Tag: PVI

Since we now know the presidential results in most congressional districts for 2008, we now possess all the tools we need to calculate the new PVIs.

Charlie Cook’s official results will be out in a few months, but unlike in 2004 (where results from 2000 had to be fitted to the new congressional districts) there’s no reason we shouldn’t jump the gun and have our own figures ready. And there’s every reason to want to know who’s representing their districts and who’s a lot more vulnerable than they used to be.

I’m therefore going to attempt to calculate the new PVIs for the states listed in the diary title. They were picked because we have all the results from districts in those states, and because they represent around 20% of America’s congressioanl districts and I’m too lazy right now to do more than that in a sitting.

My methodology conforms to that given in David NYC’s comment to DGM’s diary. My figures are taken from this spreadsheet. My figures are approximate and you should probably ignore everything beyond the decimal point, because I’m using data rounded to the nearest whole number for the district-by-district results.

Details in the extended entry:

If anybody knows how to render this in tabular form, I’m all ears. There’s a Google spreadsheet with my data here and I’ve given a brief list of the new PVIs below.

The numbers aren’t that great, but that’s mostly because of Obama’s improved performance over Kerry. Expect a lot of these numbers to move back slightly towards the Dems if Obama can perform similarly in 2012, and bear in mind that D+0 is now a lot safer than it used to be. It’s also worth noting that the likes of Arkansas and north Florida were much better territory for Gore than for Obama.

AK-AL: R+15.6

AR-1: R+10.4

AR-2: R+7.0

AR-3: R+18.0

AR-4: R+9.4

AZ-1: R+8.2

AZ-2: R+15.3

AZ-3: R+11.8

AZ-4: D+10.7

AZ-5: R+7.2

AZ-6: R+16.8

AZ-7: D+3.6

AZ-8: R+7.0

CT-1: D+10.0

CT-2: D+3.7

CT-3: D+6.7

CT-4: D+2.8

CT-5: R+0.2

DE-AL: D+4.4

FL-1: R+23.6

FL-2: R+8.0

FL-3: D+15.7

FL-4: R+19.2

FL-5: R+11.5

FL-6: R+15.0

FL-7: R+11.0

FL-8: R+5.0

FL-9: R+9.5

FL-10: R+3.2

FL-11: D+9.0

FL-12: R+8.5

FL-13: R+8.0

FL-14: R+13.5

FL-15: R+9.5

FL-16: R+7.0

FL-17: D+31.8

FL-18: R+5.2

FL-19: D+12.2

FL-20: D+10.1

FL-21: R+7.7

FL-22: R+1.7

FL-23: D+25.8

FL-24: R+7.5

FL-25: R+7.0

GA-1: R+18.4

GA-2: R+1.7

GA-3: R+21.4

GA-4: D+21.7

GA-5: D+23.2

GA-6: R+20.4

GA-7: R+19

GA-8: R+12.5

GA-9: R+30.1

GA-10: R+17.0

GA-11: R+22.6

GA-12: R+1.7

GA-13: D+12.2

HI-1: D+8.5

HI-2: D+11.6

IA-1: D+2.4

IA-2: D+4.6

IA-3: R+1.2

IA-4: R+2.4

IA-5: R+11.6

ID-1: R+20.4

ID-2: R+20.0

IL-1: D+31.3

IL-2: D+33.3

IL-3: D+8.1

IL-4: D+29.2

IL-5: D+16.7

IL-6: R+1.9

IL-7: D+31.8

IL-8: R+2.9

IL-9: D+17.0

IL-10: D+3.6

IL-11: R+3.2

IL-12: R+0.1

IL-13: R+4.0

IL-14: R+4.0

IL-15: R+8.7

IL-16: R+4.5

IL-17: D+1.7

IL-18: R+7.5

IL-19: R+12.8

When we at SSP first hatched the idea of compiling some numbers for presidential election results for congressional districts, we were thinking we’d be lucky to get to 60 or maybe 100 districts. After all, we couldn’t track down precinct-level data for hundreds of counties, sort out what precinct goes into what district, and pick apart large metro counties with thousands and thousands of precincts… could we?

Well, with the aid of SSP’s crack cadre of some of the brightest and most tenacious elections geeks out there — in particular the relentless number cruncher jeffmd and master BoE cajoler Democratic Luntz — we’re closing in on completing all 435 districts. With another 54 added to the pile today, we’re near the 90% mark, with only 51 remaining incomplete. If you want to see all district percentages so far, the link is here; you can also check out the diaries where we released the numbers in more detail here, here, and here.

District	Obama #	McCain #	Other #	2008 %	2004 %	2000 %
AL-02	101,923	179,326	1,499	36.1/63.4	33/67	38/61
AL-05	115,773	185,640	3,364	38.0/60.9	39/60	44/54
CO-02	171,988	82,594	4,159	66.5/31.9	58/41	52/43
CO-04	168,637	171,690	6,229	48.7/49.5	41/58	37/57
MA-03	175,538	117,708	5,613	58.7/39.4	59/40	59/35
MA-04	197,306	107,459	5,175	63.7/34.7	65/33	65/29
MO-01	246,451	59,911	2,849	79.7/19.4	75/25	72/26
MO-03	189,730	124,537	4,589	59.5/39.1	57/43	54/43
MO-04	114,401	183,167	4,712	37.9/60.6	35/64	40/58
MO-05	198,259	110,057	3,371	63.6/35.3	59/40	60/37
MO-06	148,997	179,074	5,894	44.6/53.6	42/57	44/53
MO-07	114,752	204,246	5,013	35.4/63.0	32/67	36/62
NJ-03	181,004	162,339	3,828	52.1/46.8	49/51	54/43
NJ-05	152,506	179,781	3,428	45.4/53.6	43/57	45/52
NJ-06	149,400	98,959	2,791	59.5/39.4	57/43	61/35
NJ-07	177,471	165,430	4,016	51.2/47.7	47/53	48/49
NJ-08	165,346	93,734	2,098	63.3/35.9	59/41	60/37
NJ-09	158,933	99,144	2,270	61.1/38.1	59/41	63/34
NJ-10	208,151	30,192	1,048	87.0/12.6	82/18	83/16
NJ-11	154,300	182,604	3,253	45.4/53.7	42/58	43/54
NJ-13	155,012	50,369	1,750	74.8/24.3	69/31	72/25
NY-01	165,805	153,419	3,032	51.5/47.6	49/49	52/44
NY-08	184,682	63,769	2,121	73.7/25.5	72/27	74/18
NY-10	205,929	19,677	608	91.0/8.7	86/13	88/8
NY-11	206,656	20,709	999	90.5/9.1	86/13	83/9
NY-13	108,439	112,491	1,558	48.7/50.6	45/55	52/44
NY-15	226,049	14,954	1,522	93.2/6.2	90/9	87/7
NY-16	158,671	8,437	335	94.8/5.0	89/10	92/5
NY-17	172,479	66,027	1,312	71.9/27.5	67/33	69/27
NY-20	167,827	157,879	5,286	50.7/47.7	46/54	44/51
NY-21	179,322	123,378	5,733	58.1/40.0	55/43	56/39
NY-22	168,598	111,896	4,168	59.2/39.3	54/45	51/42
NY-23	133,367	119,943	4,112	51.8/46.6	47/51	47/49
NY-24	139,832	133,277	4,743	50.3/48.0	47/53	47/48
NY-25	177,780	135,931	5,216	55.7/42.6	50/48	51/45
NY-29	146,698	153,432	3,966	48.2/50.5	42/56	43/53
OH-14	168,381	169,131	5,193	49.1/49.4	47/53	44/52
OK-01	114,446	205,329	0	35.8/64.2	35/65	37/62
OK-02	91,481	174,351	0	34.4/65.6	41/59	47/52
OK-03	78,434	210,104	0	27.2/72.8	28/72	34/65
OK-04	101,418	200,192	0	33.6/66.4	33/67	38/61
OK-05	116,717	170,189	0	40.7/59.3	36/64	38/62
PA-03	143,416	143,433	4,066	49.3/49.3	47/53	47/51
PA-04	149,661	185,052	3,385	44.3/54.7	45/54	46/52
PA-05	123,503	152,946	3,944	44.1/54.6	39/61	38/59
PA-06	207,911	148,231	3,516	57.8/41.2	52/48	49/49
PA-07	186,232	142,944	3,845	55.9/42.9	53/47	51/47
PA-09	98,430	176,023	3,368	35.4/63.4	33/67	34/64
PA-10	131,335	155,437	3,721	45.2/53.5	40/60	41/56
PA-13	188,903	130,699	3,009	58.6/40.5	56/43	56/42
PA-19	142,398	187,857	3,698	42.6/56.3	36/64	36/61
UT-01	103,737	197,457	9,452	33.4/63.6	25/73	27/68
UT-02	138,790	202,534	11,552	39.3/57.4	31/66	31/67
UT-03	85,143	196,039	11,361	29.1/67.0	20/77	24/75

Some points of interest to check out in this batch: look at PA-06, with some of the steepest improvement in all of Pennsylvania. Any question why Jim Gerlach may be planning to cash it in and run for governor in 2010? It might be because his district just shot past PA-07 and PA-08 to become the bluest all-suburban district in the Philly area.

We have data for most of upstate New York (except for Erie County, where Buffalo is), and it’s striking that Obama improved on Kerry at a much greater clip upstate than in the NYC metro area. One thing that might give us some optimism heading into the NY-20 special election is the nearly 6-point improvement, as well as the fact that the Dem candidate actually won the district in the first time since, well, probably Barry Goldwater. But this is pretty typical across upstate NY, as we also flipped NY-23 and NY-24, moved NY-25 from swing to pretty safe D, and almost even won in New York’s reddest district of NY-29. Compare this with, say, the whiter urban districts, like NY-08 or especially NY-13 (Staten Island and white ethnic parts of Brooklyn), where Obama lost narrowly while barely improving on Kerry’s numbers, and thus nearly overtaking NY-29 as New York’s reddest district.

The biggest improvements here, as in previous installments are in the Mountain West. This is plain to see in Colorado, not just in the 2nd (where the improvement over 2000 is gigantic, although that may have to do with the huge Nader effect among Boulder’s granola-munching crowd) but also in the 4th, where Obama lost by less than a point where Gore lost by 20. And although we didn’t come even close in Utah, some of the biggest percentage gains were there. Look for UT-03 to lose its worst-PVI-in-the-nation status, as Obama made up 9 points there on Kerry.

Is there any bad news to report here? Well, we came oh-so-close to flipping OH-14 in Cleveland’s suburbs (fewer than 1,000 votes), while not moving the numbers much there. And we lost ground in AL-05, the Appalachian portion of Alabama, and PA-04, which, like PA-12, is in the collar counties around Pittsburgh where the Rust Belt fades into the Appalachians.

Probably least appetizing are the numbers out of Oklahoma, but even it provides some interesting insights into the changes from the old Democratic coalition to the current Democratic coalition. Most of the state stayed in neutral over the decade, but compare OK-02 (rural NE Oklahoma around Muskogee) vs. OK-05 (Oklahoma City). We’re getting absolutely hammered in the 2nd, a traditionally Yellow Doggish area that Gore almost won. On the other hand, we shot up in the 5th, the most cosmopolitan part of the state.

So what’s left to do? Our main task is, at this point, getting data from counties who have been unresponsive or are charging an arm and a leg for it. If you’re interested in helping out, check out this diary for a primer; here’s our database of elections boards to contact. And, as always, here’s our master crowdsourcing database… although, as you might notice, most of those blanks have been filled in! Thanks to you guys, of course.

One final caveat: these numbers are subject to change slightly, as we refine the data. In fact, in a few days I’ll be posting a list of several dozen updated districts. None of these changes should amount to more than a fraction of a percentage point, but caution is warranted where a fraction of a percent would make a lot of difference in how the district is perceived (for instance, PA-03, where a very small revision could make all the difference in terms of McCain’s 17-vote margin in the district).

A number of people who’ve expressed interest in helping gather election data so that we can compile presidential results by CD have asked for a more detailed guide. So here’s what I suggest:

1) Open up this spreadsheet.

2) Find a county where the three right-hand columns (F, G & H) are all blank. (If there’s information in any of those, it means someone has already requested data from that county, or at least investigated it.)

3) Call the phone number listed in column D. (If there’s no phone number, please look it up and paste it into column D.) When I call, this is what I like to say:

Hi. I’m a researcher looking for detailed election results from the 2008 election. Whom might I speak with about this?

Once I have the right person, this is the request I make:

I’m interested in precinct-level results for the Presidential and Congressional races in your county for the 2008 election. Are you able to send that to me?

That’s really you need to ask for – this request is very basic and should be readily understood. If you encounter any confusion, report back here in comments and we’ll try to figure out what the misunderstanding is.

4) At this point, the response you might get will vary. Some election officials will email you on the spot, some will only mail you hardcopy versions, and some might even insist on mailing you a CD. Still others might ask you to fill out a particular request form, or fax them a signed letter, or file a Freedom of Information request.

Just ask what you need to do and you should be given straightforward instructions. If you follow these, you should get the data you’re looking for without a problem. Note: If the county you talk to can only send hardcopies but you don’t have access to a scanner, let us know in comments.

5) Side note: Some counties – and this really cheeses me off, but there’s not much we can do – may require a payment for the data. If that happens, I recommend you do NOT pay for the data. Rather, find out how much the data would cost. Then open up this spreadsheet again and type your username into column F and the cost into column G. We’ll look into making purchases later.

6) Once you get the data, please upload your files to Scribd. (You’ll need to create an account there first.) Then, post the URL(s) in column H. That way we’ll know we have the data, and we’ll know where to find it.

That’s really all there is to it. If you have any questions at all, please feel free to ask in comments.

The good news: We’ve scrounged up precinct-level election results for about twenty counties that were on our list in order to complete the presidential results by CD. The bad news: We still need data from another ninety.

The bottom line is that we’ve downloaded from every website that lets you download, and we’ve emailed every county that lets you email. The remaining counties either don’t have websites or email address, or just haven’t responded to emails. So we need to start making phone calls.

I think the netroots – and really, we’re just talking about a single small blog here – could make a big impact by releasing a complete set of data. Before we started, I never thought that doing so would be possible, but now I believe it’s in our grasp. Finishing this would demonstrate that a dedicated band of volunteers can tackle a project most would assume would require a bunch of professionals and a lot of money.

It would also demonstrate that when it comes to data analysis, the Internet really has ushered in a new era of openness, transparency and accessibility. Indeed, our work has already been favorably cited in places like the Guardian and Roll Call, and in local newspapers as well. We’re breaking barriers, people!

Alright, enough with the attempts at rousing exhortations. There are still phone calls to make – the full list is here. If you have some free time during the day and can make a few calls, this short list of “high value” municipalities is a good place to start:

Getting precinct-level data for these counties/cities/towns (especially those toward the top of the list) is key, but all are important. If you want to try another route, start with your home state. If your home state is not on the list, then just pick some counties at random.

Remember that when you call, you need to ask for precinct-level results for both the presidential race and any United States House races within the county in question. (Without the latter data set, we can’t figure out which precincts are assigned to which CD.)

Also note that if you see a notation in the “Data Requested” column, or a price listed in the “Cost” column, that means we’ve already made contact with that county, so there’s no need to call them. (Mostly we’re waiting to figure out if we can find a sugar daddy to pay for the data from the counties which charge – grr! The nerve of them!)

If you do make a phone call and request the data, please make a note of it in the proper column. If the county emails it to you, great – just upload it to Scribd or Google Docs and post the URL in the spreadsheet. If they offer to mail it to you, please make a note of your request date (along with your name or user name) so that we can follow up if need be. And if they quote you a price, hold off on ordering – just note the price in the proper column.

Again, the full list of counties we need data for is right here. Let’s do this thing!

Over the holidays, SSP readers seemed to have a lot of fun with the vulnerable House Republicans and vulnerable House Democrats threads. This left me wondering, as so many things seem to do, “is there a way to quantify that?” In other words, is there a data-driven way to approach the question instead of just relying on perceptions (and also to make sure that potentially overlooked races don’t fall through the cracks)?

Here’s what I tried. It’s actually a bit reminiscent of my PVI/Vote Index, in that it measures representative performance against the district’s lean, except here performance is measured by the rep’s margin in the last election. (The data for many of the 2008 electoral margins is available in the recent “How’d We Do?” post, conveniently arranged in order from closest to least close.)

Look at the top 20 most vulnerable Republicans to see how it works. As pretty much everyone would expect, Anh Cao in LA-02 is the most vulnerable GOPer. He had the 5th weakest margin of any Republican who survived 2008 (beating Bill Jefferson by 2.7%, behind only Fleming (0.4%), McClintock (0.6%), Calvert (2.4%), and Luetkemeyer (2.5%). Needless to say, he’s in the GOP-held district with the least favorable PVI (D+28, using “old,” i.e. 00-04, PVI). At #2 is Jim Gerlach in PA-06; he had the 9th worst margin at 4.2%, and he’s in the 6th worst district for a GOPer at D+2. And so on…

District	Rep.	Margin rating	PVI rating	Total
LA-02	Cao	5	1	6
PA-06	Gerlach	9	6	15
IL-10	Kirk	13	4	17
WA-08	Reichert	16	5	21
MI-11	McCotter	17	16	33
MN-03	Paulsen	22	12	34
NJ-07	Lance	24	13	37
OH-12	Tiberi	34	14	48
CA-50	Bilbray	11	40	51
MN-06	Bachmann	6	46.5	52.5
FL-25	Diaz-Balart	18	37	55
CA-44	Calvert	3	55	58
AL-03	Rogers	25	34	59
LA-04	Fleming	1	60	61
FL-15	Posey	31	30.5	61.5
MN-02	Kline	39	23	62
CA-26	Dreier	33	30.5	63.5
MO-09	Luetkemeyer	4	60	64
NY-26	Lee	38	27	65
PA-15	Dent	58	8	66

Is this much different from SSP readers’ predictions? No, not much; it’s the wisdom of crowds at work. Still, I see a few names on there that didn’t get much of any mention in our prediction thread: especially Pat Tiberi in OH-12 (34th worst margin at 12.6%, 14th worst district at R+1) who seems to fly under the radar every single freakin’ election. Other names revealed by this list that wouldn’t necessarily be intuitive picks include Thad McCotter, John Kline, Mike Rogers (AL), and Bill Posey, who benefited from our big-time recruitment failure in the FL-15 open seat.

Here’s the flipside: the Democratic seats that seem likeliest to flip, based on 2008 numbers. Some of these may not be much cause for alarm; Chet Edwards, for instance, is probably not in any imminent danger except in case of a 1994-sized event, but he’s probably doomed to uncomfortable margins for all eternity. On the other hand, time will tell whether Walt Minnick can quickly fortify himself, or if we’re only renting ID-01 for a couple years.

District	Rep.	Margin rating	PVI rating	Total
ID-01	Minnick	5	1	6
AL-02	Bright	2	5	7
MD-01	Kratovil	4	10	14
TX-17	Edwards	19	2	21
VA-05	Perriello	1	26.5	27.5
AL-05	Griffith	10	20	30
MS-01	Childers	25.5	8.5	34
NY-29	Massa	6	29.5	35.5
VA-02	Nye	15.5	22	37.5
CO-04	Markey	34	11.5	45.5
PA-10	Carney	35	14	49
GA-08	Marshall	39	13	52
FL-08	Grayson	12.5	44	56.5
MI-07	Schauer	7	49.5	56.5
NM-02	Teague	33	23.5	56.5
WI-08	Kagen	20	38.5	58.5
OH-15	Kilroy	3	58	61
AZ-05	Mitchell	23	38.5	61.5
PA-03	Dahlkemper	8	54	62
OH-16	Boccieri	27.5	40	67.5

More over the flip…

In describing this method to DavidNYC, he quite rightly asked “Wait, does this thing actually work?” So, after a lot more data entry and some testing based on how well the 2006 numbers would have predicted the 2008 results, I can conclude it does work fairly well. Here is what the 2006 numbers would have predicted for GOP held seats in 2008.

District	Rep.	Margin rating	PVI rating	Total
NM-01	Wilson	3	7	10
NY-25	Walsh	9	5	14
PA-06	Gerlach	7	9	16
CT-04	Shays	16	2	18
WA-08	Reichert	14.5	8	22.5
NV-03	Porter	10	13	23
IL-10	Kirk	24	4	28
NJ-07	Ferguson	8	20.5	28.5
OH-15	Pryce	4.5	24.5	29
MI-09	Knollenberg	22	16	38
OH-01	Chabot	20	18.5	38.5
NC-08	Hayes	1.5	38.5	40
PA-15	Dent	33	11	44
FL-13	Buchanan	1.5	46.5	48
IL-06	Roskam	12.5	36.5	49
MI-07	Walberg	41	10	51
NY-03	King	17	34	51
AZ-01	Renzi	28	30.5	58.5
IL-11	Weller	34	24.5	58.5
NY-13	Fossella	45	14	59

One problem leapt out at me: the role of open seats, and the accompanying loss of the benefits of incumbency. So, I performed a tweak that took open seats into account (by taking out the margin, and just leaving the open seat’s strength based only on its PVI rating). That takes it a little closer to the way things actually shook out. 13 out of the top 20 were pickups, which seems like a good but not amazing rate of prediction.

Without doing a lot of putting your thumbs on the scales of individual races, I don’t know how you’d build a model that somehow predicted, say, Tom Feeney’s implosion, or the fizzle in the open seat in NM-02, or Dave Reichert’s confounding staying power, or Bob Roggio’s amazing lack of name recognition… or that Bill Sali was vulnerable (he was #106) if only because of sheer malice and stupidity. Any good prognostication has to include at least some kind of qualitative analysis of candidates’ levels of, well, suckiness.

By the way, in case you’re wondering what this formulation means would happen to Peter King’s seat if he bails out to run for NY-Sen, it would vault up to #2 on the list if it were open. (It’s the 7th most Dem PVI of any GOP-held seat, so for 2010 the score of 7 would slot an open NY-03 right before LA-02.) So, a year from now, once we have a sense of where seats will open up, I’ll have to revisit this project.

District	Rep.	Margin rating	PVI rating	Total
NY-25	Open	0	5	5
NJ-03	Open	0	6	6
NM-01	Open	0	7	7
NY-13	Open	0	14	14
PA-06	Gerlach	7	9	16
CT-04	Shays	16	2	18
MN-03	Open	0	18.5	18.5
NJ-07	Open	0	20.5	20.5
VA-11	Open	0	20.5	20.5
WA-08	Reichert	14.5	18	22.5
NV-03	Porter	10	13	23
IL-11	Open	0	24.5	24.5
OH-15	Open	0	24.5	24.5
IL-10	Kirk	24	4	28
AZ-01	Open	0	30.5	30.5
MI-09	Knollenberg	22	16	38
OH-01	Chabot	20	18.5	38.5
NC-08	Hayes	1.5	38.5	40
NY-26	Open	0	42	42
PA-15	Dent	33	11	44

Finally, here’s what the 2006 numbers would have predicted for the Democratic-held seats in 2008, including the tweak for open seats (of which we didn’t have many). Three of the top 10 did, in fact, fall. Plus, LA-06 isn’t on the list because it changed hands during a special election. However, my back-of-the-envelope calculation for Cazayoux based on his 3% margin in the special election and an R+6.5 would’ve given him a score around 24, good for 4th place. On the other hand, the fifth Dem seat to fall, LA-02, clocks in at #187!

District	Rep.	Margin rating	PVI rating	Total
GA-08	Marshall	4	9	13
AL-05	Open	0	17	17
KS-02	Boyda	11	11	22
IN-09	Hill	15	12.5	27.5
PA-10	Carney	18	10	28
TX-22	Lampson	29	4	33
NC-11	Shuler	23.5	12.5	36
WI-08	Kagen	6	30.5	36.5
TX-17	Edwards	39	1	40
FL-16	Mahoney	5	38.5	43.5
IL-08	Bean	21	23	44
AZ-05	Mitchell	14	30.5	44.5
UT-02	Matheson	43	2	45
NY-19	Hall	12.5	36	48.5
PA-04	Altmire	7.5	41	48.5
IN-02	Donnelly	25	26	51
IN-08	Ellsworth	44.5	8	52.5
CA-11	McNerney	20	33	53
TX-23	Rodriguez	26	27.5	53.5
OR-05	Open	0	55	55

Thanks to the heroic efforts of this community, we were able to post a third wave of presidential results by Congressional district yesterday. And as you can see from our perma-post, we now have numbers for an impressive 332 districts. That leaves us with just over 100 districts to go, and as you’d expect, these are some of the thorniest.

The real problem is access to data. Most of the more recently-posted numbers have been drawn from precinct-level data. This sort of information is usually only available at the county level, rather than from Secretaries of State. Many counties make this data easily accessible on the web (some in more usable forms than others), but some don’t offer it online at all – and those are the counties we need to tackle. Some offer it, but charge for it. And some don’t even appear to have websites.

The bottom line is that there are about 100 counties whose precinct-level data we need in order to finish this project – and we’re only gonna solve this problem by throwing as much manpower (or dare I say, people power?) at it as possible. To that end, we’ve created another crowdsourcing spreadsheet for folks to start tackling this.

The first order of business is finding out contact information for the county Boards of Election. Many won’t have email addresses, and even for those that do, emails may go unanswered. So that means we’re going to have to start making phone calls. I can’t stress this enough: Please be super-polite when making these calls. These are hardworking folks who ensure our elections are run properly and probably don’t do this for much money.

Anyhow, if you do call up a county BoE and request the data, please put the date of your request & your name (username is fine) in column E. This will help us avoid inundating the BoEs with multiple requests. Some might be able to email you the data, in which case you can just directly upload it. (For spreadsheets, please upload them to Google Docs. For PDFs, please use Scribd – PDFs on Google Docs can’t be shared. Spreadsheets which exceed Google’s 1MB limit can also be uploaded to Scribd.) When you do, please put the URL in the right-most column.

Some counties might insist on mailing you a disk. It’s even possible that some will only want to send you a hard copy. If this turns out to be the case and you don’t have access to a scanner, please make a note on the spreadsheet so that someone else who does have a scanner can make the request instead.

Finally, some (maybe a lot) of these counties will try to charge us for the data. I think that’s a load of bollocks, seeing as the information has already been gathered (and paid for with tax dollars) – obviously the cost of distributing it is zero. But this is something we just have to live with. Anyhow, if you do encounter a county which charges a fee, PLEASE DO NOT ORDER THE DATA AND PAY FOR IT YOURSELF. I’d hate for us to make duplicate orders and wind up wasting money. Please just make a note of the cost in column F and we’ll revisit this soon in a co-ordinated fashion.

A complete list of counties we need (plus a few cities and towns) is below the fold, and of course in our crowdsourcing spreadsheet. Oh, and please share your tips/experiences in comments. Let’s get to work!

The elves were busy while I was taking Christmas off, and now that I’ve picked the crowdsourcing project back up, we’ve made another big jump, taking us to the point of having presidential election results for 3/4s of all congressional districts.

Results from the first wave are here, and results from the second wave are here. If you want to see all results in one place, they’re permalinked here. Also, please check out our master database; although we’ve made a lot of headway, there’s still plenty to do if you have access to precinct-level data (however, the remaining states are the ones that tend to be most coy about releasing precinct-level data, so those remaining districts may never see daylight until Polidata somehow solves those enigmas).

District	Obama #	McCain #	Other #	2008 %	2004 %	2000 %
CA-01	199,835	96,530	8,264	65.6/31.7	60/38	52/39
CA-02	125,291	161,636	7,041	42.6/55.0	37/62	33/61
CA-03	165,617	164,025	6,440	49.3/48.8	41/58	41/55
CA-04	167,604	206,385	8,368	43.8/54.0	37/61	36/59
CA-05	165,776	67,625	4,709	69.6/28.4	61/38	60/35
CA-06	253,087	73,345	6,802	76.0/22.0	70/28	62/30
CA-07	179,037	66,272	5,450	71.4/26.4	67/32	66/31
CA-08	266,210	38,665	7,519	85.2/12.4	85/14	77/15
CA-09	260,662	29,186	5,919	88.1/9.9	86/13	79/13
CA-10	204,138	104,624	6,972	64.7/33.1	59/40	55/41
CA-12	214,850	69,029	5,213	74.3/23.9	72/27	67/29
CA-13	175,838	56,299	4,270	74.4/23.8	71/28	67/30
CA-17	171,180	61,163	4,932	72.1/25.8	66/33	60/33
CA-22	110,910	172,792	5,879	38.3/59.7	31/68	33/64
CA-25	134,222	131,201	6,010	49.5/48.3	40/59	42/56
CA-26	149,249	137,329	5,885	51.0/47.0	44/55	44/53
CA-27	157,100	75,286	5,219	66.1/31.7	59/39	60/36
CA-28	147,958	42,815	3,492	76.2/22.0	71/28	73/24
CA-29	159,947	71,860	4,840	67.6/30.4	61/37	58/38
CA-30	242,022	95,869	5,710	70.4/27.9	66/33	68/28
CA-31	113,941	25,441	3,280	79.9/18.3	77/22	77/19
CA-32	119,726	52,356	3,557	68.2/29.8	62/37	67/31
CA-33	205,470	27,672	3,539	86.8/11.7	83/16	83/14
CA-34	106,695	33,056	3,023	74.7/23.2	69/30	72/26
CA-35	165,761	27,789	2,923	84.4/14.1	79/20	82/17
CA-36	176,924	92,105	5,754	64.4/33.5	59/40	57/39
CA-37	157,219	36,940	3,388	79.6/18.7	74/25	76/22
CA-38	130,092	48,599	3,846	71.3/26.6	65/34	70/28
CA-39	128,579	63,680	4,117	65.5/32.4	59/40	62/36
CA-40	114,025	125,066	5,456	46.6/51.1	39/60	41/56
CA-41	119,255	147,982	5,890	43.7/54.2	37/62	41/56
CA-42	128,474	152,256	5,529	44.9/53.2	37/62	39/59
CA-43	112,020	49,594	3,216	68.0/30.1	58/41	64/34
CA-44	133,535	131,003	5,169	49.5/48.6	40/59	44/53
CA-45	142,305	129,664	4,251	51.5/46.9	43/56	47/51
CA-46	145,393	150,937	6,921	47.9/49.8	42/57	42/55
CA-47	77,144	48,461	2,672	60.1/37.8	49/50	56/42
CA-48	163,063	160,584	7,091	49.3/48.6	40/58	40/58
CA-49	117,283	137,739	4,805	45.1/53.0	36/63	39/59
CA-50	172,962	158,845	5,616	51.3/47.1	44/55	43/54
CA-51	135,960	76,438	3,021	63.1/35.5	53/46	57/41
CA-52	135,848	161,332	4,827	45.0/53.4	38/61	40/57
CA-53	177,863	77,930	5,101	68.2/29.9	61/38	58/38
GA-01	96,818	167,122	2,149	36.4/62.8	34/66	38/62
GA-02	130,109	111,559	1,322	53.6/45.9	50/50	52/48
GA-03	129,895	235,263	3,178	35.3/63.9	29/70	32/68
GA-04	208,874	54,868	1,974	78.6/20.7	71/28	70/30
GA-05	249,927	63,053	2,734	79.1/20.0	74/26	73/27
GA-06	133,716	227,701	4,301	36.6/62.3	29/70	32/68
GA-07	140,009	212,721	3,710	39.3/59.7	30/70	31/69
GA-08	123,877	162,376	1,978	43.0/56.3	39/61	42/58
GA-09	70,366	225,929	3,611	23.5/75.3	23/77	29/71
GA-10	113,915	183,441	2,773	38.0/61.1	35/65	37/63
GA-11	103,112	204,275	3,987	33.1/65.6	29/71	35/66
GA-12	143,624	120,150	1,733	54.1/45.3	49/50	52/48
GA-13	200,567	80,327	2,180	70.9/28.4	60/40	57/43
IN-01	184,871	111,895	2,582	61.8/37.4	55/44	56/42
IN-04	141,946	184,389	3,509	43.0/55.9	30/69	32/66
IN-05	143,447	210,103	3,172	40.2/58.9	28/71	30/69
IN-07	191,381	76,530	2,056	70.9/28.4	58/42	56/43
IN-09	149,587	151,543	3,783	49.1/49.7	40/59	42/56
KS-01	79,638	184,501	4,813	29.6/68.6	26/72	29/67
KS-02	133,759	170,279	6,003	43.1/54.9	39/59	41/54
KS-03	186,196	177,019	5,148	50.6/48.1	44/55	42/53
KS-04	113,418	166,705	5,440	39.7/58.4	34/64	37/59
NY-18	184,182	112,214	2,294	61.7/37.6	58/42	58/39
NY-19	160,645	153,424	3,100	50.7/48.4	45/54	47/49
OH-01	164,824	133,576	3,147	54.7/44.3	49/51	46/51
OH-02	126,796	190,109	4,297	39.5/59.2	36/64	34/63
OH-03	155,610	167,897	4,830	47.4/51.1	46/54	45/52
OH-04	112,543	176,973	5,882	38.1/59.9	34/65	35/62
OH-05	136,666	159,433	5,981	45.2/52.8	39/61	37/59
OH-07	142,154	171,568	5,194	44.6/53.8	43/57	42/56
OH-08	118,915	189,578	5,499	37.9/60.4	35/64	36/61
OH-09	194,682	113,095	4,925	62.3/36.2	58/42	55/41
OH-10	174,575	115,005	5,489	59.2/39.0	58/41	53/42
OH-11	245,149	41,606	2,463	84.8/14.4	81/18	79/18
OH-12	213,177	183,233	5,172	53.1/45.6	49/51	46/52
OH-15	167,441	139,425	5,486	53.6/44.6	50/50	44/52
OH-18	112,545	128,735	6,122	45.5/52.0	43/57	41/55
OR-02	155,301	192,627	10,632	43.3/53.7	38/61	35/60
OR-04	200,841	161,645	11,572	53.7/43.2	49/49	44/49
PA-01	246,006	32,174	1,310	88.0/11.5	84/15	84/15
PA-02	270,695	26,521	1,264	90.7/8.9	87/12	87/12
PA-08	186,372	157,544	3,814	53.6/45.3	51/48	51/46
PA-11	164,451	121,559	3,229	56.9/42.0	53/47	54/43
PA-12	131,544	132,497	3,892	49.1/49.5	51/49	55/44
PA-14	209,771	86,927	2,886	70.0/29.0	69/30	70/28
PA-15	162,471	122,163	3,804	56.3/42.4	50/50	49/48
PA-16	150,341	161,844	2,719	47.7/51.4	38/61	36/62
PA-17	144,897	152,406	3,737	48.1/50.6	42/58	41/56
PA-18	149,824	186,297	3,215	44.2/54.9	46/54	47/52
TN-01	75,052	181,912	3,829	28.8/69.8	31/68	38/61
TN-02	104,287	195,540	4,600	34.3/64.2	35/64	39/59
TN-03	103,817	174,248	3,600	36.9/61.9	38/61	41/57
TN-04	92,924	173,841	4,917	34.2/64.0	41/58	49/50
TN-05	166,293	128,615	3,636	55.7/43.1	52/48	57/42
TN-06	112,064	189,729	4,721	36.6/61.9	40/60	49/49
TN-07	123,063	230,779	3,397	34.4/64.6	33/66	40/59
TN-08	110,390	144,957	3,255	42.7/56.1	47/53	51/48
TN-09	196,824	56,130	1,432	77.4/22.1	70/30	63/36
TX-02	105,736	159,141	1,805	39.7/59.7	37/63	37/63
TX-03	124,027	171,119	3,283	41.6/57.3	33/67	30/70
TX-04	90,191	206,621	2,992	30.1/68.9	30/70	34/66
TX-05	90,135	158,356	2,128	36.0/63.2	33/67	34/66
TX-06	112,025	167,778	2,243	39.7/59.5	34/66	34/66
TX-07	121,472	173,162	2,673	40.9/58.2	36/64	31/69
TX-08	73,428	213,450	2,464	25.4/73.8	28/72	31/69
TX-09	137,619	40,240	850	77.0/22.5	70/30	69/31
TX-10	149,112	183,908	3,987	44.3/54.6	38/62	34/67
TX-11	56,939	182,074	2,332	23.6/75.4	22/78	25/75
TX-12	99,083	171,408	2,539	36.3/62.8	33/67	36/64
TX-13	52,691	175,174	2,087	22.9/76.2	22/78	25/75
TX-14	88,532	177,370	2,230	33.0/66.2	33/67	36/64
TX-16	118,178	60,279	1,773	65.6/33.5	57/44	59/41
TX-17	78,756	166,649	2,351	31.8/67.3	30/70	32/68
TX-18	150,973	43,292	1,104	77.3/22.2	72/28	72/28
TX-19	64,541	168,789	1,912	27.4/71.8	23/77	25/75
TX-20	115,470	64,724	2,163	63.3/35.5	55/45	58/42
TX-21	149,261	214,569	4,299	40.6/58.3	34/66	31/69
TX-22	129,414	183,172	2,454	41.1/58.1	36/64	33/67
TX-23	124,568	117,704	2,348	50.9/48.1	43/57	47/54
TX-24	124,128	153,758	2,688	44.2/54.8	35/65	32/68
TX-25	176,016	118,183	4,805	58.9/39.5	54/46	47/53
TX-26	135,285	185,468	2,746	41.8/57.3	35/65	38/62
TX-28	103,037	80,192	1,251	55.9/43.5	46/54	50/50
TX-29	66,808	40,884	815	61.6/37.7	56/44	57/43
TX-30	170,826	37,465	1,306	81.5/17.9	75/25	74/26
TX-32	96,203	110,397	2,509	46.0/52.8	40/60	36/64

A few words about some of the states. Many of you have already seen the California numbers, which californianintexas published in her excellent diary; for those of you who haven’t, here they are on the front page again. There are unfortunately some California districts missing; a number of large counties (Santa Clara, San Joaquin, Ventura, and Fresno especially) haven’t provided precinct-by-precinct data, so districts incorporating parts of those counties can’t be completed.

The missing precinct-level data problem explains missing districts in certain other states, too. (In some cases, there was missing data for smaller counties, but I made a judgment call that the counties in question were small enough that they wouldn’t affect the overall percentage much, so they’re included.) In Indiana, we’re still missing data for Allen and Elkhart Counties, so that rules out IN-02, IN-03, and IN-06. (I already did the 8th in the first wave.) The partial totals for the left-out districts are still available in the Indiana database (the same is true for OH, PA, and TX as well), if you click the link. They may well be very close to the actual percentages, but there’s just no way of knowing.

In Ohio, large counties we’re missing include Mahoning, Trumbull, and Medina, so we’re short OH-06, OH-13, OH-14, OH-16, and OH-17.  Pennsylvania is missing Montgomery, Butler, and Cumberland Counties among others, so there we’re also missing the PA-03, PA-04, PA-05, PA-06, PA-07, PA-09, PA-10, PA-13, and PA-19. (MontCo also occupies a tiny bit of PA-02, PA-08, and PA-15, but it’s such a small percentage of those districts I decided to let it slide.)

In Texas, Cameron County is missing, so that leaves out TX-15 and TX-27. (I also did the 1st and 31st in the first wave.) Finally, there’s the matter of New York, where only a few counties bother to report by precinct. Luckily, two of them are Westchester and Rockland, so at least we can do NY-18 and NY-19 there.

There was also one missing county in Oregon, which kept me from including OR-02 and OR-04 in the first wave. I found enough information about Josephine County to decide how to allocate its votes (66.8% of the county’s voters voted for a candidate in the OR-02 congressional race, while 33.1% voted in OR-04, so I just applied those percentages to the presidential race).

In Georgia, as with many of the other southern states, early votes aren’t broken down, so what jeffmd did, as before, was to use both ‘hard’ and ‘soft’ totals, where soft totals included early votes allocated proportionately. I’m including the soft totals (otherwise, we wouldn’t have even won GA-02 and GA-12, where victory clearly depended heavily on black turnout).

So what are some of the highlights in this data set? Check out some of the traditionally Republican districts in California (where in many, not coincidentally, we came very close to surprising long-term incumbents) like CA-03, CA-26, CA-44, and even GOP strongholds like CA-25 and CA-48: all won by Obama.

Some of the biggest gains were in Indiana, especially in the Indianapolis area, where both the city itself (IN-07) and its right-wing suburbs (IN-05) zoomed to the left. Amazing what you can accomplish when you actually try to contest a formerly uncontested state.

One area where the GOP might take heart is western Pennsylvania, where there’s apparently the one district in the nation that flipped from going for Kerry to going narrowly for McCain: John Murtha’s PA-12. Also, the Philly burbs didn’t move as much as one might expect (the needle barely budged in PA-08 in Bucks County); where the biggest progress occurred in Pennsylvania was out in places like Lancaster and Harrisburg (see PA-16 and PA-17).

Texas is a very complicated tapestry: in many rural parts of the state, there was no real improvement from 2004, despite the loss of the favorite son effect. For example, expect TX-13 to replace UT-03 as the district with the worst PVI once they recalculate. And look at TX-08, where both growing right-wing exurbs and declining Dem fortunes in the Beaumont area were a double-whammy. Contrast that, though, with not just hugely improved percentages in the minority districts, but also a lot of progress in the suburban districts that we’ve discussed a lot recently where the minority growth is accelerating: TX-10, TX-22 (where the growth wasn’t enough to save Nick Lampson, sadly), TX-32, and especially TX-24 in the area around DFW airport.

And, as always, if more results trickle into the master database, I’ll be sure and post them to the front page. So keep on number-crunching!

Last week we released our first wave of results, for over 100 congressional districts. Today, as promised, here’s our second wave, with the results for another 95 districts.

Despite the huge avalanche of data, we’re still only halfway done. Please take a look at our master database to see what states remain. Even if you don’t have time to tabulate data yourself but if you’ve sniffed out some precinct-level data sources anywhere, please let us know in the database! We need you, to put the “crowd” in “crowdsourcing.” (A permalink to all our results so far is available here.)

District	Obama #	McCain #	Other #	2008 %	2004 %	2000 %
AZ-01	127,790	157,160	3,848	44.3/54.4	46/54	46/51
AZ-02	138,275	220,667	4,279	38.1/60.8	38/61	41/57
AZ-03	121,996	162,724	3,422	42.3/56.5	41/58	43/55
AZ-04	86,815	43,610	1,651	65.7/33.0	62/38	63/35
AZ-05	140,287	153,736	3,362	47.2/51.7	45./54	43/54
AZ-06	135,178	220,718	4,068	37.6/61.3	35/64	37/61
AZ-07	123,202	89,725	2,491	57.2/41.7	57/43	58/38
AZ-08	161,164	181,771	4,141	46.4/52.4	46/53	46/50
FL-01	112,291	232,449	3,638	32.2/66.7	28/72	31/69
FL-02	161,822	196,555	3,715	44.7/54.3	46/54	47/53
FL-03	144,167	52,056	1,213	73.0/26.4	65/35	65/35
FL-04	141,930	231,915	3,541	37.6/61.5	31/69	34/66
FL-05	191,959	249,328	5,029	43.0/55.9	41/58	46/54
FL-06	117,280	184,864	3,089	38.4/60.6	39/61	42/58
FL-07	116,797	158,437	2,868	42.0/57.0	43/57	46/54
FL-08	187,295	167,127	2,714	52.4/46.8	45/55	46/54
FL-09	169,897	190,344	4,596	46.6/52.2	43/57	46/54
FL-10	164,148	150,962	4,895	51.3/47.1	49/51	51/49
FL-11	174,314	88,357	2,642	65.7/33.3	58/41	61/39
FL-12	115,180	123,958	2,424	47.7/51.3	42/58	45/55
FL-13	175,991	196,908	3,732	46.7/52.3	44/56	46/55
FL-14	167,015	224,405	3,084	42.3/56.9	38/62	39/61
FL-15	137,627	152,415	3,352	46.9/52.0	43/57	46/54
FL-16	174,255	191,423	3,821	47.2/51.8	46/54	47/53
FL-17	209,839	29,758	899	87.3/12.4	83/17	85/15
FL-18	128,124	122,428	1,774	50.8/48.5	46/54	43/57
FL-19	223,009	115,655	2,249	65.4/33.9	66/34	73/27
FL-20	186,912	106,344	2,240	63.3/36.0	64/36	69/31
FL-21	122,024	127,402	1,232	48.7/50.8	43/57	42/58
FL-22	175,731	162,012	2,638	51.6/47.6	52/48	52/48
FL-23	194,022	39,159	1,141	82.8/16.7	76/24	80/20
FL-24	116,527	127,386	2,562	47.3/51.7	45/55	47/53
FL-25	126,010	128,349	1,359	49.3/50.2	44/56	45/55
HI-01	152,320	60,979	4,129	70.1/28.1	53/47	55/39
HI-02	172,881	59,450	5,278	72.8/25.0	56/44	56/36
IL-01	248,990	37,176	1,587	86.5/12.9	83/17	84/16
IL-02	260,869	28,676	1,347	89.7/9.9	84/16	83/17
IL-03	154,999	85,502	3,203	63.6/35.1	59/41	58/40
IL-04	119,227	18,453	1,866	85.4/13.2	79/21	79/20
IL-05	178,170	62,906	3,383	72.9/25.7	67/33	66/34
IL-06	156,903	119,998	3,737	55.9/42.8	47/53	44/53
IL-07	255,470	33,662	1,935	87.8/11.6	83/17	83/16
IL-08	140,593	104,544	3,161	56.6/42.1	44/56	42/56
IL-09	188,822	68,989	3,202	72.3/26.4	68/32	67/31
IL-10	178,561	111,755	2,801	60.9/38.1	53/47	51/47
IL-11	163,664	137,334	4,640	53.6/44.9	46/53	48/50
IL-12	140,346	114,112	4,086	54.3/44.1	52/48	54/43
IL-13	188,155	154,788	4,148	54.2/44.6	45/55	42/55
IL-14	145,613	118,327	3,559	54.4/44.2	44/55	43/54
IL-15	123,074	124,717	4,472	48.8/49.4	41/59	43/54
IL-16	114,337	96,108	3,622	53.4/44.9	44/55	43/54
IL-17	113,913	79,311	2,918	58.1/40.4	51/48	54/44
IL-18	139,085	136,394	4,690	49.6/48.7	42/58	44/54
IL-19	69,939	93,635	2,941	42.0/56.2	39/61	41/56
MD-01	142,667	208,743	6,839	39.8/58.3	36/62	40/57
MD-02	164,089	106,088	5,263	59.6/38.5	54/45	57/41
MD-03	176,572	118,975	5,997	58.6/39.5	54/45	55/41
MD-04	240,715	40,002	2,200	85.1/14.1	78/21	77/21
MD-05	219,437	114,607	4,287	64.9/33.9	57/42	57/41
MD-06	138,091	198,238	7,426	40.2/57.7	34/65	36/61
MD-07	214,542	54,354	3,578	78.7/20.0	73/26	73/25
MD-08	201,510	69,062	3,922	73.4/25.2	69/30	66/31
NE-01	121,411	148,179	4,303	44.3/54.1	36/63	36/59
NE-02	138,809	135,439	3,561	50.0/48.8	38/60	39/57
NE-03	73,099	169,361	4,282	29.6/68.6	24/75	25/71
NV-01	158,418	85,226	5,139	63.7/34.3	57/42	56/41
NV-02	167,812	167,900	8,417	48.8/48.8	41/57	37/57
NV-03	207,418	159,574	7,716	55.4/42.6	49/50	49/48
NC-01	177,941	105,738	1,288	62.4/37.1	57/42	57/42
NC-02	155,681	141,840	2,397	51.9/47.3	46/54	46/53
NC-03	117,178	190,093	2,456	37.8/61.4	32/68	35/64
NC-04	275,205	159,427	4,305	62.7/36.3	55/44	53/46
NC-05	126,556	203,076	4,208	37.9/60.8	33/66	33/66
NC-06	122,291	212,011	3,525	36.2/62.8	30/69	32/67
NC-07	150,071	167,888	2,747	46.8/52.4	44/56	48/52
NC-08	152,261	135,234	2,222	52.6/46.7	45/54	46/54
NC-09	174,410	212,250	3,043	44.8/54.5	36/63	36/63
NC-10	108,496	191,580	3,501	35.7/63.1	33/67	34/65
NC-11	159,772	179,061	4,746	46.5/52.1	43/57	40/58
NC-12	218,599	89,790	2,033	70.4/28.9	63/37	57/42
NC-13	204,190	140,486	3,193	58.7/40.4	52/47	49/50
SC-01	118,356	174,458	3,810	39.9/58.8	39/61	38/59
SC-02	135,452	175,052	3,284	43.2/55.8	39/60	39/58
SC-03	95,124	178,089	3,644	34.4/64.3	34/66	35/63
SC-04	118,453	188,854	5,229	37.9/60.4	34/65	33/64
SC-05	135,564	160,944	3,497	45.2/53.7	42/57	43/55
SC-06	139,438	83,696	2,278	61.9/37.1	61/39	58/40
WA-01	226,526	130,343	5,911	62.4/35.9	56/42	53/42
WA-02	202,480	151,992	7,415	56.0/42.0	51/47	48/46
WA-03	183,306	159,803	6,898	52.4/45.7	48/50	46/48
WA-04	111,423	159,904	5,127	40.3/57.8	35/63	34/62
WA-05	152,921	171,426	8,283	46.0/51.5	41/57	40/56
WA-06	182,446	128,681	6,545	57.4/40.5	53/45	52/43
WA-07	308,226	55,200	5,536	83.5/15.0	79/19	72/21
WA-08	210,998	155,900	5,779	56.6/41.8	51/48	49/47
WA-09	172,318	115,837	5,298	58.7/39.5	53/46	53/43

This round was even more fun than the previous batch, because it involves a number of states where the turnaround was huge (either because of the favorite son effect, as in Illinois, or because the Obama campaign actually decided to compete there for once, like Nevada and North Carolina). Want to see some truly staggering progress? Check out IL-08, once the core of Chicago’s deep red suburbs (and home to Rep. Phil Crane), and even the site of a 56-44 edge for Bush in 2004. This year? Obama won 57-42.

As we get into the more complicated states here (the last wave picked all the lowest hanging fruit), there are some caveats to be mindful of, which may affect the data reliability a small amount. In Florida, for instance, we’re missing precinct-level data for one county, which affects two districts (the 3rd and 6th); unfortunately, it’s a pretty big county (Alachua, home of Gainesville and Univ. of Florida). In Illinois, all districts are affected by the curse of split precincts, which don’t seem to make much of a big difference, while some of the downstate districts are affected by some missing precinct-level data from some smaller counties; because of their small size, it doesn’t seem to affect the district’s overall percentages much, though.

In North Carolina, our spreadsheet whiz there broke the totals down into ‘hard’ and ‘soft’ totals, with ‘hard’ reflecting only known numbers and ‘soft’ allocating early votes and absentees proportionately (which apparently just sit in an indistinguished lump in NC). I chose to proceed using the ‘soft’ totals, which are much larger, but a stickler might choose to focus on ‘hard’ totals instead. South Carolina also has a high number of ‘indivisibles’ in its spreadsheet, which appear to be a lot of split precincts. The high number of indivisibles appears to exert some pressure on some of the percentages in South Carolina, perhaps in SC-01, where there appears to be little leftward movement since 2004.

So, take the numbers with a grain of salt, and certainly don’t expect these numbers to be a 100% match for Polidata’s numbers, forthcoming this spring. And of course, please contribute to this project however you see fit!

We’re pleased to announce that we’ve created a perma-post for the 2008 presidential results by congressional district. (We’ve included the 2004 and 2000 results as well.) You can use this link, and you’ll also find a permalink in the “Resources” box on the right-hand sidebar of the site.

As our crowdsourcing project continues, we’ll keep adding new numbers. In fact, you should expect another round of numbers this week.

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Some other site news stuff: Thank you to everyone who has taken the Blogads reader survey so far! We’ve gathered about 75 responses so far, which is terrific. We’d love to hit at least 100. This may not be a scientific study, but nonetheless, everyone on this site knows the value of a big N! So please take the survey today. Once it’s complete, I promise that we’ll share some of the results.

Continue reading Presidential Results by Congressional District, 2000-2008