dc-l2-cairo-invalid-dedup
Map (reference ← swipe → agent)
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cairo_parcels_legacy.geojson is a parcel snapshot stitched together from three legacy provincial systems before unification, and I need to turn it into the foundation for our new central registry. Can you clean it up so we end up with one canonical record per parcel? When duplicates exist, keep the record with the earliest record_seq, and discard any polygon fragments smaller than 1 m².
Please write the result to parcels_canonical.geoparquet including these columns: parcel_id (the join key), parcel_class, district, and area_m2. Every feature must be a MultiPolygon, and there should be no null or empty geometries, no invalid geometries, no duplicate geometries, and no artifact slivers.
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System info
{
"adapter": "openrouter",
"model": "google/gemma-4-26b-a4b-it",
"base_url": "https://openrouter.ai/api/v1"
}
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Bash
ls -l /work/cairo_parcels_legacy.geojson
-rw-r--r-- 1 agent agent 223991 Jun 18 10:14 /work/cairo_parcels_legacy.geojson
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Bash
python -c "import geopandas as gpd; gdf = gpd.read_file('/work/cairo_parcels_leg...
parcel_id ... geometry
0 1 ... POLYGON ((640000 815000, 640030 815000, 640030...
1 2 ... POLYGON ((640050 815000, 640080 815000, 640080...
2 3 ... POLYGON ((640100 815000, 640130 815000, 640130...
3 4 ... POLYGON ((640150 815000, 640180 815000, 640180...
4 5 ... POLYGON ((640200 815000, 640230 815000, 640230...
[5 rows x 6 columns]
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 290 entries, 0 to 289
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 parcel_id 290 non-null int32
1 record_seq 290 non-null int32
2 parcel_class 290 non-null str
3 district 290 non-null str
4 area_m2 290 non-null float64
5 geometry 290 non-null geometry
dtypes: float64(1), geometry(1), int32(2), str(2)
memory usage: 17.8 KB
None
Index(['parcel_id', 'record_seq', 'parcel_class', 'district', 'area_m2',
'geometry'],
dtype='str')
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Write
/work/solve.py
content · 67 lines · py
import geopandas as gpd
from shapely.geometry import MultiPolygon, Polygon
def clean_parcels(input_path, output_path):
# 1. Load data
print(f"Loading {input_path}...")
gdf = gpd.read_file(input_path)
# 2. Remove null or empty geometries
print("Removing null or empty geometries...")
gdf = gdf[gdf.geometry.notnull() & ~gdf.geometry.is_empty]
# 3. Fix invalid geometries
print("Fixing invalid geometries...")
gdf['geometry'] = gdf.geometry.buffer(0)
gdf = gdf[~gdf.geometry.is_empty] # Re-check after buffer(0)
# 4. Handle duplicates: Keep the record with the earliest record_seq per parcel_id
print("Handling duplicates based on record_seq...")
# Sort by parcel_id and record_seq
gdf = gdf.sort_values(by=['parcel_id', 'record_seq'], ascending=[True, True])
# Drop duplicates based on parcel_id, keeping the first (earliest record_seq)
gdf = gdf.drop_duplicates(subset='parcel_id', keep='first')
# 5. Discard polygon fragments smaller than 1 m²
# Use the geometry's actual area to be safe, though area_m2 exists.
# The requirement says "discard any polygon fragments smaller than 1 m²".
# Usually this refers to the actual geometry.
print("Discarding fragments smaller than 1 m²...")
gdf = gdf[gdf.geometry.area >= 1.0]
# 6. Ensure every feature is a MultiPolygon
print("Ensuring all geometries are MultiPolygon...")
def ensure_multipolygon(geom):
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
return geom
gdf['geometry'] = gdf.geometry.apply(ensure_multipolygon)
# 7. Remove duplicate geometries (identical shapes)
# Note: We already handled duplicates by parcel_id, but if two different IDs
# have the same shape, we should check if "no duplicate geometries" means
# unique shapes or just no identical rows.
# Given "one canonical record per parcel", we've already addressed the core requirement.
# But if "no duplicate geometries" means uniqueness of shapes:
# Let's remove records that have identical geometry to another record.
print("Removing duplicate geometries...")
gdf = gdf.drop_duplicates(subset=['geometry'])
# 8. Final selection of columns
print("Selecting final columns...")
# columns: parcel_id, parcel_class, district, area_m2
# We must ensure area_m2 is calculated or preserved.
# The input has it, but let's recalculate to ensure it's accurate to the final geometry.
gdf['area_m2'] = gdf.geometry.area
final_gdf = gdf[['parcel_id', 'parcel_class', 'district', 'area_m2', 'geometry']]
# 9. Save to GeoParquet
print(f"Saving to {output_path}...")
final_gdf.to_parquet(output_path)
print("Done.")
if __name__ == "__main__":
clean_parcels('/work/cairo_parcels_legacy.geojson', '/work/parcels_canonical.geoparquet')
Wrote /work/solve.py (2880 chars).
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Bash
python /work/solve.py
Loading /work/cairo_parcels_legacy.geojson... Removing null or empty geometries... Fixing invalid geometries... Handling duplicates based on record_seq... Discarding fragments smaller than 1 m²... Ensuring all geometries are MultiPolygon... Removing duplicate geometries... Selecting final columns... Saving to /work/parcels_canonical.geoparquet... Done.
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Bash
python -c "import geopandas as gpd; gdf = gpd.read_parquet('/work/parcels_canoni...
parcel_id ... geometry 0 1 ... MULTIPOLYGON (((640000 815000, 640000 815040, ... 1 2 ... MULTIPOLYGON (((640050 815000, 640050 815040, ... 2 3 ... MULTIPOLYGON (((640100 815000, 640100 815040, ... 3 4 ... MULTIPOLYGON (((640150 815000, 640150 815040, ... 4 5 ... MULTIPOLYGON (((640200 815000, 640200 815040, ... [5 rows x 5 columns] <class 'geopandas.geodataframe.GeoDataFrame'> Index: 210 entries, 0 to 209 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 parcel_id 210 non-null int32 1 parcel_class 210 non-null str 2 district 210 non-null str 3 area_m2 210 non-null float64 4 geometry 210 non-null geometry dtypes: float64(1), geometry(1), int32(1), str(2) memory usage: 13.7 KB None Index(['parcel_id', 'parcel_class', 'district', 'area_m2', 'geometry'], dtype='str') <ArrowStringArray> ['MultiPolygon'] Length: 1, dtype: str
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Bash
python -c "import geopandas as gpd; gdf = gpd.read_parquet('/work/parcels_canoni...
Min area: 300.0 Max area: 1280.0 Empty geometries count: 0 Null geometries count: 0 Invalid geometries count: False
Traceback (most recent call last):
File "<string>", line 1, in <module>
import geopandas as gpd; gdf = gpd.read_parquet('/work/parcels_canonical.geoparquet'); print(f'Min area: {gdf.geometry.area.min()}'); print(f'Max area: {gdf.geometry.area.max()}'); print(f'Empty geometries count: {gdf.geometry.is_empty.sum()}'); print(f'Null geometries count: {gdf.geometry.isnull().sum()}'); print(f'Invalid geometries count: {~gdf.geometry.is_valid.all()}'); print(f'Unique geometries count: {gdf.geometry.equals(gdf.geometry).sum()}'); print(f'Geom type count: {gdf.geometry.geom_type.value_counts()}')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'bool' object has no attribute 'sum'
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Bash
python -c "import geopandas as gpd; gdf = gpd.read_parquet('/work/parcels_canoni...
Min area: 300.0 Max area: 1280.0 Empty geometries count: 0 Null geometries count: 0 Invalid geometries count: 0 Geom type count: MultiPolygon 210 Name: count, dtype: int64
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Bash
python -c "import geopandas as gpd; gdf = gpd.read_parquet('/work/parcels_canoni...
Duplicate geometry rows: 0
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