The 2018 WIA Data Viz competition is an opportunity to showcase your data driven artistic skills! We are displaying the top 5 finalists at our “Data Gallery” during the conference. Submissions may include everything from Tableau Dashboards and Shiny apps to maps, 3D visualizations, graphs, sculptures, drawings, and more. Submissions may be digital or physical, but must be mobile and displayable in an indoor space. You do not have to be a woman to apply!
THE POPULAR VOTE
Starting on 2/1/18 YOU can help us choose the finalists! The top 10 submissions will be featured on our website. Cast your votes to select the finalists featured at WIA
11/1/17: Submission Period Opens
1/21/18: Submission Period Ends
2/1/18: Top 10 viz chosen by our sponsors are published on WIA website and voting opens!
3/1/18: Finalists chosen and awarded tickets
3/15/18: Top 5 viz compete at the conference
Finalists (5) will win free passes to WIA.
1st place: $1000
2nd place: $750
3rd place: $500
TOP FIVE FINALISTS
Below are the top 5 finalists for the 2018 WIA Data Visualization Competition.
These finalists will attend the WIA conference for free and display their visualization at the WIA Data Gallery! They will also have the chance to win prize money as one of the top three winners voted on by the attendees of the conference.
Top 5 Finalists
Click on links below image to interact:
Women of the City
Traveling with Michelle in 2017
Final Four Men vs Women
This viz is a women empowerment story using City of Cincinnati employment data. I was inspired to pick this topic due to the social concern about gender equality and the recent women’s march all over the world. A women in data conference seemed like a perfect opportunity to showcase a piece of what is going on in the government workforce of Cincinnati. The large numbers at the top right displays the gender distribution of the work force underlined by a diverging bar illustrating the data. The color scheme for each gender runs consistently throughout the entire visualization. The first three bar charts show the population distribution, by gender segmented into age, race and job code. It is interesting to observe where there is a disparity. The next three dot plots are introduced with another number band on the far right showing the average income that each gender makes. The “underlining” of these numbers are in the form of histograms exhibiting the distribution in bin of $10,000. Here we are able to see that the males have a normal distribution whilst the females’ is skewed more to the right. The dot plots with a stacked bar for a y-axis shows the salary distribution of both genders by the number of hours they work. To conclude, I want to point out that even though there might be more men in the workforce, the women at large are significantly successful! The orange may not be always dominating the fields but when they do, it’s powerful!
“Traveling with Michelle in 2017” is an interactive Tableau dashboard created to show where I traveled to in 2017. It took about 20 minutes for me to manually create the data set in excel that showed when and where I traveled, using my work Outlook calendar that tracks all of my flights. As a consultant, I not only get the opportunity to travel for work, but I also have the opportunity to work from home when not traveling at a client. This past year, I spent a lot of time working from home in Columbus, but also working from my hometown in Dallas when I was planning my wedding in June. You can see from my calendar that earlier in the year I did not travel too frequently – about once a week per month. In mid-September however, I started traveling every week until the end of the year. Based on the map you can see that most of my personal travel time was spent in Dallas planning my wedding, while most of my work travel time was spent in New York where I started one of my largest projects. My Tableau dashboard is interactive as well – on Tableau Public, hovering over the calendar will give a tooltip that describes where I went and why I was there, whether it is for a client or for fun. Clicking on the map will highlight when I went to that location on my calendar.
I created my visualization before the 2017 NCAA Basketball Tournament because I was curious what mixture of seeds made the Final Four every year. I was also curious if the men’s and women’s tournaments followed the same patterns. To answer these questions, I created a Tableau dashboard. I placed the question I wanted the user to be able to answer at the top of the visualization, “how does the composition of the Final Four differ between men’s and women’s college basketball?” I then used callout numbers to allow for a quick way to answer the question. This shows that 55% of women’s Final Four teams are 1 seeds, while only 41% of men’s Final Four seeds are. In order to see if any one school dominated, I used a bar chart to show the number of Final Four appearances as a one seed by university. This chart shows the dominance Connecticut and Tennessee had over women’s basketball while no school was as dominated on the men’s side. Next, I wanted to detail the seed composition for every year of data, 1985-2016. I thought the best way to represent the data was through a modified lollipop chart, with a circle placed at each seed that was represented in a given year. I then sized and colored the circle by the number of teams at that seed. This approach allowed my visualization to show that low seeded women’s teams are much more likely than men’s teams to appear in the Final Four.
This is a group project that I did along with three colleagues for a Data Visualization class. We chose a dataset on world happiness to tell a story on how happy (or sad) the people around the world were. We wanted a compelling story that used multiple interactive visualizations to establish our perspective. With that in view, we designed the visualization’s interactive elements to allow the user to explore and/or engage with the analysis.
These visualizations are based on encrypted Ticketmaster data. There were three different documents used each with about 6 MM records. My friend and I set a goal to find an insight around ticket prices. The first graph was made with Spotfire. It displays how much more users are willing to pay for pre-ordered tickets. The Rock / Pop genre capitalized on this time period. However, although R&B was in higher demand, there was a shorter presale. I therefore recommend that this period begin earlier/ last longer in the future. The second viz was made with two maps laid on each other from Tableau. This shows, based on the brightest blue color, where high paying customers are being charged low prices. Although not the best news for the consumer, I recommend increasing prices in these areas.