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"
}
0:03
Let me start by examining the input file to understand its structure and content.
0:03
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:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Dtypes:')
print(gdf.dtypes)
print()
print('First few rows:')
print(gdf.head(10).to_string())
"
Shape: (290, 6) Columns: ['parcel_id', 'record_seq', 'parcel_class', 'district', 'area_m2', 'geometry'] CRS: EPSG:22992 Dtypes: parcel_id int32 record_seq int32 parcel_class str district str area_m2 float64 geometry geometry dtype: object First few rows: parcel_id record_seq parcel_class district area_m2 geometry 0 1 1 commercial Giza-East 1200.0 POLYGON ((640000 815000, 640030 815000, 640030 815040, 640000 815040, 640000 815000)) 1 2 2 industrial Qalyubia-South 1200.0 POLYGON ((640050 815000, 640080 815000, 640080 815040, 640050 815040, 640050 815000)) 2 3 3 agricultural Cairo-Central 1200.0 POLYGON ((640100 815000, 640130 815000, 640130 815040, 640100 815040, 640100 815000)) 3 4 4 residential Giza-East 1200.0 POLYGON ((640150 815000, 640180 815000, 640180 815040, 640150 815040, 640150 815000)) 4 5 5 commercial Qalyubia-South 1200.0 POLYGON ((640200 815000, 640230 815000, 640230 815040, 640200 815040, 640200 815000)) 5 6 6 industrial Cairo-Central 1200.0 POLYGON ((640250 815000, 640280 815000, 640280 815040, 640250 815040, 640250 815000)) 6 7 7 agricultural Giza-East 1200.0 POLYGON ((640300 815000, 640330 815040, 640300 815040, 640330 815000, 640300 815000)) 7 8 8 residential Qalyubia-South 1200.0 POLYGON ((640350 815000, 640380 815000, 640380 815040, 640350 815040, 640350 815000)) 8 9 9 commercial Cairo-Central 1200.0 POLYGON ((640400 815000, 640430 815000, 640430 815040, 640400 815040, 640400 815000)) 9 10 10 industrial Giza-East 1200.0 POLYGON ((640450 815000, 640480 815000, 640480 815040, 640450 815040, 640450 815000))
0:10
Let me explore the data more to understand duplicates, invalid geometries, etc.
0:10
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Check for duplicates by parcel_id
dups = gdf.groupby('parcel_id').size()
print('Duplicate parcel_ids (count > 1):')
print(dups[dups > 1].head(30))
print()
print('Number of parcel_ids with duplicates:', (dups > 1).sum())
print()
# Check for null/empty geometries
print('Null geometries:', gdf.geometry.isna().sum())
print('Empty geometries:', (~gdf.geometry.isna() & gdf.geometry.is_empty).sum())
print()
# Check for invalid geometries
print('Invalid geometries:', (~gdf.geometry.is_valid).sum())
print()
# Check geometry types
print('Geometry types:', gdf.geometry.type.value_counts().to_dict())
print()
# Check for nan values
print('Null parcel_id:', gdf['parcel_id'].isna().sum())
print('Null record_seq:', gdf['record_seq'].isna().sum())
print('Null parcel_class:', gdf['parcel_class'].isna().sum())
print('Null district:', gdf['district'].isna().sum())
print('Null area_m2:', gdf['area_m2'].isna().sum())
"
Duplicate parcel_ids (count > 1):
Series([], dtype: int64)
Number of parcel_ids with duplicates: 0
Null geometries: 0
Empty geometries: 0
Invalid geometries: 20
Geometry types: {'Polygon': 260, 'MultiPolygon': 30}
Null parcel_id: 0
Null record_seq: 0
Null parcel_class: 0
Null district: 0
Null area_m2: 0
0:17
So there are no duplicate parcel_ids - each parcel_id appears only once. But there might be duplicate geometries. Let me check more thoroughly. The task says "When duplicates exist, keep the record with the earliest record_seq." Since there are no duplicate parcel_ids, maybe duplicates are based on geometry? Or maybe there are duplicate parcel_ids but they just didn't show up because my groupby didn't catch something? Let me look more carefully. Wait, the groupby showed no duplicates with count > 1. So each parcel_id is unique. Let me check for duplicate geometries.
0:17
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
geom_wkt = gdf.geometry.to_wkt()
print('Number of duplicate geometries:', geom_wkt.duplicated(keep=False).sum())
print()
# Show the invalid geometries
invalid = gdf[~gdf.geometry.is_valid]
print('Invalid geometries:')
for idx, row in invalid.iterrows():
print(f' idx={idx} pid={row.parcel_id} seq={row.record_seq} reason={row.geometry.is_valid}')
print(f' geom: {row.geometry}')
print()
# Let's also look at area_m2 vs computed area
print('Checking area_m2 column...')
print('area_m2 describe:', gdf['area_m2'].describe())
print()
print('Computed areas (from geometry):')
areas = gdf.geometry.area
print('Computed area describe:', areas.describe())
print()
print('Smallest computed areas:')
small = gdf.iloc[areas.argsort()[:10]]
for idx, row in small.iterrows():
print(f' idx={idx} pid={row.parcel_id} area_m2={row.area_m2} computed_area={row.geometry.area:.6f}')
"
Number of duplicate geometries: 100
Invalid geometries:
idx=6 pid=7 seq=7 reason=False
geom: POLYGON ((640300 815000, 640330 815040, 640300 815040, 640330 815000, 640300 815000))
idx=16 pid=17 seq=17 reason=False
geom: POLYGON ((640050 815060, 640080 815100, 640050 815100, 640080 815060, 640050 815060))
idx=22 pid=23 seq=23 reason=False
geom: POLYGON ((640350 815060, 640380 815100, 640350 815100, 640380 815060, 640350 815060))
idx=34 pid=35 seq=35 reason=False
geom: POLYGON ((640200 815120, 640230 815160, 640200 815160, 640230 815120, 640200 815120))
idx=40 pid=41 seq=41 reason=False
geom: POLYGON ((640500 815120, 640530 815160, 640500 815160, 640530 815120, 640500 815120))
idx=52 pid=53 seq=53 reason=False
geom: POLYGON ((640350 815180, 640380 815220, 640350 815220, 640380 815180, 640350 815180))
idx=57 pid=58 seq=58 reason=False
geom: POLYGON ((640600 815180, 640630 815220, 640600 815220, 640630 815180, 640600 815180))
idx=70 pid=71 seq=71 reason=False
geom: POLYGON ((640500 815240, 640530 815280, 640500 815280, 640530 815240, 640500 815240))
idx=75 pid=76 seq=76 reason=False
geom: POLYGON ((640000 815300, 640030 815340, 640000 815340, 640030 815300, 640000 815300))
idx=88 pid=89 seq=89 reason=False
geom: POLYGON ((640650 815300, 640680 815340, 640650 815340, 640680 815300, 640650 815300))
idx=93 pid=94 seq=94 reason=False
geom: POLYGON ((640150 815360, 640180 815400, 640150 815400, 640180 815360, 640150 815360))
idx=106 pid=107 seq=107 reason=False
geom: POLYGON ((640050 815420, 640080 815460, 640050 815460, 640080 815420, 640050 815420))
idx=111 pid=112 seq=112 reason=False
geom: POLYGON ((640300 815420, 640330 815460, 640300 815460, 640330 815420, 640300 815420))
idx=124 pid=125 seq=125 reason=False
geom: POLYGON ((640200 815480, 640230 815520, 640200 815520, 640230 815480, 640200 815480))
idx=129 pid=130 seq=130 reason=False
geom: POLYGON ((640450 815480, 640480 815520, 640450 815520, 640480 815480, 640450 815480))
idx=142 pid=143 seq=143 reason=False
geom: POLYGON ((640350 815540, 640380 815580, 640350 815580, 640380 815540, 640350 815540))
idx=147 pid=148 seq=148 reason=False
geom: POLYGON ((640600 815540, 640630 815580, 640600 815580, 640630 815540, 640600 815540))
idx=160 pid=161 seq=161 reason=False
geom: POLYGON ((640500 815600, 640530 815640, 640500 815640, 640530 815600, 640500 815600))
idx=165 pid=166 seq=166 reason=False
geom: POLYGON ((640000 815660, 640030 815700, 640000 815700, 640030 815660, 640000 815660))
idx=178 pid=179 seq=179 reason=False
geom: POLYGON ((640650 815660, 640680 815700, 640650 815700, 640680 815660, 640650 815660))
Checking area_m2 column...
area_m2 describe: count 290.000000
mean 1075.912759
std 365.934414
min 0.490000
25% 1200.000000
50% 1200.000000
75% 1200.000000
max 1200.000000
Name: area_m2, dtype: float64
Computed areas (from geometry):
Computed area describe: count 290.00000
mean 1001.43000
std 458.37842
min 0.00000
25% 1200.00000
50% 1200.00000
75% 1200.00000
max 1280.00000
dtype: float64
Smallest computed areas:
idx=34 pid=35 area_m2=1200.0 computed_area=0.000000
idx=57 pid=58 area_m2=1200.0 computed_area=0.000000
idx=93 pid=94 area_m2=1200.0 computed_area=0.000000
idx=88 pid=89 area_m2=1200.0 computed_area=0.000000
idx=111 pid=112 area_m2=1200.0 computed_area=0.000000
idx=106 pid=107 area_m2=1200.0 computed_area=0.000000
idx=129 pid=130 area_m2=1200.0 computed_area=0.000000
idx=124 pid=125 area_m2=1200.0 computed_area=0.000000
idx=147 pid=148 area_m2=1200.0 computed_area=0.000000
idx=142 pid=143 area_m2=1200.0 computed_area=0.000000
0:29
Let me analyze the data: 1. **No duplicate parcel_ids** - each parcel_id appears only once. So the "earliest record_seq" rule is simple: no conflicts. 2. **100 duplicate geometries** - These need to be deduplicated. 3. **20 invalid geometries** - These are bow-tie polygons (self-intersecting). The pattern shows points like (640300 815000, 640330 815040, 640300 815040, 640330 815000, 640300 815000) - the vertices are in a crossing order, creating a self-intersecting polygon. These need to be fixed (e.g., using `buffer(0)` or `make_valid`). 4. **Some geometries have computed area = 0** (the invalid ones have zero area). 5. **There are some small fragments** - the area_m2 shows min 0.49, so we need to remove fragments < 1 m². 6. **Geometry types** - 260 Polygons, 30 MultiPolygons. We need to convert all to MultiPolygon. 7. **No null/empty geometries**. Let me first check if there are duplicate geometries (same geometry WKT) and also look more carefully at the data. Let me also check what the "duplicate geometries" means - 100 duplicate geometries might mean that 50 geometries appear twice, or some other pattern.
0:29
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Find duplicate geometries
geom_wkt = gdf.geometry.to_wkt()
dup_mask = geom_wkt.duplicated(keep=False)
dup_indices = gdf[dup_mask].index
print(f'Total rows with duplicate geometries: {len(dup_indices)}')
print()
# Group duplicate geometries
from collections import defaultdict
dup_groups = defaultdict(list)
for idx in dup_indices:
wkt = geom_wkt[idx]
dup_groups[wkt].append(idx)
print(f'Number of unique geometries that have duplicates: {len(dup_groups)}')
for wkt, indices in list(dup_groups.items())[:5]:
print(f' Geometry appears {len(indices)} times, at indices {indices}')
for i in indices:
row = gdf.loc[i]
print(f' idx={i} pid={row.parcel_id} seq={row.record_seq} area={row.area_m2}')
print()
# Check if there are any other duplicate patterns
# Let's also check for duplicated parcel_id (there were none)
print('All parcel_ids are unique:', gdf['parcel_id'].is_unique)
# Check the small fragments (< 1 m²)
small_areas = gdf[gdf['area_m2'] < 1.0]
print(f'Records with area_m2 < 1: {len(small_areas)}')
print(small_areas[['parcel_id', 'record_seq', 'area_m2']].to_string())
print()
# Also check computed area < 1
computed_areas = gdf.geometry.area
small_computed = gdf[computed_areas < 1.0]
print(f'Records with computed area < 1: {len(small_computed)}')
print(small_computed[['parcel_id', 'record_seq', 'area_m2']].to_string())
"
Total rows with duplicate geometries: 100
Number of unique geometries that have duplicates: 50
Geometry appears 2 times, at indices [0, 210]
idx=0 pid=1 seq=1 area=1200.0
idx=210 pid=900001 seq=211 area=1200.0
Geometry appears 2 times, at indices [1, 211]
idx=1 pid=2 seq=2 area=1200.0
idx=211 pid=900002 seq=212 area=1200.0
Geometry appears 2 times, at indices [2, 212]
idx=2 pid=3 seq=3 area=1200.0
idx=212 pid=900003 seq=213 area=1200.0
Geometry appears 2 times, at indices [3, 213]
idx=3 pid=4 seq=4 area=1200.0
idx=213 pid=900004 seq=214 area=1200.0
Geometry appears 2 times, at indices [4, 214]
idx=4 pid=5 seq=5 area=1200.0
idx=214 pid=900005 seq=215 area=1200.0
All parcel_ids are unique: True
Records with area_m2 < 1: 30
parcel_id record_seq area_m2
260 800001 261 0.49
261 800002 262 0.49
262 800003 263 0.49
263 800004 264 0.49
264 800005 265 0.49
265 800006 266 0.49
266 800007 267 0.49
267 800008 268 0.49
268 800009 269 0.49
269 800010 270 0.49
270 800011 271 0.49
271 800012 272 0.49
272 800013 273 0.49
273 800014 274 0.49
274 800015 275 0.49
275 800016 276 0.49
276 800017 277 0.49
277 800018 278 0.49
278 800019 279 0.49
279 800020 280 0.49
280 800021 281 0.49
281 800022 282 0.49
282 800023 283 0.49
283 800024 284 0.49
284 800025 285 0.49
285 800026 286 0.49
286 800027 287 0.49
287 800028 288 0.49
288 800029 289 0.49
289 800030 290 0.49
Records with computed area < 1: 50
parcel_id record_seq area_m2
6 7 7 1200.00
16 17 17 1200.00
22 23 23 1200.00
34 35 35 1200.00
40 41 41 1200.00
52 53 53 1200.00
57 58 58 1200.00
70 71 71 1200.00
75 76 76 1200.00
88 89 89 1200.00
93 94 94 1200.00
106 107 107 1200.00
111 112 112 1200.00
124 125 125 1200.00
129 130 130 1200.00
142 143 143 1200.00
147 148 148 1200.00
160 161 161 1200.00
165 166 166 1200.00
178 179 179 1200.00
260 800001 261 0.49
261 800002 262 0.49
262 800003 263 0.49
263 800004 264 0.49
264 800005 265 0.49
265 800006 266 0.49
266 800007 267 0.49
267 800008 268 0.49
268 800009 269 0.49
269 800010 270 0.49
270 800011 271 0.49
271 800012 272 0.49
272 800013 273 0.49
273 800014 274 0.49
274 800015 275 0.49
275 800016 276 0.49
276 800017 277 0.49
277 800018 278 0.49
278 800019 279 0.49
279 800020 280 0.49
280 800021 281 0.49
281 800022 282 0.49
282 800023 283 0.49
283 800024 284 0.49
284 800025 285 0.49
285 800026 286 0.49
286 800027 287 0.49
287 800028 288 0.49
288 800029 289 0.49
289 800030 290 0.49
0:38
Let me understand the data better: 1. **Duplicate geometries**: There are 50 unique geometries that appear twice (100 rows). Each appears with two different parcel_ids and record_seqs. Looking at the pattern, it seems like some geometries have a "legacy" copy with a different parcel_id (900001-900050 range) and a higher record_seq. So when there are duplicates (same geometry), we need to keep the one with the earliest record_seq. 2. **Invalid geometries**: 20 polygons that are self-intersecting (bow-tie shaped, like `POLYGON ((640300 815000, 640330 815040, 640300 815040, 640330 815000, 640300 815000))`). These have computed area = 0 and need to be fixed (or discarded if they can't be fixed). 3. **Small polygons**: 30 records with area_m2 = 0.49 - these are polygon fragments smaller than 1 m² and need to be discarded. 4. **Duplicate parcel_ids?** No - parcel_ids are all unique. But the instructions say "When duplicates exist, keep the record with the earliest record_seq" - this likely refers to records with duplicate geometries (same parcel represented in multiple legacy systems). Let me also check the full extent of the duplicate geometry pairs. Let me look at the full data more carefully.
0:38
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Check the duplicate geometry groups more carefully
from collections import defaultdict
geom_wkt = gdf.geometry.to_wkt()
dup_mask = geom_wkt.duplicated(keep=False)
dup_groups = defaultdict(list)
for idx in gdf[dup_mask].index:
dup_groups[geom_wkt[idx]].append(idx)
print('All duplicate geometry groups:')
for wkt, indices in sorted(dup_groups.items(), key=lambda x: x[1][0]):
for i in indices:
row = gdf.loc[i]
print(f' idx={i} pid={row.parcel_id} seq={row.record_seq} area={row.area_m2} class={row.parcel_class} dist={row.district}')
print()
"
All duplicate geometry groups: idx=0 pid=1 seq=1 area=1200.0 class=commercial dist=Giza-East idx=210 pid=900001 seq=211 area=1200.0 class=industrial dist=Giza-East idx=1 pid=2 seq=2 area=1200.0 class=industrial dist=Qalyubia-South idx=211 pid=900002 seq=212 area=1200.0 class=agricultural dist=Qalyubia-South idx=2 pid=3 seq=3 area=1200.0 class=agricultural dist=Cairo-Central idx=212 pid=900003 seq=213 area=1200.0 class=residential dist=Cairo-Central idx=3 pid=4 seq=4 area=1200.0 class=residential dist=Giza-East idx=213 pid=900004 seq=214 area=1200.0 class=commercial dist=Giza-East idx=4 pid=5 seq=5 area=1200.0 class=commercial dist=Qalyubia-South idx=214 pid=900005 seq=215 area=1200.0 class=industrial dist=Qalyubia-South idx=5 pid=6 seq=6 area=1200.0 class=industrial dist=Cairo-Central idx=215 pid=900006 seq=216 area=1200.0 class=agricultural dist=Cairo-Central idx=7 pid=8 seq=8 area=1200.0 class=residential dist=Qalyubia-South idx=216 pid=900007 seq=217 area=1200.0 class=commercial dist=Giza-East idx=8 pid=9 seq=9 area=1200.0 class=commercial dist=Cairo-Central idx=217 pid=900008 seq=218 area=1200.0 class=industrial dist=Qalyubia-South idx=9 pid=10 seq=10 area=1200.0 class=industrial dist=Giza-East idx=218 pid=900009 seq=219 area=1200.0 class=agricultural dist=Cairo-Central idx=11 pid=12 seq=12 area=1200.0 class=residential dist=Cairo-Central idx=219 pid=900010 seq=220 area=1200.0 class=commercial dist=Giza-East idx=12 pid=13 seq=13 area=1200.0 class=commercial dist=Giza-East idx=220 pid=900011 seq=221 area=1200.0 class=industrial dist=Qalyubia-South idx=14 pid=15 seq=15 area=1200.0 class=agricultural dist=Cairo-Central idx=221 pid=900012 seq=222 area=1200.0 class=residential dist=Cairo-Central idx=15 pid=16 seq=16 area=1200.0 class=residential dist=Giza-East idx=222 pid=900013 seq=223 area=1200.0 class=commercial dist=Giza-East idx=17 pid=18 seq=18 area=1200.0 class=industrial dist=Cairo-Central idx=223 pid=900014 seq=224 area=1200.0 class=agricultural dist=Qalyubia-South idx=19 pid=20 seq=20 area=1200.0 class=residential dist=Qalyubia-South idx=224 pid=900015 seq=225 area=1200.0 class=commercial dist=Cairo-Central idx=20 pid=21 seq=21 area=1200.0 class=commercial dist=Cairo-Central idx=225 pid=900016 seq=226 area=1200.0 class=industrial dist=Giza-East idx=21 pid=22 seq=22 area=1200.0 class=industrial dist=Giza-East idx=226 pid=900017 seq=227 area=1200.0 class=agricultural dist=Qalyubia-South idx=23 pid=24 seq=24 area=1200.0 class=residential dist=Cairo-Central idx=227 pid=900018 seq=228 area=1200.0 class=commercial dist=Cairo-Central idx=24 pid=25 seq=25 area=1200.0 class=commercial dist=Giza-East idx=228 pid=900019 seq=229 area=1200.0 class=industrial dist=Giza-East idx=25 pid=26 seq=26 area=1200.0 class=industrial dist=Qalyubia-South idx=229 pid=900020 seq=230 area=1200.0 class=agricultural dist=Qalyubia-South idx=26 pid=27 seq=27 area=1200.0 class=agricultural dist=Cairo-Central idx=230 pid=900021 seq=231 area=1200.0 class=residential dist=Cairo-Central idx=27 pid=28 seq=28 area=1200.0 class=residential dist=Giza-East idx=231 pid=900022 seq=232 area=1200.0 class=commercial dist=Giza-East idx=29 pid=30 seq=30 area=1200.0 class=industrial dist=Cairo-Central idx=232 pid=900023 seq=233 area=1200.0 class=agricultural dist=Qalyubia-South idx=30 pid=31 seq=31 area=1200.0 class=agricultural dist=Giza-East idx=233 pid=900024 seq=234 area=1200.0 class=residential dist=Cairo-Central idx=32 pid=33 seq=33 area=1200.0 class=commercial dist=Cairo-Central idx=234 pid=900025 seq=235 area=1200.0 class=industrial dist=Giza-East idx=33 pid=34 seq=34 area=1200.0 class=industrial dist=Giza-East idx=235 pid=900026 seq=236 area=1200.0 class=agricultural dist=Qalyubia-South idx=35 pid=36 seq=36 area=1200.0 class=residential dist=Cairo-Central idx=236 pid=900027 seq=237 area=1200.0 class=commercial dist=Cairo-Central idx=37 pid=38 seq=38 area=1200.0 class=industrial dist=Qalyubia-South idx=237 pid=900028 seq=238 area=1200.0 class=agricultural dist=Giza-East idx=38 pid=39 seq=39 area=1200.0 class=agricultural dist=Cairo-Central idx=238 pid=900029 seq=239 area=1200.0 class=residential dist=Qalyubia-South idx=39 pid=40 seq=40 area=1200.0 class=residential dist=Giza-East idx=239 pid=900030 seq=240 area=1200.0 class=commercial dist=Cairo-Central idx=41 pid=42 seq=42 area=1200.0 class=industrial dist=Cairo-Central idx=240 pid=900031 seq=241 area=1200.0 class=agricultural dist=Giza-East idx=42 pid=43 seq=43 area=1200.0 class=agricultural dist=Giza-East idx=241 pid=900032 seq=242 area=1200.0 class=residential dist=Qalyubia-South idx=43 pid=44 seq=44 area=1200.0 class=residential dist=Qalyubia-South idx=242 pid=900033 seq=243 area=1200.0 class=commercial dist=Cairo-Central idx=44 pid=45 seq=45 area=1200.0 class=commercial dist=Cairo-Central idx=243 pid=900034 seq=244 area=1200.0 class=industrial dist=Giza-East idx=45 pid=46 seq=46 area=1200.0 class=industrial dist=Giza-East idx=244 pid=900035 seq=245 area=1200.0 class=agricultural dist=Qalyubia-South idx=47 pid=48 seq=48 area=1200.0 class=residential dist=Cairo-Central idx=245 pid=900036 seq=246 area=1200.0 class=commercial dist=Cairo-Central idx=48 pid=49 seq=49 area=1200.0 class=commercial dist=Giza-East idx=246 pid=900037 seq=247 area=1200.0 class=industrial dist=Giza-East idx=50 pid=51 seq=51 area=1200.0 class=agricultural dist=Cairo-Central idx=247 pid=900038 seq=248 area=1200.0 class=residential dist=Qalyubia-South idx=51 pid=52 seq=52 area=1200.0 class=residential dist=Giza-East idx=248 pid=900039 seq=249 area=1200.0 class=commercial dist=Cairo-Central idx=53 pid=54 seq=54 area=1200.0 class=industrial dist=Cairo-Central idx=249 pid=900040 seq=250 area=1200.0 class=agricultural dist=Giza-East idx=55 pid=56 seq=56 area=1200.0 class=residential dist=Qalyubia-South idx=250 pid=900041 seq=251 area=1200.0 class=commercial dist=Qalyubia-South idx=56 pid=57 seq=57 area=1200.0 class=commercial dist=Cairo-Central idx=251 pid=900042 seq=252 area=1200.0 class=industrial dist=Cairo-Central idx=58 pid=59 seq=59 area=1200.0 class=agricultural dist=Qalyubia-South idx=252 pid=900043 seq=253 area=1200.0 class=residential dist=Giza-East idx=59 pid=60 seq=60 area=1200.0 class=residential dist=Cairo-Central idx=253 pid=900044 seq=254 area=1200.0 class=commercial dist=Qalyubia-South idx=60 pid=61 seq=61 area=1200.0 class=commercial dist=Giza-East idx=254 pid=900045 seq=255 area=1200.0 class=industrial dist=Cairo-Central idx=61 pid=62 seq=62 area=1200.0 class=industrial dist=Qalyubia-South idx=255 pid=900046 seq=256 area=1200.0 class=agricultural dist=Giza-East idx=62 pid=63 seq=63 area=1200.0 class=agricultural dist=Cairo-Central idx=256 pid=900047 seq=257 area=1200.0 class=residential dist=Qalyubia-South idx=63 pid=64 seq=64 area=1200.0 class=residential dist=Giza-East idx=257 pid=900048 seq=258 area=1200.0 class=commercial dist=Cairo-Central idx=65 pid=66 seq=66 area=1200.0 class=industrial dist=Cairo-Central idx=258 pid=900049 seq=259 area=1200.0 class=agricultural dist=Giza-East idx=66 pid=67 seq=67 area=1200.0 class=agricultural dist=Giza-East idx=259 pid=900050 seq=260 area=1200.0 class=residential dist=Qalyubia-South
0:52
Let me understand the data: 1. **Duplicate geometries**: There are 100 rows with duplicate geometries (50 unique geometries, each appearing twice). The duplicates are between the "original" parcels (pid 1-67) and "legacy" parcels (pid 900001-900050). When there are duplicates, we should keep the record with the earliest `record_seq`. 2. **Small fragments**: 30 records (pid 800001-800030) with area_m2 = 0.49 (smaller than 1 m²) should be discarded. 3. **Invalid geometries**: 20 records have invalid geometries (self-intersecting bowtie polygons). These need to be fixed (using `make_valid` or `buffer(0)`). 4. **Geometry types**: 260 are Polygon, 30 are MultiPolygon. All output must be MultiPolygon. 5. **Duplicate geometries to resolve by earliest record_seq**: Each duplicate pair has one with seq 1-67 and another with seq 211-260 (the 900000 series). So we'd keep the one with seq 1-67 (which is also the one with the original parcel_id). Let me plan the pipeline: 1. Read the GeoJSON 2. Fix invalid geometries (using `shapely.validation.make_valid` or `buffer(0)`) 3. Filter out records with area < 1 m² (the area_m2 column seems accurate for this) 4. For duplicate geometries, keep the record with the earliest record_seq 5. Convert all geometries to MultiPolygon 6. Remove any artifact slivers (polygon fragments < 1 m² within MultiPolygons) 7. Remove any remaining null/empty/invalid/duplicate geometries 8. Keep only the required columns 9. Write to GeoParquet Let me also look at the invalid geometries more closely - they seem to be bowtie polygons where the ring order is wrong. The points go in a cross pattern. Let me check what make_valid does with them.
0:52
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Check the 800000 series (small fragments)
small = gdf[gdf['parcel_id'] >= 800000]
print('Small fragments stats:')
print(small[['parcel_id', 'record_seq', 'area_m2']].describe())
print()
print('First few small fragment geometries:')
for idx, row in small.head(3).iterrows():
print(f' idx={idx} geom={row.geometry}, area={row.geometry.area}')
print()
# Check the 900000 series (duplicates)
dup_series = gdf[gdf['parcel_id'] >= 900000]
print(f'900000 series: {len(dup_series)} records')
print(f'Min record_seq: {dup_series.record_seq.min()}, Max record_seq: {dup_series.record_seq.max()}')
print()
# Check record_seq ranges
print('All record_seq ranges:')
print(' Min:', gdf['record_seq'].min(), 'Max:', gdf['record_seq'].max())
stats = gdf.groupby(pd.cut(gdf['record_seq'], bins=range(0, 301, 30))).size()
print(stats)
" 2>&1
Small fragments stats:
parcel_id record_seq area_m2
count 80.000000 80.0000 80.000000
mean 862521.750000 250.5000 750.183750
std 48722.608603 23.2379 584.374105
min 800001.000000 211.0000 0.490000
25% 800020.750000 230.7500 0.490000
50% 900010.500000 250.5000 1200.000000
75% 900030.250000 270.2500 1200.000000
max 900050.000000 290.0000 1200.000000
First few small fragment geometries:
idx=210 geom=POLYGON ((640000 815000, 640030 815000, 640030 815040, 640000 815040, 640000 815000)), area=1200.0
idx=211 geom=POLYGON ((640050 815000, 640080 815000, 640080 815040, 640050 815040, 640050 815000)), area=1200.0
idx=212 geom=POLYGON ((640100 815000, 640130 815000, 640130 815040, 640100 815040, 640100 815000)), area=1200.0
900000 series: 50 records
Min record_seq: 211, Max record_seq: 260
All record_seq ranges:
Min: 1 Max: 290
Traceback (most recent call last):
File "<string>", line 24, in <module>
stats = gdf.groupby(pd.cut(gdf['record_seq'], bins=range(0, 301, 30))).size()
^^
NameError: name 'pd' is not defined. Did you mean: 'gpd'?
1:10
Let me understand the data: 1. **Duplicate geometries**: 50 pairs of exact duplicate geometries (100 rows). Each pair has one record with parcel_id in 1-... range (record_seq 1-260) and one with parcel_id 900001-900050 (record_seq 211-260). The ones with record_seq 900000+ have HIGHER record_seq values (211-260) than the ones they duplicate (1-66). So when keeping the earliest record_seq, we keep the original ones (1-66). 2. **Small fragments (< 1 m²)**: 30 records with parcel_id 800001-800030 and area_m2=0.49. These need to be discarded. 3. **Invalid geometries**: 20 geometries (indices 6, 16, 22, 34, 40, 52, 57, 70, 75, 88, 93, 106, 111, 124, 129, 142, 147, 160, 165, 178) - these have self-intersecting rings (bow-tie shapes) and computed area of 0. These need to be fixed (made valid). 4. Also there are records (like idx=210-259) that are duplicates AND some have area_m2 < 1? No, wait - the 800001-800030 series have area_m2=0.49. Actually, let me re-examine. The 900000 series (900001-900050) are the duplicates of the original records. They have record_seq 211-260. The 800001-800030 series have record_seq 261-290 and area_m2=0.49. So there are 80 records with parcel_id >= 800000: - 50 records 900001-900050 (duplicates, seq 211-260) - 30 records 800001-800030 (tiny fragments, seq 261-290) Wait, the output above says "Small fragments stats" count = 80. But that's because I filtered `parcel_id >= 800000` which includes both 800000 series and 900000 series. Let me re-check: the 800001-800030 records have area_m2=0.49 and computed area probably also < 1. Those should be discarded. The 900001-900050 records duplicate geometries of the earlier records (indices 0-66 etc). Since they have higher record_seq, we keep the earlier ones. The 20 invalid geometries need to be made valid (likely using buffer(0) or make_valid). Let me now write the full solution script. Plan: 1. Read the data 2. Fix invalid geometries (buffer(0) or make_valid) 3. Discard polygon fragments smaller than 1 m² (based on computed area, or area_m2?) 4. For duplicate geometries, keep the one with earliest record_seq 5. Ensure all geometries are MultiPolygon 6. Ensure no null/empty geometries, invalid geometries, duplicate geometries 7. Output to parcels_canonical.geoparquet with the specified columns Wait, the requirement says "discard any polygon fragments smaller than 1 m²". This likely means both the area_m2 < 1 records AND any sub-polygons within a MultiPolygon that are < 1 m². Let me think about this. Actually, I think "polygon fragments" refers to the 30 records with area_m2=0.49. Let me also check if any MultiPolygon has sub-geometries smaller than 1 m². Let me also think about what "duplicate geometries" means in context. We have exact duplicate geometries (same WKT). For those, we keep the one with earliest record_seq. Since the 900000 series always have higher record_seq (211-260 vs 1-66), we keep the originals. But wait - the instruction says "When duplicates exist, keep the record with the earliest record_seq". This could also apply to the case where parcel_id might not be unique... but I verified all parcel_ids are unique. So the "duplicates" must refer to duplicate geometries. Let me also check: after we discard the 900000 series and the 800000 series, are there still any duplicate geometries? Let me check the remaining data after filtering. Actually, I think the cleanest approach is: 1. Fix invalid geometries 2. Discard records where area_m2 < 1 (the tiny fragments) 3. Group by geometry WKT, keep the row with minimum record_seq 4. Convert all geometries to MultiPolygon 5. Make sure everything is valid 6. Recalculate area_m2 from geometry 7. Output Let me also check if there are polygon fragments within multipolygons that are < 1 m². Actually, let me just write the full script and be thorough.
1:10
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Check if any MultiPolygons have sub-polygons < 1 m²
from shapely.geometry import MultiPolygon
mp_count = 0
small_sub = 0
for idx, row in gdf.iterrows():
geom = row.geometry
if geom.geom_type == 'MultiPolygon':
mp_count += 1
for subpoly in geom.geoms:
if subpoly.area < 1.0:
small_sub += 1
print(f'MultiPolygon count: {mp_count}')
print(f'Sub-polygons < 1 m²: {small_sub}')
"
MultiPolygon count: 30 Sub-polygons < 1 m²: 0
1:30
Let me understand the data and requirements: 1. **Duplicate geometries**: 100 rows share 50 unique geometries (each geometry appears twice - once in the original set and once in the 900000 series). The task says "When duplicates exist, keep the record with the earliest record_seq". So for each duplicate geometry, we keep the one with the smallest record_seq. Since the original records (pid 1-67) have record_seq 1-67 and the duplicates (pid 900001-900050) have record_seq 211-260, we keep the originals. 2. **Polygon fragments smaller than 1 m²**: There are 30 records with area_m2 = 0.49, all with parcel_id 800001-800030, record_seq 261-290. These need to be discarded. 3. **Invalid geometries**: 20 records have invalid geometries - they're self-intersecting bow-tie shapes. These need to be fixed (made valid). 4. **All features must be MultiPolygon**: Need to convert Polygon to MultiPolygon where applicable. 5. **No null/empty geometries, no invalid geometries, no duplicate geometries, no artifact slivers (< 1 m²)**. 6. **Output columns**: parcel_id, parcel_class, district, area_m2. Let me write the comprehensive solution script. The approach: 1. Read the GeoJSON 2. Discard polygon fragments with area_m2 < 1.0 (the 800000 series) 3. For duplicate geometries (same geometry WKT), keep only the one with smallest record_seq 4. Fix invalid geometries (make_valid) 5. Remove any residual slivers (sub-polygons < 1 m² after fixing) 6. Convert all geometries to MultiPolygon 7. Recompute area_m2 from the cleaned geometry 8. Output the required columns to GeoParquet Wait, the task says "keep the record with the earliest record_seq" for duplicates. But there are no duplicate parcel_ids - the duplicates are in terms of geometry (same geometry appears for two different parcel_ids). So I need to group by geometry and keep the row with smallest record_seq. Also, I need to handle the fact that after fixing invalid geometries (bow-tie shapes that collapse to zero area), some might become empty. Those should also be removed. Let me also think about what "artifact slivers" means - polygons with area < 1 m² that result from geometry fixing operations. Let me write the script.
1:30
1:30
Write
/work/solve.py
content · 111 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon, Polygon
from shapely import make_valid
import warnings
warnings.filterwarnings("ignore")
# 1. Read input
gdf = gpd.read_file("/work/cairo_parcels_legacy.geojson")
print(f"Input: {len(gdf)} records")
# 2. Drop polygon fragments with area_m2 < 1.0 (artifact slivers)
mask_small = gdf["area_m2"] < 1.0
print(f"Dropping {mask_small.sum()} records with area_m2 < 1.0")
gdf = gdf[~mask_small].copy()
print(f"After area filter: {len(gdf)} records")
# 3. For duplicate geometries, keep the one with the earliest record_seq
# Group by geometry WKT and keep min record_seq
geom_wkt = gdf.geometry.to_wkt()
gdf["_geom_wkt"] = geom_wkt
# For each unique geometry, pick the row with smallest record_seq
gdf = gdf.sort_values("record_seq").groupby("_geom_wkt", as_index=False).first()
gdf = gdf.drop(columns=["_geom_wkt"])
print(f"After deduplicating geometries: {len(gdf)} records")
# 4. Fix invalid geometries using make_valid
invalid_mask = ~gdf.geometry.is_valid
print(f"Invalid geometries to fix: {invalid_mask.sum()}")
gdf.loc[invalid_mask, "geometry"] = gdf.loc[invalid_mask, "geometry"].apply(make_valid)
# 5. Remove any resulting empty geometries
empty_mask = gdf.geometry.is_empty | gdf.geometry.isna()
if empty_mask.any():
print(f"Dropping {empty_mask.sum()} empty/null geometries after make_valid")
gdf = gdf[~empty_mask].copy()
# 6. Convert all geometries to MultiPolygon
def to_multipolygon(geom):
if geom is None or geom.is_empty:
return None
if geom.geom_type == "MultiPolygon":
return geom
elif geom.geom_type == "Polygon":
return MultiPolygon([geom])
elif geom.geom_type == "GeometryCollection":
# Extract only Polygon/MultiPolygon parts
polys = []
for sub in geom.geoms:
if sub.geom_type == "Polygon":
polys.append(sub)
elif sub.geom_type == "MultiPolygon":
polys.extend(list(sub.geoms))
# Skip other types (e.g. LineString, Point)
if polys:
if len(polys) == 1:
return MultiPolygon(polys)
else:
return MultiPolygon(polys)
return None
else:
return None
gdf["geometry"] = gdf["geometry"].apply(to_multipolygon)
# 7. Remove any resulting null/empty geometries
empty_mask2 = gdf.geometry.is_empty | gdf.geometry.isna()
if empty_mask2.any():
print(f"Dropping {empty_mask2.sum()} empty/null geometries after conversion")
gdf = gdf[~empty_mask2].copy()
# 8. Remove sub-polygons smaller than 1 m² (slivers inside MultiPolygons)
def remove_slivers(geom, min_area=1.0):
if geom is None or geom.is_empty:
return None
cleaned_polys = [p for p in geom.geoms if p.area >= min_area]
if not cleaned_polys:
return None
if len(cleaned_polys) == 1:
return MultiPolygon(cleaned_polys)
return MultiPolygon(cleaned_polys)
gdf["geometry"] = gdf["geometry"].apply(remove_slivers)
# Remove records that became empty after sliver removal
empty_mask3 = gdf.geometry.is_empty | gdf.geometry.isna()
if empty_mask3.any():
print(f"Dropping {empty_mask3.sum()} records that became empty after sliver removal")
gdf = gdf[~empty_mask3].copy()
# 9. Recompute area_m2 from actual cleaned geometry
gdf["area_m2"] = gdf.geometry.area
# 10. Final validation checks
print(f"\nFinal record count: {len(gdf)}")
print(f"Null geometries: {gdf.geometry.isna().sum()}")
print(f"Empty geometries: {gdf.geometry.is_empty.sum()}")
print(f"Invalid geometries: {(~gdf.geometry.is_valid).sum()}")
print(f"All MultiPolygon: {(gdf.geometry.type == 'MultiPolygon').all()}")
print(f"Min area: {gdf['area_m2'].min():.6f}")
print(f"Duplicate geometries: {gdf.geometry.to_wkt().duplicated().sum()}")
# Check for duplicate parcel_id
print(f"Duplicate parcel_ids: {gdf['parcel_id'].duplicated().sum()}")
# 11. Select output columns and write
out = gdf[["parcel_id", "parcel_class", "district", "area_m2", "geometry"]].copy()
out.to_parquet("/work/parcels_canonical.geoparquet", index=False)
print("\nWritten to /work/parcels_canonical.geoparquet")Wrote /work/solve.py (4179 chars).
1:37
Let me first check what the data looks like more carefully. The duplicates are pairs like: - idx=0 (pid=1, seq=1) and idx=210 (pid=900001, seq=211) - same geometry - ... So when parcels have duplicate geometries, we keep the one with the earliest record_seq. Record_seq 1-210 is the "original" data (including the 20 invalid geometries with seq=7,17,23,...), and seq 211-260 are the duplicate records (pid 900001-900050). Also the 30 sliver fragments (seq 261-290, pid 800001-800030) have area_m2=0.49, so they get dropped. Let me also think about: "When duplicates exist, keep the record with the earliest record_seq" Since the 900000 series has record_seq 211-260 and the matching original ones have record_seq 1-66 etc, the originals have earlier seq, so we'll keep those. Also, the original data might have the same geometries within the non-900000 set... let me check. Actually, looking at the groups, the duplicate geometries are always pairs: one from 1-200 range and one from 900000+ range. So keeping earliest seq means keeping the original. Good. But wait - the 20 invalid geometries (like idx=6, pid=7) - they have unique geometries (they're bowtie shapes). They're not duplicated. So they'll get fixed with make_valid. Let me run the script.
1:37
Bash
cd /work && python solve.py
Input: 290 records Dropping 30 records with area_m2 < 1.0 After area filter: 260 records After deduplicating geometries: 210 records Invalid geometries to fix: 20 Final record count: 210 Null geometries: 0 Empty geometries: 0 Invalid geometries: 0 All MultiPolygon: True Min area: 600.000000 Duplicate geometries: 0 Duplicate parcel_ids: 0 Written to /work/parcels_canonical.geoparquet
1:51
Wait, I need to reconsider the deduplication logic. The instruction says: "When duplicates exist, keep the record with the earliest record_seq". The question is what constitutes a "duplicate" - same geometry? Or same parcel_id? Looking at the data, the 900000 series parcels have the same geometry as the 1-200 series parcels but different parcel_ids. The 800000 series are small fragments (area_m2 = 0.49). The user says "one canonical record per parcel" and "parcel_id (the join key)". So parcel_id is the unique identifier. But there are no duplicate parcel_ids in the input data. Each record has a unique parcel_id. But the geometries are duplicated - meaning two different parcel_ids have the same geometry. So "duplicates" here means duplicate geometries, and we keep the one with the earliest record_seq. Wait, but the instruction says "When duplicates exist, keep the record with the earliest record_seq". This is about duplicate records - records representing the same parcel. Since parcel_id is supposed to be the join key (unique identifier per parcel), and the geometries are shared between the original series (1-200) and the 900000 series records... these represent the same physical parcel with two different parcel_ids. So the dedup by geometry makes sense: for the same geometry, keep the record with the smallest record_seq (which would be the original 1-200 series since those have seq 1-200 vs the 900000 series which have seq 211-260). But the 800000 series also have area_m2=0.49, less than 1, so they're dropped as fragments. Now the result has 210 records. The 20 invalid geometries (those with zero area due to bowtie shapes) were fixed by make_valid - they likely became valid polygons or multipolygons. Let me verify the output looks correct.
1:51
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_parquet('/work/parcels_canonical.geoparquet')
print('Shape:', gdf.shape)
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print()
print('Head:')
print(gdf.head(10).to_string())
print()
print('Tail:')
print(gdf.tail(10).to_string())
print()
print('Stats:')
print(gdf.describe())
print()
# Check that all parcel_ids are unique
print('All parcel_ids unique:', gdf['parcel_id'].is_unique)
print('Any nulls:', gdf.isnull().any().any())
print('Geometry types:', gdf.geometry.type.unique())
print('Min area:', gdf['area_m2'].min())
print()
# Check the 20 formerly invalid ones are now valid
print('Valid geometries all:', gdf.geometry.is_valid.all())
"
Shape: (210, 5)
Columns: ['parcel_id', 'parcel_class', 'district', 'area_m2', 'geometry']
CRS: None
Head:
parcel_id parcel_class district area_m2 geometry
0 91 agricultural Giza-East 1280.0 MULTIPOLYGON (((640000 815360, 640030 815360, 640030 815400, 640000 815400, 640000 815360)), ((640035 815365, 640043 815365, 640043 815375, 640035 815375, 640035 815365)))
1 181 commercial Giza-East 1280.0 MULTIPOLYGON (((640000 815720, 640030 815720, 640030 815760, 640000 815760, 640000 815720)), ((640035 815725, 640043 815725, 640043 815735, 640035 815735, 640035 815725)))
2 32 residential Qalyubia-South 1280.0 MULTIPOLYGON (((640050 815120, 640080 815120, 640080 815160, 640050 815160, 640050 815120)), ((640085 815125, 640093 815125, 640093 815135, 640085 815135, 640085 815125)))
3 47 agricultural Qalyubia-South 1280.0 MULTIPOLYGON (((640050 815180, 640080 815180, 640080 815220, 640050 815220, 640050 815180)), ((640085 815185, 640093 815185, 640093 815195, 640085 815195, 640085 815185)))
4 122 industrial Qalyubia-South 1280.0 MULTIPOLYGON (((640050 815480, 640080 815480, 640080 815520, 640050 815520, 640050 815480)), ((640085 815485, 640093 815485, 640093 815495, 640085 815495, 640085 815485)))
5 137 commercial Qalyubia-South 1280.0 MULTIPOLYGON (((640050 815540, 640080 815540, 640080 815580, 640050 815580, 640050 815540)), ((640085 815545, 640093 815545, 640093 815555, 640085 815555, 640085 815545)))
6 19 agricultural Giza-East 1280.0 MULTIPOLYGON (((640150 815060, 640180 815060, 640180 815100, 640150 815100, 640150 815060)), ((640185 815065, 640193 815065, 640193 815075, 640185 815075, 640185 815065)))
7 109 commercial Giza-East 1280.0 MULTIPOLYGON (((640150 815420, 640180 815420, 640180 815460, 640150 815460, 640150 815420)), ((640185 815425, 640193 815425, 640193 815435, 640185 815435, 640185 815425)))
8 50 industrial Qalyubia-South 1280.0 MULTIPOLYGON (((640200 815180, 640230 815180, 640230 815220, 640200 815220, 640200 815180)), ((640235 815185, 640243 815185, 640243 815195, 640235 815195, 640235 815185)))
9 65 commercial Qalyubia-South 1280.0 MULTIPOLYGON (((640200 815240, 640230 815240, 640230 815280, 640200 815280, 640200 815240)), ((640235 815245, 640243 815245, 640243 815255, 640235 815255, 640235 815245)))
Tail:
parcel_id parcel_class district area_m2 geometry
200 75 agricultural Cairo-Central 1200.0 MULTIPOLYGON (((640700 815240, 640730 815240, 640730 815280, 640700 815280, 640700 815240)))
201 90 industrial Cairo-Central 1200.0 MULTIPOLYGON (((640700 815300, 640730 815300, 640730 815340, 640700 815340, 640700 815300)))
202 105 commercial Cairo-Central 1200.0 MULTIPOLYGON (((640700 815360, 640730 815360, 640730 815400, 640700 815400, 640700 815360)))
203 120 residential Cairo-Central 1200.0 MULTIPOLYGON (((640700 815420, 640730 815420, 640730 815460, 640700 815460, 640700 815420)))
204 135 agricultural Cairo-Central 1200.0 MULTIPOLYGON (((640700 815480, 640730 815480, 640730 815520, 640700 815520, 640700 815480)))
205 150 industrial Cairo-Central 1200.0 MULTIPOLYGON (((640700 815540, 640730 815540, 640730 815580, 640700 815580, 640700 815540)))
206 165 commercial Cairo-Central 1200.0 MULTIPOLYGON (((640700 815600, 640730 815600, 640730 815640, 640700 815640, 640700 815600)))
207 180 residential Cairo-Central 1200.0 MULTIPOLYGON (((640700 815660, 640730 815660, 640730 815700, 640700 815700, 640700 815660)))
208 195 agricultural Cairo-Central 1200.0 MULTIPOLYGON (((640700 815720, 640730 815720, 640730 815760, 640700 815760, 640700 815720)))
209 210 industrial Cairo-Central 1200.0 MULTIPOLYGON (((640700 815780, 640730 815780, 640730 815820, 640700 815820, 640700 815780)))
Stats:
parcel_id area_m2
count 210.000000 210.000000
mean 105.500000 1154.285714
std 60.765944 182.396892
min 1.000000 600.000000
25% 53.250000 1200.000000
50% 105.500000 1200.000000
75% 157.750000 1200.000000
max 210.000000 1280.000000
All parcel_ids unique: True
Any nulls: False
Geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
Min area: 600.0
Valid geometries all: True
1:57
Let me check the CRS - it seems the output geoparquet lost the CRS. Let me fix that.
1:57
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
print('Original CRS:', gdf.crs)
"
Original CRS: EPSG:22992
2:11
The output file has CRS as None because geoparquet doesn't always preserve the CRS metadata in the same way. Let me make sure I set the CRS properly when writing.
2:11
2:11
Edit
/work/solve.py
Edited /work/solve.py.
2:18
Actually, GeoParquet might store CRS metadata differently. Let me also add the geo column metadata. Actually geopandas should handle it when saving with to_parquet if the CRS is set properly. Let me run it again.
2:18
Bash
cd /work && python solve.py
Input: 290 records Dropping 30 records with area_m2 < 1.0 After area filter: 260 records After deduplicating geometries: 210 records Invalid geometries to fix: 20 Final record count: 210 Null geometries: 0 Empty geometries: 0 Invalid geometries: 0 All MultiPolygon: True Min area: 600.000000 Duplicate geometries: 0 Duplicate parcel_ids: 0 Written to /work/parcels_canonical.geoparquet
2:26
Let me verify one more time that everything is perfect.
2:26
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_parquet('/work/parcels_canonical.geoparquet')
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print('Columns:', gdf.columns.tolist())
print()
# Final rigorous check
assert gdf['parcel_id'].is_unique, 'Duplicate parcel_ids!'
assert gdf.geometry.is_valid.all(), 'Invalid geometries remain!'
assert (gdf.geometry.type == 'MultiPolygon').all(), 'Not all MultiPolygon!'
assert gdf.isnull().any().any() == False, 'Null values!'
assert gdf.geometry.is_empty.sum() == 0, 'Empty geometries!'
assert gdf['area_m2'].min() >= 1.0, f'Found area < 1 m²: {gdf[\"area_m2\"].min()}'
assert gdf.geometry.to_wkt().duplicated().sum() == 0, 'Duplicate geometries!'
print('All checks passed!')
print(f'Canonical parcels: {len(gdf)}')
print(f'Area range: {gdf[\"area_m2\"].min():.2f} - {gdf[\"area_m2\"].max():.2f} m²')
print(f'Districts: {gdf[\"district\"].unique().tolist()}')
print(f'Classes: {gdf[\"parcel_class\"].unique().tolist()}')
"
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}}
Shape: (210, 5)
Columns: ['parcel_id', 'parcel_class', 'district', 'area_m2', 'geometry']
All checks passed!
Canonical parcels: 210
Area range: 600.00 - 1280.00 m²
Districts: ['Giza-East', 'Qalyubia-South', 'Cairo-Central']
Classes: ['agricultural', 'commercial', 'residential', 'industrial']
2:35
Everything checks out. Let me summarize what was done.
2:35