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Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology

Kinnee, Ellen J. and Tripathy, Sheila and Schinasi, Leah and Shmool, Jessie L. C. and Sheffield, Perry E. and Holguin, Fernando and Clougherty, Jane E. (2020) Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology. International Journal of Environmental Research and Public Health, 17 (16). p. 5845. ISSN 1660-4601

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Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km2), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Kinnee, Ellen J.ejk40@pitt.eduejk400000-0002-6719-2591
Tripathy, Sheila
Schinasi, Leah
Shmool, Jessie L. C.
Sheffield, Perry E.0000-0001-9156-1193
Holguin, Fernando
Clougherty, Jane E.0000-0002-9911-1565
Centers: University Centers > University Center for Social and Urban Research
Date: 12 August 2020
Date Type: Publication
Journal or Publication Title: International Journal of Environmental Research and Public Health
Volume: 17
Number: 16
Publisher: MDPI AG
Page Range: p. 5845
DOI or Unique Handle: 10.3390/ijerph17165845
Schools and Programs: School of Public Health > Environmental and Occupational Health
Refereed: Yes
Uncontrolled Keywords: geocoding error, exposure misclassification, geographic information systems (GIS), spatial analysis, spatial uncertainty, urban epidemiology
ISSN: 1660-4601
Official URL:
Funders: National Heart Lung Blood Institute, National Institute of Environmental Health Sciences
Article Type: Research Article
Date Deposited: 06 Jun 2022 15:33
Last Modified: 06 Jun 2022 15:33


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