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

Abstract

To 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.