Thomas: It is episode eight of season two of the Presentation Boss Podcast. How you going? Uh, I’m doing well. How are you doing, Kate?

Kate: I am good. I’m good

Thomas: Lovely. Look, today we are doing a presentation breakdown. What we do is we find a talk, a presentation of some kind out on the internet, and we’re gonna play it here so you can see it, you can hear it.

And at kinda key points as we’re watching it, we will pause, and Kate and I are gonna make comment on the things that we find that are working really well that we can learn from, or things that aren’t m- working so well that we can also learn from. ’cause, you know, life’s too short to make all the mistakes yourself. So what we’re gonna do today is have a watch of a talk. Now, this one was actually recommended to us. We do love a talk, uh, a recommendation. So if you, if you know of a talk out there, doesn’t have to be a TED Talk like today, but if you know something that is… we can learn from, is worth us breaking down the podcast, we would love to hear from you.

And, yeah, let us know. Uh, we’ll, we’ll, we’ll add it to the list. What we are doing today is a talk from TEDx New York back in 2014, and this one is Ben Wellington, and he is talking about a lot of datasets from within New York City. Um, Kate, have you seen this one before?

Kate: I have not, but I am quite aware of it, and I’m looking forward to watching it because I know that Ben Wellington talks about data storytelling kind of in general, um, in other talks that he’s done. The reason we use TED is because it’s accessible, it’s really clean recordings. Um, but I’m excited to see this one because, um, like I said, he talks about data storytelling, so I’m keen to see how he actually then uses the skills that he talks about

Thomas: Yeah, yeah, a little bit meta, right?

Kate: Hmm. Yeah

Thomas: many of his other talks where he talks about data

Kate: Mm-hmm.

Thomas: I’ve kind of only watched through this one where he’s just talking about the data. So, um, know, there we go Do you wanna jump in with a before we get stuck in? Go now

Kate: Um, before we do get stuck in though, I’d really love to tell you about the new community that we have launched. If you’re an analyst, if you’re a technical professional who’s really good at the technical side of your job but you want to be better at everything, those more soft skills around you, um, things like influencing decisions, making recommendations, presenting to senior stakeholders and executives and, you know, generally thinking more commercially, you might want to check out the Influential Analysts Academy.

And each month we focus on a different skill that will help you become a little bit more influential and, uh, move towards being a trusted strategic advisor. Um, every month you’ll get a live workshop, a live event with Thomas and myself. There are a lot of practical tools and, um, challenges and exercises to actually put into practice, and ongoing access to Thomas and myself at, uh, live events.

If you do need help with anything you can bring any of your work to us as well. And because it’s a community, you’ll be working alongside other analysts who have similar challenges and who are working on the same skills. Um, if you wanna keep developing those skills that we talk about mostly on this podcast really, there is a link in the show not- show notes for it.

So come and check it out. But, uh, let’s get into today’s talk

Thomas: Let’s do it. Today we are going to be watching from TEDx New York in 2014, Ben Wellington with How We Found the Worst Place to Park in New York City Using Big Data

Ben Wellington: 6,000 miles of road, miles of subway track, 400 miles of bike lane, and a half a mile of tram track, if you’ve ever been to Roosevelt Island. See, these are the numbers that make up the infrastructure of New York City. These are the statistics of our infrastructure, and they’re the kind of numbers you can find released in reports by city agencies.

For example, the Department of Transportation will proudly tell you how many miles of road they maintain. The MTA will boast how many miles of subway track there are. Right? Most city agencies give us statistics. This is from a report this year from the Taxi and Limousine Commission, where we learned that there’s about 13 and a half thousand taxis here in New York City

Kate: I love this opening. I love how easy it is to process the numbers that he’s talking about. I often hear on the news that there’s been, like, 6,522 something, and I’m like, “Oh, God.” I’m, I’m, like, trying to process that number, whereas the exact number doesn’t matter. So when he just talks about, like, 6,000, 10,000, 5,000, I can process that easily.

So I love that he has just rounded because the exact number doesn’t matter, and I think that’s just a beautiful awareness of where an exact number matters and where a, an approximate number matters. Just, yeah, excellent opening there

Thomas: Yeah, I fully agree. And I’m looking at the chart on screen now. So he’s got the different number of vehicles, different number of cars, uh, types that are on the roads. And he talked about taxis. He specifically called out taxis, and it took me a moment to hunt through and find taxis. Um, this just could have been highlighted so that the taxis…

I mean, they’re in yellow. They could remain in yellow, but maybe make the others a dim color so that I just saw them really quick and, uh, wasn’t distracted from listening to him as well. But yeah, we are, we are moving through the

Kate: Yeah. And, and interestingly, all of these bars, we’ve got 25,000, 18,000, 10,000, 7,000, all clean numbers. But taxis, because he’s specifically talking about taxis, we’ve got an exact number, 13,437. He talks about just over 13,000 taxis, but we get an exact number because here the number does matter. Again, awareness of when it matters and when it doesn’t

Ben Wellington: But did you ever think about where these numbers came from? for these numbers to exist, that means somebody at the city agency had to stop and say, “Hmm, a number that somebody might want to know,” right? “Here’s a number that our citizens want to know up. paradigm isn’t exactly working, and I think our policymakers realize that because in two thousand twelve, Mayor Bloomberg signed into law what he called the most ambitious and comprehensive open data legislation in the country. And in a lot of ways, he’s right. In, in the last two years, the city has released a thousand datasets on our open data portal, and it’s pretty awesome. you’ll go and look at data like this, and instead of just counting the number of cabs, we can start to ask different questions. So I-

Thomas: Overall tactic here that I like. We, we talked about how we liked his introduction. He could have jumped in with that kind of legislative bit, which is a couple of years ago, the mayor did this, signed into legislation, re-access, blah, blah, blah, which is kind of not the most interesting thing. Instead, he said, “Here are some numbers, here is some data, here is what we came for. Now I will give you some backstory,” and then now we’re back into the data. I really like that tactic of not starting necessarily with the context that led to the data. We sort of started, I guess you, you could say, in the present or with a bit of a result, um, straight up front

Kate: Yeah, and I think it would be very tempting to start with, “A few years ago this happened, which give us,” yeah, this. Um, it also does make me think though, they- they’ve got open data sets to be used. I’m like, “Ooh, how can I use some of that data? How can I maybe go and play with it?” Um, maybe in our academy because we’re, you know, issuing challenges, maybe this is a place to find some open data.

Thomas: Bates off to newyorkcity.gov dot

Kate: I get excited about the wrong things. All right, let’s keep going

Ben Wellington: I had a question, rush hour in New York City? I mean, it can be pretty bothersome, right? When is rush hour exactly? And I thought to myself, “Well, these cabs aren’t just numbers. These are GPS recorders driving around in our city streets recording each and every ride they take. There’s data there.” And I looked at that data, and I made a plot of the average speed of taxis in New York City throughout the day. you can see that from about midnight to around 5:18 in the morning, speed increases, and at that point, things turn around. And they get slower and slower and slower until about 8:35 in the morning when they end up at around 11 and a half miles per hour. The average taxi is going 11 and a half miles per hour on our city streets, and it turns out it stays that way for the entire day The entire day.

So I said to myself, “I guess there’s no rush hour in New York City, there’s just a rush day.” Makes sense, right? And this is important be- for a couple reasons. Like, if you’re a transportation planner, this might be pretty interesting to know. But if you wanna get somewhere quickly, you now know to set your alarm for 4:45 in the morning, you’re all set. York, right? But there’s a story behind this data. This data wasn’t just available, it turns out. It actually came from something called a Freedom of Information Law request, or a FOIL request. All right? This is a form you can find on the Taxi and Limousines Commission website.

Kate: again, I want to highlight here the order in which he presents this information. He gives us the, this is the result, this is why you should care, and then goes into this is how you get it. I think it would be very tempting for someone to start with, “Did you know you can submit a Freedom of Information request?

You can get this data, and this is what you can do with it.” And I will just say the way that he presents that timeline absolutely beautifully. He drip feeds the information in. The line is super simple. Super simple visualization, super powerful. I could follow it exactly

Thomas: and nice little call outs at the specific points he was talking about too

Kate: Yes. Yeah. So again, the structure of this goes against instinct but is way more powerful.

Ben Wellington: form, fill it out, and they will notify you. And a guy named Chris Wong did exactly that. Chris went down, and they told him, “Just bring a hard drive down, a brand-new hard drive. Bring it to our office. Leave it here for five hours. We’ll copy the data, and you can take it back.” And that’s where this data came from. Chris is the kinda guy that wants to make the data public, and so it ended up online for all to use, and that’s where this graph came from. And the fact that it exists is amazing, right? These GPS recorders, really cool. But the fact that we have citizens walking around with hard drives picking up data from city agencies to make it public.

Right, it was already kinda public. You could get to it. But it wasn’t pub- it was public, but it wasn’t public. And we can do better than that as a city, right? We don’t need our citizens walking around with hard drives. Now, every dataset’s hi- is behind the FOIL request, right? So here’s a map I made of the most dangerous intersections in New York City based on cyclist accidents. All right, so the red areas are more dangerous. And what it shows is first the East Side of Manhattan, especially in the lower, lower area of Manhattan, has more cyclist accidents. That might make sense ’cause there are more cyclists coming off the bridges over there. But there’s other hotspots worth studying, right?

There’s Williamsburg. There’s Roosevelt Avenue in Queens. And this is exactly the kinda data we need for Vision Zero, right? This is exactly what we’re looking for. And so, but there’s a story behind this data as well. This data didn’t just appear. How many of you guys know this logo? Yeah, I see some shakes.

Have you ever tried to copy and paste data out of a PDF and make sense of it? I see more shakes. More of you tried copy and pasting than knew the logo. I like that. Well, so what happened is the data that you just saw was actually on a PDF. In fact, hundreds and hundreds and hundreds of pages of PDF put out by our very own NYPD.

And in order to access it, you would either have to copy and paste for hundreds and hundreds of hours, or you could be John Krauss, ‘kay? John Krauss was like, “I’m not gonna copy and paste this data. I’m gonna write a program.” It’s called the NYPD Crash Data Band Aid. it goes to the NYPD’s website, and it would download PDFs.

Every day it would search. If it found a PDF, it would download it, and then it would run some, uh, PDF scraping program, and out would come the text. And it would go on the internet, and then people could make maps like that. And that’s, the fact that the data’s here, once again, the fact that we have access to every accident, by the way, is a row in this table.

Every single accident. You can imagine how many PDFs that is. The fact that we have access to that is great. But let’s ta-

Kate: Again, the same thing. He’s given us the problem, he’s made you want to know the answer, which is have you ever tried to take information from a PDF? And like my response to that was just like, “Oh my God, yes. I hate that. I hate having to do that.” And, and then he set up the… Like, like so it makes me invested in it, and then he talked about what actually happened.

The way that he is structuring this, it’s constantly happening. It’s like get me invested, then give me the, the part that he really wants me to know. It just, it’s happening constantly. He’s doing it so beautifully. Um, and I’m also very much in 2014 here. Like these are just kind of problems that aren’t really as much of a thing at the moment.

It’s just a, a bit of a marker in time

Thomas: Exactly what I was gonna say about, yeah, like 2014, the years before, we’re talking about building a web scraper. And think like, yeah, you know, in 2026, that is a step of analytics that AI’s now gonna do, right?

Which is scraping PDFs, putting into a spreadsheet, and even making, you know, like that heat map that we saw before, some kind of visualization. And I think, like, oh, that’s… I can appreciate why he’s talking about it, ’cause that was hours and hours and hours of work not that long ago. But what I really like is it now allows humans involved here, if, you know, l- now that we’ve got access to this data, whether it was a web scraper or it’s pulled by AI, it allows the humans to demonstrate curiosity.

What are the questions we can ask or answer about this? Like taxi information, we can use that to help answer the question: when is rush hour? And, you know, now we’re talking about the bike system. You’re right, it clearly dates it, but I think it also demonstrates where the tools we now have can accelerate the process and where now the humans are required in that process that the robots aren’t doing or aren’t doing yet

Ben Wellington: because then we’re having our citizens write PDF scrapers. It’s not the best use of our citizens’ time, and we as a city can do better than that. And the good news is, the good news is that Blasio administration actually released, recently released this data a few months ago, and so now we can actually have access to it.

But there’s a lot of data still entombed in PDF. For example, our crime data is still only available in PDF. And not just our crime data, our own city budget, Our city budget is only readable right now in PDF form. And it’s not just us that can’t analyze it. Our own legislators who vote for the budget also only get it in PDF. So our legislators cannot analyze the budget that they are voting for. And I think as a city, can do a little better than that as well. there’s a lot of data that’s not hidden in PDFs.

Thomas: Really nice example of raising the stakes, which is, hey, we all recognize that extracting data from a PDF is a bit of a pain you know, as citizens it wastes our time. Also, that’s exactly how the legislators are making decisions that affect us. I think it’s like, oh, this is actually a much bigger problem than a couple of data nerds not knowing where some bicycle accidents are. That, that is really clever and I think had you opened with, in a different universe, had he opened with, “Our legislators are trying to make decisions on a budget based on PDF,” everything that kinda comes after that is less of a stake, like lower stakes. Uh, again, the structure this guy uses is brilliant

Kate: Hmm. It really is, yeah

Ben Wellington: Of a map I made, and this is the dirtiest waterways in New York City. Now, how do I measure dirty? Well, it’s kind of a little weird, but I looked at the level of fecal coliform, uh, which is a measurement of fecal matter in each of our waterways. The larger the circle, dirtier the water. So you have the large circles here are dirty water, small circles are cleaner.

And what you see is inland waterways, this is all, uh, data that was sampled by the city over the last five years, and inland waterways are, in general, dirtier. That makes sense, right? And the bigger circles are dirtier. And I learned a few things from this. Number one, never swim in anything that ends in creek or canal. All right? But number two, I also found the dirtiest waterway in New York City by this measure. It’s one measure.

In Coney Island Creek, which is not the Coney Island you swim in luckily, it’s on the other side. Um, but Coney Island Creek, ninety four percent of samples taken over the last five years have had fecal levels so high that it would be against state law to swim in the water. And this is not the kind of fact that you-

Kate: I have to here just comment about the rate of his speech. It is so fast. I’m someone that listens to podcasts on, you know, almost double speed, and I am struggling to keep up with this guy. This is just played at one time speed, so

Thomas: This is, this is 1X speed in case you’re wondering. Yeah

Kate: Yeah. But what he’s demonstrating here i- is the insight that you can take from data, and he’s doing it so beautifully, so simply, so relatably, which is we’ve got all this data, let me give you two very specific takeaways. Because yes, it’s very interesting, like these are the cleanest waterways, but specifically here are two sentences to take out of this information, which is sorting through all of that data, which at the moment is a map is fine, but it’s a lot.

What can you do with it? You can take out these specific points

Ben Wellington: You’re not gonna see it there. But the fact that we can get to that data is awesome. But once again, it wasn’t super easy, ’cause this data was not on the open data portal. If you were to go to the open data portal, you’d see just a snippet of it, a year or a few months. It was actually on the Department of Environmental Protection’s website. And each one of these links is an Excel sheet, and each Excel sheet is different. Every heading is different. You copy, paste, reorganize, reorganize. And when you do, you can make maps, and that’s great, but once again, we can do better than that as a city.

We can normalize things, right? And we’re getting there, ’cause there’s this website that Socrata makes called the Open Data Portal in New York City. This is where 1,1100 data sets that don’t suffer from all those things I just told you live, and that number is growing. And that’s great, and you can download data in any format you want, be it CSV or PDF, if for some reason that’s what you want, or Excel document.

Whatever you want, you can download the data in that way. The problem is, once you do, find that each agency codes their addresses differently. So one is street name, intersecting street, street, borough, address, building, building, address. And so once again, you’re spending time, even when we have this portal, you’re spending time normalizing our address fields. And I think that’s not the best use of our citizens’ time, right? We can do better than that as a city. We can standardize our addresses. And if we do, we can get more maps like this. This is a map of fire hydrants in New York City, but not just any fire hydrant. These are the top 250 grossing fire hydrants in terms of parking tickets Right? So I learned a few things from this map. I, I really like this map. Number one, just don’t park on the Upper East Side. Just don’t. You’ll get a par- It doesn’t matter where you park, you will get a hydrant ticket. two, I found the two highest grossing, uh, hydrants in all of New York City, and they’re on the Lower East Side, and they were bringing in over $55,000 a year, a year, in parking tickets, that seemed a little strange to me when I noticed it.

So I did a little digging, and it turns out what you had is a hydrant and then something called a curb extension, which is like a seven-foot space to walk on, and then a parking spot. And so these cars came along, and the hydrant, “That’s all the way over there. I’m fine.” And they would … And there was actually a parking spot painted there beautifully for them. They would park there, and the NYPD disagreed with this designation and would ticket them. it wasn’t just me who found a parking ticket, right? This is the Google Street View car driving by, finding a same parking ticket. So I wrote about this on my blog, on I Want New York, and, uh, the DOT responded, and they said, “While the DOT has not received any complaints about this, uh, location, we will review the roadway markings and make any appropriate alterations.” And I thought to myself, you know, “Typical government response. All right.” And moved on with my life. But then, but then a few weeks later, something incredible happened. They repainted the spot. And for a second, I thought I saw the future of open data because think about what happened here. For five years, for five years, this spot was being ticketed. It, you know, it was confusing, and then a citizen found something. They told, uh, the city, and within a few weeks, the problem was fixed, right? It’s amazing

Kate: I love the foreshadowing of this. The comment about, “For a second I thought I saw the future of open data,” and I’m like, “Ah, this is what it could look like,” but it’s the foreshadowing of, like, something’s going to crash here. Like, there’s gonna be, there’s gonna be some kind of, like,

there’s gonna be some kind of smackdown here where everything gets reversed. Like, I’m just waiting for that now, and I’m, again, I’m invested. I love the way that he has set up almost every part of his talk to make me invested before he gets to his points

Thomas: Yeah. Such good storytelling

Kate: Mm.

Ben Wellington: And it’s not… A lot of people see open data as being a watchdog. It’s not. It’s about being a partner. And we can empower our citizens to be better partners for government, and it’s not that hard, right? All we need are a few changes. If you’re foiling data, if you’re seeing your data being foiled over and over again, let’s release it to the public.

That’s a sign that it should be made public. And if we’re going to release a PDF, if you’re a government agency releasing a PDF, let’s pass legislation that requires you to post it with the underlying data, ’cause that data’s coming from somewhere. I don’t know where, but it’s coming from somewhere, and you can release it with the PDF. And let’s adopt and share some open data standards. Let’s start with our addresses here in New York City. Let’s just start normalizing our addresses. Because you know what? New York is a leader in open data. Despite all this, we are absolutely a leader in open data. And if we start normalizing things and we set an open data standard, others will follow.

The state will follow, and maybe the federal government. And I know it’s crazy, but other countries could follow. And we’re not that far off from a time where you can write one program and map information from 100 countries. It’s not science fiction. We’re actually quite close. And by the way, who are we empowering with this, right?

‘Cause it’s not, it’s not just, uh, John Krauss, and it’s not just Chris Wong. There are hundreds of meetups going around in New York City, going on in New York City right now, active meetups. There are thousands of people attending these meetups, and these people are going after work and on weekends, and they’re attending these meetups to look at open data and make our city a better place.

Groups like who last week, just last week, released something called Citygram.nyc. That allows you to subscribe to complaints around your own home or around your office. You put in your address, and you get local complaints. And it’s not just the tech community that are after these things, right?

It’s, it’s urban planners, like the students I teach at Pratt. It’s policy advocates. It’s, it’s everyone. It’s citizens from a diverse set of backgrounds. And with some small, incremental changes, we can unlock the passion and the ability of our citizens to harness open data and make our city even better, whether it’s one dataset or one parking spot at a time. Thank you.

Kate: Hey, well, I was wrong. The smackdown did not come. We didn’t get some kind of, um, other opposing happening with data there. Um, but he did leave us with three really clear recommendations,

Thomas: Yeah

Kate: it. It’s these are the three things that need to happen to make our city and the world better. Really strong ending.

Um, I, I wouldn’t say it was the most interesting ending. I thought it was just maybe a little bit convoluted, but ultimately the essence I think is really strong

Thomas: He gave really good supporting evidence and story there for why those things should happen. And

Kate: Mm.

Thomas: writing here during, like, what is the message of this story? What… Uh, sorry, the message of this TED Talk, I should say. I think it’s in the realm of there is good data, but it’s hidden. And I think if that’s the message, I kinda think, so what?

Good data’s hidden. Um, but obviously we can pull good insights out of that if it is standardized, if it is accessible. So there’s a message there around, like, having standardized accessible data give good insights that make the life of citizens and residents better, I think is kind of

Kate: Mm. I thought he did really well at having that clear message of we can make New York and the world better. I thought that was actually quite clear

Thomas: Yeah, yeah. do wanna talk about the, his, the speed he talks at as well. Um, there’s kinda no doubt that he’s a New Yorker. He speaks quick and he speaks loud, and, uh, that rate is quite fast. So this is a 12-minute talk, just shy of 12 minutes. All I think about this is it would’ve benefited from just being a 15-minute talk, just a bit of space to slow down a little bit. Just add in some pause, ’cause there’s a lot goes on in this talk, and like you say, you’re absorbing a lot. It’s interesting. It’s engaging. Uh, it’d be nice just to have that, just a little bit of breathing space in. I dunno if, dunno if you think kinda the same?

Kate: Oh yeah, for sure. Like I said, I struggled to keep up and absorb all of the, especially b- because it was quite, um, big concepts. So yeah, I would’ve liked just… I would like for him to have left maybe something on the screen and just walk back and forward across the stage and just let me look at it and just let me, like, think about what he just said.

Just, yeah, like you said, a bit of breathing room

Thomas: classic we see, isn’t it? Somebody has made an excellent visualization. I’m thinking specifically about that, the two maps he had. One was the heat map of bicycle incidents, and the second one was the, um, the fecal matter in creeks with the, the different sized dots. And he’s made them, he’s looked at them a lot.

I haven’t. It’d be nice to put that on screen, yeah, and let me look at it. I wanna look at the pretty map for,

Kate: Yeah

Thomas: I wanna find, you know, places I know or maybe where I live, or have a little bit of a, just process it myself for just, yeah, those few moments. I think, think you’re bang on there

Kate: Hmm. Overall, my rating out of 10 for this one, oh, I’m gonna give it an eight

Thomas: That’s exactly what I was thinking, yeah.

Kate: Because the visualization was fine. His rate of speech was too fast and no breathing room. However, the setup of interest and then the answer that was just constant through the whole thing was, yeah, really, really effective. Kept me just wanting to know the next bit constantly

Thomas: Yeah. And the reason I give it that eight as well is he does a really good job of answering the, that so what question, and answering that upfront. Like you said about the… It was the creeks information again. It was, “Here’s two specific, uh, points I can pull out of this data,” rather than like, “There is the map.”

It was like, “Do not swim in anything that’s a canal,” and Coney Creek, I think it was. Like, answering that, so what do I care about this information for? There’s two arguably whimsical, um, takeaways. And then also constantly, like we said, it was the so what. Uh, when is rush hour? And then how do we get that information?

We always started with the so what. It just maintains interest and is really… It’s almost a, a, a it is a brilliant example of data storytelling

Kate: Yes, for sure. And I think that where people would do so well to emulate that and just giving that con- giving that answer straight up rather than feeling like they’ve got to go through all the background first

Thomas: Mm. And I’m sure there was heaps more background to it. He talked about the mayor who signed the legislation, and boom, this stuff was available. I’m sure that was a years-long annoying political legislative process, but it’s just dink, dink, here’s the context that matters. There was real wasted time, I guess

Kate: Yeah, for sure

Thomas: We’re giving that one an eight out of 10. Uh, that was Ben Wellington, how he found the worst place to park in New York City using big data. I’ll put a link down in the description in the show notes where you can go and watch that one, uh, without us interrupting it. Um, and as soon as it finished, I was getting recommended, uh, some of his other talks, which I am keen to go watch once we’ve finished recording. Uh, also you’ll find a link to the Influential Analyst Academy if you wanna think a little bit more about data storytelling, effective answering the so what, and hang out with Kate and I, then feel free to have a cheeky little look at it. Anything more from you today, Kate?

Kate: No, I’m happy. That was enjoyable.

Thomas: Mm-hmm.

Kate: Thank you so much for being with us today. Head to presentationboss.com.au/podcast where you’ll find, as Thomas said, all the resources and links from today

Thomas: And if you have a talk online, it doesn’t have to be a TED Talk, that you think we should break down on the podcast just like this one was recommended to us, flick us the link, podcast@presentationboss.com.au. We love a suggestion

Kate: For sure. And if you found value in today, please recommend this episode to a friend. Have a fantastic week