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"
}
0:05
Let me start by exploring the working directory to understand what files are available.
0:05
Bash
ls -la /work/
total 56 drwxr-xr-x 1 agent agent 138 Jun 15 14:30 . drwxr-xr-x 1 root root 14 Jun 15 14:30 .. -rw-r--r-- 1 agent agent 188 Jun 15 14:30 column_map.csv -rw-r--r-- 1 agent agent 6 Jun 15 14:30 parcels.cpg -rw-r--r-- 1 agent agent 25726 Jun 15 14:30 parcels.dbf -rw-r--r-- 1 agent agent 457 Jun 15 14:30 parcels.prj -rw-r--r-- 1 agent agent 8260 Jun 15 14:30 parcels.shp -rw-r--r-- 1 agent agent 580 Jun 15 14:30 parcels.shx
0:20
Let me look at the column map CSV and the shapefile's DBF fields to understand the mapping.
0:20
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
0:20
Read
/work/parcels.cpg
1 CP1252
0:20
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]]
0:35
Let me analyze what we have: 1. A shapefile `parcels.shp` with components (.shp, .shx, .dbf, .prj, .cpg) 2. `column_map.csv` that maps truncated column names (8-char limit from shapefile format) to full names 3. The shapefile uses CP1252 encoding (from .cpg file) 4. The CRS is MGI Austria Lambert Let me now write a Python script to: 1. Read the shapefile with geopandas using the correct encoding 2. Rename columns using the mapping 3. Ensure geometries are Polygon or MultiPolygon 4. Export to GeoJSON Let me also check what the actual column names are in the shapefile first.
0:35
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('parcels.shp', encoding='CP1252')
print('Columns:', list(gdf.columns))
print('Geometry types:', gdf.geometry.type.unique())
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print(gdf.head())
"
Columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
Geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
CRS: EPSG:31287
Shape: (60, 7)
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]
0:57
Now I have the data. Let me understand the column mapping: 1. KATASTRALG -> KATASTRALGEMEINDE_NAME 2. GRUNDSTUEC -> GRUNDSTUECKSNUMMER 3. EIGENTUEME -> EIGENTUEMER_NAME 4. WIDMUNG_BE -> WIDMUNG_BEZEICHNUNG 5. STRASSE_NA -> STRASSE_NAME 6. FLAECHE_M2 -> FLAECHE_M2 I need to: 1. Rename the columns according to the mapping 2. Ensure proper character encoding (CP1252 source, output as UTF-8 which is standard for GeoJSON) 3. Ensure geometries are Polygon or MultiPolygon 4. Write to `parcels.geojson` Let me write a Python script.
0:57
Write
/work/solve.py
content · 30 lines · py
import geopandas as gpd
import pandas as pd
# Read the column map
col_map = pd.read_csv('/work/column_map.csv')
col_map_dict = dict(zip(col_map['truncated'], col_map['original']))
print("Column mapping:", col_map_dict)
# Read the shapefile with proper encoding (CP1252)
gdf = gpd.read_file('/work/parcels.shp', encoding='CP1252')
# Rename columns using the mapping (only for columns that exist)
rename_dict = {k: v for k, v in col_map_dict.items() if k in gdf.columns}
gdf = gdf.rename(columns=rename_dict)
print("Renamed columns:", list(gdf.columns))
print("Geometry types before:", gdf.geometry.type.unique())
# Ensure all geometries are Polygon or MultiPolygon
# shapely can handle this; if there are mixed types we'd need to handle,
# but here they are all Polygon already
gdf = gdf.set_geometry('geometry')
# Write to GeoJSON (UTF-8 by default)
output_path = '/work/parcels.geojson'
gdf.to_file(output_path, driver='GeoJSON')
print(f"Written {len(gdf)} features to {output_path}")
print("Columns:", list(gdf.columns))
print(gdf.head())Wrote /work/solve.py (1047 chars).
1:02
Let me run the script.
1:02
Bash
cd /work && python solve.py
Column mapping: {'KATASTRALG': 'KATASTRALGEMEINDE_NAME', 'GRUNDSTUEC': 'GRUNDSTUECKSNUMMER', 'EIGENTUEME': 'EIGENTUEMER_NAME', 'WIDMUNG_BE': 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NA': 'STRASSE_NAME', 'FLAECHE_M2': 'FLAECHE_M2'}
Renamed columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry']
Geometry types before: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
Written 60 features to /work/parcels.geojson
Columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry']
KATASTRALGEMEINDE_NAME ... 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]
1:14
Let me verify the output GeoJSON is valid and properly encoded.
1:14
Bash
cd /work && python3 -c "
cd /work && python3 -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', 'none'))
print('Number of features:', len(data['features']))
f0 = data['features'][0]
print('Feature 0 props:', f0['properties'])
print('Feature 0 geom type:', f0['geometry']['type'])
print('UTF-8 check - Umlauts in props:')
for f in data['features']:
props = f['properties']
print(f' {props[\"KATASTRALGEMEINDE_NAME\"]}')
"
Type: FeatureCollection
CRS: {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:EPSG::31287'}}
Number of features: 60
Feature 0 props: {'KATASTRALGEMEINDE_NAME': 'Innere Stadt', 'GRUNDSTUECKSNUMMER': '0013/1', 'EIGENTUEMER_NAME': 'Müller GmbH', 'WIDMUNG_BEZEICHNUNG': 'Wohngebiet', 'STRASSE_NAME': 'Mariahilfer Straße', 'FLAECHE_M2': 750.0}
Feature 0 geom type: Polygon
UTF-8 check - Umlauts in props:
Innere Stadt
Mariahilf
Währing
Döbling
Hütteldorf
Floridsdorf
Brigittenau
Schönbrunn
Innere Stadt
Mariahilf
Währing
Döbling
Hütteldorf
Floridsdorf
Brigittenau
Schönbrunn
Innere Stadt
Mariahilf
Währing
Döbling
Hütteldorf
Floridsdorf
Brigittenau
Schönbrunn
Innere Stadt
Mariahilf
Währing
Döbling
Hütteldorf
Floridsdorf
Brigittenau
Schönbrunn
Innere Stadt
Mariahilf
Währing
Döbling
Hütteldorf
Floridsdorf
Brigittenau
Schönbrunn
Innere Stadt
Mariahilf
Währing
Döbling
Hütteldorf
Floridsdorf
Brigittenau
Schönbrunn
Innere Stadt
Mariahilf
Währing
Döbling
Hütteldorf
Floridsdorf
Brigittenau
Schönbrunn
Innere Stadt
Mariahilf
Währing
Döbling
1:28
The GeoJSON has been produced successfully. Let me summarize what was done: 1. Read the shapefile with CP1252 encoding (as specified in `parcels.cpg`) 2. Used `column_map.csv` to rename truncated column names to their full original names 3. All geometries are Polygon (no MultiPolygon needed, but GeoJSON handles both) 4. Written as clean GeoJSON with CRS preserved (EPSG:31287 - MGI Austria Lambert) 5. UTF-8 encoding ensures proper handling of Umlauts (ä, ö, ü, ß, etc.) The output file is `/work/parcels.geojson` with 60 features, proper full column names, and correct UTF-8 character encoding.
1:28