Lights, Water, Elections

What do watersheds, urban light pollution, and the 2012 presidential election have to do with each other? I don’t know, but I made a map.

I’ve combined these three disparate geospatial data layers to produce the image below of the lower 48 states (sorry Alaska, Hawaii, Puerto Rico, Guam, and other territories/insular areas of the US, you don’t fit neatly into a simple latitude and longitude projected rectangle).

lights_water_elections

Represented by white outlines and labels are the “United Watershed States of America” with state boundaries redrawn along major watershed boundaries. These spill over current national borders following ridges of high topography that define the large watersheds.

Beneath this are the shapes of individual counties, color coded based on the results of the 2012 presidential election (blue and red representing the Democratic and Republican party candidates respectively). Federal elections are bound to be on our minds every couple years as prescribed by the US Constitution.

Sitting below these layers is a composite image of the lower 48 states from space at night, making visible the artificial sources of light emanating from our cities and industrial regions.

More urbanized areas, identifiable by their higher density of light pollution, often match well with blue counties. There are some well-lit outliers such as seen in Arizona, Oklahoma and North Dakota. However, the lights in northern North Dakota correspond not to a large urban sprawl, but rather to the massive fossil fuel extraction efforts in the Bakken Formation. Looking at this map, one can try to imagine, for a fun thought experiment, the political trends of the “Watershed States” if lines were redrawn as shown.

blueredlights

This second map, using only the election data and nighttime imagery, shows only county colors where “illuminated” by a high density of light. The light serves almost as a proxy for population, assuming that the light output per capita is about the same in most places (which of course doesn’t hold true in comparing well-lit, well-spaced suburban areas to more densely-populated urban areas, and industrial areas with little permanent population).

These maps were made using the open source GNU Image Manipulation Program.

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