I also used to do some bloggy things at Bostonography and the Axis Maps blog.
Attention cartographers: Cindy Brewer has allowed us at Axis Maps the privilege of making some updates to the fantastic ColorBrewer tool. We’d like suggestions from the community on what to change, what to keep the same, etc.
Drop us a line over here: ColorBrewer 2.0
Tagged axis maps, color | Comments Off on What should ColorBrewer look like in 2008?
I am not by nature an angry person—in fact my friends at times find me irritatingly even-tempered—nor am I known to truly hate anything, but provocative titles have their place, right?
We’ve once again arrived at that special time of (every fourth) year when the internet abounds with maps, charts, and other graphics attempting to depict and analyze every geographic and demographic angle of the US presidential election. I am happy for these, both in the perspectives they provide on the election and in the demonstration of interesting visualization methods.
But as a cartographer in the eternal quest for the Perfect Map, I find myself complaining about some map and graphics. In particular, I take exception to this:
For anyone who doesn’t know, the above is a cartogram (’bout time I wrote about that which my site is named after!) of the United States, in this case a map by Michael Gastner, Cosma Shalizi, and Mark Newman showing the results of the 2004 presidential election by county (we’ll soon be seeing one for 2008). A cartogram is a map that does not strive for geographic accuracy, but rather in which the area of units actually represents some value. In the election map, each county’s size represents its number of voters. The point of the map is to show that while a geographic red-blue election map would show an apparent vastness of Republican votes, those “red” areas actually account for about the same number of votes as the tiny “blue” areas. (The message is further conveyed by coloring counties along a red-blue continuum to show the actual balance of votes rather than simply coloring by the winner, something first seen in Robert Vanderbei’s election maps.)
I’ve written several drafts of many paragraphs to try to explain my opinion, but really, who has time to read all my ranting? Short version: apologies to Drs. Gastner and Newman, but as a cartographer interested in clear and effective design, I really believe that cartograms generated from their method are severely over-hyped and far more popular than they should be. Consider the election map (or any number of examples)…
The bottom line is that many—perhaps even most—cartograms are essentially used for shock value, for the “holy crap, that’s a different perspective!” response, which is exactly what they get. Too frequently they can’t stand as maps on their own. I think the election cartogram is only of use when it’s next to an undistorted map. The best maps and graphics are those that tell their story clearly and elegantly, not those that simply evoke an emotional response. There are a million good reasons why I’m wrong to complain, but rather than going on and on in an attempt to counter them I will simply acknowledge that they exist and expound later if necessary.
“Put your money where your mouth is, jackass!”
Oh, actually that’s a pretty important reason I’m wrong to complain. Okay, I promise I will attempt to come up an alternative visualization of the same information as that election cartogram, as soon as I figure out where people find such detailed election data so quickly. (And, as I stressed “careful design,” it’s not going to be instant.) I’ll also keep an eye out in the coming days for maps and graphics that I think are more effective.
Having deleted most of what I previously wrote for this post, I wasn’t left with a good place to bring it up, but I must, as I seem to do in nearly every alternate post, refer to the work of my mortal enemy Zachary Johnson, who wrote his master’s thesis on cartogram designs in political maps. (He should be writing about this stuff. Maybe someday he’ll at least finally write about his findings. Eh, Joncy?) At the University of Wisconsin-Madison, specifically in room 412 Science Hall, we did not take cartographic research seriously if it could not be depicted by a cube. As such, Zach defined a cartogram typology (Cartogram3) by three characteristics: shape preservation, topology preservation (the preservation of boundaries and connectivity), and density equalization (essentially, how accurately area corresponds to value). No cartogram can be perfect in all three, and in fact most compromise all three to some degree. Zach tested several designs, each making different sacrifices, in political map-reading tasks. Note that I have bitched about shape preservation versus topology preservation. His research backs me up in some respects, but not in others. But I’m not in academia anymore; I don’t need “research” to know that I’m right!
Tagged cartograms, election maps, rants | 11 comments
Over on the Axis Maps blog I’ve written up a side project I began several months ago but never got very far on: mapping the geography of presidential campaign speeches. It’s still an interesting idea, but my colleagues and I didn’t have the time to fully develop it, so you’ll have to settle for some screenshots.
Not wanting to simply write duplicate content here, I thought I’d say a bit about what is perhaps novel about the project, which is visualizing word frequency on a map. Word clouds or tag clouds are trendy these days, especially given a tool like Wordle that can produce a damn sexy word cloud. They’re something of a simple and elegant visualization, combining data and label in one, and they’re pretty effective. (Their drawbacks should be noted, however. See, for example, the caveats at the bottom of this Many Eyes page or Zachary “Hollywood” Johnson’s experiments with tag scaling to address a potential overemphasis on long words.)
So tag clouds are hip. I thought it would be interesting to find a way to use them in a map. Easy enough: if a collection of words is associated with a location, slap it on a map. That’s the basic idea behind the campaign speeches maps. We can see how campaign word use varies across the region. Beyond that, though, I wanted to see how the patterns look at different scales by aggregating neighboring word clouds.
Aggregation is a basic and necessary cartographic generalization procedure wherein multiple point symbols are replaced with with a single areal symbol to represent them*. It’s a matter of scale; if you’ve got too many symbols close together to discern them at a small scale, you’re better off combining them. I’m a fan of the way EveryBlock maps have such scale-dependent symbolization. If there are too many points to show individually, they are combined into a larger symbol with a number on it. (It effectively becomes a proportional symbol map.) I wanted to do something similar with word clouds: if there were too many to fit in a given area, combine them into a single cloud encompassing words from all the individual clouds.
The spatial perspective it gives, then, is patterns from broad regions down to a local level. For example, zooming from the national level into my home state of Ohio on the campaign speech map, we see the word patterns for the general northeastern quadrant of the country, then the Midwest, then individual cities in Ohio.
As this project became neglected, so did my interest in learning about better aggregation algorithms. Mine was pretty basic and probably inefficient. It essentially draws all the tag clouds, finds the closest pair, checks if they overlap, if so combines them into one at the average location, and then repeats the process until no more clouds overlap. Each cloud shows only as many as 20 words, and half of them are from each candidate where possible. It’s a comparison of two texts, but not in a word-for-word way that is sometimes seen, for instance here at Many Eyes.
Anyway, I think these scale-dependent tag clouds are an interesting idea to pursue further, and I’m not sure if anyone’s done it before. Other examples would be most welcome!
* Note that the technical definition of aggregation involves “increased dimensionality” (to quote my friend Robert “The Hammer” Roth in his work on the ScaleMaster project at Penn State), e.g. from points to polygon. With my word clouds it’s more like polygon-to-polygon, but not a larger polygon, which would be closer to amalgamation. I’m not sure what a generalization expert would actually call this, as it’s sort of a content operation manifested in an geometry operation.
Tagged election maps, generalization, map projects, tag clouds | 3 comments
I wish to call attention to the fact that my humble little company Axis Maps has launched a new blog. This blog, I must stress, does not exist merely to promote ourselves, but rather to talk about practical cartography. So please stay tuned to it while our Ph.D. holders Mark Harrower and Ben Sheesley drop cartographic knowledge bombs on your face, and while Dave Heyman and I keep it real.
We begin with a single post by Mark Harrower, I’m not here to make your data look pretty. Expect more soon, though! When they’re not too inane, I’ll copy my posts from here to the Axis Maps blog.
Tagged axis maps | Comments Off on Blog-on-blog action: Axis Maps blog
So I have a single map that I need to show at several different scales in an interactive fashion. It's got to be a pretty big image to look decent when I zoom in, so that means a large file and a long download. But I only need to see the scaled-down map when it first loads. I don't want to wait for a big image to download when I just need a little one to start! The hip (or some might say "smart") way to deal with such a problem is to break the image into tiles and load said tiles on the fly as needed. But this is a simple, one-off creation, and I'm too lazy, too stupid, or too pressed for time to figure out tiling.
That was my situation recently. And I thought to myself hey, the world needs more backward-looking individuals, and patted myself on the back for avoiding learning and taking the easy way out: use Flash's timeline to fake a progressive download!
The solution takes advantage of the way a Flash movie loads: rather than waiting for the whole file to load, individual timeline frames appear as soon as their content has loaded. It's something I first exploited for my CincinnatiRoads map, embedding videos right in the timeline to allow easier playback manipulation but also have a progressive download. Flash, from what I can tell, supports progressive JPEGs but does not display them progressively.
So, for my map:
That's all. No code (except a stop() at the end), very little thinking, and maybe a half hour spent. I then drop it into my map interface. When the movie loads, it should show up as soon as the small image has finished loading, and then the high-res tiles will come in one by one. Hopefully you noticed that the simple demo map at the top of this post appeared without too much delay even though the full image is a few thousand pixels across and the file is a couple of megabytes (and if files of that size load quickly anyway because you have an amazing internet connection, shut up and I hate you).
Caveats:
Tagged flash | Comments Off on Faking a progressive image download in Flash
After a summer of long hours and occasional vagarancy, I’m happy to report that GeoCommons Maker! has launched today. FortiusOne’s CEO Sean Gorman nicely sums up what Maker (I’m going to go ahead and drop the exclamation point henceforth) is all about:
Data that was once the sole providence of GIS professionals can now be mapped by anyone. Not only can they access the data but be guided through a process of creating a cartographically and statistically accurate map.
My esteemed colleagues and I at Axis Maps teamed up with FortiusOne to build Maker, sharing the goal of bringing good cartography to a vast collection of geographic data.
Maker is integrated with the existing GeoCommons service Finder, where users can access thousands of geographic data sets in different formats and contribute their own data. Now with Maker, users can create and share some of the slightly sophisticated cartographic visualizations that were once reserved for expensive desktop GIS applications. We hope that this will encourage appreciation of cartographic design in web maps, or, to quote the FortiusOne blog’s former name, to “move past push pins.” For a good summary of the highlights and goals of Maker, check out Andrew Turner’s announcement.
Our role at Axis Maps was essentially to build the Flash front end for Maker, bringing our collected expertise in cartography to the discussions with the GeoCommons team. A good map in 5 minutes was the goal, so we tried to reduce the map-making process to a short series of decisions that produce a well-designed map allowing basic analysis that is insightful and appropriate to the data. To that end we have the “Map Brewer” that greets you in Maker when you load a data layer.
A map in four clicks if you like. This is a slight variation on the concept of a tool put forth by Professor Cindy Brewer with our own Professor Mark Harrower in ColorBrewer. Professor Brewer has described what she sees as the “brewer” concept (see #8 here). In short, a brewer is a tool that guides a mapmaker through particular cartographic design decisions, presenting reasonable options and the information necessary to critically evaluate those options. It does not make the decisions for you; it helps you make decisions. We could have tried to have the system determine the best cartographic design for the data it’s given, but we think that guiding the user through a few decisions will lead to far better maps. The Map Brewer is the salient example of the principles we’ve tried to uphold throughout the interface and workflow: allowing decent design flexibility while keeping decisions simple and encouraging effective design.
Meanwhile, something I worked with a lot was actually rendering the data on the map. The Flash application builds on Modest Maps to display geometry (retrieved from the extensive back end), making map navigation easy and providing a variety of base map choices. There is continuing work to make the rendering methods more extensible by streamlining the integration with Modest Maps and allowing more flexibility of data formats. I must stress that the powerful back end to all this reflects a lot of hard work and ingenuity at FortiusOne, and they continue to work to make these data and maps as fast and easy to access and share as possible. It’s been a valuable experience to work with them and see everything they’ve put into GeoCommons.
It’ll be exciting to see how Maker evolves and expands. For some time now I’ve perceived a geoweb community emphasis on data, data, data, and I hope that Maker can cater to that while also introducing a stronger focus on presentation.
So please pardon the kinks that are still being worked out, and enjoy making some cool maps! Here’s my fist publicly shared map, a frivolous one of course: “Freshmen are Criminals”
Tagged axis maps, map projects, online maps | 3 comments
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