Due to the large volume of recording, the complete spontaneity, and the flexible pick-up and drop-off locations, taxi data portrays a realistic and detailed picture of urban space use to a certain extent. The spatial arrangement of pick-up and drop-off hotspots reflects the organizational space, which has received attention in urban structure studies. Previous studies mainly explore the hotspots at a large scale by visual analysis or some simple indexes, where the 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. Using two taxi trajectory datasets in Wuhan and Beijing, China, this study quantitatively explores the spatial arrangement of fine-grained pick-up and drop-off local hotspots with different levels of popularity, where the sizes are adaptively set as 90m*90m in Wuhan and 105m*105m in Beijing according to the local hotspot identification method. Results show that popular hotspots tend to be surrounded by less popular hotspots, but the existence of less popular hotspots is inhibited in regions with a large number of popular hotspots. We use the terms hierarchical accompany and inhibiting patterns for these two spatial configurations. Finally, to uncover the underlying mechanism, a KNN-based model is proposed to reproduce the spatial distribution of other less popular hotspots according to the most popular ones. These findings help decision-makers construct reasonable urban minimum units for precise traffic and disease control, as well as plan a more humane spatial arrangement of points of interest.

翻译：由于记录数据量大、完全自发性以及上下车地点灵活，出租车数据在一定程度上能够真实且详细地描绘城市空间利用状况。上下车热点的空间排列反映了组织空间结构，这一直是城市结构研究的关注重点。以往研究主要通过视觉分析或简单指标在大尺度上探索热点，这些热点通常覆盖整个中央商务区、火车站或密集住宅区，半径可达数百米甚至数千米。然而，反映特定热门上下车地点的小尺度热点空间排列模式尚未得到足够关注。本研究利用中国武汉和北京两个出租车轨迹数据集，定量探索不同流行程度的细粒度局部上下车热点的空间排列，其中根据局部热点识别方法自适应设定热点尺寸为武汉90米×90米、北京105米×105米。结果表明：热门热点往往被较不热门的热点环绕，但在热门热点密集区域，较不热门热点的存在受到抑制。我们将这两种空间构型分别命名为层次伴随模式与层次抑制模式。最后，为揭示其潜在机制，本研究提出一种基于KNN的模型，根据最热门热点再现其他较不热门热点的空间分布。这些发现有助于决策者构建合理的城市最小单元，以精确实施交通与疾病控制，并规划更具人性化的兴趣点空间布局。