Lab 1

Question 1: What information does the Source tab provide about the states shapefile?

  • The Source tab provides the following:
    • Data Type: Shapefile Feature Class
    • Shapefile: Z:\Geog482_7\Student_Data\josiei\Lab1\Data\cb_2014_us_state_500k.shp
    • Geometry Type: Polygon
    • Coordinates have Z values: Yes
    • Coordinates have measures: Yes
    • Geographic Coordinate System: GCS_North_American_1983
    • Datum: D_North_American_1983
    • Prime Meridian: Greenwich
    • Angular Unit: Degree
    • Along with the extent of the data which is Top: 71.365162 dd, Left: -179.148909 dd, Bottom: -14.548699 dd, and Right: 179.778470 dd.

Question 2: 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?

  • This layer is in the GCS North American 1983 geographic coordinate system. A geographic coordinate system is defined by a 3-D surface and is measured using latitude and longitude while a projected coordinate system is defined by a 2-D surface and is measured in units or meters, feet, etc.

Question 3: Compare the different projections. How does the shape of the continental US change with each projection?

  • Albers Equal Area Conic: The shape of the continental US becomes more rounded along the sides with this projection, especially at the sides where they look like they are slightly rounded upward.
  • Mercator: The shape of the US becomes more familiar as this projection is commonly used in maps.
  • Plate Carree: Stretches the map out from the sides making most states look short and wide.
  • Robinson: Tilts and stretches the shape of the continental US to make it look similar to the shape of a parallelogram.

Question 4: How does the position of the cities in relation to each other appear to change between projections (give an example of some cities)?

  • Although the difference is quite small, you can see that depending on the projection the cities of San Antonio and Houston may be closer or farther from one another. For example, in the Robinson projection the cities are closer than in the Mercator projection.

Question 5: What spatial properties (i.e. shape, direction, area) does each projection distort?

  • Albers Equal Area Conic: This projection is best suited for area that go west-east because it distorts shape north-south.
  • Mercator: Area is increasingly distorted the farther you get from the equator making it unsuitable to show polar regions or the world as a whole.
  • Plate Carree: Shape is increasingly distorted the farther you get from the standard parallels.
  • Robinson: Area is neither conformal or equal and direction is generally distorted. This projection is only useful for world maps and not for specific countries as we have in this lab.

Question 6: Use the measure tool to measure the planar distance between cities. How does this distance change between projections? Create a table with your findings.

Projection Distance Between Phoenix and Chicago Distance Between Chicago and Philadelphia
Albers Equal Area Conic 2,315,006.111276 Meters 1,067,745.692009 Meters
Mercator 2,954,017.091543 Meters 1,423,156.657775 Meters
Plate Carree 2,868,133.908479 Meters 1,412,328.793957 Meters
Robinson 2,627,340.870925 Meters 1,037,244.857367 Meters
  • The distance between Phoenix and Chicago changed by over 600,000 meters between the Albers Equal Area Conic and Mercator projections while the distance between Chicago and Philadelphia changed by approximately 400,000 meters between the Plate Carree and Robinson projections, which are large margins.

Question 7: What variables does this dataset contain?

  • This dataset contains information on the household income in the U.S. over the last 12 months.

Question 8: What classification methods did you use? How does each classification method bias the interpretation of the data?

  • I struggled to find a dataset that would work correctly in the joining process as many of the easy to find datasets from ESRI do not download with an accessible attribute table making it impossible to join the dataset with the state dataset. With the dataset that I did find through the National Historic Geographic Information System’s database I chose to use the same graduated color classification but used a different attribute with each of the classifications. In the first map I chose to classify the information regarding households with incomes of less than $10,000, then in the second map I classified households with incomes of $200,000 or more, and for the third map I classified households with incomes between $50,000 -$59,000. I chose this bracket because according to the U.S. Census Bureau the average income in the U.S. is around $50,000. I wanted the three maps to represent what could be considered the poorest and richest people in the country as well as the middle class to see the breakup of money in the U.S. It would be interesting to see a similar dataset that is based off of country data rather than state data to see the distribution of wealth on a bigger scale. This would also make it easier for people to visualize thus eliminating some of the bias that may be present in the interpretation of the data.