Research on the Influence Mechanism of Street Vitality in Mountainous Cities Based on a Bayesian Network: A Case Study of the Main Urban Area of Chongqing

Abstract

As the main spatial carrier for people’s social activities, street space occupies an important position in the urban space. However, under the direction of traffic-driven urban planning, the social function of street space has been neglected, resulting in the gradual loss of vitality. In mountainous cities with rugged terrain, the factors influencing the vitality of streets may be different compared to those in plain areas. In order to explore the influence mechanism of street vitality in mountainous cities, a new quantitative research method based on the new data environment and a Bayesian network is proposed. In this study, Python and GIS are used to obtain spatial data of streets, and Bayesian networks are used to construct street vitality models to identify important influencing factors and causal relationships between influencing factors. The results demonstrate strong causal dependencies between the factors influencing street vitality in mountainous cities. The mechanism of influence of street vitality revolves around functionality and street texture in terms of its own environment and external environment, respectively. The combination of factor group with functional density as the root node achieved the maximum probability of high vitality of the street. The results of this study have implications for community or urban planners with respect to urban regeneration and street vitality promotion.