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"
}
0:12
Let me start by inspecting the available files to understand the data structure.
0:12
Bash
ls -la /work/
total 56 drwxr-xr-x 1 agent agent 138 Jun 16 02:05 . drwxr-xr-x 1 root root 14 Jun 16 02:05 .. -rw-r--r-- 1 agent agent 188 Jun 16 02:05 column_map.csv -rw-r--r-- 1 agent agent 6 Jun 16 02:05 parcels.cpg -rw-r--r-- 1 agent agent 25726 Jun 16 02:05 parcels.dbf -rw-r--r-- 1 agent agent 457 Jun 16 02:05 parcels.prj -rw-r--r-- 1 agent agent 8260 Jun 16 02:05 parcels.shp -rw-r--r-- 1 agent agent 580 Jun 16 02:05 parcels.shx
0:26
Let me examine the files to understand what we're working with.
0:26
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
0:26
Read
/work/parcels.cpg
1 CP1252
0:26
Read
/work/parcels.prj
1 PROJCS["MGI_Austria_Lambert",GEOGCS["GCS_MGI",DATUM["D_MGI",SPHEROID["Bessel_1841",6377397.155,299.1528128]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",400000.0],PARAMETER["False_Northing",400000.0],PARAMETER["Central_Meridian",13.3333333333333],PARAMETER["Standard_Parallel_1",49.0],PARAMETER["Standard_Parallel_2",46.0],PARAMETER["Latitude_Of_Origin",47.5],UNIT["Meter",1.0]]
0:41
Let me analyze the data: 1. **column_map.csv**: Maps truncated column names to full column names: - KATASTRALG → KATASTRALGEMEINDE_NAME - GRUNDSTUEC → GRUNDSTUECKSNUMMER - EIGENTUEME → EIGENTUEMER_NAME - WIDMUNG_BE → WIDMUNG_BEZEICHNUNG - STRASSE_NA → STRASSE_NAME - FLAECHE_M2 → FLAECHE_M2 2. **parcels.cpg**: The shapefile's character encoding is "CP1252" (Windows-1252, also known as Windows Western European Latin-1) 3. **parcels.prj**: Projection is MGI Austria Lambert (EPSG:31287? Actually, MGI Austria Lambert is EPSG:31287, but let me verify... The parameters match EPSG:31287 which is MGI / Austria Lambert) Now I need to: - Read the shapefile using geopandas, preserving the CP1252 encoding - Rename columns based on the mapping - Convert to EPSG:4326 (WGS84) for GeoJSON (standard practice) - Export as GeoJSON Let me first check the shapefile schema and data.
0:41
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('parcels.shp', encoding='CP1252')
print('Columns:', list(gdf.columns))
print('Shape:', gdf.shape)
print(gdf.dtypes)
print()
print(gdf.head())
print()
print('Geometry types:', gdf.geometry.type.unique())
print()
print('CRS:', gdf.crs)
"
Columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
Shape: (60, 7)
KATASTRALG str
GRUNDSTUEC str
EIGENTUEME str
WIDMUNG_BE str
STRASSE_NA str
FLAECHE_M2 float64
geometry geometry
dtype: object
KATASTRALG ... geometry
0 Innere Stadt ... POLYGON ((625700 483400, 625700 483425, 625730...
1 Mariahilf ... POLYGON ((625732 483400, 625732 483425, 625762...
2 Währing ... POLYGON ((625764 483400, 625764 483425, 625794...
3 Döbling ... POLYGON ((625796 483400, 625796 483425, 625826...
4 Hütteldorf ... POLYGON ((625828 483400, 625828 483425, 625858...
[5 rows x 7 columns]
Geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
CRS: EPSG:31287
0:50
Let me understand the task: 1. The shapefile `parcels.shp` has truncated column names (max 10 chars in shapefile/dbf format). 2. The `column_map.csv` maps these truncated names to full proper names: - KATASTRALG -> KATASTRALGEMEINDE_NAME - GRUNDSTUEC -> GRUNDSTUECKSNUMMER - EIGENTUEME -> EIGENTUEMER_NAME - WIDMUNG_BE -> WIDMUNG_BEZEICHNUNG - STRASSE_NA -> STRASSE_NAME - FLAECHE_M2 -> FLAECHE_M2 3. Character encoding is CP1252 (from the .cpg file). 4. I need to produce `parcels.geojson` (GeoJSON) with proper full column names and proper character encoding. Let me write a script to do this properly. I'll: 1. Read the shapefile with CP1252 encoding 2. Rename columns according to the mapping 3. Ensure geometries are Polygon or MultiPolygon 4. Write to GeoJSON I should be careful about: - Encoding: the GeoJSON output should be UTF-8 (standard for GeoJSON) - Geometry types: ensure they're Polygon/MultiPolygon - The CRS: EPSG:31287 is projected, but GeoJSON uses WGS84 (EPSG:4326) by default unless specified. Let me think about this... Actually, GeoJSON traditionally uses WGS84 (EPSG:4326) coordinates. But modern GeoJSON can have any CRS. However, for web viewers, they typically expect WGS84. Let me check what's standard. Actually, the GeoJSON specification (RFC 7946) requires coordinates to be in WGS84 (EPSG:4326). So I should reproject to EPSG:4326. But I'm not sure if the user's web viewer expects that. Let me just produce the GeoJSON with proper column names. I'll reproject to WGS84 since that's standard for GeoJSON and web viewers. Actually, let me keep it simple - just rename columns, ensure geometry types, and write as GeoJSON. The CRS can be embedded in the GeoJSON. Let me check what geopandas does by default. Let me write a script.
0:50
Write
/work/solve.py
content · 49 lines · py
import geopandas as gpd
import pandas as pd
import json
# Read the column mapping
col_map = pd.read_csv('/work/column_map.csv')
rename_dict = dict(zip(col_map['truncated'], col_map['original']))
print("Rename mapping:", rename_dict)
# Read shapefile with proper encoding
gdf = gpd.read_file('/work/parcels.shp', encoding='CP1252')
print("Original columns:", list(gdf.columns))
# Rename columns
gdf = gdf.rename(columns=rename_dict)
print("Renamed columns:", list(gdf.columns))
# Ensure geometry types are Polygon or MultiPolygon
# Check current types
print("Geometry types before:", gdf.geometry.type.unique())
# Convert any non-Polygon/MultiPolygon geometries
# (This is mostly a safety check - the data already shows Polygon)
original_types = gdf.geometry.type.unique()
for t in original_types:
if t not in ('Polygon', 'MultiPolygon'):
print(f"WARNING: Found geometry type {t}, attempting conversion")
# If needed, we can use buffer(0) to clean invalid geometries
# and ensure they are valid polygons
gdf['geometry'] = gdf.geometry.buffer(0)
# Reproject to WGS84 (EPSG:4326) for standard GeoJSON compatibility
# The original CRS is EPSG:31287 (MGI Austria Lambert)
print("Original CRS:", gdf.crs)
gdf = gdf.to_crs(epsg=4326)
print("New CRS:", gdf.crs)
# Write to GeoJSON
output_path = '/work/parcels.geojson'
gdf.to_file(output_path, driver='GeoJSON', encoding='utf-8')
print(f"\nWrote {output_path}")
print(f"Number of features: {len(gdf)}")
# Verify by reading back
verify = gpd.read_file(output_path)
print(f"Verification - columns: {list(verify.columns)}")
print(f"Verification - geometry types: {verify.geometry.type.unique()}")
print(f"Sample row:\n{verify.head(1).to_dict('records')}")Wrote /work/solve.py (1715 chars).
0:52
Let me run the script to see if it works correctly.
0:52
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'}
Original columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
Renamed columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry']
Geometry types before: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
Original CRS: EPSG:31287
New CRS: EPSG:4326
Wrote /work/parcels.geojson
Number of features: 60
Verification - columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry']
Verification - geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
Sample row:
[{'KATASTRALGEMEINDE_NAME': 'Innere Stadt', 'GRUNDSTUECKSNUMMER': '0013/1', 'EIGENTUEMER_NAME': 'Müller GmbH', 'WIDMUNG_BEZEICHNUNG': 'Wohngebiet', 'STRASSE_NAME': 'Mariahilfer Straße', 'FLAECHE_M2': 750.0, 'geometry': <POLYGON ((16.371 48.21, 16.371 48.21, 16.371 48.21, 16.371 48.21, 16.371 48...>}]
0:55
The output looks good. Let me verify the GeoJSON file is well-formed and contains the expected content.
0:55
0:55
Bash
cd /work && python -c "
cd /work && python -c "
import json
with open('parcels.geojson', 'r') as f:
data = json.load(f)
print('Type:', data['type'])
print('Number of features:', len(data['features']))
print('CRS:', data.get('crs', 'not set (OK for GeoJSON)'))
print()
print('First feature properties:')
print(json.dumps(data['features'][0]['properties'], indent=2, ensure_ascii=False))
print()
print('First feature geometry type:', data['features'][0]['geometry']['type'])
print()
print('All geometry types:')
types = set()
for f in data['features']:
types.add(f['geometry']['type'])
print(types)
"
Type: FeatureCollection
Number of features: 60
CRS: {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}}
First feature properties:
{
"KATASTRALGEMEINDE_NAME": "Innere Stadt",
"GRUNDSTUECKSNUMMER": "0013/1",
"EIGENTUEMER_NAME": "Müller GmbH",
"WIDMUNG_BEZEICHNUNG": "Wohngebiet",
"STRASSE_NAME": "Mariahilfer Straße",
"FLAECHE_M2": 750.0
}
First feature geometry type: Polygon
All geometry types:
{'Polygon'}
0:58
Everything looks good. Let me summarize what was done.
0:58