Assessing transport CO₂ emissions is important in the development of low-carbon strategies, but studies based on mixed land use are rare. This study assessed CO₂ emissions from passenger transport in traffic analysis zones (TAZs) at the community level, based on a combination of the mixed-use development model and the vehicle emission calculation model. Based on mixed land use and transport accessibility, the mixed-use development model was adopted to estimate travel demand, including travel modes and distances. As a leading low-carbon city project of international cooperation in China, Shenzhen International Low-Carbon City Core Area was chosen as a case study. The results clearly illustrate travel demand and CO₂ emissions of different travel modes between communities and show that car trips account for the vast majority of emissions in all types of travel modes in each community. Spatial emission differences are prominently associated with inadequately mixed land use layouts and unbalanced transport accessibility. The findings demonstrate the significance of the mixed land use and associated job-housing balance in reducing passenger CO₂ emissions from passenger transport, especially in per capita emissions. Policy implications are given based on the results to facilitate sophisticated transport emission control at a finer spatial scale. This new framework can be used for assessing the impacts of urban planning on transport emissions to promote sustainable urbanization in developing countries.

Urban renewal is crucial for fostering revitalization and sustainable development in cities. Accurate identification of renewal areas in urban residential districts is essential for implementing effective renewal strategies. However, existing studies struggle with the automatic large-scale spatial classification of renewal areas due to their inherent complexity. Moreover, complete demolition and reconstruction are not always conducive to long-term sustainability. This study proposes an automated framework to identify potential renewal areas in urban residential districts, using remote sensing data and GeoAI, with Shanghai as a case study. The framework began by establishing classification rules to identify different renewal modes: retention, renewal, and demolition areas. The data labeling process was performed using the Segment Anything Model, followed by a comprehensive identification of different renewal modes through deep learning models, specifically DeepLabv3+. The results showed that (1) a total of 327.18 km² of retention areas, 130.76 km² of renewal areas, and 37.31 km² of demolition areas were automatically identified; (2) retention areas were evenly distributed across the city, while potential renewal areas were concentrated in the central urban districts, and demolition areas were primarily located in the suburban regions. This automatic identification framework significantly enriches the understanding of spatial complexity in urban systems and broadens the application of remote sensing technology in urban studies. The findings provide valuable insights for urban planners, highlighting priority regions for targeted renewal efforts.

Public space quality is a multidimensional concept. Urban design quality and physical disorder represent it from the perspectives of design and maintenance, respectively. However, the practical relationship between these two factors remains unclear, raising questions about the necessity of measuring multiple dimensions of public space quality simultaneously. Based on classical studies and theories, this study developed index systems for both factors. Utilizing artificial virtual auditing and deep learning models, including FCN and SegNet, the street visual quality of two Chinese cities was assessed from the perspectives of urban design and physical disorder using extensive street view images. The correlation between these two factors was explored. The results showed that the Spearman correlation coefficients for urban design quality and physical disorder were 0.308 and −0.085 in the two cities, respectively, indicating weak or unclear correlations. Additionally, the fit and explanatory power of the linear and nonlinear regression models constructed were poor, further demonstrating that it is difficult to predict and explain one variable using the other. In the robustness test, the results were further validated by including control variables related to urban vitality, cultural characteristics, and urban development, considering the impact of the distribution of the street view images, drilling down to more granular dimensions and extending the scope of the study to a wider area. The levels of design and maintenance of streets cannot be conflated or substituted, and neither can independently represent the overall space quality alone. Both are indispensable, making it crucial to separately assess and address space quality issues from different perspectives. These results broaden our understanding of high-visual-quality street space and provide references for urban planners and stakeholders in improving street space quality.

As big data becomes increasingly integral to urban studies, the evolving profile of its applications, including the trends in data, methodologies, and contributions to urban theories, remains unclear. This study systematically reviewed literature from 57 representative journals in the fields of Urban Studies, Geography, and Environmental Studies (JCR Q1) over the past decade (2013–2023), with a focus on quantitative research involving urban spatial big data. A total of 1,425 articles were identified through automated keyword filtering and manual screening, of which 82 were selected for further analysis due to their theoretical contributions. The results revealed key thematic areas, including urban transportation, spatial quality, vitality, and structure, with a predominant focus on developed countries and large cities. The applications of big data and novel methodologies in urban studies have increased from 2013 to 2023, with increasingly higher levels of spatiotemporal resolution. However, studies linking big data to urban theories remained limited, with most quantitative research centered on applying or proposing new methods or uncovering new phenomena. Theoretical contributions primarily explored the mechanisms underlying urban environment formation and their impacts on human activities and behaviors. This study provides a comprehensive understanding of big data applications and advances in theories in urban spatial studies and highlights key directions for future research.