21 Visualization Definition and History of Visualization

T. Raghuveera

Visualization is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations. Visualization offers a method for seeing the unseen. We start with an overview of Visualization Definition and History of Visualization in this module. The learning objectives for this module are to explore the following:

Learning Objectives:

• To  introduce the concepts of Information Visualization
• To learn visualization pipeline
• To learn about the history of Visualization
• To know about the applications of Visualization

1.1 Concepts of Information Visualization

1.1.1 Definition

Visualization: The use of computer-supported, interactive, visual representations of data to amplify cognition. Transformation of data or information into pictures can be done using visualization techniques. It is a tool for learning and understanding.

Information Visualization: The use of computer-supported, interactive visual representations of abstract data to amplify cognition.

1.2 Visualization Pipeline

A visualization system is not just a system to create an image of the data but can be used to manipulate the data to create different types of images. A model of a visualization system should link the system with the model of scientific investigation discussed above. Visualization can help form the link between hypothesis and experiment and between insight and revised hypothesis.

1.2.1 Basic Visualization Models

A Basic visualization model is shown below in fig 1.1. It shows how the data is transferred into visualization.

Figure 1.1 Basic Visualization Method

The visualization pipeline describes the way from data acquisition to picture generation. Input data can be volume information (from MRI-scanners, computer tomographs) or geometric objects (CSG-, triangle data from CAD-systems) and is dragged along the pipeline. Every step depends on the result of the previous one. The visualization process takes as long as the sum of every single step. Fig 1.2 shows Basic visualization of visual Mappings. Visualized data often has dubious origins. One way to define data lineage is by describing the uncertainty.

The visualization pipeline is the computational process of converting information into a visual form that users can interact with. Fig 1.3 shows the process of visualization pipeline

   1.2.2 Visualization Flavours

The three broad classification of visualization are,

1. Scientific Visualization – User Interfaces, data representation/processing Algorithms, Visual Representations
2. Data Visualization – Include financial data and statistical methods.
3. Information Visualization – Abstract Data: WWW documents, file structures, arbitrary relationships

1.3 History of Visualization

1.3.1 History 1

The first visualizations may have taken the form of drawings in sand or scratched on rock and it is possible that the famous Palaeolithic cave paintings in Lascaux, southern France, may have functioned as both hunting guides and directions to the spirit world. The ancient Babylonians, Egyptians, Greeks and Chinese all developed sophisticated ways of representing information visually to plot the movements of the stars, produce maps to aid navigation, and develop plans for crop planting and city development.

Turin Papyrus Map (Egypt, 1150 BC)

Found in Thebes in the 19th century, this sophisticated Egyptian papyrus map displays a fascinating range of information including geographical and mineral mining data. Recent studies have shown that its colour-coded geological information is remarkably accurate.

Roman Maps, the Tabula Peutinger (366-335 BC)

The Romans were renowned mapmakers, as they needed to efficiently plan for the movement of their armies and trade throughout the empire. Although only shown here in detail the Peutinger map is a 7 metre long reproduction made by a monk in the thirteenth century.

 Ptolemy’s World Map (2nd century AD)

Later maps such as Ptolemy’s world map would revolutionize map-making through the use of latitude and longitude markings to develop a global coordinate system. By applying scientific method, maths and astronomy to specify location, these images are forerunners of modern maps.

Positions of the Sun, Moon, and Planets throughout the Year (Europe, 950 AD)

This fascinating diagram is reminiscent of a modern information visualization as it uses a grid system to combine both the time and location of planetary bodies 800 years before the first true time plotted graphics.

Su Song’s Celestial Atlas (China, 1092 AD)

Su Song was a Chinese scientist active during the Song Dynasty. This star chart uses a sophisticated projection of celestial bodies that employs techniques not introduced to Europe until the 16th century. While this chart was produced after the European chart described above, the ancient Chinese had been producing highly sophisticated visualizations of the stars for nearly a millennia.

Ramon Llull, Diagrams of Relationships between Knowledge

Llull was a Spanish philosopher who lived between the 13th and 14th centuries. He believed that there were basic concepts common to all experience, and that by arranging these ideas diagrammatically truths and insights into the human condition could be generated.

Abraham Ortelius, the First Modern Atlas (1570, Belgium)

Produced by Ortelius in Antwerp in 1570, the Theatrum Orbis Terrarum (Theatre of the World) contained 53 maps with a comprehensive descriptive text and supplementary material. Its importance lies in the fact that it was the first attempt to gather all known information and maps of the word in one printed volume.

Christoph Scheiner, Images of Sunspots

Scheiner was a Jesuit priest and mathematician working in Germany and Italy in the 17th century. Using state of the art telescopes he studied the markings on the Sun (sunspots) in order to reconcile Church teachings that the Cosmos contained no imperfections, with the visual evidence that it did.

18th Century Developments

In the 18th century the use of visualizations was used to make sense of social and historical data; a phenomenon that persists to this day. This occurred for many reasons including the invention of new measurement devices and ways of gathering data, and the need for large states to make sense of their economies and populations. The works represented with first use of bar charts, abstract symbols to represent data, and the development of meteorological and geographic symbols.

Charles de Fourcroy, Tableau Poléometrique

De Fourcroy, was a French mathematician who produced a visual analysis of the work of French civil engineers and a comparison of the demographics of European cities. His use of geometric forms predates contemporary visualization techniques such as tree maps.

19th Century Developments

Information visualization matured into a fully-fledged discipline in the 19th century with the development of many formal and technical innovations which persist to this day. During this period the many images used to analyze military campaigns, weather patterns, climate, geology, disease, social and moral behavior, and economics and trade.

Luigi Perozzo, 3D Model of the Swedish Census

An Italian statistician, Perozzo produced one of the first 3D representations of data showing the age group of Swedish population between the 18th and 19th centuries. In this representation years are measured horizontally, numbers of individuals vertically, and age groups (youngest nearest) in depth going into and out of the image. The use of 3D to represent data is now commonplace in contemporary scientific visualization, e.g. medical and engineering sciences.

20th Century, Enter the Computer

By the early to mid part of the 20th century information visualization had entered mainstream use and had become commonplace in magazines, cinemas and newspapers, but it was the emergence of computer technology that was to produce the next revolution in the subject. During the Second World War computers had proved very effective at handling the vast amounts of data needed to analyze military intelligence, providing a post-war technological platform for the development of new ways of plotting statistics graphically. By the 1970s and 80s the first full-color computer visualizations were being developed and interactive graphics were providing new ways of revealing the stories hidden in data by enabling navigation of it in three-dimensions.

21st Century and Beyond, Democratizing Visualization

The emergence of the Internet in the latter part of the 20th century, the availability of new software tools such as Flash, Google Earth and Processing, and the increase in publicly available data, has seen a huge increase in types of data visualization. Whereas in the past, various graphical aids for interpreting data have mainly been produced by specialist statisticians and scientists, in this new era we see an increasing appetite amongst members of the general public to produce their own. The BBC DataArt project is of course a good example of this.

Visualization of the Internet by the Opte Project.

Fig 1.4 shows the Opte Project

1.4 Applications of Visualization

1.4.1 Visualization Domains

Visualization Domains makes the understanding of the structure and dynamics of a knowledge domain and augment ability to identify and analyze a knowledge domain in a wide variety of quantitative and qualitative methods. It emphasizes the role of visual analytics, information visualization, exploratory data analysis, information retrieval, information science, and text mining and other enabling techniques.

1.4.2 Applications – Visualization as a Toolkit

Application tools are usually coupled with

    Haptic feedback devices

Stereo output (glasses)

Interactivity

1.4.3 Scanning – Domains

The medical visualization aims to display medical data sets for an improved diagnosis and treatment planning. In future medical experts tend to develop visualization techniques that supports further development. Fig 1.5 shows Medical scanners. Medical scanners (MRI, CT, SPECT, PET, ultrasound)

Fig 1.5 Medical Scanners

1.4.4 Scanning – Applications

Primary education: scanning visualization is used in primary education to teach the education related concepts in most practical manners.

Medical  education:  Scanning  visualization  is   used  for   surgery   and    anaesthesia. Scanning is used in medical education for surgical purpose.

Illustration of medical procedures to the patient is easier using scanning applications.

Fig 1.6 shows scanning application of visualization of Frog

1.4.5 Scanning – Applications

Surgical simulation for treatment planning

Tele-medicine

Inter-operative visualization in brain surgery, biopsies, etc.

Industrial purposes (quality control, security)

Games with realistic 3D effects.

Fig 1.7 show scanning applications in medical surgery.

Fig 1.8 Scanning Applications in medical surgery

Domain – biological scanners, electronic microscopes, confocal microscopes

Apps – paleontology, microscopic analysis

Fig 1.9 scanning applications in human being

1.4.6 Scientific Computation – Domain

Scientific computation is the use of mathematical, statistical and computer-based techniques to investigate complex systems. Scientific visualization, sometimes referred to in shorthand as SciVis, is the representation of data graphically as a means of gaining understanding and insight into the data. It is sometimes referred to as visual data analysis. Fig 1.10 show scientific computation Domain. Some of the example of scientific computation are shown below:

Mathematical analysis

ODE/PDE (ordinary and partial differential equations)

Finite element analysis (FE)

Supercomputer simulations

Fig 1.10 Scientific Computation- Domain

1.4.7 Scientific Computation – Apps

Computational fluid dynamics (CFD): Computational Fluid Dynamics (CFD) provides a qualitative (and sometimes even quantitative) prediction of fluid flows by means of mathematical modelling (partial differential equations), numerical methods (discretization and solution techniques),software tools (solvers, pre- and post processing utilities).

Computational field simulations (CFS): An important element of computational simulation is the bridge it creates between theory and experimental testing. Perhaps no other field exemplifies the significance of this link quite like pharmaceutical drug design—arguably one of the most costly and universally essential fields of scientific endeavor. Fig 1.11 shows Scientific computation applications.

1.4.8 Vector Field Viz Applications

You can visualize a vector field by plotting vectors on a regular grid, by plotting a selection of streamlines, or by using a gradient color scheme to illustrate vector and streamline densities. You can also plot a vector field from a list of vectors as opposed to a mapping. Fig 1.12 show vector field visualization applications

1.4.9 Vector Field Visualization Challenges

General Goal: Display the field’s directional information

Domain Specific:  Detect certain features like vortex cores and Swirl

1.4.10 Streamlines

Curves that connect all the particle positions are streamlines. Displaying streamlines is a local technique because you can only visualize the flow directions initiated from one or a few particles. Fig 1.13 shows Streamlines as shown below:

Fig 1.13 Streamlines

When the number of streamlines is increased, the scene becomes cluttered and you need to know where to drop the particle seeds Streamline computation is expensive Fig 1.14 shows Streamline computation

1.4.11 Measuring – Domains

Some of the applications of visualization in measuring are shown below:

Fig 1.15 show examples of Measuring –Domains in visualization

Orbiting satellites

Spacecraft

Seismic devices

Statistical Data

Fig 1.15 Measuring – Domains

1.4.12 Measuring – Applications

Some of the applications of visualization measuring are used for military intelligence, weather and atmospheric studies planetary and interplanetary exploration, oil, precious metal exploitation, and earth quake studies and Statistical Analysis – Info Vis (Financial Data …)

1.4.13 Taxonomy

Taxonomy is the science of naming, describing and classifying organisms and includes all plants, animals and microorganisms of the world. Using morphological, behavioural, genetic and biochemical observations, taxonomists identify, describe and arrange species into classifications, including those that are new to science. Taxonomy identifies and enumerates the components of biological diversity providing basic knowledge underpinning management and implementation of the Convention on Biological Diversity. Fig 1.16 show taxonomy in visualization.

1.4.14 Difference between visualization, graphics and Imaging:

Imaging – Enhance, analyze, manipulate and store 2D/3D images

Graphics – Make pictures! Digital Image Synthesis: sampling + illumination

Visualization – Exploration, transformation, viewing data as images

1.4.15 Relation to other fields

Data visualization — Graph-like image or interactive, usually tied with data exploration and analysis.

Visualization — Similar to data visualization and often is, but can also be the later described information visualization.

Information visualization — Usually encapsulates what data visualization is about, but usually makes an effort to provide “actionable insights.”

Information graphic — Serious work from journalist-type folks who provide a narrative with data.

Infographic — A toss-up between information graphic and [INFOGRAPHIC], but usually the latter and often unnecessarily big.

Chart — Typically looks very statistical and close to a table.

Graph — It is like a chart, but it sounds more visual, because it’s the root of “graphic.”

Data graphic — It’s an ambiguous term and implies that data comes first and is the driving force behind the graphic.

Fig 1.17 shows Visualization Relation to other fields

Fig 1.17 Relation to Other Fields

1.5 Summary

To summarize, we have examined the following in this module:

Concepts of Visualization

Models of Visualization

History of Visualization

Applications of Visualization

References

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2. S. Feiner and C. Beshers , “Visualizing n-dimensional virtual worlds with n-vision” , Computer Graphics , vol. 24 , no. 2 , pp.37 -38 , 1990

3.S. Kochhar , M. Friedell and M. LaPolla , “Cooperative, computer-aided design of scientific visualizations” , Proceedings IEEE Visualization \’91 , pp.306 -313 , 1991