Introduction to Data Visualization Slides

Duke Intro to Data Visualization

1. Introduction to Data Visualization Angela Zoss Data Visualization Coordinator Data & GIS Services , Research Computing September 4, 2012 LibGuide at : http ://guides.library.duke.edu/content.php?pid= 355157

2. Introduction Angela Zoss ( angela [email protected] ) Started as Data Visualization Coordinator in June Previous study in Information Science, Human -Computer Interaction, Communication, Cognitive Science, and Computer Science Primary expertise in information visualization, network analysis

3. What is visualization? Data visualization Information visualization Scientific visualization Static vs. interactive vs. dynamic Data Categorical (Nominal, Ordinal) Quantitative (Interval, Ratio)

4. Why visualize data ? Summary statistics may miss important trends Lower barrier of entry to data analysis, both for researchers and audiences Can operate as an important first stage of research into a new area of study Can preserve complexity or present multiple views of a single data set http://en.wikipedia.org/wiki/Anscombe%27s_quartet Anscombe’s Quartet

5. Two primary goals for visualization: Visualization for analysis (a.k.a. “ visual analytics ”) Exploit visual perception strengths to explore/analyze data relationships Try many views/combinations to find meaningful stories Visualization for communication Select a particular view of the data to share Construct the visualization with a goal in mind and in a way that takes into account the skills and needs of the expected audience

6. Seven Stages of Visualizing Data From Fry (2008), p. 5: Acquire : Obtain the data... Parse : Provide some structure for the data’s meaning , and order it into categories. Filter : Remove all but the data of interest . Mine : Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context. Represent : Choose a basic visual model, such as a bar graph, list, or tree. Refine : Improve the basic representation to make it clearer and more visually engaging. Interact : Add methods for manipulating the data or controlling what features are visible. Note: stages are often iterative and may have a flexible order or even be omitted in some projects. Fry, B. (2008). Visualizing data . Sebastopol, CA: O’Reilly Media, Inc.

7. From Data to Graphic What data types are present in the data source ? How are the variables likely to relate? What visualization type seems to be the best fit for the goal?

8. Matching Data Types to Visual Elements Mackinlay , J. (1986). Automating the design of graphical presentations of relational information . ACM Transactions on Graphics, 5 (2), 110-141.

9. Chart Choosers Interested in showing composition? Relationship? Distribution? (What do the charts do well?) http ://extremepresentation.typepad.com/blog/2006/09/ choosing_a_good.html Chart typically determines position of elements, with some built-in visual encodings. Additional visual encodings can often be added to incorporate more variables into charts, but beware of overwhelming the audience.

10. Common Visualization Types 1D/Linear (omitted) 2D/Planar (incl. Geospatial) 3D/Volumetric (omitted) Temporal nD /Multidimensional Tree/Hierarchical Network Shneiderman , B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. Proceedings of IEEE Symposium on Visual Languages - Boulder, CO (pp. 336-343) . See LibGuide for most up-to-date examples.

11. Style and Format Color: Grade the saturation (lightness), not the hue (color) Cultural considerations Print considerations (check in grayscale ) High saturation for small areas Not too many! (6 - 12 at most) Clarity vs. Aesthetics http://dataremixed.com/2012/05/data-visualization-clarity-or-aesthetics /

12. Questions? Angela Zoss [email protected]