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Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study

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Listed:
  • Jared A. Fisher

    (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
    These authors have contributed equally to this work.)

  • Maya Spaur

    (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
    These authors have contributed equally to this work.)

  • Ian D. Buller

    (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA)

  • Abigail R. Flory

    (Westat, 1600 Research Blvd., Rockville, MD 20850, USA)

  • Laura E. Beane Freeman

    (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA)

  • Jonathan N. Hofmann

    (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA)

  • Michael Giangrande

    (Westat, 1600 Research Blvd., Rockville, MD 20850, USA)

  • Rena R. Jones

    (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA)

  • Mary H. Ward

    (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA)

Abstract

Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012–2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using “gold standard” rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: −2 to 168) and 9 m (IQR: −80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: −1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies.

Suggested Citation

  • Jared A. Fisher & Maya Spaur & Ian D. Buller & Abigail R. Flory & Laura E. Beane Freeman & Jonathan N. Hofmann & Michael Giangrande & Rena R. Jones & Mary H. Ward, 2021. "Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study," IJERPH, MDPI, vol. 18(4), pages 1-13, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1637-:d:496079
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    References listed on IDEAS

    as
    1. Ana Isabel Ribeiro & Andreia Olhero & Hugo Teixeira & Alexandre Magalhães & Maria Fátima Pina, 2014. "Tools for Address Georeferencing – Limitations and Opportunities Every Public Health Professional Should Be Aware Of," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, December.
    2. Krieger, N. & Waterman, P. & Lemieux, K. & Zierler, S. & Hogan, J.W., 2001. "On the wrong side of the tracts? Evaluating the accuracy of geocoding in public health research," American Journal of Public Health, American Public Health Association, vol. 91(7), pages 1114-1116.
    3. Ellen J. Kinnee & Sheila Tripathy & Leah Schinasi & Jessie L. C. Shmool & Perry E. Sheffield & Fernando Holguin & Jane E. Clougherty, 2020. "Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology," IJERPH, MDPI, vol. 17(16), pages 1-23, August.
    4. Hyeongmo Koo & Yongwan Chun & Daniel A. Griffith, 2018. "Modeling Positional Uncertainty Acquired Through Street Geocoding," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 9(4), pages 1-22, October.
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