As an important indicator of urban development capacity, vitality can be affected by the human perception of street views, which is a dynamic sensory process that can differ greatly according to different transportation modes, due to their different travel speeds, distances, and routes. However, few studies have evaluated how the dynamic spatial perceptions differ between different travel modes and how these differences can affect vitality differently, due to the limitation of city-scale quantitative data on the dynamic perception of urban scenes. To fill the gap, we propose a “dynamic through-movement perception” (DTMP) measure which integrates a streetscape quality evaluation model with a network-based movement potential model. We measure the streetscape qualities from Baidu street-view images (SVI) and compare the spatial perceptions of drivers and pedestrians in central Guangzhou, China. First, more than twenty visual elements were classified from SVIs to predict human perceptions collected from visual surveys. Second, the through-movement probability of driving and walking were calculated based on classic natural movement theory in space syntax and measured as the angular betweenness for the two travel modes. Third, we accumulate the multipliers of visual perception and through-movement probability of driving and walking as the DTMP for both modes. Lastly, the DTMPs of both modes were fitted into linear regression models to explain street vitality, which is measured using Baidu mobile phone check-in data, when other control variables such as functional density, functional diversity and amenity clustering reachability are accounted for. The results show that the dynamic perception of driving overall shows a stronger correlation with street vitality, while perceived richness is significantly positive in both travel modes. This study provides the first quantitative evidence to reveal how the movement probability of different travel modes can significantly influence people’s sense of place, while in turn increasing street vitality. Our results can explain how different types of street commerce (i.e., pedestrian-oriented, and auto-oriented) aggregate spontaneously due to the dynamic movement potential, which provides an important reference for urban planners and decision makers for improving street vitality when making urban revitalization policies.

The impacts of global climate change on food systems will be broad, complex, and profoundly affected by urban context. Food-related urbanism has been investigated for decades to explore how food access influences placemaking and urban forms. With global climate change, foodscapes within urban spaces are an important consideration in urban design and planning for food security and community health. The distribution of catering businesses (restaurants and cafés), one critical method of access to food, is highly associated with urban spaces because of their high impact on diet patterns, human physical activities, travel behaviors, and the use of public spaces. This research explores the spatial associations that exist between the distribution of catering businesses and the design and planning of urban spaces in London. This quantitative research includes three parts: (1) uses Open Street Map data and the GIS spatial analysis method to study the distribution of catering businesses; (2) uses the imagery segmentation method in machine learning to categorize urban spaces into open, landscape, and conflict spaces; and (3) establishes the association between the distribution of catering businesses and the categories of urban spaces through Spearman’s correlation and a linear regression model. The results indicate that the spatial distributions of catering businesses are highly correlated with urban spaces. Conflict and landscape spaces have a significant positive influence on the distribution of catering businesses, while open space has a significant negative influence. Based on the context of global climate change, this research contributes a quantitative urban design and planning approach to promote access to food increase food options and advocate active lifestyles.

In Hong Kong, the Hakka settlements are the home of indigenous people who have been involved in agriculture and fishing for over 200 years, which has a special place in Hong Kong's history. However, these settlements are gradually being abandoned as ghost towns due to rapid urbanisation, where the city is progressively constructing high-density habitats to accommodate the exponentially increased population since the 1950s. This challenges designers to rethink means of preserving urban cultural heritage, while engaging in continuous urban regeneration processes. This study investigates workflows to detect historical building styles in one of the most densely-populated cities in the world-Hong Kong-that further deployed in human-computer interfaces in the virtual reality (VR) environment as a collaborative and suggestive design decision-making tool and co-design engagement tool for style infilling on urban regeneration to maintain urban culture inheritance. The workflow aims to discuss the preservation of historical buildings in the context of urban regeneration, urban cultural inheritance through digital archiving, bottom-up community engagement and education in urban design. This study discusses the impact of emerging technologies on the making of communities in human-centric discourse, rethinking social power and the right to the city in participatory mechanisms.

In the rapid urbanization process, climate change has a huge impact on the urban thermal environment, and the urban heat island has attracted widespread attention from society. How to better detect, analyze, and evaluate the urban heat island effect has become a hot issue in current urban environmental research. However, the correlation analysis of heat island factors mostly adopts the conventional least square method, without considering the correlation of and the interaction between spatial elements. At the same time, the single analysis method makes it difficult to analyze environmental problems scientifically, which leads to great bias. Therefore, in this paper, the spatial autoregressive confusion model was used to analyze the satellite data of Beijing, and a preliminary temperature model of Beijing for all seasons was established. The regression results show that the surface temperature of Beijing has a strong spatial autocorrelation, and that the modified normalized difference water index and the normalized differential vegetation index have a strong negative effect on the land surface temperature. The prediction models established in this study can provide accurate and sustainable data support in the urbanization process and aid in the creation of a sustainable and effective urban environment.