Next-level spatial mapping in Tableau — and spatial calcs arrive in Prep

0:01 Tore Levinsen: Um, if you have any questions, please use the Q&A. There's gonna be a lot of, uh, interesting topics presented and, uh, the speakers are n- not necessarily watching the Q&A, so we, the co- the co-chairs will do that, and then we will- we'll ask those questions to our speakers, um, in the, in the end.

0:22 So who are here today? Well, we have three speakers.

0:27 First, we have two, uh… it's allowed to say GOATs, right? You know, greatest of all time, and we have a Hall of Fame visionary that was just, uh, celebrated as at, at, at his local TUG as w- as well. Um, uh, I've met these guys, uh, several times. I've seen them, them present at TC, uh, so I'm really looking forward to l- uh, listening to Don and, and Jim, um, both from, from the States, that will talk about next level spatial mapping and some spa- uh, spatial functions that you can use to, to, uh, to clean up and Prep your data before you visualize it.

1:09 And we also have Darin, uh, we haven't met in person yet, but there's always hope, uh, that that can happen, um, from, uh, senior product manager at Tableau that will focus on my favorite tool, I, I have to say my favorite tool in the Tableau stack, which is Tableau Prep, and spatial s- spatial functions in there.

1:32 I mean, I love my Desktop and Cloud and everything else, but, I mean, give me, give me a challenge and g- put me in front of Prep and I can spend hours and hours, uh, there. Then we'll do the Q&A, uh, presentation, and close up, and hope- hopefully we're getting people signing up, uh, to present next time. So these handsome guys, uh, meaning when I say handsome guys, we also would like to have, uh, some hands…

2:01 well, ladies to present as well. We've had before, and we would like to have that as well in the future, so please remember, give us a shout-out if you would like to present something.

2:12 Don, owner, principal at DC Wise, or s- semi-retired, wasn't it, I saw somewhere, right? Ex

2:19 Don Wise: Yeah, I'm retired. Retired from, uh-

2:22 Tore Levinsen: I-

2:22 Don Wise: … the fire department.

2:23 Tore Levinsen: Yeah. Thank you for your service. I guess it's allowed to say that when you do, like, a proper job and not sit in front of a computer like the rest of us.

2:36 Or J- uh, Jim, I think you were standing, but still, we're still in front of a computer, right?

2:42 And then we have Jim, which is now a Tableau Visionary Hall of Fame, that I'm not, I'm not sure how many times we've met, but, uh, I think that I can call him a friend when I, I managed to ask him to ask his wife to do some shopping for me so that I could pick up s- up some Tableau swag when I came to the States, so I can save money on customs and taxes.

3:05 Jim Dehner: Wait, wait, wait. Before I get inundated, I would never ask my wife to do shopping. Okay?

3:12 What I asked my wife to do, I did the shopping, but my wife is the chief financial officer, and she, she dealt with the payments there.

3:22 Tore Levinsen: That's good. Thank you.

3:25 So I'm gonna, um, talk, talk about you later, Da- Darin, when we get, get to you, right? So I'm gonna stop sharing.

3:33 Most likely you're…

3:36 you've had enough of me talking now, so I'm gonna hand it over to you, Jim. Hopefully you have everything up and running.

3:42 You don't have to do a reboot again.

3:44 Jim Dehner: First of all, my apologies. For some reason my camera won't work today, and it just created a mess for everybody, and, uh, I'm sorry I took up so much time.

3:53 Uh, Don and I, uh, are going to, uh, reprise the presentation that we made at TC'26 this year.

4:02 Uh, and that was on, uh, on spatial mapping.

4:06 Don, why don't you, uh, introduce yourself, give everybody an idea of your background.

4:12 Don Wise: Good morning, everybody. Uh, Don Wise. Uh, I am a Tableau Ambassador. Uh, it, it appears is that seven times now? I found the number, Jim. I thought it was three or four times, but apparently I've been an ambassador for at least seven years. Uh, retired from the, uh, Glendale Fire Department in Glendale, California, where I ran 911 operations for 14 fire departments, and I can tell you they kept me quite busy.

4:34 Um, and as a result of all that data, we, uh, chose Tableau as our, uh, data analytics, uh, software, uh, package, and the rest is history. Uh, when I retired, uh, just kept going with the data analytics and was asked to be an ambassador, and here we are today.

4:51 Jim Dehner: Okay. And my name's Jim Dehner. I live in Nashville, Tennessee. Uh, as Tore indicated, I am, uh, I am a, uh, Tableau Visionary, and yes, I'm grateful to have, uh, been entered into the Hall o- Hall of Fame this year. Uh, Don and I spend a lot of time out on the forum, uh, and if any of you ever had a question out on the forum, uh, particularly if you had a question on mapping, Don probably answered it.

5:13 Uh, other than that, uh, y- we may have met out on, on, on the forum.

5:18 Don, you wanna take 'em through the agenda for today?

5:21 Don Wise: Sure. So, uh, Jim is gonna start us off with, uh, mapping foundations, uh, and a little background on what, uh, the different map types are and things of that nature, and discuss briefly map layers and some real world ca- use cases. I'm gonna lead into spatial files and actions, uh, and go f- through some, uh, several examples of different types of spatial files and actions.

5:44 And then, uh, Jim is hopefully gonna have some time to get into XY plots and using map layers, and then hopefully we'll have some time for questions.

5:52 Jim Dehner: Okay. Thanks, Don. Uh, first I wanna make a distinction between geospatial mapping and spatial mapping. Now, we've all used geospatial mapping. You've probably had a file, you've loaded it, uh, loaded it into your, uh- Your system there, and you've had a dimension, something like a state or a county or a postal code, and you can just drag that to the, uh, to the canvas, and it creates a map because Tableau understands the geometry for certain dimensions.

6:20 Uh, it generates its own longs and lats for those dimensions, and it can plot those dimensions. Now, the downside is those, uh, dimensions cannot be used, uh, in calculations.

6:32 Well, what we're gonna talk about today is spatial mapping, and, uh, spatial mapping is done from spatial files, and those are files that you download from the internet. Uh, they can come in a variety of formats. It might be a shapefile or a KLM file or a GeoJSON file, just, just to mention a few, and they are related to a spatial shape, and they can be a point, a line, a multiline, a polygon, a variety of shapes out there, but they're, uh, but each record is associated with a shape.

7:01 And now all of those can be used in a, uh, in spatial functions, and we're gonna, we're gonna show you some of those today.

7:07 I wanna spend just a minute and go back to this idea of geospatial mapping, and I'm go- I'm gonna use this file for it.

7:15 Uh, I have a file out here that has all of the windmill locations, uh, in the United States, and in this file, I've got two fields. One field is the state field, and you can see it's got a little globe here for an icon, and that means it's a, it's a geo field, or it's a geography field. But I also need the county, which right now is a text, uh, it's, uh, input as text, but I need that as a geo field.

7:41 So what I'm going to do is I'm gonna right-click on it, and I'm gonna come down to Geographic Role, and I can choose what it, uh, what that field represents. And these are the defaults that are out there that, uh, uh, load automatically and will create their own long and lats. And down here is County, and I'm gonna just select County, and now that field is a geo field.

8:03 I can go out to my canvas, and we can take State, and we can drag it onto the canvas, and when we go, we get a, uh, a view of all the states in the United States and those that are identified have a one, uh, it, uh, yeah, at least one wind turbine in them.

8:29 And now I can open that up, and I can get down to the county level, and we can get an idea of where these wind turbines might be located. I'm also gonna drag a field on here where I've just simply counted the number, and we can begin to take a look at a density map, and maybe that'll give us some more information.

8:47 We take a look at a density map, and we begin to get a sense of, geez, there's a lot of these in the Midwest.

8:53 And if we wanted to clean that up a little bit to make it a little bit more impactful, we could take and open up the map backgrounds up here off of Maps, and we can take a look at a satellite background, and now we're getting some impact, uh, in, uh, uh, in the visual.

9:07 Well, that's pretty cool.

9:09 We could plot that in a different way. We could take that same data, and instead of plotting it as circles on a map, we could use a field map.

9:18 And we're gonna select Field Map here.

9:20 And now we can see, uh, each county, and we have a, a, a reference to the number of, uh, windmills in e- uh, in each one of those counties.

9:28 Well, the point is that you can get a lot of information, you can do a lot of mapping off of a geo file. You don't need to use a spatial file. But you're gonna find occasions where you do need a spatial file, and, uh, we're gonna spend the rest of the day talking about those.

9:43 And probably a really good place to start is, well, where do you get this data? When we talk about spatial files, where do you get the data?

9:50 Well, uh, personally, I like to use data.gov. Uh, there's a wealth of data out there, but you can Kaggle it, or, or you can go out to Google and, uh, just simply search for downloadable, uh, downloadable spatial file and, uh, put some reference in there, and you can download the data.

10:07 When you get that data, it's going to, it's gonna arrive in a zip, uh, folder, and you have to unpack that zip folder. You have to extract the data out of it.

10:21 And when you do that, you're gonna have a, a file, maybe something like this KLM file or a shapefile, but you might also have other files in that same folder. Now, you have to keep those files. Do not delete those files.

10:34 Uh, the file that you'll actually load into Tableau will be the KLM file or the, or the shapefile.

10:41 And, uh, then you can use it to create a map. Now, the other thing we talked about is we talked about spatial functions. Well, what, what are these spatial functions that we talk about?

10:50 Well, we're all familiar with, uh, the calculation wizard and using functions in the cal- uh, in the calculation wizard, uh, to do a, a lot of our calculations.

10:59 Well, there's a heading there for Spatial, and it lists a wide variety of spatial functions, and we're gonna show you most of these this afternoon. I like to look at these spatial functions in four groups. There are those that can be used to create a point or create a line.

11:18 There are others that you can use to create a geometry, like maybe you want a buffer that goes around a point or a buffer that goes around a line that you've got, or you want to find the intersection between two spatial shapes, or you want to put an outline around them like a border, and maybe you want to group some together so that they're, uh, they're a single shape.

11:36 There's still other functions that just return a number. It's like the area of the shape or the length of the line or the distance between two points. And then there's some that we use out there to validate that, yeah, that really is a spatial shape and the type of shape that it is, or, or do two shapes intersect?

11:52 Well, I'm gonna start by using this file, and it's a spatial file. And you can see out here I've got these two f- uh, these, uh…

12:01 Two fields. Uh, one is the two-digit, uh, reference to the state name, and the other is the state name. And you see they're text files, and we're gonna leave them as text files. We're not gonna c- convert those like I did just before. We're gonna leave those as text file, but we've got this other fi- this other field out here, and this is geometry.

12:20 Now, that tells Tableau what kind of shape it is.

12:23 Uh, it could be a polygon or a multi-polygon like I've got, or it could be a point, or it could be a line, or it could be a variety of things. But that's, uh, the information that Tableau needs to actually plot, uh, your file. And I'm going to go out here, and let me just do this.

12:42 I'm gonna go out here, and I'm just gonna drag the geometry, that field that we were just looking at, I'm just gonna drag that onto the canvas, and we get a map, and that's a map of the 48 states. I put a little filter on here to filter out, uh, other information that was in here. That's a map of the 48 states. Now, what I'd like to do is I'd like to look at the four states out here in the western area, and I'm gonna start by creating a set.

13:04 And this set is nothing more than I've got Arizona and California in the set.

13:11 I've also got New Mexico in this set, and I'd like to add Nevada into the set. So I've defined those four states as being part of a set, and then I wanna create a spatial grouping of those four separate states, and I do that

13:30 with

13:33 this, uh, formula here. All this says is if the name of the state is in the set that we just created, it's one of those four, I want the geometry. I want the geometry for those four states only, and that's gonna allow us to create this little spatial dimension over here that we can take, and I can drop that onto another layer on the map, and you can see the four states.

13:55 Now, we have those four states, uh, identified individually as, uh, as the four states and created in this, uh, in this, uh, function here.

14:05 Next, what I'd like to do is I'd like to make all four of those states into a single spatial, uh, shape, and I'm gonna do that with a, with another function called Union. And all Union does is it says, "Well, tell me which shapes you wanna group together." And we just created those shapes. We created the shapes here using that, uh, uh, we created them from the set, and now we can group those together, and when we do that, it's going to look something like this.

14:34 And I'm just going over to this tab here, and it's gonna think.

14:40 Okay, now this is a single shape that repr- that, uh, grouped all those four states together. I'd like to put a border around that, and we've got a function that allows us to do that. That's called Outline, and the way that function works is just tell me the, tell me the spatial entity that you wanna draw the outline on. That's created the union, and we're gonna apply that.

15:10 And now we can come back, and I can calculate the area of that shape, and this is done rather easily here. All we do is we say we want the area of the, of the shape, of the f- of the four that have been grouped together, but we have to tell it what dimension we wanted it. I wanted it in miles. You might want kilometers or feet or something, something like that, but I used kilometers.

15:32 And then I wanna take a look at the length of that border that we put around it, and all I've done here is I take a look at the length, and this is the outline that we created, that border using that, uh, using that outline command, and I want that in miles also.

15:47 And when we do that,

15:53 we can add, uh, the area and the length of the border to our, uh, to our map. Very easily done. Uh, you've been using func- uh, uh, using the, uh, uh, the calculation wizard for doing a lot of different things. It's no different than that to use, uh, uh, to use that wizard, uh, to do your spatial functions. Now, Don's got a lot of, uh, examples that he wants to share on, uh, how to use those functions to do some really neat stuff.

16:23 Don Wise: So, uh, the scope of my portion of the presentation was, uh, definitely to all the folks in San Diego, so this is San Diego TC '26.

16:32 And the first thing that we did was we welcomed all the arrivals to San Diego. We wanted to show something fairly simple using just Make Point and Make Line, uh, and how that would work from a, from a spatial pers- perspective.

16:45 And in order for that to happen, you need two sets of latitude and longitude, in this case, to latitude and to longitude, and from latitude and from longitude. And you'll notice that all the values for to lat- latitude and to longitude are exactly the same because those values represent the city of San Diego. And then for the from latitude and from longitude, we have different values, all representing the different countries that they came from.

17:12 And then, of course, we have a count of our attendees, which is just something I mocked up. I don't, I don't have that data from Tableau.

17:19 So the calculations involved are for the from points is Make Point from latitude and from longitude, and then the to points, Make Point to latitude and to longitude, and then we combine that into Make Line from points and to points.

17:34 All of this could be combined into one calculation if you really wanted to, but I like to separate them out in the event that something breaks, and I just need to refer back to where it started from, so it makes it a little bit easier to find things.

17:47 And when we populate that on a map, we have the great circle arcs showing all of our various countries and where everybody came from. And for example, Canada, we had two hundred and fifty attendees from, uh- The province of Quebec. So this is a great way of showing where people arrive from, uh, to TC '26, and it's a great way of showing, um, some spatial elements just simply using Make Point and Make Line.

18:14 There's nothing, um, complex about doing this, and it's very, very easy to do.

18:19 From there, uh, I asked the group how many people have a smartwatch, and the majority of the people in the audience held their hands up and either had an Android device or they had an Apple smartwatch. And what's interesting about this, folks, I don't know if you know this, but if you export your health records, you're gonna find a folder called.GPX.

18:39 And if you change that extension to.TXT or.CSV, we can then take that XML file and load it into Tableau Prep, parse out the needed data, and then output that to Tableau Desktop. And I want you to keep that in mind going forward, that what we get here is we get all this waypoint date and time values, and it's recording a lot of data. It records latitude and longitude for every second that you're moving, and it's basically your GPS data.

19:07 So we have waypoint date and time, latitude, longitude. We have speed, we have our bearing, uh, we have our horizontal, uh, accuracy, our v-vertical accuracy and so on and so forth. So let's keep that in mind as we go forward.

19:19 So, uh, being in San Diego, we have great tacos, and at Tableau Conference, we have two problems: learning spatial functions and finding great tacos. And my criteria for food, folks, I don't know about you, gotta be good, cheap, and a lot. And if there's two out of the three, then I'm probably, I'm probably into that restaurant or that location. But it's gotta have at least two out of the three.

19:41 So we're gonna find some tacos in San Diego, and the first thing that I did is I went to San Diego Magazine and I found their forty best tacos.

19:49 And unfortunately, all it had was their addresses. It had absolutely no geospatial, um, identifications whatsoever. But an address is good enough, and we have to use an outside service to make this happen, and I like Geocodio, uh, for reverse geocoding. And what that allowed me to do is to populate those addresses into their app. Um, it's a web app, and, uh, if it's under twenty-five hundred addresses, you can do that for free, and that's for US, Canada, and now, uh, it, it looks like they're doing U- the United Kingdom as well, which is kind of cool.

20:24 So under twenty-five hundred addresses is free.

20:28 And so the next thing I did was I found the San Diego, uh, County, uh, road geometry, and I colored this to show Harbor Drive, which runs along the entire extension here in the front of San Diego. And this is where we were located, was Convention Way.

20:46 From there, um, populated the taco locations, uh, that I geocoded, um, and, uh, then populated just using Make Point, uh, and found the latitude and longitude for the San Diego Convention Center, so we have that guy here. And then one of my favorite places, folks, is Crack Tacos, and the reason for that is they're located outside on the wharf, um, and it's walkable from, from the Convention Center.

21:12 And I go there a lot.

21:14 And then remember we talked about that, uh, waypoint data? Um, I pop- I wanted to know if I actually went to any of the forty best tacos, um, from San Diego Magazine. And sure enough, I did. Now you notice what I'm doing here is I'm turning on layers, and that's all this is. These are just simply map marks layers, um, and being drug over independently of each other so that we can control things independently.

21:41 I can control the visibility, can control the color of it, and so on and so forth.

21:47 So it looks like I went to at least two of those locations for sure, Crack Tacos, and it looks like I also went to, uh, this guy here, Puesto. And you'll notice that I've got my speed here from my waypoint data, and I walk upwards of close to, uh, I don't know, around three miles, not quite three miles an hour, almost three miles per hour. That's pretty brisk, uh, for a sixty-eight-year-old guy.

22:10 So, uh, that was a good explanation of, uh, how to visually see where we've been spatially. And you'll notice that I took that GPS data and I honed it down to every twelve seconds because if you're using every single second, you're probably not gonna see these color ramps. So I brought it down to every twelve seconds by a calculation, and that's in the workbook.

22:34 So from there, um, I said, "Well, let's take a look at something else." This is something that I recorded back in March of, uh, uh, what was it? March of 2026.

22:47 And this is a walk that I normally do at least once a month.

22:51 Um, and I noticed that based on the color values and the color ramping, I'm doing a mile and a half here on this side, and it looks like I started around eight, uh, almost nine o'clock in the morning, and then over here about nine thirty. So, uh, going this direction and then coming back this direction.

23:08 On a flat map with no other information whatsoever, that is a little bothersome to me because I know that I walk about two and a half to three miles per hour. So what's going on?

23:21 Well, we have some additional information in that GPX data which is really, really valuable and probably something that we don't always consider, and one of those is elevation.

23:32 And you'll notice that really what this is is this is a hike up a mountain and down a mountain, and it's a good-sized hill.

23:39 Um, and I started off at five hundred feet above sea level, and I did about a seven hundred foot climb in about forty minutes.

23:47 So that's, that's some puffing and puffing.

23:49 Um, and this is skewed a little bit, so I'm gonna go ahead and close that down.

23:54 That's a bit more representative of what that, that hike looks like.

23:58 So something to consider is that with that waypoint data and just in general is- We often, uh, skip elevation, and that's a valuable thing to look at when we're looking at spatial data. All right, so back to our tacos. Um, one of the things that I wanted to do was, uh, create kind of a ranking of where we were with finding the best tacos. And so there's some distance function, an estimated walk time, and a ranking going on here.

24:25 And, um, what I did is I found some, uh, Yelp data, and their Yelp rating, and a Yelp review count, and then scraped that off. And then, uh, I have my, uh, my distance from and to the convention center just using the distance function. And, uh, loosely translated, you know, the better the ratings, the more the reviews, and the closer to the conference, the, the, the better the rating in the index.

24:48 And so we have Late Night Only, which is Kiko's place here. I don't know that I would go there unless it was…

24:55 maybe I had a few too many.

24:57 Uh, this one worth the walk, there's my Crack Tacos folks.

25:01 Uh, a conference favorite is The Taco Stand, and then our legendary tacos, these three folks here. So what we have here is we have a ranking of three dot four and a closeness rank of four. And on Lola's, we have a three dot three ranking on the index and a closeness rank of three.

25:19 And Tacos El Gordo is closest at one and a three dot four. And our Crack Tacos folks, uh, closeness rank of two. So these folks I have not visited before, uh, but obviously I need to go there next when I'm back in San Diego. So this is a great way of just, uh, ranking, uh, your findings. And don't forget that you can always right-click, duplicate this as a crosstab, and then you get a nice data table to work with in addition to that, so and start doing some additional analysis.

25:51 Our next section is Map Viewport, and I don't know how many people that are on the, on the conference, uh, this morning know about this, but this is a little feature that was introduced some time ago. Um, I'm not gonna build this out, uh, but the instructions are here for you on how to build it out. And I usually start with two different kinds of maps.

26:08 In this case, it's those taco locations and a map of our zips.

26:15 Um, and where this lives is, this is a spatial parameter, and it's already highlighted on a polygon. Um, but it's got this extra section here, dynamic value.

26:29 And you'll see here I've selected my viewport, uh, zip codes, and taco locations, and I've highlighted checkmarked taco locations because the end user is going to use the taco locations for the next step in making this, uh, kind of a dynamic, um, uh, view, something they can actually interact with. So let's pretend I'm the, uh, owner of Crack Tacos, and I want to expand my business, um, outside of the, uh, the convention center area, and I'm not sure which zip codes, uh, might be, um, uh, a good area to look at.

27:03 The user can take this and then draw a section, and it highlights. And I see here there's this big gap, and this might be all residential, but then again, it might be business, and there are just simply no taco locations here. And this might be a good area from either a marketing standpoint or maybe a new, uh, um, extension of my business. And so this gives me a good starting point to figure out where to go.

27:29 So, um, it's very easy to do, um, and it's very interactive and, uh, it's, it's, it's a nice technique to have, but it's a, it's a little hidden, and this way we wanted to show it to you today.

27:41 Don't forget that, uh, Tableau now s-supports mixed geometry. Uh, and what that means is that we can now combine polygons and points, whereas before we could not do that. Uh, and that could be a little bit limitating, li-limitating. Uh, an example of that is, uh, WKT or Well-Known Text, and I'm gonna pronounce it as wicket.

28:01 And I have a website, um, in the next, uh, screenshot here that, uh, I use for getting Well-Known Text. And you'll notice here in the example, uh, it's formatting as polygons and giving all the latitudes and longitudes. Most importantly, it's needs to be forpat– formatted in EPSG 4326 because that's what Tableau supports.

28:22 So there's a, uh, spatial reference ID number there that needs to be a part of this when bringing it in.

28:28 So what this looks like is I did a drawing using wktmap.com by Peter Prost.

28:40 Um, and so that's in the, in the caption. You can just go there, and you can draw your own polygon.

28:45 Uh, and I did one for downtown San Diego, the East Village, Little Idia– Italy, Gaslamp Quarter. And I included in that data a point, uh, so that it represents mixed geometry. So this is a polygon, and there is a point there representing, uh, Crack Tacos.

29:03 And then the parameter works as such.

29:07 So what is this good for? Uh, Jim and I talked about this, uh, at some length, and it really is great for, um, having your end user focus on a particular geometry or particular, um, uh, geography area, uh, and to exclude all other areas or really, uh, develop out your own custom territories. So you can draw your own polygons, um, and then go forward from there.

29:30 It's a little rough to, uh, try to make it work with anything else going forward. I suppose you could leverage, uh, the, um, the actual, uh, calculation here, the wicket geometry.

29:42 But, uh, it's, uh, I would work with probably a calculation that says, okay, if the dimension says, uh, it's East Village, then color it this, or if it's downtown San Diego, then color it that.

29:56 Okay.

29:57 So from here, um, we have, uh, isochrones. Uh, and I'm gonna pretend that, uh, Crack Tacos, um, is Had a, had a, uh, uh, a situation where, uh, they were not able to, uh, open like they used to back, uh, when we had the par- the, uh, pandemic.

30:16 And from there, uh, I went to Openroute Service, and I developed out, uh, some walking and driving polygons.

30:26 And so this is a ex- great example of showing, uh, what's my reach.

30:30 And the service is called Openroute Service. They have two different maps. They have a classic map, and, uh, they have their new map. Uh, the, uh, new map you can use, uh, mixed geometry. The classic map you can, um, develop out these polygons. Um, and it really shows you the population within, uh, for example, uh, this is just walking time, uh, from, from that distance.

30:51 So if they wanted to know how many people they could reach during the pandemic that might be within 15 minutes, that's a great way of determining where they're gonna do their marketing and f- and maybe do some old-fashioned flyers.

31:02 Um, from a driving perspective, uh, let me go ahead and zoom this out, then we get a much different example. Um, and it follows, uh, more of a, of a, of a drive t- drive time value. So in this case, they could reach, uh, some 2 million people within 25 minutes, um, and, uh, 166,000 within 10 minutes' drive time. So don't forget that, uh, these are available to you.

31:26 It's a separate service that you need to br- bring in, but, uh, Tableau supports that. And these are shapefiles, and they were union, just much like what Jim did earlier, uh, with a union, uh, process.

31:38 And then, uh, lastly, this one's a little bit more complex. It involves three different types of data, schools, liquor licenses, and medical cannabis shops. And this, um, uses, uh, parameter actions. And because I'm using parameter actions, there's a little bit of a criteria here that's required, and that is that it needs three sets of separate data, but all the spatial calculations are named the same.

32:04 So, for example, the, uh, points, um, calculation, uh, for the schools is named the same as the liquor stores and the cannabis medical locations, and we have a lot of those here in California. And this is all, uh… Where are my- This looks like Astra Nova School in Los Angeles, and there are three schools in the buffer, um, and there are 64 liquor licenses in the buffer.

32:33 So for this example, uh, it would take a student about less than two minutes to walk to, um, this location where they serve alcohol, and then, uh, about seven minutes to the nearest cannabis shop, and there are two cannabis licenses in the buffer. What makes this interactive is we have,

32:57 we have the selection here, and it's working off the selected point, and the collected geometry in this case is the schools, so that when I highlight, say, a different school, the buffer changes dynamically and everything updates. So that's a, uh, dynamic parameter action. This is a great way of, uh, looking at your, um, your, uh, your locations and making it interactive, uh, for the end user.

33:26 Um, what's– I'm gonna jump back here probably to…

33:31 I'm gonna go back here to the zip codes. There's something else that just, uh, came into the most recent versioning of Tableau. I'm not sure if anybody's aware of it, uh, but it's also similar to the isochrones, and it's a drive time selection tool where you can, uh, select this, uh, option and then pick a point and then drag out, and this will show you as you drag further out what your drive time is.

33:57 Now, this does not persist, so this will disappear as soon as I release it, but it gives you at least, uh, an idea of what the drive time would be, uh, as a separate option from developing out your own isochrones, and I wanted to show you that before, uh, jumping off here.

34:14 So, and then lastly, um, I, I think Darin's gonna go into this, but, uh, Tableau Prep now has a lot of, uh, spatial calculations and joins. And then in my workbook, um, all the data sources and references are there as well. So that's what I have.

34:29 Jim Dehner: That was great, Don. Uh, I've got about five more minutes. Tore, I think I've overstayed my welcome already. Do you want to go on to Darin s- to make sure he has enough time or, um-

34:41 Tore Levinsen: No, we're good. We're…

34:44 You, you'll, you'll get your five minutes.

34:47 Jim Dehner: Okay.

34:49 Tore Levinsen: From now.

34:50 Jim Dehner: From now. Okay, I'm getting there.

34:54 Old guy moving fast as he can.

34:56 One last thing we wanted to show you, and you may have seen this, uh, in other presentations before, and that's how to use, uh, map layers to do XY plotting.

35:06 And, uh, I'm just gonna dive into it very quickly here.

35:10 Uh, what we're gonna do is we're gonna create a rectangle, uh, on the map, if you will, or on layers of the map, and I want you to think of that in terms of longitude and latitude. And I'm gonna start by, uh, creating a box that's 180 degrees of longitude and 90 degrees of latitude. We're gonna change that. We're gonna narrow that down because of the effect that you get when, when you get up toward the poles, the 90 degrees on the poles where the latitudes get compressed.

35:36 But I'd like to start with 180 degrees and, and 90 degrees, uh, b- because kind of that's the way I think.

35:44 Uh, what we're gonna do is we're gonna take the daily sales from Superstore data, and those daily sales look something like this. This is a map of the daily sales, and we're gonna put it inside that box and create a proportion of the, uh, of that, of any individual point's longitude and sales value compared to, uh, the total max and min.

36:09 Let me just…

36:10 We'll just dive right into it

36:15 And, uh, first w-we need to do is we need to figure out how long that X-axis is in terms of the number of days of sales. And, uh, we can start, that's nothing more than the max order date minus the min order date, and we know the number of days of sales along the X-axis. That defines our X-axis. Now, any point along that line, uh, is the order date of that point minus the minimum order date.

36:45 So we know how far to go across that line in terms of days, and then we're gonna take that as a percent.

36:53 We've got, uh, the increment to the point divided by the total length of the axis times 180 degrees, and I multiply that by 0.1, and I'll explain that as we get into the sales here. On the sales axis or the vertical axis, all we're going to do is we're gonna take a look at any day's worth of sales using this fixed LOD.

37:15 Then we're gonna take a look at the range, and, um, because, uh, sales start at zero and, and progress upward, um, we, uh, the range is just gonna be the max value.

37:28 And the percent of sales is going to be this value here, which is the, uh, sales at any point divided by the max times 90. But I don't wanna get all the way up into that polar region where, uh, everything is compressed, so I'm gonna multiply that by 0.1, and we're actually gonna plot it on the map in a very small area close to, uh, zero longitude, zero latitude on the map.

37:55 That's what this data looks like, but if we were to look at it actually on a map, it would look something like this.

38:04 Okay?

38:05 And if I were to…

38:07 You can see it's down here.

38:09 I'm gonna pull the pin on this just real quickly, and you can see what this looks like.

38:16 We're in the zero, zero, uh, area of the map.

38:19 Now, if we can do that for a single chart, we should be able to do it for multiple parts, charts, each on their own separate layer. And what I'd like to do is I'd like to start by taking that map that we just created or that chart we… And also include a bar chart on that map.

38:39 And we can do that first by just moving that chart up. And very quickly what we would do is I want it on the top half of the map.

38:51 I want it on the top half of our chart. So I want the lower part, the lower 45 degrees, if you will, I'm gonna use for the scatter plot. The top part between 45 degrees and 90, and I just made it 85 degrees to have a little white space at the top, uh, times, uh, times the 0.1 again to, uh, get around the compression, will force those values to the top of our map.

39:19 Now we can look on a separate layer for the scatter plot, and I'm gonna use it down here in the lower part of our chart and go, uh, across the chart from zero to 90 degrees and up, uh, as high as 45 degrees.

39:32 And, uh, to create the scatter plot, we need to look at our,

39:38 look at a sales axis, and it's just, once again, the max minus the min for sales. We do the same for profit.

39:47 And our percentage for the scatter plot on, uh, sales, and we're down here in the lower part of our chart, w- I look at the, uh, percentage times 0.35. And I don't wanna go as high as 45 because I want some white space there. I want, I want my chart to have some white space in it.

40:07 And for the

40:11 horizontal, I want to go from zero to 90 degrees, so I s- and I'm offsetting at 10 to 80 degrees, and then nine- times the 0.1 once again. And we can just use Make Point

40:29 just like, uh, just like we did before, just like Don did. We'll use Make Point to create our map. And now we've got our scatter plot. We could do the same thing for a bar chart, and in the interest of time you can, you can look at, uh, how I did this, uh, downloaded, uh, in the, uh, downloaded workbook. And now we have three different charts,

40:53 each on their own map. And we know that if we actually were to look at this,

41:02 it would look something like that, but we've gotten rid of the map background

41:09 and it looks like a normal, uh, a, a normal worksheet with three different charts on it.

41:16 Thank you, Toria, for giving me, uh, the opportunity to, uh, present this last five minutes and I'll un-save this now or I'll-

41:28 Tore Levinsen: Thank you

41:28 Jim Dehner: … stop sharing.

41:30 There we go.

41:30 Tore Levinsen: And as you, we have done, we have put into the, um, into the, um, chat earlier both your workbooks.

41:39 Jim Dehner: Okay.

41:39 Tore Levinsen: So they are both downloadable and you can re-engineer if you would like to.

41:44 Jim Dehner: Yep.

41:45 Tore Levinsen: Um, do we have any questions in the…

41:50 I don't see any, uh…

41:51 Oh, I guess we haven't started the Q&A, so I guess that's why there was no questions in there. But, uh, is there any questions in the chat in itself other than Anne saying that T- Tacos El Gordo is awesome?

42:05 So agreeing with, with Don there, I guess.

42:09 Don Wise: Or I would add to anybody that's on the call, if they go to, uh, TC'27 in San Diego next year- They should probably go to Data Plus Tacos on Monday before the conference. It's a great networking event

42:22 Jim Dehner: Yeah.

42:23 Tore Levinsen: Yes, exactly

42:24 Don Wise: Spon-sponsored by San Diego TUG.

42:27 Tore Levinsen: Yeah.

42:27 Jim Dehner: Good tacos and-

42:28 Tore Levinsen: Good, good tacos and good people. So, uh,

42:33 that's for TC next year.

42:36 If there's no particular other questions, as, as I said, I think that Dennis has answered a few as we moved along.

42:44 So then I guess I will say thank you to, to, uh, Don and Jim for now, and we'll switch it over to, to Darin, who's a senior. He's a senior as well, but not a senior in the same sense, I guess. He's Senior Product Manager at Tableau.

43:03 And what, what are, what are you actually a product manager for, if I can-

43:07 Darin Bergeson: Uh, I'm the Product Manager for Tableau Prep.

43:10 Tore Levinsen: Yeah, so-

43:11 Darin Bergeson: Yeah, I am the ma- product manager on Tableau Prep. So yeah, if you have any questions, needs, uh, love to hear from all Prep users. Encourage everyone to use Tableau Prep. So, um, I'm with you Tore. I love, uh, love Prep, love talking to users, love hearing what you guys like, what you want more of. So yeah, thanks for having me.

43:30 Tore Levinsen: Cool. And you're gonna talk a bit about, uh, like the, the other guys mentioned a bit briefly, that the spatial capabilities-

43:37 Darin Bergeson: Yeah

43:38 Tore Levinsen: … that now are in Prep.

43:39 And of course, if you don't use Prep, you don't know about those. But, uh, that's what I see many times when I go to my clients is that, uh, "Well, we need to clean this data." "Yeah, but you can't do that in Desktop." "No, you can use Prep because you…"

43:51 Darin Bergeson: Use Prep.

43:51 Tore Levinsen: "Well, do, do we have Prep?" So there's a lot of people who actually don't know about it, unfortunately. So-

43:56 Darin Bergeson: Well, spread the word.

43:57 Tore Levinsen: Yeah. More love for Prep.

43:59 Darin Bergeson: Love it.

44:00 Awesome. Well, thanks again for having me. Uh, I just got a couple of slides I'm gonna walk through. Um, and then I got a, a quick demo to show, and then, yeah, happy to answer any questions.

44:10 Uh, first off, thanks for having me. Uh, again, really appreciate you, um, you know, inviting me to this, to this call. Um, love being a part of the Tableau user group here, and, uh, yeah, just thanks for being Prep users. Thanks for being interested.

44:25 So just, uh, real quick, I'll, I'll give a quick intro, um, talk about why we added spatial in Prep, um, talk about which spatial calculations are supported.

44:35 Um, I'll do a demo, and then we can end with a few questions.

44:39 Um, so just an intro for me. I'm Darin Bergeson. I've been at Tableau just about five years now. Um, I've worked on a variety of, of different areas of the product, and I've been the product manager for Prep now for about 18 months.

44:53 Uh, and uh, just so you all know, uh, we have a couple of great Scrum teams that work on Prep. Our engineers are- have all been, uh, working as part of the Prep team for, for quite a while, longer than me. Uh, so a lot of expertise, uh, in the development team, uh, which goes a long way in making the, the, the product, uh, as good as it can be.

45:13 All right, so why do we add spatial in Prep? Uh, this has been a long time coming. Uh, I know this has been a request as part of, uh, the community forums, uh, going back to different TCs. I know, uh, talking to previous, uh, Tableau Prep product managers, people wanted spatial calcs in Prep for a while.

45:31 Uh, but also, uh, most organizations work with location data in answering, um, the question where. So Prep, uh, along with Tableau Desktop, is happy to help answer those questions, uh, and bringing a lot of these spatial calc-calculations to you, um, uh, within the, the product itself.

45:50 A lot of times, spatial calcs and joins are resource costly in different analytics tools and can take a long time to compute. So again, we wanted to bring this, uh, to you within our, our Tableau ecosystem and make it easy and fast for you to leverage.

46:04 Um, again, Tableau Prep's user goal, uh, being or, uh, user's, uh, goal-oriented and smart visual interface will guide users to reach insight faster with spatial data.

46:16 All right, so which ones did we add?

46:19 So currently you can see, uh, in 26.1 we came out with area, buffer, difference, distance, intersection, intersects, length, um, Make Point, Make Line, outline, Shape Type, Symdifference, and validate.

46:35 Uh, so I know that, um, you guys had kinda talked about these previously. I won't get into the details of what they all do.

46:42 Uh, but this is what we came out with in 26.1.

46:46 Um, we've heard a lot of, uh, of good feedback. Um, and we wanna continue to, to make spatial, um, continue to work in Prep as well as we can.

46:55 So coming up in 27.1, we're adding, um, parsing Well-Known Text as well as unions, and we'll continue to add more, uh, spatial calculations and joins. So if there are things missing that you, you would like to see, um, feel free to, um, either reach out directly or make a community forum post. Um, but love to get any feedback you have on what you wanna see more of.

47:19 And yeah, so again, thanks for having me. Um, I'm gonna close this screen share and actually just let me actually… Awesome. So this is the, um, Sean Campbell and I. Uh, so Sean's one of the lead developers on Tableau Prep, but he and I presented at Tableau Conference this year, um, and wanted to showcase, uh, spatial calcs and joins as part of that presentation.

47:46 And so what we wanted to do is pull in, um, information around, uh, baseball stadiums and airports. So the story we were telling is, um, we wanna try to visit a bunch of different baseball stadiums, but we don't wanna, you know, travel too far from an airport. We wanna be in and out just to see the baseball and kinda make, uh, make it quick.

48:09 So I have both, uh, US airport data and, uh, Major League Baseball stadium data.

48:14 So right now I'm, I'm highlighting the stadium data. You can see this is, uh, already comes with a point on the stadium.

48:21 But I am gonna rename this. Rather than geometry, let's rename it to baseball stadium.

48:31 Uh, so this actually looks really good. We already have the point here for baseball stadium. We know what we're doing. Um, but let's go look at the airport data. Um, let's see. So you look over, and it looks like we got longitude, latitude.

48:44 Uh, no point, but we can go ahead and make our point here using, uh, our spatial calculations.

48:52 So I'm gonna create a clean step here, and then I'll go create a calculated field.

48:57 And first we wanna make a point for the airport.

49:00 And so we'll go to Make Point, and we just wanna use the latitude

49:06 and the longitude, and we'll call this the airport point.

49:15 And there we have it. So just like that, we got our airport point, uh, created.

49:20 But like I said, we wanna try to stay within a reasonable distance of the airport, um, when we're, when we're traveling. So, uh, five miles, um, if you think of, of, like San Diego, for example, the baseball stadium's kind of right there downtown, so is the airport. That might be fine.

49:36 Uh, other cities may not have it as close. So let's spread that out to ten miles. So what we're gonna do is create a buffer now. So again, we got buffer, and what we'll do is take that airport point,

49:53 and we wanna put it within ten

49:58 miles.

50:02 Oops. I got my error here. What did I do, what did I do wrong there? Oops.

50:06 There.

50:07 Just the extra parentheses.

50:09 And actually, let's just rename this our buffer.

50:13 Call it our ten-mile buffer.

50:18 Awesome.

50:20 So now we have, uh, our two, uh, added our airport point. We added our buffer.

50:26 And now let's take a look one more time at the data.

50:31 This all looks fine. Region.

50:34 Um, one thing I might wanna do here, so we have airport types. We got balloonport, teleports. I think we're just interested in regular airports. We'll call it large airports. So let's go ahead and filter here, and we'll just select large airport.

50:50 Um, and that should just get us where we wanna be. We just wanna find the large airports. We don't wanna do a hot air balloon or anything in.

51:00 And then what we can do is create our join. Whoops.

51:04 So just nice, easy drag and drop in Prep.

51:08 But we don't want to, um, we wanna join on our… We don't wanna join on the names of the airports and the baseball stadiums. We wanna join on our ten-mile buffer here and our baseball stadium point here.

51:24 Perfect.

51:26 And now, um, what we're gonna do, as always in Tableau, we'll do one more clean step. Or all- as in Tableau Prep, you always wanna keep your data clean, so clean it early and often.

51:38 Um, we'll go through and look. This all looks pretty good.

51:41 And so we can go ahead and create an output step.

51:46 And you can see in the join too, you can either preview this in Desktop from here, um, but for the demo purposes, I'll go ahead and output the data so I can run my flow.

51:56 Looks like it's gonna save locally here.

52:00 And I have already practiced this, so we'll replace what I've already done.

52:04 Runs my flow.

52:06 And then, oops, I'm gonna order, I'm gonna open my

52:12 Tableau Desktop.

52:16 And then we'll connect to data here.

52:18 Oof, that starts up nice and fast.

52:23 All right. So here we have our baseball stadiums and airport type profile that we just saved, so we'll go ahead and open that data here in Tableau Desktop.

52:33 And then you can go ahead and start creating your viz. So just real quick, you can pull in your airport information. So you can see our airport information here.

52:43 And then we'll pull in our baseball stadium on a new layer.

52:48 And then actually, I'm gonna pull in the ten-mile buffer on an additional layer here.

52:54 Uh, did that work?

52:57 Actually, let's not put that on a different layer. Let's put the ten-mile buffer on the airport.

53:07 No, it's not working, obviously, when I…

53:13 There we go.

53:16 Although it's not…

53:26 I just ran through this a whole bunch of times. Let's see if we put it on the baseball stadium instead.

53:42 Well, my visualization is not, skills are not working right now. But, uh, but you get the gist. Uh, you got your, um, you got your data here. Which is what Tableau Prep's supposed to do. Uh, let's see if I… Let me try this one more time. Let me restart here. So we got

54:00 airport,

54:03 airport point.

54:05 Uh, there we go. Airport points.

54:10 I have a new one for our baseball points.

54:17 Ten-mile buffer.

54:21 I'd like that to be, uh-

54:26 There we go. Here's our 10-mile buffer. So now you can see a lot of these are falling within 10 miles, so maybe we need to go back in Prep and make that five miles. Um, let's see, Seattle.

54:41 Not sure why it's not showing. Oh, there it is. Yep, so Seattle looks like our airport is… Oops, falling outside of the 10-mile buffer.

54:50 Um, I live in Minnesota. Looks like we're within the 10-mile buffer here.

54:55 So you can kind of see, um, see what we're getting at here. So this will help us plan our trip, make sure we're staying in the right, um, into the, into the right, uh, distance that we wanna be. So we're visiting the stadiums closest to the airports. That will allow us to get in and out of the stadiums as fast as possible.

55:12 So that's pretty much what I have to show, uh, for, uh, the Tableau Prep and the spatial calcs and joins. Um, I guess I'll open it up, uh, Tore or anyone else, if there's any questions, I'd be happy to answer.

55:24 Looks like there's one in the chat.

55:27 Ooh, plans for a public version of Prep.

55:31 Uh, there are… Uh, that is actually currently in discussion right now. Um, I don't wanna commit to a date, but that is, uh, actually a hot topic in mind currently. And, um, yeah, we actually just met with the public team last night talking about what that might look like.

55:47 So just, um, keep your eyes and ears out or open for a potential POC coming soon.

55:56 Tore Levinsen: That sounds good because that's something that I also wanted to ask about, and I know that a lot of people have asked about since. And I do a lot of lectures, right? And then-

56:05 Darin Bergeson: Yeah

56:05 Tore Levinsen: … at school, you give away, of course, free Tableau Desktop and, uh, and also Prep there. But, uh, I think that-

56:12 Darin Bergeson: We wanna get as many people using Prep as we can. Um, it's such a great tool to… It's useful within Tableau. Um, even if, if you're not using Tableau Desktop, uh, I think Prep is a great tool to get your data cleaned and ready to use, whatever you n- need to do with it. So we certainly, as a Prep team, wanna make sure we're getting Prep in the hands of as many people as we can.

56:31 Tore Levinsen: Yeah, and combining that with the now also published data sources on public where-

56:36 Darin Bergeson: Yep

56:36 Tore Levinsen: … people eventually then can build proper data sources that can be vetted and, and published.

56:41 Darin Bergeson: Absolutely.

56:42 Tore Levinsen: That'd be cool.

56:42 Darin Bergeson: Yep, 100%. Uh, Patrick, any plans to automatically convert addresses into lat and longitude?

56:49 Um, I guess no, but certainly something I can jot down here, um, as something that, you know, some, you know, a feature we can look into.

56:57 Um, that's one of those things. I don't wanna necessarily assume what people wanna do with their data, but, um, but yeah, I, I certainly hear you, uh, making sure we can, can convert that, uh, into that latitude and longitude automatically, or at least adding those… giving the option to add those fields automatically. So I hear you there. So let me jot that down.

57:20 Any other questions?

57:21 Tore Levinsen: I think that's a question from myself as well. Is there any limitation on the amount of polygons that can be joined or, yeah, in join, I guess?

57:29 Darin Bergeson: Uh, there's not.

57:31 Um, I mean, I think you'll hit… You could potentially run into the same data limitations that, that you might have in Prep, but, um, we don't have any limitations specifically on the shape files or the shapes themselves.

57:47 Tore Levinsen: And going back to also the chat, since we have some questions or comments. Is there any advantages of doing the spatial functions in Prep since they're now available versus Desktop?

57:58 Darin Bergeson: Um, I mean, I think it, it'll get your, your data ready to use in Desktop, and there are, um, certainly, um, if you just wanna, um…

58:07 Prep's a great place to just inspect the data to make sure it's exactly what you want it to be. Um, so again, depending what you, what your goal is with the data, um, you may just need to use Prep. But ultimately, if you're looking for deeper analysis, you're gonna wanna bring whatever you, um, create in Prep into Tableau Desktop as well, or Tableau, Tableau Desktop or Cloud.

58:32 Tore Levinsen: Yeah, and that's, uh, a, a bit similar, I guess, to what James answered for that question as well, that heavy spatial calcs can be done in Prep, so the experience in Desktop flows.

58:42 Darin Bergeson: Yep, exactly.

58:43 Tore Levinsen: You do the heavy loading. Now, that's of course what you do in general with Prep. You, you don't want-

58:48 Darin Bergeson: Yeah

58:48 Tore Levinsen: … that. You want the data to be finished when you, when you get it into Desktop.

58:51 Darin Bergeson: Exactly. Yep, you want it to be nice and polished and ready to go.

58:56 Tore Levinsen: Is there any other questions?

58:59 If not, then I'll just quickly share my-

59:03 Darin Bergeson: Thank you for having me again. This was great. I'm happy to join any time you have some Prep questions or, uh, if we have, uh, new Prep, uh, capabilities coming out, happy to jump on and, and, uh, walk through them with you guys.

59:16 Tore Levinsen: Yeah. Thank you. It's, it's always nice to, to have a, have a bit of the inside information of the potential of what's go- coming and, and, uh, also knowing that it actually something that you are focusing on. Because for us who love Prep, we would, we would know that it's not like only Cloud or only f- uh, only, uh, Tableau Next or, uh-

59:38 Darin Bergeson: Sure

59:38 Tore Levinsen: … agents and everything. And, uh, I guess-

59:40 Darin Bergeson: Prep's alive and well. We're, we're certainly investing it in the tool and trying to make it as, as useful as possible for everybody.

59:49 Tore Levinsen: So what we did earlier today, we had this, uh, this, uh, form that you could fill in. So now we, we just quickly put up a, um, a word cloud in here. So we see that we have people from, well, uh, around the world and a lot in, in, in the States at the moment. The, um… I'm just gonna go back since it was quickly just added now. This is the, the one we had from before, uh, on the, on the map as well.

1:00:15 Um, see that I'm, I'm here in Bergen on the west coast, and, uh, we, we have people a- across the globe. So the 676 members, I hope that in the future we will be more, um, people.

1:00:29 So our next meetup is planned. Uh, we haven't set a date yet, but, um- Uh, it will be in November, uh, this year. That's the plan.

1:00:39 Here's the QR code or the, the link to if you would like to, um, to re- present, if you have something exciting to share.

1:00:48 Uh, fake data, doesn't matter, but something that has to, to, to do with, uh, spatial data or with, um, some fan- nice visualization that you've done, um, that, that, that are using maps or any nice backgrounds, whatever. I mean, we're interested. Uh, we're, we have a, like, a backlog of people we are… We, we would like to speak to, uh, to, to see if they would like to present.

1:01:16 So, um, but that, that list can never be long enough. So if you're interested or if you know about somebody, uh, you think should present, uh, at this, uh, user group, or you've seen somebody that have done a lot of fun stuff, remember to, um, to let us know as well.

1:01:35 And Dennis is mentioning, um, uh, that on, um, the Newbie TUG, I think you, Jim, know something about that Newbie TUG as well, but on the 2nd of September, um, Dennis is doing a presentation on mapping, so that's more on the, on the basic stuff.

1:01:52 Jim Dehner: Yeah, I was just gonna an- I was just gonna announce it, and then Dennis, uh, Dennis, uh, jumped on. Uh, you can find us out, uh, just go out to the, uh, to the TUG groups, and we are out there. You look for, uh, Newbie TUG.

1:02:07 Or we now have a new site on LinkedIn, and if you go out to LinkedIn and look for Newbie TUG, you can find the link out there to, uh, Dennis's presentation, and we're gonna be using LinkedIn a bit more to, uh, talk about, uh, things that are happening in the Newbie TUG.

1:02:25 Tore Levinsen: That's good.

1:02:27 Nice little crossover there.

1:02:32 Okay. I think that, that should be it. We've done the Q&A. Um, there will be an email, uh, as usual, uh, where you can give your feedback, so please do give your honest feedback. Uh, we need to learn to be, uh, learn from you and see what you think, what you like, what you didn't like, so we can be better and have, uh, uh, good presentations.

1:02:54 So thank you all for joining. Uh, enjoy your rest of the day or evening or, uh, whatever time it is. It's 7…

1:03:02 It's past 7:00 PM for me now, so I think I'll go and, uh, relax a bit. And, um, see you guys next time.