{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:28:38Z","timestamp":1774445318689,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"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>Does where an incident happens affect how quickly first responders arrive? Timely emergency responses are important to urban safety. However, the combined influence of street-level environments, operational conditions, and neighborhood contexts on dispatch performance remains unclear. We examined such geographical complexity by modeling geographic predictors for whether emergency vehicles successfully arrived at incidents in the city of Dallas within the city\u2019s eight-minute benchmark. Using 250,647 incidents and 56 million GPS points along emergency dispatch routes in 2016, we compiled fourteen spatial and operational variables for every incident to train a Bayesian-optimized random forest classifier. The fourteen variables characterized street network topology, roadway attributes, land use, and socioeconomic status, and the model achieved an accuracy of 77.26% in predicting whether emergency response arrived at an incident within eight minutes. A longer distance to dispatch stations, dispatching from non-nearest stations, and low street\u2013network integration were the strongest predictors of unsuccessful responses. Higher-income areas showed slightly elevated unsuccessful rates linked to frequent construction-related disruptions. These findings highlight emergency response as a coupled spatial\u2013operational\u2013temporal process and underscore the need for context-sensitive dispatch strategies and coordinated urban planning.<\/jats:p>","DOI":"10.3390\/ijgi15040141","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T10:07:13Z","timestamp":1774433233000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Where Matters: Geographic Influences on Emergency Response\u2014A Case Study of Dallas, Texas"],"prefix":"10.3390","volume":"15","author":[{"given":"Yanan","family":"Wu","sequence":"first","affiliation":[{"name":"School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Dallas, TX 75081, USA"},{"name":"Department of Geography, University of Central Arkansas, Conway, AR 72035, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0663-252X","authenticated-orcid":false,"given":"Yalin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Dallas, TX 75081, USA"},{"name":"West Virginia GIS Technical Center, West Virginia University, Morgantown, WV 26505, USA"},{"name":"Department of Geology and Geography, West Virginia University, Morgantown, WV 26505, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9006-2920","authenticated-orcid":false,"given":"May","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Dallas, TX 75081, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"581","DOI":"10.4236\/cus.2021.93035","article-title":"EMS Response Time for Patients Critically-Injured from Automobile Accidents Using Regression Analysis","volume":"09","author":"Vanga","year":"2021","journal-title":"Curr. 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