Cargando…

Data from a survey of the Philippines’ local governments on their risk management strategies to natural disasters

This data is from a survey of Local Government Units Disaster Risk Reduction and Management (DRRM) Office in the Philippines. Conducted in 2016–2017, the survey was intended to assess the disaster risk reduction and mitigation programs and policies employed by the local government on types of disast...

Descripción completa

Detalles Bibliográficos
Autores principales: Ravago, Majah-Leah V., Mapa, Claire Dennis S., Sunglao, Jun Carlo, Aycardo, Angelie Grace
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7701180/
https://www.ncbi.nlm.nih.gov/pubmed/33294533
http://dx.doi.org/10.1016/j.dib.2020.106548
Descripción
Sumario:This data is from a survey of Local Government Units Disaster Risk Reduction and Management (DRRM) Office in the Philippines. Conducted in 2016–2017, the survey was intended to assess the disaster risk reduction and mitigation programs and policies employed by the local government on types of disaster due to natural hazards. The survey data covers 47 provinces (including Metro Manila) with 193 municipalities and cities. The sampling design followed a multi-stage probability scheme taking into account the high-risk and low-risk disaster areas. This data article describes the framework and design of the survey and highlights the creation of indices and other outcome variables based on the survey. It also provides information on the field operations including data cleaning and processing that may be useful to those undertaking similar surveys. The dataset is in comma-separated values file (.csv) with accompanying data dictionary (.txt). The questionnaire is also included in the data supplementary appendix. This data article is an adjunct to the research article, “Localized disaster risk management index for the Philippines: Is your municipality ready for the next disaster?” Ravago, et al., 2020, where data interpretation and analysis can be found.