This data uses a landslide hazard risk assessment model consisting of four modules: landslide hazard causative factors, landslide susceptibility model, exposed population and population casualty rate. The module of hazard-causing factors includes DEM, slope, rainfall, temperature, snow cover, GDP, and vegetation cover factors. The landslide hazard susceptibility model is a statistical analysis using a logistic regression model to obtain landslide susceptibility probability values. The population exposure module uses the landslide susceptibility values overlaid with population data. The population casualty rate module is based on the ratio of historical landslide casualties to the population exposed to landslides during the same period. Finally, by substituting the 2020 population data, the exposed population under different levels of landslide hazard susceptibility is calculated and multiplied with the historical period landslide hazard population casualty rate to assessIntegrated multi-hazard population risk in the peri-Himalayan and Asian water tower regions

File naming and required software

File name: the basic data of multi disaster risk in Qinghai Tibet Plateau is stored in TIF format, and the name of the file is "risk_pop. TIF".
Data reading method: the files in the data can be opened with applications that support TIF format (such as ArcGIS), and the main content is numerical value to represent the risk level.

Data Citations Data citation guideline What's data citation?

Cite as:

Wang, Y. (2022). Integrated multi-hazard population risk in the peri-Himalayan and Asian water tower regions (2021). National Tibetan Plateau Data Center, DOI: 10.11888/HumanNat.tpdc.272245. CSTR: 18406.11.HumanNat.tpdc.272245. (Download the reference： RIS | Bibtex )

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Copyright & License

To respect the intellectual property rights, protect the rights of data authors,expand servglacials of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)