Module 5- Cartography- Choropleth and Proportional Symbol Mapping

 For lab 5, I was tasked to make a map depicting the population densities of European countries as a choropleth and wine consumption for those countries as a graduated or proportional symbol using ArcGIS Pro. I incorporated data classification and map design principles learned from the past several modules.

Using Data from Eurostat and the Wine Consumption institute, I constructed a map meeting the appropriate parameters. I hit two major snags that took me awhile to resolve. At first, I could not figure out why the labels I wanted to exclude from my map were not excluded. After almost an hour I realized that I was supposed to use an “and” clause rather than the “or” clause we used earlier for data exclusion. This confused me because I understood “and” to require the associated feature to meet all the parameters listed, but I had never worked with a negative statement before and when using “is not equal to” then “and” is the appropriate choice. I anticipate that the programming course offered this summer will help me better understand this aspect of the software.

The second snag I hit was moving my symbols on the map. I didn’t realize that even though the wine consumption data was symbolized using graduated circles, the feature class itself was classed as polygon data. One of the module leaders found an article that helped clear this up for me. To be able to move the symbols I had to convert the data to a point feature. This solved my issue, but it was a headache because all my previous work (setting the classes, excluding the appropriate data, creating my labels, etc.) had to be redone on the new feature class.

I used the histogram to study the intervals made with the classification data. I used Natural Break classification for the population density data, but this posed an issue, one that I foresee would have been an issue for any classification method other than equal interval. For my inset map I needed to exclude the data from the Balkan region on my main map. By excluding this data it “altered” my natural breaks. Then, on my inset map which included those countries, the Natural breaks were representative of Europe as a whole, which meant some of the countries were classified differently and the break points were not the same for both maps. I ended up using the break points of the totality of Europe and set manual break points on the main layout.

I used equal intervals to classify my wine consumption data. Looking at the histogram I felt like this split the data in a way that was meaningful.

Overall, I found this project very insightful and learned many new strategies I feel will be helpful going forward. Even though I had several struggles that added significant time to the process I know that this further reinforced the information I took in.

Here is my map: