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.

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.

The compactness, diversity, and nested structures of the old districts in Chinese cities, in terms of their three-dimensional (3D) morphology, are particularly distinctive. However, existing multifractal measurement methods are insufficient in revealing these 3D structures. This paper introduces a 3D multifractal approach based on generalized dimension and Rényi entropy. In particular, a local indicator τ_q(h) is introduced for the analysis of the mapping of 3D units, with the Nanjing Old City serving as a case study. The results indicate the following: (1) The significant fractal characteristics of the Nanjing Old City, with a capacity dimension value of 2.344, indicating its limited 3D spatial occupancy. (2) The fluctuating generalized dimension spectrum ranges from 2.241 to 2.660, which differs from previous studies, suggesting that the 3D morphology does not exhibit typical multifractal characteristics. (3) The 3D map matrix reveals a fragmented open space system, a heterogeneous distribution of high-rise buildings, and cross-scale variations in morphological heterogeneity. This 3D multifractal method aids urban planners in assessing critical issues such as the fragmentation, crowding, and excessive heterogeneity of urban morphology, providing a spatial coordination and scaling of these issues through the 3D map matrix and enhancing the discussion of the broader mechanisms influencing morphological characteristics.

The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on urban morphology discovery have always been based on raster analysis and have been limited to the central city area. Raster analysis can lead to fragmented forms, and focusing only on the central city area ignores many representative urban forms in the suburbs and towns. In this study, a vast and complex dataset was applied to the urban morphology discovery based on the administrative community or village boundary, and a new image deformation pipeline was proposed to enhance the morphological characteristics of building groups. This allows visual neural networks to focus on extracting the morphological characteristics of building groups. Additionally, the research on urban morphology often uses unsupervised learning, which means that the learning process is difficult to control. Therefore, we refined the NT-Xent loss so that it can integrate morphological indicators. This improvement allows the visual neural network to “recognize” the similarity of samples during optimization. By defining the similarity, we can guide the network to bring samples closer or move them farther apart based on certain morphological indicators. Three Chinese cities were used for our testing. Representative urban types were identified, particularly some types located at the urban fringe. The data analysis demonstrated the effectiveness of our image deformation pipeline and loss function, and the sociological analysis illustrated the unique urban functions of these urban types.