Sunday, August 16, 2009

What's not the matter with Appalachia   posted by Razib @ 8/16/2009 12:17:00 AM
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In the post from a few days ago showing areas where Non-Hispanic white proportions over & under predicted the % for Obama there were some interesting comments. One of the issues is that lumping different regions together obscures some information. Some readers wondered about regional differences, and I did too. So I thought it might be interesting to look at the South as distinct from the non-South. For the purposes of this post the "South" means: Virginia, West Virginia, North & South Carolina, Georgia, Tennessee, Alabama, Mississippi, Louisiana, Texas, Oklahoma, Kentucky and Arkansas. I excluded Maryland, Delaware and Missouri because I don't think these can be considered culturally Southern, especially the first two. In any case, first, scatterplots and loess best fit lines for the South & the non-South. The South is red/black, the non-South is blue/green.




For me the interesting point is that the "upturn" where the % for Obama increases is notable in the South, but not the non-South. That surprised me. What counties are these? Click for the larger image.

northvssouth2.png



Some of the counties are not surprising in terms of being above the trendline, such as the "Research Triangle" region of North Carolina. But Elliott County, Kentucky? Who knew that this was the second-whitest county in the country to vote for Barack Obama. A map illustrating the trendline might be interesting. Blue is above the trendline, red below the trendline. I limited the data to the South here. Click for the larger image.

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Wednesday, August 12, 2009

Where the Whiter Folk Are   posted by Razib @ 8/12/2009 02:00:00 AM
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Today I combined some Census data with 2008 election results (thanks Cosma). Though Barack Obama won the vote last fall, he lost the Non-Hispanic white vote. It stands to reason then that the whiter and less Hispanic a county is, the more likely it would be to tilt McCain. I was curious as to geographic variation within this general rule-of-thumb. So I plotted the % who voted for Obama in a county vs. the % who were Non-Hispanic whites (according to the 2000 Census*). I then generated a line of best fit via loess, and used the deviations from the trend to generate a map shaded proportionately. In other words, the bluer a county is the more it voted for Obama above expectation based on the overall relationship of the % white Non-Hispanic within a county and vote for Obama (the converse for red naturally). Again, click the map for the larger image.

SWPLplot2.png


trendnamp1b.png




I also decided to constrain the data set to those counties which were at minimum 80% Non-Hispanic white. Mostly because the "Black Belt" counties are showing up on the above map.

trendnamp2.png



Finally, a shaded map of the results.

ElectionMapPurpleCounty.jpg



1) Don't mess with Texas Obama. Despite Obama winning the Hispanic counties in the South, those counties are always underperforming relative to other majority-minority districts. Since some of those counties are 90% Hispanic it probably isn't just Non-Hispanic white swing in the other direction.

2) Though Obama lost much of the rural North and East, he overperformed when you use the whole nation as a reference point, in particular rural areas in the West and South.

3) Pennsylvania kind of does look like Pittsburgh + Philadelphia, with Alabama in the middle.

4) Obama did well in Greater New England. Less well in the Butternut Region of the southern Midwest settled from the South.

Here are a list of the 200 counties furthest from the trendlines. The first 100 are the most pro-Obama above expectation when the predictor is Non-Hispanic white %. The last 100 the most pro-McCain.

Deviation From Trend Line % For Obama County & State
0.4 0.78 Marin, California
0.39 0.77 Multnomah, Imbler
0.38 0.78 Santa Cruz, California
0.38 0.77 San Miguel, Colorado
0.36 0.74 Sonoma, California
0.36 0.75 Dukes, Massachusetts
0.35 0.75 Berkshire, Massachusetts
0.35 0.74 Pitkin, Colorado
0.34 0.73 Dane, Wisconsin
0.34 0.72 Orange, North Carolina
0.34 0.72 Boulder, Colorado
0.33 0.73 Windham, Vermont
0.33 0.73 Franklin, Massachusetts
0.33 0.72 Hampshire, Massachusetts
0.32 0.7 Washtenaw, Michigan
0.32 0.7 Mendocino, California
0.32 0.7 King, Washington
0.31 0.71 Lamoille, Vermont
0.31 0.84 San Francisco, California
0.31 0.7 New Castle, Delaware
0.31 0.7 Johnson, Iowa
0.31 0.69 Tompkins, New York
0.3 0.7 Washington, Vermont
0.3 0.7 San Juan, Washington
0.3 0.69 Imbler, Wisconsin
0.3 0.77 Suffolk, Massachusetts
0.3 0.83 Philadelphia, Pennsylvania
0.3 0.72 Arlington, Virginia
0.3 0.69 Windsor, Vermont
0.29 0.69 Addison, Vermont
0.29 0.69 Silver Bow, Montana
0.29 0.68 Nantucket, Massachusetts
0.29 0.72 Montgomery, Maryland
0.29 0.69 Cuyahoga, Ohio
0.28 0.68 Chittenden, Vermont
0.28 0.66 Ingham, Michigan
0.28 0.66 Ramsey, Minnesota
0.28 0.75 Denver, Colorado
0.28 0.67 Iowa, Wisconsin
0.28 0.67 Camden, New Jersey
0.27 0.67 Deer Lodge, Montana
0.27 0.66 Summit, Colorado
0.27 0.66 Blaine, Idaho
0.27 0.65 Lucas, Ohio
0.27 0.65 Genesee, Michigan
0.27 0.65 Hartford, Connecticut
0.27 0.66 Monroe, Indiana
0.27 0.66 Jefferson, Washington
0.27 0.66 Bennington, Vermont
0.26 0.66 Athens, Ohio
0.26 0.76 Durham, North Carolina
0.26 0.66 Douglas, Wisconsin
0.26 0.68 Milwaukee, Wisconsin
0.26 0.67 Mercer, New Jersey
0.26 0.65 Benton, Oregon
0.26 0.76 Cook, Illinois
0.26 0.64 Hennepin, Minnesota
0.26 0.64 Muskegon, Michigan
0.26 0.65 Napa, California
0.26 0.64 Middlesex, Massachusetts
0.26 0.64 Douglas, Kansas
0.26 0.77 Santa Fe, New Mexico
0.26 0.74 Wayne, Michigan
0.26 0.65 Orange, Vermont
0.26 0.74 San Mateo, California
0.25 0.65 St. Louis, Minnesota
0.25 0.64 Hood River, Oregon
0.25 0.63 Humboldt, California
0.25 0.63 Albany, New York
0.25 0.64 Bayfield, Wisconsin
0.25 0.64 Marion, Indiana
0.25 0.64 Rock, Wisconsin
0.25 0.67 Lake, Indiana
0.25 0.79 Alameda, California
0.24 0.64 Cumberland, Maine
0.24 0.68 Contra Costa, California
0.24 0.83 Clayton, Georgia
0.24 0.62 Mahoning, Ohio
0.24 0.62 Rock Island, Illinois
0.24 0.62 Hampden, Massachusetts
0.24 0.63 Lane, Oregon
0.24 0.63 Trempealeau, Wisconsin
0.24 0.63 Carlton, Minnesota
0.24 0.63 Cheshire, New Hampshire
0.24 0.63 Grand Isle, Vermont
0.23 0.63 Crawford, Wisconsin
0.23 0.63 Orleans, Vermont
0.23 0.63 Gunnison, Colorado
0.23 0.63 Lackawanna, Pennsylvania
0.23 0.63 Grafton, New Hampshire
0.23 0.63 Portage, Wisconsin
0.23 0.63 Routt, Colorado
0.23 0.67 Broward, Florida
0.23 0.65 Clarke, Georgia
0.23 0.62 Palm Beach, Florida
0.23 0.61 New Haven, Connecticut
0.23 0.61 Eagle, Colorado
0.23 0.62 Howard, Iowa
0.23 0.62 Jackson, Iowa
0.23 0.62 Green, Wisconsin

-0.22 0.26 Ector, Texas
-0.22 0.18 Kiowa, Kansas
-0.22 0.18 Sioux, Iowa
-0.22 0.18 Piute, Utah
-0.22 0.18 Cleburne, Alabama
-0.22 0.18 Brantley, Georgia
-0.22 0.18 Campbell, Wyoming
-0.22 0.24 Upton, Texas
-0.22 0.27 Seward, Kansas
-0.22 0.16 Wichita, Kansas
-0.22 0.25 Ward, Texas
-0.22 0.17 Donley, Texas
-0.22 0.16 Morton, Kansas
-0.22 0.17 Alfalfa, Oklahoma
-0.22 0.17 Holmes, Florida
-0.22 0.17 Rock, Nebraska
-0.22 0.17 Leslie, Kentucky
-0.22 0.24 Winkler, Texas
-0.22 0.17 Box Elder, Utah
-0.22 0.17 Hooker, Nebraska
-0.22 0.16 Jack, Texas
-0.22 0.17 Crook, Wyoming
-0.22 0.17 Morgan, Utah
-0.23 0.17 Bear Lake, Idaho
-0.23 0.17 Cullman, Alabama
-0.23 0.17 Archer, Texas
-0.23 0.17 Caribou, Idaho
-0.23 0.17 Sevier, Utah
-0.23 0.16 Kingfisher, Oklahoma
-0.23 0.15 Hutchinson, Texas
-0.23 0.37 Pecos, Texas
-0.23 0.16 Jefferson, Idaho
-0.23 0.16 George, Mississippi
-0.23 0.16 Duchesne, Utah
-0.23 0.16 Sterling, Texas
-0.23 0.16 Millard, Utah
-0.23 0.16 Carter, Montana
-0.23 0.16 Roger Mills, Oklahoma
-0.23 0.16 Dewey, Oklahoma
-0.23 0.21 Garza, Texas
-0.24 0.16 Banks, Georgia
-0.24 0.16 Sioux, Nebraska
-0.24 0.16 Dawson, Georgia
-0.24 0.16 Logan, Kansas
-0.24 0.16 Cameron, Louisiana
-0.24 0.16 Haakon, South Dakota
-0.24 0.17 Clark, Idaho
-0.24 0.14 Gray, Texas
-0.24 0.14 Hemphill, Texas
-0.24 0.14 Wheeler, Texas
-0.24 0.15 Garfield, Montana
-0.24 0.15 Loving, Texas
-0.24 0.15 Glascock, Georgia
-0.24 0.15 Rich, Utah
-0.24 0.15 McPherson, Nebraska
-0.25 0.27 Hale, Texas
-0.25 0.15 Blount, Alabama
-0.25 0.15 Hayes, Nebraska
-0.25 0.14 Uintah, Utah
-0.25 0.15 Scott, Kansas
-0.25 0.15 Arthur, Nebraska
-0.25 0.15 Ellis, Oklahoma
-0.25 0.15 Major, Oklahoma
-0.25 0.14 Sherman, Texas
-0.25 0.15 Banner, Nebraska
-0.25 0.15 Texas, Oklahoma
-0.25 0.13 Hartley, Texas
-0.25 0.13 Stevens, Kansas
-0.25 0.22 Crane, Texas
-0.25 0.14 Blaine, Nebraska
-0.25 0.14 Jackson, Kentucky
-0.25 0.14 Carson, Texas
-0.25 0.28 Dawson, Texas
-0.25 0.14 Shackelford, Texas
-0.26 0.14 Harper, Oklahoma
-0.26 0.12 Lipscomb, Texas
-0.26 0.12 Cimarron, Oklahoma
-0.26 0.24 Sutton, Texas
-0.26 0.16 Gaines, Texas
-0.26 0.13 Thomas, Nebraska
-0.26 0.18 Martin, Texas
-0.27 0.13 Armstrong, Texas
-0.27 0.13 Livingston, Louisiana
-0.27 0.11 Motley, Texas
-0.27 0.11 Oldham, Texas
-0.27 0.21 Moore, Texas
-0.27 0.11 Borden, Texas
-0.28 0.12 Wallace, Kansas
-0.28 0.17 Andrews, Texas
-0.28 0.12 Franklin, Idaho
-0.28 0.12 Madison, Idaho
-0.28 0.11 Beaver, Oklahoma
-0.28 0.11 Grant, Nebraska
-0.29 0.11 Hansford, Texas
-0.29 0.26 Deaf Smith, Texas
-0.3 0.19 Parmer, Texas
-0.3 0.09 Glasscock, Texas
-0.31 0.2 Reagan, Texas
-0.32 0.08 Roberts, Texas
-0.32 0.08 Ochiltree, Texas
-0.34 0.05 King, Texas


* This is 8 years out of date, but by far the most complete and precise data set until the 2010 Census.

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