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Urbanization Analysis of Land Surface Temperature Modeling in the Peri-Urban of Surakarta

Jurnal Geografi · Vol. 16 No. 2 (2024) · pp. 273–289

DOI
10.24114/jg.v16i2.53136
ISSN
2549-7057, 2085-8167
Published
2024-07-25
Publisher
Universitas Negeri Medan
Language
en
License
creativecommons.org/licenses/by-sa/4.0

Abstract

English

The rapid development of urban areas characterized by urbanization has caused various impacts, such as LST. The Peri-Urban area of Surakarta City is one of the areas that experiences similar conditions. This study aims to measure urbanization through the urban form, population, and land urbanization and modeling urbanization of Land Surface Temperature. The method used in this study is modeling using Ordinary least squares (OLS) and Geographically Weighted Regression (GWR). Data processing tools to support this research are ArcGIS and Google Earth Engine. The combination of ArcGIS and Google Earth Engine makes LST modeling more accurate. The measurement results show that the urbanization rate in the peri-urban area of Surakarta City, based on the parameters of land urbanization and urban form, tends to move to the South and West sides with a higher population in areas close to Surakarta City. The results of this study show that GWR modeling is better at explaining the relationship of urbanization with LST than OLS models based on AICc numbers. The relationship of urbanization with population parameters, population density, percentage of built-up land, and urban form with LST using the GWR model of 66.17% and the OLS model reveals that urbanization.Keywords: GWR; LST; OLS; Urbanization; Urban form

English

The rapid development of urban areas characterized by urbanization has caused various impacts, such as LST. The Peri-Urban area of Surakarta City is one of the areas that experiences similar conditions. This study aims to measure urbanization through the urban form, population, and land urbanization and modeling urbanization of Land Surface Temperature. The method used in this study is modeling using Ordinary least squares (OLS) and Geographically Weighted Regression (GWR). Data processing tools to support this research are ArcGIS and Google Earth Engine. The combination of ArcGIS and Google Earth Engine makes LST modeling more accurate. The measurement results show that the urbanization rate in the peri-urban area of Surakarta City, based on the parameters of land urbanization and urban form, tends to move to the South and West sides with a higher population in areas close to Surakarta City. The results of this study show that GWR modeling is better at explaining the relationship of urbanization with LST than OLS models based on AICc numbers. The relationship of urbanization with population parameters, population density, percentage of built-up land, and urban form with LST using the GWR model of 66.17% and the OLS model reveals that urbanization.Keywords: GWR; LST; OLS; Urbanization; Urban form

Collaboration network of the authors

15 authors · 32 co-authorship links · 4 across different organisations

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

  • Sebelas Maret University
  • University of Technology Malaysia
  • Yogyakarta State University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Setya NugrahaAffiliation unknown2
Mahameru Rosy RochmatullahAffiliation unknown2
Chatarina MuryaniAffiliation unknown1
SugiyantoAffiliation unknown1
SarwonoAffiliation unknown1
AhmadAffiliation unknown1
Singgih PrihadiAffiliation unknown1
Natasya MaymunaSebelas Maret University1
Rahning UtomowatiSebelas Maret University1
Hairul Nizam IsmailUniversity of Technology Malaysia1
Pipit WijayantiYogyakarta State University1
Yasin YusupAffiliation 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) http://jurnal.unimed.ac.id/2012/index.php/geo/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Urbanization Analysis of Land Surface Temperature Modeling in the Peri-Urban of Surakarta</dc:title>
	<dc:title xml:lang="en-US">Urbanization Analysis of Land Surface Temperature Modeling in the Peri-Urban of Surakarta</dc:title>
	<dc:creator xml:lang="en">Noviani, Rita</dc:creator>
	<dc:creator xml:lang="en">Marfuah, Istiyanti Nur</dc:creator>
	<dc:creator xml:lang="en">Saputra, Aditya Eka</dc:creator>
	<dc:description xml:lang="en">The rapid development of urban areas characterized by urbanization has caused various impacts, such as LST. The Peri-Urban area of Surakarta City is one of the areas that experiences similar conditions. This study aims to measure urbanization through the urban form, population, and land urbanization and modeling urbanization of Land Surface Temperature. The method used in this study is modeling using Ordinary least squares (OLS) and Geographically Weighted Regression (GWR). Data processing tools to support this research are ArcGIS and Google Earth Engine. The combination of ArcGIS and Google Earth Engine makes LST modeling more accurate. The measurement results show that the urbanization rate in the peri-urban area of Surakarta City, based on the parameters of land urbanization and urban form, tends to move to the South and West sides with a higher population in areas close to Surakarta City. The results of this study show that GWR modeling is better at explaining the relationship of urbanization with LST than OLS models based on AICc numbers. The relationship of urbanization with population parameters, population density, percentage of built-up land, and urban form with LST using the GWR model of 66.17% and the OLS model reveals that urbanization.Keywords: GWR; LST; OLS; Urbanization; Urban form</dc:description>
	<dc:description xml:lang="en-US">The rapid development of urban areas characterized by urbanization has caused various impacts, such as LST. The Peri-Urban area of Surakarta City is one of the areas that experiences similar conditions. This study aims to measure urbanization through the urban form, population, and land urbanization and modeling urbanization of Land Surface Temperature. The method used in this study is modeling using Ordinary least squares (OLS) and Geographically Weighted Regression (GWR). Data processing tools to support this research are ArcGIS and Google Earth Engine. The combination of ArcGIS and Google Earth Engine makes LST modeling more accurate. The measurement results show that the urbanization rate in the peri-urban area of Surakarta City, based on the parameters of land urbanization and urban form, tends to move to the South and West sides with a higher population in areas close to Surakarta City. The results of this study show that GWR modeling is better at explaining the relationship of urbanization with LST than OLS models based on AICc numbers. The relationship of urbanization with population parameters, population density, percentage of built-up land, and urban form with LST using the GWR model of 66.17% and the OLS model reveals that urbanization.Keywords: GWR; LST; OLS; Urbanization; Urban form</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Medan</dc:publisher>
	<dc:date>2024-07-25</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://jurnal.unimed.ac.id/2012/index.php/geo/article/view/53136</dc:identifier>
	<dc:identifier>10.24114/jg.v16i2.53136</dc:identifier>
	<dc:source xml:lang="en">JURNAL GEOGRAFI; Vol. 16 No. 2 (2024): JURNAL GEOGRAFI; 273-289</dc:source>
	<dc:source>2549-7057</dc:source>
	<dc:source>2085-8167</dc:source>
	<dc:source>10.24114/jg.v16i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://jurnal.unimed.ac.id/2012/index.php/geo/article/view/53136/pdf</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 JURNAL GEOGRAFI</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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