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

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 tar...

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Published in:International journal of health geographics Vol. 19; no. 1; pp. 11 - 17
Main Authors: Oshan, Taylor M., Smith, Jordan P., Fotheringham, A. Stewart
Format: Journal Article
Language:English
Published: London BioMed Central 05.04.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Analysis
Arizona - epidemiology
Degrees of freedom
Epidemiology
Estimates
Geographic Information Systems
GWR
Health Informatics
Health Promotion and Disease Prevention
Human Geography
Humans
Hypotheses
Least-Squares Analysis
Medical Geography
Medicine
Medicine & Public Health
Metropolitan areas
Models, Theoretical
Multiscale
Obesity
Obesity - epidemiology
Public Health
Regression analysis
Spatial analysis
Spatial epidemiology
Spatial Regression
Studies
Urban health
Arizona
Obesity
Spatial epidemiology
GWR
Urban health
Multiscale
ISSN:1476-072X, 1476-072X
Online Access:Get full text
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Summary: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.
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ISSN:1476-072X
1476-072X
DOI:10.1186/s12942-020-00204-6