BIVARIATE MAPPING WITH MICROSOFT EXCEL AND ADOBE ILLUSTRATOR CS5

 

 

 

Instructions:
Build a map including two variables of my choice. They must be related to one another. Be sure data sources are reliable. Create the map in ArcMAP and export it to Adobe Illustrator. Complete the map in Adobe Illustrator. Determine approriate symbol sizes. The map must contain 30 geographic units. Include all normal map elements. Include a description of what is being mapped.

Methods:

Because the instructions were very specific, the methods identically follow the instructions.

I had to build a database with proportions of population density from the U.S. census and per capita energy use from the U.S. Department of Energy in order to determine proportions for map symbols. By dividing each data segment by the largest segment, I was able to calculate the proportional size for each of my symbols.

After determining the size of my largest symbol, I had to copy that symbol size another 49 times. By clicking on each symbol, I was able to alter the size of the symbol by entering the percentages I had calculated in my databases.

Using ColorBrewer, I selected a single-hue color scheme because my map colors would be proportions of the same value compared to one another

Challenges:

I had not anticipated as little variation in energy consumption as I found. This meant I had to carefully choos the largest symbol size so as to not overwhelm the northeast with large and illegible symbols. It was also necessary to not make the symbols too large to make the second variable (density per capita) easy to ascertain.

Another challenge was finding data for the same year for as much accuracy and comparability as possible. This took some fairly intense searching.

Because of the small size of northeastern states, it was still necessary to reduce the transparency of the energy consumption symbols to be able to make out the colors behind them.

The population density data contained a few outliers at the top. With fewer classes almost all states fell into the same category in the middle. It was necessary to push the envelope a little and include 7 data classes for population density.

Because energy consumption is measured using a relatively unknown measure, I needed to include the definition of a BTU on this map.

Energy consumption is very nuanced with many contributing variables. As a result, I needed to include some of the other factors contributing to energy consuption. That way, a reader knows not to read TOO much into the findings of the map.