Green spaces, particularly urban parks, provide essential environmental, aesthetic, and recreational benefits to human health, well-being, and happiness. However, traditional forms of investigating people’s perceptions of urban parks, such as questionnaires and interviews, are often time- and resource-intensive and do not always yield results that are transferable across sites. In this study, spatially explicit geolocational information (Sina Weibo check-in data) was utilized to analyze expressions of happiness and well-being in urban parks in Shanghai, China. The results showed significant differences in reported happiness inside and outside urban parks in Shanghai over a 6-month period. Accessibility, naturalness factors, and the frequency of park visits were positively associated with happiness. There existed both commonalities and disparities in the results between residents and non-residents. These findings can provide decision makers and urban planners with a comprehensive and timely overview of urban park use so they can accurately identify park needs and improvements.

Urban parks can be critical components of city landscapes. In the wake of COVID-19, understanding the role of urban parks in helping to elicit positive sentiment and improve the overall well-being of visitors has gained new traction in the literature. This research distinctively investigates whether the COVID-19 pandemic altered preferences regarding urban parks and identifies the key landscape attributes and environmental factors that influenced positive visitor sentiment, thereby addressing a critical gap in understanding the evolving dynamics of urban green spaces in the post-pandemic era. We use a mixed methods approach that includes natural language processing techniques to analyze crowd sourced data across more than 100 urban parks in Shanghai, China. Not surprisingly, our results highlight a post-pandemic rise in preferences for neighborhood parks and features typically associated with neighborhood parks, such as walking accessibility and surrounding residential densities. In addition, we found six park features, the presence of grasslands, water bodies, walking paths, and proximity to shopping facilities, significantly impacted the ways in which people perceived their park experience. These insights can guide urban park planning, design, and management in our evolving post-pandemic landscape to help ensure that urban parks continue to serve as essential urban spaces.

This study presents a refined approach to spatially identify carbon sequestration assets, crucial for effective climate action planning in Illinois. By integrating landscape analytical methods with species-specific carbon assessment techniques, we deliver a nuanced evaluation of forest area sequestration potential. Our methodology employs a combination of landscape imagery, deep learning analytics, Kriging interpolation, and i-Tree Planting tools to process forest sample data. The results reveal a spatial variability in sequestration capacities, highlighting significant carbon sinks in southern Illinois. This region, known for its historical woodland richness, showcases the distinct carbon sequestration abilities of various tree species. Findings emphasize the role of biodiversity in the carbon cycle and provide actionable insights for forest management and carbon neutral strategies. This study demonstrates the utility of advanced spatial analysis in environmental research, underscoring its potential to enhance accuracy in ecological quantification and conservation efforts.