Visual analytics is often used in fields where large volumes of heterogeneous and unstructured information need to be processed and analyzed. This article provides an overview of possible implementation areas.
Space and time
The analysis of spatial, temporal, and spatio-temporal data is a challenging task and requires the use of advanced tools. With recent progress in technology, data related to space and time has become an important data type in visual analytics and discovering spatial and temporal relationships and patterns within the data is now a major area of interest for many analysts.[1] For instance, the availability of GPS devices makes is possible to record and analyze the movement of people and objects at a large scale with respect to time.[2] The resulting knowledge about movement behaviors and mobility patterns can then be used to support processes such as allocation of facilities, design of transportation systems, and expansion of telecommunication networks.
Physics and astronomy
The fields of physics and astronomy also benefit from the application of visual analytics. Especially in the field of astrophysics continuous streams of large volumes of unstructured information have to be stored and analyzed. The large amount of data, that also includes a great deal of noise, exceeds the human’s ability to process it. The visual analytics approach allows astronomers to separate relevant data from noise, discover phenomena, and generate useful knowledge about the universe.[3]
Biology and medicine
Visual analytics is applied in biology and medicine as well. It allows the analysis of medical data produced during diagnostic tests such as computer tomography. The field of bio-informatics also uses visual analytics techniques to make sense of biological data. Other application areas are proteomics (study of proteins), metabolomics (study of chemical processes), and combinatorial chemistry (methods that make it possible to prepare a large number of compounds in a single process). Such a vast amount of data often cannot be analyzed using brute-force computation, but visual analytics techniques help recognizing important areas in the data and excluding irrelevant information.[4]
Economics and business
Economics and business are further application areas for visual analytics. For instance, the financial market with its different financial instruments (stocks, bonds, futures, etc.) generates a very large amount of data coming from various sources. It is important to analysts to collect and examine the data under different perspectives in order to understand the market situation and identify trends. Other applications in the area include fraud detection, analysis of consumer data, and analysis of social data.[5] Furthermore, visual analytics techniques are also advantageous for insurance agents who need to understand and consider the impact of several factors (e.g. health trends, crime rates, weather forecast, etc.) in their analyses.[6]
Security and emergency management
Visual analytics was initially focused on defense and prevention of terrorist attacks and nowadays it is still used in the fields of security and emergency management. In the domain of security, information is collected from different sources and incidents are analyzed so that dependencies and anomalies can be found.[3] Moreover, analysts are able to monitor information streams in real time and react immediately when a dangerous situation is detected.[2] With regard to emergency management, visual analytics helps determining the state of the emergency and identifying appropriate actions such as evacuation of the population. Possible scenarios include natural catastrophes, epidemics, pollution, etc.[3]
Climate and weather
Another application area is the monitoring of climate and weather changes. Visual analytics techniques support the analysis of massive amounts of data collected by satellites and sensors around the world and help discovering climate trends. Besides predicting the weather, visual analytics applications can also be used to track the global warming and ice melting at the poles or to issue hurricane and tsunami warning.[5]
Text and news
An interesting application field is the text and news analysis. Large amounts of information can be found in books, newspapers, reports, social media sites, etc. However, it often remains unexplored because of its volume.[1] Visual analytics approaches can be used to make sense of such data. For instance, news streams can be analyzed in order to detect emerging and disappearing stories. Furthermore, companies can collect textual information from blogs, forums, and other websites[7] and use visual analytics techniques to understand the public opinion about their products and services or to monitor the progress of their competitors.
All of these application areas need software solutions for visual analytics that have to be adapted to the specific needs and requirements of the respective field. Currently, many researchers and software developers focus exclusively on selected customer segments and develop tools that are suited only to a particular purpose. Nevertheless, it would be more beneficial, if they use synergy effects to develop software solutions applicable to multiple areas.[2]
[1] Sun, G.-D., Wu, Y.-C., Liang, R.-H. and Liu, S.-X. (2013) ‘A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges’, Journal of Computer Science and Technology, 28(5), pp. 852–867.
[2] Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J. and Melancon, G. (2008) ‘Visual Analytics: Definition, Process, and Challenges’, in Kerren, A., Stasko, J.T., Fekete, J.-D., and North, C. (eds.) Information Visualization. Berlin, Heidelberg: Springer-Verlag, pp. 154–175.
[3] Keim, D., Mansmann, F., Schneidewind, J., Thomas, J. and Ziegler, H. (2008) ‘Visual Analytics: Scope and Challenges’, in Simoff, S., Böhlen, M., and Mazeika, A. (eds.) Visual Data Mining. Berlin, Heidelberg: Springer, pp. 76–90.
[4] Keim, D., Mansmann, F., Stoffel, A. and Ziegler, H. (2009) ‘Visual Analytics’, in Özsu, M.T. and Liu, L. (eds.) Encyclopedia of Database Systems. New York: Springer US, pp. 3341–3346.
[5] Keim, D., Kohlhammer, J., Ellis, G. and Mansmann, F. (2010) Mastering the Information Age: Solving Problems with Visual Analytics. Goslar: Eurographics Association.
[6] Thomas, J.J. and Kielman, J. (2009) ‘Challenges for visual analytics’, Information Visualization, 8(4), pp. 309–314.
[7] Davey, J., Mansmann, F., Kohlhammer, J. and Keim, D. (2012) ‘Visual Analytics: Towards Intelligent Interactive Internet and Security Solutions’, Lecture Notes in Computer Science, 7281, pp. 93–104.