Choropleth Mapping
I thought it was interesting to make a map with proportional symbols in light of the current pandemic. I've personally seen similar maps pop up in media stories recently being used to display COVID-19 infection cases so this was a fun twist in using something I had not in the past. I'm guilty of calling these heat maps in the past, but I was taking the lead of others that were also calling these types of maps heat maps. Now having a better understanding of what proportional symbols are intended for, this is a much more appealing was of delivering data visually.
In light of everything going on, I unfortunately did not leave myself enough time to implement some ideas I had too late into this deadline. In lieu of importing a fun clip art animation, I used as a proportional symbol representing wine consumption in my map to save space. I had to reduce the max size to 45 from 100 because of the biggest outlier, Vatican City. As the smallest country in the world, it's population density and wine consumption rate (liters per capita) are incredibly high relative to the other European countries. I removed the outline and made them slightly transparent to avoid busy areas from clashing. Next time, I would include a map inset of of the area from Lichtenstein to Bosnia and Herzegovina and placed is in the northeast corner of the map. These small countries are overlooked at this scale, so providing some context and using empty map space would have been preferred. I did however cycle through the basemaps I had never used and found an oceans basemap that contrasted my green polygons great.
I was unable to get rid of the extra decimal places in my wine consumption label. Every time I would navigate to the Advanced Settings of the Format Label pane, this selection was not there (as it is in other layers and was in the previous lab). This is the reason the proportional symbols do not have any numerical reference; only the size of the circle will let the map reader know of the relative wine consumption of one European country over another.
I used a quantile classification method to better account for outlier countries in the data. Smaller, more densely populated countries had mostly high rates of wine consumption. Interestingly, the data shows these rates being even greater than the famous wine regions of France and Italy. This makes sense intuitively because of the vastly different sizes, though when analyzed using different data classification methods, the results were not as intuitive.
In light of everything going on, I unfortunately did not leave myself enough time to implement some ideas I had too late into this deadline. In lieu of importing a fun clip art animation, I used as a proportional symbol representing wine consumption in my map to save space. I had to reduce the max size to 45 from 100 because of the biggest outlier, Vatican City. As the smallest country in the world, it's population density and wine consumption rate (liters per capita) are incredibly high relative to the other European countries. I removed the outline and made them slightly transparent to avoid busy areas from clashing. Next time, I would include a map inset of of the area from Lichtenstein to Bosnia and Herzegovina and placed is in the northeast corner of the map. These small countries are overlooked at this scale, so providing some context and using empty map space would have been preferred. I did however cycle through the basemaps I had never used and found an oceans basemap that contrasted my green polygons great.
I was unable to get rid of the extra decimal places in my wine consumption label. Every time I would navigate to the Advanced Settings of the Format Label pane, this selection was not there (as it is in other layers and was in the previous lab). This is the reason the proportional symbols do not have any numerical reference; only the size of the circle will let the map reader know of the relative wine consumption of one European country over another.
I used a quantile classification method to better account for outlier countries in the data. Smaller, more densely populated countries had mostly high rates of wine consumption. Interestingly, the data shows these rates being even greater than the famous wine regions of France and Italy. This makes sense intuitively because of the vastly different sizes, though when analyzed using different data classification methods, the results were not as intuitive.
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