Spatial Regression Of Poverty In East Java Province With Distance Weighting Matrix Safaat Yulianto- Muhamad Mahdi Pangestu, Atika Nurani Ambarwati
Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang
Abstract
Poverty has emerged as a pressing issue in developing nations, such as Indonesia. It refers to a state where individuals are unable to meet their fundamental necessities. Between September 2020 and March 2021, there was a notable rise of 20.09 thousand individuals living in poverty in urban regions. This increase was the most significant in East Java Province when compared to other provinces in Indonesia. Analyze poverty cases using spatial analysis using a distance weighting matrix. Poverty in a region is influenced by poverty in the surrounding areas, resulting in poverty data that includes spatial effects or regional aspects. To analyze data with these spatial effects or regional aspects spatial regression is employed. In this research, the variable usedis the poverty level in the Eastern region of Java Province (Y), with four X variables, Average Years of Schooling (X1), Minimum Wage per Regency City (X2), Open Unemployment Rate (X3), and total population (X4). The weighting matrix utilized is the Euclidean Distance Weighting Matrix, which calculated the distance weighting matrix between different areas. The best Spatial Regression Model used is the Spatial Error Model (SEM). Modeling results using the Spatial Error Model (SEM) and the factors that influence poverty are Average Years of Schooling (X1) and Open Unemployment Rate (X3).
