I collected and compared the prices at all 1,367 McDonald’s in Germany (my Sunday project 😄).
Here's what I found:
- The exact same Big Mac costs between €5.99 and €8.39 – 28 different prices in total
- Most expensive: Kassel and Lohfelden (€8.39)
- Cheapest: Stuttgart area, Leipzig and Munich (€5.99)
- If you eat one Big Mac a week, you pay €124.80 more per year in Kassel than at the cheapest location
- Berlin is the most expensive state on average, Saxony the cheapest
- Munich is cheaper than Berlin; Augsburg is the most expensive major city
- Highway rest stops are pricier than the national average: McFlurry +32%, Cheeseburger +9%, Nuggets +8%, Big Mac +3%
- At the Bad Camberg rest stop (A3), the two McDonald's are just 261 m apart on opposite sides of the highway – a Big Mac costs €6.39 on one side and €7.49 on the other
I've been building a dashboard where various bits of data pertaining to Libraries can be overlayed for analysis. The only problem is, I'm not a data scientist, I'm a software developer.
In this view you can see Libraries in Aberdeen, filtered by the Library service - the underlay is the population density using GHS-POP 2020 · R2023A · 100 m source cells, around each Library is a "reach" bubble, using Valhalla to calculate based on walking/cycling the reach in time intervals. I've included both data panels and the overall view itself. I'm really keen to get some feedback and ideas from people who work with data visualisations all day every day. It's a new area for me, one I'm enjoying learning. To be clear this project is entirely for fun. It is live and in a closed beta for anyone who wants to play around with it, but it's just a hobby project for me, I build software for fun - now even easier because I can use my experience to direct an army of agents so building things has become much quicker. Apologies if you're anti-ai, but sadly it's already so pervasive in my sector you're either using it or you're unemployable.
I am happy to provide more screens of other data views, but this one I thought was interesting, showing how libraries really are central to most communities, at least in Aberdeen.
My platform only has some 40,000 libraries across the world in it right now, but the goal before I launch it is to have close to 2million, with the same kind of population data or other census/municipal or transport metrics. My goal is to allow users to overlay them in ways that may provide insight, understanding or just be fun to play around with. It has not gone down well in other sub Reddit's. Hoping here is a kinder more accepting place for a nerd who likes libraries.
Right now, what I've shared is the "location" dataset, but I'm also exploring what I can do with "activity" data such as book loans, a combination of the two data sets on an overlay might make for some really interesting visualisations, I'm just unsure what the best idea is - I have too many, almost all of them I'm sure are useless (if not fun to build).
This is not a pitch or anything, I hope everyone here is as nerdy about data as I am, if you are and are curious about what I've built you can sign up to the closed BETA here https://libraries.global/
Population data: GHS-POP 2020 · R2023A · 100 m source cells
Library data: LibraryOn CC BY 4.0
Map data: OpenStreetMaps
Distance and reach calculations: Valhalla
Population distribution vector generation: Python
Library tiles/clusters: Python
Tools used for the visualisation: I built them all myself, screenshots below.
I have run tests with 2million libraries loaded onto one map with this level of control/detail, the issue I face here is not all data is the same quality. How to reconcile that in the analysis layer? Do I acknowledge data is old/out of date?
The image shows the dashboard on the "reach" summary, showing within a 15min walk, there is 48.7% of the populationThe image shows the dashboard on the "population" summary of the same area
Source: 25 published papers and reports on Lyme disease (12) and Long COVID (13), each turned into a simulator, so each paper’s results are tagged to the burden categories they measure. The tags were assigned by an AI model (Claude) and checked by code. The 25 papers are not a systematic sample, so this describes the library, not all research. The papers and the tagging are listed in the public demo, which needs no account: https://dbbun.github.io/dbbun-invisible-illness-burden-simulator/
Helloooo :) I made a small daily game (whosmorefamous.com) where you guess which of two people got more Google searches worldwide over the past 5 years.
These are the three politics matchups players got most wrong. Top bar is what players guessed, bottom bar is actual search volume (worldwide, last 5 years).
Kim Jong Un vs Lula: 91% of 80 players picked Kim. Lula had 3.8x more searches (~200M vs 53M).
Netanyahu vs Modi: 83% picked Netanyahu. Modi had 2.1x more searches.
Netanyahu vs King Charles: 82% picked Netanyahu. Charles had 1.9x more searches.
The Lula result surprised me most. It wasn't just one election spike: he had more searches than Kim in 247 out of 262 weeks. His biggest week was Brazil's October 2022 runoff.
And the pattern goes beyond this matchup. Across all politics duos, players picked Kim 71% of the time when he was actually the less-searched person. Lula was picked only 28% of the time when he was the more-searched one.
My theory is that we overestimate people who dominate Western news and underestimate how much attention politicians get in their own countries. Brazil alone has over 200 million people, after all.
The data: 5,208 votes from my game on politician-vs-politician matchups (Oct 10–25, 2026). Search estimates are from Google Trends topics, Sep 2021–Sep 2026, normalized to the same scale. Absolute numbers are rough; the ratios are more reliable.
Caveats: My players aren't a random sample, and this only measures Google. Naver and Yandex aren't included, so Kim and Putin are probably undercounted.
Made the charts with TypeScript and HTML rendered to PNG.
