VISUALIZING INFORMATION: WHERE DATA MEETS DESIGN Aimée Knight, PhD

Course Description The representation of information through images is a powerful and innovative tool for extending methods of research and dissemination. In this course we will explore ways to creatively visualize data for research, while rendering information more useful, engaging and accessible to audiences.

Using a design thinking methodology we will 1) learn how to examine all kinds of data — from boutique data to big data, from fast data to slow data, from low-tech to hi-tech, from qualitative to quantitative, 2) learn how to extend insights and innovate through design as we create illustrations, maps, infographics, spark-lines, timelines, data sculptures, data murals and interactive visual exhibitions using open source data and tools.

*No prior experience with data or artistic talent necessary — just a willingness to think critically and creatively with different materials from pipe cleaners to code.

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3D Data Sculpture Prototypes from the Digital Humanities Summer Institute University of Victoria, June 2015

Goals and objectives

• To combine data with sensory forms of knowing and knowledge production

• To communicate complex information aesthetically in both form and content

• To explore how current interfaces, design tools, and choices in form & content shape the audience’s (or user’s) experience

• To get our hands dirty experimenting with an array of data viz techniques including infographics, interactive documentary, data murals and 3D data sculptures

• To explore methods in Human Centered Design, including how to successfully ideate, create content and execute creative design solutions

• To make this time and this space an inviting collaborative design studio for visual knowledge production

Major Projects (75%) There are five major projects in this course, each worth 15% of the total course grade. Each project is evaluated with a multi-media rubric focusing In the areas of a) design thinking, b) media aesthetics, c) knowledge integration and d) audience engagement. A scholarly component is required with each project submission as well as a short presentation/critique. Project revisions are accepted if there is a substantial re-seeing of the project. Detailed guidelines for projects will be presented in class.

Data diaries (10%) For this project we will collect and measure a particular type of data about our everyday lives and use this data to create hand-rendered visualizations to share each week during our class meetings (10 total).

Student Panel Presentations (15%) Once in the semester you will participate in a panel discussion in class based on course topics. Topics include 1) physical visualization, 2) the Quantified Self, 3) big data vs. boutique data and 4) *open forum.

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PART 1 DATA EXPLORATION Why do we communicate with data?

Data explorers think creatively about data to distill it, draw out its essence and communicate with it. We will examine an array of data explorers from the 18th – 21st centuries. We will also explore a variety of ways to analyze and critique data visualizations. Moving into the 21st century we will examine contemporary data visualizations and ways to analyze their form and content for aesthetic engagement. We will also become familiar with data visualization experts working within this creative medium such as Hans Rosling, Kim Crawford, Edward Tufte, Ben Fry, Jer Thorp, and Kim Rees. We will also collaborate to create our own heuristic for the critique and production of data displays. We will discuss the concept of a data diary — in which we will we collect and measure a particular type of data about our everyday lives and use this data to make a small drawing to share each week.

Readings • Chimero, Frank. Shape of Design: A Fieldguide for Makers, 2012. • Fry, Ben. Visualizing Data: Exploring and Explaining Data with the Processing

Environment Sebastopol, CA: O’Reilly Media, 2008. (Excerpt) • Heller, Steven and Rick Landes. Infographic Designers' Sketchbooks. Princeton, NJ:

Princeton Architectural Press, 2014. (Excerpt) • IDEO: The Field Guide to Human Centered Design, 2009.

Week 1 August 30 Introductions; overview; short history of data visualization

September 1 Data Explorers 18th-20th centuries; creating a heuristic Readings: A Brief History of Data Vizualization (2006); Shape of Design: A Fieldguide for Makers

Week 2 September 6 Data Explorers 21st century; creating a heuristic Readings: IDEO: The Field Guide to Human-Centered Design; NYC Cab (Project) ; Ben Fry, Visualizing Data

September 8 Visualize to analyze; explore online tools (Raw, Datawrapper, Tableau Public, Google Fusion Tables, Gapminder, Silk) Readings: Data Journalism Handbook (2012); Dear Data (Data Diary project); Infographic Designers' Sketchbooks (excerpt)

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PART 2 RETHINKING VISUALIZATIONS Why do we need to reimagine data?

This week begins with a discussion of Dear Data entries and the new human-centered heuristic we created last week to evaluate data visualizations. We will apply this heuristic to a gallery of data visualizations and make any adjustments, as needed. We will then move on to a discussion of methods as we examine the periodic table of data visualization methods to discuss various ways of representing data including information, concept, strategy, metaphor and compound visualization. In Week 4, we will get creative (and possible messy) as we build data sculptures. Playing around with data can be liberating, especially for those of us who can get stuck in our ways. This hand-made data activity builds capacity in translating words and numbers into structural forms (called physical visualization). Physical visualization allows us gain new insights from data and see it new ways.

Readings • Borner, Kate and David Polley. Visual Insights: A Practical Guide to Making Sense of

Data. Cambridge, MA: MIT Press, 2014. (Excerpt) • Cairo, Alberto. The Functional Art: An introduction to Information Graphics and

Visualization. Berkeley, CA: New Riders, 2012. (Excerpt) • Meirelles, Isabel. Design for Information: An Introduction to the Histories, Theories,

and Best Practices Behind Effective Information Visualization. Rockport, 2013. (Excerpt)

Week 3 September 13 Overview of Periodic Table; critique of available open source tools Readings: The Functional Art: An introduction to information graphics and visualization (excerpt); Periodic Table of Data Visualization Methods

September 15 Student panel discussion: Physical visualization examples Readings; Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualization (excerpt); 13 pt; Data Recipes; Timeline of Physical Visualizations

Week 4 September 19 Data sculpture activity Readings: Physical vs Virtual Prototyping; How to Build a Prototype in One Hour

September 21 3-D Data Sculpture Presentation

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PART 3 MAPPING DATA IN TIME How can we visualize data in time?

In Week 5, hands—on activities help us find, trace and tell the story of our data. After sharing our data diaries we will transform text into images, first by creating sparklines, then by analyzing + illustrating a talk in teams.

We will then turn to examining the intersections of time and data. Tufte says that this is a balance between the representation of mechanism and motion, of process and dynamics, of causes and effects, of explanation and narrative. We will apply this thinking to draw out the telling details of a story/history as we create timelines with open source tools.

Readings • Duarte, Nancy. Resonate: Present Visual Stories That Transform Audiences. Hoboken,

NJ: Wiley, 2010. (Excerpt) • Murray, Scott. Interactive Data Visualization for the Web. Sebastapol, CA: O’Reilly

Media, 2013. (Excerpt) • Rosenberg, Daniel. "A Timeline of Timelines." CABINET. Issue 13 (2004). • Yau, Nathan. Visualize This: The Flowing Data Guide to Design, Visualization, and

Statistics. Indianapolis, IN: Wiley, 2011. (Excerpt)

Week 5 September 27 Sparklines; Sparkline Generator; Text analysis and visualization with online tools; Nancy Duarte on Sparklines Readings: Interactive Data Visualization for the Web (excerpt); A Timeline of Timelines; Edward Tufte on Sparklines; Nancy Duarte: Sparklines

September 29 Discussion; Activity with Timeline JS [javascript]; “Finding a Story” worksheets Readings: Visualize This (Chapter 4); Taryn Simon: A Living Man Declared Dead (bloodlines); NYT Timeline

Week 6 October 4 Critique of open source tools, continued.

October 6 Timeline Presentation

Week 7 October 11 & 13 Fall Break

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PART 4 MAPPING DATA IN SPACE How can we visualize data in space?

As we delve into ways to represent data we will investigate a variety of maps including symbol maps, cluster maps, chart maps and 3D map projections. We will also discuss the use of legends, keys, symbols and colors before we roll up our sleeves and attempt making our own maps with open source software.

Readings • Bodenhammer, David. Deep Maps and Spatial Narratives. Bloomington, IN: Indiana

University Press, 2015. (Excerpt) • Fry, Ben. Visualizing Data: Exploring and Explaining Data with the Processing

Environment (Scatterplot Maps). Sebastapol, CA: O’Reilly Media, 2008. (Excerpt) • Moretti, Franco. Graphs, Maps and Trees: Abstract Models for Literary History. Verso,

2007. (Excerpt) • Tufte, Edward. The Visual Display of Quantitative Information. Cheshire, CT: Graphics

Press, 2001. (Excerpt) • Yau, Nathan. Data Points: Visualizations that Mean Something. Indianapolis, IN: Wiley,

2013. (Excerpt)

Week 8 October 18 Discussion: Tufte on Maps; Ben Fry’s maps; collaborative mapmaking activity Readings: The Visual Display of Quantitative Information (excerpt) ; Ben Fry Chapter 6 Scatterplot Maps

October 20 Exploring mapmaking tools Mapsdata; StoryMap JS; CartoDB; Kartograph Readings: Graphs, Maps and Trees (excerpt); Writing with Maps; Joni Seager on Maps; Data Map Tip Sheet

Week 9 October 25 Critique of open source tools, continued. Reading:Data Points: Visualizations that Mean Something (excerpt); Deep Maps and Spatial Narratives (excerpt)

October 27 Map Presentation

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PART 5 DAT(A)CTORS How can data make real change in the world?

Rather than merely visually enhancing our research (or making it pretty), the act of data visualization can bring completely new insights and unexpected findings. We will examine how data displays themselves can become (more than just beautiful information but) useful, real-world deliverables. I call these examples data(a)ctors – data visualizations that do things in the world. We will discuss variety of dat(a)ctors (including matrices, metrics and networks ). To conclude this unit, we will create a project that brings together explorations in data and design. Readings • Chambliss, Daniel and Russell Schutt. Making Sense of the Social World: Methods of

Investigation. Thousand Oaks, CA: SAGE, 2015. (Excerpt) • Kanter, Beth. Measuring the Networked Nonprofit: Using Data to Change the World.

Indianapolis, IN:Wiley, 2012. (Excerpt) • Neff, Gina and Dawn Nafus. The Quantified Self. Cambridge, MA: MIT Press, 2016.

(Excerpt) • Miles, Matthew, Michael Huberman and Johnny Saldana. Qualitative Data Analysis: A

Methods Sourcebook. Thousand Oaks, CA: SAGE, 2013. (Excerpt)

Week 10 November 1 Discussion: dat(a)ctors—visualizations that do things in the world; data murals Reading: Data murals

November 3 Data Matrices; qualitative data activities; networked visualization Reading: Qualitative Data Analysis: A Methods Sourcebook; Network Visualization Week 11 November 8 Student panel discussion: The Quantified Self Reading: Elementary quantitative data analysis; Approaches to Quantitative Data Analysis; The Quantified Self

November 10 Metrics: social media metrics and demonstrating engagement; social network analysis Readings: Beth Kanter, Measuring the Networked Nonprofit

Week 12 November 15 Student panel discussion: Big data vs boutique data November 17 Student panel discussion:Open forum= *you propose the topic.

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Week 13 November 22 Dat(a)ctor Presentation November 24 Thanksgiving Break

PART 6 DATA-DRIVEN STORYTELLING How do we tell meaningful stories with data?

We live in a world that is shaped by our interaction with data, which is increasingly complex, large and accessible to few. In the final weeks of this course we will be creating stories from datasets. For the final project, students assemble into teams to brainstorm and prototype an interactive narrative experience that tells a story with data, around data, or about data. It could be a data visualization on the web, a physical installation using hardware and human bodies, or an interactive documentary experience.

Readings • Kearney, Richard. On Stories: Thinking in Action. New York, NY: Routledge, 2001.

(Excerpt) • Pomerantz, Jeffery. Metadata. Cambridge, MA: MIT Press 2015. (Excerpt)

Week 14 November 29 Data-driven storytelling; Inspiration: Datalore Readings:Why You Need Data Storytellers; Finding the Stories Hidden Within Data; On Stories: Thinking in Action (excerpt)

December 1 Crafting Stories from Data Readings: Data Storytelling: Using Visualizations

Week 15 and 16 December 6 Group work December 8 Group work TBD Exhibition/Presentations

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Expectations I expect you to come to class on time, fully prepared to engage in the exchange of ideas. Please prepare at least one question or artifact about/from/in response to the day’s reading to bring to class. If you have an idea about how to enhance the class in any way, please share this with us during class-time (Please do not wait until the end of the semester!).

Attendance We will often work on projects, watch videos, conduct group work, and other activities during class time. As we are all part of learning community, there is no substitute for your presence during class.

Office hours and after hours I hope you will take advantage of my office hours. I am available to offer extended feedback on your projects (beyond the written feedback you formally receive). You don’t need to have a problem to come visit, but if you do find yourself having some difficulty (technical or otherwise), then I certainly want to see you sooner rather than later. If you cannot make scheduled office hours, arrange to see me at another time.

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