Visualizing the Spatiotemporal Characteristics of Dockless Bike Sharing Usage in Shenzhen, China

A comprehensive understanding of the spatiotemporal characteristics and patterns of dockless bike sharing usage is crucial in developing bike management and scheduling strategies. Recently, bike sharing-related topics have become a popular research subject. Existing studies have mainly analyzed the temporal and spatial characteristics of dockless bike sharing usage separately and have not explored how temporal patterns vary for different spatial units, even though this information is key to developing a straightforward profile of bike usage and implementing spatiotemporal scheduling strategies. To address this research gap, the space–time cube model and an emerging hot spot analysis were integrated into this study to identify the spatiotemporal patterns and hot/cold spot trends of dockless bike sharing usage in Shenzhen, China. The main goal of this study is to understand and visualize the spatiotemporal characteristics and patterns of dockless bike sharing usage with over 6.21 million GPS data processed, and to provide an analysis with integrated application of the space–time cube model and emerging hot spot analysis. We visualized the usage behavior characteristics, including riding distance, duration, and frequency, explored the spatiotemporal heterogeneity of riding origins and destinations, and identified spatiotemporal hot/cold spots for scheduling strategies. These results provide a valuable guide for developing bike spatiotemporal scheduling strategies.