干旱区生态环境知识资源中心(Arid): Predicting access to healthful food retailers with machine learning

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DOI	10.1016/j.foodpol.2020.101985
	Predicting access to healthful food retailers with machine learning
	Amin, Modhurima Dey; Badruddoza, Syed; McCluskey, Jill J.
通讯作者	McCluskey, JJ (corresponding author), Washington State Univ, Sch Econ Sci, Pullman, WA 99164 USA.
来源期刊	FOOD POLICY
ISSN	0306-9192
EISSN	1873-5657
出版年	2021
卷号	99
英文摘要	Many U.S. households lack access to healthful food and rely on inexpensive, processed food with low nutritional value. Surveying access to healthful food is costly and finding the factors that affect access remains convoluted owing to the multidimensional nature of socioeconomic variables. We utilize machine learning with census tract data to predict the modified Retail Food Environment Index (mRFEI), which refers to the percentage of healthful food retailers in a tract and agnostically extract the features of no access?corresponding to a ?food desert? and low access?corresponding to a ?food swamp.? Our model detects food deserts and food swamps with a prediction accuracy of 72% out of the sample. We find that food deserts and food swamps are intrinsically different and require separate policy attention. Food deserts are lightly populated rural tracts with low ethnic diversity, whereas swamps are predominantly small, densely populated, urban tracts, with more non-white residents who lack vehicle access. Overall access to healthful food retailers is mainly explained by population density, presence of black population, property value, and income. We also show that our model can be used to obtain sensible predictions of access to healthful food retailers for any U.S. census tract.
英文关键词	Food deserts Food swamps Machine learning
类型	Article
语种	英语
开放获取类型	Green Published
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000626623100002
WOS关键词	NEIGHBORHOOD CHARACTERISTICS ; STORE AVAILABILITY ; VEGETABLE INTAKE ; MARKET BASKET ; DIET QUALITY ; ENVIRONMENT ; DESERTS ; FRUIT ; RESIDENTS ; SUPERMARKET
WOS类目	Agricultural Economics & Policy ; Economics ; Food Science & Technology ; Nutrition & Dietetics
WOS研究方向	Agriculture ; Business & Economics ; Food Science & Technology ; Nutrition & Dietetics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350227
作者单位	[Amin, Modhurima Dey; Badruddoza, Syed] Texas Tech Univ, Dept Agr & Appl Econ, Lubbock, TX 79409 USA; [McCluskey, Jill J.] Washington State Univ, Sch Econ Sci, Pullman, WA 99164 USA
推荐引用方式 GB/T 7714	Amin, Modhurima Dey,Badruddoza, Syed,McCluskey, Jill J.. Predicting access to healthful food retailers with machine learning[J],2021,99.
APA	Amin, Modhurima Dey,Badruddoza, Syed,&McCluskey, Jill J..(2021).Predicting access to healthful food retailers with machine learning.FOOD POLICY,99.
MLA	Amin, Modhurima Dey,et al."Predicting access to healthful food retailers with machine learning".FOOD POLICY 99(2021).