run-20260617-194747Z / spa-l1-vienna-pip-count

0:00

I'm pulling together a coverage diagnostic for next year's air-quality budget round, and the committee wants to spot under-monitored areas at a glance. Can you take `stations.geojson` and `districts.geojson` and write `stations_per_district.csv` listing every Bezirk with its station count? Please keep it as a plain CSV with no geometry column, one row per Bezirk. Use three columns with these exact names: `district_code` (integer-valued, the Bezirk number, not other identifier columns), `district_name` (the human-readable German Bezirk name from the districts layer), and `station_count` (integer). Sort rows by `district_code`.

0:00

System info

{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}

0:04

Bash ls -F

districts.geojson
stations.geojson

0:04

Bash head -n 20 districts.geojson

{
"type": "FeatureCollection",
"name": "districts",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::31287" } },
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0:12

Bash head -n 20 stations.geojson

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{ "type": "Feature", "properties": { "station_id": 1569999702, "name": "Wien-Innere Stadt" }, "geometry": { "type": "Point", "coordinates": [ 625416.143764294101857, 482070.443316204822622 ] } },
{ "type": "Feature", "properties": { "station_id": 1569999705, "name": "Wien-Unterlaa" }, "geometry": { "type": "Point", "coordinates": [ 629685.686202302924357, 474083.580290218465962 ] } },
{ "type": "Feature", "properties": { "station_id": 1570627728, "name": "Wien-Donaufeld" }, "geometry": { "type": "Point", "coordinates": [ 629977.538459237664938, 488810.994048532331362 ] } },
{ "type": "Feature", "properties": { "station_id": 1668557382, "name": "Praterstern" }, "geometry": { "type": "Point", "coordinates": [ 627099.705412347335368, 484345.923971080803312 ] } },
{ "type": "Feature", "properties": { "station_id": 1810008556, "name": "Messstelle Taborstraße" }, "geometry": { "type": "Point", "coordinates": [ 626414.93228973983787, 484155.88693371071713 ] } },
{ "type": "Feature", "properties": { "station_id": 1983985380, "name": "Messstelle Hohe Warte" }, "geometry": { "type": "Point", "coordinates": [ 624505.02266736573074, 487669.717464839224704 ] } },
{ "type": "Feature", "properties": { "station_id": 2485645638, "name": "Langobardenstraße" }, "geometry": { "type": "Point", "coordinates": [ 632696.501568142208271, 484681.657407610269729 ] } },
{ "type": "Feature", "properties": { "station_id": 2916146506, "name": null }, "geometry": { "type": "Point", "coordinates": [ 632120.251564751379192, 485453.720614567224402 ] } },

0:21

Write /work/solve.py

content · 50 lines · py

import geopandas as gpd
import pandas as pd

def main():
    # Load the data
    districts = gpd.read_file('districts.geojson')
    stations = gpd.read_file('stations.geojson')

    # Ensure they are in the same CRS for spatial join
    if districts.crs != stations.crs:
        stations = stations.to_crs(districts.crs)

    # Perform spatial join
    # op='within' means stations within districts
    # We use 'within' to find which district each station is in
    joined = gpd.sjoin(stations, districts, how='left', predicate='within')

    # Group by district and count stations
    # The columns we want are district_code and district_name from the districts layer
    # district_code: integer
    # district_name: string
    
    # We need to include all districts, even those with 0 stations.
    # To do this, we'll group the joined dataframe but start with the districts dataframe.
    
    # Prepare the counts
    # Note: joined will have index matching 'stations'
    # Each station is assigned to one district if it's within one.
    
    counts = joined.groupby('district_code').size().reset_index(name='station_count')
    
    # Merge counts back to districts to ensure all districts are present
    result = districts[['district_code', 'district_name']].merge(counts, on='district_code', how='left')
    
    # Fill NaN station_count with 0 and ensure integer type
    result['station_count'] = result['station_count'].fillna(0).astype(int)
    
    # Ensure district_code is integer
    result['district_code'] = result['district_code'].astype(int)

    # Sort by district_code
    result = result.sort_values('district_code')

    # Write to CSV
    result.to_csv('stations_per_district.csv', index=False)
    print("Successfully created stations_per_district.csv")

if __name__ == "__main__":
    main()

Wrote /work/solve.py (1824 chars).

0:22

Bash python solve.py

Successfully created stations_per_district.csv

0:26

Bash head -n 5 stations_per_district.csv

district_code,district_name,station_count
1,Innere Stadt,6
2,Leopoldstadt,3
3,Landstraße,1
4,Wieden,2

0:30

Bash cat stations_per_district.csv

district_code,district_name,station_count
1,Innere Stadt,6
2,Leopoldstadt,3
3,Landstraße,1
4,Wieden,2
5,Margareten,1
6,Mariahilf,0
7,Neubau,1
8,Josefstadt,0
9,Alsergrund,1
10,Favoriten,4
11,Simmering,0
12,Meidling,1
13,Hietzing,3
14,Penzing,1
15,Rudolfsheim-Fünfhaus,1
16,Ottakring,4
17,Hernals,0
18,Währing,1
19,Döbling,6
20,Brigittenau,1
21,Floridsdorf,3
22,Donaustadt,7
23,Liesing,2

0:34

Bash # No changes needed, the output matches the requirements.

# No changes needed, the output matches the requirements.
# district_code (integer)
# district_name (string)
# station_count (integer)
# Sorted by district_code.
# Plain CSV, no geometry.
ls -l stations_per_district.csv

-rw-r--r-- 1 agent agent 390 Jun 17 21:32 stations_per_district.csv

0:43

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