Choropleth and Proportional Symbol Mapping - Computer Cartography

 Hello Cartographers!

This week's lab was for sure an interesting one. We were tasked with making a map that displays population density and the corresponding consumption of wine in each European country. To reach this final outcome, we had to reach these student learning outcomes: Choose an appropriate color scheme for a choropleth map, which entailed finding a color scheme that best represents the data we are trying to display and is color blind friendly. The next thing we were expected to gain from this map was creating an appropriate legend for the classification scheme and map type, which meant we had to consider how we wanted the legend to be presented and would be the easiest for the reader to understand. The next learning outcome was implementing an appropriate classification method for the population data. I chose quantile because I believe it best represents the population data being presented. After all, the map viewer needs to see that all classes are visibly represented. The next thing we had to do was utilize SQL Query language and query classes to manipulate data presentation. I was already familiar with this as I use this in my current job all of the time. But that basically means that if some data does not correlate well with the rest of the data we can exclude it from the overall analysis. The next few learning outcomes go hand in hand, we had to utilize proportional or graduated symbols and also learn how to create effective picture symbols. I went with the graduated symbols because it had the best overall display of the data. It didn’t block out the countries displaying population density like proportional did. I chose it because it also utilizes classes that can help people understand patterns and trends to make viewing the data easier. When it came to creating effective thematic picture symbols, I did not go this route, but I understood the concept. Basically, one way to effectively display wine consumption, you use different picture symbols of wine glasses or bottles at varying degrees of wine levels. A full wine bottle would mean a high amount of wine consumption, a near-empty bottle means that the country has very low wine consumption. It is definitely a creative way to approach this lab, but I am not very articulate so I went the boring route. The final outcome is the one we always try to achieve, compiling the map in accordance with cartographic design principles. I always make sure that my maps follow these principles.

Here is how my map turned out:

To reach this map outcome, here are the steps I took:

I added the data to the project, then right-clicked on the layer and selected the symbology pane. From there I assigned the primary symbology to be graduated colors, and then selected population density in the field section. The base classification method was natural breaks, but like I said above quantile was the best method of displaying the data. Once I completed that, I chose a blue color scheme because it shows the blue in different shades and the color is the most colorblind-friendly color. To make the data not distorted, I removed small countries that would not be visible at the scale the map is displayed in by excluding them in the advanced symbology tab. In that tab, I went to the data exclusion section and created an SQL query that included four countries too small to be viewed. Once complete, I copied the dataset and changed the primary symbology to graduated symbols. When I did that, the data displayed too many decimal places. So I went to the advanced symbology section, selected format labels, and changed the rounding of decimal places to 2. I also followed the same data exclusion steps from before to exclude the countries too small to be displayed. I then began formatting the map layout by having the main map in the center and then an inset map zooming in on the Baltics due to their close proximity to each other. One issue I had with the displaying of data was that the wine consumption points were being placed in weird locations. To combat this issue, I converted the polygons to points which allowed me to fix the placement of points to better display the data. To finish it out I converted the country names to annotations which allowed me to fix the position in which the labels were displayed. Once I completed the presentation of the data, I made sure to include all essential map elements.

This was definitely a cool map concept, and I now have a better outlook on how wine consumption is associated with population density.

Thanks for reading!