Visualization of multi layered spatio-‐temporal 3D urban datasets
Keywords: urban data, geovisualisation, user study
Background, Context:
The understanding of complex spatio-‐temporal urban data is a critical challenge in urban planning as the stakeholders have to take into account numerous constraints related to various domains embedding sustainable, social and economical concerns.
To tackle this problem, geovisualization uses methods based on human perception and cognition to show the underlying meaning of spatio-‐temporal datasets. This PhD thesis proposes to explore visualization methods in various 3D environments (Desktop, VR, AR, SAR) to find the best solution for a given user task (exploration, decision) at a given
urban scale (building, street, district, etc.).
This PhD in information visualization takes place into an pluridisciplinary environment (computer science, geoinformation, humanities): the CRENAU team of the AAU lab (http://crenau.archi.fr/),which is a founding member of the IRSTV research institute (www.irstv.fr), and the IVC team of the IRCCyN lab (http://www.irccyn.ec-‐ nantes.fr/fr/equipes/ivc).
Research subject, work plan:
The work is about displaying spatio-‐temporal urban data in an effective way. Based on the user's profile, behavior and task, the most appropriate visualization type and data quality will be estimated. More precisely, this PhD thesis proposes to consider each data as a layer that will be processed according to the data type and the required levels of detail as well as the display context. The data model will be based on standards
currently used in GIS and/or BIM communities. User tests will be conducted to evaluate
the quality of proposed solutions. The research challenges include both visualization solution proposal and the construction of an evaluation framework for information visualization.
Contacts:
-‐ Vincent Tourre: vincent.tourre@ec-‐nantes.fr
-‐ Guillaume Moreau: guillaume.moreau@ec-‐nantes.fr
-‐ Patrick Le Callet: patrick.lecallet@univ-‐nantes.fr
References:
E. Bertini, A. Tatu and D. Keim. Quality metrics in high-‐dimensional data visualization: an overview and systematization. IEEE Transactions on Visualization and Computer Graphics, pages 2203–2212, 2011.
Biljecki F, Stoter J, Ledoux H, Zlatanova S, Çöltekin A. Applications of 3D City Models: State of the Art Review. ISPRS International Journal of Geo-‐Information. 2015; 4(4):2842-‐
2889.
R. Chang, G. Wessel, R. Kosara, E. Sauda and W. Ribarsky. Legible cities: Focus-‐dependent multi-‐resolution visualization of urban relationships. IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pages 1169–1175, 2007.
S. Haroz and D. Whitney. How capacity limits of attention influence information visualization effectiveness. IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 12, pages 2402–2410, 2012.
R. Maciejewski, S. Rudolph, R. Hafen, A. Abusalah, M. Yakout, M. Ouzzani and D. S. Ebert. A visual analytics approach to understanding spatiotemporal hotspots. IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 2, pages 205–220,
2010.
B. Pan, Y. Zhao, X. Guo, X. Chen, W. Chen and Q. Peng. Perception-‐motivated visualization for 3D city scenes. The Visual Computer, vol. 29, no. 4, pages 277–286, 2013.
M. Trapp, T. Glander, H. Buchholz and J. Döllner. 3D generalization lenses for interactive focus+context visualization of virtual city models. In Proceedings of 12th International Conference on Information Visualisation, IV’08, pages 356–361. IEEE, 2008.
A. Vande Moere, M. Tomitsch, C. Wimmer, B. Christoph and T. Grechenig. Evaluating the Effect of Style in Information Visualization. IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 12, pages 2739–2748, 2012.
Required skills:
MsC in computer science. Knowledge in visualization or HCI would be a plus.
To send:
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