How to flip an election

Another reason Clinton lost Michigan: Trump was listed first on the ballot, by Josh Pasek, University of Michigan. (Disclaimer: I went to middle and high school with Pasek.) From the blog post: “The best estimate of the effect of being listed first on the ballot in a presidential election is an improvement of the first-listed individual’s vote share of 0.31%.” Trump was listed first on the Michigan ballot, because the governor of Michigan is Republican. This study is based on elections in California, which randomizes the order of the candidates on the ballot by precinct. Here’s a preprint of the paper (Pasek, J., Schneider, D., Krosnick, J. A., Tahk, A., Ophir, E., & Milligan, C. (2014). Prevalence and moderators of the candidate name-order effect evidence from statewide general elections in California. Public Opinion Quarterly, 78(2), 416-439.).

Clinton also would have won if the map of the United States looked slightly different. If you want to play around with this yourself, you can redraw the states using the tool by Kevin Hayes Wilson. Move Camden County, New Jersey into Pennsylvania and Lucas County, Ohio (i. e. roll back the Toledo War, which was a thing) into Michigan, and Clinton wins.  Each of these counties is adjacent to the state it’s being moved into. Here’s the resulting map.

I’m pretty sure that two is the minimal number of counties that have to be moved to get a Clinton win, under the constraint that the counties in each state have to remain geographically contiguous. Clinton starts out needing 37 more EV. and the only way to get that by flipping just one state is to flip Texas; but no state adjacent to Texas went blue.  There is a way to make Clinton win that involves moving one county into another state – namely, move Los Angeles County, California into Texas – but that doesn’t seem to be in the spirit.)

The natural question, then, if we want to know how much “unfairness” is due to the electoral college, is something like this: given the actual voting results, and some “random” partitioning of the US into states, what is the probability of a Trump (or Clinton) win? But what does a “random” partitioning of the US into states even mean?  It seems difficult to define this, given that we don’t have a huge number of alternate histories to run, but I’d imagine we’d want to preserve facts like:

  • some states have many more people than others, but no state is much smaller in population than the average congressional district;
  • more populous states tend to be more urban (this is relevant since the electoral college helps low-population states, and one party is more represented in urban areas);
  • states are geographically relatively compact (unlike, say, Congressional districts in some states)

But in the end this is an academic question, because we don’t get to redraw the states.  (Can you imagine the gerrymandering?)

Dressing goes on salad

Someone needs to make a better stuffing vs. dressing map than this one from Butterball. The problem is that they have a small sample: the fine print reads “This survey was conducted online with a random sample 1,000 men and women in 9 regions – all members of the CyberPulseTM Advisory Panel. Research was conducted in May 2007. The overall sampling error for the survey is +/-3% at the 95% level of confidence.” So the average state has a sample of 20, which would lead to a 21% or so margin of error. This error is enough that the map just looks wrong – Georgia and Mississippi call it stuffing, but Alabama and Tennessee call it dressing?  The Butterball map does seem to capture the regional divide, though, where the South calls it “dressing” and the North calls it “stuffing”.  We’re still fighting the linguistic Civil War in my house.  Obviously this is meant to be entertainment, but get a bigger sample, will you?

It looks like Epicurious has some internal data based on search results that led to their site, but they’re not sharing.

My Google Image Search results for “stuffing vs. dressing” find a bunch of pictures of the ambiguously named bready dish, and also this map of the largest religious denomination in US counties and this article on Josh Katz’s maps of Bert Vaux’s dialect survey. “Stuffing” vs “dressing” is not one of the questions in that survey, sadly.

And yes, I know about the compromise where it’s “stuffing” when it’s cooked in the bird and “dressing” when it’s cooked separately. But in my family of origin we generally have too much to fit in the bird, so some gets cooked in the bird and some doesn’t… does that mean we have “dressing” and “stuffing” on the table at the same time?

Use R, vote D?

David Robinson, data scientist at StackOverflow, tweeted:

Of course this is because of a confounder.  Namely, R comes out of the statistics community, which is concentrated in places with universities, which also tend to be pro-Democratic in the current political environment.   Python, he finds, is also anti-correlated with Trump voting; C# and PHP are correlated with Trump voting, he finds:

Interpret this as you will.  (Seriously, I don’t know enough about who uses C# and PHP to comment anywhere near intelligently.)

The data on language usage by county is not public, but the data on voting is, David Taylor has assembled vote counts by county, and David Robinson has some code for manipulating them and making some plots. Fun fact: the county(-equivalent) with the lowest percentage of Trump voters is the one Trump doesn’t want to move to.

A proof that π < 22/7

Note: this proof has a fatal error.

There’s a reasonably well-known proof that 22/7 > \pi, which can be written in one line:

0 < \int_0^1 {x^4 (1-x)^4 \over 1+x^2} dx = {22 \over 7} - \pi But I’ve always found this one unsatisfying because what does that integral have to do with \pi anyway? As it turns out, \pi enters through the integral \int_0^1 {1 \over (1+x^2)} = \pi/4. But let’s say I’m a purist and think that \pi is about circles. Can I do better? (Of course I can. If I couldn’t I wouldn’t be writing this post.) Start by observing that (21/11)^3 > 7, which can be shown by explicit computation: 11^3 = 1331 and 21^3 / 7 = 21^2 \times 3 = 1323.

Edited to add, November 30: of course I just showed here that (21/11)^3 < 7. This is what happens when you do arithmetic in your head…

Now, the sine function is given by the alternating series sin(x) = x - {x^3 \over 3!} + {x^5 \over 5!} - {x^7 \over 7!} + \cdots and in particular \sin(x) > x - x^3/6 by the alternating series test. Applying this with x = 11/21 gives \sin(11/21) > 11/21 - (11/21)^3/6 > 11/21 - (1/7)/6 = 1/2.

Taking the inverse sine of both sides, 11/21 > \sin^{-1} 1/2.

Finally, we have \pi = 6 \sin^{-1} (1/2). This is a geometric fact that goes back to Euclid’s construction of the hexagon. So \pi < 22/7.

On related notes:

  •  Noam Elkies, Why is π2 so close to 10?
  • Alejandro Morales, Igor Pak, and Greta Panova, Why is π < 2φ?  (This is not a particularly good approximation, but it actually admits a combinatorial proof in terms of Fibonacci and Euler numbers.)

Crossword rundown

Kurt Schlosser at Geekwire: How hard is the New York Times crossword?  This is a description of the Puzzle Difficulty Index that Puzzazz, a puzzle solving app, has been calculating.  Unsurprisingly, if you know anything about that puzzle, later-in-the-week crosswords take longer and are less frequently solved (with the exception that a few more people solve on Thursdays than Wednesdays, which I’d attribute to either noise or the fact that Thursday puzzles tend to have some sort of “gimmick” and are not just halfway between Wednesdays and Fridays).  Both links are worth reading, although there’s some redundancy.  I’ve thought for a while that this sort of thing would be possible if I had enough data.

The next frontier in this sort of analysis would be seeing which individual clues are the hardest – what do people solve immediately and what do they leave until the end, when they have a lot of crossing letters?  I’m not sure if crossword constructors would be interested in this, although anecdotally they seem to be a mathy bunch…

Of course, all of this would be irrelevant if crosswords didn’t exist, and it’s not immediately obvious that enough different strings of letters make words that crosswords should be possible.   In his book Information Theory, Inference, and Learning Algorithms, the late David MacKay analyzed this; here’s the relevant excerpt from that book (three-page PDF) and a more elaborated version of the analysis.  This actually goes back to Shannon’s founding paper although he doesn’t give the detailed analysis.  Shannon writes that:

A more detailed analysis shows that if we assume the constraints imposed by the language are of a rather chaotic and random nature, large crossword puzzles are just possible when the redundancy is 50%.

 Here “redundancy” has a specific information-theoretic meaning, and it turns out that the redundancy of English is just around 50%; MacKay’s analysis further shows that crosswords should be harder to construct (i. e. there should be fewer valid ways to fill in a given pattern of black and white squares) as words get longer.
Since I’m talking about crosswords, I’d be remiss if I didn’t point out the famous quote of Tukey:

Doing statistics is like doing crosswords except that one cannot know for sure whether one has found the solution.

Brillinger, in this paper memorializing Tukey, tells us that this quote or something like it came from books of crosswords which he gave to his students as gifts… but from which removed the answers!

And FiveThirtyEight isn’t just election news! Ollie Roeder reported on the American Crossword Puzzle Tournament in 2015.

One-fifth of Americans what?

As you probably know, there’s a (US presidential) election soon.   And there are a whole bunch of people who are predicting the probability that each candidate will win.  But as Nathan Collins has pointed out at Pacific Standardone-fifth of Americans can’t understand election predictions.

My first reaction, upon seeing this, was to think that one-fifth of Americans don’t understand what “one-fifth” means.  (I had just recently come across the old idea that Americans didn’t want a third-pound burger because they thought it was smaller than a quarter-pound burger, so I was primed to think this.)  But of course that’s too meta.

What this really is saying is that people mistake a result from one of these models like “Clinton has a 65% chance of winning the election” for “Clinton will get 65% of the vote”.   I don’t know if there’s direct evidence for this, but:

  • people have trouble interpreting, say, weather forecasts which give a probability of rain.
  • I actually made this mistake a few nights ago when listening to the FiveThirtyEight elections podcast.  For a brief moment I heard “Clinton has a 65 percent chance of winning according to our model”, thought it meant that Clinton had 65 percent of the vote, was happy, then realized that that was inconsistent with the way I’ve been feeling about the election and went back to remembering that Clinton was just a two-to-one favorite.

And if I can make that mistake, surely people who don’t have training in probability can. These examples are actually more similar than you might think: a 30% chance of rain means that there’s a 30% chance that somewhere in the forecast area there will be at least some cutoff amount of rain. Both are the case that some random variable (the difference between the two candidate’s vote percentage, the maximum amount of rain over an area) ends up over some cutoff. And complicated random variables such as these are surely hard to reason about.

Also, probabilities are used as rhetorical devices, not just pure numbers.  On Morning Joe just before the 2012 election, Joe Scarborough said:

Nate Silver says this is a 73.6 percent chance that the president is going to win? Nobody in that campaign thinks they have a 73 percent chance — they think they have a 50.1 percent chance of winning. And you talk to the Romney people, it’s the same thing […] Both sides understand that it is close, and it could go either way. And anybody that thinks that this race is anything but a tossup right now is such an ideologue, they should be kept away from typewriters, computers, laptops and microphones for the next 10 days, because they’re jokes.

What I think Scarborough intended to says here is that everyone in both campaigns acted as if they had a 50.1 percent chance of winning, i. e. they were the favorites to win but by a tiny margin. If you think someone is just on your tail that keeps you motivated. But at that time Obama did have a slight lead in the polls, and therefore was more likely to win the election. (Modulo polling errors, electoral college math…)

Oh, and go vote. But really you didn’t need me to tell you that.

Sunset times on Halloween for major cities

This is data for Sunset’s too late on Halloween.  See that post for a fuller explanation.

city lat lng pop state TZ halloweenSunset
Spokane 47.66999595 -117.4199494 272483.5 Washington America/Los_Angeles 10/31/16 17:32
Tucson 32.20499676 -110.8899862 670953.5 Arizona America/Phoenix 10/31/16 17:33
Mesa 33.42391461 -111.7360844 762217.5 Arizona America/Phoenix 10/31/16 17:35
Phoenix 33.53997988 -112.0699917 2436022.5 Arizona America/Phoenix 10/31/16 17:36
Glendale 33.58194114 -112.1958238 363360.5 Arizona America/Phoenix 10/31/16 17:36
Salem 42.5224989 -70.88309175 188982 Massachusetts America/New_York 10/31/16 17:37
Boston 42.32996014 -71.07001367 2528070.5 Massachusetts America/New_York 10/31/16 17:38
Manchester 42.99599184 -71.45528731 153221.5 New Hampshire America/New_York 10/31/16 17:38
Lowell 42.63368837 -71.31669112 415074 Massachusetts America/New_York 10/31/16 17:38
New Bedford 41.6561253 -70.94469833 115082.5 Massachusetts America/New_York 10/31/16 17:38
Providence 41.82110231 -71.4149797 663726.5 Rhode Island America/New_York 10/31/16 17:40
Worcester 42.27042889 -71.80002079 232290.5 Massachusetts America/New_York 10/31/16 17:41
Green Bay 44.5299809 -88.00001388 149811.5 Wisconsin America/Chicago 10/31/16 17:41
Appleton 44.26867902 -88.40050623 136888.5 Wisconsin America/Chicago 10/31/16 17:43
Milwaukee 43.05265505 -87.91996708 983590 Wisconsin America/Chicago 10/31/16 17:44
Racine 42.72771364 -87.81183415 105458.5 Wisconsin America/Chicago 10/31/16 17:44
Gary 41.58039349 -87.33000309 335737 Indiana America/Chicago 10/31/16 17:44
Springfield 42.12002464 -72.57999903 287003.5 Massachusetts America/New_York 10/31/16 17:44
Waukegan 42.36404075 -87.8447262 144539 Illinois America/Chicago 10/31/16 17:45
Evanston 42.04834943 -87.69995467 212243 Illinois America/Chicago 10/31/16 17:45
Waukesha 43.0116498 -88.23136926 159012 Wisconsin America/Chicago 10/31/16 17:45
Hartford 41.77002016 -72.67996708 518509.5 Connecticut America/New_York 10/31/16 17:45
Chicago 41.82999066 -87.75005497 5915976 Illinois America/Chicago 10/31/16 17:45
New Haven 41.33038291 -72.90000533 707883 Connecticut America/New_York 10/31/16 17:47
Waterbury 41.55000775 -73.05002202 174236 Connecticut America/New_York 10/31/16 17:47
Elgin 42.03946108 -88.28991866 244050 Illinois America/Chicago 10/31/16 17:47
Joliet 41.52998313 -88.10667403 362946.5 Illinois America/Chicago 10/31/16 17:47
Aurora 41.76539512 -88.29999557 273949.5 Illinois America/Chicago 10/31/16 17:48
Albany 42.67001691 -73.81994918 484286 New York America/New_York 10/31/16 17:48
Schenectady 42.81458173 -73.93996769 104767.5 New York America/New_York 10/31/16 17:48
Bridgeport 41.17997866 -73.19996118 578545 Connecticut America/New_York 10/31/16 17:48
Madison 43.07301556 -89.40111699 276036 Wisconsin America/Chicago 10/31/16 17:50
Rockford 42.26970542 -89.06969019 204371.5 Illinois America/Chicago 10/31/16 17:50
Murfreesboro 35.84596315 -86.39026717 100237 Tennessee America/Chicago 10/31/16 17:50
Stamford 41.05334556 -73.53919112 434781.5 Connecticut America/New_York 10/31/16 17:50
Poughkeepsie 41.70023114 -73.92141585 100670.5 New York America/New_York 10/31/16 17:50
Everett 47.9604175 -122.1999677 291948 Washington America/Los_Angeles 10/31/16 17:50
Nashville 36.16997438 -86.77998499 703926 Tennessee America/Chicago 10/31/16 17:51
Evansville 37.97469627 -87.5558291 144788 Indiana America/Chicago 10/31/16 17:51
Seattle 47.57000205 -122.339985 1821684.5 Washington America/Los_Angeles 10/31/16 17:52
New York 40.74997906 -73.98001693 13524139 New York America/New_York 10/31/16 17:52
Huntsville 34.71995953 -86.60999536 185474.5 Alabama America/Chicago 10/31/16 17:53
Paterson 40.91999453 -74.17000533 151205 New Jersey America/New_York 10/31/16 17:53
Clarksville 36.5300816 -87.35943282 122115 Tennessee America/Chicago 10/31/16 17:53
Newark 40.70042137 -74.17000533 280123 New Jersey America/New_York 10/31/16 17:53
Tacoma 47.21131594 -122.5150131 460273 Washington America/Los_Angeles 10/31/16 17:53
Palm Springs 33.77735557 -116.5330526 216461 California America/Los_Angeles 10/31/16 17:54
Montgomery 32.36160219 -86.27918868 194491.5 Alabama America/Chicago 10/31/16 17:55
Honolulu 21.30687644 -157.8579979 578828.5 Hawaii Pacific/Honolulu 10/31/16 17:55
Peoria 40.69998212 -89.67004114 142622 Illinois America/Chicago 10/31/16 17:55
Birmingham 33.53000633 -86.82499516 670142 Alabama America/Chicago 10/31/16 17:55
Olympia 47.03804486 -122.899434 100950 Washington America/Los_Angeles 10/31/16 17:55
Greeley 40.41919822 -104.739974 106142.5 Colorado America/Denver 10/31/16 17:56
San Bernardino 34.12038373 -117.3000342 973690.5 California America/Los_Angeles 10/31/16 17:56
Trenton 40.2169625 -74.74335535 225713 New Jersey America/New_York 10/31/16 17:56
Springfield 39.82000999 -89.65001652 125345 Illinois America/Chicago 10/31/16 17:56
Syracuse 43.04999371 -76.15001367 403873.5 New York America/New_York 10/31/16 17:57
Riverside 33.94194501 -117.3980386 297554 California America/Los_Angeles 10/31/16 17:57
Fort Collins 40.56068829 -105.0588693 178818.5 Colorado America/Denver 10/31/16 17:57
Rock Island 41.49339622 -90.53461369 102055.5 Illinois America/Chicago 10/31/16 17:57
Davenport 41.55398684 -90.58753036 178282.5 Iowa America/Chicago 10/31/16 17:57
Aurora 39.69585736 -104.808497 431966.5 Colorado America/Denver 10/31/16 17:57
Vancouver 45.63030133 -122.6399925 343796.5 Washington America/Los_Angeles 10/31/16 17:57
National City 32.67194501 -117.0980052 104291 California America/Los_Angeles 10/31/16 17:57
San Diego 32.82002382 -117.1799899 1938570.5 California America/Los_Angeles 10/31/16 17:57
Oceanside 33.2204645 -117.3349675 396474.5 California America/Los_Angeles 10/31/16 17:58
Portland 45.52002382 -122.6799901 1207756.5 Oregon America/Los_Angeles 10/31/16 17:58
Scranton 41.40929283 -75.66267908 114701 Pennsylvania America/New_York 10/31/16 17:58
Denver 39.73918805 -104.984016 1930799.5 Colorado America/Denver 10/31/16 17:58
Anchorage 61.21996991 -149.9002149 252068 Alaska America/Anchorage 10/31/16 17:58
Philadelphia 39.99997316 -75.16999597 3504775 Pennsylvania America/New_York 10/31/16 17:58
Tuscaloosa 33.22511538 -87.54417607 100594.5 Alabama America/Chicago 10/31/16 17:58
Boulder 40.03844627 -105.246093 106897.5 Colorado America/Denver 10/31/16 17:58
Lancaster 34.69804873 -118.135823 225799 California America/Los_Angeles 10/31/16 17:59
Allentown 40.59998822 -75.50002751 300980.5 Pennsylvania America/New_York 10/31/16 17:59
Colorado Springs 38.86296246 -104.7919863 427272 Colorado America/Denver 10/31/16 17:59
Irvine 33.68041058 -117.8299502 1611303.5 California America/Los_Angeles 10/31/16 17:59
Pueblo 38.2803882 -104.6300066 108244 Colorado America/Denver 10/31/16 17:59
Pasadena 34.16038129 -118.1388719 144618 California America/Los_Angeles 10/31/16 17:59
Los Angeles 33.98997825 -118.1799805 8097410 California America/Los_Angeles 10/31/16 18:00
Long Beach 33.78696739 -118.1580439 1249195.5 California America/Los_Angeles 10/31/16 18:00
Rochester 44.02205324 -92.46968937 102433 Minnesota America/Chicago 10/31/16 18:00
Wilmington 39.74626772 -75.54689803 116205.5 Delaware America/New_York 10/31/16 18:00
Salem 44.92807029 -123.0238967 187966 Oregon America/Los_Angeles 10/31/16 18:00
St. Paul 44.94398663 -93.08497481 509961 Minnesota America/Chicago 10/31/16 18:01
Billings 45.78830202 -108.5400004 102151.5 Montana America/Denver 10/31/16 18:01
Cedar Rapids 41.96998212 -91.66002303 149338.5 Iowa America/Chicago 10/31/16 18:01
Visalia 36.32502952 -119.3160094 114699.5 California America/Los_Angeles 10/31/16 18:01
St. Louis 38.63501772 -90.23998051 1259958 Missouri America/Chicago 10/31/16 18:01
Pensacola 30.42112632 -87.21693506 145319.5 Florida America/Chicago 10/31/16 18:01
Bakersfield 35.36997154 -119.0199809 367259 California America/Los_Angeles 10/31/16 18:01
Minneapolis 44.97997927 -93.25178634 1491886.5 Minnesota America/Chicago 10/31/16 18:01
St. Charles 38.78428509 -90.50616581 213139.5 Missouri America/Chicago 10/31/16 18:02
Lakeville 44.65010276 -93.24251042 156151 Minnesota America/Chicago 10/31/16 18:02
Fresno 36.7477169 -119.7729841 540768 California America/Los_Angeles 10/31/16 18:02
Rochester 43.17042564 -77.61994979 483177 New York America/New_York 10/31/16 18:02
Eugene 44.05001019 -123.1000161 195183 Oregon America/Los_Angeles 10/31/16 18:02
Lancaster 40.03777447 -76.30576644 209489 Pennsylvania America/New_York 10/31/16 18:03
Mobile 30.68002525 -88.04998499 221870 Alabama America/Chicago 10/31/16 18:04
York 39.96292116 -76.72804041 128798.5 Pennsylvania America/New_York 10/31/16 18:05
Barlett 35.22290041 -89.84109013 164843.5 Tennessee America/Chicago 10/31/16 18:05
Harrisburg 40.27359987 -76.88474919 289210 Pennsylvania America/New_York 10/31/16 18:05
Santa Barbara 34.43498985 -119.7199899 135021 California America/Los_Angeles 10/31/16 18:05
Baltimore 39.29999005 -76.61998499 1432946 Maryland America/New_York 10/31/16 18:05
Modesto 37.65541343 -120.9899899 269697 California America/Los_Angeles 10/31/16 18:05
Memphis 35.1199868 -89.99999516 753843.5 Tennessee America/Chicago 10/31/16 18:05
Sacramento 38.57502138 -121.4700381 1035949 California America/Los_Angeles 10/31/16 18:06
Stockton 37.95813397 -121.289739 488506.5 California America/Los_Angeles 10/31/16 18:06
Virginia Beach 36.85321433 -75.97831873 877475.5 Virginia America/New_York 10/31/16 18:07
Washington, D.C. 38.89954938 -77.00941858 2445216.5 District of Columbia America/New_York 10/31/16 18:08
Hampton 37.03002525 -76.34994979 256601.5 Virginia America/New_York 10/31/16 18:08
Buffalo 42.87997825 -78.88000208 647778.5 New York America/New_York 10/31/16 18:08
Norfolk 36.84995872 -76.28000574 645336 Virginia America/New_York 10/31/16 18:08
Alexandria 38.82043276 -77.09998153 127273 Virginia America/New_York 10/31/16 18:08
Niagara Falls 43.09482302 -79.0369434 117567 New York America/New_York 10/31/16 18:08
Columbia 38.95207847 -92.33390955 244754 Missouri America/Chicago 10/31/16 18:09
Des Moines 41.57998008 -93.61998092 286917.5 Iowa America/Chicago 10/31/16 18:09
San Jose 37.29998293 -121.8499891 1281471.5 California America/Los_Angeles 10/31/16 18:09
Salinas 36.68221702 -121.6416555 152737.5 California America/Los_Angeles 10/31/16 18:10
Vallejo 38.11194887 -122.258052 133367.5 California America/Los_Angeles 10/31/16 18:10
Oakland 37.76892071 -122.2211034 953044 California America/Los_Angeles 10/31/16 18:10
Berkeley 37.87390139 -122.271152 298257 California America/Los_Angeles 10/31/16 18:10
Jackson 32.29881533 -90.18499679 213799 Mississippi America/Chicago 10/31/16 18:10
Santa Cruz 36.97194629 -122.0263904 101530.5 California America/Los_Angeles 10/31/16 18:11
San Mateo 37.55691815 -122.3130616 361806.5 California America/Los_Angeles 10/31/16 18:11
Santa Rosa 38.45040367 -122.6999889 193455 California America/Los_Angeles 10/31/16 18:11
San Francisco 37.74000775 -122.4599777 2091036 California America/Los_Angeles 10/31/16 18:11
Fargo 46.8772278 -96.7894257 127472.5 North Dakota America/Chicago 10/31/16 18:11
Richmond 37.55001935 -77.449986 551443 Virginia America/New_York 10/31/16 18:11
Albuquerque 35.10497479 -106.6413308 725723 New Mexico America/Denver 10/31/16 18:12
New Orleans 29.99500246 -90.03996688 527428.5 Louisiana America/Chicago 10/31/16 18:13
Metairie 29.98386619 -90.15277653 270171 Louisiana America/Chicago 10/31/16 18:13
Erie 42.12992067 -80.08499313 138991.5 Pennsylvania America/New_York 10/31/16 18:14
Little Rock 34.73608258 -92.33109318 227555 Arkansas America/Chicago 10/31/16 18:15
Springfield 37.18001609 -93.31999923 180691 Missouri America/Chicago 10/31/16 18:16
El Paso 31.77998395 -106.5099952 658331 Texas America/Denver 10/31/16 18:16
Baton Rouge 30.45794578 -91.14015812 322710.5 Louisiana America/Chicago 10/31/16 18:17
Independence 39.09111391 -94.41528121 130695 Missouri America/Chicago 10/31/16 18:17
Pittsburgh 40.4299986 -79.99998539 1535267.5 Pennsylvania America/New_York 10/31/16 18:17
Kansas City 39.10708851 -94.60409422 955272.5 Missouri America/Chicago 10/31/16 18:18
Kansas City 39.11358052 -94.63014638 324221.5 Kansas America/Chicago 10/31/16 18:18
Sioux Falls 43.54998903 -96.7299978 148030 South Dakota America/Chicago 10/31/16 18:18
Youngstown 41.09969932 -80.64973902 194765 Ohio America/New_York 10/31/16 18:18
Wilmington 34.22551943 -77.94502039 126992 North Carolina America/New_York 10/31/16 18:19
Raleigh 35.81878135 -78.64469344 789991.5 North Carolina America/New_York 10/31/16 18:19
Omaha 41.24000083 -96.00999007 643034 Nebraska America/Chicago 10/31/16 18:19
Durham 35.99995892 -78.91999964 257114.5 North Carolina America/New_York 10/31/16 18:20
Lafayette 30.19997703 -92.01994938 135205.5 Louisiana America/Chicago 10/31/16 18:21
Fayetteville 36.06297833 -94.15720911 108267.5 Arkansas America/Chicago 10/31/16 18:21
Fayetteville 35.06293601 -78.88359359 184040.5 North Carolina America/New_York 10/31/16 18:21
Canton 40.79886497 -81.37863509 168410 Ohio America/New_York 10/31/16 18:22
Cleveland 41.4699868 -81.69499821 1169757 Ohio America/New_York 10/31/16 18:22
Akron 41.07039878 -81.51999597 451155 Ohio America/New_York 10/31/16 18:22
Topeka 39.05000531 -95.66998499 126830.5 Kansas America/Chicago 10/31/16 18:22
Roanoke 37.27119916 -79.94161686 144669.5 Virginia America/New_York 10/31/16 18:22
Lincoln 40.81997479 -96.68000086 244146 Nebraska America/Chicago 10/31/16 18:23
Ogden 41.23237856 -111.9680341 227774 Utah America/Denver 10/31/16 18:23
Greensboro 36.07000633 -79.80002344 310328 North Carolina America/New_York 10/31/16 18:23
Provo 40.24889854 -111.63777 231238 Utah America/Denver 10/31/16 18:24
Salt Lake City 40.7750163 -111.9300519 572013 Utah America/Denver 10/31/16 18:24
Shreveport 32.50001752 -93.77002344 224099 Louisiana America/Chicago 10/31/16 18:24
Winston-Salem 36.10543052 -80.25999536 237491.5 North Carolina America/New_York 10/31/16 18:25
Detroit 42.32996014 -83.08005579 2526135 Michigan America/New_York 10/31/16 18:26
Flint 43.0128642 -83.68753809 206235 Michigan America/New_York 10/31/16 18:27
Tulsa 36.12000327 -95.93002079 669434 Oklahoma America/Chicago 10/31/16 18:28
Ann Arbor 42.30037539 -83.71999089 189893 Michigan America/New_York 10/31/16 18:28
Charlotte 35.20499453 -80.83003809 943574.5 North Carolina America/New_York 10/31/16 18:29
Charleston 32.79237693 -79.99210474 254295 South Carolina America/New_York 10/31/16 18:29
Toledo 41.67002626 -83.57997359 388449 Ohio America/New_York 10/31/16 18:29
Beaumont 30.08626304 -94.10168278 107455.5 Texas America/Chicago 10/31/16 18:29
Columbus 39.97997438 -82.9900096 1003418 Ohio America/New_York 10/31/16 18:30
Wichita 37.71998313 -97.32998702 378543.5 Kansas America/Chicago 10/31/16 18:31
Columbia 34.0399752 -80.89998214 257185.5 South Carolina America/New_York 10/31/16 18:31
Tyler 32.35108604 -95.30078272 101561.5 Texas America/Chicago 10/31/16 18:31
Lansing 42.73352724 -84.54673629 198821.5 Michigan America/New_York 10/31/16 18:31
Pasadena 29.66086265 -95.14774296 388767.5 Texas America/Chicago 10/31/16 18:34
Houston 29.81997438 -95.33997929 4053287 Texas America/Chicago 10/31/16 18:34
Savannah 32.02110618 -81.10999516 155848.5 Georgia America/New_York 10/31/16 18:34
Norman 35.22791302 -97.34414636 113525 Oklahoma America/Chicago 10/31/16 18:35
Dayton 39.750376 -84.19998743 466067 Ohio America/New_York 10/31/16 18:35
Grand Rapids 42.96371991 -85.66994938 361934.5 Michigan America/New_York 10/31/16 18:35
Asheville 35.60119773 -82.55414474 105775 North Carolina America/New_York 10/31/16 18:35
Oklahoma City 35.47004295 -97.51868351 660475 Oklahoma America/Chicago 10/31/16 18:35
Greenville 34.85292299 -82.3941545 203256.5 South Carolina America/New_York 10/31/16 18:35
Boise 43.60859011 -116.2274899 242029 Idaho America/Denver 10/31/16 18:36
Kalamazoo 42.29215883 -85.58718958 128759.5 Michigan America/New_York 10/31/16 18:36
Augusta 33.46081158 -81.98498051 152895.5 Georgia America/New_York 10/31/16 18:36
Dallas 32.82002382 -96.84001693 3004852 Texas America/Chicago 10/31/16 18:36
Fort Wayne 41.08039817 -85.12998234 264793 Indiana America/Indiana/Indianapolis 10/31/16 18:36
Denton 33.21576194 -97.12883651 138952.5 Texas America/Chicago 10/31/16 18:37
Cincinnati 39.16188479 -84.45692265 971191 Ohio America/New_York 10/31/16 18:37
Arlington 32.68476076 -97.02023849 545107.5 Texas America/Chicago 10/31/16 18:37
Covington 39.0840084 -84.50859908 313064.5 Kentucky America/New_York 10/31/16 18:37
Bryan 30.67418581 -96.36968388 108156.5 Texas America/Chicago 10/31/16 18:37
Fort Pierce 27.44678591 -80.3258053 132984 Florida America/New_York 10/31/16 18:37
West Palm Beach 26.74501996 -80.12362126 675521.5 Florida America/New_York 10/31/16 18:38
Melbourne 28.08331036 -80.60832035 170870 Florida America/New_York 10/31/16 18:38
Daytona Beach 29.21055422 -81.0230754 140775.5 Florida America/New_York 10/31/16 18:38
Ft. Worth 32.73997703 -97.34003809 1090830 Texas America/Chicago 10/31/16 18:38
Fort Lauderdale 26.13606488 -80.14178552 1103781.5 Florida America/New_York 10/31/16 18:38
Elkhart 41.68294537 -85.96879419 100295 Indiana America/Indiana/Indianapolis 10/31/16 18:38
Coral Springs 26.27083701 -80.27082158 185548 Florida America/New_York 10/31/16 18:39
Miami Beach 25.80991953 -80.13178111 248538 Florida America/New_York 10/31/16 18:39
Lexington 38.05001467 -84.50002079 244972 Kentucky America/New_York 10/31/16 18:39
Jacksonville 30.33002077 -81.66998682 904953.5 Florida America/New_York 10/31/16 18:39
Miami 25.7876107 -80.22410608 2983947 Florida America/New_York 10/31/16 18:39
Waco 31.54917116 -97.14638066 143157 Texas America/Chicago 10/31/16 18:39
Sanford 28.78995974 -81.27998478 189374.5 Florida America/New_York 10/31/16 18:40
South Bend 41.68330711 -86.25001734 171791 Indiana America/Indiana/Indianapolis 10/31/16 18:40
Knoxville 35.97001243 -83.92003036 417137 Tennessee America/New_York 10/31/16 18:40
Orlando 28.50997683 -81.38003036 778985 Florida America/New_York 10/31/16 18:40
Kissimmee 28.29205731 -81.4077806 144589.5 Florida America/New_York 10/31/16 18:41
Killeen 31.11728538 -97.72748214 120464 Texas America/Chicago 10/31/16 18:42
Gainesville 29.65138002 -82.32503727 158390.5 Florida America/New_York 10/31/16 18:43
Indianapolis 39.74998842 -86.17004806 1104641.5 Indiana America/Indiana/Indianapolis 10/31/16 18:43
Macon 32.85038373 -83.63004806 104932.5 Georgia America/New_York 10/31/16 18:43
Austin 30.26694969 -97.74277836 919684 Texas America/Chicago 10/31/16 18:43
Louisville 38.22501691 -85.74870427 595819.5 Kentucky America/New_York 10/31/16 18:44
Ft. Myers 26.64029767 -81.86049199 120810.5 Florida America/New_York 10/31/16 18:45
Las Vegas 36.20999778 -115.2200061 1150717 Nevada America/Denver 10/31/16 18:45
Naples 26.14205935 -81.79499211 141902 Florida America/New_York 10/31/16 18:45
Atlanta 33.83001385 -84.39994938 2464454 Georgia America/New_York 10/31/16 18:45
Cape Coral 26.60290977 -81.97968368 117387.5 Florida America/New_York 10/31/16 18:45
Tampa 27.94698793 -82.45862085 1319232.5 Florida America/New_York 10/31/16 18:45
Corpus Christi 27.74281435 -97.40189478 249977.5 Texas America/Chicago 10/31/16 18:45
Sarasota 27.33612083 -82.53078699 321223.5 Florida America/New_York 10/31/16 18:46
St. Petersburg 27.77053876 -82.67938257 523314.5 Florida America/New_York 10/31/16 18:46
Chattanooga 35.06998985 -85.25000086 206571.5 Tennessee America/New_York 10/31/16 18:47
San Antonio 29.48733319 -98.50730534 1364905 Texas America/Chicago 10/31/16 18:47
Brownsville 25.91997988 -97.50000248 174707 Texas America/Chicago 10/31/16 18:48
Abilene 32.4486253 -99.73278609 108008 Texas America/Chicago 10/31/16 18:48
Columbus 32.47043276 -84.98001734 202225 Georgia America/New_York 10/31/16 18:49
Tallahassee 30.44998761 -84.28003422 187402.5 Florida America/New_York 10/31/16 18:49
Edinburg 26.30318646 -98.1599622 114573.5 Texas America/Chicago 10/31/16 18:50
McAllen 26.20303754 -98.22972538 243291 Texas America/Chicago 10/31/16 18:51
Amarillo 35.22998008 -101.8299966 178526 Texas America/Chicago 10/31/16 18:53
Laredo 27.50613629 -99.50721847 322768.5 Texas America/Chicago 10/31/16 18:54
Lubbock 33.58000327 -101.8799677 212343.5 Texas America/Chicago 10/31/16 18:55
Reno 39.52997601 -119.8200096 265363.5 Nevada America/Denver 10/31/16 18:58