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Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City
Rapid urbanization threatens the ecological environment and quality of life by significantly altering land use and land cover (LULC) and heat distribution. One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatio...
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| Published in: | Land (Basel) 2023-11, Vol.12 (11), p.2012 |
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| container_issue | 11 |
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| creator | Ma, Yunfei Mamitimin, Yusuyunjiang Tiemuerbieke, Bahejiayinaer Yimaer, Rebiya Huang, Meiling Chen, Han Tao, Tongtong Guo, Xinyi |
| description | Rapid urbanization threatens the ecological environment and quality of life by significantly altering land use and land cover (LULC) and heat distribution. One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range |
| doi_str_mv | 10.3390/land12112012 |
| format | article |
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One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range <30 °C decreased by 67.09%. In addition, the LST varied significantly from one category of land use to another. The average LST of built-up land and unused land was higher than the average LST of other land use types in all years, while the average LST of grassland, forest land, and water bodies was significantly lower. Finally, the results of the GWR model showed that R2 and adjusted R2 of the GWR were 0.75 and 0.73, obviously larger than the 0.58 of the OLS models. The GWR model’s higher R2 and adjusted R2 compared to the OLS model indicates that the relationship between LST and the influencing factors underlying the model may exhibit spatial non-stationarity, and the GWR model performs better than the OLS model. The results of both OLS and GWR models show that the normalized difference vegetation index (NDVI) and slope were negatively correlated with LST, while the urban index (UI) and normalized difference built-up index (NDBI) were positively correlated with LST. The findings of the study indicate that increasing green spaces and limiting the unplanned expansion of urban areas are effective measures to mitigate the UHIs in the study area. The results of the study may provide valuable insights into the spatiotemporal characteristics of the UHI and its drivers. Understanding the spatiotemporal characteristics of the UHI can help urban planners, policymakers, and scientists develop more effective urban cooling strategies and improve the urban thermal environment.</description><identifier>ISSN: 2073-445X</identifier><identifier>EISSN: 2073-445X</identifier><identifier>DOI: 10.3390/land12112012</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Air pollution ; arid and semi-arid regions ; Arid regions ; Arid zones ; Case studies ; Cities ; Climate change ; Comparative analysis ; Correlation analysis ; geographically weighted regression ; Geospatial data ; Grasslands ; Green infrastructure ; Heat ; Heat distribution ; Land cover ; Land surface temperature ; Land use ; land use and land cover ; Normalized difference vegetative index ; Outdoor air quality ; Quality of life ; Radiation ; Regression analysis ; Regression models ; Remote sensing ; Semi arid areas ; Semiarid lands ; Thermal environments ; Urban areas ; Urban climatology ; urban heat island ; Urban heat islands ; Urban planning ; Urbanization ; Urumqi City ; Vegetation</subject><ispartof>Land (Basel), 2023-11, Vol.12 (11), p.2012</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c406t-49396fdc4a642fc22f5d3d9fd58a9be9c8a2e8c2decfd3930710458a39043923</citedby><cites>FETCH-LOGICAL-c406t-49396fdc4a642fc22f5d3d9fd58a9be9c8a2e8c2decfd3930710458a39043923</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2893237256/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2893237256?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>315,783,787,25766,27937,27938,37025,44603,75461</link.rule.ids></links><search><creatorcontrib>Ma, Yunfei</creatorcontrib><creatorcontrib>Mamitimin, Yusuyunjiang</creatorcontrib><creatorcontrib>Tiemuerbieke, Bahejiayinaer</creatorcontrib><creatorcontrib>Yimaer, Rebiya</creatorcontrib><creatorcontrib>Huang, Meiling</creatorcontrib><creatorcontrib>Chen, Han</creatorcontrib><creatorcontrib>Tao, Tongtong</creatorcontrib><creatorcontrib>Guo, Xinyi</creatorcontrib><title>Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City</title><title>Land (Basel)</title><description>Rapid urbanization threatens the ecological environment and quality of life by significantly altering land use and land cover (LULC) and heat distribution. One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range <30 °C decreased by 67.09%. In addition, the LST varied significantly from one category of land use to another. The average LST of built-up land and unused land was higher than the average LST of other land use types in all years, while the average LST of grassland, forest land, and water bodies was significantly lower. Finally, the results of the GWR model showed that R2 and adjusted R2 of the GWR were 0.75 and 0.73, obviously larger than the 0.58 of the OLS models. The GWR model’s higher R2 and adjusted R2 compared to the OLS model indicates that the relationship between LST and the influencing factors underlying the model may exhibit spatial non-stationarity, and the GWR model performs better than the OLS model. The results of both OLS and GWR models show that the normalized difference vegetation index (NDVI) and slope were negatively correlated with LST, while the urban index (UI) and normalized difference built-up index (NDBI) were positively correlated with LST. The findings of the study indicate that increasing green spaces and limiting the unplanned expansion of urban areas are effective measures to mitigate the UHIs in the study area. The results of the study may provide valuable insights into the spatiotemporal characteristics of the UHI and its drivers. Understanding the spatiotemporal characteristics of the UHI can help urban planners, policymakers, and scientists develop more effective urban cooling strategies and improve the urban thermal environment.</description><subject>Air pollution</subject><subject>arid and semi-arid regions</subject><subject>Arid regions</subject><subject>Arid zones</subject><subject>Case studies</subject><subject>Cities</subject><subject>Climate change</subject><subject>Comparative analysis</subject><subject>Correlation analysis</subject><subject>geographically weighted regression</subject><subject>Geospatial data</subject><subject>Grasslands</subject><subject>Green infrastructure</subject><subject>Heat</subject><subject>Heat distribution</subject><subject>Land cover</subject><subject>Land surface temperature</subject><subject>Land use</subject><subject>land use and land cover</subject><subject>Normalized difference vegetative index</subject><subject>Outdoor air quality</subject><subject>Quality of life</subject><subject>Radiation</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Remote sensing</subject><subject>Semi arid areas</subject><subject>Semiarid lands</subject><subject>Thermal environments</subject><subject>Urban areas</subject><subject>Urban climatology</subject><subject>urban heat island</subject><subject>Urban heat islands</subject><subject>Urban planning</subject><subject>Urbanization</subject><subject>Urumqi City</subject><subject>Vegetation</subject><issn>2073-445X</issn><issn>2073-445X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNUUtr3DAQNqWFhjS3_gBBr93Uevih3ramSRZSCk1KezNjaeTV4pUcST7sX8mvrVKHEOmgYb6Hhm-K4iMtLzmX5ZcJnKaMUlZS9qY4Y2XDN0JUf9--qt8XFzEeynwk5a2ozorHuxmS9QmPsw8wkW4PAVTCYGOyKpJsSnbOTAs6Zd1IrjLoQyTekN9hAEduEBLZxaffyTeIqIl35Br9GGDeWwXTdCJ_0I77lKFfOAaM0WbKD69x-kq2pMsicpcWfVpNl-ODJZ1Npw_FOwNTxIvn97y4v_p-391sbn9e77rt7UaJsk4bIbmsjVYCasGMYsxUmmtpdNWCHFCqFhi2imlURnPJy4aWImM5M8El4-fFbrXVHg79HOwRwqn3YPv_DR_GHkLOYsKeScoENIOsTCMo8AHqqtVNbQZAMbQ8e31avebgHxaMqT_4Jbg8fc9ayRlvWFVn1uXKGiGbWmd8ypnnq_FolXdobO5vm0ZwxqpGZsHnVaCCjzGgeRmTlv3T8vvXy-f_ACT8oyI</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Ma, Yunfei</creator><creator>Mamitimin, Yusuyunjiang</creator><creator>Tiemuerbieke, Bahejiayinaer</creator><creator>Yimaer, Rebiya</creator><creator>Huang, Meiling</creator><creator>Chen, Han</creator><creator>Tao, Tongtong</creator><creator>Guo, Xinyi</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>SOI</scope><scope>DOA</scope></search><sort><creationdate>20231101</creationdate><title>Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City</title><author>Ma, Yunfei ; 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One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range <30 °C decreased by 67.09%. In addition, the LST varied significantly from one category of land use to another. The average LST of built-up land and unused land was higher than the average LST of other land use types in all years, while the average LST of grassland, forest land, and water bodies was significantly lower. Finally, the results of the GWR model showed that R2 and adjusted R2 of the GWR were 0.75 and 0.73, obviously larger than the 0.58 of the OLS models. The GWR model’s higher R2 and adjusted R2 compared to the OLS model indicates that the relationship between LST and the influencing factors underlying the model may exhibit spatial non-stationarity, and the GWR model performs better than the OLS model. The results of both OLS and GWR models show that the normalized difference vegetation index (NDVI) and slope were negatively correlated with LST, while the urban index (UI) and normalized difference built-up index (NDBI) were positively correlated with LST. The findings of the study indicate that increasing green spaces and limiting the unplanned expansion of urban areas are effective measures to mitigate the UHIs in the study area. The results of the study may provide valuable insights into the spatiotemporal characteristics of the UHI and its drivers. Understanding the spatiotemporal characteristics of the UHI can help urban planners, policymakers, and scientists develop more effective urban cooling strategies and improve the urban thermal environment.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/land12112012</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Air pollution arid and semi-arid regions Arid regions Arid zones Case studies Cities Climate change Comparative analysis Correlation analysis geographically weighted regression Geospatial data Grasslands Green infrastructure Heat Heat distribution Land cover Land surface temperature Land use land use and land cover Normalized difference vegetative index Outdoor air quality Quality of life Radiation Regression analysis Regression models Remote sensing Semi arid areas Semiarid lands Thermal environments Urban areas Urban climatology urban heat island Urban heat islands Urban planning Urbanization Urumqi City Vegetation |
| title | Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City |
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