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Episode #34: Dominikus Baur
136 Plays9 years ago
This week, I’m pleased to welcome Dominikus Baur to the program. Mobile interaction designer and developer, Dominikus creates usable, aesthetic, and responsive visualizations for all platforms. His latest project, Subspotting, logged the cell phoe reception of New York City’s four...
The post Episode #34: Dominikus Baur appeared first on PolicyViz.
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Transcript
Introduction and Sponsorship
00:00:00
Speaker
This episode of the PolicyViz podcast is brought to you by Juice Analytics. Juice is the company behind Juicebox, a new kind of platform for presenting data. It's a platform designed to deliver easy-to-read, interactive data applications and dashboards. Juicebox turns your valuable analyses into a story for everyday decision makers. For more information on Juicebox or to schedule a demo, visit juiceanalytics.com.
00:00:35
Speaker
Welcome back
Meet the Host and Guest
00:00:36
Speaker
to the PolicyViz podcast. I'm your host, John Schwabisch. I'm here with Dominikus Bauer to talk about his new project, Subspotting, a very cool project about internet access, Wi-Fi access on the New York City subway system.
00:00:49
Speaker
Yeah, exactly. So thanks for coming. Why don't we start by having you just talk a little bit about yourself, sort of backgrounds, people sort of get a flavor of who you are and then we can talk about this project in particular. Sure, sounds good. Yeah, I'm Dominikus Bauer and I work as a data visualization consultant, engineer, self-employed. So I'm based in Munich and I've been doing that for a couple of years now. This whole database business before that I did a PhD in a related topic and
00:01:17
Speaker
Yeah, I've been working with a couple of big organizations and doing workshops, the usual. The usual data is lifestyle. So I want to
Exploring Subspotting: Purpose and Challenges
00:01:27
Speaker
talk about this new project just came out a couple weeks ago, sub spotting, tracking access on the New York City subway system. So can you talk a little bit about maybe what it does first and then we can talk maybe a little bit about the back end of it and what's going on behind the scenes.
00:01:41
Speaker
Okay, so sub-spotting is a project where we captured or kept track of all of the cell phone reception on the New York subway. So the New York subway is actually a huge system. It spans 660 miles of track, which is pretty amazing. And it still works reasonably well. And anybody who's ever been in New York has probably been on the subway as well. And one thing about the subway that's kind of annoying for New Yorkers, especially
00:02:11
Speaker
is that there is not cell phone reception everywhere. But even though there is no official cell phone reception, there are certain pockets of connectivity where you can actually catch a signal and then do your internet-related stuff. I suppose it's always very important what you have planned. Anyway, what we were interested in was how bad is the situation, really? So how much cell phone reception do you get?
00:02:40
Speaker
And first we were looking for an existing dataset of this, but unfortunately there was none available, so we decided to collect this data ourselves. And then after we had done that, and after a lot of subway riding obviously, we cleared the data and turned it into two posters and an iPhone app that you can use to keep track of where you can actually get a signal on the subway.
00:03:05
Speaker
So
Technology and Collaboration Behind Subspotting
00:03:06
Speaker
can you describe now what users do with the app? So do they use it to track their own usage or are they saying, oh, I'm at 59th Street and this is what I can expect? Yeah, the latter. So actually you can use it to access the data quickly on the go. So we have this mobile visualization that's running in this app and you can use it to quickly browse through all of the lines and see where you can send that important text, for example, on the line that you're currently on.
00:03:33
Speaker
And is the app collecting people's information and then ultimately feeding back to you that you can use to update or? Actually, that was our original plan. So we thought about first collecting an initial data set and then having the app kind of collect the rest and improve our data. But unfortunately, Apple wouldn't let us. So it's not possible for an app to get this information and still be sold on the app store. Right. And why New York?
00:04:02
Speaker
So I did this project together with Daniel Godemeyer and he's a designer based in Brooklyn and that this whole idea and this whole problem was really close to his heart. Also, it's interesting that New York is this massive metropolis, but one of the few really big cities in the world that has really good public transit system, but no cell phone reception there.
00:04:25
Speaker
Yeah. Even though the mayor announced that they would change that soon. So we'll see. We'll see. So in another couple of years, you could redo the project to see if that actually works. Yeah, that's a good idea. He says with a sigh, I have to redo it. So can you talk a little bit about the data issues you ran into? So I noticed in a couple of things on the site I've heard you speak about already, some aligns sort of overlap. They're on the same track. There's some above ground versus below ground. So what were those type of issues that you faced going with the data?
00:04:54
Speaker
Yeah, so since the subway system is so massive, we decided to be smart about writing it all and capturing all this data. So there are certain lines that go on the same track and the cell phones reception is of course identical in all of these lines. So our first step was to turn
00:05:12
Speaker
this data set that we collected and that was based on the tracks themselves and mapped that to the lines. So that was one thing. But then, of course, also we tried to make this data easier to understand and add some context to that. For example, there are certain subway lines that cross the river on a bridge, for example. And there you have really good reception. You can, of course, see that in the signals.
00:05:37
Speaker
But if you don't know that there's a bridge, then you don't really understand why the why the signal gets so good all of a sudden. So we added stuff like that. You can also find if you're overground or underground in the app and you also see the borrows, for example, when you're crossing from Manhattan to Brooklyn, for example. Right.
00:05:57
Speaker
So how many times did you, or I guess your partner? I wasn't there. How many times did they travel the lines? And were you trying to be consistent in terms of the timing? And presumably the bandwidth changes when you're at rush hour versus the middle of the day versus the middle of the night. Yeah, it definitely does. So since this was a relatively big undertaking first and also, I mean, full disclosure, he didn't write all of it.
00:06:23
Speaker
but we hired a TaskRabbit to do it. So this guy had a pretty relaxed job. I don't know how bad TaskRabbit jobs usually are, but he just had to ride the subway and press a button every single time they stopped at a station.
00:06:40
Speaker
Yeah, but we wrote each piece of track twice once in either direction and we wanted to make sure that there's no difference between these directions. So actually our first test was going on each line in either direction and then seeing if there's a correlation between the signals and it was extremely high.
Overcoming Data and Licensing Challenges
00:07:01
Speaker
So we were pretty confident that not only the signal was relatively constant but also
00:07:08
Speaker
that it didn't make a difference in which direction you were going, for example. And can you talk a little bit about the hardware? We're looking at the site right now, and there's the picture of the briefcase with the four iPhones. So how did you think about actually doing the hardware for the data collection?
00:07:23
Speaker
Well, the good thing was we didn't need any special hardware since it was about the reception on the phone anyway. We just went with regular iPhone 5S actually. And the way we got it was, Daniel went into the Apple store and bought five of these guys. Then we put them in that case. So what was sort of different about this project than some of the other projects you've done?
00:07:47
Speaker
So one major difference was that we didn't have a client. So this was just you having fun. Yeah, more or less. Well, fun is always relevant. Yeah, no, it was fun. It was actually, it was more of a performance piece, I guess, since, of course, the whole process was actually the product in a way. So we were really interested in what that was like to start from the start and go the full way to some
00:08:16
Speaker
Visualization products. So that was the major difference. I guess that we were self-funded and we're still.
00:08:23
Speaker
curious
Future Plans and Technical Considerations
00:08:24
Speaker
how this will turn out. And if you talk to the MTA or any of the providers like Verizon or T-Mobile get in touch and say, ah, you did this right or wrong or this is just cool. No, actually the only feedback we got from them was that I think Sprint's New York account tweeted our website and said, check out how great the Sprint network is.
00:08:51
Speaker
So that was pretty cool. Actually, I was pretty sure that at least one of them would sue us, but maybe they just haven't seen it yet. Who knows? But with the MTA, we are an officially licensed MTA product, so that should be good. And that was also really quick and straightforward process to get this licensing.
00:09:12
Speaker
Yeah, it should be good. So you have the app. Are there other pieces of the code or the development of the project that you wanted to open up in terms of the development of the app or the code or anything that you felt should be open? Or is this a sort of thing where this is sort of a performance piece and we put a ton of work into it and so we're going to hold on to this for now and maybe repeat it somewhere?
00:09:33
Speaker
That is definitely an idea. We also get a lot of feedback that people want to have that for their city. So lots of people from Washington, for example. Also some guy tweeted, you can do that for London real quick. It's all just black.
00:09:48
Speaker
Maybe not the nicest poster to have on your wall. That's true. That's true. Just a nice lump in that. But yeah, so we still have this massive data set that's in the background of that, 1.6 million data points. And we will probably make that available at some point. Again, since we're self-funded, we try to make some money at least, get some money back.
00:10:10
Speaker
We'll see how it goes. And do you see any potential of taking your data set and matching with other MTA ridership information to look at correlations or patterns over the course of the day or where people are riding or any of that sort of thing? Oh, that's an interesting idea. Yeah.
00:10:26
Speaker
I could imagine this project going into a couple of different directions. For example, if Apple would actually let us collect data with their devices, with the apps themselves, then you could see the fluctuation of the cell phone signal over the data would be super cool.
00:10:42
Speaker
Now, are there other mobile device platforms that would allow you to do that that Apple doesn't? Yeah, it would actually work with Android. But since since my background is more on iOS development, I figured that would be quicker to do like that. Also, a lot of people are asking us to do an Android version of the app, obviously. And we were talking about that.
00:11:03
Speaker
But the problem, of course, is we have collected this data using iPhones. So we can't really say how well this maps to Android devices. But the Android version is in the works. So we'll see when we release it.
00:11:20
Speaker
Now, I also am curious about the posters a little bit. So the posters, I presume, is kind of one way to sort of recoup a bunch of the costs. But a lot of your work is sort of tremendously awesome, interactive, immersive data visualization. So was it a real switch for you to think in a sort of a static way?
00:11:36
Speaker
Yeah, actually I never did posters before and also the posters were mostly the work of Daniel. But there was a cool process where we could combine the interactive stuff that I did with his illustrator abilities. So actually our workflow was we had this initial interactive data visualization that showed all of the data. So all 45 max of it.
00:12:00
Speaker
And that was first of all useful for debugging, but Daniel could also easily export the results of that as SVGs and then import them to Illustrator and then do these beautiful posters. So what
Trends in Personal Data Visualization
00:12:14
Speaker
about going forward in terms of other projects that folks may be doing with mobile and with wearables and other sorts of personal information?
00:12:21
Speaker
Do you see these sorts of projects, and I don't know what those projects are, but these sorts of projects where we're tracking and visualizing people's behavior as the thing that we're going to see more and more of in data visualization over the next year or two or five? Yeah, I'm pretty sure that's where we're going. I mean, the main question is how much data do we actually get as users and developers of these devices?
00:12:47
Speaker
Of course, companies like Apple and Google, they collect all this data and they probably collect much more than we know about. But they're very picky about who they're giving access to for this type of info. So if it would benefit their platform, their app store, for example, to have apps who would visualize this type of information, then they would give it out.
00:13:09
Speaker
Yeah, you never know. Sometimes they just want to have their own health kit or whatever and nothing else. Right, right. So we've been talking a bit lately about telling stories with data, visual stories, and we could argue about what that phrase means. But when it comes to a project like this, do you think that people instinctively understand what this, I mean, it's still data visualization.
00:13:34
Speaker
You think they instinctively understand what this is because it's still on the map and everyone has a phone and so they can sort of, you don't even need to write out what the story is, you can just see it and get it. I have the feeling that with an app or a poster like that, people will map their own stories to this data. So especially when they come from New York, they know these lines, they know these places.
00:13:57
Speaker
And maybe they even know where the cell phone reception is good. So the app could actually show them that they were right about their intuition when it came to cell phone reception. But I guess it could trigger a lot of memories of being on the subway on a certain spot. And I don't know, everyone's pulling out their phones and starting checking Facebook. As you cross the Manhattan Bridge, just like, oh, I have a couple seconds here. I can check my signal. Right.
00:14:23
Speaker
All right, good. So really
Conclusion and Sponsor Reminder
00:14:24
Speaker
interesting project. It's called Subspotting. I'll link to it on the show pages. Domenicus, thanks for taking the time out and coming on the show. Thank you. And thanks to everyone for listening. Let me know what you think of this project. Subspotting, the project that visualizes internet access on the New York City subway lines. And if you have comments or suggestions, please let me know on the website. So until next time, this has been the Policy Vis Podcast. Thanks a lot.
00:14:58
Speaker
This episode of the PolicyViz podcast is brought to you by Juice Analytics. For 10 years, Juice has been helping clients like Aetna, the Virginia Chamber of Commerce, Notre Dame University, and US News and World Report create beautiful, easy to understand visualizations. Be sure to learn more about Juicebox, a new kind of platform for presenting data at juiceanalytics.com. And be sure to check out their book, Data Fluency, now available on Amazon.