High-density communities have proliferated globally during rapid urbanization. They are characterized by a high population density and limited per capita public spaces, making them susceptible to infectious disease risks. The impact of infectious diseases in these communities, as evident during the COVID-19 pandemic, underscores their vulnerabilities. Yet, research on disease prevention in high-density areas remains limited. This study aims to investigate the relationship between the built environment and the transmission of infectious diseases in high-density urban communities, with a particular focus on the lessons learned during the COVID-19 pandemic. Utilizing Shenzhen city as a case study, this study collected data on the built environment and epidemic trends and involved a generalized linear regression analysis, aiming to understand the key built environment factors that affect epidemic spread in high-density areas. The results from the study revealed that high-density communities experience higher rates of infectious disease transmission compared to their medium- to low-density counterparts. The significant factors identified include land use mixture and walkability, with land use mixture showing the most substantial impact on infection rates. Through a combination of qualitative analysis and empirical research, we constructed a conceptual framework linking containment measures, non-pharmaceutical interventions, and the built environment. The findings emphasize the significance to focus on the health development of high-density communities and offer valuable insights for tailored urban planning and built environment design. These insights are crucial for promoting the healthy and sustainable transformation of existing high-density communities.

Rapid urbanization has caused serious negative impacts on the ecological and human landscapes of rural areas in China. By constructing a network of multifunctional landscape corridors, we can effectively connect landscape patches, reduce the danger of landscape fragmentation, and effectively protect rural areas′ ecological and human landscape resources. With the help of the Morphological Spatial Pattern Analysis (MSPA) research method, the source sites for constructing landscape corridors were selected from the core areas that play an essential role in the performance of regional ecological functions, using the Liukeng Cultural and Ecological Tourism Area as the study area. The results of MSPA analysis are incorporated into the construction of the landscape resistance surface, the landscape corridor network is constructed using the minimum resistance model (MCR) and gravity model, and the landscape corridor network is improved by adding ecological steppingstones and humanistic landscape nodes. The results showed that ten important corridors and 13 secondary corridors were constructed based on 12 source patches in the study area; 5 ecological steppingstones and ten humanistic landscape nodes were added to the optimized network, 21 corridors were added, and 48 ecological breakpoints were proposed to be restored. The optimized network closure (0.65), line point rate (2.15), network connectivity (0.73), and other indicators indicate that the optimized study area has good connectivity of landscape corridors. The study provides a comparative analysis of landscape granularity suitable for mesoscale. Integrating historical and humanistic landscapes into the construction of landscape corridors is an optimization of previous studies that focused only on natural ecology and neglected historical and humanistic landscapes. The study can be a reference for future research on multi-functional landscape corridors and ecological networks in mesoscale and rural areas. At the same time, the construction of multifunctional landscape corridors can promote the conservation of natural and historical human landscapes and the future development of tourism in rural areas.

Land is the carrier of human economic activities, and its utilization has a profound impact on CO₂ emissions. With the advancement of urbanization, mixed land use has become a universal feature of cities. Analyzing the impact of mixed land use on CO₂ emissions is one of the prominent premises for coordinating urban development and the ecological environment. Using information entropy of land use structure (IELUS) to reflect its mixing degree, it was found that the relationship between IELUS and CO₂ emissions presents a positive U-shaped curve. Additionally, when IELUS is less than 0.351, they are negatively correlated, and vice versa. This means that cities can appropriately shift toward a higher degree of a mixed land use pattern to alleviate their environmental pressure. Further research shows that the spatial spillover effect will dilute the impact of mixed land use on CO₂ emissions. Meanwhile, improving production efficiency and increasing public transport travel are significant ways in a mixed land use model to reduce CO₂ emissions. Overall, this study provides a reference for the rational allocation of low-carbon land use systems.

Following rapid urban expansion, numerous rural villages have been requisitioned for urban development in Chinese cities. During the prevailing urbanization process, displaced farmers within a village are wholly relocated into an urban resettlement housing district in transitional China. This study aims to examine whether those farmers still retain previous widespread and dense social networks in urban environment. The study is primarily a case study in Shanghai; data were obtained through semistructured interviews, questionnaire survey and field observations. The findings show that the urbanization process led by farmland requisition gives rise to four major shifts, which include residence shift from farmhouses to urban apartments, household registration identity shift from peasantry to urbanites, occupation shift from farming to nonfarming jobs, and resident structure shift from single village to multiple sources. Such sociospatial changes have transformed the basis and number of social ties, fr...

Understanding the spatial–temporal evolution of the blue–green space (BGS) is crucial for urban planning and ecological security protection. However, the evolutionary patterns and driving factors of the BGS in new urban districts remain unclear. Based on the classical qualitative models, “patch–corridor–matrix” and “sustainability prism”, this study adopted the land use transition matrix and landscape index to quantify evolution patterns, and Pearson correlation and geographical detector analysis methods to reveal the driving factors. Taking Zhengdong New District as a case study, the results indicated the following: (1) The BGS rate was reduced by 35% from 2003 to 2021, with the most significant decrease in cultivated lands. (2) The evolution pattern of BGS alternated sequentially among ecological, disorderly, and balanced states. Among them, urban green spaces tended towards a more stable and aggregated state. Other BGSs became more fragmented and discontinuous. (3) The “sustainability prism” model consists of four driving factors, including social economy, rainwater safety, ecological liveability, and physical geography. (4) Socioeconomic factors significantly decreased the BGS, as indicated by the correlation coefficients of −0.988 and −0.978 between built-up areas and population and gross domestic products, respectively. Physical geographic factors played weak roles in the evolution of the BGS. Meanwhile, rainwater safety and ecological liveability positively affected the BGS.