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Spatial uncertainty in cluster detection

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Abstract

Advances in GIS are increasingly focused on providing more sophisticated spatial analytical capabilities. Much of this work assumes no attribute and positional uncertainties in data. While there has been considerable research devoted to enhanced data creation techniques and metadata associated with error and uncertainty, little has been done to characterize or better understand error/uncertainty impacts in spatial analysis. This paper explores issues associated with the detection and significance of clusters under known positional uncertainty. Multiple equally likely data instances in which positional certainty is not assumed are assessed for existence of clusters. Results suggest that identified patterns can vary significantly when there is error or uncertainty in spatial data.

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Authors and Affiliations

  1. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA

    Stephanie Kleinschmidt, Sergio J. Rey & Luc Anselin

  2. Department of Geography, University of California at Santa Barbara, Santa Barbara, CA, USA

    Alan T. Murray

  3. College of Public Service and Community Solutions, Arizona State University, Phoenix, AZ, USA

    Tony H. Grubesic

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  1. Stephanie Kleinschmidt
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Correspondence to Alan T. Murray.

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Kleinschmidt, S., Murray, A.T., Rey, S.J. et al. Spatial uncertainty in cluster detection. Spat. Inf. Res. 24, 181–189 (2016). https://doi.org/10.1007/s41324-016-0019-9

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