Decoupling of the Municipal Thermal Environment Using a Spatial Autoregressive Model
In the rapid urbanization process, climate change has a huge impact on the urban thermal environment, and the urban heat island has attracted widespread attention from society. How to better detect, analyze, and evaluate the urban heat island effect has become a hot issue in current urban environmen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/13/12/2059 |
| _version_ | 1797461516063080448 |
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| author | Qingrui Jiang Xiaochang Liu Zhiqiang Wu Yuankai Wang Jiahua Dong |
| author_facet | Qingrui Jiang Xiaochang Liu Zhiqiang Wu Yuankai Wang Jiahua Dong |
| author_sort | Qingrui Jiang |
| collection | DOAJ |
| description | In the rapid urbanization process, climate change has a huge impact on the urban thermal environment, and the urban heat island has attracted widespread attention from society. How to better detect, analyze, and evaluate the urban heat island effect has become a hot issue in current urban environmental research. However, the correlation analysis of heat island factors mostly adopts the conventional least square method, without considering the correlation of and the interaction between spatial elements. At the same time, the single analysis method makes it difficult to analyze environmental problems scientifically, which leads to great bias. Therefore, in this paper, the spatial autoregressive confusion model was used to analyze the satellite data of Beijing, and a preliminary temperature model of Beijing for all seasons was established. The regression results show that the surface temperature of Beijing has a strong spatial autocorrelation, and that the modified normalized difference water index and the normalized differential vegetation index have a strong negative effect on the land surface temperature. The prediction models established in this study can provide accurate and sustainable data support in the urbanization process and aid in the creation of a sustainable and effective urban environment. |
| first_indexed | 2024-03-09T17:21:29Z |
| format | Article |
| id | doaj.art-ca7205c1c3ba40218eaa70230a442641 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T17:21:29Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-ca7205c1c3ba40218eaa70230a4426412023-11-24T13:12:10ZengMDPI AGAtmosphere2073-44332022-12-011312205910.3390/atmos13122059Decoupling of the Municipal Thermal Environment Using a Spatial Autoregressive ModelQingrui Jiang0Xiaochang Liu1Zhiqiang Wu2Yuankai Wang3Jiahua Dong4Bartlett School of Architecture, University College London, 22 Gordon St, London WC1H 0QB, UKCollege of Architecture and Urban Planning, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaCollege of Architecture and Urban Planning, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaBartlett School of Architecture, University College London, 22 Gordon St, London WC1H 0QB, UKBartlett School of Architecture, University College London, 22 Gordon St, London WC1H 0QB, UKIn the rapid urbanization process, climate change has a huge impact on the urban thermal environment, and the urban heat island has attracted widespread attention from society. How to better detect, analyze, and evaluate the urban heat island effect has become a hot issue in current urban environmental research. However, the correlation analysis of heat island factors mostly adopts the conventional least square method, without considering the correlation of and the interaction between spatial elements. At the same time, the single analysis method makes it difficult to analyze environmental problems scientifically, which leads to great bias. Therefore, in this paper, the spatial autoregressive confusion model was used to analyze the satellite data of Beijing, and a preliminary temperature model of Beijing for all seasons was established. The regression results show that the surface temperature of Beijing has a strong spatial autocorrelation, and that the modified normalized difference water index and the normalized differential vegetation index have a strong negative effect on the land surface temperature. The prediction models established in this study can provide accurate and sustainable data support in the urbanization process and aid in the creation of a sustainable and effective urban environment.https://www.mdpi.com/2073-4433/13/12/2059spatial autoregressive modelspatial analysisurban heat island effect (UHI)urban land surface characteristicsland surface temperature (LST) |
| spellingShingle | Qingrui Jiang Xiaochang Liu Zhiqiang Wu Yuankai Wang Jiahua Dong Decoupling of the Municipal Thermal Environment Using a Spatial Autoregressive Model Atmosphere spatial autoregressive model spatial analysis urban heat island effect (UHI) urban land surface characteristics land surface temperature (LST) |
| title | Decoupling of the Municipal Thermal Environment Using a Spatial Autoregressive Model |
| title_full | Decoupling of the Municipal Thermal Environment Using a Spatial Autoregressive Model |
| title_fullStr | Decoupling of the Municipal Thermal Environment Using a Spatial Autoregressive Model |
| title_full_unstemmed | Decoupling of the Municipal Thermal Environment Using a Spatial Autoregressive Model |
| title_short | Decoupling of the Municipal Thermal Environment Using a Spatial Autoregressive Model |
| title_sort | decoupling of the municipal thermal environment using a spatial autoregressive model |
| topic | spatial autoregressive model spatial analysis urban heat island effect (UHI) urban land surface characteristics land surface temperature (LST) |
| url | https://www.mdpi.com/2073-4433/13/12/2059 |
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