As the main place of people’s daily activities, indoor space (its size, shape, colors, material and textures, and so on) has important physical, emotional and health-based implications on people’s behavior and quality of life. Material texture is an integral part of architectural environment perception and quality evaluation, but the effect of material texture on perceptual spaciousness lacks the support of experimental data. This research examined the effects between different wall textures on the observer’s perception of spaciousness in indoor space, the influence of wall texture changes in different room sizes, and how the associational meaning of texture affects the degree of influence of wall texture on the spaciousness of indoor space. By using VR technology and the magnitude estimation (ME) analysis method, the authors found that the effect of wall texture on perceptual spaciousness varies depending on the wall material, and the textural effect is affected by room size. The perception of spaciousness is influenced by the observer’s associational meaning of material texture, and the influence of associational meaning of material texture varies contingent on the room size. In relatively small rooms, the objective aspect (such as hardness, surface reflectivity, texture direction and texture depth) of the wall texture has a significant impact on perceived space. In contrast, the effects of subjective aspects (such as affinity and ecology) become more pronounced in relatively larger rooms. This research makes up for the lack of material texture research in perceptual spaciousness, and provides a new way for the designer to choose materials for the design of a spatial scale.

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Underground spaces connected with railway stations gather a lot of pedestrians. Clarifying pedestrians' movement in underground space is important for flow control and evacuation planning. Based on a pedestrian traffic survey data and estimated OD matrix, this study aims to clarify the pedestrians' route choice probability in underground space. Through the definition of "state" and "transition process", pedestrians' route choice model is constructed according to the utility of routes. Next, Absorbing Markov Process is adopted to solve the trip assignment in the underground pedestrian network and build the functional relation between estimated flow counts and obtained data. Through minimizing the difference between estimated and observed flow counts, unknown variables are calibrated. Finally, the influential spatial motion attributes are examined through linear regression analysis.

Coronavirus disease 2019 (COVID-19) has exposed the public safety issues. Obtaining inter-individual contact and transmission in the underground spaces is an important issue for simulating and mitigating the spread of the pandemic. Taking the underground shopping streets as an example, this study aimed to verify commercial facilities’ influence on the spatiotemporal distribution of inter-individual contact in the underground space. Based on actual surveillance data, machine learning techniques are adopted to obtain utilizers’ dynamics in underground pedestrian system and shops. Firstly, an entropy maximization approach is adopted to estimate pedestrians’ origin-destination (OD) information. Commercial utilization behaviors at different shops are modeled based on utilizers’ entering frequency and staying duration, which are obtained by re-identifying individuals’ disappearances and appearances at storefronts. Based on observed results, a simulation method is proposed to estimate utilizers’ spatiotemporal contact by recreating their space-time paths in the underground system. Inter-individual contact events and exposure duration are obtained in view of their space-time vectors in passages and shops. A social contact network is established to describe the contact relations between all individuals in the whole system. The exposure duration and weighted clustering coefficients were defined as indicators to measure the contact degree of individual and the social contact network. The simulation results show that the individual and contact graph indicators are similar across time, while the spatial distribution of inter-individual contact within shops and passages are time-varying. Through simulation experiments, the study verified the effects of self-protection and commercial type adjustment measures.

Neighborhood built environment may influence residents’ physical activity, but evidence of non-major Chinese cities is lacking. We investigated the impact of five socio-demographic characteristics, 10 objectively assessed environment characteristics, eight perceived neighborhood attributes, and social environment on physical activity and health outcomes (sense of community, body mass index, as well as self-reported health status). We also examined (1) five conceptually comparable perceived neighborhood attributes as mediators of the relationship between objective environment attributes and physical activity; (2) other perceived indicators and social environment as moderators of those relationships, using the mediation analysis in regression. Objectively assessed residential density, land use mix, street connectivity, and accessibility were curvilinearly and/or linearly related to physical activity. The slope of terrain was inversely associated with body mass index (BMI). None of the perceived attributes were found as mediators probably due to the weak associations between subjective and objective environments. High density facilitated physical activity but hindered the sense of community. Further, the perceived aesthetic and safety were associated with physical activity. Additionally, social environment moderated the positive associations of all perceived environments (except for slope) and sense of community. The present study demonstrated that both physical and social environment attributes significantly correlated with physical activity in Dalian.