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Spatial Autocorrelation of East Java's Economic Growth Using Cluster-Based Weights

Economics Development Analysis Journal · Vol. 13 No. 4 (2025)

DOI
10.15294/edaj.v13i4.19161
ISSN
2502-2725, 2252-6560
Terbit
2025-03-09
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

This study incorporates a spatial clustering technique into the formation of a spatial weight matrix as an alternative to the traditional exogenous matrix, aiming to better capture spatial dependencies. The approach is applied to analyze the spatial autocorrelation of economic growth in East Java’s regencies and municipalities using 2019–2021 data. Spatial clusters are identified based on GDP growth (GGDP), Human Development Index (HDI), population density (Dens), and geographical coordinates. These clusters are used to define a customized spatial weight matrix, where regions within the same cluster are designated as neighbors. Moran’s I, calculated using the customized spatial weight matrix, detects significant spatial autocorrelation in GDP growth for all three years, with consistently lower p-values compared to the traditional contiguity-based matrix. For example, in 2020, Moran’s I using the customized matrix yielded a p-value of 0.099 (significant at the 10% level), while the traditional matrix produced a non-significant p-value of 0.7965. These results demonstrate that spatial clustering extends the scope of spatial interaction beyond adjacent regions to include those with similar characteristics. The findings highlight the effectiveness of this method in providing a more nuanced and robust framework for analyzing spatial dependencies in economic growth.

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  • University of Brawijaya
  • Institut Agama Islam Negeri Kendari
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Rekan penulisOrganisasiArtikel bersama
Solimun SolimunUniversity of Brawijaya3
Atiek IrianyUniversity of Brawijaya2
Nabila Ayunda SoviaAfiliasi tidak diketahui2
Eni SumarminingsihUniversity of Brawijaya2
Adji Achmad Rinaldo FernandesUniversity of Brawijaya2
Muhamad Liswansyah PratamaAfiliasi tidak diketahui2
Loekito Adi SoehonoAfiliasi tidak diketahui2
Agus Fachrur RozyUniversity of Brawijaya1
Dahlia Gladiola Rurina MenufanduAfiliasi tidak diketahui1
Henny PramoedyoUniversity of Brawijaya1
Ani Budi AstutiUniversity of Brawijaya1
Wigbertus NgabuAfiliasi tidak diketahui1
Nurjannah NurjannahInstitut Agama Islam Negeri Kendari1
Yossy Maynaldi PratamaAfiliasi tidak diketahui1
Suci AstutikUniversity of Brawijaya1
Armando Jacquis Federal ZamelinaAfiliasi tidak diketahui1
Lucius RamifidisoaAfiliasi tidak diketahui1
Achmad EfendiAfiliasi tidak diketahui1
Candra R. W. S Weni UtomoAfiliasi tidak diketahui1
Hafizh Syihabuddin Al JauharAfiliasi tidak diketahui1
Maulidya MaghfiroAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/edaj/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 5 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Spatial Autocorrelation of East Java's Economic Growth Using Cluster-Based Weights</dc:title>
	<dc:creator xml:lang="en">Fitriani, Rahma</dc:creator>
	<dc:creator xml:lang="en">Wardhani, Ni Wayan Surya</dc:creator>
	<dc:creator xml:lang="en">Abdila, Naufal Shela</dc:creator>
	<dc:subject xml:lang="en">spatial weight matrix</dc:subject>
	<dc:subject xml:lang="en">spatial autocorrelation</dc:subject>
	<dc:subject xml:lang="en">spatial cluster</dc:subject>
	<dc:subject xml:lang="en">economic growth </dc:subject>
	<dc:description xml:lang="en">This study incorporates a spatial clustering technique into the formation of a spatial weight matrix as an alternative to the traditional exogenous matrix, aiming to better capture spatial dependencies. The approach is applied to analyze the spatial autocorrelation of economic growth in East Java’s regencies and municipalities using 2019–2021 data. Spatial clusters are identified based on GDP growth (GGDP), Human Development Index (HDI), population density (Dens), and geographical coordinates. These clusters are used to define a customized spatial weight matrix, where regions within the same cluster are designated as neighbors. Moran’s I, calculated using the customized spatial weight matrix, detects significant spatial autocorrelation in GDP growth for all three years, with consistently lower p-values compared to the traditional contiguity-based matrix. For example, in 2020, Moran’s I using the customized matrix yielded a p-value of 0.099 (significant at the 10% level), while the traditional matrix produced a non-significant p-value of 0.7965. These results demonstrate that spatial clustering extends the scope of spatial interaction beyond adjacent regions to include those with similar characteristics. The findings highlight the effectiveness of this method in providing a more nuanced and robust framework for analyzing spatial dependencies in economic growth.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-03-09</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/edaj/article/view/19161</dc:identifier>
	<dc:identifier>10.15294/edaj.v13i4.19161</dc:identifier>
	<dc:source xml:lang="en">Economics Development Analysis Journal; Vol. 13 No. 4 (2024): Economics Development Analysis Journal</dc:source>
	<dc:source>2502-2725</dc:source>
	<dc:source>2252-6560</dc:source>
	<dc:source>10.15294/edaj.v13i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/edaj/article/view/19161/3265</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Economics Development Analysis Journal</dc:rights>
</oai_dc:dc>