1. Usability and Human Factors
Information Visualization
Welcome to Usability and Human Factors, Information Visualization. This is lecture b.
Lecture b
This material (Comp 15 Unit 12) was developed by Columbia University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 1U24OC This material was updated by The University of Texas Health Science Center at Houston under Award Number 90WT0006.
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit
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2. Information Visualization Lecture b – Learning Objectives
Field of Information Visualization (Lecture a)
Main concepts (Lecture a)
Presentation
Interaction and dynamic queries
Hierarchies and trees
Time-series data
Information Visualization in Medicine (Lecture a)
Describe how information visualization can support and enhance the representation of trends and aggregate data (Lecture b)
By the end of this final lecture students will be able to:
Describe how information visualization can support and enhance the representation of trends and aggregate data

3. Hypervariate data sets
Common problem: many data sets are too large to visualize on one screen
Solution: overview of the whole set and details of the selected subset
Implementation: scaling (in space or time)
Scaling in space: use different portion of the screen to show overview of the whole data set and detail of the selected subset
Scaling in time: alternate between overview of the whole set and detail of the selected subset sequentially in the same space
As we mentioned earlier, real world datasets are usually quite complex. They include multiple variables, and are often very large. This creates a problem for information visualization, as often it is impossible to fit all data on the same screen.
A common solution to this problem is to include a combination of the entire dataset visualized in a very condensed way, and a view of a subset of data in greater detail.
Two common approaches to presenting overview and detail are to use scaling either in space or in time. In case of scaling in space, overview and detail occupy different portions of the screen. In case of scaling in time, they take the same space, but alternate in time.
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4. Scaling Variation in space
Can overview and detail co-exist in the same space?
Distortion
Fish-eye view
Bifocal display
Perspective wall
Let’s take a more detailed look at different scaling techniques. The three common ways to provide overview and detail on the same screen are fish-eye views, bifocal display and perspective wall.
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5. Scaling Fish-Eye Fish-eye view George Furnas 1981 Shneiderman:
Provide[s] detailed view (focus) and overview (context) without obscuring anything… The focus area (or areas) is magnified to show detail, while preserving the context, all in the single display.
Fish-eye views follow the same principles as a fish-eye view lens of cameras. They show the central focused area in great detail and all the areas outside of this focus in a condensed way to preserve context.
Furnas, G. (1986).
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6. Scaling Fish-Eye (Cont’d – 1)
Fish-eye view
Examples:
Fish-eye views for long menus
Bederson UIST’00
Fish-eye view approaches have been applied in different areas. Here is an example of using a fish-eye view method to a list of items. As you can see a few items are in focus and the rest of the sets are in a condensed way. This view allows users to examine items of interest while reminding them how long the list is overall, and where they are within the list.
Bederson, B.B. (2002).
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7. Scaling Fish-Eye (Cont’d – 2)
Fish-eye views
Examples:
Here is an example of using fish-eye view concept for graphs.
Bederson, B.B (2002).
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8. Scaling Bifocal Bifocal display
First suggested by Spence and Apperley (1980)
Bifocal display uses a similar concept, but instead of radial distortion of everything outside of the focal view, it folds the visualization in three parts. The main, focal part is then shown in great detail. The other two peripheral parts are shown in perspective.
Ying, K. Leung, R.S., Apperley, M.D. (1995).
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9. Scaling Bifocal (Cont’d – 1)
Bifocal display
Examples
Early implementation
Spence – 1980?
Here is a particular implementation of a bifocal display.
Spence, R. (1980)
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10. Scaling Perspective Wall
Perspective wall uses a variation on the same idea: it uses a real 3-dimensional perspective to distort areas outside of focal view.
Bederson, B.B (2002).
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11. Scaling Perspective Wall (Cont’d – 1)
Perspective Wall Examples:
Map work charts onto diagram
x-axis is time, y-axis is project
Here is an example of how a perspective wall could be used to show a large diagram on one screen.
Mackinay, J.D., Robertson, G.G., and Card, S.K. (1991)
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12. Scaling Perspective Wall (Cont’d – 2) File navigation
It could also be used for file navigation
Mackinay, J.D., Robertson, G.G., and Card, S.K. (1991)
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13. Time Scaling Variation in time Magic Lens Panning and Zooming
The two common approaches to using time scaling are magic lens and panning and zooming.
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14. Magic Lens Scaling Magic Lens ("Magic Lens - InfoVis:Wiki", 2014)
Magic lens follows an analogy of a magnifying glass: a user is given a tool that could be moved over the visualization. All the data covered by the tool is presented in great detail; however, it obstructs its close neighbors.
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15. Panning & Zooming Scaling
Panning – smooth movement of camera across scene (or scene moves and camera stays still)
Zooming – increasing or decreasing the magnification of the objects in a scene
Panning and zooming are two similar techniques. They allow the user to move between different levels of magnitude, zooming in on data in more detail, and zooming out to the more condensed overview.
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16. Hierarchies and Trees Definition Hierarchies in the world
Data repository in which cases are related to subcases
Hierarchies in the world
Family histories
File/directory
Organizational chart
Object-oriented software classes
Main representation
Node-link
Space-filling
Many datasets are inherently hierarchical. For example, in clinical datasets, patients’ family history is inherently hierarchical.
The two main ways to represent these types of data are node-link and space filling.
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17. Node-link Hierarchies
Root at top
Node-link hierarchies are very common in diagrammatic representations. Note that the root is at the top of the diagram.
Image source: “Data structures and file handling: Tree-Maps: a space-filling approach to the visualization of hierarchical information structures”, Brian Johnson, Ben Shneiderman
Johnson, B., Schneiderman, B. (1991).
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18. Node-link Hierarchies (Cont’d – 1)
Examples
Problems
Quickly fills out screen real estate
Difficult to maintain overview of the structure
A common way to visualize files structures is essentially a node-link hierarchy.
Some common problems with this visualization is that they quickly fill out screen real estate, and that it is difficult to maintain overview of the data.
Mamykina, L. (2012).
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19. Node-link Hierarchies (Cont’d – 2)
Potential solutions
Cone Trees (PARC, Robertson, Mackinlay, Card)
Here are some examples of node-link hierarchies.
Robertson, G.G., Mackinlay, J.D., and Card, S.K. (1991).
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20. Space-filling Treemap examples
An alternative approach to representing hierarchical data is space-filling. Smart Money was a popular example of this approach. In Smart Money high-level categories are represented as rectangles that fill the main space. As these categories are divided into lower-level ones, so are the rectangles.
Robertson, G.G., Mackinlay, J.D., and Card, S.K. (1991).
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21. Time Series Data Definition Standard Presentation
Data sets with one variable being the time of the event
Could be uni- or hypervariable
Data mining – looking for patters
Visualization – looking for visual patterns
Time series data is a very common data type in healthcare. Much of patient health data can be considered as time-series. A common way to represent time-series data is on a two-dimensional graph, with time usually indicated on the horizontal axis. The graph on the right is a paper plot of a patient’s temperature (on the vertical or Y axis) over a period of time.
Semi, (2010).
Temperature chart of a case of tuberculosis. (Wellcome Images, 2014)
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22. Time Series Data (Cont’d – 1)
Examples
Predictive calendars
Here is an example of time-series calendar data.
(Sun Microsystems Research Labs)
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23. Time Series Data (Cont’d – 2)
One issue – temporal data is often periodic (cycles – daily, weekly, yearly, etc.)
Linear display is problematic for periodic data
Potential answer – spiral display
Calis, J.V., Konstan, J.A. (1998).

24. Time Series Data (Cont’d – 3)
Additional benefit of spiral display – discovering periodicity in data
An additional benefit of spiral displays that changing their temporal cycle can help to discover periodicity in data.
Calis, J.V., Konstan, J.A. (1998)
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25. Interaction and Dynamic Queries
Data transformation
Details on demand
Dynamic query
There are many situations in which no clever distortion technique can help designers fit all data on the screen, or the data is so complex that no one visualization can help to uncover its hidden properties. In these cases, designers often use interactive visualizations that allow users to actively explore them and vary their dimensions. The two techniques we will discuss here include details on demand and dynamic query.
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26. Interaction and Dynamic Queries (Cont’d – 1)
Data transformation
Details on demand
Details could be removed from the main view for different reasons (mainly due to scaling issues)
In details on demand approach, the details could be removed from the view, however made available on demand. For example, a user might hover the mouse over a particular point on the visualization to examine its components. Mouse over is a common way to trigger details.
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27. Interaction and Dynamic Queries (Cont’d – 2)
Data transformation
Dynamic queries
Visual representation of all possible objects and actions
Rapid, incremental and reversible actions
Direct manipulation is favored (selection by pointing, not typing)
Specifying a query brings immediate display of results
Responsive interaction (< 0.1 sec)
Classic example:
HomeFinder – 1992, University of Maryland, Human Computer Interaction Lab (Ben Shneiderman)
Dynamic query is another common approach to designing interactive visualizations. In this approach, users rely on interactive elements, such as sliders or buttons to change properties of the visualization. For example they can add or remove variables, change resolution, and so forth.
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28. Information Visualization in Medicine
Purpose
Visually present medical data in more intuitive, easy to understand, learn, recognize, navigate and manage formats
Visually magnify subtle aspects of the diagnostic, therapeutic, patient management and healing process which otherwise could be difficult to notice
Prevent information overload and allow members of clinical staff to master large quantities of information
In the last part of this lecture, we will talk specifically about information visualization in medicine.
In the field visualization can help to achieve several important goals. It can help to visually present medical data in a more intuitive easy to understand way that also makes it easy to learn, recognize, navigate and manage the data. It can help to visually magnify subtle aspects of the diagnostic, therapeutic, patient management and healing process which otherwise could be difficult to notice. Finally, it can help prevent information overload and allow members of clinical staff to master large quantities of information.
Currently, information visualization is not very widespread in electronic health record systems, however they are gaining momentum. Researchers and designers explored several different approaches to visualizing clinical data.
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29. Information Visualization in Medicine (Cont’d – 1)
Examples
The Visible Human Explorer
One-Way to visualize patient data is to structure it around physical systems. For example, here you see data about the patient shown around the patient’s organs and body systems.
North, C., Shneiderman, B., Plaisant, C. (1996).
Visible Human Explorer, CU
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30. Information Visualization in Medicine (Cont’d – 2)
This visualization focuses on clinical decision-making. It shows a diagnostic flow, decisions, and plans in regards to patient care.
Aigner, W., Miksch, S. (2006).
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31. Information Visualization in Medicine (Cont’d – 3)
One of the earliest and classic approaches to visualizing clinical data is through time-series. Here, we show visualization by one of the most prominent thinkers in the field, Edward Tafte. In this little view, a lot of highly condensed data allow clinicians to quickly grasp the challenges in patient’s conditions that could lead to quick decisions.
Powsner, S.M., Tufte, E.R. (1994).
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32. Information Visualization in Medicine (Cont’d – 4)
Here is an extension of the graph we showed earlier. This visualization combines several different types of data in the same display, allowing the user to explore different components of the data simultaneously.
Clinical records (LifeLines – Medical Display,
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33. Information Visualization Summary – Lecture b
Many medical decisions are based on the ability to process information
The amount of available information is increasing exponentially, leading to information overload
Information visualization can help to make processing of information faster, more effective and more efficient
This concludes lecture b of Usability and Human Factors, Information Visualization.
To review we discussed why any medical decisions are based on the ability to process information, the amount of available information is increasing exponentially, which is leading to information overload, and final that information visualization can help to make processing of information more effective and more efficient.

34. Information Visualization References – Lecture b
PARC, a Xerox company. (2016). Parc.com. Retrieved 30 June 2016, from
Luca Chittaro, “Information Visualization and its Application to Medicine”. Artificial Intelligence in Medicine, vol.22(2), 2001, pp.81-88
Images
Slide 5: Furnas, G. (1986). "Generalized Fisheye Views", Proceedings of SIGCHI '86, New York, pp
Furnas, G. (1981). "The Fisheye View: A New Look at Structured Files", AT&T BEl Labs Technical Report, 1981.
Furnas, GW. (2006). "A fisheye follow-up: further reflections on focus + context", Proceedings of ACM CHI 2006, Montreal, Canada, April 2006, pp
Slide 6, 7, 10: Bederson, B.B (2002). Fisheye menus. Proceedings of ACM Conference on User Interface Software and Technology (UIST 20002), p
Slide 8: Ying, K. Leung, R.S., Apperley, M.D. (1995). Applying bifocal displays to topological maps. Int. J. Hum.-Comput. Interact. vol 7, 1, p
Slide 9: Spence, R. (1980). Information visualization bifocal display. Department of Electrical & Electronic Engineering, Imperial College London. Retrieved on September 10th, 2010 from /
Slide 11 & 12: Mackinay, J.D., Robertson, G.G., and Card, S.K. (1991). The perspective wall: detail and context smoothly integrated. CHI '91 Proceedings of the SIGCHI conference on Human factors in computing systems: Reaching through technology. ACM. Retrieved September 10th, 2010 from
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35. Information Visualization References – Lecture b (Cont’d – 1)
Images
Slide 17: Johnson, B., Schneiderman, B. (1991). Tree-maps: a space-filling approach to the visualization of hierarchical information structure. VIS 91 Proceedings of the 2nd conference on Visualization.
Slide 18: Mamykina, L. (2012). Personal computer: screenshot of folder structure in Windows operating system. Department of Biomedical Informatics, Columbia University Medical Center, New York, NY.
Slide 19 & 20: Robertson, G.G., Mackinlay, J.D., and Card, S.K. (1991). Cone trees: animated 3D visualizations of hierarchical information. Xerox Palo Alto Research Center. Retrieved on September 10th, 2010 from .
Slide 20: Map of the markets. The Wall Street Journal Smartmoney. Retrieved on September 10th, 2010 from
Slide 21: Semi, P.A. Orbital resonance and solar cycles. Retrieved on October 5th, 2010 from
Slide 22: Sun Microsystems Research Labs. Retrieved September 10th, 2010 from (Inactive Link)
Slide 23 & 24: Calis, J.V., Konstan, J.A. (1998). Interactive visualization of serial period data. Retrieved on September 11th, 2010 from UIST '98 Proceedings of the 11th annual ACM symposium on User interface software and technology.
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36. Information Visualization References – Lecture b (Cont’d – 2)
Images
Slide 29: North, C., Shneiderman, B., Plaisant, C. (1996). The HCIL visible human explorer. Human-Computer Interaction Lab, Computer Science Department. University of Maryland at College Park. Retrieved on September 11th, 2010 from
Slide 30: Aigner, W., Miksch, S. (2006). CareVis: integrated visualization of computerized protocol and temporal patient data. Artificial Intelligence in Medicine,v.37, 3; S
Slide 31: Powsner, S.M., Tufte, E.R. (1994). Graphical summary of patient status. The Lancet, v(344). Retreived from
Slide 32: Lifeline for visualizing patient records. Human Computer Interaction Lab. Retrieved September 11th, 2010 from .
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37. Usability and Human Factors Information Visualization Lecture b
This material was developed by Columbia University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 1U24OC This material was updated by The University of Texas Health Science Center at Houston under Award Number 90WT0006.
No Audio.
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