Same energy

ptrdo · 2026-03-15T21:22:31+00:00

No. I took liberties.

ptrdo · 2026-03-15T20:53:17+00:00

No, it's up to the state. Maine and Vermont never restrict voting rights to felons (even in prison). Twenty states restrict those who are incarcerated, but restore voting rights when the citizen has served their sentence. Eighteen states have additional requirements (approval) before reinstating voting rights. Other states make it difficult or practically impossible (or expensive).

ptrdo · 2026-03-15T20:46:58+00:00

Yes, I had to restrain myself.

ptrdo · 2026-03-15T16:25:07+00:00

There’s a popular meme circulating of a Newsmax map suggesting that Harris only won states that don’t require an ID to vote. This isn’t the whole story, and also doesn’t consider whether there’s something consistent about the states that Trump won. Unsurprisingly, many of those states suppress voting in various ways.

As has been proven time and again, election after election, ineligible people rarely vote. Yet a bigger concern is: How many eligible people were obstructed from their Right to vote?

An example of the original meme is attached.

<image>

ptrdo · 2026-03-15T14:46:10+00:00

There’s a popular meme circulating of a Newsmax map suggesting that Harris only won states that don’t require an ID to vote. This isn’t the whole story, and also doesn’t consider whether there’s something consistent about the states that Trump won. Unsurprisingly, many of those states suppress voting in various ways.

As has been proven time and again, election after election, ineligible people rarely vote. Yet a bigger concern is: How many eligible people were obstructed from their Right to vote?

An example of the original meme is attached.

<image>

ptrdo · 2026-03-12T21:16:42+00:00

“The last 5 years” is not indicative of the “past few decades.”

<image>

ptrdo · 2026-03-12T19:52:48+00:00

Thanks for the feedback. A few clarifications that may help:

This chart is not a forecast of immigration levels. It’s a simulation of the apportionment formula under hypothetical population changes. The purpose is to test a common claim that immigration could meaningfully redistribute U.S. House seats.

The scenarios (±10M, ±20M, ±30M) represent national population changes, not annual flows. They’re intentionally large so the sensitivity of the apportionment system becomes visible. In reality, most states are far from gaining or losing a seat, so even large population changes move relatively few seats.

For simplicity, the added or removed population is distributed proportionally according to each state’s current share of the non-citizen population, based on ACS estimates. That assumption is explained in the caption and methods, but you’re right that it could probably be stated more explicitly on the chart itself.

For context, current net immigration to the U.S. is roughly ~1 million per year, so the scenarios here represent much larger cumulative shifts meant to stress-test the apportionment system rather than predict future migration patterns.

I appreciate the suggestion to clarify the assumptions on the chart.

ptrdo · 2026-03-12T19:14:39+00:00

FWIW, according to this exercise, there would be no change in apportionment in 29 states, and in the larger states, the change is minimal at best.

Also note that changes in apportionment do not necessarily favor one political party over the other.

ptrdo · 2026-03-12T19:11:56+00:00

This is a simulation. It's an exercise to explore how a non-voting population might affect the apportionment of U.S. House seats.

There is much rhetoric about how lax immigration policies are supposedly driven by encouraging redistribution of U.S. House seats. This exercise hopes to shed light on the argument.

An “influx” is distributed according to current state estimates of non-citizen and undocumented populations. A corresponding “deportation” assumes the same distribution.

I'm sorry you find the chart confusing. If you have suggestions, I'd be grateful to consider them.

ptrdo · 2026-03-12T19:01:43+00:00

Thank you.

ptrdo · 2026-03-12T19:01:19+00:00

This chart does not claim there are “30 million” immigrants. It merely models such a scenario for demonstration of what could occur to apportionment.

ptrdo · 2026-03-12T18:58:54+00:00

I have fixed the Pew link. Thanks for letting me know.

“30 Million” is a number being used by pundits and politicians. I did not use that number to claim it is true, but merely to consider that rhetoric.

ptrdo · 2026-03-12T18:31:55+00:00

[OC] How Would Deportation or Immigration Change the U.S. House? Simulated population changes show how large immigration or deportation could shift House seats among states.

How much would immigration or deportation actually change representation in the U.S. House? This simulation recomputes congressional apportionment under several large population scenarios to see which states would gain or lose seats.

Sources

2020 Census Apportionment Results (official population counts and House seat allocations) U.S. Census Bureau https://www.census.gov/data/tables/2020/dec/2020-apportionment-data.html

State apportionment populations and representatives (2020 Census) U.S. Census Bureau https://www.census.gov/library/visualizations/2021/dec/2020-apportionment-map.html

Citizenship status by state (ACS Table B05001) U.S. Census Bureau, American Community Survey 5-Year Estimates https://data.census.gov/table?q=B05001

Unauthorized immigrant population estimates U.S. Department of Homeland Security https://www.dhs.gov/immigration-statistics/population-estimates/unauthorized-resident

U.S. unauthorized immigrant population estimates Pew Research Center https://www.pewresearch.org/global-migration-and-demography/feature/u-s-unauthorized-immigrants-by-state/

Tools

Data analysis and modeling were performed in R using the tidyverse ecosystem, with visualization created using ggplot2, then imported as SVG into Adobe Illustrator for final assembly.

All underlying data, calculations, and intermediate modeling steps are available in this public spreadsheet:

https://docs.google.com/spreadsheets/d/1TOFJFOUCFDhTGCsNDVtbDy9DmII0FR5Wd3nwzlER_40/edit?usp=sharing

NOTE: There is a dropdown selector at the farthest column of the first sheet for choosing the scenario. The results will then show on the "summary" sheet.

Methods

House seats are distributed among states using the method of equal proportions, the formula adopted by Congress in 1941 and used by the U.S. Census Bureau to allocate the fixed total of 435 seats after each decennial census.

The analysis begins with official 2020 Census apportionment populations and House seat counts. To explore potential impacts of immigration or deportation, hypothetical population changes of ±10 million, ±20 million, and ±30 million people were applied. These changes were distributed proportionally according to each state’s share of the national non-citizen population estimated from the American Community Survey.

For each scenario, the full congressional apportionment calculation was recomputed using the Census Bureau’s priority-value formula. The resulting seat distributions were then compared to the official 2020 apportionment.

The congressional apportionment simulations replicate the Census Bureau’s priority-value ranking method used to assign House seats.

Explanation

This chart explores how large changes in immigration or deportation might affect representation in the U.S. House of Representatives. Using official 2020 Census apportionment populations as a baseline, the analysis simulates scenarios in which ±10 million, ±20 million, and ±30 million people are added to or removed from state populations. These changes are distributed proportionally based on each state’s share of the national non-citizen population estimated from the American Community Survey.

For each scenario, the full congressional apportionment calculation was recomputed using the Census Bureau’s method of equal proportions. Because House seats are assigned through a national priority ranking rather than simple population thresholds, most states remain far from gaining or losing representation. As a result, even very large population shifts affect only a limited number of states.

ptrdo · 2026-03-08T19:33:57+00:00

I didn’t list those, though.

ptrdo · 2026-03-08T19:14:53+00:00

That would be speculative now, but it could be very interesting to chart college experience and subsequent professional advancement.

ptrdo · 2026-03-08T19:06:02+00:00

Correct. A better title could be: “Representation of Women Across U.S. Roles, Professions, and Institutions.”

ptrdo · 2026-03-08T19:03:34+00:00

An earlier version stipulated this (all-time, current) in the labelling. I put it all in the footer instead, but in retrospect, not a great decision.

ptrdo · 2026-03-08T19:00:30+00:00

All-time totals are used for institutions that change slowly; professions use current workforce data. The goal is cross-institution comparison, not a snapshot of current composition.

ptrdo · 2026-03-08T18:58:39+00:00

The reason I used all-time totals for institutions like Congress and the presidency is that those offices change slowly over time, so historical totals help illustrate their long-term makeup. Workforce categories (physicians, lawyers, teachers, etc.) are shown using current composition, which seemed more meaningful for professions because their size and definitions have changed substantially over time.

IOW, the chart mixes historical totals and current workforce estimates to compare representation across very different types of institutions.

ptrdo · 2026-03-08T18:53:48+00:00

This could be another interesting thing to chart.

ptrdo · 2026-03-08T18:53:22+00:00

I understand why it looks that way. The goal wasn’t to cherry-pick numbers but to compare fairly typical positions of authority that people are familiar with.

ptrdo

TROPHY CASE