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

S Sukanto; Bambang Juanda; Akhmad Fauzi; Sri Mulatsih

doi:10.14710/tataloka.21.4.669-677

DOI: https://doi.org/10.14710/tataloka.21.4.669-677

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

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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.

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Keywords: autocorrelation spatial; GWR; poverty; sub-district

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In Vol 21, No 4 (2019): Volume 21 No 4, November 2019

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