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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2020.2992200
|2 doi
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|a pubmed24n1032.xml
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Shi, Lei
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a UrbanMotion
|b Visual Analysis of Metropolitan-Scale Sparse Trajectories
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| 264 |
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|c 2021
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|a Text
|b txt
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|a ƒaComputermedien
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|a Date Completed 29.09.2021
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|a Date Revised 29.09.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Visualizing massive scale human movement in cities plays an important role in solving many of the problems that modern cities face (e.g., traffic optimization, business site configuration). In this article, we study a big mobile location dataset that covers millions of city residents, but is temporally sparse on the trajectory of individual user. Mapping sparse trajectories to illustrate population movement poses several challenges from both analysis and visualization perspectives. In the literature, there are a few techniques designed for sparse trajectory visualization; yet they do not consider trajectories collected from mobile apps that possess long-tailed sparsity with record intervals as long as hours. This article introduces UrbanMotion, a visual analytics system that extends the original wind map design by supporting map-matched local movements, multi-directional population flows, and population distributions. Effective methods are proposed to extract and aggregate population movements from dense parts of the trajectories leveraging their long-tailed sparsity. Both characteristic and anomalous patterns are discovered and visualized. We conducted three case studies, one comparative experiment, and collected expert feedback in the application domains of commuting analysis, event detection, and business site configuration. The study result demonstrates the significance and effectiveness of our system in helping to complete key analytics tasks for urban users
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Huang, Congcong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Meijun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Jia
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jiang, Tao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tan, Zhihao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hu, Yifan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Xiatian
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 27(2021), 10 vom: 06. Okt., Seite 3881-3899
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|x 1941-0506
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|g volume:27
|g year:2021
|g number:10
|g day:06
|g month:10
|g pages:3881-3899
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|u http://dx.doi.org/10.1109/TVCG.2020.2992200
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