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. 2021 Apr;98(2):285-295.
doi: 10.1007/s11524-020-00495-x. Epub 2020 Nov 23.

Web Data Mining: Validity of Data from Google Earth for Food Retail Evaluation

Mariana Carvalho de Menezes  1 , Vanderlei Pascoal de Matos  2 , Maria de Fátima de Pina  2 , Bruna Vieira de Lima Costa  3 , Larissa Loures Mendes  3 , Milene Cristine Pessoa  3 , Paulo Roberto Borges de Souza-Junior  2 , Amélia Augusta de Lima Friche  4 , Waleska Teixeira Caiaffa  4 , Letícia de Oliveira Cardoso  5
Affiliations

Web Data Mining: Validity of Data from Google Earth for Food Retail Evaluation

Mariana Carvalho de Menezes et al. J Urban Health. 2021 Apr.

Abstract

To overcome the challenge of obtaining accurate data on community food retail, we developed an innovative tool to automatically capture food retail data from Google Earth (GE). The proposed method is relevant 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 stratified 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 identified 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. Supermarkets, convenience stores and restaurants yielded better 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.

Keywords: Food environment; Food retail; Geocoding services; Google Earth; Urban health; Validation study.

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Figures

Fig. 1
Fig. 1
Algorithm for the acquisition of food retail outlets data in Google Earth
Fig. 2
Fig. 2
Counts of food outlets during validation process.
Fig. 3
Fig. 3
Results of the field validation across socioeconomic conditions (the Health Vulnerability Index). Key: SES = socioeconomic status (measured here through the Health Vulnerability Index); TP = true positive; FN = false negative; FP = false positive; PPV = positive predictive value
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