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Episode #161: Rob Simmon
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Robert Simmon is a data visualizer and designer working with Planet. He focuses on producing visualizations that are elegant and easily understandable, while accurately presenting the underlying data. He helped create some of NASA and Planet’s most widely-seen imagery, including...
The post Episode #161: Rob Simmon appeared first on PolicyViz.
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Transcript
Introduction to Rob Simmons and His Work
00:00:10
Speaker
Hi, everyone. Welcome back to the Policy Vis podcast. I'm your host, John Schwabisch. And on this week's episode, I get to chat with my good friend, Rob Simmons, who currently works at Planet out in California. Rob is a data visualization expert. He's a designer. He makes maps. He makes data vis. He works with satellites. He does a lot of work with color and accessibility. He basically does all the stuff that we all need to do and think about. And he gets to create cool visualizations about the world that is, you know,
00:00:39
Speaker
the stuff that we all like to look at, really.
Hockey Talk: Washington Capitals Fandom
00:00:42
Speaker
He's also the famous creator of the Blue Marble image that was on your first iPhone. Rob and I chatted a few weeks ago, so you'll notice how we start this conversation. We're talking about the caps, the Washington Capitals, that is, as we're getting into hockey season. Of course, had we done this interview a couple of days ago, we would have been talking about the Washington Nationals World Series run. So you'll hear a little bit of that, but then we get into all the good stuff that Rob works on.
00:01:08
Speaker
and all the good tools and techniques and strategies that he uses. And I've linked to those in the show notes at the bottom of the episode page, so I hope you'll be able to use them. And if you'd like to support the show, please consider sharing this episode with your friends and family.
00:01:23
Speaker
If you'd like to financially support the show, go over to my Patreon page. I've added a new tier that you can become a regular supporter of the show with some new goodies. So if you'd like to check that out, please click the link on the episode page and you can check that out. So anyways, on to my chat with Rob Simmons from
Life in Northern California
00:01:44
Speaker
Planet. Here we go. Hey, Rob. How's it going? Hi, John. It's going OK.
00:01:53
Speaker
Yeah. How's sunny Northern California? It is not sunny at all right now. It is fairly typical, somewhat cloudy morning. I live in the flatlands now, so we get a marine lair.
00:02:07
Speaker
Uh, pretty regularly. I'd say like five days out of seven that morning fog that rolls over. Yeah, it's, it's not fog. It's more like low level cloud deck. Um, and it'll probably burn off by noon or so and start getting more. Okay. So before we talk about your satellite imagery work and the date of his work, um, let's talk about hockey. So when this airs, we'll probably already be a couple of weeks into the, into the season, but we're nearing. So here's my first.
00:02:34
Speaker
And this might be the most important question I have for you today is, are you still a Caps fan or have you flipped now to like the Sharks or some other West Coast team? I am always going to be a Caps fan. All right. That's what I like to hear. Like this is never going to change. I mean, like I've tried to adopt the Sharks as a solid second team. Right. Okay. And even that's a challenge.
00:02:57
Speaker
So no, I am still all caps all the time. You know, last spring I was watching that second overtime. Yeah. It was obvious that one team had played an extra quarter season. Yes. The year before and the other team hadn't, you know, you could just tell like hurricanes were faster.
00:03:14
Speaker
Yeah, it's just it's tiring when you're doing that for that many months in a row. Yep. And then like a fraction of a second after the goal was scored,
Understanding Rob's Role at Planet
00:03:23
Speaker
I slammed my laptop closed. No hockey for like three or four weeks. And then kind of slowly like check japers rank once a week. And then
00:03:36
Speaker
every couple times a week and that's a couple times a day you know right what's going on training camp yep it's all it's all it's all good so now we're now we're good um what are our aspirations this year oh um just to hold off that inevitable falling off the cliff at least one more year
00:03:57
Speaker
Yeah, you're just waiting for it. Yeah, and and it's just like, is it this year? Um, right is this bathroom going to be reassigned? Or, you know, when does father time catch up with Ovi? Like at some point? Yeah.
00:04:12
Speaker
Um, but you know, it's, it's good. I mean, I still have optimism with this team. That's, that's the thing. I don't root for all the DC teams, but certainly in DC, it's like, you just kind of wait for the fall. And like, there's that sense of dread. I don't really have that with the cap so much until we get to the playoffs and broken through. I kind of feel like, you know, we've got, we've got that extra months of rest. So maybe again this year, you know, I thought that was going to be the case. Like they win the cup.
00:04:40
Speaker
I'm chill, I can just enjoy it. And that's more or less true for the regular season.
00:04:48
Speaker
We're talking playoffs and it's just like stress level is completely through the roof. And it's back to it. Yeah. So I thought I would grow up some and then it didn't. No. Sports fans never grow up. That's that's the beauty of being a fan. There you go. All right. So we've wasted a few minutes of people's time who do not care about hockey, but that's fine. They're just waiting to get to the good stuff. So let's get to the good stuff. Sure. How's work?
00:05:14
Speaker
Work is good. You know, Planet continues to mature as a company. We continue to launch more satellites and sort of evolve our product. And how many satellites do you guys have now? That's an interesting question. We have 15 high res satellites and we have about 100 operating of the smaller medium resolution satellites that provide the daily global coverage. And so it's this mixture of constellations and then another
00:05:43
Speaker
Five satellites that kind of continue a record that goes back to 2009, but those are reaching the end of their lifetime. Right. And when those reach the end of their lifetime, they come crashing back into into Earth or they just sort of continue floating. So they're small enough that they will probably continue orbiting.
00:06:04
Speaker
But the idea is you put them into a situation where they will deorbit as fast as possible. And then they'll burn up on reentry. Right, on reentry. Do you maybe want to tell folks a little bit about your day-to-day? I'm sure folks are familiar with the work that you do and the satellite images that you put out and your Twitter feed, which is amazing.
00:06:27
Speaker
Yeah, maybe talk about the day-to-day and what it is you do at Planet and the data vis work that you do on top of the images. Sure. So I guess I'll give a little bit of an intro to Planet. We are a satellite and analytics company based in San Francisco. We design, build our own satellites, hand them off for somebody else to launch. And then as soon as they are released into orbit, take control,
00:06:54
Speaker
start commissioning them, and then download, process, and distribute the data. And we are also on top of what could be considered to be the base data. We're also building analytics. And so it's not just imagery. It's not just multispectral data from different bands, including infrared. It's also this
00:07:17
Speaker
Second layer of information like roads and buildings and ship detection are the three things that we've launched commercially so far. And so my role there is database. I working with my colleague, Leanne Abraham.
00:07:34
Speaker
And we take the information and then try to communicate it in a way that's as clear as possible. So the basic role of data is take numbers and turn it into pictures, but really with this emphasis of how do you communicate the information behind it. And so some of that is purely taking the imagery and then processing it into something that looks like what a person would think the Earth's surface looks like.
00:08:02
Speaker
So it's not like an exact photograph because you have things like atmospheric distortion and a little bit of color shifting from that. So what you do is you kind of try to make it some blend of what it really looks like to an astronaut from orbit versus our own perception and experience of seeing things from ground level. And so that's important just so people can look at these images and then have a good chance of being able to read them.
00:08:32
Speaker
So what does that entail to do that? So you take that image that comes in and it doesn't look familiar to, you know, me, for example. What do you have to physically do to the date or to the image to make it look like something that I would recognize?
00:08:48
Speaker
Sure. So the data that's coming down is data. So it's one of those things where there's actually a very fuzzy line between like what's a picture and what's a data set. But these are calibrated data sets. So we know how much energy essentially is reaching the sensor from the top of the atmosphere. And that's coming down as a linear measurement.
00:09:13
Speaker
Our senses, including our vision, tend to be very non-linear. So sort of the first step is to non-linearize that. In sort of semi-technical terms, you can say it's applying a gamma correction. It's the same thing if you pull something in in Photoshop and adjust that gamma curve. And so basically you see bigger differences in darker areas of an image and smaller differences in brighter areas of an image. And so you're accounting for that difference.
00:09:42
Speaker
And then the slices of spectrum that the satellite is picking up on are different than sort of these broad swaths of spectrum that our eyes see. And so you're trying to adjust the red, green, and blue components of that to match
00:09:59
Speaker
sort of a more natural response and then taking out some of the atmosphere. So if you notice, I mean everybody knows this, you look up the sky's blue, that same blueness is actually captured by satellites and it really interferes with sort of the fidelity of the image again from that perspective of what do things look like to us on the ground.
The Art of Data Communication in Satellite Imagery
00:10:20
Speaker
Yeah. And so adjusting for that. And in some ways that's actually the most challenging because the atmosphere is very inconsistent. And so if I'm doing the desert over Riyadh, that's a completely different problem than Hawaii or like a smoky image over the Amazon rainforest or Indonesian Borneo. And presumably when I'm looking at an image that you have worked on versus an image that
00:10:46
Speaker
some other company or government agency or NASA has worked on. It's completely feasible, right, that those two images would look different based on the work that you and whoever your counterpart is, is doing on those, right? Yeah. So there's really kind of a couple different approaches. So first of all, everybody, NASA, Maxar, Planet,
00:11:08
Speaker
we're all generating far too much imagery, like far, far, far, several orders of magnitude of image imagery for like a person to process it, right? And so there's automated techniques of doing this work. And so basically, it's the same thing that's happening when you're taking a camera, and the camera is basically making a whole bunch of decisions. This is a digital camera, obviously, about sort of how to process the image. Like, you know, you can like pick different white balances and things like that. But
00:11:37
Speaker
In most cases, that's all happening automatically, and you can do that. Or you can take the approach of we're imaging the Earth from space, and then we should try to process everything consistently. And so one approach sort of gives you a better image. Each individual image stands on its own with a handful of exceptions.
00:11:59
Speaker
And the other approach gives you consistency so that you can stack a whole bunch of images in time. And then they're all going to look smooth and look even. So when a person comes in, you kind of can get the best of both worlds at the expense of the time and effort and expertise. And my dog hears me talking and is getting very upset. So she's coming in and squeaking.
00:12:28
Speaker
So I'm gonna pet her and hope that she stays quiet. Anyhow, let me restart a little bit of that.
00:12:36
Speaker
Okay, so if you obviously you can't have a person processing all of this imagery and so You have the two different automated approaches but sometimes for like very deep analysis for marketing purposes For getting things out to journalists. You might want to have somebody do
00:12:58
Speaker
a hand touch job. And so the advantage of that is that you can really optimize the image to show a particular feature, a particular area, and you can get a series of images that are both show what you want to show and are consistent over time or over space. And that's really, really difficult to do in an automated way. You can do it a little bit
00:13:23
Speaker
We have a mosaicking team, and obviously, if you look at Google Maps, that's almost entirely an automated process. But if you really zoom in to certain areas, you can see where that starts to break down. And so this problem of turning a very big, very heterogeneous Earth into a consistent look and feel, especially when you throw in the time dimension, is really hard to fix.
00:13:53
Speaker
And so I work both on that hand-touched and then also work with the software engineers to try to figure out the best ways to do this on a consistent basis.
00:14:03
Speaker
No, it's interesting the way you describe what you do and the fact that you are, you know, you sort of start by saying, um, uh, you know, I do data visualization. And I think, I think probably a lot of people, when they think about making maps as someone who visualizes data, they think about, you know, more of like a corpus map, right? You know, they're adding dots or circles or squares or lines or something to a map. And maybe it's just me, but you have a totally different take on.
00:14:31
Speaker
or I guess, tasks to do with maps and I think a lot of us do. When you're visualizing data, it's different than a lot of us are doing it, right? A lot of us are putting data on some sort of map, whereas for you, the map is the data. That's a fair way of describing it, at least for the majority of my work. I mean, I certainly do a certain amount of what would be considered more traditional or pure data visualization, both
00:14:59
Speaker
cartography, maps, thematic maps, graphs and charts types of things. And in fact, what I'm most interested in is blending things like that together. So take
00:15:10
Speaker
satellite imagery and then how do you make it more like what we think of as a traditional map. So I kind of have this discussion with my colleague, Lian, all the time, who is professionally trained as a cartographer, where I will say I want to make the satellite image more mappy
00:15:32
Speaker
And she'll be like, well, it is a map. So you're not making it more mapping. And what I mean by that is that I'm trying to sort of abstract and distill the information in the image to convey a certain message or to communicate an important point. If you look at just a satellite image, a lot of the time, basically, it just has far too much information.
00:15:59
Speaker
you know, if you're trying to show a road network, you know, and you also have buildings and trees and rivers and reservoirs and clouds and contrails and all the other stuff that comes with taking a straight up image.
00:16:12
Speaker
So if you start with a satellite imagery image, there's just all of this extra information. And you need to sort of pull out the relevant details or remove the extraneous details. And so that becomes super important when we're trying to do things like show off planets analytic products.
00:16:34
Speaker
and so We have these road networks. We have these building detections. So how do you sort of display the context that's around them because you might be looking at just pure roads and buildings and Without knowing things like where are the hills? Where's the water? Where are the parks, you know that this information really isn't particularly useful and informative and so it's it's that blending of
00:17:03
Speaker
a derived or extracted dataset with the more photo-like data. And how do you strike that balance? Again, how do you automate it? How do you make sure that you're doing this globally? Because it's a really different problem if you're doing it in DC or if you're doing it in Cairo, just because the surrounding landscape is so different.
00:17:25
Speaker
So different, yeah. And how do you try to convey the essence of a place through just visually? Right. And so that's another thing that sort of has been preoccupying me lately. Right. Yeah, that's really interesting. I also wonder when you are working either with colleagues at Planet or other folks when you're working with them, how they view the work that you do. I mean, I know a lot of people who are in
00:17:54
Speaker
Let's say closer to the data side of data visualization, maybe they're not making custom infographics and they're not designers by training, right? They're data scientists or what have you by training. So I wonder when you're talking to either, you know, folks that you work with or other people or whoever, do they view this work as
00:18:13
Speaker
design, do they view it as data visualization? How do they approach it? I have this thing when I hear people say, can you make this graph pop or, you know, make it look better, make it look pretty, that like, I feel like a designer loses their wings every time they hear something like that. So, like, how do people, like, how do people view your job in this role? Like, is it design? Is it data? Like, how do they approach you and sort of communicate what they want you to do?
00:18:40
Speaker
I think it's really mixed. Certainly, you know, there are times when people approach it as what I'm doing is a purely aesthetic task. Rob makes the images look pretty. And that's true, but in my opinion, that's really only a small amount of the story. Really what I'm trying to do is improve the communication.
00:19:07
Speaker
You know, I am working for Planet. Before that, I worked for NASA. And my role was to make sure that the people with a message, the scientists, the engineers, the communications team, that we convey that message in the best way possible.
00:19:26
Speaker
And the decisions I'm making are trying to make sure that that comes through loud and clear. And part of that is to make things, quote unquote, look good. But we also know that a lot of the rules in data visualization or guidelines, it's very hard to say that there are hard and fast rules in data. There's obviously a handful. But we follow these guidelines to make good design decisions
00:19:57
Speaker
And they kind of have a side effect of making things look more aesthetically pleasing, but it's not the main goal. So like a lot of the reduction of extraneous information, a lot of good choices and colors. Yes, they have an aesthetic impact, but they also focus you in on the data. They don't distract you. They make sure that you can differentiate values from one another and that your perception of that difference is accurate. And so that's really my main goal.
00:20:25
Speaker
Yeah, that's a really interesting way to think about it, right? It's it is an aesthetic approach or aesthetic decision, but you're coming at it from a different perspective. It's not coming at it from let's make this thing look pretty. Let's make this thing better. And that's how we get to sort of this end goal, where where others review it as an aesthetic change, but that's really not how we approached it. Yeah, I mean, there's a
00:20:49
Speaker
quote that I have seen floating around every once in a while, that's, I'm a designer, not an effing screwdriver. So like, yeah, I'm not just doing this one kind of simple thing. As a field, we're bringing a wealth of experience, a wealth of knowledge, a lot of it based on research to try to improve the transmission of information from one person to another. Yeah. Yeah, really interesting.
00:21:16
Speaker
Um, what else do you get to create when you're there? I obviously wait for your tweets to come out with cool satellite imagery, but like what other stuff do you get to create with all the, I mean, you've getting a lot of data that's pouring through. So do you get to create other things and toy around a little bit?
Mapping Diverse Terrains
00:21:33
Speaker
So yeah, definitely. I've been talking about these analytics products. And so like one thing that's been coming out more and more recently as examples of these and sort of how do you convey the fact that we can now run roads and building detection across the whole world?
00:21:53
Speaker
And again, I sort of touched on this that sometimes it's difficult to show the same information within completely different environments. And so like, you know, trying to optimize that process. So, you know, we take our data set. We take the abstractions on top of that.
00:22:14
Speaker
And so you have a road layer, you have a building layer, you have our imagery. And then what else can we pull in? So can we pull in a digital elevation model? So basically a terrain map with elevation described as a number, you know, meters or feet or whatever. And if we add some hill shading, you know, does that provide context? Well, in North Korea, that's a great technique.
00:22:38
Speaker
Again, in Cairo, which is pretty flat, it doesn't work so well, or anywhere where there's little terrain like the American Midwest. You don't really get much out of that. Okay, so we take that and we blend it in. Do we bring in information from open street maps and start plotting in riverways and coastlines and
00:23:01
Speaker
municipal boundaries and things like that. And so like, how much extra information do you need to use to make your core information make sense? And so this is in many ways, this is like a conventional cartographic challenge. And so, you know, I get to work with a team that has both some great engineering capabilities, some great aesthetic choices, and then, you know, a formal training.
00:23:29
Speaker
in cartography and I've learned a ton from Lian, which is awesome. And sort of like, you know, just how do you evolve this over time to both use for marketing and commercial and just like sort of advertising purposes, like, hey, this is what we're doing and isn't it cool? But then, you know, when we start to build the tools that allow our everyday users to work with this data, you know, how do you make sure that that tool is giving people the best experience possible? Yeah, right. Right, right, right.
00:23:59
Speaker
Let me ask one more question. What's the hardest terrain or area of the world that you've had to work with? Like what's the most difficult thing you've had to try to address or make?
00:24:13
Speaker
The glib answer is anywhere I haven't been. It's definitely like- So planet just needs to send you more places. That's right. Yeah. Right. So I find it much, much easier to work with a dataset of places that I'm familiar with. So like Bay Area, yeah, I can nail that. DC, DC. Yeah, DC. And like, you know, just places I visited, places I'm familiar with.
00:24:44
Speaker
And she does she's frustrated because the the under the bed is kind of filled with boxes. And I'm talking and nobody else's home room right here. And her brother probably stole her breakfast. So um, so right any place I've been or
00:25:14
Speaker
any place that has like kind of clearly familiar landmarks in it. So like if there's snow or a cloud, you know, that's a really good starting point. She used to like snow. She's complaining now. Bye bye. Come here. Okay, I bribed her with some chicken.
00:25:41
Speaker
We'll see how that works. OK, so challenging areas. OK, so easiest are places I've been. Next easiest are places that look like places I've been. So like pretty much any temperate climate with hills and woods looks like the Northeast. Sure, I can do that.
00:26:10
Speaker
I think the most challenging are deserts and that tends to be because they're very low contrast and they don't necessarily have any black or any dark areas at all in the image. And so then you end up with this really interesting challenge of you want to make an image that has contrast in it so that it's appealing so that you can actually see features.
00:26:39
Speaker
But you don't want to make it look like something it's not. And that actually, I find a lot of the time, if you create an automated algorithm to do color correction, you end up with blue deserts. And that's just because the algorithm wants there to be something white. And the image itself is actually very red or very brown. And so it's like adding in blue to try to make it whiter.
00:27:08
Speaker
and you end up with this kind of alien, possibly snow-covered look. And if you throw in like some human habitation, some vegetation, you know,
00:27:25
Speaker
Logically, it seems like that would make it easier, but I think the character of the vegetation is very different from what we consider like, you know, DC summer deciduous trees, which are like super rich green, super dark.
00:27:42
Speaker
Middle Eastern vegetation, I think, is much more like Chaparral in the California, coastal California hills. So it's lighter, it's browner.
Technical Challenges in Satellite Imagery Processing
00:27:53
Speaker
And trying to balance that plus the surface desert itself without having any real strong landmarks or features to work against makes it a little bit challenging. Interesting.
00:28:09
Speaker
And also, I've never been to the Middle East. So I don't really know what my experience would be being on the ground there. And so that's why I think I found it difficult.
00:28:23
Speaker
Yeah. Well, it'll be interesting to see like the, the Rob Simmons evaluation, right? Would be interesting to make you do some maps now of Cairo and then send you to Cairo for a couple of weeks and then, uh, get you some new imagery of Cairo and make you redo those and see if, see how different they look. Yeah. I endorsed this project, John.
00:28:42
Speaker
Good luck doing that. So we have a goal. We're going to have a Kickstarter and we're going to get Rob to Cairo. There we go. So the next cartography conference that comes up, please locate somewhere in the Middle East and we're going to have a Rob Simmons evaluation. It's going to be great. That sounds like fun.
00:28:59
Speaker
One last thing before we go. I wanted to ask about tools because I'm guessing there's not a lot of Tableau, certainly not Excel being used in your day to day. So you've mentioned Photoshop a bit, but what is the suite of tools that you're using to do your day to day?
00:29:16
Speaker
Sure. So I end up using sort of a blend of commercial tools, and that is commercial photo editing, commercial GIS, and then non-commercial both graphical user interface and command line tools. And so I think I spend most of my time in Photoshop and a plugin called geographic imager.
00:29:39
Speaker
And what that does is it allows Photoshop to maintain the geographic information that's in a satellite image or a map. And so like if you're familiar with the geotiff, it can read geotiffs, it can stitch geotiffs, it can reproject geotiffs. And then when you save an image, it preserves all of that information. And so that means when I work on an image and I export it, you can then bring it in to some other type of map making software and overlay things, or you can compare it to data.
00:30:09
Speaker
or do analysis without destroying all of that information. I assume that some of the file sizes here can get pretty huge. That just requires a little bit of patience. It requires a lot of RAM and doing things like getting SSD drives specifically to act as a cache.
00:30:36
Speaker
I had some interesting times a week or so ago where I was working with an image that was large enough that it was over the four gigabyte TIFF file size. And so I had to sort of jump through some hoops to manage to save the image that I wanted and to keep all the information in the GeoTIFF.
00:31:00
Speaker
And so one thing to remember with satellite imagery is, generally speaking, it starts at 16 bits per channel, not eight bits per channel. So right off the bat, it's twice as big as a normal image for the same dimensions.
00:31:17
Speaker
And then oftentimes there's additional channels. So you have red, green, blue, and near infrared. So that's another 25% file size. So you sort of learn the tricks and try to make as much of your process repeatable so that if something does go wrong that you can then go back and recover it without starting over from scratch. Although I have had to do that sometimes.
00:31:45
Speaker
But yeah, generally speaking, fast computers, a lot of RAM, a lot of disk space. We've actually been using Google Drive a lot lately, which has some magic caching, which seems to be working pretty well for getting files between different people. So that's definitely part of the challenge. And then compression.
00:32:09
Speaker
like make sure you're saving the images with what's called LZW compression, which is more efficient at the expense of slower to save, but can help alleviate some of these problems later on.
00:32:22
Speaker
Then let's see, also oftentimes use Illustrator with another Avenza plugin called Map Publisher. And it's basically the same thing for Illustrator that geographic imagery is for Photoshop. And so it allows you to bring in vector data sets. So shape files, GeoJSON, things like that, and then combine them with the imagery itself and keep all of the alignment, all of the scale, all of that stuff.
00:32:49
Speaker
And you can crop and you can select based on attributes. So I can like select all the roads or select all the rivers and things like that. So it's basically a mini GIS inside Illustrator. And for like producing high-end output, it's fantastic. Then on sort of the free and open source side, I use 2GIS, which is at this point pretty polished, certainly extremely powerful.
00:33:17
Speaker
tool for doing a lot of what we've been talking about, but within the context of free software.
Future Plans and Podcast Wrap-Up
00:33:26
Speaker
And then that's built on something called GDAL, which is a library for working with geographic data. And so I wrote a series of posts
00:33:35
Speaker
called the Gentle Introduction to GDAL, which basically walks through why you'd want to use it, how you use it to perform basic tasks, and then give some examples and sample data sets and things like that, which is something that I still have ideas for additional posts and really need to follow up on those. So stay tuned. I guess I'm committing myself. I'll link to the ones that you have. To a certain extent. And then I've also written about processing satellite data in Photoshop and in QGIS.
00:34:03
Speaker
so that you don't have to have the outweigh. Because some of these tools can be pretty expensive, especially to maintain them. Yeah, right. I'm sure. I'm sure. Cool. Great, man. Well, I'll put this on the site. And I'll look forward to seeing some more of your cool images. And hopefully, maybe we'll even hook up this next few months so we can go see a game together. That would be great. All right.
00:34:27
Speaker
Did you play the Sharks every once in a while? They do. Yeah, they do. They do. I was I was looking a few days ago. So I'll have to time my trip to California appropriately. Sounds like a good plan. Thanks, Rob. This was fun. Right. You're welcome. Bye.
00:34:43
Speaker
Thanks to everyone for tuning into this week's episode. I hope you enjoyed that. I hope you enjoyed the conversation. And I hope you'll keep tuning in every other week to the show. I've got some great episodes coming up for the rest of 2019. And if you'd like to support the show, please again, consider sharing it with your networks. Please consider leaving a review on iTunes or Stitcher or your favorite podcast provider. And if you'd like to financially support the show, please head over to my Patreon page where I now have a new tier, including one where you can get your own
00:35:13
Speaker
set of data visualization postcards just for a low monthly donation to help support the show. So until next time, this has been the policy of this podcast. Thanks so much for listening.