with the highest outlier scores are returned as spatial categorical outliers. A set of algorithms are further designed for single-attribute and multi-attribute spatial categorical datasets. Extensive experimental evaluations on both simulated and real datasets demonstrated the effectiveness and efficiency of our proposed approaches.

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Authors and Affiliations

  1. Traffic Science, ebay Inc, One Bellevue Center, 411-108th Avenue NE, Bellevue, WA, 98004, USA

    Xutong Liu

  2. Inter-disciplinary research center (iLab), Carnegie Mellon University, Hamburg Hall #2105B, x8-3885, 4800 Forbes Ave, Pittsburgh, PA, 15213, USA

    Feng Chen

  3. Department of Computer Science, Virginia Polytechnic Institute and State University, 7054 Haycock Road, Falls Church, VA, 22043, USA

    Chang-Tien Lu

Authors
  1. Xutong Liu
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  2. Feng Chen
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  3. Chang-Tien Lu
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Correspondence to Xutong Liu.

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Liu, X., Chen, F. & Lu, CT. On detecting spatial categorical outliers. Geoinformatica 18, 501–536 (2014). https://doi.org/10.1007/s10707-013-0188-9

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  • DOI: https://doi.org/10.1007/s10707-013-0188-9

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