The visual analytics solution in  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  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.  too.
Ballatore et al.  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 . 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.
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.
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