(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.
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.
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)