Finding a Doctor in Non-Geographic Space

I sometimes feel bad about this, but I’m going to let a map I just received today jump to the front of the queue, as it was in the right place at the right time. This one comes from my colleague Tim Wallace, who was sitting next to me filling out a health insurance form.

From the 2009 UW Health Directory, located at http://www.unityhealth.com/apps/FindADoctor/

It may surprise those of you who are not from the area to learn that Dane County, Wisconsin, does not look very much like that. The rigid lines of the Public Land Survey System, combined with the Wisconsin River, have left us with something that looks more like this:

Taken from Wikipedia article on Dane County.

Now, that being said, the fact that this map has a very high level of generalization is not inherently problematic. There are lots of very generalized, even cartoonish maps out there.  It depends on the purpose and audience of your map, and sometimes fine details are not important. But I think they significantly overdid it here. It looks rather comic, and this clashes with the professional application (a healthcare provider directory) to which it’s being put. It seems haphazard and only loosely related to reality, and that makes me doubt the rest of the information that goes in to it.

The real lesson from today’s map, which applies to a lot of maps out there, is that it should not have been made. It conveys no useful spatial information. I can’t use it to figure out how to get to any of the UW clinics, and while I can sort of tell where clinics are in relation to each other (the heavy generalization makes that an estimate at best), that’s not really useful information unless you choose your health clinic based on its proximity to other clinics or cities. “I’m sorry, the village of McFarland has asked that I stay at least 10 miles away at all times, so I’ll need a clinic an appropriate distance away.” So, barring bizarre circumstances, I cannot tell how having a map is better than having a table of clinic locations. A table would, in fact, be significantly more useful, because you could use the clinic address to actually figure out how to get there. Not everything that has a location needs to be mapped, or can be usefully mapped.

Also, if you compare the two maps above, you will notice that the city dots for the clinic map don’t really bear a lot of resemblance to where those communities actually are in Dane county. And, in case you’re wondering, I spot checked this against the addresses for the clinics in Belleville and Verona, and it still did not match. The dots have only the loosest connection to reality. If geography really means so little here, why make a map? Again, a table would be better. The only reason the author of today’s map can get away with this level of generalization and haphazard dot placement is because the map is scarcely conveying any geographic information.

The labeling could be better, though it’s not horrible. It needs more consistency in how far the label is positioned relative to the dot. Look at Cross Plains vs. Waunakee. Why not put them both directly under the dot center, if that’s what you’re going for? And this is not to mention the lack of corner positions. It is conventional, and, as I have often been taught, rather easier to read, if you put the label in a corner position — that is, up and to the right of the dot, or down and to the left, etc. Fitchburg is also ambiguously placed — the label is about the same distance from two different dots. Would have been just fine if it were off to the left of the dot. Now, to be fair, not all labeling can be ideal, because geographic realities get in the way. But, the author of this map does not appear to have been strongly tied to geographic reality anyway, so I’m not sure if that’s an excuse in this case.

This map does not need a legend.  A good map title should tell you what the map is about; since this map is about only one thing, if it’s well-titled I should be able to figure out what the red dots mean. If I’m looking at a map that’s titled “2009 UW Health Clinic Locations,” I’m not going to mistake those red dots for bowling alleys. Or cheese factories. Also, the dot in the legend is not only larger than the dots on the map, but a different shape. The ones on the map are more elliptical.

I was a little confused about the statement in the legend that says the communities of Black Earth and Cambridge are excluded — both are in Dane county. Skimming the report a bit, I think it’s because they’re not part of the UW Health Network. In which case, of course they wouldn’t be on the map — why would they bother to mention that? Chicago is also not included on this map, but they forgot to mention that one. On the other hand, if they are in the network, I have no idea why they would be left off. Maybe the author is no longer allowed to make maps of Black Earth.

In the end, this looks to me to be a classic case of “a table is too boring, let’s make a map!” But a table would have been a lot more useful for people who actually want to find a doctor.

One Nice Thing: The author set the county label in a different type than the city labels, strongly distinguishing them from each other.

The Eiffel Tower is not a Building

Good day, gentle readers. I am lately returned from a couple of trips to lands outside of Wisconsin. NACIS was wonderful, and it was great to meet many of you in Sacramento. While there, I learned that Tom Patterson, creator of the Kenai Fjords map which I praised in my last post, was slightly disappointed that I did not point out anything negative about his work.  Looking it over again, I will say that, for the elevation marker points on his map (mountain tops and sea valleys), the labels positioning could be more consistent.

I’m really nitpicky. My students love it. At least, that’s how I interpret their annoyed stares.

The subject of today’s post is once again one of my own works. This is in response to a conversation with my adviser, Mark Harrower, who pointed out that most any map, from the best to the worst, could be improved by some critique. I have previously featured one of my worst maps on here — Mark challenged me to instead show him one of my best, and then post his comments on it. So I picked my favorite, a map about the tallest buildings in Europe during the last 125+ years:

Rising Skyline, by Daniel Huffman

Rising Skyline detail

Legend

Here’s what Mark had to say (and he warns that some of these are nitpicky — his students, too, love it):

“The categorical color scheme for the kinds of buildings doesn’t work to my (r-g colorblind) eyes. Hospital and museum look identical.”

He’s quite right – I’m always embarrassed by this sort of thing, forgetting to design for people with abnormal color vision. Actually, I’m surprised that it was Hospital (pink) and Museum (grey) that got him. I would have figured on Religious (red) and Residential (green), but I suppose those two colors are distinct enough in lightness that they’re still separable.

“I don’t like that you have both vertical and horizontal timelines, it requires too much work to get this, and the vertical timeline took some time for me to understand in part because going down is more recent. Physical geographers/geologists like their vertical timelines too, but I think they arrange them with newest at top? Nonetheless, for most users I suspect a left-to-right timeline would be more graspable (oldest on left).”

When I started putting this map together, one of the people I showed it to didn’t like the “empty” spaces along the left and bottom of the map. I responded by adding in these timelines. This is probably one of the worst justifications you can give for adding something to a map: “I needed to fill space.” The vertical timeline was vertical because I needed to cover a vertical space. I think the data are interesting, to be sure, and related to the subject of the map, but it could do without such a timeline. Or a much smaller one. I did a redesign of this map recently for inclusion in a textbook. I had to shrink it from its normal size of 24″ x 18″ to about 6″ across, so I had to cut out the graphs. I think it looked better, less cluttered, with the graphs gone. Empty space is nothing to fear. Sometimes it’s a problem, but I think I went overboard in trying to fill it with graphs and little annotation boxes.

“The data are interesting but I’m not convinced they need to be mapped. Is there a spatial pattern to see here, beyond the obvious one that big cities tend to have more tall buildings? Does the spatial arrangement of these cities tell us something about the data we couldn’t learn from a table? Is space causal?”

I go back and forth on this one — I think there’s possibly something spatial going on. There’s a Communist East vs. Capitalist West story throughout part of the data, though that connection is not as clear as it would be if I mapped another data set along with it, showing something economic (which would, likewise, have to have a symbol that can convey 125+ years of data). Many times a phenomenon is not driven by where on the Earth’s surface it is, but by the fact that it happens to share a location with another phenomenon. I didn’t make that as clear as I could have (I’ve got some annotation going on which helps).  I think there’s also a story of spatial concentration going on here – big buildings becoming something that only big cities have, whereas many small towns had impressive structures prior to the 1950s. But, again, I don’t include a data set that really emphasizes the population differences between places.

“I would like to increase opacity behind the timelines so they don’t need to compete so much with the underlying (and irrelevant) basemap in the corners. The actual data (the lines) are easily upstaged by the basemap and fade effects.”

He’s quite right about that, in my opinion. The lines in the graphs would be easier to follow and focus on if Europe wasn’t going on behind them. Of course, I suspect that if I made the opacity higher, the graphs would start standing out too much — they’re already distracting from the main map. Another good reason to ditch them.

“I don’t know the names of any of the buildings – maybe you could label the lines (at least with some of the famous landmarks?). Without names of buildings, there is nothing to anchor my understanding to (e.g., I know the Eiffel Tower, etc.) – they’re just name-less lines around circles.”

People are probably going to be looking at this map for things that they know. Fun fact: the Eiffel Tower isn’t included here, because it didn’t fit my definition of “building” (which was a tricky thing to nail down). It’s a minor touch, but one that could give people a lot better connection to the data on this map.

One final issue that I have been thinking about lately with this project: it’s pretty complex. Look at that legend — reader education is definitely necessary before engaging with the map. It’s difficult to strike a balance between the transparency of the interface (how easily you get the data off the map) and the depth of the data. I wanted to design this as something you can stick on your wall — I wanted to give it enough substance and complexity that it’s worth examining at some length. Whether or not I have achieved that balance is something I can’t really answer, though.

Before I leave off, I wanted to point out just how much this map was affected by critique earlier down the line in the design process. Here’s what it looked like when I thought I was done:

I showed this to my boss, Tanya Buckingham, here at the UW Cartography Lab, to ask for her advice, as I was planning on entering this into some competitions. Looking back over the comments she made, I notice that she also suggested ditching the vertical timeline and combining it with the horizontal one. She also suggested getting rid of the really big coastal glow and making it more subtle, which advice I took. Drop shadows, glows, etc. should probably not scream “LOOK AT ME I DID SOMETHING FANCY!” The darker color scheme was also as a result of her urging. Both the scheme she suggested, and the one I eventually went with, do a better job of pulling the city dots out from the background and bringing the data to the front of the map.

Writing this up gives me the urge to go back and try and improve it the map, but the process is never done, I suppose. There just comes a time when you must decide it’s good enough.

Two Steps Removed From a Photograph

Hey everyone,

As promised, something a little different this time. We learn a lot from the mistakes of others, to be sure, but we can learn from their successes as well. There are many great maps out there which inspire me to keep going, to keep making myself better. And, of course, there’s something to be said for looking at things that are beautiful.

So, today I’ll make a few comments about one of my favorite maps, which I fell in love a couple of years ago when an instructor of mine used it on his intro cartography syllabus.

Kenai Fjords National Park, by Tom Patterson. Click to go to National Parks Service viewer where you can see the map in more detail.

Detail of Kenai Fjords Map

This is a map of Kenai Fjords National Park, in Alaska, by Tom Patterson, one of the masters of creating terrain relief. Not only is he great at it, but he has a website which helps explain his techniques to anyone interested: Shaded Relief. You can also find some nice, freely available, premade relief images for the entire globe.

The most obvious great thing about this map is the relief. I’ve provided a detail above, but you should click on the first image, which takes you to the National Park Service map viewer, and browse around the image in detail yourself. This is not just some quick, automatically generated terrain relief that you put together in ArcGIS (for an example of that, see my portfolio). Those can look decent, but the Kenai Fjords map is a huge step beyond what most people do. I am not sure as to the exact details of its creation, but he has clearly done a lot of manual work here, airbrushing in Photoshop or some similar program, carefully choosing his colors to show shadows, vegetation patterns, etc. The detail is incredible. I mean, you can even see a fine snow texturing on the top of the ice/snow dome and the glaciers. And small mountain peaks poking up through the snow. This thing is just one or two steps removed from a photograph — just far enough away from one that it doesn’t have that weird mismatched feeling that I get from looking at satellite photo that have been labeled with simple symbols and clean type, as though there are 1000-foot-high letters on the ground. He did his job well, and that means that you don’t notice most of the effort he had to go to. It looks right, it looks natural — nothing sticks out as being obviously wrong or feeling artificial. He even carries the relief into the water, so that the land doesn’t look like it’s sitting on a flat plane.

I will speculate, however, that the beauty of this relief is probably helped out a bit by the fact that the actual terrain of this region in Alaska is, itself, beautiful and interesting (applying this same technique in Kansas would likely produce something less stunning). Nonetheless, it would be easy to fail at doing justice to such terrain.

The labeling and other symbols on the map are still clear, despite what goes on underneath them — they’re not overpowered by the terrain relief. I also like the parts which show how the glaciers have receded in the last century. This is not just a pretty map — it’s a functional one that conveys data.

As I’m writing this, I’m finding it’s a lot more challenging to pick out what’s good about a map than it is to discuss what’s bad. This, again, links to what I said above about how, when things are well done, they’re harder to notice. A bad color scheme sticks out. A good color scheme draws little attention, because it just feels like it’s suppose to be that way. Likewise with the text — above, Mr. Patterson does a fine job of separating text styles. The type used for glaciers looks different than the type for islands and the type for the ranger station, because those are all different classes of things. It looks good, but you don’t think about it because it’s generally what you’re supposed to do.

As I do more of these posts, I hope to get better at pointing out the good side of things, as well. It is, in fact, one reason that I am engaging in this exercise. Meanwhile, I encourage you all to chime in about your favorite (or, if feeling critical, least favorite) parts of this map in the comments section. And keep sending me maps you like (or don’t), and tell me why.

I’m flying out to California next week to attend the annual meeting of the North American Cartography Information Society. I may be off the radar for a bit, but I hope that I will have a chance to meet some of you there.

Not Dead Yet

Hello everyone. Apologies for leaving my corner of the Internet fallow for a month — life has been rather busy as the new academic year starts, and I am going through a long bout of illness which has been sapping my energy. Things should start to pick up over the coming month, I expect. For now, a few bits and pieces of site news:

1. I’m planning on making a few changes around here, in terms of content. While we learn well from critique of others (and ourselves), it’s also nice to have a detailed look at what others have done right, as well. In service of my larger mission of helping everyone (myself included) make better maps, I’ll be putting up some really nice maps up from time to time. This is not one of those FAIL blogs, where someone posts a funny picture and we make fun of people. This is about what we can learn to do better in the future, either from good or bad cartographic examples. The site name will remain the same for now, but feel free to also send me great maps as well as not-so-great ones.
2. You may recall that last month I examined a map which showed seismic potential in the Pacific. A reader, Alistair, wrote to me recently to mention that he was familiar with the map and knew its provenance. It apparently comes from a 1979 article in Pure and Applied Geophysics, titled “Seismic gaps and plate tectonics: seismic potential for major boundaries,” by McCann, Nishenko, Sykes, and Krause. You’ll find their article on pages 1802-1147 of volume 117. There appear to be multiple versions of this map floating around, actually. Alistair sent me a link to a PowerPoint presentation which cites the article and reproduces their map, but the colors are a bit different, though the data appear the same. I’m quite pleased and amazed that someone out there was able to connect the map to a source.
3. I’ve lately received the first ever email from a victim of this blog. Mike, an employee of Kerr Wood Leidal, writes to let me know some more information about their map of hydropower potential in British Columbia. First off, it was printed at 3′ by 4′, so my concerns about the small, crowded symbols are alleviated. It’s actually a version of an earlier map that they drafted, which they could not enter in the contest due to copyright reasons. The original has a better centered projection, improved hydrography, and better point symbols. It’s worth remembering that the maps on here have a story behind their creation, a set of reasons why they turned out as they did.
4. Finally, since I’ve given some friends the benefit of a link here and there on the site, I thought I would do the same for myself. I recently put together a portfolio of my work, if you are curious to see. Feel free to subject me to the same treatment that I give others (speaking of which, one of my upcoming posts will do exactly that). While I’m at it, I’d like to plug my hosts at A Good Portfolio, which is an excellent (and free!) service. It allows you to quickly assemble an online gallery of work with an intuitive interface and minimal hassle. There are few bells and whistles, but I think it doesn’t really need them.

That’s all for now — I’ll be back with more cartographic content in the coming week.

In Need of a Dancing Banana

(Editor’s note – Continuing our series of getting cartographers to publicly criticize themselves, we next feature Mr. Andy Woodruff, proprietor of Cartogrammar and an alumnus of the famed University of Wisconsin Cartography Lab. If you’re interested in following the brave example of Mr. Woodruff, and Mr. Reynolds before him, and showing the world some of your own cartastrophes, please write me at cartastrophic@gmail.com. — DH)

Animated maps can be a delightfully cartastrophic realm, rife with dizzying excessive motion, poorly or over-designed interfaces, annoying sound effects, and (I really, really hope) perhaps a few dancing bananas. Daniel will perhaps be wise to steer clear of them here unless he is willing to give up the rest of his life to unearthing all the bad animated cartography on the internet. With this post I will only lead this blog to test the waters gingerly.

This is a map I completed for a class called Animated and Web-based Maps, instructed by Professor Mark Harrower, the king of cartography at the University of Wisconsin-Madison, and, I should add, a known expert in map animation. It’s animated, sure, and you can click the image to load and play it, but that’s not entirely necessary for what follows. Very briefly, a bit of background on the map: it was made for a lab assignment in which students were provided with a set of county-level agricultural data for the state of Wisconsin for the years 1970 to 1999 and instructed to make an animated choropleth map of a variable of their choosing.

Typically, my first reaction to viewing this map is to vomit at the sight of the colors. Unlike many of the maps that make their way onto this site, mind you, I actually used an appropriate color scheme: a diverging scheme using official Cindy Brewer ColorBrewer specs (and it’s even colorblind-friendly!). But this red-blue scheme combined with the interface color pair of green and more disgusting green… well, I hope you’re reading this on an empty stomach. Otherwise I probably owe you a new keyboard. Oh, and if your eyes aren’t completely filled with blood yet, you just might discern in the background the ghost of a photo I took in Wisconsin’s Driftless Area.

But chalk all that up to bad taste. On to the actual cartographic crimes.

First, the white color in the classification scheme, labeled “0% or No Data.” Hold on a second, 2006 self, there’s a big difference between those two! Zero is a legitimate data value that fits in the classification scheme. “No Data” is a different animal entirely. Ideally one would avoid an incomplete data set in the first place, but sometimes that’s what you’ve got (like when that’s the data provided for the assignment). In those cases, areas without data can’t be indistinguishable from areas with data, or else the map reader can never really know what’s going on. Look at the screenshot: which counties have no data, and which have a value of zero? Impossible to know. In reality perhaps some of those counties should be off-the-charts red, but you’d never know which ones. The counties without data need to be shaded with a color that is not in the map’s color scheme, probably some kind of gray. Worse, in the image above there is in fact only one county with no data, but guess what the tooltip says when you mouse over it. That’s right, “0%.”

Next, two points about the choice of variable to map, beginning with the description I wrote near the legend:

“This map shows the change in the percent of land in farms over the preceding year in each Wisconsin county from 1970 to 1999. This is a percent change of a percent– for example, a change from 50% land in farms to 48% would be shown on the map as a -4% change ( 48 is 96% of 50), not a 2% change.”

It’s as though I deliberately chose the most complicated variable possible, probably in an effort to confuse the TA into giving me a good grade. Though I clearly realized the potential confusion (hence the descriptive example), what I didn’t even think about until now is that the exact same thing could have been mapped without any of the “percent of a percent” nonsense. The percent of land in farms for a given year is the total land area in farms divided by the total land area of the county. The percent change that I mapped is that percent for the first year minus the percent for the next year, divided by the percent for the first year. But ignoring, say, erosion on the lakeshores (and I’m sure this data set did ignore it), the total land area doesn’t change from year to year. So total land area magically cancels out of the whole equation, and it would be mathematically equivalent, and a lot clearer to the reader, to simply show the percent change in agricultural land area. I haven’t taken a math class since high school, and maybe it’s starting to show.

It's mathemagical!

Beyond that, out of all the options this choice seems like a particularly strange thing to map in an animation. The whole purpose of an animated map is to show change over time. But the data are already showing change, so now we’re dealing with change in change. Watch the animation; do you get anything out of it? I sure don’t. Yes, you can see that some years are calm and some are not, but it is very difficult to get a sense of the overall trend of what’s going on with farmland in Wisconsin. It would have been a lot clearer to just map the percent of land in agriculture and watch how that changed over thirty years. Now, this point is debatable because animating change maps is not unheard of. I’ve been told that some important minds and beards have investigated such animation. It can be useful for highlighting or discovering areas of instability. For general-purpose maps such as this one, however, mapping a change variable is best left to single, static instances. If I had animated just the percent of land in farms, the same trends could have been discerned through the animation, and the user would also actually learn something about the amount of agricultural land. A more appropriate use for the change map might have been a single map showing the change over the thirty year span. In fact, the subtitle here, “Change in percent land in farms 1970-1999″ could be realized by that single map.

For a final quarrel, I would argue that the counties on this map should have been labeled. There is ample space, and whereas you might get away with not labeling states in a US map, few people know Wisconsin’s 72 counties by heart. Instead of labels on the map, I forced the user to move the mouse cursor over a county to see what its name is. The rule by which I now try to abide is: don’t lean on interactivity to solve all cartographic challenges. Interactivity as a means to reveal data is a good way to add lots of additional information to a map, but it can also make it easy to be lazy. Laziness is for the map reader, not the map maker. If the information is useful and can be accommodated without relying on interaction, then do it. The specific data value you see when hovering over a county is a good use of interactivity for extra information; the county name is not. It’d be a lot less work to visually scan persistent labels that are sitting there on the map than it is to mouse over counties to see their names one at a time.

And an extra special bonus typographic nitpick: I misused hyphens in place of both an en and an em dash in the subtitle and description, respectively.

One Nice Thing: The animated and interactive features of the map are nearly—but not quite—unbreakable. I won’t mention the one bug I did find recently.

Tectonic Junction, What’s Your Function?

My last post generated a few comments from readers out there who disagreed with some of my assessments, and I wanted to start off today by mentioning that I appreciate hearing other people’s opinions on these things, and that I hope you will all continue to weigh in whether you agree with me or not. On further reflection, I think I was perhaps unfair in some elements of my critique last week. But, I have been ill for the past while, and so I’ll just pretend that my condition impaired my judgment. Of course, I’m still a bit ill now, but we’ll try to avoid a repeat.

Today’s map was submitted by my colleague Tim Wallace, who is responsible for naming this blog. We work in a building that also houses the Arthur Robinson Map Library, which occasionally gives away unwanted materials. Tim found this one on the free map table:

Detail. Obtained from Robinson Map Library, August 2009.

The provenance is unknown – it’s printed on thin magazine paper with a torn edge, and the reverse side contains portions of two articles which don’t identify the publication, though the corner reads “September 1979.” On the off chance you happen to know where it comes from, please write to me at cartastrophic@gmail.com.

I found the logic behind the legend confusing for a good while until I noticed the numbers. It appears that we have a map here which shows seismic risk for various tectonic plate boundaries. Red is the highest seismic potential. A fine-grain black-and-white checkered pattern is the lowest. Peach and yellow are in-between. This seems to come up every week on this blog, but I’ll say it again: if you’re showing ordered data, like high-to-low seismic potential, use an ordered set of symbols (colors, in this case). This is one reason why the legend threw me. Areas marked “Plate motion subparallel to arc” are apparently of a moderate-to-low seismic potential. But, because of the fact that they use a checkerboard pattern, and because I hadn’t the damnedest what that phrase meant, I couldn’t tell that item #4 on the legend was part of a larger scheme. This is worse than just misuse of colors; patterns are being thrown in needlessly now, too.

I could, in fact, still be reading this whole legend wrong, and reflecting poorly on the institution that agreed to award me a bachelor’s degree a few years ago. Feel free to comment if you think you’ve got a more sensible interpretation than my idea of items 1-6 being part of an ordered scheme of seismic potential.

One final note on the colors/patterns: The legend does not explain what the white bands are.

On to the point features. The symbols for successful forecast (presumably explained in the article) and active volcano are overprinted directly on top of the other colors. Look again at the colored bands. The red or yellow appear no different when they are on land vs. on water. The printer simply put these colors directly onto the white paper. But look now at those two point symbols – notice how their color changes based on whether they’re sitting on land or water or on top of something else. The printer put purple ink on top of green or blue or whatever was already there, instead of leaving a white space, as they did for the bands. Not sure what happened there, though there may be a reasonable explanation that someone more familiar with late 1970s printing technology can give. It does make the points very hard to see in some areas – I originally counted four stars, but now I can find eight. It also means that the point features shown in the legend do not match the color found on the map.

I’m hoping the magazine article makes the meaning of the Tsunami symbol clearer. Is this map showing Tsunamis that happened in the last decade? Ones happening right now? Not sure.

Note that the legend refers to various filled areas as being “sites” of earthquakes. Why are these not point features? Earthquakes have an epicenter, and move more in a circular outward fashion than a wide lateral band fashion. There may be more going on, as far as data processing goes (and, again, I wish I had the article that accompanies this), but it’s perplexing. Maybe the author(s) went with bands because it’s easier to see the bands than to dig out information out of scattered points? I’ll not be too hard on this, because it’s more mysterious than bad, without information to help understand why the map author(s) may have done this.

There are exactly two labels on the main map: Oaxaca, and Gulf of Alaska. Maybe those are both significant in the article, but it seems very strange to see just those two. They should probably be set in different type, at least, so that Oaxaca doesn’t look like the name of a sea off the Mexican coast. As a general guideline, cities and bodies of water ought to look different. One of the reasons for labeling things is to help readers who don’t already know what or where these features are. It’s entirely possible that a reader out there actually did look at this and, never having heard of Oaxaca, thought it was a water feature.

A similar problem comes up in the inset. Mexico is set in the same type as Central America. Central America is not (and was not), last I knew, a country. I’m reasonably sure Mexico is, however. But look at how they’re labeled – as though the text symbols mean the same thing in each case: country. And, of course, the tectonic plates are also set in the same type as everything else. Perhaps the mapmaker had a sponsorship deal from the makers of the typeface (I am having trouble identifying exactly which it is, on account of the scan resolution looking at the actual physical document, it appears to be Helvetica). If you are a typeface designer and want to pay me more than I deserve to use your glyphs on my maps, please contact me.

The inset would be better off having some kind of marker to show where exactly it corresponds to on the main map. Perhaps this might explain why Mexico was labeled: to help the reader locate the inset.

The water on the inset is jarring -the white makes it stand out far too much, calling your eye away from the main map. Best make it blue.

Boy, sure would be nice to have a legend to explain what’s going on with the inset. Are those blue triangles historical volcanic eruptions, or maybe earthquakes? Maybe they’re places less interesting than the Cheese Factory. And what are the little round-ish zones drawn in blue, which makes them hard to notice?

If you run this map through a filter which simulates how it might look to a person with the common red-green color vision impairment, you may notice that the green for the land and the orange for seismic potential level 2 end up looking very similar, which is rather problematic if you want to know which areas are plain land, and which areas might kill you in an earthquake.

A final reiteration of the main caveat to these criticisms – the original context for the map is missing, and the magazine article which I hope accompanied it may have helped this whole thing make more sense, and explained some things which seem out of place.

One Nice Thing: Some may disagree with me and say it’s overgeneralized, but I kind of like the simplicity of the linework. I think it works here, giving it an accessible, non-technical aesthetic. Michigan is misshapen, but I’ll live.

Another Nice Thing: Tim thinks it has a nice Schoolhouse Rock sort of feeling to it. Which is another way of getting at what I was saying above.

Where Does David Wilkins Live?

Remember David Wilkins, former US ambassador to Canada? Well, if you do a Google search on him, this map from whitepages.com comes up near the top, showing the distribution of telephone directory listings matching his name:

Found at http://names.whitepages.com/David/Wilkins (click to visit).

Since they apparently generate these automatically for most any name, I thought of doing my own. But, I figured that I would take another opportunity to increase the fame and internet profile of Mr. Wilkins. Can’t pass that up.

The colors are certainly less than ideal – as with so many of the maps seen here, there’s a mismatch between an orderable data set (number of listings) and an un-orderable symbology (the colors chosen to represent those numbers). Though, I suppose one can see a weak progression in the colors, depending on your perspective. But it’s still far from a good match to the data. Running from a light to a dark blue would be perfect. It would also be more friendly to people with color vision impairments.

It would also be nice if I didn’t have to assume that white means zero listings, since it could also reasonably mean “no data available.” Troubling is the fact that some of the small states are filled in with white on the main map, but on the inset, where they are enlarged, they are given a color. The inset needs to be consistent with the main map – else it makes it harder to understand that the inset is, in fact, a zoomed-in version of the main map.

A sacrifice made with a classed choropleth map like this is that you lose some precision in getting the numbers off of it. Look at the states in light blue – they all have anywhere from 1 to 11 listings for “David Wilkins.” Grouping states like this is perfectly reasonable, to help reduce the number of colors used on the map and make it easier for someone to pick out one distinct color and match it to the legend. Some ambiguity is necessary as part of this process. But, look at Texas – the only state colored in dark red. It apparently has anywhere from 43 to 53 listings. It’s the only state in its class – why is the exact number not specified?

The classification scheme in general is a bit odd. There are a few big goals you want to try and go for when deciding how to group your states. One is to minimize intra-class differences – that is, keep the class sizes small. You don’t want a class that goes 1 to 11 listings, and one that goes 12 to 500 listings. The second one is way too broad. Another is to try and make each class roughly the same size, which this map has a problem with. There’s one state in the dark red class, two in the orange class, and twenty-five in the light blue class. A third goal for class breaks is to try and have class breaks that are relatively even in number – as an astute reader points out below, the class breaks change in size just a bit, though they’re roughly pretty even, so I think they hold up pretty well. There are a few other goals, but I’ll leave it at that. As you might expect, it’s hard to fulfill all the goals at once, but the severity of the difference between 1 red state and 25 light blue ones is still pretty bad. The two lowest classes cover most of the country, and the two upper classes cover only three states. It makes those three states stand out, but more than they should. There’s not a large, unusual, and worth-pointing-out difference between the upper and lower end states, to my mind.

These data should probably be normalized, as well. Consider Texas again: a lot of people named David Wilkins live there. This is probably because a lot of people live there in the first place – it’s one of the most populous states. More populated places will probably have more people named David Wilkins. Likewise, you can’t find anyone named David Wilkins in places like Wyoming or South Dakota, because approximately no one lives in those states. The pattern shown by this map is highly correlated to the population distribution of the United States. It does not show whether or not people from Texas are more likely than people from Wisconsin to be named David Wilkins. Instead of making a map of how many telephone listings there are in each state for David Wilkins, the author(s) should plot how many listings there are for David Wilkins per million inhabitants of the state.  Then you would find out that Delaware has 8.1 listings for David Wilkins per million inhabitants, vs. only 2.2 for Texas. The name is also particularly popular in South Carolina, which state the Ambassador calls home.

I find it a bit odd that they have region names listed for New England and the Mid Atlantic, but not the rest of the country. Also, I was under the impression that Maine was part of New England.

One Nice Thing: Those inset maps to the right sure are handy.

With that, I will leave off today’s effort to make this blog the #1 item on a Google search for David Wilkins.

Way Over in British Columbia

Today’s effort comes to us from Kerr Wood Leidal, a consulting firm in Canada:

Detail of map - click for full poster. Taken from http://kwl.bc.ca/docs/CEBC2008-RHAM-FinalAwardofMerit.pdf.

This one was brought to my attention by a reader, Eliana, who appeared particularly exasperated that this map won an award. It seems that the folks at KWL took first prize in the 2009 Map Gallery competition at GeoTec, which bills itself as the largest GIS conference in Canada. According to the GeoTec site, the winners were selected “based on overall appearance and effectiveness as maps.” So, this means they have to a) look good, and b) communicate spatial information clearly. Longtime readers may recall that one of these things is more important than the other. Though this is not to say that making your map look good is unnecessary, and in a competition like this, it’s a fair criterion for judging.

Let’s start at the legend – of the 8200 or so red spots on this map, each one encodes how much hydropower could feasibly be generated at that site. A red dot means < 1 megawatt, a red square is 1 – 10 megawatts, and a red triangle is > 10 megawatts. This is a non-orderable scheme – squares, circles, and triangles cannot be put into an inherent order. So, it doesn’t make sense to use different shapes for different numbers of megawatts — which can be put into order. Dots of different size or perhaps color brightness (but the same color hue – so, for example, a scheme from light blue to dark blue) would be more sensible here.

The other big problem with this scheme is that if you look at the map, you can’t pick out the squares from the circles from the triangles in most areas. They’re way too small to be able to tell which shape is which without staring or zooming way in, and even then it’s sometimes ambiguous. I will add the caveat that I don’t know how large this map was printed – it may be less of a problem if this thing is two or three feet across. And I’m not even sure if this map really needs to go into this much detail. It does a good job as it stands of showing the general distribution of hydropower sites, mostly clustered along the west of the province. If the authors want to add an extra level of information, about how much power might be generated at each site, their task becomes much harder, because now the dots have to be separate enough, and big enough, for people to be able to tell how they vary. And the reader cannot effectively do that, here.

Even if you could visually tell one shape from another, however, it would still be difficult to pick out the overall pattern of where the 1 megawatt sites are, and where the 10 megawatt sites are. Shape, as a visual means of encoding information, is weak in terms of what we call selectivity. It’s hard to select just one shape, and then try and find the distribution of only that shape. It’s much easier to do this sort of task with something like size – you can quickly see where the big dots are clustered and where the small ones are. A quick example:

Notice how easier it is to pick out the cluster of small squares near the center than it is to pick out the cluster of triangles near the center - size has better selectivity than shape.

Moving on from the dots, let’s consider a few other, lesser offenses. The labeling has poor contrast with the background, especially at Stewart and Port Hardy. Interestingly, some of the labels have been set in light-colored type, to better stand out against the water, thus demonstrating that the labeler was mindful of contrast issues. But not enough to make them legible against a mass of little red shapes.

Notice the white area in the northwest. That’s part of Alaska. It looks like it’s buried under an ice cap or something, given the color scheme and the fact that it’s flat, while the rest of the map shows terrain relief. I’ve never been to Alaska, so I suppose it could in fact bear a great resemblance to Antarctica. More of concern to me is the fact that the authors have possibly done one worse than the dreaded island effect. Instead of either showing British Columbia as an island with no surrounding land, or showing it in its geographic context, with the USA and other provinces around, the authors have chosen to include just one part of one of the surrounding areas. It looks very odd, and I think it would look better showing just the province, really.

Speaking of odd, it looks like someone has discovered the joy of the “glow” effect in the Adobe suite of graphic tools, because the entire coast of British Columbia is glowing white. Now, a glow effect can be a great addition to a map, but it would probably make more sense to do one a light blue one that looks like shallow coastal water, rather than giving the appearance that there’s been some sort of radioactive disaster off the Canadian Pacific coast.

Since this is a map about hydropower based on the flow of rivers, where are the rivers on this map? You can see a few here and there, in a very light blue, but the hydrography should really stand out more. Maybe not every single creek, but at least the major ones.

Finally, a note on the map projection. The authors appear to have kept the central meridian for this conic projection somewhere far east of the map area – say, around the center of Canada.  For those readers who may be confused by what I just said, I will avoid giving an entire lesson on map projections. Instead, here’s a somewhat related way of thinking about it: Consider your average map of Canada, grabbed randomly from the Internet – the kind with the curved appearance to it. Doesn’t it look like the authors of the map above took BC from the far west end of one of these typical Canada maps, and didn’t bother to rotate it so that it wasn’t tilted clockwise anymore? If it’s the only thing on the map, BC should be centered so that it’s northern border has a shallow “U” shape, instead of curving downward only. The projection on this map just constantly reminds me of the fact that BC is at the far west end of Canada.  Perhaps the authors wanted to keep that suggestion in my mind – “British Columbia: We’re way over here!” Might make a good provincial motto.

One Nice Thing: The terrain relief is not just a useless bit of decoration – it’s useful, because hydropower potential is affected by terrain. You can see the river valleys and everything (if not the actual rivers, unfortunately). So, the relief here is both a nice aesthetic component, and conveys information relevant to the topic at hand. A win-win.

Before I leave off, I’d like to thank all of you who have been writing in to me and submitting maps you have encountered. It’s a big help to have other eyes looking for these things. I may not end up using every submission, but I appreciate them nonetheless.

An Addendum

An addendum to my last post (given its own post here for the benefit of those of you who’ve already read it and moved in):

I am a bit embarrassed that I didn’t notice this at first, but these maps are very unkind to the millions of people with red-green colorblindness, as several commenters on the original OKTrends post mentioned. Here’s an approximation of what a red-green colorblind person sees when they look at one of those maps:

Run through http://colorfilter.wickline.org/ with protanopia filter

Designers cannot ignore such a vast population, and must take color vision anomalies into account.

Concerning the Value of Human Life

Today’s effort is brought to my attention by one of our readers, Robin, who previously alerted me to the problems of Moon Maps. This map appears in a post on the OKTrends blog, where the authors analyze the geographic distribution of how people answer various questions on the dating site OKCupid. We’ll take one of them as an example:

Obtained from http://blog.okcupid.com/index.php/2009/07/13/sweet-ass-american-trends/. Legend, title, and map combined by author into one PNG file.

Side note: the darkest red, for the highest percentage “No,” shows up as a single pixel at DC. So, I cannot claim that the map doesn’t follow the legend. Though, the DC dot is so unnoticeable it might as well not be. Putting an area on a map which is too small to be seen is a serious problem. Better for the authors to include an inset or two, showing the smaller states in a more magnified fashion (as we say in the business: at a larger scale).

There’s a fundamental error that a lot of people are prone to making with these maps (and, in fact, I didn’t notice myself doing it until I’d spent quite a while looking at these). If you look at the map above, or any of the others in the OKTrends post, there is always one state at each end of the scale. In the above example, Nevada is the brightest green, matching the far “Yes” end of the scale, and DC (thought you can scarcely see it) is the darkest red, matching the “No” end of the scale. This does not mean that 100% of the people in Nevada answered yes and 100% of those in DC said no. It just means that more people answered yes in Nevada than anywhere else. Maybe in DC 2% of the population said “yes,” and in Nevada 5% did, with all the other states in between. But this legend makes it appear as if DC uniformly said “No,” and Nevada “Yes,” with the vote being split in all other states. It is unintentionally misleading, which is the most tragic cartographic sin. The authors wanted to convey some information in good faith, but their communications became twisted and false.

The Yes-No labels on the legend are not the only problem. It’s the color scheme. It’s a diverging color scheme – the green and the red are opposite ends, and states shade toward one end or the other. There are many good reasons to use these schemes, but in this instance, it just makes it look like there are solid No states and solid Yes states, instead of states where a handful of people said yes, and those where a slightly larger handful of people said yes. The color scheme should be one hue, changing in lightness (say, from a pink to a dark red), and the map should show % yes votes (or % no) votes only. This still lets you pick out trends – Nevada has more Yes votes than other states, but it doesn’t mislead you in to a panic about the amoral citizens of Nevada and how they disregard human life. You can tell that “yes” is unpopular, but less so in certain areas.

Another reason to ditch the red-green scheme, which the authors use uniformly throughout their maps: It doesn’t always make sense when the options are other than yes and no. Here’s a map from later in their post:

Obtained from http://blog.okcupid.com/index.php/2009/07/13/sweet-ass-american-trends/. Legend, title, and map combined by author into one PNG file.

These two colors, green and red, have certain connotations of positive and negative in much of the english-speaking world, so here, the scheme is a problem, unless you’re going with an implied value judgment where “Right to Bear Arms = Good” and “Right to Vote = Bad.” The people on the OKTrends blog don’t quite strike me as that type. Nor the type to greenlight an unequal valuation of human lives. And, of course, the legend does again make it look like Idaho is 100% behind giving up the right to vote. I’m guessing (and it is just guessing) that if you looked at the data, the vast majority of respondents in each state said they’d rather keep the vote and lose their guns. But Idaho happened to have the most people who were more fond of guns than voting (hard to hold that against them, given the political climate). Probably just a few percent. But people will come away from this map with the sentence stuck in their mind “Idaho strongly prefers to lose the right to vote.” I have belabored the point, so let’s move on.

Note that there are two states in the lower right corner which are not actually filled in with any color. Thanks for playing, Hawaii and Alaska! It’s OK — they’re just happy to be there, given how often they’re left off of US maps.

The shapes of the states are poorly generalized, and may in fact have been drawn freehand. Possibly just traced loosely, instead. But it has that feeling to it. Many of the states look slightly-to-moderately wrong. Look at my beloved Michigan – it’s become an amorphous blob. Saginaw Bay is entirely missing. It’s a sizable body of water. Like, nearly as big as some states. Also, I’m pretty sure Ohio doesn’t look quite like that. And Maryland seems to have taken over part of Virginia. Wyoming, though, is just as rectangular as ever.

I would be tempted to complain about the map projection, but since I think it’s freehanded, it doesn’t exactly have one.

Note the line around Michigan, showing the water border between the US and Canada as it passes through the Great Lakes. Except, the line only shows the border as it passes through two of the lakes. The actual border passes through two additional ones, north of Ohio, Pennsylvania, and New York – but you don’t see that section here for some reason. So, we have part of a Canadian border. And no Canada, by the by.

The map suffers from the dreaded island effect, where the US looks like it’s floating off in the ocean with no land nearby, and Canada looks like a great and forbidding sea, much like the Gulf of Mexico or the Atlantic Ocean. I might not call this a problem for this map otherwise, but putting a portion of the Canadian border in, and then not actually drawing Canada, makes this a serious issue.

Nitpicky: Both maps have a few artifacts here and there, like in Louisiana in the bottom map. A few pixels the wrong color.

One Nice Thing: It’s an unclassed choropleth, which I rather like. Most choropleth maps organize data into classes – say, one color will represent all states with yes votes between 0% and 5%, and the next color will be for states from 6% to 10%, etc. — the states are grouped. In an unclassed choropleth, such as those above, each state is given a color in exact proportion to the number of votes for one choice or the other. A state with 1.1% yes votes gets a different color from one with 1.2%, because they have different values. They’re never grouped together into the same color, as they would be in a classed choropleth. There’s a whole debate as to which is better, classed or unclassed, and I will not get into it here. I will just say that I am a fan of the unclassed choropleth, so I think it’s a Nice Thing.

Addendum: I am a bit embarrassed that I didn’t notice this at first, but these maps are very unkind to the millions of people with red-green colorblindness, as several commenters on the original OKTrends post mentioned. Here’s an approximation of what a red-green colorblind person sees when they look at one of these maps:

Run through http://colorfilter.wickline.org/ with protanopia filter

Designers cannot ignore such a vast population, and must take color vision anomalies into account.