Spatial Distribution Pattern of Covid-19 Cases and Their Characteristics In DKI Jakarta and Surrounding Areas

Martono, Dwi Nowo Nowo; Saiya, Halvina Grasela

doi:10.22146/ijg.90542

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Spatial Distribution Pattern of Covid-19 Cases and Their Characteristics In DKI Jakarta and Surrounding Areas

Indonesian Journal of Geography · Vol. 56 No. 2 (2024) · hlm. 227–240

DOI: 10.22146/ijg.90542
ISSN: 2354-9114, 0024-9521
Terbit: 2024-08-08
Penerbit: Faculty of Geography, Universitas Gadjah Mada
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-nc/4.0

Abstrak

The COVID-19 pandemic has significantly affected various countries worldwide, including Indonesia. This study specifically examines the spatial distribution pattern of COVID-19 cases among sub-districts in DKI Jakarta and its neighboring areas. The study investigates the impact of spatial characteristics such as building density, population density, road network connectivity, and accessibility, as well as infrastructure completeness. A spatial regression model was employed to analyze the influence and pattern of COVID-19 case distribution among sub-districts. Spatial modeling indicates that geographic location has an effect on the data, often referred to as the autocorrelation effect. Moran’s Index was used to test the relationship between district locations and the number and growth rate of cases.The study findings reveal a positive spatial autocorrelation in the growth rate pattern of COVID-19 cases among sub-districts and clusters in DKI Jakarta and its surrounding areas. The spatial regression model, specifically the Spatial Autoregressive Model (SAR), identifies road connectivity, number of health centers, building density, and population density as spatial variables that significantly influence the rate of COVID-19 cases. Play This Podcast Article

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Rekan penulis	Organisasi	Artikel bersama
Suprapto Dibyosaputro	Afiliasi tidak diketahui	1
Sigit Herumurti Budi Santosa	Afiliasi tidak diketahui	1
Saiful Amri	Afiliasi tidak diketahui	1
Dwi Nowo Martono	Afiliasi tidak diketahui	1

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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 Distribution Pattern of Covid-19 Cases and Their Characteristics In DKI Jakarta and Surrounding Areas</dc:title>
	<dc:creator>Martono, Dwi Nowo Nowo</dc:creator>
	<dc:creator>Saiya, Halvina Grasela</dc:creator>
	<dc:subject xml:lang="en-US">lektor kepala;ilmu lingkungan;analisis spasial</dc:subject>
	<dc:subject xml:lang="en-US">COVID-19 Cases; Spatial Characteristics; Autocorrelation; Spatial Regression</dc:subject>
	<dc:subject xml:lang="en-US">analisis spasial</dc:subject>
	<dc:description xml:lang="en-US">The COVID-19 pandemic has significantly affected various countries worldwide, including Indonesia. This study specifically examines the spatial distribution pattern of COVID-19 cases among sub-districts in DKI Jakarta and its neighboring areas. The study investigates the impact of spatial characteristics such as building density, population density, road network connectivity, and accessibility, as well as infrastructure completeness. A spatial regression model was employed to analyze the influence and pattern of COVID-19 case distribution among sub-districts. Spatial modeling indicates that geographic location has an effect on the data, often referred to as the autocorrelation effect. Moran’s Index was used to test the relationship between district locations and the number and growth rate of cases.The study findings reveal a positive spatial autocorrelation in the growth rate pattern of COVID-19 cases among sub-districts and clusters in DKI Jakarta and its surrounding areas. The spatial regression model, specifically the Spatial Autoregressive Model (SAR), identifies road connectivity, number of health centers, building density, and population density as spatial variables that significantly influence the rate of COVID-19 cases.  Play This Podcast Article </dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Geography, Universitas Gadjah Mada</dc:publisher>
	<dc:contributor xml:lang="en-US">Sekolah Ilmu Lingkungan Universitas Indonesia jakarta Indonesia</dc:contributor>
	<dc:date>2024-08-08</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-US">Peer-reviewed Article</dc:type>
	<dc:type xml:lang="en-US">spasial analisis dan GIS</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijg/article/view/90542</dc:identifier>
	<dc:identifier>10.22146/ijg.90542</dc:identifier>
	<dc:source xml:lang="en-US">Indonesian Journal of Geography; Vol 56, No 2 (2024): Indonesian Journal of Geography; 227-240</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/90542/pdf</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/90542/29856</dc:relation>
	<dc:coverage xml:lang="en-US">urban and sub district</dc:coverage>
	<dc:coverage xml:lang="en-US"></dc:coverage>
	<dc:coverage xml:lang="en-US">data resmi dari pemerintah daerah</dc:coverage>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Authors</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>