Q1 : What information does the Source tab provide about the states shapefile?
It specifies the folder where the shapefile is saved in the R: drive.
Q2 : What coordinate system is this layer in? Is it a geographic or projected coordinate system? What is the difference between these two types of coordinate systems?
The layer is in GCS_North_American_1983, a geographic coordinate system. Geographic coordinate systems georeference locations using latitude and longitude on a curved earth, and represent the most comprehensive georeferencing system. Projected coordinate systems operate on a flattened surface of the earth in order to better translate into 2-dimensional media such as paper maps. Projections often use a cylindrical, planar or conic surface to flatten a portion of the earth’s surface. Different projections work best for different map purposes and for different regions of the earth, though they all come with inherent distortions.
Q3 : Compare the different projections. How does the shape of the continental US change with each projection?
Albers Equal Area Conic: The continental US appears horizontally shorter than Plate Carree projection, and smaller overall than the Mercator projection. It appears roughly the same size as the Robinson projection but without Robinson’s slant. In the Albers projection, the country’s upper boundary has a curvature that does not appear in the other three projections.
Mercator: The continental US becomes much larger overall than in the Albers Equal Area Conic projection, and the country’s upper boundary is straight (as opposed to the curvature visible in the Albers Equal Area Conic).
Plate Carree: The continental US remains horizontally as long as in the Mercator projection, but narrows vertically. The country’s upper boundary is straight (as opposed to the curvature visible in the Albers Equal Area Conic).
Robinson: The continental US becomes horizontally shorter than in the Mercator and Plate Carree projections and appears “italicized,” with a visible slant towards the northeastern direction. The country’s upper boundary is straight (as opposed to the curvature visible in the Albers Equal Area Conic).
Q4 : How does the position of the cities in relation to each other appear to change between projections (give an example of some cities)?
Looking at Salem and Eugene in OR…
In the Mercator and Plate Carree projections, Eugene appears almost directly South of Salem, whereas in the Albers Equal Area Conic and Robinson projections, Eugene appears Southwest of Salem, especially in the Robinson.
The distance between the cities appears largest in the Mercator projection, while the distance between the cities in the Albers Equal Area Conic projection appears the smallest.
Q5 : What spatial properties (i.e. shape, direction, area) does each projection distort?
Albers Equal Area Conic: shape
Mercator: area, distance
Plate Carree: shape, area
Robinson: shape, direction
Q6 : Use the measure tool to measure the planar distance between cities. How does this distance change between projections? Create a table with your findings.
Vertical (North-South) distances were assessed by measuring the distance between Salem, OR, and Eugene, OR. Horizontal (East-West) distances were assessed by measuring the distance between Bend, OR, and Eugene, OR. See table below:
| Projection Type | Distance (miles) between Salem and Eugene | Distance (miles) between Bend and Eugene |
| Albers Equal Area Conic | 61.23 | 89.99 |
| Mercator | 85.47 | 125.44 |
| Plate Carree | 61.36 | 125.44 |
| Robinson | 69.99 | 96.42 |
As the table reveals, the Mercator projection displayed the greatest vertical and horizontal distances. The Plate Carree projection displayed horizontal distance as elongated as the Mercator projection, but had shorter vertical distances. The Albers Equal Area Conic displayed the shortest distances (both vertical and horizontal) of any projection.
Q7 : What variables does this dataset contain?
I chose a data set from ArcGIS online quantifying the 2013 homeless population by State. The dataset contains the following variables by state: percent change in homeless population from 2012 to 2013, 2013 overall population, and 2013 homeless population.
Here is a link to an Atlantic article, as well as a report, discussing the homelessness trends that were explored by the U.S. Department of Housing and Urban Development using this data:
http://www.endhomelessness.org/page/-/files/2014_State_Of_Homelessness_final.pdf
Q8 : What classification methods did you use? How does each classification method bias the interpretation of the data?
I used natural breaks (Figure 2), quantile (Figure 3), and equal interval (Figure 4) classification methods.
A natural breaks classification method (Figure 2) works well for this particular data because it allows me to separate positive and negative values, in other words, to differentiate and have a clean break between States that experienced an increase in the homeless population and those that experienced a decrease. With natural breaks, the middle values are particularly prominent on the map, while the extremes are fewer and as such, more pronounced. In this way, the map’s audience may assume that most states either saw a slight-to-moderate increase, or a slight-to-moderate decrease in the homeless population, with only a handful of states in the extremes of either significant homelessness reduction or increase.
The quantile classification method, on the other hand, has a much more even distribution of colors, therefore placing less emphasis on any one State, even those that are on the more extreme ends of the spectrum. Additionally, there is no clean break between States who experienced an increase or decrease in the homeless population, as there was one category that had both positive and negative values. This would make legibility difficult, given the nature of the data.
Given this dataset, perhaps the most misleading classification method is equal interval. The equal interval classification is skewed dramatically by the dataset’s outlier, North Dakota’s 200% increase in homelessness, and therefore results in a map in which all other states are lumped into two categories, and in which North Dakota is the only State that visually stands out. Here, like in the quantile classification, there is no clear divide between positive and negative values. The only way in which this map is effective is in highlighting North Dakota as an outlier.
