Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/184412 
Year of Publication: 
2016
Citation: 
[Journal:] Comparative Economic Research. Central and Eastern Europe [ISSN:] 2082-6737 [Volume:] 19 [Issue:] 5 [Publisher:] De Gruyter [Place:] Warsaw [Year:] 2016 [Pages:] 5-24
Publisher: 
De Gruyter, Warsaw
Abstract: 
Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran's and the Geary's coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free) software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP), valuable in exploratory spatial data analysis.
Subjects: 
duality diagram
spatial autocorrelation
Moran'index
Moran's Eigenvector Maps
Laplace operator
spatial eigenfunction filtering
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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