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

I didn’t get my first trick-or-treater today until 6:53.  This was late enough that I was starting to wonder if any would come, despite this being our second year in this house and having gotten plenty last year.

But I live in Atlanta, where sunset today came at 6:45 because:

• despite being as far west as it is, Atlanta is on Eastern time;
• the powers that be made the decision that, starting in 2007, daylight savings time would end on the first Sunday in November instead of the last Sunday in October, so that it would be light out later on Halloween.

I think the powers that be are enemies of fun.  I told my mother this and she said that she remembered not being allowed to go out to trick or treat until it got dark.

But is this 6:45 sunset late?  It seemed like it, so I tracked down a list of cities and the maptools R package (which, among other things, can compute sunrise and sunset times) and got to work.  Here’s the result:

Lighter shades mean later sunset.  You’ll notice that one of the lightest shades is, indeed, the one for Atlanta.  There are 239 cities represented here; of these, only 17 have sunset times later than Atlanta tonight.   Full data is in Sunset times on Halloween for major cities.

The latest sunset time in a major US city tonight is 6:58 PM in Reno, Nevada.  The earliest is 5:32 PM in Spokane, Washington, which is that barely visible dark blue dot out in eastern Washington by the Idaho border.

I grew up in Philadelphia, where the sun sets at 5:58 tonight, and would have set at around 4:58 on the Halloweens of my youth. (I spent significant time in Boston and San Francisco, where the sun sets on Halloween at 5:38 and 6:11 respectively.)  So we’re off by almost two hours from what I “expect” from my formative years.  It also got up to 86 degrees today.  No wonder everything feels wrong.

An interesting article bobbed up from the ocean on Twitter today: Stan Ulam, John Von Neumann, and the Monte Carlo method, by Roger Eckhardt. This was in the Los Alamos Science Special Issue of 1987, which was dedicated to Stan Ulam. The whole issue looks quite interesting, as does indeed much of the whole publication, which was the flagship publication of the Los Alamos National Laboratory from 1980 to 2005. I suspect the articles on history would be a good place to start.

The article starts out with the now well-known story that Ulam invented the Monte Carlo method while trying to work out the win probability of a particular solitaire game, and indeed that probability has been worked out by simulation!. (Wikipedia cites the Eckhardt article, which is probably the original published source as Eckhardt only cites unpublished notes left by Ulam.)  The original scientific problem to be solved was one about neutron diffusion, which gives the Los Alamos connection.  There are also some nice inclusions of letters between Ulam and von Neumann.

Josh Barro observed on Twitter: “A single poll finding Trump up 30 in Oklahoma moves the national 538 forecast by 1.3 points. Seems… sensitive.”, and linked to 538’s page.

This got my attention, because I have been watching 538 a bit more closely than I’m proud of. I agree that this does seem sensitive. A lot of the comments here seem to explain it by saying that the move in Oklahoma implies moves in other more competitive states that Oklahoma results are historically correlated with (such as Texas?), and the model is picking up on this.

I had suspected this was just noise.  FiveThirtyEight maintains three models: “polls-only” (which attempts to predict how the election will turn out on November 8 based only on polls), “polls-plus” (which incorporates demographics and fundamental), and “now-cast” (which attempts to predict how the election would turn out today).

Each of these results is based on 20,000 simulations of the election; thus we’re looking at differences between noisy data.  However all three of fivethirtyeight’s models moved in the same direction at this time, according to the (unofficial) FiveThirtyEight forecast bot:

Update! Clinton-Trump
Polls-plus ↓ 1.3% (83.7%-16.2%)
Polls-only ↓ 1.4% (86.0%-13.9%)
Now-cast ↓ 1.9% (86.0%-13.9%)https://t.co/chUUwwNk1F

— 538 Forecast Bot (@538forecastbot) October 23, 2016

So what’s going on?  To take a quick look at this, let’s look at the last 50 updates in which all three forecasts moved (sometimes there’s a change of 0.0).  By my count, 29 of them had all three forecasts moving in the same direction, while the forecasts have been roughly flat over that time.  If the forecasts were uncorrelated, then we could think of the direction of the moves as just random flips of a coin, and one-fourth of the moves would have either three ups or three downs.  So by this simple test, the moves appear to be correlated.  In the spirit of Averaging All The Things that pervades FiveThirtyEight, perhaps the “true” motion was the average of the motion in the three models, 1.5 or 1.6 percent down.

So it seems quite reasonable that that move is “real” – at the very least all three models bear show it.  But why? There’s at least one large move based on other single polls.  Looking at the history of updates, we find this move, less than an hour ago as I write this, based on a poll that shows Nevada at Clinton +6 when the model had Clinton up by 4 previously:

Update! Clinton-Trump
Polls-plus ↑ 1.0% (84.0%-16.0%)
Polls-only ↑ 1.0% (86.1%-13.9%)
Now-cast ↑ 1.3% (86.6%-13.3%)https://t.co/chUUwwNk1F

— 538 Forecast Bot (@538forecastbot) October 24, 2016

But most big moves come from several polls coming in at the same time, and in more significant states. I think the real lesson here is that obsessive poll-watching is exhausting…

Barcelona-based independent publisher Kronecker Wallis is putting out a reissue of Newton’s Principia with very good graphic design.  (Link goes to Kickstarter, but it looks like the plan is to sell it through normal channels as well if this Kickstarter, well, kickstarts it.). 45 euros including shipping, a good deal for an object of this quality.

I’m reminded of Byrne’s graphical Euclid, although this is not the same sort of project – Byrne replaced the text with graphics, and this is about making the text look good and be a pleasure to hold and read. Are there any other similar editions of mathematical classics? (Given our history of neglecting our history, I’m not particularly hopeful, but would love to be proven wrong.)

While looking for something for work (!), I came across an interesting set of PowerPoint slides How to predict election results from Jan-Michael Frahm at UN . This nicely summarizes the methodology of the 2012 iteration of Nate Silver’s election forecast at FiveThirtyEight. The 2016 iteration is explained here but is largely similar.

If you like these sorts of models, there’s also Sam Wang (Princeton Election Consortium), Josh Katz (New York Times / Upshot), and Huffington Post, Daily Kos PredictWise is David Rothschild’s site which aggregates results from the prediction markets. Slate has been running a feature on Trump vs. Clinton: Who’s Winning Today’s Forecasts of Who Will Win the Election?, which is a bit tongue-in-cheek . They average the other forecasts. As they tell us, “averaging is a sophisticated econometric technique that combines addition and division”.

The slides come from a course COMP 066: Random Thoughts at UNC, a first-year seminar in the CS department which is as far as I can tell intended for non-majors, which is described at the epartment’s web page as follows: “explores the notions of “randomness” and its antithesis, “structure.” What does “random” mean? How do computers generate “random” numbers and just how random are they? Is the addition of random noise to a signal always bad? (The answer is no!)” I would have liked to teach a class like this back when I taught (or for that matter, to take such a class back when I took classes).

Today is Hamilton Day. (William Rowan, not Alexander.) 173 years ago today, William Rowan Hamilton figured out how to multiply quaternions. The story goes that he was trying to figure out how to multiply triples of numbers, but eventually realized he was going to have to multiply quadruples. He figured it out while walking along Broom Bridge in Dublin, where he carved the equation . There’s a plaque (you should always read the plaque), and a few years back John Baez posted some pictures. Maynooth University does an annual Hamilton Walk retracing his steps on this day, which you missed, but there’s an audio guide.

Circle packing is made harder by the following constraints: 

• The circles are not all the same size, because summer squash are not even close to being cylindrical.
• Your grill is too small, because you didn’t read the size when you registered for wedding gifts.
• The plane on which you are trying to pack circles is hot.

Bill Clinton: “if you don’t vote, it’s half a vote for the other guys” – just heard on CNN at a campaign event.

This is a property of elections that hadn’t occurred to me. The quantity Clinton cares about is C – T, where C is the number of votes for Clinton and T the number of votes for Trump.If this is positive, Clinton wins; if it’s negative, Trump wins.  If you vote for Clinton, that goes up by 1; if for nobody, it doesn’t change; if for Trump, down by 1.  So not voting is makes this margin one less than Clinton would like; voting for Trump makes this margin two less.  Hence, half a vote for the other guys.

(My vote can be inferred from my choice of sign convention in this post.)