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All Journal Journal of Mathematics UNP
Riki Wahyudi
Bengkulu University
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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ANALISIS KEMISKINAN EKSTREM PROVINSI BENGKULU MENGGUNAKAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) DENGAN PEMBOBOT ADAPTIVE GAUSSIAN KERNEL DAN ADAPTIVE BI-SQUARE Riki Wahyudi; Yulian Fauzi; Jose Rizal
Journal of Mathematics UNP Vol 8, No 2 (2023): Journal Of Mathematics UNP
Publisher : UNIVERSITAS NEGERI PADANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/unpjomath.v8i2.14914

Abstract

Extreme poverty is a condition of inability to fulfill basic needs, namely the need for food, clean drinking water, proper sanitation, health, shelter, education, and access to information which is not only limited to income, but also access to social services (United Nations, 1996).Geographically Weighted Regression (GWR) model is used in mapping extreme poverty of all level 2 regions in Bengkulu Province using Adaptive Gaussian Kernel and Adaptive Bi-Square weights as well as finding the best GWR model and analyzing the model against extreme poverty mapping of Bengkulu Province. The data used in this study is the March 2022 Susenas data. Of the 18 variables that allegedly affect extreme poverty, only 6 variables support the assumption of spatial heterogeneity in GWR modeling. Based on the selection of the best model, it is known that the GWR model with Adaptive Bisquare Kernel weighting is a suitable model for the percentage of extreme poor people in Bengkulu Province with the smallest AIC value
Co-Authors Jose Rizal Yulian Fauzi