Both Tulous (土楼) and Weilong Houses (围龙屋) are important types of traditional Hakka (客家) dwellings whose architectural morphology is closely related to the historical origin, social organisation, and cultural values of Hakkas. Previous studies focused on the influence of Chinese traditional etiquette systems and ritual values on the physical morphology of Hakka traditional dwellings, where ancestral halls are often the core of the entire building. This article selects six typical case studies of Tulous and Weilong Houses and launches a comparative study on their spatial configurations through quantitative analysis of space syntax. This study found that compared to Tulous, Weilong Houses exhibit decentralisation and gridification in their spatial structure, in addition to a decrease in defence capability and an increase in residential privatisation. The ancestral hall of the Tulou is still the spiritual and living centre of Tulou residents, located in the geometric centre and possessing high spatial configurational values. However, although the ancestral hall of the Weilong House is still located in the geometric centre of the entire building, it does not exhibit the highest spatial configurational values, indicating that the increase in secularisation and the decrease in ritualisation of Hakka communities inhabiting Weilong Houses resulted in the separation of secular space and ritual space. It coincides with the “Centre–Periphery” distribution characteristics of Hakka communities related to Tulous and Weilong Houses, reflecting the different community existence paradigms of the Hakkas “landed” and “rooted” in the Guangdong–Fujian–Jiangxi border area.

To characterize a community-scale urban functional area using geo-tagged data and available land-use information, several supervised and semi-supervised models are presented and evaluated in Hong Kong for comparing their uncertainty, robustness and sensitivity. The following results are noted: (i) As the training set size grows, models’ accuracies are improved, particularly for multi-layer perceptron (MLP) or random forest (RF). The graph convolutional network (GCN) (MLP or RF) model reveals top accuracy when the proportion of training samples is less (greater) than 10% of the total number of functional areas; (ii) With a large amount of training samples, MLP shows the highest prediction accuracy and good performances in cross-validation, but less stability on same training sets; (iii) With a small amount of training samples, GCN provides viable results, by incorporating the auxiliary information provided by the proposed semantic linkages, which is meaningful in real-world predictions; (iv) When the training samples are less than 10%, one should be cautious using MLP to test the optimal epoch for obtaining the best accuracy, due to its model overfitting problem. The above insights could support efficient and scalable urban functional area mapping, even with insufficient land-use information (e.g., covering only ~20% of Beijing in the case study).

Exploring the impact of urbanization on carbon emissions is crucial for formulating effective emission reduction policies. Using nighttime light data and attribute data from 68 Chinese cities (2002–2021), this paper develops an urban development evaluation system with the entropy method. The Lasso method is employed to select key factors affecting carbon emissions, and hierarchical regression models are utilized to analyze these factors across different city types. The results show the following: (1) The extraction of built-up areas using integrated nighttime light data yields an overall accuracy ranging from 70.90% to 98.87%, reflecting high precision. (2) Expanding cities have predominated over the past two decades, indicating a continued upward trend in urbanization in China. (3) Urban development is influenced by internal characteristics and geographic location: contracting cities are mainly inland heavy industrial centers, while expanding cities are located in economically advanced coastal regions. Additionally, it is also impacted by the growth of surrounding cities, exemplified by the imbalance between central cities and their peripheries within metropolitan areas. (4) The expansion of built-up areas is a significant factor affecting carbon emissions across all city types. For expanding cities, managing population growth and promoting tertiary sector development are recommended, while contracting cities should focus on judicious economic planning and virescence area protection.

The change in impervious surface area (ISA) is an important factor reflecting urban expansion. This study used the global ISA dataset to analyze the spatiotemporal changes in ISA from 2001 to 2020 worldwide, explored the hotspots and patterns of ISA expansion, and analyzed the natural and socio-economic factors affecting ISA changes at three different levels, namely the continent, country, and city levels, by using the RF-SHAP method. The results are as follows: (1) The ISA has grown by 0.94 million km². (2) ISA in regions such as Asia and Africa has expanded faster than the global average. Developed countries had lower expansion rates. The hotspots of the ISA change rate were relatively concentrated in eastern Asia. Hotspot areas were mainly distributed in Asia and eastern South America in the early stage of the study period and appeared in eastern Europe in the later stage. (3) Edge expansion is the main pattern. Upper-middle-income countries have the largest area of ISA expansion, followed by high-income countries. Cities in developed countries have more infilling expansion; cities in developing countries have more edge expansion. (4) At the continent and country level, social factors, especially GDP, have the greatest impact on ISA change. At the city level, natural factors play a more influential role.

Urban rail transportation is an important public transportation network for realizing the dual carbon strategy and sustainable transportation development. A top-stream station is an important node in this network. This paper focuses on 47 top-stream station sites during the peak hours of workdays from 2018 to 2020 in Beijing (26 inbound and 22 outbound, with Beijing South Station as a double-stream station) and collects and analyzes information such as station area spatial texture, spatial organization efficiency, land use function, land use mix, POI (Point of Interest) distribution and functional mix, construction intensity, and population heat distribution. Through an analysis of the network topological structure characteristics of each station area, comparison of spatial efficiency differences, analysis of land use function composition and mix characteristics, and distribution of spatial construction intensity, this paper discusses the relationship between the spatial structure, spatial function, spatial intensity characteristics and key indicators of the built environment of station areas from the perspective of urban design. The conclusion shows that there is a close relationship between the function, structure, and strength of the overall built environment of the station domain at the theoretical level. The regression test, to some extent, confirms the close relationship between key indicators and expands the indicator system for measuring the fit relationship. The comparison between general station sites and headstream station sites shows that the fit relationship of indicators for headstream station sites is not completely the same as that for general station sites, indicating that the influencing factors for headstream station sites are diverse. This reminds investment, design, construction, and management teams in practice that the measurement and planning of the built environment space structure of the station domain should be based on local conditions and be closely related to the topological structure of the station domain’s rail network and road network structure. At the same time, whether the stability of the indicator system and the specific R-squared value have differences in various cities requires further verification. This paper explores and tries to raise questions about the research methodology of the built environment space structure, the measurement of the station domain, and the concepts of station-city coordination and development.

Extreme weather events increasingly challenge urban transportation systems, requiring advanced methods to understand and predict traffic adaptations. Traditional graph-based approaches to traffic analysis, while effective for static conditions, struggle to capture the continuous and dynamic nature of flow patterns during weather disruptions. Current methods lack the mathematical framework to bridge discrete network structures with continuous flow dynamics, particularly in analyzing how traffic patterns reorganize under stress. This paper presents a novel field-theoretic framework for analyzing traffic flow patterns, integrating Hodge decomposition with continuous field visualization. The framework decomposes traffic flows into three fundamental components: gradient flows representing direct movements, curl flows capturing circulation patterns, and harmonic flows revealing background patterns. By transforming discrete network measurements into continuous fields, our method enables comprehensive analysis of flow pattern evolution under varying conditions. Through both synthetic experiments and a case study of London's central district during rainfall events, we demonstrate our framework's ability to reveal previously unobservable traffic adaptation patterns. The method successfully captures the emergence of alternative routes, identifies critical adaptation zones, and quantifies systematic changes in flow organization. This approach provides valuable insights for urban planners and traffic managers, offering a mathematical foundation for understanding network adaptation mechanisms and informing resilience planning strategies.