🤖 AI Summary
This study investigates the spatial organization of taxi pickup/drop-off hotspots at the urban microscale (90–110 m), addressing a critical gap in conventional urban structural analysis, which rarely examines centimeter-to-meter-level spatial units. Leveraging large-scale taxi trajectory data from Wuhan and Beijing, we identify a novel “hierarchical co-occurrence and suppression” spatial pattern—characterized by systematic coexistence or mutual exclusion between high-density hotspots and adjacent low-density ones. To formalize this insight, we propose an interpretable, adaptive k-nearest neighbors (k-NN) generative model that integrates radius-adaptive computation, density-based clustering, and quantitative spatial pattern analysis. The model successfully reproduces secondary hotspot distributions with high fidelity, significantly enhancing understanding of fine-grained transportation space structure. Our work establishes a new theoretical paradigm and methodological framework for microscale traffic planning and infrastructure deployment.
📝 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.