Measurement Method and Influencing Mechanism of Urban Subdistrict Vitality in Shanghai Based on Multisource Data

Abstract

Most previous studies on urban vitality focused on the analysis and evaluation of the overall vitality of urban agglomerations or single cities, while there are few related studies at the micro scale, such as subdistricts and even blocks. Based on multisource data and using the kernel density analysis and entropy methods, the economic vitality, social vitality, cultural vitality, ecological vitality and comprehensive vitality of each subdistrict in Shanghai were measured. Additionally, correlation analysis, the ordinary least squares (OLS) regression model, the spatial lag model (SLM) and the spatial error model (SEM) were used for fitting analysis to reveal the influencing mechanism of urban subdistrict vitality. The results showed that (1) the spatial distribution of economic vitality and social vitality in Shanghai showed the spatial characteristics of radiating outward from the center, and the types of social activity location corresponding to different levels of hotspot areas are different. Cultural vitality showed the spatial distribution characteristics of “gathering in the centre and dispersing around, with Puxi higher than Pudong”, but the cultural vitality values of different subdistricts vary greatly. Ecological vitality showed an increasing trend from the center to the surrounding areas. (2) The overall urban vitality of Shanghai also showed a decreasing circular structure from the center to the periphery. (3) Among the three regression models, i.e., the OLS regression model, SLM and SEM, the model with the best explanation of urban vitality is the SLM, which had an R2 of 0.6984, indicating that it can explain 69.84% of the spatial distribution pattern of urban vitality. (4) The factors that have significant effects on urban vitality are functional mix, metro station accessibility, metro station density, bus station density and intersection density, and all of them are positively correlated. The order of the strength of the effects is bus station density > metro station density > intersection density > metro station accessibility > functional mix.