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Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
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ISPRS Int. J. Geo-Inf. 2019, 8(8), 344; https://doi.org/10.3390/ijgi8080344
Received: 16 June 2019 / Revised: 24 July 2019 / Accepted: 29 July 2019 / Published: 31 July 2019
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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. View Full-Text
Keywords: taxi trajectory; urban hotspot; network-based spatiotemporal field; space–time dynamic patterns; concentration index taxi trajectory; urban hotspot; network-based spatiotemporal field; space–time dynamic patterns; concentration index
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Xia, Z.; Li, H.; Chen, Y.; Liao, W. Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method. ISPRS Int. J. Geo-Inf. 2019, 8, 344.

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