Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Impacts of the choice of distance measurement method on estimates of access to point-based resources

Journal of Exposure Science & Environmental Epidemiology volume 33, pages 237–243 (2023)Cite this article

Abstract

Background/objective

Lack of access to resources such as medical facilities and grocery stores is related to poor health outcomes and inequities, particularly in an environmental justice framework. There can be substantial differences in quantifying “access” to such resources, depending on the geospatial method used to generate distance estimates.

Methods

We compared three methods for calculating distance to the nearest grocery store to illustrate differential access at the census block-group level in the Atlanta metropolitan area, including: Euclidean distance estimation, service areas incorporating roadways and other factors, and cost distance for every point on the map.

Results

We found notable differences in access across the three estimation techniques, implying a high potential for exposure misclassification by estimation method. There was a lack of nuanced exposure in the highest- and lowest-access areas using the Euclidean distance method. We found an Intraclass Correlation Coefficient (ICC) of 0.69 (0.65, 0.73), indicating moderate agreement between estimation methods.

Significance

As compared with Euclidean distance, service areas and cost distance may represent a more meaningful characterization of “access” to resources. Each method has tradeoffs in computational resources required versus potential improvement in exposure classification. Careful consideration of the method used for determining “access” will reduce subsequent misclassifications.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Generation of gridded population estimates, by census block group.
Fig. 2: Illustration of methods for estimating access to the nearest grocery store.
Fig. 3
Fig. 4: Septiles of access to grocery stores in Atlanta at the census block-group level, by access method.
Fig. 5: Change in septile of access to grocery stores in the Atlanta metropolitan area, by access method (1—lowest access, 7—highest access).

Similar content being viewed by others

References

  1. Sexton K. Socioeconomic and racial disparities in environmental health: is risk assessment part of the problem or part of the solution? Hum Ecol Risk Assess Int J. 2000;6:561–74. http://www.tandfonline.com/doi/abs/10.1080/10807030008951330.

    Article  Google Scholar 

  2. Lee C. Environmental justice: building a unified vision of health and the environment. Environ Health Perspect. 2002;110 SUPPL. 2:141–4.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the united states: a review of food deserts literature. Heal Place. 2010;16:876–84. https://doi.org/10.1016/j.healthplace.2010.04.013.

    Article  Google Scholar 

  4. Morland K, Wing S, Diez Roux A, Poole C. Neighborhood characteristics associated with the location of food stores and food service places. Am J Prev Med. 2002;22:23–9.

    Article  PubMed  Google Scholar 

  5. Phibbs CS, Luft HS. Correlation of travel time on roads versus straight line distance. Med Care Res Rev. 1995;52:532–42. http://journals.sagepub.com/doi/10.1177/107755879505200406.

    Article  CAS  PubMed  Google Scholar 

  6. Shahid R, Bertazzon S, Knudtson ML, Ghali WA. Comparison of distance measures in spatial analytical modeling for health service planning. BMC Health Serv Res. 2009;9:200. https://bmchealthservres.biomedcentral.com/articles/10.1186/1472-6963-9-200.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Li Y, Luo M, Wu X, Xiao Q, Luo J, Jia P. Grocery store access and childhood obesity: a systematic review and meta‐analysis. Obes Rev. 2019;obr.12945. https://onlinelibrary.wiley.com/doi/abs/10.1111/obr.12945.

  8. Sparks AL, Bania N, Leete L. Comparative approaches to measuring food access in urban areas: the case of Portland, Oregon. Urban Stud. 2011;48:1715–37.

    Article  PubMed  Google Scholar 

  9. Costa BVL, Menezes MC, Oliveira CDL, Mingoti SA, Jaime PC, Caiaffa WT, et al. Does access to healthy food vary according to socioeconomic status and to food store type? an ecologic study. BMC Public Health. 2019;19:1–7.

    Article  CAS  Google Scholar 

  10. Caspi CE, Kawachi I, Subramanian SV, Adamkiewicz G, Sorensen G. The relationship between diet and perceived and objective access to supermarkets among low-income housing residents. Soc Sci Med. 2012;75:1254–62. https://doi.org/10.1016/j.socscimed.2012.05.014.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Apparicio P, Cloutier MS, Shearmur R. The case of Montréal’s missing food deserts: Evaluation of accessibility to food supermarkets. Int J Health Geogr. 2007;6:1–13.

    Article  Google Scholar 

  12. DataAxle. Leading business and consumer data for libraries and their patrons. 2020. https://www.data-axle.com/what-we-do/reference-solutions/.

  13. Centers for Disease Control and Prevention. Census tract level state maps of the modified retail food environment index. Children’s Food Environ State Indic Rep. 2011;2011:53. https://perma.cc/79QP-QTWF.

    Google Scholar 

  14. Schiavina M, Freire S, MacManus K. GHS-POP R2019A—GHS population grid multitemporal (1975-1990-2000-2015). Jt Res Centre Eur Comm. 2019. https://data.europa.eu/euodp/en/data/dataset/0c6b9751-a71f-4062-830b-43c9f432370f.

  15. Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 10. Palisades, NY: 2018.

  16. Laurini R, Thompson D. Fundamentals of spatial information system. The APIC S. London: Academic Press; 1992.

  17. OpenStreetMap Contributors. Planet dump retrieved from https://planet.osm.org. OpenStreetMap. 2020.

  18. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: 2020.

  19. Fisher R. Intraclass correlations and the analysis of variance. In: Statistical methods for research workers. Edinburgh: Tweeddale Court: Oliver and Boyd; 1934. p. 156. http://www.haghish.com/resources/materials/Statistical_Methods_for_Research_Workers.pdf.

Download references

Funding

Funding for this work was provided by Sharecare Inc (https://wellbeingindex.sharecare.com/).

Author information

Authors and Affiliations

  1. Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA

    Amruta Nori-Sarma, Keith R. Spangler & Kevin J. Lane

  2. Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA, USA

    Amruta Nori-Sarma, Keith R. Spangler, Biqi Wang, Nina Cesare, Kimberly A. Dukes & Kevin J. Lane

  3. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

    Biqi Wang & Kimberly A. Dukes

Authors
  1. Amruta Nori-Sarma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keith R. Spangler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Biqi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nina Cesare
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kimberly A. Dukes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kevin J. Lane
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ANS and KS had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: ANS, KS, and KJL. Acquisition, analysis, or interpretation of data: ANS, KS, and BW. Drafting of the paper: ANS, KS, and KJL. Critical revision of the paper for important intellectual content: All authors. Final approval of version to be published: All authors. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: All authors.

Corresponding author

Correspondence to Amruta Nori-Sarma.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nori-Sarma, A., Spangler, K.R., Wang, B. et al. Impacts of the choice of distance measurement method on estimates of access to point-based resources. J Expo Sci Environ Epidemiol 33, 237–243 (2023). https://doi.org/10.1038/s41370-022-00414-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41370-022-00414-z

Keywords

This article is cited by

Search

Advanced search

Quick links