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<p>To evaluate the adequacy of the current Mobile Emergency Care Service (SAMU) ambulance station in a medium-sized municipality and assess whether adding a second station could more effectively reduce response times in medical emergencies. An observational, cross-sectional study was conducted using geospatial simulation and the Maximal Covering Location Problem (MCLP) model, applied to 1019 emergency incidents attended by the advanced life support (ALS) ambulance from 2019 to 2022. Demand points were also derived from population counts (centroids) and 7000 simulated incidents, enabling comparative evaluation of spatial coverage and response times. The current SAMU base location covered 32.97% of real incidents and 20.03% of the population within the six-minute target time. Adding a second base increased coverage to 43.59% of incidents and 68.72% of the population. Median travel times were significantly reduced in two-base scenarios, as confirmed by Kruskal–Wallis and Dunn’s post hoc tests (<i>p</i> &lt; 0.001). While the existing base is well-positioned, it is insufficient to meet demand. A strategically placed second base improves coverage and reduces response times, providing evidence for ambulance system planning and illustrating the value of geospatial simulation and optimization modeling in spatial information science on modeling in spatial information science.</p>

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Geospatial simulation for optimizing ambulance station locations in a medium-sized city in southern Brazil: a study using the maximal covering location problem (MCLP)

  • 期刊论文

  • 发表日期：

    2025年11月12日

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  • Victor Szabo,
  • Vinícius Lopes Giacomin,
  • Carlos Eduardo Arruda,
  • Mateus de Amorim Aboboreira,
  • Felipe Hideaki Ueda,
  • Gabriela Antum de Oliveira,
  • Gustavo Cezar Wagner Leandro,
  • Sanderland José Tavares Gurgel,
  • Alessandro Filla Rosaneli,
  • Miyoko Massago,
  • Luciano de Andrade

摘要

To evaluate the adequacy of the current Mobile Emergency Care Service (SAMU) ambulance station in a medium-sized municipality and assess whether adding a second station could more effectively reduce response times in medical emergencies. An observational, cross-sectional study was conducted using geospatial simulation and the Maximal Covering Location Problem (MCLP) model, applied to 1019 emergency incidents attended by the advanced life support (ALS) ambulance from 2019 to 2022. Demand points were also derived from population counts (centroids) and 7000 simulated incidents, enabling comparative evaluation of spatial coverage and response times. The current SAMU base location covered 32.97% of real incidents and 20.03% of the population within the six-minute target time. Adding a second base increased coverage to 43.59% of incidents and 68.72% of the population. Median travel times were significantly reduced in two-base scenarios, as confirmed by Kruskal–Wallis and Dunn’s post hoc tests (p < 0.001). While the existing base is well-positioned, it is insufficient to meet demand. A strategically placed second base improves coverage and reduces response times, providing evidence for ambulance system planning and illustrating the value of geospatial simulation and optimization modeling in spatial information science on modeling in spatial information science.