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| View of three choropleth maps and one map with graduated symbols. |
Technical Details
The map above is more of a 'think-piece' than anything else... quite a bit of time was spent pondering such questions as what color schemes to use? What is the best classification scheme for each dataset? How to account for all those outliers and null values, and what to do about all those invisible micro-countries? And how do I bring this all together visually?The final product above was one I could live with... of the three data frames, I decided to make the population density/wine consumption map the centerpiece, with the two demographic maps a bit smaller. Linking all of the maps together is my neatline and the sandy/rose background color. Information that pertained to all three maps was purposely placed within the neatline but outside of the other data frames.
For each inset map I carefully considered the classification scheme. Since I had some crazy values for each dataset (and no space whatsoever with which to represent the tiny countries) I put in a separate text box for each map that hopefully explained the situation. It also just worked out that the Natural Breaks (Jenks) scheme represented all of my datasets the best, although in the case of the population density map I used 8 classes instead of the default 5. This choice was made completely due to the range of values I had... it went from 3 to 18,353 people per square kilometer. Due to the extreme range a 5 class system completely glossed over the variation on the low end which comprised the majority of the countries (with values from 3 to 518 people per square kilometer). In my opinion a quartile classification would have also showed the variation, but I wasn't too crazy about the breaks with that scheme, especially since it lumped all my outliers with what I considered to be my 'main' dataset (for example, the last class for the quartile scheme ranged from 233 to 18,353).
Each map has a circular gradient background set to a custom made blue green color ramp... it took me a bit to find, but I'm glad I was able to eventually change the default color gradient settings! For each dataset I wanted a color scheme that not only showed easily visible variations between classes but that also wouldn't clash when compared with the other data frames. In the end I kept a yellow/brown scheme for the main map, and off-set it with berry tones and blue tones for the two smaller maps. I'm not too proud of using gendered colors to represent, well... gender, but both color schemes went together and are culturally easy to interpret.
Other Thoughts...
I spent several days, off and on, working on this particular lab. I found that working on it in smaller pieces was less frustrating than trying to solve all my problems in one sitting (such as pondering what classification scheme(s) to use!).A bonus option had also been presented with this lab - if we could successfully pull off a picture symbol (for the proportional/graduated symbols), then we'd get some extra points. Well I wasn't successful - not that I mind though. Personally, I'm not a big fan of proportional/graduated symbol maps as it is. I've never liked them, and to my mind the picture symbols are even worse. Since I already have troubles telling symbol size apart on professionally made proportional maps it was probably predestined that I would not have enjoyed whatever picture creation I came up with. Truth be told I'm not a huge fan of the graduated symbols on my map, but at least these work better for me than the pictures!
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