This study examines the microclimate pattern and related spatial perception of urban green stormwater infrastructure (GSI) and the stormwater management landscape, using rain gardens as a case study. It investigates the relationship between different rain garden design factors, such as scale, depth, and planting design, and their effects on microclimate patterns and human spatial perception. Taking an area in Blacksburg, Virginia, as the study site, twelve rain garden design scenarios are generated by combining different design factors. The potential air temperature, relative humidity, and wind speed/direction are analyzed through computational simulation. Additionally, feelings of comfort, the visual beauty of the landscape, and the overall favorite are used as an evaluation index to investigate people’s perception of various rain garden design options. The study found that a multilayer and complex planting design can add more areas with moderate temperature and higher humidity. It also significantly improves people’s subjective perception of a rain garden. Furthermore, a larger scale rain garden can make people feel more comfortable and improve the visual beauty of the landscape, highlighting the importance of designing larger and recreational bioretention cells in GSI systems. Regarding depth, a relatively flatter rain garden with a complex planting design can bring stronger air flow and achieve better visual comfort and visual beauty. Overall, by examining the microclimate pattern and related perception of rain gardens, this study provides insight into better rain garden design strategies for the urban stormwater management landscape. It explores the potential of rain garden design in urban GSI and responds to climate change.

Conducting research on the spatial cognition of tourists in historical towns helps to balance cultural heritage protection and tourism development. However, the current tourist cognition research is not comprehensive enough in terms of data sources, time dimension, and spatial objects. This research takes Fengjing Ancient Town in Shanghai as an example, and through multi-source data analysis explores how tourists’ perception and cognition of the attractions changes, discusses the impacts of characteristic of spatial system and elements on perception, and then establishes a spatial cognition analysis framework involving time dimension, cognitive depth, and spatial type. On-site aerial photos, Sina Weibo check-in data, tourist memory maps, and photos from tourism websites were used to classify tourists’ spatial cognition through content analysis, theme classification, and GIS spatial analysis. This research finds that tourists have formed three cognitive levels in the travel process, from “initial spatial consciousness” to “place memory” then to “imagery perception”. Meanwhile, space is the most important object of tourists’ cognition, and it is also the carrier of other intangible cultures. In terms of spatial cognition and ancient town tourism, this research finds the tourists’ spatial cognition of Fengjing Ancient Town is related to the main river and main tourist routes that represent the image characteristics of the ancient town. This research shows that clear boundaries of tourism space, richer folk activities, and more sequential tourism routes could help tourists form a more systematic spatial cognition. Based on the findings, this research also establishes an analysis and application framework of tourists’ multilevel spatial cognition to provide optimization suggestions for development of tourism.

In China, traditional food is a significant element of culture that affects human behaviors. The point of interest (POI) of traditional food restaurants’ location and their volatile organic compounds (VOCs) emissions affect the urban ecology. Rather than examine potential urban development patch (PUDP) based on land use data, the perspective of this paper is to examine the PUDP, air quality, and food culture in urban ecology in Chengdu, China. Methods: First, the research identifies three types of PUDP models (open PUDP, landscape PUDP, and conflict PUDP) with the weighted overlay of land use data, then uses machine learning to examine the relationship between PUDP, POI of traditional food restaurant, and VOCs. Results: The study generates three types of PUDP which are open PUDP, landscape PUDP, and conflict PUDP. VOCs and POI of traditional restaurant have a strong correlation, and both have a significant negative correlation with open PUDP. However, the landscape PUDP and conflict PUDP do not show an obvious relationship with food POI and VOCs. Conclusion: The results indicate that the future urban ecology should consider restaurant location, VOCs from restaurants, and their relationship to urban land use data as they have a strong relationship.

The suburbs around Shanghai have a complex river network and a unique Chinese water-town culture. The riparian landscape in the rural Qingxi area has important regional, ecological, and social significance; it serves as an important part of the local bioclimate, but the existing studies on river vegetation did not pay enough attention to the riparian landscape in the countryside around the metropolis. The goal of this study was to examine a comprehensive evaluation model for the river plant landscape in the countryside surrounding a high-density metropolis such as Shanghai in the face of the national policy of rural revitalization and the low-carbon development problem, and to propose optimization strategies accordingly. Therefore, in this study, we selected 91 rivers in the Qingxi area and investigated their plant communities. According to the characteristics of the riparian landscape and its relationship with the river environment and local bioclimate, we classified the 91 riparian landscapes into four types of quadrats: natural landscape, residential recreation, roadside linear landscape, and agricultural landscape. In addition, based on the 13 indicator layers under the categories of ecological carrying capacity, landscape beauty, and social service, we calculated the comprehensive evaluation value (CEV) and comprehensive evaluation index (CEI) of 91 river quadrants using specific formulas to scientifically evaluate the riparian landscape in the rural Qingxi area of Shanghai. Finally, based on the existing problems summarized through data analysis, the researchers proposed five optimization directions: (1) increasing vegetation diversity, (2) choosing native and culturally representative species, (3) improving waterfront planting design, (4) achieving ecological riverbank construction, and (5) building greenway systems and recreational spaces. This study proposed an innovative evaluation model for the riparian vegetation landscape and tested its feasibility by site survey, which provided new visions for future rural landscape research.