Methodology
Computing travel time when the exact address is unknown: a comparison of point and polygon ZIP code approximation methods
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* Corresponding author: Xun Shi Xun.shi@dartmouth.edu
1 Department of Community and Family Medicine, Dartmouth Medical School, Hanover, New Hampshire, USA
2 The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth Medical School, Hanover, New Hampshire, USA
3 The Norris Cotton Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA
4 Veterans' Rural Health Research Center–Eastern Region, VA Medical Center, White River Junction, Vermont, USA
5 Department of Geography, Dartmouth College, Hanover, New Hampshire, USA
International Journal of Health Geographics 2009, 8:23 doi:10.1186/1476-072X-8-23
Published: 29 April 2009Abstract
Background
Travel time is an important metric of geographic access to health care. We compared strategies of estimating travel times when only subject ZIP code data were available.
Results
Using simulated data from New Hampshire and Arizona, we estimated travel times to nearest cancer centers by using: 1) geometric centroid of ZIP code polygons as origins, 2) population centroids as origin, 3) service area rings around each cancer center, assigning subjects to rings by assuming they are evenly distributed within their ZIP code, 4) service area rings around each center, assuming the subjects follow the population distribution within the ZIP code. We used travel times based on street addresses as true values to validate estimates. Population-based methods have smaller errors than geometry-based methods. Within categories (geometry or population), centroid and service area methods have similar errors. Errors are smaller in urban areas than in rural areas.
Conclusion
Population-based methods are superior to the geometry-based methods, with the population centroid method appearing to be the best choice for estimating travel time. Estimates in rural areas are less reliable.