In the 2016 Louisiana flood, damage data of 30 sites were selected

By integrating disaster-related data from multiple sources, this study provides a comprehensive understanding of disaster occurrence patterns, their scope, and specific needs of affected areas, thus offering a scientific basis for emergency response and resource allocation. The dataset includes detailed information on 30 specific affected locations from the 2016 Louisiana floods, capturing real data from three websites and databases（https://www.emdat.be/；https://www.gdacs.org/；https://reliefweb.int/）, such as geographic coordinates (longitude and latitude), numbers of collapsed and severely damaged buildings, fatalities and missing persons, population density, and proportions of children and pregnant women. Specific source codes of these data can be found in the Word document. However, data on the types and quantities of emergency supplies at each disaster site were unavailable, so we randomly generated these two sets of data. Based on Solomon's dataset, we also defined the latest arrival times and service durations for each disaster location's time window. This dataset helps deepen our understanding of local impacts from specific disaster events, supports detailed analysis of affected areas’ unique needs and rescue challenges, and facilitates the development of more effective and practical emergency supply distribution strategies.
These data can support various types of research and applications: (1) Following a disaster, rescue agencies can rapidly develop response plans and efficiently allocate rescue teams and resources based on information such as structural damage, casualties, and supply needs. (2) Understanding the supply requirements and demographic characteristics of different affected locations helps optimize the distribution of emergency resources, ensuring limited resources effectively meet people's needs. (3) Analysis of historical disaster data can identify vulnerable regions and high-risk disaster types, providing valuable insights for disaster prevention and mitigation planning. (4) Analyzing records of supply distribution, rescue response times, and feedback from affected populations can highlight shortcomings in rescue operations, providing lessons to improve future rescue strategies, enhance methods, and increase overall efficiency and quality.