Hierarchical accompanying and inhibiting patterns on the spatial arrangement of taxis' local hotspots (2310.11806v4)

Abstract: The spatial arrangement of taxi hotspots indicates their inherent distribution relationships, reflecting their spatial organization structure, and has received attention in urban studies. Previous studies have primarily explored large-scale hotspots through visual analysis or simple indices, which typically spans hundreds or even thousands of meters. However, the spatial arrangement patterns of small-scale hotspots representing specific popular pick-up and drop-off locations have been largely overlooked. In this study, we quantitatively examine the spatial arrangement of local hotspots in Wuhan and Beijing, China, using taxi trajectory data. Local hotspots are small-scale hotspots with the highest density near the center. Their optimal radius is adaptively calculated based on the data, which is 90 m * 90 m and 110 m * 110 m in Wuhan and Beijing, respectively. Popular hotspots are typically surrounded by less popular ones, although regions with many popular hotspots inhibit the presence of less popular ones. These configurations are termed as hierarchical accompanying and inhibiting patterns. Finally, inspired by both patterns, a KNN-based model is developed to describe these relationships and successfully reproduce the spatial distribution of less popular hotspots based on the most popular ones. These insights enhance our understanding of local urban structures and support urban planning.