{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T21:51:34Z","timestamp":1776462694882,"version":"3.51.2"},"reference-count":89,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T00:00:00Z","timestamp":1667865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Some studies have established relationships between neighborhood conditions and health. However, they neither evaluate the relative importance of neighborhood components in increasing obesity nor, more crucially, how these neighborhood factors vary geographically. We use the geographical random forest to analyze each factor\u2019s spatial variation and contribution to explaining tract-level obesity prevalence in Chicago, Illinois, United States. According to our findings, the geographical random forest outperforms the typically used nonspatial random forest model in terms of the out-of-bag prediction accuracy. In the Chicago tracts, poverty is the most important factor, whereas biking is the least important. Crime is the most critical factor in explaining obesity prevalence in Chicago\u2019s south suburbs while poverty appears to be the most important predictor in the city\u2019s south. For policy planning and evidence-based decision-making, our results suggest that social and ecological patterns of neighborhood characteristics are associated with obesity prevalence. Consequently, interventions should be devised and implemented based on local circumstances rather than generic notions of prevention strategies and healthcare barriers that apply to Chicago.<\/jats:p>","DOI":"10.3390\/ijgi11110550","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T10:53:06Z","timestamp":1667904786000},"page":"550","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2967-3626","authenticated-orcid":false,"given":"Aynaz","family":"Lotfata","sequence":"first","affiliation":[{"name":"Geography Department, Chicago State University, Chicago, IL 60605, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefanos","family":"Georganos","sequence":"additional","affiliation":[{"name":"Division of Geoinformatics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1949-6248","authenticated-orcid":false,"given":"Stamatis","family":"Kalogirou","sequence":"additional","affiliation":[{"name":"Data and Technology for Audit (DATA), European Court of Auditors, 1615 Luxembourg, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0392-8915","authenticated-orcid":false,"given":"Marco","family":"Helbich","sequence":"additional","affiliation":[{"name":"Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, 3584 CS Utrecht, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,8]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2022, January 11). 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