Just discovered a great blog post on “data illustration” versus “data visualization” at Information for Humans. AIS argues that data illustration is “for advancing theories” and “for journalism or story-telling.” By contrast data visualization “generate[s] discovery and greater perspective.” I love this distinction, although I’m not sure I like the specific language. Tukey famously argued […]
The rise of digital humanities suggests the need to rethink some basic questions in quantitative history. Why, for example, should historians use regression analysis? The conventional answer is simple: regression analysis is a social science tool, and historians should use it to do social science history. But that is a limited and constraining answer. If […]
Aaron at Plan Space from Outer Nine has a valuable insight about how standard statistics textbooks often favor technique over understanding. I think we could extend approach this from “central tendency” to the broader question of “association.” We tend to view various measures of association (for example, Chi-square χ2, Spearman’s rho ρ, Pearson r, R2, etc.) […]
Regional lords (daimyō) in early-modern Japan were grouped into several categories based on their relationship with the shogunal Tokugawa house. The two largest categories of daimyo were fudai and tozama. Lords whose ancestors had proven their loyalty to the Tokugawa before 1600 were commonly known as fudai, while lords with more suspect allegiances were known as tozama, […]
Why graph? And why, in particular, use innovative and unfamiliar graphing techniques? I started this blog without addressing these questions, but a recent blog post by Adam Crymble, critical of “shock and awe” graphs made me realize the need to explain EDA (Exploratory Data Analysis) and data visualization. Crymble wisely challenged data visualization practitioners to […]
What is clioviz? A blog devoted to data visualization in history and the humanities. What’s data visualization? An interdisciplinary approach to graphics that seeks to make trends and patterns in quantitative data visually apparent. In a well-designed data viz, patterns jump out at the viewer/reader, and results are obvious without the use of descriptive statistics. […]