Sunday, May 12, 2013

Physical Color Space

While working on the GIS project I described in this post, I had a presentation conundrum: how do I communicate my mapping of RGB (Red, Green, Blue (additive)) color space to ideology symbology to an audience that may or may not have only a passing acquaintance with the physics of color and how it relates to our current computer technology. To solve this issue, I decided I could physically model RGB space as a cube.

The cube allows one to examine the outer surface of the RGB space; as well as illustrating the relationship between additive and subtractive spaces. In terms of my ideological representation issue, I was able to communicate that the 3-axes used to represent Red, Green, and Blue values could be relabeled to represent the percent of the vote supporting Conservative, Alternative, and Liberal ideologies.


RGB Color Cube Labeled My Color Cube


Now I’m sharing, so you can make your own. You’ll need:
  • a color printer or access to one.
  • some poster board or other heavy (but flexible) art board
  • an Xacto knife or other cutting implement.
  • a ruler
  • a pencil
  • glue-stick, or other paper adhesive.
After printing, cut out and trace on to the art board one of the following PDFs (remember to add tabs if using the original PDF):
Next, you’ll cut out and fold the art board into a cube being sure to tuck the tabs in, so you have a more-or-less smooth external surface. 
Then you’ll want to do the same with the color print-out, only this time gluing it to the art board cube you’ve assembled. (Again, remember to tuck any tabs underneath.)
Optionally, you may attempt to cover your creation with a protective covering (this usually doesn’t go as well as one would like — I wrapped mine in packing tape, but it got blisters and a tiny patch of color torn, etc.)
Cube size is approx. 2.75"^3
Licensed under Creative Commons: BY-NC-SA

Friday, May 10, 2013

Introductory Political Economics with ArcGIS

 For my final Project in a GIS course I took I looked at the shifting economic fortunes and political ideology on a geographic basis.

First, politics:

I was curious how American political ideology had changed amongst the populous since 1980. (The data to do this was hard to get under the time constraints I had, so I revised it to between 2000 (i.e. before "terrorist" was a word one heard or read all too often) and 2010.) I looked at congressional district election returns as a proxy for ideology of the populous for several reasons: (1) congressional elections are the most "local" political race with national coverage, (2) they are easier for minority parties to be contenders in, and (3) due to their diversity of views and their "locality" they may be more representative. After researching more than 40 political parties' platforms, I associated values [-1, 0, +1] and labeled them as "Left/Liberal" (socially-progressive, pro-big government; +1), "Right/Conservative" (socially-conservative, anti-government; -1), and "Centrist/Alternative" (Greens, Independents, Unknowns; 0). I then produced the following maps in ArcGIS, summing the values (after weighting them by percentage of election returns) to color them (positive numbers, blue; negative numbers, red; zero, light yellow).

2000 Ideology
US Ideology 2000
2010 Ideology
US Ideology 2010

Next, Income:

I then looked at income data on a county level from the Census Bureau. Because averages can be drastically skewed by outliers, I specifically looked at median income. I was also interested in poverty so I looked to incorporate this into my economic measurements. What is the poverty threshold? Well, that depends who you ask; and if you ask the Census Bureau, it depends on the number of dependents in the household. Faced with a dilemma of which number to use, I chose the Census Bureau figure for a household of 3; this was $13,738 in 2000 and $17,374 in 2010. I then calculated the percentage of median income above these numbers, thus a low or negative percentage would indicate the median income of the county was close to or below the selected poverty threshold, and a large number would indicate how far above this threshold the median income of the county was. For example, a county with a 200% figure would indicate a median income of $27,476 in 2000. I symbolized this information as five greyscale bins (darker greys are lower (percentage) values):

2000 Median Income
US % of median income above poverty, 2000
2010 Median Income
US % of median income above poverty, 2010

 

 Overlay & Conclusions:

We can overlay these maps by year to get a composite view of Ideology and Income:

2000 Composite
Composite Map, 2000
2010 Composite Map
Composite Map, 2010

In conclusion, we can say there has been a shift to the political Right between 2000 and 2010; and, despite the Great Recession, people seem to be doing a little better economically. However, there are some flaws in my methodology and there are many complex economic interactions that could serve to make it seem like there is an improvement in income.  For example, inflation wasn't explicitly taken into account, so incomes may have risen, but their purchasing power may have stagnated or gone down. Further, the symbology of the ideology maps confuses situations in which voters voted for an alternative party and where near equal number voted for opposing parties (.i.e. increased polarity). Another way to symbolize them would be to assign the percentage of votes for each ideology as RGB (Red-Green-Blue) percentages (of 255). Doing so, we would get the following maps:

2000 Ideology as RGB
US Ideology 2000 as RGB
2010 Ideology as RGB
US Ideology 2010 as RGB

These can be hard to interpret, due to the average human's ability to perceive color differences. Further work awaits.

Saturday, February 16, 2013

Coordinates for US counties

A week or two ago an e-mail from a map cataloger looking for a list of bounding box coordinates for US counties caught my eye. Reading the discussion that followed it was quite clear if anyone had thought to make such a digital list in the library community, it was suffering from terminal bureaucratic paralysis (death-by-committee) that is despairingly common in the library world (but this is a discussion for another post).

I knew where some suitable data could be obtained and had been itching to toy around with some geospatial python, so looking through Python Geospatial Development I found some starter code. Modifying this starter code and manually correcting a couple small anomalies I have produced a list [link] of US counties and their bounding box coordinates. I have attempted to make it easy for catalogers to just copy and paste the partial fields. The file is UTF-8 encoded, sorted by state and then county, basic metadata is given at the start of the file.