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Data Desa Presisi: A new method of rural data collection

Pseudo-development in rural areas often occurs due to the lack of availability of accurate data, in addition to the closed space for citizen participation. Based on this condition, we identify and evaluate various methods of collecting rural data in Indonesia as the basis for formulating development...

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Detalles Bibliográficos
Autores principales: Sjaf, Sofyan, Sampean, Arsyad, Ahmad Aulia, Elson, La, Mahardika, Afan Ray, Hakim, Lukman, Amongjati, Sri Anom, Gandi, Rajib, Barlan, Zessy Ardinal, Aditya, I Made Godya, Maulana, Sayyid Al Bahr, Rangkuti, Muhammad Rifky
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9526142/
https://www.ncbi.nlm.nih.gov/pubmed/36193116
http://dx.doi.org/10.1016/j.mex.2022.101868
Descripción
Sumario:Pseudo-development in rural areas often occurs due to the lack of availability of accurate data, in addition to the closed space for citizen participation. Based on this condition, we identify and evaluate various methods of collecting rural data in Indonesia as the basis for formulating development policies and programs. From the results of the identification and evaluation, we conclude that a new method in rural data collection is needed, called Data Desa Presisi (DDP). DDP is a village data collection method that synthesizes a census, spatial and community participation approach. This method puts the unit of analysis of the family and the individual in the Neighborhood Association (Rukun Warga-RW) as the smallest regional unit in the rural area. The presence of DDP is expected to help villages to plan, implement, monitor, and evaluate village development based on accurate data. • We identified the village data collection methods used so far for planning and measuring village development. • DDP is used for precise planning, implementation, monitoring-evaluation, and measurement of village development. • This method can be used as basic village data because it is able to show development subjects with precision, namely: by name, by address and by coordinates.