Exploring the association between street built environment and street vitality using deep learning methods

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Metro systems and urban development: Impacts and implications

In the context of China’s recent urbanization, the agglomeration and diffusion of the strip-city spatial network are gradually being reconstructed. The ways in which the street network structure affects the underlying logic of economic and social development is worthy of in-depth consideration. This study takes Lanzhou (a typical strip city in China) as a case study, using dynamic, geographic, big data and spatial syntactic-theory models to explore the influence of street network accessibility and structure on the spatial and temporal distribution of strip-city spatial vitality. We use Hotspot Analysis (Getis-Ord Gi*) to analyze the dispersal characteristics of street space vitality. In addition, the spatial and temporal heterogeneity characteristics and mechanism of the influence of street accessibility on spatial vitality are evaluated using the spatial Durbin model (SDM). The results show that: the temporal and spatial performance of urban vitality on weekdays and weekends conforms to people’s daily activities, offering similar spatial agglomeration and dispersion effects; accessibility and pedestrian-friendly streets have better urban spatial vitality clustering; street network integration significantly affects the reshaping of urban vitality, but there is apparent temporal heterogeneity in the degree of impact.

Teng-Fei Guo

Papers

Quantitative assessment method on urban vitality of metro-led underground space based on multi-source data: A case study of Shanghai Inner Ring area