The increased frequency of extreme hot weather events in recent years poses a significant threat to the lives and health of urban residents. Consequently, the thermal comfort of urban open areas has garnered growing attention. The ground material in these urban open areas directly impacts the thermal environment, which significantly influences the comfort of crowds. This study aimed to assess the effect of land cover materials in urban center squares on the thermal comfort of people in high-temperature conditions. Eight types of land cover materials were selected from the two urban squares in the central district of Nanjing Xinjiekou. Physiological equivalent temperature (PET) calculations were performed by measuring the surface temperature, the air temperature, the humidity, and other relevant data to evaluate population thermal comfort. The findings indicated that grass provided the highest thermal comfort, with PET scores ranked as follows, from low to high: grass, permeable bricks, granite, concrete, basalt, bluestone, andesite, and asphalt. Additionally, factors such as color, roughness, and shade within the same material also impacted thermal comfort. Subsequently, using the ENVI-met 5.1 software, surface materials exhibiting superior thermal comfort were simulated for replacement, aiming to confirm the experimental results and propose retrofit strategies for improving urban square thermal comfort by optimizing material selection. The outcomes of this study hold significant implications for urban open space design and the overall well-being of city dwellers. The thermal environment in urban centers during high-temperature conditions can be improved by optimizing the choice of land cover materials in urban open areas, thereby enhancing the comfort of the population.

Crowd activity is an important indicator of commercial streets’ attractiveness and developmental potential. The development of positioning technologies such as GPS and mobile signal tracking has provided a large amount of trajectory data for studying crowd activities on commercial streets. These data can not only be used for the statistics, extraction, and visualization of crowd information, but they also facilitate the exploration of deeper insights into dynamic behaviors, choices, trajectories, and other details of crowd activities. Based on this, this article proposes a new framework for analyzing crowd activities to explore the spatial activity patterns of crowds and understand the dynamic spatial needs of people by analyzing their correlations with local formats. Specifically, we analyze the spatial activity characteristics of a crowd in the Lao Men Dong Commercial Street area by identifying the stay points and trajectory clusters of the crowd, and we establish a regression analysis model by selecting commercial street format variables to evaluate their impact on crowd activities. Through case analysis of the Lao Men Dong Commercial Street, this study confirms that our method is feasible and suitable for spatial research at different scales, thereby providing relevant ideas for format location selection, spatial layout, and other planning types, and for promoting the sustainable development of urban spaces.