The dynamic distribution of urban population density and the interaction with land use elements involve mutual constraints and guidance. However, in the existing research on the relationship between urban population density and land use, the discussion on the distribution patterns of urban population density typically spans long time periods and uses large spatial units, lacking analysis of the dynamic changes in population density within high granularity land parcels over a day. In studies related to the urban built environment, the complex relationships between different-dimensional land use elements and the dynamic distribution of population density also need further exploration. To address these bottlenecks, this study takes Shanghai’s central urban area as an example. Based on 24 h mobile signaling data on weekdays, weekends, and typical holidays, as well as urban land use data, clustering algorithms are used to summarize patterns of dynamic population density distribution. Pearson correlation analysis is then employed to study the correlation between dynamic population density distribution patterns and different land use elements. The results indicate that various urban land use factors such as locational centrality, functional diversity, transportation accessibility, compactness, and landscape quality have different impacts on the dynamic distribution of population density in spatial units, and the dynamic distribution patterns of population density in different land use types also vary. This research contributes to guiding the optimization of spatial quality and formulating planning and management measures that more effectively match construction intensity with population activity density.

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.

The photorealistic reconstruction and rendering of architectural scenes have extensive applications in industries such as film, games, and transportation. It also plays an important role in urban planning, architectural design, and the city's promotion, especially in protecting historical and cultural relics. The 3D Gaussian Splatting, due to better performance over NeRF, has become a mainstream technology in 3D reconstruction. Its only input is a set of images but it relies heavily on geometric parameters computed by the SfM process. At the same time, there is an existing abundance of raw 3D models, that could inform the structural perception of certain buildings but cannot be applied. In this paper, we propose a straightforward method to harness these raw 3D models to guide 3D Gaussians in capturing the basic shape of the building and improve the visual quality of textures and details when photos are captured non-systematically. This exploration opens up new possibilities for improving the effectiveness of 3D reconstruction techniques in the field of architectural design.

As China’s urban–rural interactions deepen, bidirectional flows of production factors reshape rural functions and social relations, giving rise to rural gentrification. The existing rural gentrification literature has focused primarily on middle-class migration and class displacement, and despite growing recognition of non-migratory external engagement in rural China, production-system restructuring in this context remains underexplored. This study selected three representative villages in Nanjing, China, for comparative case analysis and used multi-temporal point of interest (POI) and cross-sectional enterprise registration data to examine the common patterns, differentiated pathways, and formation mechanisms of production-system restructuring during rural gentrification. The results showed that rural industries, resources, and spaces were reconfigured for consumer and experiential uses as production and business activities expanded and business formats diversified, with the organizational composition of registered business entities differing across villages. Differences in production activity and business entity structures corresponded to industry extension, holistic insertion, and progressive embedding. Their formation mechanism can be summarized as the sustained integration of urban–rural flows with local production foundations. By incorporating production-system restructuring into rural gentrification analysis, this study extended conventional analysis centered on population migration and class displacement. It provided Chinese evidence for understanding diverse rural gentrification pathways in the Global South.