ID 413 - Information Graphics and Data Visualization

Instructor: Prof. Venkatesh Rajamanickam

Registration: ASC may require some students to register manually. If so, please use this registration form and take my signature by Jan 9^th, 11 am.

Timings: Wednesdays and Fridays 9:30 am to 11 am (LT 303), and some Saturdays at IDC.

Office Hours: Fridays 11:30 AM to 1:00 PM at my office in Transit Building, Room No. 330 or by appointment.

If you're on Github, you can watch the Github course repo for updates.

Course Overview

Information graphics reveal the hidden, explain the complex and illuminate the obscure. Constructing visual representation of information is not mere translation of what can be read to what can be seen. It entails filtering the information, establishing relationships, discerning patterns and representing them in a manner that enables a consumer of that information construct meaningful knowledge.

Recent advances in technology have enabled us with means for creating, recording and analysing incredible amounts of data. Where once data was scarce, it is now available in abundance. The field of Computer Science has made great strides in creating capabilities for data handling and analysis. However the techniques required to most effectively display and communicate data are somewhat neglected. This design course aims to fill the gap by teaching techniques for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science.

Course Objectives

This course will provide students with the foundations necessary for understanding and extending the current state of the art in data visualization. By the end of the course, students will be able to:

Gain an understanding of the key techniques and theory used in visualization, including data models, graphical perception and techniques for visual encoding and interaction.
Plan for data-based storytelling through charts, maps, and diagrams.
Gain practical experience building and evaluating visualization systems.
Gain the ability to read and discuss research papers from the visualization literature.

Course Contents

Information visualization overview, historical perspective
Vision, perception and cognition principles of information visualization
Data principles, models, dimensions and analysis
Information taxonomy
Visual encoding of data
Mapping, cartography, geo-visualization
Visualization tools and strategies
Data stories and presentation
Short exercises and a mini project in visual representation of data

Schedule

Date	Content
6^th Jan, 2016	Lecture 01: Introduction to Information Grahics & Data Visualization
8^th Jan, 2016	Lecture 02: Introduction to Information Grahics & Data Visualization (contd.) (View slides - both Lecture 01 & 02) Exercises: Complete the 2 class participation exercises (refer to slides) and submit them by next class (13th Jan). For exercise 1, you can simply hand draw on a A4 size paper, and for exercise 2, you should put the photographs in a PDF file (with explanatory text where required) and email it to me (subject line: ID413-exercise 2).
13^th Jan, 2016	Recommended Reading: Reflections on how designers design with data. Alex Bigelow, Steven Druckery, Danyel Fishery, Miriah Meyer. AVI '14 Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces, pp. 17-24. A Tour through the Visualization Zoo. Jeffrey Heer, Michael Bostock, Vadim Ogievetsky. Communications of the ACM, 53(6), pp. 59-67, Jun 2010. The Value of Visualization. Jarke van Wijk. Proceedings of the IEEE Visualization Conference, pp. 79-86, 2005. Lecture 03: Data Sense (View slides)
15^th Jan, 2016	Recommended Reading: On the Theory of Scales of Measurement. S. S. Stevens. Science New Series, Vol. 103, No. 2684, pp. 677-680. Jun 1946. Assignment 1: Redesign the infographic that accompanies The Hindu (Jan 14, 2016) article, ‘Increase farmers’ income, not production’. Email your submission as PDF to me (subject line: ID413-assignment 1) latest by 21 Jan mid-night. Lecture 04: Data Intelligence (View slides)
20^th Jan, 2016	Recommended Reading: Think/Classify by Perec. Read this together with The Art and Manner of Arranging One’s Books also by Perec, and The Analytical Language of John Wilkins a short story by Borges. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Shneiderman, B. IEEE Symposium on Visual Languages, 1996. Proceedings. The Structure of Information Visulaization Space. Card, S.K., Mackinlay, J. IEEE Symposium on Information Visualization, 1997. Proceedings. Lecture 05: Data Taxonomy (View slides)
22^th Jan, 2016	Recommended Reading: How NOT to Lie with Visualization. Bernice E. Rogowitz, Lloyd A. Treinish, Steve Bryson. Journal Computers in Physics, Volume 10 Issue 3, May/June 1996. The Cognitive Style of Powerpoint. Edward R. Tufte. Book, The Cognitive Style of PowerPoint: Pitching Out Corrupts Within, Second Edition, Graphis Press, 2006. The Unreasonable Effectiveness of Data. Alon Halevy, Peter Norvig, Fernando Pereira. Google. IEEE Intelligent Systems, vol.24, no. 2, March/April 2009. Lecture 06: Data Analysis (View slides) & Displaying Scientific Evidence for Making Valid Decisions: Lessons from Two Case Studies (View slides).
27^th Jan, 2016	Recommended Reading: Why “Big Data” Is a Big Deal. Jonathan Shaw, Harvard Magazine, March-April 2014. How Companies Learn Your Secrets. Charles Duhigg, New York Times, February 16, 2012. Big Data in the Big Apple. Viktor Schönberger and Kenneth Cukier, Slate, March 6 2013. Recent Critiques of Big Data: Small Bibliography. Ernest Davis, New York University. Lecture 07: Big Data (View slides).
29^th Jan, 2016	Data Visualization Project Identify narratives/aspects/issues that you want to explore and highlight through visualization of data from any India-specific dataset or datatsets. Identify datasets which when visualized with other datasets offer the greatest potential for an unique, surprising or hitherto unavailable point of view. You will analyse this data, extract relationships, discover interesting patterns and hopefully communicate an insightful story through an interactive visualization. You will work in small teams (of 2, 3 or 4) to complete this project over the rest of the semester. You will be using d3.js to implement your interactive visualization. Refer to the project page for more details.
03^rd Feb, 2016	Recommended Reading/Watching: Attention and Visual Memory in Visualization and Computer Graphics. Christopher G. Healey, James T. Enns. IEEE Trans. Vis. Comput. Graph. 2012. Gestalt principles (Part 1). Bang Wong. Nature Methods 7, pp. 863, Nov 2010. Gestalt principles (Part 2). Bang Wong. Nature Methods 7, pp. 941, Dec 2010. National Geographic: Test Your Brain Episode 1 - Pay Attention. National Geographic: Test Your Brain Episode 2 - Perception. National Geographic: Test Your Brain Episode 3 - Memory. Lecture 08: Vision Perception and Cognition (part 1) (View slides).
05^th Feb, 2016	Recommended Reading/Watching: The Science of Art: A Neurological Theory of Aesthetic Experience. V.S. Ramachandran and William Hirstein. Journal of Consciousness Studies, 6, No. 6-7, 1999, pp. 15–51. Aesthetic Universals and the Neurology of Hindu Art. Video of lecture by V. S. Ramachandran. Lecture 09: Vision Perception and Cognition (part 2) (View slides).
10^th Feb, 2016	Recommended Reading: Points of View: The design process. Bang Wong, Nature Methods, Vol.8 No.12, December 2011. Points of View: Elements of visual style. Bang Wong, Nature Methods, Vol.10 No.5, May 2013. Lecture 10: Visual Encoding (View slides).
16^th Feb, 2016	Assignment 2: In this assignment, you will design a visualization for a small data set and provide a rationale for your design choices. The choices you make will demonstrate your understanding of the data, visual and encoding principles you have learned so far. The data set is a collection of measurements related to the IITB's Million Solar Lamp project -- demographics of beneficiaries, and the assembly, distribution & repairs of solar lamps in the Jhauba Block, Jhauba District of Madhya Pradesh state. The data are summarised in multiples tables in this report. Your challenge is to combine these data in one single visualization that can fit in a A3 size paper. Submit a short write-up (1 page), providing a rigorous rationale for your design decisions. Explain the visual encodings you used and why they are appropriate for the data. The best visualization will be incorporated into the final reports and duly credited. Due: 7 Mar 2016, 11:59 pm. Recommended Reading: Chapter 4: Data-Ink and Graphical Redesign. In The Visual Display of Quantitative Information. Tufte. Chapter 9: Aesthetics and Technique in Data Graphical Design. In The Visual Display of Quantitative Information. Tufte. Design and Redesign in Data Visualization. Martin Wattenberg and Fernanda Viégas. Lecture 11: Visualization Design (View slides).
24^th & 26^th Feb, 2016	Mid-sem Week: No classes
2^nd Mar, 2016	In class review of assignment 2 CSV master file (right-click here to save) with additional data (as discussed in today's class)
4^th Mar, 2016	No class
9^th Mar, 2016	In class review of assignment 2 submissions
11^th Mar, 2016	Recommended Reading: How Maps Work. Denis Wood, Cartographica, Vol 29 Numbers 3 & 4 Autumn/Winter 1992, pp 66-74. The City Image and its Elements. Kevin Lynch, from The Image of the City, 1960. Getting There: The science of driving directions. Nick Paumgarten, The New Yorker, 24 April 2006. Rendering effective route maps: improving usability through generalization. Maneesh Agrawala and Chris Stolte, SIGGRAPH '01 Proceedings of the 28th annual conference on Computer graphics and interactive techniques. Pages 241-249. Lecture 12: Geo-Visualization (View slides).
16^th Mar, 2016	Lecture 13: Geo-Visualization (Contd.).
18^th Mar, 2016	Interim Project presentations by groups - session 1 (assessed component)
23^th Mar, 2016	Interim Project presentations by groups - session 2 (assessed component)
25^th Mar, 2016	Good Friday -- Public Holiday
29^th Mar & 1^st Apr, 2016	No classes -- Complete project and prepare presentation
6^th & 8^th Apr, 2016	Project presentations
13^th Apr, 2016	End-semester Examination (9:30 to 11:00 AM)

Links to slides will be added after the respective lecture

Assessment

For this course, you will be assessed as follows (updated):

3 individual assignments (20%)
1 group project (40%)
No midsem
Endsem (30%)
Attendance & class participation (10%)

Textbook

There is no recommended text for the course. All lecture slides will be made available as PDFs after the lectures for your reference. However lecture slides alone won’t be sufficient to prepare for the end semester exam. So keep a notebook and take notes.

Reference Books

Bertin, Jacques (1967). Semiology of Graphics: Diagrams, Networks, Maps. Esri Press.
Cairo, Alberto (2013). The Functional Art. New Riders
Few, Stephen (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press.
Fry, Ben (2012). Visualizing Data: Exploring and Explaining Data with the Processing Environment. O'Reilly Media.
Harmon, Katherine (2003). You Are Here. Princeton Architectural Press.
Harris, Robert L. (2000). Information Graphics: A Comprehensive Illustrated Reference. Oxford University Press.
Helfand, Jessica (2002). Reinventing the Wheel. Princeton Architectural Press.
Holmes, Nigel (1991). Designer's Guide to Creating Charts and Diagrams. Watson-Guptill.
Holmes, Nigel (2005). Wordless Diagrams. Bloomsbury.
Institute for Information Design Japan (2005). Information Design Source Book. Graphics-Sha.
Jacobson, Robert (2000). Information Design. The MIT Press.
McCloud, Scott (1994). McCloud, Scott (1994). Understanding Comics Understanding Comics. Harper . Harper.
Mijksenaar, Paul and Westendorp, Piet. Open Here: The Art of Instructional Design.
Mijksenaar, Paul (1997). Visual Function: An Introduction to Information Design. Princeton Architectural Press.
Myer, Eric. K. (1997). Designing Infographics. Hayden Books.
Tufte, Edward (1990). Envisioning Information. Graphics Press.
Tufte, Edward (1997). Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press.
Tufte, Edward (2001). The Visual Display of Quantitative Information. Graphics Press.
Ware, Colin (2012). Information Visualization, Third Edition: Perception for Design. Morgan Kaufmann.
Yau, Nathan (2011). Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley.
Yau, Nathan (2013). Data Points: Visualization That Means Something. Wiley.

Contact

You can reach Prof. Venkatesh Rajamanickam via email