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The impact of the weight matrix on the local indicators of spatial association: an application to per-capita value added in Italy

Author

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  • Massimo Mucciardi
  • Pietro Bertuccelli

Abstract

Making use of the local Moran index and taking as a case study the value added per capita in 103 Italian provinces in 2005, this work investigates how local measures of spatial association are affected by the use of different algorithms to calculate the weights of spatial matrices. We find that three methods we applied (adjacency, K-nn and MaxMin) provide very similar results at the global spatial level. Instead, considering the local spatial analysis, we note some differences in spatial outliers identification.

Suggested Citation

  • Massimo Mucciardi & Pietro Bertuccelli, 2012. "The impact of the weight matrix on the local indicators of spatial association: an application to per-capita value added in Italy," International Journal of Trade and Global Markets, Inderscience Enterprises Ltd, vol. 5(2), pages 133-141.
    • Handle: RePEc:ids:ijtrgm:v:5:y:2012:i:2:p:133-141
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    Cited by:

    1. M. Mucciardi & E. Otranto, 2016. "A Flexible Specification of Space–Time AutoRegressive Models," Working Paper CRENoS 201608, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    2. E. Otranto & M. Mucciardi, 2017. "Clustering Space-Time Series: A Flexible STAR Approach," Working Paper CRENoS 201707, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    3. Edoardo Otranto & Massimo Mucciardi & Pietro Bertuccelli, 2016. "Spatial effects in dynamic conditional correlations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 604-626, March.
    4. Edoardo Otranto & Massimo Mucciardi, 2019. "Clustering space-time series: FSTAR as a flexible STAR approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 175-199, March.

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