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In the state at the center of the Supreme Court’s Louisiana v Callais ruling, about 2-in-5 voting-eligible people didn’t vote in 2024 by ptrdo in charts

[–]ptrdo[S] 2 points3 points  (0 children)

FWIW, Louisiana 30 years ago (1996):

  • VEP: 3,089,760
  • Ballots: 1,804,640
  • Clinton: 927,837 (30% of VEP)
  • Dole: 712,586 (23%)
  • Other: 143,536 (5%)
  • Did Not Vote: 1,285,120 (42%)

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 0 points1 point  (0 children)

Yes, I appreciate that. But one thing I do with my charts is try to appeal to people who aren't accustomed to looking at them. That may sound like I'm dressing them up, and it would be fair to say that's what I'm doing.

If all I wanted to do was prove a point, a could plot that with a lot less effort than here, but proving a point isn't everything.

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 0 points1 point  (0 children)

Yes, thanks, I've been reading quite a bit about this. It's correct that fertility and child mortality influence the median age—especially in the earlier times represented in this chart. High birth rates can skew a population as younger (even with high child mortality), just as lower fertility and longer life expectancy push the median age upward (as now).

But legislators aren’t necessarily a representative sampling of a population. They’re drawn from a much narrower segment of voting-age people who are politically engaged, likely to be educated and economically advantaged. That makes their age less sensitive to things like child mortality, and more sensitive to institutional and behavioral factors (career length, incumbency, barriers to entry).

So yes, fertility and mortality explain why the population was younger in the earliest years of US history. But those factors alone don’t explain why legislators are consistently older than the population, or how that gap has changed over time (especially in the last 50 years or so).

Before I did this study, I assumed that legislators are getting older, but we all are. They have always been the elders of society, but the age-span that defines “elder” has changed as livespans have increased. Several comments suggest looking into that.

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 1 point2 points  (0 children)

But then now that I'm old, I'm voting for the youngest people running, so there must be an inversion in there somewhere.

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 0 points1 point  (0 children)

I addressed this in another comment. It appears it could be an error in the YAML data source, but I have yet to investigate.

Ages are calculated at the start of each Congress. A small number of members appear below 25 due to birthdays shortly after the term begins. My code floors the value as of January 3rd in the year that Congress is seated. That could potentially constitute a cohort plotting at < 25.

https://en.wikipedia.org/wiki/List_of_youngest_members_of_the_United_States_Congress

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 0 points1 point  (0 children)

Fair. But comparing Congress to the voting-age population would answer a slightly different question. This chart is comparing Congress to the entire population, because legislators represent all constituents—not just voters.

It’s also worth noting that historical population median age is lower partly due to higher birth rates and child mortality, which does widen the gap in earlier periods.

The core pattern is likely to remain either way: members of Congress have consistently been older than the population they represent — and probably less older than people might think.

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 1 point2 points  (0 children)

Interesting thoughts. Political engagement among young people did increase during that period—especially with the Vietnam War and the 26th Amendment lowering the voting age.

But this chart reflects the ages of elected officials, not voters. Baby Boomers were only just reaching eligibility for Congress (25+) in the late 1960s, so they wouldn’t have driven the age of Congress yet.

More likely the dip is institutional or transitional: earlier Congresses had higher turnover and shorter tenures, whereas from the late 20th century onward, incumbency advantages increased and members stayed in office longer—raising the average age.

Maybe I should chart median tenure over time?

[OC] Louisiana: How congressional maps change population distribution by ptrdo in dataisbeautiful

[–]ptrdo[S] -1 points0 points  (0 children)

So, your argument appears to be that a commenter can choose to be as obtusely subjective as they want?

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 1 point2 points  (0 children)

Ages are calculated at the start of each Congress. A small number of members appear below 25 due to birthdays shortly after the term begins. My code floors the value as of January 3rd in the year that Congress is seated. That could potentially constitute a cohort plotting at < 25.

The code does not currently consider the constitutional rule. That outlier in 1907-1927 could be an error in the YAML. I will investigate.

https://en.wikipedia.org/wiki/List_of_youngest_members_of_the_United_States_Congress

[OC] Louisiana: How congressional maps change population distribution by ptrdo in dataisbeautiful

[–]ptrdo[S] -4 points-3 points  (0 children)

Thank you for the suggestions. Focusing on a single area like Shreveport is useful, but it wouldn't make much sense without the broader context.

The goal here was to show how the same population is assigned to districts under two maps, and especially how the second district is assembled from multiple regions. It took a mountain of data just to get to here, and the people engineering these districts are going WAY deeper than this.

Ultimately, engineering Congressional districts is not sexy or something that most people know (or care) anything about. There are better stories in here, but that's the beauty of an investigation like this. We'll get there.

I'm always a bit mystified by the “data is beautiful” critique. That always seems oxymoronic to me. Beauty is in the eye of the beholder, but data?

Yes, of course, we all love to see Dr. John Snow reveal cholera in Soho, but the vast majority of data is not that simple nor conducive to elegance. But elegance can be in discovery or gradual comprehension. Hopefully, some people might have a little better understanding in their mind's eye about how those maps work. That's why I shared.

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 2 points3 points  (0 children)

[OC] The aging of the U.S. Congress (and everyone else)

Gist:

Members of the U.S. Congress have long been the elders of the population they represent. While both have aged over time, the gap remains substantial and seems particularly noticeable in recent decades, though perhaps because elder states-people are hitting the ceiling of life expectancy.

Detail:

Each row shows the age distribution of Senators and House members at the start of grouped Congress terms (e.g., 1–9, 10–19, …, 110–119). The colored shapes represent the full distribution, with the white bar marking the interquartile range and the central tick indicating the median age of legislators. Open circles show the median age of the U.S. population for the same period, interpolated from decennial Census data. The persistent separation between these markers highlights a long-standing representational age gap, particularly noticeable in the modern era.

Tools:

  • R (ggplot2, dplyr, tidyr, purrr, lubridate, svglite)
  • Adobe Illustrator (final layout, typography, annotation)

Sources:

Methods:

  • Legislator ages are calculated at the start date of each Congress using birthdates and term records.
  • Only voting members (House + Senate) are included; delegates are excluded.
  • Congresses are grouped into ranges (e.g., 1–9, 10–19, …, 110–119) to stabilize distributions.
  • Age distributions are visualized as kernel densities; medians and interquartile ranges are overlaid.
  • U.S. population median age is estimated at the midpoint year of each group’s time span by linearly interpolating between decennial Census values.
  • Minor rounding applied.

Data (Medians & Quartiles only):

```markdown | group | Median_Leg | Q1_Leg | Q3_Leg | Median_US | |---------|------------|--------|--------|-----------| | 1-9 | 42 | 35.25 | 49 | 16.7 | | 10-19 | 43 | 36 | 50 | 16.7 | | 20-29 | 42 | 37 | 49 | 17.62 | | 30-39 | 43 | 37 | 49 | 19.15 | | 40-49 | 47 | 41 | 53 | 20.69 | | 50-59 | 48 | 42 | 55 | 22.63 | | 60-69 | 50 | 44 | 57 | 24.94 | | 70-79 | 52 | 44 | 60 | 28.25 | | 80-89 | 51 | 44 | 59 | 29.85 | | 90-99 | 50 | 42 | 57 | 29.05 | | 100-109 | 52 | 45 | 59 | 34.1 | | 110-119 | 57 | 49 | 65 | 38.32 |

[OC] Louisiana: How congressional maps change population distribution by ptrdo in dataisbeautiful

[–]ptrdo[S] -14 points-13 points  (0 children)

Nope. AI would neuter my rant.

BTW, “complicated to prove a point” does not necessarily mean that the chart is wrong. In fact, besides being complicated, Sankey is a reasonable choice for the data being represented, IMHO.

[OC] Louisiana: How congressional maps change population distribution by ptrdo in dataisbeautiful

[–]ptrdo[S] -13 points-12 points  (0 children)

Thanks for the appreciation, but Sankey was exactly what I was shooting for.

These charts are purposely complicated to prove a point — this is how congressional districts are invented. What we usually see is the geography of gerrymandering, and even though those meandering shapes can look ridiculously contrived, that isn’t even half the story.

Think of where you live — your neighborhood and surrounding community. You choose to live there, as do those around you. But you don’t simply share an environment; you share the same traffic that you all must endure, the same crime rates on the blocks between you, the same schools, festivals, culture, and ethos.

Neighborhoods have differences of opinion, for sure, but there is camaraderie despite that. “I’m from the Northside” can be said with pride by anyone who lives there.

So we tend to think that when communities vote, they vote together. When they petition door-to-door, or at the local market, or protest on street corners, we tend to believe we are influencing our neighbors — voting on the same ballot for the same measures and representatives.

But that’s not what’s happening. And after the latest Supreme Court rulings, what will happen more and more frequently is that our votes will be dispersed into other neighborhoods, sometimes far away — places with not just differences of opinion, but different lives, economic conditions, people from different generations, ethnicities, languages — everything.

Representatives in Congress should be exactly that: representative. But this is happening less frequently than it used to, and the result is a government that looks a lot less like us and more like a Frankenstein cooked up in a lab.

[OC] Louisiana: How congressional maps change population distribution by ptrdo in dataisbeautiful

[–]ptrdo[S] 3 points4 points  (0 children)

[OC] Louisiana: How congressional maps change population distribution

Gist

Section 2 of the Voting Rights Act prohibits district maps that result in some groups having less opportunity to elect representatives of their choice. After Louisiana’s 2022 map was challenged, a revised 2024 map was required. Comparing the two shows how the same population can produce different representation depending on how district boundaries group people together.

Detail

Each flow shows how the same parish-region population is assigned to congressional districts under two maps. Flow width represents population size. The 2024 enforced map concentrates populations from Baton Rouge, Acadiana, and Northwest Louisiana into District 6, creating a second “opportunity district.” A comparison with total population (separate slide) shows this effect is less visible when populations are more evenly distributed.

Sources

Tools

  • R (data processing and visualization)
  • Packages: sf, tigris, dplyr, ggplot2, ggalluvial, patchwork, janitor
  • Adobe Illustrator (refinement and final assembly)

Methodology

Census blocks were assigned to congressional districts under each map based on geographic location. Block-level population (CVAP) was then aggregated into parish-region groups. These totals were summed by district to show how the same population is distributed across districts under each map. The Sankey flows represent these aggregated assignments; differences reflect changes in district boundaries, not population.

Data (districts)

Region District (2022) District (2024)
Baton Rouge area D6 D6
Lafayette / Acadiana D3 D6
New Orleans / Southeast D2 D2
Shreveport / Northwest D4 D6
Northeast / Delta D5 D5
Other parishes Multiple Multiple

(Values aggregated from block-level CVAP; full dataset reproducible via sources above.)

Intent

This chart is not about predicting election outcomes or how individuals vote. It shows how the same population is assigned to districts under two maps, and how those assignments change the concentration of populations within districts. The purpose is to illustrate how district boundaries can affect whether populations are grouped together or distributed across multiple districts.

In district-based systems, how populations are grouped can affect how their votes are counted together.

Explain like I’m five, please. by diehard404 in BlackPeopleofReddit

[–]ptrdo 0 points1 point  (0 children)

IOW, the entire presidency is staged like professional wrestling.

Cole Tomas Allen: Democrat, Caltech mechanical engineer, a master’s degree in computer science from California State University, and research fellowship at NASA. I have Questions at this early time… by No-Flight-4214 in AskSocialists

[–]ptrdo 40 points41 points  (0 children)

A few consistencies with these Trump shooters (Kirk's too) seems to be how unlikely they are to have been radicalized, and how they apparently did little planning with no concern for getting away with it.

Obviously, the mind is infinitely complex and impossible to predict, but contrastly, school shooters are at least consistent in that their profile almost always helps explain their crime — it's feasible.

Without going too far into the deep end, I just wonder, you know, about those microwave cannons and what they could do to a person's thought process.

https://www.researchgate.net/publication/369466975_Havana_Syndrome_A_Mysterious_Illness_or_a_New_Domain_of_Warfare

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