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Annual Review of Public Health

Volume 45, 2024

GIS-Based Assessments of Neighborhood Food Environments and Chronic Conditions: An Overview of Methodologies

Abstract

The industrial revolution and urbanization fundamentally restructured populations’ living circumstances, often with poor impacts on health. As an example, unhealthy food establishments may concentrate in some neighborhoods and, mediated by social and commercial drivers, increase local health risks. To understand the connections between neighborhood food environments and public health, researchers often use geographic information systems (GIS) and spatial statistics to analyze place-based evidence, but such tools require careful application and interpretation. In this article, we summarize the factors shaping neighborhood health in relation to local food environments and outline the use of GIS methodologies to assess associations between the two. We provide an overview of available data sources, analytical approaches, and their strengths and weaknesses. We postulate next steps in GIS integration with forecasting, prediction, and simulation measures to frame implications for local health policies.

Keyword(s): chronic health, ecological frameworks, food environment, geographic information systems, health disparities, neighborhood
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/content/journals/10.1146/annurev-publhealth-101322-031206
2024-05-20
2024-05-23
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Literature Cited

  1. 1.
    AlHasan DM, Eberth JM. 2016.. An ecological analysis of food outlet density and prevalence of type II diabetes in South Carolina counties. . BMC Public Health 16::10
     [Crossref] [Google Scholar]
  2. 2.
    Althoff T, Nilforoshan H, Hua J, Leskovec J. 2022.. Large-scale diet tracking data reveal disparate associations between food environment and diet. . Nat. Commun. 13::267
     [Crossref] [Google Scholar]
  3. 3.
    Apparicio P, Cloutier MS, Shearmur R. 2007.. The case of Montreal's missing food deserts: evaluation of accessibility to food supermarkets. . Int. J. Health Geogr. 6::4
     [Crossref] [Google Scholar]
  4. 4.
    Atanasova P, Kusuma D, Pineda E, Anjana RM, De Silva L, et al. 2022.. Food environments and obesity: a geospatial analysis of the South Asia Biobank, income and sex inequalities. . SSM Popul. Health 17::101055
     [Crossref] [Google Scholar]
  5. 5.
    Auchincloss AH, Diez Roux AV. 2008.. A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. . Am. J. Epidemiol. 168::1–8
     [Crossref] [Google Scholar]
  6. 6.
    Auchincloss AH, Gebreab SY, Mair C, Diez Roux AV. 2012.. A review of spatial methods in epidemiology, 2000–2010. . Annu. Rev. Public Health 33::107–22
     [Crossref] [Google Scholar]
  7. 7.
    Bader MD, Ailshire JA, Morenoff JD, House JS. 2010.. Measurement of the local food environment: a comparison of existing data sources. . Am. J. Epidemiol. 171::609–17
     [Crossref] [Google Scholar]
  8. 8.
    Bader MD, Purciel M, Yousefzadeh P, Neckerman KM. 2010.. Disparities in neighborhood food environments: implications of measurement strategies. . Econ. Geogr. 86::409–30
     [Crossref] [Google Scholar]
  9. 9.
    Baldock KL, Paquet C, Howard NJ, Coffee NT, Taylor AW, Daniel M. 2018.. Are perceived and objective distances to fresh food and physical activity resources associated with cardiometabolic risk?. Int. J. Environ. Res. Public Health 15::224
     [Crossref] [Google Scholar]
  10. 10.
    Belon AP, Nieuwendyk LM, Vallianatos H, Nykiforuk CI. 2016.. Perceived community environmental influences on eating behaviors: a photovoice analysis. . Soc. Sci. Med. 171::18–29
     [Crossref] [Google Scholar]
  11. 11.
    Bennion N, Redelfs AH, Spruance L, Benally S, Sloan-Aagard C. 2022.. Driving distance and food accessibility: a geospatial analysis of the food environment in the Navajo Nation and Border Towns. . Front. Nutr. 9::904119
     [Crossref] [Google Scholar]
  12. 12.
    Black C, Moon G, Baird J. 2014.. Dietary inequalities: What is the evidence for the effect of the neighbourhood food environment?. Health Place 27::229–42
     [Crossref] [Google Scholar]
  13. 13.
    Block J, Seward M, James P. 2018.. Food environment and health. . In Neighborhoods and Health, ed. DT Duncan, I Kawachi , pp. 247–77. New York:: Oxford Univ. Press
     [Google Scholar]
  14. 14.
    Block JP, Christakis NA, O'Malley AJ, Subramanian SV. 2011.. Proximity to food establishments and body mass index in the Framingham Heart Study offspring cohort over 30 years. . Am. J. Epidemiol. 174::1108–14
     [Crossref] [Google Scholar]
  15. 15.
    Bowe B, Xie Y, Li T, Yan Y, Xian H, Al-Aly Z. 2018.. The 2016 global and national burden of diabetes mellitus attributable to PM2·5 air pollution. . Lancet Planet. Health 2::e301–12
     [Crossref] [Google Scholar]
  16. 16.
    Braveman P, Egerter S, Williams DR. 2011.. The social determinants of health: coming of age. . Annu. Rev. Public Health 32::381–98
     [Crossref] [Google Scholar]
  17. 17.
    Briggs AC, Black AW, Lucas FL, Siewers AE, Fairfield KM. 2019.. Association between the food and physical activity environment, obesity, and cardiovascular health across Maine counties. . BMC Public Health 19::374
     [Crossref] [Google Scholar]
  18. 18.
    Burgoine T, Alvanides S, Lake AA. 2013.. Creating “obesogenic realities”; do our methodological choices make a difference when measuring the food environment?. Int. J. Health Geogr. 12::33
     [Crossref] [Google Scholar]
  19. 19.
    Carbonneau E, Robitaille J, Lamarche B, Corneau L, Lemieux S. 2017.. Development and validation of the Perceived Food Environment Questionnaire in a French-Canadian population. . Public Health Nutr. 20::1914–20
     [Crossref] [Google Scholar]
  20. 20.
    Carducci B, Oh C, Roth DE, Neufeld LM, Frongillo EA, et al. 2021.. Gaps and priorities in assessment of food environments for children and adolescents in low- and middle-income countries. . Nat. Food 2::396–403
     [Crossref] [Google Scholar]
  21. 21.
    Casey JA, Schwartz BS, Stewart WF, Adler NE. 2016.. Using electronic health records for population health research: a review of methods and applications. . Annu. Rev. Public Health 37::61–81
     [Crossref] [Google Scholar]
  22. 22.
    Caspi C, Friebur R. 2016.. Modified ground-truthing: an accurate and cost-effective food environment validation method for town and rural areas. . Int. J. Behav. Nutr. Phys. Activ. 13::37
     [Crossref] [Google Scholar]
  23. 23.
    Caspi CE, Sorensen G, Subramanian S, Kawachi I. 2012.. The local food environment and diet: a systematic review. . Health Place 18::1172–87
     [Crossref] [Google Scholar]
  24. 24.
    Cerin E, Saelens BE, Sallis JF, Frank LD. 2006.. Neighborhood Environment Walkability Scale: validity and development of a short form. . Med. Sci. Sports Exerc. 38::1682–91
     [Crossref] [Google Scholar]
  25. 25.
    Charreire H, Casey R, Salze P, Simon C, Chaix B, et al. 2010.. Measuring the food environment using geographical information systems: a methodological review. . Public Health Nutr. 13::1773–85
     [Crossref] [Google Scholar]
  26. 26.
    Chen X. 2017.. Take the edge off: a hybrid geographic food access measure. . Appl. Geogr. 87::149–59
     [Crossref] [Google Scholar]
  27. 27.
    Chen X. 2019.. Enhancing the two-step floating catchment area model for community food access mapping: case of the Supplemental Nutrition Assistance Program. . Prof. Geogr. 71::668–80
     [Crossref] [Google Scholar]
  28. 28.
    Chen X, Kwan M-P. 2015.. Contextual uncertainties, human mobility, and perceived food environment: the uncertain geographic context problem in food access research. . Am. J. Public Health 105::1734–37
     [Crossref] [Google Scholar]
  29. 29.
    Chen X, Ye X, Widener MJ, Delmelle E, Kwan M-P, et al. 2022.. A systematic review of the modifiable areal unit problem (MAUP) in community food environmental research. . Urban Inform. 1::22
     [Crossref] [Google Scholar]
  30. 30.
    Cohen N, Chrobok M, Caruso O. 2020.. Google-truthing to assess hot spots of food retail change: a repeat cross-sectional Street View of food environments in the Bronx, New York. . Health Place 62::102291
     [Crossref] [Google Scholar]
  31. 31.
    Cooksey-Stowers K, Schwartz MB, Brownell KD. 2017.. Food swamps predict obesity rates better than food deserts in the United States. . Int. J. Environ. Res. Public Health 14::1366
     [Crossref] [Google Scholar]
  32. 32.
    Crawford TW, Jilcott Pitts SB, McGuirt JT, Keyserling TC, Ammerman AS. 2014.. Conceptualizing and comparing neighborhood and activity space measures for food environment research. . Health Place 30::215–25
     [Crossref] [Google Scholar]
  33. 33.
    Cummins S, Flint E, Matthews SA. 2014.. New neighborhood grocery store increased awareness of food access but did not alter dietary habits or obesity. . Health Aff. 33::283–91
     [Crossref] [Google Scholar]
  34. 34.
    Cummins S, Petticrew M, Higgins C, Findlay A, Sparks L. 2005.. Large scale food retailing as an intervention for diet and health: quasi-experimental evaluation of a natural experiment. . J. Epidemiol. Community Health 59::1035–40
     [Crossref] [Google Scholar]
  35. 35.
    de Menezes MC, de Matos VP, de Pina MF, de Lima Costa BV, Mendes LL, et al. 2021.. Web data mining: validity of data from Google Earth for food retail evaluation. . J. Urban Health 98::285–95
     [Crossref] [Google Scholar]
  36. 36.
    Diez Roux AV. 2003.. Residential environments and cardiovascular risk. . J. Urban Health 80::569–89
     [Crossref] [Google Scholar]
  37. 37.
    Diez Roux AV, Merkin SS, Arnett D, Chambless L, Massing M, et al. 2001.. Neighborhood of residence and incidence of coronary heart disease. . N. Engl. J. Med. 345::99–106
     [Crossref] [Google Scholar]
  38. 38.
    Diez-Roux AV, Nieto FJ, Caulfield L, Tyroler HA, Watson RL, Szklo M. 1999.. Neighbourhood differences in diet: the Atherosclerosis Risk in Communities (ARIC) Study. . J. Epidemiol. Community Health 53::55–63
     [Crossref] [Google Scholar]
  39. 39.
    Digenis-Bury EC, Brooks DR, Chen L, Ostrem M, Horsburgh CR. 2008.. Use of a population-based survey to describe the health of Boston public housing residents. . Am. J. Public Health 98::85–91
     [Crossref] [Google Scholar]
  40. 40.
    Drewnowski A, Arterburn D, Zane J, Aggarwal A, Gupta S, et al. 2019.. The Moving to Health (M2H) approach to natural experiment research: a paradigm shift for studies on built environment and health. . SSM Popul. Health 7::100345
     [Crossref] [Google Scholar]
  41. 41.
    Dubowitz T, Ghosh-Dastidar M, Eibner C, Slaughter ME, Fernandes M, et al. 2012.. The Women's Health Initiative: the food environment, neighborhood socioeconomic status, BMI, and blood pressure. . Obesity 20::862–71
     [Crossref] [Google Scholar]
  42. 42.
    Dubowitz T, Ncube C, Leuschner K, Tharp-Gilliam S. 2015.. A natural experiment opportunity in two low-income urban food desert communities: research design, community engagement methods, and baseline results. . Health Educ. Behav. 42::87S–96S
     [Crossref] [Google Scholar]
  43. 43.
    Duncan DT, Castro MC, Blossom JC, Bennett GG, Gortmaker SL. 2011.. Evaluation of the positional difference between two common geocoding methods. . Geospat. Health 5::265–73
     [Crossref] [Google Scholar]
  44. 44.
    Duncan DT, Goedel WC, Chunara R. 2018.. Quantitative methods for measuring neighborhood characteristics in neighborhood health research. . In Neighborhoods and Health, ed. DT Duncan, I Kawachi, pp. 57–90. New York:: Oxford Univ. Press
     [Google Scholar]
  45. 45.
    Edwards KL, Clarke GP, Ransley JK, Cade J. 2010.. The neighborhood matters: studying exposures relevant to childhood obesity and the policy implications in Leeds, UK. . J. Epidemiol. Community Health 64::194–201
     [Crossref] [Google Scholar]
  46. 46.
    Elliston KG, Schuz B, Albion T, Ferguson SG. 2020.. Comparison of Geographic Information System and subjective assessments of momentary food environments as predictors of food intake: an ecological momentary assessment study. . JMIR mHealth uHealth 8::e15948
     [Crossref] [Google Scholar]
  47. 47.
    Feng X, Astell-Burt T, Badland H, Mavoa S, Giles-Corti B. 2018.. Modest ratios of fast food outlets to supermarkets and green grocers are associated with higher body mass index: longitudinal analysis of a sample of 15,229 Australians aged 45 years and older in the Australian National Liveability Study. . Health Place 49::101–10
     [Crossref] [Google Scholar]
  48. 48.
    Firth C, Jeneva B, Colin F, Grace L, Kevin M, et al. 2022.. Validity of food outlet databases from commercial and community science datasets in Vancouver and Montreal. . Findings, June 7. https://doi.org/10.32866/001c.35619
    [Google Scholar]
  49. 49.
    Fleischhacker SE, Evenson KR, Sharkey J, Pitts SB, Rodriguez DA. 2013.. Validity of secondary retail food outlet data: a systematic review. . Am. J. Prev. Med. 45::462–73
     [Crossref] [Google Scholar]
  50. 50.
    Fotheringham AS, Brunsdon C, Charlton M. 2007.. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester, UK:: Wiley
  51. 51.
    Frankenfeld CL, Leslie TF, Makara MA. 2015.. Diabetes, obesity, and recommended fruit and vegetable consumption in relation to food environment sub-types: a cross-sectional analysis of Behavioral Risk Factor Surveillance System, United States Census, and food establishment data. . BMC Public Health 15::491
     [Crossref] [Google Scholar]
  52. 52.
    Gamba RJ, Schuchter J, Rutt C, Seto EY. 2015.. Measuring the food environment and its effects on obesity in the United States: a systematic review of methods and results. . J. Community Health 40::464–75
     [Crossref] [Google Scholar]
  53. 53.
    Ghazi L, Oakes JM, MacLehose RF, Luepker RV, Osypuk TL, Drawz PE. 2021.. Neighborhood socioeconomic status and identification of patients with CKD using electronic health records. . Am. J. Kidney Dis. 78::57–65. e1
    [Crossref] [Google Scholar]
  54. 54.
    Giabbanelli PJ, Crutzen R. 2017.. Using agent-based models to develop public policy about food behaviours: future directions and recommendations. . Comput. Math. Methods Med. 2017::5742629
     [Crossref] [Google Scholar]
  55. 55.
    Glanz K, Handy SL, Henderson KE, Slater SJ, Davis EL, Powell LM. 2016.. Built environment assessment: multidisciplinary perspectives. . SSM Popul. Health 2::24–31
     [Crossref] [Google Scholar]
  56. 56.
    Gomez-Lopez IN, Clarke P, Hill AB, Romero DM, Goodspeed R, et al. 2017.. Using social media to identify sources of healthy food in urban neighborhoods. . J. Urban Health 94::429–36
     [Crossref] [Google Scholar]
  57. 57.
    Graves W, Zhang Y. 2022.. The role of demographic data bias in the under-provision of retail: a quasi-experimental case study of low food access neighborhoods in twenty-five Southern metros. . Urban Aff. Rev. 58::1466–85
     [Crossref] [Google Scholar]
  58. 58.
    Green SH, Glanz K. 2015.. Development of the Perceived Nutrition Environment Measures Survey. . Am. J. Prev. Med. 49::50–61
     [Crossref] [Google Scholar]
  59. 59.
    Gustafson A, Christian JW, Lewis S, Moore K, Jilcott S. 2013.. Food venue choice, consumer food environment, but not food venue availability within daily travel patterns are associated with dietary intake among adults, Lexington Kentucky 2011. . Nutr. J. 12::17
     [Crossref] [Google Scholar]
  60. 60.
    Hallum SH, Hughey SM, Wende ME, Stowe EW, Kaczynski AT. 2020.. Healthy and unhealthy food environments are linked with neighbourhood socio-economic disadvantage: an innovative geospatial approach to understanding food access inequities. . Public Health Nutr. 23::3190–96
     [Crossref] [Google Scholar]
  61. 61.
    Han E, Powell LM, Zenk SN, Rimkus L, Ohri-Vachaspati P, Chaloupka FJ. 2012.. Classification bias in commercial business lists for retail food stores in the U.S. . Int. J. Behav. Nutr. Phys. Activ. 9::46
     [Crossref] [Google Scholar]
  62. 62.
    Haslam A, Nikolaus CJ, Sinclair KA. 2022.. Association of food environment characteristics with health outcomes in counties with a high proportion of native American residents. . J. Hunger Environ. Nutr. https://doi.org/10.1080/19320248.2022.2101414
    [Google Scholar]
  63. 63.
    Hayanga B, Stafford M, Becares L. 2023.. Ethnic inequalities in multiple long-term health conditions in the United Kingdom: a systematic review and narrative synthesis. . BMC Public Health 23::178
     [Crossref] [Google Scholar]
  64. 64.
    Herrick CJ, Yount BW, Eyler AA. 2016.. Implications of supermarket access, neighbourhood walkability and poverty rates for diabetes risk in an employee population. . Public Health Nutr. 19::2040–48
     [Crossref] [Google Scholar]
  65. 65.
    Hill-Briggs F, Adler NE, Berkowitz SA, Chin MH, Gary-Webb TL, et al. 2020.. Social determinants of health and diabetes: a scientific review. . Diabetes Care 44::258–79
     [Crossref] [Google Scholar]
  66. 66.
    Hosler AS, Dharssi A. 2010.. Identifying retail food stores to evaluate the food environment. . Am. J. Prev. Med. 39::41–44
     [Crossref] [Google Scholar]
  67. 67.
    India-Aldana S, Kanchi R, Adhikari S, Lopez P, Schwartz MD, et al. 2022.. Impact of land use and food environment on risk of type 2 diabetes: a national study of veterans, 2008–2018. . Environ. Res. 212::113146
     [Crossref] [Google Scholar]
  68. 68.
    Inst. Med. (US) Comm. Natl. Surveill. Syst. Cardiovasc. Sel. Chronic Dis. 2011.. Existing surveillance data sources and systems. . In A Nationwide Framework for Surveillance of Cardiovascular and Chronic Lung Diseases. Washington, DC:: Natl. Acad. Press. https://www.ncbi.nlm.nih.gov/books/NBK83157/
    [Google Scholar]
  69. 69.
    James P, Seward MW, O'Malley AJ, Subramanian SV, Block JP. 2017.. Changes in the food environment over time: examining 40 years of data in the Framingham Heart Study. . Int. J. Behav. Nutr. Phys. Act. 14::84
     [Crossref] [Google Scholar]
  70. 70.
    Kanchi R, Lopez P, Rummo PE, Lee DC, Adhikari S, et al. 2021.. Longitudinal analysis of neighborhood food environment and diabetes risk in the Veterans Administration Diabetes Risk Cohort. . JAMA Netw. Open 4::e2130789
     [Crossref] [Google Scholar]
  71. 71.
    Kauhl B, Schweikart J, Krafft T, Keste A, Moskwyn M. 2016.. Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression. . Int. J. Health Geogr. 15::38
     [Crossref] [Google Scholar]
  72. 72.
    Kelly B, Flood VM, Yeatman H. 2011.. Measuring local food environments: an overview of available methods and measures. . Health Place 17::1284–93
     [Crossref] [Google Scholar]
  73. 73.
    Kolak M, Abraham G, Talen MR. 2019.. Mapping census tract clusters of type 2 diabetes in a primary care population. . Prev. Chronic Dis. 16::E59
     [Crossref] [Google Scholar]
  74. 74.
    Kusuma D, Atanasova P, Pineda E, Anjana RM, De Silva L, et al. 2022.. Food environment and diabetes mellitus in South Asia: a geospatial analysis of health outcome data. . PLOS Med. 19::e1003970
     [Crossref] [Google Scholar]
  75. 75.
    Lai P-C, So F-M, Chan K-W. 2009.. Spatial Epidemiological Approaches in Disease Mapping and Analysis. Boca Raton, FL:: CRC Press
  76. 76.
    Lee DC, Gallagher MP, Gopalan A, Osorio M, Vinson AJ, et al. 2018.. Identifying geographic disparities in diabetes prevalence among adults and children using emergency claims data. . J. Endocr. Soc. 17::460–70
     [Crossref] [Google Scholar]
  77. 77.
    Lee H, Kang HM, Ko YJ, Kim HS, Kim YJ, et al. 2015.. Influence of urban neighbourhood environment on physical activity and obesity-related diseases. . Public Health 129::1204–10
     [Crossref] [Google Scholar]
  78. 78.
    Li Y, Mallinson PAC, Bhan N, Turner C, Bhogadi S, et al. 2019.. Neighborhood physical food environment and cardiovascular risk factors in India: cross-sectional evidence from APCAPS. . Environ. Int. 132::105108
     [Crossref] [Google Scholar]
  79. 79.
    Liese AD, Barnes TL, Lamichhane AP, Hibbert JD, Colabianchi N, Lawson AB. 2013.. Characterizing the food retail environment: impact of count, type, and geospatial error in 2 secondary data sources. . J. Nutr. Educ. Behav. 45::435–42
     [Crossref] [Google Scholar]
  80. 80.
    Liese AD, Colabianchi N, Lamichhane AP, Barnes TL, Hibbert JD, et al. 2010.. Validation of 3 food outlet databases: completeness and geospatial accuracy in rural and urban food environments. . Am. J. Epidemiol. 172::1324–33
     [Crossref] [Google Scholar]
  81. 81.
    Lin HC, Hung PH, Hsieh YY, Lai TJ, Hsu HT, et al. 2022.. Long-term exposure to air pollutants and increased risk of chronic kidney disease in a community-based population using a fuzzy logic inference model. . Clin. Kidney J. 15::1872–80
     [Crossref] [Google Scholar]
  82. 82.
    Lovasi GS, Boise S, Jogi S, Hurvitz PM, Rundle AG, et al. 2023.. Time-varying food retail and incident disease in the Cardiovascular Health Study. . Am. J. Prev. Med. 64::877–87
     [Crossref] [Google Scholar]
  83. 83.
    Lucan SC, Maroko AR, Bumol J, Torrens L, Varona M, Berke EM. 2013.. Business list versus ground observation for measuring a food environment: saving time or waste of time (or worse)?. J. Acad. Nutr. Diet. 113::1332–39
     [Crossref] [Google Scholar]
  84. 84.
    Ma X, Barnes TL, Freedman DA, Bell BA, Colabianchi N, Liese AD. 2013.. Test-retest reliability of a questionnaire measuring perceptions of neighborhood food environment. . Health Place 21::65–69
     [Crossref] [Google Scholar]
  85. 85.
    Macdonald L, Cummins S, Macintyre S. 2007.. Neighbourhood fast food environment and area deprivation—substitution or concentration?. Appetite 49::251–54
     [Crossref] [Google Scholar]
  86. 86.
    Mackenbach JD, Charreire H, Glonti K, Bardos H, Rutter H, et al. 2019.. Exploring the relation of spatial access to fast food outlets with body weight: a mediation analysis. . Environ. Behav. 51::401–30
     [Crossref] [Google Scholar]
  87. 87.
    Maguire ER, Burgoine T, Monsivais P. 2015.. Area deprivation and the food environment over time: a repeated cross-sectional study on takeaway outlet density and supermarket presence in Norfolk, UK, 1990–2008. . Health Place 33::142–47
     [Crossref] [Google Scholar]
  88. 88.
    Maharana A, Nsoesie EO. 2018.. Use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity. . JAMA Netw. Open 1::e181535
     [Crossref] [Google Scholar]
  89. 89.
    Mahendra A, Polsky JY, Robitaille E, Lefebvre M, McBrien T, Minaker LM. 2017.. Geographic retail food environment measures for use in public health. . Health Promot. Chronic Dis. Prev. Can. 37::357–62
     [Crossref] [Google Scholar]
  90. 90.
    Mamiya H, Schmidt A, Moodie E, Ma Y, Buckeridge D. 2021.. Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada. . Public Health Nutr. 24::5616–28
     [Crossref] [Google Scholar]
  91. 91.
    Martin G, Mathur R, Naqvi H. 2023.. How can we make better use of ethnicity data to improve healthcare services?. BMJ 380::744
     [Crossref] [Google Scholar]
  92. 92.
    McAlexander TP, Algur Y, Schwartz BS, Rummo PE, Lee DC, et al. 2022.. Categorizing community type for epidemiologic evaluation of community factors and chronic disease across the United States. . Soc. Sci. Humanit. Open 5::100250
     [Google Scholar]
  93. 93.
    McDoom MM, Cooper LA, Hsu YJ, Singh A, Perin J, Thornton RLJ. 2020.. Neighborhood environment characteristics and control of hypertension and diabetes in a primary care patient sample. . J. Gen. Intern. Med. 35::1189–98
     [Crossref] [Google Scholar]
  94. 94.
    McGuirt JT, Gustafson A, Ammerman AS, Tucker-McLaughlin M, Enahora B, et al. 2022.. EatWellNow: formative development of a place-based behavioral “nudge” technology intervention to promote healthier food purchases among army soldiers. . Nutrients 14::1458
     [Crossref] [Google Scholar]
  95. 95.
    Mensah DO, Yeboah G, Batame M, Lillywhite R, Oyebode O. 2022.. Type, density, and healthiness of food-outlets in a university foodscape: a geographical mapping and characterization of food resources in a Ghanaian university campus. . BMC Public Health 22::1912
     [Crossref] [Google Scholar]
  96. 96.
    Mezuk B, Li X, Cederin K, Rice K, Sundquist J, Sundquist K. 2016.. Beyond access: characteristics of the food environment and risk of diabetes. . Am. J. Epidemiol. 183::1129–37
     [Crossref] [Google Scholar]
  97. 97.
    Mhasawade V, Elghafari A, Duncan DT, Chunara R. 2020.. Role of the built and online social environments on expression of dining on Instagram. . Int. J. Environ. Res. Public Health 17::735
     [Crossref] [Google Scholar]
  98. 98.
    Mohamad MS, Mahadir Naidu B, Virtanen SM, Lehtinen-Jacks S, Abdul Maulud KN. 2023.. Relationships of local food and physical activity environments with overweight in 5- to 17-year-old Malaysian children. . Asia Pac. J. Public Health 35::34–41
     [Crossref] [Google Scholar]
  99. 99.
    Moon KA, Nordberg CM, Orstad SL, Zhu A, Uddin J, et al. 2023.. Mediation of an association between neighborhood socioeconomic environment and type 2 diabetes through the leisure-time physical activity environment in an analysis of three independent samples. . BMJ Open Diabetes Res. Care 11::e003120
     [Crossref] [Google Scholar]
  100. 100.
    Moore K, Diez Roux AV, Auchincloss A, Evenson KR, Kaufman J, et al. 2013.. Home and work neighbourhood environments in relation to body mass index: the Multi-Ethnic Study of Atherosclerosis (MESA). . J. Epidemiol. Community Health 67::846–53
     [Crossref] [Google Scholar]
  101. 101.
    Moore LV, Diez Roux AV, Brines S. 2008.. Comparing perception-based and Geographic Information System (GIS)-based characterizations of the local food environment. . J. Urban Health 85::206–16
     [Crossref] [Google Scholar]
  102. 102.
    Morland K, Wing S, Diez Roux A. 2002.. The contextual effect of the local food environment on residents’ diets: the Atherosclerosis Risk in Communities study. . Am. J. Public Health 92::1761–68
     [Crossref] [Google Scholar]
  103. 103.
    Mujahid MS, Diez Roux AV, Morenoff JD, Raghunathan T. 2007.. Assessing the measurement properties of neighborhood scales: from psychometrics to ecometrics. . Am. J. Epidemiol. 165::858–67
     [Crossref] [Google Scholar]
  104. 104.
    Mulrooney T, Mcginn C, Branch B, Madumere C, Ifediora B. 2017.. A new raster-based metric to measure relative food availability in rural areas. . Southeast. Geogr. 57::151–78
     [Crossref] [Google Scholar]
  105. 105.
    Nguyen QC, Meng H, Li D, Kath S, McCullough M, et al. 2017.. Social media indicators of the food environment and state health outcomes. . Public Health 148::120–28
     [Crossref] [Google Scholar]
  106. 106.
    Ohri-Vachaspati P, Acciai F, Lloyd K, Tulloch D, DeWeese RS, et al. 2021.. Evidence that changes in community food environments lead to changes in children's weight: results from a longitudinal prospective cohort study. . J. Acad. Nutr. Diet 121::419–34
     [Crossref] [Google Scholar]
  107. 107.
    Otterbach S, Oskorouchi HR, Rogan M, Qaim M. 2021.. Using Google data to measure the role of Big Food and fast food in South Africa's obesity epidemic. . World Dev. 140::105368
     [Crossref] [Google Scholar]
  108. 108.
    Paquet C, Coffee NT, Haren MT, Howard NJ, Adams RJ, et al. 2014.. Food environment, walkability, and public open spaces are associated with incident development of cardio-metabolic risk factors in a biomedical cohort. . Health Place 28::173–76
     [Crossref] [Google Scholar]
  109. 109.
    Paquet C, Daniel M, Kestens Y, Leger K, Gauvin L. 2008.. Field validation of listings of food stores and commercial physical activity establishments from secondary data. . Int. J. Behav. Nutr. Phys. Activ. 5::58
     [Crossref] [Google Scholar]
  110. 110.
    Pearce J, Blakely T, Witten K, Bartie P. 2007.. Neighborhood deprivation and access to fast-food retailing: a national study. . Am. J. Prev. Med. 32::375–82
     [Crossref] [Google Scholar]
  111. 111.
    Pinho M, Mackenbach JD, Oppert JM, Charreire H, Bardos H, et al. 2019.. Exploring absolute and relative measures of exposure to food environments in relation to dietary patterns among European adults. . Public Health Nutr. 22::1037–47
     [Crossref] [Google Scholar]
  112. 112.
    Pliakas T, Hawkesworth S, Silverwood RJ, Nanchahal K, Grundy C, et al. 2017.. Optimising measurement of health-related characteristics of the built environment: comparing data collected by foot-based street audits, virtual street audits and routine secondary data sources. . Health Place 43::75–84
     [Crossref] [Google Scholar]
  113. 113.
    Poelman M, Strak M, Schmitz O, Hoek G, Karssenberg D, et al. 2018.. Relations between the residential fast-food environment and the individual risk of cardiovascular diseases in The Netherlands: a nationwide follow-up study. . Eur. J. Prev. Cardiol. 25::1397–405
     [Crossref] [Google Scholar]
  114. 114.
    Prager M, Kurz C, Bohm J, Laxy M, Maier W. 2019.. Using data from online geocoding services for the assessment of environmental obesogenic factors: a feasibility study. . Int. J. Health Geogr. 18::13
     [Crossref] [Google Scholar]
  115. 115.
    Rao JNK, Molina I. 2015.. Small Area Estimation. Hoboken, NJ:: Wiley
  116. 116.
    Restaur. Dive. 2022.. McDonald's is the latest QSR to embrace geofencing. . Restaurant Dive, March 28. https://www.restaurantdive.com/news/mcdonalds-implements-geofencing-technology-for-pickup/646126/
    [Google Scholar]
  117. 117.
    Rose D, Bodor JN, Hutchinson PL, Swalm CM. 2010.. The importance of a multidimensional approach for studying the links between food access and consumption. . J. Nutr. 140::1170–74
     [Crossref] [Google Scholar]
  118. 118.
    Rossen LM, Pollack KM, Curriero FC. 2012.. Verification of retail food outlet location data from a local health department using ground-truthing and remote sensing technology: assessing differences by neighborhood characteristics. . Health Place 18::956–62
     [Crossref] [Google Scholar]
  119. 119.
    Rummo P, Sze J, Elbel B. 2022.. Association between a policy to subsidize supermarkets in underserved areas and childhood obesity risk. . JAMA Pediatr. 176::646–53
     [Crossref] [Google Scholar]
  120. 120.
    Salvo D, Lemoine P, Janda KM, Ranjit N, Nielsen A, van den Berg A. 2022.. Exploring the impact of policies to improve geographic and economic access to vegetables among low-income, predominantly Latino urban residents: an agent-based model. . Nutrients 14::646
     [Crossref] [Google Scholar]
  121. 121.
    Sarkar C, Webster C, Gallacher J. 2018.. Are exposures to ready-to-eat food environments associated with type 2 diabetes? A cross-sectional study of 347 551 UK Biobank adult participants. . Lancet Planet. Health 2::e438–50
     [Crossref] [Google Scholar]
  122. 122.
    Schmidt NM, Nguyen QC, Osypuk TL. 2018.. Experimental and quasi-experimental designs in neighborhood health effects research—strengthening causal inference and promoting translation. . In Neighborhoods and Health, ed. DT Duncan, I Kawachi , pp. 155–91. New York:: Oxford Univ. Press
     [Google Scholar]
  123. 123.
    Shareck M, Lewis D, Smith NR, Clary C, Cummins S. 2018.. Associations between home and school neighbourhood food environments and adolescents' fast-food and sugar-sweetened beverage intakes: findings from the Olympic Regeneration in East London (ORiEL) Study. . Public Health Nutr. 21::2842–51
     [Crossref] [Google Scholar]
  124. 124.
    Simon PA, Kwan D, Angelescu A, Shih M, Fielding JE. 2008.. Proximity of fast food restaurants to schools: Do neighborhood income and type of school matter?. Prev. Med. 47::284–88
     [Crossref] [Google Scholar]
  125. 125.
    Singleton CR, Winata F, Adams AM, McLafferty SL, Sheehan KM, Zenk SN. 2022.. County-level associations between food retailer availability and violent crime rate. . BMC Public Health 22::2002
     [Crossref] [Google Scholar]
  126. 126.
    Springvloet L, Lechner L, Oenema A. 2014.. Planned development and evaluation protocol of two versions of a web-based computer tailored nutrition education intervention aimed at adults, including cognitive and environmental feedback. . BMC Public Health 14::47
     [Crossref] [Google Scholar]
  127. 127.
    Story M, Kaphingst KM, Robinson-O'Brien R, Glanz K. 2008.. Creating healthy food and eating environments: policy and environmental approaches. . Annu. Rev. Public Health 29::253–72
     [Crossref] [Google Scholar]
  128. 128.
    Tabaei BP, Rundle AG, Wu WY, Horowitz CR, Mayer V, et al. 2018.. Associations of residential socioeconomic, food, and built environments with glycemic control in persons with diabetes in New York City from 2007–2013. . Am. J. Epidemiol. 187::736–45
     [Crossref] [Google Scholar]
  129. 129.
    Tani Y, Suzuki N, Fujiwara T, Hanazato M, Kondo K. 2019.. Neighborhood food environment and dementia incidence: the Japan Gerontological Evaluation Study Cohort Survey. . Am. J. Prev. Med. 56::383–92
     [Crossref] [Google Scholar]
  130. 130.
    Thornton LE, Lamb KE, White SR. 2020.. The use and misuse of ratio and proportion exposure measures in food environment research. . Int. J. Behav. Nutr. Phys. Act. 17::118
     [Crossref] [Google Scholar]
  131. 131.
    Thorpe LE, Adhikari S, Lopez P, Kanchi R, McClure LA, et al. 2022.. Neighborhood socioeconomic environment and risk of type 2 diabetes: associations and mediation through food environment pathways in three independent study samples. . Diabetes Care 45::798–810
     [Crossref] [Google Scholar]
  132. 132.
    Truong K, Fernandes M, An R, Shier V, Sturm R. 2010.. Measuring the physical food environment and its relationship with obesity: evidence from California. . Public Health 124::115–18
     [Crossref] [Google Scholar]
  133. 133.
    Ursache A, Regan S, De Marco A, Duncan DT. 2021.. Measuring neighborhood deprivation for childhood health and development—scale implications in rural and urban context. . Geospat. Health 16:. https://doi.org/10.4081/gh.2021.926
    [Crossref] [Google Scholar]
  134. 134.
    Vilar-Compte M, Burrola-Mendez S, Lozano-Marrufo A, Ferre-Eguiluz I, Flores D, et al. 2021.. Urban poverty and nutrition challenges associated with accessibility to a healthy diet: a global systematic literature review. . Int. J. Equity Health 20::40
     [Crossref] [Google Scholar]
  135. 135.
    Waller LA. 2009.. Detection of clustering in spatial data. . In The SAGE Handbook of Spatial Analysis, ed. AS Fotheringham, PA Rogerson , pp. 299–320. Thousand Oaks, CA:: SAGE
     [Google Scholar]
  136. 136.
    Waller LA. 2021.. Spatial clustering and autocorrelation of health events. . In Handbook of Regional Science, ed. MM Fischer, P Nijkamp , pp. 2035–50. Berlin, Ger:.: Springer-Verlag
     [Google Scholar]
  137. 137.
    WHO (World Health Organ.), Alliance Health Policy Syst. Res. 2017.. Primary health care systems (primasys): case study from South Africa: abridged version. Geneva, Switz:: World Health Organ. https://apps.who.int/iris/handle/10665/341144
    [Google Scholar]
  138. 138.
    Widener MJ, Metcalf SS, Bar-Yam Y. 2013.. Agent-based modeling of policies to improve urban food access for low-income populations. . Appl. Geogr. 40::1–10
     [Crossref] [Google Scholar]
  139. 139.
    Wong MS, Peyton JM, Shields TM, Curriero FC, Gudzune KA. 2017.. Comparing the accuracy of food outlet datasets in an urban environment. . Geospat. Health 12::546
     [Crossref] [Google Scholar]
  140. 140.
    Yamaguchi M, Praditsorn P, Purnamasari SD, Sranacharoenpong K, Arai Y, et al. 2022.. Measures of perceived neighborhood food environments and dietary habits: a systematic review of methods and associations. . Nutrients 14::1788
     [Crossref] [Google Scholar]
  141. 141.
    Yan YH, Chien CC, Wang P, Lu MC, Wei YC, et al. 2023.. Association of exposure to air pollutants with gestational diabetes mellitus in Chiayi City, Taiwan. . Front. Endocrinol. 13::1097270
     [Crossref] [Google Scholar]
  142. 142.
    Zhang M, Zhang N, Zhou M, Ma G. 2022.. Association between neighborhood food environment and dietary diversity score among older people in Beijing, China: a cross-sectional study. . Front. Nutr. 9::903214
     [Crossref] [Google Scholar]
  143. 143.
    Zhang YT, Laraia BA, Mujahid MS, Blanchard SD, Warton EM, et al. 2016.. Is a reduction in distance to nearest supermarket associated with BMI change among type 2 diabetes patients?. Health Place 40::15–20
     [Crossref] [Google Scholar]
  144. 144.
    Zhou H, Kim G, Wang J, Wilson K. 2022.. Investigating the association between the socioeconomic environment of the service area and fast food visitation: a context-based crystal growth approach. . Health Place 76::102855
     [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-publhealth-101322-031206
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GIS-Based Assessments of Neighborhood Food Environments and Chronic Conditions: An Overview of Methodologies
Annual Review of Public Health 45, 109 (2024); https://doi.org/10.1146/annurev-publhealth-101322-031206
/content/journals/10.1146/annurev-publhealth-101322-031206
/content/journals/10.1146/annurev-publhealth-101322-031206
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