fio-l1-vienna-shapefile-recovery
Map (reference ← swipe → agent)
0:00
I'm migrating an old parcel shapefile (`parcels.shp`) into our web viewer, and a reference file `column_map.csv` is provided alongside it. Can you produce a clean GeoJSON `parcels.geojson` with the correct full column names and proper character encoding? The features should come through as Polygon or MultiPolygon.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:08
Let me start by examining the input files to understand their structure.
0:08
Bash
ls -la /work/
total 56 drwxr-xr-x 1 agent agent 138 Jun 16 06:51 . drwxr-xr-x 1 root root 14 Jun 16 06:51 .. -rw-r--r-- 1 agent agent 188 Jun 16 06:51 column_map.csv -rw-r--r-- 1 agent agent 6 Jun 16 06:51 parcels.cpg -rw-r--r-- 1 agent agent 25726 Jun 16 06:51 parcels.dbf -rw-r--r-- 1 agent agent 457 Jun 16 06:51 parcels.prj -rw-r--r-- 1 agent agent 8260 Jun 16 06:51 parcels.shp -rw-r--r-- 1 agent agent 580 Jun 16 06:51 parcels.shx
0:11
Let me first read the column_map.csv and inspect the shapefile to understand its contents.
0:11
Read
/work/column_map.csv
1 truncated,original
2 KATASTRALG,KATASTRALGEMEINDE_NAME
3 GRUNDSTUEC,GRUNDSTUECKSNUMMER
4 EIGENTUEME,EIGENTUEMER_NAME
5 WIDMUNG_BE,WIDMUNG_BEZEICHNUNG
6 STRASSE_NA,STRASSE_NAME
7 FLAECHE_M2,FLAECHE_M2
0:11
Read
/work/parcels.cpg
1 CP1252
0:11
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('parcels.shp')
print('Columns:', list(gdf.columns))
print('CRS:', gdf.crs)
print('Num features:', len(gdf))
print('Geometry types:', gdf.geometry.type.unique())
print()
print(gdf.head(3).to_string())
"
Columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
CRS: EPSG:31287
Num features: 60
Geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
KATASTRALG GRUNDSTUEC EIGENTUEME WIDMUNG_BE STRASSE_NA FLAECHE_M2 geometry
0 Innere Stadt 0013/1 Müller GmbH Wohngebiet Mariahilfer Straße 750.00 POLYGON ((625700 483400, 625700 483425, 625730 483425, 625730 483400, 625700 483400))
1 Mariahilf 0020/2 Schönbrunner Bauges. m.b.H. Wohngebiet Döblinger Hauptstraße 751.25 POLYGON ((625732 483400, 625732 483425, 625762 483425, 625762 483400, 625732 483400))
2 Währing 0027/3 Müller GmbH Wohngebiet Währinger Gürtel 752.50 POLYGON ((625764 483400, 625764 483425, 625794 483425, 625794 483400, 625764 483400))
0:20
Let me understand the task: 1. Read `parcels.shp` (shapefile) 2. Rename columns using the mapping in `column_map.csv` (truncated → original) 3. Handle character encoding - the .cpg file says CP1252, so I need to make sure text is properly decoded 4. Output as `parcels.geojson` with Polygon/MultiPolygon geometry types 5. Ensure proper CRS handling (currently EPSG:31287 which is a metric CRS, need to see if they want to keep it or reproject to WGS84 for GeoJSON) GeoJSON standard requires coordinates in WGS84 (EPSG:4326). So I should reproject to EPSG:4326. Let me also check the column mapping: - KATASTRALG → KATASTRALGEMEINDE_NAME - GRUNDSTUEC → GRUNDSTUECKSNUMMER - EIGENTUEME → EIGENTUEMER_NAME - WIDMUNG_BE → WIDMUNG_BEZEICHNUNG - STRASSE_NA → STRASSE_NAME - FLAECHE_M2 → FLAECHE_M2 (same) The .cpg file indicates CP1252 encoding, which is a Windows Central European encoding. Let me build a script. Actually, shapefile DBF encoding is tricky. geopandas should handle the CP1252 encoding when reading. Let me write a comprehensive script.
0:20
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
# Check if CP1252 encoding is being applied correctly
gdf = gpd.read_file('parcels.shp', encoding='CP1252')
print('First few rows:')
print(gdf.head(5).to_string())
# Check for special chars
for col in gdf.columns:
if gdf[col].dtype == 'object':
print(f'\n--- {col} ---')
print(gdf[col].unique())
"
First few rows:
KATASTRALG GRUNDSTUEC EIGENTUEME WIDMUNG_BE STRASSE_NA FLAECHE_M2 geometry
0 Innere Stadt 0013/1 Müller GmbH Wohngebiet Mariahilfer Straße 750.00 POLYGON ((625700 483400, 625700 483425, 625730 483425, 625730 483400, 625700 483400))
1 Mariahilf 0020/2 Schönbrunner Bauges. m.b.H. Wohngebiet Döblinger Hauptstraße 751.25 POLYGON ((625732 483400, 625732 483425, 625762 483425, 625762 483400, 625732 483400))
2 Währing 0027/3 Müller GmbH Wohngebiet Währinger Gürtel 752.50 POLYGON ((625764 483400, 625764 483425, 625794 483425, 625794 483400, 625764 483400))
3 Döbling 0034/4 Schönbrunner Bauges. m.b.H. Wohngebiet Höfergasse 753.75 POLYGON ((625796 483400, 625796 483425, 625826 483425, 625826 483400, 625796 483400))
4 Hütteldorf 0041/5 Müller GmbH Wohngebiet Schönbrunner Allee 755.00 POLYGON ((625828 483400, 625828 483425, 625858 483425, 625858 483400, 625828 483400))
0:30
Let me understand the task: 1. Read `parcels.shp` which has truncated column names (10 char limit from old Shapefile format) 2. Use `column_map.csv` to rename them to full names 3. Use proper character encoding (CP1252 as indicated by the CPG file) 4. Output to `parcels.geojson` as clean GeoJSON with proper Polygon/MultiPolygon geometries 5. The CRS should be EPSG:4326 (WGS84) for GeoJSON, since GeoJSON requires WGS84 coordinates Wait, GeoJSON technically requires coordinates in WGS84 (EPSG:4326). The source CRS is EPSG:31287 (MGI / Austria Lambert). I should reproject to WGS84. Let me write the script.
0:30
Write
/work/solve.py
content · 26 lines · py
import geopandas as gpd
import pandas as pd
import json
# 1. Read the column mapping CSV
col_map = pd.read_csv("/work/column_map.csv")
rename_dict = dict(zip(col_map["truncated"], col_map["original"]))
print("Rename mapping:", rename_dict)
# 2. Load shapefile with correct encoding (CP1252 per the .cpg file)
gdf = gpd.read_file("/work/parcels.shp", encoding="CP1252")
print(f"Loaded {len(gdf)} features, CRS: {gdf.crs}")
# 3. Rename columns
gdf = gdf.rename(columns=rename_dict)
print("Renamed columns:", list(gdf.columns))
# 4. Reproject to WGS84 (EPSG:4326) for proper GeoJSON
gdf = gdf.to_crs("EPSG:4326")
# 5. Ensure geometry types are Polygon or MultiPolygon (already are from preview)
print("Geometry types:", gdf.geometry.type.unique())
# 6. Write GeoJSON
gdf.to_file("/work/parcels.geojson", driver="GeoJSON", encoding="utf-8")
print("Done - wrote parcels.geojson")Wrote /work/solve.py (880 chars).
0:34
Let me run the script to see if it works.
0:34
Bash
cd /work && python solve.py
Rename mapping: {'KATASTRALG': 'KATASTRALGEMEINDE_NAME', 'GRUNDSTUEC': 'GRUNDSTUECKSNUMMER', 'EIGENTUEME': 'EIGENTUEMER_NAME', 'WIDMUNG_BE': 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NA': 'STRASSE_NAME', 'FLAECHE_M2': 'FLAECHE_M2'}
Loaded 60 features, CRS: EPSG:31287
Renamed columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry']
Geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
Done - wrote parcels.geojson
0:49
Let me verify the output file exists and check a snippet of it to make sure it's valid GeoJSON with proper encoding.
0:49
Bash
ls -la /work/parcels.geojson
-rw-r--r-- 1 agent agent 31875 Jun 16 06:52 /work/parcels.geojson
0:49
Bash
cd /work && python -c "
cd /work && python -c "
import json
with open('parcels.geojson', 'r', encoding='utf-8') as f:
data = json.load(f)
print('Type:', data['type'])
print('CRS:', data.get('crs', 'not present'))
print('Num features:', len(data['features']))
feat = data['features'][0]
print('Properties:', feat['properties'])
print('Geometry type:', feat['geometry']['type'])
print('Coords (sample):', feat['geometry']['coordinates'][0][:2])
# Check special chars roundtrip
print('First feature name:', feat['properties']['EIGENTUEMER_NAME'])
print('All property keys:', list(feat['properties'].keys()))
"
Type: FeatureCollection
CRS: {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}}
Num features: 60
Properties: {'KATASTRALGEMEINDE_NAME': 'Innere Stadt', 'GRUNDSTUECKSNUMMER': '0013/1', 'EIGENTUEMER_NAME': 'Müller GmbH', 'WIDMUNG_BEZEICHNUNG': 'Wohngebiet', 'STRASSE_NAME': 'Mariahilfer Straße', 'FLAECHE_M2': 750.0}
Geometry type: Polygon
Coords (sample): [[16.37090842626786, 48.210191277387686], [16.370921563632866, 48.2104159961731]]
First feature name: Müller GmbH
All property keys: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2']
1:07