Can Geographically Weighted Regression improve our contextual understanding of obesity in the US? Findings from the USDA Food Atlas
Can Geographically Weighted Regression improve our contextual understanding of obesity in the US? Findings from the USDA Food Atlas
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DOI：
10.1016/j.apgeog.2013.07.017
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发表时间：
2013-10-01
影响因子：
4.9
通讯作者：
Choi, Jinmu
中科院分区：
文献类型：
--
作者：
Chi, Sang-Hyun;Grigsby-Toussaint, Diana S.;Choi, Jinmu
人们对食物环境以及人口和社会经济因素在美国肥胖症流行中所起的作用越来越感兴趣。然而，检验食物环境和肥胖风险之间关系的现有经验证据仍然含糊不清。我们假设，空间异质性可能解释了相互矛盾的结果。使用地理加权回归，我们研究了食物环境和与肥胖相关的人口和社会经济变量之间的关联如何在美国县级的空间中变化。分析表明，在美国，便利店与杂货店的比例、贫困率和城市环境与肥胖风险呈正相关。相反，自然环境较好的地区与肥胖风险呈负相关。最重要的是，肥胖和我们分析中的所有主要解释变量之间的关联在空间上显著不同。(C)2013爱思唯尔有限公司。保留所有权利。
There is growing interest in the role the food environment as well as demographic and socio-economic factors play in the prevalence of obesity in the US. Existing empirical evidence examining the association between the food environment and obesity risk, however, remains equivocal. We hypothesized that spatial heterogeneity may account for the conflicting results. Using Geographically Weighted Regression, we examined how the associations between the food environment, and demographic and socioeconomic variables associated with obesity vary over space at the county level in the US. The analysis shows that higher ratios of convenience-to-grocery stores, poverty rate, and urban environments were positively associated with obesity risk in the US. Conversely, areas with better physical environments were negatively associated with obesity risk. Most importantly, the association between obesity and all major explanatory variables in our analysis significantly varied over space. (C) 2013 Elsevier Ltd. All rights reserved.