Exploring the 7 Different Types of Data Stories

Exploring the 7 Different Types of Data Stories

What is makes a story truly data-driven? For one, the numbers aren ' t caged in a sidebar graph. Instead, the data helps drive the narrative.

Data can help narrate as many types of stories as there is angles. My colleague Ben Jones Oftableau public inspired me to think of data stories as seven different types (à la Christopher Bo Oker ' s seven basic story plots). Jones based his idea on the analysis of numerous data stories, and the His framework helps imagine ways to free data from sidebar Graphs. These categories was meant to being a thought-starter, not a final count, which was sure to be higher.

Let's explore each category using the data compiled by Freedom House, an independent watchdog organization. The data ranks each country as "free", "" "" "" "" "partly free," or "Not free," based on a score. Using This simple dataset, we can tell seven different stories. The angle depends on what is want the data to show and how do you plan to show it.

1. Narrate change over time

How many countries were categorized as ' free ' in Versus in 2001? It turns out the number shrank over time. We can use the data to visualize the change and then explain the forces at work.

Credit:ben Jones.

CNBC's John Schoen took this approach-visualize the history of the Dow in years. The user can click on each decade and see how the index reacted to the tech boom, the inflation of the ' 70s, even the Grea T Depression. The CNBC staff paired the visualization with a future forecastsbased on past trends.

The history of the Dow 30. John Schoen

Click to visit the full interactive visualization.

2. START BIG and DRILL down

Data can guide the reader from the Big-picture view focused view. Using the Freedom House data, start the reader with a world map of the scores, the big view. Then the reader can be zoom in to a region-asia, say-and see this more than half of the countries there is labeled as "N OT free. " Zoom in even more, and the reader sees the Korea are the least free country of all.

We can guide the reader through this sequence by providing prompts in the copy as well as interactive filters.

Credit:ben Jones.

The example below shows vaccine-preventable outbreaks recorded around the world. The overview shows how many such cases exist. The filters allow the reader to drill down by country, disease, or year. The reader might be guided to see, for example, which whooping cough is more prevalent in the U.S. than elsewhere, and the Story can outline the possible reasons why.

Vaccine-preventable outbreaks. Credit:cfr

Click to visit the full interactive visualization.

3. START SMALL and ZOOM out

We can also do the reverse by starting with the molecular view and expanding to the larger view. For example, first focus on the three freest countries in the world, and which happen to being clustered in Europe. From there, zoom out of the narrative to show how and European countries stack up and then zoom out again to show the global C Omparison. Then the reader sees this just over one-third of all countries is labeled as "free."

Credit:ben Jones.

Here's an example-looks at the impact of immigration on housing value. The reader can enter his or hers zip code to start with a hyperlocal view. The interactive filter provides the statewide view, and the map overview gives the national perspective.

Immigrants and Housing. Credit:jacob Vigdor

Click to visit the full interactive visualization.

4. HIGHLIGHT contrasts

Outlining the differences in datasets can drive a powerful narrative. The freest countries is all in Europe, and happen to be located close together. The least free countries, on the other hand, is in five different regions.

Credit:ben Jones.

Of course, geography is just one of the groups ' many differences. Our stories can explore the key ways in which the and the groups differ, from government policies to culture to history. A study of contrasts could make for a captivating piece.

We can tell a similar stories with the example below. It compares the gender gap in countries around the world based on three dimensions of human development:a long and health Y life, knowledge, and a decent standard of living. Countries on one end of the spectrum has large disparities while those on the other end has near-equality.

HDI Gender Gap. Credit:ramon Martinez

Click to visit the full interactive visualization.

5. EXPLORE the intersection

When both divergent lines of data intersect and one overtakes the other, questions result. The freedom data shows that's the number of "partly free" countries overtake the ' not free ' countries, then go ' to also su Rge past the "free" countries. What caused the shift? When standings change, people want to know the reason.

Credit:ben Jones.

Sarah Ryley of New York Daily News visualized the number of summonses issued in New York since broken-windows policing too K Effect in 1993. The number rose sharply at first and held steady before starting a slow decline. Then a spike in Stop-and-frisks led to an intersection with summonses in 2010. Granted, the lines don ' t compare apples to apples. However, the crossing and its components can drive a narrative about New York's law enforcement strategy.

Summonses Since ' broken Windows. ' Credit:sarah Ryley

Click to visit the full interactive Visualizaton.

6. Dissect the factors

Sometimes factors come together like pieces of a puzzle to form the big picture. The relationship might be additive or multiplicative. For example, each country's freedom score is the sum of legal, economic and political freedom.

Credit:ben Jones.

Here's an example this shows how the sun controls the weather. The visualization draws the dot from sunspots to global weather, highlighting causal relationships along the the.

Sunspots. Credit:matt Francis.

Click to visit the full interactive visualization.

7. Profile The OUTLIERS

We are fascinated by things that aren ' t like the others. We want to know the what and the how behind the outliers.

Finding the outliers sometimes takes a bit of data exploration. Visualize the freedom data as a scatterplot, and you might say there are no outlier. But broke it down by regions in a box plot, and you start to see those that stand apart.

Credit:ben Jones.

This example shows the number of government requests Facebook received in the first half of 2013. The map makes it clear that the U.S. is the clear outlier with a margin of more than 8,200 requests. An accompanying story could highlight the U.S. government ' S stance on social media monitoring, main use cases, and POSSIBL E reasons for the feds ' unusually high number of requests.

Facebook ' s government requests. Credit:andy Kriebel

Click to visit the full interactive visualization.

Other Stories TYPES?

We just explored the beginnings of seven different stories types using one simple dataset. But as I mentioned, these categories is intended as a thought-starter. So why other types of stories should we add to this list? How else can we use data to tell stories? Share your ideas in the comments below.

Correction:this Post has been updated to correct credits on the visualizations.

Martha Kang is the editorial manager of Tableau Software where she helps Chronicle today's big Data revolution. A lifelong storyteller, she's currently focused on telling Data-driven stories so help us better understand we world, a nd ultimately, ourselves. Prior to joining Tableau, Martha worked as a journalist, first in TV news, and new media. She most recently served as the online managing editor of KPLU, a NPR affiliate in Seattle. There, she oversaw a number of projects, including the launch of Quirksee.org, a vertical site that featured in her ow n award-winning stories, as well as a Five-part, Data-driven series, in Washington state's idiosyncratic tax system. Martha have also worked at KOMO News, Northwest Cable News, and Wls-tv. In, she is chosen as a Kiplinger Fellow of public affairs journalism by Ohio State University.

Exploring the 7 Different Types of Data Stories