Assignment #5
Michael Coty
April 2, 2013
1. Folder Structure for Data Organization and Management:
Note that the shape files shown below could not be moved into the “Shape Files” folder.
2. Coordinate System and Projection:
Data Set - Counties:
Projected Coordinate System – U.S. National Atlas Equal Area
Projection – Lambert Azimuthal Equal Area
Linear Units – Meter
Data Set - States:
Projected Coordinate System – U.S. National Atlas Equal Area
Projection – Lambert Azimuthal Equal Area
Linear Units – Meter
Data Set - Power Plants:
Projected Coordinate System – U.S. National Atlas Equal Area
Projection – Lambert Azimuthal Equal Area
Linear Units – Meter
Data Set - Rivers:
Projected Coordinate System – U.S. National Atlas Equal Area
Projection – Lambert Azimuthal Equal Area
Linear Units - Meter
3. Clip and/or Select as Necessary to Reduce Data Sets to Area of Interest:
For this assignment, the “Selection” tool was utilized to reduce the data sets to include only the lower
48 states. A new “states” layer was created that did not include the states of Alaska and Hawaii. This
made our analysis simpler and reduced the amount of information. The highlighted 48 states using the
“Selection” tool is shown below:
The same technique was utilized to eliminate power plants and rivers located in Alaska and Hawaii.
However, as shown in the figure below, there appears to be some power plants that are not located in
the correct location. To find out information for the power plant, including the state where the plant
belonged, we utilized the “Identify” tool and clicked on the location. There could be several reasons for
the misplaced power plant. Locational data for the plant could have been entered incorrectly on the
eGRID data set, or the power plant may have provided incorrect information to the Environmental
Protection Agency (EPA). The plant or the EPA also may have used erroneous units of measurement in
locating the plant.
4. Calculate Areas for Polygons and Length for Lines:
Using the States polygon data set obtained at the Tufts GIS data center, a field was added to the
attribute table labeled “SquareMile”. Using the Calculate Geometry function, square miles were
calculated for each state. Note that this information was already provided in the data set, identified as
“SQMI”. However, we noticed a discrepancy in the data provided in the table and the information
generated using the Calculate Geometry function. We calculated the percent difference as shown in
the table at the bottom of the page. One reason for this difference might be in the projection format
utilized when the area was calculated. For example, we utilized the U.S. National Atlas Equal Area
projection since we are viewing the entire United States. The initial “SQMI” analysis provided in the
original attribute table may have been calculated in another projection system.
Calculated Area for States (in square miles):
Percent Difference in Area between States Data Set and Calculate Geometry Function:
Calculated Area for County Tracts (in square miles and acres):
Using the Calculate Geometry function, the length of the Rivers database was calculated in kilometers.
See screen shot below:
5. Use at Least 4 Tools to Document your Area
Viewing Statistics in a Table – For this assignment, we wondered how many power plants are located in
the lower 48 states. Based on the 2009 U.S. eGRID data (the latest information available) obtained from
the EPA website, the number of power plants in the lower 48 states is 5,492. This information was
obtained using the Statistics function on the power plant attribute table. This information could be
incorrect, depending on the data that was provided from the eGRID website. A screen shot of the table
is provided below:
For this analysis, we also wondered what the population is for the lower 48 states. Based on the
“States” file obtained at the Tufts GIS center, and using the Statistics function, we learned that the
population for the lower 48 states is approximately 309,207,532. This is using the 2010 Census Data
(POP2010). As shown in the window below, the count for the states equals 49. A review of the
attribute table shows that this information includes Washington D.C. Errors for this calculation could
occur for many reasons. The information relies on the U.S. Census data, for example, which itself
includes a margin of error.
Summarize Data in an Attribute Table - We selected specific data and summarized it in an attribute
table. For this table, we summarized the number of power plants per state (Count_PSTATABB) and the
amount of CO2 emissions from the power plants (Sum_PLCO2AN). The “Summarize” window is shown
directly below, with the sum of the annual plant CO2 emissions box checked. As we see, there are 59
power plants in New Hampshire, for example. The total CO2 emissions from the power plants located in
New Hampshire is approximately 6,056,353 tons per year. Again, this data relies upon information
provided on the eGRID website which could contain errors. A screen shot of a portion of the Table
summarizing CO2 emissions in tons for each state is shown on the following page.
Select by Attribute - For this analysis, we were only concerned with the lower 48 states. Therefore, we
utilized the “Select by Attribute” function to remove the states of Alaska and Hawaii from the Power
Plant data set. The screen shot of this tool, highlighting the “Power Plants” selection is provided below:
Spatial Join - A Spatial Join was utilized to join the Power Plants and States data sets. This information
allowed us to determine the number of power plants by state. For example, Massachusetts has
between 86 and 135 power plants. This information can be compared to the rest of the country to
evaluate states with a similar number of power plants. Do these states have a similar number of power
plants because they have the same population? Or does one state use more energy than another?
Does geographic location and climate matter? For example, do states in the northeast have more power
plants per capita? Potential errors for this analysis could occur from data that was provided on the
eGRID website. A screen shot of this information is provided below: