From time to time, I make quick, one-off projects for my own entertainment, and I usually don’t have anywhere for them to go once complete. So, instead, I will post a few of my recent ones here in hopes that they will amuse you, gentle reader.
Some weeks ago, a friend of mine was looking at a choropleth map and commenting that she liked the mosaic of colors. This got me to thinking about using enumeration units as actual mosaic tiles to create an image. I’m sure someone’s done something like this before, but here it is:
Picturesque New England
While working on one of my river transit maps, I was reading about a lot of various New England towns, and it seemed to me that approximately every other one was described as “picturesque” on Wikipedia. So, I decided to do some wholly unsound research to map which parts of Massachusetts were most picturesque.
Each town is assigned a “picturesqueness score,” which is simply the number of search hits for “(townname) Massachusetts picturesque” divided by the search hits for “(townname) Massachusetts. So, it’s the percentage of pages which mention the town that also contain the word “picturesque.” I used Bing for the searches, mainly because Google kept searching for the word “beautiful” instead of “picturesque,” and I couldn’t stop it.
When I was done, I hastily assembled these maps using Indiemapper, which makes doing unclassed choropleths and noncontiguous cartograms a snap.
This last week I was a guest lecturer in a cartography course, and as part of the lecture I showed a map extracted from one of the old Apple versions of Oregon Trail (to show the representation of mountains). Thinking about the game later that day, I decided to map out the route taken by my intrepid, dysentery-ravaged party, in a way different than the game uses. Unsurprisingly, I fell back on the Tube map style. I swear I know how to make other kinds of maps, and once I finish my river atlas I may never do them again.
The type is set in a bitmap face called Apple ][, which I found at dafont.com. If you use it, be careful of the kerning — it needs some adjustment. But it was, otherwise, exactly what I needed to make this work.
Making this reminded me that I’d been meaning to do one other map in this style, for some months…
I have a friend who is very much into wine, and some way or another, the idea came up that I ought to do an Oregon Trail-style map of the various designated American Viticultural Areas of Oregon.
The image above was created at a size of 320 x 200. If you click, you can view a copy which I tripled to 960 x 600, so it was large enough to actually see. Pixels have gotten a lot smaller since the 80’s.
There is a group of five very small AVAs that are situated in the Willamette Valley, which I had to leave out due to size constraints. Suppose I’ll need to make a second map sometime.
It was a fun challenge to try and fit everything into such a small space and with no color to work with. I can’t say it’s wholly successful, but it was good enough for its purpose as an amusement. I have a mind to institute some sort of bitmap-mapping competition, but my last efforts at competition-sponsoring went poorly, so I may just skip it.
That’s all for the time being. I’m sure I’ll have more amusements in a few months.
Today I’d like to give a little publicity to a couple of new projects I’m involved in, and which need help from people like you. Both of these are organized through NACIS, the North American Cartographic Information Society.
Atlas of Design
First off, NACIS is creating a new publication, the Atlas of Design, which is intended to be a showcase for top-notch cartographic work around the world. We need help from you, though, to make it happen. If you know of some great work out there, let us know at firstname.lastname@example.org. It doesn’t have to be something you’ve made — if you’ve seen a great map out there that someone else has made, encourage them to submit to us, or let us know and we’ll get in touch. We want work out there that gets to the heart of great cartography and makes us think about what beauty and design are.
As the announcement says:
The Atlas will feature a gallery of full-color maps showcasing cartography at its most beautiful, its cleverest, its sharpest, and its most intriguing. But it will be more than a museum of images; each map will be accompanied by thoughtful commentary that guides the reader toward a deeper understanding of the work: its inspiration and message, the ways it means to influence us. It is well to look upon something beautiful and good, but once we understand how it is beautiful and good, we can carry those lessons into our own work and advance the craft of mapmaking.
For more information, including guidelines, go to nacis.org/atlas.
Initiative for Cartographic Education
NACIS is also launching a new education program, the Initiative for Cartographic Education. The aim of ICE is to improve the quality and reach of cartography education at all levels (primary through college through professional training). As its first project, ICE will be assembling a curated database of education resources: labs, lesson plans, images, tutorials, etc. Wondering how other people teach projections? You’ll be able to look at lecture notes and slides from other educators, using them to inspire improvements in your own practice. Creating a new a lab section and need some content? Ready-to-use lab exercises will be available to help get you started. We want to make it easier for colleagues to share best practices with each other, and create an ongoing conversation about how cartography should be taught.
To do this, we need your help. If you have resources you’d be willing to share (preferably under a Creative Commons license), contact me at email@example.com. We can host materials, but if you already happen to have them online, we’ll also be putting URL entries into the database as well.
NACIS is about cartographers coming together to do great things, and both of these projects are going to be awesome. Please consider participating. And please pass this along to as many people possible. We want everyone to know what we’re up to.
Let me tell you about one of my favorite maps.
I’ve seen it on various t-shirts around Madison, Wisconsin, the city in which I have lived for the past four years or so. It’s an emblem of sorts for we who are proud of living on an isthmus.
I love this map because of its simplicity, and how that simplicity exemplifies good and clever cartographic design.
I like to bring this map up as an example to people because it helps explore the edge of the term “map.” It certainly doesn’t look like most maps (except, of course, the increasingly-poplar typographic maps). But to me, its unusual appearance simply brings into focus what a map is and is not. This is a representation of space, and one in which there is a correspondence between space on the page and space on the Earth. The isthmus of Madison runs roughly southwest-northeast, and to either side there are lakes. This relationship is preserved in the representation above. It’s authored, like any map, and it is graphical (it functions through its appearance). Those are the four components of map-ness, to me: authored, graphic, representation, spatial.
So, first off, I appreciate it because it permits me to be needlessly pedantic about what makes something a map. Beyond letting me show off in front of others, though, I appreciate it even when no one else is around. I enjoy its simplicity and its economy. It’s a very highly generalized map, breaking down the area into but two categories: lake and city. It’s a reminder to me that you can still convey a message with a map that is ridiculously simplified. Adding any more detail here would get in the way. All maps tell stories. Some are short, some are long. This map is a slogan. Unfortunately, there are a lot of maps out there that don’t say much, but take a lot of space to say it, and they could learn a thing or two from this one. Here, the message is paired perfectly with the level of visual complexity used to express it. I imagine there are a lot of more detailed maps out there that could stand to be distilled down into three words. I have some problems with those who worship Tufte’s doctrine of minimalistic, ink-efficient design, but those ideas can certainly be instructive.
Finally, I appreciate the fact that, like typographic maps generally, it needs no legend. When you look at this map, you see what it is. The interpretation of map symbols should ideally be seamless. As mapmakers, we work in the zone of representations. People see through maps — they don’t look at Google Maps and see yellow lines and blue polygons. They see roads and water. It is our job to make that representational layer transparent, so that people see what our symbols mean, not how they were constructed. A legend absolutely destroys that transparency, because it makes someone aware of the symbol’s mediation, and forces them to scramble for its meaning.
I’m not a big fan of legends. While they are certainly necessary and useful at times, I’d rather mark things directly on the page if I can. Too often, you find maps like this one, in which the legend serves only to waste everyone’s time. A lot of people have been unfortunately indoctrinated with the notion that a map must have a legend, when they should be used sparingly.
What I would be keen to see is something which expresses this economy of design, and this easy legibility, but does not use type. I’m sure there are examples out there, and it’s something I will have to ponder in my own future work.
Today I have decided to begin offering free PDFs of all the maps that I sell prints of.
There’s a fine line that a lot of people walk when putting their art online. You want people to be able to see (or hear) your work, but you also want to maintain some control over your intellectual property so that people don’t go passing it off as their own or profiting from it while you see nothing. And, if you’re selling something, why would people pay you for it if they can get it free? But, then again, people are less likely to buy when you only share a sample of your work — they can’t be wholly sure of what they’re getting until they’ve handed over their money. And so the arguments go back and forth.
Setting aside my fears, and feeling filled with a bit of faith in humanity, I have decided to embrace openness in the belief that the positive will outweigh the negative, that most people will not harm me, and they will be offset by those who will be kind to me. I have seen it work for others (though, it should be noted, that success stories tend to circulate; artists who are harmed by this model probably don’t get a lot of press).
If you click the link near the top of the page that says “Storefront,” you can see a PDF of any of the works that I’m selling at any level of detail you want. If you want to download the PDF and pay nothing, so be it. If you wish, though, you can also voluntarily donate to me via PayPal based on what you think my work is worth (and what you can afford). So, if you’d like to just print the map yourself and pay me directly, rather than ordering through Zazzle, now this is easy to do. Or if you’d like to print the map off and pay me nothing, that’s fine, too.
I also dreamed once of my river maps having some sort of educational use, so putting them out there free may encourage that far-off dream, as well.
I admittedly have little to lose from this — I rarely sell prints, and I am making these for my own satisfaction first and foremost. I’m slowly generating an atlas, and while I may offer copies of it to interested purchasers, I’m mostly doing it because I want to be able to hold a book of maps in my hand and know that I made them all.
But I’m also doing this because I’m secretly an idealist (with all the inherent irrationality), and I find the notion of a world in which people pay what they want for art to be attractive. Others have gone down this path, and I thought it was time I tried it, as well.
Edit: Now with extra licensing! As per Marty’s suggestion below, I have marked the download links with a Creative Commons license, specifically the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
I have nearly recovered sufficiently from an amazing NACIS conference, and I think I’m ready to get back to a little blogging. This time around, I’d like to present you all with an unfinished concept, and to ask you for your help in carrying it to completion. Specifically, I’d like to show you some attempts I’ve made at improving digital hillshades (I’ll be randomly switching terminology between ‘hillshade’ and ‘shaded relief’ throughout).
Automated, straight-out-of-your-GIS hillshades are usually terrible, and it generally takes some extra cleanup work to get them to the point where they aren’t embarrassing or won’t burst into flames simply by being put next to a well-executed manual shaded relief. Here’s an example I stole from shadedreliefarchive.com which illustrates the problem:
The computer doesn’t see the big picture — that every little bump in elevation can sum to a large mountain, or that some bumps are more critical than others. It treats everything the same, because it can’t generalize. What we’re left with is noise, rather than an image. But most of us, including myself, haven’t the talent to do a manual hillshade. We are left with two options: steal one from shadedreliefarchive.com, or do a digital one and try to find ways to make it look not terrible. In this post, I’m going to talk about some new (or, at least new to me) ways of doing the latter.
To begin, here’s a bit of Mars, from a project I’m doing about Olympus Mons, given an automated hillshade through ArcMap’s Spatial Analyst tools.
As in the earlier example, this image is way too noisy and detailed, especially in the rough area west of the mountain, Lycus Sulci. The common answer to these problems is to find ways of reducing the detail in the DEM so that those annoying little bumps go away, but the big stuff remains. Usually this is done by downsampling, blurring, median filters, and a few other more sophisticated methods that I don’t have time to explain in detail. For starters, check out Tom Patterson’s excellent tutorials at shadedrelief.com, and Bernhard Jenny’s gasp-inducing tools at terraincartography.com — both of these resources can take you a long way toward improving a digital hillshade.
Both of these are an improvement over the original. The major valleys in Lycus Sulci become more apparent, and the flatter plateau regions there are no longer obscured by a myriad of tiny bumps. At the same time, though, while we’re losing unwanted details in the Sulci, we’re also losing desirable details elsewhere, especially along the escarpment of Olympus Mons and the gently sloping mountain face. In places like these, where the terrain is not so rough, we can support a finer level of detail than in the Sulci.
What we need is a way to keep 100% of the original detail in the smooth places where we can support it, and to generalize the terrain where it’s too rough. To do this, we need a way of figuring out where the terrain is rough and where it isn’t. To do this, I originally started looking at variations in terrain aspect — which way things are facing, since the rough areas have a lot of variation in aspect, and the smooth areas have relatively constant aspect in one direction. But, that’s a somewhat complicated path to go down (though it works well), so instead I’m going with a simpler method that’s probably just as effective: I’m going to look at the variation in my initial hillshade, above. If I do some analysis to find out where the hillshade is seeing a lot of variation — many dark and light pixels in close proximity, then that will give me a mathematical way of separating the smooth from the rough areas.
Here, I’ve calculated the standard deviation of the hillshade (using a 12px diameter circle window), and also blurred it a bit just to smooth things. The darker areas correspond to the smoothest terrain, and the bright areas are where we find a lot of jagged changes, such as in the rugged Sulci. Notice that even though the escarpment is steep, and the plain at the top center of the image is flat, both are dark because they’re relatively smooth and would be good places to keep lots of detail in our final image. In the end, what I’ve really done here is take a look at my initial, poor hillshade, and find out where the noisiest sections are. I think the analogy to image noise reduction is valuable here — we’re trying to reduce noise in our image, so that the major features become clear.
So now I’ve got a data set which tells me a degree of ruggedness or noisiness for different parts of the terrain. There are other ways to get the same effect — you could do a high-pass filter, or the aspect analysis I mentioned above, or perhaps look at curvature. This is just my way of measuring things.
Once I have this data set, I can move on to the fun part. What I want to do is use this to figure out where to keep details and where to lose them. I’m going to use this thing to do a weighted average of my original, high-detail DEM, and a much more generalized DEM. Where the terrain is very rough, I want the resulting data set to draw from the generalized DEM. Where it’s very smooth, I want it to use the detailed DEM. Where it’s in-between, I want it to mix both of them together, adjusting the level of detail in the final product based on the level of roughness in the terrain. In more mathematical terms, I want to use this thing as the weight in a weighted average of my original DEM and the generalized one.
The general formula looks kind of like this:
((Generalized DEM * Weight) + (Detailed DEM * (WeightMax – Weight))) / WeightMax — where Weight is the value of our noisiness data set. Each pixel in the final output is a mix of the original DEM and the generalized one. Where there’s a lot of variation in the terrain, our Weight is very high, so we get a result that’s mostly the generalized DEM and very little of the detailed DEM. Where terrain is smooth, Weight is low, and we see mostly our detailed DEM.
Here’s the output, once it’s been hillshaded:
The smoothest areas retain all of their original detail, and the roughest areas are much more generalized. It’s a combination of the first two hillshades near the top of this post, with the best of both worlds. It could still use some tweaking. For example, in the Lycus Sulci, it’s still blending in some of the initial DEM into the generalized one, so I could tweak my setup a bit, by requiring the noisiness index to fall below a certain number before we even begin to blend in the detailed DEM. Right now the index runs from 0 to 100. So, an area with a noisiness of 80 would mean that we blend in 20% of the detailed DEM and 80% of the generalized. If I tweak the data set so that the new maximum is 40 (and all values above 40 are replaced with 40), then more of my terrain will get the highest level of generalization. Any place that’s at 40 (or was higher than 40 and has become 40) will get 100% of the generalized DEM and 0% of the detailed one.
Here’s what we get:
And here it is compared to the original relief:
Notice how seamlessly the two images blend together along the mountain slope — each of them has the same high level of detail. But in the Sulci, where we need more generalization, the improvement is manifest. For comparison, here’s my generalized DEM vs. the original before blending the two. The loss of fine texture detail on the mountain slope and especially along the cliff face becomes apparent here:
So, there you have it. I feel like this is still a work in progress, that there are some other places it could go. Is this the best way to figure out how to blend the two DEMs together? Should I even be blending at all? Is this even a problem that needs solving? I am a bit unhappy with the median filter, I will say — it’s a classic of noise reduction, but it tends to leave things a bit…geometric. Here’s a more extreme example:
There’s a balance still between cutting out detail and the artificial look of the median filter. I have also tried blurring, but then everything looks blurry, unsurprisingly. I’d like something that can cut out details, but keep sharpness. I may go back and use Terrain Equalizer some more to generate the blending base. But all this fits more on the side of “things you can use to blend into your detailed DEM,” and the main point I am writing about here is the blending concept.
So, I invite you, gentle reader, to give me your input on where this can go, if it has any potential, and how to improve it. I think, after some weeks of work on this and a number of dead ends, my brain can take this no further without a break.
I’ve been meaning for some time to share these videos that I produced last year to assist in teaching projections to my students. Specifically, I wanted to use them to emphasize the importance of choosing projection parameters carefully to reduce distortions in the subject area, and to show how two different-looking maps can really be the same projection.
The first video is of an Azimuthal Equidistant projection. The standard point moves around the map, beginning in the central US and ending near the southern end of Africa. I try to point out, when showing it, that the pattern of distortion remains the same because it’s the same projection, but that the location of those distortions on the earth changes as the standard point moves, and how the map at the beginning and the map at the end are appropriate for showing different locations.
The second is of an Albers Equal Area Conic. First the central meridian moves, then the two standard parallels. Here I point out that the areas of the land features never change throughout the movie. Their shapes shift around significantly, but area is always preserved. The angle distortion moves with the standard parallels, and we can choose a set of standard parallels to best depict each area. We begin with a projection best suited for India and end with one adjusted for Sweden.
By the time I show these videos, I’ve already gone over all these projection concepts — they’re just a nice way to reinforce what we’ve already discussed. Student responses suggest that the videos have been helpful in teaching distortions and the importance of choosing projection parameters. It can be a tough thing to get your head around, and I like to approach it from several different angles to make sure I’m reaching as many of them as I can.
I made these using GeoCart (and Tom Patterson’s lovely Natural Earth raster), in a painstaking process which consisted of: 1) adjust projection parameters by a small amount (I think it was .25 degrees), 2) export image, 3) repeat 1-2 several hundred times, 4) use some Photoshop automation to mark the standard point/central meridian (though I had to add the standard parallels manually), 5) stitch together with FrameByFrame
It took many hours. Soon thereafter daan Strebe, GeoCart’s author, pointed out at the 2010 NACIS meeting that he’d added an animation feature to the program, which probably would have saved me a lot of time.
If you’d like the originals (each a bit under 40 MB, in .mov format), drop me a line.
I made a post recently on my other blog, Cartastrophe, about the misuse of map elements. I feel like it belongs here, too, as it’s somewhat about cartography education, so here’s a link if you’d like to head on over.
Gentle readers, welcome back. Forgive my prolonged absence (even lengthier on Cartastrophe). I’m unemployed, and it turns out that being unemployed can be a great deal of work, as I’ve been working harder these past couple months than when I was actually being paid. Much of my time has gone to building an atlas of my river transit maps, but I’ve also been taking some time to work on other projects.
One of those projects which I’ve lately taken on as an amusing diversion is making Tweet Maps, which are simply maps that can be constructed within a post on Twitter. Here’s one I put up earlier today on my account, @pinakographos:
Prime Meridian: North Sea (((GBR))) English Channel (((FRA-ESP))) Mediterranean Sea (((DZA-MLI-BFA-TGO-GHA))) Gulf of Guinea
It’s a fun challenge, and it gives cause to think a bit more deeply about how representations are constructed, and what a map really is. Something I used to tell my students was that map readers are used to looking through maps — ignoring the representation and instead seeing the place it stands for. When most of us look at a map of Iceland, we don’t see patches of colors and lines and letters. We just see Iceland. But cartographers work in the layer of representation, and don’t have the luxury of looking through it. We have to create that transition between seeing bits of ink and imagining a territory.
Making these Tweet Maps is a nice way for me to break out of the standard cartographic visual paradigm and think about how little it can really take to convey a space. I also hope that the unfamiliarity of this map style will make it just a bit harder for readers to simply look through the representation, and become more aware of that intermediate step that occurs between seeing some marks on a page and seeing the place that it symbolizes.
But mostly I just do them because they amuse me.
For more maps in the series, look for the #TweetMaps hashtag on Twitter.
We seem to like naming things after people; buildings, streets, awards, etc. Everywhere you look there are names on the landscape, meant to memorialize some historic figure deemed worthy. But it rarely works. Generations pass, and we no longer apprehend the significance of the fact that we live on Adams Street or walk past DeWaters Hall on our trip through campus. That’s just what they’re called; it doesn’t even occur to us that they share names with specific human beings.
When James Doty platted the first streets of Madison, Wisconsin, in 1836, he named them after the signers of the U.S. Constitution. Today, though, that connection is lost on many of its residents. They have no idea whom their street was meant to honor, nor know that all the street names share this common theme. This last month I’ve been working on a map, The Ways of the Framers, which aims to reconnect Madison’s modern citizens with the people their city was intended to memorialize.
The street grid is rendered using signatures traced off of a scan of the U.S. Constitution. Handwriting is personal, and it putting it on the map is a way to give the reader a more direct human connection with the historical figure. It’s different than simply reading a webpage about each figure. It puts a little bit of George Washington’s personality into the landscape, into the place where the reader lives.
Only the streets which are named after the Framers of the Constitution are shown, so it’s probably not the best map to use for navigational purposes. I almost included other streets on the map, such as those named after non-Framers, but ultimately decided to keep it focused and simple.
Fun fact: Three of the framers no longer have streets named for them. Robert Morris, Gouverneur Morris, and George Clymer. Morris St. was later renamed to Main St., and Clymer St. was renamed Doty St.
Click on the thumbnails for a few images of the map.
Click here to purchase a 36″ x 18″ print.
Click to download a free PDF (~25MB), which you may use according to the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
If you wish, you can pay me what you think my work is worth: click here to donate via PayPal.
Important: Zazzle will let you shrink the map down from the original size if you ask, but I cannot guarantee it will look good if you do.
10% of profits from the sale of prints of this map will be given to the University of Wisconsin Cartography Lab, which trains students in the cartographic arts. I was one such student, and I would not be where I am today without their support. It is a small way to repay the vast debt I owe them and help give other students similar opportunities.
Note: This version of the map does not include streets that are not named for signers of the constitution. I have an alternate version, where those streets are shown with thin lines. Contact me if you would like to obtain a print or a PDF of this version.
At the instigation of my colleague Tim Wallace, the UW Cartography Diaspora has been lately abuzz with a debate on the role of art and science in cartography (particularly web cartography). Today’s post is my contribution to the discussion.
For some background, I recommend you first read through the comments of my colleagues on the subject:
Tim Wallace: “Web Cartography in Relation to Art & Science”
Tim Wallace: “On Art & Science in Web Cartography”
Andy Woodruff: “Apart from being dead, Art and Science are strong in web cartography”
Tim has challenged several of us to respond to him in writing, so more of my colleagues may be chiming in later. I’ll add their posts here as they come up.
Onward to my own comments…
I’m going to stray a bit from where my colleagues have focused and talk about art in cartography generally, not just where it fits in web cartography, because that’s what caught my attention initially. For me, this whole debate started like this:
Tim: “…my commentary is on the displacement of art in web cart[ography].”
Me: “If art’s being displaced from web cartography, that makes it not cartography anymore.”
Caveat: Tim may have been talking about horse carts and I just assumed he meant cartography.
Among all this discussion of “what is the role of art in cartography,” my proposition is this: cartography is a form of art. Art is not simply a component of cartography, alloyed with a liberal dose of science or technology or hackery. Art is what cartography is made of. It belongs on the same list as sculpture, as poetry, as painting.
What of science? Doesn’t a lot of that go into mapmaking? We cartographers use fancy digital tools that can calculate and render smooth bezier curves or instantly translate a color from an RGB space into CMYK process colors and determine how much ink to lay down based on print materials and coatings, etc. We also use math to analyze and manipulate our data: map projections, interpolations, calculating buffer zones, etc. Does this make cartography a science as well as an art? Not necessarily.
A ceramicist relies on redox chemistry in order to produce colorful glaze patterns, firing everything in carefully controlled kilns to ensure that they achieve a desired appearance. A metal sculptor welds and files and cuts with various modern technological implements. A painter employs different varieties of paints, blended with precision in modern factories. Does this mean that all of these pursuits rely on both art and science, sitting at the intersection of those two august concepts?
The argument that cartography is, or involves, science boils down to two things: tools and data. Cartographers use tools and techniques that were developed through scientific experimentation and research. But so do other arts. The synthetic painter’s brush didn’t invent itself. The other half of the argument is that cartographers use math and science to manipulate data. Again, that doesn’t make us unique. The data are our clay, the raw material input that our art requires. We manipulate our data the way a sculptor shapes their medium of choice into a final expressive work. I might use some mathematical formulae to transform a dataset, but a ceramicist will use a modern human-built kiln to change the chemical properties of their clay into something more desirable. Both require education and experience, and an understanding of the raw materials and how they are best manipulate.
If cartography is both an art and science, so is sculpture. So is painting. So is photography. So is architecture. It goes on. We cannot declare that cartography is both an art and a science without claiming the same for many other fields. If we’re all willing to do that, then, yes, I agree cartography is an art and science. But if sculpture is “just art,” then so is cartography.
There may be a science to the tools or the data or the materials, but the art is in what the artist does with those inputs. That is where cartography lies. Cartography is about creating something out of spatial data, just as painting is about creating something out of pigment and canvas. Art is in the doing.
Back to Tim’s prompt. If art is missing from web cartography, or is at least not as present as we’d like, it’s because art requires people. What’s really missing from web cartography, and a lot of digital cartography generally, is humanity. Cartography is a fundamentally human practice. Machines don’t need maps — they can understand their environment through a series of databases and formulae. They don’t need a visual expression of space to help them interpret and interact with places, the way that people do. For most of human history, the maps people read were made by fellow human beings who drew everything out by hand and with at least a modicum of thought to how it looked. Every mark on the page involved a decision and an intent; an artist making use of the inputs at hand to try and evoke the desired reaction from a reader — maybe to create an understanding (this is where the river is), a judgment (the country across the river is a threat), or a feeling (worry that said country is going to harm us).
Now, however, we have machines that make the maps for us. Through automated or semi-automated processes, people are involved less and less in the creation of the final map. Click a button and the computer will place everything for you, and color it, too. Most of the marks on the final page can make it there with no active human decision behind them. No more intent. No human brain considering how the typeface or the line color or weight will affect a fellow human reader. There is less art now because there is less humanity. Machines do not express, or create, or understand how to evoke a reaction. Machines do not make art.
When humans made maps for each other, the cartographer had at least some understanding of how their work might influence a reader’s thoughts and feelings, by virtue of being the same species. But now the creator of the map is part digital, a human-machine hybrid, and that connection with the reader is fading. So many maps today are unattractive because they are alienating, because they were not made by people, but by insensate machines. There is no sapience behind the lines they draw, no appreciation for mood, for aesthetics. The machine does not desire to make you think or feel or learn anything in particular, as the artist does, and this is the heart of what is wrong with so much of cartography today. Only humans can make maps for other humans. Digital tools are all well and good, but they must remain just that: tools, in the hands of a human capable of wielding them wisely and with a purpose.
Therefore if there is no art in web cartography, it is no longer cartography, because cartography is an art. Instead, we are seeing something new, the rise of the map made without humans. That’s a recent development, and it certainly has its own value as far as things like production speed, accessibility, and cost go. But the lack of human intent, of art, means that it is a fundamentally different thing than cartography. Related, to be sure, but separate. I’ll leave it to someone else to think of a name for it. Just like I wouldn’t call it art when an automated algorithm paints a painting based on a digital photograph, it’s not cartography when a server tosses together a map based on a spatial database. Any art that inheres in that process was left there in the form of the lingering human intelligence of the programmers who helped the computer figure out how to make the map/painting, and that’s usually not too much. There is no art without creative intention. Therefore there is no cartography without a human creator.
In the end, I and the other bloggers involved in this discussion are neither right nor wrong. There are a lot of different ways to think about cartography; this one is mine, based on my self-image as a spatial artist. I don’t think any major decisions need to be made about what cartography really is. There are just different models that help us all figure out what it is we’re doing, and how to do it better. In an increasingly digital world, this is how I am personally trying to articulate the relevance of my role as a human cartographer.
EDIT: A tweet from @shashashasha points out that I neglected to say anything about that other tricky term, “design.” To me, design means making decisions based on goals. It’s again about using our human brains to see something we want to do, then making cartographic choices to get there. The random and the organic are undesigned. Where there is intelligence and intention, there is design, which ties back into most of what I said above.