Module 7 Lab: Choropleth & Proportional Symbol Mapping

This week, we were instructed to create a choropleth map that compares population density to wine consumption in Europe. A choropleth map is a map with shaded areas that are representative of a variable being displayed on the map. This map was fun to make because it was based off of skills we learned last week, but introduced new functions in ArcMap that I will definitely use in the future. The data did not need to be normalized because it displays population density for each country, which can be accurately compared. 

The first stages of map creation included adding the provided European countries file to the table of contents. From there I had to decide which classification method to use for the population density. This was, in my opinion, the trickiest part of this map because such a major part of the map presentation was left up to our own judgement. After cycling through the methods to see how many classes they created and how well the data was presented several times, I settled on the quantile method. I believe this method is the most appropriate because it separates the population into four straightforward equal-observation classes that ultimately mean low-high population density with enough detail to specify which countries/population densities fall into which class and why. This is easily compared to wine consumption which was covered in the next step. I chose a neutral color tone  keeping in mind that one of the variables should not distract from the other as they are being compared. 

The next step was to add the original european countries file into the table of contents again to begin creation of the wine consumption layer. For this layer, we were to determine whether to use graduated or proportional symbols. I picked graduated because it created five classes that were appropriate to compare to the population densities. The proportional method created symbols with so many observations in the largest class that it was impossible to locate patterns within the data. I chose a "wine bottle green" color and used standard circles because they are easy to compare visually. The Flannery Compensation was not utilized because it could skew interpretation of the data as the larger symbols for the big class do not mean the large class represents dramatically more wine consumption.

The last step in ArcMap was to add essential map elements which included a legend that had a scale with no spaces between the colors of the gradient and effectively presented the wine consumption labels, and to add an inset map.

The finishing touches on this map were done in Adobe Illustrator. Due to problems I ran into with label conversion in earlier labs, I opted to insert them all in AI. I also added my title, subtitle, and data source information on this software.

The map this week required attention to detail for accuracy. I am satisfied with the way my map turned out although the data is not displayed exactly like the example we were given. I feel that it does its job well in effectively communicating patterns among population density and wine consuption.




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