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Spatial Heterogeneity of Pandemic Risk in Urban Areas: A Comparative Analysis of Risk, Spatial Planning, and Urban Preparedness Using OLS and GWR Models

Indonesian Journal of Geography (2026)

DOI
10.22146/ijg.120307
ISSN
2354-9114, 0024-9521
Terbit
2026-11-03
Penerbit
Faculty of Geography, Universitas Gadjah Mada
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

The COVID-19 pandemic has highlighted the limitations of conventional case-based zoning, emphasizing the need for spatially detailed risk mapping in heterogeneous urban environments such as Medan, where density, mobility, and socioeconomic conditions vary considerably across neighborhoods. This study develops a neighborhood-level pandemic risk mapping framework for Medan City using fuzzy logic and multi-criteria analysis, examining relationships among pandemic risk, spatial planning, and city preparedness across 151 urban village in 21 sub-districts. Primary data were gathered through structured interviews with 151 urban villages (kelurahan) representatives and field observations, complemented by secondary statistical data from official government agencies. Data accuracy was verified through data triangulation across primary and secondary sources and expert consistency ratio checks prior to multi-criteria evaluation. Threat, vulnerability, capacity, spatial planning, and preparedness data were standardized using fuzzy membership functions, with risk weights determined through Principal Component Analysis and planning weights derived from the Analytical Hierarchy Process. Spatial layers were integrated through Multi-Criteria Evaluation and weighted overlay in a Geographic Information System, with inter-index relationships assessed using Ordinary Least Squares and Geographically Weighted Regression. The pandemic risk index ranged from 0.015 to 0.809, with 40.44% of urban villages classified as low risk, 46.36% as moderate risk, and 13.25% as high risk. High-risk areas concentrated in the city center and eastern zones, reflecting elevated density and mobility. While the global OLS model showed weak direct effects of risk and spatial planning on preparedness, the local GWR model revealed substantially stronger spatial relationships, with risk demonstrating a significant influence on spatial planning at an R² of 0.7182. In conclusion, this study demonstrates that combining fuzzy multi-criteria risk mapping with local spatial regression provides a robust, spatially targeted framework for urban spatial planners and policy makers to strengthen disaster resilience and prioritize pandemic interventions in highly vulnerable urban neighborhoods.Received: 2026-06-08 Revised: 2026-07-08 Accepted: 2026-09-29 Published: 2026-11-03

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17 penulis · 26 tautan kepenulisan bersama · 6 lintas organisasi

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  • State University of Medan
  • Universitas Sumatera Utara
  • Malikussaleh University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Restu RestuAfiliasi tidak diketahui2
Wesli WesliMalikussaleh University1
A. Rahim MatondangUniversitas Sumatera Utara1
Suwardi LubisUniversitas Sumatera Utara1
Rizky Harrianto NainggolanState University of Medan1
Walbiden LumbantoruanAfiliasi tidak diketahui1
Minah SinuhajiAfiliasi tidak diketahui1
Ali NurmanState University of Medan1
Asnidar AsnidarAfiliasi tidak diketahui1
Budi Ali MukminState University of Medan1
Dedy Sutrisno HarianjaState University of Medan1
Teguh Febri SudarmaState University of Medan1
Eko PrasetyaState University of Medan1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://jurnal.ugm.ac.id/ijg/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Spatial Heterogeneity of Pandemic Risk in Urban Areas: A Comparative Analysis of Risk, Spatial Planning, and Urban Preparedness Using OLS and GWR Models</dc:title>
	<dc:creator>Damanik, Muhammad Ridha Syafii</dc:creator>
	<dc:creator>Sirojuzilam, Sirojuzilam</dc:creator>
	<dc:creator>Santosa, Heru</dc:creator>
	<dc:creator>Samsuri, Samsuri</dc:creator>
	<dc:subject xml:lang="en-US">Regional Planning</dc:subject>
	<dc:subject xml:lang="en-US">COVID-19; risk; spatial planning; urban preparedness; OLS; GWR</dc:subject>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:description xml:lang="en-US">The COVID-19 pandemic has highlighted the limitations of conventional case-based zoning, emphasizing the need for spatially detailed risk mapping in heterogeneous urban environments such as Medan, where density, mobility, and socioeconomic conditions vary considerably across neighborhoods. This study develops a neighborhood-level pandemic risk mapping framework for Medan City using fuzzy logic and multi-criteria analysis, examining relationships among pandemic risk, spatial planning, and city preparedness across 151 urban village in 21 sub-districts. Primary data were gathered through structured interviews with 151 urban villages (kelurahan) representatives and field observations, complemented by secondary statistical data from official government agencies. Data accuracy was verified through data triangulation across primary and secondary sources and expert consistency ratio checks prior to multi-criteria evaluation. Threat, vulnerability, capacity, spatial planning, and preparedness data were standardized using fuzzy membership functions, with risk weights determined through Principal Component Analysis and planning weights derived from the Analytical Hierarchy Process. Spatial layers were integrated through Multi-Criteria Evaluation and weighted overlay in a Geographic Information System, with inter-index relationships assessed using Ordinary Least Squares and Geographically Weighted Regression. The pandemic risk index ranged from 0.015 to 0.809, with 40.44% of urban villages classified as low risk, 46.36% as moderate risk, and 13.25% as high risk. High-risk areas concentrated in the city center and eastern zones, reflecting elevated density and mobility. While the global OLS model showed weak direct effects of risk and spatial planning on preparedness, the local GWR model revealed substantially stronger spatial relationships, with risk demonstrating a significant influence on spatial planning at an R² of 0.7182. In conclusion, this study demonstrates that combining fuzzy multi-criteria risk mapping with local spatial regression provides a robust, spatially targeted framework for urban spatial planners and policy makers to strengthen disaster resilience and prioritize pandemic interventions in highly vulnerable urban neighborhoods.Received: 2026-06-08 Revised: 2026-07-08 Accepted: 2026-09-29 Published: 2026-11-03</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Geography, Universitas Gadjah Mada</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-11-03</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijg/article/view/120307</dc:identifier>
	<dc:identifier>10.22146/ijg.120307</dc:identifier>
	<dc:source xml:lang="en-US">Indonesian Journal of Geography; ARTICLE IN PRESS</dc:source>
	<dc:source>2354-9114</dc:source>
	<dc:source>0024-9521</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/view/120307/pdf</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/120307/47555</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/120307/47556</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/120307/47557</dc:relation>
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	<dc:rights xml:lang="en-US">Copyright (c) 2026 Authors</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
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