DEVELOPMENT OF DATA WORK AND HOUSEHOLD INFORMATION MANAGEMENT SYSTEMS TO SUPPORT SPATIAL DEVELOPMENT INITIATIVES FOR POOR AND DISADVANTAGED HOUSEHOLDS

Abstract

This research aims to: 1) Develop tools for collecting household information, and 2) Study the procedures involved in managing this information to support the promotion of spatial development initiatives targeting poor households and disadvantaged group. The study employs a research and development model utilizing action research methods. The tools employed include group discussions and brainstorming forums, with a focus on purposive selection criteria. Key informants for this study included academics specializing in household information from the three southern border provinces, project leaders involved in the Rajabhat University for Local Development Project, and 46 volunteer graduate representatives from the project area. Content data were analyzed, and an overview was summarized. The results of the study revealed that the tools used for collecting household information encompassed data on household members, health, economic status, environmental conditions, political and administrative aspects,
and household needs for addressing life challenges. Notably, this data collection approach was designed to alleviate anxiety among participants. Besides, the household information management process consists of 4 essential steps. 1) Data planning, involves collaborating with relevant agencies directly responsible for the area and notifying the local security agency, as this process requires caution in providing sensitive information. 2) Data collection, necessitates that the data collector be a local individual who is familiar with impoverished households and possesses effective communication skills, particularly in using local dialects to inquire about information. 3) Processing, entails fostering understanding within the context of the target group by organizing, comparing, and evaluating the collected information. Finally, and 4) Utilization, requires proficiency in interpreting the dataset to draw concrete conclusions and effectively communicate the findings