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

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RAZA OWAIS; MANSOURNIA MOHAMMAD ALI; RAHIMI FOROUSHANI ABBAS; HOLAKOUIE NAIENI KOUROSH

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Journal Paper

Paper Information

Journal: ARCHIVES OF IRANIAN MEDICINE Year:2019 | Volume:22 | Issue:3 Page(s): 155-160

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Title

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

Author(s)

RAZA OWAIS | MANSOURNIA MOHAMMAD ALI | RAHIMI FOROUSHANI ABBAS | HOLAKOUIE NAIENI KOUROSH | Issue Writer Certificate

Keywords

Acute Respiratory Infection (ARI)

Geographically weighted regression

Ordinary linear regression

Tanzania

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.

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An ecological on the spatially varying relationship between county-level suicide rates and altitude in the United States

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APA: Copy

RAZA, OWAIS, MANSOURNIA, MOHAMMAD ALI, RAHIMI FOROUSHANI, ABBAS, & HOLAKOUIE NAIENI, KOUROSH. (2019). Geographically Weighted Regression Analysis: A Statistical Method to Account for Spatial Heterogeneity. ARCHIVES OF IRANIAN MEDICINE, 22(3), 155-160. SID. https://sid.ir/paper/281827/en

Vancouver: Copy

RAZA OWAIS, MANSOURNIA MOHAMMAD ALI, RAHIMI FOROUSHANI ABBAS, HOLAKOUIE NAIENI KOUROSH. Geographically Weighted Regression Analysis: A Statistical Method to Account for Spatial Heterogeneity. ARCHIVES OF IRANIAN MEDICINE[Internet]. 2019;22(3):155-160. Available from: https://sid.ir/paper/281827/en

IEEE: Copy

OWAIS RAZA, MOHAMMAD ALI MANSOURNIA, ABBAS RAHIMI FOROUSHANI, and KOUROSH HOLAKOUIE NAIENI, “Geographically Weighted Regression Analysis: A Statistical Method to Account for Spatial Heterogeneity,” ARCHIVES OF IRANIAN MEDICINE, vol. 22, no. 3, pp. 155–160, 2019, [Online]. Available: https://sid.ir/paper/281827/en

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