After years working at different newsrooms as a writer, fact-checker and researcher, I always felt something was missing. I loved reporting, finding new sources and connecting the dots for readers, but I craved the challenge of discovering more information that could be buried in numbers.
I studied at The University of Texas at Austin where I amassed clips as I wrote for multiple publications on everything related to Texas politics and public policy. I went to the Texas Capitol almost every day and covered legislative debates that would go on well into the night. I then took a job as a researcher for Type Investigations, a nonprofit that produces long-form investigative projects. It only took a few Excel tasks with some formulas and pivot tables when I worked on a story about sexual harassment in Wall Street for me to understand the importance of empirical evidence in journalism –– and the unmeasurable opportunity that came with it.
I started reading stories with large methodologies, bespoke graphics and many, many datasets. I learned about The Guardian’s data team after reading their investigation on How American Moves Its Homeless. I admired the craft of map-making after following Reuters’ coverage of the Rohingya crisis, and I followed FiveThirtyEight’s series The Gerrymandering Project, a viz-driven analysis that somehow managed to simplify an incredibly complex topic.
After receiving my acceptance letter at Columbia University for the MS in Data Journalism, my original goal was to learn how to analyze data and incorporate that with my journalism experience. Since I never gave myself an opportunity to experiment with visualization or design, I never thought I’d succeed as a visual journalist. I figured I would take the program’s only data visualization class to learn more about it, but otherwise focus mainly on analysis, as I had carefully planned.
It took just one visuals class to completely change the career path I thought I would strictly follow. I spent hours selecting the right colors, the best chart forms and crafting visual cues to better explain what I was trying to say in my writing. I studied data teams from different newsrooms and compared their styles, and tried incorporating some techniques for better design in my own work. The conversations I enjoyed the most with one of my data professors, Jonathan Soma, wasn’t that much about the data behind the story, but how best I could make sure readers would leave with at least one takeaway after looking only at the graphics. I was skeptical about applying to data visualization jobs, but my teachers pushed me because they were convinced my projects were good enough.
They were right. I got hired at FiveThirtyEight as an associate visual journalist. After only one visuals class and virtually no design experience, I became a part of one of the best teams of graphics and interactives. In only six months, I sketched, designed and coded our series on the presidential and vice-presidential debates, another project on how the electoral college has changed, and even contributed to FiveThirtyEight’s 2020 Election Forecast in collaboration with other web developers on my team. I created static graphics for our politics, sports, and science platforms, and even experimented with abstract design to show COVID-19 spread in Thanksgiving.
I intend to continue learning about the art of visualizing data, striving to find new ways to help readers understand important issues, elevating storytelling with thoughtful design, and giving an audience more tools to help make informed decisions. I enjoy the challenge of finding new visual ways to communicate findings in data, and I no longer feel like something is missing.
Description of portfolio:
We created a guide to election night to show how initial returns could be misleading. I designed and coded an interactive that would allow readers to navigate through different states and learn more about when polls would close, the potential shift in results, and their timing.
We polled voters before and after debates to look at the potential impact these had upon viewers’ opinions of the candidates. I helped craft the questions for voters, created different chart forms and worked in front-end development. I had to design graphics always thinking about the “before” and “after”, a challenge I addressed by designing histograms with clear outlines, slope charts and a waffle chart form that used legends and change in color for an “after” effect.
I created graphics using a blue-red gradient to enhance the battleground swings year by year and highlight the bigger and smaller changes in margin. The biggest challenge was figuring out how to apply the gradient to the lines in d3.js, and how to implement the arrows for the annotations for better design.
The biggest challenge for this story was coming up with a visual strategy that could best drive readers through a complex analysis with different variables. I opted for a waffle chart map to show diversity in the suburbs while avoiding substantial grey areas in a choropleth map. I also designed callouts in a beeswarm to merge variables without overwhelming the reader and worked with labeling to avoid confusion.
The multiple line chart needed multiple annotations and labels, so I experimented with different colors, opacities and dotted lines to create smart design without overwhelming readers with a packed graphic.
This is one of the most challenging graphics I’ve done. We wanted to create a transmission scenario that could be applied to virtually any city or state, regardless of specific COVID-19 transmission rates. I overcame this challenge by using Illustrator as my main tool, experimenting with different shapes and color palettes until I landed the final product.
For this story, I opted for bar charts to show both the positive and negative values and easily mark the months affected by COVID-19. I decided to flip both bar charts to help readers better understand the big difference between percent change in both services to the year prior, using color-coding for positive and negative values.
I created maps where states were sized by the number of congressional districts states would have after the 2020 census. The different-sized states presented challenges when it came to positioning in a way that would not hugely compromise their geographic location.
I used data to show how voter registration dropped dramatically in some states during the pandemic. I experimented with percent change from the 2016 election and other strategies to maintain the same y-axis across all states, but California and Texas undermined the changes in other states. I overcame this challenge by adjusting each chart to its own axis, effectively showing how voter registration dropped dramatically since 2016, no matter how big or small the state in question is.