Integrating multiple environmental variables to identify potential urban heat island risk areas based on the maxent model
The urbanization process has led to the continuous formation of urban heat island risk (UHIR). While existing studies on UHIR have focused mainly on qualitative assessment and analysis, limited attention has been directed toward identifying potential UHIR areas. Additionally, there is a gap in under...
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Taylor & Francis Group
2025-03-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2459135 |
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| author | Bin Li Changxiu Cheng |
| author_facet | Bin Li Changxiu Cheng |
| author_sort | Bin Li |
| collection | DOAJ |
| description | The urbanization process has led to the continuous formation of urban heat island risk (UHIR). While existing studies on UHIR have focused mainly on qualitative assessment and analysis, limited attention has been directed toward identifying potential UHIR areas. Additionally, there is a gap in understanding how complex urban environments contribute to the formation of potential UHIR areas. Therefore, this study proposes a framework based on maximum entropy (MaxEnt) modeling that integrates multiple environmental variables to identify potential UHIR areas. First, a multilevel screening mechanism was developed to delineate the stable UHIR distribution by coupling high-temperature areas, importance, connectivity, and population distribution. Subsequently, an environmental variable list was constructed at the building, accessibility, and landscape levels to fully consider natural and human factors. Finally, MaxEnt was used to derive the probability distribution of potential UHIR areas based on the stable distribution and environmental variables. The results within the fifth ring road of Beijing reveal the following: (1) The high-UHIR areas exhibit a circular distribution with a northwest‒southeast axis, primarily located in the western regions of Xicheng District and the border areas of Dongcheng District, Chaoyang District, and Fengtai District, whereas the UHIR is lower in the southern part of the study area. (2) Human variables play a pivotal role in influencing the formation of potential UHIR areas, with Distance from trunk roads demonstrating the highest regularization training gain at 0.346, followed by Distance from parks (0.203), LPI (0.163), DIVISION (0.154), and BH (0.149). (3) UHIR areas cover 30% of the study area and 60% of the population. The population density in high-UHIR areas is 19,113 people per square kilometer, surpassing that in non-high-risk areas by 5,805, thus increasing the impact of potential UHIR. Importantly, the framework of this study is transferable and may provide new insights into urban climate adaptation planning. |
| format | Article |
| id | doaj-art-0cc55a4ebdda42b7b90470f9d56157ae |
| institution | DOAJ |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-0cc55a4ebdda42b7b90470f9d56157ae2025-08-20T03:12:19ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-03-0111510.1080/10095020.2025.2459135Integrating multiple environmental variables to identify potential urban heat island risk areas based on the maxent modelBin Li0Changxiu Cheng1State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, ChinaThe urbanization process has led to the continuous formation of urban heat island risk (UHIR). While existing studies on UHIR have focused mainly on qualitative assessment and analysis, limited attention has been directed toward identifying potential UHIR areas. Additionally, there is a gap in understanding how complex urban environments contribute to the formation of potential UHIR areas. Therefore, this study proposes a framework based on maximum entropy (MaxEnt) modeling that integrates multiple environmental variables to identify potential UHIR areas. First, a multilevel screening mechanism was developed to delineate the stable UHIR distribution by coupling high-temperature areas, importance, connectivity, and population distribution. Subsequently, an environmental variable list was constructed at the building, accessibility, and landscape levels to fully consider natural and human factors. Finally, MaxEnt was used to derive the probability distribution of potential UHIR areas based on the stable distribution and environmental variables. The results within the fifth ring road of Beijing reveal the following: (1) The high-UHIR areas exhibit a circular distribution with a northwest‒southeast axis, primarily located in the western regions of Xicheng District and the border areas of Dongcheng District, Chaoyang District, and Fengtai District, whereas the UHIR is lower in the southern part of the study area. (2) Human variables play a pivotal role in influencing the formation of potential UHIR areas, with Distance from trunk roads demonstrating the highest regularization training gain at 0.346, followed by Distance from parks (0.203), LPI (0.163), DIVISION (0.154), and BH (0.149). (3) UHIR areas cover 30% of the study area and 60% of the population. The population density in high-UHIR areas is 19,113 people per square kilometer, surpassing that in non-high-risk areas by 5,805, thus increasing the impact of potential UHIR. Importantly, the framework of this study is transferable and may provide new insights into urban climate adaptation planning.https://www.tandfonline.com/doi/10.1080/10095020.2025.2459135Urban heat Island risk (UHIR)environmental variablesMaxEnt modelurban climatemultilevel screening |
| spellingShingle | Bin Li Changxiu Cheng Integrating multiple environmental variables to identify potential urban heat island risk areas based on the maxent model Geo-spatial Information Science Urban heat Island risk (UHIR) environmental variables MaxEnt model urban climate multilevel screening |
| title | Integrating multiple environmental variables to identify potential urban heat island risk areas based on the maxent model |
| title_full | Integrating multiple environmental variables to identify potential urban heat island risk areas based on the maxent model |
| title_fullStr | Integrating multiple environmental variables to identify potential urban heat island risk areas based on the maxent model |
| title_full_unstemmed | Integrating multiple environmental variables to identify potential urban heat island risk areas based on the maxent model |
| title_short | Integrating multiple environmental variables to identify potential urban heat island risk areas based on the maxent model |
| title_sort | integrating multiple environmental variables to identify potential urban heat island risk areas based on the maxent model |
| topic | Urban heat Island risk (UHIR) environmental variables MaxEnt model urban climate multilevel screening |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2459135 |
| work_keys_str_mv | AT binli integratingmultipleenvironmentalvariablestoidentifypotentialurbanheatislandriskareasbasedonthemaxentmodel AT changxiucheng integratingmultipleenvironmentalvariablestoidentifypotentialurbanheatislandriskareasbasedonthemaxentmodel |