Data Revisualizations
Personal edits of published charts
Data journalism adheres to the constraints of a brand aesthetic as well as the constraints of its data.
With all data re-visualizations, I attempt to adhere to the aesthetic of the original publisher.
When a fanciful aesthetic comes to mind, I create additional revisions using a more ~artistic~ design.
I would gladly discuss any of these edits, my thought processes, and possible improvements. I am currently creating, and will be adding more revisualizations in the near future. Enjoy!
The Economist: Still a man’s world
In this version of the revisualization, I remove the original truncated axis, and replace the original tick marks with opaque & translucent bars to intuitively indicate that the numbers are percentages of a whole, as opposed to integers.
I also include fewer countries/groups of countries, by removing all countries in the EU28, and leaving the EU28 group visible for comparison. This decision sacrifices data granularity for the sake of creating a digestible graphic.
In this version of the revisualization, I remove all but four entries: Portugal and Brazil, the two countries with the most gender parity in published papers, Japan, the country with the least gender parity in published papers, and EU28, a data point I assumed was most relevant to the Economist's audience. This decision creates a simpler graph that is less likely to lose a reader's attention before they can process its information.
In this revisualization I change the colors of lines to match colors associated with the social media platforms with which they correlate.
I also de-truncate the y-axis and add redundant '%' signs to each tick label.
These decisions are inspired by my work as a data designer at xtown.la where, I learned through user testing that a significant portion of data journalism readers tend to skim graphs and misread axes. I remind my readers the y-axis represents percentage by including redundant '%' symbols and always maintaining a full axis. I never want my audience to view a graph and assume that numbers that correspond to '80%' on the y-axis are actually '100%', as the original graph allows.
Additionally , I add line-type redundancy to forecasted data.
I make this design decision because depicting collected data and predicted data using the same line can be dangerous. Because I found that some data vis consumers skim graphs, looking at general shapes and colors before (/without) bothering to study the axes of a graph, it is important to double (or triple) encode forecasting to avoid misleading my audience.
Lastly, I add intuitive coloring to each graph's title.
By designing a bright-to-dark green color scheme to the titles of each graph, I use visual methods to remind the audience that these graphs are separated into a progression of various ages. This double encoding not only solidifies the visual grid of the graphic (shout-out to design principles!) but also draws attention to the graphs' labeling, which is visually lost in the original version.