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Just maps

Just maps

February 21, 2026

TL;DR

Intro

I just got to know these:

On top of:

PostGIS: Free Open Source Spatial Database

Openrouteservice: Free Open Source Geo-services API

Which sounds like some how to reitti that we covered here

Reitti | Docker Config 🐋 ↗ wanderer | Docker Config 🐋 ↗

Reitti UI

Wanderer supports creating routes and GPX

Geo and R

Some time ago I was impressed but R language and its amazing maps analytics toolset.

When working around consumer intelligence and FMCG, I was mapping population to consumptions patterns.

Quite interesting stuff.

Luckily, the population part is open information for the public.

So here we go again and building around it:

#sudo apt install gh
gh auth login
#gh repo create eda-geospatial --private --source=. --remote=origin --push
    
git init && git add . && git commit -m "Initial commit: simple eda-geospatial" && gh repo create eda-geospatial --private --source=. --remote=origin --push
find . -type f -size +100M -exec ls -lh {} +

head -n 100 ValeursFoncieres-2024.txt > french2024subset.txt
uv run python -c "import pandas as pd; 
> df = pd.read_csv('french2024subset.txt', sep='|', low_memory=False); 
> res = df[(df['Nature mutation'] == 'Vente') & (df['Type local'].isin(['Maison', 'Appartement']))].copy(); 
> res['Value'] = res['Valeur fonciere'].astype(str).str.replace(',', '.').astype(float); 
> res['Surface'] = res['Surface reelle bati'].astype(str).str.replace(',', '.').astype(float); 
> res = res[res['Surface'] > 0]; 
> res.loc[:, 'price_m2'] = res['Value'] / res['Surface']; 
> print(f'Count of Residential Sales: {len(res)}'); 
> if len(res) > 0:
>     print(f'Average Price per m2: {res[\"price_m2\"].mean():.2f} €/m2'); 
>     print(f'Average Transaction Price: {res[\"Value\"].mean():.2f} €'); 
> else:
>     print('No valid residential sales found in this subset.');"
Count of Residential Sales: 9
Average Price per m2: 2551.91 €/m2
Average Transaction Price: 172833.33 €
find . -name "*.shx" -o -name "*.dbf" -o -name "*.prj"

alt text

Powered with ine data (derived) which you can get here under CC4.0

wget https://raw.githubusercontent.com/gregoiredavid/france-geojson/master/communes-version-simplifiee.geojson -O france_communes.geojson
uv run calculate_french_averages.py
Calculating national averages and analyzing outliers (2024 DVF)...
Processing in chunks - please wait...

==================================================
FRANCE REAL ESTATE ROBUST STATS (2024 DVF)
==================================================
Total Sales Processed: 986,176

--- Means (Sensitive to Outliers) ---
Avg Price per m²:          16,850.34 €/m²
Avg Transaction Price:     898,225.51 €

--- Medians (Robust) ---
Median Price per m²:       2,664.19 €/m²
Median Transaction Price:  195,600.00 €

--- Percentiles ---
5th Percentile (Price):    47,500.00 €
25th Percentile (Price):    120,000.00 €
75th Percentile (Price):    325,000.00 €
95th Percentile (Price):    1,209,850.00 €
99th Percentile (Price):    15,500,000.00 €
==================================================

Generating outlier scatter plot...
Scatter plot saved as french_price_scatter.png

french communes 2024 price per squared meter

Thanks again to https://www.data.gouv.fr/datasets/demandes-de-valeurs-foncieres where you can take 5 years back of TRANSACTIONS!

Conclusions

Few years ago with R I enjoyed their geo capabilities.

Maps and data are cool, lets agree on that.

And in the upcoming days, I will be sharing few posts about geo + time = motion data :)

Video | Docs ↗ Racing | Docs ↗

If you were expecting a post about the geometric series of improvements of LLMs, this was not your post.

Going all in Business via AI Assisted SDLC