1. Usability and Human Factors
Information Visualization
Welcome to Usability and Human Factors, Information Visualization. This is lecture a.
In this lecture we will be talking about how information visualization can support and enhance the representation of trends and aggregate data.
Lecture a
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 a – 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 unit students will be able to:
Define what information visualization is and what it is not.
Describe different ways to present different types of information, from simple to complex sets with multiple variables
Discuss how to visualize information that is inherently hierarchical
Apply ways to design not only static visualizations that don’t change over time and dynamic visualizations that also allow users to interact with them.
Identify dimensions of visualizing medical data.

3. Field of Information Visualization
Data explosion: Cisco predicts that by 2020, global internet traffic will reach 2.3 zettabytes per year (10 21 bytes) (Cisco, 2016)
Knowledge workers
Reliance on information
Ability to process information to make correct decision or select appropriate action
Medical applications
Access to large volumes of various patient-related information
Assess the case, select appropriate action
The amount of information people consume in their daily lives is increasing every year. The information technology company Cisco estimates by the year 2020, global internet traffic will reach 2.3 zettabytes, or 10 to the 21st power, bytes. There will be over 3 networked devices per person.
Printed documents constitute only a very small portion of that data, leaving the majority of it to the digital realm that does not have direct physical manifestation.
This is particularly the case for a person whose jobs it is to process and create knowledge. They have to rely on information coming from multiples sources. The ability to process it and make correct decisions or select appropriate action is essential to their performance.
Not surprisingly, clinicians are increasingly included in that category. Now days they rely not only on directly collected information from their patients, but also on large volumes of aggregated patient-related data. Multitudes of clinical journals discuss the advances in medical sciences, new monitoring devices that are capable of collecting more information about each patient, and integration of electronic health records make available patient data over the course of their lives. And clinicians are expected to be able to access it, analyze it, and select appropriate action.
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4. Field of Information Visualization (Cont’d – 1)
Human Vision
Highest bandwidth (100 MB/sec)
Fast, parallel
Pattern recognition
Pre-attentive
Extends memory and cognitive capacity
People think visually
Great resource to use in application design
The good news is that human beings developed very complex perceptual systems to deal with the complex physical environment around them. Human vision is incredibly fast; it is highly parallel, we can perceive many things at once; we have a great ability to recognize patterns; often time we notice things without consciously looking for them, which is call pre-attentive perception; it far extends our memory and cognitive capacity; and there is a growing body of evidence that people think visually. All these properties make vision a great resource to use in the design of computing applications.
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5. Field of Information Visualization (Cont’d – 2)
Which state has the highest income? Is there any relationship between income and education?
(tables and graph – lectures in InfoVis Chris North, UV)
Let’s look at this example. This is a very common way of presenting data, in a table. This table contains census data, specifically average education level and income in different states. Can you tell which state has the highest income? Is there any relationship between income and education?
North, (2010)
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6. Field of Information Visualization (Cont’d – 3)
Which state has the highest income? Is there any relationship between income and education?
(tables and graph – lectures in InfoVis Chris North, UV
Let’s try this again, now looking at a relatively simple visualization of the same data. Now its salient properties become much more apparent; we no longer need to rely on serial search, scanning the table one item after another. We can quickly grasp the relationship in the data just by looking at the graph.
North, (2010)
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7. Field of Information Visualization (Cont’d – 4)
Information Visualization IS
Cognitive process
Understand decision making practices in a particular domain
Understand data used in decision making
Provide tools that present data in a way that helps people understand it and gain insight from it (data  information  knowledge)
Information Visualization IS NOT
Visual design
UI design or Information Architecture (although can be related)
Scientific visualization (visual representation of physical objects – medical imaging)
Given that example, we can say that information visualization is really a way to support human cognition. It requires understanding of decision making practices in a particular domain; understanding data used in that decision-making. This understanding helps designers to develop tools that present data in a way that improves understanding and insight, and helps to convert data to information and then to knowledge.
Now a few words about what it is not. Information Visualization is different from visual design, in that it really primarily focuses on supporting cognitive processes. It is different from User Interface Design or Information architecture; not all user interfaces involve information visualization, although many do. Finally, it is different from scientific visualization, which is primarily concerned with physical representation of physical objects, for example, medical imaging. In contrast, information visualization is concerned with information, which does not have physical properties.
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8. Field of Information Visualization (Cont’d – 5)
Components:
Mapping items with no direct physical correspondence to 1, 2 or 3-D physical space
Highlighting important aspects of data that support its comprehension
Analysis tasks:
Identify, Locate, Distinguish, Categorize, Cluster, Distribution, Rank, Compare (within relations), Compare (between relations), Associate, Correlate
Overview, Zoom, Filter, Details on demand, Relate, History, Extract
Visualizing information includes two main components:
First, it involves taking items with no direct physical correspondence and mapping them to 1, 2, or 3-dimensional physical space.
Second, it involves giving information a visual representation that is useful for analysis and decision support.
Since visualizations are meant to support different cognitive tasks, it is important to understand the nature of these tasks. Researchers in information visualization identify a large number of these basic tasks that include for example, identifying objects, distinguishing between different objects, and more. It also includes mapping them to functions of visualization displays, such as overview, zoom, and so on. We will talk about these functions in greater detail later in the lecture.
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9. Examples
Here are two examples of information visualization that became popular. One was Smart Money, which visualized complex data on performance of financial markets. The other one is an architectural history of Manhattan. While the Smart Money site is no longer available, the Skyscraper.org site can still be explored.
 ("Stock Research & MarketWatch Investment Tools - MarketWatch", n.d.)
 ("Transparent New York", n.d.)
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10. Multivariate data sets
Data tables
Most popular way of presenting information in information-rich interfaces (tabular)
Disadvantage – provide access to data, but minimal decision support
Data types
Nominal (qualitative)
Ordinal
Numeric (quantitative)
Meta-data (data about data, i.e. type)
Data sets – number of variables per class
1 – Univariable sets 2 – Bivariate sets
3 – Trivariate sets >3 – Hypervariate sets
At the heart of information visualization is data.
The most popular way to present data is in a table. Tables are great for databases, where you can write automated scripts to query them; however, they provide minimal decision support for people.
There are several types of data and it is important to distinguish between them when thinking about visualization. There are many ways to think about data types; we will talk about one of the simplest taxonomies, which is Nominal data.
Nominal data is any qualitative data that simply includes categories, for example, names of states or people.
Ordinal data presupposes some order to it, for example age categories (18-20, 21-35, etc.).
Numeric data is any data that has a numeric value, for example age of individuals.
Finally, meta-data is data about data, for example its type.
Once we have a set of data, one of the most critical characteristics of this set that will suggest particular approaches to visualization is how many variables it includes. We will differentiate between univariate sets that include only one variable, bivariate, trivariate, and multivariate sets. The reason we don’t really distinguish beyond 3 is that by then we have utilized all the dimensions of our 3-dimensional space for developing visualizations, so anything beyond that requires creative thinking.
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11. Multivariate data sets (Cont’d – 1)
Representation
Graphs
Charts (structure and relationships between entities are important)
Maps (spatial organization)
Diagrams (schematic picture of object or entity)
Glyphs (metaphors – mapping variables to particular visual properties)
As you can probably guess, the majority of datasets you might encounter in the real world are multivariate, and they present a set of challenges. The ways to represent them usually include graphs, charts, in which structure and relationship between entities are important, maps that favor spatial organization, diagrams, which feature schematic properties of objects or entities, and glyphs, or visual metaphors, that allow us to map variables to particular visual properties.
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12. Representations of data sets: Simple data sets
Univariate sets
Univariate sets are the simplest sets you will encounter. Many of the everyday visualizations you see, for example line or bar graphs, deal with univariate data.
Mamykina, (2010).
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13. Representations of data sets: Simple data sets (Cont’d – 1)
Bivariate sets (scatter plot is common)
Bivariate sets are more complex. They include two variables that often have some dependency. The purpose of the visualization is to make that dependency apparent to the naked eye. For example, if there is any dependency between price of a car and its mileage, a scatter plot would make it possible to see it.
Mamykina, (2010)
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14. Representations of data sets: Simple data sets (Cont’d – 2)
Trivariate sets (3D scatter plot – quickly becomes messy)
As you add more dimensions, simple visualizations quickly become messier. For example, here we add another dimension, to include another component, horsepower, to our scatter plot. However, 3-dimensional plots usually are relatively difficult to interpret due to such issues as occlusion (when some elements of visualization block others).
Mamykina, (2010).
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15. Representations of data sets: Simple data sets (Cont’d – 3)
Trivariate sets (3D scatter plot – quickly becomes messy)
An alternative to increasing a number of dimensions is to manipulate other visual properties. For example, here we use spatial orientation for price and mileages, and size of a visual element for the third dimension, such as horsepower.
Mamykina, (2010).
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16. Multivariate data sets (Cont’d – 2)
Each variable in its own display
As we discussed earlier, most of the real world datasets include multiple variables. Visualizing these datasets is nontrivial and often requires creative thinking. Most importantly, the visualization should be designed to support the tasks and activities of users.
The easiest way to represent multiple variables is to give each a separate display, and combine these displays on the same screen. For example, stacked timelines are a common way to represent several types of time-series data together.
Mamykina, (2010).
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17. Hypervariate data sets
Scatterplot matrix
Here is one example of different variables displayed in different scatter plots. While quite complex for a novice, this rich visualization could allow an expert to distinguish important properties of data.
("ArcObjects Online", n.d.)
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18. Hypervariate data sets (Cont’d – 1) Star Plot
A star plot is another way of visualizing sets with many variables. In this visualization, each variable receives its own coordinate axis, all coming from one central point. The small charts on the right show how star plots can be used to help monitor changes in many variables over time simultaneously.
Stasko, J. (2005).
Star Plot
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19. Hypervariate data sets (Cont’d – 2) Parallel coordinates
(VRVIS, 2012)

20. Hypervariate data sets (Cont’d – 3) Metaphor-based
Another way to represent multiple properties of data in the same visualization is to use metaphors. Here, is an example of planned attendance meetings represented by schematic tables with coffee cups. Each table represents a meeting. Full cups represent how many individuals of those invited accepted the invitation, empty cups show individuals who rejected meetings, and upside down cups show individuals who have not responded. This visualization has been shown as promising for individuals of older age and lower education.
Mamykina, L., Mynatt, E.D. (2008)
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21. Information Visualization Summary – Lecture a
History of information visualization
Multivariate data sets and graph representations
Hypervariate data sets
This concludes lecture a of Usability and Human Factors, Information Visualization.
In this section we reviewed the history of Information visualization, and then we examined two examples of data sets, multivariate data sets and hypervariate data sets. Our next unit we will discuss scaling and time scaling.

22. Information Visualization References – Lecture a
Mamykina, L., Mynatt, E.D., 2008 (forthcoming). Two Approaches to Interpreting Health and Wellness Information, in International Journal of Advanced Pervasive and Ubiquitous Computing (JAPUC), 2009.
Provider, S. & Papers, W. (2016). The Zettabyte Era—Trends and Analysis. Cisco. Retrieved 30 June 2016, from
Images
Slide 5 & 6: North, Chris. Information visualization (CPSC) Class Lecture. Retrieved September 10th, 2010 from
Slide 9: Stock Research & MarketWatch Investment Tools - MarketWatch. Smartmoney.com. Retrieved 30 June 2016, from
Slide 9: Transparent New York. Skyscraper.org. Retrieved 30 June 2016, from
Slides 12-16: Mamykina, L. Data graphics. Department of Biomedical Informatics, Columbia University Medical Center, New York, NY.
Slide 17: ArcObjects Online. Edndoc.esri.com. Retrieved 30 June 2016, from
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23. Information Visualization References – Lecture a (Cont’d – 1)
Images
Slide 18: Stasko, J. (2005). Multivariate data and representation: CS Information Visualization. PowerPoint Presentation, retrieved on Septmber 10th, 2010 from
Slide 19: Retrieved on September 10th, 2010 from
Slide 20: Mamykina, L., Mynatt, E.D. (2008). Two Approaches to Interpreting Health and Wellness Information, in International Journal of Advanced Pervasive and Ubiquitous Computing (JAPUC).(forthcoming)
No audio.

24. Usability and Human Factors Information Visualization Lecture a
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.
Health IT Workforce Curriculum Version 4.0