Improving vitality has been a major bottleneck in the revitalization of traditional village heritage worldwide. The vitality of traditional village (VTV) varies greatly depending on socioeconomic factors and natural conditions. Significant spatial variation exists in VTV, even within the same urban jurisdictions in China; however, the main determinants for this have not yet been quantified owing to the difficulty of obtaining data from large rural samples, making targeted invigoration difficult. Thus, we applied point of interest data, which are easily accessible big data, to bridge the data source gap. To assess the VTV’s influencing factors and analyze the spatial variations among the factors’ impacting intensity, we used the Ordinary Least Squares and Geographically Weighted Regression models and conducted empirical studies involving 148 traditional villages in Lishui, China. Seven factors influenced the vitality of traditional villages in the study area, with the most significant being topographic relief, elevation, scenic spots and commercial industry. Moreover, the factors’ impacting intensity varied by region. Topographic relief and elevation had the greatest impact intensity in the north and south of Lishui, whereas primary education, transportation facility and agricultural bases had the greatest impact in the north, and scenic spots and commercial industry had the greatest impact in the middle of Lishui. Taken together, this method makes a large sample of VTV’s impact factor analysis feasible, has global implications, and can provide a foundation for the scientific and precise regional promotion of VTV, which is beneficial for rural heritage revitalization.

Tourist preferences for public spaces in historic districts can reflect whether renovated spaces and functional structures meet tourism demands. However, conventional big data lack the spatio-temporal accuracy needed to support a refined, dynamic study of small-scale public spaces inside historic districts. This paper, therefore, proposes using a Wi-Fi probe to evaluate the spatio-temporal dynamics of tourists’ spatial preferences in historic districts. We conducted a one-week measurement in the Xiaohe Street Historic Block in Hangzhou, China. Three indicators—visit time preference, aggregation preference, and stay preference—were used to examine the dynamic change in tourists’ spatial preferences, with 15 min as the time unit and public spaces with a radius of 25 m as the spatial unit. Our research demonstrates that, compared with conventional big data, the Wi-Fi probe offers a more reasonable and accurate method to measure tourists’ spatial preferences in historic districts at a smaller time and spatial granularity. The research findings can be applied to evaluate the effectiveness of spatial regeneration and diagnose renewal-related issues in historic districts. It can also serve as a foundation for more precise planning of public spaces in historic districts, as well as the modification of functional structures.

Urban development boundaries are efficient tools for coordinating urban–rural relations and ensuring sustainable development. From 2000 to 2020, the expansion rate of the built-up area in cities and towns throughout China reached 177%, far exceeding the urban population growth rate of 96.5% in the same period. As this spatial expansion seems to continue, there is a need to intervene and control urban boundaries. We believe using the urban–rural integration perspective to set (or reset) and maintain urban development boundaries will help manage urban expansion more effectively than present methods. This research, therefore, develops an urban development boundary delineation method from a macroscopic view for China. A new model for defining boundaries was developed based on the four dimensions of urban–rural interaction: economic demand, environmental protection, urban carrying capacity, and urban development resistance. And an empirical study was conducted in Guiyang City as an example. The results show that the resultant urban boundary can provide a more comprehensive and realistic growth model than current methods, making it more applicable for controlling and fostering sustainable urban and rural development.

With the rise in rural tourism, public space use has become more complex, causing significant weekday-weekend vitality imbalances. However, the factors shaping these dynamics in traditional villages remain unclear. This study uses Wi-Fi sensing method to analyze vitality variations across weekdays and weekends, and it develops a 13-metric evaluation framework to examine how built environment factors, from both internal and external dimensions, differentially influence the vitality of public spaces in traditional villages across various time periods. Using 17 public spaces in Yantou Village, Lishui, China, as a case, it finds: (1) Historical Element Proximity consistently and significantly drives public space vitality across all periods; (2) Leisure Facility Count and Decorative Element Count demonstrate strong positive effects during weekend morning peaks. (3) Retail Facility Count significantly reduces vitality during weekend morning peak but enhances it during midday off-peak, whereas Street Vendor Count shows the opposite pattern—increasing vitality in morning peak and decreasing it in midday off-peak. Using Wi-Fi sensing’s high-resolution, real-time, and non-invasive capabilities, this study provides a scientific method to accurately assess the variations in public space vitality and their impact factors between weekdays and weekends in traditional villages, offering technical support for enhancing public space vitality and sustainably revitalizing rural heritage.