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Geographically Weighted Panel Regression Modelling of Human Development Index Data in East Kalimantan Province in 2017-2020

Pemodelan Geographically Weighted Panel Regression pada Data Indeks Pembangunan Manusia di Provinsi Kalimantan Timur Tahun 2017-2020

Jurnal Matematika, Statistika dan Komputasi · Vol. 19 No. 2 (2023) · pp. 323–341

DOI
10.20956/j.v19i2.23775
ISSN
2614-8811, 1858-1382
Published
2023-01-05
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

Geographically Weighted Panel Regression (GWPR) model is a panel regression model applied on spatial data. This research applied Fixed Effect Model (FEM) on panel regression as the global model and GWPR as the local model for Human Development Index (HDI) regencies/municipalities in East Kalimantan Province data over the years 2017-2020. The aim of this research is to obtain the GWPR model of HDI data, as well as to acquire factors that influence it. The parameter of GWPR model was estimated on each observation location using the Weighted Least Square (WLS) method, namely Ordinary Least Square (OLS) with addition of spatial weighting. The spatial weighting on GWPR model was calculated using fixed bisquare, fixed tricube, adaptive bisquare and adaptive tricube. After the selection process, the optimum weighting function is adaptive tricube which provides a minimum Cross Validation (CV) value of 5.1419. Based on GWPR parameter testing, factors that affect HDI are local and diverse in each 10 regencies/municipalities in East Kalimantan Province. These factors are the labor force participation rate, number of health facilities, Gini ratio, population growth rate, open unemployment rate, poverty gap index and percentage of food expenditure. The coefficient of determination of the GWPR model obtains a value of 94.36% with the RMSE value of 0.1221.

Bahasa Indonesia

Model Geographically Weighted Panel Regression (GWPR) merupakan model regresi panel yang diterapkan pada data spasial. Penelitian ini menerapkan Fixed Effect Model (FEM) pada regresi panel sebagai model global dan GWPR sebagai model lokal untuk data Indeks Pembangunan Manusia (IPM) kabupaten/kota di Provinsi Kalimantan Timur tahun 2017-2020. Penelitian ini bertujuan untuk memperoleh model GWPR untuk data IPM, serta faktor-faktor yang mempengaruhinya. Parameter model GWPR diestimasi pada setiap lokasi pengamatan menggunakan metode Weighted Least Square (WLS) yaitu Ordinary Least Square (OLS) dengan penambahan pembobotan spasial. Pembobotan spasial pada model GWPR dihitung menggunakan fungsi pembobot fixed bisquare, fixed tricube, adaptive bisquare dan adaptive tricube. Setelah proses penyeleksian, fungsi pembobot optimum adalah adaptive tricube yang memberikan nilai Cross Validation (CV) minimum sebesar 5,1419. Berdasarkan pengujian parameter model GWPR, faktor-faktor yang mempengaruhi IPM bersifat lokal dan beragam di setiap 10 kabupaten/kota di Provinsi Kalimantan Timur. Faktor-faktor tersebut adalah tingkat partisipasi angkatan kerja, jumlah fasilitas kesehatan, Gini ratio, laju pertumbuhan penduduk, tingkat pengangguran terbuka, indeks kedalaman kemiskinan dan persentase pengeluaran per kapita kelompok makanan. Koefisien determinasi model GWPR sebesar 94,36% dengan nilai RMSE sebesar 0,1221.

Keywords

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Co-authorOrganisationJoint articles
Sri WahyuningsihAffiliation unknown3
Sifriyani SifriyaniAffiliation unknown1
Ghina Fadhilla MumtazAffiliation unknown1
Melisa ArumsariAffiliation unknown1
Novita AndrianiAffiliation unknown1
Adelia RamadhaniAffiliation unknown1
Memi Nor HayatiAffiliation unknown1
Ineu SintiaAffiliation unknown1
Siti MahmudaAffiliation unknown1
Siti Fatimah KhairunnisaAffiliation unknown1
Meirinda FauziyahAffiliation unknown1
Adelia MirandaAffiliation unknown1
Darnah DarnahAffiliation unknown1
Azkadiaannzumi AzkadiaannzumiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Geographically Weighted Panel Regression Modelling  of Human Development Index Data  in East Kalimantan Province in 2017-2020</dc:title>
	<dc:title xml:lang="id">Pemodelan Geographically Weighted Panel Regression pada Data Indeks Pembangunan Manusia di Provinsi Kalimantan Timur Tahun 2017-2020</dc:title>
	<dc:creator xml:lang="en">Ananda, Ni Made Shantia </dc:creator>
	<dc:creator xml:lang="en">Suyitno, Suyitno</dc:creator>
	<dc:creator xml:lang="en">Siringoringo, Meiliyani </dc:creator>
	<dc:creator xml:lang="id">Suyitno</dc:creator>
	<dc:creator xml:lang="id">Meiliyani Siringoringo</dc:creator>
	<dc:subject xml:lang="en">Adaptive Bisquare</dc:subject>
	<dc:subject xml:lang="en">Fixed Effect Model (FEM)</dc:subject>
	<dc:subject xml:lang="en">Geographically Weighted Panel Regression (GWPR)</dc:subject>
	<dc:subject xml:lang="en">Human Development Index (HDI)</dc:subject>
	<dc:subject xml:lang="id">Adaptive Bisquare</dc:subject>
	<dc:subject xml:lang="id">Fixed Effect Model (FEM)</dc:subject>
	<dc:subject xml:lang="id">Geographically Weighted Panel Regression (GWPR)</dc:subject>
	<dc:subject xml:lang="id">Indeks Pembangunan Manusia</dc:subject>
	<dc:description xml:lang="en">Geographically Weighted Panel Regression (GWPR) model is a panel regression model applied on spatial data. This research applied Fixed Effect Model (FEM) on panel regression as the global model and GWPR as the local model for Human Development Index (HDI) regencies/municipalities in East Kalimantan Province data over the years 2017-2020. The aim of this research is to obtain the GWPR model of HDI data, as well as to acquire factors that influence it. The parameter of GWPR model was estimated on each observation location using the Weighted Least Square (WLS) method, namely Ordinary Least Square (OLS) with addition of spatial weighting. The spatial weighting on GWPR model was calculated using fixed bisquare, fixed tricube, adaptive bisquare and adaptive tricube. After the selection process, the optimum weighting function is adaptive tricube which provides a minimum Cross Validation (CV) value of 5.1419. Based on GWPR parameter testing, factors that affect HDI are local and diverse in each 10 regencies/municipalities in East Kalimantan Province. These factors are the labor force participation rate, number of health facilities, Gini ratio, population growth rate, open unemployment rate, poverty gap index and percentage of food expenditure. The coefficient of determination of the GWPR model obtains a value of 94.36% with the RMSE value of 0.1221.</dc:description>
	<dc:description xml:lang="id">Model Geographically Weighted Panel Regression (GWPR) merupakan model regresi panel yang diterapkan pada data spasial. Penelitian ini menerapkan Fixed Effect Model (FEM) pada regresi panel sebagai model global dan GWPR sebagai model lokal untuk data Indeks Pembangunan Manusia (IPM) kabupaten/kota di Provinsi Kalimantan Timur tahun 2017-2020. Penelitian ini bertujuan untuk memperoleh model GWPR untuk data IPM, serta faktor-faktor yang mempengaruhinya. Parameter model GWPR diestimasi pada setiap lokasi pengamatan menggunakan metode Weighted Least Square (WLS) yaitu Ordinary Least Square (OLS) dengan penambahan pembobotan spasial. Pembobotan spasial pada model GWPR dihitung menggunakan fungsi pembobot fixed bisquare, fixed tricube, adaptive bisquare dan adaptive tricube. Setelah proses penyeleksian, fungsi pembobot optimum adalah adaptive tricube yang memberikan nilai Cross Validation (CV) minimum sebesar 5,1419. Berdasarkan pengujian parameter model GWPR, faktor-faktor yang mempengaruhi IPM bersifat lokal dan beragam di setiap 10 kabupaten/kota di Provinsi Kalimantan Timur. Faktor-faktor tersebut adalah tingkat partisipasi angkatan kerja, jumlah fasilitas kesehatan, Gini ratio, laju pertumbuhan penduduk, tingkat pengangguran terbuka, indeks kedalaman kemiskinan dan persentase pengeluaran per kapita kelompok makanan. Koefisien determinasi model GWPR sebesar 94,36% dengan nilai RMSE sebesar 0,1221.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2023-01-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/23775</dc:identifier>
	<dc:identifier>10.20956/j.v19i2.23775</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 19 No. 2 (2023): JANUARY 2023; 323-341</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/23775/9156</dc:relation>
	<dc:relation>10.20956/j.v19i2.23775.g9156</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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