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

Ellen J. Kinnee, Sheila Tripathy, Leah Schinasi, Jessie L. C. Shmool, Perry E. Sheffield, Fernando Holguin and Jane E. Clougherty
Additional contact information
Ellen J. Kinnee: University Center for Social and Urban Research, University of Pittsburgh, Pittsburgh, PA 15260, USA
Sheila Tripathy: Department of Environmental and Occupational Health, Drexel University Dornsife School of Public Health, Philadelphia, PA 19104, USA
Leah Schinasi: Department of Environmental and Occupational Health, Drexel University Dornsife School of Public Health, Philadelphia, PA 19104, USA
Jessie L. C. Shmool: Department of Environmental and Occupational Health, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA 15260, USA
Perry E. Sheffield: Environmental Medicine and Public Health and Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Fernando Holguin: Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA
Jane E. Clougherty: Department of Environmental and Occupational Health, Drexel University Dornsife School of Public Health, Philadelphia, PA 19104, USA

IJERPH, 2020, vol. 17, issue 16, 1-23

Abstract: 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/km 2 ), 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.

Keywords: geocoding error; exposure misclassification; geographic information systems (GIS); spatial analysis; spatial uncertainty; urban epidemiology (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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