Validating activity, time, and space diversity as essential components of urban vitality - Kang, Chaogui; Fan, Dongwan; Jiao, Hongzan (Environment and planning. B, Urban analytics and city science, 2021)
Urbanization’s rapid progress presents an urgent challenge for developing a predictive, quantitative theory of “the death and life of cities” (a.k.a. “the essential diversity conditions for the urban built environment”). Despite the importance of activity diversity (i.e. serving different primary functions), existing works ignored that time diversity (i.e. attracting people at different times of the day) and space diversity (i.e. attracting people from different districts) also play important roles in promoting urban life in large cities. With assistance of human mobility and crowdsourcing data, this article thoroughly validates whether activity, time, and space diversity are essential and inseparable components of urban vitality in the Wuhan, China context. To achieve the goal, point of interest (POI) data are utilized to quantitatively measure activity diversity, human mobility data are adopted for building quantitative metrics of time diversity and space diversity, and a detailed urban perception map is crowdsourced as ground truth data for establishing a regression model between urban diversity indicators and urban vitality. The resultant regression model succeeds to decouple the relationship between population concentration, activity diversity, time diversity, space diversity, and urban vitality. It confirms that activity diversity together with time diversity and space diversity has stronger association with urban vitality than any single diversity indicator. Our contributions are threefold: (a) we provided a comprehensive collection of metrics for measuring urban diversity, (b) we confirmed that activity, time, and space diversity are essential components of urban vitality, and (c) our methodology can be replicated at scale to understand urban vitality under various geographic, societal, and economic contexts due to easy accessibility of similar datasets.