Effective data viz example:
This is a big data piece titled “Hindsight Is Always 20/20,” commissioned for the 2008 Democratic National Convention, by one of our professors Luke DuBois. He culled the State of the Union addresses of every president for their most frequent words. Then, playing with the idea of testing the presidents’ visions, he presented the top 66 words used by each one in descending order of frequency on eye charts.
Each eye chart represents one US president with the most frequent word in the biggest type at the top. George W. Bush: Terror. Richard Nixon: Truly.
The visual aesthetic of the charts is super clean and the information provided is intuitive enough, and the scales of the words in each line are consistent. It also provides relevant information at the corner of each chart to show the president’s serving year. There is interactivity where people can switch to other charts at the bottom of a line of thumbnails which are aligned chronologically.
Effective data visualizations enable the user to discover unexpected patterns and invite a different perspective of the data. This visualization combines the visual form and information with metaphor and hence tells an interesting story through data. The phrase ‘Hindsight Is Always 20/20’ is used to describe the fact that it is easy for one to be knowledgeable about an event after it has happened.
Ineffective data viz example:
The graph represents the entire bitcoin market, which has a value of around $60 billion. As described on its website, ‘then divided the value of the bitcoin market by address’. As you can see, over 95% of all bitcoins in circulation is owned by about 4% of the market. In fact, 1% of the addresses control half the entire market.
Why slicing a pie chart using Voronoi? Shattered polygons make it harder to read than the usual pie chart.
Color: What is the point of using blue vs pink and differences in transparency if they are not intuitive enough?
Two variables in a single pie chart: addresses vs BTC, both represented with figures.
The Connected Worlds exhibition shows a large-scale immersive, interactive ecosystem which encourages visitors to explore the interconnectedness of different environments and strategize to keep systems in balance. Continue reading “NYSCI Visit”
View here online.
Code on GitHub: https://github.com/HereIsJade/Hate-Crime-News-DataViz
Data source: https://projects.propublica.org/hate-news-index/
A hate crime is a criminal offense against a person or property motivated in whole or in part by an offender’s bias against a race, religion, disability, sexual orientation, ethnicity, gender, or gender identity.
Hate itself is not a crime—and the FBI is mindful of protecting freedom of speech and other civil liberties.
Google News Lab, recently released a tool meant to further that mission: the Hate Crime Index. Created with data viz studio Pitch Interactive, the Index uses machine learning to automatically collect articles that cover racism, bigotry, and abuse.
In Rogers’s view, it’s the job of data journalists, like his team at Google News Lab, to fill in those gaps and bring accurate and complete data to people. The Documenting Hate Index leverages machine learning and Google’s trove of search data to surface the first layer of data quickly and easily so that journalists can use it to bring the increase in hate crimes since the election under greater scrutiny.
As Rogers puts it, there are plenty of amazing local reporters who are picking up on hate crime incidents in their region, but those reports never get seen elsewhere. The Index offers journalists the ability to connect isolated incidents to the bigger picture of what’s going on in our country today. “Anything that brings truth and facts and data to this issue is important,” he says.