Home > Geovisual Analytics > Paving the Way for Geovisual Analytics

Paving the Way for Geovisual Analytics

The visual analytics solution in [27] relies on the current state-of-the-art in both geovisual analytics as well as virtual reality technologies. Much work is yet to be done here where it becomes the most preferred interface for the users for geovisual analytics. The separation of model from the user interactions may not be possible always. Hence, new strategies must be discovered for incorporating immersive characteristics throughout the data analytic workflow.

5. Road Ahead: Research Challenges

 

Geovisual analytics is the most recent successor in the evolution of visualization of geographic data, which had humble beginnings in cartography. Geovisualizations and geovisual analytics have matured in terms of methodologies and implementations across different types, complexities, and sizes of datasets, with geo-referencing. However, despite around 2 to 5 decades4 of work in geovisualizations, there remain multiple persistent challenges [7] in the area, which stem from gaps in:

(a) better understanding of scope of domain, how it interacts with other domains;
(b) a systematic understanding of human factors, e.g. cognitive, social, geographical, etc.; and
(c) a set of guidelines which can templatize the visualizations to data types, so that the practitioner is guided in designing appropriate and helpful visualizations corresponding to the tasks.
The reason for the persistence in these challenges is due to the interdisciplinary nature of the area of geovisualizations. As is the challenging with visualization in itself, evaluation or assessment of geovisualizations need work in future. These challenges have been documented by Keim et al. [17] too.
Ballatore et al. [5] have proposed the method for information search using spatial approaches. They argue for cross-fertilization of ideas between cognitive psychology, computer science and GIS to enable the use of spatial approaches. This cross-pollination is exactly the need of the hour for geovisualizations too [7]. Additionally, at the conceptual level, we find geovisualizations to be synonymous to spatial approaches for information search, by virtue of the inherent spatial nature of data.

In summary, we have provided a brief introduction to geovisualizaton, and how it is increasingly being resolved using geovisual analytics. We have discussed a sampling of the research challenge we often see in the area. The new advent in technology and more accessibility to datasets show promise in more innovations in this field.

Acknowledgements

 

The author is thankful to the members of the Graphics-Visualization-Computing Laboratory (GVCL) at the International Institute of Information Technology Bangalore (IIITB), both current and alumni, for inspiring the need for this article and developing body of work in geovisual analytics. The author is grateful to her colleagues at IIIT-B, and financial support for research in geovisual analytics from the Government of India, namely the Early Research Career Award from SERB, and other sponsored projects from DST, and INCOIS, and non-governmental organizations, such as Foundation for Research in Health Systems (FRHS), Bangalore.

References

 

[1]  Andrienko, G., Andrienko, N., Jankowski, P., Keim, D., Kraak, M.J., MacEachren, A., Wrobel,
S.: Geovisual analytics for spatial decision support: Setting the research agenda. International
Journal of Geographical Information Science 21(8), 839–857 (2007)

[2]  Andrienko, N., Andrienko, G., Gatalsky, P.: Exploratory spatio-temporal visualization: an
analytical review. Journal of Visual Languages & Computing 14(6), 503–541 (2003)

[3] Bach, B., Dragicevic, P., Archambault, D., Hurter, C., Carpendale, S.: A descriptive framework
for temporal data visualizations based on generalized space-time cubes. In: Computer
Graphics Forum. vol. 36, pp. 36–61. Wiley Online Library (2017)

[4] Badam, S.K., Elmqvist, N., Fekete, J.D.: Steering the craft: Ui elements and visualizations
for supporting progressive visual analytics. In: Computer Graphics Forum. vol. 36, pp. 491–
502. Wiley Online Library (2017)

[5] Ballatore, A., Kuhn, W., Hegarty, M., Parsons, E.: Special issue introduction: Spatial approaches
to information search (2016)

[6] Blumenfeld, J.: Getting Ready for NISAR – and for Managing Big Data using the Commercial
Cloud (December 2017), https://earthdata.nasa.gov/getting-ready-for-nisar, last accessed
on December 13, 2017

[7] Çöltekin, A., Bleisch, S., Andrienko, G., Dykes, J.: Persistent challenges in geovisualization–
a community perspective. International Journal of Cartography pp. 1–25 (2017)
[8] Fekete, J.D., Primet, R.: Progressive analytics: A computation paradigm for exploratory data
analysis. arXiv preprint arXiv:1607.05162 (2016)

[9] Hägerstraand, T.: What about people in regional science? Papers in regional science 24(1),
7–24 (1970)

[10] Hahmann, S., Burghardt, D.: How much information is geospatially referenced? networks
and cognition. International Journal of Geographical Information Science 27(6), 1171–1189
(2013)

[11]  Hargrove, W.W., Hoffman, F.M.: Potential of multivariate quantitative methods for delineation
and visualization of ecoregions. Environmental management 34(1), S39–S60 (2004)

[12] Harrower, M., Fabrikant, S.: The role of map animation for geographic visualization. Geographic
visualization: concepts, tools and applications pp. 49–65 (2008)

[13] Heer, J., Bostock, M., Ogievetsky, V.: A tour through the visualization zoo. Queue 8(5), 20
(2010)

[14] Huang, X., Zhao, Y., Ma, C., Yang, J., Ye, X., Zhang, C.: Trajgraph: A graph-based visual
analytics approach to studying urban network centralities using taxi trajectory data. IEEE
transactions on visualization and computer graphics 22(1), 160–169 (2016)

[15] Javed, W., Elmqvist, N.: Exploring the design space of composite visualization. In: Pacific
Visualization Symposium (PacificVis), 2012 IEEE. pp. 1–8. IEEE (2012)

[16] Jin, H., Guo, D.: Understanding climate change patterns with multivariate geovisualization.
In: Data MiningWorkshops, 2009. ICDMW’09. IEEE International Conference on. pp. 217–
222. IEEE (2009)

[17] Keim, D., Andrienko, G., Fekete, J.D., Gorg, C., Kohlhammer, J., Melançon, G.: Visual
analytics: Definition, process, and challenges. Lecture notes in computer science 4950, 154–
176 (2008)

[18] Kramer, J.: Is abstraction the key to computing? Communications of the ACM 50(4), 36–42
(2007)

[19] Lanir, J., Bak, P., Kuflik, T.: Visualizing proximity-based spatiotemporal behavior of museum
visitors using tangram diagrams. In: Computer Graphics Forum. vol. 33, pp. 261–270.
Wiley Online Library (2014)

[20] Li, S., Dragicevic, S., Castro, F.A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B.,
Haworth, J., Stein, A., et al.: Geospatial big data handling theory and methods: A review and
research challenges. ISPRS Journal of Photogrammetry and Remote Sensing 115, 119–133
(2016)

[21] Liiv, I.: Seriation and matrix reordering methods: An historical overview. Statistical Analysis
and Data Mining: The ASA Data Science Journal 3(2), 70–91 (2010)

[22] Liu, S., Pu, J., Luo, Q., Qu, H., Ni, L.M., Krishnan, R.: Vait: A visual analytics system for
metropolitan transportation. IEEE Transactions on Intelligent Transportation Systems 14(4),
1586–1596 (2013)

[23] Lokuge, I., Ishizaki, S.: Geospace: An interactive visualization system for exploring complex
information spaces. In: Proceedings of the SIGCHI conference on Human factors in
computing systems. pp. 409–414. ACM Press/Addison-Wesley Publishing Co. (1995)

[24] Luo, W., MacEachren, A.M.: Geo-social visual analytics. Journal of spatial information science
2014(8), 27–66 (2014)

[25] MacEachren, A.M., Gahegan, M., Pike, W., Brewer, I., Cai, G., Lengerich, E., Hardistry, F.:
Geovisualization for knowledge construction and decision support. IEEE computer graphics
and applications 24(1), 13–17 (2004)

[26] MacEachren, A.M., Kraak, M.J.: Research challenges in geovisualization. Cartography and
geographic information science 28(1), 3–12 (2001)

[27] Moran, A., Gadepally, V., Hubbell, M., Kepner, J.: Improving big data visual analytics with
interactive virtual reality. In: High Performance Extreme Computing Conference (HPEC),
2015 IEEE. pp. 1–6. IEEE (2015)

[28] Mulder, J.D., VanWijk, J.J., Van Liere, R.: A survey of computational steering environments.
Future generation computer systems 15(1), 119–129 (1999)

[29] Parker, S.G., Johnson, C.R.: Scirun: a scientific programming environment for computational
steering. In: Proceedings of the 1995 ACM/IEEE conference on Supercomputing. p. 52.
ACM (1995)

[30] Rosen, P.A., Hensley, S., Shaffer, S., Veilleux, L., Chakraborty, M., Misra, T., Bhan, R., Sagi,
V.R., Satish, R.: The nasa-isro sar mission-an international space partnership for science and
societal benefit. In: Radar Conference (RadarCon), 2015 IEEE. pp. 1610–1613. IEEE (2015)

[31] Schulz, H.J., Angelini, M., Santucci, G., Schumann, H.: An enhanced visualization process
model for incremental visualization. IEEE transactions on visualization and computer graphics
22(7), 1830–1842 (2016)

[32] Sreevalsan-Nair, J., Agarwal, S., Vangimalla, R.R., Ramesh, S., Murthy, N.: Collaborative
design of visual analytic techniques for survey data for community-based research in public
health. In: Proceedings of 8th Workshop of Visual Analytics in Healthcare (VAHC 2017).
pp. 1–2 (2017)

[33] Stolper, C.D., Perer, A., Gotz, D.: Progressive visual analytics: User-driven visual exploration
of in-progress analytics. IEEE Transactions on Visualization and Computer Graphics
20(12), 1653–1662 (2014)

[34] Tateosian, L., Amindarbari, R., Healey, C., Kosik, P., Enns, J.: The utility of beautiful visualizations.
In: Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings.
vol. 17, p. 18 (2017)

[35] Van Gog, T., Paas, F., Marcus, N., Ayres, P., Sweller, J.: The mirror neuron system and observational
learning: Implications for the effectiveness of dynamic visualizations. Educational
Psychology Review 21(1), 21–30 (2009)

[36] Zhang, Y., Luo, W., Mack, E.A., Maciejewski, R.: Visualizing the impact of geographical
variations on multivariate clustering. In: Computer Graphics Forum. vol. 35, pp. 101–110.
Wiley Online Library (2016)

Pages ( 8 of 8 ): « Previous1 ... 67 8