The map above is a choropleth map designed to show two data sets. The first is the population density of European countries and the second liters of wine drank annually per capita. Population density is represented by a graduated color set of red in each representative country boundary. The grapes on the map are the symbology that represents the volume on wine drank per capita in each nation. They are scaled with their size communicating the amount consumed. Small being the lower values and then four more classes each going up in size to the largest.
Our lab this week was an exercise in designing choropleth maps and using graduated and proportional symbology to display data. We were tasked with presenting European population density per square kilometer, classifying it, and proving a unipolar color scheme for it. In addition, we were provided with the 2012 data of liters of wine drank per capita as reported by The Wine Institute. The population density was to be designed as a choropleth map underlaid by the wine consumption data in either a proportional symbol or graduated symbol map.
In the above map, population densities are separated into five classes. A natural breaks classification method was utilized to better show the disparities in densities across each country. Other methods were tested, such as quantile methodology and even-intervals, however, they proved to group more disparate data values creating a misrepresented and mono-classified map. The color red was used as the selection for our graduated color. Not only do the varying shades contrast well against each other, the lighter shades provided by the lesser values were rather pleasing to the eye. Since a majority of the region is surrounded by water, the red provides a brilliantly tame contrast between the two geographies.
To map the wine consumption per capita, an equal interval classification method was used. To echo the sentiments described above, it too best compartmentalized the natural data groups to best communicate the overall differentials between each country. A graduated symbology was used over the proportional option as the graduated better clustered the intended information and the proportional may have required the utilization of more than 5 classes due to the relative created shape of the proportional circles.
The map was created in ArcGis Pro. The datasets were uploaded in shapefile form into the software and the symbology, as described above, was appropriately adjusted. The attribute table for our data was scanned to remove any outliers of regions that drank no wine and were in conjunction too small to be clearly represented on the map. Through the use of a SQL query in our data exclusion function of our feature symbology, these areas were removed. Next, label classes were created for the countries so names could be displayed and manipulated on our map. A annotated label class was then created so the country names could be adjusted as our layout was designed.
After much trial and error to select the appropriate symbology for our wine feature, it was decided that a .svg file would be downloaded, utilized, and subsequently scaled by our software. A figure of grapes was chosen to represent the wine consumption.
A new map was created and data copied from the previous map to design an inset map highlighting the smaller countries east of the Adriatic Sea. These areas were too small to prevent from being cluttered with the data displayed on the entire map. The aforementioned labelling properties were also performed within this inset map. Lastly, the labels for these smaller regions were removed from the original map through the use of an SQL query in the label class ribbon. A new layer was created from the omitted county polygons and was placed over and shaded gray.
After writing a small summary of the map, legends were created and attempted to be balanced within the map frame without compromising any of the communicative integrity of the map.
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