Map Makeovers: How to Make Your Map Great

Charlie Frye and Jim Herries share best practices for creating maps that are cartographically accurate and visually appealing to your audience.

Embed
Download
Transcript
480x270
960x540
Custom
Width:
Height:
Start From:
Player Color:

Right-click on these links to download and save this video.

Transcript

00:02Welcome to Map Makeovers - How to Make Your Map Great.

00:03My name is Charlie Frye, I'm the chief cartographer at Esri, and with me is one of my colleagues, Jim Herries...

00:08...who is a project manager, geographer, cartographer, data expert on my team.

00:13And a few other things that you'll see come out in today's talk.

00:18So first, I'd like to welcome you to the second half of the Technical Users Conference.

00:21As you've gone so far, we're now - we just passed the halfway point...

00:27...and so hopefully everything has been going well for you...

00:31...and then we'll try to keep things on that same track throughout this session.

00:37So what we were thinking with this session is that we wanted to take some maps that we had either...

00:42...worked on or intercepted kind of halfway to getting finished and share some of how we thought about them.

00:51Make sure my PowerPoint's working here; there we go.

00:59And the idea was is that what we do in-house is that we offer a critique...

01:04...and the idea if somebody is making a map and they're not a cartographer...

01:09...in terms of their background, we want our team to take a look at them...

01:12...and get a sense for what could we do in five minutes, ten minutes, couple days to fix the map.

01:20And we want to give you kind of an insider's look under the thinking we use when we do that.

01:25And so what I've started this out with was a little section on how we think cartographically.

01:31And this is more opinion and experience rather than kind of textbook...

01:34...knowledge and the kind of the academic distillation of cartographic thinking.

01:40The purpose is to give you all some confidence to do this same kind of thing...

01:44...or build your confidence if you are already doing this same sort of thing.

01:50So when I'm doing some of this sort of critique is that I look at maps holistically.

01:57I'm looking for a geographic rationale for why you're making the map...

02:01...in other words, as a geographer I'm looking for, is there something that's being informed by geographic thinking?

02:08So you know why is what where, what's the rationale, is it something that's insightful that you're seeing in the map.

02:18As a designer, my background was architecture before I got into geography...

02:23...and so one of the things we learned in there was, as we learned geography, was that...

02:28...design by pulling things out until the design breaks.

02:31And then put whatever it was back in, and that's a good place to start thinking about a minimalist design.

02:38For cartographers, we think of what we do in terms of communicating graphically.

02:44So putting, we call, graphic marks on a map and those are the things that communicate to people.

02:49So the idea is every graphic mark should do something on the map...

02:53...and if it's on there and it's not doing anything useful or contributing, it could probably be dropped off.

02:59Readability or legibility is a big thing for what we look at in our maps, particularly for web maps.

03:05The idea is you should be able to look at that map and immediately see the most important thing...

03:10...read it, understand it, there's no extra work in trying to move the map in or out...

03:15...or spend time reading the legend or trying to translate the title to what's actually going on.

03:23So all of those things should just happen for you.

03:27One of the things that's kind of interesting I've put down here is that, and we talk about this...

03:31...in terms of getting maps reviewed, is having good judgment about what needs to be on the map...

03:35...what the concept of the map is.

03:38We started to talk about how we do our maps in terms of specifications as if the map is a product.

03:45And what we would do is literally have a top-down summary of what the map's about...

03:48...like an executive summary, like you'd put together for any other product specification.

03:52So you know, what does the map do, who is it for, and get very specific about those things.

03:57What that does then is it allows us as we're going through the mapmaking processing to go back to that...

04:03...and ensure we're well grounded as we go along.

04:08It's also good when somebody else sees that map for the first time and says something similar about that map.

04:13So if you're making a map of - we have a map of radiation that we'll show you in a little bit...

04:19...that if somebody looks at that map and says something pretty close to what's in the executive summary...

04:25...after about 30 seconds of looking at that map.

04:29One of the things that's a little harder to do, and it's a skill we try to encourage on, for folks on our team to do...

04:35...is to be blatantly objective, so not just take a step back, you know, like let something sit for a few days...

04:40...but actually be ruthless about it. We'll show you some examples of that.

04:46Clarity is another thing. Last year, for the 60 percent of you who were here maybe last year...

04:52... we had a keynote speaker name Saul Wurman, who's - I guess he's a designer and an information architect...

04:58...and about a half dozen or a dozen other things, and his big thing was clarity.

05:03And he distinguishes clarity from simplicity in a nice way...

05:07...where he looks at simplicity as something that might be dumbing something down...

05:12...so moving material to a less educated audience...

05:15...whereas clarity is making something easy for the audience that you intend to communicate to, to understand.

05:25And kind of the bottom quote down here is that I don't like to think about some of this as cartography so much as...

05:32...and one of the things I've done in the last six months or so is stop using the word map so much around Esri.

05:38What we've found or I find and a few others who have been doing the same thing...

05:41...is that maps are cheap and free and easy and people don't have a high opinion of them...

05:47...but when you talk about an information product...

05:51...just the idea of that sounding more important engages the rest of the organization in an interesting way.

05:57Our marketing people feel like they have a vested interest then, so there's information product...

06:01...okay, well, I could care about the information, and I have a job because it's a product.

06:06So just turning that kind of thing around and getting people out of their usual pattern...

06:11...helps everybody, I think, do a better job of producing maps.

06:19So what all of this really boils down to is that your map should tell a story that your audience can easily interpret...

06:27...and relate to, so it's really about kind of accessibility to the message...

06:32...and that the message works in as an efficient way as possible.

06:43I guess where I learned cartography, I was at Kansas State...

06:47...and one of the things that they really emphasized was communication first.

06:52In other words, whatever you're doing, you're doing it for a reason, to pass some information along.

06:59With web maps in particular, overtly communicating is important.

07:03It's okay to make 28-point text if you need to to get somebody to read the two most important words about a map...

07:09...and put it front and center and then they'll figure the rest out.

07:18What I feel like is that when we do a lot of our maps and we iterate on them is that...

07:22...the more time we spend doing that and having people just look at what is their first impression...

07:30...and evaluating that kind of thing that we do a lot better in the long run...

07:34...for how well that map does when we finally do give it to the intended audience.

07:44I can't remember where I was, I was trying to remember who was telling me about self-evidence in communication...

07:47...but basically what it boils down to is that obvious stuff should be obviously right...

07:52...so blue for water, green for vegetation, and so on.

07:57Sometimes we get kind of lost in the process of being creative and trying to create something new and innovative...

08:03...when making sure it works first is most important.

08:09And I guess back to the middle bullets.

08:12One of the things that is interesting about making maps or doing anything creative is it's hard to force it.

08:19The bit about inspiration there is that we do a lot of maps after maybe years of sitting them on the table...

08:26...because we didn't quite figure out what we needed or we didn't have the data that we needed...

08:29...the right way rather than trying to force something out the door because somebody was asking for it...

08:35...or some other external influence.

08:37But at the same time there's days when you do need to make your inspiration happen...

08:41...or at least some portion of it to get a project done, and that's where the real work in mapping ends up being.

08:49So to do these makeovers, we're going to go through three different maps.

08:54I'm going to talk about the first couple, then Jim's going to talk about the food deserts map.

09:04So the history behind this is that shortly after the earthquake and tsunami in Japan...

09:09...we had a group of people at Esri who were asked to work on a map to show what was potentially...

09:15...happening with the radiation that was reported as being emanating from the nuclear power plants...

09:24...and in particular, they were looking at the Fukushima Daiichi plant.

09:28That team was also - had kind of an ulterior motive, which was that they had been creating...

09:34...a new analysis method in ArcGIS for empirical Bayesian kriging.

09:39And so this on the screen is what they initially sent me as can you please take a look at this.

09:44We know the cartography is not too great, you know, give some suggestions to help us out.

09:50And so what I've initially looked at was, what's the story of this map?

09:54I mean if you look at it, I know where it is now thanks to the title, but I don't know what I'm looking at.

10:03What are we empirically Bayesian kriging on this map? I don't understand that.

10:09I know there's a date on there, and the legend gets into a little bit of the story...

10:13...but at the same time I'm also looking at the colors, I'm trying to figure out, what am I supposed to feel about this map?

10:21Basically, it doesn't work very well, but you know, they're not cartographers producing it, they're analysts...

10:25...and so we actually started way back in the early part of what they were thinking about as a result.

10:35So the map turned into something that looks a bit like this.

10:40One of the things that we realized is that the map did need to tell a story about what we were analyzing.

10:45As it turned out, we were analyzing predicted ionizing radiation levels, so this is a certain level of radiation being measured.

10:54You can even see some of, if you go back to the before here, look at the shape of the analysis.

11:04The analysts were actually learning a bit about the data as we went through some of the process.

11:07You could see the shape of what was the hot spot in this area so to speak; it took on a different character.

11:16Now let me go through a bit of what we did to get from the beginning to this point.

11:23So the big things that changed are the colors, then the map elements and a lot of the wording...

11:28...and what you would read about this map.

11:29So in other words, how we told the story.

11:34So the original colors, I'm looking at this, I think it's...

11:39...I don't know how to say maybe it's popular to choose a full spectrum color ramp...

11:44...these days with heat maps and so on, and at the same time that's a bit loud in a lot of cases...

11:49...so the team was trying to do something like make them transparent so at least it's not loud.

11:54But I'm not understanding what's going on, and actually you can't see it very well here, but the colors of the points...

12:00...were also given the same color scheme, and what was happening...

12:03...is that points were different colors than what the color bands were showing.

12:08And so that's one of the things that I pushed back, like, okay, I'm not buying your analysis model here...

12:13...because there's clearly points in different classes and different color bands; what's going on with that?

12:18So you don't want to present something that's confusing to people.

12:21So that gets into that kind of legibility and clarity. It's like, okay, if you look at a map...

12:25... and you see immediately conflicting information, that's the sort of thing that it's easy to be kind of blatantly objective about.

12:33And you can't let that kind of stuff slip by as you review maps.

12:39Alright, so the final color scheme, this is one of the earlier suggestions...

12:42...was, okay, this is something that's measuring intensity.

12:44And one of the basic cartographic principles for showing this kind of information is that intensity is best understood...

12:50...through a value change, not a color change.

12:53So they started using a single color scheme and then they also started to add a few other things to the map.

13:00One of them was these distance bands, so that you could see distance...

13:05...and make sure that you weren't confusing distance with color change.

13:09And this represents, this map overall represents kind of a middle ground in the process...

13:16...so we wanted to see what else was going on with this.

13:20And there are a few other things that happened on this map.

13:22One is that they started adding some city points, they added some political boundaries to give better context...

13:29...we added an error map instead of just a legend for the error data.

13:36A good title makes a big difference.

13:38I see a lot of titles where it's either the place-name or it's the data provider.

13:45Both of those have different places to go on the map than the title in most cases.

13:50In this case we're mixing a place-name with an analysis method.

13:54The only thing that was really good about the title was the date, and that's an important thing to have on a map like this.

14:01And one of the things about this sort of map is that we're presenting actually some pretty complicated information...

14:06...to an audience that, odds are, didn't have a college degree that included the word nuclear.

14:12So the difficulty here is, you know, how do we measure radiation?

14:15Turns out that's actually really complicated too.

14:18There're several mainstream methods, or measurement types, and even those vary quite a bit.

14:30So the first attempt was, okay, make it clearer by removing the Bayesian kriging from the title; that didn't help much either.

14:39We finally had a geographer get involved on the analysis team...

14:42...and she took this from the standpoint of, well, what was she actually analyzing?

14:49And that turns into a much stronger title, and this is, I think, close to the final title.

14:53I think we may have picked a different day of values to analyze.

15:01Actually, let me go back here.

15:02So one other piece to this that we'll cover a little bit later is, looking at this title, you know...

15:08...how many of you would actually know what that means?

15:15So maybe 15 or 20 percent of you are feeling somewhat comfortable; nobody like, I know what that means.

15:23So keep that in mind for a little while.

15:26So back to the color schemes, the nice piece of art was a comment of somebody made looking at the map...

15:33... it's like, yeah, it looks pretty, but I don't understand it, and the dots didn't match.

15:38I think that was something I brought up earlier.

15:41Then there were some things that as we looked at the next kind of level of changes that the team made...

15:48...does all of this make sense? So one of the things that we were looking at was the hillshade.

15:51And it turns out that for whatever reasons, whether you're measuring values, just made the hillshade...

15:57...look like it was showing the values at being out of context.

16:02So we were pretty sure that the government was actually and the people doing the measurement...

16:05...were actually recording accurate measurements.

16:07But they had the hillshade there making it seem a little out of place, 'cause sometimes you get different measurements...

16:14Actually added some confusion to the story.

16:16So by taking the hillshade out, that kept people from having to think about one additional piece of information.

16:24The rings actually ended up getting labeled after a while...

16:26...that was these were five-kilometer rings coming out from around the Fukushima Daiichi reactor.

16:40And so we changed things to this kind of arrangement...

16:44...so now each of the rings was labeled like I was saying.

16:47We stopped showing the additional reactor that wasn't having a problem. It was really a story about this reactor.

16:53So again, just focusing the map on what was actually needed.

16:59Alright, so back to the original map elements, so this was the original legend, it doesn't tell you a whole lot.

17:07One of my first comments was that there's - in parentheses right up over here, here in the legend there is a unit...

17:20...and it's USV per H, and so that was not very helpful to me, being the educated layperson without a nuclear degree...

17:32...and so I asked what that was. Nobody on the team actually knew a lot about it so we started doing some investigating...

17:40...and the team actually did a credible job of educating themselves, so these are ultra-sieverts.

17:46It's one of the most common measuring systems for radiation.

17:50The difficulty with ultra-sieverts is there's also millisieverts, and then you'll often see variables expressed...

17:56...per second, per hour, per week, per month, and per year...

17:59...in giving you a sense for how much radiation happens with different things.

18:07And what I wanted to see in this legend was, okay, well, tell me what's normal radiation.

18:12Tell me what radiation I should worry about for, you know...

18:17...if I have a predilection for cancer in my family, what should I be worried about?

18:24And it turned out that that was even debatable; I had a friend of mine who was a nuclear engineer...

18:30...he said don't worry about it, radiation coming over from Japan. Well, fine.

18:34Another person on the team, actually his father was a, and still is actually...

18:39...a doctor who specializes in nuclear medicine, and he had been studying in Japan for decades.

18:45And so he was actually quite helpful in giving us some additional perspective.

18:48So the lesson there is be honest with yourself and that kind of blatant objectivity.

18:55When you don't know something, don't keep going; go back and learn what you need to know.

19:04So part of the analytical method, and actually it's something that's useful for understanding the map, is the error map...

19:12...or the error that can be produced from this Bayesian kriging method.

19:19And so what we ended up deciding to do is just put a map of that on as an inset on here.

19:24So that way, you could easily compare to know where we were most confident of our analysis.

19:33Another question we had, and we decided we weren't going to worry so much about it was that we didn't make a locator map...

19:39...well, yes, we did a much smaller locator map of the island of Japan...

19:45...just to show where we were looking. Even though we were kind of debating, most people were seeing this area in the news.

19:49We're now familiar with this shape of the coastline, and we could've said, okay, well, maybe we drop that.

19:55But in the end, we decided that more people than were watching the news might see this map and it'd be useful to have it.

20:05You can see that small locator map there. And we also added a lot of text to the map.

20:11Part of it was that we wanted to explain what we were doing...

20:16...so how did we analyze it, and then we also needed to explain what we analyzed.

20:21So back to that ionizing radiation thing.

20:25And you can also see the legend took on a lot more text in terms of its explanatory capabilities.

20:33So one of the things about this map is it is actually a complicated topic, so it's okay to put enough text on it.

20:41However, this was kind of a first attempt at doing that, and this gets into that legibility and readability thing.

20:48If you're looking at this block of text, it's really pretty difficult to follow.

20:54It's a little bit wide for what I would want to see on a map.

20:58So what I draw from that is, look at most newspapers, you get a narrow column of information.

21:03And the idea is that you're able to scan down that and read. It's sort of like accelerated speed reading.

21:10In speed reading, you're scanning down the page in a book...

21:11...and your eyes are just pulling off to the sides a little bit to pick up extra words.

21:16This is a little too much to do that with, so what we want to do is just clean that up so it's got a little bit more hierarchy to it.

21:22And you'd want to know, depending what you're interested in, where you could focus.

21:31So we changed that a bit, and also - but before...go back here once.

21:35This was all about the methodology, so there was nothing else.

21:39In this case, we introduced it with what predicted ionizing radiation was...

21:45...then cut the text in about half for just explaining the method...

21:49...and not worrying so much about the technology so much as what the value of the method was to this particular analysis...

21:54...and a little bit of what we did.

21:58We also toned the language down from PhD level to kind of a smart high school student or entry level college student level.

22:07So there's still some, as my drill sergeant in the Army once said, "20-dollar words" in there.

22:13But the idea is that most people can follow this and get something valuable out of it.

22:22So the legend shifted quite a bit, so we actually dropped the observed values for just being confusing and not contributing.

22:31We also had a shorter definition of ionizing radiation here. And we explained what other symbols were on the map.

22:38In both cases for the map's legend as well as the standard error, it turns out they were using ultra-sieverts per hour.

22:45So it was just making sure that everybody knew that that was what was going on.

22:51What was kind of interesting about this - and this just gets back to the story for this map...

22:57...is that, okay, what did we want to say when we showed this information?

23:00Should people be worried? Should they be scared? Should they be reassured?

23:07One of the things we learned about ionizing radiation was that...

23:11...the first two classes in this legend are actually normal values for everyday Japan; it just depends on where you are.

23:21And actually well into the second class is normal in Sweden. Or excuse me, into the third class is normal in Sweden.

23:26So it just depends on where you are in the world as to what normal radiation levels are.

23:32So that meant just really it was just the last three classes of radiation were things for people to be concerned about.

23:39So what we included in those descriptions was actually mandated levels of concern that the Japanese government had.

23:45So these were things that when you measured this level of radiation, that some kind of action was required.

23:53So we actually went out and found what those were.

23:55So that way it's not us just making up our classification or taking the default natural breaks value.

24:01It was actually going in and finding something meaningful, and making sure that we could communicate that to the audience.

24:16Alright, so the final result, the major things changed...

24:20...the colors went from a multihue scheme to a single hue scheme that showed intensity.

24:25I think what was interesting here is that only these three darkest values were areas of concern.

24:32And really just within 10 kilometers. And so you remember there was a 25-kilometer kind of cordon around the area.

24:43So that kind of showed you the second band here. So that got out to about there.

24:50So it showed that, okay, it was well intended, may not have been perfect, as you can see.

24:55There was probably ionizing radiation getting well beyond that, but not at a high level.

25:00So this wasn't going to be at a level that was going to be worrisome for medical problems.

25:05But at the same time, you know, you don't want people staying in that area for prolonged amounts of time.

25:12And actually just to give you an idea, if you flew here from Japan, you got almost as much radiation from that fourth class.

25:17So that's how little radiation actually was out...

25:22...but again, you don't fly for weeks and months at a time, so you're stacking up a lot of flight hours by living there.

25:34Alright, so this next map is kind of a mystery map, with the idea that - Do you have a question?

25:40[Audience question] Is there a reason why you chose not to put the north arrow on there? Is there [inaudible]?

25:45That's a good question. So he is asking if there was a reason why we chose not to put the north arrow on here.

25:50And for the most part, we were expecting that because we had the locator map on with Japan...

25:55...people would be able to orient themselves.

25:58I'm not a big fan of putting north arrows on maps when it should be obvious.

26:02Particularly when you get to a small enough scale, there's an assumption on the audience's part...

26:07...in this case where they are fairly well educated, that they could figure that out.

26:11So this wasn't intended to be a map that was going to go into, say, a fifth grade textbook...

26:16...which maybe I would say, maybe that's where a north arrow would have been helpful.

26:19This was something that, I call it a higher order news agency or magazine might put out...

26:25...so it wasn't as necessary to add some extra piece of graphics to the map. It's a good question though.

26:33[Audience question] Is there anyone else who would like to see the distance rings be concentric circles?

26:41That's a good question, which was, Is anyone else out here who would as you're looking at this...

26:47...feel like distance rings should be concentric circles rather than just arcs on the land?

26:55Nobody brought that up; that's a good thought. 'Cause if you're out looking...

26:59...for instance, if you're looking for people who were washed offshore from the tsunami, that's a consideration in that area.

27:10It's interesting, 'cause that depends on, you know, now - we were looking at historical data in this case...

27:16...but definitely if we were thinking about making this map within a day or two of getting these values...

27:21...that would have been an important consideration.

27:23[Audience question] [Inaudible] projection, you see a lot of maps that are [inaudible]. Should be.

27:31Right, and I'm not - So the concern that's being brought up is that are those rings circles which aren't equidistant...

27:39...or are they actually equidistant buffers?

27:42And I don't remember the answer. I can't remember if the projection supported having circular looking buffers.

27:52Alright, so the next map we're going to look at is a little bit of a mystery map to start with.

27:57And I'm doing that on purpose 'cause what I want to do is to see what your impression of the map is first.

28:04So this is before, and it goes back to that initial comment of a nice piece of art.

28:12And I'm thinking it does look a little bit like something Jackson Pollock might have done in his early days or something.

28:18But, so how many people have any idea what this might be?

28:26[Audience comment] Remote sensing map?

28:27Yeah, so I'm hearing things that we said around our hallway - land cover, remote sensing.

28:32[Audience comment] [Inaudible] map of rainfall or...

28:34Yeah, so I'm hearing like rainfall, and so far everybody is wrong.

28:42[Inaudible audience comment]

28:45So what we were looking at, what we're actually looking at here is soil moisture potential.

28:51So this is how much water, what the soil in these areas contains.

28:55The reporting units are soil unit polygons; the location is in the Corvallis, Oregon, area.

29:02It kind of points out in the US when we do soil surveys, we do them on a county basis...

29:07...where what can happen is that soil scientists in one county will do one thing...

29:11...and then the adjacent county will have another thing happen.

29:15And you can see that fairly clearly here. So one of the things we wanted to do is change the map a little bit...

29:22...so that we would go from something that had a little more hue change than what was, I think, useful.

29:29And what was happening is that because you had all that hue change it was looking...

29:33...most people in our hallway said, "It looks like a land cover map."

29:36Just 'cause they were expecting a lot of different classes of something going on, and they were certainly...

29:40...not seeing a sequence of colors indicating how well drained soils were.

29:45And so the idea is that we wanted two things with this color ramp, not just show you how well drained the soils were...

29:51...but we also wanted to show a little bit of which soils were going to be useful for agricultural production or holding vegetation.

29:58So the notion there is that the soils that are best drained are in green, and it doesn't come out real well on this projector...

30:05...so you're not seeing some of the yellows that are on top of hills...

30:07...that are going to be more aired where it's either running off or it's well drained.

30:12But it does look a lot better, and people, when they look at the new map, see that there is a sequence.

30:17So that was one of the fundamental things that was needing to be changed here. So the color scheme was a big deal.

30:24Initially, we didn't have any labels on the map.

30:26So there was no way to tell where it was or what - for instance, in Corvallis there is a lot of urbanized area as well...

30:33...which will change your interpretation of what's going on.

30:37What we were finding out is that this was done by a cartographer on my team, who was cranking out maps of soils...

30:43...and does a great job of picking colors most days.

30:45It's just he picked colors quickly on this one and nobody else got to see it until after the fact.

30:50And when we looked at it, was like, well, we should've reviewed that, so we went back and fixed it.

31:00So this is the original color scheme, and it looks fairly qualitative. You can see a lot of different hues...

31:06...and that's something that, like I said, people thought it was a land cover map looking at it.

31:12Even when they were familiar with the area, they still thought it was a land cover map.

31:19So by using a more sequential scheme, we're essentially using three hues, going from green to yellow to blue.

31:27And what begins to happen, particularly when you get out of this particular area...

31:30...where the soil scientists weren't in so much agreement...

31:34...is it does take on a pretty obvious sequential look to the map as well, not just to the color scheme in the legend.

31:51So one of the things we were thinking too was that, okay, in this map it's, you know...

31:53...without any other features on top of it, it's really difficult to know the significance of what you're seeing.

32:02And so by adding the additional information...

32:05...so for instance, what you're seeing here is we put our hydrographic reference overlay map on top of this.

32:11So what it's got in there are a little bit of cultural place-names and information...

32:15...but it's also emphasizing the rivers and streams as a network.

32:19And then the dashed and dotted - or excuse me - the dotted lines are watershed boundaries.

32:23So what you expect to see is that where the watershed boundaries are...

32:27...you generally have lighter colors where things are going to be more drained so there is less water in the soil...

32:33...and then as you get towards the rivers, you're going to see more water in the soil, and it's going to be less well drained.

32:45So one of the things that I'd say not enough people know...

32:48...but Esri does require that we do a review of all content we put out to the public.

32:53It includes maps. So like, for instance, on our blogs, we have somebody - there're two sets of eyes looking at everything.

32:59And the same thing is true for our maps.

33:01And that's one thing I'd encourage every organization to make sure they are doing is...

33:06...as you put things out, you could have a well-intended person produce a product that's not quite right.

33:13And you don't want to have to deal with the ramifications of having to do that.

33:22Alright, so there is the final result for that map with the colors on it, and then in general I think that it turned out quite well...

33:31...and it fit actually better with the family of soils maps that were being produced online as well at the time.

33:37So I'm going to go ahead and switch over to having Jim present the work he's done on the food desert maps now.

33:47Great, thank you, Charlie. Thank you, everyone, for coming.

33:52How many of you live in a food desert?

33:56How many of you know definitively what a food desert is?

34:01How many of you do not know definitively what a - there you go. Come on.

34:05Alright, good. The first step is acknowledgment.

34:09So in the Japan example, we had a very scientifically studied locational problem.

34:15Things emanating, people are here; get out of this area. Okay.

34:20And with soils we have a very interesting set of data, very detailed.

34:26This is something that touches on human geography, upon commerce, upon how we travel...

34:36...how we move about; access, if you will.

34:39This is the popular definition that you'll find if you do a literature review.

34:44Food desert is defined by areas anywhere in the world or in a city with limited access to affordable...

34:50...and nutritious food; it's frequently associated with health issues, like obesity or diabetes.

34:58Often it's associated with demographic characteristics, such as income or access to transportation.

35:06Not everyone owns a car. Sometimes competing destinations are included in the definition of what a food desert is.

35:12So it's not just that there isn't good food in a given area, it's that there's a concentration of other types of food.

35:21Clearly there is a behavioral choice.

35:22You can put me a thousand miles away from an In-N-Out and I'll find my way back there.

35:29It's not a problem, for me. I know what I'm choosing, I've passed 40, I've had the talk, I got it.

35:37I'm still going to have my In-N-Out, once in a while. Is my wife going to - no, she won't get access to this.

35:45Finally, access does not automatically result in good choices; we recognize that, this is not about that.

35:51So as you can tell, there's lots of room to define what a food desert is.

35:54So in the year and a half that I've been involved with this at Esri, I've been looking at, well, how important is it...

36:01...that whatever maps that get produced are relatable and they connect the user to a definition that has meaning?

36:10I like to use real-world examples to help create some understanding.

36:13So the map on the left you see...if I can get this mouse rolling.

36:18Let's say, theoretically, I live here. And let's say my two kids love going to this park.

36:23And, sorry, the type's a little small; I see I should have made that bigger.

36:26But the road we take to walk to this park is lined to here, and it says its 1.2 miles.

36:34And the crow fly distance you might get from drawing a ring using a popular software package like, I don't know, ArcMap...

36:42...is .6 miles. So let's think about this for a second - .6 miles is a very easy way to measure that distance...

36:49...and 1.2 miles is what I actually experience as a human being, if I want to get access to that park.

36:56So what's the error rate on that? .6 versus 1.2, it's a 100 percent error rate.

37:02So if I were to analyze every single house in this neighbor, calculating its access to that park as measured by distance...

37:11...I did it with crow fly distance, or I did it with network distance...

37:15...do you think distance might enter into a regression equation if I was trying to analyze some dependent variable...

37:21...like do people in this area use this park?

37:26It might behave a little differently if I'm using something that's a little more reality based.

37:31I don't get to the park, my kids would love to shoot me through a cannon to the park; I might sign up for it.

37:38And so in the case of food deserts, what I started looking at about a year ago is what's out there?

37:43And there's a lot of good small-scale maps out there showing you state or county level information...

37:49...the results of good analysis done by folks who are really interested in human geography and the factors that drive this topic.

38:00And the advantage of small-scale maps is they're easy to work with.

38:03There is a lot of good county data out there from CDC and others.

38:06Medium-scale maps start to give you a little bit more detail at a level where you live.

38:13When I asked you all who lives in a food desert, I've never been in a crowd where...

38:18...I've had more than about a handful of people raise their hands, because it's not a well-defined thing.

38:23There is a whole political argument out there; we're not going to go there today.

38:28What I'm here to talk about is when you start to zoom in a little bit, you start to see different things going on.

38:36Things people have published where on the top left...

38:39...there's a nice grid that's representing various demographic information in context to food deserts.

38:45The food desert locator from USDA just came out recently showing tract boundaries...

38:50...where the results of the analysis done over here is represented as a binary decision; tracts in pink are food deserts.

38:59And similar, Mari Gallagher on the top right has done this map of Chicago...

39:04...where she's translated census tracts into things we can relate to - neighborhoods.

39:10That long list of items on the left, those are all neighborhoods that Chicagoans would recognize.

39:16So that was one of my first clues - oh, so if you make this topic localizable and relatable to the person, okay, they start to get it.

39:25As a colleague, Randy Jackson at Ohio State when I was there, said, "I've never seen a census tract get on a bus."

39:34And so that kind of inspired me a year ago to start looking at large-scale maps.

39:38Kirk Goldsberry out of Michigan State is doing some very interesting things in the Lansing area...

39:43...showing here are the locations of various sources of food...

39:46...and here are the households that participated in my survey, and here is my analysis.

39:50So really good local study, and the map helps you develop kind of confidence about, okay, this is your methodology...

39:58...but, you know, I don't live in Lansing, so I can't quite relate to that.

40:03So one of my inspirations I guess last year was to look at - I'm sorry, let me make sure.

40:09Oh, one of my inspirations I guess last year was, we all have this mental map of food.

40:14I know where I shop, and when I see a walkability index for Redlands, the first thing I do is look at, well, here is my house...

40:21...can I walk to that Albertson's that I go to in the distance they specify?

40:27Oh, they're using straight line distance, I can tell.

40:30And I don't walk to a grocery store.

40:31It's about one and a half miles away, and there's some topography that's unfavorable to my 40-year-old knees.

40:38So I think that relatable aspect, how can I relate to this?

40:42We all have a mental map of our own areas.

40:44And like when you travel, you go to San Diego, for example...

40:47...how many of you know where the nearest grocery store is from you where you sit?

40:50Where is it, what's its name? Shout it out. Ralph's.

40:53Name the second closest? Yep, yep. Similar to how gas prices work, right?

41:03When you need gas, doesn't matter if the cheap gas is one mile further down the road...

41:07...what's convenient to you, what your eye can see.

41:09Probably saw the Ralph's sign; maybe some friendly Esri person pointed it out to you...

41:13...or a colleague, somebody made a map.

41:15But when you are in a condition where you don't have all the options you'd like to have to get the optimal...

41:23...access to what you want, that's where you're sort of at the mercy of the market, the market conditions. What's here?

41:30There's one supermarket that 500 of you just named near you. That's kind of interesting. What if there were 10?

41:37We'd say, wow, there's a lot of choice in this area.

41:39I can go to Ralph's, I can go to Albertson's. It turns out there is just one.

41:42So it's just that mental map we all have in our home, how does that translate to areas you don't know about?

41:50How many of you are from Kansas City?

41:54We have a very nice image on-screen of Kansas City right there; you probably recognized it.

42:00And square in the middle is a proposed supermarket.

42:04If you put a supermarket there in downtown Kansas City...

42:07...that map's telling you you would serve 4,400 additional people living in poverty.

42:11[Audience comment] It's there.

42:12Is it there now? Oh, good. Well, this is a year old, don't tell anybody.

42:17My point is that scale matters; all scales have their purpose. Not all scales can serve the same purpose.

42:24Large-scale maps definitely, showing neighborhood level information...

42:28...tend to be something the public can relate to a little bit more.

42:30Anyone here use Zillow.com? Yeah, Redfin, when you're looking for a house?

42:36So what if Zillow looked like this map on the right?

42:39That's an actual screen shot of a friend's house in San Bernardino County...

42:44...and the price of that house, I think he's buying it like 300,000 or something.

42:48And the median price for San Bernardino County is lower than that. So he's, I don't know, is he getting a good deal?

42:57The large-scale map would give him better context, right, about...

43:00...well, does he have access to other homes for sale in the area...

43:03...that are of similar characteristics and quality and price and availability and terms? No.

43:08So the scale definitely matters.

43:12So Charlie's indicated that the Japan map, someone else made it, brought it to him for review.

43:18The soil water retention map, someone in his own group brought it to him for review.

43:24My name's Jim, and this is my map.

43:26I got it out last year with - it was reviewed, but we didn't get it to where we wanted it to be.

43:32Let me explain what we were going for in this map.

43:35Because the definition of food desert is pretty murky, complex...

43:39...we don't quite have agreement about it, similar to the radiation situation...

43:42...I targeted one main topic with a little something slipped in on the side.

43:49I just wanted to show access, so in blue, the little Ss are supermarkets.

43:54And that's your typical dots on the map.

43:58And in green are all the areas that are within I think it's a one-mile walk to that supermarket.

44:05So all the areas in green one could argue, well, you can drive to it, you can walk to it.

44:08If you're the "typical" person, you're probably well served by this.

44:13So we're going to call the areas in green not a food desert, just based on access.

44:19But the red dots, what's that about?

44:22Well, not only did we draw a half-mile walk time around each or one-mile walk time around each supermarket...

44:35...we also did a 10-minute drive time.

44:37Those dots in red are people who...

44:41The dots represent census block groups with more than 30 percent of the population living below poverty.

44:50So if I know this block group 30 percent of X is below poverty, what's X?

44:56Oh, well, X is a dot. Each dot represents 50 people or 100 people or 25 people.

45:03And so you get both the density of the pattern, as well as just the visual.

45:08Okay, so these areas are not walkable, and because they live in poverty...

45:12...could be the case they don't have a car or reliable car transportation; they have to rely on something else.

45:17So what we wanted to do is just kind of illustrate access.

45:22And so if that's the before, just focusing on access...

45:26...knowing that the food desert topic needs a little bit more to it than just access.

45:32Many of the comments I received about this map were, hey, don't you know there's more to this than supermarkets? I sure do.

45:41You know, farmer's markets, convenience stores, fast food, fast food carrying good food...

45:46...nice restaurants, diners; again, back to personal choice.

45:49There's food everywhere, so how can we have a problem?

45:52How can - who raised their hand that said they live in a food...

45:54How can you possibly raise your hand that you live in a food desert?

45:57So to me that's terribly interesting; why can't this map explain it?

46:01So we were trying at this iteration to blend many of those socioeconomic things.

46:07Rather than just show raw socioeconomic data as dots on a map...

46:12...which it shocks people but it doesn't really grab them the right way, let's try to actually do some modeling a little bit differently...

46:19...to blend that into the underlying surface and create a little bit of a 3D view like this.

46:26So what was changed? We changed methodology, and we changed colors.

46:29Our original methodology was, which census blocks have walkable access to a supermarket.

46:35We used Network Analyst with NAVTEQ data, you can get it in StreetMap Premium...

46:40...and we analyzed from this block, how many supermarkets are within X miles or X minutes.

46:46You know, I ran a half mile, I ran one kilometer, I ran one mile.

46:50Looked at all those results and scored each census block.

46:54From this block representing all these households in this block, there are...

46:58...I mean if we talk about this, I bet the Ralph's is within a half mile.

47:02So that would be a score of one.

47:05So that's what is represented on the map. You do the same thing with restaurants.

47:11In New York City, there's a census block in Manhattan that has access to 611 restaurants within a half mile.

47:20I'm going to try not to let that drive my legend too much.

47:24The goal here was to put distance measurements in full view...

47:27...but again acknowledging it's only a partial view of how people access food.

47:32My purpose in this map was simply to get people to stop using rings and start looking at access.

47:38From where you live, how would you get to these, this network of dots on the map?

47:44'Cause after all, when you look at that map, with all these supermarkets...

47:46...there's 26,000 supermarkets that I used in my nationwide study.

47:51How can you say there's a food - look at all of these supermarkets. How can you say there's a problem?

47:55Well, Kansas since 2007, the state of Kansas lost, I think it was, 84 out of about 200 supermarkets...

48:03...more than a third; since 2007, they're gone, they don't exist.

48:08And the article I read said, the reason that's interesting among other reasons...

48:12...is that the hardware store right next to it is now gone too.

48:15So it's an economic development. It's, you know, this is part of the bad cycle, so it is what it is.

48:22And if we want to talk about food deserts effectively, we need to be able to talk about the components effectively.

48:29So our goal with this revised methodology was to start to put some of those components into the map and see...

48:36...if it rings true for people.

48:38'Cause what rings true in New Orleans might be for different causes than what rings true in Philadelphia.

48:44So we worked in Philadelphia in the time available before User Conference for this project.

48:50Our goal is to, again, create a relatable measure of the food environment where access is just part of the story.

48:56We wanted to create a number of socioeconomic conditions and make them explicitly part of the map...

49:02...poverty, car ownership, percent income spent on food at home...

49:06...from the Consumer Expenditure Survey that the US government puts out...

49:10...measure of crime, rural population, distance to and from restaurants, distance to and number of supermarkets.

49:19So you can see already what we've added, in addition to supermarkets access...

49:22... now we are thinking about restaurants and farmer's markets.

49:25Our approach was to take census block group data for those various categories and interpolate a surface using natural neighbor...

49:32...and then classify the surface into three categories.

49:36If it's a problem area, because its poverty rate 30 percent or higher in that census block group...

49:44...that's probably - I think most people would agree, that should be an area we look at.

49:48One is likely not a problem area...

49:51...but we just want to have a buffer between total problem area and definitely not a problem area.

49:57I live in the suburbs, I've a couple of cars; if one breaks down I can take the other one.

50:01It's not a problem for me to get to the supermarket anytime I need to.

50:05So I'm in that green area, and that's the difference between the fact that I don't have walkable access to a supermarket.

50:12I have a couple of cars, I can get there, it's not a problem.

50:15In other situations, I have a 25-year-old sister-in-law who lives in Manhattan, she walks 10 minutes.

50:24I loved it when I asked her, "How far do you walk to your grocery store?" "Ten minutes." I just wanted to hug her, thank you.

50:29That's exactly what the literature says is typical.

50:33What we did then is create these surfaces for each of those demographic factors that we talked about earlier...

50:39...and then summed them together.

50:42Whereas our original color scheme, green is good, red is bad...

50:46...and people of a certain socioeconomic condition are represented as dots to show who has access walkable, who does not.

50:54What we actually did in this map, we were showing two measures of access, so at this scale...

50:59...and this map is up on ArcGIS.com - you just type in supermarket and you'll find it.

51:03This is a nationwide map showing access.

51:06And if I could ask the - I don't know who's here.

51:09Yeah, my legend, sorry. I should've moved the legend, I should've made the legend bigger.

51:13The areas in brown, you can't walk to a supermarket, but also you can't drive to it in 10 minutes.

51:18You could drive to it in 20 minutes, but you can't do it in 10 minutes.

51:22And that's just again used as a measure of access.

51:25The areas in gray have drivable access to one supermarket. Why did I choose to show those in gray?

51:35Because, I don't know, if they closed in the worst recession since most of us have been alive...

51:39...then those are areas you would kind of want to focus on.

51:43There're areas of opportunities if they close for whatever reason, somebody else could open.

51:46And then the areas in white at this scale, they are totally drivable.

51:50So as you moved in, notice that almost of this map is white because the whole city is traversable within 10 minutes...

51:57...from each of those individual block groups.

51:59So again, I'm taking each census block, rather, and saying, Can I get to at least two supermarkets within 10 minutes?

52:07The answer is yes, you can drive it. So does anybody in this town not have access to a car?

52:12Yeah, I think that the folks at that end over there in south Coatesville, those are the areas of concern.

52:17So the trick for us was to figure out how to represent that.

52:22Again, we used the classification approach to blend all this together...

52:24...all the socioeconomic conditions that might be of concern, and put it together in a map.

52:29And we had this pre-shaded relief version, this is the point at which Charlie dropped by and said...

52:37...Yeah, I've had this idea for a while. Why don't you try and put some hillshading under it and see what happens.

52:42Okay, that sounds good. That's why it's good to have a cartographer nearby.

52:46So we tried it. And the initial hillshading on the left looked like that, and the one on the right...

52:50...represents what we did with a piece of the software called focal statistics.

52:55We ran what's called a focal min, and we looked at every cell in the blended raster...

53:01...and we said, What's the lowest value nearby? And you get a map result like this. It's very pretty.

53:07What it does for us is it takes a relatively flat surface like this and puts some depth to it.

53:14And that's an effect we wanted to have, right? We wanted to kind of show we as a country, we're doing great.

53:20There are little pockets that we think might be of concern for this set of specific reasons.

53:26I'm not going to publish a paper about what we did in a three- or four-week scramble...

53:30...because the point is not our methodology is magic.

53:33The point is we took a very conservative approach about what we're defining as areas of concern...

53:39...put it together so that we could surface right across the street from or the river from Philadelphia.

53:45It might be pretty challenging to get a supermarket chain to agree to build something there.

53:50So what other options are there?

53:51And again, driving back to if we can't describe the individual components and layers...

53:56...we probably can't solve the food desert problem, whatever it actually is, in city by city...

54:02...unless we can kind of show and visualize and have people who are Tweeting, I live in a food desert...

54:08...raising their hands at a public session, I live in a food desert.

54:10What I want you to do is go type in your address to a map like this, see what it says, give us feedback.

54:15And that's, I think, one of the points of doing this on the web.

54:20The Japan example was a print map.

54:22We hope that maps like this allow us to put together a user experience where you can kind of validate...

54:28...yeah, does this work for you, does this not work for you, similar to the USDA food locator.

54:34In this case we applied a 60 percent transparency on the final result.

54:38And the effect, I'm afraid it probably works well from midrow back.

54:44I think these are the 100-meter cells, I'm not sure. But these, the idea is that those red areas should be kind of recessed.

54:53And that's an effect we're going for to kind of grab ya.

54:59I want to give a special thanks to Clare Steiner - can you stand up? - our intern extraordinaire...

55:05...who did the reclassifications and the blending for us for this project.

55:10Clare's been a good sport putting all this together under pressure.

55:15She graduates in May of next year, so Clare will be up here.

55:20I want to thank you for attending; we're going to take questions now. And I'll remind you to take your - fill out your evaluations.

55:27There's an online; it's at esri.com/evaluations I think.

55:33Thank you.

Copyright 2014 Esri
Auto Scroll (on)Enable or disable the automatic scrolling of the transcript text when the video is playing. You can save this option if you login

Comments

Thank you for your message. We have tested the video and are not able to duplicate this problem. Do you mind refreshing your browser and trying the video again? If the error message still displays, could you please tell me what browser/version you are using?

We appreciate your patience.

Karen Jaffarian
Esri Product Marketing

KarenJaffarian

Dec 22nd, 2011. 1:29:40 PM

videos not working properly along with picture

choudharipp

Dec 7th, 2011. 4:13:55 AM

You're welcome. We're glad to hear you're finding the video helpful in your work. Thank you for your feedback.

KarenJaffarian

Nov 15th, 2011. 3:01:11 PM

Thanks for this video, creating great "Geographic Information Products" is something that will make all the analysis we're all so busy doing worthwhile. It is a great confidence boost to know all these best practice approaches you've learned over the years is very in-keeping with my own map making culture. Thanks for bringing it to the masses!! Love it.

nitesky

Nov 15th, 2011. 2:55:09 PM

Comment on this Video