Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

Abstract

Background: Obesity rates are recognized to be at epidemic levels throughout much of the world, posing significant threats to both the health and financial security of many nations. The causes of obesity can vary but are often complex and multifactorial, and while many contributing factors can be targeted for intervention, an understanding of where these interventions are needed is necessary in order to implement effective policy. This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants, especially through the use of geographically weighted regression (GWR).

Method: This paper provides a critical review of previous GWR models of obesogenic processes and then presents a novel application of multiscale (M)GWR using the Phoenix metropolitan area as a case study.

Results: Though the MGWR model consumes more degrees of freedom than OLS, it consumes far fewer degrees of freedom than GWR, ultimately resulting in a more nuanced analysis that can incorporate spatial context but does not force every relationship to become local a priori. In addition, MGWR yields a lower AIC and AICc value than GWR and is also less prone to issues of multicollinearity. Consequently, MGWR is able to improve our understanding of the factors that influence obesity rates by providing determinant-specific spatial contexts.

Conclusion: The results show that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants compared to both GWR and ordinary least squares.

Keywords: GWR; Multiscale; Obesity; Spatial epidemiology; Urban health.

Fig. 1

The Phoenix metropolitan area as covered in the 500 Cities Project. Obesity rate data is available for 10 individual cities

Fig. 2

Percentage of obese population by census tract in the Phoenix metropolitan area

Fig. 3

Composite maps for GWR (left) and MGWR (right) parameter estimate surfaces for percent Supplemental Nutrition Assistance Program (SNAP) (top), and percent college (bottom), which tend to show global patterns of spatial heterogeneity. Grey tracts are not statistically different from zero

Fig. 4

Composite maps for GWR (left) and MGWR (right) parameter estimate surfaces for percent African American (top), and percent Hispanic (bottom), which tend to show regional patterns of spatial heterogeneity. Grey tracts are not statistically different from zero

Fig. 5

Composite maps for GWR (left) and MGWR (right) parameter estimate surfaces for the intercept (top), and annual checkup (bottom), which tend to show local patterns of spatial heterogeneity. Grey tracts are not statistically different from zero

Fig. 6

Composite maps for GWR (left) and MGWR (right) parameter estimate surfaces for food desert (top), and mean normalized difference vegetation index (NDVI) (bottom), which show no distinct patterns. Grey tracts are not statistically different from zero

Fig. 7

Maps of local condition numbers for GWR (left) and MGWR (right)