Pennsylvania Rabbit Hole Part IX

Unhackthevote December 26, 2017

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Households that Defy Common Sense

After finding so many Pennsylvania voters registered with the same name and same birth-date at the same address, we decided to look further at households. So, we made a list of all addresses in each data set and searched for duplicate records.

Nearly 500,000 voters were added to the voter rolls in the seven months before the election; but, fewer than 100,000 new addresses were added during this same time period.

We then examined the addresses in which there were more voters in November than either August 15 (3 months prior) or February 27 (four months after).

By the Numbers

We found 220,000 registration records at addresses where there were more voters in November than either three months prior or four months after.

In April 2016, there were on average 2.08 voters per household. The day before the election, there were 2.14 voters per household. By February 2017, households were almost back to their pre-election state, at 2.09 voters per household.

We made a list of these “over-full” households, and what we saw is quite interesting. For instance, one address in Philadelphia housed six people on November 7. Three of the residents were named Carmen and two of those Carmens shared a birthday as well as a last name. But the birthdays were exactly three years apart. One of these latter two Carmens appears to have moved to that address between August 15, 2016 and November 7. Between November 7 and February 27, 2017, she disappeared from the voter rolls, along with one of the other housemates. A third housemate also moved in between August and November but stayed there through at least February.

We found over 100,000 households that displayed a similar pattern: voters who were not clearly related to each other by name appearing just in time for the November election, then disappearing again within a few months. Peculiar, indeed.

Six sets of data. And nine parts so far in this series because we found so many anomalies in that data. And we aren’t quite done.