Analysis of space and classification poverty in Semarang City using spatial-logistic regression. (May 2019)

• Record Type: Journal Article
• Authors: Ispriyanti, D; Prahutama, A; Mustafid,
• Is Part Of: Journal of physics. Volume 1217(2019)
• Publication Date: 2019-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1217/1/012106
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The declining poverty rate becomes the priority of the Sustainable Development Goals (SDGs). In the period of 2002 - 2017 poverty rate in Central Java experienced a decrease, as similarly found in Semarang city. Examining the factors affecting the poverty classification in Semarang city, therefore, is deemed important. The analysis used in this research was conducted by examining the spatial factors and classification of poverty levels by means of the logistic model. The logistic model resulted was found to be capable of classifying the poverty levels, and the spatial model used was capable of modeling each region. In modelling the binary logistic regression, a number of factors affecting the poverty classification included the number of family members, education certificates of the head of family, the main fuel used for cooking, and listed as the receiver of the rice for poor or not. Accuracy on the classification of binary logistic regression models reached 94.9%. For spatial modeling by means of Geographically Weighted Regression (GWR), the dependent variable used referred to the number of underprivileged families in each sub-district. In GWR modeling, each sub-district had its own characteristics in modeling the number of underprivileged families in each sub-district in Semarang city.