The application of location-based social media big data in urban contexts offers new and alternative strategies for understanding city liveliness in developing countries where traditional census data are poor. This paper demonstrates how the spatial-temporal distribution of China's Tencent social media usage intensities can be effectively used as a proxy for modelling the geographic patterns of human activity at fine scales. Our results suggest that the spatially-temporally contextualized nature of human activity is dependent upon land use mixing characteristics. With billions of social media data being collected in the virtual world, findings of this study suggest that land use policies to delineating the density, orderly or disorderly geographic patterns of human activity are important for city liveliness.
- Big data
- Human activity
- Land use
ASJC Scopus subject areas
- Sociology and Political Science
- Urban Studies
- Tourism, Leisure and Hospitality Management