Choropleth Mapping

This lab involved creating two choropleth maps drawing from the same dataset. A choropleth map is a type that should be familiar to most people these days. It's a thematic map that uses color, shaded or pattern fills of bounded areas (often the borders of counties, states or nations) to show the variation in the data being considered. The dataset that I worked with in the lab was the change in population by state from the year 1990 to the year 2000, expressed by the percentage change over that interval.

The critical portions of this assignment involved determining how to divide the data points up into a classification scheme and then creating a color scheme to express that classification. How should the data be chunked into groups (if it should be, at all)? How many groups (or classes)? How should the fills for each class be determined? None of these questions have straightforward answers, and details like these, if chosen poorly, can lead to maps that mislead rather than inform.

For a map showing growth rates by state, to be displayed in color, I chose a classification scheme based on an algorithm that attempts to create classes that match natural groupings in the data, in theory matching like with like. I only made my choice after trying several methods to ascertain how well they expressed the data. I also viewed the data as a histogram in order to see how the data was spread out. The data displayed a significant skew to the left (the lower range of growth rates), with a few strong outliers to the right. I wanted to make sure that those outliers were visible on the maps and not obscured by being classed with regions with significantly lower rates of change.

I also made the choice to remove the District of Columbia's data from the classification calculations, although I do show it and its negative growth rate on the map. As it's not only an outlier, but also the only negative growth area on the map, I picked a color well outside the color scheme I chose for the classification scheme used with the rest of the data.

The other map I produced calculated growth rates over the same time period by arbitrary regions. For this map, I was instructed to build my classification scheme outside of ArcGIS, and thus had to calculate it by hand. This was not as difficult as it might have been as the data was limited to nine regions. I ended up with a basic scheme that placed two regions in all but the highest growth region, which got a class all its own.

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The base map for this assignment uses a conical equal area projection. As a result, the direction of north on the map varies greatly by longitude. Because of this, I chose to indicate the orientation of the map without using a north arrow. Instead, I drew a graticule (grid lines showing a range of latitude and longitude) on the main map and also in the inset maps for Alaska and Hawaii. The area covered for the Washington D.C. inset was of a large enough scale that a small north arrow within the inset was appropriate.

Color Map of Population Growth in the United States from 1990 to 2000 by State

Displayed as Percentage Change from 1990 Levels

Greyscale Map of Population Growth in the United States from 1990 to 2000 by Region

Displayed as Percentage Change from 1990 Levels