The making of a making of

I recently had the pleasure of working with Gilda Santana, the head of the University of Miami’s architecture library. She needed a poster about her research on the educational genealogy of the architecture department.

Here’s a ten-thousand-foot view of the final product:

archGenealogy proof

If that’s hard to read, it’s because the print version is huge. Really huge. Visualization designer included for scale:

big chart energy

The full version can be downloaded from my portfolio here.

As Alli Torban recently pointed out on Data Viz Today, narrative charts (which depict individuals as lines moving through different stages or points in time) are great for spotting large-scale patterns while keeping an eye on the individual. They also take a lot of careful, painstaking work.

Here are my biggest takeaways:

  1. Design for the story you’re trying to tell. I made the diagram twice because the first time, I got caught up in my own interpretation instead of in my client’s research.
  2. Talk to your clients a lot. Like, a lot. I met with Gilda several times during this project to make sure I was on track–and I wasn’t! Those midway checkpoints helped me to course correct and get her what she needed.
  3. Take whatever help you can get from your tools. There isn’t a chart-building tool alive that can automatically generate a narrative chart, but starting with an alluvial diagram in RAWGraphs made the final version possible. (Do the RAWGraphs folks like pie-as-food? I want to send them a pie.)
  4. Leave the little details for last. Keeping the color scheme and background highlighting for last saved me from having to recolor the graphic a dozen times.

For the full process (and pictures!), read on.

Gilda initially brought her research and a preliminary graphic to my professor, Alberto Cairo:


She wanted to show the educational genealogy of faculty members in the School of Architecture, their research interests, and the years they worked at UM. Alberto suggested and sketched out the following design:


And then he sent her to me to make the graphic.

Which I did! Twice. I laid it out a second time because I forgot the cardinal rule of data visualization: design for the story you’re trying to tell. Both versions of the graphic tell interesting stories, but only one matches Gilda’s focus and vision.

My first attempt grouped schools by geographic region. Because I had an order in mind for the schools, I drew the graphic directly in Illustrator. Here’s an ugly progress shot along the way:


I brought this (very) rough draft to Gilda at our first check-in:


I’m proud of this graphic in a lot of ways: the heavy presence of northeastern schools is clearly visible, and it’s easy to find and trace specific schools. However, there is not a research focus to be seen. There’s a heavy focus on regionalism–while Gilda had mentioned regions in our conversation, they weren’t the headline of her work. And as Gilda pointed out, it looks like every faculty member has a Ph.D. unless the reader is paying very close attention to the little black boxes.

Talking to Gilda during the design process helped bring the project back on track. Faculty research focus needed to be the actual focus of the diagram! We sat together and determined the primary and secondary research interests of each professor, and then I went back to work.

The graphic would be absolute criss-crossing chaos if I added research focus, but kept the schools in a rigid order. So I pulled the data into RawGraphs and started tinkering with alluvial charts. I couldn’t see a line for each individual person, but I could get a sense of how much crossover I would have.

The rawest of the RawGraphs, which sorted by frequency, looked like this:fromRawSizeSOrt.jpg


Cleaning up the data helped a little, but the biggest boon was RawGraph’s automatic sorting option. That function automatically minimizes crossover, and gave me this:


I still needed to tweak and rearrange, but this starting point was critical. The tool couldn’t take me all the way there, but the alluvial charts made a huge difference in my workload and the quality of the final product.

I exported the alluvial as an SVG and reordered the nodes in Illustrator. I added lines for individual people, then reordered the nodes some more. After all that manual mucking around, I paused to do some critical fact-checking. Finally, I added in notes about secondary research interests and doctoral degrees.

That brought me here:


I made the hiring timeline in R using geom_rect in ggplot2:

timeline copy

I exported the image to PDF and placed each timeline with its faculty member.

I wanted to make the institutional and focus groups clearer, so I drew in some background shapes to highlight which lines went together. And I thought individual faculty members might want to look themselves up, so I added a legend in alphabetical order at the bottom.


And finally, I took care of the last detail: color. Based on Alli Torban’s wonderful advice, I design in grayscale and then add color as the very last step. This is a diagram about Miami, so turquoise/teal seemed appropriate.

Leaving the little details for last really saved my skin (and my sleep schedule) . Color is hugely important in data visualization, but if I had started out working with color, I would still be tinkering with sliders. And if I hadn’t left the background highlighting for second-to-last, I would have redrawn those shapes every time I tweaked line arrangement or spacing. Getting the content right and then adding those design elements helped me to focus on what really mattered.

I brought the diagram to Gilda, who had a few suggestions:


And here is the final product:

archGenealogy proof.png

Thanks to Gilda for sharing her research with me, and thanks to Alberto for the initial idea and opportunity! This was a really rewarding project, and I’m looking forward to collaborating in the future.


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