Datasets Comparison

The datasets are derived from a detailed case study of the emergency response to the severe rainstorm and geological disaster in Longchuan County, China, in 2019. The whole event is extensively documented in the published monograph, ''Comprehensive Response to Emergencies: Lessons from the 2019 Severe Rainstorm and Geological Disaster in Longchuan County''. The datasets include a comprehensive record of emergency management activities. They are divided into four stages: Monitoring and Early Warning, Prevention and Emergency Preparedness, Emergency Response and Rescue Activities and Recovery and Reconstruction. The data details the collaborative interactions among various human and non-human agents within the disaster response systems at each stage. We provide codes to construct the hypergraph representation of the disaster response system in each stage. The codes include hypergraph clustering and dismantling, which can be used to analyze the robustness of inter-community structure and system reliability. The code used for generating graphics can also be found. The detailed descriptions and step-by-step guidelines can be found in READ_ME.md.

University of the Chinese Academy of Sciences School of Engineering Science

Hypergraph, Social Network Analysis, Disaster Management, Emergency Response

Creative Commons Attribution 4.0 International

The datasets are derived from a detailed case study of the emergency response to the severe rainstorm and geological disaster in Longchuan County, China, in 2019. The whole event is extensively documented in the published monograph, ''Comprehensive Response to Emergencies: Lessons from the 2019 Severe Rainstorm and Geological Disaster in Longchuan County''. The datasets include a comprehensive record of emergency management activities. They are divided into four stages: Monitoring and Early Warning, Prevention and Emergency Preparedness, Emergency Response and Rescue Activities and Recovery and Reconstruction. The data details the collaborative interactions among various human and non-human agents within the disaster response systems at each stage. We provide codes to construct the hypergraph representation of the disaster response system in each stage. The codes include hypergraph clustering and dismantling, which can be used to analyze the robustness of inter-community structure and system reliability. The code used for generating graphics can also be found. The detailed descriptions and step-by-step guidelines can be found in READ_ME.md. About the updates of Version 2: we developed new codes to address the robustness of the disaster response system at a macroscopic scale. These analysis codes are included in the updates. Detailed descriptions can be found in READ_ME_About_Updates.md.

University of the Chinese Academy of Sciences School of Engineering Science

Hypergraph, Social Network Analysis, Disaster Management, Emergency Response

Creative Commons Attribution 4.0 International