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Targeting the spatial context of obesity determinants via multiscale geographically weighted regression
by
Oshan, Taylor M.
, Fotheringham, A. Stewart
, Smith, Jordan P.
in
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
2020
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Targeting the spatial context of obesity determinants via multiscale geographically weighted regression
by
Oshan, Taylor M.
, Fotheringham, A. Stewart
, Smith, Jordan P.
in
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
2020
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Do you wish to request the book?
Targeting the spatial context of obesity determinants via multiscale geographically weighted regression
by
Oshan, Taylor M.
, Fotheringham, A. Stewart
, Smith, Jordan P.
in
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
2020
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Targeting the spatial context of obesity determinants via multiscale geographically weighted regression
Journal Article
Targeting the spatial context of obesity determinants via multiscale geographically weighted regression
2020
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Overview
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.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
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