Archive for November, 2013

Enjoy the Holiday

I got out the globe last night to visualize the flight path one of my holiday visitors will be taking from Singapore to Colorado via Tokyo. Sometimes you just want to learn old-school style.

A moment to give thanks…I’m thankful for the people who put the cartography books’ principles into action. @vtcraghead recently tweeted one of his maps, which uses a palette from Cartographer’s Toolkit.

I’m also thankful for cupcakes.

I hope everyone in the U.S. has a great Thanksgiving. I know my relatives and I will if the amount of butter currently residing in the fridge is any indication.

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Today’s Colorado snow seemed like a good backdrop for GlobeMan. It wasn’t until I looked at this picture that I realized that, wow, that is one bad generalization effort. Africa and Europe look very sad indeed.

Don’t forget to tune in to James Fee’s GIS hangout today. It’s at 11am PST. PostGIS Day Extravaganza Panel.


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Effective Data Visualization: The Argument


I hate taking strong positions. There are no strong positions that are correct!* Here’s one stated on the ol’ social media circles today:

beautiful data visualization < effective data visualization**

Give it some thought. No really, do. Here’s the real deal:

About 90% of the time a beautiful data visualization is an effective data visualization. If it’s beautiful people are going to look at it, which gets you almost fully to the “effective” state. I realize that’s a bit cynical, but really, just getting people’s attention is the hardest part. I’m thinking 10% of the time you might have a beauty of a map but it tells the wrong story, in which case it’s not just ineffective, it’s misleading at best, unethical at worst.

However, I truly believe that the opposite situation favors the visualization side of things a bit more. Therefore, I’d be comfortable saying that 80% of the time an effective visualization is beautiful (rather than 90%). I’m going to get flack for this, especially as a teacher of effective design, but truthfulness requires even myself to admit that there are times that less than attractive visualizations are still effective.

This is especially true for visualizations of NEW data–data that nobody has ever mapped before. These can get away with a bit of ugliness just by their sheer inventiveness. Here’s a great example from this week:

I tweeted this morning about a new visualization of data surrounding the purchase of BitCoin in various parts of the world. I tweeted it because the subject matter and the implications are fascinating. I didn’t tweet it because of the visualization being “pretty”–which it most assuredly isn’t. A map of something more typical, say population movement, I would’ve wanted to look better. But this one was so new and unique that I was willing to put aside thoughts of the rudimentary map style and overlapping text.

If you want to maximize your effectiveness, though, it is better to err on the side of design: fonts, colors, text placement, line width, projection knowledge, and the like, since there aren’t going to be many times that you get the chance (or think of) such a great new visualization that allows the rest of us to momentarily forget our taste.


**no disrespect meant, ht @Option_Explicit

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Are Creative Products the Result of Randomness?


How do you become a good cartographer? This is the question I was ruminating on yesterday. If you really think about it long enough it’s amazing how far this one question takes you into psychology, philosophy, and the science of teaching. The easy answer, and the one that I’m known to espouse, is that it takes (1) study and (2) practice.

But what needs more attention is the pathway to get from study and practice to true creative success. For one thing, you can’t simply copy what you study in order to be a good cartographer. Indeed, the map products you create have to be yours–original. I’m guilty of being rather flippant in one of the books, where I simply say that through lots of studying and practice you’ll eventually get to a point where the particularities of your data and study area and audience will all contribute to the uniqueness of the product.

It’s not that that’s not true. But we still have a serious issue of how you get from study/practice to original and noteworthy cartographic design.

Random. I feel that randomness has a lot to do with it. Some ingenious efforts are a result of randomness. The practitioner who makes a creative breakthrough somehow has enough sense to duplicate the result of that random design breakthrough and publicize it. This is how new techniques come into being and its also how new “good” cartographers are made.

Now, the implication of this conclusion is decidedly painful. It implies that every great creative achievement through time is essentially the result of one or more people, who have had the requisite study and practice, randomly discovering something new. So why bother trying? The key here is that you can’t randomly come up with something creative without that experience and study. That’s where the trying comes in. Then, if you hit yourself regularly with a healthy dose of idleness in order to foster those random synapse connections, the creative successes will might come. I still think its a rather depressing idea. Let me know what you think.

The main inspiration for writing about all this was actually an opposing idea: does a lack of ability to notice detail mean that you can’t be a good designer? If you are someone who never remembers where the car keys are, a big picture kind of person, what chance have you?


Depiction of Variables on Maps, Methods


In “Some Truth With Maps: A Primer on Symbolization & Design,” by Alan M. MacEachren, the various means of mapping a variable’s intensity are listed and described. Most of these are relevant to mapping today. I’ve included my interpretations and modern updates to these below. I’m using my usual method of illustration here–map slices–just so we get the essence of the idea rather than getting caught up in the map itself.


Size map readers assume that large symbols denote high values, medium symbols denote moderate values, and small symbols denote low values. Don’t mess with the reader’s expectations on this. Symbol size differences, as long as there are only a few, are easily differentiated by the human eye. This makes this form of mapping fairly ideal. It’s pretty obvious, but only use symbol size differentiation for ordinal data. The example below is a recent implementation of the concept, utilizing hexbins to query and display Walmart location data.


Shape Shape is used to denote type, or nominal, data. Because shapes are harder for the human eye to distinguish, the map maker must ensure that they are large and relatively few in order to enable the map reader to more quickly decipher the map. The argument is that a map with a lot of small shapes to look at is not going to be very effective since it will take too long to understand. Icons to depict place-type is probably the most common implementation of shape-based map design. Here, Matt Stevenson’s trail map has only a few, large, icons.


Color Value The lightness of a single color is varied to denote the magnitude of an ordinal variable. This is most useful for heat maps, where the map reader doesn’t need to check the map legend to determine an individual value, but rather only needs to know where the general trend is high or low. With choropleth maps, however, where the reader may want to check the legend to see what each shade means, color value is less useful. More than five shades and it’s too difficult to match the color to the legend key.



Color Hue Nominal data is well suited for display via different colors as long as (1) audience color deficiency is accounted for and (2) the features are large enough to enable the colors to be adequately seen. MacEachren warns against using color differences for ordinal data, saying that people don’t understand logical order of hues (as indicated by the order in the electromagnetic spectrum). However, I differ with him on this point, as I believe that a diverging color scheme–two colors that vary in intensity–can be effective. Blues and greens are common in diverging schemes and are fairly easy for the modern map reader to decipher. This dot map of septic system locations colored by age uses two colors: yellow denotes the youngest age and dark orange denotes the oldest.


Color Saturation The purity of a color is its saturation. This is easily confused with value. The most highly saturated colors (e.g., pure red, green, blue) have medium values. A color palette with a lot of muddy looking colors is a low saturation palette. The infamous fruit palette that we used to see a lot of in GIS maps is a high saturation palette. Depicting the magnitude of something based on saturation will result in a palette of a single hue that ranges from grayish to the least gray. The heat map below is part of a kernel density analysis of aged septic systems.


Texture Maps that use texture to differentiate types in area data are thankfully few and far between today. The fact that it is listed in MacEachren’s book is due to its vintage–when we could only publish maps in black and white we had to use texture a lot more. That didn’t mean it was good, just that it was one of the few options. I always say that if you have to use texture, use it as sparingly as possible by confining it to categories that have the least number of areas or the smallest areas. Data that often requires the use of texture includes geology and soils data, simply due to the fact that there are too many types of things to distinguish to rely on color hue alone. This older map of Yosemite does an okay job of it.


Arrangement Pattern arrangement can indicate type. Different line/dash patterns can differentiate horse riding trails from bike riding trails, for example. MacEachren points out that area data can have a different arrangement of, say, dots, in different areas to distinguish them. Honestly, I’m not seeing much use for this and while I think that density (discussed later) is an important graphic variable, the use of arrangement is antiquated. The example below, therefore, is just a very simple road vs. trail map.


Orientation While we don’t see this too often, it’s by far one of the most visually stunning effects that we can do with our data. MacEachren states that humans are able to quickly and effectively decipher orientation differences in symbols. I feel that orientation is best reserved for data that has a real orientation to map (e.g., flight routes, ship traffic). I assert that there is no need to differentiate non-directional data with orientation as more suitable methods exist. For example, MacEachren’s book has a map that differentiates oil, lumber, reactor, and coal point locations with variously oriented bars when it could be more easily accomplished with hue or even shape. Ocean surface currents, however, is a dataset that has innate orientation, as in the example below, and is particularly suited for orientation-based symbology.


Focus The fuzziness of the features can be manipulated via fade-outs to create a contrast between sharp and imprecise that can indicate unambiguous to ambiguous, respectively. While MacEachren lamented that focus hadn’t made an appearance :) in many maps at the time of his book, we see it more and more on digital maps now, and with great effect. This canopy height map shows nothing for areas that have (presumably) low or no canopy.

As you can see, in practice, cartographers very often employ more than one of these techniques at a time. Quite simply, if multiple techniques enhance clarity rather than muddy it, then they work.


Density MacEachren doesn’t list density in the book, but it can’t be left out today. Dot density mapping, where one dot is equal to a percentage of what’s inside an area, or even where there is a 1:1 relationship between the dot and the variable, is used a lot today. These have implications for method in terms of how many dots are grouped and at which zoom levels, for digital interactive mapping. Each implementation will differ depending on the data and the interpretation of the cartographer, and most importantly: the size of the dots.


Extrusion Also not listed in the book is extrusion, which is the 3D depiction of a variable via “height.” The early implementations of extrusion weren’t very appealing. Software could only extrude simple features, for one thing, and often those features wound up obscuring important data behind them (e.g., city population maps with sky scraper symbols for each city). However, the cartographer can fine-tune this now and make it work for some data. If the data is suited for it, it is a great technique, since people readily grasp the idea that a taller feature denotes more of the quantity.



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On brevity, Comics, and Learning Anew

Cicero said, “brevity is a great charm of eloquence.”* A recent client project led to the perfect chance to put this idea in action. We’ve put together an entirely new concept that is difficult to explain to people in a few sentences or less. But place the concept in cartoon form, and the main idea becomes much more clear.

Now, I’m no cartoonist, but it seemed like a comic was in order for the script that my client set out. Originally he had wanted it just to be in text. But what’s the fun in that? People need visuals. That’s what we cartographers are here for. And if that cartographic role takes us into comics once in a while, then all the better!

Thankfully, a bit of knowledge in a graphics program is all you need to do something like this. I did it the same way any newbie would approach a map design project: look for an inspiration piece, then find the tutorials to make it happen. My inspiration was xkcd. Some experimentation led me to use the calligraphy tool and a few tutorials on comic bubble making (which I later discarded for the simpler looking xkcd style) and a glance at how others were doing their stick figure line art got me to this point.

It’s an extremely simple cartoon on the face of it. But as a person who’d never created one before, I even had to look up such seemingly mundane things as how other cartoonists draw the frames and how they deal with characters who aren’t really there. Professionals, I’m sure, think of these things as second-nature. We cartographers, too, have to remember that what comes second-nature to us after all these hard years of trial-and-error, research, and practice have gotten us to a great place.

I once had a professor who suffered a stroke. His class was almost 100% memorization of material–large woody plants and their scientific names, to be precise. It wasn’t until he himself had to relearn all the names after the stroke that he realize how hard it was to memorize everything from scratch. At that point he was slightly (only slightly, unfortunately) easier on his students.

The cartoons I made:


One thing I love: clients who have work that’s never boring.

*For a most amusing pronunciation of “Cicero” click here. I clicked on that link when the room was quiet and my sound was turned up. The subtle haughtiness was hilarious. Maybe you had to be there. Also, if you haven’t before, pick up a free e-copy of Cicero’s Treatises on Friendship and Old Age and read a bit of it each day. Redeeming.

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