Election Data Analysis Election Forensics Election Integrity mathematics programming technical

Potential Duplicate Registrants in VA RVL by Locality

Previously I posted the computation of potential duplicate records based on string comparisons in the registered voter list. As a follow up to that article, I’ve compiled the statistics of the number of potential pairs for each locality in VA.

I tallied the number of registrant pairs with the reference match criteria defined by the MOU between ELECT and the DMV along with the two highest confidence (most stringent) match criteria that I computed. I also stratified the results by Active registrant records only or either Active or Inactive records. I also stratified by if the pairs crossed a locality boundary or not.

The table below is organized into the following computed columns, and has been sorted in decreasing order according to column 5.

  1. Exactly matching First + Last + DOB, which is equivalent to the MOU between ELECT and DMV.
  2. Exactly matching First + Middle + Last + Suffix + DOB
  3. Exactly matching First + Middle + Last + Suffix + DOB + Gender + Street Address
  4. The same as #1, but filtering for only ACTIVE voter records
  5. The same as #2, but filtering for only ACTIVE voter records
  6. The same as #3, but filtering for only ACTIVE voter records
  7. The same as #1, but filtering for only pairs that cross a locality boundary.
  8. The same as #2, but filtering for only pairs that cross a locality boundary.
  9. The same as #3, but filtering for only pairs that cross a locality boundary.
  10. The same as #4, but filtering for only pairs that cross a locality boundary.
  11. The same as #5, but filtering for only pairs that cross a locality boundary.
  12. The same as #6, but filtering for only pairs that cross a locality boundary.

LOCALITY_NAMENum Registrant RecordsPct Same First Last DobPct Same Full Name DobPct Same Full Name Dob AddressPct Same First Last Dob _ Active OnlyPct Same Full Name Dob _ Active OnlyPct Same Full Name Dob Address _ Active OnlyPct Same First Last Dob _ xLocPct Same Full Name Dob _ xLocPct Same Full Name Dob Address _ xLocPct Same First Last Dob _ Active Only _ xLocPct Same Full Name Dob _ Active Only _ xLocPct Same Full Name Dob Address _ Active Only _ xLoc
NORTON CITY26040.2304%0.2304%0.1536%0.1920%0.1920%0.1536%0.0768%0.0768%0.0000%0.0384%0.0384%0.0000%
NOTTOWAY COUNTY97040.2988%0.2061%0.0618%0.2473%0.1752%0.0618%0.2370%0.1649%0.0206%0.1855%0.1340%0.0206%
RADFORD CITY95510.4293%0.2827%0.0000%0.2827%0.1675%0.0000%0.4293%0.2827%0.0000%0.2827%0.1675%0.0000%
HIGHLAND COUNTY19030.2627%0.1576%0.1051%0.2627%0.1576%0.1051%0.1576%0.0525%0.0000%0.1576%0.0525%0.0000%
WILLIAMSBURG CITY104800.2195%0.1336%0.0000%0.2004%0.1336%0.0000%0.2004%0.1336%0.0000%0.1813%0.1336%0.0000%
LYNCHBURG CITY563190.3072%0.1829%0.0533%0.2255%0.1296%0.0533%0.1616%0.0764%0.0000%0.1190%0.0479%0.0000%
EMPORIA CITY40230.3480%0.1740%0.0000%0.2983%0.1243%0.0000%0.2486%0.0746%0.0000%0.1989%0.0249%0.0000%
SUFFOLK CITY715800.2403%0.1229%0.0754%0.2249%0.1187%0.0754%0.1229%0.0307%0.0000%0.1104%0.0265%0.0000%
FALLS CHURCH CITY112130.1784%0.1338%0.0357%0.1516%0.1159%0.0178%0.0892%0.0624%0.0000%0.0803%0.0624%0.0000%
SUSSEX COUNTY71490.2658%0.1259%0.0839%0.2238%0.1119%0.0839%0.1539%0.0140%0.0000%0.1119%0.0000%0.0000%
FRANKLIN CITY59240.2026%0.1182%0.0338%0.1857%0.1013%0.0338%0.1688%0.0844%0.0000%0.1519%0.0675%0.0000%
APPOMATTOX COUNTY121950.2542%0.1230%0.0328%0.2214%0.0902%0.0328%0.2050%0.0738%0.0000%0.1886%0.0574%0.0000%
LEE COUNTY156190.2497%0.0960%0.0128%0.2305%0.0832%0.0128%0.1473%0.0192%0.0000%0.1409%0.0192%0.0000%
ALBEMARLE COUNTY848890.1920%0.1001%0.0212%0.1590%0.0825%0.0188%0.1402%0.0554%0.0000%0.1096%0.0401%0.0000%
AMHERST COUNTY229060.1965%0.0829%0.0437%0.1790%0.0742%0.0437%0.1441%0.0393%0.0000%0.1266%0.0306%0.0000%
PRINCE EDWARD COUNTY135950.2280%0.0883%0.0000%0.1912%0.0662%0.0000%0.2133%0.0883%0.0000%0.1765%0.0662%0.0000%
STAUNTON CITY181800.1980%0.0935%0.0000%0.1595%0.0605%0.0000%0.1650%0.0605%0.0000%0.1265%0.0275%0.0000%
NELSON COUNTY118950.1765%0.0673%0.0168%0.1513%0.0588%0.0168%0.1261%0.0504%0.0000%0.1177%0.0420%0.0000%
ARLINGTON COUNTY1770920.1378%0.0683%0.0113%0.1146%0.0576%0.0102%0.0870%0.0344%0.0000%0.0683%0.0260%0.0000%
NORTHUMBERLAND COUNTY104570.1339%0.0574%0.0191%0.1243%0.0574%0.0191%0.0956%0.0191%0.0000%0.0861%0.0191%0.0000%
SOUTHAMPTON COUNTY132180.2194%0.0757%0.0000%0.1740%0.0530%0.0000%0.1589%0.0454%0.0000%0.1286%0.0227%0.0000%
HOPEWELL CITY158250.2401%0.0695%0.0253%0.2085%0.0506%0.0253%0.1390%0.0190%0.0000%0.1201%0.0126%0.0000%
LUNENBURG COUNTY80970.1853%0.0618%0.0000%0.1729%0.0494%0.0000%0.1853%0.0618%0.0000%0.1729%0.0494%0.0000%
AMELIA COUNTY101790.1375%0.0884%0.0098%0.0884%0.0491%0.0098%0.1375%0.0884%0.0098%0.0884%0.0491%0.0098%
RICHMOND CITY1610970.1707%0.0639%0.0000%0.1316%0.0490%0.0000%0.1459%0.0528%0.0000%0.1155%0.0416%0.0000%
CHARLOTTESVILLE CITY347890.1265%0.0604%0.0000%0.1064%0.0489%0.0000%0.1150%0.0489%0.0000%0.0949%0.0374%0.0000%
LEXINGTON CITY42110.2612%0.1187%0.0000%0.1900%0.0475%0.0000%0.2612%0.1187%0.0000%0.1900%0.0475%0.0000%
FAIRFAX COUNTY7877270.1143%0.0559%0.0053%0.0988%0.0474%0.0053%0.0665%0.0236%0.0000%0.0546%0.0171%0.0000%
CHARLOTTE COUNTY84740.2242%0.0708%0.0236%0.1652%0.0472%0.0236%0.2006%0.0472%0.0000%0.1416%0.0236%0.0000%
HARRISONBURG CITY264430.1777%0.0870%0.0000%0.1210%0.0454%0.0000%0.1324%0.0567%0.0000%0.0908%0.0303%0.0000%
BRUNSWICK COUNTY110980.2253%0.0631%0.0000%0.1982%0.0451%0.0000%0.2072%0.0451%0.0000%0.1802%0.0270%0.0000%
HAMPTON CITY1008070.2044%0.0764%0.0060%0.1468%0.0446%0.0040%0.1210%0.0387%0.0000%0.0972%0.0268%0.0000%
WISE COUNTY247500.1455%0.0525%0.0000%0.1333%0.0444%0.0000%0.1212%0.0364%0.0000%0.1091%0.0283%0.0000%
WYTHE COUNTY209500.1480%0.0525%0.0191%0.1289%0.0430%0.0191%0.1002%0.0143%0.0000%0.0907%0.0143%0.0000%
CHESAPEAKE CITY1780050.1258%0.0433%0.0303%0.1140%0.0410%0.0303%0.0843%0.0062%0.0000%0.0747%0.0051%0.0000%
NEWPORT NEWS CITY1247780.1354%0.0537%0.0016%0.1122%0.0409%0.0016%0.1002%0.0313%0.0000%0.0850%0.0216%0.0000%
CUMBERLAND COUNTY74160.1483%0.0539%0.0270%0.1214%0.0405%0.0270%0.1214%0.0270%0.0000%0.0944%0.0135%0.0000%
PRINCE GEORGE COUNTY249570.1643%0.0401%0.0000%0.1322%0.0401%0.0000%0.1643%0.0401%0.0000%0.1322%0.0401%0.0000%
HALIFAX COUNTY250860.1196%0.0438%0.0239%0.1156%0.0399%0.0239%0.0877%0.0120%0.0000%0.0837%0.0080%0.0000%
SMYTH COUNTY201590.1339%0.0397%0.0000%0.1290%0.0397%0.0000%0.1141%0.0198%0.0000%0.1091%0.0198%0.0000%
FAIRFAX CITY178250.1234%0.0617%0.0000%0.0954%0.0393%0.0000%0.1122%0.0617%0.0000%0.0842%0.0393%0.0000%
CAMPBELL COUNTY413180.1380%0.0508%0.0048%0.1186%0.0387%0.0048%0.1283%0.0411%0.0000%0.1089%0.0290%0.0000%
COLONIAL HEIGHTS CITY130660.0918%0.0383%0.0000%0.0918%0.0383%0.0000%0.0918%0.0383%0.0000%0.0918%0.0383%0.0000%
CHESTERFIELD COUNTY2700840.1529%0.0478%0.0067%0.1300%0.0381%0.0059%0.1107%0.0248%0.0000%0.0937%0.0196%0.0000%
PETERSBURG CITY237400.1685%0.0421%0.0000%0.1559%0.0379%0.0000%0.1601%0.0421%0.0000%0.1474%0.0379%0.0000%
SURRY COUNTY56750.1762%0.0352%0.0000%0.1410%0.0352%0.0000%0.1410%0.0000%0.0000%0.1057%0.0000%0.0000%
STAFFORD COUNTY1112610.1222%0.0440%0.0072%0.1079%0.0351%0.0072%0.1007%0.0279%0.0000%0.0881%0.0207%0.0000%
BUCHANAN COUNTY148360.0876%0.0337%0.0000%0.0876%0.0337%0.0000%0.0607%0.0067%0.0000%0.0607%0.0067%0.0000%
PORTSMOUTH CITY683810.1536%0.0409%0.0058%0.1375%0.0336%0.0058%0.1185%0.0263%0.0000%0.1024%0.0190%0.0000%
PITTSYLVANIA COUNTY453220.1677%0.0441%0.0044%0.1522%0.0331%0.0044%0.1324%0.0221%0.0000%0.1214%0.0154%0.0000%
MECKLENBURG COUNTY229960.1522%0.0478%0.0000%0.1305%0.0304%0.0000%0.1261%0.0391%0.0000%0.1131%0.0304%0.0000%
NORTHAMPTON COUNTY98770.0911%0.0304%0.0202%0.0810%0.0304%0.0202%0.0911%0.0101%0.0000%0.0810%0.0101%0.0000%
PAGE COUNTY170950.1872%0.0351%0.0000%0.1521%0.0292%0.0000%0.1521%0.0117%0.0000%0.1170%0.0058%0.0000%
ACCOMACK COUNTY254830.1216%0.0275%0.0000%0.1020%0.0275%0.0000%0.1138%0.0275%0.0000%0.0942%0.0275%0.0000%
GRAYSON COUNTY109410.1645%0.0274%0.0000%0.1554%0.0274%0.0000%0.1462%0.0274%0.0000%0.1371%0.0274%0.0000%
ALLEGHANY COUNTY110690.1355%0.0271%0.0000%0.1084%0.0271%0.0000%0.0994%0.0090%0.0000%0.0723%0.0090%0.0000%
MATHEWS COUNTY73780.0949%0.0271%0.0271%0.0678%0.0271%0.0271%0.0678%0.0000%0.0000%0.0407%0.0000%0.0000%
BEDFORD COUNTY632400.1233%0.0300%0.0063%0.1154%0.0269%0.0063%0.1012%0.0142%0.0000%0.0933%0.0111%0.0000%
HENRICO COUNTY2404360.1152%0.0299%0.0083%0.0998%0.0258%0.0083%0.0944%0.0175%0.0000%0.0807%0.0133%0.0000%
WAYNESBORO CITY155610.1735%0.0450%0.0000%0.1285%0.0257%0.0000%0.1735%0.0450%0.0000%0.1285%0.0257%0.0000%
HANOVER COUNTY870000.1092%0.0287%0.0023%0.1011%0.0253%0.0023%0.1023%0.0218%0.0000%0.0943%0.0184%0.0000%
CRAIG COUNTY39720.1007%0.0252%0.0000%0.1007%0.0252%0.0000%0.1007%0.0252%0.0000%0.1007%0.0252%0.0000%
GALAX CITY40670.1229%0.0246%0.0000%0.1229%0.0246%0.0000%0.1229%0.0246%0.0000%0.1229%0.0246%0.0000%
ORANGE COUNTY284820.1299%0.0351%0.0000%0.1194%0.0246%0.0000%0.1299%0.0351%0.0000%0.1194%0.0246%0.0000%
DANVILLE CITY288380.1040%0.0312%0.0000%0.0902%0.0243%0.0000%0.1040%0.0312%0.0000%0.0902%0.0243%0.0000%
CARROLL COUNTY211630.1040%0.0236%0.0095%0.1040%0.0236%0.0095%0.0945%0.0142%0.0000%0.0945%0.0142%0.0000%
FREDERICK COUNTY679120.1075%0.0324%0.0088%0.0883%0.0236%0.0059%0.0898%0.0206%0.0000%0.0736%0.0147%0.0000%
MANASSAS PARK CITY90180.0665%0.0222%0.0000%0.0554%0.0222%0.0000%0.0444%0.0222%0.0000%0.0333%0.0222%0.0000%
HENRY COUNTY365390.1259%0.0246%0.0000%0.1122%0.0219%0.0000%0.0931%0.0082%0.0000%0.0848%0.0055%0.0000%
BLAND COUNTY45810.1091%0.0218%0.0000%0.1091%0.0218%0.0000%0.1091%0.0218%0.0000%0.1091%0.0218%0.0000%
SPOTSYLVANIA COUNTY1053610.0987%0.0247%0.0057%0.0873%0.0218%0.0057%0.0816%0.0095%0.0000%0.0702%0.0066%0.0000%
WINCHESTER CITY183520.1035%0.0381%0.0000%0.0708%0.0218%0.0000%0.0926%0.0381%0.0000%0.0599%0.0218%0.0000%
LANCASTER COUNTY92670.0755%0.0216%0.0000%0.0755%0.0216%0.0000%0.0755%0.0216%0.0000%0.0755%0.0216%0.0000%
KING WILLIAM COUNTY139960.1286%0.0214%0.0000%0.1143%0.0214%0.0000%0.1286%0.0214%0.0000%0.1143%0.0214%0.0000%
WESTMORELAND COUNTY142330.1827%0.0211%0.0000%0.1756%0.0211%0.0000%0.1546%0.0211%0.0000%0.1475%0.0211%0.0000%
VIRGINIA BEACH CITY3319140.1118%0.0259%0.0066%0.0967%0.0208%0.0066%0.0883%0.0114%0.0000%0.0762%0.0081%0.0000%
POWHATAN COUNTY242870.1400%0.0371%0.0000%0.1153%0.0206%0.0000%0.1400%0.0371%0.0000%0.1153%0.0206%0.0000%
BOTETOURT COUNTY263110.1178%0.0190%0.0076%0.1102%0.0190%0.0076%0.1102%0.0114%0.0000%0.1026%0.0114%0.0000%
FLUVANNA COUNTY210010.1286%0.0238%0.0000%0.1190%0.0190%0.0000%0.1095%0.0238%0.0000%0.1000%0.0190%0.0000%
SCOTT COUNTY160590.1121%0.0249%0.0000%0.1059%0.0187%0.0000%0.0996%0.0125%0.0000%0.0934%0.0062%0.0000%
ALEXANDRIA CITY1122120.0820%0.0205%0.0000%0.0686%0.0178%0.0000%0.0784%0.0169%0.0000%0.0651%0.0143%0.0000%
TAZEWELL COUNTY281470.0995%0.0178%0.0142%0.0959%0.0178%0.0142%0.0853%0.0036%0.0000%0.0817%0.0036%0.0000%
RICHMOND COUNTY56490.2301%0.0354%0.0000%0.1947%0.0177%0.0000%0.1947%0.0354%0.0000%0.1593%0.0177%0.0000%
ROCKINGHAM COUNTY568170.0845%0.0246%0.0035%0.0739%0.0176%0.0000%0.0739%0.0176%0.0000%0.0669%0.0141%0.0000%
LOUISA COUNTY295670.1150%0.0271%0.0135%0.1015%0.0169%0.0135%0.1082%0.0135%0.0000%0.0947%0.0034%0.0000%
LOUDOUN COUNTY2919140.0740%0.0219%0.0041%0.0620%0.0164%0.0041%0.0651%0.0171%0.0000%0.0531%0.0116%0.0000%
RAPPAHANNOCK COUNTY62390.0962%0.0160%0.0000%0.0801%0.0160%0.0000%0.0962%0.0160%0.0000%0.0801%0.0160%0.0000%
JAMES CITY COUNTY643900.0745%0.0186%0.0000%0.0668%0.0155%0.0000%0.0621%0.0124%0.0000%0.0544%0.0093%0.0000%
PATRICK COUNTY128620.0855%0.0155%0.0000%0.0777%0.0155%0.0000%0.0855%0.0155%0.0000%0.0777%0.0155%0.0000%
PRINCE WILLIAM COUNTY3165300.0812%0.0186%0.0000%0.0663%0.0148%0.0000%0.0711%0.0142%0.0000%0.0581%0.0104%0.0000%
AUGUSTA COUNTY549930.1455%0.0218%0.0036%0.1255%0.0145%0.0036%0.1346%0.0182%0.0000%0.1146%0.0109%0.0000%
DINWIDDIE COUNTY208350.1584%0.0384%0.0048%0.1152%0.0144%0.0048%0.1488%0.0288%0.0048%0.1152%0.0144%0.0048%
GOOCHLAND COUNTY214100.1261%0.0187%0.0000%0.1121%0.0140%0.0000%0.1261%0.0187%0.0000%0.1121%0.0140%0.0000%
MONTGOMERY COUNTY619440.0936%0.0145%0.0000%0.0807%0.0129%0.0000%0.0904%0.0145%0.0000%0.0775%0.0129%0.0000%
SHENANDOAH COUNTY323040.0960%0.0155%0.0000%0.0743%0.0124%0.0000%0.0960%0.0155%0.0000%0.0743%0.0124%0.0000%
ROANOKE COUNTY734670.0953%0.0163%0.0027%0.0830%0.0123%0.0027%0.0817%0.0109%0.0000%0.0694%0.0068%0.0000%
SALEM CITY179320.0892%0.0112%0.0000%0.0781%0.0112%0.0000%0.0892%0.0112%0.0000%0.0781%0.0112%0.0000%
NEW KENT COUNTY190220.1051%0.0210%0.0000%0.0894%0.0105%0.0000%0.0946%0.0210%0.0000%0.0789%0.0105%0.0000%
WASHINGTON COUNTY394490.1014%0.0152%0.0000%0.0887%0.0101%0.0000%0.0862%0.0051%0.0000%0.0786%0.0051%0.0000%
MADISON COUNTY104070.0865%0.0192%0.0000%0.0769%0.0096%0.0000%0.0865%0.0192%0.0000%0.0769%0.0096%0.0000%
NORFOLK CITY1412360.0984%0.0092%0.0000%0.0864%0.0085%0.0000%0.0899%0.0064%0.0000%0.0793%0.0057%0.0000%
PULASKI COUNTY238250.0881%0.0126%0.0000%0.0756%0.0084%0.0000%0.0881%0.0126%0.0000%0.0756%0.0084%0.0000%
CLARKE COUNTY122690.1060%0.0163%0.0000%0.0978%0.0082%0.0000%0.1060%0.0163%0.0000%0.0978%0.0082%0.0000%
GREENE COUNTY149260.1072%0.0067%0.0000%0.1072%0.0067%0.0000%0.1072%0.0067%0.0000%0.1072%0.0067%0.0000%
GLOUCESTER COUNTY302840.0859%0.0066%0.0000%0.0859%0.0066%0.0000%0.0859%0.0066%0.0000%0.0859%0.0066%0.0000%
WARREN COUNTY305170.0885%0.0066%0.0000%0.0852%0.0066%0.0000%0.0819%0.0066%0.0000%0.0786%0.0066%0.0000%
ISLE OF WIGHT COUNTY311790.0898%0.0064%0.0000%0.0834%0.0064%0.0000%0.0898%0.0064%0.0000%0.0834%0.0064%0.0000%
ROCKBRIDGE COUNTY162660.1230%0.0123%0.0000%0.1045%0.0061%0.0000%0.1230%0.0123%0.0000%0.1045%0.0061%0.0000%
CULPEPER COUNTY371170.0943%0.0108%0.0000%0.0808%0.0054%0.0000%0.0889%0.0108%0.0000%0.0754%0.0054%0.0000%
FAUQUIER COUNTY563960.0887%0.0071%0.0000%0.0762%0.0053%0.0000%0.0887%0.0071%0.0000%0.0762%0.0053%0.0000%
FREDERICKSBURG CITY194550.0874%0.0051%0.0000%0.0720%0.0051%0.0000%0.0874%0.0051%0.0000%0.0720%0.0051%0.0000%
FRANKLIN COUNTY398660.0602%0.0050%0.0050%0.0502%0.0050%0.0050%0.0552%0.0000%0.0000%0.0452%0.0000%0.0000%
MANASSAS CITY238150.1008%0.0042%0.0000%0.0966%0.0042%0.0000%0.0840%0.0042%0.0000%0.0798%0.0042%0.0000%
YORK COUNTY508380.0925%0.0157%0.0000%0.0669%0.0039%0.0000%0.0885%0.0157%0.0000%0.0629%0.0039%0.0000%
BATH COUNTY33580.0893%0.0000%0.0000%0.0893%0.0000%0.0000%0.0893%0.0000%0.0000%0.0893%0.0000%0.0000%
BRISTOL CITY123450.0729%0.0000%0.0000%0.0567%0.0000%0.0000%0.0567%0.0000%0.0000%0.0567%0.0000%0.0000%
BUCKINGHAM COUNTY110630.1356%0.0271%0.0000%0.0904%0.0000%0.0000%0.1356%0.0271%0.0000%0.0904%0.0000%0.0000%
BUENA VISTA CITY44320.0903%0.0000%0.0000%0.0903%0.0000%0.0000%0.0903%0.0000%0.0000%0.0903%0.0000%0.0000%
CAROLINE COUNTY228940.1005%0.0087%0.0000%0.0830%0.0000%0.0000%0.1005%0.0087%0.0000%0.0830%0.0000%0.0000%
CHARLES CITY COUNTY57200.0524%0.0000%0.0000%0.0350%0.0000%0.0000%0.0524%0.0000%0.0000%0.0350%0.0000%0.0000%
COVINGTON CITY38880.1029%0.0000%0.0000%0.0772%0.0000%0.0000%0.1029%0.0000%0.0000%0.0772%0.0000%0.0000%
DICKENSON COUNTY101440.1084%0.0000%0.0000%0.0887%0.0000%0.0000%0.1084%0.0000%0.0000%0.0887%0.0000%0.0000%
ESSEX COUNTY83180.1443%0.0000%0.0000%0.1443%0.0000%0.0000%0.1443%0.0000%0.0000%0.1443%0.0000%0.0000%
FLOYD COUNTY118520.0759%0.0000%0.0000%0.0759%0.0000%0.0000%0.0759%0.0000%0.0000%0.0759%0.0000%0.0000%
GILES COUNTY120930.0413%0.0000%0.0000%0.0331%0.0000%0.0000%0.0413%0.0000%0.0000%0.0331%0.0000%0.0000%
GREENSVILLE COUNTY64350.1709%0.0155%0.0000%0.1399%0.0000%0.0000%0.1709%0.0155%0.0000%0.1399%0.0000%0.0000%
KING AND QUEEN COUNTY54030.0740%0.0000%0.0000%0.0740%0.0000%0.0000%0.0740%0.0000%0.0000%0.0740%0.0000%0.0000%
KING GEORGE COUNTY197800.1314%0.0000%0.0000%0.0910%0.0000%0.0000%0.1314%0.0000%0.0000%0.0910%0.0000%0.0000%
MARTINSVILLE CITY90700.0992%0.0000%0.0000%0.0882%0.0000%0.0000%0.0992%0.0000%0.0000%0.0882%0.0000%0.0000%
MIDDLESEX COUNTY87460.1029%0.0114%0.0000%0.0800%0.0000%0.0000%0.1029%0.0114%0.0000%0.0800%0.0000%0.0000%
POQUOSON CITY96350.0934%0.0000%0.0000%0.0934%0.0000%0.0000%0.0934%0.0000%0.0000%0.0934%0.0000%0.0000%
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Election Data Analysis Election Forensics Election Integrity mathematics programming technical

Potential duplicate registrants in VA voter list

I previously documented the utilization of the Hamming string distance measure to identify candidate pairs of duplicate registrants in voter lists. While a good first attempt at quantifying the numbers of potential duplicates in the voter rolls, using a hamming distance metric is less than ideal for reasons discussed below and in the previous article. I have since been able to update the processing functions to use a more complete Levenshtein distance (LD) metric, and made some improvements to parsers and other code utilities, etc., but otherwise the the analysis followed the same procedure, and is described below.

Using the 2022-11-23 Registered Voter List (RVL) and the 2023-01-26 Voter History List (VHL) purchased from the VA Department of Elections (ELECT) I wrote up an analysis script to check for potentially duplicated registrant records in the RVL and cross reference duplicate pairings with the VHL to identify potential duplicate votes. The details are summarized below.

Please note that I will not publish voter Personally Identifiable Information (PII) on this blog. I have substituted fictitious PII information for all examples given below, and cryptographically hashed all voter information in the downloadable results file. I will make available the detailed information to those that have the authorization to receive and process voter data upon request (contact us).

Summary of Results:

As a baseline, there were 6,464 records for STATUS=’Active’ registrants that adhered to the definition of a “duplicate” when Social Security Number (SSN) is not available, as defined by the MOU between DMV and ELECT (section 7.3) of having the same First Name + Last Name + Full Date of Birth (DOB). I’ve included a copy of the MOU between the VA DMV and ELECT at the end of this article for reference. It should be noted that most records held by DMV and ELECT have a SSN associated with them (or at least they should). SSN information is not distributed as part of the data purchased from ELECT, however, so this is the appropriate standard baseline for this work.

Upgrading our definition of a potential duplicate to [First + Middle + Last + Suffix + DOB] and using a LevenshteinDistance=0 drops the number of potential duplicates to 1,982, with each identified registrant in a pair having an exactly matching string result and unique voter ID numbers.

According to my derivations and simulations that are described in detail here, we should only expect to see an average of 11 (+/- 3) potential duplicate pairs (a.k.a. “collisions”) at a distance of 0. This is over two orders of magnitude different than what we observe in the compiled results. Such a discrepancy deserves further investigation and verification.

Allowing for a single string difference by setting LevenshteinDistance<=1 increases the pool of potential duplicates to 5,568. While this relaxation of the filter does allow us to find certain issues (described below) it also increases our chances of finding false positives as well. The LD metric results should not be viewed as a final determination, but as simply a useful tool to make an initial pass through the data and find candidate matches that still require further review, verification and validation.

Increasing to LevenshteinDistance<=2 brings the number of potential duplicates up to 32,610. When we increase to LD <= 3 we get an explosion of 183,130 potential duplicates.


For every entry in the latest RVL, I performed a string distance comparison, based on Levenshtein distance, between every possible pair of strings of (FIRST NAME + MIDDLE NAME + LAST NAME + SUFFIX + FULL DOB).  For the ~6M different RVL entries, we therefore need to compute ~3.8 x 10^13 different string comparisons, and each string comparison can require upwards of 75 x 75 individual character comparisons, meaning the total number of character operations is on the order of 202.5 Quadrillion, not including logging and I/O.

A distance of 0 indicates the strings being compared are identical, a distance of 1 indicates that there a single character can be changed, inserted or removed that would convert one string into the other. A distance of 2 indicates that 2 modifications are required, etc. 

Example: The string pair of “ALISHA” –> “ALISHIA” has an LD of 1, corresponding to the addition of an “I” before the final “A”.

I aggregated all of the Levenshtein distance pairings that were less than or equal to 3 characters different in order to identify potential (key word) duplicated registrants, and additionally for each pairing looked at the voter history information for each registrant in the pair to determine if there was a potential (again … key word) for multiple ballots to be cast by the same person in any given election.  As we allow for more characters to be different, we potentially are including many more likely false positive matches, even if we are catching more true positives.

For example: At a distance of 4 the strings of “Dave Joseph Smith M 10/01/1981” and “Tony Joseph Smith M 10/01/1981” at the same address would produce a potential match, but so would “Davey Joseph Smith M 10/01/1981” and “David Josiph Smith M 10/02/1981”. The first pair is more likely to be a false positive due to twins, while the second is more likely to be due to typo’s, mistakes, or use of nicknames and might warrant further investigation. A much stronger potential match would be something like “David Josiph Smith M 10/01/1981” and “David Joseph Smith M 10/01/1981”, with a distance of 1 at the same address. In an attempt to limit false positives, I have clamped the distance checks to <= 3 in this analysis.

The Levenshtein distance measure is importantly able to identify potential insertions or deletions as well as character changes, which is an improvement over the Hamming distance measure. This is exampled by the following pairing: “David Joseph Smith M 10/01/1981” and “Dave Joseph Smith M 10/01/1981”. The change from “id” to “e” in the first name adds/subtracts a character making the rest of the characters in the remainder of the string shift position. A Levenshtein metric would correctly return a small distance of 2, whereas the hamming distance returns 27.

Note that with the official records obtained from ELECT, and in accordance with the laws of VA, I do not have access to the social security number or drivers license numbers for each registration record, which would help in identifying and discriminating potential duplicate errors vs things like twins, etc. I only have the first name, middle name, last name, suffix, month of birth, day of birth, year of birth, gender, and address information that I can work with.  I can therefore only take things so far before someone else (with investigative authority and ability to access those other fields) would need to step in and confirm and validate these findings.


The summary totals are as follows, with detailed examples.

DMV_ELECT MOU StandardLD <= 0LD <= 1LD <= 2LD <= 3
Number of Potential Duplicate Registrant Pairs7,586 (0.12%)2,472 (0.04%)6,620 (0.11%)32,610 (0.53%)183,130 (2.99%)
Number of Potential Duplicate Registrant Pairs (Active Only)6,464 (0.11%)1,982 (0.03%)5,568 (0.10%)28,884 (0.50%)164,302 (2.85%)
Number of Potential Duplicate Ballots6,3621123,57637,028236,254
Number of Potential Duplicate Ballots (Active Only)6,2281103,54236,434232,394

Examples of Types of Issues Observed:


Example #1: The following set of records has the exact match (distance = 0) of full name and full birthdate (including year), but different address and different voter ID numbers AND there was a vote cast from each of those unique voter ID’s in the 2020 General Election.  While it’s remotely possible that two individuals share the exact same name, month, day and year of birth … it is probabilistically unlikely (see here), and should warrant further scrutiny.

Voter Record A:

AMY BETH McVOTER 12/05/1970 F 12345 CITIZEN CT

Voter Record B:

AMY BETH McVOTER 12/05/1970 F 5678 McPUBLIC DR

Example #2: This set of records has a single character different (distance of 1) in their first name, but middle name, last name, birthdate and address are identical AND both records are associated with votes that were cast in the 2020, 2021, and 2022 November General Elections.  While it is possible that this is a pair of 23 year old twins (with same middle names) that live together, it at least bears looking into.

Voter Record A:


Voter Record B:


Example #3: This set of records has two characters different (distance of 2) in their birthdate, but name and address are identical AND the birth years are too close together for a child/parent relationship, AND both records are associated with votes that were cast in the 2020 and 2022 November General Elections. 

Voter Record A:


Voter Record B:


Example #4: This set of records has again a single character different (distance of 1) in the first name (but not the first letter this time) and the last name, birthdate and address are identical.  There were also multiple votes cast in the 2019 and 2022 November General from these registrants.

Voter Record A:


Voter Record B:


Example #5: This set of records has two characters different (distance of 2) in the first and middle names and the last name, birthdate, gender and address are identical.  There were also multiple votes cast in the 2021 and 2022 November General from these registrants. Again it is possible that these records represent a set of twins given the information that ELECT provides.

Voter Record A:


Voter Record B:


Example #6: The following set of records has the exact match (Distance = 0) of full name and full birthdate (including year), and same address but different voter ID numbers.  There was no duplicated votes in the same election detected between the two ID numbers.

Voter Record A:

JAMES TIBERIUS KIRK 03/22/2223 M 1701 Enterprise Bridge

Voter Record B:

JAMES TIBERIUS KIRK 03/22/2223 M 1701 Enterprise Bridge

Example #7: The following set of records has the exact match (distance = 0) of full name and full birthdate (including year), same address but different gender and voter ID numbers.  There was no duplicated votes in the same election detected between the two ID numbers.

Voter Record A:


Voter Record B:


Example #8: The following set of records has a single punctuation character different, with the same address but different voter ID numbers.  There was no duplicated votes in the same election detected between the two ID numbers.

Voter Record A:


Voter Record B:


Results Dataset:

A full version of the aggregated excel data is provided below, however all voter information (ID, first name, middle name, last name, dob, gender, address) have been removed and replaced by a one-way hash number, with randomized salt, based on the voter ID. The full file with specific voter information can be provided to parties authorized by ELECT to receive and process voter information, Election Officials, or Law Enforcement upon request.


The MOU between the VA Department of Elections (ELECT) and the VA Department of Motor Vehicles (DMV) is also provided below for reference. Section 7.3 defines the minimal standards for determining a match when no social security number is present.

Election Data Analysis mathematics programming technical

Derivation of Expected number of String Collisions in VA Registered Voter Data

Below I present the theory and derivation as to how I arrived at the expected value of 11 collisions (+/- 3) as mentioned in my posts discussing string distance analysis (here and here). I’ve tried to make the derivation below as digestible as possible, with accessible references, but it is admittedly still a very technical read. I think its important to “show my work” on the subject, though, and I present it here and am happy to take comments and criticism (contact).

Q: How much of a chance do we actually have of getting an exact (Hamming distance of 0) collision in the full name and full date of birth? Well, there is a similar and well known probability puzzle that asks how many random people do you need to approximately have a 50% chance of 2 of them sharing the same birthday (not including the year of birth). This is known as the “Birthday Problem” in probability theory, and rather surprisingly, the answer is that you only need about 23 people in your population sample to have a 50% probability that 2 of those people will share a day-of-year of birth. To quote the wikipedia article on the matter “… While it may seem surprising that only 23 individuals are required to reach a 50% probability of a shared birthday, this result is made more intuitive by considering that the birthday comparisons will be made between every possible pair of individuals. With 23 individuals, there are 23 × 22/2 = 253 pairs to consider, far more than half the number of days in a year.” The same mathematics of the birthday problem is the basis of the Birthday Attack cryptographic exploit, and it is therefore a well-studied problem in cryptography and cyber security.

Figure 1: The computed probability of at least two people sharing a birthday versus the number of people. A recreation of the classic “Birthday Problem”.

Now, as interesting as the toy birthday problem is as described above, it is over simplified for the problem we are looking at here. Firstly, the problem setup assumes independent and identically distributed random variable (e.g. an “IID” set of variables). While this is not exactly the case, the IID assumption provides for a computable first order estimate, and in the case of the classical birthday problem the estimate has been shown to be fairly accurate under experimental conditions.

Secondly, when we start additionally considering the year of birth, or sharing of first names, middle names and last names, things get much more complicated to compute, but the method is the same. We want to determine the probability of 2 people sharing the same First Name, Middle Name, Last Name, Suffix, Month-of-Birth, Day-of-Birth and Year-of-Birth in the population of unique registrants in the Registered Voter List. This means that in addition to the 365 day-of-birth possibilities, we need to estimate the number of possible years to cover, the number of possible first names, the number of possible middle names, the number of possible last names, the number of possible suffix strings and then include these possibilities into the same formulation as the birthday problem setup.

For determining how many years we should cover, I will simply use the average life expectancy of approximately 79 years. We can therefore update our N value of the birthday problem from 365 to 365 * 79 = 28835. When we perform the same analysis as the standard birthday problem with just this new parameter included, we end up needing 200 people in our sample population to have a 50% probability of of 2 people having a match.

Figure 2: The computed probability of at least two people sharing a birthday versus the number of people in the sample population. A recreation of the classic “Birthday Problem”, but we’ve updated the analysis to include the year of birth, and assumed the average life expectancy of 79 years. This moves the 50% crossover point to a population size of 200 from 23 for the standard Birthday Problem setup.

A similar analysis can be done with the number of names being considered, etc. For each (assumed independent and uniform) variable we add to the setup, we multiply the number of possible states (N) by the number of unique variable settings.

We can estimate the universe of possible names using the frequentist method from the RVL data itself: We know that we have 6,127,859 unique voter ID’s in the RVL, and there are 14 unique SUFFIX entries, 291,368 unique FIRST names, 405,591 unique MIDDLE names, and 465,185 unique LAST names. So multiplying out 365 x 79 x 14 x 291368 x 405591 x 465185 = 2.22 x 10^22 potential states to consider.

Now unfortunately, as we start dealing with bigger and bigger N values the ability of computers to maintain the necessary precision to carry out the mathematics for direct computation becomes harder and harder, eventually resulting in Infinite or divide-by-zero answers as the probabilities get smaller and smaller. So lets begin by first determining if we can find the 50% crossover point for the unique voter ID population size. We find that we only need 410 unique First, Middle, and Last names (each) to break the 50% probability limit.

Figure 3: The computed probability of at least two people sharing a first name, middle name, last name, suffix, month-of-birth, day-of-birth, year-of-birth versus the number of people in the sample population. This assumes the Nyears = 79, Nsuffix = 14, Nfirst = 410, Nmiddle = 410, Nlast = 410.

As we increase the number of unique (first, middle, last) names under consideration, we find that we very quickly reduce the probability to near zero (again … this is assuming an IID set of variables … more on that later). In fact we only need to assume that there are 1300 unique first names, middle names and last names before the probability drops to under 1%. This is two full orders of magnitude below the actual number of unique first names, middle names and last names (each) that we find by simple examination of the RVL file, so the actual probability of a collision under these conditions should be much, much, much lower. While not exactly zero, it is computationally indistinguishable from zero given machine precision. Note (again) that this formulation is still simplified in that it assumes a uniform distribution within the N possible states, but it still serves to give a first order approximation and sanity check.

Figure 4: The computed probability of at least two people sharing a first name, middle name, last name, suffix, month-of-birth, day-of-birth, year-of-birth versus the number of people in the sample population. This assumes the Nyears = 79, Nsuffix = 14, Nfirst = 1300, Nmiddle = 1300, Nlast = 1300.

As we start approaching the limit of computational precision we have to resort to approximation methods for computing the very small, but non-zero probability of collision given the actual number of unique first, middle and last names observed in the RVL dataset. We can use the Taylor series expansion for small powers in order to do this, and our equation for computing the probability becomes: Pb = 1 – exp(-k*(k-1) / (2 *N)).

Replicating our earlier example in Figure 4 above with Nfirst == Nmiddle == Nlast == 1300 to show the comparison of the Taylor expansion to the explicit computation produces the graphic in Figure 5 below. We see that the small value approximation is close, but slightly over-estimates the directly computed probability for IID variables.

Figure 5: The computed probability of at least two people sharing a first name, middle name, last name, suffix, month-of-birth, day-of-birth, year-of-birth versus the number of people in the sample population. This assumes the Nyears = 79, Nsuffix = 14, Nfirst = 1300, Nmiddle = 1300, Nlast = 1300.

When we perform this Taylor series approximation and look to find the number of records required in order to obtain a 50% probability that any 2 records would match given our updated universe of possible matches, we end up with requiring K = 176,000,000,000, or 176 Billion records. When we again try to evaluate the Taylor series for the explicit number of unique Voter ID’s present in the RVL file, which is just over 6M, we again obtain a number that is computationally indistinguishable from 0. (To be absolutely meticulous … its a bigger number that is indistinguishable from 0 than we previously computed, but it is still indistinguishable from zero.)

Figure 5: The computed probability of at least two people sharing a first name, middle name, last name, suffix, month-of-birth, day-of-birth, year-of-birth versus the number of people in the sample population. This assumes the Nyears = 79, Nsuffix = 14, Nfirst = 291368, Nmiddle = 405591, Nlast = 465185, and the computed 50% crossover point occurs at approximately 176 Billion samples required.

Another Implementation note: In order to explicitly code the above direct computations we also need to do some clever tricks with logarithms in order to avoid numerical overflow / underflow issues as much as possible. The formula for computing the permutations, which is N! / (N-K)! = N x (N-1) x … x (N-K+1) can have numerical issues when N becomes large. However if we take the base-10 logarithm of the equation, we can use the product and quotient rules of logarithms to compute the result and avoid numerical overflow: log10( N! / (N-K)! ) = log10(N!) – log((N-K)!) = log10(N) + log10(N-1) + … + … log10(N-K+1), which is a much more stable computation.

We can perform a similar trick in order to compute the denominator of N^k by using the power property of logarithms such that log10( N^k ) = k x log10(N).

You must of course remember to reverse the logarithm once you’ve computed the log-sums. So the final computation of Pb becomes the following:

Vnr = log10(N) + log10(N-1) + … + … log10(N-K+1), where N is the number of possible states N = 365 x Nyears x Nfirst x Nmiddle x Nlast x Nsuffix.

Vt = k x log10(N)

Pa_log10 = Vnr – Vt = log10(Pa) = log10(Vnr/Vt)

Pb = 1 – 10^(Pa)

Updating from uniform distributions to non-uniform distributions

So what happens when we take into account the fact that names and birthdays are not uniformly distributed? (e.g. the last name of “Smith” is more frequent than “Sandeval”) This fact increases the probability of a collision occurring in the dataset. This increase also makes intuitive sense as we can anecdotally observe that coincident names and birthdates, while still rare … do actually happen in real life with common names.

However, in the non-uniform case we don’t have as nearly of a nice closed set of formulas for computing the probability. What we can do instead to estimate the probability is perform a number of Monte Carlo simulations of selecting K values from the weighted possibilities, and determine how many collisions occurred in each simulation trial. By setting K equal to the number of unique Voter ID values in the RVL dataset, we can directly answer the question via simulation of “how many collisions of First+Middle+Last+Suffix+DOB should we expect when looking at the VA Registered Voter List file“?

We can determine the weightings for each variable easily enough from the distributions of unique values in the data itself.

The below MATLAB weightedCollisionSim(…) function is a program that can be used to perform this analysis. It assumes that the RVL table object is a global variable to setup the trials, and uses the MATLAB built-in randsample(…) function to perform each draw.

After 100 simulation runs, the results are that for the K=6,127,859 unique voter ID’s in the RVL, we should expect to have an average of about 11 collisions at Hamming distance of 0, with a standard deviation of roughly 3.

I will note that as a validation and verification step, the MATLAB simulation code below, when used with uniform sampling, produces similar results to what we analytically derived above.

function [p,m,s] = weightedCollisionSim(k,ntrials,varargin)
% To compute the probability the ntrials must be >> 1:
% [p,m,s] = weightedCollisionSim(k,ntrials,values1,weights1,...,values2,weights2)
% [p,m,s] = weightedCollisionSim(k,ntrials,Nvalues1,weights1,...,Nvalues2,weights2)
% p = Probability of a collision
% m = mean number of collisions
% s = standard deviation of collisions

if nargin == 0
    global rvl; % Assume the RVL is an available global var

    ntrials = 100; % Number of trials
    % Population size set as num of unique voter IDs in RVL
    npop = numel(unique(rvl.IDENTIFICATION_NUMBER));

    % Convert the DOB strings to datetime objects
    dob = datetime(rvl.DOB);

    % How many unique days of the year are there?
    [ud,uda,udb] = unique(day(dob,'dayofyear'));
    % How often do they occur?
    nud = accumarray(udb,1,[numel(ud),1]);
    Ndays = numel(ud);

    % How many unique years of birth are there?
    [uy,uya,uyb] = unique(year(dob));
    % How often do they occur?
    nuy = accumarray(uyb,1,[numel(uy),1]);
    Nyears = numel(uy);

    % How many unique suffix strings are there?
    [us,usa,usb] = unique(rvl.SUFFIX);
    % How often do they occur?
    nus = accumarray(usb,1,[numel(us),1]);
    Nsuffix = numel(us);

    % How many unique first names are there?
    [uf,ufa,ufb] = unique(rvl.FIRST_NAME);
    % How often do they occur?
    nuf = accumarray(ufb,1,[numel(uf),1]);
    Nfirst = numel(uf);

    % How many unique middle names are there?
    [um,uma,umb] = unique(rvl.MIDDLE_NAME);
    % How often do they occur?
    num = accumarray(umb,1,[numel(um),1])
    Nmiddle = numel(um);

    % How many unique last names are there?
    [ul,ula,ulb] = unique(rvl.LAST_NAME);
    % How often do they occur?
    nul = accumarray(ulb,1,[numel(ul),1]);
    Nlast = numel(ul);
    % Initializing the weighting vectors
    w0 = nus;
    w1 = nud;
    w2 = nuy;
    w3 = nuf;
    w4 = num;
    w5 = nul;

    % Recursively compute results and return
    [p,m,s] = weightedCollisionSim(npop,ntrials,1:Nsuffix,w0,1:Ndays,w1,1:Nyears,w2,...

if nargin < 2 || isempty(ntrials)
    ntrials = 1;

nc = zeros(ntrials,1);
for j = 1:ntrials
    fprintf('Trial %d\n',j);
    y = zeros(k,numel(varargin)/2);
    m = 1;
    for i = 1:2:numel(varargin)
        w = varargin{i+1};
        v = varargin{i};
        if ~isempty(w) && isvector(w)
            % Non-uniform weightings
            y(:,m) = randsample(v,k,true,w);
            % Uniform sampling
            y(:,m) = randsample(v,k,true);
        m = m+1;
    [u,~,ib] = unique(y,'rows');
    nu = accumarray(ib,1,[size(u,1),1]);
    nc(j) = sum(nu > 1);
p = mean(nc>0);
m = mean(nc);
s = std(nc);