Meet Jon Schleuss, a graphics and data journalist for the Los Angeles Times. He has been at the Los Angeles Times for 5 years creating interactive graphics and data visualizations. Jon is fascinated by discovering stories within datasets, spreadsheets, and maps. He’s been a longtime member of a civic-focused mapping community called Maptime LA, where he gets to pursue his personal passion of creating at the intersection of open data, maps, and civic technology.
How’d you get into data?
I was working at a small-town publication before working for the Los Angeles Times. I knew how to use social media and build websites. But that job gave me the opportunity to branch out and start building interactive maps, digital projects, etc.
One of my colleagues, back then, told me about a conference called NICAR (National Institute of Computer-Assisted Reporting) and recommended I go. It seemed like a conference that spawned in the 1980s, just based off of its name. Who doesn’t use computers for reporting? But I went and discovered a great conference filled with journalists who had wonky skillsets like me.
These were journalists who also knew how to write code, build maps, or analyze large datasets. I learned so much from these people and found this sweet spot between basic journalism and tech-influenced journalism. This conference was my first exposure to journalists who code or handle data but were still journalists by profession.
In your opinion, what gives a dataset value?
The dataset needs to be interesting. I’m asking myself, “Is there an interesting story that can be found in this dataset?” Part of what’s interesting is the information’s proximity to the audience.
I ask: “Is the data relevant to people in Los Angeles?” or “Is there a timeliness element to the data that I can capitalize on?” For example, if we look at the last baseball season and the fact that the Dodgers were playing in the playoffs, that data related to baseball and specifically the Dodgers would be very interesting to people in Los Angeles. But after the World Series ends, that data wouldn’t be as interesting. We’d need to wait until the next season when the Dodgers are in the news.
What issue in Los Angeles do you think has the most potential for a data-driven solution?
The reality is that there is an unlimited number of issues because data is everywhere and has the potential to solve any issue. I’m personally interested in the transportation space, I think there are tons of data like performance metrics, metrics for bus vs rail, data on scooters and smaller modes of transportation. Most importantly, these types of data are important for telling stories.
I don’t have an opinion on what decisions should be made or what should happen, but there are interesting consequences and data surrounding these issues. Take Bird scooters, for instance, the company will need to limit the number of scooters operating in the city of Los Angeles to keep from violating its permit. But how will we know if it’s in violation? The data!
That’s another part of the newsworthiness of an issue, that conflict. Conflict plus transportation usually ends up being a good story (if it’s there).
Share and walk through an example of your work related to data.
Last month, the Mega Millions Jackpot reached a record level. This was news about 2 years ago, I had published a simulator on LATimes.com called “Here’s $100. Can you win the Powerball jackpot?” As a result of this latest jackpot news, I went in and updated our simulator to reflect the most current odds. Turns out, the odds are even worse now than they used to be 2 years ago.
Since 300 million is a large number to visualize, we try to simplify the complexity of “1 in 300 million” by pretending to give people $100 to buy pretend lottery tickets. As people play with the simulator, they begin to quickly realize that they keep on buying tickets and never win. The fact is that you’ll never really win the lottery.
This simulator is one of the most popular things I have ever built. It’s always extremely popular when the lottery hits the news cycle. Over the past few years, this simulator has seen over 1 million users play the game.
What’s your favorite “data-story”? Why?
I love the New York Times’ data story, “Good, Evil, Ugly, Beautiful: Help Us Make a ‘Game of Thrones’ Chart” because it asks people to rate Game of Thrones characters on a matrix comparing Goodness and Attractiveness.
It’s so basic. But as you plot each character on this scatterplot, you get to see what other people thought and how your answers matched up. I think they capitalized on the timeliness of Game of Thrones culture hitting mass market appeal. Another reason why I like this visualization is that I think it’s harder to do data stories around entertainment news. This story did a good job in quantifying opinions and thoughts while capitalizing on the timeliness of the culture.
What advice do you have for someone looking to start using LA Counts datasets to tell their own stories?
Be curious! Interview your data. When we interview people, we will ask questions to know if we can trust the person and the story we’re hearing. Likewise with a dataset, I always ask questions and treat the data like it’s a subject in a story and I’m going to interview it. I’ll ask questions like, “Is there a pattern here?” or “Is there an outlier?” because I want to find out if the data is accurate. Just as people can lie and mislead you, so can data. As a data user, you have to be able to judge the truthfulness of the data otherwise you can’t accurately use it. That’s a big challenge. By being skeptical and curious about what stories are revealed in the data, you’re taking the first step towards telling a good data story.
Another big piece of advice is that in California, we have a Public Records Act that states public records held by governing agencies belong to the people. As a resident of California, you should take advantage of this. But how do we take advantage of this? When you want a specific public record dataset, you simply need to find the correct governing entity and ask for the record. There can be key differences between open data and public records requests too. A government office can collect datasets and omit or truncate parts of the datasets prior to placing them on open data portals for viewing. In those times, the agencies won’t provide a full and raw view of the data, but that type of raw data is accessible via a Public Records request. Sometimes all it means is picking up the phone and asking. It’s your right and it’s a part of the government code, so cite it when necessary!