Composition and context drivers of residential property location value as a socioeconomic status measure

Coffee, Neil; Lockwood, Tony; Rossini, Peter; Niyonsenga, Théophile; McGreal, Stanley

doi:10.1177/2399808318805489

Cited by 12 publications

(9 citation statements)

References 31 publications

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“…The goal of this study was to determine whether BE measures at baseline had any influence on changes in body mass index (BMI) at year 1 and/or at year 2. We further compared these associations with that of residential property values, a proxy measure of socioeconomic status (SES) associated with individual accumulated wealth, and BMI change ( Berrigan et al, 2015 ; Coffee, Lockwood, Rossini, Niyonsenga, & McGreal, 2020 ; Drewnowski et al, 2016 , 2020 ; Leonard et al, 2016 , 2017 ; Ware, 2019 ). We evaluated these associations in a prospective cohort of adult respondents from three urban and rural counties in WA State, which differed in both racial/ethnic composition and in urban form ( Buszkiewicz et al, 2020 ; Gupta et al, 2021 ; Rose et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Associations between neighborhood built environment, residential property values, and adult BMI change: The Seattle Obesity Study III

Buszkiewicz

Rose

et al. 2022

SSM - Population Health

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Section: Introductionmentioning

confidence: 99%

Associations between neighborhood built environment, residential property values, and adult BMI change: The Seattle Obesity Study III

Buszkiewicz

Rose

et al. 2022

SSM - Population Health

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“…These spatial patterns may include both the effect of geography, as well as the effect of geographic patterning associated with omitted variables. For example, spatial context may play a distinct role in shaping human behavior (e.g., [96,97]). Alternatively, the intercept may be useful for identifying additional determinants, policy formation, and informing follow-up investigations.…”

Section: Discussionmentioning

confidence: 99%

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

2020

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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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“…These spatial patterns may include both the effect of geography, as well as the effect of geographic patterning associated with omitted variables. For example, spatial context may play a distinct role in shaping human behavior (e.g., Enos, 2017;Coffee et al, 2018). Alternatively, the intercept may be useful for identifying additional determinants, policy formation, and informing follow-up investigations.…”

Section: Discussionmentioning

confidence: 99%

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

Oshan¹,

Smith²,

Fotheringham³

2020

Preprint

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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.

show abstract

Composition and context drivers of residential property location value as a socioeconomic status measure

Cited by 12 publications

References 31 publications

Associations between neighborhood built environment, residential property values, and adult BMI change: The Seattle Obesity Study III

Associations between neighborhood built environment, residential property values, and adult BMI change: The Seattle Obesity Study III

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

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

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