Food accessibility has been a subject of growing interest due to its impact on public health outcomes. This paper describes a spatial analysis method to identify gaps in geographic food access and correlate them with a variety of demographic and socioeconomic factors. The proposed food accessibility metric is the square footage of supermarkets that can be reached within 10 min travel time by walking, biking, driving, and 30 min travel time by walk/transit. The spatial analysis is conducted for the centroids of each census tract within a study area, and the approach is illustrated with an application for the state of Massachusetts. Correlations between demographic and socioeconomic explanatory variables and food accessibility are explored using the Gradient Boosted machine learning model. More specifically, the explanatory variables are percent minority population, percent of population in poverty, vehicle ownership, and population density. The spatial analysis shows a strong correlation between food accessibility and population density. The machine learning model is then used to identify gaps in food accessibility for each transportation mode while controlling for community characteristics. The residuals of the model reveal which communities have the lowest food accessibility relative to other similar communities within the state. This research provides a quantitative method to identify communities that have reduced access to food relative to state-wide trends. Lastly, it provides insights for where policy interventions would be valuable for improving food access in addition to recommendations on increasing food accessibility.

中文翻译：

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通过交通方式测量和建模食品可及性


由于食品可及性对公共卫生结果的影响，它已成为人们日益关注的话题。本文描述了一种空间分析方法，用于识别地理食品获取方面的差距，并将其与各种人口和社会经济因素相关联。拟议的食品可达性指标是步行、骑自行车、驾车 10 分钟内可以到达的超市的平方英尺，步行/公共交通 30 分钟内可以到达的超市的平方英尺。对研究区域内每个人口普查区的质心进行空间分析，并通过马萨诸塞州的应用来说明该方法。使用梯度提升机器学习模型探索人口和社会经济解释变量与食物可及性之间的相关性。更具体地说，解释变量是少数民族人口百分比、贫困人口百分比、车辆拥有量和人口密度。空间分析显示食物可及性与人口密度之间存在很强的相关性。然后，使用机器学习模型来确定每种交通方式在食物可及性方面的差距，同时控制社区特征。模型的残差揭示了哪些社区相对于州内其他类似社区的食物可及性最低。这项研究提供了一种定量方法来识别相对于全州趋势而言食物获取机会减少的社区。最后，除了关于增加粮食可及性的建议之外，它还提供了政策干预对于改善粮食可及性有价值的见解。