Spatial Modeling for Poverty: The Comparison of Spatial Error Model and Geographic Weighted Regression

Achi Rinaldi, Yuni Susianto, Budi Santoso, Wahyu Kusumaningtyas


This study aims to analyze poverty using spatial models. The researchers also compared the Spatial Error Model (SEM) and Geographically Weighted Regression (GWR). The comparison of the two models was based on the estimation evaluation criteria and the constructed spatial associations. Spatial regression is considered very appropriate to be used to model the relationship pattern between poverty and explanatory variables when the observed data has a spatial effect caused by the proximity between the observation areas. The spatial dependence of errors on observational data can be overcome using SEM, while the effect of heterogeneity of spatial variance can overcome using GWR.


Geographically Weighted Regression, poverty, regression, spatial dependence, Spatial Error Model.

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