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

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

DOI
10.24114/jg.v16i2.53136
ISSN
2549-7057, 2085-8167
Terbit
2024-07-25
Penerbit
Universitas Negeri Medan
Bahasa
en
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Inggris

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

Inggris

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

Jaringan kolaborasi para penulis

15 penulis · 32 tautan kepenulisan bersama · 5 lintas organisasi

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  • Sebelas Maret University
  • University of Technology Malaysia
  • Muhammadiyah University of Surakarta
  • 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
Setya NugrahaSebelas Maret University2
Mahameru Rosy RochmatullahMuhammadiyah University of Surakarta2
Chatarina MuryaniSebelas Maret University1
SugiyantoAfiliasi tidak diketahui1
SarwonoAfiliasi tidak diketahui1
AhmadAfiliasi tidak diketahui1
Singgih PrihadiSebelas Maret University1
Natasya MaymunaSebelas Maret University1
Rahning UtomowatiSebelas Maret University1
Hairul Nizam IsmailUniversity of Technology Malaysia1
Pipit WijayantiSebelas Maret University1
Yasin YusupSebelas Maret University1

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.unimed.ac.id/2012/index.php/geo/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (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>
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	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
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	<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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