LAUSR

The emergence of geographical ‘big data’ provides new opportunities for studying urban issues. This study uses geographical ‘big data’ on point of interest density (POID), degree of urban function mixing (MIX), location check-in density (CIQD), housing prices (HP), and population change (POPC) to measure the urban vitality of patches of new development that occurred in Chinese cities from 2005 to 2015. The study uses association rule analysis to explore the relationship between different urban growth patterns on urban vitality, and the results indicate that different forms of urban growth have different effects on urban vitality. Infilling is characterized by high values for point of interest density and location check-in density with low values for urban function mixing and mixed values for population change. Edge-expansion is associated with high values for population change and urban function mixtures. Outlying expansion is associated with several negative values for urban vitality, particularly variables related to interactions between people and the environment around them (CIQD). The results indicate that cities may utilize these different forms of urban growth patterns to achieve different goals; for example, infilling may be more effective for office-style development in areas with existing higher population density and urban function mixtures, and edge-expansion may be effective for rapidly absorbing large populations and hosting urban functions that require larger footprints. As such, Chinese cities currently undergoing early stages of development should pursue high-intensity edge-expansion development. To the best of the authors’ knowledge, this is the first attempt to study the relationship between urban expansion types and urban vitality through the use of ‘big data,’ and the results of this study can provide guidance on urban spatial development for government leaders and researchers in the future.