| id |
caadria2024_465 |
| authors |
Li, Jinze, Song, Zhehao, Wen, Jian, Cai, Chenyi and Tang, Peng |
| year |
2024 |
| title |
Exploring Nonlinear Relationship Between Built Environment and Street Vitality Using Machine Learning: A Case Study of Ding Shu, China |
| 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. 375–384 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.375
|
| summary |
Urban vitality serves as the linchpin for sustainable urban development. Being the most extensively utilized public space within cities, augmenting street vitality bears paramount importance in accelerating design in human-centric habitats. This study employs spatial analysis and machine learning methods to explore the potential nonlinear relationships and local threshold effects between the built environment (BE) and street vitality based on multi-source data. This investigation provides support for the quantitative assessment and optimization of street vitality. Initially, using collected street view images, street spatial elements are extracted through deep learning algorithms. Subsequently, integrating multiple data sources, machine learning methods are employed to quantify the impact and interactions of the built environment on street vitality. Illustrated with the case of Dingshu, the feasibility of this process is demonstrated. By examining the correlation and underlying mechanisms between the built environment and street vitality, this study aids decision-makers in leveraging technological means to expedite design processes and create human-centric cities. |
| keywords |
Nonlinear Relationship, Built Environment, Street Vitality, GBDT-SHAP, Interaction Effect |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,219,352 bytes) |
| references |
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2024/11/17 22:05 |
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