- [MUSIC PLAYING]
- Hello, and welcome to the Data Journalism Podcast.
- My name is Simon Rogers.
- I am a Data Journalist, speaker, and teacher, and data
- editor at Google.
- And my name is Alberto Cairo.
- I am a professor of Visualization
- at the University of Miami, an Infographics designer,
- and journalist, and also a book author.
- We love using data to tell stories.
- And the music you can hear is the sound of data,
- made with two-tone, an app that turns numbers into tunes.
- There's also the sound of football, American football,
- that is, with a changing football school since 1920,
- care of one of our guests today.
- And this is the Data Journalism Podcast
- that dissects the latest trends in data journalism
- around the world.
- In each episode, we will explore the latest in data journalism,
- and we will chat with some of the world's top data journalists.
- You'll get to find out how they do what they do.
- So subscribe at datajournalismpodcast.com
- to see how data is changing the world of journalism forever.
- Hi, Simon.
- Hey, Alberto.
- How are you doing?
- So good.
- I'm really thrilled to have the chat that we had this week.
- It was a lot of fun.
- It was a very interesting one, right?
- Can you tell us a little bit about our guests?
- Yeah, so we have two doyen of local data journalism.
- We've got Ben Welsh from the LA Times, who
- has been just one of the first people out there
- to really see the power of using data to tell stories.
- The Los Angeles Times have one of the first great data
- journalism sections, and he has forgotten more than we will ever
- learn about how data can just improve local reporting.
- And we also are lucky enough to have Mary Jo Webster, who
- is a great local reporter who's just taught herself
- how to work in this area and really created
- some interesting work.
- It was a very interesting conversation with both of them,
- and we got into sort of how they got started,
- how they discovered the tools that they use nowadays,
- beginning with Excel.
- They tell a couple of funny stories
- about how they discovered Excel and then later more
- sophisticated data analysis tools.
- I think that it was a very enlightening conversation,
- and listeners will enjoy it a lot.
- We should do an episode one day about Excel
- and the power of this tool in getting people
- into data journalism.
- Love it or hate it.
- It's just become this kind of like entry-level drug
- for people who are getting into data and reporting.
- I mean, it's super useful.
- You can do amazing things in Excel, right?
- I mean, both Ben and Mary Jo, they
- mentioned AR, the programming language, and Python,
- and many other tools.
- But both of them say, you know, if you
- can get away with doing this very simple thing with Excel,
- why don't do it with Excel?
- Or if you are getting started in this field,
- it's better to begin with the tools that you already know
- and many people already know that it's Excel, right?
- Yeah, and one of the things we wanted to do with this episode
- was really getting some of the nitty-gritty.
- So we have a rapid-fire series of questions at the end,
- really about the kind of people's top tools,
- some of their foibles, some of the stories they care about.
- It's a lot of fun.
- And I would really advise sticking around to that,
- because you'll learn a lot about how each of them works.
- It was an excellent episode, I think.
- So let's get into it.
- Let's get into it.
- [MUSIC PLAYING]
- [MUSIC PLAYING]
- Hi, I'm Mary Jo Webster.
- I'm the data editor at the Star Tribune in Minneapolis.
- And hello, I'm Ben Welsh.
- I live in Los Angeles, where I work on a project
- at Stanford University called Big Local News
- and at the Los Angeles Times.
- Thank you so much for joining us, Mary Jo and Ben.
- We're really happy to have you both here today to talk really
- about local data journalism and how it's
- growing around the country, and I think around the world
- as well.
- But I would love you to talk a little bit first about how
- you got into this world.
- Now, Ben, when I was at The Guardian back in the day,
- when we were setting up the data blog,
- we were definitely looking at the work
- that you guys were doing.
- And I feel like you were actually really
- one of the innovators in this field.
- So talk to me a little bit about how
- you got into data journalism and the kind of work that you do.
- Well, it definitely wasn't the plan.
- It wasn't anything that happened on purpose,
- but I'm really glad it did.
- I was a pretty listless undergraduate in Chicago,
- Illinois at DePaul University and kind of unclear
- of where I was going with my life and career,
- like perhaps many undergraduates that Alberto
- encounters from time to time, and maybe himself as well.
- And I won't bore you with the whole long story,
- but through sort of an unusual series of events,
- I became the assistant to two local television journalists
- in Chicago, who were both excellent practitioners.
- And I became what was known at that time
- as the foot man, which was a very glorified term
- for the intern or the helper around the office.
- And so as they prepare a weekly newspaper column
- and television piece, I would do research and scheduling
- and a little bit of television editing too.
- And in that, I kind of caught the journalism bug.
- And one of the early stories we worked on
- was about corruption in a Chicago suburb.
- Locals could probably guess which ones.
- And we did my first public records request
- for legal fees from a sort of local heavy
- who was milking the city out of money.
- And it came as a really, really long printout.
- Remember when printouts used to have the perforated tears
- along each side and a single print over 20, 30 pages
- will be connected through these long strips in the side
- that you have to rip off, right?
- And we got that back, this epic printout
- of all the legal fees.
- And we thought, well, what are we gonna do with this?
- How are we gonna turn this into a story?
- And I don't even know from where,
- but I got the idea of just typing it into a spreadsheet.
- I think it was probably the first spreadsheet
- of my entire life.
- And I spent having nothing better to do.
- I spent a good chunk of time just hammering all that in
- and we added it up.
- And at the end, that number got read out on the local news.
- The anchor even climbed up a step ladder
- and dropped the sort of accordion of paper live on camera,
- which was kind of a funny little gag.
- And I kind of caught the bug.
- You know what I mean?
- And within that, I saw that through technology and data,
- it was kind of like a way I could help.
- It's something I could chip in and do
- as a place for me to contribute.
- And it also was a gateway to doing more ambitious journalism
- than just the average run of the mill story.
- We did a story nobody else was doing
- that was more ambitious and analytical,
- and that was exciting.
- And so I kind of just got into it there.
- This is now 20 years ago, I guess,
- or more than that actually.
- And I just followed that and followed that.
- And without boring you with my whole life story,
- I mean, I kind of ended up on this podcast today.
- - Mary Jo, tell us about how you caught the bug.
- - Mine is very much a local news-based situation.
- I was, I don't know, three or four years into my career
- working as a reporter in Oshkosh, Wisconsin,
- a very small daily paper.
- I was tasked with covering the police and sheriff's department
- both on a kind of a policy standpoint,
- but also, you know, I went out to the crime scenes
- and the fires and whatnot.
- But I was covering the city budget
- and it looked like the police department
- was growing its budget dramatically one year to the next.
- And I was sitting there with a calculator
- trying to calculate the percent change
- between the two years for all of the departments.
- And a colleague of mine came over and said,
- do you know what Excel is?
- And I was like, no, no idea.
- He opened it up.
- He showed me how to do a simple percent change formula.
- My eyes were bugging out.
- I was like, wow, this is so cool.
- Where did you learn this?
- You see, he told me I went to this IRE&NICAR bootcamp
- and they teach you all this stuff called,
- back then it was called computer-assisted reporting.
- And I was immediately hooked
- and I decided I had to keep going.
- And sometime later that year,
- I was able to attend a training that Sarah Cohen did.
- She was the IRE trainer at the time.
- And I remember sitting in this room.
- I don't think it was a hands-on training.
- My memory serves.
- But I just remember being enthralled
- by everything she was saying
- and mind being blown by this entirely different way
- of reporting that I had not learned in college
- that my editors had not taught me in my early years.
- And it was a complete turning point for me.
- - That's amazing.
- Both historical, welcome to the podcast in the first place.
- So thank you for being here.
- But the stories that you are telling
- are similar to some of the stories
- that I tell my students that
- when you see data journalism or my own field,
- data visualization from the outside,
- it looks like these very shiny, super advanced,
- high technology kind of endeavor.
- But when you see how this software is made,
- it's actually sometimes pretty pedestrian, right?
- It's like you essentially type the data
- into an Excel spreadsheet
- or you don't use machine learning algorithm.
- No, you just use a simple Excel pivot table, right?
- So I find all that absolutely fascinating.
- I mean, we are going to go back and forth
- between your professional careers,
- but also other sites of your professional endeavors.
- I'm really curious about your educational endeavors
- because both of you have been involved
- in educational initiatives, right?
- Can you talk a little bit about that?
- Both of you?
- - You mean what training we have taken or the teaching?
- - The training that you offer,
- because for example, Mary Jo,
- you have a data journalism academy, right?
- So both of you have been involved
- in these types of initiatives.
- Not only, I mean, you didn't take advantage
- as of things such as like IRE NYCAR,
- but you also offered to the community.
- - Yes, I am a huge believer in teaching.
- I think it has helped me
- as much as it helps the other people
- because I believe very much in the learn, do, teach approach.
- I found all the holes in my knowledge
- by having to turn around and teach it to somebody else.
- So I've also gotten so much back from other people.
- That's how I learned.
- So I feel the need to turn around
- and teach it to other people.
- I have taught for about 10 years as an adjunct
- at the University of Minnesota,
- a data journalism class that was fully dedicated
- just to data journalism.
- I'm currently a senior fellow
- with the Center for Health Journalism's
- Data Journalism Program,
- in that we do a week-long training
- and then we do six months of mentoring
- and additional training throughout that time.
- I'm particularly enjoying the mentoring part of that
- because it's another level of teaching.
- You're helping them get a project off the ground.
- You didn't just teach them how to run things in Excel
- and then walk away.
- You keep it going and help them get a story out the door.
- And it's quite fulfilling, actually.
- And then I taught at IRE and Nicar a lot
- and I built up so many materials
- that I've gradually decided I should share those materials.
- And at the start of the pandemic
- when I had a bunch of free time, I thought,
- you know what, it's time to turn some of these materials
- into videos.
- So I made a new website and posted a lot of stuff there,
- mainly Excel and other teaching things.
- I am doing a new thing right now
- that I'm actually gonna present about this
- at the Nicar conference this year.
- I've created a little data for editors training module
- for the assignment editors in our room.
- And so these are the people who work directly
- with the reporters, mainly in our local news section.
- And I've broken it down.
- I'm not teaching them how to use Excel
- or how to run data or any of that.
- I've broken it down into little sections
- of the different points in a data-driven story
- that the editor needs to weigh in and look at
- and what they should be doing at each point
- and what they should be looking for.
- So like the key ones are that brainstorming process
- with their reporters when they're talking
- about what story should I do?
- What should I tackle?
- Looking, having, I'm giving them some clues
- that they should be watching for
- that maybe there's a data opportunity here.
- What questions they should be asking, things like that.
- And then the bulletproofing process.
- Okay, the data has come in.
- We've got findings.
- We're showing you a bunch of charts and tables.
- What do you do with it?
- Too often the editors just look at it and go,
- oh, that's pretty.
- Oh, that's kind of interesting.
- And I'm trying to teach them like,
- how about we take it to another level
- and you really help bulletproof it.
- And then the third level is the editing the written story.
- One of the things I've seen a lot as editors
- see a paragraph full of numbers
- and they just glaze right over it.
- And they don't think about like,
- what are we really saying?
- What are the key points we're getting across?
- So I'm trying to teach them some editing things around,
- you know, what's the key finding?
- You know, not lumping too many numbers in a paragraph.
- All these kinds of things to try to help
- make the story more powerful.
- And then we're also gonna do a graphic session.
- My colleague at the head of our graphics department
- is gonna help me with that.
- And that'll be more about, okay, when should we use graphics?
- What kinds of graphics should we use?
- What should you as an editor be doing
- when we send you those proofs?
- Instead of just saying, oh, that looks beautiful.
- What else should you be doing?
- And I'm really anxious to see what kind of outcome that gets.
- If it leads to more data-driven stories on a beat basis
- and more daily basis or a weekly basis,
- not just the big enterprise kind of stories.
- - What about you, Ben?
- Can I talk a little bit about your educational efforts?
- - Well, I think Mary Jo said something
- very sort of succinct and wise there
- with the learn D2, learn, do, teach formulation.
- You know what I mean?
- It's a great way.
- It definitely benefits yourself.
- But I also think there's another great thing
- about that kind of approach or model
- is there's really a very sort of like stimulating
- and actually rewarding creative process
- of converting something that you've learned how to do
- into like a lesson or something
- that you can teach someone else.
- And it actually is like, it kind of gets the neurons firing
- the same way when you're like kind of innovating the code
- in the first place for the idea.
- And so for me along those lines, you know,
- one like sort of approach to teaching that I've enjoyed
- or found fruitful is taking emerging new techniques
- that we're developing in the newsroom
- that I feel like are kind of like a big deal for me
- or a big deal for our team,
- but that I don't see like being taught
- and then trying to convert that
- into like a night-card bootcamp or class
- to try to spread it to other people.
- And so, you know, examples I've done this with not alone.
- I always try to seek out other folks
- to help me like really strengthen the idea.
- But with others, we've created some of the earliest
- night-card bootcamps on how to use Jupyter Notebooks,
- how to use the static site frameworks
- that so many newsrooms use to make standalone visual stories
- but like nobody ever talks about, right?
- And this year at night-card, we're gonna have,
- I think the first kind of three hour bootcamp
- on how to automate a scraper
- within the GitHub actions framework.
- And so to me, like those three things, Jupyter Notebooks,
- static site page building, Git scraping
- are three really important like technologies
- that have taken off in our world
- in the last five or 10 years.
- And it's just been fun to try to like think about
- and pin down, well, how exactly do we use these?
- What are the fundamental skills?
- How can I pass that on to someone else?
- - It's really interesting to me
- how you're both so involved in teaching.
- And I wonder, does that reflect towards,
- I wonder if this is particularly applies to people
- working with data on a local level,
- is often people are kind of isolated.
- Often it's like one or two people on their own,
- they've got no support, there's no network.
- But the colleagues from other organizations
- are kind of struggling in the same way.
- So there's a kind of a need there, is that fair?
- - I love the psychoanalytical take here.
- I mean, I did grow up as a lonely, nerdy kid
- on a grand full road side,
- who didn't have a lot of friends.
- And so maybe this is me still trying to connect,
- you know what I mean, and get over.
- I was not crazy.
- - Well, I think you're right.
- There's so many that, you know,
- these days they refer to themselves as lonely coders.
- The majority of my 20 years in data journalism
- was spent in a newsroom where I didn't have support
- from anybody else.
- And so I sought it out from other people
- and learned from other people.
- And then, you know, I turned around and taught.
- But I think another factor is the Nykar community
- has always been a sharing community.
- If you even look at how Nykar was formed in the first place,
- it was really a group of investigative reporters
- who realized they needed to learn how to work with data
- and they didn't know.
- And a few of them learned somehow, I'm not sure how,
- and then they decided to share it with the others
- to try to pass it along.
- And so it was really, that's how Nykar got started.
- And that has been infused throughout all the years.
- I remember so many boot camps,
- and I think Ben is guilty of this,
- of people sitting at the bar at the end of the conference
- helping somebody with their data, you know, hovering over
- a laptop, helping someone with their data
- when they should be, you know, having fun
- and enjoying themselves.
- But that's how much I think our community likes to share
- and teach each other and it just, it's wonderful.
- - Yeah, and just to add to that,
- I mean, I think if you follow the origins back
- like Mary Jo was doing, I mean, data journalism's roots
- go a lot further than I think is commonly recognized.
- You know what I mean?
- It's really a practice that stretches back decades
- into the invention of the computer in the '60s and '70s.
- And I think is that culture of those early days
- is probably in like an anthropological sense,
- like indistinguishable from the hacker culture of MIT
- that's so celebrated.
- You know what I mean?
- It really was a very similar people
- with very similar sort of ethos of sharing
- that is the wellspring for a lot of what we do today.
- And I think that, you know, as an inheritor of that,
- I'm continuing to be inspired by it
- and I feel some obligation to uphold it, you know?
- - I think that we all do.
- We all have the sort of like feel the need
- to contribute back to the community that helped us
- at the beginning of our career, right?
- I have a question related to what you were both saying
- about, you know, growing up as a nerdy kid, right?
- I sort of sympathize with that because, I mean,
- people don't see where I'm sitting right now,
- but I'm sitting in a room with my kid
- and then surrounded by comic books, science fiction novels,
- you know, books about history and psychology
- and then some tabletop games.
- So the nerdery comes naturally to me.
- So has that improved or has that changed?
- It's like, are we still seeing or are you still seeing
- as sort of like the nerds in the newsroom
- or has that situation changed
- and data journalism, data visualization,
- computer assistant reporting,
- has it has become more widespread and more accepted
- or more popular within local newsrooms?
- What do you think?
- - I think it is more widely accepted
- as a tool that every reporter should have in their toolbox.
- I like to talk about data analysis
- as really another form of interviewing a source.
- That's all we're doing really.
- And it just requires kind of a little bit
- of a foreign language to do it.
- I also see a data journalists increasingly
- as the innovators in the room.
- In all the newsrooms I've been in
- for quite a few years now,
- the data journalists have been at the forefront
- of whatever new digital innovation we're gonna do.
- And they're the ones pushing for it and seeking it out.
- And in my newsroom right now,
- my team is the one often called upon
- for like, what do we do next?
- Where do we go?
- And I think that's wonderful.
- And I think that takes us out of the nerdy idea
- into kind of more of the leaders.
- - Yeah, and you see this in the product realm too,
- where a lot of people out of the data journalism world
- have become leaders in the next generation
- of digital business products
- their companies are putting together,
- which is a whole other level beyond
- just like what's happening in the newsroom.
- To be frank with you,
- I think in the last two or three years,
- we've seen a quantum leap with data journalism in newsrooms.
- The takeover has happened.
- You see it at the prestige level with those metrics.
- You look at the Pulitzer Prizes last year,
- five of the Pulitzer Prizes
- went to data journalism projects,
- including the public service highest award,
- which went to the New York Times COVID tracking effort, right?
- Which on its own was a gigantic public service
- and an incredible feat of like data gathering and analysis.
- But it also was the most popular
- and I'll say it most lucrative thing
- the New York Times has ever published.
- And those COVID tracking things
- I bet at the Minneapolis paper
- was the most popular thing ever published.
- I'll just throw that out there.
- Mary Jo can correct me.
- It's the most popular thing
- the Washington Post ever published.
- It's the most popular thing
- the Los Angeles Times published.
- And I don't mean to demean the news by saying that,
- but once the product you're creating
- is both the high prestige thing
- and more importantly, the economic and economic engine.
- You know what I mean?
- That's growing the digital audience
- of which there's a desperate need to grow.
- The entire way the organization looks at you changes
- just fundamentally.
- And that's happening.
- - Yes, the tracker we'd put up was,
- is hands down the historically most read page
- in the history of the Star Tribune.
- And the numbers on it are so astronomical
- that I don't envision it being that record being broken.
- So it would take something pretty massive,
- I think to break it.
- But the other interesting thing we saw
- is that it was also the most popular page
- among our digital readers who are not subscribers,
- but they come to us pretty regularly
- and we would like them to be subscribers.
- We call them in tenders.
- And it was the most popular page among them.
- And now we have conversations going around
- what else like that, maybe not data,
- but more in the service journalism realm,
- but data is one of the things you can do
- for service journalism.
- What can we do to help get more of those people
- to become subscribers?
- - Exactly.
- This all changed.
- And Steve Kornacki was named
- one of the sexiest men alive guys.
- Come on, we're on top.
- Yeah, the dog agrees with that assessment.
- So we have talked a lot about COVID on this podcast.
- And I wonder what you think the longer term effects
- are gonna be for your field
- of that very data intensive experience
- of working with COVID data in a way that readers
- and users actually really responded to.
- - I mean, the tracker thing is here to stay.
- And I think the creating dashboards for the general public
- on major topics they care about is not new.
- I mean, it's something that's been around with weather
- and sports for a long time.
- But I think that it's a type of product
- that clearly our audience wants
- that is in my opinion, like capital J journalism
- in most circumstances, but requires a new assembly line
- to be constructed inside the metaphorical factory
- of your newsroom to create that.
- You know what I mean?
- It requires a little bit more
- of a product development mindset,
- a longer development cycle, a more iterative approach,
- you know what I mean?
- Than juggling what's on Sunday's front page
- and the sort of like day-to-day heartbeat of the newsroom.
- And so I think there's a way in which
- to produce more of those kinds of widgets,
- data journals and teams are gonna have to find a way
- to create a kind of app workflow
- that's able to support that.
- And I think that's becoming more
- like a product development team in some ways.
- - From a different perspective,
- I think it's gonna have a long-term effect in newsrooms
- because I think it really woke up a lot of people
- to the need and benefits to being able
- to analyze your own data
- and not rely on a government official
- to hand you some numbers on a press release.
- Especially in those early days
- where we kind of had to pull stuff together ourselves.
- But then even more long-term is data literacy,
- increasing understanding among reporters and editors
- for the need to understand
- all the weird little caveats behind data
- and the problems we have with denominators and whatnot.
- And I think there's gonna be,
- I'm expecting to see greater appetite
- for data training in my newsroom.
- - We talked a lot about how data journalism is affecting
- the bottom line for news organizations
- and how they're all seeing the value now.
- If you're a reader or a user,
- you don't necessarily realize
- you're looking at data journalism, I guess.
- But what difference does it make to them?
- Do you think it helps people understand
- the local areas better
- in ways that we haven't quite kind of grasped
- the long-term impact of yet?
- - Well, I think the way we present data journalism,
- especially in charts and graphs and things like that,
- allow people who maybe need a visual aid like that
- to see something rather than reading
- long sentences of text,
- that some readers just do better
- with absorbing information that way than others.
- And I think also the readers maybe
- who aren't naturally inclined to do that
- are getting better at it because there is so much of it.
- - We all have, oh, sorry, Ben.
- Were you going to say something?
- We can make a pause.
- Let's set this up, okay, perfect.
- I was about to say, let's begin there,
- we all have projects in our past as professionals
- that we are very fond of,
- even if they are not perhaps the best projects
- that we have ever done,
- but there are certain projects that we hold dear, right?
- So if you had to choose just one of your local
- data journalism projects in the past, right,
- what would that be and why?
- What was the story about?
- What do you do to gather the data or visualize
- or display the data, explain the data,
- and what type of impact that story had?
- - In 2018, we did a series called Denied Justice
- that looked at how the criminal justice system
- in Minnesota was handling sex assault investigations.
- A reporter came to me and said he was hearing
- that very few cases that get reported to police
- actually result in a conviction, but nobody tracks that.
- It's not a number that someone calculates.
- So we put in requests for thousands of police
- investigative reports and we built our own database,
- tracking key bits of information off these cases
- and then hunting down, was this referred to the prosecutor?
- Were charges filed?
- Was somebody convicted?
- And a mountain of work in order to get a few numbers.
- And what we found is like 8% of reported cases
- result in a conviction.
- And we ended up doing a series,
- it ended up being a nine part series,
- which we did not expect,
- because it ended up being so powerful.
- The response was overwhelming from readers and policymakers.
- And we had hundreds of victims come forward
- to share their stories because they read our first story
- and were like, oh my gosh, I'm not the only one
- who had this bad experience with the police department.
- I'm not the only one.
- This is so cathartic to hear this
- and they wanted to share their stories.
- And as a result, Minnesota's sexual assault laws
- have been overhauled quite substantially.
- Several police departments have added additional staff
- or like victim advocates embedded into their department,
- prosecutors embedded into the department
- to help improve the quality of the investigations.
- And it was one of the most,
- probably hands down the most rewarding
- piece of work I've ever done.
- At the end of it, we gathered all these victims
- at the state Capitol and took a picture
- of all of them together.
- And these women came out in the bitter cold in Minnesota
- in December to do this photo.
- And they came up to me and the other reporters
- and they were so thankful.
- And I've never had that experience before
- that the people just thanking us for the work.
- And that story is a really big combination
- of data being the spine holding up the key findings
- and then these powerful emotional stories from victims.
- And policy makers told us that the two things combined
- were so powerful that they could not ignore it.
- - Yeah, when the data comes together
- with real life in a way, you know what I mean?
- Or when the data leads you to the doorstep
- you didn't know you needed to go to, right?
- That is just this sort of like unforgettable feeling.
- You know what I mean?
- It's kind of like one of the deeper work hits.
- It's better than any hack, you know what I mean?
- And so like for me, like an example of that was
- we had a circumstance where there was like
- a little political fight in LA government
- over the fire department that was really pretty superficial
- and was just a couple of people
- trying to score political points.
- But it raised some questions about the efficacy
- of the 911 system in the city of Los Angeles.
- And so my colleague and I, Robert Lopez,
- decided to get a copy of the entire 911 calls database
- and analyze what was happening with the 911 system.
- And you know, there's a lot of ins and outs
- to how it all worked out.
- But the reality was is by doing our own
- independent analysis of the data,
- we were able to identify several structural flaws
- in how the 911 center operates,
- how they handle calls near the city border
- with neighboring fire departments,
- and then go out into the real world tracking real calls,
- knock on doors and find out what happened.
- And in that we discovered these kind of deeper problems
- with the system that nobody was talking about
- in city politics.
- And we were able to really point to where the system
- needed to improve to like make a difference.
- And then a change actually did happen, you know.
- And to me, that's sort of like that feeling of
- where the statistics lead you to the store,
- you listen to the numbers,
- and then you follow them out into the real world.
- And then you connect that with what's really happening there.
- And then you bring that back to your audience.
- That's better than any chart.
- That's better.
- You know, I love charts, you know what I mean?
- But like, that's better than any little dinky thing.
- That's the deeper like thing
- that numbers and statistics can lead you to,
- which is the sort of hidden truth,
- but the deeper story that isn't being told.
- And that's the hit, you know,
- I'm still looking for it on every story to get back to.
- I'm still chasing.
- - Those are both examples of stories where a human
- is probably not gonna come forward and tell us
- that there's a story there.
- Maybe they'd have an inkling,
- but probably nobody did the data analysis
- to know for sure that that's what it is.
- And it takes us to do these kinds of lifts
- to heavy lifts on data to find those stories.
- And, you know, as I said, interview the source
- to get at the meat of that story that needs to be told.
- - And then sometimes I think this may have happened
- in Mary Jo's case too.
- Your finding, your synthesis of what's really going on
- actually gives a name to and some meaning to something
- really negative that happened to somebody.
- And then they decide, then your sources
- and the people you're writing about
- find this deeper reward in the whole thing.
- And it's very rare, you know what I mean?
- But to me, that's as good as it gets.
- - That combination of like traditional reporting
- with the data and that sweet spot event have impact as well
- is just everything you can ask for, isn't it?
- So we are in February, this is our first part of the year.
- If you two were to make predictions,
- I know everybody does make predictions,
- but you two make predictions
- about what your big exercise is gonna be this year.
- - Let's let Mary Jo off the hook here.
- I don't want you to make her pop up too much.
- So since I'm working on this new project at Stanford
- called Big Local News,
- which is really an open source collaborative effort
- to try to be a data backbone for all the local newsrooms
- who maybe need help or some consulting
- or some technology to really pull off big data projects,
- we're looking to take it up a notch, Simon.
- I mean, I think this year, this team I'm on
- with Cheryl and Surdar and everybody up on the farm
- in Palo Alto, I'll say it here,
- we are going to build the biggest web scrapers
- in the history of data journalism.
- We're gonna, we have a project called Agenda Watch
- where we're gonna scrape hundreds of city council agendas
- across the country, put them into one database,
- and we're gonna try to do the same thing
- with county courthouses.
- And you know, those types of projects,
- scraping your local county courthouse or city council,
- I mean, every local newspaper in America
- has wanted to do it.
- Some of them have tried to do it.
- A very few have succeeded in doing it
- for like a short period of time,
- but I just don't think that there's been a lasting
- and sort of broad solution to that challenge
- because it's a lot of work, there's competing priorities,
- you know, da, da, da, da, da.
- And we're really looking to solve that or try to
- by being the sort of database library
- that attacks those big things.
- And so by December 31st,
- we'll be the biggest scraper you've ever seen,
- outside of Google, of course.
- - I love the Local News Project,
- I'm really glad to hear that.
- (upbeat music)
- Okay, so we are gonna finish off with a new section,
- which I'm gonna just call no dumb questions
- because these are quite dumb questions.
- And they're one word answers,
- but I'm gonna go first.
- And my one to Mary Jo is,
- what book or class had an impact on you
- as a professional?
- - The Investigative Reporter's Handbook.
- - Very good, how about you, Ben?
- - Oh, just one?
- - Yeah, just one, I'm afraid, sorry.
- - Oh, man.
- - Be concise, we are all journalists,
- we are supposed to be concise.
- - Gotta be concise, all right, I'm gonna do it.
- I will say the one that had an effect on me,
- I will say The Autobiography of Malcolm X
- by Alex Haley, guys.
- How about that?
- It's like a young teenage me,
- you know what I mean, moving from the country to Chicago,
- getting a broader view of the world.
- It was nonfiction journalism
- that helped me like see what was up,
- you know, and its book is an alternative version
- of American history that I hadn't heard before.
- It's also a religious conversion story,
- not unlike one I've been taught in church,
- you know, as a kid who went to Sunday school.
- And it shows the power of stories
- that go against the grain
- that aren't otherwise being told.
- And it's a great piece of literature.
- So if you haven't read it, you know,
- you should check it out.
- I think sadly in some ways, you know,
- we've regressed since then.
- You know, that was a time when Alex Haley
- could have the most popular show on television
- and write a really provocative book.
- And as a reminder of the power,
- and I would say virtue of the mainstream media,
- when it's properly focused, you know.
- - Those were the times.
- Okay, next quick question for both of you, actually.
- So if you could keep just one of the tools
- that you commonly use in your daily job,
- what would that tool be?
- - R.
- - I like that answer.
- What about you, Ben?
- - Oh, a spreadsheet, guys.
- It all begins with a spreadsheet.
- You can't play baseball without a bat
- and you can't crush data without a spreadsheet.
- And--
- - Oh, you can, you can, R can do everything.
- There's so many things that R can do that Excel can't.
- - These R people, they really love R.
- Have you guys noticed this?
- Like, I think--
- - I have actually noticed that.
- - Yeah, I think that, you know,
- not unlike, that it's like R conversion stories.
- It has an evangelical fervor and flavor,
- not unlike the Nation of Islam.
- - You are only saying that because you're a Python person.
- That's the only reason why you're saying that.
- I thought you were gonna say Python for sure.
- - No, I think it all comes down to the fundamentals.
- You know, you can't dribble.
- You can't play basketball if you can't dribble.
- Can't play baseball without a bat.
- I'll give you the religious flavor,
- is I'll pick Libre Office's Calc,
- the open source free alternative spreadsheet,
- which is just as good as the ones from those companies
- you maybe have heard of.
- - Okay, if you rephrase the question to
- what should any journalist have a tool,
- I would say Excel.
- But what should I, as a professional data journalist,
- doing this full-time, crunching numbers like non-stopped,
- then it's R.
- - All right, okay.
- Next one, what is your favorite hack
- that you are too embarrassed to admit?
- I can tell you my one is,
- and I still do this occasionally,
- is the way you can use the finder and place
- in Microsoft Word as a shortcut to getting stuff done
- if you're in a hurry.
- I still use it.
- I'm slightly embarrassed about it.
- So there you go, that's mine.
- Ben, do you go first?
- - Well, I'm not easily embarrassed,
- which maybe is a bug, not a feature in my personality.
- But I mean, a hack,
- saying no to other people in the newsroom.
- You know what I mean?
- I think that the data journalists,
- especially in smaller newsrooms,
- can sometimes get consumed by helping things
- just go the way they've always been going
- or sort of chipping in around the edges.
- And I think that being a helper
- and assisting that is noble work and often really valuable.
- But I see it pulled back a lot of other data journalists
- from really reaching their potential from taking risks
- and from doing the more ambitious, powerful work
- that our skills are capable of.
- So never be afraid to say no
- and never be afraid to bet on yourself
- and to take a chance.
- - I guess I don't know if I'd call this a hack,
- but I am probably most embarrassed to admit
- that I have data import scripts
- that I have not automated, but I probably should.
- And I just still keep running them.
- It's not exactly by hand, but they're not automated.
- I feel like I should be moving into the 21st century,
- but, oh well.
- - Well, I still run out of patience
- whenever I need to style graphs or charts in R
- and I export them as PDFs and I style them in Illustrator
- just because I find it faster.
- So that would be my personal hack.
- And I'm not embarrassed at all in admitting it.
- So we all have a skeletons in our closet, I guess.
- - I had trouble making a graphic in R yesterday
- and I was like, ugh, okay, I'm just gonna put it in Excel
- and I will make it in Excel.
- - I do the same thing with Illustrator.
- It's like R is not working, fine.
- Let me go to the Illustrator's graphing tool,
- which is sort of like the clunkiest graphing tool ever.
- So anyway, so let's go to the next one.
- What would you have done
- if you hadn't gone into journalism?
- - Oh, MG, I have no idea.
- I've wanted to be a journalist since I was 16, no clue.
- - Well, I was not in a very motivated place
- before I found journalism as we covered already, Alberto.
- But one thing I did like to do at that time in my young life
- was read pretentious books and be like a pretentious student.
- And so I think at that time, if you had asked me,
- I probably would have told you
- I would have become a high school English teacher
- or something, you know what I mean?
- And I don't think that that's a crazy outcome for me.
- And maybe I would have been happy doing that.
- It's hard to say.
- I probably would have stayed in Chicago
- and now that I think about it, never met my wife,
- which is making me sad to even contemplate.
- Like, you know, that's probably it.
- - So glad you didn't do that, Ben.
- (laughing)
- - Oh my God.
- My monologue to like high school students
- about of mice and men, it would be just terrible.
- - I would hope I need to hear that.
- Okay, last one, pie charts or tree maps.
- And I'm going to go to Mary Jo for this one.
- - Tree maps.
- - Like anything with visualizations, it depends.
- You know what I mean?
- Like, so like, I would say like,
- well, at first I would say never pie charts
- in the circular form.
- However, if you have like a single,
- like a categorical thing,
- I think the like underrated pie chart alternative
- is a single stacked bar.
- You know what I mean?
- Just like one rectangle sliced up.
- You can do the nice little labels on the top and bottom.
- And I guess it's technically a stacked bar chart,
- but it's just a single bar.
- It's also a pie chart.
- I think those are great.
- I think those really work and you can stack them.
- It doesn't like Pew do that on their quizzes.
- I think that's good.
- Cause like, cause maybe that is a tree map.
- I don't know.
- You guys tell me in some way it is a tree map, I guess,
- but like to me the tree map works
- when there's a lot of slices,
- like there's like 50 slices
- and like one or two of them are huge, you know?
- And you want to be like, these ones are big
- and these ones are small.
- But like, if you only have like five things, you know,
- you don't need that vertical part of the tree map.
- If it's just like a fixed wide height, it works fine.
- Sorry, I just did that.
- But that's really how I feel about it.
- - It's a great, it's a great answer.
- I mean, tree maps were created to show a nested hierarchies,
- right?
- So you have a total
- and then you subdivide that total into large divisions,
- for example, population of the world.
- And then you start dividing them into continents
- and each one of the continent pieces
- gets further subdivided into the country.
- So that's what tree maps are for.
- So it's an excellent answer.
- I would advocate for the pie chart though.
- So just to be the contrarian,
- I would vote for pie charts if I could.
- Simon, can I do that?
- - You don't get advocate for pie charts.
- - I did not expect that, Alberto.
- - I'm getting old.
- Getting older, you know, on a simple pie chart.
- I tweeted the other day, if a graphic works, it's good.
- And pie charts unfortunately are fortunate, often work.
- It's like if there are two, three subdivisions,
- four subdivisions perhaps, readers like them,
- readers understand them, readers read them,
- they bring attention to the data.
- So, you know, I'm getting older, more flexible,
- more pragmatic in some sense.
- So go for the pie chart.
- - Even the three-dimensional pie chart?
- - No, three-dimensional.
- That's going to, maybe when I'm 80.
- - That's the limit so far.
- - Yeah, that's, no, maybe when I'm about to retire,
- maybe I will accept three pie charts.
- - Wow, now that Alberto has been switched with somebody else,
- I think that's a good point to end.
- Ben Welsh, MaryJo Webster, thank you so much.
- - Thank you.
- - Thank you for having us.
- (upbeat music)
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The state of local data journalism
By Ben Welsh • • The Data Journalism Podcast in Zoom
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