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This research presents and applies a new procedure to elaborate a spatial hierarchy of poverty. The proposed method simultaneously combines variables of magnitude and intensity based on recent (bootstrapping & spatial autocorrelation) and traditional statistical techniques, and overlay routines of Geographical Information Systems. While magnitude and intensity refer to absolute and relative data, respectively, each variable may be concentrated or agglomerated in space. In this study, concentration is the presence of high global values, regardless of their location, and ag glomeration is the concentration of spatially contiguous high local values. Both agglomeration and concentration are merged through a geographical overlay procedure to create conglomerates of magnitude or intensity of poverty. Cases inside these conglomerates are classified by gaussian (natural breaks) or paretian (heads and tails) procedures to set up a spatial hierarchy.

Mapa y jerarquía espacial de la pobreza en México. Un nuevo procedimiento para identificar el patrón espacial de los problemas sociales