DATA(VIS) JOURNALISM

-Why and how to start

Nakho Kim (nkim3@wisc.edu)Nov 2011

Why Data?

• In fact, journalism has always been about DATA.– Collecting facts– Parsing out meaningful patterns from facts– Telling stories with patterns– Opening up for feedback

• The change:– We have so much more data on hand, both quan &

qual• Open public data (data.gov), whistleblowers (Wikileaks.org),

open collective data (Twitter, FB, Wikipedia…)

– We can process that data better• Computing power (and labor)

– We have better ways to present them• In a nutshell: more diverse, intuitive ways to go in-

depth

Why Visualization?

• Two main functions (often both)– Interface for “thick” information– Pattern finding

Information interface

• Goal: – To organize or filter some information from

the vast whole • Example: Madison Commons neighborhood

map (link), timelines– To show the process or mechanism• Example: Dynamic flowcharts (link)

– …or just to get attention

Pattern Finding

• 4 major patterns better found with Vis: – Difference • position: Voter heatmap (link), scale: Money chart (link)

– Clustering • Social networks (link)

– Overlap • Guardian’s London riot map (link): pol-econ spaces

– Change over time • Rosling’s Gapminder: Health & Wealth (link)

The Best Datavis Ever

“Napoleon’s March” by Minard (1861)

The process

• “4 steps to raise value to public”– Data –> Filter –> Vis –> Story (Lorenz, 2010)

• Inverted pyramid process (Bradshow, 2011)– Compile -> Clean -> Context -> Combine ->

Communicate• Visualise -> Narrate -> Socialize -> Humanize ->

Personalize -> Utilize

• The core: (Plan), Collect, Process, Show-tell

• Planning the story : – What do you WANT to find? – DO NOT try to cramp too much info• Afghanistan disaster (link)

• Collecting data– Locating• Data.gov, Guardian DataStore, etc

– Mining• We Feel Fine (link)

– Sourcing • Ushahidi Kenya (link)

• Processing data– Excel rules (macros and formulas are helpful)– Good stat tools are useful, too• SPSS, SAS, Matlab, R…• Parsing often includes statistical analysis.

– Google docs rising• Easy to connect different tools and gadgets

– Export to csv for versatility

• Showing the data– Selecting the vis style : little changes big

differences • Network layouts: round vs force-layout (link: NodeXL)• Mapping vs chart (link: NYT 2008 election)

– Does it tell a good story? • If yes, is it the story you planned?

– Yes: Congrats.– No: Pretend that it was your plan.

• If no, select something else

• Final check– Proof-check for unintended biases

• One code for one data type • Color as misleading connotations• Patterns distorting the vision• Exaggeration by unintended hyperbole, figure size, etc• The greatest sin of all: overpacking(examples of bad vis: link)

Now, tell (write, talk, draw) your story around it. - The case of infovis (link) : good and bad- Sometimes, straightforward is the best (link)

Tools

• Menu-based

• Query-coding

• Hard-coding

Easy to learn / clunky

Hard to learn / flexible

Many Eyes

Fusion Table

Gephi

R

Processing

Python, Java, Ruby…

Apps (Dipity, Wordle, etc)

Don’t Be Over-ambitious

• Most likely, any journalist will be starting with menu-based online tools– Many Eyes (link)• Great for on-the-fly charts

– Fusion table (link)• Great for maps and mashups

• If you know what you’re doing– Start using dedicated tools • E.g. Networks: Gephi (link)

Be ambitious only if…

• … your team can afford a programmer.• … you are willing to learn some

foreign language.

Now, let’s discuss

• What are the stories you want to visualize?• What kind of data do you want to see?• What are some of the good datavis examples

you’ve seen?• How about some really bad ones?• Which kind of tools would you like to learn?

Some Suggestions

• Watch: – “Journalism in the Age of Data”

http://datajournalism.stanford.edu• Read:

– Many Eyes FAQ (link)– 10 tools for data journos (link)

• Play: – Draw one network graph, one chart, one map with Many Eyes

• Prepare:– Explain 1 or 2 Examples of good / bad datavis– One real journalism task to discuss about