Module 6: Proportional Symbol and Bi-variate Choropleth

This week we learned about proportional symbol mapping and bi-variate choropleth (above).

Proportional symbol mapping is a quantitative map that varies the representation of a  feature, and the size, shape, and color vary with the particular variable. This type of map is very appropriate to map data counts.
Our first assignment was to utilize a proportional symbol map to represent the population size of cities in India.  First we were asked to determine the appropriate variables for a custom conic projection for the area.  I utilized a central meridian of 80E and parallels of 30.8N and 12.0N. The symbol properties for the proportional symbol were set with consideration of the background colors and visibility.  Finally a custom legend was created to show the range of symbols utilizing Flannery Appearance Compensation to accentuate the differences in the larger symbols.

Next we moved on to divergent proportional symbology to display jobs gained or lost by state in a time frame of December 2007 and July 2015.  This map creation had many challenges.  First the use of negative numbers does not work for this process.  The original shape file was divided into two shape files utilizing Select by Attribute SQL for <0 and >0.  In the file with the data <0, a new field was created, the field calculator was utilized to obtain the absolute value of those numbers (essentially making them positive).  Due to technical difficulties with loading this data (load times were extreme for even the smallest changes), and the two layers (gains and losses) being set to the same minimum size, line width, and no maximum created different size reference I tried to make the map as completely basic as possible. I am not satisfied with this map, but in the time frame and technical issues this is it.

Update:  Due to the technical issues with ArcPro with proportional symbol for raw count data that I encountered for the above map, I chose to change the symbolization for the raw count data to a dot density map.  More confident in the display of the data in this format.

Finally, we were asked to create a bi-variate choropleth map displaying two variables (% obesity and % physical inactivity)  Bi-variate choropleth maps display two variables (sometimes three = Tri- or Multi-variate) on the same map.  Variables for choropleth maps should be normalized.  These were in the form of %.  Each variable was classed into 3 class quantile classification in order to obtain the break values for the classes.  From the attribute table use of SQL to obtain the records for these classifications were then classed in new fields.  The completion of a column for each variable were then combined using concatenate to a third/final field.  The map was symbolized

with unique values from this final field.  Adjustments were performed to those unique colors by color ramp, complementary color wheel and filling in changes based on Hue (top left and bottom right are complementary - opposite on the color wheel, top right lies between on color wheel) , Saturation (very low or 0 in the bottom left and gradually increase to top right) and Value (lowest in the top right and lowest in the bottom left).  Here is a close up of the legend.  Map appears at top of this post.