PDF(Pubmed)
Abstract：
BACKGROUND: It has been hypothesized that low access to healthy and nutritious food increases health disparities. Low-accessibility areas, called food deserts, are particularly commonplace in lower-income neighborhoods. The metrics for measuring the food environment\'s health, called food desert indices, are primarily based on decadal census data, limiting their frequency and geographical resolution to that of the census. We aimed to create a food desert index with finer geographic resolution than census data and better responsiveness to environmental changes.
METHODS: We augmented decadal census data with real-time data from platforms such as Yelp and Google Maps and crowd-sourced answers to questionnaires by the Amazon Mechanical Turks to create a real-time, context-aware, and geographically refined food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better food environments.
RESULTS: We made 139,000 pull requests to Yelp, analyzing 15,000 unique food retailers in the metro Atlanta area. In addition, we performed 248,000 walking and driving route analyses on these retailers using Google Maps\' API. As a result, we discovered that the metro Atlanta food environment creates a strong bias towards eating out rather than preparing a meal at home when access to vehicles is limited. Contrary to the food desert index that we started with, which changed values only at neighborhood boundaries, the food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the environment that occurred after the census data was collected.
CONCLUSIONS: Research on the environmental components of health disparities is flourishing. New machine learning models have the potential to augment various information sources and create fine-tuned models of the environment. This opens the way to better understanding the environment and its effects on health and suggesting better interventions.
METHODS: We augmented decadal census data with real-time data from platforms such as Yelp and Google Maps and crowd-sourced answers to questionnaires by the Amazon Mechanical Turks to create a real-time, context-aware, and geographically refined food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better food environments.
RESULTS: We made 139,000 pull requests to Yelp, analyzing 15,000 unique food retailers in the metro Atlanta area. In addition, we performed 248,000 walking and driving route analyses on these retailers using Google Maps\' API. As a result, we discovered that the metro Atlanta food environment creates a strong bias towards eating out rather than preparing a meal at home when access to vehicles is limited. Contrary to the food desert index that we started with, which changed values only at neighborhood boundaries, the food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the environment that occurred after the census data was collected.
CONCLUSIONS: Research on the environmental components of health disparities is flourishing. New machine learning models have the potential to augment various information sources and create fine-tuned models of the environment. This opens the way to better understanding the environment and its effects on health and suggesting better interventions.
摘要：
背景：据推测，获得健康和营养食品的机会不足会增加健康差异。低可达性地区，叫做食物沙漠，在低收入社区尤其普遍。衡量食物环境健康状况的指标，叫做食物沙漠指数，主要基于十年人口普查数据，将其频率和地理分辨率限制为人口普查的频率和地理分辨率。我们的目标是创建一个比人口普查数据具有更好地理分辨率的食物沙漠指数，并且对环境变化具有更好的响应能力。
方法：我们使用来自Yelp和GoogleMaps等平台的实时数据以及AmazonMechanicalTurks对问卷的众包答案来增强十年人口普查数据，以创建实时，上下文感知，和地理上精致的食物沙漠指数。最后,我们在一个概念应用程序中使用了这个完善的索引，该概念应用程序建议在亚特兰大都市区的来源和目的地之间具有类似ETA的替代路线，作为干预措施，以使旅行者接触到更好的食物环境。
结果：我们向Yelp发出了139,000个拉取请求，分析亚特兰大都会区的15,000家独特的食品零售商。此外,我们使用GoogleMaps\'API对这些零售商进行了248,000条步行和驾驶路线分析。因此,我们发现，亚特兰大都会区的食物环境会产生强烈的偏见，倾向于外出就餐，而不是在车辆有限的情况下在家做饭。与我们开始的食物沙漠指数相反，只在邻域边界改变了值，我们建立的食物沙漠指数记录了一个主题在城市中行走或开车时不断变化的暴露情况。该模型对收集人口普查数据后发生的环境变化也很敏感。
结论：关于健康差异的环境因素的研究正在蓬勃发展。新的机器学习模型有可能增加各种信息源并创建环境的微调模型。这为更好地了解环境及其对健康的影响并提出更好的干预措施开辟了道路。
方法：我们使用来自Yelp和GoogleMaps等平台的实时数据以及AmazonMechanicalTurks对问卷的众包答案来增强十年人口普查数据，以创建实时，上下文感知，和地理上精致的食物沙漠指数。最后,我们在一个概念应用程序中使用了这个完善的索引，该概念应用程序建议在亚特兰大都市区的来源和目的地之间具有类似ETA的替代路线，作为干预措施，以使旅行者接触到更好的食物环境。
结果：我们向Yelp发出了139,000个拉取请求，分析亚特兰大都会区的15,000家独特的食品零售商。此外,我们使用GoogleMaps\'API对这些零售商进行了248,000条步行和驾驶路线分析。因此,我们发现，亚特兰大都会区的食物环境会产生强烈的偏见，倾向于外出就餐，而不是在车辆有限的情况下在家做饭。与我们开始的食物沙漠指数相反，只在邻域边界改变了值，我们建立的食物沙漠指数记录了一个主题在城市中行走或开车时不断变化的暴露情况。该模型对收集人口普查数据后发生的环境变化也很敏感。
结论：关于健康差异的环境因素的研究正在蓬勃发展。新的机器学习模型有可能增加各种信息源并创建环境的微调模型。这为更好地了解环境及其对健康的影响并提出更好的干预措施开辟了道路。
