Socio-Spatial Analysis of Poverty: A Comprehensive Study on Integrating Multidimensional Poverty Indices with Geographic Conditions in Krucil District, Probolinggo, Indonesia
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Abstract
The role of geography in population studies is represented by the utilization of space in studying social issues. The study explores the intersection of geography and population studies by employing spatial analysis to examine social problems, particularly poverty. Focusing on the Krucil District in Probolinggo Regency, East Java, Indonesia, the research integrates multidimensional indicators of well-being to provide a comprehensive understanding of poverty. The Multidimensional Poverty Index (MPI) is a comprehensive poverty measurement tool at the individual and household levels. The urgent integration of spatial analysis into social sciences is essential for addressing the significant poverty level as a socioeconomic problem. Poverty measurement was analyzed using the Alkire-Foster (AF) method with primary data from 132 households across 11 sub-districts. Results reveal the MPI score of 0.19, indicating significant poverty levels, with the health dimension most affected. Moran’s index of -0.134, indicating no spatial autocorrelation (p value > alpha, .574 > 0.05), suggesting that high multidimensional poverty areas are surrounded by low poverty areas and vice versa, with geographic, spatial, and physical conditions significantly contributing to multidimensional poverty. These findings suggest that poverty alleviation efforts commence with Seneng Village, which has been designated as a pilot project. This approach will allow for the testing and refinement of strategies in a controlled environment, providing valuable insights and data that can be applied to broader initiatives.
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