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"
}
0:03
Let me start by reading the input files to understand what we're working with.
0:03
Bash
ls -la /work/
total 56 drwxr-xr-x 1 agent agent 138 Jun 18 00:56 . drwxr-xr-x 1 root root 14 Jun 18 00:56 .. -rw-r--r-- 1 agent agent 188 Jun 18 00:56 column_map.csv -rw-r--r-- 1 agent agent 6 Jun 18 00:56 parcels.cpg -rw-r--r-- 1 agent agent 25726 Jun 18 00:56 parcels.dbf -rw-r--r-- 1 agent agent 457 Jun 18 00:56 parcels.prj -rw-r--r-- 1 agent agent 8260 Jun 18 00:56 parcels.shp -rw-r--r-- 1 agent agent 580 Jun 18 00:56 parcels.shx
0:12
Let me start by reading the column map and inspecting the shapefile's current attributes.
0:12
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
0:12
Read
/work/parcels.cpg
1 CP1252
0:18
Let me understand the setup: 1. `parcels.shp` has 7 columns with truncated names (10-character DBF limit): KATASTRALG, GRUNDSTUEC, EIGENTUEME, WIDMUNG_BE, STRASSE_NA, FLAECHE_M2 2. `column_map.csv` maps truncated → original names: - KATASTRALG → KATASTRALGEMEINDE_NAME - GRUNDSTUEC → GRUNDSTUECKSNUMMER - EIGENTUEME → EIGENTUEMER_NAME - WIDMUNG_BE → WIDMUNG_BEZEICHNUNG - STRASSE_NA → STRASSE_NAME - FLAECHE_M2 → FLAECHE_M2 (stays the same) 3. The CPG file says CP1252 encoding. Let me also check the shapefile's geometry type and CRS.
0:18
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('CRS:', gdf.crs)
print('Geometry types:', gdf.geometry.type.value_counts())
print('Shape:', gdf.shape)
print('---')
print(gdf.head())
print('---')
print(gdf.dtypes)
"
Columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
CRS: EPSG:31287
Geometry types: Polygon 60
Name: count, dtype: int64
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]
---
KATASTRALG str
GRUNDSTUEC str
EIGENTUEME str
WIDMUNG_BE str
STRASSE_NA str
FLAECHE_M2 float64
geometry geometry
dtype: object
0:33
Let me understand the requirements: 1. The shapefile has truncated column names (10 chars max for DBF). The `column_map.csv` maps truncated names to full names. 2. The encoding is CP1252 (as indicated by the .cpg file). 3. Need to output a clean GeoJSON `parcels.geojson` with the correct full column names. 4. Features should be Polygon or MultiPolygon (they're already Polygon, so that's fine). Let me look at the column map more carefully: - KATASTRALG → KATASTRALGEMEINDE_NAME - GRUNDSTUEC → GRUNDSTUECKSNUMMER - EIGENTUEME → EIGENTUEMER_NAME - WIDMUNG_BE → WIDMUNG_BEZEICHNUNG - STRASSE_NA → STRASSE_NAME - FLAECHE_M2 → FLAECHE_M2 (already matching) Let me write the script.
0:33
0:33
Write
/work/solve.py
content · 26 lines · py
import geopandas as gpd
import pandas as pd
import json
# 1. Read the column mapping
col_map = pd.read_csv("/work/column_map.csv")
# Build dict: truncated -> original
rename_dict = dict(zip(col_map["truncated"], col_map["original"]))
print("Rename mapping:", rename_dict)
# 2. Read shapefile with proper encoding
gdf = gpd.read_file("/work/parcels.shp", encoding="CP1252")
print("Original columns:", list(gdf.columns))
# 3. Rename columns using the mapping (skip geometry)
rename_actual = {k: v for k, v in rename_dict.items() if k in gdf.columns}
gdf = gdf.rename(columns=rename_actual)
print("Renamed columns:", list(gdf.columns))
# 4. Ensure geometry is Polygon or MultiPolygon (it already is, but just in case)
# GeoJSON spec: if polygons, they should be Polygon or MultiPolygon
# The data is already Polygon type.
# 5. Write GeoJSON
gdf.to_file("/work/parcels.geojson", driver="GeoJSON", encoding="utf-8")
print("Done. Geojson written.")Wrote /work/solve.py (953 chars).
0:36
Let me run it and then verify the output.
0:36
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']
Done. Geojson written.
0:41
Let me verify the output file exists and check its contents.
0:41
Bash
ls -la /work/parcels.geojson
-rw-r--r-- 1 agent agent 25942 Jun 18 00:57 /work/parcels.geojson
0:41
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('Number of features:', len(data['features']))
print('Feature keys:', list(data['features'][0].keys()))
print('Properties:', list(data['features'][0]['properties'].keys()))
print('Geometry type:', data['features'][0]['geometry']['type'])
print()
# Show first 2 features
for f in data['features'][:2]:
print(f['properties'])
"
Type: FeatureCollection
Number of features: 60
Feature keys: ['type', 'properties', 'geometry']
Properties: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2']
Geometry type: Polygon
{'KATASTRALGEMEINDE_NAME': 'Innere Stadt', 'GRUNDSTUECKSNUMMER': '0013/1', 'EIGENTUEMER_NAME': 'Müller GmbH', 'WIDMUNG_BEZEICHNUNG': 'Wohngebiet', 'STRASSE_NAME': 'Mariahilfer Straße', 'FLAECHE_M2': 750.0}
{'KATASTRALGEMEINDE_NAME': 'Mariahilf', 'GRUNDSTUECKSNUMMER': '0020/2', 'EIGENTUEMER_NAME': 'Schönbrunner Bauges. m.b.H.', 'WIDMUNG_BEZEICHNUNG': 'Wohngebiet', 'STRASSE_NAME': 'Döblinger Hauptstraße', 'FLAECHE_M2': 751.25}
0:46
Everything looks great: 1. **Encoding**: The shapefile was read with `CP1252` encoding (matching the `.cpg` file), and the GeoJSON is written as UTF-8. 2. **Column names**: All truncated DBF column names have been replaced with the full names from `column_map.csv`. 3. **Geometry types**: All features are `Polygon` (they can be read as MultiPolygon too if needed, but Polygon is valid). 4. **60 features** — matches the input. 5. **CRS**: The GeoJSON is in WGS84 (as GeoJSON convention requires) — actually wait, let me check that. GeoJSON requires coordinates in WGS84 (EPSG:4326). The original data is EPSG:31287 (MGI / Austria Lambert). Let me verify how `to_file` with driver GeoJSON handles CRS.
0:46
0:46
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('parcels.geojson')
print('CRS:', gdf.crs)
print('First coords:', list(gdf.geometry.iloc[0].exterior.coords)[:3])
"
CRS: EPSG:31287 First coords: [(625700.0, 483400.0), (625700.0, 483425.0), (625730.0, 483425.0)]
0:54
The CRS is EPSG:31287 (MGI / Austria GK M31), which is a metric CRS. GeoJSON technically should be in WGS84 (EPSG:4326) per the spec. However, many viewers accept any CRS since GeoJSON doesn't have a formal CRS field in the spec (RFC 7946 says coordinates must be WGS84). Let me think about what the user asked: "produce a clean GeoJSON parcels.geojson with the correct full column names and proper character encoding". They didn't explicitly ask to reproject to WGS84. The existing coordinates are in EPSG:31287 which is a metric CRS. However, the GeoJSON specification (RFC 7946) requires WGS84. Modern GIS viewers like web viewers typically expect WGS84 coordinates. Since the user mentioned a "web viewer", it's likely they need WGS84. Let me reproject to EPSG:4326.
0:54
0:54
Write
/work/solve.py
content · 26 lines · py
import geopandas as gpd
import pandas as pd
# 1. 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)
# 2. Read shapefile with proper encoding (CP1252 as declared in .cpg)
gdf = gpd.read_file("/work/parcels.shp", encoding="CP1252")
print("Original columns:", list(gdf.columns))
print("Original CRS:", gdf.crs)
# 3. Rename columns using the mapping (skip geometry)
rename_actual = {k: v for k, v in rename_dict.items() if k in gdf.columns}
gdf = gdf.rename(columns=rename_actual)
print("Renamed columns:", list(gdf.columns))
# 4. Reproject to WGS84 (EPSG:4326) — required for proper GeoJSON (RFC 7946)
gdf = gdf.to_crs("EPSG:4326")
print("Output CRS:", gdf.crs)
# 5. Write GeoJSON with UTF-8 encoding
gdf.to_file("/work/parcels.geojson", driver="GeoJSON", encoding="utf-8")
print("Done. parcels.geojson written.")
Wrote /work/solve.py (940 chars).
0:59
Wait, the user didn't explicitly say they want WGS84. But GeoJSON per RFC 7946 requires WGS84 coordinates. In practice, web viewers (like Leaflet, Mapbox, OpenLayers etc.) expect GeoJSON with WGS84 (lon/lat). So reprojecting is the right thing. Let me double check: "proper character encoding" was asked, and the .cpg says CP1252. The script handles that. Let me run it.
0:59
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']
Original CRS: EPSG:31287
Renamed columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry']
Output CRS: EPSG:4326
Done. parcels.geojson written.
1:10
Let me do a final verification to ensure everything looks correct.
1:10
Bash
cd /work && python -c "
cd /work && python -c "
import json, geopandas as gpd
# Check GeoJSON itself
with open('parcels.geojson') as f:
gj = json.load(f)
print('=== GeoJSON structural check ===')
print('Type:', gj['type'])
print('Features:', len(gj['features']))
print('First feature type:', gj['features'][0]['type'])
print('Geometry type:', gj['features'][0]['geometry']['type'])
print('Properties:', list(gj['features'][0]['properties'].keys()))
print()
# Check coords are in degrees now
gdf = gpd.read_file('parcels.geojson')
print('=== Coordinate check ===')
c = list(gdf.geometry.iloc[0].exterior.coords)
print('First 3 coords:', c[:3])
print('Longitude range:', round(min(c[i][0] for i in range(len(c))), 4), 'to', round(max(c[i][0] for i in range(len(c))), 4))
print('Latitude range:', round(min(c[i][1] for i in range(len(c))), 4), 'to', round(max(c[i][1] for i in range(len(c))), 4))
print()
# Check encoding of German characters
print('=== Encoding check ===')
for feat in gj['features'][:3]:
for k, v in feat['properties'].items():
if isinstance(v, str) and any(ord(c) > 127 for c in v):
print(f' {k}: {v}')
"
=== GeoJSON structural check === Type: FeatureCollection Features: 60 First feature type: Feature Geometry type: Polygon Properties: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2'] === Coordinate check === First 3 coords: [(16.37090842626786, 48.210191277387686), (16.370921563632866, 48.2104159961731), (16.371325013587484, 48.210405458345434)] Longitude range: 16.3709 to 16.3713 Latitude range: 48.2102 to 48.2104 === Encoding check === EIGENTUEMER_NAME: Müller GmbH STRASSE_NAME: Mariahilfer Straße EIGENTUEMER_NAME: Schönbrunner Bauges. m.b.H. STRASSE_NAME: Döblinger Hauptstraße KATASTRALGEMEINDE_NAME: Währing EIGENTUEMER_NAME: Müller GmbH STRASSE_NAME: Währinger Gürtel
1:22
Everything looks clean: 1. **Correct full column names** — KATASTRALGEMEINDE_NAME, GRUNDSTUECKSNUMMER, etc. 2. **Proper CRS** — reprojected to EPSG:4326 (WGS84) with longitude ~16.37°, latitude ~48.21° (Vienna area) 3. **Polygon geometry** — all 60 features are Polygon type 4. **UTF-8 encoding** — German special characters (ü, ß, ö) are properly preserved 5. **GeoJSON format** — valid FeatureCollection with correct structure Let me also confirm the file exists.
1:22