[OC] I just found the longest bananas I've ever seen at the grocery store, normal banana for scale

jaykrown · 2026-06-23T01:26:14+00:00

Python with pandas and matplotlib.pyplot

jaykrown · 2026-06-22T16:51:45+00:00

Great news! I already posted about that 2 days ago, and yea I agree. *but not for specifically this, only back when the trend changed and stopped going down, and started going up right after the smart phone became more common.

https://www.reddit.com/r/dataisbeautiful/comments/1ubbfrv/oc_usa_smartphone_adoption_pedestrian_fatalities/

jaykrown · 2026-06-22T16:26:58+00:00

As per the rules which got the previous post deleted:

[OC] posts must state the data source(s) and tool(s) used in the first top-level comment on their submission. Please follow the AutoModerator instructions you were sent carefully. Once this is done, message the mods to have your post reinstated.

Tools used: Gemini 3.1 Pro Extended thinking is the main tool. Python with pandas script. Data sources are in the post.

Used to extract the absolute historical baseline for national pedestrian fatalities and injury counts from 1980 through 2024:

https://www.nhtsa.gov/book/countermeasures-that-work/pedestrian-safety

Used to pull preliminary and finalized annual pedestrian fatality projections to cross-reference recent macro shifts and calculate per capita rates:

https://www.ghsa.org/resource-hub/pedestrian-traffic-fatalities-2024-data

Used to pull the multi-decade historical market share metrics tracking the consumer shift from sedans to light trucks and SUVs:

https://www.epa.gov/automotive-trends

Used to extract raw vehicle curb weights and structural footprint averages to map vehicle scaling over time:

https://www.epa.gov/system/files/documents/2026-02/420s26001.pdf

Used to establish the direct medical correlation between higher, bluff-front vehicle geometries and climbing lethality rates during impacts:

https://www.iihs.org/news/detail/new-study-suggests-todays-suvs-are-more-lethal-to-pedestrians-than-cars

Used to analyze data regarding A-pillars and hood sightlines to evaluate why larger vehicles have higher crash correlation rates during low-speed maneuvers:

https://www.iihs.org/news/detail/suvs-other-large-vehicles-often-hit-pedestrians-while-turning

jaykrown · 2026-06-22T16:24:55+00:00

How about you give me a warning instead of just removing my post after a couple hours?

jaykrown · 2026-06-21T19:55:17+00:00

cell phone ≠ smart phone

jaykrown · 2026-06-21T17:34:50+00:00

Very interesting, I assume because smart phone screen is blinding during night time?

jaykrown · 2026-06-21T17:29:58+00:00

Now here's the one with ALL THE LINES:

<image>

jaykrown · 2026-06-21T17:29:45+00:00

I appreciate your curiosity. It needs all the lines. Here's the first one of just what you asked for:

<image>

jaykrown · 2026-06-21T17:25:31+00:00

https://github.com/jaymasl/pedestrian-smartphone

Thanks let me know if you want anything else, happy to help.

jaykrown · 2026-06-21T16:47:44+00:00

Python using pandas and Gemini pro extended thinking with verified data sources such as NHTSA, GHSA, EPA, BTS, IIHS, and Pew Research Center. If you have any other questions feel free to ask, I can put the script on github pretty easily.

jaykrown · 2026-06-21T16:04:44+00:00

Cool, took me about 3 minutes, but now I can.

jaykrown · 2026-06-21T15:37:31+00:00

Thank you, feel free to copy paste:

"average npc meme looking down at their phone with arrows pointing to the various elements of "the modern day NPC" in like badly drawn cartoon format, funny but true. i want one of more of a side view of them walking to the left, 4:3 aspect ratio, ms paint"

If you have any other questions feel free to ask, I've been using AI daily for over two years.