President Barack Obama defeated Former Massachusetts Governor Mitt Romney on Tuesday, November 6, 2012 to remain in office as President of the United States. He won by about 5 million votes, 126 electoral college votes, and two states (plus DC).
In some cities, Obama did well. In others, he fared badly. What about those places made the president more or less favorable?
We look at demographic data, specifically population density and eduational attainment, in the hopes to find trends that correlate with the election results.
import pandas as pd
pd.set_option('display.max_rows', 0)
import numpy as np
import seaborn as sns
from datetime import datetime
from matplotlib import pyplot as plt
import difflib
import sklearn.linear_model as linear_model
import sklearn
import six
from matplotlib import colors
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from numpy import hstack
from numpy import atleast_2d, where
# Matplotlib preferences
%matplotlib inline
plt.rcParams['figure.figsize'] = (15,12)
plt.rc('xtick', labelsize=20)
plt.rc('ytick', labelsize=20)
plt.rc('axes', titlesize=25, labelsize=18)
# Code for setting the style of the notebook
from IPython.core.display import HTML
def css_styling():
styles = open("theme/custom.css", "r").read()
return HTML(styles)
css_styling()
# Output text to an external file
def output(text):
with open('ouput.txt', "a") as output:
output.write("{}\n".format(text))
The Guardian published a file containing the 2012 election results, let's take a look.
http://www.theguardian.com/news/datablog/2012/nov/07/us-2012-election-county-results-download
elect_res = pd.read_csv("csv_in/US_elect_county.csv")
elect_res = elect_res[elect_res.FIPS > 0]
elect_res.OBAMA_NUM = elect_res.OBAMA_NUM.str.replace(",", "")
elect_res.ROMNEY_NUM = elect_res.ROMNEY_NUM.str.replace(",", "")
elect_res = elect_res.convert_objects(convert_numeric=True)
elect_res["OBAMA_PCT"] = elect_res.OBAMA_NUM / (elect_res.OBAMA_NUM + elect_res.ROMNEY_NUM)
elect_res["OBAMA_WIN"] = elect_res.OBAMA_PCT > .5
elect_res.head(10)
ind | STATE_POST | COUNTY_NAME | FIPS | OBAMA_NUM | ROMNEY_NUM | OBAMA_PCT | OBAMA_WIN | |
---|---|---|---|---|---|---|---|---|
1 | 1 | AL | Autauga | 1001 | 6354 | 17366 | 0.267875 | False |
2 | 2 | AL | Baldwin | 1003 | 18329 | 65772 | 0.217940 | False |
3 | 3 | AL | Barbour | 1005 | 5873 | 5539 | 0.514634 | True |
4 | 4 | AL | Bibb | 1007 | 2200 | 6131 | 0.264074 | False |
5 | 5 | AL | Blount | 1009 | 2961 | 20741 | 0.124926 | False |
6 | 6 | AL | Bullock | 1011 | 4058 | 1250 | 0.764506 | True |
7 | 7 | AL | Butler | 1013 | 4367 | 5081 | 0.462214 | False |
8 | 8 | AL | Calhoun | 1015 | 15500 | 30272 | 0.338635 | False |
9 | 9 | AL | Chambers | 1017 | 6853 | 7596 | 0.474289 | False |
10 | 10 | AL | Cherokee | 1019 | 2126 | 7494 | 0.220998 | False |
Is this the full data? Let's see if we're missing any states
states = [("AK", "Alaska"), ("AL", "Alabama"), ("AR", "Arkansas"), ("AZ", "Arizona"), ("CA", "California"), ("CO", "Colorado"), ("CT", "Connecticut"), ("DE", "Delaware"), ("FL", "Florida"), ("GA", "Georgia"), ("HI", "Hawaii"), ("IA", "Iowa"), ("ID", "Idaho"), ("IL", "Illinois"), ("IN", "Indiana"), ("KS", "Kansas"), ("KY", "Kentucky"), ("LA", "Louisiana"), ("MA", "Massachusetts"), ("MD", "Maryland"), ("ME", "Maine"), ("MI", "Michigan"), ("MN", "Minnesota"), ("MO", "Missouri"), ("MS", "Mississippi"), ("MT", "Montana"), ("NC", "North Carolina"), ("ND", "North Dakota"), ("NE", "Nebraska"), ("NH", "New Hampshire"), ("NJ", "New Jersey"), ("NM", "New Mexico"), ("NV", "Nevada"), ("NY", "New York"), ("OH", "Ohio"), ("OK", "Oklahoma"), ("OR", "Oregon"), ("PA", "Pennsylvania"), ("RI", "Rhode Island"), ("SC", "South Carolina"), ("SD", "South Dakota"), ("TN", "Tennessee"), ("TX", "Texas"), ("UT", "Utah"), ("VA", "Virginia"), ("VT", "Vermont"), ("WA", "Washington"), ("WI", "Wisconsin"), ("WV", "West Virginia"), ("WY", "Wyoming")]
have = elect_res.STATE_POST.unique()
[state for (abbv, state) in states if abbv not in have]
['Alaska', 'Colorado', 'Connecticut', 'Florida', 'Georgia', 'South Carolina', 'Utah', 'Wyoming']
Not too bad. We can look at these results more closely after we've added on interesting demographic data.
This section is a little technical, feel free to skip to Part 2
We will be using the "Metropolitian Statistical Area" (MSA) as our demographical reference point.
An MSA is "a geographical region with a relatively high population density at its core and close economic ties throughout the area." (More info here)
We must distribute the election results from each county or town to their respective MSA.
(Definitions file taken from first two rows of 'msa_population.csv'. We will use the rest of the data from that dataset later on)
msa_def = pd.read_csv("csv_in/msa_definitions.csv")
msa_def.MSA_AGG_NUM = msa_def.MSA_AGG_NUM.fillna(0)
msa_def = msa_def.convert_objects(convert_numeric=True)
msa_def.TOWNSHIP_NAME = msa_def.TOWNSHIP_NAME.str.rstrip("town")
msa_def.TOWNSHIP_NAME = msa_def.TOWNSHIP_NAME.str.rstrip("city")
msa_def.MSA_AGG_NUM = msa_def.MSA_AGG_NUM.astype(int)
msa_def.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 4983 entries, 0 to 4982 Data columns (total 11 columns): STATE_NUM 4983 non-null int64 STATE_NAME 4983 non-null object MSA_NUM 4983 non-null int64 MSA_NAME 4983 non-null object MSA_AGG_NUM 4983 non-null int64 MSA_AGG_NAME 295 non-null object COUNTY_NUM 4983 non-null int64 TOWNSHIP_NUM 4983 non-null int64 COUNTY_NAME 4983 non-null object TOWNSHIP_NAME 1767 non-null object FIPS 4983 non-null int64 dtypes: int64(6), object(5)
fips_to_msa = {}
for ind, data in msa_def.iterrows():
if pd.isnull(data.TOWNSHIP_NAME):
if pd.isnull(data.MSA_AGG_NAME):
# MSA
fips_to_msa[data.FIPS] = data.MSA_NUM
else:
# MSA_AGG
fips_to_msa[data.FIPS] = data.MSA_AGG_NUM
# Towns
else:
try:
fips_to_msa[data.FIPS][data.TOWNSHIP_NAME] = data.MSA_NUM
except:
fips_to_msa[data.FIPS] = {data.TOWNSHIP_NAME:data.MSA_NUM}
# Create new dataframe
df = pd.read_csv("csv_in/msa_list.csv")
df["OBAMA_NUM"] = 0
df["ROMNEY_NUM"] = 0
df.index = df.MSA_NUM
# Distribute votes
for ind, data in elect_res.iterrows():
try:
msa = fips_to_msa[data.FIPS]
except:
continue
if type(msa) == dict:
town = difflib.get_close_matches(data.COUNTY_NAME, msa.keys(), 1, .2)
if town == []:
continue
msa = msa[town[0]]
try:
df.loc[msa, "OBAMA_NUM"] += data.OBAMA_NUM
df.loc[msa, "ROMNEY_NUM"] += data.ROMNEY_NUM
except:
continue
df = df[df.OBAMA_NUM > 0]
df["TOTAL_VOTES"] = df.OBAMA_NUM + df.ROMNEY_NUM
df["OBAMA_PCT"] = df.OBAMA_NUM / (df.OBAMA_NUM + df.ROMNEY_NUM)
df["OBAMA_WIN"] = df.OBAMA_PCT > .5
df = df.sort()
df.head()
MSA_NUM | MSA_NAME | OBAMA_NUM | ROMNEY_NUM | TOTAL_VOTES | OBAMA_PCT | OBAMA_WIN | |
---|---|---|---|---|---|---|---|
MSA_NUM | |||||||
10180 | 10180 | Abilene, TX Metro Area | 11701 | 41487 | 53188 | 0.219993 | False |
10420 | 10420 | Akron, OH Metro Area | 185228 | 143165 | 328393 | 0.564044 | True |
10580 | 10580 | Albany-Schenectady-Troy, NY Metro Area | 203119 | 147555 | 350674 | 0.579225 | True |
10740 | 10740 | Albuquerque, NM Metro Area | 189901 | 144675 | 334576 | 0.567587 | True |
10780 | 10780 | Alexandria, LA Metro Area | 21461 | 44269 | 65730 | 0.326502 | False |
The US Census publishes population data. We will specifically be looking at 2010 Population Density ("POP_DENSITY_2010")
http://www.census.gov/population/metro/data/pop_pro.html
msa_pop = pd.read_csv("csv_in/msa_population.csv")
msa_pop = msa_pop[pd.isnull(msa_pop.MSA_NUM) == False]
for c in ["POP_2000","POP_2010","POP_DENSITY_2000","POP_DENSITY_2010"]:
msa_pop[c] = msa_pop[c].str.replace(",","")
msa_pop = msa_pop.convert_objects(convert_numeric=True)
msa_pop.head()
MSA_NUM | MSA_NAME | POP_2000 | POP_2010 | POP_PCT_CHANGE | POP_DENSITY_2000 | POP_DENSITY_2010 | POP_DENSITY_CHANGE | |
---|---|---|---|---|---|---|---|---|
7 | 10180 | Abilene, TX Metro Area | 160245 | 165252 | 3.1 | 1903.0 | 1771.8 | -131.2 |
8 | 10420 | Akron, OH Metro Area | 694960 | 703200 | 1.2 | 2682.9 | 2412.8 | -270.1 |
9 | 10500 | Albany, GA Metro Area | 157833 | 157308 | -0.3 | 952.4 | 922.7 | -29.7 |
10 | 10580 | Albany-Schenectady-Troy, NY Metro Area | 825875 | 870716 | 5.4 | 2858.6 | 2944.9 | 86.3 |
11 | 10740 | Albuquerque, NM Metro Area | 729649 | 887077 | 21.6 | 3310.2 | 3518.6 | 208.4 |
Basic statistics about our data:
print ("2010 Population Size\n\tAverage: {}\n\tStd. Deviation: {}\n\n2010 Population Density (People / Square Mile)\n\tAverage: {}\n\tStd. Deviation: {}".format(round(msa_pop.POP_2010.mean(), 2), round(msa_pop.POP_2010.std(), 2), round(msa_pop.POP_DENSITY_2010.mean(), 2),round(msa_pop.POP_DENSITY_2010.std(), 2) ))
2010 Population Size Average: 705786.24 Std. Deviation: 1579548.52 2010 Population Density (People / Square Mile) Average: 2373.83 Std. Deviation: 2229.82
msa_pop.sort("POP_DENSITY_2010").plot(x = "MSA_NAME", y="POP_DENSITY_2010", xticks=(), title="Population Density Distribution")
<matplotlib.axes._subplots.AxesSubplot at 0x10ed7b310>
Largest density MSA's:
msa_pop.sort("POP_DENSITY_2010", ascending = False).head()
MSA_NUM | MSA_NAME | POP_2000 | POP_2010 | POP_PCT_CHANGE | POP_DENSITY_2000 | POP_DENSITY_2010 | POP_DENSITY_CHANGE | |
---|---|---|---|---|---|---|---|---|
243 | 35620 | New York-Northern New Jersey-Long Island, NY-N... | 18323002 | 18897109 | 3.1 | 31683.6 | 31251.4 | -432.2 |
308 | 41860 | San Francisco-Oakland-Fremont, CA Metro Area | 4123740 | 4335391 | 5.1 | 12438.4 | 12144.9 | -293.4 |
206 | 31100 | Los Angeles-Long Beach-Santa Ana, CA Metro Area | 12365627 | 12828837 | 3.7 | 12442.0 | 12113.9 | -328.1 |
153 | 26180 | Honolulu, HI Metro Area | 876156 | 953207 | 8.8 | 10977.5 | 11548.2 | 570.7 |
71 | 16980 | Chicago-Joliet-Naperville, IL-IN-WI Metro Area | 9098316 | 9461105 | 4.0 | 9829.6 | 8613.4 | NaN |
Smallest density MSA's:
msa_pop.sort("POP_DENSITY_2010").head()
MSA_NUM | MSA_NAME | POP_2000 | POP_2010 | POP_PCT_CHANGE | POP_DENSITY_2000 | POP_DENSITY_2010 | POP_DENSITY_CHANGE | |
---|---|---|---|---|---|---|---|---|
169 | 27620 | Jefferson City, MO Metro Area | 140052 | 149807 | 7.0 | 587.7 | 522.7 | -64.9 |
292 | 40580 | Rocky Mount, NC Metro Area | 143026 | 152392 | 6.5 | 567.4 | 525.7 | -41.7 |
54 | 15260 | Brunswick, GA Metro Area | 93044 | 112370 | 20.8 | 581.7 | 539.0 | -42.6 |
233 | 34100 | Morristown, TN Metro Area | 123081 | 136608 | 11.0 | 511.9 | 554.2 | 42.3 |
21 | 11500 | Anniston-Oxford, AL Metro Area | 112249 | 118572 | 5.6 | 614.4 | 566.6 | -47.8 |
The US Census via the American Community Survey publishes educational data for each MSA. This data is from the ACS 2013 3 Year Estimate. Some original columns have been removed or renamed.
10 = Less then high school
12 = High school
14 = Some college / Associates degree
16 = Bachelors degree and beyond
msa_edu = pd.read_csv("csv_in/msa_educational-attainment.csv")
msa_edu.EDU_10 = msa_edu.EDU_10 / msa_edu.EDU_TOTAL
msa_edu.EDU_12 = msa_edu.EDU_12 / msa_edu.EDU_TOTAL
msa_edu.EDU_14 = msa_edu.EDU_14 / msa_edu.EDU_TOTAL
msa_edu.EDU_16 = msa_edu.EDU_16 / msa_edu.EDU_TOTAL
msa_edu["EDU_AVG"] = (msa_edu.EDU_10 * 10 + msa_edu.EDU_12 * 12 + msa_edu.EDU_14 * 14 + msa_edu.EDU_16 * 16)
msa_edu.head()
MSA_NUM | MSA_NAME | EDU_TOTAL | EDU_10 | EDU_12 | EDU_14 | EDU_16 | EDU_AVG | |
---|---|---|---|---|---|---|---|---|
0 | 10020 | Abbeville, LA Micro Area | 30138 | 0.188367 | 0.443759 | 0.223671 | 0.144203 | 12.647422 |
1 | 10100 | Aberdeen, SD Micro Area | 20712 | 0.047605 | 0.309965 | 0.351101 | 0.291329 | 13.772306 |
2 | 10140 | Aberdeen, WA Micro Area | 38464 | 0.142679 | 0.293365 | 0.409630 | 0.154326 | 13.151206 |
3 | 10180 | Abilene, TX Metro Area | 82667 | 0.161588 | 0.300035 | 0.331595 | 0.206781 | 13.167140 |
4 | 10220 | Ada, OK Micro Area | 18407 | 0.121693 | 0.309556 | 0.285109 | 0.283642 | 13.461401 |
Basic statistics:
print ("Educational Attainment\n\tAverage: {}\n\tStd. Deviation: {}".format(round(msa_edu.EDU_AVG.mean(), 3), round(msa_edu.EDU_AVG.std(), 3)))
Educational Attainment Average: 13.3 Std. Deviation: 0.44
msa_edu.sort("EDU_AVG").plot(x = "MSA_NAME", y="EDU_AVG", xticks=(), title="Educational Attainment Distribution")
<matplotlib.axes._subplots.AxesSubplot at 0x10e588e90>
Highest Educated MSA's:
msa_edu.sort("EDU_AVG", ascending = False).head()
MSA_NUM | MSA_NAME | EDU_TOTAL | EDU_10 | EDU_12 | EDU_14 | EDU_16 | EDU_AVG | |
---|---|---|---|---|---|---|---|---|
104 | 14500 | Boulder, CO Metro Area | 161239 | 0.057561 | 0.116268 | 0.224108 | 0.602063 | 14.741347 |
457 | 29660 | Laramie, WY Micro Area | 16822 | 0.034122 | 0.111758 | 0.346570 | 0.507550 | 14.655095 |
692 | 39420 | Pullman, WA Micro Area | 17401 | 0.028044 | 0.158612 | 0.276478 | 0.536866 | 14.644331 |
27 | 11180 | Ames, IA Metro Area | 38589 | 0.031097 | 0.151416 | 0.307523 | 0.509964 | 14.592708 |
33 | 11460 | Ann Arbor, MI Metro Area | 179603 | 0.046029 | 0.151751 | 0.277167 | 0.525052 | 14.562485 |
Lowest Educated MSA's:
msa_edu.sort("EDU_AVG").head()
MSA_NUM | MSA_NAME | EDU_TOTAL | EDU_10 | EDU_12 | EDU_14 | EDU_16 | EDU_AVG | |
---|---|---|---|---|---|---|---|---|
708 | 40100 | Rio Grande City-Roma, TX Micro Area | 27570 | 0.495901 | 0.223105 | 0.175190 | 0.105803 | 11.781792 |
889 | 48100 | Wauchula, FL Micro Area | 13020 | 0.378571 | 0.331874 | 0.198003 | 0.091551 | 12.005069 |
698 | 39700 | Raymondville, TX Micro Area | 10948 | 0.357143 | 0.340427 | 0.213281 | 0.089149 | 12.068871 |
172 | 17500 | Clewiston, FL Micro Area | 18875 | 0.350675 | 0.335470 | 0.207894 | 0.105960 | 12.138278 |
36 | 11580 | Arcadia, FL Micro Area | 16607 | 0.314807 | 0.375685 | 0.225568 | 0.083941 | 12.157283 |
Merge the population and educational data onto the election data to create one large DataFrame
msa_pop = msa_pop.drop("MSA_NAME", 1)
df = df.merge(right=msa_pop, how='left', on="MSA_NUM")
msa_edu = msa_edu.drop(["MSA_NAME","EDU_TOTAL"],1)
df = df.merge(right=msa_edu, how='left', on="MSA_NUM")
df.head()
MSA_NUM | MSA_NAME | OBAMA_NUM | ROMNEY_NUM | TOTAL_VOTES | OBAMA_PCT | OBAMA_WIN | POP_2000 | POP_2010 | POP_PCT_CHANGE | POP_DENSITY_2000 | POP_DENSITY_2010 | POP_DENSITY_CHANGE | EDU_10 | EDU_12 | EDU_14 | EDU_16 | EDU_AVG | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 10180 | Abilene, TX Metro Area | 11701 | 41487 | 53188 | 0.219993 | False | 160245 | 165252 | 3.1 | 1903.0 | 1771.8 | -131.2 | 0.161588 | 0.300035 | 0.331595 | 0.206781 | 13.167140 |
1 | 10420 | Akron, OH Metro Area | 185228 | 143165 | 328393 | 0.564044 | True | 694960 | 703200 | 1.2 | 2682.9 | 2412.8 | -270.1 | 0.072797 | 0.321001 | 0.301059 | 0.305143 | 13.677097 |
2 | 10580 | Albany-Schenectady-Troy, NY Metro Area | 203119 | 147555 | 350674 | 0.579225 | True | 825875 | 870716 | 5.4 | 2858.6 | 2944.9 | 86.3 | 0.063663 | 0.254003 | 0.318971 | 0.363364 | 13.964072 |
3 | 10740 | Albuquerque, NM Metro Area | 189901 | 144675 | 334576 | 0.567587 | True | 729649 | 887077 | 21.6 | 3310.2 | 3518.6 | 208.4 | 0.116345 | 0.243287 | 0.341785 | 0.298582 | 13.645208 |
4 | 10780 | Alexandria, LA Metro Area | 21461 | 44269 | 65730 | 0.326502 | False | 145035 | 153922 | 6.1 | 845.0 | 803.2 | -41.8 | 0.170721 | 0.363467 | 0.307582 | 0.158230 | 12.906644 |
def plotLinReg(title, X_col, xlabel, Y, ylabel):
X = X_col[:, np.newaxis]
X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split(X, Y, test_size = .5, random_state = 5)
regr = linear_model.LinearRegression()
regr.fit(X_train, Y_train)
plt.axhline(y=0.5,xmin=0,xmax=max(X),c="grey",linewidth=3, zorder=3)
plt.scatter(X, Y, color='blue', zorder=5)
plt.plot(X_test, regr.predict(X_test), color='red',
linewidth=3, zorder=4)
plt.ylabel(ylabel)
plt.xlabel(xlabel)
plt.title(title)
plotLinReg("Population Density vs Obama Percentage",
df.POP_DENSITY_2010, "Popululation Density (people per square mile)",
df.OBAMA_PCT, "% of votes for Obama")
plt.savefig("images/votes-density-unfiltered.jpg")
As we can see, there are a few very population dense outliers.
df[df.POP_DENSITY_2010 > 10000].sort("POP_DENSITY_2010", ascending=False)
MSA_NUM | MSA_NAME | OBAMA_NUM | ROMNEY_NUM | TOTAL_VOTES | OBAMA_PCT | OBAMA_WIN | POP_2000 | POP_2010 | POP_PCT_CHANGE | POP_DENSITY_2000 | POP_DENSITY_2010 | POP_DENSITY_CHANGE | EDU_10 | EDU_12 | EDU_14 | EDU_16 | EDU_AVG | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
194 | 35620 | New York-Northern New Jersey-Long Island, NY-N... | 3852335 | 2059511 | 5911846 | 0.651630 | True | 18323002 | 18897109 | 3.1 | 31683.6 | 31251.4 | -432.2 | 0.129354 | 0.243847 | 0.234864 | 0.391935 | 13.778762 |
240 | 41860 | San Francisco-Oakland-Fremont, CA Metro Area | 1161260 | 339122 | 1500382 | 0.773976 | True | 4123740 | 4335391 | 5.1 | 12438.4 | 12144.9 | -293.4 | 0.110650 | 0.163766 | 0.262930 | 0.462654 | 14.155177 |
163 | 31100 | Los Angeles-Long Beach-Santa Ana, CA Metro Area | 2129241 | 1241192 | 3370433 | 0.631741 | True | 12365627 | 12828837 | 3.7 | 12442.0 | 12113.9 | -328.1 | 0.207889 | 0.195260 | 0.275322 | 0.321528 | 13.420979 |
113 | 26180 | Honolulu, HI Metro Area | 201803 | 87255 | 289058 | 0.698140 | True | 876156 | 953207 | 8.8 | 10977.5 | 11548.2 | 570.7 | 0.068770 | 0.248472 | 0.348207 | 0.334550 | 13.897076 |
Let's see if the correlation still holds when we remove these highly dense MSA's.
The chart below shows our new highest population dense MSA's.
df_original = df.copy()
df = df[pd.isnull(df.POP_DENSITY_2010) == False]
df = df[pd.isnull(df.EDU_AVG) == False]
df = df[df.POP_DENSITY_2010 < 10000]
df.sort("POP_DENSITY_2010", ascending=False).head()
MSA_NUM | MSA_NAME | OBAMA_NUM | ROMNEY_NUM | TOTAL_VOTES | OBAMA_PCT | OBAMA_WIN | POP_2000 | POP_2010 | POP_PCT_CHANGE | POP_DENSITY_2000 | POP_DENSITY_2010 | POP_DENSITY_CHANGE | EDU_10 | EDU_12 | EDU_14 | EDU_16 | EDU_AVG | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
47 | 16980 | Chicago-Joliet-Naperville, IL-IN-WI Metro Area | 2337624 | 1282464 | 3620088 | 0.645737 | True | 9098316 | 9461105 | 4.0 | 9829.6 | 8613.4 | NaN | 0.116868 | 0.234737 | 0.279802 | 0.368593 | 13.800239 |
241 | 41940 | San Jose-Sunnyvale-Santa Clara, CA Metro Area | 351856 | 142399 | 494255 | 0.711892 | True | 1735819 | 1836911 | 5.8 | 8300.4 | 8417.7 | 117.3 | 0.122293 | 0.152168 | 0.250334 | 0.475204 | 14.156899 |
207 | 37980 | Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Me... | 1754922 | 966623 | 2721545 | 0.644826 | True | 5687147 | 5965343 | 4.9 | 8064.3 | 7773.2 | -291.1 | 0.088707 | 0.292180 | 0.260006 | 0.359108 | 13.779030 |
238 | 41740 | San Diego-Carlsbad-San Marcos, CA Metro Area | 478386 | 431046 | 909432 | 0.526027 | True | 2813833 | 3095313 | 10.0 | 7094.4 | 6920.5 | -173.9 | 0.136432 | 0.186498 | 0.330293 | 0.346778 | 13.774835 |
151 | 29820 | Las Vegas-Paradise, NV Metro Area | 387978 | 288223 | 676201 | 0.573761 | True | 1375765 | 1951269 | 41.8 | 6781.8 | 6527.2 | -254.6 | 0.155833 | 0.293786 | 0.331123 | 0.219258 | 13.227614 |
Even when we remove those four, there is still a strong correlation.
plotLinReg("Population Density vs Obama Percentage (filtered)",
df.POP_DENSITY_2010, "Popululation Density (people per square mile)",
df.OBAMA_PCT, "% of votes for Obama")
plt.savefig("images/votes-density-filtered.jpg")
plt.show()
We can see from glancing at the above graph that Obama didn't lose a single MSA above about 5000 people per square mile.
The chart below shows the densest MSA's in which Obama lost. We can see that the 2010 density of Reading, PA (the densest) is only 4,657 people per mile
df[df.OBAMA_WIN == False].sort("POP_DENSITY_2010", ascending = False).head()
MSA_NUM | MSA_NAME | OBAMA_NUM | ROMNEY_NUM | TOTAL_VOTES | OBAMA_PCT | OBAMA_WIN | POP_2000 | POP_2010 | POP_PCT_CHANGE | POP_DENSITY_2000 | POP_DENSITY_2010 | POP_DENSITY_CHANGE | EDU_10 | EDU_12 | EDU_14 | EDU_16 | EDU_AVG | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
218 | 39740 | Reading, PA Metro Area | 79895 | 80857 | 160752 | 0.497008 | False | 373638 | 411442 | 10.1 | 4316.3 | 4656.8 | 340.5 | 0.125915 | 0.365747 | 0.254981 | 0.253356 | 13.271558 |
208 | 38060 | Phoenix-Mesa-Glendale, AZ Metro Area | 553436 | 713782 | 1267218 | 0.436733 | False | 3251876 | 4192887 | 28.9 | 5028.4 | 4394.9 | -633.4 | 0.136396 | 0.228553 | 0.346807 | 0.288244 | 13.573796 |
193 | 35380 | New Orleans-Metairie-Kenner, LA Metro Area | 252643 | 253524 | 506167 | 0.499130 | False | 1316510 | 1167764 | -11.3 | 6118.9 | 4370.2 | NaN | 0.136166 | 0.283776 | 0.302023 | 0.278035 | 13.443854 |
259 | 44300 | State College, PA Metro Area | 33677 | 33697 | 67374 | 0.499852 | False | 135758 | 153990 | 13.4 | 4106.6 | 4366.2 | 259.6 | 0.054689 | 0.298789 | 0.217584 | 0.428938 | 14.041541 |
95 | 23420 | Fresno, CA Metro Area | 85862 | 91143 | 177005 | 0.485082 | False | 799407 | 930450 | 16.4 | 4168.7 | 4216.1 | 47.4 | 0.261997 | 0.226484 | 0.317965 | 0.193554 | 12.886153 |
plotLinReg("Educational Attainment vs Obama Percentage",
df.EDU_AVG, "Educational Attainment",
df.OBAMA_PCT, "% of votes for Obama")
plt.savefig("images/votes-education.jpg")
plt.show()
2-dimensional clustering K-means clustering based on Population Density and Educational Attainment
(The population dense outliers are so far out they create their own cluster, so we will again ignore those four areas that we ignored in the population density correlations)
We evaluate the quality of clusters for different numbers of clusters used
X = df.POP_DENSITY_2010.values
Y = df.EDU_AVG.values
X_scaler = StandardScaler(with_std=True)
scaled_X = X_scaler.fit_transform(X)
Y_scaler = StandardScaler(with_std=True)
scaled_Y = Y_scaler.fit_transform(Y)
def evaluate_clustering(X, max_k):
inertia = []
inertia.append(0)
for k in range(1, max_k + 1):
kmeans = KMeans(init='k-means++', n_clusters=k, n_init=5)
kmeans.fit_predict(X)
inertia.append(kmeans.inertia_)
return inertia
data = hstack((atleast_2d(scaled_X).T, atleast_2d(scaled_Y).T))
inertia_k = evaluate_clustering(data, 15)
ax = plt.subplot(111)
ax.plot(inertia_k)
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
ax.set_xlim(1, len(inertia_k))
plt.savefig("images/cluster-eval.jpg")
plt.show()
Two clusters are of course a vast improvement over one cluster, but four clusters is even better. We will calculate and analyze both a 2-Clustering and a 4-Clustering
color_list_full = [("#D2691E", "Brown"), ("#1E90FF", "Blue"), ("#3CB371", "Green"), ("#9400D3", "Purple")]
color_list = [a for (a, b) in color_list_full]
def cluster(n_clusters):
k_means = KMeans(init='k-means++', n_clusters=n_clusters, n_init=10)
k_means.fit_predict(data)
k_means_labels = k_means.labels_
df["CLUSTER_{}".format(n_clusters)] = k_means_labels
k_means_cluster_centers = k_means.cluster_centers_
k_means_labels_unique = np.unique(k_means_labels)
# KMeans
for k, col in zip(range(n_clusters), color_list[:n_clusters]):
my_members = k_means_labels == k
cluster_center = k_means_cluster_centers[k]
plt.plot(data[my_members, 0], data[my_members, 1], 'w',
markerfacecolor=col, marker='.')
plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor="#2F4F4F",
markeredgecolor='k', markersize=6)
# plt.text(-3.5, 1.8, 'train time: %.2fs\ninertia: %f' % (
# t_batch, k_means.inertia_))
plt.xlabel("Population Density", fontsize="18")
plt.ylabel("Educational Attainment", fontsize="18")
plt.savefig("images/{}-cluster.jpg".format(n_clusters))
plt.show()
return k_means_labels
# analyzeCluster(n_clusters, k_means_labels)
def analyzeCluster(n_clusters, labels):
titles=["Population Density", "Educational Attainment"]
titles = ["Population Density"]
plt.rcParams['figure.figsize'] = (14,7 * n_clusters)
for i, label in enumerate(set(labels)):
point_indices = where(labels == label)[0]
point_indices = point_indices.tolist()
cluster = df.iloc[point_indices]
obama = cluster.OBAMA_PCT.mean()
print "Cluster {} ({}):\n\tSize: {}\n\tAvg Pop Density: {}\n\tAvg Edu Lvl: {}\n\tObama Avg Pct: {}%".format(i+1, color_list_full[i][1], len(point_indices), round(cluster.POP_DENSITY_2010.mean(), 2), round(cluster.EDU_AVG.mean(), 2), round(cluster.OBAMA_PCT.mean() * 100, 2))
with sns.axes_style('darkgrid'):
ax = plt.subplot(n_clusters, 3, 3 * i + 1)
sns.violinplot(cluster[['OBAMA_PCT']],
color=color_list[i],
ax=ax,
names=["% Of Obama Votes"])
ax.set_title("Cluster %d" % (i + 1))
ax = plt.subplot(n_clusters, 3, 3 * i + 2)
sns.violinplot(cluster[['POP_DENSITY_2010']],
color=color_list[i],
ax=ax,
names=["Population Density"])
ax = plt.subplot(n_clusters, 3, 3 * i + 3)
sns.violinplot(cluster[['EDU_AVG']],
ax=ax,
color=color_list[i],
names=["Educational Attainment"])
plt.rcParams['figure.figsize'] = (15,12)
plt.savefig("images/{}-cluster-analysis.jpg".format(n_clusters))
labels = cluster(2)
analyzeCluster(2, labels)
Cluster 1 (Brown): Size: 110 Avg Pop Density: 3279.06 Avg Edu Lvl: 13.82 Obama Avg Pct: 53.62% Cluster 2 (Blue): Size: 181 Avg Pop Density: 1596.0 Avg Edu Lvl: 13.29 Obama Avg Pct: 42.34%
The 2-clustering groups these MSA's into a higher educated higher density cluster, and a low density low educated cluster. We can see that Obama did better in the high education high density cluster, about 53%. Versus the other cluster where he fared only about 42%.
labels = cluster(4)
analyzeCluster(4, labels)
Cluster 1 (Brown): Size: 113 Avg Pop Density: 2191.07 Avg Edu Lvl: 13.8 Obama Avg Pct: 49.58% Cluster 2 (Blue): Size: 124 Avg Pop Density: 1336.16 Avg Edu Lvl: 13.26 Obama Avg Pct: 40.31% Cluster 3 (Green): Size: 23 Avg Pop Density: 3443.21 Avg Edu Lvl: 12.81 Obama Avg Pct: 51.88% Cluster 4 (Purple): Size: 31 Avg Pop Density: 5067.87 Avg Edu Lvl: 13.73 Obama Avg Pct: 57.02%
We can see that two clusters are especially interesting to us. One is a smaller cluster, very dense, and highly educated. In that cluster, Obama fared well, about 57%. On the other hand we see a low density low educated cluster where Obama fared poorly, about 41%.
The Center for Climate and Energy Solutions published the geolocations for US MSA's. In order to map the clusterings, we import this data and merge it with the existing data set.
Check out the resulting maps imported into CartoDB:
2 Clustering
4 Clustering
msa_list = pd.read_csv("csv_in/msa_list.csv")
msa_name_to_num = {}
for ind, data in msa_list.iterrows():
msa_name_to_num[data.MSA_NAME] = data.MSA_NUM
msa_carto = pd.read_csv("csv_in/msa_carto.csv")
msa_carto.rename(columns={'name': 'MSA_NAME'}, inplace=True)
msa_carto = msa_carto.drop_duplicates(subset="MSA_NAME")
msa_carto = msa_carto.drop(["msacmsa", "type", "created_at", "updated_at", "cartodb_id"], 1)
msa_carto = msa_carto[msa_carto.MSA_NAME.str.contains(", PR") == False]
for ind, data in msa_carto.iterrows():
name = difflib.get_close_matches(str(data.MSA_NAME), msa_name_to_num.keys(), 1, .2)
num = msa_name_to_num[name[0]]
msa_carto.loc[ind, "MSA_NAME_FOUND"] = name[0]
msa_carto.loc[ind, "MSA_NUM"] = num
msa_carto = msa_carto.drop(["MSA_NAME", "MSA_NAME_FOUND"], 1)
df_carto = df.merge(right=msa_carto, on="MSA_NUM")
df_carto.to_csv("csv_out/df_carto.csv")
df_carto.head()
MSA_NUM | MSA_NAME | OBAMA_NUM | ROMNEY_NUM | TOTAL_VOTES | OBAMA_PCT | OBAMA_WIN | POP_2000 | POP_2010 | POP_PCT_CHANGE | ... | EDU_14 | EDU_16 | EDU_AVG | CLUSTER_2 | CLUSTER_4 | area | perimeter | longitude | latitude | the_geom | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 10180 | Abilene, TX Metro Area | 11701 | 41487 | 53188 | 0.219993 | False | 160245 | 165252 | 3.1 | ... | 0.331595 | 0.206781 | 13.167140 | 1 | 1 | 0.227950 | 1.925191 | -99.890071 | 32.302139 | 0106000020E6100000010000000103000000010000005A... |
1 | 10580 | Albany-Schenectady-Troy, NY Metro Area | 203119 | 147555 | 350674 | 0.579225 | True | 825875 | 870716 | 5.4 | ... | 0.318971 | 0.363364 | 13.964072 | 0 | 0 | 0.937307 | 5.338503 | -74.018758 | 42.876503 | 0106000020E61000000100000001030000000100000016... |
2 | 10740 | Albuquerque, NM Metro Area | 189901 | 144675 | 334576 | 0.567587 | True | 729649 | 887077 | 21.6 | ... | 0.341785 | 0.298582 | 13.645208 | 0 | 0 | 0.000206 | 0.082351 | -106.255318 | 35.847518 | 0106000020E6100000010000000103000000010000000D... |
3 | 10780 | Alexandria, LA Metro Area | 21461 | 44269 | 65730 | 0.326502 | False | 145035 | 153922 | 6.1 | ... | 0.307582 | 0.158230 | 12.906644 | 1 | 1 | 0.333852 | 3.398299 | -92.531215 | 31.203393 | 0106000020E610000001000000010300000001000000D5... |
4 | 10900 | Allentown-Bethlehem-Easton, PA-NJ Metro Area | 170952 | 162738 | 333690 | 0.512308 | True | 740395 | 821173 | 10.9 | ... | 0.282193 | 0.295476 | 13.564746 | 0 | 3 | 0.307474 | 3.318848 | -75.497769 | 40.776243 | 0106000020E61000000100000001030000000100000031... |
5 rows × 25 columns
The linear regressions showed us that there are correlations between certain demographic features and election results. The clusterings showed us that the groupings of these regions by their features can indicate electoral success.
While it is up to the sociologists and political scientists to explain why this phenomenon occurs, we have seen that it certainly does.
Including election results, population density, and educational attainment:
df_original
MSA_NUM | MSA_NAME | OBAMA_NUM | ROMNEY_NUM | TOTAL_VOTES | OBAMA_PCT | OBAMA_WIN | POP_2000 | POP_2010 | POP_PCT_CHANGE | POP_DENSITY_2000 | POP_DENSITY_2010 | POP_DENSITY_CHANGE | EDU_10 | EDU_12 | EDU_14 | EDU_16 | EDU_AVG | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 10180 | Abilene, TX Metro Area | 11701 | 41487 | 53188 | 0.219993 | False | 160245 | 165252 | 3.1 | 1903.0 | 1771.8 | -131.2 | 0.161588 | 0.300035 | 0.331595 | 0.206781 | 13.167140 |
1 | 10420 | Akron, OH Metro Area | 185228 | 143165 | 328393 | 0.564044 | True | 694960 | 703200 | 1.2 | 2682.9 | 2412.8 | -270.1 | 0.072797 | 0.321001 | 0.301059 | 0.305143 | 13.677097 |
2 | 10580 | Albany-Schenectady-Troy, NY Metro Area | 203119 | 147555 | 350674 | 0.579225 | True | 825875 | 870716 | 5.4 | 2858.6 | 2944.9 | 86.3 | 0.063663 | 0.254003 | 0.318971 | 0.363364 | 13.964072 |
3 | 10740 | Albuquerque, NM Metro Area | 189901 | 144675 | 334576 | 0.567587 | True | 729649 | 887077 | 21.6 | 3310.2 | 3518.6 | 208.4 | 0.116345 | 0.243287 | 0.341785 | 0.298582 | 13.645208 |
4 | 10780 | Alexandria, LA Metro Area | 21461 | 44269 | 65730 | 0.326502 | False | 145035 | 153922 | 6.1 | 845.0 | 803.2 | -41.8 | 0.170721 | 0.363467 | 0.307582 | 0.158230 | 12.906644 |
5 | 10900 | Allentown-Bethlehem-Easton, PA-NJ Metro Area | 170952 | 162738 | 333690 | 0.512308 | True | 740395 | 821173 | 10.9 | 3643.6 | 3889.3 | 245.7 | 0.090772 | 0.331558 | 0.282193 | 0.295476 | 13.564746 |
6 | 11020 | Altoona, PA Metro Area | 15516 | 31500 | 47016 | 0.330015 | False | 129144 | 127089 | -1.6 | 2755.7 | 2428.7 | -327.0 | 0.059314 | 0.484242 | 0.256035 | 0.200410 | 13.195081 |
7 | 11100 | Amarillo, TX Metro Area | 15057 | 63549 | 78606 | 0.191550 | False | 226522 | 249881 | 10.3 | 2743.6 | 2591.4 | -152.2 | 0.152422 | 0.243828 | 0.371029 | 0.232721 | 13.368099 |
8 | 11180 | Ames, IA Metro Area | 25940 | 19557 | 45497 | 0.570147 | True | 79981 | 89542 | 12.0 | 2382.0 | 2343.4 | -38.6 | 0.031097 | 0.151416 | 0.307523 | 0.509964 | 14.592708 |
9 | 11300 | Anderson, IN Metro Area | 24404 | 26755 | 51159 | 0.477023 | False | 133358 | 131636 | -1.3 | 1524.6 | 1327.9 | -196.8 | 0.098599 | 0.379664 | 0.331626 | 0.190111 | 13.226498 |
10 | 11460 | Ann Arbor, MI Metro Area | 120791 | 56401 | 177192 | 0.681696 | True | 322895 | 344791 | 6.8 | 3606.4 | 3363.5 | -242.9 | 0.046029 | 0.151751 | 0.277167 | 0.525052 | 14.562485 |
11 | 11500 | Anniston-Oxford, AL Metro Area | 15500 | 30272 | 45772 | 0.338635 | False | 112249 | 118572 | 5.6 | 614.4 | 566.6 | -47.8 | 0.187500 | 0.328577 | 0.323043 | 0.160881 | 12.914609 |
12 | 11540 | Appleton, WI Metro Area | 57042 | 61813 | 118855 | 0.479929 | False | 201602 | 225666 | 11.9 | 2156.5 | 1950.0 | -206.4 | 0.051646 | 0.307609 | 0.348533 | 0.292212 | 13.762625 |
13 | 11700 | Asheville, NC Metro Area | 104793 | 108221 | 213014 | 0.491954 | False | 369171 | 424858 | 15.1 | 662.2 | 728.7 | 66.5 | 0.104986 | 0.252890 | 0.340284 | 0.301841 | 13.677959 |
14 | 12100 | Atlantic City-Hammonton, NJ Metro Area | 59509 | 41918 | 101427 | 0.586718 | True | 252552 | 274549 | 8.7 | 3601.0 | 3412.3 | -188.7 | 0.146883 | 0.317288 | 0.282583 | 0.253246 | 13.284384 |
15 | 12220 | Auburn-Opelika, AL Metro Area | 21274 | 32062 | 53336 | 0.398868 | False | 115092 | 140247 | 21.9 | 1198.5 | 1220.9 | 22.4 | 0.108189 | 0.275312 | 0.278624 | 0.337875 | 13.692370 |
16 | 12420 | Austin-Round Rock-San Marcos, TX Metro Area | 333447 | 288027 | 621474 | 0.536542 | True | 1249763 | 1716289 | 37.3 | 3259.2 | 3131.5 | -127.8 | 0.115132 | 0.187690 | 0.288158 | 0.409020 | 13.982131 |
17 | 12540 | Bakersfield-Delano, CA Metro Area | 64690 | 99840 | 164530 | 0.393181 | False | 661645 | 839631 | 26.9 | 3631.2 | 3711.5 | 80.3 | 0.276269 | 0.258272 | 0.316818 | 0.148641 | 12.675662 |
18 | 12580 | Baltimore-Towson, MD Metro Area | 684107 | 480781 | 1164888 | 0.587273 | True | 2552994 | 2710489 | 6.2 | 5890.9 | 5435.7 | -455.2 | 0.089392 | 0.251771 | 0.278724 | 0.380113 | 13.899115 |
19 | 12940 | Baton Rouge, LA Metro Area | 158249 | 203718 | 361967 | 0.437192 | False | 705973 | 802484 | 13.7 | 1640.2 | 1603.3 | -36.9 | 0.123842 | 0.311356 | 0.287258 | 0.277544 | 13.437009 |
20 | 12980 | Battle Creek, MI Metro Area | 29270 | 28338 | 57608 | 0.508089 | True | 137985 | 136146 | -1.3 | 1313.9 | 1179.4 | -134.5 | 0.090930 | 0.336023 | 0.372157 | 0.200891 | 13.366016 |
21 | 13020 | Bay City, MI Metro Area | 26797 | 23735 | 50532 | 0.530298 | True | 110157 | 107771 | -2.2 | 1920.5 | 1780.5 | -140.0 | 0.081664 | 0.328151 | 0.384856 | 0.205329 | 13.427702 |
22 | 13140 | Beaumont-Port Arthur, TX Metro Area | 54781 | 84319 | 139100 | 0.393825 | False | 385090 | 388745 | 0.9 | 1779.2 | 1711.2 | -68.0 | 0.148767 | 0.349531 | 0.338327 | 0.163374 | 13.032617 |
23 | 13380 | Bellingham, WA Metro Area | 49743 | 38764 | 88507 | 0.562023 | True | 166814 | 201140 | 20.6 | 1678.6 | 1775.1 | 96.4 | 0.077640 | 0.212026 | 0.380249 | 0.330084 | 13.925554 |
24 | 13460 | Bend, OR Metro Area | 34985 | 40670 | 75655 | 0.462428 | False | 115367 | 157733 | 36.7 | 960.6 | 1154.6 | 194.0 | 0.061335 | 0.219531 | 0.407330 | 0.311804 | 13.939205 |
25 | 13740 | Billings, MT Metro Area | 28401 | 43934 | 72335 | 0.392632 | False | 138904 | 158050 | 13.8 | 2148.5 | 2109.9 | -38.6 | 0.057420 | 0.294385 | 0.336392 | 0.311803 | 13.805158 |
26 | 13780 | Binghamton, NY Metro Area | 46578 | 46156 | 92734 | 0.502275 | True | 252320 | 251725 | -0.2 | 2141.3 | 2226.1 | 84.8 | 0.079017 | 0.313136 | 0.328974 | 0.278872 | 13.615401 |
27 | 13820 | Birmingham-Hoover, AL Metro Area | 200495 | 304072 | 504567 | 0.397361 | False | 1052238 | 1128047 | 7.2 | 1508.5 | 1314.2 | -194.4 | 0.125090 | 0.260453 | 0.323972 | 0.290486 | 13.559707 |
28 | 13900 | Bismarck, ND Metro Area | 18503 | 36480 | 54983 | 0.336522 | False | 94719 | 108779 | 14.8 | 2066.4 | 1802.5 | -263.9 | 0.029533 | 0.245902 | 0.384064 | 0.340501 | 14.071067 |
29 | 13980 | Blacksburg-Christiansburg-Radford, VA Metro Area | 30652 | 36103 | 66755 | 0.459172 | False | 151272 | 162958 | 7.7 | 1199.6 | 1228.1 | 28.5 | 0.093393 | 0.263142 | 0.295922 | 0.347543 | 13.795227 |
30 | 14020 | Bloomington, IN Metro Area | 30048 | 29773 | 59821 | 0.502299 | True | 175506 | 192714 | 9.8 | 2082.9 | 2331.1 | 248.2 | 0.081357 | 0.283851 | 0.293108 | 0.341683 | 13.790234 |
31 | 14060 | Bloomington-Normal, IL Metro Area | 31590 | 39638 | 71228 | 0.443505 | False | 150433 | 169572 | 12.7 | 3279.9 | 3046.9 | -233.0 | 0.043534 | 0.233425 | 0.279048 | 0.443992 | 14.246997 |
32 | 14260 | Boise City-Nampa, ID Metro Area | 100652 | 152229 | 252881 | 0.398021 | False | 464840 | 616561 | 32.6 | 2373.5 | 2310.2 | -63.2 | 0.090095 | 0.242154 | 0.361167 | 0.306583 | 13.768476 |
33 | 14540 | Bowling Green, KY Metro Area | 18179 | 29616 | 47795 | 0.380354 | False | 104166 | 125953 | 20.9 | 1548.7 | 1730.3 | 181.5 | 0.121359 | 0.314346 | 0.288050 | 0.276245 | 13.438362 |
34 | 14740 | Bremerton-Silverdale, WA Metro Area | 59701 | 47468 | 107169 | 0.557073 | True | 231969 | 251133 | 8.3 | 1953.4 | 1835.9 | -117.5 | 0.056185 | 0.236956 | 0.419949 | 0.286909 | 13.875165 |
35 | 15180 | Brownsville-Harlingen, TX Metro Area | 49159 | 24955 | 74114 | 0.663289 | True | 335227 | 406220 | 21.2 | 3104.5 | 2842.9 | -261.6 | 0.322438 | 0.256246 | 0.262565 | 0.158751 | 12.515257 |
36 | 15380 | Buffalo-Niagara Falls, NY Metro Area | 261046 | 200075 | 461121 | 0.566112 | True | 1170111 | 1135509 | -3.0 | 4601.5 | 4129.4 | -472.1 | 0.074073 | 0.272227 | 0.331874 | 0.321826 | 13.802906 |
37 | 15500 | Burlington, NC Metro Area | 28341 | 37712 | 66053 | 0.429065 | False | 130800 | 151131 | 15.5 | 1096.7 | 1126.7 | 29.9 | 0.167674 | 0.275696 | 0.342321 | 0.214309 | 13.206530 |
38 | 15940 | Canton-Massillon, OH Metro Area | 91710 | 94135 | 185845 | 0.493476 | False | 406934 | 404422 | -0.6 | 2128.1 | 1919.9 | -208.1 | 0.080115 | 0.374432 | 0.323544 | 0.221909 | 13.374494 |
39 | 16020 | Cape Girardeau-Jackson, MO-IL Metro Area | 12887 | 30922 | 43809 | 0.294163 | False | 90312 | 96275 | 6.6 | 1014.5 | 1093.8 | 79.3 | 0.107983 | 0.340447 | 0.292986 | 0.258585 | 13.404345 |
40 | 16180 | Carson City, NV Metro Area | 10290 | 12394 | 22684 | 0.453624 | False | 52457 | 55274 | 5.4 | 3247.1 | 3285.4 | 38.3 | 0.136594 | 0.292510 | 0.373051 | 0.197845 | 13.264295 |
41 | 16300 | Cedar Rapids, IA Metro Area | 80392 | 59074 | 139466 | 0.576427 | True | 237230 | 257940 | 8.7 | 1874.6 | 1661.3 | -213.2 | 0.050499 | 0.253774 | 0.378006 | 0.317722 | 13.925901 |
42 | 16580 | Champaign-Urbana, IL Metro Area | 45113 | 44723 | 89836 | 0.502171 | True | 210275 | 231891 | 10.3 | 4043.9 | 4405.2 | 361.3 | 0.057510 | 0.222029 | 0.299165 | 0.421297 | 14.168497 |
43 | 16620 | Charleston, WV Metro Area | 45291 | 68670 | 113961 | 0.397425 | False | 309635 | 304284 | -1.7 | 1099.5 | 1033.7 | -65.8 | 0.118504 | 0.374394 | 0.269812 | 0.237290 | 13.251777 |
44 | 16740 | Charlotte-Gastonia-Rock Hill, NC-SC Metro Area | 374987 | 340025 | 715012 | 0.524449 | True | 1330448 | 1758038 | 32.1 | 1738.1 | 1881.3 | 143.2 | 0.110440 | 0.231132 | 0.306446 | 0.351982 | 13.799937 |
45 | 16820 | Charlottesville, VA Metro Area | 59319 | 44244 | 103563 | 0.572782 | True | 174021 | 201559 | 15.8 | 1979.4 | 2050.7 | 71.3 | 0.101875 | 0.220843 | 0.222572 | 0.454709 | 14.060230 |
46 | 16860 | Chattanooga, TN-GA Metro Area | 64234 | 89686 | 153920 | 0.417321 | False | 476531 | 528143 | 10.8 | 1095.4 | 1126.8 | 31.5 | 0.129820 | 0.300622 | 0.323799 | 0.245759 | 13.370993 |
47 | 16980 | Chicago-Joliet-Naperville, IL-IN-WI Metro Area | 2337624 | 1282464 | 3620088 | 0.645737 | True | 9098316 | 9461105 | 4.0 | 9829.6 | 8613.4 | NaN | 0.116868 | 0.234737 | 0.279802 | 0.368593 | 13.800239 |
48 | 17020 | Chico, CA Metro Area | 32567 | 35312 | 67879 | 0.479780 | False | 203171 | 220000 | 8.3 | 2124.2 | 2144.7 | 20.5 | 0.125288 | 0.217878 | 0.415499 | 0.241335 | 13.545763 |
49 | 17140 | Cincinnati-Middletown, OH-KY-IN Metro Area | 409059 | 578826 | 987885 | 0.414076 | False | 2009632 | 2130151 | 6.0 | 2864.8 | 2563.6 | -301.1 | 0.087554 | 0.300529 | 0.297424 | 0.314494 | 13.677717 |
50 | 17300 | Clarksville, TN-KY Metro Area | 36785 | 51117 | 87902 | 0.418477 | False | 232000 | 273949 | 18.1 | 1135.0 | 1151.8 | 16.7 | 0.093663 | 0.321000 | 0.375135 | 0.210202 | 13.403753 |
51 | 17420 | Cleveland, TN Metro Area | 9891 | 31525 | 41416 | 0.238821 | False | 104015 | 115788 | 11.3 | 766.7 | 811.3 | 44.6 | 0.146371 | 0.354450 | 0.317225 | 0.181954 | 13.069524 |
52 | 17460 | Cleveland-Elyria-Mentor, OH Metro Area | 611420 | 379288 | 990708 | 0.617155 | True | 2148143 | 2077240 | -3.3 | 4581.7 | 3808.4 | -773.3 | 0.086892 | 0.284001 | 0.322921 | 0.306186 | 13.696801 |
53 | 17660 | Coeur d'Alene, ID Metro Area | 18824 | 39347 | 58171 | 0.323598 | False | 108685 | 138494 | 27.4 | 1230.2 | 1357.8 | 127.6 | 0.062052 | 0.281993 | 0.423604 | 0.232352 | 13.652511 |
54 | 17780 | College Station-Bryan, TX Metro Area | 21937 | 46239 | 68176 | 0.321770 | False | 184885 | 228660 | 23.7 | 2660.7 | 2804.2 | 143.5 | 0.149706 | 0.240768 | 0.267306 | 0.342220 | 13.604079 |
55 | 17860 | Columbia, MO Metro Area | 41504 | 40355 | 81859 | 0.507018 | True | 145666 | 172786 | 18.6 | 1633.1 | 1630.5 | -2.6 | 0.054679 | 0.191586 | 0.251339 | 0.502396 | 14.402906 |
56 | 17980 | Columbus, GA-AL Metro Area | 10495 | 8276 | 18771 | 0.559107 | True | 281768 | 294865 | 4.6 | 1859.4 | 1680.8 | -178.5 | 0.123912 | 0.280777 | 0.368359 | 0.226952 | 13.396703 |
57 | 18020 | Columbus, IN Metro Area | 10622 | 18083 | 28705 | 0.370040 | False | 71435 | 76794 | 7.5 | 1086.6 | 1083.8 | -2.8 | 0.092038 | 0.310636 | 0.299088 | 0.298238 | 13.607050 |
58 | 18140 | Columbus, OH Metro Area | 453241 | 400196 | 853437 | 0.531077 | True | 1612694 | 1836536 | 13.9 | 3416.4 | 3186.0 | -230.4 | 0.083834 | 0.266212 | 0.294817 | 0.355137 | 13.842515 |
59 | 18580 | Corpus Christi, TX Metro Area | 56223 | 67602 | 123825 | 0.454052 | False | 403280 | 428185 | 6.2 | 3148.9 | 3204.0 | 55.1 | 0.175235 | 0.275434 | 0.353292 | 0.196039 | 13.140269 |
60 | 18700 | Corvallis, OR Metro Area | 27662 | 14922 | 42584 | 0.649587 | True | 78153 | 85579 | 9.5 | 2548.9 | 2653.2 | 104.4 | 0.058870 | 0.143737 | 0.298036 | 0.499357 | 14.475759 |
61 | 19060 | Cumberland, MD-WV Metro Area | 11837 | 25845 | 37682 | 0.314129 | False | 102008 | 103299 | 1.3 | 878.5 | 845.1 | -33.4 | 0.100424 | 0.441803 | 0.284850 | 0.172924 | 13.060545 |
62 | 19100 | Dallas-Fort Worth-Arlington, TX Metro Area | 896612 | 1202585 | 2099197 | 0.427121 | False | 5161544 | 6371773 | 23.4 | 4295.2 | 3909.3 | -385.9 | 0.156883 | 0.221216 | 0.298913 | 0.322988 | 13.576010 |
63 | 19180 | Danville, IL Metro Area | 10773 | 14888 | 25661 | 0.419820 | False | 83919 | 81625 | -2.7 | 745.1 | 684.6 | -60.5 | 0.121640 | 0.373678 | 0.360252 | 0.144430 | 13.054941 |
64 | 19260 | Danville, VA Metro Area | 23070 | 27031 | 50101 | 0.460470 | False | 110156 | 106561 | -3.3 | 965.6 | 794.0 | -171.5 | 0.152773 | 0.328045 | 0.353339 | 0.165843 | 13.064506 |
65 | 19340 | Davenport-Moline-Rock Island, IA-IL Metro Area | 106481 | 78549 | 185030 | 0.575480 | True | 376019 | 379690 | 1.0 | 2306.0 | 2218.6 | -87.4 | 0.080605 | 0.278257 | 0.371745 | 0.269393 | 13.659854 |
66 | 19380 | Dayton, OH Metro Area | 181747 | 216723 | 398470 | 0.456112 | False | 848153 | 841502 | -0.8 | 2565.7 | 2243.4 | -322.3 | 0.089714 | 0.293820 | 0.351436 | 0.265030 | 13.583564 |
67 | 19460 | Decatur, AL Metro Area | 18456 | 44164 | 62620 | 0.294730 | False | 145867 | 153829 | 5.5 | 793.8 | 818.7 | 24.9 | 0.169653 | 0.330969 | 0.312771 | 0.186607 | 13.032665 |
68 | 19500 | Decatur, IL Metro Area | 22688 | 25249 | 47937 | 0.473288 | False | 114706 | 110768 | -3.4 | 1978.6 | 1730.3 | -248.3 | 0.102106 | 0.304768 | 0.343716 | 0.249410 | 13.480861 |
69 | 19780 | Des Moines-West Des Moines, IA Metro Area | 163324 | 137637 | 300961 | 0.542675 | True | 481394 | 569633 | 18.3 | 2576.4 | 2375.0 | -201.4 | 0.067031 | 0.240071 | 0.323213 | 0.369686 | 13.991108 |
70 | 19820 | Detroit-Warren-Livonia, MI Metro Area | 1242117 | 824814 | 2066931 | 0.600947 | True | 4452557 | 4296250 | -3.5 | 4534.2 | 3800.4 | -733.9 | 0.096636 | 0.263540 | 0.342266 | 0.297558 | 13.681493 |
71 | 20020 | Dothan, AL Metro Area | 17433 | 43992 | 61425 | 0.283810 | False | 130861 | 145639 | 11.3 | 601.9 | 623.4 | 21.5 | 0.149552 | 0.348941 | 0.304949 | 0.196558 | 13.097026 |
72 | 20100 | Dover, DE Metro Area | 35525 | 32134 | 67659 | 0.525059 | True | 126697 | 162310 | 28.1 | 828.1 | 912.7 | 84.6 | 0.122355 | 0.332810 | 0.328434 | 0.216402 | 13.277765 |
73 | 20220 | Dubuque, IA Metro Area | 28600 | 21265 | 49865 | 0.573549 | True | 89143 | 93653 | 5.1 | 2466.3 | 2242.7 | -223.6 | 0.053686 | 0.323469 | 0.316160 | 0.306685 | 13.751687 |
74 | 20260 | Duluth, MN-WI Metro Area | 100421 | 53959 | 154380 | 0.650479 | True | 275486 | 279771 | 1.6 | 1428.7 | 1377.1 | -51.6 | 0.048860 | 0.279888 | 0.416194 | 0.255058 | 13.754901 |
75 | 20500 | Durham-Chapel Hill, NC Metro Area | 189225 | 81769 | 270994 | 0.698263 | True | 426493 | 504357 | 18.3 | 1858.7 | 1860.4 | 1.7 | 0.110484 | 0.186388 | 0.251308 | 0.451820 | 14.088927 |
76 | 20740 | Eau Claire, WI Metro Area | 45829 | 38589 | 84418 | 0.542882 | True | 148337 | 161151 | 8.6 | 1408.6 | 1345.6 | -63.0 | 0.059887 | 0.292886 | 0.364729 | 0.282498 | 13.739676 |
77 | 20940 | El Centro, CA Metro Area | 17464 | 9545 | 27009 | 0.646599 | True | 142361 | 174528 | 22.6 | 3347.6 | 3062.6 | -285.1 | 0.302470 | 0.234479 | 0.318969 | 0.144082 | 12.609326 |
78 | 21060 | Elizabethtown, KY Metro Area | 16947 | 27268 | 44215 | 0.383286 | False | 107547 | 119736 | 11.3 | 839.2 | 841.3 | 2.1 | 0.094291 | 0.338180 | 0.359562 | 0.207968 | 13.362410 |
79 | 21140 | Elkhart-Goshen, IN Metro Area | 24376 | 42348 | 66724 | 0.365326 | False | 182791 | 197559 | 8.1 | 1551.7 | 1369.5 | -182.2 | 0.197272 | 0.358217 | 0.265362 | 0.179149 | 12.852776 |
80 | 21300 | Elmira, NY Metro Area | 15285 | 16207 | 31492 | 0.485361 | False | 91070 | 88830 | -2.5 | 2354.2 | 2162.6 | -191.5 | 0.093550 | 0.336521 | 0.351266 | 0.218663 | 13.390087 |
81 | 21340 | El Paso, TX Metro Area | 112273 | 56517 | 168790 | 0.665164 | True | 679622 | 800647 | 17.8 | 4521.0 | 4318.3 | -202.7 | 0.207791 | 0.245670 | 0.324428 | 0.222110 | 13.121716 |
82 | 21500 | Erie, PA Metro Area | 65136 | 46102 | 111238 | 0.585555 | True | 280843 | 280566 | -0.1 | 3402.8 | 3298.5 | -104.4 | 0.074909 | 0.392221 | 0.269365 | 0.263506 | 13.442933 |
83 | 21660 | Eugene-Springfield, OR Metro Area | 99891 | 60979 | 160870 | 0.620942 | True | 322959 | 351715 | 8.9 | 2558.7 | 2787.1 | 228.4 | 0.086618 | 0.236012 | 0.401969 | 0.275401 | 13.732308 |
84 | 21780 | Evansville, IN-KY Metro Area | 59765 | 85671 | 145436 | 0.410937 | False | 342815 | 358676 | 4.6 | 1785.4 | 1660.1 | -125.3 | 0.087123 | 0.335042 | 0.351410 | 0.226425 | 13.434272 |
85 | 22020 | Fargo, ND-MN Metro Area | 49842 | 49712 | 99554 | 0.500653 | True | 174367 | 208777 | 19.7 | 3050.3 | 2770.1 | -280.2 | 0.034665 | 0.198014 | 0.384253 | 0.383069 | 14.231449 |
86 | 22140 | Farmington, NM Metro Area | 15688 | 28779 | 44467 | 0.352801 | False | 113801 | 130044 | 14.3 | 763.9 | 831.1 | 67.2 | 0.152749 | 0.326224 | 0.366554 | 0.154472 | 13.045500 |
87 | 22180 | Fayetteville, NC Metro Area | 84906 | 56912 | 141818 | 0.598697 | True | 336609 | 366383 | 8.8 | 1765.2 | 1694.0 | -71.2 | 0.095209 | 0.259348 | 0.417470 | 0.227973 | 13.556412 |
88 | 22220 | Fayetteville-Springdale-Rogers, AR-MO Metro Area | 54299 | 103003 | 157302 | 0.345190 | False | 347045 | 463204 | 33.5 | 933.4 | 1114.8 | 181.4 | 0.154589 | 0.290937 | 0.270025 | 0.284449 | 13.368671 |
89 | 22380 | Flagstaff, AZ Metro Area | 26316 | 19368 | 45684 | 0.576044 | True | 116320 | 134421 | 15.6 | 1002.6 | 1348.4 | 345.7 | 0.110661 | 0.234348 | 0.341232 | 0.313759 | 13.716177 |
90 | 22420 | Flint, MI Metro Area | 128972 | 71807 | 200779 | 0.642358 | True | 436141 | 425790 | -2.4 | 2271.4 | 1814.2 | -457.3 | 0.082573 | 0.325609 | 0.392270 | 0.199547 | 13.417583 |
91 | 22520 | Florence-Muscle Shoals, AL Metro Area | 21664 | 37833 | 59497 | 0.364119 | False | 142950 | 147137 | 2.9 | 746.2 | 772.3 | 26.1 | 0.132374 | 0.338808 | 0.314556 | 0.214262 | 13.221412 |
92 | 22540 | Fond du Lac, WI Metro Area | 22356 | 30329 | 52685 | 0.424333 | False | 97296 | 101633 | 4.5 | 1773.6 | 1658.6 | -115.0 | 0.079673 | 0.365859 | 0.345411 | 0.209057 | 13.367703 |
93 | 22900 | Fort Smith, AR-OK Metro Area | 28531 | 69659 | 98190 | 0.290569 | False | 273170 | 298592 | 9.3 | 832.0 | 832.9 | 0.9 | 0.152696 | 0.351994 | 0.329047 | 0.166263 | 13.017752 |
94 | 23060 | Fort Wayne, IN Metro Area | 67688 | 103890 | 171578 | 0.394503 | False | 390156 | 416257 | 6.7 | 2155.0 | 1964.2 | -190.8 | 0.092088 | 0.302220 | 0.345120 | 0.260572 | 13.548352 |
95 | 23420 | Fresno, CA Metro Area | 85862 | 91143 | 177005 | 0.485082 | False | 799407 | 930450 | 16.4 | 4168.7 | 4216.1 | 47.4 | 0.261997 | 0.226484 | 0.317965 | 0.193554 | 12.886153 |
96 | 23460 | Gadsden, AL Metro Area | 12792 | 29102 | 41894 | 0.305342 | False | 103459 | 104430 | 0.9 | 702.6 | 664.7 | -37.9 | 0.171337 | 0.318575 | 0.364529 | 0.145560 | 12.968622 |
97 | 24020 | Glens Falls, NY Metro Area | 23708 | 22825 | 46533 | 0.509488 | True | 124345 | 128923 | 3.7 | 893.1 | 923.4 | 30.3 | 0.093455 | 0.369742 | 0.296362 | 0.240442 | 13.367582 |
98 | 24140 | Goldsboro, NC Metro Area | 23154 | 27536 | 50690 | 0.456776 | False | 113329 | 122623 | 8.2 | 646.5 | 592.8 | -53.7 | 0.158399 | 0.285725 | 0.398709 | 0.157167 | 13.109289 |
99 | 24220 | Grand Forks, ND-MN Metro Area | 20781 | 22659 | 43440 | 0.478384 | False | 97478 | 98461 | 1.0 | 1970.1 | 2043.9 | 73.9 | 0.055470 | 0.247741 | 0.377410 | 0.319379 | 13.921398 |
100 | 24340 | Grand Rapids-Wyoming, MI Metro Area | 165795 | 201229 | 367024 | 0.451728 | False | 740482 | 774160 | 4.5 | 2583.4 | 2330.9 | -252.5 | 0.095003 | 0.279702 | 0.330951 | 0.294344 | 13.649272 |
101 | 24500 | Great Falls, MT Metro Area | 14949 | 18039 | 32988 | 0.453165 | False | 80357 | 81327 | 1.2 | 1912.6 | 1857.1 | -55.5 | 0.078859 | 0.286167 | 0.398373 | 0.236600 | 13.585430 |
102 | 24580 | Green Bay, WI Metro Area | 76394 | 81175 | 157569 | 0.484829 | False | 282599 | 306241 | 8.4 | 2159.0 | 1869.1 | -289.9 | 0.077410 | 0.322011 | 0.339662 | 0.260916 | 13.568170 |
103 | 24660 | Greensboro-High Point, NC Metro Area | 176328 | 173914 | 350242 | 0.503446 | True | 643430 | 723801 | 12.5 | 1483.2 | 1507.7 | 24.5 | 0.128708 | 0.276423 | 0.314657 | 0.280212 | 13.492746 |
104 | 24780 | Greenville, NC Metro Area | 44462 | 39927 | 84389 | 0.526870 | True | 152772 | 189510 | 24.0 | 1275.3 | 1380.9 | 105.5 | 0.125124 | 0.247403 | 0.343587 | 0.283886 | 13.572470 |
105 | 25060 | Gulfport-Biloxi, MS Metro Area | 27915 | 56429 | 84344 | 0.330966 | False | 246190 | 248820 | 1.1 | 1352.8 | 1092.3 | -260.5 | 0.137683 | 0.280542 | 0.374844 | 0.206932 | 13.302049 |
106 | 25180 | Hagerstown-Martinsburg, MD-WV Metro Area | 39765 | 60577 | 100342 | 0.396295 | False | 222771 | 269140 | 20.8 | 1418.0 | 1299.6 | -118.4 | 0.118794 | 0.377580 | 0.300253 | 0.203373 | 13.176408 |
107 | 25260 | Hanford-Corcoran, CA Metro Area | 12282 | 17184 | 29466 | 0.416819 | False | 129461 | 152982 | 18.2 | 2761.1 | 2996.2 | 235.1 | 0.284896 | 0.253604 | 0.334736 | 0.126764 | 12.606736 |
108 | 25420 | Harrisburg-Carlisle, PA Metro Area | 113622 | 133714 | 247336 | 0.459383 | False | 509074 | 549475 | 7.9 | 2480.3 | 2446.0 | -34.3 | 0.079071 | 0.342555 | 0.268190 | 0.310184 | 13.618976 |
109 | 25500 | Harrisonburg, VA Metro Area | 18709 | 30737 | 49446 | 0.378372 | False | 108193 | 125228 | 15.7 | 1525.4 | 1861.9 | 336.5 | 0.156985 | 0.336571 | 0.211417 | 0.295027 | 13.288973 |
110 | 25620 | Hattiesburg, MS Metro Area | 18635 | 36921 | 55556 | 0.335427 | False | 123812 | 142842 | 15.4 | 1010.0 | 828.0 | -182.0 | 0.127205 | 0.272682 | 0.328380 | 0.271733 | 13.489281 |
111 | 25860 | Hickory-Lenoir-Morganton, NC Metro Area | 52917 | 101771 | 154688 | 0.342089 | False | 341851 | 365497 | 6.9 | 581.5 | 578.6 | -2.9 | 0.172584 | 0.313662 | 0.328289 | 0.185464 | 13.053267 |
112 | 26100 | Holland-Grand Haven, MI Metro Area | 43068 | 88503 | 131571 | 0.327337 | False | 238314 | 263801 | 10.7 | 1547.9 | 1539.7 | -8.2 | 0.066765 | 0.288410 | 0.331624 | 0.313201 | 13.782522 |
113 | 26180 | Honolulu, HI Metro Area | 201803 | 87255 | 289058 | 0.698140 | True | 876156 | 953207 | 8.8 | 10977.5 | 11548.2 | 570.7 | 0.068770 | 0.248472 | 0.348207 | 0.334550 | 13.897076 |
114 | 26300 | Hot Springs, AR Metro Area | 13317 | 25031 | 38348 | 0.347267 | False | 88068 | 96024 | 9.0 | 638.5 | 627.2 | -11.3 | 0.118080 | 0.318088 | 0.360085 | 0.203747 | 13.299000 |
115 | 26380 | Houma-Bayou Cane-Thibodaux, LA Metro Area | 21614 | 57832 | 79446 | 0.272059 | False | 194477 | 208178 | 7.0 | 971.8 | 952.6 | -19.2 | 0.232170 | 0.395914 | 0.228244 | 0.143672 | 12.566836 |
116 | 26420 | Houston-Sugar Land-Baytown, TX Metro Area | 811874 | 1032582 | 1844456 | 0.440170 | False | 4715407 | 5946800 | 26.1 | 4258.4 | 4109.6 | -148.8 | 0.183387 | 0.233254 | 0.287666 | 0.295693 | 13.391331 |
117 | 26580 | Huntington-Ashland, WV-KY-OH Metro Area | 42552 | 60561 | 103113 | 0.412673 | False | 288649 | 287702 | -0.3 | 1447.2 | 1376.7 | -70.4 | 0.120083 | 0.371982 | 0.306395 | 0.201541 | 13.178787 |
118 | 26620 | Huntsville, AL Metro Area | 71611 | 115942 | 187553 | 0.381817 | False | 342376 | 417593 | 22.0 | 1199.4 | 1182.5 | -16.9 | 0.104334 | 0.224449 | 0.310229 | 0.360988 | 13.855742 |
119 | 26820 | Idaho Falls, ID Metro Area | 11232 | 42231 | 53463 | 0.210089 | False | 101677 | 130374 | 28.2 | 1678.8 | 1690.7 | 11.9 | 0.089599 | 0.261204 | 0.384757 | 0.264440 | 13.648077 |
120 | 26900 | Indianapolis-Carmel, IN Metro Area | 335963 | 395769 | 731732 | 0.459134 | False | 1525104 | 1756241 | 15.2 | 2493.0 | 2285.6 | -207.3 | 0.101529 | 0.274315 | 0.289820 | 0.334335 | 13.713924 |
121 | 26980 | Iowa City, IA Metro Area | 55171 | 29096 | 84267 | 0.654717 | True | 131676 | 152586 | 15.9 | 2849.6 | 2707.7 | -141.8 | 0.050289 | 0.175730 | 0.277412 | 0.496568 | 14.440519 |
122 | 27060 | Ithaca, NY Metro Area | 24447 | 10202 | 34649 | 0.705561 | True | 96501 | 101564 | 5.2 | 2788.7 | 3030.8 | 242.1 | 0.051408 | 0.158327 | 0.256976 | 0.533289 | 14.544293 |
123 | 27100 | Jackson, MI Metro Area | 32301 | 36298 | 68599 | 0.470867 | False | 158422 | 160248 | 1.2 | 1422.5 | 1462.8 | 40.2 | 0.085940 | 0.331178 | 0.386245 | 0.196637 | 13.387158 |
124 | 27140 | Jackson, MS Metro Area | 117583 | 114413 | 231996 | 0.506832 | True | 497197 | 539057 | 8.4 | 1435.9 | 1277.9 | -157.9 | 0.111891 | 0.251090 | 0.331809 | 0.305210 | 13.660676 |
125 | 27180 | Jackson, TN Metro Area | 19982 | 26661 | 46643 | 0.428403 | False | 107377 | 115425 | 7.5 | 999.1 | 943.8 | -55.2 | 0.116725 | 0.349045 | 0.295001 | 0.239230 | 13.313472 |
126 | 27340 | Jacksonville, NC Metro Area | 18115 | 31695 | 49810 | 0.363682 | False | 150355 | 177772 | 18.2 | 1023.9 | 962.8 | -61.1 | 0.072658 | 0.284516 | 0.456141 | 0.186684 | 13.513703 |
127 | 27500 | Janesville, WI Metro Area | 49158 | 30492 | 79650 | 0.617175 | True | 152307 | 160331 | 5.3 | 2019.8 | 1894.7 | -125.2 | 0.119942 | 0.324632 | 0.348859 | 0.206567 | 13.284100 |
128 | 27620 | Jefferson City, MO Metro Area | 21153 | 46263 | 67416 | 0.313768 | False | 140052 | 149807 | 7.0 | 587.7 | 522.7 | -64.9 | 0.099899 | 0.332254 | 0.294307 | 0.273541 | 13.482979 |
129 | 27740 | Johnson City, TN Metro Area | 21013 | 53311 | 74324 | 0.282722 | False | 181607 | 198716 | 9.4 | 836.3 | 879.1 | 42.8 | 0.129053 | 0.315830 | 0.289324 | 0.265793 | 13.383713 |
130 | 27780 | Johnstown, PA Metro Area | 23181 | 33464 | 56645 | 0.409233 | False | 152598 | 143679 | -5.8 | 1491.0 | 1322.1 | -168.9 | 0.071119 | 0.441415 | 0.277588 | 0.209879 | 13.252453 |
131 | 27860 | Jonesboro, AR Metro Area | 12738 | 25011 | 37749 | 0.337439 | False | 107762 | 121026 | 12.3 | 797.4 | 856.9 | 59.5 | 0.133656 | 0.346992 | 0.297968 | 0.221384 | 13.214159 |
132 | 27900 | Joplin, MO Metro Area | 19233 | 49524 | 68757 | 0.279724 | False | 157322 | 175518 | 11.6 | 866.1 | 872.5 | 6.3 | 0.143619 | 0.314575 | 0.324901 | 0.216905 | 13.230185 |
133 | 28020 | Kalamazoo-Portage, MI Metro Area | 85333 | 68800 | 154133 | 0.553632 | True | 314866 | 326589 | 3.7 | 1716.9 | 1603.7 | -113.2 | 0.077227 | 0.243993 | 0.362155 | 0.316625 | 13.836356 |
134 | 28100 | Kankakee-Bradley, IL Metro Area | 21574 | 23115 | 44689 | 0.482759 | False | 103833 | 113449 | 9.3 | 1591.6 | 1528.5 | -63.1 | 0.118071 | 0.312665 | 0.381972 | 0.187292 | 13.276967 |
135 | 28140 | Kansas City, MO-KS Metro Area | 442376 | 471042 | 913418 | 0.484308 | False | 1836038 | 2035334 | 10.9 | 2505.8 | 2326.1 | -179.7 | 0.083639 | 0.242955 | 0.321724 | 0.351682 | 13.882897 |
136 | 28420 | Kennewick-Pasco-Richland, WA Metro Area | 34722 | 60762 | 95484 | 0.363642 | False | 191822 | 253340 | 32.1 | 2039.9 | 2212.4 | 172.5 | 0.163809 | 0.237992 | 0.351088 | 0.247112 | 13.363007 |
137 | 28660 | Killeen-Temple-Fort Hood, TX Metro Area | 41913 | 66145 | 108058 | 0.387875 | False | 330714 | 405300 | 22.6 | 2020.9 | 2001.8 | -19.2 | 0.095046 | 0.297603 | 0.406107 | 0.201244 | 13.427100 |
138 | 28700 | Kingsport-Bristol-Bristol, TN-VA Metro Area | 32347 | 88260 | 120607 | 0.268202 | False | 298484 | 309544 | 3.7 | 626.6 | 633.5 | 6.8 | 0.133760 | 0.363642 | 0.301130 | 0.201467 | 13.140610 |
139 | 28740 | Kingston, NY Metro Area | 43164 | 27340 | 70504 | 0.612221 | True | 177749 | 182493 | 2.7 | 909.3 | 958.6 | 49.3 | 0.100017 | 0.276014 | 0.314776 | 0.309193 | 13.666289 |
140 | 28940 | Knoxville, TN Metro Area | 87162 | 181748 | 268910 | 0.324131 | False | 616079 | 698030 | 13.3 | 1282.3 | 1343.3 | 60.9 | 0.096875 | 0.291514 | 0.303321 | 0.308290 | 13.646054 |
141 | 29020 | Kokomo, IN Metro Area | 17529 | 25057 | 42586 | 0.411614 | False | 101541 | 98688 | -2.8 | 1577.7 | 1480.3 | -97.4 | 0.088255 | 0.366982 | 0.342850 | 0.201913 | 13.316841 |
142 | 29100 | La Crosse, WI-MN Metro Area | 41913 | 30662 | 72575 | 0.577513 | True | 126838 | 133665 | 5.4 | 1959.7 | 1877.9 | -81.8 | 0.040121 | 0.268085 | 0.375687 | 0.316108 | 13.935563 |
143 | 29140 | Lafayette, IN Metro Area | 30432 | 36030 | 66462 | 0.457886 | False | 178541 | 201789 | 13.0 | 3417.9 | 3227.7 | -190.2 | 0.086209 | 0.283959 | 0.285782 | 0.344050 | 13.775348 |
144 | 29180 | Lafayette, LA Metro Area | 41185 | 80642 | 121827 | 0.338061 | False | 239086 | 273738 | 14.5 | 1434.9 | 1524.5 | 89.6 | 0.125888 | 0.344889 | 0.274514 | 0.254709 | 13.316091 |
145 | 29340 | Lake Charles, LA Metro Area | 28765 | 55104 | 83869 | 0.342975 | False | 193568 | 199607 | 3.1 | 1359.2 | 1309.9 | -49.3 | 0.123748 | 0.367409 | 0.301555 | 0.207288 | 13.184765 |
146 | 29420 | Lake Havasu City-Kingman, AZ Metro Area | 18324 | 46442 | 64766 | 0.282926 | False | 155032 | 200186 | 29.1 | 1134.6 | 1143.6 | 9.0 | 0.160259 | 0.318619 | 0.400749 | 0.120372 | 12.962469 |
147 | 29540 | Lancaster, PA Metro Area | 87108 | 129364 | 216472 | 0.402398 | False | 470658 | 519445 | 10.4 | 2869.5 | 2940.2 | 70.8 | 0.136783 | 0.380693 | 0.231350 | 0.251174 | 13.193829 |
148 | 29620 | Lansing-East Lansing, MI Metro Area | 127002 | 92188 | 219190 | 0.579415 | True | 447728 | 464036 | 3.6 | 3259.9 | 3222.2 | -37.7 | 0.068769 | 0.227032 | 0.378293 | 0.325906 | 13.922673 |
149 | 29700 | Laredo, TX Metro Area | 37592 | 11074 | 48666 | 0.772449 | True | 193117 | 250304 | 29.6 | 5923.6 | 5300.1 | -623.5 | 0.315830 | 0.231332 | 0.276911 | 0.175926 | 12.625867 |
150 | 29740 | Las Cruces, NM Metro Area | 36778 | 26988 | 63766 | 0.576765 | True | 174682 | 209233 | 19.8 | 1824.5 | 1782.3 | -42.2 | 0.197369 | 0.224595 | 0.316628 | 0.261408 | 13.284151 |
151 | 29820 | Las Vegas-Paradise, NV Metro Area | 387978 | 288223 | 676201 | 0.573761 | True | 1375765 | 1951269 | 41.8 | 6781.8 | 6527.2 | -254.6 | 0.155833 | 0.293786 | 0.331123 | 0.219258 | 13.227614 |
152 | 29940 | Lawrence, KS Metro Area | 28012 | 17028 | 45040 | 0.621936 | True | 99962 | 110826 | 10.9 | 2963.5 | 2795.5 | -168.0 | 0.051574 | 0.192786 | 0.260640 | 0.495000 | 14.398133 |
153 | 30020 | Lawton, OK Metro Area | 12517 | 17657 | 30174 | 0.414827 | False | 114996 | 124098 | 7.9 | 1920.5 | 1830.3 | -90.2 | 0.092335 | 0.337814 | 0.367482 | 0.202369 | 13.359771 |
154 | 30140 | Lebanon, PA Metro Area | 18975 | 34010 | 52985 | 0.358120 | False | 120327 | 133568 | 11.0 | 2136.4 | 2053.6 | -82.8 | 0.110475 | 0.445355 | 0.231186 | 0.212983 | 13.093356 |
155 | 30300 | Lewiston, ID-WA Metro Area | 10444 | 15614 | 26058 | 0.400798 | False | 57961 | 60888 | 5.0 | 1431.3 | 1443.6 | 12.3 | 0.083831 | 0.292392 | 0.414700 | 0.209077 | 13.498047 |
156 | 30460 | Lexington-Fayette, KY Metro Area | 88797 | 109545 | 198342 | 0.447696 | False | 408326 | 472099 | 15.6 | 3041.9 | 2918.6 | -123.2 | 0.111108 | 0.243812 | 0.287951 | 0.357129 | 13.782202 |
157 | 30620 | Lima, OH Metro Area | 16869 | 28802 | 45671 | 0.369359 | False | 108473 | 106331 | -2.0 | 1688.3 | 1497.7 | -190.6 | 0.085593 | 0.392360 | 0.338492 | 0.183556 | 13.240021 |
158 | 30700 | Lincoln, NE Metro Area | 62332 | 66124 | 128456 | 0.485240 | False | 266787 | 302157 | 13.3 | 3939.9 | 3748.0 | -192.0 | 0.060857 | 0.205338 | 0.361877 | 0.371928 | 14.089753 |
159 | 30780 | Little Rock-North Little Rock-Conway, AR Metro... | 121748 | 153743 | 275491 | 0.441931 | False | 610518 | 699757 | 14.6 | 1455.4 | 1404.3 | -51.0 | 0.093959 | 0.296249 | 0.318923 | 0.290869 | 13.613401 |
160 | 30860 | Logan, UT-ID Metro Area | 325 | 5195 | 5520 | 0.058877 | False | 102720 | 125442 | 22.1 | 2110.4 | 1915.9 | -194.4 | 0.082302 | 0.225486 | 0.353240 | 0.338973 | 13.897767 |
161 | 30980 | Longview, TX Metro Area | 19740 | 54537 | 74277 | 0.265762 | False | 194042 | 214369 | 10.5 | 834.5 | 876.0 | 41.5 | 0.174978 | 0.295103 | 0.359825 | 0.170093 | 13.050067 |
162 | 31020 | Longview, WA Metro Area | 20897 | 19107 | 40004 | 0.522373 | True | 92948 | 102410 | 10.2 | 2036.9 | 2005.7 | -31.2 | 0.124275 | 0.294578 | 0.428447 | 0.152700 | 13.219144 |
163 | 31100 | Los Angeles-Long Beach-Santa Ana, CA Metro Area | 2129241 | 1241192 | 3370433 | 0.631741 | True | 12365627 | 12828837 | 3.7 | 12442.0 | 12113.9 | -328.1 | 0.207889 | 0.195260 | 0.275322 | 0.321528 | 13.420979 |
164 | 31140 | Louisville/Jefferson County, KY-IN Metro Area | 276264 | 293219 | 569483 | 0.485114 | False | 1161975 | 1283566 | 10.5 | 2618.8 | 2477.3 | -141.4 | 0.101122 | 0.300291 | 0.320301 | 0.278285 | 13.551498 |
165 | 31180 | Lubbock, TX Metro Area | 26727 | 64265 | 90992 | 0.293729 | False | 249700 | 284890 | 14.1 | 3107.3 | 3077.6 | -29.7 | 0.135585 | 0.254623 | 0.326282 | 0.283510 | 13.515434 |
166 | 31340 | Lynchburg, VA Metro Area | 43997 | 82452 | 126449 | 0.347943 | False | 228616 | 252634 | 10.5 | 741.4 | 808.3 | 66.9 | 0.113597 | 0.335088 | 0.307624 | 0.243691 | 13.362819 |
167 | 31460 | Madera-Chowchilla, CA Metro Area | 12698 | 18990 | 31688 | 0.400720 | False | 123109 | 150865 | 22.5 | 1894.9 | 2391.0 | 496.1 | 0.318071 | 0.230815 | 0.315252 | 0.135862 | 12.537811 |
168 | 31540 | Madison, WI Metro Area | 240646 | 100775 | 341421 | 0.704837 | True | 501774 | 568593 | 13.3 | 3728.7 | 3502.2 | -226.5 | 0.044871 | 0.199615 | 0.307233 | 0.448280 | 14.317846 |
169 | 31740 | Manhattan, KS Metro Area | 14036 | 22091 | 36127 | 0.388518 | False | 108999 | 127081 | 16.6 | 1786.3 | 1766.3 | -19.9 | 0.048614 | 0.222721 | 0.370729 | 0.357936 | 14.075974 |
170 | 31860 | Mankato-North Mankato, MN Metro Area | 27816 | 23130 | 50946 | 0.545990 | True | 85712 | 96740 | 12.9 | 1759.4 | 1933.4 | 174.0 | 0.044645 | 0.252731 | 0.373591 | 0.329032 | 13.974022 |
171 | 31900 | Mansfield, OH Metro Area | 21785 | 33057 | 54842 | 0.397232 | False | 128852 | 124475 | -3.4 | 1358.6 | 1105.9 | -252.7 | 0.112995 | 0.386160 | 0.340276 | 0.160569 | 13.096840 |
172 | 32580 | McAllen-Edinburg-Mission, TX Metro Area | 97879 | 39786 | 137665 | 0.710994 | True | 569463 | 774769 | 36.1 | 1863.9 | 2083.8 | 219.9 | 0.338360 | 0.251814 | 0.238821 | 0.171005 | 12.484941 |
173 | 32780 | Medford, OR Metro Area | 42341 | 46974 | 89315 | 0.474064 | False | 181269 | 203206 | 12.1 | 1946.1 | 1939.4 | -6.7 | 0.103290 | 0.272907 | 0.387613 | 0.236190 | 13.513405 |
174 | 32820 | Memphis, TN-MS-AR Metro Area | 293499 | 227181 | 520680 | 0.563684 | True | 1205204 | 1316100 | 9.2 | 2723.5 | 2372.3 | -351.3 | 0.119209 | 0.285065 | 0.325064 | 0.270662 | 13.494360 |
175 | 32900 | Merced, CA Metro Area | 26128 | 23403 | 49531 | 0.527508 | True | 210554 | 255793 | 21.5 | 2447.4 | 2455.0 | 7.7 | 0.316541 | 0.255532 | 0.304581 | 0.123346 | 12.469463 |
176 | 33140 | Michigan City-La Porte, IN Metro Area | 24104 | 18609 | 42713 | 0.564325 | True | 110106 | 111467 | 1.2 | 1257.5 | 1177.3 | -80.3 | 0.114020 | 0.375877 | 0.343816 | 0.166288 | 13.124745 |
177 | 33260 | Midland, TX Metro Area | 8223 | 35452 | 43675 | 0.188277 | False | 116009 | 136872 | 18.0 | 2535.7 | 2684.2 | 148.5 | 0.172840 | 0.231884 | 0.365188 | 0.230088 | 13.305048 |
178 | 33340 | Milwaukee-Waukesha-West Allis, WI Metro Area | 447918 | 410697 | 858615 | 0.521675 | True | 1500741 | 1555908 | 3.7 | 5454.8 | 5257.6 | -197.2 | 0.088384 | 0.253305 | 0.314605 | 0.343706 | 13.827266 |
179 | 33460 | Minneapolis-St. Paul-Bloomington, MN-WI Metro ... | 1029256 | 803497 | 1832753 | 0.561590 | True | 2968806 | 3279833 | 10.5 | 3617.3 | 3383.4 | -233.9 | 0.060399 | 0.204283 | 0.326666 | 0.408652 | 14.167142 |
180 | 33540 | Missoula, MT Metro Area | 34380 | 23285 | 57665 | 0.596202 | True | 95802 | 109299 | 14.1 | 1827.5 | 1908.0 | 80.5 | 0.034504 | 0.212188 | 0.347502 | 0.405807 | 14.249223 |
181 | 33660 | Mobile, AL Metro Area | 78417 | 94566 | 172983 | 0.453322 | False | 399843 | 412992 | 3.3 | 1890.9 | 1659.3 | -231.7 | 0.139078 | 0.316103 | 0.324864 | 0.219955 | 13.251390 |
182 | 33700 | Modesto, CA Metro Area | 70609 | 69549 | 140158 | 0.503781 | True | 446997 | 514453 | 15.1 | 4301.8 | 4322.7 | 21.0 | 0.218580 | 0.286012 | 0.329368 | 0.166040 | 12.885736 |
183 | 33740 | Monroe, LA Metro Area | 29704 | 48488 | 78192 | 0.379885 | False | 170053 | 176441 | 3.8 | 1085.9 | 995.8 | -90.0 | 0.142592 | 0.346133 | 0.297119 | 0.214156 | 13.165679 |
184 | 33780 | Monroe, MI Metro Area | 36310 | 35593 | 71903 | 0.504986 | True | 145945 | 152021 | 4.2 | 969.8 | 919.0 | -50.8 | 0.079649 | 0.349176 | 0.381604 | 0.189571 | 13.362194 |
185 | 33860 | Montgomery, AL Metro Area | 84034 | 83626 | 167660 | 0.501217 | True | 346528 | 374536 | 8.1 | 1603.8 | 1492.7 | -111.2 | 0.132634 | 0.283355 | 0.304873 | 0.279139 | 13.461034 |
186 | 34060 | Morgantown, WV Metro Area | 16580 | 24513 | 41093 | 0.403475 | False | 111200 | 129709 | 16.6 | 2156.1 | 2034.2 | -121.9 | 0.086276 | 0.346937 | 0.234277 | 0.332510 | 13.626042 |
187 | 34100 | Morristown, TN Metro Area | 11127 | 33021 | 44148 | 0.252039 | False | 123081 | 136608 | 11.0 | 511.9 | 554.2 | 42.3 | 0.172106 | 0.410787 | 0.266046 | 0.151061 | 12.792125 |
188 | 34580 | Mount Vernon-Anacortes, WA Metro Area | 21875 | 19418 | 41293 | 0.529751 | True | 102979 | 116901 | 13.5 | 1208.3 | 1286.5 | 78.2 | 0.107947 | 0.250776 | 0.407630 | 0.233648 | 13.533958 |
189 | 34620 | Muncie, IN Metro Area | 22630 | 21231 | 43861 | 0.515948 | True | 118769 | 117671 | -0.9 | 2219.5 | 2377.0 | 157.5 | 0.095452 | 0.338470 | 0.321974 | 0.244105 | 13.429461 |
190 | 34740 | Muskegon-Norton Shores, MI Metro Area | 44436 | 30882 | 75318 | 0.589978 | True | 170200 | 172188 | 1.2 | 1736.3 | 1563.9 | -172.4 | 0.103380 | 0.334608 | 0.384571 | 0.177442 | 13.272148 |
191 | 34900 | Napa, CA Metro Area | 19964 | 11647 | 31611 | 0.631552 | True | 124279 | 136484 | 9.8 | 3739.2 | 4045.9 | 306.7 | 0.186308 | 0.184906 | 0.320450 | 0.308336 | 13.501629 |
192 | 34980 | Nashville-Davidson--Murfreesboro--Franklin, TN... | 261550 | 364057 | 625607 | 0.418074 | False | 1311789 | 1589934 | 21.2 | 1660.6 | 1695.3 | 34.6 | 0.108614 | 0.279311 | 0.282466 | 0.329609 | 13.666139 |
193 | 35380 | New Orleans-Metairie-Kenner, LA Metro Area | 252643 | 253524 | 506167 | 0.499130 | False | 1316510 | 1167764 | -11.3 | 6118.9 | 4370.2 | NaN | 0.136166 | 0.283776 | 0.302023 | 0.278035 | 13.443854 |
194 | 35620 | New York-Northern New Jersey-Long Island, NY-N... | 3852335 | 2059511 | 5911846 | 0.651630 | True | 18323002 | 18897109 | 3.1 | 31683.6 | 31251.4 | -432.2 | 0.129354 | 0.243847 | 0.234864 | 0.391935 | 13.778762 |
195 | 35660 | Niles-Benton Harbor, MI Metro Area | 33465 | 38209 | 71674 | 0.466906 | False | 162453 | 156813 | -3.5 | 1149.5 | 1056.1 | -93.4 | 0.103681 | 0.301612 | 0.337027 | 0.257680 | 13.497411 |
196 | 36140 | Ocean City, NJ Metro Area | 21275 | 25488 | 46763 | 0.454954 | False | 102326 | 97265 | -4.9 | 1709.2 | 1424.6 | -284.6 | 0.092862 | 0.328773 | 0.270808 | 0.307557 | 13.586122 |
197 | 36220 | Odessa, TX Metro Area | 8095 | 23936 | 32031 | 0.252724 | False | 121123 | 137130 | 13.2 | 2484.8 | 2519.6 | 34.8 | 0.249028 | 0.288231 | 0.331665 | 0.131077 | 12.689579 |
198 | 36420 | Oklahoma City, OK Metro Area | 61156 | 142399 | 203555 | 0.300440 | False | 1095421 | 1252987 | 14.4 | 2588.7 | 2568.8 | -19.9 | 0.115840 | 0.262797 | 0.328757 | 0.292605 | 13.596256 |
199 | 36500 | Olympia, WA Metro Area | 64879 | 43703 | 108582 | 0.597512 | True | 207355 | 252264 | 21.7 | 1556.6 | 1702.9 | 146.3 | 0.052032 | 0.229579 | 0.391880 | 0.326510 | 13.985737 |
200 | 36540 | Omaha-Council Bluffs, NE-IA Metro Area | 159970 | 199693 | 359663 | 0.444777 | False | 767041 | 865350 | 12.8 | 3284.5 | 3138.1 | -146.4 | 0.078624 | 0.228108 | 0.339578 | 0.353691 | 13.936672 |
201 | 36780 | Oshkosh-Neenah, WI Metro Area | 45410 | 42073 | 87483 | 0.519072 | True | 156763 | 166994 | 6.5 | 2866.4 | 2701.8 | -164.6 | 0.068720 | 0.317610 | 0.348130 | 0.265541 | 13.620983 |
202 | 36980 | Owensboro, KY Metro Area | 19473 | 30009 | 49482 | 0.393537 | False | 109875 | 114752 | 4.4 | 1350.5 | 1347.4 | -3.1 | 0.101848 | 0.396077 | 0.315966 | 0.186108 | 13.172668 |
203 | 37100 | Oxnard-Thousand Oaks-Ventura, CA Metro Area | 138119 | 123919 | 262038 | 0.527095 | True | 753197 | 823318 | 9.3 | 5218.3 | 5542.2 | 323.9 | 0.172682 | 0.178690 | 0.330470 | 0.318159 | 13.588211 |
204 | 37620 | Parkersburg-Marietta-Vienna, WV-OH Metro Area | 24015 | 42128 | 66143 | 0.363077 | False | 164624 | 162056 | -1.6 | 1215.0 | 1135.9 | -79.1 | 0.083396 | 0.382533 | 0.351066 | 0.183004 | 13.267359 |
205 | 37700 | Pascagoula, MS Metro Area | 16804 | 41615 | 58419 | 0.287646 | False | 150564 | 162246 | 7.8 | 1007.2 | 862.8 | -144.4 | 0.122710 | 0.324880 | 0.362815 | 0.189595 | 13.238590 |
206 | 37900 | Peoria, IL Metro Area | 73546 | 89644 | 163190 | 0.450677 | False | 366899 | 379186 | 3.3 | 1862.2 | 1651.2 | -211.0 | 0.066741 | 0.294018 | 0.356551 | 0.282690 | 13.710381 |
207 | 37980 | Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Me... | 1754922 | 966623 | 2721545 | 0.644826 | True | 5687147 | 5965343 | 4.9 | 8064.3 | 7773.2 | -291.1 | 0.088707 | 0.292180 | 0.260006 | 0.359108 | 13.779030 |
208 | 38060 | Phoenix-Mesa-Glendale, AZ Metro Area | 553436 | 713782 | 1267218 | 0.436733 | False | 3251876 | 4192887 | 28.9 | 5028.4 | 4394.9 | -633.4 | 0.136396 | 0.228553 | 0.346807 | 0.288244 | 13.573796 |
209 | 38220 | Pine Bluff, AR Metro Area | 19725 | 14027 | 33752 | 0.584410 | True | 107341 | 100258 | -6.6 | 1021.7 | 847.3 | -174.5 | 0.141340 | 0.394656 | 0.302499 | 0.161504 | 12.968336 |
210 | 38300 | Pittsburgh, PA Metro Area | 545787 | 559798 | 1105585 | 0.493664 | False | 2431087 | 2356285 | -3.1 | 3290.0 | 2990.8 | -299.3 | 0.051924 | 0.319489 | 0.290632 | 0.337956 | 13.829239 |
211 | 38540 | Pocatello, ID Metro Area | 14196 | 22880 | 37076 | 0.382889 | False | 83103 | 90656 | 9.1 | 1730.3 | 1803.9 | 73.6 | 0.073882 | 0.281004 | 0.373244 | 0.271870 | 13.686205 |
212 | 38900 | Portland-Vancouver-Hillsboro, OR-WA Metro Area | 601791 | 369658 | 971449 | 0.619478 | True | 1927881 | 2226009 | 15.5 | 3994.4 | 4372.6 | 378.2 | 0.088416 | 0.210254 | 0.347512 | 0.353818 | 13.933464 |
213 | 39100 | Poughkeepsie-Newburgh-Middletown, NY Metro Area | 127594 | 113304 | 240898 | 0.529660 | True | 621517 | 670301 | 7.8 | 2078.5 | 2319.5 | 241.0 | 0.098064 | 0.262666 | 0.314530 | 0.324740 | 13.731891 |
214 | 39140 | Prescott, AZ Metro Area | 32624 | 62385 | 95009 | 0.343378 | False | 167517 | 211033 | 26.0 | 690.1 | 724.5 | 34.4 | 0.104864 | 0.267409 | 0.403822 | 0.223904 | 13.493532 |
215 | 39540 | Racine, WI Metro Area | 52887 | 49173 | 102060 | 0.518195 | True | 188831 | 195408 | 3.5 | 3151.6 | 2905.0 | -246.6 | 0.099867 | 0.307108 | 0.352435 | 0.240590 | 13.467494 |
216 | 39580 | Raleigh-Cary, NC Metro Area | 327308 | 295619 | 622927 | 0.525436 | True | 797071 | 1130490 | 41.8 | 1694.1 | 1850.1 | 156.1 | 0.090093 | 0.186015 | 0.290728 | 0.433164 | 14.133924 |
217 | 39660 | Rapid City, SD Metro Area | 18050 | 35789 | 53839 | 0.335259 | False | 112818 | 126382 | 12.0 | 1253.6 | 1170.8 | -82.8 | 0.073462 | 0.276567 | 0.380153 | 0.269818 | 13.692654 |
218 | 39740 | Reading, PA Metro Area | 79895 | 80857 | 160752 | 0.497008 | False | 373638 | 411442 | 10.1 | 4316.3 | 4656.8 | 340.5 | 0.125915 | 0.365747 | 0.254981 | 0.253356 | 13.271558 |
219 | 39820 | Redding, CA Metro Area | 18184 | 34561 | 52745 | 0.344753 | False | 163256 | 177223 | 8.6 | 1227.5 | 1334.2 | 106.8 | 0.107729 | 0.252293 | 0.443810 | 0.196168 | 13.456834 |
220 | 39900 | Reno-Sparks, NV Metro Area | 95726 | 89472 | 185198 | 0.516885 | True | 342885 | 425417 | 24.1 | 4268.8 | 3714.7 | -554.1 | 0.138357 | 0.237331 | 0.350073 | 0.274239 | 13.520387 |
221 | 40060 | Richmond, VA Metro Area | 336187 | 308208 | 644395 | 0.521710 | True | 1096957 | 1258251 | 14.7 | 2159.1 | 2175.7 | 16.6 | 0.113296 | 0.260603 | 0.286089 | 0.340012 | 13.705635 |
222 | 40140 | Riverside-San Bernardino-Ontario, CA Metro Area | 561957 | 535695 | 1097652 | 0.511963 | True | 3254821 | 4224851 | 29.8 | 4060.4 | 4299.6 | 239.2 | 0.208626 | 0.255236 | 0.343530 | 0.192609 | 13.040242 |
223 | 40220 | Roanoke, VA Metro Area | 61885 | 85662 | 147547 | 0.419426 | False | 288309 | 308707 | 7.1 | 1519.5 | 1483.4 | -36.1 | 0.105264 | 0.295380 | 0.313882 | 0.285474 | 13.559132 |
224 | 40340 | Rochester, MN Metro Area | 49240 | 48403 | 97643 | 0.504286 | True | 163618 | 186011 | 13.7 | 1663.0 | 1549.6 | -113.4 | 0.051365 | 0.210971 | 0.354380 | 0.383284 | 14.139167 |
225 | 40380 | Rochester, NY Metro Area | 231212 | 186368 | 417580 | 0.553695 | True | 1037831 | 1054323 | 1.6 | 3096.9 | 2909.0 | -187.9 | 0.084545 | 0.255542 | 0.315812 | 0.344101 | 13.838936 |
226 | 40420 | Rockford, IL Metro Area | 70202 | 64806 | 135008 | 0.519984 | True | 320204 | 349431 | 9.1 | 2592.2 | 2381.4 | -210.7 | 0.122510 | 0.332209 | 0.329210 | 0.216071 | 13.277683 |
227 | 40580 | Rocky Mount, NC Metro Area | 42463 | 32263 | 74726 | 0.568249 | True | 143026 | 152392 | 6.5 | 567.4 | 525.7 | -41.7 | 0.155924 | 0.349374 | 0.335828 | 0.158874 | 12.995306 |
228 | 40900 | Sacramento--Arden-Arcade--Roseville, CA Metro ... | 343078 | 300889 | 643967 | 0.532757 | True | 1796857 | 2149127 | 19.6 | 4584.3 | 4538.5 | -45.8 | 0.113690 | 0.204999 | 0.372665 | 0.308646 | 13.752535 |
229 | 40980 | Saginaw-Saginaw Township North, MI Metro Area | 54372 | 42716 | 97088 | 0.560028 | True | 210039 | 200169 | -4.7 | 1910.1 | 1601.2 | -308.9 | 0.101235 | 0.321575 | 0.368221 | 0.208969 | 13.369845 |
230 | 41060 | St. Cloud, MN Metro Area | 41725 | 53864 | 95589 | 0.436504 | False | 167392 | 189093 | 13.0 | 1231.3 | 1260.7 | 29.4 | 0.054567 | 0.278178 | 0.410757 | 0.256497 | 13.738370 |
231 | 41140 | St. Joseph, MO-KS Metro Area | 20322 | 29570 | 49892 | 0.407320 | False | 122336 | 127329 | 4.1 | 1609.9 | 1621.5 | 11.6 | 0.111487 | 0.382960 | 0.303095 | 0.202458 | 13.193046 |
232 | 41180 | St. Louis, MO-IL Metro Area | 711940 | 622117 | 1334057 | 0.533665 | True | 2698687 | 2812896 | 4.2 | 3015.9 | 2742.5 | -273.5 | 0.079379 | 0.256544 | 0.334408 | 0.329668 | 13.828729 |
233 | 41420 | Salem, OR Metro Area | 70098 | 75812 | 145910 | 0.480419 | False | 347214 | 390738 | 12.5 | 2660.4 | 2958.1 | 297.7 | 0.160829 | 0.254213 | 0.361548 | 0.223411 | 13.295080 |
234 | 41500 | Salinas, CA Metro Area | 51712 | 25087 | 76799 | 0.673342 | True | 401762 | 415057 | 3.3 | 6735.2 | 6402.3 | -332.9 | 0.308936 | 0.202308 | 0.272461 | 0.216294 | 12.792227 |
235 | 41540 | Salisbury, MD Metro Area | 21959 | 24842 | 46801 | 0.469199 | False | 109391 | 125203 | 14.5 | 1057.7 | 1089.8 | 32.1 | 0.139157 | 0.334192 | 0.273081 | 0.253570 | 13.282130 |
236 | 41660 | San Angelo, TX Metro Area | 9358 | 27417 | 36775 | 0.254466 | False | 105781 | 111823 | 5.7 | 1909.2 | 1905.7 | -3.5 | 0.148698 | 0.326161 | 0.308887 | 0.216253 | 13.185393 |
237 | 41700 | San Antonio-New Braunfels, TX Metro Area | 310563 | 365331 | 675894 | 0.459485 | False | 1711703 | 2142508 | 25.2 | 3570.4 | 3475.4 | -94.9 | 0.148868 | 0.253108 | 0.330056 | 0.267969 | 13.434252 |
238 | 41740 | San Diego-Carlsbad-San Marcos, CA Metro Area | 478386 | 431046 | 909432 | 0.526027 | True | 2813833 | 3095313 | 10.0 | 7094.4 | 6920.5 | -173.9 | 0.136432 | 0.186498 | 0.330293 | 0.346778 | 13.774835 |
239 | 41780 | Sandusky, OH Metro Area | 20969 | 16655 | 37624 | 0.557330 | True | 79551 | 77079 | -3.1 | 2019.8 | 1782.7 | -237.1 | 0.084344 | 0.390294 | 0.317312 | 0.208050 | 13.298137 |
240 | 41860 | San Francisco-Oakland-Fremont, CA Metro Area | 1161260 | 339122 | 1500382 | 0.773976 | True | 4123740 | 4335391 | 5.1 | 12438.4 | 12144.9 | -293.4 | 0.110650 | 0.163766 | 0.262930 | 0.462654 | 14.155177 |
241 | 41940 | San Jose-Sunnyvale-Santa Clara, CA Metro Area | 351856 | 142399 | 494255 | 0.711892 | True | 1735819 | 1836911 | 5.8 | 8300.4 | 8417.7 | 117.3 | 0.122293 | 0.152168 | 0.250334 | 0.475204 | 14.156899 |
242 | 42020 | San Luis Obispo-Paso Robles, CA Metro Area | 57655 | 56500 | 114155 | 0.505059 | True | 246681 | 269637 | 9.3 | 2352.4 | 2293.4 | -59.0 | 0.105677 | 0.198341 | 0.388413 | 0.307569 | 13.795747 |
243 | 42060 | Santa Barbara-Santa Maria-Goleta, CA Metro Area | 84279 | 59096 | 143375 | 0.587822 | True | 399347 | 423895 | 6.1 | 6041.4 | 6242.8 | 201.4 | 0.217592 | 0.173816 | 0.306379 | 0.302212 | 13.386424 |
244 | 42100 | Santa Cruz-Watsonville, CA Metro Area | 61106 | 17132 | 78238 | 0.781027 | True | 255602 | 262382 | 2.7 | 4479.6 | 4553.7 | 74.1 | 0.151527 | 0.150062 | 0.333661 | 0.364751 | 13.823272 |
245 | 42140 | Santa Fe, NM Metro Area | 49668 | 15145 | 64813 | 0.766328 | True | 129292 | 144170 | 11.5 | 1845.2 | 1770.8 | -74.4 | 0.140824 | 0.201445 | 0.286408 | 0.371322 | 13.776456 |
246 | 42220 | Santa Rosa-Petaluma, CA Metro Area | 124504 | 45666 | 170170 | 0.731645 | True | 458614 | 483878 | 5.5 | 3163.0 | 3253.6 | 90.6 | 0.135071 | 0.197645 | 0.355295 | 0.311988 | 13.688401 |
247 | 42540 | Scranton--Wilkes-Barre, PA Metro Area | 130095 | 98888 | 228983 | 0.568143 | True | 560625 | 563631 | 0.5 | 2954.4 | 2889.1 | -65.4 | 0.084136 | 0.364955 | 0.293491 | 0.257418 | 13.448384 |
248 | 42660 | Seattle-Tacoma-Bellevue, WA Metro Area | 950201 | 517758 | 1467959 | 0.647294 | True | 3043878 | 3439809 | 13.0 | 4474.3 | 4721.6 | 247.3 | 0.077441 | 0.205373 | 0.332532 | 0.384654 | 14.048796 |
249 | 43100 | Sheboygan, WI Metro Area | 28356 | 34282 | 62638 | 0.452696 | False | 112646 | 115507 | 2.5 | 2309.5 | 2088.2 | -221.3 | 0.078796 | 0.330789 | 0.338292 | 0.252123 | 13.527481 |
250 | 43300 | Sherman-Denison, TX Metro Area | 10665 | 30907 | 41572 | 0.256543 | False | 110595 | 120877 | 9.3 | 769.1 | 722.4 | -46.7 | 0.138292 | 0.304481 | 0.355199 | 0.202028 | 13.241926 |
251 | 43340 | Shreveport-Bossier City, LA Metro Area | 76386 | 94717 | 171103 | 0.446433 | False | 375965 | 398604 | 6.0 | 1711.8 | 1566.4 | -145.4 | 0.123727 | 0.330188 | 0.311325 | 0.234761 | 13.314239 |
252 | 43580 | Sioux City, IA-NE-SD Metro Area | 28509 | 31627 | 60136 | 0.474075 | False | 143053 | 143577 | 0.4 | 2218.8 | 2105.7 | -113.1 | 0.139705 | 0.300324 | 0.335894 | 0.224077 | 13.288684 |
253 | 43620 | Sioux Falls, SD Metro Area | 44966 | 58311 | 103277 | 0.435392 | False | 187093 | 228261 | 22.0 | 2247.0 | 2215.0 | -32.0 | 0.066449 | 0.281508 | 0.331974 | 0.320070 | 13.811328 |
254 | 43780 | South Bend-Mishawaka, IN-MI Metro Area | 66045 | 65235 | 131280 | 0.503085 | True | 316663 | 319224 | 0.8 | 2282.5 | 2015.6 | -266.9 | 0.100903 | 0.304990 | 0.326217 | 0.267890 | 13.522189 |
255 | 44060 | Spokane, WA Metro Area | 91977 | 104797 | 196774 | 0.467425 | False | 417939 | 471221 | 12.7 | 2936.4 | 2861.7 | -74.7 | 0.058239 | 0.237230 | 0.407349 | 0.297182 | 13.886947 |
256 | 44100 | Springfield, IL Metro Area | 44056 | 54075 | 98131 | 0.448951 | False | 201437 | 210170 | 4.3 | 1893.8 | 1719.1 | -174.7 | 0.064312 | 0.264218 | 0.334113 | 0.337357 | 13.889029 |
257 | 44180 | Springfield, MO Metro Area | 66048 | 129197 | 195245 | 0.338283 | False | 368374 | 436712 | 18.6 | 1489.9 | 1434.4 | -55.5 | 0.092728 | 0.286218 | 0.348960 | 0.272094 | 13.600839 |
258 | 44220 | Springfield, OH Metro Area | 30022 | 31116 | 61138 | 0.491053 | False | 144742 | 138333 | -4.4 | 2146.3 | 1928.1 | -218.2 | 0.118426 | 0.361748 | 0.332993 | 0.186832 | 13.176462 |
259 | 44300 | State College, PA Metro Area | 33677 | 33697 | 67374 | 0.499852 | False | 135758 | 153990 | 13.4 | 4106.6 | 4366.2 | 259.6 | 0.054689 | 0.298789 | 0.217584 | 0.428938 | 14.041541 |
260 | 44600 | Steubenville-Weirton, OH-WV Metro Area | 23515 | 28902 | 52417 | 0.448614 | False | 132008 | 124454 | -5.7 | 1108.4 | 1008.5 | -99.9 | 0.075449 | 0.412243 | 0.331172 | 0.181136 | 13.235990 |
261 | 44700 | Stockton, CA Metro Area | 80005 | 66742 | 146747 | 0.545190 | True | 563598 | 685306 | 21.6 | 5197.5 | 4889.1 | -308.4 | 0.215298 | 0.256879 | 0.342062 | 0.185761 | 12.996574 |
262 | 45060 | Syracuse, NY Metro Area | 147095 | 103921 | 251016 | 0.585999 | True | 650154 | 662577 | 1.9 | 2762.7 | 2749.3 | -13.4 | 0.087819 | 0.285791 | 0.313984 | 0.312406 | 13.701954 |
263 | 45460 | Terre Haute, IN Metro Area | 29507 | 34761 | 64268 | 0.459124 | False | 170943 | 172425 | 0.9 | 1245.1 | 1173.5 | -71.6 | 0.119093 | 0.359676 | 0.324554 | 0.196677 | 13.197629 |
264 | 45500 | Texarkana, TX-Texarkana, AR Metro Area | 14702 | 35462 | 50164 | 0.293079 | False | 129749 | 136027 | 4.8 | 946.9 | 921.6 | -25.3 | 0.113483 | 0.374862 | 0.338048 | 0.173607 | 13.143559 |
265 | 45780 | Toledo, OH Metro Area | 180829 | 118941 | 299770 | 0.603226 | True | 659188 | 651429 | -1.2 | 3099.2 | 2701.9 | -397.3 | 0.087293 | 0.305602 | 0.352926 | 0.254178 | 13.547980 |
266 | 45820 | Topeka, KS Metro Area | 43848 | 51915 | 95763 | 0.457880 | False | 224551 | 233870 | 4.2 | 1672.3 | 1595.3 | -76.9 | 0.078944 | 0.310521 | 0.324263 | 0.286272 | 13.635725 |
267 | 45940 | Trenton-Ewing, NJ Metro Area | 94300 | 43155 | 137455 | 0.686043 | True | 350761 | 366513 | 4.5 | 5783.5 | 5864.6 | 81.1 | 0.106446 | 0.254337 | 0.234204 | 0.405013 | 13.875569 |
268 | 46060 | Tucson, AZ Metro Area | 183470 | 162927 | 346397 | 0.529652 | True | 843746 | 980263 | 16.2 | 3416.7 | 3213.0 | -203.6 | 0.119172 | 0.219541 | 0.369899 | 0.291388 | 13.667007 |
269 | 46140 | Tulsa, OK Metro Area | 120590 | 235409 | 355999 | 0.338737 | False | 859532 | 937478 | 9.1 | 2053.7 | 1980.2 | -73.5 | 0.103006 | 0.284723 | 0.341287 | 0.270984 | 13.560500 |
270 | 46220 | Tuscaloosa, AL Metro Area | 41918 | 49707 | 91625 | 0.457495 | False | 192034 | 219461 | 14.3 | 1213.7 | 1308.2 | 94.5 | 0.123826 | 0.306443 | 0.301100 | 0.268630 | 13.429071 |
271 | 46340 | Tyler, TX Metro Area | 22101 | 61858 | 83959 | 0.263236 | False | 174706 | 209714 | 20.0 | 1206.5 | 1167.0 | -39.5 | 0.144735 | 0.250043 | 0.355179 | 0.250043 | 13.421062 |
272 | 46540 | Utica-Rome, NY Metro Area | 47821 | 54028 | 101849 | 0.469528 | False | 299896 | 299397 | -0.2 | 2080.8 | 2186.4 | 105.5 | 0.101976 | 0.302545 | 0.347535 | 0.247944 | 13.482891 |
273 | 46700 | Vallejo-Fairfield, CA Metro Area | 87225 | 48364 | 135589 | 0.643304 | True | 394542 | 413344 | 4.8 | 4883.4 | 4502.1 | -381.4 | 0.118616 | 0.240918 | 0.400078 | 0.240388 | 13.524476 |
274 | 47020 | Victoria, TX Metro Area | 12432 | 26123 | 38555 | 0.322448 | False | 111663 | 115384 | 3.3 | 1685.8 | 1729.1 | 43.3 | 0.174667 | 0.307767 | 0.361347 | 0.156219 | 12.998236 |
275 | 47220 | Vineland-Millville-Bridgeton, NJ Metro Area | 30172 | 18767 | 48939 | 0.616523 | True | 146438 | 156898 | 7.1 | 2181.9 | 2523.6 | 341.7 | 0.205449 | 0.394213 | 0.252037 | 0.148301 | 12.686382 |
276 | 47260 | Virginia Beach-Norfolk-Newport News, VA-NC Met... | 421746 | 338446 | 760192 | 0.554789 | True | 1576370 | 1671683 | 6.0 | 3589.3 | 4084.1 | 494.8 | 0.083585 | 0.250979 | 0.369294 | 0.296141 | 13.755982 |
277 | 47300 | Visalia-Porterville, CA Metro Area | 29191 | 42186 | 71377 | 0.408969 | False | 368021 | 442179 | 20.2 | 2708.7 | 2804.1 | 95.4 | 0.311333 | 0.241616 | 0.312089 | 0.134962 | 12.541358 |
278 | 47380 | Waco, TX Metro Area | 25688 | 47885 | 73573 | 0.349150 | False | 213517 | 234906 | 10.0 | 1891.2 | 1743.0 | -148.2 | 0.163835 | 0.266548 | 0.340790 | 0.228827 | 13.269220 |
279 | 47900 | Washington-Arlington-Alexandria, DC-VA-MD-WV M... | 1415293 | 751878 | 2167171 | 0.653060 | True | 4796183 | 5582170 | 16.4 | 6251.8 | 6388.1 | 136.3 | 0.089853 | 0.183638 | 0.235093 | 0.491416 | 14.256147 |
280 | 47940 | Waterloo-Cedar Falls, IA Metro Area | 48663 | 36666 | 85329 | 0.570298 | True | 163706 | 167819 | 2.5 | 2038.2 | 1947.3 | -90.8 | 0.082703 | 0.304255 | 0.327199 | 0.285842 | 13.632359 |
281 | 48140 | Wausau, WI Metro Area | 32330 | 36568 | 68898 | 0.469244 | False | 125834 | 134063 | 6.5 | 1007.9 | 912.3 | -95.6 | 0.065375 | 0.367574 | 0.329139 | 0.237912 | 13.479177 |
282 | 48300 | Wenatchee-East Wenatchee, WA Metro Area | 17566 | 27028 | 44594 | 0.393909 | False | 99219 | 110884 | 11.8 | 1365.5 | 1520.2 | 154.7 | 0.186815 | 0.277684 | 0.332528 | 0.202972 | 13.103315 |
283 | 48540 | Wheeling, WV-OH Metro Area | 23844 | 33203 | 57047 | 0.417971 | False | 153172 | 147950 | -3.4 | 978.5 | 842.0 | -136.5 | 0.086261 | 0.385651 | 0.322839 | 0.205248 | 13.294149 |
284 | 48620 | Wichita, KS Metro Area | 84276 | 136689 | 220965 | 0.381400 | False | 571166 | 623061 | 9.1 | 2368.7 | 2265.6 | -103.1 | 0.099484 | 0.257094 | 0.348598 | 0.294824 | 13.677524 |
285 | 48660 | Wichita Falls, TX Metro Area | 11755 | 37621 | 49376 | 0.238071 | False | 151524 | 151306 | -0.1 | 1592.3 | 1526.2 | -66.0 | 0.133156 | 0.326593 | 0.338620 | 0.201631 | 13.217452 |
286 | 48700 | Williamsport, PA Metro Area | 15104 | 30490 | 45594 | 0.331272 | False | 120044 | 116111 | -3.3 | 1864.8 | 1747.5 | -117.3 | 0.096242 | 0.412864 | 0.285823 | 0.205071 | 13.199446 |
287 | 48900 | Wilmington, NC Metro Area | 79425 | 101747 | 181172 | 0.438396 | False | 274532 | 362315 | 32.0 | 1073.7 | 1232.1 | 158.4 | 0.103960 | 0.239871 | 0.342155 | 0.314014 | 13.732445 |
288 | 49020 | Winchester, VA-WV Metro Area | 20066 | 33308 | 53374 | 0.375951 | False | 102997 | 128472 | 24.7 | 1030.1 | 1165.0 | 134.9 | 0.127309 | 0.339114 | 0.267346 | 0.266231 | 13.345000 |
289 | 49180 | Winston-Salem, NC Metro Area | 107155 | 121387 | 228542 | 0.468863 | False | 421961 | 477717 | 13.2 | 1134.4 | 1202.6 | 68.2 | 0.126417 | 0.279334 | 0.301605 | 0.292643 | 13.520950 |
290 | 49420 | Yakima, WA Metro Area | 25945 | 35381 | 61326 | 0.423067 | False | 222581 | 243231 | 9.3 | 2265.5 | 2377.1 | 111.5 | 0.283835 | 0.260865 | 0.298772 | 0.156527 | 12.655983 |
291 | 49620 | York-Hanover, PA Metro Area | 72126 | 111576 | 183702 | 0.392625 | False | 381751 | 434972 | 13.9 | 2244.0 | 2306.4 | 62.4 | 0.084914 | 0.407527 | 0.265592 | 0.241967 | 13.329223 |
292 | 49660 | Youngstown-Warren-Boardman, OH-PA Metro Area | 156826 | 103551 | 260377 | 0.602304 | True | 602964 | 565773 | -6.2 | 1841.7 | 1569.8 | -271.9 | 0.083712 | 0.400838 | 0.299440 | 0.216009 | 13.295492 |
293 | 49700 | Yuba City, CA Metro Area | 13821 | 21322 | 35143 | 0.393279 | False | 139149 | 166892 | 19.9 | 2073.0 | 2206.1 | 133.1 | 0.204681 | 0.236241 | 0.395122 | 0.163956 | 13.036707 |
294 | 49740 | Yuma, AZ Metro Area | 14025 | 19500 | 33525 | 0.418345 | False | 160026 | 195751 | 22.3 | 2982.2 | 2836.3 | -145.9 | 0.261426 | 0.224209 | 0.360208 | 0.154157 | 12.814191 |
css_styling()