Abstract
Pick-up and drop-off events of taxi trajectory data contain rich information about residents’ travel activities and road traffic. Such data have been widely applied in urban hotspot detection in recent years. However, few studies have attempted to delimitate the urban hotspot scope using taxi trajectory data. On this basis, the current study firstly introduces a network-based spatiotemporal field (NSF) clustering approach to discover and identify hotspots. Our proposed method expands the notion from spatial to space–time dimension and from Euclidean to network space by comparing with traditional spatial clustering analyses. In addition, a concentration index of hotspot areas is presented to refine the surface of centredness to delimitate the hotspot scope further. This index supports the quantitative depiction of hotspot areas by generating two standard deviation isolines. In the case study, we analyze the spatiotemporal dynamic patterns of hotspots at different days and times of day using the NSF method. Meanwhile, we also validate the effectiveness of the proposed method in identifying hotspots to evaluate the delimitating results. Experimental results reveal that the proposed approach can not only help detect detailed microscale characteristics of urban hotspots but also identify high-concentration patterns of pick-up incidents in specific places.
Highlights
Urban hotspots refer to regions with frequent human mobility, heavy traffic flow and prosperous economic activities
We evaluate the effectiveness of our proposed network-based spatiotemporal field (NSF) method in delimitating urban hotspots
FrInamthewisosrekction, we propose a systemic methodology framework and detailed process (Figure 4) for identifying and delimitating urban hotspots based on taxi trajectory data
Summary
Urban hotspots refer to regions with frequent human mobility, heavy traffic flow and prosperous economic activities. The typical techniques for measuring the distribution of hotspot intensity are over a 2D planar space These approaches ignore many urban geographical phenomena associated with human activities that occur on or along the road network (e.g., points of interest, traffic crashes and street crime). A network-based spatiotemporal field (NSF) clustering approach is proposed to identify hotspots using the pick-up and drop-off events from GPS trajectories. A concentration index is presented to delimitate the urban hotspot on network-constrained centredness surface to capture the true ‘hotspot core’. This index enables the clear identification of high-intensity pick-up events based on the derivation of a key isoline model.
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