Using Social Media to Identify Sources of Healthy Food in Urban Neighborhoods
An established body of research has used secondary data sources (such as proprietary business databases) to demonstrate the importance of the neighborhood food environment for multiple health outcomes. However, documenting food availability using secondary sources in low-income urban neighborhoods can be particularly challenging since small businesses play a crucial role in food availability. These small businesses are typically underrepresented in national databases, which rely on secondary sources to develop data for marketing purposes. Using social media and other crowdsourced data to account for these smaller businesses holds promise, but the quality of these data remains unknown. This paper compares the quality of full-line grocery store information from Yelp, a crowdsourced content service, to a “ground truth” data set (Detroit Food Map) and a commercially-available dataset (Reference USA) for the greater Detroit area. Results suggest that Yelp is more accurate than Reference USA in identifying healthy food stores in urban areas. Researchers investigating the relationship between the nutrition environment and health may consider Yelp as a reliable and valid source for identifying sources of healthy food in urban environments.
KeywordsSocial media Neighborhood Food sources Grocery stores Yelp Reference USA
- 4.Auchincloss AH, Roux AVD, Mujahid MS, Shen M, Bertoni AG, Carnethon MR. Neighborhood resources for physical activity and healthy foods and incidence of type 2 diabetes mellitus: the multi-ethnic study of atherosclerosis. Arch Intern Med. 2009;169(18):1698–704.CrossRefPubMedPubMedCentralGoogle Scholar
- 8.Mantovani R, Daft L, Macaluso T, Welsh J, Hoffman K. Authorized food retailers’ characteristics and access study. US Department of Agriculture: Alexandria VA; 1997.Google Scholar
- 14.McKinnon RA, Reedy J, Morrissette MA, Lytle LA, Yaroch AL. Measures of the food environment. A compilation of the literature, 1990-2007. Am J Prev Med. [Review]. 2009;36(4 SUPPL):S124–S33.Google Scholar
- 17.Manduca R, Spielman SE, Folch D, editors. Fast food data: where user-generated content works and where it doesn't. Chicago, IL: Workshops on Big Data and Urban Informatics; 2014.Google Scholar
- 19.Michigan Department of Agriculture. Michigan’s food & agriculture industry. 2012. Retrieved from http://www.michigan.gov/documents/mdard/1262-AgReport-2012_2_404589_7. Accessed 12 March 2016.
- 23.Winkler WE. String comparator metrics and enhanced decision rules in the Fellegi-Sunter model of record linkage. Washington DC: US Census Bureau, Division SR; 1990.Google Scholar
- 25.National Academies of Sciences, Engineering, Medicine. A framework for educating health professionals to address the social determinants of health. Washington, DC: The National Academies Press; 2016.Google Scholar
- 26.LaRose R, Gregg JL, Strover S, Straubhaar J, Carpenter S. Closing the rural broadband gap. Sage -- Thousand Oaks, CA: promoting adoption of the internet in rural America. Telecommun Policy. 2007;31(6–7):359–73.Google Scholar
- 27.Spyratos S, Stathakis D, Lutz M, Tsinaraki C. Using Foursquare place data for estimating building block use. Environment and Planning B. Sage -- Thousand Oaks, CA: Planning and Design. Article first published online: July 27, 2016. doi:10.1177/0265813516637607.
- 28.Spyratos S, Stathakis D. Evaluating the services and facilities of European cities using crowdsourced place data. Environment and Planning B: Urban Analytics and City Science. Article first published online: January 2, 2017. doi:10.1177/0265813516686070