A Dataset for the Medical Support Vehicle Location-Allocation Problem

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 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. This repository contains eight structured Jupyter Notebooks located within the notebooks/ directory, designed for reproducible data analysis. The entire workflow must be executed sequentially by the user within the Google Colaboratory (Colab) platform. The process begins with the notebooks/PreProcessing.ipynb file, which handles the initial data preparation. The precise order of execution for all eight notebooks is detailed in the Materials and Methods section of the associated published article.