The research on the impact of urban blue spaces on residents’ mental health has attracted great attention from scholars internationally, and quantitative studies of the effects dominate the current academia. This study, on the basis of reviewing the theories of urban blue spaces and residents’ mental health, conducted a meta-analysis of 47 key studies by systematically selecting and examining the literature from Web of Science, CNKI, and other databases. This paper analyzed the measuring indicators and research models among the literature and standardized the effect size of the research findings. The meta-analysis results include that: 1) the measurements of the characteristics of urban blue spaces are mainly conducted in space-based and individual-based dimensions; 2) residents’ mental health is mainly measured from aspects of general mental health, positive psychology, and negative psychology; 3) the proximity of blue space has a significant positive effect in improving residents’ general mental health and positive psychology; 4) the availability of blue space is significantly positively correlated with general mental health and positive psychology; 5) although there are studies confirming that factors such as blue space visibility, frequency of visit, and exposure types have an impact on mental health, the relevant studies are still limited; and 6) research on the effect of blue spaces on negative psychology is controversial, especially on mental disorders such as depression, and the findings among existing studies vary significantly. The results of this meta-analysis can provide guidelines for future research and the construction of healthy cities.

This research investigated the impact of social-networking service posts on the formation of image structure of cities, focusing on the spatial distribution of images and their content similarity. It aimed to delineate the image structure of cities created by numerous users, moving beyond traditional qualitative methods towards a more quantitative and objective approach with big data. Taking central Tokyo as an example, this study extracted geotagged image data of 33 major railway station areas from Flickr’s API (Application Programming Interface). Four coverage types of viewpoint distribution, namely planar, intersecting linear, linear, and nodal, were identified, reflecting the unique urban structures respectively. Further investigation of the image contents, primarily consisting of “urban landscape” and “landscape/street trees,” showed that such contents significantly influenced the formation of the image structure of cities. The study concluded that as the number of photo posts increased and the representative viewpoints concentrated, the digital information received by users became more homogeneous, leading to strongly stereotyped images of urban landscapes. These findings highlight the role of social networking services in shaping perceptions of the urban environment and provide insights into the image structure of cities as formed by digital information.

The establishment and development of the Guangdong–Hong Kong–Macao Greater Bay Area have demonstrated the collaborative relationship of the globally regional reconstructing. As a major strategy of the Greater Bay Area, Northern Metropolis faces challenges in transboundary ecological collaborative management. Based on the analysis of the ecological issues and the complexity of the transboundary management in Shenzhen Bay, this article proposes a co-governed “Special Ecological Conservation Zone” under the context of “one country, two systems” by zoning areas with varied protection levels. The development and operation framework includes: establishing an independent Joint Work Group, building consensus and standardizing collaboration procedure, conducting hierarchical management and regulating development, leveraging and motivating knowledge and technological innovation, and strengthening community engagement and emphasizing shared benefits. By case-studying three sites along the Shenzhen Bay, this article also provides place-making strategies for different levels of protection zones. Exploring innovative collaboration models for Northern Metropolis, this article is expected to provide new solutions for the sustainable development of the Greater Bay Area and creative insights for global transboundary ecological collaborative management.

With the continuous advance of big data and artificial intelligence technologies, various data-driven machine learning algorithms have been widely applied in the studies of urban resilience, particularly in addressing the challenging issue of urban waterlogging. Currently, it is a pressing task to understand the influencing factors of waterlogging from the perspective of built environment, and provide guidance on dynamic monitoring and early alarm services. Focusing on Shenzhen, China, a typical high-density urbanized city, this research constructed a multifactorial dataset encompassing hydrological, meteorological, urban morphology, and waterlogging event data. Then, this research assessed and compared the performance of four mainstream machine learning models—LightGBM, RF, SVR, and BPDNN—in predicting urban waterlogging risks. The results showed that LightGBM had the best accuracy and robustness in predicting waterlogging depths and risk levels in urban areas. The research also employed interpretability algorithm—Shapley Additive Explanations (SHAP)—for decoupling analysis. The results indicated that hydro-meteorological factors (the total rainfall volume and the rainfall lasting time) and several architectural configuration factors (e.g., density of buildings, building congestion degree) are the main influencing factors. In addition, the percentage of water body is vital to waterlogging regulation and retention, especially exhibiting a significant mitigating effect when exceeding 2.5%. This research provides a new technical method for urban waterlogging prediction and reveals the influencing factors and intrinsic mechanisms from the perspective of built environment, which is of great significance for the enhancement of the resilience of high-density cities.

As important cultural service providers in urban ecosystems, historical blocks play a crucial role in ensuring the well-being of multiple stakeholders during the urban renewal process. Based on theories of Landsenses Ecology and literature review, this study systematically examined the landsense elements of historical blocks and their related cultural ecosystem services (CES) by proposing a landsense design practice framework for CES enhancement and further constructing a landsense evaluation indicator system. Using the Three-Lanes and Seven-Alleys Block in Fuzhou, China as a case study, this research analyzed the status quo and existing problems of the block according to the evaluation indicator system, and then proposed suggestions for landsense creation to enhance, preserve, and improve CES of the block. Finally, it qualitatively elaborated the evaluation indicators upon the practice framework of landsense design. This study provides an essential theoretical foundation and evaluation basis for enhancing CES in historical blocks from a perspective of multi-dimensional perceptions.