Visual Data Analytics - sorbonne-universite · Figure 15: Lengler R., Eppler M. (2007), Towards A Periodic Table of Visualization Methods for Management, IASTED Proceedings of the

Visual Data Analytics

Part 1: Introduction to Scientific Visualization

Pascal Frey

Institut des sciences du calcul et des donnéesLaboratoire Jacques Louis LionsSorbonne Université, Paris - France

Section 1.1

Visualisation at large

Data Visualisation

• Visualisation involves the creation of a mental image of data to communicate infor-mation clearly and e�ciently and to produce insight.

• To convey data e�ectively, both aesthetic form and functionality need to go hand byhand.

• According to F. Viegas and M. Wattenberg, an ideal visualisation should not onlycommunicate clearly but stimulate viewer engagement and attention.

• The human visual system represents the sensory path with by far the highest band-width.

• Visual representations lend themselves well to interaction( interactive data exploration).

• Data visualization is closely related to information graphics, information visualiza-tion, scienti�c visualization, exploratory data analysis and statistical graphics.

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Visual Perception and Data Visualization

• Human distinguish di�erences in line length, shape, orientation, and color withoutsigni�cant processing e�ort; these are referred to as "pre-attentive attributes".

• E�ective graphics take advantage of pre-attentive processing and attributes and therelative strength of these attributes: use a bar chart (lines) rather than pie charts(surface area).

• Human perception/cognition and data visualization◦ Knowledge of human perception and cognition is necessary when designing in-tuitive visualizations.

◦ Cognition refers to processes in human beings like perception, attention, learn-ing, memory, thought, concept formation, reading, and problem solving.

◦ When properties of symbolic data are mapped to visual few properties, humanscan browse through large amounts of data e�ciently.

◦ Roughly 2/3 of the brain’s neurons can be involved in visual processing.◦ Proper visualization provides a di�erent approach to show potential connections,relationships, etc. which are not as obvious in non-visualized quantitative data.


Visualisation Examples

Figure 1: Visualisation of weather data using scalar / vector values, glyphs, contours (TriVis Weather Graphix).



Figure 2: Anscombe’s Quartet: four datasets appear statistically very similar, but visualization reveals signi�cant di�erences(Anscombe, F. J. (1973), Graphs in Statistical Analysis. American Statistician 27 (1): 17–21).



Figure 3: Chart junk (E. Tufte): Which of these visualizations will you remember later? (Credit: Michelle Borkin, Harvard SEAS).



Figure 4: In 1644, Michael Florent van Langren, a Flemmish astronomer, is believed to have provided the �rst visual representationof statistical data. The one dimensional line graph below shows the twelve known estimates at the time of the di�erence inlongitude between Toledo and Rome as well as the name of each astronomer who provided the estimate.


Visualisation examples

Figure 5: Charles Joseph Minard’s vectorized map (1869) displaying the movements and the number of Napoleonic troops duringthe Russian campaign (1812-1813), as well as the temperature on the return path.



Figure 6: This �gure illustrates the oligomeric structure of the protein and highlights that the catalytic site (which binds fructose-1,6-bisphosphate) and the regulatory site (which binds NADH) are topographically distinct and located at di�erent subunitinterfaces (Changeux J.P. (2013), 50 years of allosteric interactions: the twists and turns of the models, Nature Reviews MolecularCell Biology 14, 819–829).



Figure 7: Contour maps are used to visualize terrains since ca. 1750 (US Topographic map 3D).



Figure 8: Pantheon is a project developed by the Macro Connections group at The MIT Media Lab that’s collecting, analyzing,and visualizing data on historical cultural popularity and production.



Figure 9: Graph representing the metadata of thousands of archive documents, documenting the social network of hundreds ofLeague of Nations personals. Published in: Grandjean, Martin (2014). "La connaissance est un réseau". Les Cahiers du Numérique10 (3): 37-54.



Figure 10: Storyline visualization is a technique used to depict the temporal dynamics of social interactions. This visualizationtechnique was �rst introduced as a hand-drawn illustration in XKCD’s “Movie Narrative Charts”. If properly constructed, thevisualization can convey both global trends and local interactions in the data (Y. Tanahashi, UC Davis).



Figure 11: Arc diagram of Les Misérables character co-occurences (J. Heer, A survey of powerful visualization techniques, fromthe obvious to the obscure, ACM 53(6), 59-67, 2010).



Figure 12: Smart Blade GmbH: section view of the CAD model of the wind tunnel (left), �ow visualization (velocity contours) fromthe CFD simulations of the wind tunnel (right).



Figure 13: CT scan diagnostic imaging and coloured MRI brain scan showing lissencephaly -smooth brain- (Siemens 2010).



Figure 14: A new method of analyzing our brain’s electrical activity �nds neural signatures of consciousness. At right is a healthyperson’s activity; at left and center are two people with brain injuries. Both are in vegetative states, but one displays patternssuggestive of healthy function (Chennu et al., PLOS Computational Biology).



Figure 15: Lengler R., Eppler M. (2007), Towards A Periodic Table of Visualization Methods for Management, IASTED Proceedingsof the Conference on Graphics and Visualization in Engineering.


New science, old concern

The use of visualization to present information is not new, it has been used in maps,scienti�c drawings, and data plots for over a thousand years.

Figure 16: A rigid obstacle in �owing water creates wake turbulence, sketch by Leonardo da Vinci in 1509 (credit: image TheRoyal Collection, HRM Queen Elizabeth II).


Visualisation History

Early maps& diagrams Measurement

& Theory

New graphicforms Begin modern

period

Golden ageModern dark

ages

Re-birth

High-D Vis

Milestones: Time course of developmentsDensity

0.000

0.001

0.002

0.003

0.004

0.005

Year1500 1600 1700 1800 1900 2000

Figure 1: The time distribution of events considered milestones in the history of data visualiza-tion, shown by a rug plot and density estimate.

Project, http://www.math.yorku.ca/SCS/Gallery/milestone/, where a colorversion of this chapter will also be found.

2.1 Pre-17th Century: Early maps and diagrams

The earliest seeds of visualization arose in geometric diagrams, in tables of the positions of starsand other celestial bodies, and in the making of maps to aid in navigation and exploration. Theidea of coordinates was used by ancient Egyptian surveyors in laying out towns, earthly andheavenly positions were located by something akin to latitude and longitude at least by 200 BC,and the map projection of a spherical earth into latitude and longitude by Claudius Ptolemy [c.85–c. 165] in Alexandria would serve as reference standards until the 14th century.

Among the earliest graphical depictions of quantitative information is an anonymous 10th

century multiple time-series graph of the changing position of the seven most prominent heav-enly bodies over space and time (Figure 2), described by Funkhouser (1936) and reproducedin Tufte (1983, p. 28). The vertical axis represents the inclination of the planetary orbits, thehorizontal axis shows time, divided into thirty intervals. The sinusoidal variation, with differentperiods is notable, as is the use of a grid, suggesting both an implicit notion of a coordinatesystem, and something akin to graph paper, ideas that would not be fully developed until the1600–1700s.

In the 14th century, the idea of a plotting a theoretical function (as a proto bar graph), and

3

Figure 17: The time distribution of events considered milestones in the history of data visualization, shown by a rug plot anddensity estimate (Friendly M. (2006), A Brief History of Data Visualization, Springer).


Visualisation History

ca. 6200 bc oldest known maps

ca. 1780 terrain & astrological maps

architectural & construction plans

1896 �rst X-ray image

ca. 1900 �rst statistical graphs

anatomical atlasses

ca. 1940 electronic measurement devices (CT, MRI, satellites)

scienti�c computing (numerical methods, simulations)

1972 �rst computer tomography (CT) device

1986 US NSF committee on “Graphics, Image Processing, and Worksta-tions” recommends to develop new concepts and techniques for“Visualization in Scienti�c Computing” (ViSC).


Section 1.2

De�nitions and Purposes

Definitions and Purposes

• classical de�nition: visualisation is the formation of a mental vision, image, or pic-ture of (something not visible or present to sight, or of an abstraction); to makevisible to the mind or imagination (the Oxford English Dictionary, 1989)

• digital de�nition: a tool or method for interpreting image data fed into a computerand for generating images from complex multi-dimensional data sets (1987).

• In general, visualization is essentially a mapping process from computer represen-tations to perceptual representations, choosing encoding techniques to maximizehuman understanding and communication.

• The goal of a viewer might be a deeper understanding of physical phenomena ormathematical concepts, but it also might be a visual proof of computer representa-tions derived from such an initial stage.


Quotes.Scienti�c Visualization is concerned with exploring data and in-formation in such a way as to gain understanding and insightinto the data. The goal of scienti�c visualization is to promotea deeper level of understanding of the data under investigationand to foster new insight into the underlying processes, relyingon the humans’ powerful ability to visualize.

R.A. Earnshaw

A useful de�nition of visualization might be the binding (or map-ping) of data to representations that can be perceived. The typesof bindings could be visual, auditory, tactile, etc., or a combina-tion of these.

J. Foley and B. Ribarsky


Quotes..The standard argument to promote scienti�c visualization is thattoday’s researchers must consume ever higher volumes of num-bers that gush, as if from a �re hose, out of supercomputer simu-lations or high-powered scienti�c instruments. If researchers tryto read the data, usually presented as vast numeric matrices, theywill take in the information at snail’s pace. If the information isrendered graphically, however, they can assimilate it at a muchfaster rate.

R. Friedho� and T. Kiley

Visualization is a method of computing. It transforms the sym-bolic into the geometric, enabling researchers to observe theirsimulations and computations. Visualization o�ers a method forseeing the unseen. It enriches the process of scienti�c discoveryand fosters profound and unexpected insights. In many �elds itis already revolutionizing the way scientists do science.

B. McCormick, T. DeFanti, and M. Brown


Quotes...Underlying the concept of visualization is the idea that an ob-server can build a mental model, the visual attributes of whichrepresent data attributes in a de�nable manner. This raises sev-eral questions:

• What mental models most e�ectively carry various kinds ofinformations ?

• Which de�nable and recognizable visual attributes of thesemodels are most useful for conveying speci�c informationeither independently or in conjunction with other attributes?

• How can we most e�ectively induce chosen mental modelsin the mind of an observer ?

• How can we provide guidance on choosing appropriate mod-els and their attributes to a human or automated display de-signer ?

Choosing the appropriate representation can provide the key tocritical and comprehensive appreciation of the data, thus bene-�ting subsequent analysis, processing, or decision making.

P.K. RobertsonISCD Summer School 2019, Marine station Rosco�, 22-26 june 2019 27/ 63

Quotes....In their understanding, scienti�c data visualization supports sci-entists and relations, prove or disprove hypotheses, and discovernew phenomena using graphical techniques. The primary objec-tive in data visualization is to gain insight into an informationspace by mapping data onto graphical primitives.

E. Ignatius and H. Senay

In their understanding Visualization technology is based on theintegration of older technologies, including computer graphics,image processing, computer vision, computer-aided design ge-ometric modeling, approximation theory, perceptual psychology,and user interface studies.

R.B. Haber and D.A. McNabb

Progress in scienti�c visualization can be accelerated if work-ers could more readily �nd visualization techniques relevant toa given problem.

S. Wehrend and C. LewisISCD Summer School 2019, Marine station Rosco�, 22-26 june 2019 28/ 63

Visualisation Process

B. Fry sets up the Data Visualisation process as the series of steps:

Acquire Obtain the data, whether from a �le on a disk or a source over anetwork.

Parse Provide some structure for the data’s meaning, and order it intocategories.

Filter Remove all but the data of interest.

Mine Apply methods from statistics or data mining as a way to discernpatterns or place the data in mathematical context.

Represent Choose a basic visual model, such as a bar graph, list, or tree.

Re�ne Improve the basic representation to make it clearer and morevisually engaging.

Interact Add methods for manipulating the data or controlling what fea-tures are visible.


Visualisation Process..

Figure 18: Representation of the visualisation process (C. Garth, 2016).


Visualisation Process...

Figure 19: Representation of the visualisation process (Tableau Software).


Visualisation Goals

External CognitionVisualization supports understanding and insight by actingas an extension of human memory and depicting complexfacts.

CommunicationVisualization is used to communicate information quicklyand e�ciently.

Depiction of abstract informationVisualization can depict abstract data without a natural orinherent representation (e.g. networks).

ExplorationInteractive representations allow viewers to explore thedata according to their interests and highlight speci�c as-pects.

Increase of productivityVisual information can be absorbed much quicker thanmost other forms of information. Visualizations should bedesigned to help rapid comprehension.


Visualisation Requirements

Visual representations should

• represent the data,

• encourage viewers to think about it,

• make good use of available space (e.g. screen resolution)

• o�er di�erent levels of interaction, and

• support the requirements of the underlying application.

They should not

• distort the data, or

• distract the viewer from the contained information.

cf. E.R. Tufte, The Visual Display of Quantitative InformationISCD Summer School 2019, Marine station Rosco�, 22-26 june 2019 33/ 63

Bad Visualisation

Figure 20: Two examples of poor visualisation: as a substitute for substance, one can try lots of color, 3D e�ects, or disguisedredundancy. The graph on the right uses all three techniques, to display just �ve numbers.


The Scientific Visualisation Pipeline

Step1 Problem de�nition• Work�ow analysis

◦ Where is visualization applicable to solve problem?• Requirements analysis

◦ In which speci�c tasks to incorporate visualization?

Step 2 Abstraction of Tasks and Data• Translation of tasks from application-speci�c togeneric

◦ high-level: e.g. visualization of uncertainty, con-nectivity, cause-and-e�ect, ...

◦ low-level: read and �lter data, �nd minima, sort, ...• Data conventions and conversion

◦ e.g. tables, lists, trees, sampling, ...

Step 3 Visualisation and Interaction Design

Step 4 Algorithm implementation


Example

Let’s consider the steady-state simulation of �uid �ow around a complex geometry.

Step1 Problem de�nitionModel �ow simulation to produce pressures at a set ofpoints.Data = scalar �eld (pressure)

Step 2 Abstraction of Tasks and DataCompute the gradient of pressure to produce vectors ateach of the set of input data points.High-level task: compute gradients.

Step 3 Visualisation and Interaction DesignDrop virtual particles into this vector �eld, and computethe path they would follow in the �eld.Produce a set of polygons representing a tube bent tomatch the path.Create a digital image from a virtual camera of the polyg-onal model.


Example..

Figure 21: Simulation of a �uid �ow around a complex geometry.


Scientific Visualisation

What happens when display precedes data analysis!


Additional Motivations.

• Through the availability of increasingly powerful computers with increasing amountsof internal and external memory, it is possible to investigate incredibly complexdynamics by means of ever more realistic simulations.However, this brings with it vast amounts of data.

• To analyze these data it is imperative to have software tools which can visualizethese multi-dimensional data sets.

• Comparing this with experiment and theory it becomes clear that visualization ofscienti�c data is useful yet di�cult.For complicated, time-dependent simulations, the running of the simulation may involve the calcu-lation of many time steps, which requires a substantial amount of CPU time , and memory resourcesare still limited, one cannot save the results of every time step. Hence, it will be necessary to visualizeand store the results selectively in ‘real time’ so as to avoid recomputing the dynamics.


Additional Motivations..

• Main reasons for scienti�c visualisation:◦ it will compress a lot of data into one picture (data browsing),◦ it can reveal correlations between di�erent quantities both in space and time,◦ it can furnish new space-like structures beside the ones which are already knownfrom previous calculations,

◦ it opens up the possibility to view the data selectively and interactively in ‘realtime’. By following the formation and the deformation as well as the motions ofthese structures in time, one will gain insight into the complicated dynamics.

• The aim is to integrate the simulation codes into a visualisation environment in orderto analyze the data ’real time’ and to by-pass the need to store every intermediateresult for later analysis.It is also very useful to have the possibility to interactively change the simulationparameters and immediately see the e�ect of this change through the new data.


Questions

• The discussion is focussed on the following questions:◦ What is the improvement in the understanding of the data as compared to thesituation without visualization?

◦ Which visualization techniques are suitable for one’s data?◦ Are direct volume rendering techniques to be preferred over surface renderingtechniques?

◦ Can current techniques, like streamline and particle advection methods, be usedto appropriately outline the known visual phenomena in the system?


Concerns

• The success of visualization not only depends on the results which it produces, butalso depends on the environment in which it has to be done.

• This environment is determined by the available hardware, like graphical worksta-tions, disk space, color printers, video editing hardware, and network bandwidth,and by the visualization software.For example, the graphical hardware imposes constraints on interactive speed ofvisualization and on the size of the data sets which can be handled.

• Many di�erent problems encountered with visualization software must be taken intoaccount: the user interface, programming model, data input, data output, data ma-nipulation facilities, and other related items are all important.The way in which these items are implemented determines the convenience ande�ectiveness of the use of the software package as seen by the scientist.


Perception of visuals

• Visual attributes:◦ a clever choice of visual attributes is paramount to visualization process◦ redundancy of visual attributes enhances interpretability

• Interpretation of visual attributes:◦ natural to interpret, simple,◦ acquired reactions to visual attributes: through education (color ranking, isosur-faces, etc)

◦ illusory visual attributes

• Color:◦ psychophysical process: relates to wavelengths, spectral distribution and amountof light entering eye (physics) and perceived sensation with no linear relation tophysics (psychology).

◦ no complete theory, variety of color spaces, perceptual dimensions of color (hue,saturation, intensity).


Perception of visuals

Figure 22: M.C. Escher optical illusion.


Skills.

A student who wants to specialize in visualization needs a broadly based background.

Ideally, he or she will need:

• a strongmathematical background, with calculus, linear algebra, ordinary and partialdi�erential equations, and numerical analysis.

• in addition to a regular computer science background, the student would also needa strong grounding in computer graphics plus some experience in computer anima-tion.

• he or she should have some art courses such as graphics design, photography, draw-ing, or painting to obtain the general principles of design from an artistic viewpoint.Plus, the student need some science courses such as biology, chemistry, or physics,to be able to communicate with the scientists.

While it might be extremely di�cult, if not impossible to �t all of this into anundergraduate curriculum, a program oriented towards a Masters Degree inVisualization would be quite feasible.


Skills..

In science, novelty emerges only with di�culty, manifested by re-sistance, against a background provided by expectations.

Thomas KuhnThe eye sees only what the mind is prepared to comprehend.

Henri Bergson


Section 1.3

Technical Features

Visualisation techniques

• A visualization technique is used to create and manipulate a graphic representationfrom a set of data.

• Some techniques will be appropriate only for speci�c applications while others aremore generic and can be used in many applications.

• It should always be kept in mind that the goal of visualization is not to understandthe data but to understand the underlying phenomenon.

• There are three steps in the visualisation pipeline:1. The construction of an empirical model from the data. This construction mayinvolve sampling theory considerations, and general mathematical interpolation/ approximation schemes. If the data contains errors then this must be takeninto account.

2. The selection of some schematic means of depicting the model as some abstractvisualization object, such as an image of a contour map.

3. The rendering of the image on a graphics display.


Graphics Processing Unit

Figure 23: Modern day computer has dedicated Graphics Processing Unit (GPU) to produce images for the display, with its owngraphics memory (or Video RAM or VRAM).


Visualization techniques

• For many areas of science and engineering, visualization has become more than aconvenient tool: it has become a necessity for interpreting the enormous amounts ofdata being produced by large-scale instrumentation, experiments and simulations.

• Fortunately, scienti�c visualization bene�ts from technological outcomes.For example, one of the most creative and useful ways of presenting scienti�c datais the large-scale (tiled) display.

• Ranging from tabletop versions to fully immersive 3D stereo virtual reality environ-ments, these displays enable users to explore features that may be hard or impos-sible to identify with conventional, personal computers.


Visualisation devices.

Graphics cards: the primary suppliers of the GPUs (video chips or chipsets) used invideo cards are AMD and Nvidia.

Nvidia GeForce GTX Titan Z AMD Radeon R9 295X2


Visualisation devices..

Graphics cards: speci�cations and features

Nvidia GTX Titan Z AMD Radeon R9 295X2dual chip dual chip

Stream processors 5,760 5,632Engine clock 705 Mhz 1018 MhzCompute performances 21.7 T�ops 11.5 T�opsTexture �ll-rate 338 GT/s 358.3 GT/sMemory con�guration 12 GB 8 GB

Retail price 2,999 USD 1,499 USD


Visualisation devices...

• Large-screen displays: found in industries and research centers.The ability to display created environments at "full scale" can be of tremendousvalue to researchers and scientists.These displays can be curved for an even more immersive experience.

Figure 24: Immersive environment for design review created by a large-screen curved display.


Visualisation devices....

Figure 25: Large display (6.5 × 2.5 m) in use at the Institut des sciences du calcul et des données (SU, Paris).


Visualisation devices.....

• Immersive environments: this system allows the user to walk in the space, providinga totally immersive, high-resolution experience.Built from 4 to 6 walls with rear projection, the user experiences the environment infull scale and in real time.

Figure 26: Research in immersive environments works to virtually place the viewer in a space.


Visualisation devices......

• Active shutter 3D system: is a technique of displaying stereoscopic 3D images.It works by only presenting the image intended for the left eye while blocking theright eye’s view, then presenting the right-eye image while blocking the left eye, andrepeating this so rapidly that the interruptions do not interfere with the perceivedfusion of the two images into a single 3D image.

• Real-time tracking: concerns the positional measurement of bodies (subjects or ob-jects) that move in a de�ned space. Position and/or orientation of the body can bemeasured.3 degrees of freedom (3DOF or 3D) tracking consists in measuring only x, y, z po-sitions. If position (3 coordinates) and orientation (3 independent angular coordi-nates) are measured simultaneously, this is called 6 degrees of freedom (6DOF or6D) tracking.

• Haptic technology: is a tactile feedback technology which recreates the sense oftouch by applying forces, vibrations, or motions to the user.


Software packages

• Non-exhaustive set of tools which cover the functions needed by most researchersfor their scienti�c visualization needs.1. Matlab: is a numerical computing package which has gained wide popularity inthe science and engineering communities. It provides a large set of plottingoptions, aw well as the standard basic visualization tools.

2. VTK-Paraview: the Visualization Toolkit is an open source set of graphics libraries,accessible using C++, Tcl, Perl, Python, or Java. ParaView is an open source, freelyavailable visualization application built on top of VTK.

3. OpenGL: the Open Graphics Library is a standard specifying an API for applica-tions that produce 2-d and 3-d computer graphics.

4. Open Scene Graph: is an open source high performance 3-d graphics toolkit. Ascene graph is a hierarchical representation of all elements of a visual sceneÐ once you have constructed this directed graph, the underlying libraries willtraverse it and render the objects represented by it.

5. Maya: is an interactive, professional 3-d graphics, modeling, and animation pro-gram, used in the movie, television, and game industries, as well as for designand architectural rendering.


Software packages

The table below shows where the packages fall in the visualization pipeline:

1. Produce input data: use a CFD simulation program

2. Analyze, �lter, reformat: compute the gradient of pressure

3. Apply scienti�c visualization techniques: compute the pathlines

4. Map to geometry: produce a set of polygons representing the domain

5. Rendering: create an image from a virtual camera of the polygonal model

6. Viewing: use a web browser to view the resulting image

Tool input data analysis sci-vis geometry rendering viewingexperiments xMatlab x x x x x xVTK-Paraview x x x x x xOpenGL x xOpenScene Graph x xMaya x x


Software packages

Paraview (Sandia Natl. Lab., Los Alamos Natl. Lab., Kitware Inc.).

Figure 27: Visualization of the result of OpenFOAM combustion calculations (background color represents the temperature, thearrows represents the gas velocity and their colors represent the concentration of oxygen.


Software packages

OpenSceneGraph (open source 3D application programming interface).

Figure 28: TerrainView is the free 3D viewer of ViewTec for interactive visualization od 3D data.


Software packages

Maya Autodesk (3D computer graphics software).

Figure 29: Autodesk Maya 2013 (Alias Systems Corp., Autodesk Inc.).


Software packages

Ensight (FEA and CFD post-processing).

Figure 30: Ensight 10 (CEI-Computational Engineering International, Inc.).


Numerical Experiments

Objectives of the numerical experiments: complex simulation + advanced rendering


Documents

Visual Data Analytics - sorbonne-universite · Figure 15: Lengler R., Eppler M. (2007), Towards A Periodic Table of Visualization Methods for Management, IASTED Proceedings of the