Urban density

Big data

Engineering

Urban planning

Urban studies

Dynamics (music)

Cartography

Pedagogy

Telecommunications

Economics

Data mining

Civil engineering

Computer science

Economic geography

Regional science

Geography

Mobile phone

Sociology

Data science

Economic growth

Computers Environment and Urban Systems

Understanding urban vibrancy aids policy-making to foster urban space and therefore has long been a goal of urban studies. Recently, the emerging urban big data and urban analytic methods have enabled us to portray citywide vibrancy. From the social sensing perspective, this study presents a comprehensive and comparative framework to cross-validate urban vibrancy and uncover associated spatial effects. Spatial patterns of urban vibrancy indicated by multisource urban sensing data (points-of-interest, social media check-ins, and mobile phone records) were investigated. A comprehensive urban vibrancy metric was formed by adaptively weighting these metrics. The association between urban vibrancy and demographic, economic, and built environmental factors was revealed with global regression models and local regression models. An empirical experiment was conducted in Shenzhen. The results demonstrate that four urban vibrancy metrics are all higher in the special economic zone (SEZ) and lower in non-SEZs but with different degrees of spatial aggregation. The influences of employment and road density on all vibrancy metrics are significant and positive. However, the effects of metro stations, land use mix, building footprints, and distance to district center depend on the vibrancy indicator and location. These findings unravel the commonalities and differences in urban vibrancy metrics derived from multisource urban big data and the hidden spatial dynamics of the influences of associated factors. They further suggest that urban policies should be proposed to foster vibrancy in Shenzhen therefore benefit social wellbeing and urban development in the long term. They also provide valuable insights into the reliability of urban big data-driven urban studies.

Portraying the spatial dynamics of urban vibrancy using multisource urban big data