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Clustering multivariate spatial data based on local measures of spatial autocorrelation

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  • Luca Scrucca

Abstract

A growing interest in clustering spatial data is emerging in several areas, from local economic development to epidemiology, from remote sensing data to environment analyses. However, methods and procedures to face such problem are still lacking. Local measures of spatial autocorrelation aim at identifying patterns of spatial dependence within the study region. Mapping these measures provide the basic building block for identifying spatial clusters of units. If this may work satisfactorily in the univariate case, most of the real problems have a multidimensional nature. Thus, we need a clustering method based on both the multivariate data information and the spatial distribution of units. In this paper we propose a procedure for exploring and discover patterns of spatial clustering. We discuss an implementation of the popular partitioning algorithm known as K-means which incorporates the spatial structure of the data through the use of local measures of spatial autocorrelation. An example based on a set of variables related to the labour market of the Italian region Umbria is presented and deeply discussed.

Suggested Citation

  • Luca Scrucca, 2005. "Clustering multivariate spatial data based on local measures of spatial autocorrelation," Quaderni del Dipartimento di Economia, Finanza e Statistica 20/2005, Università di Perugia, Dipartimento Economia.
    • Handle: RePEc:pia:wpaper:20/2005
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    File URL: http://www2.ec.unipg.it/quaderni/spatcluster.pdf
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    Cited by:

    1. Getayeneh Antehunegn Tesema & Zemenu Tadesse Tessema & Stephane Heritier & Rob G. Stirling & Arul Earnest, 2023. "A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research," IJERPH, MDPI, vol. 20(7), pages 1-24, March.
    2. Damiani, Mirella & Pompei, Fabrizio & Ricci, Andrea, 2011. "Temporary job protection and productivity growth in EU economies," MPRA Paper 29698, University Library of Munich, Germany.
    3. Silvia Micheli, 2010. "Learning Curve and Wind Power," Quaderni del Dipartimento di Economia, Finanza e Statistica 81/2010, Università di Perugia, Dipartimento Economia.
    4. Francesco Venturini, 2011. "Product variety, product quality, and evidence of Schumpeterian endogenous growth: a note," Quaderni del Dipartimento di Economia, Finanza e Statistica 93/2011, Università di Perugia, Dipartimento Economia.
    5. Stefano Herzel & Marco Nicolosi & Cătălin Stărică, 2012. "The cost of sustainability in optimal portfolio decisions," The European Journal of Finance, Taylor & Francis Journals, vol. 18(3-4), pages 333-349, May.
    6. Mirella Damiani, 2010. "Labour regulation, corporate governance and varieties of capitalism," Quaderni del Dipartimento di Economia, Finanza e Statistica 76/2010, Università di Perugia, Dipartimento Economia.
    7. Davide Castellani & Fabio Pieri, 2011. "Foreign Investments and Productivity Evidence from European Regions," Quaderni del Dipartimento di Economia, Finanza e Statistica 83/2011, Università di Perugia, Dipartimento Economia.

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