Module 6 Choropleth and Proportional Symbol Mapping - or, best places in Europe to blend if you're a heavy wine drinker!

     This module was designed to introduce us to Choropleth and Proportional Symbol Mapping.  It was a great exercise to pull together what we have learned with regard to cartographic principles, data classification, SQL queries and additional time with Adobe Illustrator.
   

Once I imported my data, I used Color Brewer to help select a color ramp for the choropleth portion.  I wanted to use brown tones for the land masses and wanted to make sure the hues were suitable for individuals with colorblindness.  After confirming my choices, I downloaded the style for future use with ArcMap.  My next task was to determine a classification scheme for the population density data.  After previewing several options, I chose a 5 class quantile scheme.  Since we had removed four countries whose small size and large density would have skewed the presentation, I felt that keeping an equal number of features within each class was a good means of presenting the data.  This method allowed me to see density spread among lower ranges much more clearly while still allowing visualization of the higher end densities. With regard to the wine consumption, I chose a deep red/purple color resembling red wine to apply to my graduated symbol choice.  I used graduated symbols because the symbol sizes are discriminated by the range to which they belong making them easily recognizable in the legend.  Classification was a bit trickier here as I did not like the results of the standard options.  While certainly subjective, I chose to use a 5 class manual interval classification to determine a set of ranges which kept consumption levels similar to others in its class (rare, occasional, social, moderate and heavy consumption) while still depicting the high end Vatican City outlier.  Because of the subjectivity of this method, I divided the consumption by weeks, months and days to help determine where to best place my breaks as well as using information on the graph within the classification window.  Before finalizing the map, I inserted a world ocean base layer from ESRI to fill in the blank space where the oceans would be as well as to account for the location of less important, non-European countries.  I placed the basic map elements and labels within ArcMap and then exported to Adobe Illustrator to apply clean-up.  Before beginning any edits in AI, I was careful to organize my layers and move items into more appropriate categories.  I also removed any unnecessary layers, in this case the wine consumption country linework, and clipping planes.  Once my layers were renamed and organized I could begin the clean-up process.  Since I had already translated my country names via my attribute table, all I needed to do with the text in AI was to move and rotate as required to make it legible.  I applied a drop shadow to raise the legend above other items, since space was tight.  I screened the small tightly grouped countries west of Italy and north of Greece on the main map and directed the user to the enlarged, inset map.  The color scheme, symbology an labels for theses countries was only visible in the inset map.  I kept effects to a minimum so as not to shift focus away from the content of the map.  Overall, this was an enjoyable means of implementing choropleth and graduated symbols to compare data.