Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing
Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (U...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-03-01
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| Series: | Computational Urban Science |
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| Online Access: | https://doi.org/10.1007/s43762-024-00119-z |
| _version_ | 1797259407099166720 |
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| author | Yanrong Zhu Juan Wang Yuting Yuan Bin Meng Ming Luo Changsheng Shi Huimin Ji |
| author_facet | Yanrong Zhu Juan Wang Yuting Yuan Bin Meng Ming Luo Changsheng Shi Huimin Ji |
| author_sort | Yanrong Zhu |
| collection | DOAJ |
| description | Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. |
| first_indexed | 2024-04-24T23:08:56Z |
| format | Article |
| id | doaj.art-17627ce298d846f0b08542fbb75dfdc0 |
| institution | Directory Open Access Journal |
| issn | 2730-6852 |
| language | English |
| last_indexed | 2024-04-24T23:08:56Z |
| publishDate | 2024-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj.art-17627ce298d846f0b08542fbb75dfdc02024-03-17T12:18:27ZengSpringerComputational Urban Science2730-68522024-03-014111510.1007/s43762-024-00119-zSpatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in BeijingYanrong Zhu0Juan Wang1Yuting Yuan2Bin Meng3Ming Luo4Changsheng Shi5Huimin Ji6College of Applied Arts and Science, Beijing Union UniversityCollege of Applied Arts and Science, Beijing Union UniversityCollege of Applied Arts and Science, Beijing Union UniversityCollege of Applied Arts and Science, Beijing Union UniversitySchool of Geography and Planning, Sun Yat-Sen UniversityCollege of Applied Arts and Science, Beijing Union UniversityCollege of Applied Arts and Science, Beijing Union UniversityAbstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management.https://doi.org/10.1007/s43762-024-00119-zGeographical big dataSentiment analysisUrban functional areaAssociation rules analysisBeijing |
| spellingShingle | Yanrong Zhu Juan Wang Yuting Yuan Bin Meng Ming Luo Changsheng Shi Huimin Ji Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing Computational Urban Science Geographical big data Sentiment analysis Urban functional area Association rules analysis Beijing |
| title | Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing |
| title_full | Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing |
| title_fullStr | Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing |
| title_full_unstemmed | Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing |
| title_short | Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing |
| title_sort | spatial heterogeneities of residents sentiments and their associations with urban functional areas during heat waves a case study in beijing |
| topic | Geographical big data Sentiment analysis Urban functional area Association rules analysis Beijing |
| url | https://doi.org/10.1007/s43762-024-00119-z |
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