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.
Once upon a time, there was a website called MapBlast. This was during the wild frontier days of online road maps about ten years ago, when MapQuest was king and Google Maps was but a gleam in the eye of a couple of Danish guys in Australia. MapBlast never seemed to me to be more than a minor player during these days, but it had one special feature that made it my website of choice for route planning: LineDrive directions.
Then, as now, all the other online mapping services gave you route maps that looked like this, a highlighted route drawn onto a standard road map:
MapBlast, though, could give you what they called LineDrive directions, a linear cartogram of your trip that looked like this:
LineDrive was developed by Maneesh Agrawala, Chris Stolte, and Christian Chabot at Stanford University, and they describe their system in a 2001 paper if you’re interested in the details. What is most interesting to me is the creators’ inspiration for LineDrive: hand-drawn route maps. While most online mapping services were, and still are, patterned after paper road atlases, LineDrive was designed to look like what you might quickly sketch on a napkin.
This starting point leads to something quite remarkable. Hand-drawn route maps are custom products — they’re for just a few people, and are about going between one specific place and another. A road atlas, on the other hand, is the same for everyone. It doesn’t change based on the situation. It’s multipurpose, which is quite valuable, but it’s not as effective for a given route as something customized.
When the Web came along, the mapping services that came online simply translated the idea of a paper road atlas into its digital equivalent. They added a few enhancements — you could zoom in and out or draw routes on them, but it was fundamentally the same thing. It was still multipurpose, not customized. When mapping services today talk about customization, they mean that you get to draw a blue line on top of their map or add a picture of a pushpin. But the map is always the same for everyone. LineDrive’s most remarkable feature was that it gave everyone their own map, fully customized to their specific situation. It should have been revolutionary, but it turns out that nothing ever came of it, at least in the realm of online driving directions.
I did not apprehend the significance of this development at the time. Instead, I merely loved the design. It was brilliant. Clean, simple, effective. It tells you everything you need to know about how to get from A to B, and it tells you nothing else. There is no clutter. It does not take up my time or printer ink with roads I won’t be using, or cities hundreds of miles from those I’ll be passing through. I can look at this map quickly while driving. I don’t have to hunt around the page to find the little blue line that contains the path information I need — everything on this page is there because it’s essential.
The LineDrive map shows every detail of the whole route at once by distorting scale. It makes the short legs of the trip look longer, and the long legs shorter, so that everything is visible on the same page at once. With a more traditional road map, either in an atlas or printed off of Google/Bing/etc., I would actually need several maps at different scales to cover each part of the route — zooming in to Madison to show how I get to the highway, then zooming out to show the highway portion of the trip, etc. LineDrive fits everything on the same page, and it does so legibly. Again, it’s customized, which is what gives it value.
Since the scale changes, the length of each leg drawn on the map doesn’t correspond to the same distance. Therefore each line is marked with its distance, so you don’t lose that valuable information. In fact, distance is presented more clearly than on a standard road map. If I wanted to figure out how long each leg of my trip was on Google’s map, I’d have to go compare each one to the scale bar in the corner, measuring it out bit by bit. Or, I’d have to check the written directions. It’s not usually presented clearly on the map itself. For me, LineDrive eliminates the need for a verbal listing of directions accompanying the map. They can be clearly read out from the map itself, distances and turn directions and all.
This map doesn’t do everything, to be sure. It’s only good for getting from one place to another. It’s very purpose-specific, and there are plenty of things that more standard traditional print and online road maps can do that this can’t. It won’t tell you where the city you’re driving to actually is, or what’s nearby. You can’t deviate from the path, so you can’t react to road closures or changes of plan by switching roads. You don’t know the names of the towns you’ll be passing through. Within its particular niche, though, LineDrive was very, very effective, just like the hand-drawn maps it was inspired by.
I have never understood why it did not catch on. Perhaps most people don’t usually use the map to drive the route — the verbal directions tell them where to go more clearly. Perhaps they only have the map to plan routes, not to follow them. The map may be used for reviewing the route before you begin, or re-routing in case of emergency. I’m not sure if any of this is true; it’s just idle speculation. If it is true, though, then LineDrive doesn’t offer any advantages — it doesn’t explain the route any more clearly than verbal directions, and it doesn’t let you do any route planning.
Alas, it died too soon. Microsoft bought MapBlast in 2002 and closed them down. They took the LineDrive technology and kept it going on their own map service, MSN Maps & Directions, until 2005. The site stopped updating in 2005, however, and Microsoft’s subsequent mapping endeavors don’t appear to offer LineDrive as an option.
You can still access the old 2005 site, however, at http://mapblast.com/DirectionsFind.aspx. (EDIT 10/19/11: Microsoft appears to have finally taken MapBlast offline. Maybe traffic from this post reminded them that they’d left it up.) In generating a sample image for this post, I noticed that the database has quite a few holes and errors in it, so I wouldn’t trust the directions it gives. It’s merely a relic of a different era. If you try it, be sure to check out the standard map directions, too, in addition to the LineDrive ones — it’s a nice reminder of just how far online road map design has come in the past few years.
LineDrive was something truly different. MapBlast’s competitors offered a slightly enhanced version of the paper road atlas. MapBlast offered this, too, but they would also give you your own version of a hand-drawn route map for anywhere you wanted to go, at a moment’s notice. I feel like LineDrive made much more effective use of the power computers can bring to cartography than other online mapping services have. It was a more creative re-thinking of what the digital revolution could do for map-making.
I never used to draw route maps by hand; I would simply write verbal directions and bring an atlas. Discovering LineDrive changed that, though. Every long and unfamiliar trip since then has started with me taking pen to paper and sketching out a simple linear cartogram to get me where I want to go.
Let me tell you about how I was saved by maps.
I used to be a chemist some years ago. I worked at a mom & pop pharmaceutical laboratory in my home town of Kalamazoo, Michigan. From the time I was ten or eleven, I had planned on this job. I blame Mr. Wizard — I loved watching all the seemingly magic things he could do with brightly-colored liquids in test tubes. Now that I had my childhood dream job, however, I was disillusioned. There was no magic, there was only routine humdrum. The work was hard, it was stressful, and it was frequently dull. I started the slide into depression. I came home every day feeling far too tired for the number of hours I was working. Time off seemed fleeting, and I spent the whole weekend worried about the fact that Monday was approaching, and wondering sometimes if I could face another week. I felt constantly pursued, and unable to relax even when given respite.
I decided to get out.
I had planned on going to graduate school right after college. I liked being in school and I succeeded there. It seemed natural to continue. But inertia kept me in the workforce for about three years before I finally managed to get enough forward momentum to return to school. My other love in school besides chemistry had been history. I can’t blame Mr. Wizard for this one, but I’ll blame my high school teacher, Mr. Cahow, instead. My bachelor’s degree at Kalamazoo College was in both subjects. At first I tried, and failed, to get in to graduate school to study classical history. The next year, though, I switched focus and applied to History of Science departments. Given my background, it seemed a pretty sensible fit, and the University of Wisconsin agreed. They let me in, and I was off to Madison in the summer of 2007.
Coming along with me was my girlfriend of three years, an amazing and brilliant woman whom I had met in college, and with whom I was very much in love. We got a small apartment together about a mile from campus, she found a job, and we settled in to an unfamiliar city. It was about here that my life started completely unravelling.
I did not fit in to my new graduate department at all. I was wholly out of my depth; my background was insufficient to match the demands of the program. I had gone to graduate school because I liked school, not because I was deeply passionate about the history of science in particular. It just seemed interesting. My peers, on the other hand, seemingly had been pursuing this track for far longer than I, and had put in enough extracurricular effort through their college years that by the time we all started at Madison they were talking over my head. I fell swiftly behind and lost heart. I realize now that while I might have had the relevant skills, I lacked the critical element of passion. I did not feel like spending all week trying to get through 400+ pages of reading, because the end goal was simply not enticing enough, nor was the journey greatly intriguing.
This had been my grand escape plan. My job had depressed me, and I was going to return to school, something I was good at, and I was going to enjoy getting an advanced degree and then spend the rest of my life in academia. My depression returned, much stronger, as this escape plan crumbled away.
My girlfriend, meanwhile, made a bunch of new friends in town, and started spending more and more time with them. Sometimes she wouldn’t come home, or even communicate with me, for days. Eventually, she told me that it was because she didn’t like being around me when I was depressed. She kept getting more distant, and moved into her own separate room. She would have parties at our apartment and introduce me as her “roommate,” and then close me off into one room so that I couldn’t hear what was going on, and so that no one could see me. Then she’d leave for a few days and I’d have to clean up the mess. I did not generally have the wherewithal to stand up for myself in the face of her steadily worsening treatment of me. Her behavior eventually reached the point where I started reading online to determine if I was in an emotionally abusive relationship. None of this helped my depression.
If you’ve never been depressed, I’ll just say that it’s much worse than it sounds, and I imagine everyone manifests it a bit differently. I would stay in bed for hours. I would avoid doing any of my school work. It took a significant effort to scrape together the energy needed to do any sort of housework or cooking. I was constantly bored, but could not muster myself to do anything to make me less bored, and I did not have the courage to face the growing pile of assignments that I was falling behind on. I felt trapped and powerless.
I made a lot of maps during that period; it was one of the only activities that gave me any sort of positive emotion. I had actually started a cartographic hobby a few months earlier, before I moved to Madison. As far back as I can recall, I liked reading maps. I used to be the navigator when my family would take trips. I like paging through atlases for fun. So it was natural enough that I eventually determined to learn a bit about how to make them. One of my first efforts was for Wikipedia, a map of the Kalamazoo River:
I was terribly proud of that map. I still am, despite the many, many flaws I can see in it today.
I kept up my new mapmaking hobby when I moved to Madison. It gave me something creative to do, and I have since learned that, for me, being creative is critical to keeping a positive emotional state. Making maps was the light of my day, in a time when my days were very, very dark. I probably talked more to my colleagues in History of Science about the maps I made than anything actually having to do with my graduate work. I did not construct anything particularly interesting or attractive. Mostly I just put together choropleths of census data to answer idle curiosities. But I was making something, and it felt good. And it involved learning geography, which was fun and new. It was avoidance behavior, to be sure — there were important things I really needed to be doing, and spending eight hours on a map was just a way of procrastinating, but I needed the escape. I could not always face my life outside of my cartographic refuge.
I was still trapped in my ill-fitting graduate program. I was adrift, and didn’t know what to do. I did not thrive as a chemist; I did not thrive as a historian of science. What now? I had no other obvious options, and I considered dropping out of school. But one day a friend of mine in the History of Science department pointed out that, being as I liked maps so much, perhaps I could go to school to learn about them. And, it so happened that there was a first-rate cartography program at Wisconsin. I spent my second semester in graduate school taking a cartography and a GIS class while applying for a transfer to the cartography program. I found that I liked these new classes, and that it was no longer a massive chore to get up every day and try and get to campus and accomplish something. I had more energy. I felt like I had a future. Somehow, despite no background or training, despite a weak performance in History of Science, and despite an application letter that didn’t say much more than “I really like maps,” the University of Wisconsin-Madison Department of Geography accepted me, and I began a Master’s program in Cartography & GIS. They took a chance on me, and I cannot hope to repay them for that.
I threw away everything I had ever thought I wanted to do with my life and leapt blindly into the unknown. I abandoned the safe, clear path I had been plotting since I was a child. As simple as it sounds, that was probably the most courageous thing I’ve ever done in my life.
I thrived immediately in my new program. I found that I was part of something I had lacked before: a community. I was surrounded by supportive friends and colleagues from whom I learned much, and whose input can be seen in everything that I design. I had a place to belong, and a future that I was passionate about. I came out of my depression. I worked up the fortitude to bring about an end to my relationship with my girlfriend, who by now was living on her own yet was unwilling to formally let go. I began to move on from my old life, to the new one I have now. I am a cartographer, and a teacher (another chance that the Geography department took on me). I love what I do. I draw strength from it. It feels like I should have been doing this all along, like I was made for it.
I write this story because I want people to understand what maps mean to me. Cartography is not just a hobby, or a job. It has taken me through the darkest times of my life. It has helped me overcome depression. It has given me a renaissance and a calling. It has given me a community which has enriched me personally and professionally. It has saved me from a life I would prefer not to contemplate, one which I cannot believe would be as fulfilling.
I am very glad that I made that terrible map for Wikipedia, one winter in 2007.
Last week, a map which I made about swearing on Twitter gained its fifteen minutes of Internet fame. I heard a lot of comments on the design, and one of the things that many of the more negative commenters (on sites other than mine) were displeased by was the color scheme. It was, as they said, very hard to distinguish between the fifteen different shades of red used to indicate the profanity rate. This complaint was probably a good thing, because I did not particularly want readers to tell the shades of red apart and trace them back to a specific number.
In designing the map, I took a couple of steps which made it more difficult for people to get specific data off of it. Before I can explain why I would want to do this, first you need a quick, general background on how the map was made.
This is a map based on a very limited sample of tweets. Twitter will give you a live feed of tweets people are making, but they will only give you about 10% of them, chosen randomly. On top of that, I could only use tweets that are geocoded, which means the user had to have a smart phone that could add a GPS reading to the tweet. A third limitation was that I could only use tweets which were flagged as being in English, being as I don’t know any curse words in other languages besides Latin. Finally, there were occasional technical glitches in collecting the data, which caused the program my colleagues and I were using to stop listening to the live feed from time to time. If you add those four limitations up, it means that I made use of somewhere between about 0.5% and 1% of all tweets going on in the US during the time period analyzed. Possibly not a strongly representative sample, but still a large one at 1.5 million data points.
In that limited sample, I searched for profanities. This is based on my subjective assessment of what may be a profanity (as many readers sought to remind me), and the simple word searches I did may have missed more creative uses of language. Once I had the number and location of profanities, I could start to do some spatial analysis. I didn’t want to make a map of simply the number of profanities, because that just shows where most people live, not how likely they are to be swearing. So, I set up some calculations in my software so that each isoline gives the number of profanities in the nearest 500 tweets, giving a rate of profanity instead of a raw total. Unfortunately, for places that are really sparsely populated, like Utah, the algorithm had to search pretty far, sometimes 100 miles, to get 500 tweets, meaning the lines you see there are based partially on swearing people did (or, didn’t) in places far away. If I hadn’t done this, then there would be too few data points in Utah and similar places to get a good, robust rate (counting the # of profanities in 10 tweets is probably not the most representative sample, we need something much bigger to be stable). Maybe I should have just put “no data” in those low areas, but that’s another debate.
So, the map is based on a limited sample of tweets, and the analysis requires some subjective judgments of what’s a swear word, and then some heavy smoothing and borrowing of data from areas nearby in order to get a good number. What all that means is: you shouldn’t take this as a really precise assessment of the swearing rate in your city. If I had chosen to look for different words, or if the Twitter feed had sent a different random 10% of tweets, or if I had chosen to search profanities in the nearest 300, rather than 500 tweets, then the map would end up looking different. Peaks would drift around some and change shape. But my feeling is that the big picture would not change significantly. Under different conditions, you’d still see a general trend of more profanity in the southeast, a low area around Utah, etc. The finer details of the distribution are the most shaky.
Okay, back to my main point about trying to make it difficult to get specific numbers. What I wanted readers to do is focus on that big picture, which I think is much more stable and reliable. And so I made some decisions in the design that were intended to gently push them toward my desired reading. First off is that color scheme, which has only small changes between each level of swearing, which makes it hard to look at your home and tell if it’s in the zone for 12 or 13 or 14 profanities per 100 tweets. What’s important is that you know your home is at the high end. Whether it measured at 12 or 14 doesn’t matter, because that number is based on a lot of assumptions and limitations, and is likely to go up or down some if I collected on a different day. The color scheme makes the overall patterns pretty clear — bright areas, dark areas, medium areas, which is where I want the reader to focus. It’s weaker in showing the details I would rather they avoid.
The other thing I did was to smooth out the isolines manually. The isolines I got from my software had a very precise look to them. Lots of little detailed bends and jogs, which makes it look like I knew exactly where the line between 8 and 9 profanities per 100 tweets was. It lends an impression of precision which is at odds with the reality of the data set, so I generalized them to look broader and more sweeping. The line’s exact location is not entirely reflective of reality, so there’s no harm in moving it a bit, since it would shift around quite a bit on its own if the sample had been different.
This is a subtler change, but I hope it helped make the map feel a bit less like 100% truth and more like a general idea of swearing. Readers have a rather frightening propensity for assuming what they see in a map is true (myself included), and I’d rather than not take the little details as though they were fact.
Had I to do it over again I probably would have made it smaller (it’s 18″ x 24″). Doing it at 8.5″ x 11″ would have taken the small details even further out of focus and perhaps kept people thinking about regional, rather than local patterns. Maybe I shouldn’t have used isolines at all, but rather a continuous-tone raster surface. There are many ways to second-guess how I made the map.
Anyway, the point I mostly want to make about all of this is that it’s sometimes preferable to make the design focus on the big picture, and to do so you may need to obfuscate the little things. Certainly, though, a number of people were unhappy that I impaired their preferred reading of the map. People like precision, and getting specific information about places. But I didn’t feel like I had the data to support that, and I would be misleading readers if I allowed them to do so easily.