Maladministration of Voter Rolls?

Part 3 in the "Nothin To See Here" Series

If a system is corruptible it should be FIXED. There is no need to PROVE corruption actually occurred with a corruptible system.

If the bank leaves open the door to the safe, it doesn’t take an actual THEFT to prove the door should be locked at all times.

What a concept.

Maladministration is about poor governance or the mismanagement of public resources or functions that might have serious implications for an agency and/or the community.

The data shows the registration roll and consequently the mail-in ballot system is so rife with OPPORTUNITIES for fraud, that it needs to be eliminated or severely curtailed.

Here is the proof I would offer.

First, I obtained 4 snapshots of a counties voter rolls between August 2023 through November 2023. This is a period of time when ballots are being mailed out in this universal mail in ballot state. This time period of snapshots is critical to proving the point of corruptibility.

Then I created two sets of HASH values

• [VOTER ID]&[MAILING ADDRESS]

• [VOTER ID]&[RESIDENTIAL ADDRESS]

The purpose of this HASH is to track through time, in this case four months, how Voters records are added, deleted and changed from period to period.

Ask yourself, would you expect 1000’s of changes in the voter rolls where address are deleted and added back with different values for the same Voter ID then deleted again, in a span of 4 months?

Or what about a Voter ID WITHOUT a mailing address being deleted and when it is added back, it has a mailing address for only 1 month then it is again deleted and added back WITHOUT a mailing address in a span of 4 months? Does that make sense?

In the above scenario, let’s assume someone with nefarious intentions identifies a low propensity voter, they have access to the roll, they take a NULL value for mailing address, change it to a value where people are harvesting ballots, the ballot is sent out, it is delivered to the harvester address, then the mailing address is changed back to the original NULL values. The low propensity voter does not even think about the fact that the ballot did not show up in the mail.

This is not far fetched. I have proof that the data was changed in this way.

Therefore the fraud COULD happen and that means this mail-in ballot system is corruptible.

We don’t NEED to prove anything else.

Methodology

This method is very heavy on data transformation techniques and I am not going to describe everything involved. First let’s look at how you do this type of analysis. (You can skip to results if you are not into data transformation.)

1. Obtain 4 snapshots of voter rolls during the period of time when ballots are being mailed out.

2. Create 2 HASH columns on each copy of the roll.

   1. HASH1: [Voter ID]&[Residential Address]

   2. HASH2: [Voter ID]&[mailing Address]

3. Now the difficult part, analyze the deletions and additions between each period of the roll for both HASH sets. Your data set will then look like this.

   1. Snapshot 1 to 2

      1. deletion between Snapshot 1 and 2 of HASH 1

      2. deletion between Snapshot 1 and 2 of HASH 2

   2. Snapshot 2 to 1

      1. additions between Snapshot 2 and 1 of HASH 1

      2. additions between Snapshot 2 and 1 of HASH 2

   3. Snapshot 2 to 3

      1. deletion between Snapshot 2 and 3 of HASH 1

      2. deletion between Snapshot 2 and 3 of HASH 2

   4. Snapshot 3 to 2

      1. additions between Snapshot 3 and 2 of HASH 1

      2. additions between Snapshot 3 and 2 of HASH 2

   5. Snapshot 3 to 4

      1. deletion between Snapshot 3 and 4 of HASH 1

      2. deletion between Snapshot 3 and 4 of HASH 2

   6. Snapshot 4 to 3

      1. additions between Snapshot 4 and 3 of HASH 1

      2. additions between Snapshot 4 and 3 of HASH 2

4. Now create what I am going to call a “merge set” or a union set of the changes made above. In other words, all of the records that show up as being deleted or added. When you do this, make sure to carry forward with your master merged set several other fields like the “mailing address” or “residential address” and “what period of time the record is from”.

5. Now this merged set can be analyzed because the HASH includes the ID, you can track through time for single voter the changes. You need to ask your self the following:

   1. What values are being changed?

   2. Does this represent human behavior or database manipulation or other?

   3. Are the address in these records legitimate?

   4. What types of address exist in this change set? Is it a shelter? A home? A vacant lot?

6. If you know how to parse the Voter ID out of the HASH, I would also recommend counting the instances of the Voter ID. Then you can analyze the merge set on that ID. You might find, as I did, data that can ONLY be explained as corrupted.

7. Also, again if you know how to parse the Voter ID out of the HASH, you can count the instances of each address. These are the suspect address involved in this maladministration, the easy targets so to speak. I show examples of this below.

Summary of Results

The first conclusions are below and they are the most important.

• Why can’t our elected officials produce an audit trail of changes and monitor it for nefarious behavior? It should not take a private citizen to do it. Don’t believe me? Ask your clerk for an audit trail of all changes on the roll.

• The fact that they cannot (or will not) is an indication that the database is MALADMINISTERED.

• The fact that the database is maladministered means that it should not be used to produce BALLOTS with correct address 100% of the time. Full stop.

• The fact that we cannot reliably produce good ballot address means that we should not be using mail in ballots and should instead show up with an ID to a polling place.

• The above points do not require PROOF of a ballot harvesting (aka 2000 Mules whatever happened to that?). The PROOF is in the MALADMINISTRATION of a database.

Detailed Analysis - Address Counts

The total voter roll for the period between Aug-Nov 2023 was ~ 550,000 for each month. Over the four months, there are 47,160 address impacted by changes. These address are the “easy targets” for maladministration.

For those ~ 47k address the break down is as follows:

• 32,905 changes to mailing addresses

• 48,999 changes to residential addresses

• 81,904 was the total number of changes (32,905+48,999)

To be clear, let’s define what “change” means in the above context:

• Add - a HASH is added to a later period that did not exit in prior periods

• Delete - a HASH was removed from the later period that exists in the prior period

• What I am not saying is that the VOTER was added or deleted. I am referring to the HASH combination in this study.

Here is a sample of the top 25 address instances where changes occur and the address type. These are the easy targets. This happens in 4 months.

What this means for example is that address #4 is a Social Services Center that had 102 instances of adds and deletes associated with this address….in 4 months. Any good database administrator would be able to answer the following rudimentary questions.

• Who made these changes?

• What date and time where the changes made?

• Where were the changes made from? What is the IP address or from which computer terminal?

• What is the reason for the change?

• Did the change result in an Undeliverable ballot?

If these basic questions cannot be answered then this is an example of MALADMINISTRATION.

But it gets worse…..and for this you really have to do a micro examination of each change compared to the last change and detect what characters changed between versions of the record. To illustrate this, I am going to post 3 examples.

For all the examples below, I have made the HASH records anonymous.

The periods of time when the snapshot was OBTAINED are:

• Period 1 - Aug 2023

• Period 2 - Sep 2023

• Period 3 - Oct 2023

• Period 4 - Nov 2023

Case 1

This is a Republican voter born in 1987 and registered to vote in Oct 2018.

For this first example, I will explain it line by line.

This person’s HASH starts out with a NULL mailing address (line 2) and the residential address being removed from period 1 compared to 2 (line 3). The specified residential address that is removed (line 3) is (or was) a vacant lot. So was the initial residential address was bad? How did that get entered in the first place?

Then the HASH is added back in period 2 with a different mailing address (line 4) and different residential address (line 5). The mailing address is an apartment but the residential address is a single family home. How does that work? These address are 10 miles apart.

Then both are deleted again (lines 6 & 7).

Then they are added pack again to period 3 (lines 8 & 9). This time the mailing address was added back without the complete zip code as found in the prior period. The residential address REVERTS back to what it was in period 1, the vacant lot.

Then both are deleted again and added back (line 12 & 13).

When the mailing address is added back it REVERTS to the original NULL values. When the residential address is added back it takes the value (almost) of the original mailing address with zip code changes. Compare line 13 to line 4 to confirm. Did you catch that? The residential address takes the value of the old mailing address.

Got all of that. Does it make sense that a human being would behave that way? If you believe that it does, ask yourself these questions:

• Who made these changes? Was it a state agency? An NGO? Other?

• Is the voter aware these changes are being made?

• What date and time were the changes made?

• Why were the changes made only to be returned (almost to the original values)?

• Which address was the mail in ballot sent to?

• Did the voter receive the ballot?

• Did the legal voter vote the ballot?

• Does an audit trail exist to answer all these questions?

• Most importantly, could all of this mess be avoided if people showed up with an ID and voted in person?

The following case studies will be more abbreviated.

Case 2

This is an Unaffiliated voter born in 1981 and registered to vote in Dec 2022.

This person starts out with a mailing address and a residential address that are 7 miles apart being removed. Red flag #1. Then added back without a mailing address but the residential address takes on the mailing address value (almost) except the complete zip code is not included. Then just the mailing address is deleted and added back but this time with slightly different syntax. Then both the mailing and residential address are deleted again (lines 8 & 9). Finally the mailing address is added back with NULL values and the residential address is returned to the original value.

How do we know the ballot was mailed to the correct address in all of this mess? Many of the same questions apply from Case #1.

Case 3

This is an Unaffiliated voter born in 1971 and registered to vote in Sep 2021.

Both address involved in this case study are social service centers.

This person starts out with no mailing address and is registered at a social services center. Then is added back at a different social services center the next month. The 2 months later is added back with a NULL mailing address and the worst part, a residential address that is clearly UNDELIVERABLE.

Where did the mail in ballots go? Do we have 100% assurance with this type of address churn, particularly at a social service center that the ballot is put into the correct hands?

Conclusion

I wanted to go through 10 case studies of the 1000’s that I have identified. But this process would take months to do. You get the idea at this point of how to look at the data. What are the simple points to remember?

• Registration roll data have a lot of “churn” month to month that can only be examined looking at snapshots.

• Make sure to obtain snapshots of your voter rolls every month if possible.

• It is rarely possible to understand the source of this churn.

• It is rarely possible to obtain any sort of audit trail on the churn.

• The churn has the ability generate UNDELIVERABLE ballots that fall into the hands of ballot harvesters.

All of this mess could be avoided if people were required to show up in person with an ID to vote.

How else are we going to solve this problem of voter roll churn?