[00:00:00] or maybe he's still trying to get his zoom in order.
[00:00:06] - Can you hear me?
[00:00:08] - I can hear you.
[00:00:09] - Yeah.
[00:00:11] - This has been a truly dramatic, like, point of view.
[00:00:14] (laughing)
[00:00:16] I'm zooming you from my phone
[00:00:17] because I could not get my computer to zoom in.
[00:00:20] I'm so sorry.
[00:00:22] I am one of the recalcitrant nerds who insists
[00:00:25] on continuing to use Linux,
[00:00:27] even though everyone else has abandoned it.
[00:00:30] And I use zoom every day with no trouble,
[00:00:34] but I was told by your system
[00:00:38] that I needed to upgrade my zoom.
[00:00:40] And so I've spent the last 20 minutes
[00:00:41] trying to upgrade my zoom.
[00:00:42] (laughing)
[00:00:44] So hard, so hard.
[00:00:48] I know, so now I have zoomed you from my phone,
[00:00:51] which is probably what I should have done in the first place.
[00:00:53] And so I'm really, really, really, really sorry.
[00:00:56] Thank you for your patience.
[00:00:58] - Oh, no worries, Ben.
[00:00:59] We are so thrilled to have you here.
[00:01:02] I just wanted to give you a little bit of a preview
[00:01:09] of what's been going on prior to you arriving.
[00:01:13] You know, this event started on Wednesday
[00:01:15] and it's a humanitarian mapathon event.
[00:01:18] And the goal was to digitize 20,000 buildings
[00:01:22] using hot OSM and open street maps.
[00:01:26] I don't know if you can see from my virtual background,
[00:01:28] but between our two campuses, UCLA and USC,
[00:01:32] we each digitized collectively 10,000 buildings
[00:01:38] at each school.
[00:01:39] So we reach our miles to 20,000 buildings,
[00:01:42] which were digitized mostly.
[00:01:45] We were working in Indonesia and Sudan, South Sudan.
[00:01:50] And yesterday we had back to back to back workshops
[00:01:55] around topics of humanitarian mapping,
[00:01:58] data science, Python, Jupyter Notebooks, Tableau, QGIS,
[00:02:03] all these kinds of tools that I know you're familiar with
[00:02:07] with the work that you do and the team that you manage.
[00:02:12] So we're all very excited to have you here.
[00:02:15] I just wanted to quickly kind of introduce you
[00:02:22] with the work that I know that you've done.
[00:02:25] So let me just quickly,
[00:02:27] I'm just very briefly share my screen.
[00:02:32] So you look quite different from your picture here.
[00:02:37] - Yeah, COVID's been hard on us all.
[00:02:39] - Yeah, yeah, you have this kind of rock star
[00:02:42] vibe about you today.
[00:02:45] COVID has made us all into, you know, punk stars.
[00:02:48] But Ben, you know, if you're from Los Angeles
[00:02:54] and are familiar with the type of work
[00:02:57] that the LA Times has done over the years,
[00:02:59] we know Ben has this kind of data journalist
[00:03:04] who has put together a lot of the data,
[00:03:07] you know, journalistic stories around the LA Times
[00:03:11] and our local communities.
[00:03:13] I happen to, let me see,
[00:03:18] know about Ben's work primarily through the pandemic
[00:03:22] because when this came about tracking the coronavirus
[00:03:25] in California was a site launched by the LA Times
[00:03:31] earlier this year.
[00:03:33] And Ben led the effort to work with community organizers,
[00:03:37] with hospitals to kind of have a one-stop shop
[00:03:41] where all this information about the coronavirus was collected.
[00:03:45] It's really an amazing resource if you haven't been there.
[00:03:48] It has all the almost real-time data statistics
[00:03:53] about coronavirus, kind of more focused locally.
[00:03:57] So we know we have data from Johns Hopkins,
[00:04:00] that's global and kind of more national,
[00:04:02] but where do we find neighborhood level data
[00:04:06] about what's going on in Los Angeles?
[00:04:08] And this is just an incredible resource.
[00:04:11] You know, it's full of real-time data graphics
[00:04:15] that's updated hourly.
[00:04:18] And, you know, unfortunately, it's still relevant today.
[00:04:21] You know, back in March, when I first talked to Ben
[00:04:25] about this, I was actually thinking, you know,
[00:04:28] maybe the last of the summer.
[00:04:30] But that's the reality of the moment.
[00:04:33] It's an incredible resource with data charts, maps.
[00:04:39] And also, what makes this resource so useful
[00:04:43] for us in academia is that, you know, Ben's a huge,
[00:04:50] as he said, you know, Linux operating systems
[00:04:53] about open data.
[00:04:54] So, you know, this week we've been thinking so much
[00:04:56] about open data, crowdsourced information,
[00:04:59] using open street maps.
[00:05:01] And the data desk information provided by the LA Times
[00:05:06] allows us to really work with data that's transparent,
[00:05:11] that's made publicly available.
[00:05:13] And this Coronavirus GitHub page is where I believe
[00:05:17] Ben's team is contributing to the community
[00:05:21] by providing almost real-time data and statistics
[00:05:24] about all the information that they're collecting.
[00:05:29] So, I'll leave my talk to that
[00:05:33] and let Ben take over now.
[00:05:37] And once again, thank you, Ben, for joining us today.
[00:05:41] - Yeah, thank you.
[00:05:42] I had just emailed you a link to my deck.
[00:05:46] So, I'm stuck on my phone.
[00:05:47] Would you mind calling that up
[00:05:48] and just sharing your screen if it's not too much hassle?
[00:05:51] - Oh, no problem.
[00:05:52] Hold on one second.
[00:05:54] - Sure.
[00:05:55] I'll spiel for, I don't know, not too long,
[00:05:57] but I was hoping to give everybody here an overview
[00:05:59] of our team at the LA Times,
[00:06:01] the data and graphics department,
[00:06:02] kind of what we do, the types of people who are on the team,
[00:06:05] the types of things we make,
[00:06:08] so you guys can get a sense of, you know,
[00:06:10] how these kind of data skills live
[00:06:13] within the journalism world.
[00:06:14] And then, you know, we can answer any questions
[00:06:17] anybody has that really talk about anything
[00:06:19] you guys wanna talk about.
[00:06:22] And along the way, if you have anything
[00:06:24] you wanna know more about,
[00:06:25] or if I'm not explaining something good,
[00:06:27] just stop me, just talk.
[00:06:28] I'm happy to have a conversation
[00:06:31] and glad to be here.
[00:06:32] So, I also think it's really cool
[00:06:34] you guys are doing open street map stuff,
[00:06:36] which I myself have dabbled in a little bit,
[00:06:40] trying to do one of my hometowns in Eastern Iowa,
[00:06:42] where I grew up.
[00:06:43] So, well, where to begin?
[00:06:47] So, my name is Ben Welsh.
[00:06:48] I'm the editor of the data and graphics department
[00:06:50] at the LA Times.
[00:06:51] And we're a team of about 20 people
[00:06:57] who try to use our computer skills to like,
[00:07:00] make stuff happen.
[00:07:01] And this presentation will give you kind of an overview
[00:07:04] of what that ends up being.
[00:07:07] So, go ahead and click on.
[00:07:10] So, there's me before COVID.
[00:07:12] My wife calls this my Bible salesman photo.
[00:07:16] And obviously, next is me after COVID.
[00:07:19] We already got to this joke though,
[00:07:20] so we don't need to stick with it.
[00:07:22] Shout out to all the Twin Peaks fans out there.
[00:07:25] We can keep going.
[00:07:26] And this deck is one that I have presented internally
[00:07:31] at our company,
[00:07:31] and it initially included some private business figures,
[00:07:35] which I've redacted just to not get in trouble.
[00:07:38] But I'll give you the gist of what I was trying to get across
[00:07:41] when we get to that point, okay?
[00:07:43] Keep clicking.
[00:07:44] So, the data and graphics department,
[00:07:45] its mission, as best as I can articulate it,
[00:07:48] is for us to create digital journalism
[00:07:51] that's important to the readers of the LA Times,
[00:07:54] both in what they want to read
[00:07:55] and what they ought to read, right?
[00:07:58] With using data development and design.
[00:08:01] And by development,
[00:08:02] I mean actual computer code development, right?
[00:08:05] So the people that are on this team, if you hit the next slide,
[00:08:08] are not just reporters and editors and journalists
[00:08:12] and people like me who went to journalism school.
[00:08:15] They're also at the same time,
[00:08:17] computer programmers, data analysts,
[00:08:20] information designers, data scientists,
[00:08:23] whatever you want to call it, they're nerds, right?
[00:08:25] And so, we're kind of one of the more multi-hyphened teams
[00:08:30] at the LA Times and that people are called upon
[00:08:32] to not just do the traditional skills of journalism,
[00:08:35] but also to have these technical skills
[00:08:38] that let them find and tell stories in other ways.
[00:08:42] And this is something that's increasingly common
[00:08:44] across the journalism industry.
[00:08:46] Our team is not unique.
[00:08:48] There's kind of an evolution happening
[00:08:50] in graphics and data departments across the country
[00:08:53] where graphics is becoming kind of a higher-skill
[00:09:00] data and programming profession.
[00:09:02] And our team is caught in the same currents
[00:09:05] that are driving that, right?
[00:09:06] And so, the people on our team have to be pretty darn flexible.
[00:09:10] And so, if you hit the next slide, you can get a look at them.
[00:09:13] Like I said, it's about 20 different people.
[00:09:17] They come from many of them from Southern California,
[00:09:20] many of them from UCLA and USC, or at least a couple,
[00:09:23] but also from all across America.
[00:09:25] And they're people with a lot of different backgrounds
[00:09:28] and skills.
[00:09:29] We try to have everybody learn as many
[00:09:32] of the different types of things we do as possible,
[00:09:34] but the way it works out is some people tend to be
[00:09:37] a little better at analysis or maybe a little better
[00:09:40] at visualization, but we don't specialize.
[00:09:43] We really do kind of try to treat everybody
[00:09:46] as being able to take on all of the challenges.
[00:09:51] And so, we're always learning and we're always experimenting
[00:09:55] and trying to take on new skills.
[00:09:58] Just this morning, I was helping someone learn
[00:10:01] how to write some spelt, if anybody here is a spelt fan,
[00:10:04] to make some of our election graphics for next week.
[00:10:07] So, if you hit the next slide, we're gonna get into
[00:10:09] what are these things we actually make?
[00:10:12] In a lot of ways, to me, a newspaper where I've worked
[00:10:15] at the LA Times for 13 years now and seen a lot of change,
[00:10:18] but even though we're making different things
[00:10:20] and different products, it is kind of an information factory.
[00:10:24] So, in a certain sense, a news organization
[00:10:27] takes the raw materials of life, of things
[00:10:31] that are happening in the world,
[00:10:33] and it refines those raw materials into products,
[00:10:36] news products that you can sell,
[00:10:38] and the people want that help them make sense of the world.
[00:10:41] And traditionally, in my painful metaphor,
[00:10:46] the assembly lines at this factory were really focused
[00:10:48] on making a print newspaper that had blocks of text, right?
[00:10:54] With headlines and photos, and that was like,
[00:10:58] what the majority of the assembly lines were set up to do.
[00:11:00] There were other ones to have sports scores and boxes
[00:11:03] and to give you the movie listings
[00:11:05] and all these other things.
[00:11:06] But in the rewiring of our economy,
[00:11:09] that's brought on by the internet and all that,
[00:11:11] the factory of the LA Times has to be refitted
[00:11:15] with new assembly lines to make different products, right?
[00:11:18] That are more in line with what people want
[00:11:21] that can compete with other factories out there
[00:11:23] in the marketplace, and that can help us shift
[00:11:27] to a different type of business,
[00:11:31] which is more focused on getting money from subscriptions
[00:11:34] and readers who like us enough to pay us
[00:11:36] and less so on advertising,
[00:11:38] which if you guys are interested in all that business stuff,
[00:11:41] we can talk about it later, but I won't be too boring.
[00:11:43] And in our little department, it's my view that our assembly,
[00:11:47] we basically have three assembly lines
[00:11:49] that the people you saw before kind of interchangeably work
[00:11:52] on.
[00:11:53] The first one makes applications, software websites, right?
[00:11:59] The second makes visual stories.
[00:12:02] These are stand up, we'll get to those.
[00:12:03] And then the third is making digital designs.
[00:12:06] And so let's go through those one, two, three,
[00:12:08] and I'll show you a bunch of examples of each.
[00:12:10] So first up is applications.
[00:12:13] This is where we use our software skills
[00:12:16] to gather and refine data to serve our digital audience,
[00:12:19] writing computer code to go get data,
[00:12:21] filing public records requests to get it,
[00:12:23] otherwise building databases ourselves from the ground up
[00:12:27] that we can then turn into a web application
[00:12:31] that is not a traditional story
[00:12:33] that people will wanna read and maybe even pay for, right?
[00:12:36] And so if you hit the next slide, the first example,
[00:12:39] I guess would be the coronavirus tracker.
[00:12:41] This is a recent one.
[00:12:42] This is where we're gathering data
[00:12:44] from a ton of different sources
[00:12:45] and turning it into all these pages
[00:12:47] that update as frequently as we can get them to,
[00:12:52] and let people slice and dice and tailor them.
[00:12:54] There's also 58 pages, one for each local county, right?
[00:12:58] And we're always working to look and expand on this.
[00:13:01] There's a few things I'm hoping we can get out soon.
[00:13:03] Yes, that is Netscape.
[00:13:04] This is my visual pun for this presentation.
[00:13:06] If you'll bear with me, I'm showing my age.
[00:13:11] Okay, so then like another example,
[00:13:12] if we keep going would be our live wildfires map.
[00:13:16] So this is where we're pulling in data
[00:13:17] from a lot of public sources and one private one
[00:13:21] to try to give people the most comprehensive,
[00:13:23] kind of composite view of what's happening
[00:13:26] with wildfires this minute in California
[00:13:29] or the most recent one possible.
[00:13:33] And it's in no way a traditional story, right?
[00:13:35] It's a single webpage that updates and stands along.
[00:13:38] If you hit the next one,
[00:13:40] another example would be our quake bot.
[00:13:42] So this is something that isn't like a standalone webpage.
[00:13:45] It's something else.
[00:13:46] It's a piece of automation.
[00:13:48] And so every time there's an earthquake detected
[00:13:50] by the USGS, it sends out like a data feed
[00:13:53] to the whole world, like kind of little pulses
[00:13:56] that say here's the latest earthquake
[00:13:57] and how big it was and where it was.
[00:13:59] And we have an algorithm that reads every one of those
[00:14:03] based on how close they are to Los Angeles
[00:14:05] and how high the magnitude is.
[00:14:07] We have some sort of editorial barriers.
[00:14:10] And then if the data surpasses those barriers,
[00:14:12] this blog post is automatically written
[00:14:14] and this map is automatically made
[00:14:16] and prepared for publication.
[00:14:18] And so within a minute of an earthquake happening,
[00:14:21] we can have the complete post,
[00:14:23] including the map ready to roll.
[00:14:25] We still have a human look at it, of course,
[00:14:27] before it goes live,
[00:14:28] but this is where automation gives us
[00:14:30] kind of a quick step out the door immediately.
[00:14:36] And then the next one would be, I think,
[00:14:38] our most recent police shootings database,
[00:14:42] we have a long running project called the homicide report
[00:14:44] where we have built from the ground up
[00:14:46] our own independent database of every person
[00:14:49] killed by another in LA County since the year 2000.
[00:14:52] And this year we decided to repurpose a slice of that data
[00:14:55] to be a standalone database that just tracks police shootings
[00:14:58] and gives people a sense of the latest trends
[00:15:01] and that now, like the homicide report,
[00:15:03] live updates every time we get new records in.
[00:15:07] These databases, they power these standalone applications
[00:15:11] and they would probably worth doing just for that reason,
[00:15:13] but they also have the side benefit
[00:15:16] of allowing us to do enterprise analysis
[00:15:19] into these issues that we otherwise couldn't do
[00:15:21] if we didn't have the data, right?
[00:15:23] So on the next slide, you can see,
[00:15:25] since we've been gathering all this COVID data
[00:15:27] for months and months,
[00:15:28] we've been able to do tons of stories about COVID
[00:15:31] that are a little more traditional,
[00:15:33] but that have stronger claims and more insightful analysis
[00:15:38] because we have this data to draw from.
[00:15:39] So one example would be this kind of step back piece
[00:15:43] that we did after LA County's sort of botched reopening,
[00:15:47] where we were able to use all the data we gathered
[00:15:49] and other things we pulled in to kind of tell that story
[00:15:53] of what happened here in LA
[00:15:55] in a way that wasn't just a data application, right?
[00:15:59] And then the next one would come from our homicide report
[00:16:01] database.
[00:16:03] If you guys follow the news,
[00:16:04] you may have heard about the case of Deshawn Kizzie,
[00:16:06] who was a young man,
[00:16:08] killed following a bicycle stop in South LA
[00:16:10] by sheriff's deputies.
[00:16:15] And when that happened,
[00:16:16] it sort of raised a question of,
[00:16:18] well, how common are these bicycle stops
[00:16:20] that lead to fatal encounters?
[00:16:22] And because we had gathered this database
[00:16:25] and had all this information,
[00:16:26] we were the only media outlet that was able to give
[00:16:29] the public the news that, hey, this isn't a loan incident.
[00:16:33] This is something that's happened 15 times
[00:16:35] in the last 15 years.
[00:16:37] And so it's not especially common,
[00:16:39] but it's more common than this being the first time.
[00:16:41] And you're able to bring context and insight to it.
[00:16:45] You can see if you see that map fly by really quick
[00:16:48] that these incidents have been concentrated
[00:16:49] in certain areas too.
[00:16:52] We have some public records requests out
[00:16:54] that I believe will allow us to look into that further
[00:16:57] in the future.
[00:16:59] So that's thing one,
[00:17:00] that's the kind of the applications and the analysis.
[00:17:03] We have live election results coming on Tuesday.
[00:17:06] So this is all the flowing data feeds
[00:17:08] of votes coming in from across America.
[00:17:10] We'll go into maps and tables and charts
[00:17:12] to help you kind of make sense of what's going on, right?
[00:17:17] And so all right, so now we can do thing two.
[00:17:19] So assembly line number two is visual stories
[00:17:24] is what I call them.
[00:17:25] This is kind of an emerging story type
[00:17:27] in the world of data and graphics online
[00:17:30] that nobody really has a good name for.
[00:17:32] So if anybody here has a better name for it,
[00:17:33] I'd love to hear it.
[00:17:34] You can hit the next slide.
[00:17:39] And so visual stories are when graphics reporter reports
[00:17:43] and designs visual coverage that is substantial enough
[00:17:46] to stand on its own.
[00:17:48] Traditionally graphics departments
[00:17:49] had sort of made accompanying graphics.
[00:17:52] So on the old assembly line,
[00:17:53] you might be making a front page story
[00:17:56] and you got to have 1600 words of text
[00:17:59] and you got to have a headline
[00:18:00] and you got to have maybe a couple photos.
[00:18:02] And hey, if you're really going to dress up
[00:18:04] that front page story, you might get a bar chart
[00:18:06] and go with it, right?
[00:18:08] And so a lot of graphics departments
[00:18:12] had been oriented around being a support desk
[00:18:14] that was producing these sort of supplementary graphics
[00:18:18] for traditional stories.
[00:18:19] And we love those graphics.
[00:18:20] They're a good thing, I like them.
[00:18:22] But I think we have the potential to do quite a bit more
[00:18:24] and I think our audience wants something a little more.
[00:18:27] And so one thing I bet everybody here has noticed
[00:18:30] reading some of the leading news outlets in America
[00:18:33] is that they now produce these visual stories
[00:18:36] that are primarily just a combination of graphics
[00:18:39] that are big enough that they can just stand on their own, right?
[00:18:42] And that's what I call a visual story.
[00:18:45] And these are pieces that at most places
[00:18:47] are reported, pitched and executed primarily
[00:18:51] by the graphics reporter.
[00:18:53] It's not a case where a writer of a traditional story
[00:18:57] comes to the graphics department and asks for a graphic, right?
[00:19:01] So the entire process of how things kind of come together
[00:19:03] has changed and the graphics reporter
[00:19:07] is more in the driver's seat, okay?
[00:19:10] And so go ahead and hit the next slide
[00:19:14] and we'll look at some examples.
[00:19:15] So early this year, as I'm sure everyone here knows,
[00:19:18] Kobe Bryant died in a tragic helicopter accident
[00:19:21] near Calabasas and immediately we thought
[00:19:25] there's a visual component to the story.
[00:19:27] For people to understand what happened,
[00:19:30] you need to know the path of his helicopter, right?
[00:19:33] And so on day one, it was like a map of where did he crash
[00:19:37] and it's just like an X.
[00:19:38] And then between day one and day two,
[00:19:42] it's like, well, what was the path of the helicopter
[00:19:44] and what was really going on and what's the play-by-play, right?
[00:19:48] And then what we kind of learned on,
[00:19:49] it happened on a Sunday, what we kind of learned on Monday
[00:19:52] is like, oh, well, it seems like the final like part
[00:19:55] of this helicopter flight is where things really went wrong.
[00:19:59] And this was kind of the crucial moment.
[00:20:01] And so we decide, well, let's try to do a story
[00:20:03] that helps people see or understand as best as we can
[00:20:06] what happened.
[00:20:07] And so we took the publicly available data about the helicopter
[00:20:11] and we turned it into like a three or four step
[00:20:14] little graphic here that has, for each chapter in the story,
[00:20:17] has like a 3D map that helps you see what's going on.
[00:20:20] And by the end, you sort of are able to see
[00:20:24] this sort of important thing where the helicopter
[00:20:27] began climbing very rapidly in the last few seconds
[00:20:30] before a quick turn and then a crash.
[00:20:32] And that's the sort of thing that you can describe in words
[00:20:34] but is maybe better understood by seeing a 3D model, right?
[00:20:40] So the next example on the next slide
[00:20:43] would be something from politics.
[00:20:46] So we have this very tight DA race, we think.
[00:20:49] And after the primary, when we knew we were going
[00:20:52] to run off, we get the precinct level results
[00:20:55] that show you where people voted in every neighborhood.
[00:20:58] And we kind of did an analysis that looked at,
[00:21:00] well, where is the incumbent strong?
[00:21:02] Where is the challenger strong?
[00:21:04] And what would an upset coalition have to look like
[00:21:07] based on past election results
[00:21:09] and what we kind of know about this election?
[00:21:10] So this piece is really about helping people understand
[00:21:14] the political calculus of this local race through maps, right?
[00:21:19] And then the next one is we did a local mask survey.
[00:21:26] So there was this thing over the summer,
[00:21:28] the question of how many people are actually really wearing masks
[00:21:31] and rather than just quote public opinion polls
[00:21:34] or take data from other places,
[00:21:36] we decided to conduct our own survey.
[00:21:38] So a dozen of our reporters went to three different locations
[00:21:41] of multiple times over a few weeks.
[00:21:44] We followed a method that Jill Darling at USC
[00:21:47] helped us like figure out.
[00:21:48] And then we were able to kind of come back
[00:21:50] with our own provisional conclusions
[00:21:52] about how many people are wearing masks roughly
[00:21:56] and the variations between the different locations we looked at.
[00:22:00] And then that's presented here
[00:22:02] as sort of a designee standalone thing
[00:22:04] that's really more about charts and maps
[00:22:06] than it is about text, right?
[00:22:09] Next slide would be something that's not really data at all.
[00:22:14] It's more visual.
[00:22:15] And so in the George Floyd protests,
[00:22:17] I'm sure everyone saw on social media,
[00:22:19] there was a lot of footage of the police here in LA
[00:22:22] being pretty violent.
[00:22:24] And so we did a piece where we tried to aggregate
[00:22:28] as much as that as possible
[00:22:30] and then help compare it to the actual standards
[00:22:35] for use of force the police are supposed to follow
[00:22:38] to show the contrast.
[00:22:40] And so this is a case where we're using the design
[00:22:42] and the visual graph and the videos to tell the story
[00:22:46] and it's not really like charts and maps, you know what I mean?
[00:22:49] But it still falls within kind of this
[00:22:51] under this visual umbrella that I think is part of what we do
[00:22:56] and it's why I call it a visual story
[00:22:58] and not a graphic story or something, right?
[00:23:02] And we have another example I'm trying to remember.
[00:23:05] Oh, and then here's one we did on homeless housing.
[00:23:07] This is a type of visual story I bet you've all seen too
[00:23:10] that we call scrolly telling, which is a terrible term,
[00:23:14] which is where as you scroll things sort of move and happen
[00:23:17] and this is another type of visual technique
[00:23:19] that we use from time to time, depending on whether it fits
[00:23:22] the story we're trying to tell, okay?
[00:23:26] I think that's it for those examples.
[00:23:27] And then I think we get to things three,
[00:23:29] if I remember my deck right, which is digital designs.
[00:23:31] Oh, no, it doesn't fit the screen size.
[00:23:35] My gosh, you found a bug in my deck,
[00:23:37] but this is a case where we're using our digital design skills
[00:23:42] to elevate the newsroom's most ambitious journalism
[00:23:45] through like what we think
[00:23:46] are cool digital custom presentations.
[00:23:49] And in this case, we're usually helping to elevate stories
[00:23:53] developed by other departments rather than our own work.
[00:23:56] And a lot of it has some, you know,
[00:23:57] nothing to do with data or graphics.
[00:23:59] We're kind of the boutique web designers
[00:24:02] for these like flagship projects of the newsroom, okay?
[00:24:06] So examples of that wouldn't be on the following slide.
[00:24:08] So the first one would be we did an obituaries package
[00:24:12] of all the people who died from COVID.
[00:24:17] Not all of them of many people who died in COVID,
[00:24:20] of COVID here locally.
[00:24:21] And then that package has sort of a custom design
[00:24:23] to try to make it more dramatic and interesting
[00:24:25] and engaging for the reader.
[00:24:27] The next example is one from Sunday.
[00:24:29] I wonder if anybody here saw it.
[00:24:31] We had a piece by Rosanna Shee about DDT
[00:24:34] being dumped off the coast back in the day here in LA.
[00:24:37] And to try to make that story more impactful and grabby
[00:24:40] and to get people we did custom design for it,
[00:24:43] which included like a video topper,
[00:24:45] which you see here, a little scrolly telling in the middle.
[00:24:48] And then the sort of overall design and theme of the page
[00:24:52] is customized to fit the story.
[00:24:54] It isn't just the generic LA times kind of look, right?
[00:24:58] Now the next one is the Chicano moratorium package
[00:25:02] over the summer, we had an anniversary
[00:25:04] of a famous protest in East LA against the Vietnam War,
[00:25:08] which ended up being a series of stories.
[00:25:12] And in the past, this might have just been
[00:25:13] like a newspaper special suction,
[00:25:16] and then a bunch of pages on our website
[00:25:18] that aren't really connected.
[00:25:19] But what we try to do is to bring a style and theme
[00:25:22] to the whole package.
[00:25:23] This is kind of the cover page where all the stories
[00:25:26] were collected, and then each of the individual stories
[00:25:28] was given sort of a variation on the theme
[00:25:31] that you see here is the idea.
[00:25:34] And I think next up is the one I'm gonna,
[00:25:36] oh, then sometimes we do these for other platforms,
[00:25:39] not just for the open web.
[00:25:40] The Linux nerd in me would love it all just to be
[00:25:42] on RWW, but as far as our business goes,
[00:25:47] we do need to cross publish these things
[00:25:49] on monopolistic corporate platforms, guys, like Apple News.
[00:25:54] And so sometimes we will customize stuff
[00:25:57] to work on those other places as well,
[00:25:59] as you see here with our voter guide,
[00:26:02] which ran inside of Apple News.
[00:26:06] And then we have, I think the next slide
[00:26:08] will just tease one we have coming,
[00:26:09] we're gonna try to do a crazier year in review package
[00:26:14] that will be more visual and fun
[00:26:16] than what we typically done in the past.
[00:26:18] So those are the three types of things we do.
[00:26:21] The next section kind of gets into what that requires
[00:26:24] for a change, and some of this is a little more
[00:26:26] in my message for people eternally at the LA Times,
[00:26:29] but I think it'll work for you guys too,
[00:26:31] which is for this, for our data and graphics department
[00:26:34] to make these things and to achieve this mission,
[00:26:38] they have to be more independent and ambitious
[00:26:41] than maybe a traditional graphics department has been,
[00:26:44] and they need to have a higher skill level.
[00:26:46] So that requires us to seek out
[00:26:50] and develop our own ideas,
[00:26:52] to look at the major storylines of the day,
[00:26:55] be it COVID or COB or the Dodgers winning
[00:26:58] or the election or the George Floyd protests
[00:27:01] or homelessness here locally
[00:27:02] or whatever those major topics are
[00:27:04] that we know both matter to our democracy,
[00:27:08] but also to our audience in terms of what they want to pay for.
[00:27:12] We need to look at those topics and say,
[00:27:14] our job is to develop striking visual and data coverage
[00:27:17] that people are going to want,
[00:27:18] and that means we have to be more ambitious
[00:27:20] than some departments have been in the past.
[00:27:22] We need to take more initiative,
[00:27:24] and we need to operate more like a traditional news department
[00:27:27] in having our own sort of editing system and people in place.
[00:27:31] So that's kind of this section.
[00:27:32] And you can just jump through the next few slides
[00:27:34] and we'll get to where the next section,
[00:27:39] I just talk about the results.
[00:27:40] So just, you can jump ahead here a couple.
[00:27:45] There's Alex Tatuzzi and my deputy editor, my hero.
[00:27:48] And then the next slide is our listing.
[00:27:50] We have a job listing for a second deputy editor
[00:27:52] as we try to increase our editing muscle on our team,
[00:27:56] which is one of my major strategic goals
[00:27:58] for the current moment.
[00:28:01] And the reason we pursue all these goals
[00:28:03] is not just 'cause we're nerds and we like to do it,
[00:28:05] which is maybe a good enough reason
[00:28:07] if you're not expecting to get paid.
[00:28:11] But we also are pursuing this different type of journalism.
[00:28:16] One, because we like doing it.
[00:28:17] Two, because it's better journalism.
[00:28:19] The three, because it pays and it's better for our business.
[00:28:21] And so the next section is really kind of covers
[00:28:26] that these types of things I've been showing you,
[00:28:29] they're more popular with audiences.
[00:28:31] They're more valuable in that they are seen
[00:28:34] to convert more new subscribers to the digital product,
[00:28:38] which is our number one business goal than a typical story.
[00:28:41] And they're more durable in that they continue
[00:28:44] to draw an audience long after they're initially published,
[00:28:47] where most new stories kind of peter out within a day or two.
[00:28:50] So this next section is all redacted,
[00:28:54] so we don't need to look at it, but I can give you the gist.
[00:28:56] I mean, you probably aren't gonna be surprised
[00:28:58] to hear this if you think about it,
[00:29:00] but our coronavirus tracker,
[00:29:02] as humble as it may appear to you,
[00:29:04] is the most popular page in the history
[00:29:06] of the Los Angeles Times website.
[00:29:08] No page has ever been visited by more people.
[00:29:10] And if you compare it to our top traditional stories,
[00:29:13] it is multiple times more popular
[00:29:15] than the most popular stories of the year.
[00:29:19] And then when you look at conversions
[00:29:21] in terms of what's valuable in attracting new paying subscribers,
[00:29:25] it is an order of magnitude higher
[00:29:30] than the number two story of the entire year.
[00:29:33] And so you see that when things like this connect,
[00:29:36] they have a sort of home run possibility for the business
[00:29:40] that a traditional story very rarely can have.
[00:29:44] And then if you look at that over time,
[00:29:47] you'll see that it didn't become the top converting story
[00:29:50] until three or four months after it had initially launched,
[00:29:53] which just shows that it's continuing to draw audience
[00:29:56] and new paying customers over time
[00:29:59] and that sort of durability I was talking about.
[00:30:01] And so for those reasons,
[00:30:04] we think that there's a strong business logic
[00:30:06] behind investing in what we do.
[00:30:08] And I'll kind of close with this, I guess maybe,
[00:30:10] but having been a news nerd, as people say,
[00:30:14] and having done this kind of thing for nearly 20 years now,
[00:30:18] that has been the biggest change
[00:30:19] that I have seen in the last five or 10 years.
[00:30:22] When I began this as like a computer programmer journalist
[00:30:26] in 2000, we were curiosities.
[00:30:29] We were either the one person on the investigative team
[00:30:34] who had learned to code to try to do a cool data analysis
[00:30:38] for a traditional print investigative piece,
[00:30:40] or you were some weird person in the corner
[00:30:44] who said the internet's gonna be big one day,
[00:30:46] and we need to learn how to make new weird things.
[00:30:49] And so the justifications for most of the teams
[00:30:51] that I've been on in my entire career
[00:30:54] were either this is data analysis
[00:30:57] to do more impactful and important investigative reporting.
[00:31:00] That's actually what got me into it initially
[00:31:02] and is still my passion, right?
[00:31:04] Or it was this is sort of boutique internet experimentation
[00:31:08] to kind of see what comes of it, right?
[00:31:11] And we're now far enough into the digital transition
[00:31:16] to the information economy
[00:31:17] that that's just fundamentally changed.
[00:31:19] And what we've seen is that there are certain types
[00:31:21] of these products, these data and graphics products
[00:31:25] that are so successful
[00:31:26] that there's now like a capitalistic economic logic
[00:31:29] for producing them.
[00:31:31] And there's less and less of a logic
[00:31:34] for producing what these departments produced before.
[00:31:36] And so that's why I think not to get,
[00:31:39] I guess I am at the university
[00:31:40] so I can get all Marxist on you guys, you know,
[00:31:42] like I think that that is this economic logic
[00:31:45] that is driving kind of the evolution of our team
[00:31:48] and the justification for growing it,
[00:31:50] as well as similar changes you see in other departments
[00:31:55] like ours at other news organizations,
[00:31:57] some of which are like us in the middle of the transition
[00:32:01] and some others are further along down the road.
[00:32:04] Like for instance, the New York Times graphics desk
[00:32:07] which is already fully converted into this type of thing
[00:32:10] that I've been talking about here today.
[00:32:12] And I don't work there so I don't know the numbers
[00:32:15] but I would estimate based on what we see
[00:32:18] and based upon how the number of things they put out
[00:32:21] and the quality of what they put out
[00:32:23] that like the New York Times graphics department
[00:32:25] may be the most read and most profitable department
[00:32:29] in the whole newsroom, like that wouldn't surprise me
[00:32:31] with maybe the exception of one or two people
[00:32:33] who write about Trump, you know.
[00:32:37] And that is something that just was not true in the past
[00:32:41] when these departments were seen as supplementary, right?
[00:32:45] So that's my spiel.
[00:32:47] I'm happy to talk about really any of these issues
[00:32:50] that were raised, any of the stories in particular,
[00:32:52] any, you guys want to talk about Python?
[00:32:54] I'll talk D3, I'll talk about that.
[00:32:57] Whatever you guys want to talk about,
[00:32:58] I'm happy to just jam and hang out.
[00:33:03] - All right, well, that was fascinating.
[00:33:07] I do certainly want to open it up to the audience
[00:33:10] because this is about the students who are here
[00:33:13] participating this week.
[00:33:15] Maybe I'll have Andy kind of summarize the questions
[00:33:18] in the chat room, but in the meantime,
[00:33:21] well, you know, go ahead and post questions
[00:33:23] that you have for Ben in the chat room,
[00:33:26] but maybe I'll kick it off a little bit here, Ben.
[00:33:29] You really talk about this kind of transformative age
[00:33:32] of data journalism through this kind of visual evolution.
[00:33:38] What's just kind of like mind boggling to me
[00:33:40] is the fluidity by which you guys go about your business.
[00:33:45] Thinking like any one of the products
[00:33:47] that you have showcased in your presentation,
[00:33:50] if we were to do any one of those in academic institution,
[00:33:54] it'll probably take six months to create.
[00:33:56] And just kind of, you know, be followed by this,
[00:34:01] your ability to, you know, pivot so quickly
[00:34:05] with a new story.
[00:34:06] And then the next day you have like something
[00:34:08] that looks like it took six months to create.
[00:34:12] Can you talk a little bit about this kind of this environment
[00:34:15] that you work in, the speed that you have to work in?
[00:34:18] And you ever sleep?
[00:34:20] - Well, I'm glad you think that during COVID, very rarely,
[00:34:23] as you can tell by looking at me,
[00:34:24] but like after the selection,
[00:34:27] the dream is like December, like maybe a day off,
[00:34:30] but like,
[00:34:33] but more seriously, it's really like any,
[00:34:37] like, you know, I like the factory metaphor,
[00:34:39] but I'm sure there's other ways to talk about it,
[00:34:41] but it's like any process, any like manufacturing process,
[00:34:46] you break it down into pieces
[00:34:48] and then you like get really good at like, you know,
[00:34:51] getting each of those pieces of things
[00:34:53] that need to get done kind of like handled, you know,
[00:34:55] and you then try to speed up the process as much as you can.
[00:35:00] And so like in the case of like a visual story, for instance,
[00:35:04] you can think about it in that abstract manufacturing way
[00:35:07] in the sense that you need a toolkit
[00:35:10] that lets you stand up a single static page
[00:35:14] that's outside of like a traditional
[00:35:16] rigid content management system, right?
[00:35:19] That can be custom coded and designed.
[00:35:22] And so what you'll find is that at our department
[00:35:24] and every other one like it,
[00:35:26] that people have their little quote framework
[00:35:28] or their little, you know, publishing rig
[00:35:31] that lets them spin up a standalone web page.
[00:35:36] And then within that, there's like components
[00:35:37] of web development that make it easier for you
[00:35:39] to move quickly like a template inheritance.
[00:35:42] Well, within that system,
[00:35:44] we have a base template that has all the LA time styles
[00:35:47] and fonts and the header and footer
[00:35:49] and like all the basic pieces.
[00:35:51] And so then those are just like there for you
[00:35:53] when you like get the page like rolling, you know what I mean?
[00:35:56] And you're within that already.
[00:35:59] And then by the push of a button,
[00:36:00] you can trigger a little deploy
[00:36:02] that will send that like blank page live.
[00:36:04] And then within the framework, you, you know,
[00:36:08] depending on how you set it up,
[00:36:09] you have ways to import data files
[00:36:11] and then to quickly get them into JavaScript
[00:36:14] and other tools so that you can lay out the page
[00:36:16] kind of as quickly as possible.
[00:36:18] And our system isn't as streamlined as it could be
[00:36:20] or as it should be, you know,
[00:36:21] but like every, we've developed it ourselves internally
[00:36:25] and we're trying to make it better like all the time.
[00:36:28] Like this week we were talking about,
[00:36:29] well, how could we get it better at producing a headless page
[00:36:33] that's for iframing in other places?
[00:36:35] You know, how could we make a template
[00:36:37] that's just like ready to make an iframe page
[00:36:39] that could go on the homepage in 10 minutes, right?
[00:36:42] And so you have to have like that toolkit that's there.
[00:36:46] You have to have someone on staff
[00:36:48] who can like build and maintain such a toolkit
[00:36:50] because they don't really exist.
[00:36:52] One of my goals is for there to be
[00:36:54] a more standard open source sort of like thing for that.
[00:36:58] You know what I mean?
[00:36:59] You look at tool like when we make an application
[00:37:01] like the live election results or the tracker,
[00:37:04] we might use Django or some of these
[00:37:06] pretty highly developed web frameworks for databases.
[00:37:09] But when it comes to static pages,
[00:37:11] you really just have kind of like a hodgepodge
[00:37:15] of like node framework thingies
[00:37:17] that like can kind of halfway make a page
[00:37:20] but they don't have the 30 different things
[00:37:22] you actually need to make a real page
[00:37:23] that you want to publish, right?
[00:37:25] And like kind of, I think that there's the possibility
[00:37:28] for a stronger open source framework
[00:37:31] that goes beyond just the basic tools
[00:37:34] and gets into a little more of the opinionated decisions
[00:37:36] about how to manage deployment
[00:37:38] and static assets and data files and stuff.
[00:37:41] Wow, that was way too nerdy.
[00:37:43] But like that's part of it.
[00:37:45] That's like the technology part.
[00:37:47] And then there's the kind of editing and journalism part
[00:37:49] which is identifying either planned events
[00:37:53] that are ahead on the calendar
[00:37:54] that you know are coming like the election
[00:37:56] or pivoting when a major storyline emerges
[00:38:00] and then focusing your assignment on it.
[00:38:03] And so like in the case of like the Kobe thing,
[00:38:05] it's like Kobe's helicopter crashes on Sunday.
[00:38:08] In a breaking news situation like that,
[00:38:10] we tend to think in terms of like rounds of coverage
[00:38:14] or stages.
[00:38:15] So on day one, we're going to do this.
[00:38:16] On day two, we're going to do this.
[00:38:18] And then we're going to aim for something
[00:38:19] that's like a little more, you know what I mean?
[00:38:21] And so like in the case of Kobe,
[00:38:23] day one was just like, where is the crash?
[00:38:25] What kind of helicopter was it?
[00:38:27] You know what I mean?
[00:38:28] The basic facts and can you visually do that?
[00:38:30] Day two was, well let's get the play-by-play.
[00:38:33] What was the like?
[00:38:35] When did they take off?
[00:38:36] Where did they go?
[00:38:37] But you know, so we had a graphic
[00:38:39] with the day two story that had that stuff.
[00:38:41] Was there a route, a typical route?
[00:38:43] Was it an unusual route, you know?
[00:38:45] And then what happens typically in that is
[00:38:49] after you learn the basic facts,
[00:38:50] you kind of get to a point where you're like,
[00:38:52] okay, here's what we think the story is.
[00:38:54] You know what I mean?
[00:38:55] So like within these facts,
[00:38:56] here's what we think is worth sort of teasing out
[00:39:00] visually for people.
[00:39:01] So both has to be something insightful
[00:39:03] that goes beyond the basic facts for people,
[00:39:05] but it also has to have like a visual component to it, right?
[00:39:09] And so in the case of Kobe,
[00:39:10] our decision was to focus on the final turns of the helicopter.
[00:39:13] All this is of course debatable,
[00:39:15] but those were sort of our judgments we made
[00:39:16] like in those moments.
[00:39:18] Like the conception boat crash,
[00:39:20] the fire off Santa Barbara coast,
[00:39:22] I guess a year ago now,
[00:39:24] was another example of that where day one,
[00:39:27] it's just, well, where was the boat? What was the boats route?
[00:39:30] Day two, or there are like advanced,
[00:39:33] our piece for the weekend then was like,
[00:39:34] well, what were the escape routes on this boat?
[00:39:37] Where that was what seemed to us to be like the story.
[00:39:39] You're kind of betting that that's how it's gonna shake out.
[00:39:42] You know what I mean?
[00:39:43] As the facts come in.
[00:39:44] And then let's make a 3D model of the boats
[00:39:46] so we can show people what these escape routes were like
[00:39:49] and emphasize that they weren't very good.
[00:39:51] You know, it's what it ended up being.
[00:39:53] So that's like a breaking new situation.
[00:39:55] And then there's kind of in between ones
[00:39:57] where it's like there's gonna be this long storyline.
[00:40:00] And this is something that we're just trying to really
[00:40:02] get better at in our department this year.
[00:40:04] And is what I really admire about the New York Times
[00:40:07] and some of the more mature departments,
[00:40:09] is it seems to me that they have kind of decided on
[00:40:12] for the year, here's what their major storylines are gonna be.
[00:40:15] And then they come up with a string of visual coverage
[00:40:17] to try to attack those major storylines.
[00:40:20] And sometimes those can be planned like the election.
[00:40:23] And other times like COVID, they just occur
[00:40:25] and then you have to be smart pivot, right?
[00:40:28] And so for us, we've done that this year
[00:40:30] for the Floyd protests and for the COVID coverage
[00:40:33] where we put together our own independent visual coverage plan
[00:40:38] of like here's ideas.
[00:40:39] Everybody in the team pitches things in.
[00:40:41] We think about are there applications we could do?
[00:40:43] We think about are there visual stories we could do?
[00:40:46] And then we try to make them happen.
[00:40:50] - Yeah, that's wonderful.
[00:40:51] I just wanna jump in.
[00:40:52] There's a really, really great question.
[00:40:54] I think it can kind of build on what you were,
[00:40:56] I mean, what you just talked about.
[00:40:58] But the question is about just sort of thought process
[00:41:01] and developing stories and data,
[00:41:03] but specifically about dealing with biases and storytelling.
[00:41:08] And then the follow up to it is,
[00:41:10] are there any specific examples that you could share
[00:41:13] where you did face pushback on a story
[00:41:18] that is sort of data driven,
[00:41:19] but maybe there's some sort of part of it
[00:41:22] that kind of, you know, there was a stop to it.
[00:41:26] - Like internally at the LA Times, that's-
[00:41:28] - Yeah.
[00:41:29] - Well, you know, or maybe just from you.
[00:41:34] Maybe you kind of, yeah.
[00:41:35] - I think I have an example, it might not be exactly that.
[00:41:38] I will just say I've been here 13 years
[00:41:39] and you know, like any institution,
[00:41:42] people have disagreements and conflicts and struggles,
[00:41:45] you know what I mean?
[00:41:46] And I've had my own.
[00:41:47] But I will say that in that time,
[00:41:48] I have never once felt that if I had a good story
[00:41:51] that I knew was true, we couldn't get it published.
[00:41:53] That's never happened.
[00:41:55] There are people I really disagree with
[00:41:57] and to be honest with you, I don't like
[00:41:59] and they might not like me,
[00:41:59] but I have at different times been able to come to them
[00:42:04] with we have the story, check this out, let's do it.
[00:42:06] And those people, they rise above all the personal stuff
[00:42:09] and they're like, yeah, let's do it.
[00:42:10] You know what I mean?
[00:42:11] And to me, that's one of the great things about a newsroom
[00:42:13] is that people who disagree or who are different
[00:42:17] can unite around like a good story.
[00:42:19] And like I have nothing but good things to say
[00:42:21] about that personally, but like this is an example though
[00:42:23] and of something slightly different but related,
[00:42:26] which is how and one thing I really love about statistics
[00:42:30] is how it can sometimes they can sometimes lead you
[00:42:33] to conclusions that you do you would never imagine,
[00:42:36] you know what I mean?
[00:42:36] Or maybe we're counter to your initial bias
[00:42:40] and then you end up telling a different story
[00:42:41] than the one you thought you were gonna tell
[00:42:43] when it started out, right?
[00:42:45] And so you guys remember Hurricane Harvey in Houston.
[00:42:50] Oh, I have to send a Slack message really quick.
[00:42:51] I have to,
[00:43:02] this will just take a second.
[00:43:03] I have bought a mechanical keyboard
[00:43:05] during this like period at home guys
[00:43:07] and it is so loud when I type that this might be deafening
[00:43:10] but you guys remember Hurricane Harvey.
[00:43:13] This was in Houston a couple years ago.
[00:43:15] They had kind of like a big storm come through
[00:43:18] and kind of flood the whole place
[00:43:19] and it led to these sort of crazy images on television
[00:43:22] of people trying to cross the city
[00:43:24] in this sort of biblical flood kind of situation.
[00:43:27] And it was a Friday night and we were sitting
[00:43:28] in the newsroom at the LA Times
[00:43:30] and an editor said to me, well, we gotta do something.
[00:43:33] Let's come up with something.
[00:43:34] It was one of these moments, right?
[00:43:35] Like what are we gonna do?
[00:43:37] This nuts thing is happening.
[00:43:38] We have to come up with a way to cover it.
[00:43:42] And like a lot of those situations,
[00:43:45] you kind of start with what you're seeing in the news,
[00:43:47] what you hear from other people and your own biases.
[00:43:50] You know what I mean?
[00:43:51] Like what you kind of think about the world.
[00:43:53] And like, and then we're trying to come up
[00:43:56] with a data story.
[00:43:57] And so our initial thought was, hey,
[00:43:58] if we get the data about where the FEMA flood zones are,
[00:44:05] these officially defined flood zones from the government,
[00:44:07] right?
[00:44:08] And we compare it to the early damage data
[00:44:11] that we see coming in from the government
[00:44:12] about where buildings are damaged.
[00:44:14] We're gonna find that all the people in these zones
[00:44:17] got wiped out and we're gonna be able to add them up
[00:44:19] into some big number and try to do a story
[00:44:22] about the like extent of the damage, right?
[00:44:25] And like, we didn't know exactly what it was gonna be,
[00:44:27] but that was kind of our methodological bias
[00:44:30] or frame that we like came to it with, right?
[00:44:36] Let me just send this message, sorry.
[00:44:38] And we're all fired up to do that.
[00:44:42] I stayed up to like one in the morning, like it's like,
[00:44:44] oh my gosh, the Harris County Property Assessors Office
[00:44:47] has all the data online.
[00:44:49] Yeah, grab it.
[00:44:50] And the FEMA is having a conference call
[00:44:52] and they're gonna give out the damage data.
[00:44:54] Wow, there's property values.
[00:44:56] Here's the flood zones.
[00:44:57] I'm gonna put them all together and like find it out.
[00:44:59] And like, and then we pitched that story to an editor
[00:45:02] and we say, this is what story is gonna be.
[00:45:03] It's gonna be about how screwed up Houston got
[00:45:05] 'cause all this.
[00:45:06] And then I did the analysis
[00:45:09] and what I quickly found, or not quickly, not so quickly,
[00:45:12] what I soon found was that most of the damage
[00:45:15] was actually outside of the official FEMA flood zones, right?
[00:45:19] So that was an interesting thing.
[00:45:21] And then we're like, okay, so let's start calling people
[00:45:24] and let's figure out what's going on.
[00:45:25] So we're just getting on the phone calling, texting
[00:45:27] 'cause we have all these addresses
[00:45:28] from the property assessors.
[00:45:29] Just call every person and just see what's going on.
[00:45:32] And we did dozens and dozens of phone calls.
[00:45:34] And what we found is that people inside
[00:45:38] the official FEMA risk zones were actually like fine.
[00:45:42] Like every one of them we could try to was like,
[00:45:44] no, I'm good.
[00:45:45] We were high.
[00:45:46] We had put up like, we had elevated our house
[00:45:49] or we had put in a retaining pond
[00:45:52] and that caught all the water or our roads got redone.
[00:45:55] And it was the people outside who had the problems.
[00:45:57] And what we kind of pieced together then
[00:46:00] from talking to experts and more and more people
[00:46:03] was that our initial assumption was actually reverse.
[00:46:06] And that's because believe it or not,
[00:46:08] public policy works guys or it can.
[00:46:11] It's that if you're inside an official FEMA flood zone,
[00:46:14] there were all these additional building requirements
[00:46:16] and retrofitting requirements for like taking care
[00:46:19] and getting ready.
[00:46:20] And so the people who lived in the quote unquote
[00:46:22] riskiest areas actually ended up having the best outcomes
[00:46:26] because they had to take all these preventive measures.
[00:46:29] And so we ended up writing a story about that,
[00:46:30] about how, hey, these regulations actually work,
[00:46:33] but they're not nearly widespread enough
[00:46:34] to save all these other people, you know what I mean?
[00:46:37] It's kind of what the story ended up being.
[00:46:39] And we had to kind of go back to the editor
[00:46:40] and be like, oh, our initial pitch, it's like not that at all.
[00:46:43] And that was this, it's a little hard to summarize,
[00:46:47] but that was this sort of like weird process
[00:46:50] of gathering numbers, listening to them,
[00:46:53] talking to people on the other end of the data
[00:46:55] and gradually having like a light bulb go on like,
[00:46:57] oh, what everything I think is wrong.
[00:47:00] And then you have, there's, whenever that happens,
[00:47:02] there's always this like fun meeting
[00:47:03] like in the middle of it where like you get together
[00:47:05] and you're like, hey, we're totally wrong, right?
[00:47:07] (laughing)
[00:47:09] And you have to, okay, well, we can't just give up.
[00:47:11] We gotta come up with like some story.
[00:47:13] And so then oftentimes it's a more interesting story,
[00:47:15] you know what I mean?
[00:47:16] Because it's something you didn't expect, you know?
[00:47:21] - Yeah, that's, yeah, that is actually amazing.
[00:47:24] And it's really, I think it's just sort of really helpful
[00:47:26] to kind of get that perspective on how you develop
[00:47:31] and really work through these stories.
[00:47:33] Another question, and I know we wanna be really sensitive
[00:47:37] of having you here for too, too long,
[00:47:40] but we have a couple of really good questions.
[00:47:42] One, I think that would maybe really also be helpful
[00:47:45] for students is just sort of thinking about
[00:47:48] that tipping point between when you're creating a story,
[00:47:51] when is it interactive and when is it static
[00:47:55] and kind of like, how does that decision process get made
[00:47:58] and how does that sort of, how does that happen?
[00:48:01] - That's a tough one, and there's not one answer,
[00:48:04] but I mean, if the question is when is something interactive?
[00:48:08] Look, if you ask it that way,
[00:48:09] the answer is less and less so every month and year.
[00:48:13] And that has a lot to do with the changes we've seen
[00:48:16] in consumer technology over the last five or 10 years.
[00:48:19] As more and more people have moved to mobile phones.
[00:48:22] Believe it or not, you probably will believe it.
[00:48:24] You guys are into tech.
[00:48:27] The majority of people who read the LA Times online
[00:48:30] read it on a phone, like well more than 50%.
[00:48:33] And so in a circumstance where the majority
[00:48:36] of your audience is visiting you on a very small screen,
[00:48:40] which they mostly just like thumb up and down on, right?
[00:48:45] A lot of the interactive graphics that were very intricate
[00:48:48] and made in Adobe Flash or whatever, 10 years ago,
[00:48:51] really no longer makes sense because most people
[00:48:54] aren't gonna interact with them and use them.
[00:48:56] And the reality is, is what we learned from web analytics,
[00:48:58] even before the mobile changes,
[00:49:00] that most people never interacted with them anyway.
[00:49:03] And so that's why you've seen a shift in,
[00:49:06] if you follow kind of our little niche,
[00:49:08] you've seen a shift away from most interactive graphics.
[00:49:11] And so for the most part, we're looking for graphics
[00:49:14] that can work static and small on mobile
[00:49:19] and the way to make them more insightful
[00:49:22] is usually through adding an annotation layer,
[00:49:24] so like a little swoopy arrow or something.
[00:49:28] Or a scrolling change, like you see
[00:49:30] in that scrolling technique or the chart sort of changes
[00:49:33] as the user continues to read the page.
[00:49:36] Where interactivity still makes a lot of sense,
[00:49:38] I think is in cases of personalization or of discovery.
[00:49:43] So we're gonna have a chart that shows you
[00:49:47] the total number of deaths at nursing homes from COVID.
[00:49:51] But then when we have the, we're gonna have a table
[00:49:52] that lets you look up the nursing home and care about, right?
[00:49:55] And then that table should have like a search
[00:49:57] and a filter and a way for you to like dig into it.
[00:50:00] And then there's something like the live wildfires map,
[00:50:02] which is really about one, giving you an overview
[00:50:05] of like how many fires are there right now.
[00:50:08] But then two,
[00:50:11] two, letting you drill down into areas that you care about
[00:50:14] to see kind of the latest data within those areas.
[00:50:17] And so that's the map being there for personalization
[00:50:20] and discovery, you know.
[00:50:23] I think a lot of the interactivity that was just
[00:50:25] like about fiddling, you've probably noticed
[00:50:28] has like mostly disappeared.
[00:50:29] Plus it's a lot, it takes more time to code.
[00:50:32] - Yeah, very cool.
[00:50:34] You know, we definitely wanna ask you this one question
[00:50:36] for sure, because we have so many students here
[00:50:38] who are interested in data and storytelling.
[00:50:41] And sort of what is like, if you had one piece of advice
[00:50:44] for students going out, working on school projects,
[00:50:47] or really just wanting to break into
[00:50:49] Ellie Times newsroom or other places, what is it?
[00:50:54] What should they know?
[00:50:55] - One piece of advice, I mean, I don't know.
[00:50:57] - Or two or three.
[00:50:58] - I can give you a call.
[00:50:59] - Yeah, please.
[00:51:00] - I mean, to me, this is a thing that has become cliche
[00:51:03] and is said by some people who I think are kind of jerks,
[00:51:06] but if you race them from your mind, it is true.
[00:51:10] You gotta learn to code.
[00:51:11] You don't have any, it's just like to really
[00:51:13] do this well, you have to learn to code.
[00:51:16] And you have to learn to code certain types of things,
[00:51:19] not just like coding in general,
[00:51:20] but I do think the fundamentals of programming
[00:51:22] are so important.
[00:51:24] The fundamentals of just like software and package design
[00:51:26] are really underrated, just like how do I write a function
[00:51:29] and then import it into another file and then reuse it
[00:51:32] and like package code.
[00:51:34] Like the basics of coding, I think are super important.
[00:51:38] And then the sort of applied application of coding
[00:51:40] to the types of things we do.
[00:51:42] And so that, I think on the one hand,
[00:51:44] is kind of data gathering and analysis.
[00:51:47] So this is, how do I get data by scraping it
[00:51:50] or reading it out of a database or building my own database?
[00:51:53] And then how do I analyze it to like interview the data
[00:51:57] is how we think about it.
[00:51:58] What questions do I have for the data
[00:51:59] and how can I write code that will help me get the answers
[00:52:02] to those questions?
[00:52:03] And so the tool that I teach a lot of classes on
[00:52:06] that I like, but it's all debatable
[00:52:09] is the Jupyter Notebook with Python data analysis tools.
[00:52:13] They're free is kind of the best thing about it.
[00:52:15] And the Jupyter environment once you figure out
[00:52:18] how to get it running is just a really handy way
[00:52:22] to do like step by step kind of data work.
[00:52:25] And then on the other side,
[00:52:26] there's the sort of how do we tell stories visually
[00:52:31] through web development, right?
[00:52:33] And that really involves learning fundamentals of HTML and CSS.
[00:52:39] You know, again, just the basics are so important.
[00:52:42] They're not even in HTML's case.
[00:52:43] It's not even that hard, right?
[00:52:46] And then how do you use JavaScript to make interactivity
[00:52:50] and data visualizations?
[00:52:54] I'm a big fan of D3.
[00:52:56] And it's also a great way to get better at JavaScript, too,
[00:53:01] because the D3 culture is really built around excellence
[00:53:05] and programming, which I think is important.
[00:53:06] I think there's a lot of free tools that are just about
[00:53:09] cutting corners that don't really help you
[00:53:12] over the long run become to master that type of thing.
[00:53:16] So to me, there's the coding part,
[00:53:18] but then there's the journalism part, which is thing two.
[00:53:20] And this is where, like when I get applicants for internships
[00:53:24] and stuff from data science programs,
[00:53:26] which I'm always glad to get and I love to see,
[00:53:29] you know, sometimes the people just haven't had the time
[00:53:31] or opportunity to really apply their work in a way
[00:53:34] that they're making something for the general public
[00:53:36] or that has kind of a headline take away like point, you know?
[00:53:41] A big part of editing and putting out a piece of journalism
[00:53:44] is you kind of have to have like something to say.
[00:53:47] And just writing a notebook that asks 10 questions
[00:53:50] of the data or just making a graphic
[00:53:52] that lets you fiddle with the numbers isn't enough, right?
[00:53:55] The things that you're making need to kind of take
[00:53:56] that next level of kind of journalistic whatever, you know,
[00:54:01] by saying my finding is this.
[00:54:03] And here's the headline that says it
[00:54:05] and here's the three charts that show it, you know what I mean?
[00:54:08] And you kind of like have a takeaway and a point
[00:54:10] so that you have something to share.
[00:54:12] And so when I see applicants who,
[00:54:15] and I don't expect anyone who's being out
[00:54:17] to really master any of this,
[00:54:18] but when I see people who kind of have clearly put in the effort
[00:54:22] to do both of those two things,
[00:54:24] to like learn how to do the coding stuff to make stuff,
[00:54:26] but also learn how to boil it down
[00:54:28] and to make something out of it
[00:54:29] for regular people, you know what I mean?
[00:54:32] That's when I think you're a strong applicant
[00:54:35] and you're sort of beginning the road to become do what we do.
[00:54:39] But the reality is is learning how to code takes a while
[00:54:41] and the things we're learning,
[00:54:42] we're trying to do are emerging and experimental.
[00:54:45] And so no one including myself really gets kind of good
[00:54:48] at what we're doing until they've spent a couple of years
[00:54:51] really apprenticing at it.
[00:54:52] And that's just, that's just being human, you know?
[00:54:57] - That's great.
[00:54:58] That's really humbling.
[00:54:59] And I think really honest to share that.
[00:55:01] And I hope all the students out there
[00:55:04] really get a lot out of that.
[00:55:06] That's really helpful.
[00:55:07] You know, I don't know if you wanted to kind of
[00:55:10] have a wrap up question or...
[00:55:13] - Yeah, whatever you guys got,
[00:55:14] I'll take, I got a couple of minutes yet.
[00:55:17] - Yeah, okay.
[00:55:18] Well, well, first of all, thank you for kind of validating
[00:55:22] this week for us from what you just said,
[00:55:25] the value of coding, open data.
[00:55:29] This is all that we've been kind of preaching
[00:55:31] in the last couple of days.
[00:55:33] Also should mention that if I look up
[00:55:35] visual storyteller in the dictionary,
[00:55:37] I think I'll find you in there.
[00:55:40] You seem to embody that kind of visual storyteller mentality.
[00:55:46] But I don't know if there's like one or two final questions.
[00:55:50] Maybe somebody can unmute themselves
[00:55:53] if you're out there and introduce yourself to Ben
[00:55:57] and ask a question, hoping somebody can do that.
[00:56:07] - I have a quick question.
[00:56:09] - Please.
[00:56:10] - And that's about, Ben, I see that you're active
[00:56:13] in an organization called Data for Progress.
[00:56:16] And I'm wondering if you'd talk about that a little bit.
[00:56:19] It sort of ties in with what we've been doing
[00:56:22] with humanitarian mapping.
[00:56:24] And sort of how you see that kind of mission
[00:56:28] converging with what you're doing at the LA Times,
[00:56:32] which is obviously more of a commercial than--
[00:56:35] - Yeah.
[00:56:37] I don't know if I'm familiar with Data for Progress,
[00:56:39] but I'm guessing it came up linked
[00:56:40] to some other things that I do, you know what I mean?
[00:56:42] But like for me personally, open source software
[00:56:46] is probably as close as I get to religion.
[00:56:49] Nice, but like, and I think is a really underrated
[00:56:53] as a solution to a lot of problems.
[00:56:56] It just needs more institutional support
[00:56:58] from places like UCLA.
[00:57:00] But what you guys are providing today, which is great,
[00:57:03] you know, but like, and so I've been involved
[00:57:08] in a number of kind of open source data gathering
[00:57:12] and cleaning efforts related to helping journalists
[00:57:15] both improve their skills, but also to kind of
[00:57:18] build software and data infrastructure.
[00:57:20] So we're not all like replicating each other's work
[00:57:23] and we can benefit from kind of group collaboration.
[00:57:27] One of those is called the California Civic Data Coalition.
[00:57:30] And so this was a foundation funded open source
[00:57:33] software project that refines and cleans up
[00:57:37] the campaign finance data put up
[00:57:39] by the California state government.
[00:57:40] So this is the money in our state politics,
[00:57:42] like all these Uber ads you're seeing
[00:57:44] for Prop 22 right now.
[00:57:46] And the data is there, it's just really difficult to access
[00:57:50] because it's just in crappy shape.
[00:57:52] And so there's kind of like this problem of who's going
[00:57:56] to clean it up, right?
[00:57:57] And so we created an open source group
[00:57:59] to write the software that would be the refinery
[00:58:02] for that like raw data, and then all the code
[00:58:04] and all the effort is open source as well as the result.
[00:58:07] Along the way we invented some bulk data loading
[00:58:10] open source software tools, which people now use
[00:58:12] for like totally different purposes, you know what I mean?
[00:58:14] There's like side benefits to doing that.
[00:58:17] And we're replicating that model currently
[00:58:19] when it comes to tracking COVID data,
[00:58:21] where initially the LA Times was doing 100%
[00:58:23] of this COVID tracking ourselves in California,
[00:58:26] but we now have a team of people from eight newsrooms
[00:58:28] across the state who are helping us kind of gather it
[00:58:31] and consolidate this cleaned up database of COVID data.
[00:58:37] And I think that there's one, a lot of potential for that,
[00:58:41] but two, I think as news organizations get smaller
[00:58:43] and struggle, I don't know if there's a better alternative,
[00:58:46] like who, you know what I mean?
[00:58:47] Other than we just hope the New York Times gets big enough,
[00:58:50] they can do it for everybody, you know what I mean?
[00:58:52] Like that's really, the commercial solution
[00:58:54] is going to be news monopoly, you know,
[00:58:57] that's just big enough to do that is what I think.
[00:58:59] But, you know, maybe I'm being too pessimistic.
[00:59:02] So I think we need to band together
[00:59:04] to come up with this alternative.
[00:59:10] - Thank you.
[00:59:12] Great, how about one last question for Ben?
[00:59:16] How about one of the questions?
[00:59:18] - I have a question.
[00:59:19] - There we go, please.
[00:59:22] - So I love your neighborhood map,
[00:59:26] which is, I know, which has been on the page
[00:59:29] for a while now, it's so great.
[00:59:31] And I use it in my classes to talk to instruct students,
[00:59:36] well, instruct students about two things,
[00:59:38] one about the concept of place
[00:59:40] and another about the concept
[00:59:42] of volunteer geographic information.
[00:59:46] And I'm curious if you guys have done other projects
[00:59:49] that you think are within the realm
[00:59:52] of like volunteer geographic information.
[00:59:54] So like people actually map like-
[00:59:57] - Yep, that project is one of my great white whales
[01:00:00] and that we haven't really revived it.
[01:00:02] There was a, the plan was to do it last year
[01:00:06] for this census, but other things have come up.
[01:00:11] And just so people know,
[01:00:13] there is no official source of neighborhoods in LA County,
[01:00:16] even though we all feel that we live in one.
[01:00:19] And so about 10 years ago with some of my colleagues,
[01:00:22] we sort of tried to pull the public
[01:00:25] and then divide up LA County into, you know,
[01:00:28] the LA Times neighborhoods at least.
[01:00:30] And then we connected those kind of like
[01:00:32] with the legos of census tracts.
[01:00:34] So then all the areas have all this metadata
[01:00:37] about demographics that are then merged with them,
[01:00:40] which has allowed us to do dozens of investigative
[01:00:43] and analytical stories to compare East LA to West LA,
[01:00:46] you know what I mean?
[01:00:48] It's not more in social science.
[01:00:50] And one of my personal ambitions is for our team
[01:00:53] to be more of a social science data provider.
[01:00:56] We're trying to do that a little bit here with COVID,
[01:00:58] but I would love to sort of be able to publish
[01:01:01] sort of like the machine readable atlas
[01:01:05] of LA demographics by neighborhood or something
[01:01:07] so that we could see more and more people
[01:01:09] able to do that type of analytical work.
[01:01:12] But at the core of it was this crowdsourcing effort
[01:01:14] 'cause we didn't want to do it entirely alone.
[01:01:16] So we sort of put out our own version of the maps
[01:01:17] and then we had people tell us how wrong we were
[01:01:20] and we modified them and you can't please everybody.
[01:01:23] There's definitely flaws,
[01:01:24] but that was kind of a collaborative process.
[01:01:27] You know, that's the type of thing
[01:01:28] that we haven't turned into like it.
[01:01:31] We're going to do something like that every month,
[01:01:32] but there's a few examples through the years
[01:01:34] that I've had a lot of fun working on.
[01:01:35] Like we finally, we're going to do the like a few years ago,
[01:01:40] the like, what is the East Side story in the LA Times?
[01:01:43] You know what I mean?
[01:01:44] Where everybody brings out their grievances, you know,
[01:01:46] and I will tell you as long as I am the data
[01:01:49] and graphics editor, the East Side begins at the LA River.
[01:01:52] I'm sorry, everybody who lives on Sunset,
[01:01:54] you don't live on the East Side, sorry, you know what I mean?
[01:01:57] But like, but we know people disagree, you know what I mean?
[01:02:03] Like there is no answer.
[01:02:04] And so we did like a one-off that was sort of
[01:02:06] in that same tradition where it was,
[01:02:08] here's a map of LA, draw the East Side, right?
[01:02:11] And we had hundreds of people like draw them all.
[01:02:13] And then we made this composite map
[01:02:14] that was like everybody's East Side like overlaid.
[01:02:18] And it was a little abstract and already, you know what I mean?
[01:02:23] I don't know if it was like the clearest data visualization
[01:02:26] ever drawn, but it sort of had this fun effort of like,
[01:02:29] here's the splatter of like what everybody thinks, you know?
[01:02:32] And though I'm sure our readership is biased
[01:02:34] in a certain direction.
[01:02:36] But so like that was an example of that, I guess.
[01:02:41] - Yeah.
[01:02:44] - You know, to me, the crowdsourcing thing that's out there
[01:02:47] that nobody does much with Viz,
[01:02:48] which we could do with Quakebot,
[01:02:49] is the like, did you feel an earthquake data from like USGS?
[01:02:53] I think it's like all of there for every earthquake
[01:02:55] and like nobody really does much with it, you know?
[01:02:59] I know the scientists at USGS do,
[01:03:01] but like, I don't know if the news media does.
[01:03:03] Yeah.
[01:03:04] - Right.
[01:03:06] Cool.
[01:03:06] Thank you.
[01:03:11] - All right.
[01:03:11] Yeah, no, there's a whole bunch of chatter
[01:03:13] about that project you're talking about mapping Los Angeles.
[01:03:17] - Yeah.
[01:03:18] - It's many of us fondly remember that kind of,
[01:03:20] hey, we can contribute to defining boundaries,
[01:03:22] which is not something we're used to.
[01:03:25] Boundaries are usually defined by somebody up there.
[01:03:28] And we abide by those boundaries.
[01:03:30] And here's the LA Times saying,
[01:03:31] hey, you define your own boundaries.
[01:03:33] So that was a really neat project.
[01:03:35] And still to this day in the urban planning department,
[01:03:39] that's a go to resource for doing neighborhood level analysis.
[01:03:43] - Yeah.
[01:03:44] I was absolutely just going to say that.
[01:03:46] I mean, I think the impact of that project in the classroom,
[01:03:51] I just don't know if that kind of reaches you all,
[01:03:53] but like, I mean, that is in so many different classes,
[01:03:55] so many different students use that as sort of like,
[01:03:58] understanding LA at this point.
[01:04:00] - Yes, we have census tracts.
[01:04:01] Yes, we have these other boundaries,
[01:04:02] but those particular boundaries in that work
[01:04:06] is just super, super influential in the classroom.
[01:04:09] - Yeah.
[01:04:09] I mean, to me, I just see all the times we fail to revive it.
[01:04:12] So I feel a little guilty with that.
[01:04:14] But like, you know, we kind of have, I have some ideas.
[01:04:17] I might be like, this is one of these things
[01:04:20] where like internally it gets caught up in the politics of like,
[01:04:23] do we need a hyper local neighborhood news product?
[01:04:25] You know what I mean?
[01:04:26] And like, it's hard to have a conversation internally
[01:04:28] without it being caught up with like traditional coverage.
[01:04:31] It's my belief that it should be more of a data application
[01:04:34] and like kind of like a social science hub
[01:04:37] that is like easy enough for the average person
[01:04:40] to understand, you know what I mean?
[01:04:41] That's just like what is, you know,
[01:04:44] Chevy bills or whatever, you know?
[01:04:46] And I would like to kind of narrow kind of the mission
[01:04:50] of it to really focus on that kind of popularizing
[01:04:53] the demographics thing.
[01:04:54] And so my hope is, is that we can do that
[01:04:57] when the new census data comes out.
[01:04:59] But as you know, it's the ACS is where the action really is.
[01:05:03] So it doesn't matter.
[01:05:06] I kind of wonder if we could partner that with like,
[01:05:08] can I give you my pet idea?
[01:05:10] And you guys can tell me if it's terrible.
[01:05:13] With like a gentrification analysis.
[01:05:15] - Oh, yeah.
[01:05:17] - If you like that, yeah.
[01:05:18] 'Cause I did some reading
[01:05:19] and you guys may know this better than me,
[01:05:20] but there's these like academic definitions
[01:05:22] of gentrification, which as far as I can tell,
[01:05:26] are kind of limited to a sort of post-war redevelopment
[01:05:30] sort of framework of like, how old is the housing stock?
[01:05:33] And how recently has it been replenished?
[01:05:36] And what is the change in housing value been, right?
[01:05:40] And so like, you could, I look at those methods
[01:05:42] and I could take these longitudinal census databases.
[01:05:45] I can do that for every census tract
[01:05:46] and then I could tell you what's the most gentrified neighborhood
[01:05:48] in LA like that way, right?
[01:05:49] That would be one way.
[01:05:50] But like my hunch is that that's not enough.
[01:05:53] Like in our 21st century life,
[01:05:58] there's sort of this Brooklynized idea of gentrification,
[01:06:01] which has to do with cultural products, right?
[01:06:04] And commerce and like that kind of thing, you know what I mean?
[01:06:07] Like what is the business in Highland Park now
[01:06:10] on that strip on Fig versus 10 years ago, right?
[01:06:13] Used to be a Latino typewriter shop
[01:06:15] and now it's a hipster cocktail bar or whatever, you know?
[01:06:19] And I think that's what people have in mind
[01:06:21] when they send you an interpretation today.
[01:06:23] And so like what I'm kind of reaching for
[01:06:25] is could you come up with a metric
[01:06:27] that would try to capture that
[01:06:29] and then integrate it with the older metrics
[01:06:31] into like a kind of more up-to-date gentrification index
[01:06:35] or something, do anybody have any ideas
[01:06:38] or have read anything that smiles ahead of me on this?
[01:06:42] - Well, you know, there's a Center for Neighborhood Knowledge
[01:06:44] here at UCLA that does a lot of research.
[01:06:47] I don't know if you know Paul Wong,
[01:06:48] he's a professor who--
[01:06:49] - I don't know. - Wong worked with models
[01:06:53] of gentrification and they have a site,
[01:06:56] a project called Urban Displacement,
[01:06:59] collaboration with Berkeley and UCLA
[01:07:01] where they have index S for gentrification.
[01:07:04] - No, really?
[01:07:05] - It just talks about the need for us, you know,
[01:07:08] in academia and also to let you know,
[01:07:11] like UCLA is launching this kind of data acts initiative.
[01:07:14] So it sounds like we are,
[01:07:17] once that behind you guys, but we're right there
[01:07:21] in terms of our efforts to think about,
[01:07:25] like you said, you know, the importance of coding
[01:07:27] in kind of the more social sciences,
[01:07:30] which isn't traditionally been the case.
[01:07:32] So just to think about how us in academia,
[01:07:36] I mean, I feel such a stronger bond with you
[01:07:39] through what we've been discussing today
[01:07:41] and the importance that we work together
[01:07:45] to create some meaningful stories
[01:07:49] that can inform policy decisions across our communities.
[01:07:55] - Yeah, I also love, I really love the idea
[01:07:58] of kind of looking at sort of alternative data sources.
[01:08:01] I was on a call yesterday and a workshop yesterday
[01:08:06] actually looking at the Getty's new Riche project.
[01:08:10] And they're putting all these images out there.
[01:08:12] And this workshop was really trying to understand
[01:08:15] how to look at those images, you know,
[01:08:17] in this, you know, cultural production,
[01:08:20] the streets that were photographed,
[01:08:22] historically looking at, you know, racial change over time
[01:08:25] and really talking about gentrification
[01:08:29] and how a data set like that with those images
[01:08:32] could be used with, you know, all these sources
[01:08:35] that you're talking about, but other data sets
[01:08:37] that are cultural and kind of trying to, you know,
[01:08:40] look at that and also how to sort of code those images
[01:08:45] and doing that coding so that it actually is, you know,
[01:08:49] doing the work that we want it to do
[01:08:51] in terms of understanding, you know, the displacement
[01:08:55] or whatever's happening.
[01:08:57] And not just sort of saying like, oh, you know,
[01:08:59] this was here before and now it's gone,
[01:09:00] but like, well, why is it gone?
[01:09:02] And really kind of, you know, putting the point onto that.
[01:09:05] So yeah, it's really interesting.
[01:09:09] And I think you're right.
[01:09:10] I mean, I think what we've been talking about
[01:09:11] with, you know, really learning how to, you know,
[01:09:15] build coding and, you know, also just stepping back
[01:09:19] and being able to kind of piece, I think,
[01:09:22] like really different data sets.
[01:09:25] And the thing that we were talking about yesterday too
[01:09:27] is just sort of understanding that world of data
[01:09:30] and kind of, especially I think for things
[01:09:33] like gentrification, kind of stepping out
[01:09:36] and kind of looking at or trying to understand it
[01:09:42] in a way that's local and trying to find sources
[01:09:49] that maybe aren't that typical, you know, census,
[01:09:53] you know, way of understanding it.
[01:09:54] So I don't know, I love it.
[01:09:55] Yeah.
[01:09:56] - There's a guy at Harvard I found he did a paper read
[01:09:59] used Yelp data and then he tried to like correlate
[01:10:02] the arrival of certain types of businesses
[01:10:05] with other gentrification indicators, you know,
[01:10:07] like educated population or whatever.
[01:10:10] And so there might be something there, you know,
[01:10:15] but I kind of imagine the index.
[01:10:18] You take like the traditional metric of like buildings
[01:10:21] or whatever, you know, you add in the racial dynamic
[01:10:25] of, you know, white invasion, as I've learned,
[01:10:27] it's called in some papers.
[01:10:29] And then you maybe add like a third cultural dimension
[01:10:32] of these products and then you like mix those into like
[01:10:35] a radar chart or like an index or something
[01:10:38] and that'd be kind of cool.
[01:10:40] My bet is what Adams would be number one.
[01:10:42] That's my bet guys.
[01:10:44] - I would say from personal experience
[01:10:45] with Brooklyn and here in LA, the first major indicator
[01:10:49] should be when the real estate agent decides
[01:10:51] to change the name of the name.
[01:10:53] - There you go.
[01:10:54] That's another one.
[01:10:55] - That's an indicator.
[01:10:56] - One thing I learned looking at the literature too,
[01:10:58] I thought was interesting is traditionally only like
[01:11:01] the lowest quartile could ever be thought of
[01:11:03] as having gentrified.
[01:11:04] So you had to be depressed to ever undergo the process.
[01:11:08] And I don't think that people think about it that way
[01:11:10] today anymore.
[01:11:11] Like, 'cause when I ran the traditional numbers,
[01:11:13] I saw that Venice Beach had a really high score
[01:11:16] on some of the values but wouldn't have been considered
[01:11:19] because it was already a pretty wealthy place to begin with.
[01:11:21] Right?
[01:11:22] And so like there's this way in which I think
[01:11:23] the traditional numeric framework doesn't mesh
[01:11:27] with how people really think about it
[01:11:29] or talk about it today.
[01:11:30] And like, that's the thing I just feel like
[01:11:32] there must be some way to wrestle that.
[01:11:34] But.
[01:11:36] - That could be a,
[01:11:38] and this is a very geographically nerdy
[01:11:40] but a modifiable aerial unit problem issue
[01:11:43] where because you're looking at Venice as a whole.
[01:11:47] - Right, we've predefined it.
[01:11:49] - Yeah.
[01:11:50] - Very wealthy parts get, you know,
[01:11:51] averaged with the less wealthy parts.
[01:11:54] So those.
[01:11:56] - That's a good point.
[01:11:57] - Yeah.
[01:12:01] - Mm.
[01:12:02] - But I'm thinking also that with the sort of combining
[01:12:06] what Andy was talking about with the coding
[01:12:08] and like maybe taking for each neighborhood,
[01:12:11] choosing kind of like the commercial strip
[01:12:16] and looking at Google Street View
[01:12:17] and sort of like coding that over time
[01:12:20] for the main, like the main drags of different neighborhoods.
[01:12:25] - Yeah.
[01:12:26] Or even if we did the number for the whole county,
[01:12:28] you could pick the place that had the highest
[01:12:30] and up new merit score and then go in
[01:12:32] and do that closer study on that place.
[01:12:34] - Yeah.
[01:12:35] - You know, and then you wouldn't have to do everywhere.
[01:12:37] - Right.
[01:12:38] - I also have to think about how to do less.
[01:12:39] Sorry.
[01:12:40] (laughing)
[01:12:42] - Now, I mean, I think that's one of the things
[01:12:43] that we all struggle to is sort of kind of, you know,
[01:12:46] looking at LA, but I think a lot of us push our students
[01:12:50] and just people that we work with to focus in on areas.
[01:12:52] And I think that's sort of like a nice bridge to,
[01:12:55] you know, I think what you at the times do
[01:12:56] is kind of putting in the person into our data stories.
[01:13:01] And like, you know, not always sort of like having that
[01:13:04] bird's eye view of everybody, but like, let's zoom in.
[01:13:07] Let's like really kind of understand what this data set
[01:13:10] is doing on this street, on this block.
[01:13:12] - Yep.
[01:13:13] We talk about them and like, there's usually,
[01:13:15] there's like two different couples we're looking for.
[01:13:18] There's like, there's either Mr. and Mrs. Outlier
[01:13:21] or there's Mr. and Mrs. Central Tendency.
[01:13:23] And like kind of depending on like which story
[01:13:25] you're telling, you're sort of like aiming
[01:13:27] for those people, you know.
[01:13:31] - That's great.
[01:13:32] I love that.
[01:13:33] - I feel like we're in a newsroom right now
[01:13:36] making decisions for the next story.
[01:13:41] But I wanna wrap us up here,
[01:13:44] taking too much of your time.
[01:13:48] Thank you so much.
[01:13:49] The conversations in the chat room has been just nonstop.
[01:13:54] You've touched a lot of topics that really mean
[01:13:56] so much to many of us.
[01:13:59] So just wanna say a big thank you to Ben.
[01:14:03] I'm sure it's shared by all of us here.
[01:14:06] There you go.
[01:14:09] And Ben, let's definitely get together again.
[01:14:13] When there's a little bit more time to relax.
[01:14:16] I don't know when that would be.
[01:14:18] But I'd love to kind of have a conversation
[01:14:20] about how we in academia can work closer together
[01:14:23] with you guys at the LA Times.
[01:14:27] - Great.
[01:14:27] - Yeah.
[01:14:28] - For having me.
[01:14:29] - Thank you.
[01:14:30] Thank you for being so generous with your time.
[01:14:31] It's really amazing.
[01:14:33] - Mm-hmm.
[01:14:34] Thank you so much.
[01:14:35] - Sorry, I was late.
[01:14:38] - I love the new luck.
[01:14:39] I love the-
[01:14:40] (laughing)
[01:14:42] - I'm gonna report that to my wife.
[01:14:46] - All right, thank you so much, Ben.
[01:14:47] - Thank you, Ben.
[01:14:49] - Mm-hmm.
[01:14:49] - Bye.