hughdbrown · April 2, 2017 21:27 · hughdbrown · Apr 2, 2017
diff --git a/mobility.py b/mobility.py
 #!/usr/bin/env python
 from __future__ import print_function

 from re import compile
 from abc import abstractmethod

 from scipy.stats import spearmanr, pearsonr, linregress
 import numpy as np
 import matplotlib.pyplot as plt

 table_re1 = compile(r"""^\d+\s+(?P<city>[^,]+),""")
 table_re2 = compile(r"""^(?P<city>[^,\(]+)""")

 class MobileForeignAbstract(object):
    def __init__(self, mobile_data, foreign_data):
        self.mobile_cities, self.foreign_born_cities = mobile_data, foreign_data
    @abstractmethod
    def mobile(self):
        pass
    @abstractmethod
    def foreign(self):
        pass
    @abstractmethod
    def compare(self, shared, mobile, foreign):
        pass
    @abstractmethod
    def correlation(self, x, y):
        pass
    def graph(self, x, y):
        reg = linregress(x, y)
        slope, intercept = reg.slope, reg.intercept
        ybar = np.array([slope * xx + intercept for xx in x])

        plt.scatter(x, y)
        plt.plot(x, ybar)
        ax = plt.gca()
        ax.set_xlabel("Mobility")
        ax.set_ylabel("Foreign born")
        ax.set_title("Cities: mobility versus % foreign-born")
        regression = self.correlation(x, y)
        ax.text(min(x), max(y), r'correlation {0:.3f}'.format(regression))
        plt.show()

    def shared(self, mobile, foreign):
        # Find the 42 cities common to the two sets
        # Dropped out:
        #   ['Dayton', 'Bridgeport', 'Nashville', 'Providence', 'Grand Rapids',
        #   'Salt Lake City', 'Port St. Lucie', 'Manchester']
        return set(mobile).intersection(set(foreign))


 class MobileForeignOrdinal(MobileForeignAbstract):
    def mobile(self):
        return [
            table_re1.match(line).groupdict()["city"]
            for line in self.mobile_cities.splitlines()
        ]

    def foreign(self):
        return [
            table_re2.match(line).groupdict()["city"].strip()
            for line in self.foreign_born_cities.splitlines()
        ]

    def compare(self, shared, mobile, foreign):
        shared_mobile = [city for city in mobile if city in shared]
        shared_foreign = [city for city in foreign if city in shared]
        mapped = [
            (i, shared_foreign.index(city))
            for i, city in enumerate(shared_mobile)
        ]
        mapped = np.array(mapped)
        return mapped[:, 0], mapped[:, 1]

    def correlation(self, x, y):
        return spearmanr(x, y).correlation


 class MobileForeignNumeric(MobileForeignAbstract):
    def mobile(self):
        return {
            table_re1.match(line).groupdict()["city"]: float(line.split(' ')[-4])
            for line in self.mobile_cities.splitlines()
        }

    def foreign(self):
        return {
            table_re2.match(line).groupdict()["city"].strip(): float(line.split('\t')[-2])
            for line in self.foreign_born_cities.splitlines()
        }

    def compare(self, shared, mobile, foreign):
        mapped = {
            city: (mobile[city], foreign[city])
            for city in shared
        }
        mapped = np.array(mapped.values())
        return mapped[:, 0], mapped[:, 1]

    def correlation(self, x, y):
        return pearsonr(x, y)[0]

 # From http://www.rajchetty.com/chettyfiles/mobility_geo.pdf
 mobile_cities = """1 Salt Lake City, Utah 1,426,729 46.2 10.8 77.3 0.264
 2 Pittsburgh, Pennsylvania 2,561,364 45.2 9.5 74.9 0.359
 3 San Jose, California 2,393,183 44.7 12.9 73.5 0.235
 4 Boston, Massachusetts 4,974,945 44.6 10.5 73.7 0.322
 5 San Francisco, California 4,642,561 44.4 12.2 72.5 0.250
 6 San Diego, California 2,813,833 44.3 10.4 74.3 0.237
 7 Manchester, New Hampshire 1,193,391 44.2 10.0 75.0 0.296
 8 Minneapolis, Minnesota 2,904,389 44.2 8.5 75.2 0.338
 9 Newark, New Jersey 5,822,286 44.1 10.2 73.7 0.350
 10 New York, New York 11,781,395 43.8 10.5 72.2 0.330
 11 Los Angeles, California 16,393,360 43.4 9.6 73.8 0.231
 12 Providence, Rhode Island 1,582,997 43.4 8.2 73.6 0.333
 13 Washington, DC 4,632,415 43.2 11.0 72.2 0.330
 14 Seattle, Washington 3,775,744 43.2 10.9 72.0 0.273
 15 Houston, Texas 4,504,013 42.8 9.3 74.7 0.325
 16 Sacramento, California 2,570,609 42.7 9.7 71.3 0.257
 17 Bridgeport, Connecticut 3,405,565 42.4 7.9 72.4 0.359
 18 Fort Worth, Texas 1,804,370 42.3 9.1 73.6 0.320
 19 Denver, Colorado 2,449,044 42.2 8.7 73.3 0.294
 20 Buffalo, New York 2,369,699 42.0 6.7 73.1 0.368
 21 Miami, Florida 3,955,969 41.5 7.3 76.3 0.267
 22 Fresno, California 1,419,998 41.3 7.5 71.3 0.295
 23 Portland, Oregon 1,842,889 41.3 9.3 70.5 0.277
 24 San Antonio, Texas 1,724,863 41.1 6.4 74.3 0.320
 25 Philadelphia, Pennsylvania 5,602,247 40.8 7.4 69.6 0.393
 26 Austin, Texas 1,298,076 40.4 6.9 71.9 0.323
 27 Dallas, Texas 3,405,666 40.4 7.1 72.6 0.347
 28 Phoenix, Arizona 3,303,211 40.3 7.5 70.6 0.294
 29 Grand Rapids, Michigan 1,286,045 40.1 6.4 71.3 0.378
 30 Kansas City, Missouri 1,762,873 40.1 7.0 70.4 0.365
 31 Las Vegas, Nevada 1,568,418 40.0 8.0 71.1 0.259
 32 Chicago, Illinois 8,183,799 39.4 6.5 70.8 0.393
 33 Milwaukee, Wisconsin 1,660,659 39.3 4.5 70.3 0.424
 34 Tampa, Florida 2,395,997 39.1 6.0 71.3 0.335
 35 Orlando, Florida 1,697,906 39.1 5.8 71.5 0.326
 36 Port St. Lucie, Florida 1,533,306 39.0 6.2 71.2 0.303
 37 Baltimore, Maryland 2,512,431 38.8 6.4 67.7 0.412
 38 St. Louis, Missouri 2,325,609 38.4 5.1 69.0 0.413
 39 Dayton, Ohio 1,179,009 38.3 4.9 68.2 0.397
 40 Cleveland, Ohio 2,661,167 38.2 5.1 68.7 0.405
 41 Nashville, Tennessee 1,246,338 38.2 5.7 67.9 0.357
 42 New Orleans, Louisiana 1,381,652 38.2 5.1 69.5 0.397
 43 Cincinnati, Ohio 1,954,800 37.9 5.1 66.4 0.429
 44 Columbus, Ohio 1,663,807 37.7 4.9 67.1 0.406
 45 Jacksonville, Florida 1,176,696 37.5 4.9 68.9 0.361
 46 Detroit, Michigan 5,327,827 37.3 5.5 68.5 0.358
 47 Indianapolis, Indiana 1,507,346 37.2 4.9 67.5 0.398
 48 Raleigh, North Carolina 1,412,127 36.9 5.0 67.3 0.389
 49 Atlanta, Georgia 3,798,017 36.0 4.5 69.4 0.366
 50 Charlotte, North Carolina 1,423,942 35.8 4.4 67.0 0.397"""


 # From https://en.wikipedia.org/wiki/List_of_United_States_cities_by_foreign-born_population
 foreign_born_cities = """Hialeah, Florida	218,901	162,951	74.4	1
 Miami, Florida	433,143	244,352	56.4	2
 Santa Ana, California	340,378	166,960	49.1	3
 Fremont, California	205,521	88,211	42.9	4
 Los Angeles, California	3,831,880	1,521,119	39.7	5
 San Jose, California	964,679	367,711	38.1	6
 Anaheim, California	337,899	127,111	37.6	7
 New York, New York	8,391,881	2,996,580	35.7	8
 Irvine, California	209,707	74,225	35.4	9
 Jersey City, New Jersey	242,513	84,583	34.9	10
 Irving, Texas	205,549	70,263	34.2	11
 San Francisco, California	815,358	278,369	34.1	12
 Chula Vista, California	223,746	71,797	32.1	13
 Yonkers, New York	201,073	62,818	31.2	14
 Oakland, California	409,151	116,794	28.5	15
 Houston, Texas	2,260,918	644,167	28.5	16
 Laredo, Texas	226,419	62,653	27.7	17
 Stockton, California	287,584	79,511	27.6	18
 Garland, Texas	222,013	60,187	27.1	19
 Long Beach, California	462,594	124,340	26.9	20
 Newark, New Jersey	278,157	74,762	26.9	21
 Honolulu, Hawaii (CDP)	374,658	96,752	25.8	22
 Paradise, Nevada (CDP)	202,987	51,090	25.2	23
 Boston, Massachusetts	645,187	161,740	25.1	24
 San Diego, California	1,306,228	325,819	24.9	25
 North Las Vegas, Nevada	224,416	55,805	24.9	26
 Dallas, Texas	1,299,590	322,072	24.8	27
 El Paso, Texas	620,440	151,295	24.4	28
 Riverside, California	297,863	69,792	23.4	29
 Plano, Texas	273,381	62,217	22.8	30
 Aurora, Colorado	323,288	70,747	21.9	31
 Sacramento, California	466,685	102,076	21.9	32
 Arlington, Virginia (CDP)	217,483	47,379	21.8	33
 Phoenix, Arizona	1,593,660	346,430	21.7	34
 Las Vegas, Nevada	567,610	119,437	21	35
 Chicago, Illinois	2,850,502	588,480	20.6	36
 Fresno, California	479,911	97,316	20.3	37
 Austin, Texas	790,593	159,353	20.2	38
 Arlington, Texas	380,072	76,440	20.1	39
 Fort Worth, Texas	731,588	131,197	17.9	40
 Reno, Nevada	219,649	37,964	17.3	41
 Seattle, Washington	616,669	105,154	17.1	42
 Tucson, Arizona	543,907	90,794	16.7	43
 Orlando, Florida	235,876	39,210	16.6	44
 Bakersfield, California	324,479	53,480	16.5	45
 Denver, Colorado	610,345	95,585	15.7	46
 Minneapolis, Minnesota	385,384	59,093	15.3	47
 St. Paul, Minnesota	281,244	42,669	15.2	48
 Modesto, California	202,740	30,464	15	49
 Tampa, Florida	343,879	50,377	14.6	50
 Glendale, Arizona	253,210	36,197	14.3	51
 Charlotte, North Carolina	704,417	96,734	13.7	52
 San Antonio, Texas	1,373,677	180,895	13.2	53
 Durham, North Carolina	229,147	30,174	13.2	54
 Raleigh, North Carolina	405,197	53,154	13.1	55
 Scottsdale, Arizona	237,834	30,074	12.6	56
 Chandler, Arizona	249,515	31,551	12.6	57
 Portland, Oregon	566,606	71,380	12.6	58
 Mesa, Arizona	467,178	56,895	12.2	59
 Washington, D.C.	599,657	72,110	12	60
 Henderson, Nevada	256,424	30,462	11.9	61
 Nashville-Davidson, Tennessee	605,466	70,404	11.6	62
 Philadelphia, Pennsylvania	1,547,297	179,444	11.6	63
 Winston-Salem, North Carolina	229,826	26,064	11.3	64
 Oklahoma City, Oklahoma	560,226	63,199	11.3	65
 Albuquerque, New Mexico	529,216	57,298	10.8	66
 Columbus, Ohio	773,021	83,091	10.7	67
 Des Moines, Iowa	200,569	21,050	10.5	68
 Greensboro, North Carolina	255,141	26,410	10.4	69
 St. Petersburg, Florida	244,318	25,094	10.3	70
 Milwaukee, Wisconsin	605,027	59,785	9.9	71
 Anchorage, Alaska (municipality)	286,174	27,107	9.5	72
 Omaha, Nebraska	454,714	43,029	9.5	73
 Tulsa, Oklahoma	389,369	36,514	9.4	74
 Gilbert, Arizona (town)	222,092	20,606	9.3	75
 Jacksonville, Florida	813,518	73,992	9.1	76
 Wichita, Kansas	372,194	33,608	9	77
 Boise City, Idaho	205,698	18,221	8.9	78
 Madison, Wisconsin	235,410	20,736	8.8	79
 Virginia Beach, Virginia	433,575	36,727	8.5	80
 Corpus Christi, Texas	287,231	23,315	8.1	81
 Lexington-Fayette, Kentucky (urban county)	296,545	23,967	8.1	82
 Indianapolis (balance), Indiana	807,640	63,241	7.8	83
 Colorado Springs, Colorado	399,803	31,266	7.8	84
 Fort Wayne, Indiana	251,825	19,055	7.6	85
 Atlanta, Georgia	540,932	39,733	7.3	86
 Buffalo, New York	270,221	19,618	7.3	87
 Kansas City, Missouri	482,228	34,085	7.1	88
 Lincoln, Nebraska	254,008	17,128	6.7	89
 Detroit, Michigan	910,848	60,170	6.6	90
 Rochester, New York	207,291	13,454	6.5	91
 Baltimore, Maryland	637,418	41,343	6.5	92
 Pittsburgh, Pennsylvania	311,640	19,993	6.4	93
 Norfolk, Virginia	233,333	14,562	6.2	94
 Richmond, Virginia	204,451	12,301	6	95
 St. Louis, Missouri	356,587	21,177	5.9	96
 Spokane, Washington	203,268	11,562	5.7	97
 Louisville, Kentucky ("balance")	566,492	31,315	5.5	98
 Memphis, Tennessee	676,646	36,519	5.4	99
 New Orleans, Louisiana	354,850	18,968	5.3	100
 Lubbock, Texas	225,865	10,709	4.7	101
 Akron, Ohio	207,208	9,526	4.6	102
 Baton Rouge, Louisiana	225,388	10,235	4.5	103
 Cleveland, Ohio	431,369	19,495	4.5	104
 Chesapeake, Virginia	222,455	9,838	4.4	105
 Birmingham, Alabama	231,824	8,883	3.8	106
 Cincinnati, Ohio	333,013	11,471	3.4	107
 Toledo, Ohio	316,164	8,793	2.8	108
 Montgomery, Alabama	201,465	5,454	2.7	109"""


 def main(model):
    m = model(mobile_cities, foreign_born_cities)
    mobile = m.mobile()
    foreign = m.foreign()
    shared = m.shared(mobile, foreign)
    x, y = m.compare(shared, mobile, foreign)
    m.graph(x, y)


 if __name__ == '__main__':
    #main(MobileForeignOrdinal)
    main(MobileForeignNumeric)
	#!/usr/bin/env python
	from __future__ import print_function

	from re import compile
	from abc import abstractmethod

	from scipy.stats import spearmanr, pearsonr, linregress
	import numpy as np
	import matplotlib.pyplot as plt

	table_re1 = compile(r"""^\d+\s+(?P<city>[^,]+),""")
	table_re2 = compile(r"""^(?P<city>[^,\(]+)""")

	class MobileForeignAbstract(object):
	def __init__(self, mobile_data, foreign_data):
	self.mobile_cities, self.foreign_born_cities = mobile_data, foreign_data
	@abstractmethod
	def mobile(self):
	pass
	@abstractmethod
	def foreign(self):
	pass
	@abstractmethod
	def compare(self, shared, mobile, foreign):
	pass
	@abstractmethod
	def correlation(self, x, y):
	pass
	def graph(self, x, y):
	reg = linregress(x, y)
	slope, intercept = reg.slope, reg.intercept
	ybar = np.array([slope * xx + intercept for xx in x])

	plt.scatter(x, y)
	plt.plot(x, ybar)
	ax = plt.gca()
	ax.set_xlabel("Mobility")
	ax.set_ylabel("Foreign born")
	ax.set_title("Cities: mobility versus % foreign-born")
	regression = self.correlation(x, y)
	ax.text(min(x), max(y), r'correlation {0:.3f}'.format(regression))
	plt.show()

	def shared(self, mobile, foreign):
	# Find the 42 cities common to the two sets
	# Dropped out:
	# ['Dayton', 'Bridgeport', 'Nashville', 'Providence', 'Grand Rapids',
	# 'Salt Lake City', 'Port St. Lucie', 'Manchester']
	return set(mobile).intersection(set(foreign))


	class MobileForeignOrdinal(MobileForeignAbstract):
	def mobile(self):
	return [
	table_re1.match(line).groupdict()["city"]
	for line in self.mobile_cities.splitlines()
	]

	def foreign(self):
	return [
	table_re2.match(line).groupdict()["city"].strip()
	for line in self.foreign_born_cities.splitlines()
	]

	def compare(self, shared, mobile, foreign):
	shared_mobile = [city for city in mobile if city in shared]
	shared_foreign = [city for city in foreign if city in shared]
	mapped = [
	(i, shared_foreign.index(city))
	for i, city in enumerate(shared_mobile)
	]
	mapped = np.array(mapped)
	return mapped[:, 0], mapped[:, 1]

	def correlation(self, x, y):
	return spearmanr(x, y).correlation


	class MobileForeignNumeric(MobileForeignAbstract):
	def mobile(self):
	return {
	table_re1.match(line).groupdict()["city"]: float(line.split(' ')[-4])
	for line in self.mobile_cities.splitlines()
	}

	def foreign(self):
	return {
	table_re2.match(line).groupdict()["city"].strip(): float(line.split('\t')[-2])
	for line in self.foreign_born_cities.splitlines()
	}

	def compare(self, shared, mobile, foreign):
	mapped = {
	city: (mobile[city], foreign[city])
	for city in shared
	}
	mapped = np.array(mapped.values())
	return mapped[:, 0], mapped[:, 1]

	def correlation(self, x, y):
	return pearsonr(x, y)[0]

	# From http://www.rajchetty.com/chettyfiles/mobility_geo.pdf
	mobile_cities = """1 Salt Lake City, Utah 1,426,729 46.2 10.8 77.3 0.264
	2 Pittsburgh, Pennsylvania 2,561,364 45.2 9.5 74.9 0.359
	3 San Jose, California 2,393,183 44.7 12.9 73.5 0.235
	4 Boston, Massachusetts 4,974,945 44.6 10.5 73.7 0.322
	5 San Francisco, California 4,642,561 44.4 12.2 72.5 0.250
	6 San Diego, California 2,813,833 44.3 10.4 74.3 0.237
	7 Manchester, New Hampshire 1,193,391 44.2 10.0 75.0 0.296
	8 Minneapolis, Minnesota 2,904,389 44.2 8.5 75.2 0.338
	9 Newark, New Jersey 5,822,286 44.1 10.2 73.7 0.350
	10 New York, New York 11,781,395 43.8 10.5 72.2 0.330
	11 Los Angeles, California 16,393,360 43.4 9.6 73.8 0.231
	12 Providence, Rhode Island 1,582,997 43.4 8.2 73.6 0.333
	13 Washington, DC 4,632,415 43.2 11.0 72.2 0.330
	14 Seattle, Washington 3,775,744 43.2 10.9 72.0 0.273
	15 Houston, Texas 4,504,013 42.8 9.3 74.7 0.325
	16 Sacramento, California 2,570,609 42.7 9.7 71.3 0.257
	17 Bridgeport, Connecticut 3,405,565 42.4 7.9 72.4 0.359
	18 Fort Worth, Texas 1,804,370 42.3 9.1 73.6 0.320
	19 Denver, Colorado 2,449,044 42.2 8.7 73.3 0.294
	20 Buffalo, New York 2,369,699 42.0 6.7 73.1 0.368
	21 Miami, Florida 3,955,969 41.5 7.3 76.3 0.267
	22 Fresno, California 1,419,998 41.3 7.5 71.3 0.295
	23 Portland, Oregon 1,842,889 41.3 9.3 70.5 0.277
	24 San Antonio, Texas 1,724,863 41.1 6.4 74.3 0.320
	25 Philadelphia, Pennsylvania 5,602,247 40.8 7.4 69.6 0.393
	26 Austin, Texas 1,298,076 40.4 6.9 71.9 0.323
	27 Dallas, Texas 3,405,666 40.4 7.1 72.6 0.347
	28 Phoenix, Arizona 3,303,211 40.3 7.5 70.6 0.294
	29 Grand Rapids, Michigan 1,286,045 40.1 6.4 71.3 0.378
	30 Kansas City, Missouri 1,762,873 40.1 7.0 70.4 0.365
	31 Las Vegas, Nevada 1,568,418 40.0 8.0 71.1 0.259
	32 Chicago, Illinois 8,183,799 39.4 6.5 70.8 0.393
	33 Milwaukee, Wisconsin 1,660,659 39.3 4.5 70.3 0.424
	34 Tampa, Florida 2,395,997 39.1 6.0 71.3 0.335
	35 Orlando, Florida 1,697,906 39.1 5.8 71.5 0.326
	36 Port St. Lucie, Florida 1,533,306 39.0 6.2 71.2 0.303
	37 Baltimore, Maryland 2,512,431 38.8 6.4 67.7 0.412
	38 St. Louis, Missouri 2,325,609 38.4 5.1 69.0 0.413
	39 Dayton, Ohio 1,179,009 38.3 4.9 68.2 0.397
	40 Cleveland, Ohio 2,661,167 38.2 5.1 68.7 0.405
	41 Nashville, Tennessee 1,246,338 38.2 5.7 67.9 0.357
	42 New Orleans, Louisiana 1,381,652 38.2 5.1 69.5 0.397
	43 Cincinnati, Ohio 1,954,800 37.9 5.1 66.4 0.429
	44 Columbus, Ohio 1,663,807 37.7 4.9 67.1 0.406
	45 Jacksonville, Florida 1,176,696 37.5 4.9 68.9 0.361
	46 Detroit, Michigan 5,327,827 37.3 5.5 68.5 0.358
	47 Indianapolis, Indiana 1,507,346 37.2 4.9 67.5 0.398
	48 Raleigh, North Carolina 1,412,127 36.9 5.0 67.3 0.389
	49 Atlanta, Georgia 3,798,017 36.0 4.5 69.4 0.366
	50 Charlotte, North Carolina 1,423,942 35.8 4.4 67.0 0.397"""


	# From https://en.wikipedia.org/wiki/List_of_United_States_cities_by_foreign-born_population
	foreign_born_cities = """Hialeah, Florida 218,901 162,951 74.4 1
	Miami, Florida 433,143 244,352 56.4 2
	Santa Ana, California 340,378 166,960 49.1 3
	Fremont, California 205,521 88,211 42.9 4
	Los Angeles, California 3,831,880 1,521,119 39.7 5
	San Jose, California 964,679 367,711 38.1 6
	Anaheim, California 337,899 127,111 37.6 7
	New York, New York 8,391,881 2,996,580 35.7 8
	Irvine, California 209,707 74,225 35.4 9
	Jersey City, New Jersey 242,513 84,583 34.9 10
	Irving, Texas 205,549 70,263 34.2 11
	San Francisco, California 815,358 278,369 34.1 12
	Chula Vista, California 223,746 71,797 32.1 13
	Yonkers, New York 201,073 62,818 31.2 14
	Oakland, California 409,151 116,794 28.5 15
	Houston, Texas 2,260,918 644,167 28.5 16
	Laredo, Texas 226,419 62,653 27.7 17
	Stockton, California 287,584 79,511 27.6 18
	Garland, Texas 222,013 60,187 27.1 19
	Long Beach, California 462,594 124,340 26.9 20
	Newark, New Jersey 278,157 74,762 26.9 21
	Honolulu, Hawaii (CDP) 374,658 96,752 25.8 22
	Paradise, Nevada (CDP) 202,987 51,090 25.2 23
	Boston, Massachusetts 645,187 161,740 25.1 24
	San Diego, California 1,306,228 325,819 24.9 25
	North Las Vegas, Nevada 224,416 55,805 24.9 26
	Dallas, Texas 1,299,590 322,072 24.8 27
	El Paso, Texas 620,440 151,295 24.4 28
	Riverside, California 297,863 69,792 23.4 29
	Plano, Texas 273,381 62,217 22.8 30
	Aurora, Colorado 323,288 70,747 21.9 31
	Sacramento, California 466,685 102,076 21.9 32
	Arlington, Virginia (CDP) 217,483 47,379 21.8 33
	Phoenix, Arizona 1,593,660 346,430 21.7 34
	Las Vegas, Nevada 567,610 119,437 21 35
	Chicago, Illinois 2,850,502 588,480 20.6 36
	Fresno, California 479,911 97,316 20.3 37
	Austin, Texas 790,593 159,353 20.2 38
	Arlington, Texas 380,072 76,440 20.1 39
	Fort Worth, Texas 731,588 131,197 17.9 40
	Reno, Nevada 219,649 37,964 17.3 41
	Seattle, Washington 616,669 105,154 17.1 42
	Tucson, Arizona 543,907 90,794 16.7 43
	Orlando, Florida 235,876 39,210 16.6 44
	Bakersfield, California 324,479 53,480 16.5 45
	Denver, Colorado 610,345 95,585 15.7 46
	Minneapolis, Minnesota 385,384 59,093 15.3 47
	St. Paul, Minnesota 281,244 42,669 15.2 48
	Modesto, California 202,740 30,464 15 49
	Tampa, Florida 343,879 50,377 14.6 50
	Glendale, Arizona 253,210 36,197 14.3 51
	Charlotte, North Carolina 704,417 96,734 13.7 52
	San Antonio, Texas 1,373,677 180,895 13.2 53
	Durham, North Carolina 229,147 30,174 13.2 54
	Raleigh, North Carolina 405,197 53,154 13.1 55
	Scottsdale, Arizona 237,834 30,074 12.6 56
	Chandler, Arizona 249,515 31,551 12.6 57
	Portland, Oregon 566,606 71,380 12.6 58
	Mesa, Arizona 467,178 56,895 12.2 59
	Washington, D.C. 599,657 72,110 12 60
	Henderson, Nevada 256,424 30,462 11.9 61
	Nashville-Davidson, Tennessee 605,466 70,404 11.6 62
	Philadelphia, Pennsylvania 1,547,297 179,444 11.6 63
	Winston-Salem, North Carolina 229,826 26,064 11.3 64
	Oklahoma City, Oklahoma 560,226 63,199 11.3 65
	Albuquerque, New Mexico 529,216 57,298 10.8 66
	Columbus, Ohio 773,021 83,091 10.7 67
	Des Moines, Iowa 200,569 21,050 10.5 68
	Greensboro, North Carolina 255,141 26,410 10.4 69
	St. Petersburg, Florida 244,318 25,094 10.3 70
	Milwaukee, Wisconsin 605,027 59,785 9.9 71
	Anchorage, Alaska (municipality) 286,174 27,107 9.5 72
	Omaha, Nebraska 454,714 43,029 9.5 73
	Tulsa, Oklahoma 389,369 36,514 9.4 74
	Gilbert, Arizona (town) 222,092 20,606 9.3 75
	Jacksonville, Florida 813,518 73,992 9.1 76
	Wichita, Kansas 372,194 33,608 9 77
	Boise City, Idaho 205,698 18,221 8.9 78
	Madison, Wisconsin 235,410 20,736 8.8 79
	Virginia Beach, Virginia 433,575 36,727 8.5 80
	Corpus Christi, Texas 287,231 23,315 8.1 81
	Lexington-Fayette, Kentucky (urban county) 296,545 23,967 8.1 82
	Indianapolis (balance), Indiana 807,640 63,241 7.8 83
	Colorado Springs, Colorado 399,803 31,266 7.8 84
	Fort Wayne, Indiana 251,825 19,055 7.6 85
	Atlanta, Georgia 540,932 39,733 7.3 86
	Buffalo, New York 270,221 19,618 7.3 87
	Kansas City, Missouri 482,228 34,085 7.1 88
	Lincoln, Nebraska 254,008 17,128 6.7 89
	Detroit, Michigan 910,848 60,170 6.6 90
	Rochester, New York 207,291 13,454 6.5 91
	Baltimore, Maryland 637,418 41,343 6.5 92
	Pittsburgh, Pennsylvania 311,640 19,993 6.4 93
	Norfolk, Virginia 233,333 14,562 6.2 94
	Richmond, Virginia 204,451 12,301 6 95
	St. Louis, Missouri 356,587 21,177 5.9 96
	Spokane, Washington 203,268 11,562 5.7 97
	Louisville, Kentucky ("balance") 566,492 31,315 5.5 98
	Memphis, Tennessee 676,646 36,519 5.4 99
	New Orleans, Louisiana 354,850 18,968 5.3 100
	Lubbock, Texas 225,865 10,709 4.7 101
	Akron, Ohio 207,208 9,526 4.6 102
	Baton Rouge, Louisiana 225,388 10,235 4.5 103
	Cleveland, Ohio 431,369 19,495 4.5 104
	Chesapeake, Virginia 222,455 9,838 4.4 105
	Birmingham, Alabama 231,824 8,883 3.8 106
	Cincinnati, Ohio 333,013 11,471 3.4 107
	Toledo, Ohio 316,164 8,793 2.8 108
	Montgomery, Alabama 201,465 5,454 2.7 109"""


	def main(model):
	m = model(mobile_cities, foreign_born_cities)
	mobile = m.mobile()
	foreign = m.foreign()
	shared = m.shared(mobile, foreign)
	x, y = m.compare(shared, mobile, foreign)
	m.graph(x, y)


	if __name__ == '__main__':
	#main(MobileForeignOrdinal)
	main(MobileForeignNumeric)