spa-l1-vienna-pip-count
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`.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:05
Let me start by reading the input files to understand their structure.
0:05
Bash
ls /work/*.geojson
/work/districts.geojson /work/stations.geojson
0:10
Let me inspect both files to understand their structure.
0:10
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import json
# Inspect districts
d = gpd.read_file('districts.geojson')
print('=== DISTRICTS ===')
print('Columns:', list(d.columns))
print('Shape:', d.shape)
print(d.head(3).to_string())
print()
print(d.dtypes)
"
=== DISTRICTS === Columns: ['district_code', 'district_name', 'osm_relation_id', 'geometry'] Shape: (23, 4) district_code district_name osm_relation_id geometry 0 1 Innere Stadt 1990592 POLYGON ((626064.247 482275.213, 626169.413 482320.024, 626308.187 482540.416, 626329.255 482579.935, 626339.075 482592.757, 626339.625 482592.578, 626344.529 482599.334, 626354.366 482612.87, 626354.724 482612.65, 626411.713 482690.656, 626440.292 482729.957, 626445.208 482736.769, 626455.241 482751.725, 626463.387 482765.317, 626471.907 482780.77, 626477.229 482791.269, 626482.57 482802.615, 626534.045 482919.277, 626536.391 482924.308, 626538.642 482929.692, 626552.437 482960.961, 626556.056 482969.558, 626579.861 483024.147, 626582.106 483029.286, 626583.259 483031.924, 626586.216 483038.837, 626588.397 483043.884, 626589.856 483047.268, 626591.683 483051.489, 626649.042 483181.834, 626666.735 483221.955, 626667.378 483223.404, 626671.024 483231.669, 626674.502 483239.493, 626675.329 483241.361, 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482470.768, 628449.04 482417.697, 628488.361 482362.803, 628543.521 482265.326, 628642.712 482071.69, 628665.91 482032.446, 628689.101 481999.566, 628728.453 481953.608, 628807.276 481872.801, 628927.679 481767.842, 629519.817 481287.875, 629592.186 481234.467, 629821 481106.089, 630204.088 480954.415, 630274.402 480927.246)) district_code int32 district_name str osm_relation_id int32 geometry geometry dtype: object
0:25
Now let's look at the stations file.
0:25
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
s = gpd.read_file('stations.geojson')
print('=== STATIONS ===')
print('Columns:', list(s.columns))
print('Shape:', s.shape)
print(s.head(5).to_string())
print()
print(s.dtypes)
"
=== STATIONS === Columns: ['station_id', 'name', 'geometry'] Shape: (49, 3) 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) station_id int64 name str geometry geometry dtype: object
0:32
Now I have the picture. Let me write a Python script that: 1. Reads both GeoJSON files 2. Performs a spatial join to count stations within each district 3. Outputs a CSV with columns: district_code, district_name, station_count 4. Sorted by district_code Let me also check if districts and stations have the same CRS.
0:32
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
d = gpd.read_file('districts.geojson')
s = gpd.read_file('stations.geojson')
print('Districts CRS:', d.crs)
print('Stations CRS:', s.crs)
"
Districts CRS: EPSG:31287 Stations CRS: EPSG:31287
0:50
Both files are in EPSG:31287 (Austria Lambert). I can use a spatial join to assign each station to a district, then count per district. Let me write the script.
0:50
0:50
Write
/work/solve.py
content · 27 lines · py
import geopandas as gpd
# Read inputs
districts = gpd.read_file("/work/districts.geojson")
stations = gpd.read_file("/work/stations.geojson")
# Spatial join — each station gets the district it falls in
# Both are in EPSG:31287 (same CRS), so direct spatial predicate works
joined = gpd.sjoin(stations, districts, how="left", predicate="within")
# Some stations might fall outside all districts; count only those matched
counts = (
joined.groupby(["district_code", "district_name"])
.size()
.reset_index(name="station_count")
)
# Ensure district_code is int32
counts["district_code"] = counts["district_code"].astype(int)
# Sort by district_code
counts = counts.sort_values("district_code").reset_index(drop=True)
# Write plain CSV (no geometry)
counts.to_csv("/work/stations_per_district.csv", index=False)
print(counts.to_string())Wrote /work/solve.py (851 chars).
1:02
Let me run it and check the output.
1:02
Bash
cd /work && python solve.py
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 7 Neubau 1 6 9 Alsergrund 1 7 10 Favoriten 4 8 12 Meidling 1 9 13 Hietzing 3 10 14 Penzing 1 11 15 Rudolfsheim-Fünfhaus 1 12 16 Ottakring 4 13 18 Währing 1 14 19 Döbling 6 15 20 Brigittenau 1 16 21 Floridsdorf 3 17 22 Donaustadt 7 18 23 Liesing 2
1:17
The output looks clean. Let me double-check a few things: 1. The columns are named correctly: `district_code`, `district_name`, `station_count` ✓ 2. `district_code` is integer ✓ 3. Sorted by `district_code` ✓ 4. Plain CSV with no geometry ✓ 5. Only 19 districts have stations — some districts (6, 8, 11, 17) have 0 stations. The instruction says "one row per Bezirk", so let me reconsider. The spatial join with `how="left"` on the stations side won't give districts with 0 stations. Let me change the logic to ensure all 23 districts appear, even with 0 station counts. Wait, the instruction says "listing every Bezirk with its station count" — so yes, I should include districts with 0 stations. Let me fix this.
1:17
1:17
Edit
/work/solve.py
Edited /work/solve.py.
1:30
Wait, the districts might have 23 rows but some district_code values might be missing from the groupby if no stations land in them. Let me re-check. The groupby is on the joined dataframe which uses `how="left"` from stations to districts — so districts with zero stations won't appear in the groupby result. Then I merge the full districts list back. That should work. But hold on — the groupby columns are `district_code` and `district_name` from the joined result. If a district has no stations, it won't be in that groupby. Then I left-merge the full districts list onto the counts. That gives me all districts. Good. But wait — I used `districts[["district_code", "district_name"]]` as the left table in the merge, so I get all districts. And then I fill NaN with 0. That's correct. Let me just double-check the column order: `district_code` (int), `district_name` (str), `station_count` (int). Sorted by `district_code`.
1:30
Bash
cd /work && python solve.py
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
1:43
All 23 districts are present. Let me verify the output CSV to make sure it matches requirements. Requirements: - Plain CSV no geometry column ✓ - One row per Bezirk ✓ - Columns: `district_code` (integer), `district_name`, `station_count` (integer) ✓ - `district_code` is the Bezirk number (integer-valued) ✓ - `district_name` is human-readable German Bezirk name ✓ - Sorted by `district_code` ✓
1:43
1:43
Read
/work/stations_per_district.csv
1 district_code,district_name,station_count
2 1,Innere Stadt,6
3 2,Leopoldstadt,3
4 3,Landstraße,1
5 4,Wieden,2
6 5,Margareten,1
7 6,Mariahilf,0
8 7,Neubau,1
9 8,Josefstadt,0
10 9,Alsergrund,1
11 10,Favoriten,4
12 11,Simmering,0
13 12,Meidling,1
14 13,Hietzing,3
15 14,Penzing,1
16 15,Rudolfsheim-Fünfhaus,1
17 16,Ottakring,4
18 17,Hernals,0
19 18,Währing,1
20 19,Döbling,6
21 20,Brigittenau,1
22 21,Floridsdorf,3
23 22,Donaustadt,7
24 23,Liesing,2
1:43
Bash
ls -la /work/stations_per_district.csv
-rw-r--r-- 1 agent agent 390 Jun 16 20:15 /work/stations_per_district.csv
2:01
The CSV looks correct. Let me verify the column names exactly match: `district_code`, `district_name`, `station_count`. Yes. The district_code values are integers. The rows are sorted by district_code. Everything checks out.
2:01