Census population estimates 2011-2019
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Brittain33
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« Reply #400 on: January 02, 2018, 09:07:36 PM »

Alabama has inflow?!
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jimrtex
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« Reply #401 on: January 03, 2018, 02:00:59 AM »

Do you have or know of a spreadsheet where one could input population numbers to determine the overall seat apportionment?

Column A(2:51): State Names
Column B(2:51): Populations (leave out DC and PR, unless you want to experiment)
A52: Cell with number of representatives, set to 435, but you can vary.
B52: Quota =SUM(B2:B51)/A52
Column C(2:51): Estimate. =B2/$B$52, etc.
Column D(2:51): Minimum number of seats. =MAX(INT(C2),1), etc, ensures all states get one representative, and avoids division by zero.
D52: Minimum seats = SUM(D2:D51)
Column E(2:51): Divisor for next seat:

Huntington-Hill: =SQRT(D2*(D2+1)), etc. (Geometric mean of n and n+1)
Webster's = D2 + 0.5 (Arithmetic mean of n and n+1)
Dean's = D2*(D2+1)*2/(D2+D2+1)  (Harmonic mean)
Jefferson's = D2+1
Adams's = D2

Webster's is same as St.Lague, Jefferson's the same ad D'Hondt

Column F(2:F51): Quotient = B2/E2, etc. May also use C2/E2, etc.
Column G(2:G51): Quotient Rank = RANK(F2,F$2:F$51), etc.
G52 extra seats to apportion A52-D52

Column H(2:H51):= D2 + IF(G2 <= $G$52, 1, 0), etc. add one for largest quotients.
H52 total apportionment = SUM(H2:H51), should match A52

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jimrtex
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« Reply #402 on: January 03, 2018, 12:13:02 PM »

I don't know.

The population estimates include estimates of components of change (births, deaths, domestic net migration, international net migration).

Some years this was positive, others negative. Over 7 years, the net was 1153 persons. 2017 had the biggest inflow, but only 3K.

The other number is from the 2016 ACS, and estimates the state-to--state flows. The margin-of-error is quite large 122,220 +/- 9,811 into Alabama, 99,892 +/- 7,271 out of Alabama (90% confidence levels). If you take the smallest differentials 112,409 to 107,163 is a ratio of 105%.

For previous years the ratio would have been 1.13, 0.96, 1.07, 0.96, 1.09. 1.10, which are pretty consistent with not much flow.

Based on the 5-year ACS, the 122K is an outlier (or alternatively, the other 4 years were on the low side.  105, 104, 108, 114, 122 could represent an upward trend, an exaggerated upward trend, or an outlier in the 5th year.

The Census Bureau reports a lot more data about inward flows, since the question asked is whether the person lived in a different residence 1-year ago, and if not where you lived. This gets reported as: same house, same county, same state, not in the US.

Some counties with high interstate movers as percentage of population are Dale and Russell, which are likely tied to Fort Rucker and Fort Benning. Russell will get "interstate" movers who simply crossed the state line from Columbus.

Others include Lee and Tuscaloosa. The Census Bureau considers college students living in dorms to be residing at that location, and the same would be true for students living off campus. Freshmen would report that they had moved in the last year. Upper-classmen might, depending on how they interpreted the question. There may be procedural differences as well. Group quarters at college campuses are only surveyed in January-April, and September-December. The Census Bureau really has no way of knowing whether an apartment is occupied by a seasonal student, or a permanent resident.

There may be biases introduced. If a student does not complete college, they are somewhat likely to return to their hometown. If they are living at their parent's home, Mom when she fills out the census form may not remember that Johnny was "residing" at Auburn 12 months previously.

And the skipping of the summer months may have a bias. Let's say those who would have been surveyed in July were shifted to September/October; August to November/December; May to January/February; and June to March/April. Those who would have been surveyed in July and August, will be surveyed after they have arrived on campus. Those who would have left by May and June, will be surveyed before they have left.

This sort of shifting would also happen for Alabamians who go out of state, but the student flow might be towards Alabama. Where a parent attended school may influence the choice of school for their children. If you graduated from Auburn and lived in Atlanta, you might be willing to pay out-of-state tuition to Auburn, particularly if you viewed Athens as too liberal, and if your child wanted to go to Clemson, you might not only refuse to pay their tuition, you would probably disinherit them. There are likely more Alabama and Auburn graduates in Atlanta, than UGA grads in Birmingham.

Other counties with high domestic inflows are Lauderdale, on the state boundary, and probably with more jobs than adjacent areas of Tennessee or Mississippi; Madison, where Huntsville will actually attract college-educated persons; and Baldwin, next to Pensacola, and actually has a coastline, which would be attractive to retirees. Far southern Alabama may be attractive to retirees who want a warm climate with low housing costs.

If you take the states north from Virginia, Kentucky, Missouri, and Kansas, only ME, NH, DE, SD, and ND had domestic inflows cumulative over the the estimate period. Of the remaining states in the southeast and west, only MS, LA, NM, WY, AK, and HI had domestic outflows. Alabama barely makes it into the positive side because it is in the southeast, and better off than Mississippi and Louisiana.
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« Reply #403 on: January 04, 2018, 07:58:45 PM »

Do you have or know of a spreadsheet where one could input population numbers to determine the overall seat apportionment?

Column A(2:51): State Names
Column B(2:51): Populations (leave out DC and PR, unless you want to experiment)
A52: Cell with number of representatives, set to 435, but you can vary.
B52: Quota =SUM(B2:B51)/A52
Column C(2:51): Estimate. =B2/$B$52, etc.
Column D(2:51): Minimum number of seats. =MAX(INT(C2),1), etc, ensures all states get one representative, and avoids division by zero.
D52: Minimum seats = SUM(D2:D51)
Column E(2:51): Divisor for next seat:

Huntington-Hill: =SQRT(D2*(D2+1)), etc. (Geometric mean of n and n+1)
Webster's = D2 + 0.5 (Arithmetic mean of n and n+1)
Dean's = D2*(D2+1)*2/(D2+D2+1)  (Harmonic mean)
Jefferson's = D2+1
Adams's = D2

Webster's is same as St.Lague, Jefferson's the same ad D'Hondt

Column F(2:F51): Quotient = B2/E2, etc. May also use C2/E2, etc.
Column G(2:G51): Quotient Rank = RANK(F2,F$2:F$51), etc.
G52 extra seats to apportion A52-D52

Column H(2:H51):= D2 + IF(G2 <= $G$52, 1, 0), etc. add one for largest quotients.
H52 total apportionment = SUM(H2:H51), should match A52


I just lost a whole lot of what I had composed here online, but your method caps the number of seats each state has between its integer "floor" and its integer "ceiling" (or just the "ceiling" for a state with a fractional quota of less than 1, which is correct of course), while these methods, when interpreted as divisor methods rather than fixed ratio methods some of them started out as, don't.  I was starting to explain the virtues of each when my web browser closed down for me for some reason.
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jimrtex
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« Reply #404 on: January 05, 2018, 06:46:25 AM »

Do you have or know of a spreadsheet where one could input population numbers to determine the overall seat apportionment?

Column A(2:51): State Names
Column B(2:51): Populations (leave out DC and PR, unless you want to experiment)
A52: Cell with number of representatives, set to 435, but you can vary.
B52: Quota =SUM(B2:B51)/A52
Column C(2:51): Estimate. =B2/$B$52, etc.
Column D(2:51): Minimum number of seats. =MAX(INT(C2),1), etc, ensures all states get one representative, and avoids division by zero.
D52: Minimum seats = SUM(D2:D51)
Column E(2:51): Divisor for next seat:

Huntington-Hill: =SQRT(D2*(D2+1)), etc. (Geometric mean of n and n+1)
Webster's = D2 + 0.5 (Arithmetic mean of n and n+1)
Dean's = D2*(D2+1)*2/(D2+D2+1)  (Harmonic mean)
Jefferson's = D2+1
Adams's = D2

Webster's is same as St.Lague, Jefferson's the same ad D'Hondt

Column F(2:F51): Quotient = B2/E2, etc. May also use C2/E2, etc.
Column G(2:G51): Quotient Rank = RANK(F2,F$2:F$51), etc.
G52 extra seats to apportion A52-D52

Column H(2:H51):= D2 + IF(G2 <= $G$52, 1, 0), etc. add one for largest quotients.
H52 total apportionment = SUM(H2:H51), should match A52


I just lost a whole lot of what I had composed here online, but your method caps the number of seats each state has between its integer "floor" and its integer "ceiling" (or just the "ceiling" for a state with a fractional quota of less than 1, which is correct of course), while these methods, when interpreted as divisor methods rather than fixed ratio methods some of them started out as, don't.  I was starting to explain the virtues of each when my web browser closed down for me for some reason.

I forgot a caveat and a limitation.

The short cut I used assumes there will not be quota violations, where a state would not be apportioned either n or n+1 districts when:

n/435 <= Pstate / PUSA < (n+1)/435

This is estimated to be about a 1/1600 occurrence for the USA (the probability is based on the distribution of state populations, that is effectively random, and can only be simulated). For other uses, such as the apportionment of the British Parliament it is quite likely.

While the shortcut could be regarded as a defect in my implementation, the fact that a quota violation could occur, should be considered a defect of the divisor methods.

The limitation of my method is that it does not identify all the seats near the 435th seat. California should be apportioned roughly every 8th seat. While its quotients will be quite regular and periodic, other states will have different periods, so the rankings will not be periodic. My method will identify the ranking for California's 53rd seat, but not its 54th or 52nd, etc. And some of the rankings of other states will be in error because of this (but not, barring quota violations, whether or not they will be apportioned a particular number of seats).

To generate a full ranking with a spreadsheet requires more of a brute force approach. For example, one could generate, say the first 63 divisors, and calculate the quotients for all 50 states for each divisor. This would produce the top 500 or so rankings.

500/435 * 53 = 60.92 is an estimate of California's representation in a 500 member House. 63 is a fudge of 60.92, to make sure that California's rankings in the top 500 are generated. This method would also produce a lot of extraneous results quotients (e.g that for Wyoming's 63rd seat).
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jimrtex
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« Reply #405 on: January 06, 2018, 03:18:45 PM »

If it was desired that 100% of districts be within 5% of the mean, this could be done with a House of 3031. The final piece is Vermont six seats being underpopulated to above 95% of the average of  109,328 persons. CA would have 368 seats, TX 269, FL 201. NY, PA, IL, OH would each have 100+; 15 others (down to CO and MN) would have 50+; 13 others (down to KS) would have 20+; 9 others (down to MT, RI) would have 10+. The final six would be DE 9, SD 8, ND 7, AK 7, VT 6, and WY 5. DC would have 7 electoral votes. 3031 + 50x2 + 7 = 3138 electoral votes. A speculators might want to acquire 3138.com.
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« Reply #406 on: January 06, 2018, 03:21:56 PM »

If it was desired that 100% of districts be within 5% of the mean, this could be done with a House of 3031. The final piece is Vermont six seats being underpopulated to above 95% of the average of  109,328 persons. CA would have 368 seats, TX 269, FL 201. NY, PA, IL, OH would each have 100+; 15 others (down to CO and MN) would have 50+; 13 others (down to KS) would have 20+; 9 others (down to MT, RI) would have 10+. The final six would be DE 9, SD 8, ND 7, AK 7, VT 6, and WY 5. DC would have 7 electoral votes. 3031 + 50x2 + 7 = 3138 electoral votes. A speculators might want to acquire 3138.com.

That is a real website; its a Chinese Chess website.
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jimrtex
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« Reply #407 on: January 06, 2018, 07:01:24 PM »

If it was desired that 100% of districts be within 5% of the mean, this could be done with a House of 3031. The final piece is Vermont six seats being underpopulated to above 95% of the average of  109,328 persons. CA would have 368 seats, TX 269, FL 201. NY, PA, IL, OH would each have 100+; 15 others (down to CO and MN) would have 50+; 13 others (down to KS) would have 20+; 9 others (down to MT, RI) would have 10+. The final six would be DE 9, SD 8, ND 7, AK 7, VT 6, and WY 5. DC would have 7 electoral votes. 3031 + 50x2 + 7 = 3138 electoral votes. A speculators might want to acquire 3138.com.

That is a real website; its a Chinese Chess website.
It appears to be a game development company. If only other companies had similar press releases:

"Volkswagen is not a liar..."

"Enron - Some friends and relatives are worried about their initial investment..."


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Kevinstat
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« Reply #408 on: January 06, 2018, 11:29:22 PM »

Do you have or know of a spreadsheet where one could input population numbers to determine the overall seat apportionment?

Column A(2:51): State Names
Column B(2:51): Populations (leave out DC and PR, unless you want to experiment)
A52: Cell with number of representatives, set to 435, but you can vary.
B52: Quota =SUM(B2:B51)/A52
Column C(2:51): Estimate. =B2/$B$52, etc.
Column D(2:51): Minimum number of seats. =MAX(INT(C2),1), etc, ensures all states get one representative, and avoids division by zero.
D52: Minimum seats = SUM(D2:D51)
Column E(2:51): Divisor for next seat:

Huntington-Hill: =SQRT(D2*(D2+1)), etc. (Geometric mean of n and n+1)
Webster's = D2 + 0.5 (Arithmetic mean of n and n+1)
Dean's = D2*(D2+1)*2/(D2+D2+1)  (Harmonic mean)
Jefferson's = D2+1
Adams's = D2

Webster's is same as St.Lague, Jefferson's the same ad D'Hondt

Column F(2:F51): Quotient = B2/E2, etc. May also use C2/E2, etc.
Column G(2:G51): Quotient Rank = RANK(F2,F$2:F$51), etc.
G52 extra seats to apportion A52-D52

Column H(2:H51):= D2 + IF(G2 <= $G$52, 1, 0), etc. add one for largest quotients.
H52 total apportionment = SUM(H2:H51), should match A52


I just lost a whole lot of what I had composed here online, but your method caps the number of seats each state has between its integer "floor" and its integer "ceiling" (or just the "ceiling" for a state with a fractional quota of less than 1, which is correct of course), while these methods, when interpreted as divisor methods rather than fixed ratio methods some of them started out as, don't.  I was starting to explain the virtues of each when my web browser closed down for me for some reason.

I forgot a caveat and a limitation.

The short cut I used assumes there will not be quota violations, where a state would not be apportioned either n or n+1 districts when:

n/435 <= Pstate / PUSA < (n+1)/435

This is estimated to be about a 1/1600 occurrence for the USA (the probability is based on the distribution of state populations, that is effectively random, and can only be simulated). For other uses, such as the apportionment of the British Parliament it is quite likely.

While the shortcut could be regarded as a defect in my implementation, the fact that a quota violation could occur, should be considered a defect of the divisor methods.

The limitation of my method is that it does not identify all the seats near the 435th seat. California should be apportioned roughly every 8th seat. While its quotients will be quite regular and periodic, other states will have different periods, so the rankings will not be periodic. My method will identify the ranking for California's 53rd seat, but not its 54th or 52nd, etc. And some of the rankings of other states will be in error because of this (but not, barring quota violations, whether or not they will be apportioned a particular number of seats).

To generate a full ranking with a spreadsheet requires more of a brute force approach. For example, one could generate, say the first 63 divisors, and calculate the quotients for all 50 states for each divisor. This would produce the top 500 or so rankings.

500/435 * 53 = 60.92 is an estimate of California's representation in a 500 member House. 63 is a fudge of 60.92, to make sure that California's rankings in the top 500 are generated. This method would also produce a lot of extraneous results quotients (e.g that for Wyoming's 63rd seat).
California, with 52.53/435 of the United States's apportionment population as of and according to the 2010 census, would have had 55 seats under Jefferson's method and 50 seats under Adams's.  Texas and New York must also have had quota violations in one or both of those methods as the difference in the apportionment between the them is 3 for Texas and 2 for New York (it would be 2 for Florida if not for the guarantee of 1 seat for each state, which gives Vermont and Wyoming seats that would otherwise go to Florida and Washington).
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jimrtex
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« Reply #409 on: January 08, 2018, 01:51:29 PM »

Do you have or know of a spreadsheet where one could input population numbers to determine the overall seat apportionment?

Column A(2:51): State Names
Column B(2:51): Populations (leave out DC and PR, unless you want to experiment)
A52: Cell with number of representatives, set to 435, but you can vary.
B52: Quota =SUM(B2:B51)/A52
Column C(2:51): Estimate. =B2/$B$52, etc.
Column D(2:51): Minimum number of seats. =MAX(INT(C2),1), etc, ensures all states get one representative, and avoids division by zero.
D52: Minimum seats = SUM(D2:D51)
Column E(2:51): Divisor for next seat:

Huntington-Hill: =SQRT(D2*(D2+1)), etc. (Geometric mean of n and n+1)
Webster's = D2 + 0.5 (Arithmetic mean of n and n+1)
Dean's = D2*(D2+1)*2/(D2+D2+1)  (Harmonic mean)
Jefferson's = D2+1
Adams's = D2

Webster's is same as St.Lague, Jefferson's the same ad D'Hondt

Column F(2:F51): Quotient = B2/E2, etc. May also use C2/E2, etc.
Column G(2:G51): Quotient Rank = RANK(F2,F$2:F$51), etc.
G52 extra seats to apportion A52-D52

Column H(2:H51):= D2 + IF(G2 <= $G$52, 1, 0), etc. add one for largest quotients.
H52 total apportionment = SUM(H2:H51), should match A52


I just lost a whole lot of what I had composed here online, but your method caps the number of seats each state has between its integer "floor" and its integer "ceiling" (or just the "ceiling" for a state with a fractional quota of less than 1, which is correct of course), while these methods, when interpreted as divisor methods rather than fixed ratio methods some of them started out as, don't.  I was starting to explain the virtues of each when my web browser closed down for me for some reason.

I forgot a caveat and a limitation.

The short cut I used assumes there will not be quota violations, where a state would not be apportioned either n or n+1 districts when:

n/435 <= Pstate / PUSA < (n+1)/435

This is estimated to be about a 1/1600 occurrence for the USA (the probability is based on the distribution of state populations, that is effectively random, and can only be simulated). For other uses, such as the apportionment of the British Parliament it is quite likely.

While the shortcut could be regarded as a defect in my implementation, the fact that a quota violation could occur, should be considered a defect of the divisor methods.

The limitation of my method is that it does not identify all the seats near the 435th seat. California should be apportioned roughly every 8th seat. While its quotients will be quite regular and periodic, other states will have different periods, so the rankings will not be periodic. My method will identify the ranking for California's 53rd seat, but not its 54th or 52nd, etc. And some of the rankings of other states will be in error because of this (but not, barring quota violations, whether or not they will be apportioned a particular number of seats).

To generate a full ranking with a spreadsheet requires more of a brute force approach. For example, one could generate, say the first 63 divisors, and calculate the quotients for all 50 states for each divisor. This would produce the top 500 or so rankings.

500/435 * 53 = 60.92 is an estimate of California's representation in a 500 member House. 63 is a fudge of 60.92, to make sure that California's rankings in the top 500 are generated. This method would also produce a lot of extraneous results quotients (e.g that for Wyoming's 63rd seat).
California, with 52.53/435 of the United States's apportionment population as of and according to the 2010 census, would have had 55 seats under Jefferson's method and 50 seats under Adams's.  Texas and New York must also have had quota violations in one or both of those methods as the difference in the apportionment between the them is 3 for Texas and 2 for New York (it would be 2 for Florida if not for the guarantee of 1 seat for each state, which gives Vermont and Wyoming seats that would otherwise go to Florida and Washington).

I messed up. Jefferson's method would give two extra seats to California, and one extra to TX, NY, IL, OH, NC, and NJ; and take one from WA, MN, SC, WV, NE, ME, NH, RI; compared to Huntington-Hill (based on 2010 population).

Huntington-Hill, Webster's, and Dean's method often give the same result. After Congress failed to make an apportionment in 1920, they came up with the current procedure of using a divisor method, and fixing the size of the House, such that Congress could do what it does best, do nothing. But they did not make a decision between Huntington-Hill and Webster's methods. Under the 1930 apportionment they gave the same results. In 1940, there was a different result, with Arkansas gaining a seventh representative at the expense of Michigan. Since Arkansas was a reliable Democratic state at the time, Congress adopted Huntington-Hill on a party line vote (except Democrats from Michigan) and Huntington-Hill has been used ever since.

Huntington-Hill is estimated to have quota violations quite rarely (perhaps once in 1600 apportionments over 16000 years for USA-like population distributions), and I had conflated this to include Jefferson and Adams methods.

Perhaps contributing to this was a recollection of the 1790 apportionment. The initial apportionment based on Webster's method was vetoed, by President Washington, the first ever presidential veto. He did so on the basis of the advice of Secretary of State Thomas Jefferson, who pointed out that some States would have more than one representative per 30,000 persons.

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The Constitution is ambiguous. One reading is that the total number of representatives for the United States may not be greater than one per 30,000 - this was the interpretation by Congress. The other was that of Jefferson, which was adopted by Washington, that the limit was on a per state basis. Congress was unwilling to challenge George Washington, who was revered, on so important a subject as the interpretation of the Constitution.

But what they did was to use Jefferson's method, but chose a quota so that it would produce only minimal changes from Webster's method (one representative from Delaware to Virginia).

For my short cut method to work, an estimate of the number of representative to which a state is entitled must be made.

Let w = Pstate/quota, where quota = PUSA/435

Then for Webster's: est = w

For Huntington-Hill: est = sqrt( w2 + 1/4 )

For Dean's: est = ( w + sqrt ( w2 + 1) ) / 2

For Jefferson's: est = w - 1/2

For Adam's: est = w + 1/2

But for all nethod's other than Webster's the estimates would sum to something other than 435.

So we need to adjust the quota. This can be done in an iterative manner:

Where quota1 is the quota from above, and estusa is the total representation for the USA based on a particular quota.

quotan+1 = quotan * estusa(quotan) /435

Convergence is quite fast for Huntington-Hill with estusa ~= 435.000 after two iterations. About 4 iterations are needed for Jefferson's and Adam's methods.

For the 2010 Census (disregarding overseas population) the quota for Webster's method is 708,405; for Huntington-Hill is 711,428; and for Jefferson's method 669,906. Calculate the estimates using these adjusted quotas to to in the form n.xxx. The state will get n or n+1 seats by that method.
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« Reply #410 on: January 29, 2018, 09:47:21 AM »

If nobody has posted the 2017 estimates yet, I got u fam.

DC - 601,723 --> 693,972; +15.33%
1. TX - 25,145,561 --> 28,304,596; +12.56%
2. ND - 672,591 --> 755,393; +12.31%
3. UT - 2,763,885 --> 3,101,833; +12.23%
4. FL - 18,801,310 -->20,984,400; +11.61%
5. CO - 5,029,196 --> 5,607,154; +11.49%
6. NV - 2,700,551 --> 2,998,039; +11.02%
7. WA - 6,724,540 --> 7,405,743; +10.13%
8. AZ - 6,392,017 --> 7,016,270; +9.77%
9. ID   - 1,567,582 --> 1,716,943; +9.53%
10. SC - 4,625,364 --> 5,024,369; +8.63%
11. OR - 3,831,074 --> 4,142,776; +8.14%
12. NC - 9,535,483 --> 10,273,419; +7.74%
13. GA - 9,687,653 --> 10,429,379; +7.66%
14. DE - 897,934 --> 961,939; +7.13%
15. SD - 814,180 --> 869,666; +6.81%
16. MT - 989,415 --> 1,050,493; +6.17%
17. CA - 37,253,956 --> 39,536,653; +6.13%
18. VA - 8,001,024 --> 8,470,020; +5.86%
19. TN - 6,346,105 --> 6,715,984; +5.83%
50 States + DC - 308,745,538 --> 325,719,178; +5.50%
20. MN - 5,303,925 --> 5,576,606; +5.14%
21. NE - 1,826,341 --> 1,920,076; +5.13%
22. HI - 1,360,301 --> 1,427,538; +4.94%
23. MD - 5,773,552 --> 6,052,177; +4.83%
24. OK - 3,751,351 --> 3,930,864; +4.79%
25. MA - 6,547,629 --> 6,859,819; +4.77%
26. AK - 710,231 --> 739,795; +4.16%
27. LA - 4,533,372 --> 4,684,333; +3.33%
28. IA - 3,046,355 --> 3,145,711; +3.26%
29. AR - 2,915,918 --> 3,004,279; +3.03%
30. IN - 6,483,802 --> 6,666,818; +2.82%
31. WY - 563,626 --> 579,315; +2.78%
32. KY - 4,339,367 --> 4,454,189; +2.65%
33. NY - 19,378,102 -> 19,849,399; +2.43%
34. NJ - 8,791,894 --> 9,005,644; +2.43%
35. KS - 2,853,118 --> 2,913,123; +2.10%
36. MO - 5,988,927 --> 6,113,532; +2.08%
37. NH - 1,316,470 --> 1,342,795; +2.00%
38. AL - 4,779,736 --> 4,874,747; +1.99%
39. WI - 5,686,986 --> 5,795,483; +1.91%
40. NM - 2,059,179 --> 2,088,070; +1.40%
41. OH - 11,536,504 --> 11,658,609; +1.06%
42. PA - 12,702,379 --> 12,805,537; +0.81%
43. MI - 9,883,640 --> 9,962,311; +0.80%
44. RI - 1,052,567 --> 1,059,639; +0.67%
45. ME - 1,328,361 --> 1,335,907; +0.57%
46. MS - 2,967,297 --> 2,984,100; +0.57%
47. CT - 3,574,097 --> 3,588,184; +0.39%
48. IL - 12,830,632 --> 12,802,023; −0.22%
49. VT - 625,741 --> 623,657; −0.33%
50. WV - 1,852,994 --> 1,815,857; −2.00%
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Dr. Arch
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« Reply #411 on: January 30, 2018, 12:21:25 AM »

If nobody has posted the 2017 estimates yet, I got u fam.

DC - 601,723 --> 693,972; +15.33%
1. TX - 25,145,561 --> 28,304,596; +12.56%
2. ND - 672,591 --> 755,393; +12.31%
3. UT - 2,763,885 --> 3,101,833; +12.23%
4. FL - 18,801,310 -->20,984,400; +11.61%
5. CO - 5,029,196 --> 5,607,154; +11.49%
6. NV - 2,700,551 --> 2,998,039; +11.02%
7. WA - 6,724,540 --> 7,405,743; +10.13%
8. AZ - 6,392,017 --> 7,016,270; +9.77%
9. ID   - 1,567,582 --> 1,716,943; +9.53%
10. SC - 4,625,364 --> 5,024,369; +8.63%
11. OR - 3,831,074 --> 4,142,776; +8.14%
12. NC - 9,535,483 --> 10,273,419; +7.74%
13. GA - 9,687,653 --> 10,429,379; +7.66%
14. DE - 897,934 --> 961,939; +7.13%
15. SD - 814,180 --> 869,666; +6.81%
16. MT - 989,415 --> 1,050,493; +6.17%
17. CA - 37,253,956 --> 39,536,653; +6.13%
18. VA - 8,001,024 --> 8,470,020; +5.86%
19. TN - 6,346,105 --> 6,715,984; +5.83%
50 States + DC - 308,745,538 --> 325,719,178; +5.50%
20. MN - 5,303,925 --> 5,576,606; +5.14%
21. NE - 1,826,341 --> 1,920,076; +5.13%
22. HI - 1,360,301 --> 1,427,538; +4.94%
23. MD - 5,773,552 --> 6,052,177; +4.83%
24. OK - 3,751,351 --> 3,930,864; +4.79%
25. MA - 6,547,629 --> 6,859,819; +4.77%
26. AK - 710,231 --> 739,795; +4.16%
27. LA - 4,533,372 --> 4,684,333; +3.33%
28. IA - 3,046,355 --> 3,145,711; +3.26%
29. AR - 2,915,918 --> 3,004,279; +3.03%
30. IN - 6,483,802 --> 6,666,818; +2.82%
31. WY - 563,626 --> 579,315; +2.78%
32. KY - 4,339,367 --> 4,454,189; +2.65%
33. NY - 19,378,102 -> 19,849,399; +2.43%
34. NJ - 8,791,894 --> 9,005,644; +2.43%
35. KS - 2,853,118 --> 2,913,123; +2.10%
36. MO - 5,988,927 --> 6,113,532; +2.08%
37. NH - 1,316,470 --> 1,342,795; +2.00%
38. AL - 4,779,736 --> 4,874,747; +1.99%
39. WI - 5,686,986 --> 5,795,483; +1.91%
40. NM - 2,059,179 --> 2,088,070; +1.40%
41. OH - 11,536,504 --> 11,658,609; +1.06%
42. PA - 12,702,379 --> 12,805,537; +0.81%
43. MI - 9,883,640 --> 9,962,311; +0.80%
44. RI - 1,052,567 --> 1,059,639; +0.67%
45. ME - 1,328,361 --> 1,335,907; +0.57%
46. MS - 2,967,297 --> 2,984,100; +0.57%
47. CT - 3,574,097 --> 3,588,184; +0.39%
48. IL - 12,830,632 --> 12,802,023; −0.22%
49. VT - 625,741 --> 623,657; −0.33%
50. WV - 1,852,994 --> 1,815,857; −2.00%

Puerto Rico?
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Cokeland Saxton
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« Reply #412 on: January 30, 2018, 08:05:52 AM »

I just did the 50 states, DC, and the US as a whole. But here's Puerto Rico and the rest of the territories:

Guam - 159,358 --> 162,742; +2.12%
American Samoa - 55,519 --> 54,194; -2.39%
Northern Mariana Islands - 53,833 --> 52,263; -2.92%
US Virgin Islands - 106,405 --> 102,951; -3.25%
Puerto Rico - 3,725,789 --> 3,337,177; -10.43%


RIP Puerto Rico
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« Reply #413 on: January 30, 2018, 08:07:55 AM »

I just did the 50 states, DC, and the US as a whole. But here's Puerto Rico and the rest of the territories:

Guam - 159,358 --> 162,742; +2.12%
American Samoa - 55,519 --> 54,194; -2.39%
Northern Mariana Islands - 53,833 --> 52,263; -2.92%
US Virgin Islands - 106,405 --> 102,951; -3.25%
Puerto Rico - 3,725,789 --> 3,337,177; -10.43%


RIP Puerto Rico

And that's pre Hurricane Maria.
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Cokeland Saxton
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« Reply #414 on: January 30, 2018, 02:22:27 PM »

That is exactly why I said RIP
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Dr. Arch
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« Reply #415 on: February 02, 2018, 02:08:32 AM »
« Edited: February 02, 2018, 02:10:23 AM by Arch »

I just did the 50 states, DC, and the US as a whole. But here's Puerto Rico and the rest of the territories:

Guam - 159,358 --> 162,742; +2.12%
American Samoa - 55,519 --> 54,194; -2.39%
Northern Mariana Islands - 53,833 --> 52,263; -2.92%
US Virgin Islands - 106,405 --> 102,951; -3.25%
Puerto Rico - 3,725,789 --> 3,337,177; -10.43%


RIP Puerto Rico

And that's pre Hurricane Maria.

Sigh... yeah, as I expected. The population right now should be around 2.7 million or so, another 20% drop that's unaccounted for (in terms of months).
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Young Conservative
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« Reply #416 on: February 03, 2018, 02:14:21 PM »
« Edited: February 03, 2018, 02:16:59 PM by Young Conservative »

1. TX - 25,145,561 --> 28,304,596; +12.56%
2. ND - 672,591 --> 755,393; +12.31%
3. UT - 2,763,885 --> 3,101,833; +12.23%
4. FL - 18,801,310 -->20,984,400; +11.61%

5. CO - 5,029,196 --> 5,607,154; +11.49%
6. NV - 2,700,551 --> 2,998,039; +11.02%
7. WA - 6,724,540 --> 7,405,743; +10.13%

8. AZ - 6,392,017 --> 7,016,270; +9.77%
9. ID   - 1,567,582 --> 1,716,943; +9.53%
10. SC - 4,625,364 --> 5,024,369; +8.63%

11. OR - 3,831,074 --> 4,142,776; +8.14%
12. NC - 9,535,483 --> 10,273,419; +7.74%
13. GA - 9,687,653 --> 10,429,379; +7.66%

14. DE - 897,934 --> 961,939; +7.13%
15. SD - 814,180 --> 869,666; +6.81%
16. MT - 989,415 --> 1,050,493; +6.17%

17. CA - 37,253,956 --> 39,536,653; +6.13%
18. VA - 8,001,024 --> 8,470,020; +5.86%

19. TN - 6,346,105 --> 6,715,984; +5.83%

50 States + DC - 308,745,538 --> 325,719,178; +5.50%



All the states growing faster than the US as a whole, by partisan lean.

12/19 Republican.
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DINGO Joe
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« Reply #417 on: February 03, 2018, 02:20:39 PM »

1. TX - 25,145,561 --> 28,304,596; +12.56%
2. ND - 672,591 --> 755,393; +12.31%
3. UT - 2,763,885 --> 3,101,833; +12.23%
4. FL - 18,801,310 -->20,984,400; +11.61%

5. CO - 5,029,196 --> 5,607,154; +11.49%
6. NV - 2,700,551 --> 2,998,039; +11.02%
7. WA - 6,724,540 --> 7,405,743; +10.13%

8. AZ - 6,392,017 --> 7,016,270; +9.77%
9. ID   - 1,567,582 --> 1,716,943; +9.53%
10. SC - 4,625,364 --> 5,024,369; +8.63%

11. OR - 3,831,074 --> 4,142,776; +8.14%
12. NC - 9,535,483 --> 10,273,419; +7.74%
13. GA - 9,687,653 --> 10,429,379; +7.66%

14. DE - 897,934 --> 961,939; +7.13%
15. SD - 814,180 --> 869,666; +6.81%
16. MT - 989,415 --> 1,050,493; +6.17%

17. CA - 37,253,956 --> 39,536,653; +6.13%
18. VA - 8,001,024 --> 8,470,020; +5.86%

19. TN - 6,346,105 --> 6,715,984; +5.83%

50 States + DC - 308,745,538 --> 325,719,178; +5.50%



All the states growing faster than the US as a whole, by partisan lean.

12/19 Republican.

Outside of Utah, and maybe Idaho, the only way states can grow fast is through immigration, so D can flip them all.
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Nyvin
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« Reply #418 on: February 03, 2018, 07:32:27 PM »

1. TX - 25,145,561 --> 28,304,596; +12.56%
2. ND - 672,591 --> 755,393; +12.31%
3. UT - 2,763,885 --> 3,101,833; +12.23%
4. FL - 18,801,310 -->20,984,400; +11.61%

5. CO - 5,029,196 --> 5,607,154; +11.49%
6. NV - 2,700,551 --> 2,998,039; +11.02%
7. WA - 6,724,540 --> 7,405,743; +10.13%

8. AZ - 6,392,017 --> 7,016,270; +9.77%
9. ID   - 1,567,582 --> 1,716,943; +9.53%
10. SC - 4,625,364 --> 5,024,369; +8.63%

11. OR - 3,831,074 --> 4,142,776; +8.14%
12. NC - 9,535,483 --> 10,273,419; +7.74%
13. GA - 9,687,653 --> 10,429,379; +7.66%

14. DE - 897,934 --> 961,939; +7.13%
15. SD - 814,180 --> 869,666; +6.81%
16. MT - 989,415 --> 1,050,493; +6.17%

17. CA - 37,253,956 --> 39,536,653; +6.13%
18. VA - 8,001,024 --> 8,470,020; +5.86%

19. TN - 6,346,105 --> 6,715,984; +5.83%

50 States + DC - 308,745,538 --> 325,719,178; +5.50%



All the states growing faster than the US as a whole, by partisan lean.

12/19 Republican.

Showing partisan trend from 2012 to 2016 for those states would be more useful.

Places like Arizona, North Carolina, Georgia, Florida and Texas are Republican "now" (barely) but that's certainly not a guarantee for the future.
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Cokeland Saxton
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« Reply #419 on: February 28, 2018, 01:32:43 PM »

county level estimates from 2017 will be out in a couple weeks. What should we expect to see?
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krazen1211
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« Reply #420 on: March 03, 2018, 03:44:44 PM »
« Edited: March 05, 2018, 08:20:17 AM by muon2 »

1. TX - 25,145,561 --> 28,304,596; +12.56%
2. ND - 672,591 --> 755,393; +12.31%
3. UT - 2,763,885 --> 3,101,833; +12.23%
4. FL - 18,801,310 -->20,984,400; +11.61%

5. CO - 5,029,196 --> 5,607,154; +11.49%
6. NV - 2,700,551 --> 2,998,039; +11.02%
7. WA - 6,724,540 --> 7,405,743; +10.13%

8. AZ - 6,392,017 --> 7,016,270; +9.77%
9. ID   - 1,567,582 --> 1,716,943; +9.53%
10. SC - 4,625,364 --> 5,024,369; +8.63%

11. OR - 3,831,074 --> 4,142,776; +8.14%
12. NC - 9,535,483 --> 10,273,419; +7.74%
13. GA - 9,687,653 --> 10,429,379; +7.66%

14. DE - 897,934 --> 961,939; +7.13%
15. SD - 814,180 --> 869,666; +6.81%
16. MT - 989,415 --> 1,050,493; +6.17%

17. CA - 37,253,956 --> 39,536,653; +6.13%
18. VA - 8,001,024 --> 8,470,020; +5.86%

19. TN - 6,346,105 --> 6,715,984; +5.83%

50 States + DC - 308,745,538 --> 325,719,178; +5.50%



All the states growing faster than the US as a whole, by partisan lean.

12/19 Republican.

Trump won 30/51 (DC) states. Big growth in favor of the GOP. Trump was +500k votes in Florida compared to Romney.
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« Reply #421 on: March 05, 2018, 12:59:31 PM »

1. TX - 25,145,561 --> 28,304,596; +12.56%
2. ND - 672,591 --> 755,393; +12.31%
3. UT - 2,763,885 --> 3,101,833; +12.23%
4. FL - 18,801,310 -->20,984,400; +11.61%

5. CO - 5,029,196 --> 5,607,154; +11.49%
6. NV - 2,700,551 --> 2,998,039; +11.02%
7. WA - 6,724,540 --> 7,405,743; +10.13%

8. AZ - 6,392,017 --> 7,016,270; +9.77%
9. ID   - 1,567,582 --> 1,716,943; +9.53%
10. SC - 4,625,364 --> 5,024,369; +8.63%

11. OR - 3,831,074 --> 4,142,776; +8.14%
12. NC - 9,535,483 --> 10,273,419; +7.74%
13. GA - 9,687,653 --> 10,429,379; +7.66%

14. DE - 897,934 --> 961,939; +7.13%
15. SD - 814,180 --> 869,666; +6.81%
16. MT - 989,415 --> 1,050,493; +6.17%

17. CA - 37,253,956 --> 39,536,653; +6.13%
18. VA - 8,001,024 --> 8,470,020; +5.86%

19. TN - 6,346,105 --> 6,715,984; +5.83%

50 States + DC - 308,745,538 --> 325,719,178; +5.50%



All the states growing faster than the US as a whole, by partisan lean.

12/19 Republican.

Trump won 30/51 (DC) states. Big growth in favor of the GOP. Trump was +500k votes in Florida compared to Romney.

Florida 2012 R: 49.03%
Florida 2016 R: 48.60%
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Cokeland Saxton
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« Reply #422 on: March 06, 2018, 08:28:33 AM »

the +500k was like because Florida's population was higher in 2016 than in 2012 Tongue
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cvparty
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« Reply #423 on: March 06, 2018, 09:05:30 AM »

the +500k was like because Florida's population was higher in 2016 than in 2012 Tongue
ye the population increased like 1,300,000
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krazen1211
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« Reply #424 on: March 06, 2018, 10:29:16 PM »

the +500k was like because Florida's population was higher in 2016 than in 2012 Tongue

The Dem party gained half that. Big swing towards the GOP.
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