Urban green spaces play a critical role in regulating air temperature, reducing air pollution and enhancing people’s well being. Yet, existing data and research on potential drivers of urban green space availability in Latin America are limited. Here, focusing on 371 large cities in 11 countries in Latin America, we described the total and per capita variability of urban green space, its spatial configuration and green urban parks across the categories of cities’ natural, built and socioeconomic environments. We tested the relative importance of geographic (climate) versus city-level built environment (population, population density, street intersection density) and socioeconomic (city gross domestic product per capita, unemployment, education) drivers in explaining urban green space availability. We found a high level of heterogeneity in green space quantity across cities and across categories of cities’ environments. Relative to other city factors, climate zone had the largest influence in explaining the quantity of green space, whereas education, street intersection density and population density were the most important drivers of urban park availability. The significance of climate for green space availability, combined with the inequitable quantity of green space, indicates that cities have differing capacities to implement nature-based solutions for heat mitigation and health promotion.

Nighttime light (NTL) is a critical indicator of urban vitality and development. Using NTL as a representation of urban vitality and development, the study explores how different fresh-made beverage shops, namely boba and coffee shops, proxy various facets of urban vitality and development in four megacities in China. Existing studies mostly discuss urban vitality as a broad concept and seldom investigate the diverse urban vitality and development represented by different indicators. This study selects Beijing, Shanghai, Guangzhou, and Shenzhen as case study regions and explores (1) their urban vitality pattern represented by NTL. (2) the heterogeneous spatial distribution of boba and coffee shops; (3) how boba and coffee shops represent urban vitality differently; and (4) how boba and coffee shops portray the economy and population growth aspect of urban development differently. We acquired NTL data from remote sensing images to measure urban vitality and development. Cross-sectionally, the majority of urban vitality and development represented by NTL concentrates in urban centers. The distribution of coffee shops assimilates the spatial pattern of urban vitality represented by NTL while boba shops have a greater spatial extent in metropolitan fringes. Longitudinally, from 2012 to 2020, the global and local bivariate Moran’s I analysis between NTL and beverage shops shows that the coffee shops capture urban vitality and development better than boba shops in Beijing, while the pattern is reversed in Guangzhou and Shenzhen. Examining the evolving spatial dynamics between beverage shops’ growth and urban development using bivariate Moran’s I and Getis–Ord Gi/Mann–Kendall emerging hot spot analysis, we found that the locations with the most intense economic growth have seen the most spatial expansion of coffee shops. In contrast, those with the fastest population growth have seen the greatest spatial development of boba businesses. These results indicate that coffee shops represent the economic aspect of urban vitality while boba shops emphasize the population growth aspects. By examining the dynamic spatio–temporal relationship between small beverage shops and urban vitality and development represented by NTL data, this study broadens the usage of remote sensing data in urban studies and expands on previous research and offers insights for urban planners and geographers to reference when choosing indicators of urban vitality and growth.

Identifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data, and population migration data based on wavelet transform, then identified the polycentric spatial structure of the GBA by carrying out cluster and outlier analysis, and evaluated the level of different urban centers byconducting geographical weighted regression analysis. Using data fusion, we identified 4579.81 km² of the urban poly-center area in the GBA, with an identification accuracy of 93.22%. Although the number and spatial extent of the identified urban poly-centers are consistent with the GBA development plan outline, the poly-center level evaluation results are inconsistent with the development plan, which shows there are great differences in actual development levels among different cities in the GBA. By identifying and grading the polycentric spatial structure of the GBA, this study accurately analyzed the current spatial distribution and could provide policy implications for the GBA’s future development and planning.

Blue spaces is associated with self-rated health (SRH), but little is known about the pathways underlying this association among Chinese urban elderly individuals. Based on neighborhood effect theory, this study examined the relationship between neighborhood blue spaces and SRH among elderly individuals using data from a questionnaire survey conducted in Guangzhou, remote sensing images, street views, and environmental information in the context of a Chinese megacity. In addition, multilevel linear model and mediating effect model empirical analyses were performed. Results showed that first, the SRH of the elderly was associated with individual- and neighborhood-level factors. Second, the multilevel mediation model revealed that multiple biopsychosocial pathways existed between neighborhood blue spaces and the SRH of the elderly, specifically, the blue space characteristics related to the SRH of the elderly via the mediating effect of stress. Third, owing to demographic characteristics and socioeconomic status, the stratified analyses also indicated a strong association between neighborhood blue spaces and SRH outcomes in the older and low-income groups. The mediating effect of stress in the age and income groups was also observed, and the mediation pathways and group differences were confirmed in the context of Chinese cities. This research enriches the empirical literature on blue spaces and elderly health from a multidisciplinary perspective and suggests the need for “healthy neighborhood” and “health-aging” planning in Chinese settings.

The rise of mass tourism has encouraged rapid economic growth; meanwhile, the eco-environmental system has come under increasing pressure. To achieve sustainable development, it is critical to deeply explore the relationship and evolution characteristics between three subsystems: tourism, the economy, and the eco-environment. This study aims to develop a more comprehensive indicator system for evaluating the coupling coordination degree (CCD) of the tourism–economy–environment (TEE) system using statistical data from nine cities in the Pearl River Delta (PRD) urban agglomeration from 2010 to 2019. We investigated the spatial–temporal evolution characteristics and driving forces of the TEE system in the PRD using the CCD model and the geo-detector model. The research results show the following: (1) The comprehensive benefits of the TEE system have increased steadily over the past 10 years, whereas the benefits of the eco-environment subsystem have fluctuated and been relatively unstable. (2) Spatially, in terms of tourism development, the eastern regions of the PRD are more developed than the western regions, and the regions with the greatest tourism benefits have gradually shifted to the northeastern regions of the PRD. Economic development presented an imbalanced but relatively stable spatial pattern. Guangzhou and Shenzhen have been the two most economically developed cities over the past 10 years. The eco-environment development has fluctuated over time, revealing a spatial pattern of cities with low environmental benefits in the center and cities with high eco-environmental benefits in the surrounding regions. (3) The PRD’s TEE system has become more integrated, moving from moderate disorder to a model of high-quality coordinated development, demonstrating a spatial pattern in which the cities of high development coordination are located near the Pearl River Estuary, and the coordination decreases the further away they are from the estuary. (4) The major driving factors of heterogeneous TEE coordination development include eco-environment protection, opening-up policies, education investment, technological innovation level, and the regional economic development level. The results are expected to effectively promote economic, tourism, and environmental improvement in the PRD, as well as to provide policy recommendations for coordinated TEE development in other similar urban agglomerations.