Archive for the 'Author Criticism' Category

24
Jan
11

No Swearing in Utah

I’ve got a map on the cover of the latest issue of Cartographic Perspectives, and some colleagues of mine have been so kind as to spread it around Twitter and Facebook and all those other popular social media which I’ve never gotten in to. It’s been a while since I’ve subjected my own work to this blog, so I thought I’d take advantage of its temporary boost in popularity in a small corner of the Internet to do so again.

Click to download PDF (~12MB)

This time, though I’d like to try an experiment. If you would be so kind, gentle readers, I would like to turn this critique over to you. This afternoon I am feeling unrealistically optimistic about the number of readers who might be willing to provide comments. If you’re so inclined, click the link above to download a PDF, and then let me know what you think. Here at Cartastrophe, my goal is to enlighten myself (and, hopefully others), through critique and analysis; anything you can add to the discussion is always welcome.

Among other things, I am particularly interested to hear thoughts on the GIS work (described in the lower left corner); I am no expert in spatial analysis, and I feel I was somewhat arbitrary in my methods. Basically, I generated a raster surface in which each pixel gave the average number of profanities for the nearest 500 tweets that could be located. This should account for variation in population density around the US. Perhaps you have a better suggestion for how to go about it. Comments, be they negative or positive, on non-GIS things are welcome, as well.

And I encourage everyone to have a look at the new issue of Cartographic Perspectiveshttp://www.nacis.org/CP/CP66/CP66.pdf. Especially if you want to hear me go on at length about reviving the historical technique of waterlining.

23
Oct
09

The Eiffel Tower is not a Building

Good day, gentle readers. I am lately returned from a couple of trips to lands outside of Wisconsin. NACIS was wonderful, and it was great to meet many of you in Sacramento. While there, I learned that Tom Patterson, creator of the Kenai Fjords map which I praised in my last post, was slightly disappointed that I did not point out anything negative about his work.  Looking it over again, I will say that, for the elevation marker points on his map (mountain tops and sea valleys), the labels positioning could be more consistent.

I’m really nitpicky. My students love it. At least, that’s how I interpret their annoyed stares.

The subject of today’s post is once again one of my own works. This is in response to a conversation with my adviser, Mark Harrower, who pointed out that most any map, from the best to the worst, could be improved by some critique. I have previously featured one of my worst maps on here — Mark challenged me to instead show him one of my best, and then post his comments on it. So I picked my favorite, a map about the tallest buildings in Europe during the last 125+ years:

Rising Skyline, by Daniel Huffman

Rising Skyline, by Daniel Huffman

Rising Skyline detail

Rising Skyline detail

RS_Legend

Legend

Here’s what Mark had to say (and he warns that some of these are nitpicky — his students, too, love it):

“The categorical color scheme for the kinds of buildings doesn’t work to my (r-g colorblind) eyes. Hospital and museum look identical.”

He’s quite right – I’m always embarrassed by this sort of thing, forgetting to design for people with abnormal color vision. Actually, I’m surprised that it was Hospital (pink) and Museum (grey) that got him. I would have figured on Religious (red) and Residential (green), but I suppose those two colors are distinct enough in lightness that they’re still separable.

“I don’t like that you have both vertical and horizontal timelines, it requires too much work to get this, and the vertical timeline took some time for me to understand in part because going down is more recent. Physical geographers/geologists like their vertical timelines too, but I think they arrange them with newest at top? Nonetheless, for most users I suspect a left-to-right timeline would be more graspable (oldest on left).”

When I started putting this map together, one of the people I showed it to didn’t like the “empty” spaces along the left and bottom of the map. I responded by adding in these timelines. This is probably one of the worst justifications you can give for adding something to a map: “I needed to fill space.” The vertical timeline was vertical because I needed to cover a vertical space. I think the data are interesting, to be sure, and related to the subject of the map, but it could do without such a timeline. Or a much smaller one. I did a redesign of this map recently for inclusion in a textbook. I had to shrink it from its normal size of 24″ x 18″ to about 6″ across, so I had to cut out the graphs. I think it looked better, less cluttered, with the graphs gone. Empty space is nothing to fear. Sometimes it’s a problem, but I think I went overboard in trying to fill it with graphs and little annotation boxes.

“The data are interesting but I’m not convinced they need to be mapped. Is there a spatial pattern to see here, beyond the obvious one that big cities tend to have more tall buildings? Does the spatial arrangement of these cities tell us something about the data we couldn’t learn from a table? Is space causal?”

I go back and forth on this one — I think there’s possibly something spatial going on. There’s a Communist East vs. Capitalist West story throughout part of the data, though that connection is not as clear as it would be if I mapped another data set along with it, showing something economic (which would, likewise, have to have a symbol that can convey 125+ years of data). Many times a phenomenon is not driven by where on the Earth’s surface it is, but by the fact that it happens to share a location with another phenomenon. I didn’t make that as clear as I could have (I’ve got some annotation going on which helps).  I think there’s also a story of spatial concentration going on here – big buildings becoming something that only big cities have, whereas many small towns had impressive structures prior to the 1950s. But, again, I don’t include a data set that really emphasizes the population differences between places.

“I would like to increase opacity behind the timelines so they don’t need to compete so much with the underlying (and irrelevant) basemap in the corners. The actual data (the lines) are easily upstaged by the basemap and fade effects.”

He’s quite right about that, in my opinion. The lines in the graphs would be easier to follow and focus on if Europe wasn’t going on behind them. Of course, I suspect that if I made the opacity higher, the graphs would start standing out too much — they’re already distracting from the main map. Another good reason to ditch them.

“I don’t know the names of any of the buildings – maybe you could label the lines (at least with some of the famous landmarks?). Without names of buildings, there is nothing to anchor my understanding to (e.g., I know the Eiffel Tower, etc.) – they’re just name-less lines around circles.”

People are probably going to be looking at this map for things that they know. Fun fact: the Eiffel Tower isn’t included here, because it didn’t fit my definition of “building” (which was a tricky thing to nail down). It’s a minor touch, but one that could give people a lot better connection to the data on this map.

One final issue that I have been thinking about lately with this project: it’s pretty complex. Look at that legend — reader education is definitely necessary before engaging with the map. It’s difficult to strike a balance between the transparency of the interface (how easily you get the data off the map) and the depth of the data. I wanted to design this as something you can stick on your wall — I wanted to give it enough substance and complexity that it’s worth examining at some length. Whether or not I have achieved that balance is something I can’t really answer, though.

Before I leave off, I wanted to point out just how much this map was affected by critique earlier down the line in the design process. Here’s what it looked like when I thought I was done:

OldEuropeI showed this to my boss, Tanya Buckingham, here at the UW Cartography Lab, to ask for her advice, as I was planning on entering this into some competitions. Looking back over the comments she made, I notice that she also suggested ditching the vertical timeline and combining it with the horizontal one. She also suggested getting rid of the really big coastal glow and making it more subtle, which advice I took. Drop shadows, glows, etc. should probably not scream “LOOK AT ME I DID SOMETHING FANCY!” The darker color scheme was also as a result of her urging. Both the scheme she suggested, and the one I eventually went with, do a better job of pulling the city dots out from the background and bringing the data to the front of the map.

Writing this up gives me the urge to go back and try and improve it the map, but the process is never done, I suppose. There just comes a time when you must decide it’s good enough.

31
Aug
09

In Need of a Dancing Banana

(Editor’s note – Continuing our series of getting cartographers to publicly criticize themselves, we next feature Mr. Andy Woodruff, proprietor of Cartogrammar and an alumnus of the famed University of Wisconsin Cartography Lab. If you’re interested in following the brave example of Mr. Woodruff, and Mr. Reynolds before him, and showing the world some of your own cartastrophes, please write me at cartastrophic@gmail.com. — DH)

Animated maps can be a delightfully cartastrophic realm, rife with dizzying excessive motion, poorly or over-designed interfaces, annoying sound effects, and (I really, really hope) perhaps a few dancing bananas. Daniel will perhaps be wise to steer clear of them here unless he is willing to give up the rest of his life to unearthing all the bad animated cartography on the internet. With this post I will only lead this blog to test the waters gingerly.

An animated map of Wisconsin farmland

This is a map I completed for a class called Animated and Web-based Maps, instructed by Professor Mark Harrower, the king of cartography at the University of Wisconsin-Madison, and, I should add, a known expert in map animation. It’s animated, sure, and you can click the image to load and play it, but that’s not entirely necessary for what follows. Very briefly, a bit of background on the map: it was made for a lab assignment in which students were provided with a set of county-level agricultural data for the state of Wisconsin for the years 1970 to 1999 and instructed to make an animated choropleth map of a variable of their choosing.

Typically, my first reaction to viewing this map is to vomit at the sight of the colors. Unlike many of the maps that make their way onto this site, mind you, I actually used an appropriate color scheme: a diverging scheme using official Cindy Brewer ColorBrewer specs (and it’s even colorblind-friendly!). But this red-blue scheme combined with the interface color pair of green and more disgusting green… well, I hope you’re reading this on an empty stomach. Otherwise I probably owe you a new keyboard. Oh, and if your eyes aren’t completely filled with blood yet, you just might discern in the background the ghost of a photo I took in Wisconsin’s Driftless Area.

But chalk all that up to bad taste. On to the actual cartographic crimes.

First, the white color in the classification scheme, labeled “0% or No Data.” Hold on a second, 2006 self, there’s a big difference between those two! Zero is a legitimate data value that fits in the classification scheme. “No Data” is a different animal entirely. Ideally one would avoid an incomplete data set in the first place, but sometimes that’s what you’ve got (like when that’s the data provided for the assignment). In those cases, areas without data can’t be indistinguishable from areas with data, or else the map reader can never really know what’s going on. Look at the screenshot: which counties have no data, and which have a value of zero? Impossible to know. In reality perhaps some of those counties should be off-the-charts red, but you’d never know which ones. The counties without data need to be shaded with a color that is not in the map’s color scheme, probably some kind of gray. Worse, in the image above there is in fact only one county with no data, but guess what the tooltip says when you mouse over it. That’s right, “0%.”

Next, two points about the choice of variable to map, beginning with the description I wrote near the legend:

“This map shows the change in the percent of land in farms over the preceding year in each Wisconsin county from 1970 to 1999. This is a percent change of a percent– for example, a change from 50% land in farms to 48% would be shown on the map as a -4% change ( 48 is 96% of 50), not a 2% change.”

It’s as though I deliberately chose the most complicated variable possible, probably in an effort to confuse the TA into giving me a good grade. Though I clearly realized the potential confusion (hence the descriptive example), what I didn’t even think about until now is that the exact same thing could have been mapped without any of the “percent of a percent” nonsense. The percent of land in farms for a given year is the total land area in farms divided by the total land area of the county. The percent change that I mapped is that percent for the first year minus the percent for the next year, divided by the percent for the first year. But ignoring, say, erosion on the lakeshores (and I’m sure this data set did ignore it), the total land area doesn’t change from year to year. So total land area magically cancels out of the whole equation, and it would be mathematically equivalent, and a lot clearer to the reader, to simply show the percent change in agricultural land area. I haven’t taken a math class since high school, and maybe it’s starting to show.

It's mathemagical!

It's mathemagical!

Beyond that, out of all the options this choice seems like a particularly strange thing to map in an animation. The whole purpose of an animated map is to show change over time. But the data are already showing change, so now we’re dealing with change in change. Watch the animation; do you get anything out of it? I sure don’t. Yes, you can see that some years are calm and some are not, but it is very difficult to get a sense of the overall trend of what’s going on with farmland in Wisconsin. It would have been a lot clearer to just map the percent of land in agriculture and watch how that changed over thirty years. Now, this point is debatable because animating change maps is not unheard of. I’ve been told that some important minds and beards have investigated such animation. It can be useful for highlighting or discovering areas of instability. For general-purpose maps such as this one, however, mapping a change variable is best left to single, static instances. If I had animated just the percent of land in farms, the same trends could have been discerned through the animation, and the user would also actually learn something about the amount of agricultural land. A more appropriate use for the change map might have been a single map showing the change over the thirty year span. In fact, the subtitle here, “Change in percent land in farms 1970-1999″ could be realized by that single map.

For a final quarrel, I would argue that the counties on this map should have been labeled. There is ample space, and whereas you might get away with not labeling states in a US map, few people know Wisconsin’s 72 counties by heart. Instead of labels on the map, I forced the user to move the mouse cursor over a county to see what its name is. The rule by which I now try to abide is: don’t lean on interactivity to solve all cartographic challenges. Interactivity as a means to reveal data is a good way to add lots of additional information to a map, but it can also make it easy to be lazy. Laziness is for the map reader, not the map maker. If the information is useful and can be accommodated without relying on interaction, then do it. The specific data value you see when hovering over a county is a good use of interactivity for extra information; the county name is not. It’d be a lot less work to visually scan persistent labels that are sitting there on the map than it is to mouse over counties to see their names one at a time.

And an extra special bonus typographic nitpick: I misused hyphens in place of both an en and an em dash in the subtitle and description, respectively.

One Nice Thing: The animated and interactive features of the map are nearly—but not quite—unbreakable. I won’t mention the one bug I did find recently.

04
Aug
09

True Confessions of a Trained Cartographer, Part 1: My First Map

(Editor’s note: Daniel Reynolds is a colleague of mine in the University of Wisconsin Cartography Laboratory. He has graciously agreed to act as a guest poster, publicly criticizing some of his own works for your edification and entertainment  — DH)

Okay, so the title is a lie. This wasn’t my first map. Or even my second. But this was the first map I made where I wasn’t trying to copy a lab step by step. I cringe every time I see this thing.

4850_map1

In my introductory cartography class at Utah State University, the first assignment was to do a combination choropleth and dot density layout with the given data in ArcView 3.3. Why not something else, something not quite so dated given that it was 2006? Or perhaps something with better tools to make stuff pretty? Well, the instructor had never used ArcGIS 9.1 (the then-current standard at USU) since he hadn’t been working in the geospatial field for a number of years. All that is to say that I didn’t have the best software to produce a good looking map.  My excuses stop there…

When I made this map, I didn’t know much about color theory. That is unfortunate, as I’m pretty sure I picked one of the worst possible color schemes. By that, I mean the choropleth makes me want to stab my eyes out. I’m pretty sure it was a default scheme that the software spit out. Color  is, in my opinion one of the easier ways to categorize a map as a ‘GIS map’ or a ‘Cartographic Map';  a good number of the default color schemes in the GIS packages I have used are passable, but need work. Other schemes make Cynthia Brewer cry…like the one I used. While it makes sense to use a diverging color scheme to highlight areas of population growth and population decline, I managed to abuse this concept pretty thoroughly. A diverging scheme is meant to highlight data in relation to a critical value. A good diverging scheme is made of two hues changing in saturation and/or brightness as you move away from the critical value.

diverging

Instead of following this sensible approach, I used four hues (although I think at the time I felt that orange/yellow and green/blue fit together reasonably well). This results in a color scheme that could be interpreted as categorical even though the data beg for an ordinal scheme. On top of all that, I don’t think there is enough differentiation between some of the colors (the two blues, the two greens). A little less obvious is that I really goofed up the relationship between the color scheme and the critical value. The second lowest category (0-11%) should be part of the color values above the critical break. In other words, it should be some shade of green. As it is, the critical value appears to be 11% instead of the more sensible 0%. While we’re examining those values, I should probably point out that the break values were likely determined by the software without much thought on my part.  My best guess is that I used a standard deviation classification. Why? I don’t know!

A few last parting shots at the choropleth map:

  • Not only did I fail to include surrounding landmasses to avoid the island effect, but I also managed to leave an enormous amount of empty space on the page.
  • The legend drives me nuts. Again, I didn’t effectively use the space at hand.  I think it would be much more aesthetically pleasing if it were more compact (make the legend title two lines instead of one).
  • The labels could be much smaller and less distracting. They disappear on the darker states.
  • The topic of the map is population change, but we have no frame of reference. Is this change from 1930 to 2030?

On to the dot density map:

Part of the assignment was to symbolize the population density of hurricane-impacted states on a county level. While I think this is an appropriate scale for the map in question, I had no clue at the time.  I remember the concepts of dot size versus dot value being very vague and somewhat hard to comprehend. The basic goal with a dot map is to pick a value that is easy to wrap your head around and a size that allows you to display that value effectively. I won’t go into more detail here. I managed to pick a decent value but then made the dots so small that it looks like someone tipped over a pepper shaker on my map.

My other major problem with the dot-density map is that the data set we were given is not appropriate for the given scale.  The coastlines are quite jagged which is a result of using a dataset that has been generalized (poorly in my opinion) for a smaller scale. (Yes, I do mean smaller scale).

Lastly, the island effect is avoided this time around, but the fill color ended up with a bizarre and entirely unnecessary pattern. I’m not quite sure why this happened as I don’t recall choosing this.  My best guess is that there was some image integrity loss since it is stored as a jpeg.

One Nice Thing: I had the presence of mind to generalize the legend values in the choropleth rather than using precise but essentially useless decimals.

PS.  At the time I made this map, I was really proud of the background color. I had never really used image editing software (other than doodling in MS Paint) so realizing I could draw a box, make it blue, and drop it behind everything else made me very happy. Unfortunately, the particular shade I chose makes me gag.

PPS. If you are wondering why we mapped predicted population change for the entire US with population density of hurricane impacted states, I have no clue.

(Editor’s note: To avoid leaving the Internet with the impression that Mr. Reynolds still regularly makes maps worthy of such criticism, I leave you with a link to his portfolio, where you can see his current, and much better works. — DH)

30
Jun
09

What Have I Done?

It seems only fair for me to start this blog out with one of my own works, which still graces the Wikipedia page on the Kalamazoo River:

Kalamazoo River Map

Yeah.

The labeling is troubling, and the whole thing has this “I threw it together in MS Paint” look (which, actually, I didn’t), but the real problem is the inset in the lower left.  See, there’s a problem on a lot of maps called the island effect, where you just show one state or country in your map, and completely leave off the other geographic context. Look at the area to the immediate west of Michigan. It’s brown. That’s where Lake Michigan is. Also, Wisconsin. Apparently, Wisconsin (not to mention other bordering states) blends seamlessly and stealthily into the Great Lakes. So does Canada – they have, in fact, hidden their entire country in camouflage in preparation for the big invasion.

Also, in the inset, Michigan is surrounded by the same brown used for land on the main map…making it look more like a hole in the middle of the land rather than an island. And like there’s no water around it at all, only barren wasteland.

To be nice (to myself), I did make this some time before I actually had any cartographic training, or even access to the proper tools. I’ve been planning for years to replace this with a better effort. Someday, perhaps that will happen.

One Nice Thing: The city label size varies by city population. Though, you know, it would be nice if I indicated that in some way.




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