Let's Talk About Projections

The following link is a youtube clip from a 90's show called the West Wing: http://www.youtube.com/watch?v=vVX-PrBRtTY 

In the 4:00 minute clip in which the Organizations of Cartographers for Social Equality lobbies to have the show's president (President Barlett) encourage legislation to support elementary and secondary geography education using the Gall-Peters projections.

Wait a second, there is more than one map of the world?? Heck yes there is, because depending upon the location/country/city being mapped and the scale used, the projection changes. For example, the lambert conformal conic projection rocks for depicting the United States:

http://myweb.unomaha.edu/~kstuart/CartGIS/MapProjection/USALambertsConformalConic.png

http://www.geoatlas.com/en/maps/world-maps-0/mercator-projection-24

The above map uses a Mercator projection, and I'm willing to bet this is the projection the average American is most familiar with. Wait a second... is Russia really that big? Why does Greenland look bigger than South America? What the heck is going on? Without out getting into serious geography nerd jargon, the simple answer is that the further from the equator a landmass happens to be, the more distorted it's size and area will be.

Below is an image of the Gall-Peters projection: 

http://theirondandelion.files.wordpress.com/2013/08/gall-peters-projection-world-map.jpg

If you're used to the Mercator or even Robinson projection, the Gall-Peters projection probably comes off as a little trippy. "Like whoa, Afrca is HUGE, and Europe looks so small!". Now bear in mind, no world scale map is going to be 100% accurate. Ever. Where the Mercator projection was created predominately for the purpose of maritime navigation, the Gall-Peters is a cylindrical equal area projection. Wait a second, let me translate that into English: this kind of map is designed to depict each landmass based upon it's actual area. Hence why Greenland looks smaller than South America.

Map projections is one of those geography topics that could be discussed forever and eternity. However, it's a Saturday night, so let me leave you all with something more entertaining: http://www.youtube.com/watch?v=esbS_vT25GU

US Choropleth Map

The above map is an choropleth from earlier on in the semester. I went with the light blue to dark purple color ramp to represent the sequence of owner occupied housing data. Perhaps with the end of the semester around the corner I will be able to create a bivariate map of the number of owner occupied houses versus the number of rented houses per state (as both data sets were available to me at the time).

Dot Density Example

The above map is the U.S. Census Bureau's 1980 population dot density map, or what also appears to be their "night time map" due to the fact it looks like the night time illumination one would expect to see with highly developed/urban areas. This map strikes a nice balance in that there is clearly a clustering, almost whiting out, of highly dense areas, but due to the smaller dot size there is not a vast white out effect being produced.

Isoline Map example

Isolines rock at depicting continuous data. From temperature maps on your local news station, to topographic maps, isolines are your friend in showing continuous data over a spatial plane. The above map forgoes the isoline boundaries, and instead the cartographer made the choice to simply allow the colors to flow from the low values to high values. It is important to note that the above map does not have set intervals for labeling it's isoline values, and since this is a temperature map for non-research purposes this is not terribly surprising -- most temperature maps produced at this scale are labeling temperature at the rough location of major cities (New York City, Washington D.C., Denver, CO, etc).

Bivariate Map example

The above map is an example of a bivariate map, utilizing proportional symbols with a chloropleth underlay. While the data choices are certainly interesting, the map's design deserves some consideration as well. Nicaragua is pushed forward by it's neighboring, grayed out countries, while it itself colorfully standouts with it's yellows, oranges, and blues. Particularly clever of the cartographer is the use of complementary colors (blue and orange) to help both data sets really stand out.

Typhoon Maps

Mapping storm systems is an interesting task that leaves the cartographer with a variety of questions. How much detail does the base map need? How should the storm trajectory be mapped? Should multiple potential storm trajectories be shown, or an extending buffer?

          The reason I chose these two maps in particular is because both find different ways to depict the storm path. The first map simply shows the storm path as a dotted line, with typhoon category bench marks as the storm passes over the Philippines, crosses the South China Sea, makes land fall in Vietnam, and proceeds north the China. On the other hand, the second map chooses to use color and symbol to depict the typhoon's intensity, and the storm trajectory is depicted as an expanding buffer, to underline the reality that the storm could shift course. The second map at depicts estimated times of the typhoon's position, which is also important information  in helping stage potential evacuations that may need to occur for coastal populations.

Bivariate Map

The above map was created using 2010 U.S. Census data, and I chose to represent data concerning the population density of each county in North Carolina, as well as what percentage of each county's population has a bachelors degree or higher. I was initially curious to use those data sets because I wanted to see how population density correlates with higher level of education.

Dot Density Map

The above map depicts the population density and rough distribution of persons under the age of 18 (also referred to as minors). Each dot represents 400 individuals, with a total of 1,005 dots for the entire map.

Final Map Proposal

Area of Interest: The United States of America

Tpoic: The number of breweries and beer consumed by state. 

Plan: To make a bivariate a chloropleth of the number of breweries per state, and overlay that with a proportional symbol of beer consumption per capita (perhaps beer steins, perhaps kegs).

Data: The wonderful thing about beer is that there's a plethora of data available (such as the brewer's almanac), and data is available for at least the past decade. 

Data resources include:
http://www.beerinstitute.org/assets/uploads/2012_Beer_Consumption_By_State.pdf
http://www.beerinstitute.org/br
http://www.brewersassociation.org/pages/business-tools/craft-brewing-statistics/breweries-per-capita

Audience: Aimed to be utilized by casual beer drinkers and beer enthusiasts alike.