spa-l1-vienna-pip-count

Status: done
Score: 100
Duration: 0:52
Cost: 0.16¢
Model: google/gemma-4-26b-a4b-it

Map (reference ← swipe → agent)

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`.
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System info 
{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
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Bash ls -F 
districts.geojson
stations.geojson
0:03
Write /work/inspect_data.py
content · 12 lines · py 
import geopandas as gpd

# Load the datasets
stations = gpd.read_file('stations.geojson')
districts = gpd.read_file('districts.geojson')

# Display information to understand the structure
print("Stations columns:", stations.columns.tolist())
print("Districts columns:", districts.columns.tolist())
print("\nStations head:\n", stations.head())
print("\nDistricts head:\n", districts.head())

Wrote /work/inspect_data.py (390 chars).
0:04
Bash python /work/inspect_data.py 
Stations columns: ['station_id', 'name', 'geometry']
Districts columns: ['district_code', 'district_name', 'osm_relation_id', 'geometry']

Stations head:
    station_id                 name                       geometry
0   252548493         Neubaugürtel  POINT (623360.378 482029.348)
1   441581664           Operngasse  POINT (625453.509 482337.109)
2  1328552671    Argentinierstraße  POINT (625963.743 481655.536)
3  1569815300    Wien/Stammersdorf  POINT (627853.106 494117.279)
4  1569941334  Wien-Jubiläumswarte  POINT (617815.807 484317.253)

Districts head:
    district_code  ...                                           geometry
0              1  ...  POLYGON ((626064.247 482275.213, 626169.413 48...
1              2  ...  POLYGON ((627606.815 486384.807, 627747.697 48...
2              3  ...  POLYGON ((630274.402 480927.246, 630260.17 480...
3              4  ...  POLYGON ((625107.183 481597.703, 625103.736 48...
4              5  ...  POLYGON ((624887.555 481892.415, 624901.681 48...

[5 rows x 4 columns]
0:37
Write /work/solve.py
content · 45 lines · py 
import geopandas as gpd
import pandas as pd

# Load the datasets
stations = gpd.read_file('stations.geojson')
districts = gpd.read_file('districts.geojson')

# Ensure both use the same CRS
if stations.crs != districts.crs:
    stations = stations.to_crs(districts.crs)

# Spatial join to find which district each station belongs to
# op='within' because stations are points
joined = gpd.sjoin(stations, districts, how='left', predicate='within')

# Count stations per district
# We group by the columns we need from the districts layer
# Since we want all districts (even those with 0 stations), 
# it's better to start with the districts dataframe.

# Create a count dataframe from the join
counts = joined.groupby('district_code').size().reset_index(name='station_count')

# Merge back to districts to ensure we have all districts, even those with 0 stations
# We also need the district_name
result = districts[['district_code', 'district_name']].merge(counts, on='district_code', how='left')

# Fill NaN station counts with 0
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')

# Select only requested columns
result = result[['district_code', 'district_name', 'station_count']]

# Save to CSV
result.to_csv('stations_per_district.csv', index=False)

print("Successfully created stations_per_district.csv")
print(result)

Wrote /work/solve.py (1512 chars).
0:38
Bash python /work/solve.py 
Successfully created stations_per_district.csv
    district_code         district_name  station_count
0               1          Innere Stadt              6
1               2          Leopoldstadt              3
2               3            Landstraße              1
3               4                Wieden              2
4               5            Margareten              1
5               6             Mariahilf              0
6               7                Neubau              1
7               8            Josefstadt              0
8               9            Alsergrund              1
9              10             Favoriten              4
10             11             Simmering              0
11             12              Meidling              1
12             13              Hietzing              3
13             14               Penzing              1
14             15  Rudolfsheim-Fünfhaus              1
15             16             Ottakring              4
16             17               Hernals              0
17             18               Währing              1
18             19               Döbling              6
19             20           Brigittenau              1
20             21           Floridsdorf              3
21             22            Donaustadt              7
22             23               Liesing              2
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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
0:51