Urban parks are one of the most common spaces for social interactions in modern cities. The design of park spaces, especially space configuration, has significant influences on people’s social behaviors in parks. In this study, the associations between space configurational attributes and social interactions were investigated using space syntax theory. An observation analysis of social behaviors was carried out in two urban parks in Beijing, China. Nine space configurational attributes, including depth to the gate, depth to the main road, connectivity, normalized angular integration (NAIN), and normalized angular choice (NACH) with three radii, were calculated using a segment model. The variance analysis and regression analysis reveal the strong joint effect of space type, space scale factors, and space configurational attributes on social interaction behaviors in parks. The personal interaction group contained 23% of the total observed people involved in social interactions. Pathway length, zone area, and NACH-10K (NACH with a radius of 10,000 m) are positively associated with the number of people involved in personal interactions. For the social interaction group (77% of the total observed people), the space scale and depth to main city road were found to have a positive and negative influence on social interaction intensity.

This paper investigates the impact of street pattern, metro stations, and density of urban functions on pedestrian distribution in Tianjin, China. Thirteen neighborhoods are selected from the city center and suburbs. Pedestrian and vehicle volumes are observed through detailed gate count from 703 street segments in these neighborhoods. Regression models are constructed to analyze the impact of the street pattern, points of interest (POIs), and vehicle and metro accessibility on pedestrian volumes in each neighborhood and across the city. The results show that when analyzing all neighborhoods together, local street connectivity and POIs had a strong influence on pedestrian distribution. Proximity to metro stations and vehicle accessibility had a minor impact. When analyzing each neighborhood separately, both local- and city-scale street patterns affect pedestrian distributions. These findings suggest that the street pattern provides a base layer for metro stations to attract both the emergence of active urban functions and pedestrian movement.

The urban road spatial structure is a crucial and complex component of urban design. Generative design models, such as the Stable Diffusion model, can rapidly and massively produce designs. However, the opacity of their internal architecture and the uncertainty of their outcomes mean that the results generated do not meet specific disciplinary assessment criteria, thus limiting their widespread application in planar design and planning. Additionally, traditional software processes targeting specific indicators are time-consuming and do not allow for rapid evaluation. To address these challenges, we utilized several areas of the road spatial structures in six cities and their corresponding four space-syntax parameters as training samples. We simultaneously trained two models: one is a LoRA Model based on the Stable Diffusion architecture used for generating road networks similar to those of various city road spatial structures; the other is a CoAtNet Model (Convolution + Transformer) used as an evaluation model to predict the space-syntax parameters of road structures and calculate the Mean Absolute Percentage Error (MAPE) relative to real urban samples. Subsequently, by linking these two models end-to-end, we were able to filter out generated samples with the smallest MAPE, thereby enhancing the structural similarity between the generated results and the actual urban road spatial structures. This process of rapid generation and swift evaluation of network configurations marks a critical advancement towards better performance and more customized design solutions.

Against the background of rapid global urbanization, the renewal and renovation of historic districts have become an increasingly important concern. As a city with a long and rich history, Beijing contains numerous historic districts that are in urgent need of systematic renewal and renovation. This study proposes a functional enhancement and renovation design methodology for urban historic districts based on space syntax theory and analytical methods, applying it to the Qianmen Historic District in Beijing. Through traffic flow and business format analysis, the research examines traffic patterns and business format distribution characteristics in the Qianmen area and ultimately guides the design based on these findings. Research indicates that restrooms and attractions in Beijing’s Qianmen historic district exhibit dispersed space distribution, broad service coverage, high metric step depth (447 m and 436 m, respectively), and low topological connectivity. In contrast, hotels and restaurants feature smaller service areas, lower metric step depth (395 m and 297 m, respectively), and higher topological connectivity. Based on these findings, this study proposes targeted design recommendations for Qianmen’s street renovations based on traffic flow analysis results. Considering the need for vehicle parking and pedestrian rest demands in urban functional renewal, rest seats and shared charging piles are set up on the streets with big pedestrian flow to meet the needs of pedestrians. Moreover, cycling routes are designed to connect big-traffic-flow streets with small-traffic-flow ones. These renewal measures aim to enhance the overall vitality of the Qianmen district. The renovation approach and methodology proposed in this study can serve as a reference for future updates and renovations of historic districts.

The integration of railway stations into urban environments necessitates a detailed examination of their vitality and influencing factors. This study assesses urban vitality around four major railway stations in Beijing utilizing a variety of analytical models including Ordinary Least Squares, Geographically Weighted Regression, Multi-Scale Geographically Weighted Regression, and machine learning approaches such as XGBoost 2.0.3, Random Forest 1.4.1.post1, and LightGBM 4.3.0. These analyses are grounded in Baidu heatmaps and examine relationships with spatial form, functional distribution, and spatial configuration. The results indicate significant associations between urban vitality and variables such as commercial density, average number of floors, integration, residential density, and housing prices, particularly in predicting weekday vitality. The MGWR model demonstrates enhanced fit and robustness, explaining 84.8% of the variability in vitality, while the Random Forest model displays the highest stability among the machine learning options, accounting for 76.9% of vitality variation. The integration of SHAP values with MGWR coefficients identifies commercial density as the most critical predictor, with the average number of floors and residential density also being key. These findings offer important insights for spatial planning in areas surrounding railway stations.

Network information technology provides a variety of choices and convenient channels for daily shopping. Most of the existing studies focus on the distribution of life service functions such as fresh market, and there is still a lack of market differentiation analysis with different degrees of network. Based on field research and open network data, this paper selects four regions in Beijing and divides fresh market into three types according to the degree of network: review distribution, review only and offline. Space Syntax is used to analyze the spatial structure characteristics and influence mechanism of fresh market distribution under the development of information technology. The results show that the influence of network information technology on urban function distribution is different in different scales. The proportion of various markets in each region is highly stable. Meanwhile, the Internet does not remove the influence of geographical factors, and the markets with high network degree are still distributed in the streets with relatively high accessibility. As far as the market distribution mechanism is concerned, the network market is affected by the surrounding population factors and traffic conditions.