  The spatial arrangement of taxi hotspots indicates their inherent
distribution relationships, reflecting spatial organization structure and has
received attention in urban studies. Previous studies mainly explore
large-scale hotspots by visual analysis or simple indexes, where hotspots
usually cover the entire central business district, train stations, or dense
residential areas, reaching a radius of hundreds or even thousands of meters.
However, the spatial arrangement patterns of small-scale hotspots, reflecting
the specific popular pick-up and drop-off locations, have not received much
attention. This study quantitatively examines the spatial arrangement of
fine-grained local hotspots in Wuhan and Beijing, China, using taxi trajectory
data. Hotspots are adaptatively identified with sizes of 90m*90m in Wuhan and
105m*105m in Beijing according to identification method. Findings show popular
hotspots are typically surrounded by less popular ones, though regions with
many popular hotspots inhibit the presence of less popular ones. We term these
configurations as hierarchical accompany and inhibiting patterns. Finally,
inspired by both patterns, a KNN-based model is developed to describe these
relationships, successfully reproducing the spatial distribution of less
popular hotspots based on the most popular ones. These insights enhance
understanding of local urban structures and support urban planning.
