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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DOI：
10.1186/s12942-020-00204-6
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发表时间：
2020-04-05
影响因子：
4.9
通讯作者：
Fotheringham, A. Stewart
中科院分区：
文献类型：
--
作者：
Oshan, Taylor M.;Smith, Jordan P.;Fotheringham, A. Stewart
背景 全世界大部分地区的肥胖率已达到流行病水平，对许多国家的健康和金融安全构成重大威胁。肥胖的原因可能各不相同，但往往很复杂和多因素，虽然可以针对许多促成因素进行干预，但为了实施有效的政策，有必要了解哪些地方需要这些干预措施。这引发了人们对将空间背景纳入肥胖决定因素的分析和建模的兴趣，特别是通过使用地理加权回归（GWR）。方法 本文对之前的肥胖过程 GWR 模型进行了批判性回顾，然后以凤凰城大都市区作为案例研究，提出了多尺度 (M)GWR 的新应用。结果 虽然 MGWR 模型比 OLS 消耗更多的自由度，但它比 GWR 消耗的自由度少得多，最终导致更细致的分析，可以纳入空间上下文，但不会强制每个关系都成为局部先验。此外，MGWR 产生的 AIC 和 AICc 值比 GWR 低，并且也不易出现多重共线性问题。因此，MGWR 能够通过提供特定决定因素的空间背景来提高我们对影响肥胖率的因素的理解。结论 结果表明，全局和局部过程的组合能够最好地模拟肥胖率，并且与 GWR 和普通最小二乘法相比，MGWR 提供了更丰富但更简洁的肥胖率决定因素的定量表示。
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.