Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods
The representational image of the city has attracted people’s long-term attention. Nevertheless, the mechanism of interaction between the image and the built environment (BE) and image studies at the town scale have not been fully explored. In this study, we collected multi-source data from 26 chara...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/13/3/291 |
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| author | Weixing Xu Peng Zeng Beibei Liu Liangwa Cai Zongyao Sun Sicheng Liu Fengliang Tang |
| author_facet | Weixing Xu Peng Zeng Beibei Liu Liangwa Cai Zongyao Sun Sicheng Liu Fengliang Tang |
| author_sort | Weixing Xu |
| collection | DOAJ |
| description | The representational image of the city has attracted people’s long-term attention. Nevertheless, the mechanism of interaction between the image and the built environment (BE) and image studies at the town scale have not been fully explored. In this study, we collected multi-source data from 26 characteristic towns in Tianjin, China. We explored a deep learning approach to recognize social media data, which led to the development of quantifiable town uniqueness image (UI) variables. We studied the influence of the BE on the town UI and the moderating effects of positive emotions on the relationship between the two. The results showed that positive emotions had significantly positive moderating effects on the water system ratio’s effect on UI, but weakened sidewalk density and tourist attraction density. They also inhibited the negative effects of road connectivity but could strengthen the negative effects of the sky view factor and points of interest (POI) mix. The moderating effects on other variables are relatively mediocre. This study helps to reveal the inner mechanism of BE and town image. It is conducive to accurately coordinating the relationship between planning policies and design strategies, optimizing resource allocation, and promoting sustainable town development. |
| first_indexed | 2024-04-24T18:05:48Z |
| format | Article |
| id | doaj.art-3e4ad58046424670af2b6f2127a2eb71 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-04-24T18:05:48Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-3e4ad58046424670af2b6f2127a2eb712024-03-27T13:50:34ZengMDPI AGLand2073-445X2024-02-0113329110.3390/land13030291Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning MethodsWeixing Xu0Peng Zeng1Beibei Liu2Liangwa Cai3Zongyao Sun4Sicheng Liu5Fengliang Tang6School of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, ChinaSchool of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, ChinaSchool of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, ChinaSchool of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, ChinaSchool of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, ChinaSchool of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, ChinaSchool of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, ChinaThe representational image of the city has attracted people’s long-term attention. Nevertheless, the mechanism of interaction between the image and the built environment (BE) and image studies at the town scale have not been fully explored. In this study, we collected multi-source data from 26 characteristic towns in Tianjin, China. We explored a deep learning approach to recognize social media data, which led to the development of quantifiable town uniqueness image (UI) variables. We studied the influence of the BE on the town UI and the moderating effects of positive emotions on the relationship between the two. The results showed that positive emotions had significantly positive moderating effects on the water system ratio’s effect on UI, but weakened sidewalk density and tourist attraction density. They also inhibited the negative effects of road connectivity but could strengthen the negative effects of the sky view factor and points of interest (POI) mix. The moderating effects on other variables are relatively mediocre. This study helps to reveal the inner mechanism of BE and town image. It is conducive to accurately coordinating the relationship between planning policies and design strategies, optimizing resource allocation, and promoting sustainable town development.https://www.mdpi.com/2073-445X/13/3/291town uniqueness imagebuilt environmentpositive emotionsmoderating effectsTianjin |
| spellingShingle | Weixing Xu Peng Zeng Beibei Liu Liangwa Cai Zongyao Sun Sicheng Liu Fengliang Tang Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods Land town uniqueness image built environment positive emotions moderating effects Tianjin |
| title | Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods |
| title_full | Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods |
| title_fullStr | Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods |
| title_full_unstemmed | Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods |
| title_short | Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods |
| title_sort | exploring the built environment factors influencing town image using social media data and deep learning methods |
| topic | town uniqueness image built environment positive emotions moderating effects Tianjin |
| url | https://www.mdpi.com/2073-445X/13/3/291 |
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