The continuous worsening of urban thermal environments poses a severe threat to human health and is among the main problems associated with urban climate change and sustainable development. This issue is particularly severe in high-density built-up areas. Existing studies on the thermal environments (temperature data extracted from satellite remote sensing images) are mainly focused on urban canopy areas (airspace below the average height of trees or buildings) rather than the near surface region (at pedestrian height). However, the main outdoor activity space of urban residents is the area near surface region. Hence, this study aims to investigate the influence of urban form (i.e., building density, height, and openness) on thermal environment near the surface region. The high-density built-up areas of a typical megacity (i.e., Nanjing) in China were selected, and the thermal environments of 26 typical blocks were simulated using ENVI-met software. Temperature field measurements were carried out for simulation validation. On this basis, a classified and comparative study was conducted by selecting the key spatial form elements that affect thermal environments. The results showed that in actual high-density built-up areas, single urban form parameter does not determine the thermal environments near the urban surface but mainly affected by the use (function) of space. For this study, the overall thermal environment of a street block is optimal when the building density is between 40% and 50% and the average building height is between 8 and 17 stories. Nonetheless, the urban form can be improved to optimize the overall effects on building functions and thermal environments. Furthermore, function-specific urban form optimization strategies were proposed to optimize thermal environments according to specific functional needs.

CO₂ is the primary contributor to global warming, and also the most significant anthropogenic emission gas in cities. This study investigates near-surface CO₂ spatiotemporal variability patterns at the community scale to address the critical gap in urban CO₂ high-resolution measurement and promote urban carbon neutrality. Combining fixed and mobile monitoring across five representative communities (1-km² coverage) with two-hour temporal precision and 20 m spatial resolution, results revealed average CO₂ concentrations of 440–480 ppm, exhibiting bimodal diurnal cycles and highlighting spatiotemporal divergent emission behaviors. Three communities peaked during 17:00–19:00 LT, while two peaked during 08:00–10:00 LT. Spatial correlation analysis identified two dominant patterns: road-adjacent “externally dominated” hotspots and “internally dominated” zones with elevated intra-community levels. Spearman correlation analysis, Random Forest, and Geographically and Temporally Weighted Regression models quantified spatial morphology and element contributions, demonstrating that building morphology exerted time-varying impacts across communities. Meanwhile, external traffic contributed 18–39% to concentration variability, while internal traffic and energy consumption drove localized peaks. The findings indicated that apart from the emission sources, the micro-scale urban spatial design elements also regulate the near-surface CO₂ distribution. This high-resolution approach provides actionable insights for optimizing community layouts and infrastructure to mitigate localized emissions, advancing carbon neutrality targeted urban planning.

Based on multi-source data including mobile signaling data and remote sensing imagery, this research constructed human activity indicators and applied spatial analysis methods to identify gender-specific patterns in green space usage across different types of days. A Standardized Gender Difference Index (SDI) was developed to quantify gender-based disparities in both temporal and spatial dimensions. Correlation analysis was employed to examine spatial influencing factors and their interrelationships. The results indicate that on holidays, a stable male-dominant usage pattern was observed across all parks, with males showing a preference for cultural parks. On weekdays, although male users remain predominant overall, considerable variations exist among parks. On a daily scale, males exhibit earlier and more flexible activity patterns, whereas females demonstrate significantly lower presence during evening hours, indicating a “nocturnal poverty” phenomenon. Spatially, community parks display the greatest gender disparity, whereas comprehensive parks show relative gender balance. Although both genders value barrier-free facilities, females place additional emphasis on service facilities, while males are more influenced by mixed land-use functions surrounding the parks. This study demonstrates that high-precision, large-coverage multi-source data can accurately and dynamically identify gender-based differences in green space usage and their influencing factors, providing valuable insights for inclusive urban planning.

Urban high-density development has led to the emergence of complex three-dimensional pedestrian networks. As a crucial component of city centers, these networks significantly influence the spatial distribution of service industries. Understanding the correlation between pedestrian networks and service industry layouts is vital for effective planning and development. This study proposes a technical framework for analyzing the relationship between three-dimensional pedestrian networks and service industry layouts. Using the Xinjiekou central area in Nanjing as a case study, we constructed a three-dimensional pedestrian network model using the sDNA method. Focusing on catering formats, we introduced a method to study the spatial distribution characteristics of service industries in three-dimensional spaces and employed a graph convolutional network model to systematically analyze the correlation between pedestrian network closeness and betweenness with catering formats. The results indicate that pedestrian network closeness is significantly positively correlated with the number and average spending of catering formats, while betweenness shows almost no correlation. High-closeness areas, due to their traffic convenience and walkability, are more conducive to the concentration of catering formats and higher spending levels. Our findings provide valuable insights for catering format location decisions and the optimization of three-dimensional pedestrian networks, contributing to sustainable urban development.