Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of Hangzhou
Despite emerging empirical findings and computational tools that extend city image research to include social dimensions beyond visual perception, methodologies for effectively identifying and analyzing the relationships between the five city image elements remain underdeveloped. This paper addresse...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/13/12/2194 |
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| author | Jiaqi Wang Yu Shi Weishun Xu Yue Wu |
| author_facet | Jiaqi Wang Yu Shi Weishun Xu Yue Wu |
| author_sort | Jiaqi Wang |
| collection | DOAJ |
| description | Despite emerging empirical findings and computational tools that extend city image research to include social dimensions beyond visual perception, methodologies for effectively identifying and analyzing the relationships between the five city image elements remain underdeveloped. This paper addresses the gap by proposing a big data-driven method, integrating Weibo check-in data, Baidu Map POI, and ArcGIS algorithms to identify city image elements and further reveal a city’s overall morphological characteristics. Based on different modes of observation, city image elements are categorized as spatial descriptors (“districts”, “nodes”, and “paths”) and symbolic descriptors (“landmarks” and “edges”). Taking Hangzhou as a case study, the findings show a strong alignment between urban development achievements and the distribution patterns of city image elements. “Districts” and “landmarks” stand out as the most prominent, reflecting functional zoning and urban maturity, while “nodes” emphasize the city’s polycentric structure. “Paths” offer clear insight into the city’s development trajectory, while “edges” appear to be legible only in relation to other elements. This method innovates cognitive mapping by merging real-world perceptions with algorithmic precision, offering a valuable tool for understanding urban morphology, monitoring development changes, and fostering participatory urban design. |
| format | Article |
| id | doaj-art-b721c49a9c8043ce88af5399c3d26eaf |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-b721c49a9c8043ce88af5399c3d26eaf2024-12-27T14:35:25ZengMDPI AGLand2073-445X2024-12-011312219410.3390/land13122194Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of HangzhouJiaqi Wang0Yu Shi1Weishun Xu2Yue Wu3College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaThe Architectural Design & Research Institute of Zhejiang University Co., Ltd., Hangzhou 310028, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaDespite emerging empirical findings and computational tools that extend city image research to include social dimensions beyond visual perception, methodologies for effectively identifying and analyzing the relationships between the five city image elements remain underdeveloped. This paper addresses the gap by proposing a big data-driven method, integrating Weibo check-in data, Baidu Map POI, and ArcGIS algorithms to identify city image elements and further reveal a city’s overall morphological characteristics. Based on different modes of observation, city image elements are categorized as spatial descriptors (“districts”, “nodes”, and “paths”) and symbolic descriptors (“landmarks” and “edges”). Taking Hangzhou as a case study, the findings show a strong alignment between urban development achievements and the distribution patterns of city image elements. “Districts” and “landmarks” stand out as the most prominent, reflecting functional zoning and urban maturity, while “nodes” emphasize the city’s polycentric structure. “Paths” offer clear insight into the city’s development trajectory, while “edges” appear to be legible only in relation to other elements. This method innovates cognitive mapping by merging real-world perceptions with algorithmic precision, offering a valuable tool for understanding urban morphology, monitoring development changes, and fostering participatory urban design.https://www.mdpi.com/2073-445X/13/12/2194image of the citycity image elementscognitive mappingsocial media check-ins |
| spellingShingle | Jiaqi Wang Yu Shi Weishun Xu Yue Wu Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of Hangzhou Land image of the city city image elements cognitive mapping social media check-ins |
| title | Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of Hangzhou |
| title_full | Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of Hangzhou |
| title_fullStr | Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of Hangzhou |
| title_full_unstemmed | Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of Hangzhou |
| title_short | Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of Hangzhou |
| title_sort | identification of spatial and symbolic city image elements through social media data a case study of hangzhou |
| topic | image of the city city image elements cognitive mapping social media check-ins |
| url | https://www.mdpi.com/2073-445X/13/12/2194 |
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