title: A methodology for combining multiple commercial data sources to improve measurement of the food and alcohol environment: applications of geographical information systems.
abstract: Commercial data sources have been increasingly used to measure and locate community resources. We describe a methodology for combining and comparing the differences in commercial data of the food and alcohol environment. We used commercial data from two commercial databases (InfoUSA and Dun&Bradstreet) for 2003 and 2009 to obtain information on food and alcohol establishments and developed a matching process using computer algorithms and manual review by applying ArcGIS to geocode addresses, standard industrial classification and North American industry classification taxonomy for type of establishment and establishment name. We constructed population and area-based density measures (e.g. grocery stores) and assessed differences across data sources and used ArcGIS to map the densities. The matching process resulted in 8,705 and 7,078 unique establishments for 2003 and 2009, respectively. There were more establishments captured in the combined dataset than relying on one data source alone, and the additional establishments captured ranged from 1,255 to 2,752 in 2009. The correlations for the density measures between the two data sources was highest for alcohol outlets (r = 0.75 and 0.79 for per capita and area, respectively) and lowest for grocery stores/supermarkets (r = 0.32 for both). This process for applying geographical information systems to combine multiple commercial data sources and develop measures of the food and alcohol environment captured more establishments than relying on one data source alone. This replicable methodology was found to be useful for understanding the food and alcohol environment when local or public data are limited.
authors.affiliation: University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, USA.
authors.forename: Dara D
authors.initials: DD
authors.lastname: Mendez
authors.affiliation: University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, USA.
authors.forename: Jessica
authors.initials: J
authors.lastname: Duell
authors.affiliation: University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, USA.
authors.forename: Sarah
authors.initials: S
authors.lastname: Reiser
authors.affiliation: University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, USA.
authors.forename: Deborah
authors.initials: D
authors.lastname: Martin
authors.affiliation: University of Pittsburgh, Center for Social and Urban Research, Pittsburgh, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Gradeck
authors.affiliation: University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, USA.
authors.forename: Anthony
authors.initials: A
authors.lastname: Fabio
mesh_terms: D000434:Alcoholic Beverages / Q000600:supply & distribution*; D003132:Commerce / Q000706:statistics & numerical data; D016208:Databases, Factual; D005524:Food Technology / Q000706:statistics & numerical data*; D040362:Geographic Information Systems; D006801:Humans; D016247:Information Storage and Retrieval; D011156:Population Density; D012111:Residence Characteristics / Q000706:statistics & numerical data; D014481:United States / Q000453:epidemiology