Geographically Weighted Regression Analysis: A Statistical Method to Account for Spatial Heterogeneity Pubmed



Raza O¹ ; Mansournia MA¹ ; Rahimi Foroushani A¹ ; Holakouienaieni K¹ Show Affiliations
Source: Archives of Iranian Medicine Published:2019

Abstract

Ordinary linear regression (OLR) is one of the most common statistical techniques used in determining the association between the outcome variable and its related factors. This method determines the association that is assumed to be true for the whole study area - a global association. In the field of public health and social sciences, this assumption is not always true, especially when it is known that the relationship between variables varies across the study area. Therefore, in such a scenario, an OLR should be calibrated in a way to account for this spatial variability. In this paper, we demonstrate use of the geographically weighted regression (GWR) method to account for spatial heterogeneity. In GWR, local models are reported in which association varies according to the location accounting for the local variation in variables. This technique utilizes geographical weights in determining association between the outcome variable and its related factors. These geographical weights are relatively large (i.e. close to 1) for observations located near regression point than for the observations located farther from the regression point. In this paper, we demonstrated the application of GWR and its comparison with OLR using demographic and health survey (DHS) data from Tanzania. Here we have focused on determining the association between percentages of acute respiratory infection (ARI) in children with its related factors. From OLR, we found that the percentage of female with higher education had the largest significant association with ARI (P = 0.027). On the other hand, result from the GWR returned coefficients varying from -0.15 to -0.01 (P < 0.001) over the study area in contrast to the global coefficient from OLR model. We advocate that identifying significant spatially-varying association will help policymaker to recognize the local areas of interest and design targeted interventions. © 2019 The Author(s).