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A Dataset for the Medical Support Vehicle Location–Allocation Problem.

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Název: A Dataset for the Medical Support Vehicle Location–Allocation Problem.
Autoři: Medina-Perez, Miguel, Guzmán, Giovanni, Saldana-Perez, Magdalena, Lara, Adriana, Torres-Ruiz, Miguel
Zdroj: Data (2306-5729); Dec2025, Vol. 10 Issue 12, p206, 21p
Témata: EMERGENCY medical services, RESOURCE allocation, MATHEMATICAL optimization, GEODATABASES, ACQUISITION of data, ASSIGNMENT problems (Programming)
Geografický termín: MEXICO City (Mexico)
Abstrakt: In mass-casualty incidents, emergency responders require access to accurate and timely information to support informed decision-making and ensure the efficient allocation of resources. This article presents a dataset derived from a case study conducted in Mexico City (CDMX) based on the earthquake of 19 September 2017. The dataset presents hypothetical scenarios involving multiple demand points and large numbers of victims, making it suitable for analysis using optimization techniques. It integrates voluntary collaborative geographic information, open government data sources, and historical records, and details the data collection, cleaning, and preprocessing stages. The accompanying Python 3 source code enables users to update the original data for consistent analysis and processing. Researchers can adapt this dataset to other cities with similar risk characteristics, such as Santiago (Chile), Los Angeles (USA), or Tokyo (Japan), and extend it to other types of catastrophic events, including floods, landslides, or epidemics, to support emergency response and resource allocation planning. Dataset:  https://doi.org/10.5281/zenodo.17845383 (accessed on 4 December 2025). Dataset License: All publicly shared data and derived layers are provided under CC BY-NC 4.0. All accompanying scripts are licensed under GPL-3.0. Traffic information obtained from the TomTom Traffic API is not redistributed in this dataset. Users must retrieve these data themselves using their own API key, in accordance with TomTom's licensing terms. Only the source code required to reproduce the traffic extraction process is provided. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:In mass-casualty incidents, emergency responders require access to accurate and timely information to support informed decision-making and ensure the efficient allocation of resources. This article presents a dataset derived from a case study conducted in Mexico City (CDMX) based on the earthquake of 19 September 2017. The dataset presents hypothetical scenarios involving multiple demand points and large numbers of victims, making it suitable for analysis using optimization techniques. It integrates voluntary collaborative geographic information, open government data sources, and historical records, and details the data collection, cleaning, and preprocessing stages. The accompanying Python 3 source code enables users to update the original data for consistent analysis and processing. Researchers can adapt this dataset to other cities with similar risk characteristics, such as Santiago (Chile), Los Angeles (USA), or Tokyo (Japan), and extend it to other types of catastrophic events, including floods, landslides, or epidemics, to support emergency response and resource allocation planning. Dataset:  https://doi.org/10.5281/zenodo.17845383 (accessed on 4 December 2025). Dataset License: All publicly shared data and derived layers are provided under CC BY-NC 4.0. All accompanying scripts are licensed under GPL-3.0. Traffic information obtained from the TomTom Traffic API is not redistributed in this dataset. Users must retrieve these data themselves using their own API key, in accordance with TomTom's licensing terms. Only the source code required to reproduce the traffic extraction process is provided. [ABSTRACT FROM AUTHOR]
ISSN:23065729
DOI:10.3390/data10120206