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Published on: February 25, 2013
Positional error and time-activity patterns in near-highway proximity studies: an exposure misclassification
Kevin J Lane1, Madeleine Kangsen Scammell, Jonathan I Levy
1Boston University School of Public Health, Boston, MA, USA. KLane@BU.edu.
Geocoding errors in residential proximity studies can lead to inaccurate exposure assessments for near-highway air pollutants. Parcel geocoding offers more precise location data than street network methods, reducing potential misclassification.
Area of Science:
- Environmental Health
- Epidemiology
- Geographic Information Systems (GIS)
Background:
- Research on near-highway air pollutant health effects is increasing.
- Accurate exposure assignment is crucial for these studies.
- Residential proximity to roadways is a common exposure assessment technique.
Purpose of the Study:
- To assess positional error in different geocoding methods for residential proximity studies.
- To evaluate the impact of geocoding error on exposure misclassification.
- To examine time-activity patterns in relation to highway proximity and demographics.
Main Methods:
- Compared positional error of parcel, TIGER, and StreetMap USA geocoding against a gold standard.
- Assessed error in a Boston-area highway proximity study (N=703).
- Collected time-activity data across five micro-environments for weekdays and weekends.
Main Results:
- Parcel geocoding had significantly lower median positional error (8 m) than street network methods (TIGER=22 m, StreetMap USA=23 m).
- Street network geocoding introduced greater error in the 0-50 m proximity to highway category.
- Time-activity patterns differed significantly by demographics and highway proximity, with closer residents spending more time at work/school.
Conclusions:
- Geocoding errors and time-activity patterns can cause differential and non-differential exposure misclassification in highway proximity studies.
- A multi-stage manual correction process is proposed to minimize positional error.
- Further research in diverse populations and settings is recommended.
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