MEGATHREAD. Be patient, there's a lot of terminology. We…

Last night I mapped the book votes visually at a precinct level and found curious correlations that bears public scrutiny.

Screencaps are not from a public site, but underlying data given is real, public, and verifiable.

We'll be taking a precinct-by-precinct look at Fulton county excess votes. Excess meaning more recorded votes than book.

Fulton has 10K+ excess votes. This has been blamed on stale record keeping. At a precinct level, there are 14700 "excess" votes and 3900 "missing" votes.

This is a precinct map of Fulton County. There'll be a lot of data, so refer to these maps over and over until you understand. Here, the red and yellow/orange areas are where the most excess votes are (by ratio).

And for background context - this is a map showing where Trump votes are. This is a *relative* coloring not an absolute coloring (otherwise it'd be all blue). Fulton is strongly Democratic so the red areas are actually like 55% Trump at best whereas the blue areas are <5% Trump.

Here's where the interesting part begins.

This is a map of where the lowest vote increases relative to 2016 are. In other words, these are areas that simply didn't have much vote growth vs. 2016 (possibly didn't grow much in population).

In other words, if you take the excess vote map and the vote growth map side-by-side, you see a high correlation. For some reason, *excess votes* correlates to *low vote growth.*

Let's look at two clusters of excess votes in particular. One in the north and one in the south.

Fulton County is highly segregated. The left map shows Black population by percent, the right map shows white population by percent. This will be relevant.

The south cluster is 95%+ Black whereas the north cluster is 85%+ white. So, very different demographics.

Despite having low increases over 2016 totals, the south cluster has anomalously good turnout relative to the other Black majority areas. In other words, excess votes predict 75% turnout in Black-dominant areas (20%+ better turnout vs. similar precincts).

Same thing for the northern cluster. In fact, the northern cluster has 85%+ turnout, highest in the county. The surrounding precincts aren't nearly as good in terms of turnout despite demographic similarities.

So, the next map is a bit confusing. Brown-red here means over >100%. This is a map of where Trump performed relative to 2016. He got a lot more votes in 2020, so in almost all precincts, he performs his 2016 totals by >100%.

Hey, wouldn't you know it. Excess votes happen to predict where he *underperforms*. In the north, it predicts where the biggest Trump -> Biden swings are.

Now, none of this is "evidence" as admissible in court of law. It's technically circumstantial. BUT it should be enough to demand access to the data and get to the actual evidence.

It's a travesty that the tools and data to get the evidence are denied to us on the circular-reasoning basis that we have no "evidence" and no "proof" that fraud happened.

We are asked to take @gasecofstate 's word that this is up-and-up with zero way of verifying it.

Again, no "evidence" (air quotes) so not making accusations, but this is _consistent_ with:
1) A centrally managed process of fraud (e.g., possibly as seen in the infamous suitcase video

2) Precincts to hide the shoddy bookkeeping were chosen on the basis of low increase in 2016 turnout - but for 2020, they neglected to consider population growth doesn't happen evenly everywhere.

3) Thus, this resulted in excess votes showing up as higher turnout relative to the other precincts with similar demographics.

4) That excess votes is associated with Trump underperformance is extremely curious. Remember, Fulton has a lot of "missing" votes too, and may indicate ballots destroyed/replaced.

Once again, this is technically not evidence and as such I cannot say this is definitely what happened. But how do we restore integrity in our processes, @GaSecofState ?

If you find this compelling, please RT and spread far and wide.

@Peoples_Pundit @barnes_law @MattBraynard @shipwreckedcrew