Web data mining : validity of data from Google Earth for food retail evaluation.

dc.contributor.authorMenezes, Mariana Carvalho de
dc.contributor.authorMatos, Vanderlei Pascoal de
dc.contributor.authorPina, Maria de Fátima de
dc.contributor.authorCosta, Bruna Vieira de Lima
dc.contributor.authorMendes, Larissa Loures
dc.contributor.authorPessoa, Milene Cristine
dc.contributor.authorSouza Junior, Paulo Roberto Borges de
dc.contributor.authorFriche, Amélia Augusta de Lima
dc.contributor.authorCaiaffa, Waleska Teixeira
dc.contributor.authorCardoso, Leticia de Oliveira
dc.date.accessioned2023-03-13T19:21:11Z
dc.date.available2023-03-13T19:21:11Z
dc.date.issued2020pt_BR
dc.description.abstractTo overcome the challenge of obtaining accu- rate data on community food retail, we developed an innovative tool to automatically capture food retail data from Google Earth (GE). The proposed method is rele- vant to non-commercial use or scholarly purposes. We aimed to test the validity of web sources data for the assessment of community food retail environment by comparison to ground-truth observations (gold standard). A secondary aim was to test whether validity differs by type of food outlet and socioeconomic status (SES). The study area included a sample of 300 census tracts strati- fied by SES in two of the largest cities in Brazil, Rio de Janeiro and Belo Horizonte. The GE web service was used to develop a tool for automatic acquisition of food retail data through the generation of a regular grid of points. To test its validity, this data was compared with the ground-truth data. Compared to the 856 outlets iden- tified in 285 census tracts by the ground-truth method, the GE interface identified 731 outlets. In both cities, the GE interface scored moderate to excellent compared to the ground-truth data across all of the validity measures: sensitivity, specificity, positive predictive value, negative predictive value and accuracy (ranging from 66.3 to 100%). The validity did not differ by SES strata. Super- markets, convenience stores and restaurants yielded bet- ter results than other store types. To our knowledge, this research is the first to investigate using GE as a tool to capture community food retail data. Our results suggest that the GE interface could be used to measure the community food environment. Validity was satisfactory for different SES areas and types of outlets.pt_BR
dc.identifier.citationMENEZES, M. C. de et al. Web data mining: validity of data from Google Earth for food retail evaluation. Journal of Urban Health, New York, v. 98, p. 285–295, nov. 2020. Disponível em: <https://link.springer.com/article/10.1007/s11524-020-00495-x>. Acesso em: 11 out. 2022.pt_BR
dc.identifier.doihttps://doi.org/10.1007/s11524-020-00495-xpt_BR
dc.identifier.issn1468-2869
dc.identifier.urihttp://www.repositorio.ufop.br/jspui/handle/123456789/16337
dc.identifier.uri2https://link.springer.com/article/10.1007/s11524-020-00495-xpt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectFood environmentpt_BR
dc.subjectValidation studypt_BR
dc.subjectGeocoding servicespt_BR
dc.subjectUrban healthpt_BR
dc.titleWeb data mining : validity of data from Google Earth for food retail evaluation.pt_BR
dc.typeArtigo publicado em periodicopt_BR

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