ANALISIS SPASIAL KEMISKINAN DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION: STUDI KASUS KABUPATEN PANDEGLANG DAN LEBAK

*S Sukanto -  Program Studi Ekonomi Pembangunan Fakultas Ekonomi, Universitas Sriwijaya, Indonesia
Bambang Juanda -  Program Studi Ilmu Perencanaan Pembangunan Wilayah dan Perdesaan, Institut Pertanian Bogor, Indonesia
Akhmad Fauzi -  Program Studi Ilmu Perencanaan Pembangunan Wilayah dan Perdesaan, Institut Pertanian Bogor, Indonesia
Sri Mulatsih -  Program Studi Ilmu Perencanaan Pembangunan Wilayah dan Perdesaan, Institut Pertanian Bogor, Indonesia
Received: 5 Mar 2018; Published: 29 Nov 2019.
Open Access
Citation Format:
Abstract
Poverty is the main problem both at the national and regional development.  Existing poverty alleviation programs have not paid attention to the spatial aspect. Thus the policies are often poorly targeted. This study aims to find spatial patterns of poverty in Pandeglang and Lebak districts. Geographically weighted regression (GWR) is used to analyze the poverty data in 2016. Based on the analysis, positive spatial autocorrelation is found and clustered in 25 sub-districts. Net enrollment rates tend to reduce poverty in all sub-districts. Meanwhile, village funds, electricity, and roads tend to reduce poverty rates in more than 80% of sub-districts. Independent variables have a different response in each sub-district. Therefore, the poverty alleviation program of each sub-district is adjusting to its influencing factor.
Keywords
autocorrelation spatial; GWR; poverty; sub-district

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