Importance of Normalization: Worldwide Alcohol Consumption

This lab consisted of mapping worldwide alcohol consumption data. The first of the two maps shows the worldwide total consumption of alcohol in millions of liters per country. The second map is normalized per person in each country, calculated in liters per person. Each map displays different colors per class, varying from lighter colors for the lower values to darker colors for the higher values. I used the “Color Brewer” website (http://colorbrewer2.org/) for the first map and chose a 5 class, sequential, multi-hue CMYK scheme which ranges from yellow, to green, to blue. The second uses the standard colors which arcmap had set for the Jenks classification. I decided to keep it since it ranges from a yellow, to an orangish-tan, to brown, the typical colors of beer, an alcoholic drink. Both color schemes have colors greatly distributed to clearly show the differences between classes which can be important when looking at a world map with countries varying in size. 

In creating my maps, I had to choose a classification to map the data in a way that makes sense. When choosing a classification, I took the type of data and the type  of distribution into account. I decided that the type of classification that would best distribute the intervals in each class was the Natural Breaks aka Jenks classification because “the purpose of natural breaks is to minimize differences between data values in the same class and maximize differences between classes.” (Slocum: Data Classification, pg. 84)  Jenks is typically a good type of classification for many types of distributions and data types and helps tell a “good” story of the information being mapped, this is probably why arcmap has it as the default classification. 

Taking these things into consideration, I can conclude that both maps are true for what they are trying to display, but they can also be misleading depending on the information the author is trying to convey to the reader. For example, if someone were to look at the first map and tried to figure out which country drinks the most, they would assume it was China because it is the darkest country on the map. This is true because as a whole, they drink the most alcohol, but this is only because they have a big population. However, if we take a look at the second map we will see that China is not the darkest country on the map anymore. This is because the second map is a normalized map of alcohol consumption per capita. The same can be said for India due to its big population. Personally I prefer the second map because I believe it tells a better story of alcohol consumption per country. When people think of alcohol consumption, they usually think of how much alcohol is being consumed per person not so much the country because we generally do not really know how big or small the population is per country. Another reason I’d prefer the second map is because it is easier to understand liters/person as opposed to the millions of liters/country in the first map. Not everyone enjoys math which would make the first map’s units a nightmare to try to visualize. Overall, the second map is just easier to understand and probably easier to relate to when trying to understand the data.