GIS 5007 Module 5 - Choropleth and Proportional/Graduated Symbol Mapping

This week's module focused on choropleth and proportional/graduated symbol mapping; choropleth mapping is a mapping technique that uses a graduated shading scheme across predefined enumeration units [typically political boundaries, such as counties, states, or countries in this case]. Proportional/Graduated symbol mapping is a mapping technique that places dots at geographic locations where occurrences take place, and uses progressively bigger symbols to portray differences between classes; also, these dots can be placed at either true or conceptual points during the mapping process. For this exercise, we used the choropleth method to map the population density of many major European countries and we used the proportional/graduated symbol method to map how much wine is consumed in each of these same countries. 

Overall, this assignment was a great introduction to these two mapping schemes, and many challenges were met throughout the entirety of this assignment. ArcGIS has been the software platform used for each module's assignment, and has never presented any issues while running over a virtual desktop environment - until Module 5. For extra credit, we were allowed to use pictorial symbols for our "dots" on this map, and I found a fun little clip art file at Freepik. Once this little .SVG file was introduced to the ArcGIS project file, every action taken to create this map was slowed by an incredible amount.

The process in classifying the data was relatively simple; I chose the natural breaks method, but needed to include a fifth class so detail was not lost having only four classes. However, to create the inset maps, data had to be excluded from the main map. This regenerated new class values, so I ended up selecting the manual intervals option where I could input the original class values. 

Finally, converting all labels to annotations and converting the wine consumption feature class to a point [it was originally a polygonal feature class] also proved to be quite burdensome while working over a virtual desktop. Slowly, I was able to get each label and grape cluster positioned in a manner that was easy to read and understand.

I am very happy with the quality of this map, and I feel that this exercise proved to quite helpful in grasping an understanding of these mapping methods.