We are so pleased to accounce that indiemapper.com has launched and is ready for everyone to start making beautiful maps right away. Sign-up for you 30-day free trial. Watch screencasts of what indiemapper can do for you. Once you sign-up, you can always browse our easy-to-follow tutorials and support site to get you started, or if you are like most of us, just dive in and have fun.
Happy map making!
The title was one of the opening statements made by Google’s “technology evangelist” Ed Parsons in a recent talk for the British Computer Society. In the talk he argues traditional street maps are bad (all of them) because they fail to engender a sense of place and because they abstract the world using map symbols. He goes on to say Streetview is good and doesn’t suffer any of these problems. So is Google Earth. The take-home message is that 2D is bad! Maps symbols are bad! Photos are good! And paper is bad! [subtext: Google doesn't make paper, but if we did, we might soften our stance].
Here is my concern: I’m not aware of any research to support such simplistic claims. Merely saying them, repeatedly, doesn’t make them true. The wayfinding research that I have seen shows that for some users, for some map reading tasks, yes, absolutely Streetview and Virtual Earths and geo-tagged photos can help. And for some users and some situations paper is better than pixels. And for some users, and some kinds of data, 2D is better than 3D. But none of those statements is a blanket truth and by outright rejecting all traditional maps in his talk–even if just for wayfinding on mobile devices–an otherwise solid argument is overshadowed by hyperbole.
If drug companies made arguments like these they might try to convince us by saying “Aspirin is bad. Aspirin may make your arms fall off. But our new drug has none of these problems. Use our new drug.” The difference is drug companies are legally obligated to back-up their claims. It is perhaps the reason they don’t employ “evangelists.”
The deeper, more troubling message that we hear again and again is that cartography is little more than making street maps. And the flip side of that coin is the only reason we use maps is for wayfinding. Streetview is very cool (it really is), but it is also pretty specialized in its uses and the advent of it does not in fact “kill cartography.”
Cartography is more than taking photographs of a street. It’s a shame that someone with this level of influence at Google has such a limited view of why we map.
In case you haven’t seen it over on the indiemapper blog, this is a composite view of all the data loaded into indieprojector since it was launched earlier this summer.
I’m pleased to announce we’ve launched ColorBrewer2.org! After 8 years, which is about 80 in web years, it was time to update and overhaul the much-loved ColorBrewer. I was lucky to be a co-designer on the original and with the Flex development talents of Andy Woodruff we were finally able to implement ideas that had been kicking around. This remains totally free and adds some new features that’ll make using this easier and faster.
New Features include:
1. EXPORT: We never really had this before and now you have four ways to get colors out of ColorBrewer: export Adobe ASE color swatches directly into Illustrator or Photoshop, copy and paste color specs, download an Excel file of specs, or even run ColorBrewer right inside ArcGIS (thanks to the folks at the NCS).
2. MILLIONS OF SPOT/ACCENT COLORS: You can now check any spot color against the schemes, not just the pre-defined 8 we use to include. For example, you can now see how well your specific company colors work against any scheme – just type in the hex/rgb/cmyk values and take them for a test drive.
3. FILTERING: You can now narrow your search and find what you’re looking for much faster using filtering by colorblind-safe, print friendly, and photocopy-able check boxes.
4. TRANSPARENCY: This one was much requested, especially by folks who wanted to preview how well the color schemes worked on mash-up tiles and terrain/hillshading. This one was tough becuase the quality guarantee (and testing) behind the schemes was done with fully opaque colors and white backgrounds. So be carefully not to assume that the schemes will work as well once you start changing their opacity and merge them with other map layers, but if you are cautious (e.g., 3 or 4 colors) it may work for your needs.
One of our core ideas of our company is that we can and should donate some a portion of our time to fun side projects. Updating ColorBrewer was just such a labor of love and we believe, deeply, in the need for tools to support the on-going democratization of cartography and also the need for good design in the world. Cheers!
I live with a couple of cyclists, who spend many of their summer days out on the trails west and south of Madison. A few months ago, one of them asked me to make a bike map for him, pointing out what he felt was a shortcoming of the ones available to him: it’s hard to figure out where the hills are. This is particularly critical if you ride in places like the Driftless Area, as my roomates do. A map showing you where to turn and which roads have wide shoulders and low auto traffic is very useful, but it doesn’t tell you how rough the next hill is going to be.
Figure 1: The above is a draft of one of my first attempts, in this case depicting a particular ride that one of my roommates hopes to participate in this summer. click to see fullsize
So I set to work considering how best to show elevation changes along a route, and I came up with a relatively simple concept: encode the elevation of points along the route using line width.
The symbology here is, I think, fairly efficient. By varying the width I am encoding three pieces of information: the elevation of the path, the slope, and the aspect. The first is not particularly useful, but the other two are the critical pieces of information for the cyclist. Importantly, both need to be on the map together – knowing the slope of a hill is great, but you also have to know whether, as you head along the road, you’ll be climbing up that steep grade or coasting down. Getting all that information into one symbol is not necessarily that hard. Both slope and aspect are derived from elevation, so it’s really just a matter of producing a map which shows elevation in a way that makes it easy to see the pattern of how elevation changes. Show the one variable, and your brain fills in the other two. But, it works a lot better if the symbology makes it easier for your brain to figure out how elevation changes. Compare the two maps below:
Figure 2: Using the visual variables of lightness or size to encode data
One encodes elevation along the path by width, and the other by color value. In my opinion, slope is much easier to figure out when line width changes than when the color value does. The color at A is darker than the color at B – but can you quantify how much darker? And can you do it as easily as you can tell how much wider the line is it at A vs B? Speed and ease of understanding are, I think, particularly important given how the maps are to be used. I am told that these will be read by people who don’t even stop their bikes while reading the map (I don’t really know anything about biking – I’m not usually permitted outside the confines of the UW Cartography Lab). So, the map has to work when they’re not looking closely or long at it. The second advantage of line widths over something like color variations is that line widths are more robust – they won’t vary according to lighting conditions, as the users bike in and out of the shade of trees and in varying levels of cloud cover.
The map on the left (using lightness) does have a couple advantages of its own. A small one: by not changing line width, we don’t have to worry about lines getting too wide (causing crowding) or too narrow (and thus being hard to see). The other advantage is really more of a lack of a disadvantage – the highest elevations are not dominant. Look back at Figure One for a moment – notice how the south-center part of the map stands out the most. It’s at the highest elevation, so it has the widest lines. But it’s also mostly flat stretches, which means that it’s not a big deal to our cyclist – they want to know about the hills, about the changes. Encoding elevation by colors keeps the reader from focusing attention as much on the high elevations, which won’t stand out quite so badly.
Instead of encoding something the cyclist doesn’t care about (elevation) and letting them figure out the things they do want to know (slope and aspect), we could just encode the latter directly. Again, though, we need both for it to be useful, and so here’s where it gets tricky. Slope isn’t so bad – it’s just however many degrees the angle is, so that’s something we can pretty easily put into a color ramp, for example. But aspect is the hard part, since it depends on which way you’re going down the road. It’s uphill one way, and downhill the other. You could put little arrows or some other indicator next to the road to indicate which way is uphill. Or perhaps encode the aspect in color hue (red for north, blue for east, etc.) while changing the lightness of the color to indicate the grade. Or, you might try this:
Figure 3: The arrow points downhill, and larger arrows or darker ones indicate steeper slopes.
There are more possibilities, obviously. But I am of the opinion that these solutions are somewhat weaker than simply showing elevation directly – the reader has to process two different symbols (or two properties of the same symbol) and extract two pieces of information. Maybe that’s still easier than processing the symbols to extract elevation, and then calculating slope and aspect internally. But I do not think so. If you present someone with a map they intend to use to figure out the lay of the land, they’re expecting to see the terrain – hills, valleys, etc. Figure 3 above is getting too abstract. It doesn’t feel like land anymore, and so it’s harder to interpret. This is why people like hillshading – mountains look like mountains, and that’s something we can understand without a lot of processing.
I imagine a reader could train themselves to interpret something like Figure 3 faster and easier, since it does show what they want to know with about the least amount of ink possible, and without showing anything extraneous. But that will take effort and learning. Right out of the gate, I think a map showing elevation is easier to understand, because it’s a lot easier to figure out what the landscape is going to look like.
Thinking about the landscape was, in fact, what led me to the initial technique of encoding elevation by line weight. I had simply thought of it in terms of looking down on the world from high up. Roads which are at a higher elevation would be closer to your eye, and so appear larger than those far down in valleys. Whether or not this particular concept is working in the back of people’s minds when they see these maps is another matter, but it at least provided the inspiration. The more academic analysis came later, much of it while I was writing this up.
While I appreciate any general feedback readers would be so kind as to provide, I’m particularly interested to know if anyone’s seen anything like this before. It’s not a terribly complicated symbology idea, so I imagine someone somewhere must have thought of this.
Google Earth is amazing. As we’ve commented here before, it continues to blow our minds and has also done wonders for the popularity of maps. And let’s be honest, it looks super cool. There is no doubt that Google Earth is much sexier than that boring old atlas collecting dust on your shelf: It’s interactive, seamlessly integrates distributed data sources, animates the surface of the earth over time, facilities virtual communities, can be customized by both developer and user, etc, etc. It’s hard to not be impressed.
So all of our maps should be in Google Earth, right?
Wrong.
In fact, despite recent efforts to create a suite of thematic mapping approaches, Google Earth is a terrible environment for presenting many kinds of thematic maps. I’d go so far as to say that the 3D prism maps and 3D graduated symbol maps we see popping up in Google Earth are pure chart junk, of the kind Tufte warned us about repeated for past 25 years.
3D prism map in Google Earth (blog.thematicmapping.org)
3D human figures as proportional symbols (blog.thematicmapping.org)
CHART JUNK
Chart junk takes what should have been a simple-to-read graphic and makes extracting information (1) slower, (2) more difficult, and (3) more prone to reading errors, because of excessive ornamentation and unnecessary design additions—like adding a 3D effect that communicates nothing in and of itself but simply “looks cool.” This is not idle speculation: Research consistently shows chart junk and “redundant ink” hurt otherwise fine graphics.
Want to see for yourself? Download these two example KML/KMZ files from blog.thematicmapping.org and run them in Google Earth. While you’re looking at them try to extract numbers or compare places: KMZ File 1 | KML File 2
“BUT THEY LOOK COOL”: A TECHNOLOGY IN SEARCH OF A PROBLEM
As Abraham Maslow said, “If the only tool you have is a hammer, you will see every problem as a nail.” This seems to be the case with virtual globes and the developers who love them and insist that any and all kinds of thematic data belong there. Instead, I’d challenge us to take a step back and ask,
WHY DO WE MAKE THEMATIC MAPS?
For a long time folks like Robinson, Dent, and MacEachren have been arguing that thematic maps exist to support two basic tasks: (1) the ability to extract numbers/facts about specific places (e.g., 15C in Paris) and (2) the ability to judge those values in geographic relation to other places (e.g., 5C warmer than London, about the same as Milan). In other words, we want both specific details and overall patterns to be obvious on our thematic maps. And we want all of that AT A GLANCE.
The problem with digital globes (as with all globes) is you can’t see half the planet and, due to curvature, really only about a 1/3 of the planet clearly at once. Which leaves us with a conundrum: If you’re only mapping a small place (e.g., a country), why do you need to have it on a globe? And if you have a global dataset, why would you allow your readers to only ever see ½ the data at once? They can rotate the globe (more on this later) but they’ll never be able to see the entire dataset at once. That makes understanding overall patterns very difficult, and asking folks to “remember” half of a global dataset while they spin the globe to the other side is far, far beyond the meager limits of our working memory. If you’re not convinced, just try it.
KNOW YOUR HISTORY
What makes these recent developments even more frustrating is that in the 70s and 80s, with the advent of digital map making, cartographers flirted with, and largely rejected, faux 3D prism maps and 3D graduated symbol maps (like the two examples above) since they suffered from several limits:
Many a thesis and dissertation was written in the past 40 years demonstrating these limits to human visual processing.
The nice thing about Virtual Earths is that you can rotate them, so the problem of visual occlusion is solved, right? Yes and no. Yes, interactivity and the ability to rotate the globe can help reveal hidden places, but no, these virtual globes introduce a significant extraneous cognitive load because the user must now think about controlling the globe (not always easy with a mouse) while also trying to focus on the thematic content. In fact, adding a complex task, like visually acquiring the Google Earth controls and then trying to figure out how to move/scale/reposition the globe between two other tasks effectively “flushes” short-term working memory. It’s a kind of mental sorbet, which is why giving folks something distracting to do is a common trick in memory tests (they lose their train of thought). Why would we deliberately do this to our map-readers?
BIG PROBLEM: INCONSISTENT SCALE
In the examples above it is really hard to judge relative sizes. Why? Because the scale of the symbols is constantly changing, and the ones closer to the viewer are much larger (and at a different scale) than the ones far away. Given that it has been long established in cartography that people are terrible at estimating sizes, and even worse at estimating volumes, it is utterly inane to compound this failure by drawing the symbols at different scales. Of course it is worse than this: Rotating the globe slides each symbol through its own scale transformation path, changing in size with every pixel the maps are moved.
This is an absolute rule: If you want to give people the best chance to judge the relative sizes of objects, they should all be drawn at the same scale.
STILL NOT CONVINCED? LET’S DO SOME USER TESTING
Judging height is critical to the success of this map, yet most heights are obscured
TASK #1: As quickly as you can, how does Nepal compare to Uzbekistan?
TASK #2: As quickly as you can, find all of the other places on the map similar to Nepal? Which place is most similar? Which one least?
Hard, isn’t it? To be honest, it shouldn’t be: A regular 2D classed choropleth map or proportional symbol map would make short work of those questions. So what did we gain by extruding the countries up into space? Not much that I can see.
SOLUTIONS?
I need to be convinced of two things: (1) something is fundamentally wrong with our proven and highly efficient planimetric thematic maps, and (2) that reprojecting this data onto a virtual globe somehow solves those problems. Otherwise, we truly have a cool new technology in search of an application, and that’s just putting the cart before the horse.
Some suggestions: First, unless the 3rd dimension communicates something and isn’t merely redundant data already encoded in the colors, sizes, etc., do not include it (for all the reasons outlined above). Second, if you want folks to perform “analytical map reading tasks” such as estimating relative sizes, distances, or densities, keep scale constant. Third, do not obscure parts of the map behind other parts if that isn’t inherently relevant to the data (e.g., this is fine for terrain visualization). Fourth, and most importantly, do some user testing before presenting a new technique as the best thing ever: It’s how research works and why it is important.
So what things are Google Earth (and other Virtual Globes) good for? The consensus around here is (1) to engender, quite powerfully at times, a qualitative “sense of place” or “immersion”; (2) for virtual tourism (e.g., sit on top of Mt Everest) or virtual architecture/planning; and (3) to perform a kind of viewshed analysis and see what can and cannot be seen from locations (line-of-sight). All of those are inherently 3D-map reading tasks in which the immersive, 3D nature of the map is important. By comparison, population data (one number per country) is NOT inherently 3-dimensional and is only made to suffer when dressed-up in prism maps and 3D figurines.
Cartography, like all good design, is about communicating the maximum amount of information with the least amount of ink (or pixels). The world is just too complex and interesting to be wasting our ink/pixels on non-functioning ornamentation.
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