Studying the spatiotemporal pattern of urban leisure activities helps us to understand the development and utilization of urban public space, people’s quality of life, and the happiness index. It has outstanding value for improving rational resource allocation, stimulating urban vitality, and promoting sustainable urban development. This study aims at discovering the spatiotemporal distribution patterns and people’s behavioral preferences of urban leisure activities using quantitative technology merging ubiquitous sensing big data. On the basis of modeling individual activity traces using mobile signaling data (MSD), we developed a space-time constrained dasymetric interpolation method to refine the urban leisure activity spatiotemporal distribution. We conducted an empirical study in Nanjing, China. The results indicate that leisure plays an essential role in daily human life, both on weekdays and weekends. Significant differences exist in spatiotemporal and type selection in urban leisure. The weekend afternoon is the breakout period of leisure, and entertainment is the most popular leisure activity. Furthermore, the correlation between leisure resource allocation and leisure activity participation was argued. Our findings confirm that data-driven approaches would be a promising method for analyzing human behavior patterns; therefore, assisting in land planning decisions and promoting social justice and sustainability.
Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China
