The staying activity in shopping street has an inseparable relation with the spatial scale of the street. Neither too narrow nor too wide scale of street could bring a good walking experience to pedestrian, so it is especially important to find a suitable scale. Most of the researches are qualitative investigations related to human psychology, and few of them are about underground shopping street. This research made a quantitative investigation on the relationship between the spatial scale of underground shopping street and the staying activity there, defined the concept of staying activity coefficient to quantify the staying activity in shopping street, and used virtual reality experimental method to study the influence of spatial scale changes on the staying activity in shopping street. The research results show that the height and width of underground shopping street as well as the aspect ratio could significantly affect human activity in the pedestrian spaces of underground shopping street. Meanwhile, the most suitable scale of pedestrian spaces in underground street for people to have staying activity is also derived.

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.

Based on the research on the current situation and transformation path of the spatial construction of villages and towns in northern Jiangsu, “compactness” and “convenience” are extracted as the elements of spatial evaluation. With multi-source data, comprehensively using ENVI remote sensing image interpretation, GIS spatial analysis, Fragstats landscape index calculation, entropy weight–TOPSIS comprehensive evaluation, and SPSS cluster analysis, a “digital full cycle” of a research framework for the spatial evaluation of villages and towns is built. In this paper, Suining County is taken as the research object, and the spatial construction level of its villages and towns is studied. The research results show that at the county level, the spatial compactness of villages and towns roughly presents the characteristics of an “X” pattern, decreasing from the middle to the four sides, while facility convenience generally presents the characteristics of a right “人” (Chinese character) pattern. At the town level, facility convenience basically presents the pattern characteristics of the “center-node” differentiation structure. The research aims to guide villages and towns to solve the current dilemma of spatial construction, promote the construction of digital villages and towns, and impel the digital transformation of the village and town evaluation system, data, and methods, so as to provide real-time, quantitative, and accurate data and method support for planning and decision-making in villages and towns.

In the context of the rapid development of the construction of urban underground spaces in China, people’s demands for the quality of these spaces have become increasingly prominent. As an important part of underground spaces, the different materials used in underground street interfaces give rise to different spatial experiences, which are important for creating a comfortable underground space atmosphere. However, current research on the perception of underground street spaces lacks an exploration of the influence of materials, and most research on materials focuses on the interface design of above-ground commercial streets. In this study, material characteristics were extracted as independent variables, 17 scenes were orthogonalized, and the efficient simulation characteristics of VR were used to build a scene model for the experiment. Participants’ evaluations of the scenes were measured during the experiment, and conclusions were drawn through scene ranking and one-way ANOVA. The results showed that the use of white, high-finish materials with non-directional textures and low bumpiness for the side interface was more in line with the participants’ preferences. This study provides new ideas for the design of high-quality underground commercial streets.

The complex spatial environment of underground commercial street spaces will affect users’ behavior and information needs. As a medium to coordinate the interaction between the underground commercial street space environment and people, guide signs can provide useful information for users. However, the visual saliency of guide signs is the fundamental premise for determining the transmission of information to users. Based on field research, this study identified and examined the factors influencing the significance of guide signs in underground commercial streets from the user’s perspective using the order relation analysis method (G1 method) and with the help of screen-based eye tracking and virtual reality (VR) eye-tracking technology, In addition, we explored the design relationship between critical influencing factors and the space between underground commercial streets, and the visual significance of the differences between each important influencing variable. The study showed that the set position, set height, and design color of underground commercial street guide signs are essential factors in their visual prominence. The prominence was more significant when the position of guide signs was located in the middle and upper area of the space, and the prominence was more significant when the set height was 2.56~2.75 m and 3.12~3.31 m. This study can provide data and theoretical support for the visual saliency design of underground commercial street guide signs and provide a reference for the humanized design of underground commercial street guide signs for public facilities in cities.

Developing and utilizing underground space is a vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards more significant, more terrific refinement, applying architectural theories and excessively subjective designs has resulted in a deficient human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with the perception evaluation, and a regression analysis was employed to identify appropriate scale ranges, which were superimposed with the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces was established, providing a reference for the human-centered design of these environments.