Shortly after posting some updated WI data analysis (here) I got pointed to an even better set of data to use for generating the fingerprints. This updated dataset uses the registration numbers as of Nov 3 and the per ward vote tallies provided on the https://elections.wi.gov/ site.

I also made some code modifications in order to compute an idealized model of what the Fingerprint “should” look like, similar to what was done in the original National Academy of Sciences paper. The ideal model is based on a 2D Gaussian Mixture Model fit to the locations of top histogram bins that are >= 70% of the max histogram value. The model uses a shared covariance matrix for all of the components based off of the sample covariance of the data. In the statewide plot below the Election Fingerprint is in the top right, with the ideal model in the bottom left. On the bottom right and top left are the sums of the Election Fingerprint image across the different dimensions.

Statewide Election Fingerprint, per Ward group

We see what looks like two (or even three) superimposed lobes around the 90% turnout and 40% per Biden bin, which is consistent with my earlier low-res version, and we have a lot more detail as to the upward kurtotic smear off the main distribution. There also are a few Ward Groupings that have exceptionally high % for Biden (rarely, if ever, does a candidate receive near 100% vote share), and there are Ward Groupings that are near/over the 100% turnout marker, with the addition that some of those are ALSO either 100% Biden or 0% Biden (all big red flags)!

As discussed in the National Academy of Sciences paper that all of this work is based on, an idealized Election Fingerprint should look like a 2D Gaussian (or a multi-variate normal distribution). Or maybe, in an extremely divided populace, 2 overlapping Gaussian distributions. Deviations from this are, by definition, irregular and an indication that there might be an issue with the election. Distinct linear or non-linear features sloping away from the central lobes(s) could indicate voter manipulation or “ballot stuffing”; isolated regions at high turnout +/or very high vote percentage for one candidate could be an indication of extreme manipulation or vote substitution. Now, its true that no real Election Fingerprint will look perfectly Gaussian due to the realities of how elections operate, data errors, etc … but they should at least be close!

Testing specific Wards to check for outliers

Just like before, I went through each individual counties data to make individual county specific Fingerprints. I also used the model to find a set of “interesting” wards by testing how well the ward fit against the probability density function (pdf) of the model. Wards are deemed outliers from the model if the ward group entry had (over 25 votes total) AND (it had over 97% turnout OR it had over 97% votes for Biden OR it had an evaluated pdf of < 1E-9 against the GMM Model). The complete list of outliers according to this model is below, along with the sums for the outlier wards.

	BidenVotes	TrumpVotes	TotalVotes	RegisteredVotes
Sum	213312	139381	359130	446866

Locality	BidenVotes	TrumpVotes	TotalVotes	RegisteredVoters	Turnout	BidenPct	TrumpPct	Model Probability
ASHLAND COUNTY TOWN OF LA POINTE WARD 1	236	52	294	324	90.7407407407407	80.2721088435374	17.687074829932	1.92477676888761E-12
ASHLAND COUNTY TOWN OF SANBORN WARDS 1-2	442	85	530	705	75.177304964539	83.3962264150943	16.0377358490566	6.11329669551507E-18
BARRON COUNTY CITY OF BARRON WARDS 1-7	683	837	1532	1963	78.0438104941416	44.5822454308094	54.6344647519582	5.63608603904006E-10
BAYFIELD COUNTY CITY OF BAYFIELD WARDS 1-4	314	57	373	454	82.15859030837	84.1823056300268	15.2815013404826	1.02971568650162E-21
BAYFIELD COUNTY CITY OF WASHBURN WARDS 1-4	944	320	1283	1493	85.934360348292	73.5775526110678	24.9415432579891	4.1581192007984E-12
BAYFIELD COUNTY TOWN OF BAYFIELD WARD 1	399	158	564	640	88.125	70.7446808510638	28.0141843971631	6.44035024538831E-10
BAYFIELD COUNTY TOWN OF RUSSELL WARDS 1-2	601	91	699	912	76.6447368421053	85.9799713876967	13.0185979971388	7.1789626891056E-28
BROWN COUNTY TOWN OF MORRISON WARDS 1-2	196	768	979	1112	88.0395683453237	20.0204290091931	78.4473953013279	5.20055002328544E-11
COLUMBIA COUNTY TOWN OF RANDOLPH WARD 1	92	371	470	526	89.3536121673004	19.5744680851064	78.936170212766	1.49308448006707E-12
DANE COUNTY CITY OF MADISON WARD 127	77	13	92	93	98.9247311827957	83.695652173913	14.1304347826087	6.39561194230751E-06
DANE COUNTY CITY OF MADISON WARD 140	37	8	46	47	97.8723404255319	80.4347826086957	17.3913043478261	2.44340709557608E-05
DANE COUNTY CITY OF MADISON WARD 48	659	148	819	1426	57.4333800841515	80.4639804639805	18.0708180708181	3.52762840567405E-10
DANE COUNTY CITY OF MADISON WARD 49	1968	423	2427	4227	57.416607523066	81.0877626699629	17.4289245982695	3.68273621333911E-10
DANE COUNTY CITY OF MADISON WARD 50	1462	238	1741	3074	56.6363044892648	83.9747271682941	13.6703044227455	1.25242937905889E-10
DANE COUNTY CITY OF MADISON WARD 54	233	68	304	637	47.723704866562	76.6447368421053	22.3684210526316	5.70258703816946E-18
DANE COUNTY CITY OF MADISON WARD 55	1346	270	1649	2939	56.1075195644777	81.6252274105518	16.3735597331716	5.509685735556E-11
DANE COUNTY CITY OF MADISON WARD 56	655	195	866	1799	48.1378543635353	75.635103926097	22.5173210161663	9.6228505281742E-18
DANE COUNTY CITY OF MADISON WARD 57	750	156	923	1791	51.535455053043	81.2567713976165	16.9014084507042	2.47205132607457E-14
DANE COUNTY CITY OF MADISON WARD 58	341	58	406	760	53.4210526315789	83.9901477832512	14.2857142857143	7.30999920891661E-13
DANE COUNTY CITY OF MADISON WARD 59	365	77	452	930	48.6021505376344	80.7522123893805	17.0353982300885	8.17744524385459E-17
DANE COUNTY CITY OF SUN PRAIRIE WARDS 20-22,24-25,28	175	77	256	258	99.2248062015504	68.359375	30.078125	0.000158078462324593
DOUGLAS COUNTY CITY OF SUPERIOR WARDS 17-19,30-32	1173	620	1856	2528	73.4177215189873	63.2004310344828	33.4051724137931	3.37051056052282E-11
DOUGLAS COUNTY TOWN OF DAIRYLAND WARD 1	25	101	129	153	84.3137254901961	19.3798449612403	78.2945736434108	2.75440521379353E-13
DUNN COUNTY CITY OF MENOMONIE WARD 6	353	257	620	831	74.6089049338147	56.9354838709677	41.4516129032258	4.75782477468469E-10
DUNN COUNTY CITY OF MENOMONIE WARDS 3-4	523	369	919	1568	58.609693877551	56.9096844396083	40.1523394994559	1.08064146240878E-21
DUNN COUNTY CITY OF MENOMONIE WARDS 5,7	361	279	673	1276	52.7429467084639	53.6404160475483	41.4561664190193	8.60680131606536E-27
DUNN COUNTY CITY OF MENOMONIE WARDS 8-9	751	528	1308	1790	73.072625698324	57.4159021406728	40.3669724770642	5.1671988172534E-11
EAU CLAIRE COUNTY CITY OF ALTOONA WARDS 12-16	16	11	27	26	103.846153846154	59.2592592592593	40.7407407407407	7.25926716445429E-07
EAU CLAIRE COUNTY CITY OF EAU CLAIRE WARD 20	360	215	596	1246	47.8330658105939	60.4026845637584	36.0738255033557	6.79322707506469E-21
EAU CLAIRE COUNTY CITY OF EAU CLAIRE WARD 5	451	209	679	1255	54.1035856573705	66.4212076583211	30.780559646539	3.54653273188261E-16
EAU CLAIRE COUNTY CITY OF EAU CLAIRE WARD 6	535	186	743	1169	63.5585970915312	72.0053835800807	25.0336473755047	5.48820883152761E-11
EAU CLAIRE COUNTY TOWN OF FAIRCHILD WARD 1	35	135	174	195	89.2307692307692	20.1149425287356	77.5862068965517	9.210877887475E-13
EAU CLAIRE COUNTY TOWN OF WILSON WARD 1	49	172	223	257	86.7704280155642	21.9730941704036	77.1300448430493	1.18540801589859E-11
FOND DU LAC COUNTY CITY OF FOND DU LAC WARD 1	360	424	788	988	79.7570850202429	45.6852791878173	53.8071065989848	6.79364522819134E-11
FOND DU LAC COUNTY CITY OF FOND DU LAC WARD 11	310	298	618	810	76.2962962962963	50.1618122977346	48.2200647249191	2.55555808340175E-16
FOND DU LAC COUNTY CITY OF FOND DU LAC WARD 13	309	322	649	809	80.2224969097651	47.6117103235747	49.6147919876733	1.11701213986908E-11
FOND DU LAC COUNTY CITY OF FOND DU LAC WARD 16	338	351	698	830	84.0963855421687	48.4240687679083	50.2865329512894	2.97370363102557E-10
FOND DU LAC COUNTY CITY OF FOND DU LAC WARD 17	114	104	222	262	84.7328244274809	51.3513513513513	46.8468468468468	5.93415306364651E-12
FOND DU LAC COUNTY CITY OF FOND DU LAC WARD 19	336	371	721	879	82.0250284414107	46.6019417475728	51.4563106796116	4.52742910745579E-10
FOND DU LAC COUNTY CITY OF FOND DU LAC WARD 2	329	316	659	830	79.3975903614458	49.9241274658574	47.9514415781487	1.15316292964617E-13
FOND DU LAC COUNTY CITY OF FOND DU LAC WARD 5	683	661	1378	1626	84.7478474784748	49.5645863570392	47.9680696661829	9.37767055176873E-11
FOND DU LAC COUNTY CITY OF FOND DU LAC WARD 9	341	326	679	885	76.7231638418079	50.220913107511	48.0117820324006	5.66905415025079E-16
FOND DU LAC COUNTY CITY OF RIPON WARDS 4-6	415	403	829	1085	76.4055299539171	50.0603136308806	48.6127864897467	3.75104914617071E-16
FOND DU LAC COUNTY CITY OF RIPON WARDS 9-11	540	592	1152	1396	82.5214899713467	46.875	51.3888888888889	5.52488291943727E-10
FOREST COUNTY TOWN OF NASHVILLE WARD 2	131	54	186	250	74.4	70.4301075268817	29.0322580645161	5.58741907395928E-23
GRANT COUNTY CITY OF PLATTEVILLE WARDS 5-6	335	294	662	1149	57.615317667537	50.6042296072508	44.4108761329305	3.46889241295325E-26
GRANT COUNTY CITY OF PLATTEVILLE WARDS 7-9	855	625	1547	2129	72.6632221700329	55.2682611506141	40.4007756948933	1.43548705444654E-10
GRANT COUNTY VILLAGE OF WOODMAN WARD 1	12	35	47	69	68.1159420289855	25.531914893617	74.468085106383	1.82801386794522E-13
JACKSON COUNTY TOWN OF BEAR BLUFF WARD 1	11	89	100	108	92.5925925925926	11	89	1.36352878176187E-10
JACKSON COUNTY TOWN OF KOMENSKY WARD 1	146	30	176	222	79.2792792792793	82.9545454545455	17.0454545454545	1.32889222362522E-25
JEFFERSON COUNTY CITY OF WHITEWATER WARD 12	115	87	205	501	40.9181636726547	56.0975609756098	42.4390243902439	2.12872002012183E-42
JEFFERSON COUNTY CITY OF WHITEWATER WARDS 10-11	351	223	590	758	77.8364116094987	59.4915254237288	37.7966101694915	2.13791245784257E-10
JEFFERSON COUNTY VILLAGE OF CAMBRIDGE WARD 1	44	20	65	74	87.8378378378378	67.6923076923077	30.7692307692308	8.22755433390597E-15
KENOSHA COUNTY CITY OF KENOSHA WARD 10	291	113	413	619	66.7205169628433	70.4600484261501	27.3607748184019	2.06776570315375E-10
KENOSHA COUNTY CITY OF KENOSHA WARD 3	162	60	230	530	43.3962264150943	70.4347826086957	26.0869565217391	3.43956135655265E-34
KENOSHA COUNTY CITY OF KENOSHA WARD 33	23	3	26	33	78.7878787878788	88.4615384615385	11.5384615384615	5.42133845876956E-13
KENOSHA COUNTY CITY OF KENOSHA WARD 7	273	94	374	557	67.1454219030521	72.9946524064171	25.1336898395722	6.87529928579931E-11
KEWAUNEE COUNTY CITY OF ALGOMA WARDS 1-6	825	819	1685	1980	85.1010101010101	48.9614243323442	48.6053412462908	9.13667635453448E-11
LA CROSSE COUNTY CITY OF LA CROSSE WARD 11	200	144	349	614	56.8403908794788	57.3065902578797	41.2607449856734	4.45768797779824E-11
LA CROSSE COUNTY CITY OF LA CROSSE WARD 8	518	398	936	2035	45.995085995086	55.3418803418803	42.5213675213675	3.353166809769E-20
LANGLADE COUNTY CITY OF ANTIGO WARD 4	201	203	406	497	81.6901408450704	49.5073891625616	50	6.0338889427495E-11
LANGLADE COUNTY CITY OF ANTIGO WARD 5	128	171	307	411	74.6958637469586	41.6938110749186	55.700325732899	4.05169423409582E-10
MANITOWOC COUNTY CITY OF MANITOWOC WARDS 5-6	807	678	1520	1842	82.5190010857763	53.0921052631579	44.6052631578947	3.74295368009036E-12
MANITOWOC COUNTY CITY OF MANITOWOC WARDS 7-8	498	540	1061	1371	77.3887673231218	46.9368520263902	50.8953817153629	2.85889433338131E-12
MARATHON COUNTY CITY OF WAUSAU WARD 6	315	196	531	691	76.8451519536903	59.3220338983051	36.9114877589454	2.35008171069057E-11
MARATHON COUNTY CITY OF WAUSAU WARD 8	279	208	500	712	70.2247191011236	55.8	41.6	1.41787361998521E-14
MILWAUKEE COUNTY CITY OF CUDAHY WARDS 1-3	1225	729	1984	2433	81.5454171804357	61.7439516129032	36.7439516129032	7.55840876244649E-16
MILWAUKEE COUNTY CITY OF CUDAHY WARDS 10-12	963	936	1924	2374	81.044650379107	50.051975051975	48.6486486486487	1.74595382047536E-23
MILWAUKEE COUNTY CITY OF CUDAHY WARDS 13-15	1118	1050	2189	2633	83.1371059627801	51.0735495660119	47.9671082686158	1.04744228724223E-23
MILWAUKEE COUNTY CITY OF CUDAHY WARDS 4-6	983	797	1819	2295	79.2592592592593	54.040681693238	43.8152831225948	9.06241072063201E-20
MILWAUKEE COUNTY CITY OF CUDAHY WARDS 7-9	857	715	1595	2102	75.8801141769743	53.730407523511	44.8275862068966	8.54298236281862E-19
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 10	242	197	446	481	92.7234927234927	54.2600896860987	44.1704035874439	1.46465970777214E-27
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 11	540	653	1203	1354	88.8478581979321	44.8877805486284	54.2809642560266	4.20290665007461E-32
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 12	337	502	849	981	86.5443425076453	39.6937573616019	59.1283863368669	2.24476659505987E-35
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 13	552	635	1200	1344	89.2857142857143	46	52.9166666666667	1.90914119352679E-31
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 14	494	528	1033	1149	89.9042645778938	47.8218780251694	51.1132623426912	2.33294864294759E-30
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 15A	209	330	543	650	83.5384615384615	38.4898710865562	60.7734806629834	7.5447895506994E-35
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 15B	177	207	390	450	86.6666666666667	45.3846153846154	53.0769230769231	2.80359406780404E-30
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 16	528	692	1239	1387	89.3294881038212	42.6150121065375	55.8514931396287	1.90956073819003E-34
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 17	396	350	757	839	90.2264600715137	52.3117569352708	46.2351387054161	4.20442722572625E-27
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 18	212	256	472	517	91.2959381044487	44.9152542372881	54.2372881355932	8.35213158350655E-34
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 19	557	712	1279	1441	88.7578070784178	43.5496481626271	55.6684910086005	3.21836172741495E-33
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 2	631	878	1531	1668	91.7865707434053	41.2148922273024	57.3481384715872	1.68975552016341E-37
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 20	329	339	677	772	87.6943005181347	48.5967503692762	50.0738552437223	2.97205717497328E-28
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 21	174	127	311	402	77.363184079602	55.9485530546624	40.8360128617363	9.56678900628984E-18
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 22A	317	396	718	803	89.4146948941469	44.150417827298	55.1532033426184	3.93357547643516E-33
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 22B	280	386	677	739	91.6102841677943	41.3589364844904	57.0162481536189	3.12040163864428E-37
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 23	493	763	1275	1410	90.4255319148936	38.6666666666667	59.843137254902	6.22957831868089E-39
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 3	751	823	1598	1774	90.0789177001127	46.9962453066333	51.5018773466834	3.68125386891159E-31
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 4	321	470	798	951	83.9116719242902	40.2255639097744	58.8972431077694	2.2686537468804E-33
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 5	286	369	659	720	91.5277777777778	43.3990895295903	55.9939301972686	2.60219138859018E-35
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 6	495	582	1093	1222	89.443535188216	45.288197621226	53.2479414455627	3.67278895928314E-32
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 7	650	777	1437	1573	91.3541004450095	45.2331245650661	54.0709812108559	1.42346954750186E-33
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 8	553	428	1002	1172	85.4948805460751	55.189620758483	42.7145708582834	5.12738299716137E-22
MILWAUKEE COUNTY CITY OF FRANKLIN WARD 9	522	469	1003	1154	86.9150779896014	52.0438683948156	46.7597208374875	4.17082445761439E-25
MILWAUKEE COUNTY CITY OF GLENDALE WARD 8	356	172	529	592	89.3581081081081	67.296786389414	32.5141776937618	1.89068769493298E-17
MILWAUKEE COUNTY CITY OF GLENDALE WARDS 1,7	984	347	1363	1555	87.6527331189711	72.1936903888481	25.4585473220836	3.16744713268672E-14
MILWAUKEE COUNTY CITY OF GLENDALE WARDS 2,8S	712	212	935	1076	86.8959107806692	76.1497326203208	22.6737967914439	2.28904788740795E-12
MILWAUKEE COUNTY CITY OF GLENDALE WARDS 3,9	1053	459	1546	1764	87.6417233560091	68.111254851229	29.6895213454075	6.14813246126453E-16
MILWAUKEE COUNTY CITY OF GLENDALE WARDS 4,10	1172	468	1657	1854	89.3743257820928	70.7302353651177	28.2438141219071	6.02930527494358E-16
MILWAUKEE COUNTY CITY OF GLENDALE WARDS 5,11	934	357	1312	1480	88.6486486486487	71.1890243902439	27.2103658536585	2.86273338591949E-15
MILWAUKEE COUNTY CITY OF GLENDALE WARDS 6,12	866	270	1153	1349	85.470719051149	75.1084128360798	23.417172593235	7.42416867151683E-12
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 1	576	438	1035	1261	82.0777160983346	55.6521739130435	42.3188405797101	6.75719273960832E-20
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 10	565	518	1102	1297	84.9653045489591	51.2704174228675	47.005444646098	1.54815447380789E-24
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 11	619	689	1323	1457	90.8030199039122	46.7876039304611	52.0786092214664	7.49110216197986E-32
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 12	609	767	1392	1576	88.3248730964467	43.75	55.1005747126437	9.38170717809966E-33
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 13	352	317	675	780	86.5384615384615	52.1481481481481	46.962962962963	8.46101282137081E-25
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 14	469	428	910	1124	80.9608540925267	51.5384615384615	47.032967032967	2.5923776457702E-22
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 15	431	467	912	1086	83.9779005524862	47.2587719298246	51.2061403508772	3.64452002818065E-27
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 16	182	200	385	475	81.0526315789474	47.2727272727273	51.9480519480519	1.03185649345929E-25
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 17	461	346	815	992	82.1572580645161	56.5644171779141	42.4539877300613	2.52239916433965E-19
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 18	426	448	888	1014	87.5739644970414	47.972972972973	50.4504504504504	1.11207447361166E-28
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 19	764	525	1298	1508	86.0742705570292	58.8597842835131	40.4468412942989	5.85375850169128E-20
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 2	445	447	903	1068	84.5505617977528	49.2801771871539	49.5016611295681	7.65010167785277E-26
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 20	685	665	1364	1590	85.7861635220126	50.2199413489736	48.7536656891496	8.26628576102177E-26
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 21	450	372	838	1027	81.5968841285297	53.6992840095465	44.3914081145585	5.02825498303287E-21
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 3	495	343	852	1071	79.5518207282913	58.0985915492958	40.2582159624413	3.60103037564101E-17
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 4	516	306	832	1100	75.6363636363636	62.0192307692308	36.7788461538462	1.96038822277719E-13
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 5	520	439	972	1211	80.2642444260941	53.4979423868313	45.1646090534979	1.42770280944536E-20
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 6	448	530	996	1143	87.1391076115486	44.9799196787149	53.2128514056225	6.42641640780174E-31
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 7	629	643	1296	1518	85.3754940711462	48.5339506172839	49.6141975308642	6.70730734893469E-27
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 8	612	504	1127	1361	82.8067597354886	54.3034605146406	44.7204968944099	3.49143853336436E-21
MILWAUKEE COUNTY CITY OF GREENFIELD WARD 9	550	499	1065	1244	85.6109324758843	51.6431924882629	46.8544600938967	1.26351449266701E-24
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 107	588	10	604	909	66.4466446644664	97.3509933774834	1.65562913907285	0.00607290997487753
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 116	395	10	407	663	61.3876319758673	97.051597051597	2.45700245700246	0.00442900875815082
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 130	267	78	352	928	37.9310344827586	75.8522727272727	22.1590909090909	5.06449448879178E-16
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 132	1255	359	1633	1969	82.935500253936	76.852418860992	21.9840783833435	6.22621280143243E-10
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 144	334	6	344	559	61.5384615384615	97.0930232558139	1.74418604651163	0.0045609717682438
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 15	535	218	768	921	83.3876221498371	69.6614583333333	28.3854166666667	8.50493764715297E-13
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 186	1795	696	2541	3271	77.6826658514216	70.6414797323888	27.3907910271547	6.8232933072707E-10
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 191	399	141	550	1204	45.6810631229236	72.5454545454545	25.6363636363636	3.99914771473777E-12
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 205	572	197	792	965	82.0725388601036	72.2222222222222	24.8737373737374	4.24373565136365E-11
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 206	479	163	654	757	86.3936591809776	73.2415902140673	24.9235474006116	4.72113232055249E-13
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 207	446	270	730	928	78.6637931034483	61.0958904109589	36.986301369863	5.36175276901475E-15
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 208	512	302	834	1027	81.207400194742	61.3908872901679	36.21103117506	6.79004252066778E-16
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 209	453	295	762	907	84.013230429989	59.4488188976378	38.7139107611549	1.95546986299735E-18
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 210	355	315	689	921	74.8099891422367	51.5239477503628	45.7184325108853	4.27579491035909E-20
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 211	461	285	770	998	77.1543086172345	59.8701298701299	37.012987012987	3.56231589937219E-15
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 212	514	310	842	1123	74.9777382012467	61.0451306413302	36.8171021377672	8.4094687651029E-14
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 213	569	294	883	1155	76.4502164502164	64.4394110985277	33.2955832389581	2.2623703277218E-12
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 243	852	215	1083	1238	87.4798061389338	78.6703601108033	19.8522622345337	5.41723117940624E-12
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 245	556	135	702	828	84.7826086956522	79.2022792022792	19.2307692307692	3.00331841376544E-10
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 261	337	174	523	632	82.753164556962	64.4359464627151	33.2695984703633	5.82555622850465E-15
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 262	665	388	1064	1305	81.5325670498084	62.5	36.4661654135338	2.04151668899336E-15
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 263	374	199	579	757	76.4861294583884	64.594127806563	34.3696027633851	2.65171157727609E-12
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 264	676	485	1185	1444	82.0637119113573	57.0464135021097	40.9282700421941	5.79902239092725E-19
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 265	351	267	628	776	80.9278350515464	55.8917197452229	42.515923566879	3.34925682107936E-19
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 266	340	360	721	900	80.1111111111111	47.1567267683773	49.9306518723994	2.13915754083083E-25
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 267	362	285	657	792	82.9545454545455	55.0989345509893	43.37899543379	1.04876863667821E-20
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 268	798	634	1452	1788	81.2080536912752	54.9586776859504	43.66391184573	5.79255390176922E-20
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 269	451	388	857	1076	79.6468401486989	52.6254375729288	45.2742123687281	6.07513589006184E-21
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 270	411	376	799	992	80.5443548387097	51.4392991239049	47.0588235294118	3.33979654311459E-22
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 271	817	634	1472	1834	80.2617230098146	55.5027173913043	43.070652173913	3.58022181504136E-19
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 272	512	567	1096	1266	86.5718799368089	46.7153284671533	51.7335766423358	4.28503366875072E-29
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 273	313	290	610	719	84.8400556328234	51.3114754098361	47.5409836065574	1.95369959606737E-24
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 274	296	297	610	763	79.9475753604194	48.5245901639344	48.6885245901639	3.26718700327756E-24
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 275	772	633	1423	1854	76.7529665587918	54.2515811665495	44.4834855938159	1.05215494863984E-18
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 276	672	600	1295	1527	84.8068107400131	51.8918918918919	46.3320463320463	5.54486888719382E-24
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 277	688	602	1320	1640	80.4878048780488	52.1212121212121	45.6060606060606	1.1395279941763E-21
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 278	370	359	740	916	80.7860262008734	50	48.5135135135135	2.07680960722687E-23
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 279	573	445	1040	1265	82.2134387351779	55.0961538461539	42.7884615384615	2.42738130289664E-20
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 280	1124	481	1630	2065	78.9346246973366	68.9570552147239	29.5092024539877	4.4239076774363E-11
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 281	474	265	747	957	78.0564263322884	63.4538152610442	35.475234270415	1.89778550535121E-13
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 283	473	252	736	983	74.8728382502543	64.2663043478261	34.2391304347826	5.45437704403475E-12
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 286	277	136	420	580	72.4137931034483	65.9523809523809	32.3809523809524	1.56448201263501E-10
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 287	331	141	483	597	80.9045226130653	68.5300207039337	29.1925465838509	4.2570324619449E-12
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 288	673	386	1077	1350	79.7777777777778	62.4883936861653	35.8402971216342	1.19076439246385E-14
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 292	428	198	632	797	79.297365119197	67.7215189873418	31.3291139240506	8.75090412574107E-12
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 293	495	277	789	953	82.791185729276	62.7376425855513	35.1077313054499	6.82907019712108E-16
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 294	418	191	621	733	84.7203274215553	67.3107890499195	30.7568438003221	1.43489007810167E-14
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 295	541	191	750	931	80.5585392051557	72.1333333333333	25.4666666666667	1.9406997206457E-10
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 296	671	288	982	1154	85.0953206239168	68.3299389002037	29.3279022403259	2.61839983551383E-14
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 297	637	197	849	1021	83.1537708129285	75.0294464075383	23.2037691401649	1.26537229672712E-10
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 298	542	162	723	856	84.4626168224299	74.9654218533887	22.4066390041494	2.44713694663658E-11
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 299	653	249	921	1081	85.1988899167438	70.9011943539631	27.0358306188925	2.92696461989792E-13
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 300	967	224	1215	1410	86.1702127659574	79.5884773662551	18.4362139917695	6.00326630881646E-11
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 301	769	361	1153	1359	84.8417954378219	66.6955767562879	31.3096270598439	6.25191697293189E-15
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 302	476	309	792	951	83.2807570977918	60.1010101010101	39.0151515151515	1.17316695988034E-17
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 303	722	429	1172	1365	85.8608058608059	61.6040955631399	36.6040955631399	3.38458923045215E-18
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 304	531	379	928	1165	79.656652360515	57.2198275862069	40.8405172413793	8.90270006142794E-18
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 305	567	344	932	1159	80.4141501294219	60.8369098712446	36.9098712446352	7.29125476826321E-16
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 306	476	215	700	931	75.187969924812	68	30.7142857142857	3.05481514364101E-10
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 307	486	302	802	986	81.3387423935091	60.5985037406484	37.6558603491272	2.04179924025003E-16
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 309	645	453	1114	1378	80.8417997097242	57.8994614003591	40.6642728904847	7.56341300405146E-18
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 311	368	277	656	847	77.4498229043684	56.0975609756098	42.2256097560976	1.12321636456187E-17
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 312	553	389	950	1077	88.2079851439183	58.2105263157895	40.9473684210526	1.07820265316607E-21
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 313	950	762	1743	2182	79.8808432630614	54.5037292025244	43.7177280550775	1.06521933847579E-19
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 314	347	320	680	812	83.743842364532	51.0294117647059	47.0588235294118	4.70010708286927E-24
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 315	466	436	916	1604	57.1072319201995	50.8733624454148	47.5982532751092	4.13471102377506E-20
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 316	373	370	750	925	81.0810810810811	49.7333333333333	49.3333333333333	9.49352292125111E-24
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 317	533	352	901	1087	82.8886844526219	59.1564927857936	39.0677025527192	4.96013279980341E-18
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 33	1324	684	2039	2510	81.2350597609562	64.9337910740559	33.5458558116724	5.60180949248076E-14
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 81	384	230	632	779	81.1296534017972	60.7594936708861	36.3924050632911	3.1634316099815E-16
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 82	528	301	843	975	86.4615384615385	62.6334519572954	35.70581257414	5.64360612254358E-18
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 83	662	323	1009	1222	82.569558101473	65.609514370664	32.01189296333	2.86383247830138E-14
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 84	465	224	710	867	81.8915801614764	65.4929577464789	31.5492957746479	5.33119079644663E-14
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 85	623	302	945	1146	82.4607329842932	65.9259259259259	31.957671957672	4.65866813999447E-14
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 86	629	274	926	1110	83.4234234234234	67.92656587473	29.5896328293737	1.37729902611045E-13
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 87	434	150	593	729	81.3443072702332	73.1871838111299	25.2951096121417	2.15106739253755E-10
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 88	605	183	804	964	83.402489626556	75.2487562189055	22.7611940298507	1.11543465562254E-10
MILWAUKEE COUNTY CITY OF MILWAUKEE WARD 93	495	170	676	790	85.5696202531646	73.2248520710059	25.1479289940828	1.42556748420486E-12
MILWAUKEE COUNTY CITY OF OAK CREEK WARDS 1-3	1701	1744	3522	4009	87.852332252432	48.2964224872232	49.5173197047132	1.34995380021904E-28
MILWAUKEE COUNTY CITY OF OAK CREEK WARDS 10-12	1596	2090	3758	4268	88.0506091846298	42.4693986162853	55.6146886641831	1.00089661325203E-33
MILWAUKEE COUNTY CITY OF OAK CREEK WARDS 13-15	1559	2056	3664	4075	89.9141104294479	42.5491266375546	56.1135371179039	6.55292766071753E-35
MILWAUKEE COUNTY CITY OF OAK CREEK WARDS 16-19	1565	1307	2929	3521	83.1865947174098	53.4312051894845	44.6227381358826	5.40429558312339E-22
MILWAUKEE COUNTY CITY OF OAK CREEK WARDS 4-6	1777	1607	3453	4087	84.4873990702227	51.4624963799595	46.5392412395019	3.97596480799759E-24
MILWAUKEE COUNTY CITY OF OAK CREEK WARDS 7-9	1652	1758	3461	3954	87.5316135558928	47.731869401907	50.7945680439179	7.52545559662631E-29
MILWAUKEE COUNTY CITY OF SOUTH MILWAUKEE WARDS 1-4	1502	1252	2824	3348	84.3488649940263	53.1869688385269	44.3342776203966	8.79048436758186E-23
MILWAUKEE COUNTY CITY OF SOUTH MILWAUKEE WARDS 13-16	1354	1345	2753	3303	83.3484710868907	49.1827097711587	48.8557936796222	2.79916556081515E-25
MILWAUKEE COUNTY CITY OF SOUTH MILWAUKEE WARDS 5-8	1240	1179	2464	3013	81.7789578493196	50.3246753246753	47.849025974026	1.3006965302369E-23
MILWAUKEE COUNTY CITY OF SOUTH MILWAUKEE WARDS 9-12	1543	1570	3154	3679	85.7298178852949	48.9220038046925	49.7780596068484	8.55077535471153E-27
MILWAUKEE COUNTY CITY OF ST. FRANCIS WARDS 1-4	966	688	1688	2022	83.4817012858556	57.2274881516588	40.7582938388626	1.48833787683868E-19
MILWAUKEE COUNTY CITY OF ST. FRANCIS WARDS 5-8	1045	742	1818	2111	86.120322122217	57.4807480748075	40.8140814081408	7.37392790023734E-21
MILWAUKEE COUNTY CITY OF ST. FRANCIS WARDS 9-12	1370	807	2206	2541	86.8162140889414	62.1033544877607	36.5820489573889	1.73670652459223E-18
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 1	935	352	1313	1461	89.8699520876112	71.2109672505712	26.8088347296268	4.29199717483575E-16
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 10	1009	379	1425	1682	84.7205707491082	70.8070175438597	26.5964912280702	4.96976046265056E-13
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 11	942	370	1335	1541	86.6320571057755	70.561797752809	27.7153558052434	3.04969908063255E-14
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 12	1009	540	1578	1743	90.5335628227194	63.9416983523447	34.2205323193916	5.90182535799617E-20
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 13	538	175	734	852	86.150234741784	73.2970027247956	23.841961852861	6.91680405682294E-13
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 14	1019	333	1386	1599	86.6791744840525	73.5209235209235	24.025974025974	4.00745124345971E-13
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 15	1128	325	1487	1701	87.419165196943	75.857431069267	21.8560860793544	8.72613807561488E-13
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 16	1056	489	1590	1753	90.7016543069025	66.4150943396226	30.7547169811321	8.10244077319936E-19
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 17	796	527	1349	1485	90.8417508417509	59.006671608599	39.0659747961453	5.17912850085271E-23
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 18	756	369	1137	1345	84.5353159851301	66.490765171504	32.4538258575198	7.35436406286319E-15
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 19	836	455	1315	1527	86.1165684348396	63.574144486692	34.6007604562738	2.95934324139749E-17
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 2	1006	298	1321	1505	87.7740863787375	76.1544284632854	22.558667676003	6.45221760442866E-13
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 20	998	686	1716	1990	86.2311557788945	58.1585081585082	39.97668997669	1.71666108579506E-20
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 21	683	459	1167	1350	86.4444444444444	58.5261353898886	39.3316195372751	2.17866869555251E-20
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 22	859	484	1377	1552	88.7242268041237	62.3819898329702	35.1488743645606	1.49463701960189E-19
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 23	668	519	1215	1384	87.7890173410405	54.9794238683128	42.7160493827161	1.47783902838351E-23
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 24	702	338	1058	1276	82.9153605015674	66.351606805293	31.9470699432892	4.48324575884219E-14
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 3	981	401	1404	1625	86.4	69.8717948717949	28.5612535612536	2.16209341470747E-14
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 4	1264	589	1896	2116	89.6030245746692	66.6666666666667	31.0654008438819	6.41887343588786E-18
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 5	747	333	1095	1331	82.2689706987228	68.2191780821918	30.4109589041096	7.11539536094584E-13
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 6	733	267	1019	1213	84.0065952184666	71.9332679097154	26.2021589793916	3.42786865328096E-12
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 7	1020	509	1564	1777	88.0135059088351	65.2173913043478	32.5447570332481	1.42365789266778E-17
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 8	414	276	698	853	81.8288393903869	59.3123209169054	39.541547277937	2.03410688133861E-17
MILWAUKEE COUNTY CITY OF WAUWATOSA WARD 9	783	631	1447	1698	85.2179034157833	54.1119557705598	43.6074637180373	1.31727251710136E-22
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 1	552	302	875	1165	75.107296137339	63.0857142857143	34.5142857142857	1.09341656007839E-12
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 10	671	414	1107	1453	76.18719889883	60.6142728093948	37.3983739837398	2.05397477403839E-14
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 11	702	679	1402	1726	81.2282734646582	50.0713266761769	48.4308131241084	1.49191405531741E-23
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 12	582	663	1265	1554	81.4028314028314	46.0079051383399	52.4110671936759	6.25448622497098E-27
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 13	843	829	1715	2012	85.2385685884692	49.1545189504373	48.3381924198251	2.50898219645981E-26
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 14	531	355	906	1204	75.2491694352159	58.6092715231788	39.1832229580574	2.38701616746422E-15
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 15	527	434	987	1264	78.0854430379747	53.3941236068896	43.9716312056738	8.88322217068542E-20
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 16	496	473	996	1249	79.7437950360288	49.7991967871486	47.4899598393574	4.05804724716096E-23
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 17	680	641	1348	1668	80.8153477218225	50.4451038575668	47.5519287833828	4.45060932552017E-23
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 18	754	771	1550	1882	82.3591923485654	48.6451612903226	49.741935483871	3.27839997095381E-25
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 19	736	649	1413	1707	82.7768014059754	52.0877565463553	45.930644019816	9.1547815357996E-23
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 2	838	365	1223	1619	75.5404570722668	68.5200327064595	29.8446443172527	4.15549638821068E-10
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 20	559	453	1025	1242	82.5281803542673	54.5365853658537	44.1951219512195	6.98704542708123E-21
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 21	683	572	1279	1609	79.4903666873835	53.4010946051603	44.7224394057858	2.56434663338589E-20
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 22	566	410	990	1288	76.8633540372671	57.1717171717172	41.4141414141414	9.11106723907263E-17
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 23	686	730	1440	1739	82.8062104657849	47.6388888888889	50.6944444444444	3.01938597867455E-26
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 24	552	357	922	1199	76.8974145120934	59.8698481561822	38.7201735357918	4.34927337209287E-15
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 25	654	686	1365	1579	86.4471184293857	47.9120879120879	50.2564102564103	4.94212313828082E-28
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 3	523	280	828	1103	75.0679963735267	63.1642512077295	33.8164251207729	1.2380705237555E-12
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 4	714	488	1225	1565	78.2747603833866	58.2857142857143	39.8367346938776	1.49370039073485E-16
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 5	585	401	1000	1276	78.3699059561128	58.5	40.1	1.87650274487038E-16
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 6	490	399	916	1230	74.4715447154472	53.4934497816594	43.5589519650655	1.46418702181846E-18
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 7	675	470	1175	1534	76.5971316818774	57.4468085106383	40	1.68103981731446E-16
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 8	565	337	925	1231	75.1421608448416	61.0810810810811	36.4324324324324	7.92817234717319E-14
MILWAUKEE COUNTY CITY OF WEST ALLIS WARD 9	735	523	1280	1653	77.4349667271627	57.421875	40.859375	8.44200178825207E-17
MILWAUKEE COUNTY VILLAGE OF BAYSIDE WARD 5	508	192	706	779	90.6290115532734	71.9546742209632	27.1954674220963	2.44456563873148E-16
MILWAUKEE COUNTY VILLAGE OF BAYSIDE WARDS 1,3	719	323	1049	1242	84.4605475040258	68.5414680648236	30.791229742612	7.3531014466122E-14
MILWAUKEE COUNTY VILLAGE OF BAYSIDE WARDS 1S,3S	47	24	71	75	94.6666666666667	66.1971830985916	33.8028169014084	5.20072614807201E-22
MILWAUKEE COUNTY VILLAGE OF BAYSIDE WARDS 2,4	911	327	1254	1416	88.5593220338983	72.6475279106858	26.0765550239234	1.21537753246468E-14
MILWAUKEE COUNTY VILLAGE OF BROWN DEER WARDS 1-2	1891	553	2488	2949	84.3675822312648	76.0048231511254	22.2266881028939	6.03006145464191E-11
MILWAUKEE COUNTY VILLAGE OF BROWN DEER WARDS 3-4	1684	772	2490	2892	86.0995850622407	67.6305220883534	31.004016064257	3.2746666274357E-15
MILWAUKEE COUNTY VILLAGE OF BROWN DEER WARDS 5-6	1749	686	2477	2925	84.6837606837607	70.6096083972547	27.6947920872023	4.32055702907867E-13
MILWAUKEE COUNTY VILLAGE OF FOX POINT WARDS 1-4	1533	641	2201	2468	89.1815235008104	69.6501590186279	29.1231258518855	2.8694532937401E-16
MILWAUKEE COUNTY VILLAGE OF FOX POINT WARDS 5-9	1959	788	2786	3098	89.9289864428664	70.3158650394831	28.2842785355348	1.67529969347659E-16
MILWAUKEE COUNTY VILLAGE OF GREENDALE WARDS 1-2	824	916	1765	1954	90.3275332650972	46.685552407932	51.8980169971671	1.35248528592402E-31
MILWAUKEE COUNTY VILLAGE OF GREENDALE WARDS 3-4	1061	904	2007	2324	86.3597246127367	52.8649725959143	45.0423517688092	3.63389937076315E-24
MILWAUKEE COUNTY VILLAGE OF GREENDALE WARDS 5-6	912	845	1782	2030	87.7832512315271	51.1784511784512	47.4186307519641	2.67310357712138E-26
MILWAUKEE COUNTY VILLAGE OF GREENDALE WARDS 7-8	900	824	1756	1990	88.2412060301507	51.25284738041	46.9248291571754	1.53793346484194E-26
MILWAUKEE COUNTY VILLAGE OF GREENDALE WARDS 9-10	1087	1024	2127	2409	88.293897882939	51.1048425011754	48.142924306535	1.0979100727427E-26
MILWAUKEE COUNTY VILLAGE OF HALES CORNERS WARDS 1-3	726	878	1637	1877	87.2136387852957	44.3494196701283	53.6346976175932	1.63033371187735E-31
MILWAUKEE COUNTY VILLAGE OF HALES CORNERS WARDS 4-6	751	829	1603	1860	86.1827956989247	46.849656893325	51.715533374922	9.50129837887944E-29
MILWAUKEE COUNTY VILLAGE OF HALES CORNERS WARDS 7-9	763	772	1552	1763	88.0317640385706	49.1623711340206	49.7422680412371	5.09051745202475E-28
MILWAUKEE COUNTY VILLAGE OF RIVER HILLS WARDS 1-3	715	535	1259	1413	89.101203113942	56.791104050834	42.4940428911835	3.34151147982707E-23
MILWAUKEE COUNTY VILLAGE OF SHOREWOOD WARDS 1-4	2851	606	3509	4079	86.0259867614611	81.2482188657737	17.2698774579652	1.82789251970963E-10
MILWAUKEE COUNTY VILLAGE OF SHOREWOOD WARDS 5-8	2460	435	2939	3365	87.3402674591382	83.701939435182	14.8009527050017	9.12181429933463E-11
MILWAUKEE COUNTY VILLAGE OF SHOREWOOD WARDS 9-12	2389	419	2845	3236	87.9171817058096	83.9718804920914	14.7275922671353	4.38025974823043E-11
MILWAUKEE COUNTY VILLAGE OF WEST MILWAUKEE WARDS 1-2,5	519	249	781	1075	72.6511627906977	66.4532650448143	31.8822023047375	2.45436618393263E-10
MILWAUKEE COUNTY VILLAGE OF WEST MILWAUKEE WARDS 3-4,6	611	290	920	1168	78.7671232876712	66.4130434782609	31.5217391304348	3.38065145202773E-12
MILWAUKEE COUNTY VILLAGE OF WHITEFISH BAY WARD 12	667	310	997	1084	91.9741697416974	66.900702106319	31.0932798395186	1.58783909593186E-19
MILWAUKEE COUNTY VILLAGE OF WHITEFISH BAY WARD 7	614	208	833	942	88.4288747346072	73.7094837935174	24.9699879951981	3.64155504598521E-14
MILWAUKEE COUNTY VILLAGE OF WHITEFISH BAY WARDS 1-2	1044	550	1614	1787	90.3189703413542	64.6840148698885	34.0768277571252	2.05819451025194E-19
MILWAUKEE COUNTY VILLAGE OF WHITEFISH BAY WARDS 3-4	1052	437	1508	1674	90.0836320191159	69.7612732095491	28.9787798408488	7.59867496717096E-17
MILWAUKEE COUNTY VILLAGE OF WHITEFISH BAY WARDS 5-6	1148	501	1668	1875	88.96	68.8249400479616	30.0359712230216	1.76565509275726E-16
MILWAUKEE COUNTY VILLAGE OF WHITEFISH BAY WARDS 8,10	1344	483	1844	2064	89.3410852713178	72.885032537961	26.1930585683297	4.439862753817E-15
MILWAUKEE COUNTY VILLAGE OF WHITEFISH BAY WARDS 9,11	1211	378	1615	1871	86.3174772848744	74.984520123839	23.405572755418	2.1467920231762E-12
MONROE COUNTY VILLAGE OF WILTON WARD 1	123	122	247	311	79.4212218649518	49.7975708502024	49.3927125506073	1.20704010552853E-10
OUTAGAMIE COUNTY CITY OF APPLETON WARD 8	663	144	820	1415	57.9505300353357	80.8536585365854	17.5609756097561	1.45780801269311E-21
OZAUKEE COUNTY TOWN OF FREDONIA WARDS 1-4	284	1073	1380	1542	89.4941634241245	20.5797101449275	77.7536231884058	1.38655200184393E-14
OZAUKEE COUNTY VILLAGE OF BAYSIDE WARD 6	59	33	93	94	98.936170212766	63.4408602150538	35.4838709677419	2.00129596830969E-11
OZAUKEE COUNTY VILLAGE OF NEWBURG WARD 3	9	46	55	60	91.6666666666667	16.3636363636364	83.6363636363636	5.63503296502046E-19
PIERCE COUNTY CITY OF RIVER FALLS WARDS 12-14	932	724	1712	2223	77.0130454340981	54.4392523364486	42.2897196261682	5.61509915169131E-11
PIERCE COUNTY CITY OF RIVER FALLS WARDS 6-8	1260	770	2092	2613	80.0612323000383	60.2294455066922	36.8068833652008	5.8496683463879E-13
PIERCE COUNTY CITY OF RIVER FALLS WARDS 9-11	665	537	1264	1923	65.730629225169	52.6107594936709	42.4841772151899	1.09797839673096E-20
POLK COUNTY CITY OF ST. CROIX FALLS WARDS 1-4	635	621	1278	1560	81.9230769230769	49.6870109546166	48.5915492957746	4.87553890759147E-10
PORTAGE COUNTY CITY OF STEVENS POINT WARDS 1-3	718	306	1065	1435	74.2160278745645	67.4178403755869	28.7323943661972	5.75731642041891E-16
PORTAGE COUNTY CITY OF STEVENS POINT WARDS 10-12	707	375	1113	1404	79.2735042735043	63.5220125786163	33.6927223719677	3.13559487680873E-11
PORTAGE COUNTY CITY OF STEVENS POINT WARDS 13-15	820	393	1252	1549	80.8263395739187	65.4952076677316	31.3897763578275	1.05043079569372E-11
PORTAGE COUNTY CITY OF STEVENS POINT WARDS 31-33	474	244	747	1112	67.1762589928058	63.4538152610442	32.6639892904953	3.85556315207787E-19
PORTAGE COUNTY CITY OF STEVENS POINT WARDS 4-6	569	245	841	1248	67.3878205128205	67.6575505350773	29.1319857312723	2.78068480164296E-21
PORTAGE COUNTY CITY OF STEVENS POINT WARDS 7-9	512	207	743	1149	64.6649260226284	68.9098250336474	27.8600269179004	1.19090420839269E-24
RACINE COUNTY CITY OF RACINE WARD 18	325	35	363	596	60.9060402684564	89.5316804407714	9.64187327823691	1.61620200534854E-18
RACINE COUNTY CITY OF RACINE WARD 19	269	47	321	522	61.4942528735632	83.8006230529595	14.6417445482866	1.45532182383773E-15
RACINE COUNTY CITY OF RACINE WARD 2	536	98	644	1007	63.9523336643495	83.2298136645963	15.2173913043478	1.42908976350886E-13
RACINE COUNTY CITY OF RACINE WARD 4	516	49	569	859	66.2398137369034	90.6854130052724	8.6115992970123	2.22596252082422E-15
RACINE COUNTY CITY OF RACINE WARD 5	414	43	463	664	69.7289156626506	89.4168466522678	9.28725701943845	6.42199163428323E-13
RACINE COUNTY CITY OF RACINE WARD 7	385	74	470	684	68.7134502923977	81.9148936170213	15.7446808510638	2.65763243490565E-10
RACINE COUNTY CITY OF RACINE WARD 8	492	106	604	887	68.0947012401353	81.4569536423841	17.5496688741722	1.8659463263179E-10
ROCK COUNTY CITY OF BELOIT WARD 13	350	76	438	660	66.3636363636364	79.9086757990868	17.351598173516	3.86795309941425E-11
ROCK COUNTY CITY OF BELOIT WARD 14	422	121	552	844	65.4028436018957	76.4492753623188	21.9202898550725	5.01839456532688E-10
ROCK COUNTY CITY OF BELOIT WARD 16	454	117	579	1188	48.7373737373737	78.4110535405872	20.2072538860104	5.60799598953891E-24
ROCK COUNTY CITY OF BELOIT WARD 8	34	8	43	72	59.7222222222222	79.0697674418605	18.6046511627907	1.58318773614765E-14
SHAWANO COUNTY TOWN OF BARTELME WARD 1	260	88	354	454	77.9735682819383	73.4463276836158	24.8587570621469	8.27419151174227E-30
SHAWANO COUNTY TOWN OF RED SPRINGS WARDS 1-2	328	210	540	628	85.9872611464968	60.7407407407407	38.8888888888889	9.12515507152146E-16
ST. CROIX COUNTY CITY OF HUDSON WARDS 1-2	553	538	1117	1402	79.6718972895863	49.5076096687556	48.16472694718	3.01168489100185E-10
ST. CROIX COUNTY CITY OF HUDSON WARDS 9-10	823	512	1371	1621	84.5774213448489	60.0291757840992	37.345003646973	4.66935435278649E-13
TAYLOR COUNTY TOWN OF MAPLEHURST WARD 1	36	179	218	199	109.547738693467	16.5137614678899	82.1100917431193	1.08923188875317E-14
TAYLOR COUNTY TOWN OF PERSHING WARD 1	34	52	88	100	88	38.6363636363636	59.0909090909091	4.20619321566663E-10
VILAS COUNTY TOWN OF LAC DU FLAMBEAU WARDS 1-7	977	634	1624	2191	74.1214057507987	60.1600985221675	39.0394088669951	2.71781088739366E-22
WALWORTH COUNTY CITY OF WHITEWATER WARD 7	147	111	267	597	44.7236180904523	55.0561797752809	41.5730337078652	4.20346919413067E-28
WALWORTH COUNTY CITY OF WHITEWATER WARD 9	194	96	296	576	51.3888888888889	65.5405405405405	32.4324324324324	1.08258816705731E-25
WALWORTH COUNTY CITY OF WHITEWATER WARDS 3-4	547	305	875	1242	70.450885668277	62.5142857142857	34.8571428571429	1.21363190304917E-11
WALWORTH COUNTY CITY OF WHITEWATER WARDS 8,13	92	35	129	256	50.390625	71.3178294573643	27.1317829457364	6.78349828881846E-30
WAUKESHA COUNTY CITY OF WAUKESHA WARD 25	330	290	641	868	73.8479262672811	51.4820592823713	45.2418096723869	5.93869651342477E-10
WAUKESHA COUNTY CITY OF WAUKESHA WARD 27	53	32	89	119	74.7899159663866	59.5505617977528	35.9550561797753	5.34798400477303E-12
WAUKESHA COUNTY VILLAGE OF MERTON WARDS 5-6	20	71	92	94	97.8723404255319	21.7391304347826	77.1739130434783	4.6967624756401E-11
WAUPACA COUNTY CITY OF WAUPACA WARDS 1-13	1409	1557	3030	3676	82.4265505984766	46.5016501650165	51.3861386138614	1.098732231727E-10
WINNEBAGO COUNTY CITY OF OSHKOSH WARD 3	193	137	339	903	37.5415282392027	56.9321533923304	40.4129793510324	1.12053944116867E-30
WINNEBAGO COUNTY CITY OF OSHKOSH WARD 4	312	203	525	1028	51.0700389105058	59.4285714285714	38.6666666666667	6.31136222801884E-17
WOOD COUNTY CITY OF MARSHFIELD WARDS 4,14	571	553	1138	1342	84.7988077496274	50.1757469244288	48.5940246045694	6.46363645172154E-10

Per County Election Fingerprints

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Previously (see here, and here) I built a set of Election Fingerprints including WI that was based on the County data that I extracted from the NYT/Edison research election tracker, which was the best data that I had available at the time. While the results did have some interesting features, there wasn’t really enough data points to make any sort of inference one way or the other. Well … this last weekend (07/30/21) I received a dataset that had all of the vote counts and registered voters by Ward Grouping in the state, which is a much higher fidelity dataset than I had been working with.

Update 8/4/21: Not 24 hours after posting, my data source for the WI data has discovered some more accurate voter registration numbers for me to use which more accurately account for same day registrations. (The one I used was dated as of 12/1/20) I will be updating my analysis ASAP. The updated plots and analysis is posted here, but I’ll keep this post up for historic purposes.

The original image I created is replicated below for reference. As you can see, its a very blocky histogram created from the County level data points. There is a main lobe at about 90% turnout and 40% for Biden with some kurtotic streaking up and to the left with another grouping of Counties. While those features are indeed irregular and interesting, due to the coarse nature of the original per County data, it was plausible that the shape is just due to sampling issues so I didn’t flag WI as one of my issue states.

The data set I received this weekend however, is a drastic improvement in fidelity. It’s not without its data quality issues, as there was a good bit of data normalization needing to be done to get the vote tally data per ward group and the voter registration data per ward to marry up. Besides some different spellings, abbreviations and shorthand that needed to be rectified between the dataset, the registration data had any municipality that would straddle county lines labeled similar to the following format: “MULTIPLE COUNTIES, CITY OF NOWHERESVILLE, WARD 5A” along with a registered voter count. The vote count tally data had grouped multiple wards into different groupings such as “SOMEWHERE COUNTY, CITY OF NOWHERESVILLE, WARDS 1-3,4,5A,6C,13-15” along with a set of vote totals for Biden, Trump and TotalVotesCast. So I had to go through the files by hand and group each files corresponding data with the smallest size groupings as possible. There also seems to be a few (not many, though) Wards that are not accounted for by the Vote count result as well and others that are missing from the registration data. That being said, we can see much more detail in the generated statewide fingerprint (below), which also matches the “low-res” version that I had done previously. (Hooray … my results are consistent!)

We see what looks like two (or even three) superimposed lobes around the 90% turnout and 40% per Biden bin, which is consistent with my earlier low-res version, and we have a lot more detail as to the upward kurtotic smear off the main distribution. There also are a few Ward Groupings that have exceptionally high % for Biden (rarely, if ever, does a candidate receive near 100% vote share), and there are Ward Groupings that are near/over the 100% turnout marker, with the addition that some of those are ALSO either 100% Biden or 0% Biden (all big red flags)!

As discussed in the National Academy of Sciences paper that all of this work is based on, an idealized Election Fingerprint should look like a 2D Gaussian (or a multi-variate normal distribution). Or maybe, in an extremely divided populace, 2 overlapping Gaussian distributions. Deviations from this are, by definition, irregular and an indication that there might be an issue with the election. Distinct linear or non-linear features sloping away from the central lobes(s) could indicate voter manipulation or “ballot stuffing”; isolated regions at high turnout +/or very high vote percentage for one candidate could be an indication of extreme manipulation or vote substitution. Now, its true that no real Election Fingerprint will look perfectly Gaussian due to the realities of how elections operate, data errors, etc … but they should at least be close!

So that moves the needle for me as to considering WI as a problematic state based off Election Fingerprint generation. I would definitely say now that I think there are concrete signs of election irregularity in WI that deserve to be rigorously investigated. I can’t tell you if the shape of the above plot is due to incompetence, or outright fraud … but there is definitely something wrong!

Per County Data

Since I now have all of this rich data to play with … It’d be a shame to only do things statewide! I also produced fingerprints for every single county. One note here is that there is one plot that is the group of “MULTIPLE COUNTIES” that I had mentioned above due to the way the source registration data was organized and labelled. Some of the counties, look actually pretty good with nice, localized, near-gaussian distributions. Some are just an absolute mess!

My list of “interesting” counties:

ASHLAND, BAYFIELD, BROWN, CALUMET, CHIPPEWA, CLARK, DANE, DOGE, DOOR, DOUGLAS, DUNN, EAU CLAIRE, FOREST, GRANT, IOWA, JACKSON, JUNEAU, KENOSHA, KEWAUNEE, LACROSSE, LAFAYETTE, MARATHON, MILWAULKEE, MONROE, ONEIDA, OUTGAMIE, OZAUKEE, PIERCE, PORTAGE, RACINE, RICHLAND, ROCK, RUSK, SAULK, SHAWANO, SHEBOYGEN, St. CROIX, TAYLOR, VILAS, WASHINGTON, WAUKESHA, WAUPACA, WINNEBAGO (43/72 WI Counties)

Of that list, I’d label the following as “VERY interesting”:

BAYFIELD, BROWN, CALUMET, DANE, DOOR, EAU CLAIRE, KENOSHA, LACROSSE, LAFAYETTE, MARATHON, MILWAULKEE, OUTGAMIE, OZAUKEE, PORTAGE, RACINE, ROCK, SAULK, SHAWANO, SHEBOYGEN, St. CROIX, VILAS, WAUKESHA, WINNEBAGO (23/72 WI Counties)

The Election Fingerprints of all of the counties (plus the “MULTIPLE COUNTIES” super-group) are listed below:

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Here is the link to the consolidated dataset used to generate all of the graphics on this page.

BLUF: Discovered 55,196 vote spike in Alexandria City absentee ballots with 0 (… as in … zilch, zip, nada) for Trump or Jorgensen sometime between 11:30pm 11/3/2020 and 01:00am 11/4/2020. Generated time series of VA per county election fingerprints, and cumulative vote counts. Data sources: VA Dept of Elections voter registration data by locality file, NYT Edison time series datafeed snapshots from wayback machine.

Introduction:

So I’m still puzzled by the shape of the per county election fingerprint in VA. Multiple scientists I’ve talked to and worked with on this are all scratching their heads as to what would explain the significant structures observed. As a reminder, the election fingerprint “should” look like one (maaaaybe two for a really split electorate) Gaussian lobes, without a lot of smearing, linear features or structures. They will of course not be perfect Gaussians, but we would expect them to be somewhat close. I go into the theory and details of generating these fingerprints here, and the VA per county data is replicated below as well. It does not look Gaussian at all and is by definition an “irregularity” in the election data.

For reference I show the MN plot below as well, which still shows some slight irregularities, but for the most part it looks very clean. It’s got a little bit of kurtotic energy in the tails, but it looks like a pretty well defined 2D Gaussian main lobe otherwise. It looks like it could even have two distribution centers: one distribution center for a large number of the smaller (presumably suburban + rural) localities that didn’t go for Biden, and much fewer (but more populous) larger localities that went just slightly above 50% for Biden. Both distribution centers had about 90% turnout. The MN data makes sense to me, and other data scientists that I’ve shown it too, and it doesn’t set off any blaring alarm bells. The VA data however, remains inexplicable.

So, in trying to decipher what happened in VA, I was interested in taking a look at how the election fingerprints for VA evolved over time. Does the whole structure move fairly uniformly from left to right as more turnout occurs? Does it look Gaussian at any point in the counting process and then shift? In order to attempt to do that I need the per county vote tallies over time and unfortunately I didn’t anticipate needing to do analysis like this before the election, and so I didn’t setup anything to capture the updates to the VA Dept of Elections site or the NYT Edison datafeed. I tried to do an after-the-fact FOIA request, but it has been denied by VA Dept of Elections stating that they do not keep those records.

However … thankfully, the wayback machine does have a few snapshots of the VA datafeed. Not nearly what I would like to be able to make a full sequence to watch how the fingerprint takes shape, but its better than nothing. And while we’re at it, lets keep our eye’s out for individual counties that have large vote swings and also have statistically improbable results for any candidate (> 95%).

The earliest data file I could find on the wayback machine was from 2020-11-04T04:06:57.160Z and shows results through 11:30pm on the 3rd. While thats not ideal, that at least starts us off while Trump was still ahead.

2020-11-04T04:06:57.160Z

We can already see our “boomerang” structure fairly well formed in this initial plot, and I didn’t find any individual counties that had a large update with over 95% Biden. (Now that doesn’t mean such updates didn’t happen before this snapshot, but since my snapshot sampling rate is so low, each update includes more than one set of batch updates, and specific outlier batches might be just getting rolled into the sums.)

2020-11-04T05:34:34.206Z

We can pretty clearly see that just after the last snapshot, right around midnight, there’s obviously some sort of issue that happened with the data, with a couple of really large bumps for Biden that end up getting reversed and reverted. I have no idea if there’s any specific event that this can be correlated to in news reporting, if this is a glitch in the NYT feed, etc. But it looks like this event occurred between the updates to the NYT datafeed that I could find on the wayback machine. So whatever it is that happened, it got baked into the cake already as far as this datafeed snapshot is concerned. (If anyone has the files for these timestamps, please share!)

In addition to that we see that we’ve had a noticeable shift in our fingerprint as it looks like more low-turnout areas have been shifted into the boomerang.

Whats really interesting to me is that there was one locality (Alexandria) that had a large change of 55,196 Biden votes and ZERO for Trump or Jorgensen votes in the near hour and a half since the previous data points. From the data, it looks like these were all absentee votes, which we admittedly expect to be a higher turnout percentage for Biden … but 100% of such a large sample size … c’mon man! According to VA Dept of Elections, the absentee vote in Alexandria broke overall 84.78% (55,940 / 65,985) for Biden and 13.57% (8,951 / 65,985) for Trump. Now we know the underlying distribution is not I.I.D., so we should not expect exactly 13.57% of this batch to have gone for Trump, but we would still expect at the very least a small handful of this batch should have been for Trump. Additionally, this batch of absentee ballots makes up 83.65% of Biden’s total absentee haul, meaning Tump must have received 8,951 / (65,985 – 55,196) = 82.96% of ALL the remaining absentee votes in Alexandria, which also seems pretty unlikely.

2020-11-05T02:45:04.745Z and Beyond

After the previous batch there aren’t any more large outliers at least that I caught. But I only have a few snapshots from the wayback machine to work with. We see the remaining straggler counties start to shift to underneath the “boomerang”. If there was a point in time where the data was looking like a 2D Gaussian, it was before the first snapshot that I was able to find and get my hands on.

Complete Galleries Below:

See also my previous election fingerprint blog posts here, and here. There is another, related, discrepancy in the Daily Absentee List data that I document here.

Originally Posted 2020-12-01 10:58:00 (Multiple Updates)

After computing the VA election fingerprints which clearly indicate that more investigation is required into the 2020 VA vote data, I took a look at the official VA daily absentee ballot count file and compared it with the JSON reports from VA dept of elections.  The JSON data provides a summary total of the votes recorded at each virtual absentee precinct, and the daily absentee list (here) gives all of the absentee ballot registrations for each precinct. For each (virtual) absentee precinct in a locality I summed all of the daily absentee numbers from component real precincts in order to compare those numbers with the reported total absentee votes. If everything has been tallied and recorded correctly they should be equal, or at least close.

I then compute the “excess absentee vote” by taking the difference of the number of total absentee votes reported in each virtual absentee precinct minus the cumulative (‘Marked’ + ‘Pre-Processed’ + ‘OnMachine’ + ‘FWAB’) numbers for the absentee precinct.  

I finally plotted the “excess absentee” vote totals vs the % of the absentee precinct vote that went for Biden.  Ideally we would like to see excess absentee vote == 0, meaning that the daily absentee reports and the number of absentee ballots that were counted in each virtual precinct match perfectly.  (I’ll give you a hint … they don’t.)

Outstanding Question: Is there a description document someplace where the BALLOT_STATUS (‘Marked’, etc) meanings are described.  I’m using my best guess as to which categories map to valid ballots, and I’d like to double check that.

Each dot in the plot above represents one of the virtual absentee precincts across the commonwealth. Unsurprisingly, we can clearly see that as the number of excess absentee votes increases more of the absentee vote totals go to Biden. Summing over all of the excess votes, there were 1,334,968 excess absentee votes across VA in the 2020 election using this method. Where did these 1,334,968 votes come from? (See latest updates below)

All source data comes directly from the VA Dept of Elections. My tabulated results are posted below. I welcome any / all peer review and will gladly make my code and compiled datasets available.

Note: Removed incorrect files ... see updates below

Update 2020-12-01 19:30:00

In discussions with The Virginia Project and others that have been churning and burning through this data, there was a question as to weather or not the Daily Absentee Tally dataset (which is available to campaigns by request from the dept of elections), which is what I used above includes the In-Person early vote. My understanding is that the early vote was treated the same as No Excuse Absentee, so early vote numbers *should* be included in the Daily Absentee Tally dataset. But it looks like that might not be true. The JSON files published by VA dept of elections only give the sum totals per candidate per precinct (including absentee precincts), and I used the Daily Absentee Tally dataset to map how many absentee (and early votes) should correspond to each precinct.

However, there is another dataset that I was pointed to that could be a useful comparison and way to compute the missing numbers: that is the Summary Absentee Dataset located here. This dataset DOES contain the In-Person early vote absentee totals (‘ApplicationType’ == ‘In Person’), but it is only generated per absentee counting precinct and not mapped back to the voters physical precinct. It does however break down the Absentee votes per age group, gender, and type of absentee ballot … which is nice … but not what I’m looking for at the moment.

So, we have two datasets, both summaries of the absentee vote numbers with different breakdowns, that *should* sum to give the same totals. Surprise … they don’t. Why? Unknown. The ‘In Person’ vote not being included in the Daily tally does not reconcile the numbers between the two datasets, though.

The plot I generated above, redone with this other dataset is shown below. I’ve included new csv files with the additional data (I’ve labeled as “_V2”). The excess votes per absentee district are similarly computed as the totalVote reported by each absentee precinct (from the JSON files) minus the sum count of all of the Absentee/Mail-In numbers from the Summary Absentee Dataset for that absentee precinct. This new plot does not show positive excess votes, but instead shows negative excess votes, with higher negative excess votes also showing higher Biden total vote percentage. I don’t know which one is correct, but both seem to show high positive / negative excess vote numbers that align with higher Biden percent of the vote. Summing over all of the negative excess votes gives -121,049 votes that are unaccounted for.

One additional question I have is how to tease out the rejection rate of Absentee/Mail-In votes, which might be a contributing factor for both plots. Are the numbers in the Daily or Summary absentee stats files before or after rejection? I have so far not been able to find a dataset that captures the rejection rates for Absentee/Main-In ballots.

Note: Removed incorrect files ... see updates below

Update 2020-12-04 02:00:00

So I’ve been able to confirm that the Daily Absentee List DOES, in fact, contain the In-Person “early” votes. I did this by finding my name and address in the list as I voted early on Oct 26th. The early votes are the ones marked as “On Machine”. I will also note that the date reported for my early vote was incorrectly set to Oct 28th (I voted on the 26th), which is why I had difficulty finding it at first.

So this means that:

1. My first plot above *should* be correct in its computation of the “excess absentee vote”. Save for a fat finger bug in my code (I’m in the process of double-checking, btw).
2. The Summary Absentee count does not match the totals from the Daily Absentee List. Why?
3. If the Daily list is correct, and the excess vote is computed correctly above in the first plot, then how to account for the 1,334,968 excess votes?

Update 2020-12-06 00:05:00

I have revised my previously computed excess vote number of 185,713 to 1,334,968 due to finding some “fat finger” errors, and idiosyncrasies with the DAL file.

Yes, I know … thats a really big number! I’m going back over my code again to see where / if I screwed something up. I will continue to update if I find anything else.

I’ve also taken the time to clean up the plots, both with and without annotations as to which precincts were the most egregious offenders.

Update 2020-12-10 23:12:00

Happy to report that I found a logical bug in my code.  I was subtracting PreProcessed ballots when I should have been adding when computing the excess vote with the DAL data (D’Oh!).  It’s a simple bug, but produces a big difference.  Result is a much smaller order of magnitude difference in the excess vote numbers, and a much more believable excess vote tally.
This line of code:

>> abCntVotes(j) = tdata.marked(j) + tdata.onMachine(j) - tdata.preProcessed(j) + tdata.fwab(j) ;

Should have read:  

>> abCntVotes(j) = tdata.marked(j) + tdata.onMachine(j) + tdata.preProcessed(j) + tdata.fwab(j) ;

That’s the good news.

The first bit of bad news is that the Summary Absentee List (SAL) still doesn’t make much sense, as discussed in my 2020-12-01 update above.  The other bad news is there are two absentee precincts that still stick out like a sore thumb from the DAL derived excess vote numbers. Guess which ones they are … PRINCE WILLIAM COUNTY (11) and PRINCE WILLIAM COUNTY (1)!  PWC district 1 and 11 are both waaay outside the standard deviation on the plot below.  Another curious fact is that we see the deviation on the Biden precincts (blue) looks markedly higher than the deviation in the Trump precincts (red)

Now if we look at the summary totals of the PWC absentee precincts we see that the sum of the negative excess absentee counts of district 1 and 10, are almost a perfect complement of the district 11 excess count (far right column).  That’s also curious.  By itself I’d call that just a coincidence, but combined with the fact that PWC 11 and 01 are also so outside the general trends of all other precincts in the plot above that gets my worry beads out.

Per discussion with PWC staff, there was apparently an error in reporting election data that got caught and corrected where all absentee votes were being sent to the district 01 absentee precinct, so this might be an artifact of that issue.

Additionally, and this is something we see in all of the precinct data and in the JSON data itself.  The JSON data files directly report the Trump, Jorgensen and Biden vote totals, as well as the ‘totalVotes’ numbers. The ‘sumVotes’ column below is the sum of the Trump, Jorgensen and Biden votes which should equal the ‘totalVotes’ column, but it doesn’t.  It’s not usually a dramatic difference, but its a difference all the same.

Again, per discussion with PWC staff, the ‘totalVotes’ numbers reported by the Dept. of Elections JSON data feed includes items such as overvotes / undervotes / unmarked ballots, which count for turnout reasons, but don’t get attributed to a specific candidate.

localityStr	precinctStr	Nreg	Donald J. Trump	Jo Jorgensen	Joseph R. Biden	sumVotes	totalVotes	issued	marked	onMachine	cancelled	deleted	fwab	late	notIssued	preProcessed	provisional	unmarked	absenteeVotesSum	excessAbsenteeVotes
PRINCE WILLIAM COUNTY	# AB – Central Absentee Precinct (01)	115475	22720	781	46939	70440	70721	2590	3371	47143	0	6106	62	0	3	21449	0	91	72025	-1304
PRINCE WILLIAM COUNTY	# AB – Central Absentee Precinct (10)	45383	10588	443	21371	32402	32525	968	1443	22543	0	2954	14	0	0	8842	1	42	32842	-317
PRINCE WILLIAM COUNTY	# AB – Central Absentee Precinct (11)	106455	19579	782	46040	66401	66683	3303	3228	40443	0	5626	52	0	0	21338	0	111	65061	1622

So taking all of the above into account, and assuming that PWC 11 and 01 are artifacts of a data entry error, using the DAL and the JSON vote tallies we see that there is a good bit, but not extreme amounts, of variation in the “excess vote” and that the deviations in excess vote seems greater in blue precincts than in red precincts.

The SAL data still does not agree with the JSON or the DAL data files, and I’m still working to figure out why.

My tabulated results are posted below. Note that I do not include the raw DAL data in my results below, only my summarized results, as the raw data contains personal address information. I welcome any / all peer review and will gladly make my code and compiled datasets available.

Update 2020-12-13 17:44:00

Per twitter comment, looking at the data with normalized x-axis (as percent of absentee vote total) in order to compensate for different precinct sizes produces the plot below. Newport News City and Richmond County now also stand out, and we still see a difference in deviation of excess votes between Biden precincts and Trump precincts.

Discovered today that my county PWC, VA uses the HART voting machine systems and I have been doing a little research. Right now I’m just seeing what I can find and verify and collecting them on this page.

Biggest Find:

I found a very recent paper (2019) discussing and demonstrating methods by which to hack all of the major brand voting machines that use ballot images. It’s effective enough to be able to defeat any ballot image recounts as well. It’s a fairly simple man-in-the-middle attack vector, which needs to only install a wrapper around the windows scanner driver for the scanning systems. … and given all of these systems are using unencrypted(!) USB sticks(!!), thats pretty easy to do!!!

The wrapped adversarial payload can use some standard “textbook” (… no really … I own multiple textbooks that reference them) image processing tricks to selectively switch where the voter has marked the ballot in the image as its scanned, keeping the voters handwriting and style intact. The user sees their vote recorded, but the ballot image is altered before its recorded or counted. And auditing, or recounting from ballot images just recounts the swapped image. With a well designed and deterministic coding of this attack, re-running the same ballots through the scanner to perform a recount just re-applies the same deterministic conversion to the stored ballot images, and hence the recount stays the same. It’s a pretty devilishly elegant attack vector.

Now I’m not saying that that took place in the election, but it is a direct example of a pretty scary scenario and a working example.

Other Finds:

Article on VA audit process, and why it can’t possibly affect the 2020 results. https://www.wusa9.com/article/news/politics/elections/virginia-election-audit-happens-after-results-are-certified-a-state-law-renders-the-election-check-powerless-to-fix-mistakes/65-cbfa162c-63fb-49c3-b298-cf84af01fc9d

HART Verity Systems Administrator Guide: https://sos.idaho.gov/elect/clerk/Hart/Verity2/6641-011%20A_Verity_2.X_Administrators%20Guide_Central.pdf

Feb 5 2016 PWC Elections Board Meeting Agenda discussing Acquisition of HART systems: https://sos.idaho.gov/elect/clerk/Hart/Verity2/6641-011%20A_Verity_2.X_Administrators%20Guide_Central.pdf

Romney Financial Ties to HART (from 2012): https://www.forbes.com/sites/rickungar/2012/10/20/romney-family-investment-ties-to-voting-machine-company-that-could-decide-the-election-causes-concern/?sh=5cba429e1afa

Another follow-the-money story from 2019: https://federalnewsnetwork.com/government-news/2019/07/whos-behind-voting-machine-makers-money-of-unclear-origins/

NBC Jan 2020 Story on Voting Machine makers testifying before congress: https://www.nbcnews.com/tech/security/voting-machine-makers-face-questions-house-lawmakers-more-remain-n1113181

2018 Story on Vulnerabilities of multiple Voting Machines: https://www.forbes.com/sites/rickungar/2012/10/20/romney-family-investment-ties-to-voting-machine-company-that-could-decide-the-election-causes-concern/?sh=5cba429e1afa

Whistleblower detailing HART shady business practices (2008): https://www.wired.com/2008/03/whistleblower-v/

HART Systems Security Issues (2006): https://hovav.net/ucsd/papers/ttbr-hart.html

Computing the VA 2020 Senate Election data time series from the NYT Edison data feed. This is direct from the NYT data feed without any processing.

source: https://static01.nyt.com/elections-assets/2020/data/api/2020-11-03/race-page/virginia/senate.json

Computing the VA 2020 Presidential Election data time series from the NYT Edison data feed. This is direct from the NYT data feed without any processing.

source: https://static01.nyt.com/elections-assets/2020/data/api/2020-11-03/race-page/virginia/president.json

Adding onto the work I’ve already done looking at the VA Daily Absentee List I wanted to compute the rejection rate for absentee ballots and some other basic statistics just from the Daily Absentee Ballot List (DAL) produced by VA Dept of Elections. (https://www.elections.virginia.gov/candidatepac-info/client-services/)

Keep in mind I’ve already been able to show:

• There were 166 Absentee Ballot Applications that were received and accepted AFTER the corresponding ballot was received or accepted (here).
• There is a discrepancy in the number of “excess” absentee votes counted vs. the total accepted registrations from the Daily Absentee List (here).
• The 2020 VA election fingerprints, as well as other states, have significant structural irregularities and indications of vote manipulation (here).
• The VA election fingerprints show a trend of increasing irregularity starting with 2008, 2012, 2016 and now 2020 (here).

Basic Info:

There are 3335335 entries, with 36 columns in the Daily Absentee List.

The column headings are: ELECTION_NAME, LOCALITY_CODE, LOCALITY_NAME, PRECINCT_CODE, PRECINCT_NAME, LAST_NAME, FIRST_NAME, MIDDLE_NAME, SUFFIX, ADDRESS_LINE_1, ADDRESS_LINE_2, ADDRESS_LINE_3, CITY, STATE, ZIP, COUNTRY, INTERNATIONAL, EMAIL_ADDRESS, FAX, VOTER_TYPE, ONGOING, APP_RECIEPT_DATE, APP_STATUS, BALLOT_RECEIPT_DATE, BALLOT_STATUS, identification_number, PROTECTED, CONG_CODE_VALUE, STSENATE_CODE_VALUE, STHOUSE_CODE_VALUE, AB_ADDRESS_LINE_1, AB_ADDRESS_LINE_2, AB_ADDRESS_LINE_3, AB_CITY, AB_STATE, AB_ZIP

Computing the Absentee Ballot Rejection Rate:

So (in MATLAB), with the full list directly imported into the ‘DailyAbsenteeList’ table variable:

% Lets see if we can teas out the rejection rate for 
% absentee ballots
>> ineligable = DailyAbsenteeList.APP_STATUS == "Denied/Not Eligible";
>> incomplete = DailyAbsenteeList.APP_STATUS == "Denied/Incomplete";
>> rejectionRate = 100 * mean(ineligable | incomplete)
>> ans = 0.0425

RESULT: The absentee ballot rejection rate was an exceptionally low 0.0425%

Finding the Counted ballots:

As we did in the “excess vote” computation, we need to find the indices into the list of all of the ballots that were counted, meaning their BALLOT_STATUS is in one of the following states: {‘On Machine’, ‘Marked’, ‘Pre-Processed’, ‘FWAB’}. So the ‘idxv’ variable below is a boolean flag indicating that the BALLOT_STATUS of each entry in the list is in a countable state.

% For each entry in the Daily Absentee List, test if 
% BALLOT_STATUS is in a valid state 
>> idxv = DailyAbsenteeList.BALLOT_STATUS=='Marked' | ...
    DailyAbsenteeList.BALLOT_STATUS=='Pre-Processed' | ...
    DailyAbsenteeList.BALLOT_STATUS=='On Machine' | ...
    DailyAbsenteeList.BALLOT_STATUS=='FWAB';

Now one would think that if a ballot has been marked into one of those BALLOT_STATUS categories, then that ballot application should have been ‘Approved’, right? Well lets double check that shall we:

% For each entry in the Daily Absentee List, test if 
% BALLOT_STATUS is in a valid state AND APP_STATUS is set to 
% 'Approved'.  (One would think these would match.)
>> idxav = (DailyAbsenteeList.BALLOT_STATUS=='Marked' | ...
    DailyAbsenteeList.BALLOT_STATUS=='Pre-Processed' | ...
    DailyAbsenteeList.BALLOT_STATUS=='On Machine' | ...
    DailyAbsenteeList.BALLOT_STATUS=='FWAB') & ...
    DailyAbsenteeList.APP_STATUS=='Approved';

We can check for mismatches by counting the number of flags that are not equal between ‘idxv’ and ‘idxav’.

% How many absentee ballots have their BALLOT_STATUS set to 
% a valid (i.e. "countable") state, but are not marked as 
% 'Approved'
>> numUnapprovedButCountableVotes = sum(idxv)-sum(idxav)
>> ans = 1437

RESULT: There are 1437 entries that have BALLOT_STATUS set to a valid state, but don’t have an approved absentee ballot application

Well … this begs some follow up questions…

Q1: Of the ballots that have been set to a valid BALLOT_STATUS state but were not set to ‘Approved’, what is the breakdown by APP_STATUS?

>> cancelled = sum(DailyAbsenteeList.APP_STATUS=='Cancelled or Duplicate' & (idxv~=idxav))
>> deniedInc = sum(DailyAbsenteeList.APP_STATUS=='Denied/Incomplete' & (idxv~=idxav))
>> deniedIne = sum(DailyAbsenteeList.APP_STATUS=='Denied/Not Eligible' & (idxv~=idxav))
>> issued = sum(DailyAbsenteeList.APP_STATUS=='Issued' & (idxv~=idxav))
>> onHold = sum(DailyAbsenteeList.APP_STATUS=='On Hold' & (idxv~=idxav))
>> pending = sum(DailyAbsenteeList.APP_STATUS=='Pending Approval' & (idxv~=idxav))
>> provisional = sum(DailyAbsenteeList.APP_STATUS=='Provisional' & (idxv~=idxav))

RESULTS: Of the 1437 ballots that were in a countable state but not marked as having APP_STATUS=’Accepted’, their APP_STATUS was:
cancelled = 444
deniedInc = 568
deniedIne = 4
issued = 28
onHold = 101
pending = 288
provisional = 4

Q2: Of the ballots that have been set to a valid BALLOT_STATUS state but were not set to ‘Approved’, what is the breakdown by BALLOT_STATUS?

>> unapproved_mailIn = sum(DailyAbsenteeList.BALLOT_STATUS=='Marked' & ...
    DailyAbsenteeList.APP_STATUS~='Approved')
>> unapproved_preProcessed = sum(DailyAbsenteeList.BALLOT_STATUS=='Pre-Processed' & ...
    DailyAbsenteeList.APP_STATUS~='Approved')
>> unapproved_earlyInPerson = sum(DailyAbsenteeList.BALLOT_STATUS=='On Machine' & ...
    DailyAbsenteeList.APP_STATUS~='Approved')
>> unapproved_FWAB = sum(DailyAbsenteeList.BALLOT_STATUS=='FWAB' & ...
    DailyAbsenteeList.APP_STATUS~='Approved')

RESULTS: Of the 1437 ballots that were in a countable state but not marked as having APP_STATUS=’Accepted’, their BALLOT_STATUS was:
unapproved_MailIn (‘Marked’) = 512
unapproved_preProcessed (‘Pre-Processed’) = 578
unapproved_earlyInPerson (‘On Machine’) = 347
unapproved_FWAB (‘FWAB’) = 0

Checking for duplicate identities:

Another check we can do is try to determine the amount of duplicated voters that have been accepted.

% What about the possibility of the same person being marked 
% with multiple ballots?  We will now go through 
% the'Accepted' and countable ballots and
% look for duplicate names and address information.
>> idxvs = find(idxav);
>> fnames = cellstr(DailyAbsenteeList.FIRST_NAME(idxvs));
>> lnames = cellstr(DailyAbsenteeList.LAST_NAME(idxvs));
>> mnames = cellstr(DailyAbsenteeList.MIDDLE_NAME(idxvs));
>> sffx = cellstr(DailyAbsenteeList.SUFFIX(idxvs));
>> addy1 = cellstr(DailyAbsenteeList.ADDRESS_LINE_1(idxvs));
>> addy2 = cellstr(num2str(DailyAbsenteeList.ADDRESS_LINE_2(idxvs)));
>> addy3 = cellstr(num2str(DailyAbsenteeList.ADDRESS_LINE_3(idxvs)));
>> zips = cellstr(num2str(DailyAbsenteeList.ZIP(idxvs)));
>> addy2 = strrep(strrep(addy2,'NaN',''),' ','');
>> addy3 = strrep(strrep(addy3,'NaN',''),' ','');
>> zips = strrep(strrep(zips,'NaN',''),' ','');
>> namesAndAddys = join([fnames, mnames, lnames, sffx, addy1, addy2, addy3, zips]);
>> [unames,unr,uni] = unique(namesAndAddys);
>> duplicateVotes = numel(namesAndAddys) - numel(unames)
>> ans = 166

RESULT: The number of repeated accepted absentee ballots is 166 … now where have we seen that number pop up before … ahhhh yes … its the same number of ballots that have ballot applications that we received and accepted AFTER the actual ballot was accepted and cast!