- or maybe he's still trying to get his zoom in order.
- - Can you hear me?
- - I can hear you.
- - Yeah.
- - This has been a truly dramatic, like, point of view.
- (laughing)
- I'm zooming you from my phone
- because I could not get my computer to zoom in.
- I'm so sorry.
- I am one of the recalcitrant nerds who insists
- on continuing to use Linux,
- even though everyone else has abandoned it.
- And I use zoom every day with no trouble,
- but I was told by your system
- that I needed to upgrade my zoom.
- And so I've spent the last 20 minutes
- trying to upgrade my zoom.
- (laughing)
- So hard, so hard.
- I know, so now I have zoomed you from my phone,
- which is probably what I should have done in the first place.
- And so I'm really, really, really, really sorry.
- Thank you for your patience.
- - Oh, no worries, Ben.
- We are so thrilled to have you here.
- I just wanted to give you a little bit of a preview
- of what's been going on prior to you arriving.
- You know, this event started on Wednesday
- and it's a humanitarian mapathon event.
- And the goal was to digitize 20,000 buildings
- using hot OSM and open street maps.
- I don't know if you can see from my virtual background,
- but between our two campuses, UCLA and USC,
- we each digitized collectively 10,000 buildings
- at each school.
- So we reach our miles to 20,000 buildings,
- which were digitized mostly.
- We were working in Indonesia and Sudan, South Sudan.
- And yesterday we had back to back to back workshops
- around topics of humanitarian mapping,
- data science, Python, Jupyter Notebooks, Tableau, QGIS,
- all these kinds of tools that I know you're familiar with
- with the work that you do and the team that you manage.
- So we're all very excited to have you here.
- I just wanted to quickly kind of introduce you
- with the work that I know that you've done.
- So let me just quickly,
- I'm just very briefly share my screen.
- So you look quite different from your picture here.
- - Yeah, COVID's been hard on us all.
- - Yeah, yeah, you have this kind of rock star
- vibe about you today.
- COVID has made us all into, you know, punk stars.
- But Ben, you know, if you're from Los Angeles
- and are familiar with the type of work
- that the LA Times has done over the years,
- we know Ben has this kind of data journalist
- who has put together a lot of the data,
- you know, journalistic stories around the LA Times
- and our local communities.
- I happen to, let me see,
- know about Ben's work primarily through the pandemic
- because when this came about tracking the coronavirus
- in California was a site launched by the LA Times
- earlier this year.
- And Ben led the effort to work with community organizers,
- with hospitals to kind of have a one-stop shop
- where all this information about the coronavirus was collected.
- It's really an amazing resource if you haven't been there.
- It has all the almost real-time data statistics
- about coronavirus, kind of more focused locally.
- So we know we have data from Johns Hopkins,
- that's global and kind of more national,
- but where do we find neighborhood level data
- about what's going on in Los Angeles?
- And this is just an incredible resource.
- You know, it's full of real-time data graphics
- that's updated hourly.
- And, you know, unfortunately, it's still relevant today.
- You know, back in March, when I first talked to Ben
- about this, I was actually thinking, you know,
- maybe the last of the summer.
- But that's the reality of the moment.
- It's an incredible resource with data charts, maps.
- And also, what makes this resource so useful
- for us in academia is that, you know, Ben's a huge,
- as he said, you know, Linux operating systems
- about open data.
- So, you know, this week we've been thinking so much
- about open data, crowdsourced information,
- using open street maps.
- And the data desk information provided by the LA Times
- allows us to really work with data that's transparent,
- that's made publicly available.
- And this Coronavirus GitHub page is where I believe
- Ben's team is contributing to the community
- by providing almost real-time data and statistics
- about all the information that they're collecting.
- So, I'll leave my talk to that
- and let Ben take over now.
- And once again, thank you, Ben, for joining us today.
- - Yeah, thank you.
- I had just emailed you a link to my deck.
- So, I'm stuck on my phone.
- Would you mind calling that up
- and just sharing your screen if it's not too much hassle?
- - Oh, no problem.
- Hold on one second.
- - Sure.
- I'll spiel for, I don't know, not too long,
- but I was hoping to give everybody here an overview
- of our team at the LA Times,
- the data and graphics department,
- kind of what we do, the types of people who are on the team,
- the types of things we make,
- so you guys can get a sense of, you know,
- how these kind of data skills live
- within the journalism world.
- And then, you know, we can answer any questions
- anybody has that really talk about anything
- you guys wanna talk about.
- And along the way, if you have anything
- you wanna know more about,
- or if I'm not explaining something good,
- just stop me, just talk.
- I'm happy to have a conversation
- and glad to be here.
- So, I also think it's really cool
- you guys are doing open street map stuff,
- which I myself have dabbled in a little bit,
- trying to do one of my hometowns in Eastern Iowa,
- where I grew up.
- So, well, where to begin?
- So, my name is Ben Welsh.
- I'm the editor of the data and graphics department
- at the LA Times.
- And we're a team of about 20 people
- who try to use our computer skills to like,
- make stuff happen.
- And this presentation will give you kind of an overview
- of what that ends up being.
- So, go ahead and click on.
- So, there's me before COVID.
- My wife calls this my Bible salesman photo.
- And obviously, next is me after COVID.
- We already got to this joke though,
- so we don't need to stick with it.
- Shout out to all the Twin Peaks fans out there.
- We can keep going.
- And this deck is one that I have presented internally
- at our company,
- and it initially included some private business figures,
- which I've redacted just to not get in trouble.
- But I'll give you the gist of what I was trying to get across
- when we get to that point, okay?
- Keep clicking.
- So, the data and graphics department,
- its mission, as best as I can articulate it,
- is for us to create digital journalism
- that's important to the readers of the LA Times,
- both in what they want to read
- and what they ought to read, right?
- With using data development and design.
- And by development,
- I mean actual computer code development, right?
- So the people that are on this team, if you hit the next slide,
- are not just reporters and editors and journalists
- and people like me who went to journalism school.
- They're also at the same time,
- computer programmers, data analysts,
- information designers, data scientists,
- whatever you want to call it, they're nerds, right?
- And so, we're kind of one of the more multi-hyphened teams
- at the LA Times and that people are called upon
- to not just do the traditional skills of journalism,
- but also to have these technical skills
- that let them find and tell stories in other ways.
- And this is something that's increasingly common
- across the journalism industry.
- Our team is not unique.
- There's kind of an evolution happening
- in graphics and data departments across the country
- where graphics is becoming kind of a higher-skill
- data and programming profession.
- And our team is caught in the same currents
- that are driving that, right?
- And so, the people on our team have to be pretty darn flexible.
- And so, if you hit the next slide, you can get a look at them.
- Like I said, it's about 20 different people.
- They come from many of them from Southern California,
- many of them from UCLA and USC, or at least a couple,
- but also from all across America.
- And they're people with a lot of different backgrounds
- and skills.
- We try to have everybody learn as many
- of the different types of things we do as possible,
- but the way it works out is some people tend to be
- a little better at analysis or maybe a little better
- at visualization, but we don't specialize.
- We really do kind of try to treat everybody
- as being able to take on all of the challenges.
- And so, we're always learning and we're always experimenting
- and trying to take on new skills.
- Just this morning, I was helping someone learn
- how to write some spelt, if anybody here is a spelt fan,
- to make some of our election graphics for next week.
- So, if you hit the next slide, we're gonna get into
- what are these things we actually make?
- In a lot of ways, to me, a newspaper where I've worked
- at the LA Times for 13 years now and seen a lot of change,
- but even though we're making different things
- and different products, it is kind of an information factory.
- So, in a certain sense, a news organization
- takes the raw materials of life, of things
- that are happening in the world,
- and it refines those raw materials into products,
- news products that you can sell,
- and the people want that help them make sense of the world.
- And traditionally, in my painful metaphor,
- the assembly lines at this factory were really focused
- on making a print newspaper that had blocks of text, right?
- With headlines and photos, and that was like,
- what the majority of the assembly lines were set up to do.
- There were other ones to have sports scores and boxes
- and to give you the movie listings
- and all these other things.
- But in the rewiring of our economy,
- that's brought on by the internet and all that,
- the factory of the LA Times has to be refitted
- with new assembly lines to make different products, right?
- That are more in line with what people want
- that can compete with other factories out there
- in the marketplace, and that can help us shift
- to a different type of business,
- which is more focused on getting money from subscriptions
- and readers who like us enough to pay us
- and less so on advertising,
- which if you guys are interested in all that business stuff,
- we can talk about it later, but I won't be too boring.
- And in our little department, it's my view that our assembly,
- we basically have three assembly lines
- that the people you saw before kind of interchangeably work
- on.
- The first one makes applications, software websites, right?
- The second makes visual stories.
- These are stand up, we'll get to those.
- And then the third is making digital designs.
- And so let's go through those one, two, three,
- and I'll show you a bunch of examples of each.
- So first up is applications.
- This is where we use our software skills
- to gather and refine data to serve our digital audience,
- writing computer code to go get data,
- filing public records requests to get it,
- otherwise building databases ourselves from the ground up
- that we can then turn into a web application
- that is not a traditional story
- that people will wanna read and maybe even pay for, right?
- And so if you hit the next slide, the first example,
- I guess would be the coronavirus tracker.
- This is a recent one.
- This is where we're gathering data
- from a ton of different sources
- and turning it into all these pages
- that update as frequently as we can get them to,
- and let people slice and dice and tailor them.
- There's also 58 pages, one for each local county, right?
- And we're always working to look and expand on this.
- There's a few things I'm hoping we can get out soon.
- Yes, that is Netscape.
- This is my visual pun for this presentation.
- If you'll bear with me, I'm showing my age.
- Okay, so then like another example,
- if we keep going would be our live wildfires map.
- So this is where we're pulling in data
- from a lot of public sources and one private one
- to try to give people the most comprehensive,
- kind of composite view of what's happening
- with wildfires this minute in California
- or the most recent one possible.
- And it's in no way a traditional story, right?
- It's a single webpage that updates and stands along.
- If you hit the next one,
- another example would be our quake bot.
- So this is something that isn't like a standalone webpage.
- It's something else.
- It's a piece of automation.
- And so every time there's an earthquake detected
- by the USGS, it sends out like a data feed
- to the whole world, like kind of little pulses
- that say here's the latest earthquake
- and how big it was and where it was.
- And we have an algorithm that reads every one of those
- based on how close they are to Los Angeles
- and how high the magnitude is.
- We have some sort of editorial barriers.
- And then if the data surpasses those barriers,
- this blog post is automatically written
- and this map is automatically made
- and prepared for publication.
- And so within a minute of an earthquake happening,
- we can have the complete post,
- including the map ready to roll.
- We still have a human look at it, of course,
- before it goes live,
- but this is where automation gives us
- kind of a quick step out the door immediately.
- And then the next one would be, I think,
- our most recent police shootings database,
- we have a long running project called the homicide report
- where we have built from the ground up
- our own independent database of every person
- killed by another in LA County since the year 2000.
- And this year we decided to repurpose a slice of that data
- to be a standalone database that just tracks police shootings
- and gives people a sense of the latest trends
- and that now, like the homicide report,
- live updates every time we get new records in.
- These databases, they power these standalone applications
- and they would probably worth doing just for that reason,
- but they also have the side benefit
- of allowing us to do enterprise analysis
- into these issues that we otherwise couldn't do
- if we didn't have the data, right?
- So on the next slide, you can see,
- since we've been gathering all this COVID data
- for months and months,
- we've been able to do tons of stories about COVID
- that are a little more traditional,
- but that have stronger claims and more insightful analysis
- because we have this data to draw from.
- So one example would be this kind of step back piece
- that we did after LA County's sort of botched reopening,
- where we were able to use all the data we gathered
- and other things we pulled in to kind of tell that story
- of what happened here in LA
- in a way that wasn't just a data application, right?
- And then the next one would come from our homicide report
- database.
- If you guys follow the news,
- you may have heard about the case of Deshawn Kizzie,
- who was a young man,
- killed following a bicycle stop in South LA
- by sheriff's deputies.
- And when that happened,
- it sort of raised a question of,
- well, how common are these bicycle stops
- that lead to fatal encounters?
- And because we had gathered this database
- and had all this information,
- we were the only media outlet that was able to give
- the public the news that, hey, this isn't a loan incident.
- This is something that's happened 15 times
- in the last 15 years.
- And so it's not especially common,
- but it's more common than this being the first time.
- And you're able to bring context and insight to it.
- You can see if you see that map fly by really quick
- that these incidents have been concentrated
- in certain areas too.
- We have some public records requests out
- that I believe will allow us to look into that further
- in the future.
- So that's thing one,
- that's the kind of the applications and the analysis.
- We have live election results coming on Tuesday.
- So this is all the flowing data feeds
- of votes coming in from across America.
- We'll go into maps and tables and charts
- to help you kind of make sense of what's going on, right?
- And so all right, so now we can do thing two.
- So assembly line number two is visual stories
- is what I call them.
- This is kind of an emerging story type
- in the world of data and graphics online
- that nobody really has a good name for.
- So if anybody here has a better name for it,
- I'd love to hear it.
- You can hit the next slide.
- And so visual stories are when graphics reporter reports
- and designs visual coverage that is substantial enough
- to stand on its own.
- Traditionally graphics departments
- had sort of made accompanying graphics.
- So on the old assembly line,
- you might be making a front page story
- and you got to have 1600 words of text
- and you got to have a headline
- and you got to have maybe a couple photos.
- And hey, if you're really going to dress up
- that front page story, you might get a bar chart
- and go with it, right?
- And so a lot of graphics departments
- had been oriented around being a support desk
- that was producing these sort of supplementary graphics
- for traditional stories.
- And we love those graphics.
- They're a good thing, I like them.
- But I think we have the potential to do quite a bit more
- and I think our audience wants something a little more.
- And so one thing I bet everybody here has noticed
- reading some of the leading news outlets in America
- is that they now produce these visual stories
- that are primarily just a combination of graphics
- that are big enough that they can just stand on their own, right?
- And that's what I call a visual story.
- And these are pieces that at most places
- are reported, pitched and executed primarily
- by the graphics reporter.
- It's not a case where a writer of a traditional story
- comes to the graphics department and asks for a graphic, right?
- So the entire process of how things kind of come together
- has changed and the graphics reporter
- is more in the driver's seat, okay?
- And so go ahead and hit the next slide
- and we'll look at some examples.
- So early this year, as I'm sure everyone here knows,
- Kobe Bryant died in a tragic helicopter accident
- near Calabasas and immediately we thought
- there's a visual component to the story.
- For people to understand what happened,
- you need to know the path of his helicopter, right?
- And so on day one, it was like a map of where did he crash
- and it's just like an X.
- And then between day one and day two,
- it's like, well, what was the path of the helicopter
- and what was really going on and what's the play-by-play, right?
- And then what we kind of learned on,
- it happened on a Sunday, what we kind of learned on Monday
- is like, oh, well, it seems like the final like part
- of this helicopter flight is where things really went wrong.
- And this was kind of the crucial moment.
- And so we decide, well, let's try to do a story
- that helps people see or understand as best as we can
- what happened.
- And so we took the publicly available data about the helicopter
- and we turned it into like a three or four step
- little graphic here that has, for each chapter in the story,
- has like a 3D map that helps you see what's going on.
- And by the end, you sort of are able to see
- this sort of important thing where the helicopter
- began climbing very rapidly in the last few seconds
- before a quick turn and then a crash.
- And that's the sort of thing that you can describe in words
- but is maybe better understood by seeing a 3D model, right?
- So the next example on the next slide
- would be something from politics.
- So we have this very tight DA race, we think.
- And after the primary, when we knew we were going
- to run off, we get the precinct level results
- that show you where people voted in every neighborhood.
- And we kind of did an analysis that looked at,
- well, where is the incumbent strong?
- Where is the challenger strong?
- And what would an upset coalition have to look like
- based on past election results
- and what we kind of know about this election?
- So this piece is really about helping people understand
- the political calculus of this local race through maps, right?
- And then the next one is we did a local mask survey.
- So there was this thing over the summer,
- the question of how many people are actually really wearing masks
- and rather than just quote public opinion polls
- or take data from other places,
- we decided to conduct our own survey.
- So a dozen of our reporters went to three different locations
- of multiple times over a few weeks.
- We followed a method that Jill Darling at USC
- helped us like figure out.
- And then we were able to kind of come back
- with our own provisional conclusions
- about how many people are wearing masks roughly
- and the variations between the different locations we looked at.
- And then that's presented here
- as sort of a designee standalone thing
- that's really more about charts and maps
- than it is about text, right?
- Next slide would be something that's not really data at all.
- It's more visual.
- And so in the George Floyd protests,
- I'm sure everyone saw on social media,
- there was a lot of footage of the police here in LA
- being pretty violent.
- And so we did a piece where we tried to aggregate
- as much as that as possible
- and then help compare it to the actual standards
- for use of force the police are supposed to follow
- to show the contrast.
- And so this is a case where we're using the design
- and the visual graph and the videos to tell the story
- and it's not really like charts and maps, you know what I mean?
- But it still falls within kind of this
- under this visual umbrella that I think is part of what we do
- and it's why I call it a visual story
- and not a graphic story or something, right?
- And we have another example I'm trying to remember.
- Oh, and then here's one we did on homeless housing.
- This is a type of visual story I bet you've all seen too
- that we call scrolly telling, which is a terrible term,
- which is where as you scroll things sort of move and happen
- and this is another type of visual technique
- that we use from time to time, depending on whether it fits
- the story we're trying to tell, okay?
- I think that's it for those examples.
- And then I think we get to things three,
- if I remember my deck right, which is digital designs.
- Oh, no, it doesn't fit the screen size.
- My gosh, you found a bug in my deck,
- but this is a case where we're using our digital design skills
- to elevate the newsroom's most ambitious journalism
- through like what we think
- are cool digital custom presentations.
- And in this case, we're usually helping to elevate stories
- developed by other departments rather than our own work.
- And a lot of it has some, you know,
- nothing to do with data or graphics.
- We're kind of the boutique web designers
- for these like flagship projects of the newsroom, okay?
- So examples of that wouldn't be on the following slide.
- So the first one would be we did an obituaries package
- of all the people who died from COVID.
- Not all of them of many people who died in COVID,
- of COVID here locally.
- And then that package has sort of a custom design
- to try to make it more dramatic and interesting
- and engaging for the reader.
- The next example is one from Sunday.
- I wonder if anybody here saw it.
- We had a piece by Rosanna Shee about DDT
- being dumped off the coast back in the day here in LA.
- And to try to make that story more impactful and grabby
- and to get people we did custom design for it,
- which included like a video topper,
- which you see here, a little scrolly telling in the middle.
- And then the sort of overall design and theme of the page
- is customized to fit the story.
- It isn't just the generic LA times kind of look, right?
- Now the next one is the Chicano moratorium package
- over the summer, we had an anniversary
- of a famous protest in East LA against the Vietnam War,
- which ended up being a series of stories.
- And in the past, this might have just been
- like a newspaper special suction,
- and then a bunch of pages on our website
- that aren't really connected.
- But what we try to do is to bring a style and theme
- to the whole package.
- This is kind of the cover page where all the stories
- were collected, and then each of the individual stories
- was given sort of a variation on the theme
- that you see here is the idea.
- And I think next up is the one I'm gonna,
- oh, then sometimes we do these for other platforms,
- not just for the open web.
- The Linux nerd in me would love it all just to be
- on RWW, but as far as our business goes,
- we do need to cross publish these things
- on monopolistic corporate platforms, guys, like Apple News.
- And so sometimes we will customize stuff
- to work on those other places as well,
- as you see here with our voter guide,
- which ran inside of Apple News.
- And then we have, I think the next slide
- will just tease one we have coming,
- we're gonna try to do a crazier year in review package
- that will be more visual and fun
- than what we typically done in the past.
- So those are the three types of things we do.
- The next section kind of gets into what that requires
- for a change, and some of this is a little more
- in my message for people eternally at the LA Times,
- but I think it'll work for you guys too,
- which is for this, for our data and graphics department
- to make these things and to achieve this mission,
- they have to be more independent and ambitious
- than maybe a traditional graphics department has been,
- and they need to have a higher skill level.
- So that requires us to seek out
- and develop our own ideas,
- to look at the major storylines of the day,
- be it COVID or COB or the Dodgers winning
- or the election or the George Floyd protests
- or homelessness here locally
- or whatever those major topics are
- that we know both matter to our democracy,
- but also to our audience in terms of what they want to pay for.
- We need to look at those topics and say,
- our job is to develop striking visual and data coverage
- that people are going to want,
- and that means we have to be more ambitious
- than some departments have been in the past.
- We need to take more initiative,
- and we need to operate more like a traditional news department
- in having our own sort of editing system and people in place.
- So that's kind of this section.
- And you can just jump through the next few slides
- and we'll get to where the next section,
- I just talk about the results.
- So just, you can jump ahead here a couple.
- There's Alex Tatuzzi and my deputy editor, my hero.
- And then the next slide is our listing.
- We have a job listing for a second deputy editor
- as we try to increase our editing muscle on our team,
- which is one of my major strategic goals
- for the current moment.
- And the reason we pursue all these goals
- is not just 'cause we're nerds and we like to do it,
- which is maybe a good enough reason
- if you're not expecting to get paid.
- But we also are pursuing this different type of journalism.
- One, because we like doing it.
- Two, because it's better journalism.
- The three, because it pays and it's better for our business.
- And so the next section is really kind of covers
- that these types of things I've been showing you,
- they're more popular with audiences.
- They're more valuable in that they are seen
- to convert more new subscribers to the digital product,
- which is our number one business goal than a typical story.
- And they're more durable in that they continue
- to draw an audience long after they're initially published,
- where most new stories kind of peter out within a day or two.
- So this next section is all redacted,
- so we don't need to look at it, but I can give you the gist.
- I mean, you probably aren't gonna be surprised
- to hear this if you think about it,
- but our coronavirus tracker,
- as humble as it may appear to you,
- is the most popular page in the history
- of the Los Angeles Times website.
- No page has ever been visited by more people.
- And if you compare it to our top traditional stories,
- it is multiple times more popular
- than the most popular stories of the year.
- And then when you look at conversions
- in terms of what's valuable in attracting new paying subscribers,
- it is an order of magnitude higher
- than the number two story of the entire year.
- And so you see that when things like this connect,
- they have a sort of home run possibility for the business
- that a traditional story very rarely can have.
- And then if you look at that over time,
- you'll see that it didn't become the top converting story
- until three or four months after it had initially launched,
- which just shows that it's continuing to draw audience
- and new paying customers over time
- and that sort of durability I was talking about.
- And so for those reasons,
- we think that there's a strong business logic
- behind investing in what we do.
- And I'll kind of close with this, I guess maybe,
- but having been a news nerd, as people say,
- and having done this kind of thing for nearly 20 years now,
- that has been the biggest change
- that I have seen in the last five or 10 years.
- When I began this as like a computer programmer journalist
- in 2000, we were curiosities.
- We were either the one person on the investigative team
- who had learned to code to try to do a cool data analysis
- for a traditional print investigative piece,
- or you were some weird person in the corner
- who said the internet's gonna be big one day,
- and we need to learn how to make new weird things.
- And so the justifications for most of the teams
- that I've been on in my entire career
- were either this is data analysis
- to do more impactful and important investigative reporting.
- That's actually what got me into it initially
- and is still my passion, right?
- Or it was this is sort of boutique internet experimentation
- to kind of see what comes of it, right?
- And we're now far enough into the digital transition
- to the information economy
- that that's just fundamentally changed.
- And what we've seen is that there are certain types
- of these products, these data and graphics products
- that are so successful
- that there's now like a capitalistic economic logic
- for producing them.
- And there's less and less of a logic
- for producing what these departments produced before.
- And so that's why I think not to get,
- I guess I am at the university
- so I can get all Marxist on you guys, you know,
- like I think that that is this economic logic
- that is driving kind of the evolution of our team
- and the justification for growing it,
- as well as similar changes you see in other departments
- like ours at other news organizations,
- some of which are like us in the middle of the transition
- and some others are further along down the road.
- Like for instance, the New York Times graphics desk
- which is already fully converted into this type of thing
- that I've been talking about here today.
- And I don't work there so I don't know the numbers
- but I would estimate based on what we see
- and based upon how the number of things they put out
- and the quality of what they put out
- that like the New York Times graphics department
- may be the most read and most profitable department
- in the whole newsroom, like that wouldn't surprise me
- with maybe the exception of one or two people
- who write about Trump, you know.
- And that is something that just was not true in the past
- when these departments were seen as supplementary, right?
- So that's my spiel.
- I'm happy to talk about really any of these issues
- that were raised, any of the stories in particular,
- any, you guys want to talk about Python?
- I'll talk D3, I'll talk about that.
- Whatever you guys want to talk about,
- I'm happy to just jam and hang out.
- - All right, well, that was fascinating.
- I do certainly want to open it up to the audience
- because this is about the students who are here
- participating this week.
- Maybe I'll have Andy kind of summarize the questions
- in the chat room, but in the meantime,
- well, you know, go ahead and post questions
- that you have for Ben in the chat room,
- but maybe I'll kick it off a little bit here, Ben.
- You really talk about this kind of transformative age
- of data journalism through this kind of visual evolution.
- What's just kind of like mind boggling to me
- is the fluidity by which you guys go about your business.
- Thinking like any one of the products
- that you have showcased in your presentation,
- if we were to do any one of those in academic institution,
- it'll probably take six months to create.
- And just kind of, you know, be followed by this,
- your ability to, you know, pivot so quickly
- with a new story.
- And then the next day you have like something
- that looks like it took six months to create.
- Can you talk a little bit about this kind of this environment
- that you work in, the speed that you have to work in?
- And you ever sleep?
- - Well, I'm glad you think that during COVID, very rarely,
- as you can tell by looking at me,
- but like after the selection,
- the dream is like December, like maybe a day off,
- but like,
- but more seriously, it's really like any,
- like, you know, I like the factory metaphor,
- but I'm sure there's other ways to talk about it,
- but it's like any process, any like manufacturing process,
- you break it down into pieces
- and then you like get really good at like, you know,
- getting each of those pieces of things
- that need to get done kind of like handled, you know,
- and you then try to speed up the process as much as you can.
- And so like in the case of like a visual story, for instance,
- you can think about it in that abstract manufacturing way
- in the sense that you need a toolkit
- that lets you stand up a single static page
- that's outside of like a traditional
- rigid content management system, right?
- That can be custom coded and designed.
- And so what you'll find is that at our department
- and every other one like it,
- that people have their little quote framework
- or their little, you know, publishing rig
- that lets them spin up a standalone web page.
- And then within that, there's like components
- of web development that make it easier for you
- to move quickly like a template inheritance.
- Well, within that system,
- we have a base template that has all the LA time styles
- and fonts and the header and footer
- and like all the basic pieces.
- And so then those are just like there for you
- when you like get the page like rolling, you know what I mean?
- And you're within that already.
- And then by the push of a button,
- you can trigger a little deploy
- that will send that like blank page live.
- And then within the framework, you, you know,
- depending on how you set it up,
- you have ways to import data files
- and then to quickly get them into JavaScript
- and other tools so that you can lay out the page
- kind of as quickly as possible.
- And our system isn't as streamlined as it could be
- or as it should be, you know,
- but like every, we've developed it ourselves internally
- and we're trying to make it better like all the time.
- Like this week we were talking about,
- well, how could we get it better at producing a headless page
- that's for iframing in other places?
- You know, how could we make a template
- that's just like ready to make an iframe page
- that could go on the homepage in 10 minutes, right?
- And so you have to have like that toolkit that's there.
- You have to have someone on staff
- who can like build and maintain such a toolkit
- because they don't really exist.
- One of my goals is for there to be
- a more standard open source sort of like thing for that.
- You know what I mean?
- You look at tool like when we make an application
- like the live election results or the tracker,
- we might use Django or some of these
- pretty highly developed web frameworks for databases.
- But when it comes to static pages,
- you really just have kind of like a hodgepodge
- of like node framework thingies
- that like can kind of halfway make a page
- but they don't have the 30 different things
- you actually need to make a real page
- that you want to publish, right?
- And like kind of, I think that there's the possibility
- for a stronger open source framework
- that goes beyond just the basic tools
- and gets into a little more of the opinionated decisions
- about how to manage deployment
- and static assets and data files and stuff.
- Wow, that was way too nerdy.
- But like that's part of it.
- That's like the technology part.
- And then there's the kind of editing and journalism part
- which is identifying either planned events
- that are ahead on the calendar
- that you know are coming like the election
- or pivoting when a major storyline emerges
- and then focusing your assignment on it.
- And so like in the case of like the Kobe thing,
- it's like Kobe's helicopter crashes on Sunday.
- In a breaking news situation like that,
- we tend to think in terms of like rounds of coverage
- or stages.
- So on day one, we're going to do this.
- On day two, we're going to do this.
- And then we're going to aim for something
- that's like a little more, you know what I mean?
- And so like in the case of Kobe,
- day one was just like, where is the crash?
- What kind of helicopter was it?
- You know what I mean?
- The basic facts and can you visually do that?
- Day two was, well let's get the play-by-play.
- What was the like?
- When did they take off?
- Where did they go?
- But you know, so we had a graphic
- with the day two story that had that stuff.
- Was there a route, a typical route?
- Was it an unusual route, you know?
- And then what happens typically in that is
- after you learn the basic facts,
- you kind of get to a point where you're like,
- okay, here's what we think the story is.
- You know what I mean?
- So like within these facts,
- here's what we think is worth sort of teasing out
- visually for people.
- So both has to be something insightful
- that goes beyond the basic facts for people,
- but it also has to have like a visual component to it, right?
- And so in the case of Kobe,
- our decision was to focus on the final turns of the helicopter.
- All this is of course debatable,
- but those were sort of our judgments we made
- like in those moments.
- Like the conception boat crash,
- the fire off Santa Barbara coast,
- I guess a year ago now,
- was another example of that where day one,
- it's just, well, where was the boat? What was the boats route?
- Day two, or there are like advanced,
- our piece for the weekend then was like,
- well, what were the escape routes on this boat?
- Where that was what seemed to us to be like the story.
- You're kind of betting that that's how it's gonna shake out.
- You know what I mean?
- As the facts come in.
- And then let's make a 3D model of the boats
- so we can show people what these escape routes were like
- and emphasize that they weren't very good.
- You know, it's what it ended up being.
- So that's like a breaking new situation.
- And then there's kind of in between ones
- where it's like there's gonna be this long storyline.
- And this is something that we're just trying to really
- get better at in our department this year.
- And is what I really admire about the New York Times
- and some of the more mature departments,
- is it seems to me that they have kind of decided on
- for the year, here's what their major storylines are gonna be.
- And then they come up with a string of visual coverage
- to try to attack those major storylines.
- And sometimes those can be planned like the election.
- And other times like COVID, they just occur
- and then you have to be smart pivot, right?
- And so for us, we've done that this year
- for the Floyd protests and for the COVID coverage
- where we put together our own independent visual coverage plan
- of like here's ideas.
- Everybody in the team pitches things in.
- We think about are there applications we could do?
- We think about are there visual stories we could do?
- And then we try to make them happen.
- - Yeah, that's wonderful.
- I just wanna jump in.
- There's a really, really great question.
- I think it can kind of build on what you were,
- I mean, what you just talked about.
- But the question is about just sort of thought process
- and developing stories and data,
- but specifically about dealing with biases and storytelling.
- And then the follow up to it is,
- are there any specific examples that you could share
- where you did face pushback on a story
- that is sort of data driven,
- but maybe there's some sort of part of it
- that kind of, you know, there was a stop to it.
- - Like internally at the LA Times, that's-
- - Yeah.
- - Well, you know, or maybe just from you.
- Maybe you kind of, yeah.
- - I think I have an example, it might not be exactly that.
- I will just say I've been here 13 years
- and you know, like any institution,
- people have disagreements and conflicts and struggles,
- you know what I mean?
- And I've had my own.
- But I will say that in that time,
- I have never once felt that if I had a good story
- that I knew was true, we couldn't get it published.
- That's never happened.
- There are people I really disagree with
- and to be honest with you, I don't like
- and they might not like me,
- but I have at different times been able to come to them
- with we have the story, check this out, let's do it.
- And those people, they rise above all the personal stuff
- and they're like, yeah, let's do it.
- You know what I mean?
- And to me, that's one of the great things about a newsroom
- is that people who disagree or who are different
- can unite around like a good story.
- And like I have nothing but good things to say
- about that personally, but like this is an example though
- and of something slightly different but related,
- which is how and one thing I really love about statistics
- is how it can sometimes they can sometimes lead you
- to conclusions that you do you would never imagine,
- you know what I mean?
- Or maybe we're counter to your initial bias
- and then you end up telling a different story
- than the one you thought you were gonna tell
- when it started out, right?
- And so you guys remember Hurricane Harvey in Houston.
- Oh, I have to send a Slack message really quick.
- I have to,
- this will just take a second.
- I have bought a mechanical keyboard
- during this like period at home guys
- and it is so loud when I type that this might be deafening
- but you guys remember Hurricane Harvey.
- This was in Houston a couple years ago.
- They had kind of like a big storm come through
- and kind of flood the whole place
- and it led to these sort of crazy images on television
- of people trying to cross the city
- in this sort of biblical flood kind of situation.
- And it was a Friday night and we were sitting
- in the newsroom at the LA Times
- and an editor said to me, well, we gotta do something.
- Let's come up with something.
- It was one of these moments, right?
- Like what are we gonna do?
- This nuts thing is happening.
- We have to come up with a way to cover it.
- And like a lot of those situations,
- you kind of start with what you're seeing in the news,
- what you hear from other people and your own biases.
- You know what I mean?
- Like what you kind of think about the world.
- And like, and then we're trying to come up
- with a data story.
- And so our initial thought was, hey,
- if we get the data about where the FEMA flood zones are,
- these officially defined flood zones from the government,
- right?
- And we compare it to the early damage data
- that we see coming in from the government
- about where buildings are damaged.
- We're gonna find that all the people in these zones
- got wiped out and we're gonna be able to add them up
- into some big number and try to do a story
- about the like extent of the damage, right?
- And like, we didn't know exactly what it was gonna be,
- but that was kind of our methodological bias
- or frame that we like came to it with, right?
- Let me just send this message, sorry.
- And we're all fired up to do that.
- I stayed up to like one in the morning, like it's like,
- oh my gosh, the Harris County Property Assessors Office
- has all the data online.
- Yeah, grab it.
- And the FEMA is having a conference call
- and they're gonna give out the damage data.
- Wow, there's property values.
- Here's the flood zones.
- I'm gonna put them all together and like find it out.
- And like, and then we pitched that story to an editor
- and we say, this is what story is gonna be.
- It's gonna be about how screwed up Houston got
- 'cause all this.
- And then I did the analysis
- and what I quickly found, or not quickly, not so quickly,
- what I soon found was that most of the damage
- was actually outside of the official FEMA flood zones, right?
- So that was an interesting thing.
- And then we're like, okay, so let's start calling people
- and let's figure out what's going on.
- So we're just getting on the phone calling, texting
- 'cause we have all these addresses
- from the property assessors.
- Just call every person and just see what's going on.
- And we did dozens and dozens of phone calls.
- And what we found is that people inside
- the official FEMA risk zones were actually like fine.
- Like every one of them we could try to was like,
- no, I'm good.
- We were high.
- We had put up like, we had elevated our house
- or we had put in a retaining pond
- and that caught all the water or our roads got redone.
- And it was the people outside who had the problems.
- And what we kind of pieced together then
- from talking to experts and more and more people
- was that our initial assumption was actually reverse.
- And that's because believe it or not,
- public policy works guys or it can.
- It's that if you're inside an official FEMA flood zone,
- there were all these additional building requirements
- and retrofitting requirements for like taking care
- and getting ready.
- And so the people who lived in the quote unquote
- riskiest areas actually ended up having the best outcomes
- because they had to take all these preventive measures.
- And so we ended up writing a story about that,
- about how, hey, these regulations actually work,
- but they're not nearly widespread enough
- to save all these other people, you know what I mean?
- It's kind of what the story ended up being.
- And we had to kind of go back to the editor
- and be like, oh, our initial pitch, it's like not that at all.
- And that was this, it's a little hard to summarize,
- but that was this sort of like weird process
- of gathering numbers, listening to them,
- talking to people on the other end of the data
- and gradually having like a light bulb go on like,
- oh, what everything I think is wrong.
- And then you have, there's, whenever that happens,
- there's always this like fun meeting
- like in the middle of it where like you get together
- and you're like, hey, we're totally wrong, right?
- (laughing)
- And you have to, okay, well, we can't just give up.
- We gotta come up with like some story.
- And so then oftentimes it's a more interesting story,
- you know what I mean?
- Because it's something you didn't expect, you know?
- - Yeah, that's, yeah, that is actually amazing.
- And it's really, I think it's just sort of really helpful
- to kind of get that perspective on how you develop
- and really work through these stories.
- Another question, and I know we wanna be really sensitive
- of having you here for too, too long,
- but we have a couple of really good questions.
- One, I think that would maybe really also be helpful
- for students is just sort of thinking about
- that tipping point between when you're creating a story,
- when is it interactive and when is it static
- and kind of like, how does that decision process get made
- and how does that sort of, how does that happen?
- - That's a tough one, and there's not one answer,
- but I mean, if the question is when is something interactive?
- Look, if you ask it that way,
- the answer is less and less so every month and year.
- And that has a lot to do with the changes we've seen
- in consumer technology over the last five or 10 years.
- As more and more people have moved to mobile phones.
- Believe it or not, you probably will believe it.
- You guys are into tech.
- The majority of people who read the LA Times online
- read it on a phone, like well more than 50%.
- And so in a circumstance where the majority
- of your audience is visiting you on a very small screen,
- which they mostly just like thumb up and down on, right?
- A lot of the interactive graphics that were very intricate
- and made in Adobe Flash or whatever, 10 years ago,
- really no longer makes sense because most people
- aren't gonna interact with them and use them.
- And the reality is, is what we learned from web analytics,
- even before the mobile changes,
- that most people never interacted with them anyway.
- And so that's why you've seen a shift in,
- if you follow kind of our little niche,
- you've seen a shift away from most interactive graphics.
- And so for the most part, we're looking for graphics
- that can work static and small on mobile
- and the way to make them more insightful
- is usually through adding an annotation layer,
- so like a little swoopy arrow or something.
- Or a scrolling change, like you see
- in that scrolling technique or the chart sort of changes
- as the user continues to read the page.
- Where interactivity still makes a lot of sense,
- I think is in cases of personalization or of discovery.
- So we're gonna have a chart that shows you
- the total number of deaths at nursing homes from COVID.
- But then when we have the, we're gonna have a table
- that lets you look up the nursing home and care about, right?
- And then that table should have like a search
- and a filter and a way for you to like dig into it.
- And then there's something like the live wildfires map,
- which is really about one, giving you an overview
- of like how many fires are there right now.
- But then two,
- two, letting you drill down into areas that you care about
- to see kind of the latest data within those areas.
- And so that's the map being there for personalization
- and discovery, you know.
- I think a lot of the interactivity that was just
- like about fiddling, you've probably noticed
- has like mostly disappeared.
- Plus it's a lot, it takes more time to code.
- - Yeah, very cool.
- You know, we definitely wanna ask you this one question
- for sure, because we have so many students here
- who are interested in data and storytelling.
- And sort of what is like, if you had one piece of advice
- for students going out, working on school projects,
- or really just wanting to break into
- Ellie Times newsroom or other places, what is it?
- What should they know?
- - One piece of advice, I mean, I don't know.
- - Or two or three.
- - I can give you a call.
- - Yeah, please.
- - I mean, to me, this is a thing that has become cliche
- and is said by some people who I think are kind of jerks,
- but if you race them from your mind, it is true.
- You gotta learn to code.
- You don't have any, it's just like to really
- do this well, you have to learn to code.
- And you have to learn to code certain types of things,
- not just like coding in general,
- but I do think the fundamentals of programming
- are so important.
- The fundamentals of just like software and package design
- are really underrated, just like how do I write a function
- and then import it into another file and then reuse it
- and like package code.
- Like the basics of coding, I think are super important.
- And then the sort of applied application of coding
- to the types of things we do.
- And so that, I think on the one hand,
- is kind of data gathering and analysis.
- So this is, how do I get data by scraping it
- or reading it out of a database or building my own database?
- And then how do I analyze it to like interview the data
- is how we think about it.
- What questions do I have for the data
- and how can I write code that will help me get the answers
- to those questions?
- And so the tool that I teach a lot of classes on
- that I like, but it's all debatable
- is the Jupyter Notebook with Python data analysis tools.
- They're free is kind of the best thing about it.
- And the Jupyter environment once you figure out
- how to get it running is just a really handy way
- to do like step by step kind of data work.
- And then on the other side,
- there's the sort of how do we tell stories visually
- through web development, right?
- And that really involves learning fundamentals of HTML and CSS.
- You know, again, just the basics are so important.
- They're not even in HTML's case.
- It's not even that hard, right?
- And then how do you use JavaScript to make interactivity
- and data visualizations?
- I'm a big fan of D3.
- And it's also a great way to get better at JavaScript, too,
- because the D3 culture is really built around excellence
- and programming, which I think is important.
- I think there's a lot of free tools that are just about
- cutting corners that don't really help you
- over the long run become to master that type of thing.
- So to me, there's the coding part,
- but then there's the journalism part, which is thing two.
- And this is where, like when I get applicants for internships
- and stuff from data science programs,
- which I'm always glad to get and I love to see,
- you know, sometimes the people just haven't had the time
- or opportunity to really apply their work in a way
- that they're making something for the general public
- or that has kind of a headline take away like point, you know?
- A big part of editing and putting out a piece of journalism
- is you kind of have to have like something to say.
- And just writing a notebook that asks 10 questions
- of the data or just making a graphic
- that lets you fiddle with the numbers isn't enough, right?
- The things that you're making need to kind of take
- that next level of kind of journalistic whatever, you know,
- by saying my finding is this.
- And here's the headline that says it
- and here's the three charts that show it, you know what I mean?
- And you kind of like have a takeaway and a point
- so that you have something to share.
- And so when I see applicants who,
- and I don't expect anyone who's being out
- to really master any of this,
- but when I see people who kind of have clearly put in the effort
- to do both of those two things,
- to like learn how to do the coding stuff to make stuff,
- but also learn how to boil it down
- and to make something out of it
- for regular people, you know what I mean?
- That's when I think you're a strong applicant
- and you're sort of beginning the road to become do what we do.
- But the reality is is learning how to code takes a while
- and the things we're learning,
- we're trying to do are emerging and experimental.
- And so no one including myself really gets kind of good
- at what we're doing until they've spent a couple of years
- really apprenticing at it.
- And that's just, that's just being human, you know?
- - That's great.
- That's really humbling.
- And I think really honest to share that.
- And I hope all the students out there
- really get a lot out of that.
- That's really helpful.
- You know, I don't know if you wanted to kind of
- have a wrap up question or...
- - Yeah, whatever you guys got,
- I'll take, I got a couple of minutes yet.
- - Yeah, okay.
- Well, well, first of all, thank you for kind of validating
- this week for us from what you just said,
- the value of coding, open data.
- This is all that we've been kind of preaching
- in the last couple of days.
- Also should mention that if I look up
- visual storyteller in the dictionary,
- I think I'll find you in there.
- You seem to embody that kind of visual storyteller mentality.
- But I don't know if there's like one or two final questions.
- Maybe somebody can unmute themselves
- if you're out there and introduce yourself to Ben
- and ask a question, hoping somebody can do that.
- - I have a quick question.
- - Please.
- - And that's about, Ben, I see that you're active
- in an organization called Data for Progress.
- And I'm wondering if you'd talk about that a little bit.
- It sort of ties in with what we've been doing
- with humanitarian mapping.
- And sort of how you see that kind of mission
- converging with what you're doing at the LA Times,
- which is obviously more of a commercial than--
- - Yeah.
- I don't know if I'm familiar with Data for Progress,
- but I'm guessing it came up linked
- to some other things that I do, you know what I mean?
- But like for me personally, open source software
- is probably as close as I get to religion.
- Nice, but like, and I think is a really underrated
- as a solution to a lot of problems.
- It just needs more institutional support
- from places like UCLA.
- But what you guys are providing today, which is great,
- you know, but like, and so I've been involved
- in a number of kind of open source data gathering
- and cleaning efforts related to helping journalists
- both improve their skills, but also to kind of
- build software and data infrastructure.
- So we're not all like replicating each other's work
- and we can benefit from kind of group collaboration.
- One of those is called the California Civic Data Coalition.
- And so this was a foundation funded open source
- software project that refines and cleans up
- the campaign finance data put up
- by the California state government.
- So this is the money in our state politics,
- like all these Uber ads you're seeing
- for Prop 22 right now.
- And the data is there, it's just really difficult to access
- because it's just in crappy shape.
- And so there's kind of like this problem of who's going
- to clean it up, right?
- And so we created an open source group
- to write the software that would be the refinery
- for that like raw data, and then all the code
- and all the effort is open source as well as the result.
- Along the way we invented some bulk data loading
- open source software tools, which people now use
- for like totally different purposes, you know what I mean?
- There's like side benefits to doing that.
- And we're replicating that model currently
- when it comes to tracking COVID data,
- where initially the LA Times was doing 100%
- of this COVID tracking ourselves in California,
- but we now have a team of people from eight newsrooms
- across the state who are helping us kind of gather it
- and consolidate this cleaned up database of COVID data.
- And I think that there's one, a lot of potential for that,
- but two, I think as news organizations get smaller
- and struggle, I don't know if there's a better alternative,
- like who, you know what I mean?
- Other than we just hope the New York Times gets big enough,
- they can do it for everybody, you know what I mean?
- Like that's really, the commercial solution
- is going to be news monopoly, you know,
- that's just big enough to do that is what I think.
- But, you know, maybe I'm being too pessimistic.
- So I think we need to band together
- to come up with this alternative.
- - Thank you.
- Great, how about one last question for Ben?
- How about one of the questions?
- - I have a question.
- - There we go, please.
- - So I love your neighborhood map,
- which is, I know, which has been on the page
- for a while now, it's so great.
- And I use it in my classes to talk to instruct students,
- well, instruct students about two things,
- one about the concept of place
- and another about the concept
- of volunteer geographic information.
- And I'm curious if you guys have done other projects
- that you think are within the realm
- of like volunteer geographic information.
- So like people actually map like-
- - Yep, that project is one of my great white whales
- and that we haven't really revived it.
- There was a, the plan was to do it last year
- for this census, but other things have come up.
- And just so people know,
- there is no official source of neighborhoods in LA County,
- even though we all feel that we live in one.
- And so about 10 years ago with some of my colleagues,
- we sort of tried to pull the public
- and then divide up LA County into, you know,
- the LA Times neighborhoods at least.
- And then we connected those kind of like
- with the legos of census tracts.
- So then all the areas have all this metadata
- about demographics that are then merged with them,
- which has allowed us to do dozens of investigative
- and analytical stories to compare East LA to West LA,
- you know what I mean?
- It's not more in social science.
- And one of my personal ambitions is for our team
- to be more of a social science data provider.
- We're trying to do that a little bit here with COVID,
- but I would love to sort of be able to publish
- sort of like the machine readable atlas
- of LA demographics by neighborhood or something
- so that we could see more and more people
- able to do that type of analytical work.
- But at the core of it was this crowdsourcing effort
- 'cause we didn't want to do it entirely alone.
- So we sort of put out our own version of the maps
- and then we had people tell us how wrong we were
- and we modified them and you can't please everybody.
- There's definitely flaws,
- but that was kind of a collaborative process.
- You know, that's the type of thing
- that we haven't turned into like it.
- We're going to do something like that every month,
- but there's a few examples through the years
- that I've had a lot of fun working on.
- Like we finally, we're going to do the like a few years ago,
- the like, what is the East Side story in the LA Times?
- You know what I mean?
- Where everybody brings out their grievances, you know,
- and I will tell you as long as I am the data
- and graphics editor, the East Side begins at the LA River.
- I'm sorry, everybody who lives on Sunset,
- you don't live on the East Side, sorry, you know what I mean?
- But like, but we know people disagree, you know what I mean?
- Like there is no answer.
- And so we did like a one-off that was sort of
- in that same tradition where it was,
- here's a map of LA, draw the East Side, right?
- And we had hundreds of people like draw them all.
- And then we made this composite map
- that was like everybody's East Side like overlaid.
- And it was a little abstract and already, you know what I mean?
- I don't know if it was like the clearest data visualization
- ever drawn, but it sort of had this fun effort of like,
- here's the splatter of like what everybody thinks, you know?
- And though I'm sure our readership is biased
- in a certain direction.
- But so like that was an example of that, I guess.
- - Yeah.
- - You know, to me, the crowdsourcing thing that's out there
- that nobody does much with Viz,
- which we could do with Quakebot,
- is the like, did you feel an earthquake data from like USGS?
- I think it's like all of there for every earthquake
- and like nobody really does much with it, you know?
- I know the scientists at USGS do,
- but like, I don't know if the news media does.
- Yeah.
- - Right.
- Cool.
- Thank you.
- - All right.
- Yeah, no, there's a whole bunch of chatter
- about that project you're talking about mapping Los Angeles.
- - Yeah.
- - It's many of us fondly remember that kind of,
- hey, we can contribute to defining boundaries,
- which is not something we're used to.
- Boundaries are usually defined by somebody up there.
- And we abide by those boundaries.
- And here's the LA Times saying,
- hey, you define your own boundaries.
- So that was a really neat project.
- And still to this day in the urban planning department,
- that's a go to resource for doing neighborhood level analysis.
- - Yeah.
- I was absolutely just going to say that.
- I mean, I think the impact of that project in the classroom,
- I just don't know if that kind of reaches you all,
- but like, I mean, that is in so many different classes,
- so many different students use that as sort of like,
- understanding LA at this point.
- - Yes, we have census tracts.
- Yes, we have these other boundaries,
- but those particular boundaries in that work
- is just super, super influential in the classroom.
- - Yeah.
- I mean, to me, I just see all the times we fail to revive it.
- So I feel a little guilty with that.
- But like, you know, we kind of have, I have some ideas.
- I might be like, this is one of these things
- where like internally it gets caught up in the politics of like,
- do we need a hyper local neighborhood news product?
- You know what I mean?
- And like, it's hard to have a conversation internally
- without it being caught up with like traditional coverage.
- It's my belief that it should be more of a data application
- and like kind of like a social science hub
- that is like easy enough for the average person
- to understand, you know what I mean?
- That's just like what is, you know,
- Chevy bills or whatever, you know?
- And I would like to kind of narrow kind of the mission
- of it to really focus on that kind of popularizing
- the demographics thing.
- And so my hope is, is that we can do that
- when the new census data comes out.
- But as you know, it's the ACS is where the action really is.
- So it doesn't matter.
- I kind of wonder if we could partner that with like,
- can I give you my pet idea?
- And you guys can tell me if it's terrible.
- With like a gentrification analysis.
- - Oh, yeah.
- - If you like that, yeah.
- 'Cause I did some reading
- and you guys may know this better than me,
- but there's these like academic definitions
- of gentrification, which as far as I can tell,
- are kind of limited to a sort of post-war redevelopment
- sort of framework of like, how old is the housing stock?
- And how recently has it been replenished?
- And what is the change in housing value been, right?
- And so like, you could, I look at those methods
- and I could take these longitudinal census databases.
- I can do that for every census tract
- and then I could tell you what's the most gentrified neighborhood
- in LA like that way, right?
- That would be one way.
- But like my hunch is that that's not enough.
- Like in our 21st century life,
- there's sort of this Brooklynized idea of gentrification,
- which has to do with cultural products, right?
- And commerce and like that kind of thing, you know what I mean?
- Like what is the business in Highland Park now
- on that strip on Fig versus 10 years ago, right?
- Used to be a Latino typewriter shop
- and now it's a hipster cocktail bar or whatever, you know?
- And I think that's what people have in mind
- when they send you an interpretation today.
- And so like what I'm kind of reaching for
- is could you come up with a metric
- that would try to capture that
- and then integrate it with the older metrics
- into like a kind of more up-to-date gentrification index
- or something, do anybody have any ideas
- or have read anything that smiles ahead of me on this?
- - Well, you know, there's a Center for Neighborhood Knowledge
- here at UCLA that does a lot of research.
- I don't know if you know Paul Wong,
- he's a professor who--
- - I don't know. - Wong worked with models
- of gentrification and they have a site,
- a project called Urban Displacement,
- collaboration with Berkeley and UCLA
- where they have index S for gentrification.
- - No, really?
- - It just talks about the need for us, you know,
- in academia and also to let you know,
- like UCLA is launching this kind of data acts initiative.
- So it sounds like we are,
- once that behind you guys, but we're right there
- in terms of our efforts to think about,
- like you said, you know, the importance of coding
- in kind of the more social sciences,
- which isn't traditionally been the case.
- So just to think about how us in academia,
- I mean, I feel such a stronger bond with you
- through what we've been discussing today
- and the importance that we work together
- to create some meaningful stories
- that can inform policy decisions across our communities.
- - Yeah, I also love, I really love the idea
- of kind of looking at sort of alternative data sources.
- I was on a call yesterday and a workshop yesterday
- actually looking at the Getty's new Riche project.
- And they're putting all these images out there.
- And this workshop was really trying to understand
- how to look at those images, you know,
- in this, you know, cultural production,
- the streets that were photographed,
- historically looking at, you know, racial change over time
- and really talking about gentrification
- and how a data set like that with those images
- could be used with, you know, all these sources
- that you're talking about, but other data sets
- that are cultural and kind of trying to, you know,
- look at that and also how to sort of code those images
- and doing that coding so that it actually is, you know,
- doing the work that we want it to do
- in terms of understanding, you know, the displacement
- or whatever's happening.
- And not just sort of saying like, oh, you know,
- this was here before and now it's gone,
- but like, well, why is it gone?
- And really kind of, you know, putting the point onto that.
- So yeah, it's really interesting.
- And I think you're right.
- I mean, I think what we've been talking about
- with, you know, really learning how to, you know,
- build coding and, you know, also just stepping back
- and being able to kind of piece, I think,
- like really different data sets.
- And the thing that we were talking about yesterday too
- is just sort of understanding that world of data
- and kind of, especially I think for things
- like gentrification, kind of stepping out
- and kind of looking at or trying to understand it
- in a way that's local and trying to find sources
- that maybe aren't that typical, you know, census,
- you know, way of understanding it.
- So I don't know, I love it.
- Yeah.
- - There's a guy at Harvard I found he did a paper read
- used Yelp data and then he tried to like correlate
- the arrival of certain types of businesses
- with other gentrification indicators, you know,
- like educated population or whatever.
- And so there might be something there, you know,
- but I kind of imagine the index.
- You take like the traditional metric of like buildings
- or whatever, you know, you add in the racial dynamic
- of, you know, white invasion, as I've learned,
- it's called in some papers.
- And then you maybe add like a third cultural dimension
- of these products and then you like mix those into like
- a radar chart or like an index or something
- and that'd be kind of cool.
- My bet is what Adams would be number one.
- That's my bet guys.
- - I would say from personal experience
- with Brooklyn and here in LA, the first major indicator
- should be when the real estate agent decides
- to change the name of the name.
- - There you go.
- That's another one.
- - That's an indicator.
- - One thing I learned looking at the literature too,
- I thought was interesting is traditionally only like
- the lowest quartile could ever be thought of
- as having gentrified.
- So you had to be depressed to ever undergo the process.
- And I don't think that people think about it that way
- today anymore.
- Like, 'cause when I ran the traditional numbers,
- I saw that Venice Beach had a really high score
- on some of the values but wouldn't have been considered
- because it was already a pretty wealthy place to begin with.
- Right?
- And so like there's this way in which I think
- the traditional numeric framework doesn't mesh
- with how people really think about it
- or talk about it today.
- And like, that's the thing I just feel like
- there must be some way to wrestle that.
- But.
- - That could be a,
- and this is a very geographically nerdy
- but a modifiable aerial unit problem issue
- where because you're looking at Venice as a whole.
- - Right, we've predefined it.
- - Yeah.
- - Very wealthy parts get, you know,
- averaged with the less wealthy parts.
- So those.
- - That's a good point.
- - Yeah.
- - Mm.
- - But I'm thinking also that with the sort of combining
- what Andy was talking about with the coding
- and like maybe taking for each neighborhood,
- choosing kind of like the commercial strip
- and looking at Google Street View
- and sort of like coding that over time
- for the main, like the main drags of different neighborhoods.
- - Yeah.
- Or even if we did the number for the whole county,
- you could pick the place that had the highest
- and up new merit score and then go in
- and do that closer study on that place.
- - Yeah.
- - You know, and then you wouldn't have to do everywhere.
- - Right.
- - I also have to think about how to do less.
- Sorry.
- (laughing)
- - Now, I mean, I think that's one of the things
- that we all struggle to is sort of kind of, you know,
- looking at LA, but I think a lot of us push our students
- and just people that we work with to focus in on areas.
- And I think that's sort of like a nice bridge to,
- you know, I think what you at the times do
- is kind of putting in the person into our data stories.
- And like, you know, not always sort of like having that
- bird's eye view of everybody, but like, let's zoom in.
- Let's like really kind of understand what this data set
- is doing on this street, on this block.
- - Yep.
- We talk about them and like, there's usually,
- there's like two different couples we're looking for.
- There's like, there's either Mr. and Mrs. Outlier
- or there's Mr. and Mrs. Central Tendency.
- And like kind of depending on like which story
- you're telling, you're sort of like aiming
- for those people, you know.
- - That's great.
- I love that.
- - I feel like we're in a newsroom right now
- making decisions for the next story.
- But I wanna wrap us up here,
- taking too much of your time.
- Thank you so much.
- The conversations in the chat room has been just nonstop.
- You've touched a lot of topics that really mean
- so much to many of us.
- So just wanna say a big thank you to Ben.
- I'm sure it's shared by all of us here.
- There you go.
- And Ben, let's definitely get together again.
- When there's a little bit more time to relax.
- I don't know when that would be.
- But I'd love to kind of have a conversation
- about how we in academia can work closer together
- with you guys at the LA Times.
- - Great.
- - Yeah.
- - For having me.
- - Thank you.
- Thank you for being so generous with your time.
- It's really amazing.
- - Mm-hmm.
- Thank you so much.
- - Sorry, I was late.
- - I love the new luck.
- I love the-
- (laughing)
- - I'm gonna report that to my wife.
- - All right, thank you so much, Ben.
- - Thank you, Ben.
- - Mm-hmm.
- - Bye.
DATA AND GRAPHICS: An introduction
By Ben Welsh • • Humanitarian Mapathon in UCLA