- The source tab explains the data type, where it came from, and some of its properties.
- This layer is in GCS North American 1983 geographic coordinate system. Geographic coordinate systems use a grid on a sphere, describing absolute location but not good for measurements of for small scale uses. Projected coordinate systems are on flat, two-dimensional surfaces with constant lengths, angles, and areas.
- The shape of the continental United States is distorted differently in each projection. The Albers Equal Area Conic is a projection that preserves area well and shows little distortion. Mercator projections are based on the cylindrical map projection. Mercator projections show reasonably true shapes and distances, with minimal distortion at the equator and increased distortion at the poles. The Plate Carree projection has less distortion at the poles than Mercator and is a cylindrical projection. Shape and area are most distorted in Plate Carree while distance and direction remain true. The Robinson projection has strong distortion at the poles. The longitude lines are concaved and the latitude lines are parallel and straight. The Robinson is best used to show the whole world at once and not for smaller scale uses.
- The distances between cities seems to vary with different projections. For example, on the Plate Carree projection Philadelphia and Chicago seem to have a greater distance between them than on the Albers Equal Area Conic projection.
- Albers Equal Area Conic projection distorts distance and size. Mercator distorts area and distance. Robinson distorts size, distance, and shape. Plate Carree distorts size, distance and shape.
| Projection | Distance between Philadelphia and Chicago (meters) |
| Plate Carree | 1,412,328.793957 |
| Robinson | 1,037,244.857367 |
| Mercator | 1,423,156.657775 |
| Albers Equal Area Conic | 1,067,745.692009 |
- The data set I chose is from the US Energy Information Administration. It contains data about industrial CO2 emissions in the United States in millions of metric tons for the years 1980 to 2013 and is organized by state. For my maps, I chose to show emissions for the year 2012.
- I used equal interval, quantile, and standard deviation classification systems. The equal interval makes it seem that there is little CO2 emitted in most states while Texas, Louisiana, and California emit the most, with Texas having the highest. It puts the rest of the US all together, even though emissions may vary significantly between them.
The quantile method gives a more even distributed look to CO2 emissions. It breaks it up more, but the section with the highest emissions shows a range of more than 150 while the other colors show ranges much smaller.
The standard deviation method may be not the best for this purpose, as there are outliers in the data that can greatly influence the mean and not provide an accurate depiction of a representative average.
