From Sketching to Coding: Visualization as a Thinking Process

This DH’23 workshop will explore and discuss strategies for teaching visualization literacy - from sketching by hand to creating visualizations using computational tools. Led by instructors from the humanities, visual analytics, design, and computer science, sessions will cover different methodologies and challenges of teaching visualization to audiences with varied technical and disciplinary expertise and goals. We will walk participants through visualization exercises, reflecting on how tools (both analog and digital) can enhance but also constrain our thinking.

Workshop topics & outline

Introduction

• Workshop Introduction & Goals
• Visualizing 2 Quantities [exercise]
• Data Visualization in a Nutshell

Hands-on Approaches to Creating Visualizations

• Sketching Data Visualizations [exercise]
• Creating Digital Data Visualization [exercise]

Discussion of Teaching and Applying Visualization Approaches in DH

• Strategies for ideation, creation, and critical interpretation
• Designing assignments and syllabi
• Open discussion of participants’ past instruction experiences and approaches

Workshop Materials

• Full workshop proposal (PDF)
• Observable Tutorial
• RawGraphs Tutorial (PDF)
• Workshop Slides

Resources & Links

Data Visualization Theory

• Tamara Munzner, Visualization Analysis & Design, AK Peters, 2015
• Isabelle Mireilles, Design for Information, Rockport, 2013
• Jaques Bertin, Semiology of Graphics, Esri Press, 2010 (original version 1967)

Design Process and Theory

• Hinrichs, U., Forlini, S., & Moynihan, B. (2019). In defense of sandcastles: Research thinking through visualization in digital humanities. Digital Scholarship in the Humanities, 34(Supplement_1), i80-i99.
• Sedlmair, M., Meyer, M., & Munzner, T. (2012). Design study methodology: Reflections from the trenches and the stacks. IEEE transactions on visualization and computer graphics, 18(12), 2431-2440.
• Franconeri, S. L., Padilla, L. M., Shah, P., Zacks, J. M., & Hullman, J. (2021). The science of visual data communication: What works. Psychological Science in the public interest, 22(3), 110-161.
• Aragon, C., Guha, S., Kogan, M., Muller, M., & Neff, G. Human-Centered Data Science: An Introduction. MIT Press, 2022.
• D’Ignazio, C. & Klein, L. F. Data Feminism. MIT Press, 2020.

Techniques, Visualization Types & General Resources

• DataViz_Project
• ChartMaker Directory @ Visualising Data
• The Data Visualization Catalogue
• Directory of Visualizations
• Data Viz Project
• Text Visualization Browser

Teaching

• Word Embedding Models: A Very Short Introduction (an introduction to representing words’ meanings as vectors for a DH audience)
• Python for data analysis and visualization: GLAM Workbench; NLS Jupyter Notebooks

Visualization Tools (unsorted)

• VisTools
• RAWGraphs: RAWGraphs is an open source data visualization framework built with the goal of making the visual representation of complex data easy for everyone.
• Observable: JavaScript-based notebook interface with charts that can be customized from existing code or programmed from scratch Observable libraries for visualization (Plot isn’t the only one!)

DH Specific Tools

• https://vistorian.net/
• https://hdlab.stanford.edu/palladio/
• https://github.com/cpietsch/vikus-viewer
• http://timelinecurator.org/
• https://github.com/YaleDHLab/pix-plot

Sketching

• Steven Heller & Rick Landers. Raw Data. Infographic Designers’ Sketchbooks. Thames and Hudson Ltd
• 5 Design Sheet Method

Color

• Color Brewer
• Viz Palette
• I want hue
• 2D Colormaps
• ColorCat
• Coloring for Color Blindness
• Colorgorical

Other Examples & Inspirations

• The Pudding (interactive data visualization + journalism)
• Delayed Gratification (journalism + mainly infographics)
• We Are What We Steal (text analysis + data viz + reflections on bias)