Data Visualization Literacy

Transcription

1 Data Visualization Literacy Katy Victor H. Yngve Distinguished Professor of Engineering and Information Science Director, Cyberinfrastructure for Network Science Center Indiana University Informing Environmental Health Decisions Through Data Integration National Academies Keck Center, Room Fifth Street, NW, Washington February 20, 2018 Data Visualization Literacy Data visualization literacy (ability to read, make, and explain data visualizations) requires literacy (ability to read and write text, e.g., in titles, axis labels, legend), visual literacy (ability to find, interpret, evaluate, use, and create images and visual media), and data literacy (ability to read, create, and communicate data). Being able to read and write data visualizations is becoming as important as being able to read and write text. Understanding, measuring, and improving data and visualization literacy is important for understanding STEAM developments and to strategically approach global issues. 2

2 Tasks See Atlas of Science: Anyone Can Map, page 5 3 Needs-Driven Workflow Design Validation Interpretation DEPLOY Stakeholders Types and levels of analysis determine data, algorithms & parameters, and deployment Visually encode data Overlay data Data Select visualiz. type READ ANALYZE VISUALIZE 4

3 Visualization Framework See Atlas of Science: Anyone Can Map, page 24 5 Visualization Framework See BIG TABLE: See Atlas of Science: Anyone Can Map, pages

4 Sci2 Tool Interface Components Implement Vis Framework Download tool for free at 7 Load One File and Run Many Analyses and Visualizations Times Cited Publication Year City of Publisher Country Journal Title (Full) Title Subject Category Authors NEW YORK USA COMMUNICATI Plug-and-Play Macroscopes Computer Science Borner, K ONS OF THE ACM MALDEN USA CTS-CLINICAL AND TRANSLATIONA L SCIENCE Advancing the Science of Team Science Research & Experimental Medicine Falk-Krzesinski, HJ Borner, K Contractor, N Fiore, SM Hall, KL Keyton, J Spring, B Stokols, D Trochim, W Uzzi, B WASHINGTON USA SCIENCE A Multi-Level Systems Cell Biology Borner, K Contractor, N Falk- TRANSLATIONA Perspective for the Science Research & Krzesinski, HJ Fiore, SM Hall, L MEDICINE of Team Science Experimental Medicine KL Keyton, J Spring, B Stokols, D Trochim, W Uzzi, B 8

5 Load One File and Run Many Analyses and Visualizations Times Cited Publication Year City of Publisher Country Journal Title (Full) Title Subject Category Authors NEW YORK USA COMMUNICATI Plug-and-Play Macroscopes Computer Science Borner, K ONS OF THE ACM MALDEN USA CTS-CLINICAL AND TRANSLATIONA L SCIENCE Advancing the Science of Team Science Research & Experimental Medicine Falk-Krzesinski, HJ Borner, K Contractor, N Fiore, SM Hall, KL Keyton, J Spring, B Stokols, D Trochim, W Uzzi, B WASHINGTON USA SCIENCE A Multi-Level Systems Cell Biology Borner, K Contractor, N Falk- TRANSLATIONA Perspective for the Science Research & Krzesinski, HJ Fiore, SM Hall, L MEDICINE of Team Science Experimental Medicine KL Keyton, J Spring, B Stokols, D Trochim, W Uzzi, B Co-author and many other bi-modal networks. 9 10

6 Fig. 1. From: ToxPi GUI: an interactive visualization tool for transparent integration of data from diverse sources of evidence. Example of relationship between Results and Chart windows. The upper panel shows sorted ToxPi results, with the highlighted reference chemical (rank #2) signified by the bold square and cross-hairs on the lower panel. Inset: popup high-resolution window showing individual chemical s ToxPi and information David M. Reif, et al. Bioinformatics Feb 1;29(3): References Börner, Katy, Chen, Chaomei, and Boyack, Kevin. (2003). Visualizing Knowledge Domains. In Blaise Cronin (Ed.), ARIST, Medford, NJ: Information Today, Volume 37, Chapter 5, pp Shiffrin, Richard M. and Börner, Katy (Eds.) (2004). Mapping Knowledge Domains. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl_1). Börner, Katy (2010) Atlas of Science: Visualizing What We Know. The MIT Press. Scharnhorst, Andrea, Börner, Katy, van den Besselaar, Peter (2012) Models of Science Dynamics. Springer Verlag. Katy Börner, Michael Conlon, Jon Corson-Rikert, Cornell, Ying Ding (2012) VIVO: A Semantic Approach to Scholarly Networking and Discovery. Morgan & Claypool. Katy Börner and David E Polley (2014) Visual Insights: A Practical Guide to Making Sense of Data. The MIT Press. Börner, Katy (2015) Atlas of Knowledge: Anyone Can Map. The MIT Press. 12

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11 DATA TYPES Datasets Attributes Data Types Items Attributes Links Positions Grids Attribute Types Categorical Data and Dataset Types Tables Networks & Trees Fields Geometry Clusters, Sets, Lists Ordered Ordinal Items Items (nodes) Grids Items Items Attributes Links Attributes Positions Attributes Positions Quantitative Dataset Types Tables Attributes (columns) Networks Fields Geometry (Continuous) (Spatial) Grid of positions Ordering Direction Sequential Items (rows) Cell containing value Link Node (item) Cell Position Attributes (columns) Diverging Multidimensional Table Trees Value in cell Cyclic Value in cell Visualization Analysis and Design. Tamara Munzner, CRC Press, 2014.

12 VISUAL ENCODINGS Magnitude Channels: Ordered Attributes Position on common scale Position on unaligned scale Length (1D size) Tilt/angle Identity Channels: Categorical Attributes Spatial region Color hue Motion Shape Area (2D size) Depth (3D position) Color luminance Color saturation Curvature Volume (3D size) Visualization Analysis and Design. Tamara Munzner, CRC Press, 2014.

13 EXERCISE - Data Types

14 EXERCISE - Deconstruction

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