dc-l2-cairo-invalid-dedup
Map (reference ← swipe → agent)
0:00
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.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:04
Let me start by understanding the input data. I'll read the GeoJSON file to see its structure.
0:04
Bash
ls -lh /work/cairo_parcels_legacy.geojson
-rw-r--r-- 1 agent agent 219K Jun 18 12:36 /work/cairo_parcels_legacy.geojson
0:04
Bash
head -c 5000 /work/cairo_parcels_legacy.geojson
{
"type": "FeatureCollection",
"name": "cairo_parcels_legacy",
"crs": {
"type": "name",
"properties": {
"name": "urn:ogc:def:crs:EPSG::22992"
}
},
"features": [
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
640000.0,
815000.0
],
[
640030.0,
815000.0
],
[
640030.0,
815040.0
],
[
640000.0,
815040.0
],
[
640000.0,
815000.0
]
]
]
},
"properties": {
"parcel_id": 1,
"record_seq": 1,
"parcel_class": "commercial",
"district": "Giza-East",
"area_m2": 1200.0
}
},
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
640050.0,
815000.0
],
[
640080.0,
815000.0
],
[
640080.0,
815040.0
],
[
640050.0,
815040.0
],
[
640050.0,
815000.0
]
]
]
},
"properties": {
"parcel_id": 2,
"record_seq": 2,
"parcel_class": "industrial",
"district": "Qalyubia-South",
"area_m2": 1200.0
}
},
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
640100.0,
815000.0
],
[
640130.0,
815000.0
],
[
640130.0,
815040.0
],
[
640100.0,
815040.0
],
[
640100.0,
815000.0
]
]
]
},
"properties": {
"parcel_id": 3,
"record_seq": 3,
"parcel_class": "agricultural",
"district": "Cairo-Central",
"area_m2": 1200.0
}
},
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
640150.0,
815000.0
],
[
640180.0,
815000.0
],
[
640180.0,
815040.0
],
[
640150.0,
815040.0
],
[
640150.0,
815000.0
]
]
]
},
"properties": {
"parcel_id": 4,
"record_seq": 4,
"parcel_class": "residential",
"district": "Giza-East",
"area_m2": 1200.0
}
},
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
640200.0,
815000.0
],
[
640230.0,
815000.0
],
[
640230.0,
815040.0
],
[
640200.0,
815040.0
],
[
640200.0,
815000.0
]
]
]
},
"properties": {
"parcel_id": 5,
"record_seq": 5,
"parcel_class": "commercial",
"district": "Qalyubia-South",
"area_m2": 1200.0
}
},
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
640250.0,
815000.0
],
[
640280.0,
815000.0
],
[
640280.0,
815040.0
],
[
640250.0,
815040.0
],
[
640250.0,
815000.0
]
]
]
},
"properties": {
"parcel_id": 6,
"record_seq": 6,
"parcel_class": "industrial",
"district": "Cairo-Central",
"area_m2": 1200.0
}
},
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
640300.0,
815000.0
],
[
640330.0,
815040.0
],
[
640300.0,
815040.0
],
[
640330.0,
815000.0
],
[
640300.0,
815000.0
]
]
]
},
"properties
0:11
Let me get a better picture of the data - how many features, what columns, what types of duplicates exist, etc.
0:11
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
print('Shape:', gdf.shape)
print('Columns:', list(gdf.columns))
print('CRS:', gdf.crs)
print('Geometry types:', gdf.geometry.type.value_counts())
print()
print('Null geoms:', gdf.geometry.isna().sum())
print('Null parcel_id:', gdf['parcel_id'].isna().sum())
print()
print('parcel_id value counts (top 30):')
print(gdf['parcel_id'].value_counts().head(30))
print()
print('Is valid:', gdf.is_valid.sum(), 'of', len(gdf))
print('Invalid:', (~gdf.is_valid).sum())
"
Shape: (290, 6) Columns: ['parcel_id', 'record_seq', 'parcel_class', 'district', 'area_m2', 'geometry'] CRS: EPSG:22992 Geometry types: Polygon 260 MultiPolygon 30 Name: count, dtype: int64 Null geoms: 0 Null parcel_id: 0 parcel_id value counts (top 30): parcel_id 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 10 1 11 1 12 1 13 1 14 1 15 1 16 1 17 1 18 1 19 1 20 1 21 1 22 1 23 1 24 1 25 1 26 1 27 1 28 1 29 1 30 1 Name: count, dtype: int64 Is valid: 270 of 290 Invalid: 20
0:18
Let me dig deeper to understand the duplicates and the data better.
0:18
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Check for duplicate parcel_ids
dupes = gdf['parcel_id'].value_counts()
dupes = dupes[dupes > 1]
print('Duplicate parcel_ids and count:')
print(dupes.head(50))
print(f'Total duplicate parcels: {len(dupes)}')
print(f'Total features with duplicate parcel_ids: {dupes.sum()}')
"
Duplicate parcel_ids and count: Series([], Name: count, dtype: int64) Total duplicate parcels: 0 Total features with duplicate parcel_ids: 0
0:25
Hmm, no duplicate `parcel_id`s found. But the prompt says "When duplicates exist, keep the record with the earliest record_seq". Let me look more carefully - maybe there's a different kind of duplication. Let me check for duplicate geometries or overlapping parcels.
0:25
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Check for duplicate geometries
dupe_geoms = gdf.geometry.value_counts()
dupe_geoms = dupe_geoms[dupe_geoms > 1]
print('Duplicate geometries count:', len(dupe_geoms))
print()
print('Showing duplicates:')
for idx, count in dupe_geoms.items():
print(f' Count: {count}, geom wkt: {idx.wkt[:100]}')
# Show which parcel_ids have this geom
matching = gdf[gdf.geometry == idx]
print(f' parcel_ids: {list(matching.parcel_id)}, record_seq: {list(matching.record_seq)}')
"
Duplicate geometries count: 50 Showing duplicates: Count: 2, geom wkt: POLYGON ((640000 815000, 640030 815000, 640030 815040, 640000 815040, 640000 815000)) parcel_ids: [1, 900001], record_seq: [1, 211] Count: 2, geom wkt: POLYGON ((640050 815000, 640080 815000, 640080 815040, 640050 815040, 640050 815000)) parcel_ids: [2, 900002], record_seq: [2, 212] Count: 2, geom wkt: POLYGON ((640100 815000, 640130 815000, 640130 815040, 640100 815040, 640100 815000)) parcel_ids: [3, 900003], record_seq: [3, 213] Count: 2, geom wkt: POLYGON ((640150 815000, 640180 815000, 640180 815040, 640150 815040, 640150 815000)) parcel_ids: [4, 900004], record_seq: [4, 214] Count: 2, geom wkt: POLYGON ((640200 815000, 640230 815000, 640230 815040, 640200 815040, 640200 815000)) parcel_ids: [5, 900005], record_seq: [5, 215] Count: 2, geom wkt: POLYGON ((640250 815000, 640280 815000, 640280 815040, 640250 815040, 640250 815000)) parcel_ids: [6, 900006], record_seq: [6, 216] Count: 2, geom wkt: POLYGON ((640350 815000, 640380 815000, 640380 815040, 640350 815040, 640350 815000)) parcel_ids: [8, 900007], record_seq: [8, 217] Count: 2, geom wkt: POLYGON ((640400 815000, 640430 815000, 640430 815040, 640400 815040, 640400 815000)) parcel_ids: [9, 900008], record_seq: [9, 218] Count: 2, geom wkt: POLYGON ((640450 815000, 640480 815000, 640480 815040, 640450 815040, 640450 815000)) parcel_ids: [10, 900009], record_seq: [10, 219] Count: 2, geom wkt: POLYGON ((640550 815000, 640580 815000, 640580 815040, 640550 815040, 640550 815000)) parcel_ids: [12, 900010], record_seq: [12, 220] Count: 2, geom wkt: POLYGON ((640600 815000, 640630 815000, 640630 815040, 640600 815040, 640600 815000)) parcel_ids: [13, 900011], record_seq: [13, 221] Count: 2, geom wkt: POLYGON ((640700 815000, 640730 815000, 640730 815040, 640700 815040, 640700 815000)) parcel_ids: [15, 900012], record_seq: [15, 222] Count: 2, geom wkt: POLYGON ((640000 815060, 640030 815060, 640030 815100, 640000 815100, 640000 815060)) parcel_ids: [16, 900013], record_seq: [16, 223] Count: 2, geom wkt: POLYGON ((640100 815060, 640130 815060, 640130 815100, 640100 815100, 640100 815060)) parcel_ids: [18, 900014], record_seq: [18, 224] Count: 2, geom wkt: POLYGON ((640200 815060, 640230 815060, 640230 815100, 640200 815100, 640200 815060)) parcel_ids: [20, 900015], record_seq: [20, 225] Count: 2, geom wkt: POLYGON ((640250 815060, 640280 815060, 640280 815100, 640250 815100, 640250 815060)) parcel_ids: [21, 900016], record_seq: [21, 226] Count: 2, geom wkt: POLYGON ((640300 815060, 640330 815060, 640330 815100, 640300 815100, 640300 815060)) parcel_ids: [22, 900017], record_seq: [22, 227] Count: 2, geom wkt: POLYGON ((640400 815060, 640430 815060, 640430 815100, 640400 815100, 640400 815060)) parcel_ids: [24, 900018], record_seq: [24, 228] Count: 2, geom wkt: POLYGON ((640450 815060, 640480 815060, 640480 815100, 640450 815100, 640450 815060)) parcel_ids: [25, 900019], record_seq: [25, 229] Count: 2, geom wkt: POLYGON ((640500 815060, 640530 815060, 640530 815100, 640500 815100, 640500 815060)) parcel_ids: [26, 900020], record_seq: [26, 230] Count: 2, geom wkt: POLYGON ((640550 815060, 640580 815060, 640580 815100, 640550 815100, 640550 815060)) parcel_ids: [27, 900021], record_seq: [27, 231] Count: 2, geom wkt: POLYGON ((640600 815060, 640630 815060, 640630 815100, 640600 815100, 640600 815060)) parcel_ids: [28, 900022], record_seq: [28, 232] Count: 2, geom wkt: POLYGON ((640700 815060, 640730 815060, 640730 815100, 640700 815100, 640700 815060)) parcel_ids: [30, 900023], record_seq: [30, 233] Count: 2, geom wkt: POLYGON ((640000 815120, 640030 815120, 640030 815160, 640000 815160, 640000 815120)) parcel_ids: [31, 900024], record_seq: [31, 234] Count: 2, geom wkt: POLYGON ((640100 815120, 640130 815120, 640130 815160, 640100 815160, 640100 815120)) parcel_ids: [33, 900025], record_seq: [33, 235] Count: 2, geom wkt: POLYGON ((640150 815120, 640180 815120, 640180 815160, 640150 815160, 640150 815120)) parcel_ids: [34, 900026], record_seq: [34, 236] Count: 2, geom wkt: POLYGON ((640250 815120, 640280 815120, 640280 815160, 640250 815160, 640250 815120)) parcel_ids: [36, 900027], record_seq: [36, 237] Count: 2, geom wkt: POLYGON ((640350 815120, 640380 815120, 640380 815160, 640350 815160, 640350 815120)) parcel_ids: [38, 900028], record_seq: [38, 238] Count: 2, geom wkt: POLYGON ((640400 815120, 640430 815120, 640430 815160, 640400 815160, 640400 815120)) parcel_ids: [39, 900029], record_seq: [39, 239] Count: 2, geom wkt: POLYGON ((640450 815120, 640480 815120, 640480 815160, 640450 815160, 640450 815120)) parcel_ids: [40, 900030], record_seq: [40, 240] Count: 2, geom wkt: POLYGON ((640550 815120, 640580 815120, 640580 815160, 640550 815160, 640550 815120)) parcel_ids: [42, 900031], record_seq: [42, 241] Count: 2, geom wkt: POLYGON ((640600 815120, 640630 815120, 640630 815160, 640600 815160, 640600 815120)) parcel_ids: [43, 900032], record_seq: [43, 242] Count: 2, geom wkt: POLYGON ((640650 815120, 640680 815120, 640680 815160, 640650 815160, 640650 815120)) parcel_ids: [44, 900033], record_seq: [44, 243] Count: 2, geom wkt: POLYGON ((640700 815120, 640730 815120, 640730 815160, 640700 815160, 640700 815120)) parcel_ids: [45, 900034], record_seq: [45, 244] Count: 2, geom wkt: POLYGON ((640000 815180, 640030 815180, 640030 815220, 640000 815220, 640000 815180)) parcel_ids: [46, 900035], record_seq: [46, 245] Count: 2, geom wkt: POLYGON ((640100 815180, 640130 815180, 640130 815220, 640100 815220, 640100 815180)) parcel_ids: [48, 900036], record_seq: [48, 246] Count: 2, geom wkt: POLYGON ((640150 815180, 640180 815180, 640180 815220, 640150 815220, 640150 815180)) parcel_ids: [49, 900037], record_seq: [49, 247] Count: 2, geom wkt: POLYGON ((640250 815180, 640280 815180, 640280 815220, 640250 815220, 640250 815180)) parcel_ids: [51, 900038], record_seq: [51, 248] Count: 2, geom wkt: POLYGON ((640300 815180, 640330 815180, 640330 815220, 640300 815220, 640300 815180)) parcel_ids: [52, 900039], record_seq: [52, 249] Count: 2, geom wkt: POLYGON ((640400 815180, 640430 815180, 640430 815220, 640400 815220, 640400 815180)) parcel_ids: [54, 900040], record_seq: [54, 250] Count: 2, geom wkt: POLYGON ((640500 815180, 640530 815180, 640530 815220, 640500 815220, 640500 815180)) parcel_ids: [56, 900041], record_seq: [56, 251] Count: 2, geom wkt: POLYGON ((640550 815180, 640580 815180, 640580 815220, 640550 815220, 640550 815180)) parcel_ids: [57, 900042], record_seq: [57, 252] Count: 2, geom wkt: POLYGON ((640650 815180, 640680 815180, 640680 815220, 640650 815220, 640650 815180)) parcel_ids: [59, 900043], record_seq: [59, 253] Count: 2, geom wkt: POLYGON ((640700 815180, 640730 815180, 640730 815220, 640700 815220, 640700 815180)) parcel_ids: [60, 900044], record_seq: [60, 254] Count: 2, geom wkt: POLYGON ((640000 815240, 640030 815240, 640030 815280, 640000 815280, 640000 815240)) parcel_ids: [61, 900045], record_seq: [61, 255] Count: 2, geom wkt: POLYGON ((640050 815240, 640080 815240, 640080 815280, 640050 815280, 640050 815240)) parcel_ids: [62, 900046], record_seq: [62, 256] Count: 2, geom wkt: POLYGON ((640100 815240, 640130 815240, 640130 815280, 640100 815280, 640100 815240)) parcel_ids: [63, 900047], record_seq: [63, 257] Count: 2, geom wkt: POLYGON ((640150 815240, 640180 815240, 640180 815280, 640150 815280, 640150 815240)) parcel_ids: [64, 900048], record_seq: [64, 258] Count: 2, geom wkt: POLYGON ((640250 815240, 640280 815240, 640280 815280, 640250 815280, 640250 815240)) parcel_ids: [66, 900049], record_seq: [66, 259] Count: 2, geom wkt: POLYGON ((640300 815240, 640330 815240, 640330 815280, 640300 815280, 640300 815240)) parcel_ids: [67, 900050], record_seq: [67, 260]
0:37
Let me understand the data: 1. **290 features** total 2. **CRS**: EPSG:22992 (a metric CRS for Egypt) 3. **Geometry types**: 260 Polygon, 30 MultiPolygon 4. **20 invalid geometries** 5. **Duplicate geometries**: 50 pairs of duplicate geometries (same shape but different parcel_ids and record_seqs). The duplicates seem to be pairs: parcel_id 1 & 900001 (record_seq 1 & 211), etc. 6. **No duplicate parcel_ids** - each parcel_id is unique, but the same geometry appears under two different parcel_ids Wait, but the task says: > "When duplicates exist, keep the record with the earliest record_seq" So the duplicate geometries have different parcel_ids but the same geometry. "Duplicates" here likely means duplicate geometries sharing the same geometry shape. We need to keep the one with the smallest record_seq. Let me also check for: - Tiny polygons (< 1 m²) - need to filter those out - Invalid geometries - need to fix/buffer(0) - Non-MultiPolygon geometries - need to convert to MultiPolygon Let me check the data more thoroughly.
0:37
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Check for tiny polygons
# CRS is metric, so area is in m²
areas = gdf.geometry.area
print('Area stats (m²):')
print(f' Min: {areas.min():.4f}')
print(f' Max: {areas.max():.4f}')
print(f' Features with area < 1: {(areas < 1).sum()}')
# Look at some small ones
small = gdf[areas < 1]
print(f' Small features parcel_ids: {list(small.parcel_id)}')
# Check invalid geometries
invalid = gdf[~gdf.is_valid]
print(f'Invalid features: {len(invalid)}')
print(invalid[['parcel_id', 'record_seq', 'parcel_class', 'district']].head(30))
for idx, row in invalid.iterrows():
print(f' parcel_id={row.parcel_id}, geom type={row.geometry.type}, area={row.geometry.area:.4f}')
"
Area stats (m²):
Min: 0.0000
Max: 1280.0000
Features with area < 1: 50
Small features parcel_ids: [7, 17, 23, 35, 41, 53, 58, 71, 76, 89, 94, 107, 112, 125, 130, 143, 148, 161, 166, 179, 800001, 800002, 800003, 800004, 800005, 800006, 800007, 800008, 800009, 800010, 800011, 800012, 800013, 800014, 800015, 800016, 800017, 800018, 800019, 800020, 800021, 800022, 800023, 800024, 800025, 800026, 800027, 800028, 800029, 800030]
Invalid features: 20
parcel_id record_seq parcel_class district
6 7 7 agricultural Giza-East
16 17 17 commercial Qalyubia-South
22 23 23 agricultural Qalyubia-South
34 35 35 agricultural Qalyubia-South
40 41 41 commercial Qalyubia-South
52 53 53 commercial Qalyubia-South
57 58 58 industrial Giza-East
70 71 71 agricultural Qalyubia-South
75 76 76 residential Giza-East
88 89 89 commercial Qalyubia-South
93 94 94 industrial Giza-East
106 107 107 agricultural Qalyubia-South
111 112 112 residential Giza-East
124 125 125 commercial Qalyubia-South
129 130 130 industrial Giza-East
142 143 143 agricultural Qalyubia-South
147 148 148 residential Giza-East
160 161 161 commercial Qalyubia-South
165 166 166 industrial Giza-East
178 179 179 agricultural Qalyubia-South
parcel_id=7, geom type=Polygon, area=0.0000
parcel_id=17, geom type=Polygon, area=0.0000
parcel_id=23, geom type=Polygon, area=0.0000
parcel_id=35, geom type=Polygon, area=0.0000
parcel_id=41, geom type=Polygon, area=0.0000
parcel_id=53, geom type=Polygon, area=0.0000
parcel_id=58, geom type=Polygon, area=0.0000
parcel_id=71, geom type=Polygon, area=0.0000
parcel_id=76, geom type=Polygon, area=0.0000
parcel_id=89, geom type=Polygon, area=0.0000
parcel_id=94, geom type=Polygon, area=0.0000
parcel_id=107, geom type=Polygon, area=0.0000
parcel_id=112, geom type=Polygon, area=0.0000
parcel_id=125, geom type=Polygon, area=0.0000
parcel_id=130, geom type=Polygon, area=0.0000
parcel_id=143, geom type=Polygon, area=0.0000
parcel_id=148, geom type=Polygon, area=0.0000
parcel_id=161, geom type=Polygon, area=0.0000
parcel_id=166, geom type=Polygon, area=0.0000
parcel_id=179, geom type=Polygon, area=0.0000
<string>:22: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
0:47
Let me understand the data: 1. **Duplicate geometries**: There are 50 pairs of duplicate geometries (100 features). Each duplicate has a different `parcel_id` (e.g., 1 & 900001) and different `record_seq` (e.g., 1 & 211). The rule says: "keep the record with the earliest record_seq". So when there are duplicates (same geometry), keep the one with the smaller `record_seq`. 2. **Polygon fragments smaller than 1 m²**: There are 50 features with area < 1 m². 20 of those are also invalid (zero-area geometries that self-intersect). These should be discarded entirely. 3. **Invalid geometries**: 20 invalid geometries (all zero-area self-intersecting polygons). These get discarded as they're < 1 m². 4. The output needs: - Columns: `parcel_id`, `parcel_class`, `district`, `area_m2` - All geometries must be MultiPolygon - No null/empty geometries, no invalid geometries, no duplicate geometries - Write to `parcels_canonical.geoparquet` Let me now write the solve script. The duplicate geometries are between the "normal" parcel_ids (1,2,3,...) and the 900xxx series. Since record_seq for the normal ones (1,2,3...) is smaller than for the 900xxx series (211,212,...), we'd keep the normal ones. Also, there are 50 small features - some are the invalid ones (zero area) and some are legitimate small ones (area < 1 but > 0). Let me check what those are. Wait - 50 features with area < 1 m² matches 20 invalid (0 area) + potentially 30 more small fragments? Let me look more carefully. Actually, the invalid ones all have area=0.0000 and there are 20 of them. The small features list includes all 20 invalid ones plus 30 more (the 800xxx series). Let me check the 800xxx series.
0:47
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Check 800xxx series
d8 = gdf[gdf['parcel_id'] >= 800000]
print(f'800xxx features: {len(d8)}')
print(d8[['parcel_id', 'record_seq', 'parcel_class', 'district', 'area_m2']].head(60))
print()
# Their areas
print('Area stats for 800xxx:')
print(d8.geometry.area.describe())
print()
# Check small ones <1 m²
small_area = gdf[gdf.geometry.area < 1]
print('All small features (<1m²):')
print(small_area[['parcel_id', 'record_seq', 'parcel_class', 'district']].to_string())
print()
print(f'Count of <1m²: {len(small_area)}')
"
800xxx features: 80
parcel_id record_seq parcel_class district area_m2
210 900001 211 industrial Giza-East 1200.00
211 900002 212 agricultural Qalyubia-South 1200.00
212 900003 213 residential Cairo-Central 1200.00
213 900004 214 commercial Giza-East 1200.00
214 900005 215 industrial Qalyubia-South 1200.00
215 900006 216 agricultural Cairo-Central 1200.00
216 900007 217 commercial Giza-East 1200.00
217 900008 218 industrial Qalyubia-South 1200.00
218 900009 219 agricultural Cairo-Central 1200.00
219 900010 220 commercial Giza-East 1200.00
220 900011 221 industrial Qalyubia-South 1200.00
221 900012 222 residential Cairo-Central 1200.00
222 900013 223 commercial Giza-East 1200.00
223 900014 224 agricultural Qalyubia-South 1200.00
224 900015 225 commercial Cairo-Central 1200.00
225 900016 226 industrial Giza-East 1200.00
226 900017 227 agricultural Qalyubia-South 1200.00
227 900018 228 commercial Cairo-Central 1200.00
228 900019 229 industrial Giza-East 1200.00
229 900020 230 agricultural Qalyubia-South 1200.00
230 900021 231 residential Cairo-Central 1200.00
231 900022 232 commercial Giza-East 1200.00
232 900023 233 agricultural Qalyubia-South 1200.00
233 900024 234 residential Cairo-Central 1200.00
234 900025 235 industrial Giza-East 1200.00
235 900026 236 agricultural Qalyubia-South 1200.00
236 900027 237 commercial Cairo-Central 1200.00
237 900028 238 agricultural Giza-East 1200.00
238 900029 239 residential Qalyubia-South 1200.00
239 900030 240 commercial Cairo-Central 1200.00
240 900031 241 agricultural Giza-East 1200.00
241 900032 242 residential Qalyubia-South 1200.00
242 900033 243 commercial Cairo-Central 1200.00
243 900034 244 industrial Giza-East 1200.00
244 900035 245 agricultural Qalyubia-South 1200.00
245 900036 246 commercial Cairo-Central 1200.00
246 900037 247 industrial Giza-East 1200.00
247 900038 248 residential Qalyubia-South 1200.00
248 900039 249 commercial Cairo-Central 1200.00
249 900040 250 agricultural Giza-East 1200.00
250 900041 251 commercial Qalyubia-South 1200.00
251 900042 252 industrial Cairo-Central 1200.00
252 900043 253 residential Giza-East 1200.00
253 900044 254 commercial Qalyubia-South 1200.00
254 900045 255 industrial Cairo-Central 1200.00
255 900046 256 agricultural Giza-East 1200.00
256 900047 257 residential Qalyubia-South 1200.00
257 900048 258 commercial Cairo-Central 1200.00
258 900049 259 agricultural Giza-East 1200.00
259 900050 260 residential Qalyubia-South 1200.00
260 800001 261 unknown border-sliver 0.49
261 800002 262 unknown border-sliver 0.49
262 800003 263 unknown border-sliver 0.49
263 800004 264 unknown border-sliver 0.49
264 800005 265 unknown border-sliver 0.49
265 800006 266 unknown border-sliver 0.49
266 800007 267 unknown border-sliver 0.49
267 800008 268 unknown border-sliver 0.49
268 800009 269 unknown border-sliver 0.49
269 800010 270 unknown border-sliver 0.49
Area stats for 800xxx:
count 80.000000
mean 750.183750
std 584.374105
min 0.490000
25% 0.490000
50% 1200.000000
75% 1200.000000
max 1200.000000
dtype: float64
All small features (<1m²):
parcel_id record_seq parcel_class district
6 7 7 agricultural Giza-East
16 17 17 commercial Qalyubia-South
22 23 23 agricultural Qalyubia-South
34 35 35 agricultural Qalyubia-South
40 41 41 commercial Qalyubia-South
52 53 53 commercial Qalyubia-South
57 58 58 industrial Giza-East
70 71 71 agricultural Qalyubia-South
75 76 76 residential Giza-East
88 89 89 commercial Qalyubia-South
93 94 94 industrial Giza-East
106 107 107 agricultural Qalyubia-South
111 112 112 residential Giza-East
124 125 125 commercial Qalyubia-South
129 130 130 industrial Giza-East
142 143 143 agricultural Qalyubia-South
147 148 148 residential Giza-East
160 161 161 commercial Qalyubia-South
165 166 166 industrial Giza-East
178 179 179 agricultural Qalyubia-South
260 800001 261 unknown border-sliver
261 800002 262 unknown border-sliver
262 800003 263 unknown border-sliver
263 800004 264 unknown border-sliver
264 800005 265 unknown border-sliver
265 800006 266 unknown border-sliver
266 800007 267 unknown border-sliver
267 800008 268 unknown border-sliver
268 800009 269 unknown border-sliver
269 800010 270 unknown border-sliver
270 800011 271 unknown border-sliver
271 800012 272 unknown border-sliver
272 800013 273 unknown border-sliver
273 800014 274 unknown border-sliver
274 800015 275 unknown border-sliver
275 800016 276 unknown border-sliver
276 800017 277 unknown border-sliver
277 800018 278 unknown border-sliver
278 800019 279 unknown border-sliver
279 800020 280 unknown border-sliver
280 800021 281 unknown border-sliver
281 800022 282 unknown border-sliver
282 800023 283 unknown border-sliver
283 800024 284 unknown border-sliver
284 800025 285 unknown border-sliver
285 800026 286 unknown border-sliver
286 800027 287 unknown border-sliver
287 800028 288 unknown border-sliver
288 800029 289 unknown border-sliver
289 800030 290 unknown border-sliver
Count of <1m²: 50
1:34
Now I have a clear picture of the data:
1. **290 features** total
2. **Duplicate geometries**: There are 50 pairs (100 features) where two different parcel_ids share the same geometry. For example, parcel_id=1 and 900001 have the same geometry, record_seq=1 and 211. So the duplicates are between the main series (1-200ish) and the 900xxx series.
3. **Tiny polygons < 1 m²**: 50 features, including:
- 20 features with area=0 (parcel_ids 7, 17, 23, 35, 41, 53, 58, 71, 76, 89, 94, 107, 112, 125, 130, 143, 148, 161, 166, 179) — these are also invalid (zero-area)
- 30 features in the 800xxx series with area=0.49 (border-slivers)
4. **Invalid geometries**: 20 of them, all the zero-area ones listed above.
The task says:
- "When duplicates exist, keep the record with the earliest record_seq"
- "discard any polygon fragments smaller than 1 m²"
- "one canonical record per parcel"
- Output includes: parcel_id, parcel_class, district, area_m2
- Every feature must be a MultiPolygon
- No null/empty geometries, no invalid geometries, no duplicate geometries, no artifact slivers
Now "duplicates" — the task mentions "duplicates exist" and "keep the record with the earliest record_seq". Looking at the data:
- The duplicate geometries (same geometry appearing twice) — parcel_ids like 1 and 900001 share the same geometry. The task says "one canonical record per parcel" — but these have different parcel_ids. The question is: what constitutes "duplicates" here?
Since the task says "parcel_id (the join key)", it seems parcel_id is the unique identifier. But there are no duplicate parcel_ids in the data. However, there are duplicate geometries with different parcel_ids.
Let me re-read: "When duplicates exist, keep the record with the earliest record_seq". This suggests the dedup is based on some notion of duplicates. Given the context of "stitched together from three legacy provincial systems", the 900xxx records are duplicates of the 1-200ish records — same geometry but from a different legacy system. Since these have different parcel_ids, they represent the same parcel from different sources.
I think the intent is:
- Duplicate records (same geometry, same real-world parcel) → keep the one with lowest record_seq
- The 900xxx series seem to be later records (higher record_seq) from a different legacy system that duplicated the geometry
- The 800xxx series seem to be artifact slivers (border-sliver district, unknown class, area 0.49)
Let me think more carefully. The task says "one canonical record per parcel". If the same geometry appears twice with different parcel_ids but the geometry is identical, then these are representing the same parcel from different legacy systems. But they have different parcel_ids.
Hmm, maybe the "parcel_id" is supposed to be the unique join key but there could be multiple records with the same parcel_id? Let me check again...
Wait, I already checked and there are NO duplicate parcel_ids. Every parcel_id is unique. So the "duplicates" must refer to duplicate geometries.
Actually, re-reading more carefully: "one canonical record per parcel" — if the same parcel (same geometry) appears twice from different legacy sources, we should keep one. The parcel_id to keep would be the one from the record with the lowest record_seq.
So the logic should be:
1. Group by geometry (same parcel)
2. For each group, keep the record with the lowest record_seq
3. Remove features with area < 1 m² (the 800xxx slivers and the zero-area invalid ones)
4. Convert to MultiPolygon
5. Make all geometries valid
6. Recalculate area_m2
7. Output to GeoParquet
Let me also look at whether the 800xxx (border-sliver) and 900xxx series are supposed to be completely removed or handled differently.
The 800xxx series (30 features) are all labeled "border-sliver" district, "unknown" class, area 0.49 m² — clearly artifact slivers that should be discarded since they're < 1 m².
For the duplicate geometries between main series and 900xxx series — these have the same geometry, so we keep the one with earliest record_seq (which is the main series one with lower record_seq).
Let me write the script.
Wait, but it's grouping by geometry. Two features with the exact same geometry but different parcel_ids. The task says "one canonical record per parcel" — so if the geometry is the same, it's the same parcel. We keep the one with lowest record_seq.
But parcels 1 and 900001 have the same geometry. parcel 1 has record_seq=1, parcel 900001 has record_seq=211. So we keep parcel_id=1.
Let me write the solution:
```python
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely.geometry import MultiPolygon
from shapely.validation import make_valid
# Read
gdf = gpd.read_file('/work/cairo_parcels_legacy.geojson')
print(f'Loaded {len(gdf)} features')
# 1. Handle duplicates: group by geometry, keep smallest record_seq
# Sort by record_seq so first in each group is the one to keep
gdf_sorted = gdf.sort_values('record_seq')
# Group by geometry (WKT)
gdf_sorted['geom_wkt'] = gdf_sorted.geometry.apply(lambda g: g.wkt)
gdf_dedup = gdf_sorted.groupby('geom_wkt').first().reset_index(drop=True)
# Drop the helper column
gdf_dedup = gdf_dedup.drop(columns=['geom_wkt'])
print(f'After dedup: {len(gdf_dedup)} features')
# 2. Remove features with area < 1 m² (discard artifact slivers)
areas = gdf_dedup.geometry.area
gdf_clean = gdf_dedup[areas >= 1.0].copy()
print(f'After removing <1m²: {len(gdf_clean)} features')
# 3. Fix invalid geometries
invalid_mask = ~gdf_clean.is_valid
print(f'Invalid geometries to fix: {invalid_mask.sum()}')
for idx in gdf_clean[invalid_mask].index:
gdf_clean.at[idx, 'geometry'] = make_valid(gdf_clean.at[idx, 'geometry'])
# 4. Convert all to MultiPolygon
def to_multipolygon(geom):
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
elif geom.geom_type == 'MultiPolygon':
return geom
else:
# If it's a GeometryCollection or something else, try to extract polygons
if hasattr(geom, 'geoms'):
polygons = [g for g in geom.geoms if g.geom_type == 'Polygon']
if polygons:
return MultiPolygon(polygons)
# Fallback - shouldn't happen after cleaning
return geom
gdf_clean['geometry'] = gdf_clean['geometry'].apply(to_multipolygon)
# 5. Recalculate area_m2 in the same CRS (EPSG:22992 - metric)
gdf_clean['area_m2'] = gdf_clean.geometry.area
# 6. Select final columns
gdf_final = gdf_clean[['parcel_id', 'parcel_class', 'district', 'area_m2', 'geometry']].copy()
# 7. Final validation
print(f'Final features: {len(gdf_final)}')
print(f'Null geometries: {gdf_final.geometry.isna().sum()}')
print(f'Empty geometries: {gdf_final.geometry.is_empty.sum()}')
print(f'Invalid geometries: {(~gdf_final.is_valid).sum()}')
print(f'All MultiPolygon: {(gdf_final.geometry.geom_type == "MultiPolygon").all()}')
# 8. Write
gdf_final.to_parquet('/work/parcels_canonical.geoparquet', index=False)
print('Done!')
```
Actually, let me think about this more carefully. The "duplicate" case might be about same parcel_id. Let me check if any parcel_ids appear more than once...
No, I confirmed no duplicate parcel_ids exist. So "duplicates" refers to duplicate geometries.
Also, I need to check for duplicate geometries after dedup. After grouping by geometry and keeping the first, there should be no duplicate geometries left. But let me verify in the script.
Also, I want to make sure that after make_valid we don't introduce issues. Some zero-area polygons might become empty or points/lines after make_valid. But we already filtered by area < 1, and the zero-area ones ARE the invalid ones... wait, let me check:
The invalid ones have area=0. So they'll be filtered out by the <1 check. Good.
Let me also think about whether we need to check for "duplicate geometries" after dedup. After groupby first by geometry, there shouldn't be duplicates. But let me also check for any remaining issues.
Actually wait - the 20 invalid features have area=0 (zero area). These are likely degenerate polygons (collapsed to a line or point). They'll be removed by the <1 check. But maybe I should still run make_valid on remaining features to make sure.
Let me also be more careful about the GeoParquet format. Using `to_parquet` with geopandas should produce the right format.
1:34
1:34
Write
/work/solve.py
content · 88 lines · py
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely.geometry import MultiPolygon
from shapely.validation import make_valid
# ── 1. Read source data ──────────────────────────────────────────────
gdf = gpd.read_file('/work/cairo_parcels_legacy.geojson')
print(f'Loaded {len(gdf)} features, CRS={gdf.crs}')
# ── 2. Deduplicate by geometry — keep record with lowest record_seq ──
# A parcel is identified by its geometry (the same real-world parcel
# appears from multiple legacy systems with different parcel_ids).
gdf_sorted = gdf.sort_values('record_seq')
gdf_sorted['_geom_key'] = gdf_sorted.geometry.apply(lambda g: g.wkt)
gdf_dedup = gdf_sorted.groupby('_geom_key', as_index=False).first()
gdf_dedup.drop(columns=['_geom_key'], inplace=True)
print(f'After dedup (same geometry → earliest record_seq): {len(gdf_dedup)}')
# ── 3. Discard polygon fragments smaller than 1 m² ──────────────────
areas = gdf_dedup.geometry.area
gdf_clean = gdf_dedup[areas >= 1.0].copy()
print(f'After removing <1 m² fragments: {len(gdf_clean)} (removed {(areas < 1.0).sum()})')
# ── 4. Fix invalid geometries ────────────────────────────────────────
invalid_mask = ~gdf_clean.is_valid
print(f'Fixing {invalid_mask.sum()} invalid geometries...')
for idx in gdf_clean[invalid_mask].index:
gdf_clean.at[idx, 'geometry'] = make_valid(gdf_clean.at[idx, 'geometry'])
# ── 5. Ensure every feature is a MultiPolygon ────────────────────────
def ensure_multipolygon(geom):
"""Convert any geometry to MultiPolygon."""
if geom is None or geom.is_empty:
return geom
t = geom.geom_type
if t == 'MultiPolygon':
return geom
if t == 'Polygon':
return MultiPolygon([geom])
# GeometryCollection or other — extract Polygon components
if hasattr(geom, 'geoms'):
polys = [g for g in geom.geoms if g.geom_type == 'Polygon']
if polys:
return MultiPolygon(polys)
# Last resort: if we got a Point/LineString, skip (but shouldn't happen
# after area filter)
return geom
gdf_clean['geometry'] = gdf_clean['geometry'].apply(ensure_multipolygon)
# ── 6. Recalculate area_m2 from cleaned geometry ─────────────────────
gdf_clean['area_m2'] = gdf_clean.geometry.area.round(4)
# ── 7. Keep only the requested columns ───────────────────────────────
gdf_final = gdf_clean[['parcel_id', 'parcel_class', 'district',
'area_m2', 'geometry']].copy()
gdf_final.reset_index(drop=True, inplace=True)
# ── 8. Final quality checks ──────────────────────────────────────────
print(f'\n── Final quality report ──')
print(f' Features: {len(gdf_final)}')
null_mask = gdf_final.geometry.isna()
print(f' Null geometries: {null_mask.sum()}')
empty_mask = gdf_final.geometry.is_empty
print(f' Empty geometries: {empty_mask.sum()}')
invalid_mask = ~gdf_final.is_valid
print(f' Invalid geometries: {invalid_mask.sum()}')
dup_geom = gdf_final.geometry.value_counts()
print(f' Duplicate geometries: {(dup_geom > 1).sum()}')
non_mp = gdf_final.geometry.geom_type != 'MultiPolygon'
print(f' Non-MultiPolygon: {non_mp.sum()}')
tiny = gdf_final.geometry.area < 1.0
print(f' Area < 1 m²: {tiny.sum()}')
print(f' Area range: {gdf_final.geometry.area.min():.4f} – {gdf_final.geometry.area.max():.4f} m²')
print(f' CRS: {gdf_final.crs}')
# If anything failed, abort
assert null_mask.sum() == 0, 'Null geometries present!'
assert empty_mask.sum() == 0, 'Empty geometries present!'
assert invalid_mask.sum() == 0, 'Invalid geometries present!'
assert (dup_geom > 1).sum() == 0, 'Duplicate geometries present!'
assert non_mp.sum() == 0, 'Non-MultiPolygon geometries present!'
assert tiny.sum() == 0, 'Tiny fragments (<1 m²) still present!'
# ── 9. Write output ──────────────────────────────────────────────────
gdf_final.to_parquet('/work/parcels_canonical.geoparquet', index=False)
print('\n✓ Written to /work/parcels_canonical.geoparquet')Wrote /work/solve.py (3994 chars).
1:37
Let me run the script and see if it works.
1:37
Bash
cd /work && python solve.py
Loaded 290 features, CRS=EPSG:22992 After dedup (same geometry → earliest record_seq): 240 After removing <1 m² fragments: 190 (removed 50) Fixing 0 invalid geometries... ── Final quality report ── Features: 190 Null geometries: 0 Empty geometries: 0 Invalid geometries: 0 Duplicate geometries: 0 Non-MultiPolygon: 0 Area < 1 m²: 0 Area range: 1200.0000 – 1280.0000 m² CRS: None ✓ Written to /work/parcels_canonical.geoparquet
1:44
The CRS shows None because GeoJSON has a CRS field but when we convert to GeoPandas and then to Parquet, the CRS might not be preserved properly. Let me check if the output file has the CRS set correctly. Actually, GeoParquet stores CRS via the "geo" metadata. The CRS from the original GeoJSON was EPSG:22992. Let me set it explicitly before writing.
1:44
1:44
Read
/work/solve.py
73 tiny = gdf_final.geometry.area < 1.0
74 print(f' Area < 1 m²: {tiny.sum()}')
75 print(f' Area range: {gdf_final.geometry.area.min():.4f} – {gdf_final.geometry.area.max():.4f} m²')
76 print(f' CRS: {gdf_final.crs}')
77
78 # If anything failed, abort
79 assert null_mask.sum() == 0, 'Null geometries present!'
80 assert empty_mask.sum() == 0, 'Empty geometries present!'
81 assert invalid_mask.sum() == 0, 'Invalid geometries present!'
82 assert (dup_geom > 1).sum() == 0, 'Duplicate geometries present!'
83 assert non_mp.sum() == 0, 'Non-MultiPolygon geometries present!'
84 assert tiny.sum() == 0, 'Tiny fragments (<1 m²) still present!'
85
86 # ── 9. Write output ──────────────────────────────────────────────────
87 gdf_final.to_parquet('/work/parcels_canonical.geoparquet', index=False)
88 print('\n✓ Written to /work/parcels_canonical.geoparquet')
1:52
The issue is that the CRS (which was EPSG:22992) was lost. I need to set it explicitly. Let me look at the earlier part - I think gdf_dedup lost the CRS because the groupby operation may have stripped it. Let me check.
1:52
Edit
/work/solve.py
Edited /work/solve.py.
1:58
Now let me re-run to verify CRS is preserved.
1:58
Bash
cd /work && python solve.py
Loaded 290 features, CRS=EPSG:22992 After dedup (same geometry → earliest record_seq): 240 After removing <1 m² fragments: 190 (removed 50) Fixing 0 invalid geometries... ── Final quality report ── Features: 190 Null geometries: 0 Empty geometries: 0 Invalid geometries: 0 Duplicate geometries: 0 Non-MultiPolygon: 0 Area < 1 m²: 0 Area range: 1200.0000 – 1280.0000 m² CRS: EPSG:22992 ✓ Written to /work/parcels_canonical.geoparquet
2:12
Wait - I need to re-check the logic. The user says "parcel_id (the join key)" and "When duplicates exist, keep the record with the earliest record_seq". The duplicate geometries are pairs like: - parcel_id=1, record_seq=1 - parcel_id=900001, record_seq=211 They have the same geometry. When I dedup by geometry, I keep the one with the lowest record_seq - that's parcel_id=1 (record_seq=1), which is correct. But wait - the user said "parcel_id (the join key)". So the parcel_id is the unique identifier for a parcel. Looking at the duplicate pairs, the 900xxx series seems to be a duplicate system's mapping of the same geometries. So dropping them by geometry (keeping earliest record_seq) makes sense. But there are also the invalid self-intersecting polygons (parcel_id 7, 17, 23, etc.) - these have area 0.0, so they'll be removed by the <1 m² filter anyway. That's correct. And the 800xxx series (border-sliver features) all have area 0.49, so they'll be removed too. Good. But wait - the invalid geometries with area 0.0 - I'm "fixing 0 invalid geometries". That's because they're all filtered out already by the <1 m² check. The valid check only runs on the remaining 190 features. Let me verify no invalid geometries slipped through by checking the final output more carefully. Actually the report shows 0 invalid geometries, 0 null, 0 empty, 0 duplicate geometries, 0 non-MultiPolygon, 0 area < 1. All checks pass. The CRS is set. Let me verify the output file exists and verify some content.
2:12
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_parquet('parcels_canonical.geoparquet')
print(f'Shape: {gdf.shape}')
print(f'Columns: {list(gdf.columns)}')
print(f'CRS: {gdf.crs}')
print(f'Dtypes:')
print(gdf.dtypes)
print()
print('First 10 rows:')
print(gdf.head(10))
print()
print('parcel_id range:', gdf['parcel_id'].min(), '-', gdf['parcel_id'].max())
print('Districts:', gdf['district'].unique())
print('Classes:', gdf['parcel_class'].unique())
print()
# Spot check: parcel_id 1 should be present (record_seq 1, not 900001)
p1 = gdf[gdf['parcel_id'] == 1]
print(f'parcel_id=1 present: {len(p1) > 0}')
p900001 = gdf[gdf['parcel_id'] == 900001]
print(f'parcel_id=900001 (duplicate) present: {len(p900001) > 0}')
p800001 = gdf[gdf['parcel_id'] == 800001]
print(f'parcel_id=800001 (sliver) present: {len(p800001) > 0}')
"
Shape: (190, 5)
Columns: ['parcel_id', 'parcel_class', 'district', 'area_m2', 'geometry']
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "Egypt 1907 / Red Belt", "base_crs": {"name": "Egypt 1907", "datum": {"type": "GeodeticReferenceFrame", "name": "Egypt 1907", "ellipsoid": {"name": "Helmert 1906", "semi_major_axis": 6378200, "inverse_flattening": 298.3}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "id": {"authority": "EPSG", "code": 4229}}, "conversion": {"name": "Egypt Red Belt", "method": {"name": "Transverse Mercator", "id": {"authority": "EPSG", "code": 9807}}, "parameters": [{"name": "Latitude of natural origin", "value": 30, "unit": "degree", "id": {"authority": "EPSG", "code": 8801}}, {"name": "Longitude of natural origin", "value": 31, "unit": "degree", "id": {"authority": "EPSG", "code": 8802}}, {"name": "Scale factor at natural origin", "value": 1, "unit": "unity", "id": {"authority": "EPSG", "code": 8805}}, {"name": "False easting", "value": 615000, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 810000, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "east", "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "unit": "metre"}]}, "scope": "Engineering survey, topographic mapping.", "area": "Egypt - onshore between 29\u00b0E and 33\u00b0E, offshore Mediterranean east of 29\u00b0E and offshore Gulf of Suez.", "bbox": {"south_latitude": 21.99, "west_longitude": 29, "north_latitude": 33.82, "east_longitude": 34.27}, "id": {"authority": "EPSG", "code": 22992}}
Dtypes:
parcel_id int32
parcel_class str
district str
area_m2 float64
geometry geometry
dtype: object
First 10 rows:
parcel_id ... geometry
0 91 ... MULTIPOLYGON (((640000 815360, 640030 815360, ...
1 181 ... MULTIPOLYGON (((640000 815720, 640030 815720, ...
2 32 ... MULTIPOLYGON (((640050 815120, 640080 815120, ...
3 47 ... MULTIPOLYGON (((640050 815180, 640080 815180, ...
4 122 ... MULTIPOLYGON (((640050 815480, 640080 815480, ...
5 137 ... MULTIPOLYGON (((640050 815540, 640080 815540, ...
6 19 ... MULTIPOLYGON (((640150 815060, 640180 815060, ...
7 109 ... MULTIPOLYGON (((640150 815420, 640180 815420, ...
8 50 ... MULTIPOLYGON (((640200 815180, 640230 815180, ...
9 65 ... MULTIPOLYGON (((640200 815240, 640230 815240, ...
[10 rows x 5 columns]
parcel_id range: 1 - 210
Districts: <ArrowStringArray>
['Giza-East', 'Qalyubia-South', 'Cairo-Central']
Length: 3, dtype: str
Classes: <ArrowStringArray>
['agricultural', 'commercial', 'residential', 'industrial']
Length: 4, dtype: str
parcel_id=1 present: True
parcel_id=900001 (duplicate) present: False
parcel_id=800001 (sliver) present: False
2:20
Everything looks good! Let me verify the full quality checklist one more time: 1. ✅ `parcels_canonical.geoparquet` exists 2. ✅ Columns: parcel_id, parcel_class, district, area_m2, geometry 3. ✅ CRS is EPSG:22992 (correctly preserved) 4. ✅ Every geometry is MultiPolygon 5. ✅ No null geometries 6. ✅ No empty geometries 7. ✅ No invalid geometries 8. ✅ No duplicate geometries 9. ✅ No slivers (<1 m²) — min area is 1200 m² 10. ✅ Deduplication worked: parcel_id=1 kept (earlier record_seq=1) instead of duplicate parcel_id=900001 11. ✅ Slivers (800xxx series) removed Let me also verify the file exists.
2:20
Bash
ls -lh /work/parcels_canonical.geoparquet
-rw-r--r-- 1 agent agent 18K Jun 18 12:38 /work/parcels_canonical.geoparquet
2:35
Let me verify everything the user asked for: 1. ✅ One canonical record per parcel (deduplicated by geometry, keeping earliest record_seq) 2. ✅ Discarded polygon fragments < 1 m² (50 features removed — the 20 invalid zero-area ones + 30 border slivers of 0.49 m²) 3. ✅ Written to parcels_canonical.geoparquet 4. ✅ Columns: parcel_id, parcel_class, district, area_m2 5. ✅ Every feature is a MultiPolygon 6. ✅ No null/empty geometries 7. ✅ No invalid geometries 8. ✅ No duplicate geometries 9. ✅ No artifact slivers (< 1 m²) 10. ✅ CRS preserved as EPSG:22992 The output looks clean. Let me provide a summary.
2:35