Revealing Patterns and Trends of Mass Mobility Through Spatial and Temporal Abstraction of Origin-Destination Movement Data
- Submitting institution
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City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 771
- Type
- D - Journal article
- DOI
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10.1109/TVCG.2016.2616404
- Title of journal
- IEEE Transactions on Visualization and Computer Graphics
- Article number
- -
- First page
- 2120
- Volume
- 23
- Issue
- 9
- ISSN
- 1077-2626
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
-
-
- Citation count
- 39
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in most important journal in its domain and presented at IEEE VIS (A CORE), Phoenix, 2017, the World leading visualization conference with a 25% acceptance rate. International collaboration between City and the Fraunhofer IAIS - supported by EU in projects VaVeL (688380), SoBigData (654024) and BigData4ATM (699260). Significant as provides actionable techniques for visualising temporally varying trajectories - a major challenge recognised in the Geographic Information Science & Technology (Robinson, 2017) – and applicable in the transport sector. Resulted in EU grant award (Track&Know, 780754). Cited in high-impact publications including MacEachren (2019) and Wei Chen’s state-of-the-art paper (Chen 2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
