Archive Page 5

20
Aug
09

Where Does David Wilkins Live?

Remember David Wilkins, former US ambassador to Canada? Well, if you do a Google search on him, this map from whitepages.com comes up near the top, showing the distribution of telephone directory listings matching his name:

Since they apparently generate these automatically for most any name, I thought of doing my own. But, I figured that I would take another opportunity to increase the fame and internet profile of Mr. Wilkins. Can’t pass that up.

The colors are certainly less than ideal – as with so many of the maps seen here, there’s a mismatch between an orderable data set (number of listings) and an un-orderable symbology (the colors chosen to represent those numbers). Though, I suppose one can see a weak progression in the colors, depending on your perspective. But it’s still far from a good match to the data. Running from a light to a dark blue would be perfect. It would also be more friendly to people with color vision impairments.

It would also be nice if I didn’t have to assume that white means zero listings, since it could also reasonably mean “no data available.” Troubling is the fact that some of the small states are filled in with white on the main map, but on the inset, where they are enlarged, they are given a color. The inset needs to be consistent with the main map – else it makes it harder to understand that the inset is, in fact, a zoomed-in version of the main map.

A sacrifice made with a classed choropleth map like this is that you lose some precision in getting the numbers off of it. Look at the states in light blue – they all have anywhere from 1 to 11 listings for “David Wilkins.” Grouping states like this is perfectly reasonable, to help reduce the number of colors used on the map and make it easier for someone to pick out one distinct color and match it to the legend. Some ambiguity is necessary as part of this process. But, look at Texas – the only state colored in dark red. It apparently has anywhere from 43 to 53 listings. It’s the only state in its class – why is the exact number not specified?

The classification scheme in general is a bit odd. There are a few big goals you want to try and go for when deciding how to group your states. One is to minimize intra-class differences – that is, keep the class sizes small. You don’t want a class that goes 1 to 11 listings, and one that goes 12 to 500 listings. The second one is way too broad. Another is to try and make each class roughly the same size, which this map has a problem with. There’s one state in the dark red class, two in the orange class, and twenty-five in the light blue class. A third goal for class breaks is to try and have class breaks that are relatively even in number – as an astute reader points out below, the class breaks change in size just a bit, though they’re roughly pretty even, so I think they hold up pretty well. There are a few other goals, but I’ll leave it at that. As you might expect, it’s hard to fulfill all the goals at once, but the severity of the difference between 1 red state and 25 light blue ones is still pretty bad. The two lowest classes cover most of the country, and the two upper classes cover only three states. It makes those three states stand out, but more than they should. There’s not a large, unusual, and worth-pointing-out difference between the upper and lower end states, to my mind.

These data should probably be normalized, as well. Consider Texas again: a lot of people named David Wilkins live there. This is probably because a lot of people live there in the first place – it’s one of the most populous states. More populated places will probably have more people named David Wilkins. Likewise, you can’t find anyone named David Wilkins in places like Wyoming or South Dakota, because approximately no one lives in those states. The pattern shown by this map is highly correlated to the population distribution of the United States. It does not show whether or not people from Texas are more likely than people from Wisconsin to be named David Wilkins. Instead of making a map of how many telephone listings there are in each state for David Wilkins, the author(s) should plot how many listings there are for David Wilkins per million inhabitants of the state.  Then you would find out that Delaware has 8.1 listings for David Wilkins per million inhabitants, vs. only 2.2 for Texas. The name is also particularly popular in South Carolina, which state the Ambassador calls home.

I find it a bit odd that they have region names listed for New England and the Mid Atlantic, but not the rest of the country. Also, I was under the impression that Maine was part of New England.

One Nice Thing: Those inset maps to the right sure are handy.

With that, I will leave off today’s effort to make this blog the #1 item on a Google search for David Wilkins.

14
Aug
09

Way Over in British Columbia

Today’s effort comes to us from Kerr Wood Leidal, a consulting firm in Canada:

Detail of map - click for full poster. Taken from http://kwl.bc.ca/docs/CEBC2008-RHAM-FinalAwardofMerit.pdf.

Detail of map - click for full poster. Taken from http://kwl.bc.ca/docs/CEBC2008-RHAM-FinalAwardofMerit.pdf.

This one was brought to my attention by a reader, Eliana, who appeared particularly exasperated that this map won an award. It seems that the folks at KWL took first prize in the 2009 Map Gallery competition at GeoTec, which bills itself as the largest GIS conference in Canada. According to the GeoTec site, the winners were selected “based on overall appearance and effectiveness as maps.” So, this means they have to a) look good, and b) communicate spatial information clearly. Longtime readers may recall that one of these things is more important than the other. Though this is not to say that making your map look good is unnecessary, and in a competition like this, it’s a fair criterion for judging.

Let’s start at the legend – of the 8200 or so red spots on this map, each one encodes how much hydropower could feasibly be generated at that site. A red dot means < 1 megawatt, a red square is 1 – 10 megawatts, and a red triangle is > 10 megawatts. This is a non-orderable scheme – squares, circles, and triangles cannot be put into an inherent order. So, it doesn’t make sense to use different shapes for different numbers of megawatts — which can be put into order. Dots of different size or perhaps color brightness (but the same color hue – so, for example, a scheme from light blue to dark blue) would be more sensible here.

The other big problem with this scheme is that if you look at the map, you can’t pick out the squares from the circles from the triangles in most areas. They’re way too small to be able to tell which shape is which without staring or zooming way in, and even then it’s sometimes ambiguous. I will add the caveat that I don’t know how large this map was printed – it may be less of a problem if this thing is two or three feet across. And I’m not even sure if this map really needs to go into this much detail. It does a good job as it stands of showing the general distribution of hydropower sites, mostly clustered along the west of the province. If the authors want to add an extra level of information, about how much power might be generated at each site, their task becomes much harder, because now the dots have to be separate enough, and big enough, for people to be able to tell how they vary. And the reader cannot effectively do that, here.

Even if you could visually tell one shape from another, however, it would still be difficult to pick out the overall pattern of where the 1 megawatt sites are, and where the 10 megawatt sites are. Shape, as a visual means of encoding information, is weak in terms of what we call selectivity. It’s hard to select just one shape, and then try and find the distribution of only that shape. It’s much easier to do this sort of task with something like size – you can quickly see where the big dots are clustered and where the small ones are. A quick example:

Selectivity Diagram

Notice how easier it is to pick out the cluster of small squares near the center than it is to pick out the cluster of triangles near the center - size has better selectivity than shape.

Moving on from the dots, let’s consider a few other, lesser offenses. The labeling has poor contrast with the background, especially at Stewart and Port Hardy. Interestingly, some of the labels have been set in light-colored type, to better stand out against the water, thus demonstrating that the labeler was mindful of contrast issues. But not enough to make them legible against a mass of little red shapes.

Notice the white area in the northwest. That’s part of Alaska. It looks like it’s buried under an ice cap or something, given the color scheme and the fact that it’s flat, while the rest of the map shows terrain relief. I’ve never been to Alaska, so I suppose it could in fact bear a great resemblance to Antarctica. More of concern to me is the fact that the authors have possibly done one worse than the dreaded island effect. Instead of either showing British Columbia as an island with no surrounding land, or showing it in its geographic context, with the USA and other provinces around, the authors have chosen to include just one part of one of the surrounding areas. It looks very odd, and I think it would look better showing just the province, really.

Speaking of odd, it looks like someone has discovered the joy of the “glow” effect in the Adobe suite of graphic tools, because the entire coast of British Columbia is glowing white. Now, a glow effect can be a great addition to a map, but it would probably make more sense to do one a light blue one that looks like shallow coastal water, rather than giving the appearance that there’s been some sort of radioactive disaster off the Canadian Pacific coast.

Since this is a map about hydropower based on the flow of rivers, where are the rivers on this map? You can see a few here and there, in a very light blue, but the hydrography should really stand out more. Maybe not every single creek, but at least the major ones.

Finally, a note on the map projection. The authors appear to have kept the central meridian for this conic projection somewhere far east of the map area – say, around the center of Canada.  For those readers who may be confused by what I just said, I will avoid giving an entire lesson on map projections. Instead, here’s a somewhat related way of thinking about it: Consider your average map of Canada, grabbed randomly from the Internet – the kind with the curved appearance to it. Doesn’t it look like the authors of the map above took BC from the far west end of one of these typical Canada maps, and didn’t bother to rotate it so that it wasn’t tilted clockwise anymore? If it’s the only thing on the map, BC should be centered so that it’s northern border has a shallow “U” shape, instead of curving downward only. The projection on this map just constantly reminds me of the fact that BC is at the far west end of Canada.  Perhaps the authors wanted to keep that suggestion in my mind – “British Columbia: We’re way over here!” Might make a good provincial motto.

One Nice Thing: The terrain relief is not just a useless bit of decoration – it’s useful, because hydropower potential is affected by terrain. You can see the river valleys and everything (if not the actual rivers, unfortunately). So, the relief here is both a nice aesthetic component, and conveys information relevant to the topic at hand. A win-win.

Before I leave off, I’d like to thank all of you who have been writing in to me and submitting maps you have encountered. It’s a big help to have other eyes looking for these things. I may not end up using every submission, but I appreciate them nonetheless.

06
Aug
09

An Addendum

An addendum to my last post (given its own post here for the benefit of those of you who’ve already read it and moved in):

I am a bit embarrassed that I didn’t notice this at first, but these maps are very unkind to the millions of people with red-green colorblindness, as several commenters on the original OKTrends post mentioned. Here’s an approximation of what a red-green colorblind person sees when they look at one of those maps:

Run through http://colorfilter.wickline.org/ with protanopia filter

Run through http://colorfilter.wickline.org/ with protanopia filter

Designers cannot ignore such a vast population, and must take color vision anomalies into account.

05
Aug
09

Concerning the Value of Human Life

Today’s effort is brought to my attention by one of our readers, Robin, who previously alerted me to the problems of Moon Maps. This map appears in a post on the OKTrends blog, where the authors analyze the geographic distribution of how people answer various questions on the dating site OKCupid. We’ll take one of them as an example:

human_lives_map

Obtained from http://blog.okcupid.com/index.php/2009/07/13/sweet-ass-american-trends/. Legend, title, and map combined by author into one PNG file.

Side note: the darkest red, for the highest percentage “No,” shows up as a single pixel at DC. So, I cannot claim that the map doesn’t follow the legend. Though, the DC dot is so unnoticeable it might as well not be. Putting an area on a map which is too small to be seen is a serious problem. Better for the authors to include an inset or two, showing the smaller states in a more magnified fashion (as we say in the business: at a larger scale).

There’s a fundamental error that a lot of people are prone to making with these maps (and, in fact, I didn’t notice myself doing it until I’d spent quite a while looking at these). If you look at the map above, or any of the others in the OKTrends post, there is always one state at each end of the scale. In the above example, Nevada is the brightest green, matching the far “Yes” end of the scale, and DC (thought you can scarcely see it) is the darkest red, matching the “No” end of the scale. This does not mean that 100% of the people in Nevada answered yes and 100% of those in DC said no. It just means that more people answered yes in Nevada than anywhere else. Maybe in DC 2% of the population said “yes,” and in Nevada 5% did, with all the other states in between. But this legend makes it appear as if DC uniformly said “No,” and Nevada “Yes,” with the vote being split in all other states. It is unintentionally misleading, which is the most tragic cartographic sin. The authors wanted to convey some information in good faith, but their communications became twisted and false.

The Yes-No labels on the legend are not the only problem. It’s the color scheme. It’s a diverging color scheme – the green and the red are opposite ends, and states shade toward one end or the other. There are many good reasons to use these schemes, but in this instance, it just makes it look like there are solid No states and solid Yes states, instead of states where a handful of people said yes, and those where a slightly larger handful of people said yes. The color scheme should be one hue, changing in lightness (say, from a pink to a dark red), and the map should show % yes votes (or % no) votes only. This still lets you pick out trends – Nevada has more Yes votes than other states, but it doesn’t mislead you in to a panic about the amoral citizens of Nevada and how they disregard human life. You can tell that “yes” is unpopular, but less so in certain areas.

Another reason to ditch the red-green scheme, which the authors use uniformly throughout their maps: It doesn’t always make sense when the options are other than yes and no. Here’s a map from later in their post:

Obtained from http://blog.okcupid.com/index.php/2009/07/13/sweet-ass-american-trends/. Legend, title, and map combined by author into one PNG file.

Obtained from http://blog.okcupid.com/index.php/2009/07/13/sweet-ass-american-trends/. Legend, title, and map combined by author into one PNG file.

These two colors, green and red, have certain connotations of positive and negative in much of the english-speaking world, so here, the scheme is a problem, unless you’re going with an implied value judgment where “Right to Bear Arms = Good” and “Right to Vote = Bad.” The people on the OKTrends blog don’t quite strike me as that type. Nor the type to greenlight an unequal valuation of human lives. And, of course, the legend does again make it look like Idaho is 100% behind giving up the right to vote. I’m guessing (and it is just guessing) that if you looked at the data, the vast majority of respondents in each state said they’d rather keep the vote and lose their guns. But Idaho happened to have the most people who were more fond of guns than voting (hard to hold that against them, given the political climate). Probably just a few percent. But people will come away from this map with the sentence stuck in their mind “Idaho strongly prefers to lose the right to vote.” I have belabored the point, so let’s move on.

Note that there are two states in the lower right corner which are not actually filled in with any color. Thanks for playing, Hawaii and Alaska! It’s OK — they’re just happy to be there, given how often they’re left off of US maps.

The shapes of the states are poorly generalized, and may in fact have been drawn freehand. Possibly just traced loosely, instead. But it has that feeling to it. Many of the states look slightly-to-moderately wrong. Look at my beloved Michigan – it’s become an amorphous blob. Saginaw Bay is entirely missing. It’s a sizable body of water. Like, nearly as big as some states. Also, I’m pretty sure Ohio doesn’t look quite like that. And Maryland seems to have taken over part of Virginia. Wyoming, though, is just as rectangular as ever.

I would be tempted to complain about the map projection, but since I think it’s freehanded, it doesn’t exactly have one.

Note the line around Michigan, showing the water border between the US and Canada as it passes through the Great Lakes. Except, the line only shows the border as it passes through two of the lakes. The actual border passes through two additional ones, north of Ohio, Pennsylvania, and New York – but you don’t see that section here for some reason. So, we have part of a Canadian border. And no Canada, by the by.

The map suffers from the dreaded island effect, where the US looks like it’s floating off in the ocean with no land nearby, and Canada looks like a great and forbidding sea, much like the Gulf of Mexico or the Atlantic Ocean. I might not call this a problem for this map otherwise, but putting a portion of the Canadian border in, and then not actually drawing Canada, makes this a serious issue.

Nitpicky: Both maps have a few artifacts here and there, like in Louisiana in the bottom map. A few pixels the wrong color.

One Nice Thing: It’s an unclassed choropleth, which I rather like. Most choropleth maps organize data into classes – say, one color will represent all states with yes votes between 0% and 5%, and the next color will be for states from 6% to 10%, etc. — the states are grouped. In an unclassed choropleth, such as those above, each state is given a color in exact proportion to the number of votes for one choice or the other. A state with 1.1% yes votes gets a different color from one with 1.2%, because they have different values. They’re never grouped together into the same color, as they would be in a classed choropleth. There’s a whole debate as to which is better, classed or unclassed, and I will not get into it here. I will just say that I am a fan of the unclassed choropleth, so I think it’s a Nice Thing.

Addendum: I am a bit embarrassed that I didn’t notice this at first, but these maps are very unkind to the millions of people with red-green colorblindness, as several commenters on the original OKTrends post mentioned. Here’s an approximation of what a red-green colorblind person sees when they look at one of these maps:

Run through http://colorfilter.wickline.org/ with protanopia filter

Run through http://colorfilter.wickline.org/ with protanopia filter

Designers cannot ignore such a vast population, and must take color vision anomalies into account.

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
Jul
09

Misplacing Egypt

A quick post today for the 1 month anniversiary of Cartastrophe. Most of you have probably already seen this map make the rounds of the cartography blogs during the last couple of days, but it’s worth reposting.

Issues of accuracy aside, my question is why do they use that satellite photomosaic as the background? There’s no call for it – a simple solid fill would work better, and would be less distracting to the eye for a map that’s likely being flashed on the screen for a handful of seconds. It’s needlessly cool and fancy.

Also, Iran is set in much larger type than everything else. I think Israel might actually also be a bit larger than the other countries, despite being the smallest one marked on the map.

One Nice Thing: They did highlight countries of interest to the news story.

Hat tip to Daniel Reynolds for pointing this one out to me.

29
Jul
09

On the Canadian Adventures of David Wilkins

David Wilkins, the 21st United States Ambassador to Canada, stepped down a few months ago with the election of Barack Obama as President. While both countries eagerly await his replacement at Rockcliffe Park, let’s have a look at this handy map which shows where Mr. Wilkins traveled during his nearly three years in the country:

Obtained from http://ottawa.usembassy.gov/. No author listed.

Obtained from http://ottawa.usembassy.gov. No author listed.

This map can be found on the official website of the US Embassy in Canada, by going to the page for the Ambassador.

The obvious fault, first: Putting big red dots on top of labels tends to defeat the purpose of having those labels. A lot of the things I talk about on this site might not be immediately obvious to a lot of people making maps – good color choices, projections, etc. But it’s hard to imagine how the problematic nature of obliterating text escaped the notice of the vigilant staff of the US Embassy. Maybe they’re trying to subtly insult the people of Saskatchewan or Labrador. I’m sorry, I mean “Lab  dor.” Misread the map for a second.

Speaking of Labrador – it’s not a separate province, last I checked. Not sure why it’s labeled separately from Newfoundland, since all the other labels on this map are provinces. If they’re going to label major physical features, why not label Baffin Island while they’re at it?

The provinces are filled in using different colors. This is a perfectly reasonable idea, to help tell them apart. But, for some reason, British Columbia (BC), Nova Scotia (NS), Prince Edward Island (PE), and Newfound and Labrador (NL / Labrador) are all the same color, whereas the other nine provinces and territories are given different colors from each other. Perhaps the maritime provinces on the east coast have joined forces with British Columbia for some nefarious purpose, and the US Ambassador is secretly trying to alert the world without exposing the fact that he knows.

The dots are different sizes. Insofar as I can tell, this is only so that they fit better – there are many small ones in New Brunswick (NB), for example, to avoid overlaps. But, then there are still small overlaps here and there, anyway, such as in Manitoba (MB). It would be better if the dots were of uniform size – else, it implies that certain places are more important than others, or that the Ambassador visited some places more than others. Smaller would be better – since the ones in NB are quite legible, and there would be minimal overlap.

Me being unreasonably nitpicky: The abbreviations are not all standard two-letter Canadian postal abbreviations. They could be using their own system, but I’m not sure why they would. NWT should be NT, NF should be NL, etc.

Finally, it’s arguable whether or not the island effect is a problem here. Personally, I think it would look better if Canada was placed in a geographic context – especially one that shows the US, since this is a page for the US embassy. Emphasizing that relationship makes sense here, and it seems a missed opportunity.

One Nice Thing: The linework is well-generalized. It’s not overly detailed, but has a clean simplicity appropriate to the purpose.




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