Edward Tufte's principles of data visualization, as articulated in his influential book "The Visual Display of Quantitative Information" (first edition in 1983) remain profoundly relevant (although in recent years his theories were also regularly criticized) and insightful for the field of Data Visualization. His exploration provides a rich collection of principles and insights aimed at creating clear, precise, and effective data graphics. Tufte's approach combines statistical integrity with design efficiency, advocating for graphical excellence that communicates complex ideas with clarity and minimal distortion. Through detailed examples, he illustrates both successful and flawed data visualizations, emphasizing the importance of truthful representation and the avoidance of misleading presentations.

Tufte’s principles - although not clearly presented as such - can be summarized as follows:

• Graphical excellence:
  • present interesting data with well-designed graphics
  • communicate complex ideas with clarity, precision, and efficiency
  • maximize information transfer with minimal ink and space
  • use multivariate displays to convey multiple dimensions
  • ensure truthful representation of data
    
    
• Graphical integrity:
  • represent numbers proportionally
  • use clear, thorough labeling
  • focus on data variation, not design variation
  • prefer deflated and standardized units for monetary time-series
  • avoid exceeding the data's dimensional constraints
  • contextualize data appropriately
    
    
• Data-ink and Chartjunk:
  • maximize the data-ink ratio (data-ink / total ink)
  • remove non-data-ink and redundant data-ink
  • avoid "chartjunk"—decorations that do not enhance understanding
    
    
• Data density and small multiples:
  • maximize data density and graphical space efficiency
  • use small multiples to facilitate comparison and reveal interactions
    
    
• Graphical elegance:
  • combine simplicity of design with complexity of data
  • integrate words, numbers, and drawings effectively
  • balance scale, detail, and narrative quality
  • avoid content-free decorations

Professor Edward "Ted" Andrew Miguel (University of California, Berkeley) discusses in 5 videos the work of statistician Dr. Edward Tufte and his book 'The Visual Display of Quantitative Information'

Tufte’s principles underscore the significance of thoughtful, integrity-driven design in data visualization, aiming to reveal the complexities of data without distortion or unnecessary decoration. In addition, he also highlights the importance of historical context and practical examples, citing early pioneers like William Playfair, who invented several types of statistical graphics, and the impactful work of John Tukey in Exploratory Data Analysis (EDA). Visualizations should be designed thoughtfully, considering both the perception of the viewer and the truthfulness of the data presented. Poorly designed graphs can mislead and misinform, as illustrated by numerous examples in his book. 

In Digital Humanities, data visualization is often understood as the process of transforming data into graphical or spatial forms, such as charts, graphs, maps, timelines, networks, animations, etc. Data visualization is sometimes also distinguished from Information Visualization, the art of representing data so that it is easy to understand and manipulate, thus making the information useful. Visualizations can make sense of information by helping to find relationships in the data and support - or disprove - ideas about the data.

Visualization tools can offer a powerful pathway to explore, understand, and communicate historical or other humanities data in a more effective and appealing way. For instance, data visualizations can be used to show patterns and trends in historical data, compare different sources or perspectives, highlight key events, actors, or themes in narratives, and provide context and background for the data. Ultimately, it can also invite questions and feedback from its users.

Examples of DH methods and tools in which data visualizations plays an important role can be found in the following - not exhaustive - list:

• Data Management and data cleaning (e.g. OpenRefine, R, etc.)
• Social Network Analysis (e.g., Gephi, UCINET, R, etc.)
• GIS software (e.g. QGIS, ArcGIS, etc.)
• Text Analysis (e.g. Named Entity Recognition, Sentiment Analysis, word frequencies, word clouds, etc.)
• ...

Finally, Tufte also leaves all readers with some well-considered advice in the Epilogue of his book (Tufte 2001, p. 191):

The theory of the visual display of quantitative information consists of principles that generate design options and that guide choices among options. The principles should not be applied rigidly or in a peevish spirit; they are not logically or mathematically certain; and it is better to violate any principle than to place graceless or inelegant marks on paper. Most principles of design should be greeted with some skepticism, for word authority can dominate our vision, and we may come to see only though the lenses of word authority rather than with our own eyes.

References

• Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Cheshire, CT: Graphics Press.
• Tukey, J. W. (1977). Exploratory data analysis. Pearson. ISBN 978-0201076165.

Further reading

• Cairo, A. (2016). The truthful art: Data, charts, and maps for communication.
• Kirk, A. (2019). Data visualization: A handbook for data driven design (2nd ed.). SAGE Publications Ltd.
• Wilke, C. O. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures. O'Reilly Media.
• Meirelles, I. (2013). Design for information: An introduction to the histories, theories, and best practices behind effective information visualizations. Rockport Publishers.
• Drucker, J. (2011). Humanities approaches to graphical display. Digital Humanities Quarterly, 5(1). Retrieved from http://digitalhumanities.org:8081/dhq/vol/5/1/000091/000091.html
• Sinclair, S., Ruecker, S., & Radzikowska, M. (2013). Information visualization for humanities scholars. In K. M. Price & R. Siemens (Eds.), Literary studies in the digital age: An evolving anthology. MLA Commons. Retrieved from https://dlsanthology.mla.hcommons.org/information-visualization-for-humanities-scholars/

Resources

• Pelagios Visualisation Activity: https://pelagios.org/activities/visualisation/