Throughout this week's sessions, we engaged in an in-depth exploration of various methods and techniques for visualizing and presenting data within the broader context of data analysis and design practices. We examined multiple approaches to transforming raw information into meaningful visual representations, considering how different visualization methods can effectively communicate complex datasets and enhance understanding through visual storytelling techniques.
In 1869, Russian chemist Dmitri Mendeleev sought to organize the known elements in a logical way. While many variations had been created before, Mendeleev's organizational method used the atomic number of the elements to arrange them, which also left space for undiscovered elements. His table remains the definitive version today due to its elegant data organization. What's significant about Mendeleev's table is that he understood which aspect of the data should drive the organization—a lesson that's crucial for my project. For instance, while mapping locations in my data, I first tried creating a geographically accurate map. However, this proved messy and difficult to follow. When I redrew it with less geographic accuracy, it worked better because my data focused on distances and locations rather than specific street routes. Studying the periodic table helped me understand this principle: just as Mendeleev recognized atomic numbers as his key organizing factor, I realized my data centered on locations and distances.
I encountered Fritz Kahn's work at the Pompidou Centre, though I regret not studying it more carefully at the time. Kahn, a physician, is best known for his industrial-style illustration depicting the human body's inner workings. His genius lay in using relatable imagery to create an easily comprehensible visualization. Though highly detailed and illustrative—unusual traits for a chart—the detail serves a clear purpose: making complex information relatable and understandable. While this approach may not directly apply to my project, it demonstrated how a visualisation can effectively employ extensive visual data and taught me the importance of determining the right amount of data to include in visualisations.
"The Practical Guide to Designing with Data" proved invaluable for my dashboard project. While the entire book is fascinating, two points particularly stood out. The first section emphasises removing "chart junk"—any stylistic elements that distract or impair visibility. This principle resonates with me since I tend to add decorative elements, but it helps me strike the right balance between style and clarity. The book's insights on mapping are especially relevant to my data analysis. It explains how understanding the purpose of your map shapes its design, using metro maps as an example. These maps intentionally simplify geographic accuracy to enhance clarity, showing connections between locations rather than exact routes. This approach could be particularly useful in making my data more comprehensible.
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This week has been invaluable for my project work. Examining different approaches to detail and style has helped me understand why specific stylistic choices are made based on a chart's purpose and audience. Additionally, studying the periodic table and Brian Suda's book has deepened my understanding of how to identify key data points—a crucial skill for my project.
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