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 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.