Positional error in automated geocoding of residential addresses

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Positional error in automated geocoding of residential addresses

Publisher
BioMed Central
Publication Date
Dec 19, 2003
Source
PMC
Keywords
Disciplines
  • Ecology
  • Geography
License
Unknown

Abstract

1476-072x-2-10.fm ral International Journal of Health ss BioMed CentGeographics Open AcceMethodology Positional error in automated geocoding of residential addresses Michael R Cayo* and Thomas O Talbot Address: Geographic Research and Analysis Section, Bureau of Environmental and Occupational Epidemiology, New York State Department of Health, 547 River Street, Room 200, Troy, NY 12180-2216, USA Email: Michael R Cayo* - [email protected]; Thomas O Talbot - [email protected] * Corresponding author Abstract Background: Public health applications using geographic information system (GIS) technology are steadily increasing. Many of these rely on the ability to locate where people live with respect to areas of exposure from environmental contaminants. Automated geocoding is a method used to assign geographic coordinates to an individual based on their street address. This method often relies on street centerline files as a geographic reference. Such a process introduces positional error in the geocoded point. Our study evaluated the positional error caused during automated geocoding of residential addresses and how this error varies between population densities. We also evaluated an alternative method of geocoding using residential property parcel data. Results: Positional error was determined for 3,000 residential addresses using the distance between each geocoded point and its true location as determined with aerial imagery. Error was found to increase as population density decreased. In rural areas of an upstate New York study area, 95 percent of the addresses geocoded to within 2,872 m of their true location. Suburban areas revealed less error where 95 percent of the addresses geocoded to within 421 m. Urban areas demonstrated the least error where 95 percent of the addresses geocoded to within 152 m of their true location. As an alternative to using street centerline files for geocoding, we used residential property parcel points to locate the addresses.

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