Module 7 - Choropleth & Proportional Symbol Mapping

You have probably seen choropleth and proportional symbol maps before, they are everywhere – on local TV news, BuzzFeed articles, newspapers, magazines, and much more. Not only are they fun to look at, but they also provide quick and powerful information at a glance. Choropleth maps are thematic maps (that is, a map with a theme), and are used to depict aggregated counts within a polygon of a large area (often referred to as ‘enumeration units’). The polygons are shaded or patterned in proportion to the measurement of the standardized data being displayed, such as population density or per capita consumption. Proportional/graduated symbol maps are used to depict size variables to represent differences in magnitude of discrete information in polygons, such as counts of dogs per counties. The symbols are sized in a graduated manner representing the proportions.

For our lab assignment, we used ArcMap to produce a choropleth map showing overall population densities in European countries using 2013 Census Data. Then, we tied-in wine consumption data using proportional or graduated symbology as a fun comparison.

For the choropleth portion, I chose a sequential color scheme called "Brown Light to Dark". This color ramp works great for people who are color blind. I was able to verify this accessibility design at www.Vischeck.com where it allowed me to simulate what people with color deficits (specifically Deuteranope and Propotanope) would see it as, and it was the same! Also, I really like this color scheme because it is very much like the natural color of land, and it looks nice against the blue ocean color hues of ESRI's Ocean Base Map (which I included in my map).

Furthermore, for the choropleth portion, I chose the Quantile data classification method because I felt it best represented all the classes in my map, making it easier for an audience to ascertain the population dynamics of most countries in Europe. I played around with the other classification methods to see what the data looked like. Some methods had too many countries in it, which defeated the purpose of even classifying the data to being with. The Quantile method does a great job at differentiating.

The value we used to classify our data in our choropleth map was 'population density', and not 'raw population counts'. This brings up a huge topic we covered in this week's lesson: you are lying with maps if you use unstandardized data. Choropleth mapping is correctly used if we are mapping data that is evenly distributed amongst a boundary (enumeration unit). The population density column in our attribute table is a more accurate representation of the information we are trying to convey because the data has been standardized taking into account the different area sizes of European countries, and their different population counts.

For the symbology portion (wine consumption), I chose to use graduated symbols (using the 'wine_cnsmp' as the value field) over proportional symbols because the proportional symbols completely overlapped all of Europe even after making adjustments and configurations on the back end. The graduated symbology was more appropriate, aesthetically pleasing, and comprehensible for this map project. Additionally, with the graduated symbology I had more flexibility with changing the sizes of each dot invidually and was able to classify my data. The wine consumption data did not need to be normalized because graduated and/or proportional symbology is used to represent numeral data associated with point locations, and not area.

I finalized my map product in Adobe Illustrator by using drop shadow styling effects on a few features: symbology, legends, and title. I felt it gave the map a little pop. This is definitely one of my favorite maps I've created in class. I spent a lot of time designing it, and I feel really proud of it.