Cycling GPS trajectories generated by dockless shared bike users offer valuable opportunities to investigate human travel behaviour and the cycling-related urban structure within cities. This chapter introduces an innovative concept known as ‘biking islands’ to highlight the potential insights that can be drawn from cycling trajectory data. A biking island refers to a distinct geographical area characterised by a high density of cycling activities, thereby reflecting zones that naturally attract cyclists. Using GPS-tracked trajectory data, this study identifies biking islands through the application of percolation theory, with Shanghai serving as a case study. The results reveal a hierarchical organisation of biking islands, where the size and distribution of these islands change according to varying thresholds. As the threshold increases, biking islands shrink and fragment into smaller ones. Notably, larger biking islands are predominantly found in Shanghai’s central urban area, while the Huangpu River emerges as a natural boundary, limiting seamless cycling movement across its banks. Furthermore, the formation and extent of biking islands are closely influenced by the surrounding land uses, suggesting a strong correlation between cycling intensity and urban functionality. The proposed concept and methodology not only enhance the understanding of cyclists’ travel behaviour and the spatial dynamics of cycling infrastructure but also hold practical implications for urban and transport planning. These insights can support the designation of non-motorised zones, the strategic placement of biking facilities, and the identification of critical road segments essential for improving the overall efficiency and connectivity of the cycling network.