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A Two-Phase Clustering Approach for Urban Hotspot Detection With Spatiotemporal and Network Constraints
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2021-03-23 , DOI: 10.1109/jstars.2021.3068308
Feng Li , Wenzhong Shi , Hua Zhang

Urban hotspots are regions with intensive passenger flow, sound infrastructure, and thriving business during a certain period of time, which mirror the travel behavior of residents. Taxi trajectory is one of the important data sources for urban hotspot detection. Unfortunately, it should be pointed out that quite a few of the relevant studies have ignored the temporal dynamics or network-constrained characteristics of urban hotspots, making the detecting results less reasonable and reliable. In this study, a two-phase clustering approach is proposed to detect urban hotspot with taxi trajectory. Concretely, in the first phase, spatiotemporal hierarchical density-based spatial clustering of applications with noise is utilized to cluster the trajectory points with spatial and temporal attributes, which is essential for understanding the evolution of urban hotspots over time. In the second phase, the idea of region growing is introduced to further filter noise, in which the spatial similarity between data points is measured by the route distance, considering that the trajectory data are constrained by the road network. A case study is carried out by the proposed method. Meanwhile, in combination with the Luojia1-01 night-time light remote sensing data and POI data, the reliability of the clustering results is verified and the semantic meaning of the discovered clusters is enriched. Furthermore, not only the spatiotemporal distribution but also the trip lengths and directions of the detected hotspots are explored. These findings can serve as a scientific basis for policymakers in traffic control, public facilities planning, as well as location-based service.

中文翻译:

时空和网络约束的城市热点两阶段聚类方法

城市热点是指在一定时期内客流密集,基础设施完善,业务蓬勃发展的地区,这反映了居民的出行行为。出租车轨迹是城市热点检测的重要数据来源之一。不幸的是,应该指出的是,许多相关研究都忽略了城市热点的时间动态或网络约束特征,从而使检测结果不太合理和可靠。在这项研究中,提出了一种两阶段聚类方法来检测具有出租车轨迹的城市热点。具体而言,在第一阶段,利用时空分层基于密度的应用程序对噪声进行空间聚类,以将轨迹点与空间和时间属性进行聚类,这对于了解城市热点随时间的演变必不可少。在第二阶段,引入区域增长的思想来进一步滤除噪声,其中考虑路线数据受道路网络约束,通过路线距离来测量数据点之间的空间相似性。通过提出的方法进行了案例研究。同时,结合罗家1-01夜间光遥感数据和POI数据,验证了聚类结果的可靠性,丰富了所发现聚类的语义。此外,不仅探索时空分布,而且还探索探测到的热点的行程长度和方向。这些发现可为决策者制定交通控制,公共设施规划,
更新日期:2021-04-16
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