Melola Discovery

← Melola Discovery

Journal

Model Robust Geographically Weighted Regression pada Data Kemiskinan di Sulawesi Selatan Tahun 2019

ESTIMASI: Journal of Statistics and Its Application · Vol. 6 No. 2 (2025) · pp. 144–151

DOI
10.20956/ejsa.v6i2.18046
ISSN
2721-379X, 2721-3803
Published
2025-08-04
Publisher
Hasanuddin University
Language
en

Abstract

Geographically Weighted Regression (GWR) is a method of spatial analysis that can be used to perform analysis by assigning weights based on the geographical distance of each observation location and the assumption of having spatial heterogenity. The result of this analysis is an equation model whose parameter values apply only to each observation location and are different from other observation locations. However, when there are outliers at the observation location, a more robust estimation method is needed. One of the robust methods that can be applied to the GWR model is the Least Absolute Deviation method. In this study, model estimation was carried out on the factors that affect poverty in South Sulawesi in 2019 using Robust Geographically Weighted Regression (RGWR) with the Least Absolute Deviation (LAD) method. Determination of weighting is done by using the adaptive kernel bisquare weighting function. The results obtained are RGWR models which are different and apply only to each district/city in South Sulawesi. In addition, it was also found that the RGWR model with the LAD method was the best model for data that experienced spatial heterogenity and contained outliers.

Keywords

Collaboration network of the authors

40 authors · 91 co-authorship links · 1 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Hasanuddin University
  • Lampung University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nasrah SirajangHasanuddin University6
Nurtiti SunusiHasanuddin University5
RaupongAffiliation unknown2
Saleh AfAffiliation unknown2
Sitti SahrimanHasanuddin University2
Ika FitrianaHasanuddin University2
Andi Kresna JayaHasanuddin University2
Nurhidaya LAffiliation unknown2
Sonya SonyaAffiliation unknown2
Alimatun NajihaHasanuddin University2
Aidawayati RangkutiHasanuddin University2
Iis Cendrah KasihHasanuddin University1
Rizki AdiputraHasanuddin University1
Asti Inayati MagfirahAffiliation unknown1
Mega Selvia TjahayaAffiliation unknown1
Andi Isna YunitaHasanuddin University1
Ratmila MammiAffiliation unknown1
Nur Alfianingsih RajaHasanuddin University1
Riska RasyidHasanuddin University1
Hendriete Tiur Marowi WaibusiHasanuddin University1
Anisa AnisaAffiliation unknown1
Victor LimanAffiliation unknown1
Sitti SahrimanAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Anisa KalondengHasanuddin University1
Surya Cahyadi SyamAffiliation unknown1
Nuralyatussa’ AdaAffiliation unknown1
Siswanto SiswantoHasanuddin University1
Taufiq AkbarHasanuddin University1
Fadhila Febriyanti NajamuddinHasanuddin University1
Mayashari MayashariHasanuddin University1
Anisa AnisaHasanuddin University1
Mustabsyirah MustabsyirahHasanuddin University1
Agung Muhammad TakdirHasanuddin University1
Sri Astuti ThamrinHasanuddin University1
Nuravia NuraviaAffiliation unknown1
Fitriani FitrianiLampung University1

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/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Model Robust Geographically Weighted Regression pada Data Kemiskinan di Sulawesi Selatan Tahun 2019</dc:title>
	<dc:creator xml:lang="en">Rahman, Aqilah Salsabila</dc:creator>
	<dc:creator xml:lang="en">Tinungki, Georgina Maria</dc:creator>
	<dc:creator xml:lang="en">Herdiani, Erna Tri</dc:creator>
	<dc:subject xml:lang="en">Adaptive Kernel Bisquare</dc:subject>
	<dc:subject xml:lang="en">Geographically Weighted Regression</dc:subject>
	<dc:subject xml:lang="en">Least Absolute Deviation</dc:subject>
	<dc:subject xml:lang="en">Poverty</dc:subject>
	<dc:subject xml:lang="en">Robust</dc:subject>
	<dc:description xml:lang="en">Geographically Weighted Regression (GWR) is a method of spatial analysis that can be used to perform analysis by assigning weights based on the geographical distance of each observation location and the assumption of having spatial heterogenity. The result of this analysis is an equation model whose parameter values apply only to each observation location and are different from other observation locations. However, when there are outliers at the observation location, a more robust estimation method is needed. One of the robust methods that can be applied to the GWR model is the Least Absolute Deviation method. In this study, model estimation was carried out on the factors that affect poverty in South Sulawesi in 2019 using Robust Geographically Weighted Regression (RGWR) with the Least Absolute Deviation (LAD) method. Determination of weighting is done by using the adaptive kernel bisquare weighting function. The results obtained are RGWR models which are different and apply only to each district/city in South Sulawesi. In addition, it was also found that the RGWR model with the LAD method was the best model for data that experienced spatial heterogenity and contained outliers. </dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2025-08-04</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/18046</dc:identifier>
	<dc:identifier>10.20956/ejsa.v6i2.18046</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 6, No. 2, Juli, 2025 : Estimasi; 144-151</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/18046/13218</dc:relation>
</oai_dc:dc>