Error and Bias in Determining Exposure Potential of Children at School Locations Using Proximity-Based GIS Techniques

• Published in: Environmental health perspectives 2007-09, Vol.115 (9), p.1363-1370
• Main Authors: Zandbergen, Paul A., Joseph W. Green
• Format: Article
• Language: English
• Subjects: Air Pollutants - analysis; Air pollution; Asthma; Bias; Child; Children; Children's Health; Environmental Exposure - analysis; Environmental health; Environmental monitoring; Environmental Monitoring - methods; Epidemiology; False positive errors; Florida; Geographic Information Systems; Health aspects; Humans; Location; Motor vehicle traffic; Research Design; Roads; School location; Schools; United States; Vehicle Emissions - analysis
• ISSN: 0091-6765; 1552-9924
• DOI: 10.1289/ehp.9668

Background: The widespread availability of powerful tools in commercial geographic information system (GIS) software has made address geocoding a widely employed technique in spatial epidemiologic studies. Objective: The objective of this study was to determine the effect of the positional error in geocoding on the analysis of exposure to traffic-related air pollution of children at school locations. Methods: For a case study of Orange County, Florida, we determined the positional error of geocoding of school locations through comparisons with a parcel database and digital orthophotography. We used four different geocoding techniques for comparison to establish the repeatability of geocoding, and an analysis of proximity to major roads to determine bias and error in environmental exposure assessment. Results: Results indicate that the positional error in geocoding of schools is very substantial: We found that the 95% root mean square error was 196 m using street centerlines, 306 m using TIGER roads, and 210 and 235 m for two commercial geocoding firms. We found bias and error in proximity analysis to major roads to be unacceptably large at distances of < 500 m. Bias and error are introduced by lack of positional accuracy and lack of repeatability of geocoding of school locations. Conclusions: These results suggest that typical geocoding is insufficient for fine-scale analysis of school locations and more accurate alternatives need to be considered.