MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 757
- Type
- D - Journal article
- DOI
-
10.1109/TVCG.2015.2468111
- Title of journal
- IEEE Transactions on Visualization and Computer Graphics
- Article number
- -
- First page
- 11
- Volume
- 22
- Issue
- 1
- ISSN
- 1077-2626
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2015
- 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
-
5
- Research group(s)
-
-
- Citation count
- 89
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Flow maps, derived from massive amounts of movement data are valuable for understanding human mobility and improving city transportation. In this output we employ an interdisciplinary approach that allows new patterns to mobility to be identified from complex, high-dimensional data. Funded by Deutsche Forschungsgemeinschaft (DFG) priority research program (273827070) and EU-H2020 SoBigData (the EU research infrastructure for social big data, 654024) the work contributed to SoBigData++ (871042), and the techniques are being used in transportation, migration, epidemiology, etc. The output is published in the most prestigious journal in DataViz and presented at IEEE VIS (A CORE).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
