Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street

Tianning Yao, Yao Xu, Liang Sun (), Pan Liao and Jin Wang
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Tianning Yao: School of Architecture & Design, China University of Mining and Technology, Xuzhou 221116, China
Yao Xu: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Liang Sun: School of Architecture & Design, China University of Mining and Technology, Xuzhou 221116, China
Pan Liao: School of Architecture & Design, China University of Mining and Technology, Xuzhou 221116, China
Jin Wang: School of Civil Engineering, Yancheng Institute of Technology, Yancheng 224051, China

Land, 2024, vol. 13, issue 9, 1-27

Abstract: The exploitation of urban subsurface space in urban inventory planning is closely connected to the quality of urban environments. Currently, the construction of underground pedestrian streets is characterised by inefficiency and traffic congestion, making them insufficient for fulfilling the demand for well-designed and human-centred spaces. In the study of spatial quality, traditional evaluation methods, such as satellite remote sensing and street maps, often suffer from low accuracy and slow updating rates, and they frequently overlook human perceptual evaluations. Consequently, there is a pressing need to develop a set of spatial quality evaluation methods incorporating pedestrian perspectives, thereby addressing the neglect of subjective human experiences in spatial quality research. This study first quantifies and clusters the characteristics of underground pedestrian spaces using spatial syntax. It then gathers multidimensional perception data from selected locations and ultimately analyses and predicts the results employing machine learning techniques, specifically Random Forest and XGBoost. The research results indicate variability in pedestrians’ evaluations of spatial quality across different functionally oriented spaces. Key factors influencing these evaluations include Gorgeous, Warm, Good Ventilation, and Flavour indicators. The study proposes a comprehensive and applicable spatial quality evaluation model integrating spatial quantification methods, machine learning algorithms, and multidimensional perception measurements. The development of this model offers valuable scientific guidance for the planning and construction of high-quality urban public spaces.

Keywords: underground space; multidimensional perception; spatial quality; space syntax; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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