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11.188: Urban Planning and Social Science
Laboratory |
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11.205: Introduction to Spatial Analysis |
For this test, we will use US county-level data from the November 2016, US presidential election (between Donald Trump and Hillary Clinton). The relevant data for the test is bundled together into a zipped-filer called, test21data.zip on Stellar (and in the class locker). (Yes, some of these same data were used in last year's test, but we will focus this year on Texas results.) Extract all the files from test21data.zip into a local folder such as C:\temp\11.188\test21.
Look in your copy of the folder for a QGIS project document called 11.188_test21_start.qgz that has already included some of the shapefiles and tables that you will need. This QGIS Document utilizes a Metropolitan Statistical Area shapefile from the US Census plus several shapefiles that were obtained from the MIT Library Geodata Repository and then projected to a North_America_Albers_Equal_Area_Conic (NAD 1983) projection (in meters). (The standard EPSG code for this coordinate system is 102008 so I have included it in the shapefile names, but you will not need to use this fact.) For each of these shapefile, only the contiguous 48 United States were saved (the "lower 48").
Filename
Description
us_a1city_2000_lower48
_epsg102008.shpPoint feature shapefile showing the location of major US cities (within the lower 48 states) having 2000 US Census population of at least 50,000.
us_f7states_2006_lower48
_epsg102008.shpPolygon feature shapefile showing state boundaries for the lower 48 states.
us_e25msa_2010_lower48
_epsg102008.shpPolygon feature shapefile from US Census Bureau of the US 2010 Census boundaries of metropolitan statistical areas (MSAs). The original shapefile has been limited to the lower 48 states and projected to the EPSG=102008 coordinate system.
us_f7counties_1996_lower48
_epsg102008.shpPolygon feature shapefile from US Census Bureau of the 1996 County boundaries within the lower 48 states together with the state, county name, Federal Information Processing Standard code (FIPS), and estimated 1990 and 1996 population, projected to EPSG=102008.
Point feature shapefile of the centroids of all US counties where centroid is within the boundary of Texas, projected to EPSG=102008. USinterstates.shp Line feature shapefile of the US interstate highways. Obtained from ESRI (the ArcGIS vendor) and stored in geographic coordinates (NAD83). Not required for any questions but available, if desired, for context. USboundary
_epsg102008.shpLine feature shapefile of US borders converted to EPSG=102008 after extraction from World country border shapefile downloaded from Natural Earth (http://www.naturalearthdata.com/downloads/110m-cultural-vectors).
election16_county_meta.xlsx(for use with QGIS)Spreadsheet with County level 2016 presidential election results plus a few 5-year 2015 ACS census variables
Data dictionary for columns in election16_county.xlsx11.188_test21_start.qgz
QGIS project document with several shapefiles already added.
Within the QGIS document, 11.188_test21_start.qgz, the several state, county, MSA, and city shapefiles have already been added. The counties are shown thematically using quantile classification broken into 10 categories based on the 1996 population of each county. The county boundaries are omitted in order to avoid map clutter and make the map more readable. Data are shown for the 3111 counties in the lower 48 states.In addition to the shapefiles and QGIS document, test21data.zip also contains a spreadsheet, ELECTION16_COUNTY.xlsx, with county-level presidential election results along with selected census data extracted from the 5-year 2015 ACS census. For your information, another spreadsheet, election16_county_meta, provides a data-dictionary explaining the columns in the election16_county table.
The 2016 election dataset was obtained from https://github.com/tonmcg/US_County_Level_Election_Results_08-20. Portions of the CSV file with election data were then joined with American Community Survey US census data to produce ELECTION16_COUNTY. The 5-year 2015 ACS census data included with the election data come from the S1701 table on Poverty Status and the S2301 table on Employment. In addition, we have added US Census population counts for years 2000 and 2010 to the MSA shapefile, us_e25msa_2010_lower48_epsg102008.shp.
Note that some MSAs cross State boundaries and the MSA and State boundaries come from different sources and do not exactly line up even when the share a common boundary.
When you open the
map document, the county map is displayed using a North_America_Albers_Equal_Area_Conic
projection (NAD 1983) rather than in geographic latitude-longitude
coordinates. As we know, local planning agencies typically use
projected coordinate systems. For example, the MassGIS
shapefiles that we have used are generally
saved in the Massachusetts (mainland) State Plane coordinate system.
Part II-1a
(4 points): Explain
briefly why national and world geographic datasets are usually
distributed in geographic coordinates (e.g., latitude/longitude data
in decimal degrees using WGS84), but local agencies such as MassGIS
and Boston or Cambridge prefer to store and display geographic
datasets in State Plane coordinates. Provide at least one reason
for each case.
Part II-1b (4 points): Suppose we were to view the same US County map in geographic coordinates. Compared with the original view (using EPSG 102008) would the lat/lon map look stretched in the east/west (horizontal) direction or the north/south vertical direction? Explain briefly why this is the case. [Note: You are welcome to change the display coordinate system of your QGIS window, but there is no need and no map to submit for this question. Just provide a brief answer and explanation. If you do change your display coordinates, we suggest that you do so in a instance of the startup QGIS document since the maps we ask you to submit should be in EPSG=102008, which (in QGS) is North_America_Albers_Equal_Area_Conic projection (NAD 1983) in meters.]
Let's develop a chloropleth (thematic) map showing the percentage of votes (per_gop) for Donald Trump in each county. Add into QGIS the table ELECTION16_COUNTY table and, join it to the shapefile of 'lower-48' counties and design your map. Since we will only ask questions about Texas counties, you may want to filter the layer to show only those Texas counties. Note that ELECTION16_COUNTY lists the county FIPS code as text (field= fips_txt) as well as an integer (field=combined_fips). We added the fips_txt field for your convenience. It is not in the original file. Be careful to select the appropriate data type when you join the table to your shapefile.
In addition, please notice that ELECTION16_COUNTY has many columns, most of which are not needed for the test. We have made these available in case you want to explore this data beyond the test. This may be interesting for some of you, depending on your research interests.
MAP #1: Prepare and submit a map layout of the counties in Texas and shade the counties based on the percentage of votes that were for Donald Trump. Be sure to:
- Zoom in to a little beyond the Texas boundary, show the other State boundaries, and include the usual scale bar, source, legend, north arrow, as well as your name and an appropriate title,
- Leave the map in the original (EPSG=102008) projection,
- Indicate your choice of classification method for your thematic map of 'per_gop',
- Overlay a point map of major cities (in the us_a1city_2000_lower48_epsg102008 shapefile) with the size of the city 'point' proportional to the population of the city. Only include cities that have POP >= 100000,
- Include only those MSAs that you determined earlier to intersect Texas and also have MSA pop2010 >= 250000. To facilitate readability, make them 50% transparent and fill them with some type of 'hash' pattern,
- Adjust color ramps, choice of background and foreground colors, outlines, etc. to improve the readability of the map.
NOTE: This question is worth 15 points and provides an opportunity to demonstrate the cartographic skill that you have acquited to develop an informative, readable map.
Part III-2a (3 points): Briefly discuss your choice of classification scheme and number of categories for your election results map for Texas.
Part III-2b (3 points): Briefly discuss any spatial pattern that you observe in your map regarding Trump's percentage of votes among the Texas counties and MSAs.
Part III-3a (2 points): What is the average (mean) value of the percentage of votes for Trump (per_gop) among all the Texas counties for which you have the voting results joined to your map: _____________?
Part III-3b (3 points): Select those counties whose centroids are within those MSAs that have pop2010 >= 250000 and were determined earlier to have any-part-in-Texas. How many counties are in your 'big metro' county selection? ________ What is the total number of votes for Trump ___________ and for Clinton ___________ within these 'big metro' counties?
Part III-3c (4 points): Using the county-level ACS census data joined to your county shapefile, determine the average (mean) percentage of persons below the poverty level (pct_below_pov) for the 'big metro' Texas counties ___________ and for all the other Texas counties ___________.
Next, let's examine the presidential voting pattern for counties near the US-Mexico border as shown in the US_boundary_epsg102008 shapefile.
Create a buffer of 50 km radius around the US-Mexico border. Select all the Texas counties that intersect your buffer. (That is, the counties that have any or all of the county area within your border buffer.)
Part IV-1a (4 points): How many Texas counties have any or all of the county within the 50 km buffer: __________?
Part IV-1b (2 points): Among those Texas counties that overlap the border buffer, what is the total number of votes for each candidate (i.e., the sum of votes_gop, and the sum of votes_dem): Trump votes: ____________, and Clinton votes: ______________?
Using the county-level ACS census data joined to your county shapefile, lets estimate the fraction of persons in each county who are categorized as Hispanic or Latino (of any race). Use 'pop_16plus' for the total number of persons aged 16 and older, and use 'pop_16p_hispanic' for the number of persons aged 16 and older who are of Hispanic or Latino origin (of any race).
Part IV-2a (4 points): What is the total number of persons aged 16 and older (pop_16plus) in those Texas counties within your 50 km buffer of the Mexican border ____________ and in those Texas counties beyond the 50 km buffer _________ ?
Part IV-2b (3 points): Compute for every county in Texas a new variable, pct_hispanic, that is the percentage of persons aged 16 and older who are Hispanic or Latino (of any race). For the County of El Paso, FIPS=48141, in the Western most tip of Texas along the Mexican border, what is the pop_16plus __________, pop_16p_hispanic __________, and pct_hispanic __________?
Create a scatterplot graphic that plots for every Texas county the pct_hispanic vs. pct_gop. (Hint: the Tool to create a scatterplot is available in the 'Graphics' section of 'Processing / Toolbox'.)
MAP #2 (10 points): Prepare and submit a second QGIS layout of the Texas counties and again shade the counties based on the percentage of votes that were for Donald Trump. This time, be sure to:
- Include the outline of the States, and the usual scale bar, source, legend, north arrow, and an appropriate title,
- You may use many of the same layers as in the first Map, but add the following:
- Overlay your 50 km buffer of the Mexico border with enough transparency to allow the chloropleth map of per_gop to be visible underneath.
- Highlight those Texas counties that overlap any part of the border buffer.
- Include in your layout the scatterplot graphic of Texas counties showing the percentage vote for Trump (pct_gop) vs. pct_hispanic, the percent of persons aged 16 and older who are Hispanic or Latino origin (of any race).
Part IV-3b (3 points): Briefly discuss any spatial pattern that you observe in your scatterplot graphic of pct_gop vs. pct_hispanic.
That's all for the test, but feel free to keep the election data and explore some of the election patterns further when you have time. We do not have time on the test to examine further the relationship between voting, demographics, and health exchange participation and subsidies.
Please note:
Last modified 27 March 20021.
(jf)
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