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Using Geographically Weighted Models to Explore Obesity Prevalence Association with Air Temperature, Socioeconomic Factors, and Unhealthy Behavior in the USA

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Abstract

The literature informs assumptions about the relationship between obesity prevalence and socioeconomic and environmental factors. Little is known about the relationship between obesity, air temperature, unhealthy behaviors, and socioeconomic factors in the USA. We used regressive models like Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multi-Scale Geographical Weighted Regression (MGWR). The OLS provided a baseline model for analyzing public data, revealing the presence of spatially autocorrelated residuals, implying that geographically weighted models (GWM) may be appropriate. The results showed that MGWR performed better than GWR with an adjusted R2 of 0.75 and AICc = 4,777. The findings indicate that household composition (elder, younger, single parents, and people with disability), multi-unit housing, crowded housing, mobile homes, access to a private vehicle, smoking, and air temperature vary geographically and are related to obesity prevalence. Among them, household composition and smoking prevalence are significantly associated with obesity prevalence. MGWR specifies a different spatial scale for each explanatory variable, whereas classical GWR assumes that all modeled processes operate on the same spatial scale. By framing obesity determinants, MGWR facilitates the development of a more specific policy.

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  1. Department of Geography, Chicago State University, Chicago, IL, USA

    Aynaz Lotfata

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Lotfata, A. Using Geographically Weighted Models to Explore Obesity Prevalence Association with Air Temperature, Socioeconomic Factors, and Unhealthy Behavior in the USA. J geovis spat anal 6, 14 (2022). https://doi.org/10.1007/s41651-022-00108-y

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