Integrating street view imagery and taxi trajectory for identifying urban function of street space
Street space is a crucial component of public space, serving as a site for a variety of human activities. However, prior studies have primarily focused on the traffic function of street space, neglecting other functional types, such as residential and commercial. To address this gap, this study prop...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-05-01
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| Series: | Geo-spatial Information Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2311866 |
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| author | Mianxin Gao Haijing Guo Longwei Liu Yongyu Zeng Wenkai Liu Yefei Liu Hanfa Xing |
| author_facet | Mianxin Gao Haijing Guo Longwei Liu Yongyu Zeng Wenkai Liu Yefei Liu Hanfa Xing |
| author_sort | Mianxin Gao |
| collection | DOAJ |
| description | Street space is a crucial component of public space, serving as a site for a variety of human activities. However, prior studies have primarily focused on the traffic function of street space, neglecting other functional types, such as residential and commercial. To address this gap, this study proposes a classification method for street space by integrating taxi trajectory and street view imagery. The dynamic travel features of the residents are extracted from taxi trajectory data and the multi-level physical environment features of the streets are constructed based on street view imagery data. Then, the street space with same urban functions is assigned to the same cluster based on the dynamic travel features of the residents, the multi-level physical environment features of streets and K-means method. The proposed method is empirically applied to the Futian District of Shenzhen to demonstrate its validity, and the street space is successfully classified into five types, namely public, traffic, commercial, residential, and mixed commercial and residential. This work contributes to the evaluation of street space quality, facilitating scientific planning and efficient governance of street space. |
| format | Article |
| id | doaj-art-7bacfa29034d4ff59cc851adda33b196 |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-7bacfa29034d4ff59cc851adda33b1962025-08-20T03:59:32ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-05-012831085110710.1080/10095020.2024.2311866Integrating street view imagery and taxi trajectory for identifying urban function of street spaceMianxin Gao0Haijing Guo1Longwei Liu2Yongyu Zeng3Wenkai Liu4Yefei Liu5Hanfa Xing6Department of Guangdong Province, Surveying and Mapping Institute Lands and Resource, Guangzhou, ChinaDepartment of Guangdong Province, Surveying and Mapping Institute Lands and Resource, Guangzhou, ChinaDepartment of Guangdong Province, Surveying and Mapping Institute Lands and Resource, Guangzhou, ChinaDepartment of Guangdong Province, Surveying and Mapping Institute Lands and Resource, Guangzhou, ChinaBeidou Research Institute, Faculty of Engineering, South China Normal University, Foshan, ChinaSchool of Geography, South China Normal University, Guangzhou, ChinaBeidou Research Institute, Faculty of Engineering, South China Normal University, Foshan, ChinaStreet space is a crucial component of public space, serving as a site for a variety of human activities. However, prior studies have primarily focused on the traffic function of street space, neglecting other functional types, such as residential and commercial. To address this gap, this study proposes a classification method for street space by integrating taxi trajectory and street view imagery. The dynamic travel features of the residents are extracted from taxi trajectory data and the multi-level physical environment features of the streets are constructed based on street view imagery data. Then, the street space with same urban functions is assigned to the same cluster based on the dynamic travel features of the residents, the multi-level physical environment features of streets and K-means method. The proposed method is empirically applied to the Futian District of Shenzhen to demonstrate its validity, and the street space is successfully classified into five types, namely public, traffic, commercial, residential, and mixed commercial and residential. This work contributes to the evaluation of street space quality, facilitating scientific planning and efficient governance of street space.https://www.tandfonline.com/doi/10.1080/10095020.2024.2311866Urban functionstreettaxi trajectorystreet view |
| spellingShingle | Mianxin Gao Haijing Guo Longwei Liu Yongyu Zeng Wenkai Liu Yefei Liu Hanfa Xing Integrating street view imagery and taxi trajectory for identifying urban function of street space Geo-spatial Information Science Urban function street taxi trajectory street view |
| title | Integrating street view imagery and taxi trajectory for identifying urban function of street space |
| title_full | Integrating street view imagery and taxi trajectory for identifying urban function of street space |
| title_fullStr | Integrating street view imagery and taxi trajectory for identifying urban function of street space |
| title_full_unstemmed | Integrating street view imagery and taxi trajectory for identifying urban function of street space |
| title_short | Integrating street view imagery and taxi trajectory for identifying urban function of street space |
| title_sort | integrating street view imagery and taxi trajectory for identifying urban function of street space |
| topic | Urban function street taxi trajectory street view |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2024.2311866 |
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