Laying the Foundation for Resident-Centered Welfare Policies
Early Identification of At-Risk Households Through Analysis of Single-Person Households and Residential Environments at the Neighborhood Level
Jungnang-gu (Mayor Ryu Gyeong-gi) will actively work to eliminate welfare blind spots starting in April by utilizing AI-based big data analysis.
This analysis will first be conducted in Myeonmokbon-dong, an area with a large population and a high proportion of elderly residents, and the project will be gradually expanded based on the analysis results.
As welfare risk factors diversify due to the increase in single-person households, deepening aging, and deterioration of residential environments, there have been limitations in proactively identifying households in need of support using traditional administrative methods alone.
Accordingly, the district aims to eliminate welfare blind spots and provide customized welfare services to residents by utilizing AI and big data.
The analysis is based on key data such as the distribution of single-person households by gender and age, housing deterioration, and housing types. In particular, it uses machine learning techniques that analyze and learn the relationships between risk factors based on large volumes of data to predict households with a high likelihood of welfare risk. This is expected to enable early detection and prompt support for potential at-risk households that were difficult to identify using only existing administrative data.
Additionally, spatial analysis using Geographic Information Systems (GIS) will be conducted concurrently. This will visually identify welfare blind spots, allowing welfare officers at neighborhood community centers to intuitively recognize problem areas and enhance on-site responsiveness.
The district will closely cooperate with the Myeonmokbon-dong Community Service Center to maximize the effectiveness of this analysis and actively incorporate the field experience and opinions of practitioners.
Ryu Gyeong-gi, Mayor of Jungnang-gu, stated, “Through big data analysis using AI technology, we expect to more precisely identify welfare blind spots and effectively allocate limited welfare resources.” He added, “We will continue to actively adopt data-driven administration to ensure that all residents can live safe and warm lives within a tightly woven welfare network.”
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