Choropleth & Graduated/Proportional Symbol Mapping

Data classification appears to be an ongoing theme here- and with good reason.  Simply put: it is not simple, nor easy to do.  Choropleth maps sound very complicated, and most people are not familiar with the term "choropleth," and as such it is a word I like to throw around in casual discussion of my classes. It makes the work I'm doing sound very difficult and technical (which it is, to some degree).  The truth is that it's a term used for a map that displays some characteristic within administrative boundaries, or enumeration units.  Graduated or proportional symbols are another method of displaying some characteristic, with their placement on a map coinciding with their incident geographic location.

Choropleth maps are, as the one here, often used to display population characteristics based on administrative units, such as countries, states, cities, etc.  The population density in the uppermost frame is shown in shades of green, with the highest density values being the darker shades, and the values decreasing with the lightness of the color.  The bottom frames, with the population percentages by gender, follow this pattern as well- darker shade means higher value, lighter means lower.  With this type of map population patterns are easily revealed, as the top map clearly shows higher population density in western Europe in comparison with the countries in the east.  Again, as we found last week, the classification of the data values is of paramount importance here.  We want to display on the map what is actually true in the real-world, and thus must take care to place each country in a class with members as alike to each other as possible, and as different to values in other classes as we can.  The top map had a few statistical outliers in the population density measure, as there are countries in Europe that are very, very small (Vatican City, Malta, etc.) and have very, very high density values.  These anomalous nations are not visible in a map of this scale, and so are placed in a class with a few countries with much lower values, but easier visibility on the map.  The wine consumption, in liters per capita, is displayed by circles that increase in magnitude as the per capita consumption increases.  Again, placing the countries in classes of different value ranges helps depict a visual pattern, with consumption generally greater in western Europe.  I employed the natural breaks method of classification for that, as I feel it is an accurate way to group the countries by that value.  

All in all, mapping statistics isn't the simplest thing to do, but, like anything else, it becomes easier with practice.  This week's maps seemed daunting at first, but through creating them I can honestly say I have, once again, broadened my understanding of this subject.  I will probably still occasionally obliquely refer to the complicated nature of the choropleth map in casual conversation though, as opportunities to successfully slip such arcane and technical terms into a discussion will always be somewhat gratifying.