| id |
caadria2024_81 |
| authors |
Cheng, Sifan and van Ameijde, Jeroen |
| year |
2024 |
| title |
Sensing Streets: Exploring the Association Between Cityscape Qualities and Street Perceptions Using Street View Imagery and Natural Language Processing |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.139
|
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 139–148 |
| summary |
Assessing the perception of street environments and understanding the relationships between their aesthetic qualities and pedestrian experiences are critical to promoting walking behaviour and enhancing urban residents’ long-term well-being. While Machine Learning-based analysis of Street View Imagery (SVI) has enabled a range of streetscape studies, the relationship between the visual qualities of cityscapes and people’s emotional responses is still under studied. This study used recently developed computational methods to quantify urban street qualities and related sentiments. It collected online reviews and employed Natural Language Processing (NLP) methods to understand how people perceive streets and which environmental features contribute to positive and negative street perceptions. The analytical framework developed in this study can support other high-resolution studies into the spatial-temporal perception of cityscapes in high-density cities across the world. |
| keywords |
Cityscape Quality, Street Perception, Social Media Data, Sentiment Analysis, Natural Language Processing |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (950,820 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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