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Developing local measures of spatial association for categorical data

Author

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  • Barry Boots

Abstract

This paper describes a procedure for extending local statistics to categorical spatial data. The approach is based on the notion that there are two fundamental characteristics of categorical spatial data; composition and configuration. Further, it is argued that, when considered locally, the latter should be measured conditionally with respect to the former. These ideas are developed for binary, gridded data. Local composition is measured by counting the numbers of cells of a particular type, while local configuration is measured by join counts. The approach is illustrated using a small, empirical data set and an ad hoc procedure is developed to deal with the impact of global spatial autocorrelation on the local statistics. Copyright Springer-Verlag Berlin Heidelberg 2003

Suggested Citation

  • Barry Boots, 2003. "Developing local measures of spatial association for categorical data," Journal of Geographical Systems, Springer, vol. 5(2), pages 139-160, August.
    • Handle: RePEc:kap:jgeosy:v:5:y:2003:i:2:p:139-160
    DOI: 10.1007/s10109-003-0110-3
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    Citations

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    Cited by:

    1. Luc Anselin & Xun Li, 2019. "Operational local join count statistics for cluster detection," Journal of Geographical Systems, Springer, vol. 21(2), pages 189-210, June.
    2. Brajendra C. Sutradhar & R. Prabhakar Rao, 2023. "Asymptotic Inferences in a Multinomial Logit Mixed Model for Spatial Categorical Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 885-930, February.
    3. Pietrzak Michał B. & Wilk Justyna & Bivand Roger S. & Kossowski Tomasz, 2014. "The Application Of Local Indicators For Categorical Data (LICD) In The Spatial Analysis Of Economic Development," Comparative Economic Research, Sciendo, vol. 17(4), pages 203-220, December.
    4. Qing Luo & Daniel A. Griffith & Huayi Wu, 2019. "Spatial autocorrelation for massive spatial data: verification of efficiency and statistical power asymptotics," Journal of Geographical Systems, Springer, vol. 21(2), pages 237-269, June.
    5. Herrera Gómez, Marcos, 2013. "Análisis de Estructuras Espaciales Persistentes. Desempleo Departamental en Argentina [Persistent Spatial Structure Analysis. Regional Unemployment in Argentina]," MPRA Paper 49407, University Library of Munich, Germany.
    6. Francesco Riccioli & Roberto Fratini & Fabio Boncinelli, 2021. "The Impacts in Real Estate of Landscape Values: Evidence from Tuscany (Italy)," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    7. Luc Anselin, 2019. "Quantile local spatial autocorrelation," Letters in Spatial and Resource Sciences, Springer, vol. 12(2), pages 155-166, August.

    More about this item

    Keywords

    categorical spatial data; local spatial statistics; spatial autocorrelation; C0; C15; C49;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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