Introduction and definition

The explosion in computing techniques led to the generation of a tremendous amount of data which are stored in the internet and processed in the IT infrastructures all over the world. Some examples of new technologies for data collections2 are: web logs; RFID; sensor networks; social networks; social data (due to the Social data revolution), Internet text and documents; Internet search indexing; call detail records; astronomy, atmospheric science, genomics, biogeochemical, biological; military surveillance; medical records; photography archives; video archives; large-scale eCommerce.

In managing this huge amount of data, when it comes to human-computer interaction there is a need to distil the most important information to be presented it in a humanly understandable and comprehensive way. Here it comes visualisation, which is a way to interpret and translate data from computer understandable formats to human ones by employing graphical models, charts, graphs and other images that are conventional for humans (Bederson and Shneiderman 2003). In a sense we can define visualisation as any technique for creating images, diagrams, or animations to communicate a message or an idea.

In contrast with visualisation traditionally seen as the output of the analytical process, visual analytics considers visualisation as a dynamic tool that aims at integrating the outstanding capabilities of humans in terms of visual information exploration and the enormous processing power of computers to form a powerful knowledge discovery environment. In this view visual analytics is useful for tackling the increasing amount of data available, and for using in the best way the information contained in the data itself. Moreover visual analytics aims at present the data in way suitable for informing the policy making process.

More in particular the interdisciplinary field of visual analytics aims at combining human perception and computing power in order to solve the information overload problem. In Thomas and Cook’ (2003) definition, visual analytics is “the science of analytical reasoning supported by interactive visual interfaces”. Precisely visual analytics is an iterative process that involves information gathering, data preprocessing, knowledge representation, interaction and decision making.

The characteristic of this field is that it entails the association of data-mining and text-mining technologies, used for preprocessing massive amounts of data, and information visualisation3, which is useful for disentangling important from trivial and useless information. In a certain way information visualisation becomes a tool in a semi-automated analytical process characterized by the cooperation between humans and computers, in which is the user who decides the direction of the analysis relating to a particular task, while the system works as an interaction tool. It is somehow difficult to distinguish among information visualisation, scientific visualization4 and visual analytics. In poor terms we can say that scientific visualisation deals with data having a natural geometric structure, while information visualization handles abstract data structures such as trees or graphs, and finally visual analytics deals properly with sense-making and reasoning. More in particular information visualization is mostly applied to data not belonging to scientific inquiry, e.g. graphical representations of data for business, government, news and social media.

Visualization work does not necessarily deal with an analysis task nor does it always use advanced data analysis algorithms. On the other hand visual analytics can be seen as an integral approach to decision-making, combining visualization, human factors and data analysis. It entails identifying the best algorithm for a given analysis task, to be integrated with the best automated analysis algorithms with appropriate visualization and interaction techniques.

Visualization and visual analytics should be considered in strict integration with other research areas, such as modelling and simulation5, social network analysis, participatory sensing, open linked data, visual computing.

The disciplines in the domain of visualization and visual analytics are: Human-Computer Interaction (HCI), Usability Engineering, Cognitive and Perceptual Science, Decision Science, Information Visualisation, Scientific Visualisation, Databases, Data Mining, Statistics, Knowledge Discovery, Data Management & Knowledge Representation, Presentation, Production and Dissemination, Statistics, Interaction, Geospatial Analytics, Graphics and Rendering, Cognition, Perception, and Interaction.

As far the visual analytics methodologies are concerned, in the CROSSOVER taxonomy we can identify the following: visualisation of a single, static, embedded data set; visualisation of multiple static data sets; visualisation of a single live data feed or updating data set; and finally visualisation of multiple data points, including live feeds or updates.

Why it matters in governance

Today’s governments face the challenge of understanding an increasingly complex and interdependent world, and the fast pace of change and increased instability in all the areas of regulation requires rapid decision making able to draw on the wider amount of available evidence in real-time. How can visual analytics help?

1. Generate high involvement of citizens in policy-making. One of the main application of visualization is in making sense of large datasets and identifying key variables and causal relationships in a non-technical way. Similarly, it enables non-technical users to make sense of data and interact with them. For instance, the GapMinder software helps to understand the main global demographic changes and raise awareness on the implications of sound health policies in developing countries. Visualization is a fundamental part of simulation tools as it helps exploring and understanding better the data under different scenarios (Keim 2011).
2. Understand the impact of policies: visualisation is instrumental in making evaluation of policy impact more effective. For instance, farmsubsidy.org helps understanding who are the main beneficiaries of the common agricultural policy by geo-referencing the single beneficiary.
3. Identify problems at an early stage, detect the “unknown unknown” and anticipate crisis: visual analytics are largely used in the intelligence community because they help exploiting the human capacity to detect unexpected patterns and connections between data. Thereby they help early detection of potential threats at an early stage. For instance, the VisAware project in the US provides situational awareness in situation of emergencies, helping the coordination of different resources involved in emergencies Livnat et al.(2005).

History and trends

Since from the beginning of human history, visualisation has been an effective way to communicate both abstract and concrete ideas. The appearance of digital visualisation led to the development of graphic hardware as well as to a wide array of technique used to visualize data in a number of ways (van Wijk. 2005). Often visualisation is needed to enable interaction (Thomas and Ahrweiler Eds. 2005) and to demonstrate how an operation works and which results are generated and derivable. In this view visualisation is massively used to provide the results of a simulation to users as well as to receive feedback and promote interaction.

Traditionally the first examples of visualization date back to the 19th century with the drawings6 by Charles Joseph Minard, who developed a format to show data tied to a timescale with a landscape background. In 1869 Minard applied its drawings to show the march of Napoleon’s army towards Moscow, starting with 422,000 and ending with 10,000 men, and Hannibal's crossing of the Alps, starting with 97,000 and ending with 6,000 men. The modern visualization field, making use of computer graphics, originated in the late 1980s with the studies on scientific visualization applied to fluid dynamics, volume visualization, molecular modelling, imaging remote-sensing data, and medical imaging (Rosenblum 1994). From scientific visualisation took place some more recent areas, such as information visualization, mobile visualization, location-aware computing and visual analytics. Information visualization arose when Robertson, Card and Mackinlay in the 1980s started to use the work of Bertin (1967)7 and Tufte (1983)8 in interactive computer applications. Later Shneiderman (1996) inter alia formalized the process of information visualization. Finally Ware (2004) emphasized the important of human perception in information visualization. In parallel with information visualization rose the field of data mining, aimed at discovering information hidden in massive amounts of data. The problem with the field, is that it aimed at substituting the human analysis with automatic computer operations, not supporting human perception with interactive visualization. Visualization of data soon showed its limitations due to the complexity of required analytical reasoning. In order to avoid that was developed the interdisciplinary field of visual analytics, which combines human perception abilities with computers’ processing power in order to tackle the information overload problem. Visual analytics can therefore be seen as the combination between data mining and text-mining technologies on one side, and information visualisation on the other side: “Visual analytics is more than only visualization. It can rather be seen as an integral approach combining visualization, human factors and data analysis” (Keim 2008). Future developments of visual analytics include the fields of enhanced collaboration capabilities, more intuitive interaction, support of non-computing devices, as well as the integration of quantitative and qualitative data. In fact visual analytics require particular technological advances, as traditional data mining tools are unsuitable for some necessary functionalities such as the algorithm speed required for iterative visualisation.

Inspiring cases

• GapMinder9
• US Labour Force Visualization 10
• State Cancer Profiles11
• Instant Atlas12

Policy applications of visualization and visual analytics tools

With regard to the governance and policy making context, some visualization tools can be applicable to a wide array of issues and situation (education, environment, public health, urban growth, national defense, etc. etc.). In the public context, visual analytics of public data is an exploding field, with particular relation to the open data movement, in order to monitor policy context and evaluate government policies. Most basic mash-up tools are available to visualize government.

Let us see some other examples :

Demographics visualizations, allowing stakeholders and decision makers to have a clear picture of the data and of their trends over time. Visualisation of demographic data make easier the design and evaluation of various policies, as there is no need to dig through acres of numbers. In fact advanced algorithms are able to create figures and illustrations easy to interpret. Typical examples are the aforementioned GapMinder13 (which embeds visualizations of various demographic data at global level), as well as Dynamic Choropleth Maps14, DataPlace15, Hive Group16, Name Voyager17, State Cancer Profiles18

Legal Arguments visualisation: text analysis, argumentation mappings and visualisation algorithms can be applied to legal documents in order to simplify legislation making it more accessible and comprehensible to the general public, or in order to visually represent corroborative evidence (e.g. the tools Carneades19 and Deflog20)

Discussion Arguments visualisation, making use of visualisation techniques for visualizing the flow of a discussion that include various arguments, in order to instantly get awareness of the topics discussed, as well as of the arguments and the support such arguments gain. In this view visualisation supports all interested stakeholders to understand the flow of a discussion, which is presented to them in a structured and interactive format, avoiding numerous discussion threads. Example of such visualisation tools include DebateGraph21, which is intensively used for building argumentation maps, as well as Araucaria22, Compendium23, Argublogging24 and Rationale25

Geovisualization, which is based on the provision of theory, tools and methods for visual analysis, synthesis, exploration and representation of geographical data and information in order to derive problem specific models and design task specific maps for incorporating geographical knowledge into planning and decision making. Some examples of such tools include ESTAT26, GeoViz Toolkit27, the geovisualization tools at the US National Cancer Institute28, some applications of InstantAtlas29

Advanced visualization applications used for security and national defense. In this fields, software advances are being led both on the military and on the corporate front. In fact business organizations also have urgent information visualization requirements that support their business intelligence and situational awareness capability, data mining and reporting requirements. In this view many of the software innovations are being targeted at financial and corporate requirements, but are also applicable to the defense domain due to common data mining and information visualization challenges. Examples of such tools are: DataMontage30, HoneyComb31, Oculus GeoTime32 and Starlight33. Other very interesting examples are Analyst’s Notebook34 is Visual Sentinel Visualizer35, adopted by intelligence agencies such as the CIA

Visualization applications adopted for financial markets monitoring and visualizing in real time. An example of such tool is SmartMoney36

Tools on the market

There is a massive quantity of visualization tools in the market, both freely available and enterprise level, critical for analysts and researchers, but also for common people, is now available online.

Freely available tools

First of all we have visualization websites useful for sharing and presenting data, provide clear context on important cultural, environmental, social and economic issue, build chart and share visualization and discoveries. Such examples are Data360, FlowingData, Hohli, IBM Many Eyes.

Then we have data visualization tools used for plotting data on maps, frameworks for creating charts, graphs and diagrams and tools to simplify the handling of data transforming them into spreadsheets, visual data mining and database exploration system, data visualization system for high-dimensional data, visualization framework for animating data. Some examples of those tools are: Data Wrangler, JavaScript InfoVis Toolkit, VisDB, Graphviz, IBM OpenDX, Gephi, GeoCommons, Miso Dataset, Polymaps, Processing, Protovis, Raphael, Tableau Public.

Enterprise-level software

Apart from free visualization tools, there are also many more advanced software which are used by firms in order to satisfy their information visualization requirements for business intelligence support and situational awareness capability, as well as data mining and reporting requirements. Other uses include enterprise knowledge visualization, linking knowledge to spatial data, online analytical processing and data mining, advanced social network analysis and visualization, data mining and interactive visualization, communication of location-based statistical data, on-line and batch environment for business graphics, information visualization tools for high dimensional non-linear data, visual analysis of data in spreadsheet format, analysis of high volumes of unstructured text, analysis of high-dimensional data in large complex data sets and of multivariate time-oriented data.

Some examples of such software are: CViz Cluster Visualization, IBM ILOG Visualization, Spotfire, Survey Visualizer, Infoscope, Inspire, Sentinel Visualizer, Grapheur 2.0, InstantAtlas™, Miner3D, VisuMap, Drillet, Eaagle, GraphInsight, Gsharp

Other examples of visualization software can be found in

http://groups.diigo.com/group/CROSSOVERproject/content/tag/visualization


Key Challenges and Gaps

New tools like the Word Tree (Wattenberg 2008), Treemap, Tag Cloud and Bubble Chart37 are available but lack interactivity. What is also missing is a better interaction of visualization approaches and analytical processes of text mining, as well as a better integration between new opportunities for data collection, such as open data and participatory sensing, policy modelling and visual analytics tools. Most applications related to visual analytics of public data remain at the level of visualisation only, with limited analytical functionalities. Geo-visualisation is a fast growing application area in the government context, but there is little integration with other related areas such as participatory sensing.

Visualisation tools are still largely design for analyst and are not accessible to non-experts. Intuitive interfaces and devices are needed to interact with data results through clear visualizations and meaningful representations. User acceptability is a challenge in this sense, and clear comparisons with previous systems to assess its adequacy and objective rules of thumbs to facilitate design decisions would be a great contribution to the community.

Scalability of visualisation in face of big data availability is a permanent challenge, since visualisation require additional performances with respect to traditional analytics in order to allow for real time interaction and reduce latency.

Finally, visualisation is largely a demand- and design-driven research area. In this sense one of the main challenge is to ensure the multidisciplinary collaboration of engineering, statistics, computer science and graphic design.

A relevant challenge of visualization and visual analytics is to adapt existing techniques to policy modelling:

RelaNet (Landesberger et al. 2008), which displays the network relations and thereby is able to show the connections and co-variances of the different opinions overtime

CirVis3D (Landesberger et al. 2009), which can visualize clustered opinion snippets as well as display time series in order to show the opinion trends over time

Current research

1. Close the loop of information selection, preparation and visualisation
2. Simultaneous multiple visualisation
3. Integration of visualisation with comments / wiki / blogs
4. Collaborative platform display
5. Interaction between visualisation and models
6. Mobile visual analytics tools
7. Geo-visualisation of government data
8. Integration with opinion mining and participatory sensing
9. Evaluation framework for visualisation effectiveness
10. Visualisation infrastructures for policy modelling issues

A list of EU funded projects in visual analytics include:

1. VisMaster-Visual Analytics: Mastering the Information Age38 
2. VisSense- Visual Analytic Representation of Large Datasets for Enhancing Network Security39
3. CUBIST- Combining and Uniting Business Intelligence and Semantic Technologies40 
4. WATTALIST – Modelling and Analysing Demand Response Systems41
5. CODE – Commercially empowered Linked Open Data Ecosystems in Research42
6. SemSeg-4D Space-Time Topology for Semantic Flow Segmentation43

Future research: long term and short term issues

Short-term research

1. Reusability of mashup tools (mashup is a web application which combines data from one or more sources into a single integrated tool or application) for visual analytics
2. Tighter integration between automatic computation and interactive visualisation, which consists in the availability of complex and powerful algorithms that allow for manipulating the data under analysis, transforming it in order to feed suitable visualizations
3. Bias identification and signalling in visualisation
4. Techniques and algorithms for creating effective visualization tools based on perceptual psychology (dealing with the process by which the physical energy received by sense organs forms the basis of perceptual experience), cognitive science (focusing on how information is represented, processed, and transformed) and graphical principles
5. Visualisations enabling interactive exploration techniques such as focus & context, in order for the viewers to be able to see the object of primary interest presented in full detail while at the same time getting a overview–impression of all the surrounding information — or context — available
6. Impact evaluation of visual analytics on policy choices

Long-term research

1. Learning adaptive algorithm for users intent. Note that learning/adaptive algorithms are defined as being capable to automatically change behaviour based on its execution context (data handled by the algorithm, configuration parameters of the runtime environment, resources used) in order to obtain optimal performances
2. Advanced visual analytics interfaces: visual interfaces in which neither the analytics nor the visualization needs to be advanced in itself but synergy between automation and visualization is in fact advanced
3. Intuitive and affordable visual analytics interface for citizens
4. Development of novel interaction algorithms incorporating machine recognition of the actual user intent and appropriate adaptation of main display parameters such as the level of detail, data selection, etc. by which the data is presented