Rigging Pennsylvania: Obama 2008 by county compared with Biden 2020 by county

Pennsylvania 2008 election by county and Pennsylvania 2020 election by county ^ | May 16, 2022 | my own calculations

[kiryandil]

A Comparison of Obama 2008 with Biden 2020

Blue highlighted counties were won by the Democrat.
pop column is the county population rank in the state.
A negative number in the Obama minus Biden column means that Biden "turnout" was greater than Obama 2008

Metropolitan areas [grouped counties] follow the main table.

													p	Obama	Biden
													o	minus	div
		Obama 2008		McCain 2008					Biden		Trump		p	Biden	Obama
Adams	40%	17,633	59%	26,349	44,491		Adams	32%	18,207	66%	37,523	56,540		-574	103%
Allegheny	57%	373,153	42%	272,347	651,436		Allegheny	60%	429,065	39%	282,324	719,733	2	-55,912	115%
Armstrong	37%	11,138	62%	18,542	30,081		Armstrong	23%	8,457	76%	27,489	36,370		2,681	76%
Beaver	48%	40,499	51%	42,895	84,488		Beaver	41%	38,122	58%	54,759	94,122	21	2,377	94%
Bedford	27%	6,059	72%	16,124	22,443		Bedford	16%	4,367	84%	23,025	27,574		1,692	72%
Berks	54%	97,047	45%	80,513	180,000		Berks	45%	92,895	53%	109,736	205,540	9	4,152	96%
Blair	37%	19,813	62%	32,708	53,102		Blair	28%	17,636	71%	45,306	63,595		2,177	89%
Bradford	40%	10,306	58%	15,057	25,787		Bradford	27%	8,046	72%	21,600	30,159		2,260	78%
Bucks	54%	179,031	45%	150,248	332,924		Bucks	52%	204,712	47%	187,367	396,234	4	-25,681	114%
Butler	36%	32,260	63%	57,074	90,425		Butler	33%	37,508	66%	74,359	113,305	19	-5,248	116%
Cambria	49%	32,451	49%	31,995	65,670		Cambria	31%	21,730	68%	48,085	70,574		10,721	67%
Cameron	39%	879	59%	1,323	2,245		Cameron	26%	634	73%	1,771	2,434		245	72%
Carbon	50%	13,464	48%	12,957	26,923		Carbon	33%	11,212	65%	21,984	33,629		2,252	83%
Centre	55%	41,950	44%	32,992	75,763		Centre	52%	40,055	47%	36,372	77,493		1,895	95%
Chester	54%	137,833	45%	114,421	254,354		Chester	58%	182,372	41%	128,565	314,502	7	-44,539	132%
Clarion	38%	6,756	60%	10,737	17,766		Clarion	24%	4,678	75%	14,578	19,493		2,078	69%
Clearfield	43%	14,555	55%	18,662	33,813		Clearfield	25%	9,673	74%	29,203	39,422		4,882	66%
Clinton	48%	7,097	51%	7,504	14,791		Clinton	31%	5,502	68%	11,902	17,625		1,595	78%
Columbia	47%	13,230	52%	14,477	28,063		Columbia	34%	10,532	64%	20,098	31,171		2,698	80%
Crawford	44%	16,780	54%	20,750	38,134		Crawford	31%	12,924	68%	28,559	42,004		3,856	77%
Cumberland	43%	48,306	56%	63,739	113,304		Cumberland	44%	62,245	55%	77,212	141,595	16	-13,939	129%
Dauphin	54%	69,975	45%	58,238	129,529		Dauphin	54%	78,983	45%	66,408	147,368	14	-9,008	113%
Delaware	60%	178,870	39%	115,273	297,004		Delaware	63%	206,423	36%	118,532	327,931	5	-27,553	115%
Elk	51%	7,290	47%	6,676	14,271		Elk	27%	4,522	72%	12,140	16,906		2,768	62%
Erie	59%	75,775	39%	50,351	127,691		Erie	50%	68,286	49%	66,869	137,083	15	7,489	90%
Fayette	49%	25,866	50%	26,081	52,560		Fayette	33%	20,444	66%	41,227	62,139		5,422	79%
Forest	42%	1,038	56%	1,366	2,444		Forest	28%	728	71%	1,882	2,646		310	70%
Franklin	33%	21,169	66%	41,906	63,641		Franklin	28%	22,422	71%	57,245	80,783		-1,253	106%
Fulton	25%	1,576	74%	4,642	6,306		Fulton	14%	1,085	86%	6,824	7,977		491	69%
Greene	49%	7,829	49%	7,889	15,976		Greene	28%	4,911	71%	12,579	17,669		2,918	63%
Huntingdon	36%	6,621	63%	11,745	18,632		Huntingdon	24%	5,445	75%	17,061	22,792		1,176	82%
Indiana	46%	17,065	53%	19,727	37,302		Indiana	31%	12,634	68%	28,089	41,198		4,431	74%
Jefferson	34%	6,447	64%	12,057	18,802		Jefferson	20%	4,527	79%	17,960	22,824		1,920	70%
Juniata	32%	3,068	67%	6,484	9,711		Juniata	19%	2,253	80%	9,649	12,043		815	73%
Lackawanna	63%	67,520	37%	39,488	107,876		Lackawanna	54%	61,991	45%	52,334	115,410	17	5,529	92%
Lancaster	44%	99,586	55%	126,568	228,137		Lancaster	41%	115,847	57%	160,209	280,239	6	-16,261	116%
Lawrence	47%	19,711	52%	21,851	42,103		Lawrence	35%	15,978	64%	29,597	46,076		3,733	81%
Lebanon	40%	23,310	59%	34,314	58,297		Lebanon	33%	23,932	65%	46,731	71,652		-622	103%
Lehigh	57%	87,089	42%	63,382	152,473		Lehigh	53%	98,288	46%	84,259	184,713	10	-11,199	113%
Luzerne	54%	72,492	45%	61,127	135,175		Luzerne	42%	64,873	57%	86,929	153,321	13	7,619	89%
Lycoming	37%	18,381	62%	30,280	49,237		Lycoming	29%	16,971	70%	41,462	59,254		1,410	92%
Mckean	41%	6,465	58%	9,224	15,947		McKean	26%	5,098	72%	14,083	19,466		1,367	79%
Mercer	49%	26,411	49%	26,565	53,821		Mercer	36%	21,067	62%	36,143	57,954		5,344	80%
Mifflin	33%	5,375	66%	10,929	16,502		Mifflin	21%	4,603	78%	16,670	21,502		772	86%
Monroe	58%	39,453	41%	28,293	68,443		Monroe	53%	44,060	46%	38,726	83,829	20	-4,607	112%
Montgomery	60%	253,393	39%	165,552	422,419		Montgomery	63%	319,511	36%	185,460	510,157	3	-66,118	126%
Montour	42%	3,364	57%	4,574	8,023		Montour	39%	3,771	60%	5,844	9,771		-407	112%
Northampton	56%	75,255	43%	58,551	135,587		Northampton	50%	85,087	49%	83,854	170,942	13	-9,832	113%
Northumberland	42%	14,329	56%	19,018	33,939		Northumberland	30%	12,677	68%	28,952	42,283		1,652	88%
Perry	32%	6,396	66%	13,058	19,745		Perry	24%	5,950	74%	18,293	24,652		446	93%
Philadelphia	83%	595,980	16%	117,221	717,329		Philadelphia	81%	603,790	18%	132,740	741,377	1	-7,810	101%
Pike	47%	11,493	52%	12,518	24,284		Pike	40%	13,019	59%	19,213	32,554		-1,526	113%
Potter	31%	2,300	68%	5,109	7,507		Potter	19%	1,726	80%	7,239	9,064		574	75%
Schuylkill	45%	28,300	54%	33,767	63,057		Schuylkill	29%	20,727	69%	48,871	70,603		7,573	73%
Snyder	35%	5,382	64%	9,900	15,479		Snyder	26%	4,910	73%	13,983	19,140		472	91%
Somerset	37%	12,878	62%	21,686	35,168		Somerset	21%	8,654	78%	31,466	40,543		4,224	67%
Sullivan	40%	1,233	59%	1,841	3,120		Sullivan	26%	921	73%	2,619	3,595		312	75%
Susquehanna	43%	8,381	55%	10,633	19,286		Susquehanna	29%	6,236	70%	15,207	21,752		2,145	74%
Tioga	36%	6,390	63%	11,326	17,984		Tioga	24%	4,955	75%	15,742	21,075		1,435	78%
Union	42%	7,333	57%	9,859	17,400		Union	37%	7,475	61%	12,356	20,115		-142	102%
Venango	40%	9,238	59%	13,718	23,307		Venango	29%	7,585	70%	18,569	26,528		1,653	82%
Warren	46%	8,537	52%	9,685	18,517		Warren	29%	6,066	69%	14,237	20,650		2,471	71%
Washington	47%	46,122	52%	50,752	98,047		Washington	38%	45,088	61%	72,080	118,478	18	1,034	98%
Wayne	43%	9,892	56%	12,702	22,835		Wayne	33%	9,191	66%	18,637	28,089		701	93%
Westmoreland	41%	72,721	58%	102,294	176,873		Westmoreland	35%	72,129	64%	130,218	204,697	11	592	99%
Wyoming	46%	5,985	53%	6,983	13,138		Wyoming	32%	4,704	67%	9,936	14,858		1,281	79%
York	43%	82,839	56%	109,268	194,210		York	37%	88,114	62%	146,733	238,471	8	-5,275	106%
		3,276,363		2,655,885					3,458,229		3,377,674
													p	Obama	Biden
													o	minus	div
Pittsburgh		Obama 2008		McCain 2008					Biden		Trump		p	Biden	Obama
Allegheny	57%	373,153	42%	272,347	651,436		Allegheny	60%	429,065	39%	282,324	719,733	2	-55,912	115%
Butler	36%	32,260	63%	57,074	90,425		Butler	33%	37,508	66%	74,359	113,305	19	-5,248	116%
Beaver	48%	40,499	51%	42,895	84,488		Beaver	41%	38,122	58%	54,759	94,122	21	2,377	94%
Washington	47%	46,122	52%	50,752	98,047		Washington	38%	45,088	61%	72,080	118,478	18	1,034	98%
Westmoreland	41%	72,721	58%	102,294	176,873		Westmoreland	35%	72,129	64%	130,218	204,697	11	592	99%
													p	Obama	Biden
													o	minus	div
Harrisburg		Obama 2008		McCain 2008					Biden		Trump		p	Biden	Obama
Dauphin	54%	69,975	45%	58,238	129,529		Dauphin	54%	78,983	45%	66,408	147,368	14	-9,008	113%
Cumberland	43%	48,306	56%	63,739	113,304		Cumberland	44%	62,245	55%	77,212	141,595	16	-13,939	129%
Lebanon	40%	23,310	59%	34,314	58,297		Lebanon	33%	23,932	65%	46,731	71,652		-622	103%
Franklin	33%	21,169	66%	41,906	63,641		Franklin	28%	22,422	71%	57,245	80,783		-1,253	106%
Adams	40%	17,633	59%	26,349	44,491		Adams	32%	18,207	66%	37,523	56,540		-574	103%
York	43%	82,839	56%	109,268	194,210		York	37%	88,114	62%	146,733	238,471	8	-5,275	106%
Perry	32%	6,396	66%	13,058	19,745		Perry	24%	5,950	74%	18,293	24,652		446	93%
													p	Obama	Biden
													o	minus	div
Williamsport		Obama 2008		McCain 2008					Biden		Trump		p	Biden	Obama
Montour	42%	3,364	57%	4,574	8,023		Montour	39%	3,771	60%	5,844	9,771		-407	112%
Lycoming	37%	18,381	62%	30,280	49,237		Lycoming	29%	16,971	70%	41,462	59,254		1,410	92%
Northumberland	42%	14,329	56%	19,018	33,939		Northumberland	30%	12,677	68%	28,952	42,283		1,652	88%
Snyder	35%	5,382	64%	9,900	15,479		Snyder	26%	4,910	73%	13,983	19,140		472	91%
Union	42%	7,333	57%	9,859	17,400		Union	37%	7,475	61%	12,356	20,115		-142	102%
													p	Obama	Biden
													o	minus	div
Philadelphia		Obama 2008		McCain 2008					Biden		Trump		p	Biden	Obama
Philadelphia	83%	595,980	16%	117,221	717,329		Philadelphia	81%	603,790	18%	132,740	741,377	1	-7,810	101%
Bucks	54%	179,031	45%	150,248	332,924		Bucks	52%	204,712	47%	187,367	396,234	4	-25,681	114%
Delaware	60%	178,870	39%	115,273	297,004		Delaware	63%	206,423	36%	118,532	327,931	5	-27,553	115%
Chester	54%	137,833	45%	114,421	254,354		Chester	58%	182,372	41%	128,565	314,502	7	-44,539	132%
Montgomery	60%	253,393	39%	165,552	422,419		Montgomery	63%	319,511	36%	185,460	510,157	3	-66,118	126%
Lancaster	44%	99,586	55%	126,568	228,137		Lancaster	41%	115,847	57%	160,209	280,239	6	-16,261	116%
													p	Obama	Biden
													o	minus	div
Allentown		Obama 2008		McCain 2008					Biden		Trump		p	Biden	Obama
Lehigh	57%	87,089	42%	63,382	152,473		Lehigh	53%	98,288	46%	84,259	184,713	10	-11,199	113%
Berks	54%	97,047	45%	80,513	180,000		Berks	45%	92,895	53%	109,736	205,540	9	4,152	96%
Northampton	56%	75,255	43%	58,551	135,587		Northampton	50%	85,087	49%	83,854	170,942	13	-9,832	113%
Monroe	58%	39,453	41%	28,293	68,443		Monroe	53%	44,060	46%	38,726	83,829	20	-4,607	112%
Pike	47%	11,493	52%	12,518	24,284		Pike	40%	13,019	59%	19,213	32,554		-1,526	113%

[Berlin_Freeper]

you are becoming more and more unhinged.

[Michael.SF.]

That would be a huge task to undertake. Do you have any specific examples? But as I think on that, if the fraud was undertaken at specific drop boxes by mules, then precinct boundaries may not necessarily be a factor.

[kiryandil]

But as I think on that, if the fraud was undertaken at specific drop boxes by mules, then precinct boundaries may not necessarily be a factor.

Some states have very usable precinct numbers, whereas others have extremely unfriendly data, if it's available at all.

We have to remember also [as Erik points out with precinct data being better] that the county data is not cut-and-dried, because some counties are split between congressional districts, both locally and at the national level.

[Hiddigeigei]

Thank you for your informative post, and I appreciate what must have been considerable effort in putting it all together.

[Erik Latranyi]

That would be a huge task to undertake. Do you have any specific examples? But as I think on that, if the fraud was undertaken at specific drop boxes by mules, then precinct boundaries may not necessarily be a factor.

They had to use ballots made out to registered voters, otherwise, they ran the risk of the fraud being uncovered.

So, you can see turnout in blue precincts was near 100%.

[kiryandil]

You're welcome.   

[Albion Wilde]

Please! Use Post #1 next time?

[Gay State Conservative]

Lots to digest here!

[kiryandil]

right.

I was focused on getting the four separate parts of the post up before a troll weighed in to disrupt the thread. Plus, you don't get to see how it looks on the main page before you pull the trigger.

As it was, I was right to be wary of vote-fraud-denying trolls - one jumped on within 5 minutes of the last of my initial posts, to cr@p up the thread and obfuscate the point.

[kiryandil]

Lots to digest here!

I didn't even do the Senate or House races.

There's hanky-panky there, too. As Erik pointed out, ALL the state-wide races are at risk with this method.

I discovered a Minnesota US House race [Minnesota's 7th CD and Collin Peterson] where a 30-year conservative Democrat lost because he was outside the rigging zone of the Minnesota ballot mules.

[kiryandil]

Lots to digest here!

I didn't even do the Senate or House races.

There's hanky-panky there, too. As Erik pointed out, ALL the state-wide races are at risk with this method.

I discovered a Minnesota US House race [Minnesota's 7th CD and Collin Peterson] where a 30-year conservative Democrat lost because he was outside the rigging zone of the Minnesota ballot mules.

[InterceptPoint]

I didn’t check in to good old FR until about an hour ago. So I missed all the fun.

But the post itself is brilliant and, I would say, invaluable to we Freepers. The fraud just jumps off the page.

Donations are fine but that’s nobody’s business but yours. It is posts like this one that are the real value that FR provides. It is what brings us all back everyday.

[Albion Wilde]

Well, you did a monumental job, and sorry to be a pest over it. Thank you for sharing all your work, kiryandil.

[kiryandil]

No worries! I appreciate the scrutiny.

And thanks for the thanks! :)

[SunkenCiv]

Thanks kiryandil.

• LIVE Today: Primaries in Idaho, Kentucky, North Carolina, Pennsylvania, and Oregon (GO VOTE!) [05/17/2022]
• Pennsylvania's Lancaster County Bans Ballot Drop Boxes [05/16/2022]
• Special Report: Pennsylvania Primary 2022 The Real Doug Mastriano, The Real Kathy Barnette [05/16/2022]
• House GOP leaders say white nationalism accusations are all politics [05/16/2022]
• Pennsylvania 2022 Poll: Oz Leads by 5 [05/16/2022]
• Pennsylvania Senate - Republican Primary [05/16/2022]

[Robert A Cook PE]

?? ??

[Robert A Cook PE]

The ability to gerrymander by race and culture (er, number of illegal aliens) begins in the 2000 census under Clinton’s minions and NGO “paid census volunteers” and was expanded by 2010 census under Obama when his walkers went around completely un-monitored and unobserved inventing people in abandoned buildings and burned out wrecks.

Thus, the lawsuits by radicals: “Imputed counts” - since “everybody knows” the poor and downtrodden are afraid to talk to the government!

[Nifty]

This is good. Thanks for doing this.