A multi-stage approach to maximizing geocoding success in a large population-based cohort study through automated and interactive processes

Geospat Health. 2012 May;6(2):273-84. doi: 10.4081/gh.2012.145.


To enable spatial analyses within a large, prospective cohort study of nearly 86,000 adults enrolled in a 12-state area in the southeastern United States of America from 2002-2009, a multi-stage geocoding protocol was developed to efficiently maximize the proportion of participants assigned an address level geographic coordinate. Addresses were parsed, cleaned and standardized before applying a combination of automated and interactive geocoding tools. Our full protocol increased the non-Post Office (PO) Box match rate from 74.5% to 97.6%. Overall, we geocoded 99.96% of participant addresses, with only 5.2% at the ZIP code centroid level (2.8% PO Box and 2.3% non-PO Box addresses). One key to reducing the need for interactive geocoding was the use of multiple base maps. Still, addresses in areas with population density <44 persons/km2 were much more likely to require resource-intensive interactive geocoding than those in areas with >920 persons/km2 (odds ratio (OR) = 5.24; 95% confidence interval (CI) = 4.23, 6.49), as were addresses collected from participants during in-person interviews compared with mailed questionnaires (OR = 1.83; 95% CI = 1.59, 2.11). This study demonstrates that population density and address ascertainment method can influence automated geocoding results and that high success in address level geocoding is achievable for large-scale studies covering wide geographical areas.

Publication types

  • Research Support, American Recovery and Reinvestment Act
  • Research Support, N.I.H., Extramural

MeSH terms

  • Confidence Intervals
  • Data Interpretation, Statistical
  • Epidemiologic Methods*
  • Female
  • Geographic Information Systems*
  • Humans
  • Logistic Models
  • Male
  • Odds Ratio
  • Population Density
  • Prospective Studies
  • Residence Characteristics / statistics & numerical data*
  • United States