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"
}
0:13
Let me start by reading the GeoJSON file to understand its structure and content.
0:13
Read
/work/cairo_parcels_legacy.geojson
1 {
2 "type": "FeatureCollection",
3 "name": "cairo_parcels_legacy",
4 "crs": {
5 "type": "name",
6 "properties": {
7 "name": "urn:ogc:def:crs:EPSG::22992"
8 }
9 },
10 "features": [
11 {
12 "type": "Feature",
13 "geometry": {
14 "type": "Polygon",
15 "coordinates": [
16 [
17 [
18 640000.0,
19 815000.0
20 ],
21 [
22 640030.0,
23 815000.0
24 ],
25 [
26 640030.0,
27 815040.0
28 ],
29 [
30 640000.0,
31 815040.0
32 ],
33 [
34 640000.0,
35 815000.0
36 ]
37 ]
38 ]
39 },
40 "properties": {
41 "parcel_id": 1,
42 "record_seq": 1,
43 "parcel_class": "commercial",
44 "district": "Giza-East",
45 "area_m2": 1200.0
46 }
47 },
48 {
49 "type": "Feature",
50 "geometry": {
51 "type": "Polygon",
52 "coordinates": [
53 [
54 [
55 640050.0,
56 815000.0
57 ],
58 [
59 640080.0,
60 815000.0
61 ],
62 [
63 640080.0,
64 815040.0
65 ],
66 [
67 640050.0,
68 815040.0
69 ],
70 [
71 640050.0,
72 815000.0
73 ]
74 ]
75 ]
76 },
77 "properties": {
78 "parcel_id": 2,
79 "record_seq": 2,
80 "parcel_class": "industrial",
<system-reminder>Showed lines 1-80 of 11522. Re-call Read with offset=81 to continue, or raise limit.</system-reminder>
0:16
Let me read more of the file to understand the data better.
0:16
Read
/work/cairo_parcels_legacy.geojson
81 "district": "Qalyubia-South",
82 "area_m2": 1200.0
83 }
84 },
85 {
86 "type": "Feature",
87 "geometry": {
88 "type": "Polygon",
89 "coordinates": [
90 [
91 [
92 640100.0,
93 815000.0
94 ],
95 [
96 640130.0,
97 815000.0
98 ],
99 [
100 640130.0,
101 815040.0
102 ],
103 [
104 640100.0,
105 815040.0
106 ],
107 [
108 640100.0,
109 815000.0
110 ]
111 ]
112 ]
113 },
114 "properties": {
115 "parcel_id": 3,
116 "record_seq": 3,
117 "parcel_class": "agricultural",
118 "district": "Cairo-Central",
119 "area_m2": 1200.0
120 }
121 },
122 {
123 "type": "Feature",
124 "geometry": {
125 "type": "Polygon",
126 "coordinates": [
127 [
128 [
129 640150.0,
130 815000.0
131 ],
132 [
133 640180.0,
134 815000.0
135 ],
136 [
137 640180.0,
138 815040.0
139 ],
140 [
141 640150.0,
142 815040.0
143 ],
144 [
145 640150.0,
146 815000.0
147 ]
148 ]
149 ]
150 },
151 "properties": {
152 "parcel_id": 4,
153 "record_seq": 4,
154 "parcel_class": "residential",
155 "district": "Giza-East",
156 "area_m2": 1200.0
157 }
158 },
159 {
160 "type": "Feature",
161 "geometry": {
162 "type": "Polygon",
163 "coordinates": [
164 [
165 [
166 640200.0,
167 815000.0
168 ],
169 [
170 640230.0,
171 815000.0
172 ],
173 [
174 640230.0,
175 815040.0
176 ],
177 [
178 640200.0,
179 815040.0
180 ],
<system-reminder>Showed lines 81-180 of 11522. Re-call Read with offset=181 to continue, or raise limit.</system-reminder>
0:22
Let me explore the data more to understand the structure - especially duplicate scenarios, polygons with area < 1 m², etc.
0:22
0:22
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
print('Columns:', gdf.columns.tolist())
print('Dtypes:')
print(gdf.dtypes)
print()
print('Shape:', gdf.shape)
print()
print('CRS:', gdf.crs)
print()
print('Geometry types:', gdf.geometry.type.value_counts())
print()
print('Null geometries:', gdf.geometry.isna().sum())
print('Empty geometries:', gdf.geometry.is_empty.sum())
print('Invalid geometries:', (~gdf.geometry.is_valid).sum())
print()
print('parcel_id nunique:', gdf['parcel_id'].nunique())
print('record_seq nunique:', gdf['record_seq'].nunique())
print()
print('Duplicated parcel_id counts:')
dup = gdf['parcel_id'].value_counts()
print(dup[dup > 1].head(20))
print()
print('Sample of duplicates:')
dup_ids = dup[dup > 1].index[:5]
for pid in dup_ids:
subset = gdf[gdf['parcel_id'] == pid]
print(f'parcel_id={pid}: {len(subset)} records, record_seqs={subset[\"record_seq\"].tolist()}')
print(subset[['parcel_id','record_seq','parcel_class','district','area_m2']].to_string())
print()
"
Columns: ['parcel_id', 'record_seq', 'parcel_class', 'district', 'area_m2', 'geometry'] Dtypes: parcel_id int32 record_seq int32 parcel_class str district str area_m2 float64 geometry geometry dtype: object Shape: (290, 6) CRS: EPSG:22992 Geometry types: Polygon 260 MultiPolygon 30 Name: count, dtype: int64 Null geometries: 0 Empty geometries: 0 Invalid geometries: 20 parcel_id nunique: 290 record_seq nunique: 290 Duplicated parcel_id counts: Series([], Name: count, dtype: int64) Sample of duplicates:
0:30
Interesting - there are no duplicate parcel_ids, but 290 features with 290 unique parcel_ids and 290 unique record_seqs. So the task says "When duplicates exist, keep the record with the earliest record_seq" - but there are no duplicates by parcel_id. Let me re-read the instruction. "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." Hmm, maybe they mean duplicate geometries, not duplicate parcel_ids? Or perhaps the data is cleaner than expected. Let me check for duplicate geometries. Also, there are 20 invalid geometries to fix, and 260 Polygons that need to be converted to MultiPolygon. Let me do a deeper analysis.
0:30
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
print('Duplicate geometries:', gdf.geometry.duplicated().sum())
print()
# Check for records with same parcel_id (should be none based on earlier)
dup_pids = gdf['parcel_id'].value_counts()
print('parcel_id value counts > 1:', (dup_pids > 1).sum())
print()
# Check for records with same geometry
from shapely import wkt
geoms_series = gdf.geometry.apply(lambda g: g.wkt)
dup_geoms = geoms_series.value_counts()
dup_geoms_multi = dup_geoms[dup_geoms > 1]
print(f'{len(dup_geoms_multi)} geometries appear more than once')
for wkt_str, cnt in dup_geoms_multi.items():
idx = geoms_series[geoms_series == wkt_str].index
print(f'Count={cnt}, indices={idx.tolist()}, parcel_ids={gdf.loc[idx,\"parcel_id\"].tolist()}, record_seqs={gdf.loc[idx,\"record_seq\"].tolist()}')
print()
# Check small geometries
gdf['calc_area'] = gdf.geometry.area
small = gdf[gdf['calc_area'] < 1.0]
print(f'Geometries with area < 1 m²: {len(small)}')
if len(small) > 0:
print(small[['parcel_id','record_seq','calc_area','area_m2']].to_string())
print()
# Check for small area features
small2 = gdf[gdf['calc_area'] < 1.0]
print(f'Geometries with calculated area < 1: {len(small2)}')
small3 = gdf[gdf['calc_area'] < 0.5]
print(f'Geometries with calculated area < 0.5: {len(small3)}')
"
Duplicate geometries: 50
parcel_id value counts > 1: 0
50 geometries appear more than once
Count=2, indices=[0, 210], parcel_ids=[1, 900001], record_seqs=[1, 211]
Count=2, indices=[1, 211], parcel_ids=[2, 900002], record_seqs=[2, 212]
Count=2, indices=[2, 212], parcel_ids=[3, 900003], record_seqs=[3, 213]
Count=2, indices=[3, 213], parcel_ids=[4, 900004], record_seqs=[4, 214]
Count=2, indices=[4, 214], parcel_ids=[5, 900005], record_seqs=[5, 215]
Count=2, indices=[5, 215], parcel_ids=[6, 900006], record_seqs=[6, 216]
Count=2, indices=[7, 216], parcel_ids=[8, 900007], record_seqs=[8, 217]
Count=2, indices=[8, 217], parcel_ids=[9, 900008], record_seqs=[9, 218]
Count=2, indices=[9, 218], parcel_ids=[10, 900009], record_seqs=[10, 219]
Count=2, indices=[11, 219], parcel_ids=[12, 900010], record_seqs=[12, 220]
Count=2, indices=[12, 220], parcel_ids=[13, 900011], record_seqs=[13, 221]
Count=2, indices=[14, 221], parcel_ids=[15, 900012], record_seqs=[15, 222]
Count=2, indices=[15, 222], parcel_ids=[16, 900013], record_seqs=[16, 223]
Count=2, indices=[17, 223], parcel_ids=[18, 900014], record_seqs=[18, 224]
Count=2, indices=[19, 224], parcel_ids=[20, 900015], record_seqs=[20, 225]
Count=2, indices=[20, 225], parcel_ids=[21, 900016], record_seqs=[21, 226]
Count=2, indices=[21, 226], parcel_ids=[22, 900017], record_seqs=[22, 227]
Count=2, indices=[23, 227], parcel_ids=[24, 900018], record_seqs=[24, 228]
Count=2, indices=[24, 228], parcel_ids=[25, 900019], record_seqs=[25, 229]
Count=2, indices=[25, 229], parcel_ids=[26, 900020], record_seqs=[26, 230]
Count=2, indices=[26, 230], parcel_ids=[27, 900021], record_seqs=[27, 231]
Count=2, indices=[27, 231], parcel_ids=[28, 900022], record_seqs=[28, 232]
Count=2, indices=[29, 232], parcel_ids=[30, 900023], record_seqs=[30, 233]
Count=2, indices=[30, 233], parcel_ids=[31, 900024], record_seqs=[31, 234]
Count=2, indices=[32, 234], parcel_ids=[33, 900025], record_seqs=[33, 235]
Count=2, indices=[33, 235], parcel_ids=[34, 900026], record_seqs=[34, 236]
Count=2, indices=[35, 236], parcel_ids=[36, 900027], record_seqs=[36, 237]
Count=2, indices=[37, 237], parcel_ids=[38, 900028], record_seqs=[38, 238]
Count=2, indices=[38, 238], parcel_ids=[39, 900029], record_seqs=[39, 239]
Count=2, indices=[39, 239], parcel_ids=[40, 900030], record_seqs=[40, 240]
Count=2, indices=[41, 240], parcel_ids=[42, 900031], record_seqs=[42, 241]
Count=2, indices=[42, 241], parcel_ids=[43, 900032], record_seqs=[43, 242]
Count=2, indices=[43, 242], parcel_ids=[44, 900033], record_seqs=[44, 243]
Count=2, indices=[44, 243], parcel_ids=[45, 900034], record_seqs=[45, 244]
Count=2, indices=[45, 244], parcel_ids=[46, 900035], record_seqs=[46, 245]
Count=2, indices=[47, 245], parcel_ids=[48, 900036], record_seqs=[48, 246]
Count=2, indices=[48, 246], parcel_ids=[49, 900037], record_seqs=[49, 247]
Count=2, indices=[50, 247], parcel_ids=[51, 900038], record_seqs=[51, 248]
Count=2, indices=[51, 248], parcel_ids=[52, 900039], record_seqs=[52, 249]
Count=2, indices=[53, 249], parcel_ids=[54, 900040], record_seqs=[54, 250]
Count=2, indices=[55, 250], parcel_ids=[56, 900041], record_seqs=[56, 251]
Count=2, indices=[56, 251], parcel_ids=[57, 900042], record_seqs=[57, 252]
Count=2, indices=[58, 252], parcel_ids=[59, 900043], record_seqs=[59, 253]
Count=2, indices=[59, 253], parcel_ids=[60, 900044], record_seqs=[60, 254]
Count=2, indices=[60, 254], parcel_ids=[61, 900045], record_seqs=[61, 255]
Count=2, indices=[61, 255], parcel_ids=[62, 900046], record_seqs=[62, 256]
Count=2, indices=[62, 256], parcel_ids=[63, 900047], record_seqs=[63, 257]
Count=2, indices=[63, 257], parcel_ids=[64, 900048], record_seqs=[64, 258]
Count=2, indices=[65, 258], parcel_ids=[66, 900049], record_seqs=[66, 259]
Count=2, indices=[66, 259], parcel_ids=[67, 900050], record_seqs=[67, 260]
Geometries with area < 1 m²: 50
parcel_id record_seq calc_area area_m2
6 7 7 0.00 1200.00
16 17 17 0.00 1200.00
22 23 23 0.00 1200.00
34 35 35 0.00 1200.00
40 41 41 0.00 1200.00
52 53 53 0.00 1200.00
57 58 58 0.00 1200.00
70 71 71 0.00 1200.00
75 76 76 0.00 1200.00
88 89 89 0.00 1200.00
93 94 94 0.00 1200.00
106 107 107 0.00 1200.00
111 112 112 0.00 1200.00
124 125 125 0.00 1200.00
129 130 130 0.00 1200.00
142 143 143 0.00 1200.00
147 148 148 0.00 1200.00
160 161 161 0.00 1200.00
165 166 166 0.00 1200.00
178 179 179 0.00 1200.00
260 800001 261 0.49 0.49
261 800002 262 0.49 0.49
262 800003 263 0.49 0.49
263 800004 264 0.49 0.49
264 800005 265 0.49 0.49
265 800006 266 0.49 0.49
266 800007 267 0.49 0.49
267 800008 268 0.49 0.49
268 800009 269 0.49 0.49
269 800010 270 0.49 0.49
270 800011 271 0.49 0.49
271 800012 272 0.49 0.49
272 800013 273 0.49 0.49
273 800014 274 0.49 0.49
274 800015 275 0.49 0.49
275 800016 276 0.49 0.49
276 800017 277 0.49 0.49
277 800018 278 0.49 0.49
278 800019 279 0.49 0.49
279 800020 280 0.49 0.49
280 800021 281 0.49 0.49
281 800022 282 0.49 0.49
282 800023 283 0.49 0.49
283 800024 284 0.49 0.49
284 800025 285 0.49 0.49
285 800026 286 0.49 0.49
286 800027 287 0.49 0.49
287 800028 288 0.49 0.49
288 800029 289 0.49 0.49
289 800030 290 0.49 0.49
Geometries with calculated area < 1: 50
Geometries with calculated area < 0.5: 50
0:48
Now I have a clear picture of the data. Let me analyze: 1. **290 features** total, 260 Polygon + 30 MultiPolygon 2. **parcel_id** has 290 unique values - so there are no duplicate parcel_ids in the traditional sense 3. **record_seq** also has 290 unique values 4. But there are **50 duplicate geometries** - the same geometry appears twice with different parcel_ids and record_seqs 5. There are **20 invalid geometries** 6. There are geometries with **area = 0** (some original features) and **area = 0.49** (artifacts) Wait, the user says "duplicates" - they mention "When duplicates exist, keep the record with the earliest record_seq". Since there are no duplicate parcel_ids, what does "duplicate" mean here? Looking at the duplicate geometries: - Index 0 (parcel_id=1, record_seq=1) and index 210 (parcel_id=900001, record_seq=211) have the same geometry - Index 1 (parcel_id=2, record_seq=2) and index 211 (parcel_id=900002, record_seq=212) have the same geometry - etc. So it seems like some parcels from different legacy systems have the same geometry. The user wants to deduplicate based on geometries - when the same geometry appears, keep the record with the lowest record_seq. Let me also look at the invalid geometries and the zero-area ones. Let me look more carefully at the parcels with area=0 and the invalid ones.
0:48
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
# Let's look at the 0-area geometries
zero_area = gdf[gdf.geometry.area == 0]
print('Zero-area features:')
for idx in zero_area.index:
g = gdf.loc[idx, 'geometry']
print(f'Index {idx}: parcel_id={gdf.loc[idx,\"parcel_id\"]}, record_seq={gdf.loc[idx,\"record_seq\"]}, type={g.type}')
if g.type == 'Polygon':
print(f' exterior coords: {list(g.exterior.coords)}')
print()
# Look at invalid geometries
invalid = gdf[~gdf.geometry.is_valid]
print(f'Invalid geometries ({len(invalid)}):')
for idx in invalid.index:
g = gdf.loc[idx, 'geometry']
print(f'Index {idx}: parcel_id={gdf.loc[idx,\"parcel_id\"]}, record_seq={gdf.loc[idx,\"record_seq\"]}, type={g.type}, area={g.area}')
if hasattr(g, 'exterior'):
from shapely.validation import explain_validity
print(f' validity: {explain_validity(g)}')
print()
# Check what the duplicate geometry records look like
dup_geoms = gdf[gdf.geometry.duplicated(keep=False)]
print(f'Total features involved in duplicated geometries: {len(dup_geoms)}')
# Group by geometry WKT
dup_geoms['geom_wkt'] = dup_geoms.geometry.apply(lambda g: g.wkt)
for wkt_str, grp in dup_geoms.groupby('geom_wkt'):
print(f' Geometry: {grp.iloc[0].parcel_id} - {grp.iloc[0].record_seq} vs {grp.iloc[1].parcel_id} - {grp.iloc[1].record_seq}')
if len(grp) > 2:
print(f' More than 2! {len(grp)}')
"
Zero-area features: Index 6: parcel_id=7, record_seq=7, type=Polygon exterior coords: [(640300.0, 815000.0), (640330.0, 815040.0), (640300.0, 815040.0), (640330.0, 815000.0), (640300.0, 815000.0)] Index 16: parcel_id=17, record_seq=17, type=Polygon exterior coords: [(640050.0, 815060.0), (640080.0, 815100.0), (640050.0, 815100.0), (640080.0, 815060.0), (640050.0, 815060.0)] Index 22: parcel_id=23, record_seq=23, type=Polygon exterior coords: [(640350.0, 815060.0), (640380.0, 815100.0), (640350.0, 815100.0), (640380.0, 815060.0), (640350.0, 815060.0)] Index 34: parcel_id=35, record_seq=35, type=Polygon exterior coords: [(640200.0, 815120.0), (640230.0, 815160.0), (640200.0, 815160.0), (640230.0, 815120.0), (640200.0, 815120.0)] Index 40: parcel_id=41, record_seq=41, type=Polygon exterior coords: [(640500.0, 815120.0), (640530.0, 815160.0), (640500.0, 815160.0), (640530.0, 815120.0), (640500.0, 815120.0)] Index 52: parcel_id=53, record_seq=53, type=Polygon exterior coords: [(640350.0, 815180.0), (640380.0, 815220.0), (640350.0, 815220.0), (640380.0, 815180.0), (640350.0, 815180.0)] Index 57: parcel_id=58, record_seq=58, type=Polygon exterior coords: [(640600.0, 815180.0), (640630.0, 815220.0), (640600.0, 815220.0), (640630.0, 815180.0), (640600.0, 815180.0)] Index 70: parcel_id=71, record_seq=71, type=Polygon exterior coords: [(640500.0, 815240.0), (640530.0, 815280.0), (640500.0, 815280.0), (640530.0, 815240.0), (640500.0, 815240.0)] Index 75: parcel_id=76, record_seq=76, type=Polygon exterior coords: [(640000.0, 815300.0), (640030.0, 815340.0), (640000.0, 815340.0), (640030.0, 815300.0), (640000.0, 815300.0)] Index 88: parcel_id=89, record_seq=89, type=Polygon exterior coords: [(640650.0, 815300.0), (640680.0, 815340.0), (640650.0, 815340.0), (640680.0, 815300.0), (640650.0, 815300.0)] Index 93: parcel_id=94, record_seq=94, type=Polygon exterior coords: [(640150.0, 815360.0), (640180.0, 815400.0), (640150.0, 815400.0), (640180.0, 815360.0), (640150.0, 815360.0)] Index 106: parcel_id=107, record_seq=107, type=Polygon exterior coords: [(640050.0, 815420.0), (640080.0, 815460.0), (640050.0, 815460.0), (640080.0, 815420.0), (640050.0, 815420.0)] Index 111: parcel_id=112, record_seq=112, type=Polygon exterior coords: [(640300.0, 815420.0), (640330.0, 815460.0), (640300.0, 815460.0), (640330.0, 815420.0), (640300.0, 815420.0)] Index 124: parcel_id=125, record_seq=125, type=Polygon exterior coords: [(640200.0, 815480.0), (640230.0, 815520.0), (640200.0, 815520.0), (640230.0, 815480.0), (640200.0, 815480.0)] Index 129: parcel_id=130, record_seq=130, type=Polygon exterior coords: [(640450.0, 815480.0), (640480.0, 815520.0), (640450.0, 815520.0), (640480.0, 815480.0), (640450.0, 815480.0)] Index 142: parcel_id=143, record_seq=143, type=Polygon exterior coords: [(640350.0, 815540.0), (640380.0, 815580.0), (640350.0, 815580.0), (640380.0, 815540.0), (640350.0, 815540.0)] Index 147: parcel_id=148, record_seq=148, type=Polygon exterior coords: [(640600.0, 815540.0), (640630.0, 815580.0), (640600.0, 815580.0), (640630.0, 815540.0), (640600.0, 815540.0)] Index 160: parcel_id=161, record_seq=161, type=Polygon exterior coords: [(640500.0, 815600.0), (640530.0, 815640.0), (640500.0, 815640.0), (640530.0, 815600.0), (640500.0, 815600.0)] Index 165: parcel_id=166, record_seq=166, type=Polygon exterior coords: [(640000.0, 815660.0), (640030.0, 815700.0), (640000.0, 815700.0), (640030.0, 815660.0), (640000.0, 815660.0)] Index 178: parcel_id=179, record_seq=179, type=Polygon exterior coords: [(640650.0, 815660.0), (640680.0, 815700.0), (640650.0, 815700.0), (640680.0, 815660.0), (640650.0, 815660.0)] Invalid geometries (20): Index 6: parcel_id=7, record_seq=7, type=Polygon, area=0.0 validity: Self-intersection[640315 815020] Index 16: parcel_id=17, record_seq=17, type=Polygon, area=0.0 validity: Self-intersection[640065 815080] Index 22: parcel_id=23, record_seq=23, type=Polygon, area=0.0 validity: Self-intersection[640365 815080] Index 34: parcel_id=35, record_seq=35, type=Polygon, area=0.0 validity: Self-intersection[640215 815140] Index 40: parcel_id=41, record_seq=41, type=Polygon, area=0.0 validity: Self-intersection[640515 815140] Index 52: parcel_id=53, record_seq=53, type=Polygon, area=0.0 validity: Self-intersection[640365 815200] Index 57: parcel_id=58, record_seq=58, type=Polygon, area=0.0 validity: Self-intersection[640615 815200] Index 70: parcel_id=71, record_seq=71, type=Polygon, area=0.0 validity: Self-intersection[640515 815260] Index 75: parcel_id=76, record_seq=76, type=Polygon, area=0.0 validity: Self-intersection[640015 815320] Index 88: parcel_id=89, record_seq=89, type=Polygon, area=0.0 validity: Self-intersection[640665 815320] Index 93: parcel_id=94, record_seq=94, type=Polygon, area=0.0 validity: Self-intersection[640165 815380] Index 106: parcel_id=107, record_seq=107, type=Polygon, area=0.0 validity: Self-intersection[640065 815440] Index 111: parcel_id=112, record_seq=112, type=Polygon, area=0.0 validity: Self-intersection[640315 815440] Index 124: parcel_id=125, record_seq=125, type=Polygon, area=0.0 validity: Self-intersection[640215 815500] Index 129: parcel_id=130, record_seq=130, type=Polygon, area=0.0 validity: Self-intersection[640465 815500] Index 142: parcel_id=143, record_seq=143, type=Polygon, area=0.0 validity: Self-intersection[640365 815560] Index 147: parcel_id=148, record_seq=148, type=Polygon, area=0.0 validity: Self-intersection[640615 815560] Index 160: parcel_id=161, record_seq=161, type=Polygon, area=0.0 validity: Self-intersection[640515 815620] Index 165: parcel_id=166, record_seq=166, type=Polygon, area=0.0 validity: Self-intersection[640015 815680] Index 178: parcel_id=179, record_seq=179, type=Polygon, area=0.0 validity: Self-intersection[640665 815680] Total features involved in duplicated geometries: 100 Geometry: 1 - 1 vs 900001 - 211 Geometry: 16 - 16 vs 900013 - 223 Geometry: 31 - 31 vs 900024 - 234 Geometry: 46 - 46 vs 900035 - 245 Geometry: 61 - 61 vs 900045 - 255 Geometry: 2 - 2 vs 900002 - 212 Geometry: 62 - 62 vs 900046 - 256 Geometry: 3 - 3 vs 900003 - 213 Geometry: 18 - 18 vs 900014 - 224 Geometry: 33 - 33 vs 900025 - 235 Geometry: 48 - 48 vs 900036 - 246 Geometry: 63 - 63 vs 900047 - 257 Geometry: 4 - 4 vs 900004 - 214 Geometry: 34 - 34 vs 900026 - 236 Geometry: 49 - 49 vs 900037 - 247 Geometry: 64 - 64 vs 900048 - 258 Geometry: 5 - 5 vs 900005 - 215 Geometry: 20 - 20 vs 900015 - 225 Geometry: 6 - 6 vs 900006 - 216 Geometry: 21 - 21 vs 900016 - 226 Geometry: 36 - 36 vs 900027 - 237 Geometry: 51 - 51 vs 900038 - 248 Geometry: 66 - 66 vs 900049 - 259 Geometry: 22 - 22 vs 900017 - 227 Geometry: 52 - 52 vs 900039 - 249 Geometry: 67 - 67 vs 900050 - 260 Geometry: 8 - 8 vs 900007 - 217 Geometry: 38 - 38 vs 900028 - 238 Geometry: 9 - 9 vs 900008 - 218 Geometry: 24 - 24 vs 900018 - 228 Geometry: 39 - 39 vs 900029 - 239 Geometry: 54 - 54 vs 900040 - 250 Geometry: 10 - 10 vs 900009 - 219 Geometry: 25 - 25 vs 900019 - 229 Geometry: 40 - 40 vs 900030 - 240 Geometry: 26 - 26 vs 900020 - 230 Geometry: 56 - 56 vs 900041 - 251 Geometry: 12 - 12 vs 900010 - 220 Geometry: 27 - 27 vs 900021 - 231 Geometry: 42 - 42 vs 900031 - 241 Geometry: 57 - 57 vs 900042 - 252 Geometry: 13 - 13 vs 900011 - 221 Geometry: 28 - 28 vs 900022 - 232 Geometry: 43 - 43 vs 900032 - 242 Geometry: 44 - 44 vs 900033 - 243 Geometry: 59 - 59 vs 900043 - 253 Geometry: 15 - 15 vs 900012 - 222 Geometry: 30 - 30 vs 900023 - 233 Geometry: 45 - 45 vs 900034 - 244 Geometry: 60 - 60 vs 900044 - 254
<string>:10: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead. <string>:11: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead. <string>:20: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
1:08
Let me analyze the data: 1. **290 features** total, **290 unique parcel_ids** - so no duplicate parcel_ids per se. 2. **50 duplicate geometries** - meaning 50 pairs of identical geometries with different parcel_ids. For example, parcel_id=1 (record_seq=1) has same geometry as parcel_id=900001 (record_seq=211). So these are "duplicate records" in the sense that the same geometry appears twice with different parcel_ids. 3. The task says "When duplicates exist, keep the record with the earliest record_seq." So for each pair of identical geometries, we keep the one with the smaller record_seq. 4. **20 invalid geometries** - these are the self-intersecting "bowtie" polygons with area 0. We need to fix them (make_valid) or discard them. 5. **50 features with area < 1 m²** - 20 are zero-area (self-intersecting), and 30 are 0.49 m² slivers (parcel_ids 800001-800030). 6. Some features already are MultiPolygon (30 of them). We need to ensure ALL output features are MultiPolygon. The task says: - "When duplicates exist, keep the record with the earliest record_seq" - So for the 50 pairs of identical geometries, keep the one with lower record_seq. - "discard any polygon fragments smaller than 1 m²" - Remove any geometry with area < 1 m². - Output columns: parcel_id, parcel_class, district, area_m2 - Every feature must be a MultiPolygon - No null or empty geometries - No invalid geometries - No duplicate geometries - No artifact slivers Let me think about what "duplicates" means more carefully. The task says "When duplicates exist" - I think this refers to duplicate geometries. The 50 pairs of identical geometries have different parcel_ids but same shape. For each such pair, the instruction says to keep the one with the earliest record_seq. But wait - each parcel_id is unique, so these aren't "duplicate records" for the same parcel. They're just identical geometries tagged to different parcel_ids. The instruction says to keep one canonical record per parcel... Hmm, but each parcel_id is unique. Let me re-read: "turn it into the foundation for our new central registry" and "one canonical record per parcel". So I think the idea is: - Each unique geometry should be a single record - When the same geometry appears more than once (with different parcel_ids), keep the one with the earliest record_seq - This means we take the record with the smallest record_seq for each unique geometry Let me also consider discarding features with area < 1 m²: - The 20 self-intersecting zero-area polygons: These are invalid and area < 1 m², so they should be discarded. - The 30 slivers with 0.49 m²: These are < 1 m² so they should be discarded. So the process: 1. Fix invalid geometries (make_valid) or just remove them since they're already < 1 m² 2. Remove any geometry with area < 1 m² 3. For duplicate geometries, keep the record with earliest record_seq 4. Ensure all geometries are MultiPolygon 5. Write to GeoParquet with the specified columns Actually, let me think step by step more carefully. The 20 zero-area features are also invalid. Should I try to fix them? Their area is 0, so they'd be discarded anyway since < 1 m². So just discard them. The 30 slivers (0.49 m²) should also be discarded. But wait - some of the slivers might also be duplicates with valid records? Let me check. Looking at the data: - The 20 zero-area features have parcel_ids: 7, 17, 23, 35, 41, 53, 58, 71, 76, 89, 94, 107, 112, 125, 130, 143, 148, 161, 166, 179 - These don't appear in the duplicate geometry list (which involves parcel IDs like 1,2,3... paired with 900001, 900002...) The 30 slivers with 0.49 m² have parcel_ids: 800001-800030. These also don't appear in the duplicate list. So: 1. Remove the 20 zero-area features (area < 1 m²) 2. Remove the 30 slivers (area < 1 m²) 3. For the 50 geometry duplicates, keep the one with lowest record_seq (which are the ones with smaller record_seq values: 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 15, 16, 18, 20, 21, 22, 24, 25, 26, 27, 28, 30, 31, 33, 34, 36, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 51, 52, 54, 56, 57, 59, 60, 61, 62, 63, 64, 66, 67) 4. Convert any Polygon to MultiPolygon 5. Ensure valid geometries Let me write the comprehensive script. Actually, wait. Let me also think about what "duplicate geometries" means in the context of the instructions. The user says "one canonical record per parcel" - but since parcel_id is unique per record, there's no duplicate parcel_id. However, there are duplicate geometries. So "parcel" in this context might refer to a unique geographic parcel (geometry), not the parcel_id attribute. So when the same geometry appears with two different parcel_ids, we treat it as a duplicate and keep the one with lowest record_seq. Let me now write the script.
1:08
1:08
Write
/work/solve.py
content · 115 lines · py
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely.geometry import MultiPolygon, Polygon
from shapely.validation import make_valid
# ---------------------------------------------------------------------------
# 1. Read source data
# ---------------------------------------------------------------------------
gdf = gpd.read_file("/work/cairo_parcels_legacy.geojson")
print(f"Input features: {len(gdf)}")
print(f"CRS: {gdf.crs}")
# ---------------------------------------------------------------------------
# 2. Set a working copy
# ---------------------------------------------------------------------------
df = gdf.copy()
# ---------------------------------------------------------------------------
# 3. Drop null / empty geometries (safety check)
# ---------------------------------------------------------------------------
before = len(df)
df = df[df.geometry.notna()].copy()
df = df[~df.geometry.is_empty].copy()
print(f"After removing null/empty: {len(df)} (removed {before - len(df)})")
# ---------------------------------------------------------------------------
# 4. Fix invalid geometries with make_valid, then check area
# ---------------------------------------------------------------------------
n_invalid = (~df.geometry.is_valid).sum()
print(f"Invalid geometries before fix: {n_invalid}")
# Fix invalid geometries
df.loc[~df.geometry.is_valid, "geometry"] = df.loc[~df.geometry.is_valid, "geometry"].apply(make_valid)
n_invalid_after = (~df.geometry.is_valid).sum()
print(f"Invalid geometries after fix: {n_invalid_after}")
# ---------------------------------------------------------------------------
# 5. Discard any polygon fragments smaller than 1 m²
# ---------------------------------------------------------------------------
df["_area"] = df.geometry.area
small_mask = df["_area"] < 1.0
print(f"Features with area < 1 m²: {small_mask.sum()}")
df = df[~small_mask].copy()
print(f"After removing small fragments: {len(df)}")
# ---------------------------------------------------------------------------
# 6. Drop duplicate geometries, keeping earliest record_seq
# ---------------------------------------------------------------------------
# Sort by record_seq so that the first occurrence of each geometry is kept
df = df.sort_values("record_seq")
# Use drop_duplicates on geometry WKT for reliable dedup
# But first check: are there any exact geometry duplicates?
# We can use geopandas' duplicated method
n_dup_before = df.geometry.duplicated(keep=False).sum()
print(f"Features involved in geometry duplicates: {n_dup_before}")
# Keep first occurrence (which has smallest record_seq after sort)
df = df[~df.geometry.duplicated(keep="first")].copy()
print(f"After dedup: {len(df)}")
# ---------------------------------------------------------------------------
# 7. Ensure every feature is MultiPolygon
# ---------------------------------------------------------------------------
def to_multipolygon(geom):
"""Convert Polygon to MultiPolygon, leave MultiPolygon as-is."""
if geom.geom_type == "Polygon":
return MultiPolygon([geom])
elif geom.geom_type == "MultiPolygon":
return geom
else:
# Unexpected geometry type – skip
return None
df["geometry"] = df["geometry"].apply(to_multipolygon)
# Drop any that came back None
df = df[df.geometry.notna()].copy()
print(f"After MultiPolygon conversion: {len(df)}")
# ---------------------------------------------------------------------------
# 8. Recalculate area_m2 in the projected CRS (EPSG:22992 uses metres)
# ---------------------------------------------------------------------------
df["area_m2"] = df.geometry.area.round(2)
# ---------------------------------------------------------------------------
# 9. Select desired columns
# ---------------------------------------------------------------------------
out = df[["parcel_id", "parcel_class", "district", "area_m2", "geometry"]].copy()
out = out.reset_index(drop=True)
# ---------------------------------------------------------------------------
# 10. Final quality checks
# ---------------------------------------------------------------------------
print("\n=== FINAL QUALITY CHECKS ===")
print(f"Total records: {len(out)}")
print(f"Null geometries: {out.geometry.isna().sum()}")
print(f"Empty geometries: {out.geometry.is_empty.sum()}")
print(f"Invalid geometries: {(~out.geometry.is_valid).sum()}")
print(f"Duplicate geometries: {out.geometry.duplicated().sum()}")
print(f"Features with area < 1 m²: {(out.geometry.area < 1.0).sum()}")
print(f"All MultiPolygon: {(out.geometry.geom_type == 'MultiPolygon').all()}")
# ---------------------------------------------------------------------------
# 11. Write to GeoParquet
# ---------------------------------------------------------------------------
out.to_parquet("/work/parcels_canonical.geoparquet", index=False)
print("\nWritten to /work/parcels_canonical.geoparquet")
# Print summary of output content
print("\n=== OUTPUT PREVIEW ===")
print(out[["parcel_id", "parcel_class", "district", "area_m2"]].head(10).to_string())
print(f"\nTotal output features: {len(out)}")Wrote /work/solve.py (5152 chars).
1:13
Let me run this script to see if it works.
1:13
Bash
cd /work && python solve.py
Input features: 290 CRS: EPSG:22992 After removing null/empty: 290 (removed 0) Invalid geometries before fix: 20 Invalid geometries after fix: 0 Features with area < 1 m²: 30 After removing small fragments: 260 Features involved in geometry duplicates: 100 After dedup: 210 After MultiPolygon conversion: 210 === FINAL QUALITY CHECKS === Total records: 210 Null geometries: 0 Empty geometries: 0 Invalid geometries: 0 Duplicate geometries: 0 Features with area < 1 m²: 0 All MultiPolygon: True Written to /work/parcels_canonical.geoparquet === OUTPUT PREVIEW === parcel_id parcel_class district area_m2 0 1 commercial Giza-East 1200.0 1 2 industrial Qalyubia-South 1200.0 2 3 agricultural Cairo-Central 1200.0 3 4 residential Giza-East 1200.0 4 5 commercial Qalyubia-South 1200.0 5 6 industrial Cairo-Central 1200.0 6 7 agricultural Giza-East 600.0 7 8 residential Qalyubia-South 1200.0 8 9 commercial Cairo-Central 1200.0 9 10 industrial Giza-East 1200.0 Total output features: 210
1:28
Wait - I need to re-check the logic. The instructions say: "When duplicates exist, keep the record with the earliest record_seq" This is about duplicate geometries. But looking at the data more carefully: The "duplicate geometries" are pairs like index 0 (parcel_id=1, record_seq=1) and index 210 (parcel_id=900001, record_seq=211). They have different parcel_ids. So which one to keep? The one with the earliest record_seq - which is record_seq=1 (index 0). But also there are "duplicates" from the same geometry being assigned to different parcel_ids. In a canonical registry, we want one record per parcel. But the task says "one canonical record per parcel" and "when duplicates exist, keep the record with the earliest record_seq". Wait, looking at it again: each parcel_id is unique (290 parcel_ids, 290 nunique). The duplicates are in the geometry column - same geometry shared by two different parcel_ids. The instruction says "When duplicates exist, keep the record with the earliest record_seq" - so among the duplicate geometries, keep the record (row) that has the smallest record_seq. But hold on - the zero-area self-intersecting polygons (like parcel_id=7, record_seq=7) - after make_valid, those become valid but still might have area 0. Let me check what make_valid does to those. Actually, looking at the output: - Before: 290 features - Invalid: 20 (all zero-area bow-tie shapes) - After make_valid: 0 invalid - they became valid - Area < 1: 30 (20 of the former bow-ties plus 10 of the 0.49 m² fragments... wait, 30 total?) Let me check: the zero-area features were 20 (indices 6, 16, 22, 34, 40, 52, 57, 70, 75, 88, 93, 106, 111, 124, 129, 142, 147, 160, 165, 178). After make_valid, they probably got converted to LineString or Point, or became very thin polygons? Wait, make_valid on a bow-tie polygon (self-intersecting) typically converts it to a MultiPolygon of the two valid triangles. Let's check what area those became. Then the 0.49 m² fragments (parcel_ids 800001-800030, record_seq 261-290) - that's 30 features with area 0.49. So total removed as <1 m² should be: the 20 bow-ties (which after make_valid might have some area but <1?) + the 30 tiny fragments = 50. But the output says "Features with area < 1 m²: 30" - so the bow-ties after make_valid had area >= 1? Wait actually 30 = 30 tiny fragments. The 20 bow-ties after make_valid might have produced valid polygons with area > 1. Let me calculate - the bow-ties have coordinates like (640300,815000), (640330,815040), (640300,815040), (640330,815000). If make_valid resolves the self-intersection, it would split into two triangles, each with area = 0.5 * 30 * 40 = 600 m². So they'd be 600 m² each, well above 1 m². So the output area_m2=600.0 for parcel_id=7 makes sense. Actually wait, let me check the output more carefully. Parcel_id=7 has area_m2=600.0 - that's the area after make_valid. But the original data said area_m2=1200.0 for that record. I'm recalculating area from the valid geometry, which is correct. The 0.49 m² fragments (30 of them) got removed. And the 20 zero-area bow-ties got fixed by make_valid and became ~600 m² each. Now about the deduplication: 100 features in duplicate geometry pairs (50 pairs). After dedup by geometry keeping the one with earliest record_seq, we keep 50 features (the ones with record_seq 1-67) and drop the other 50 (record_seq 211-260, the 900000-series parcel_ids). So: 290 - 30 (removed small) - 50 (removed duplicates) = 210. That checks out. But wait - I need to double check the duplicates. The duplicate geometry pairs have parcel_ids like [1, 900001], [2, 900002], etc. The first has record_seq 1-67, the second has record_seq 211-260. So we keep record_seq 1-67 ones and drop 211-260 ones. That's correct. The output looks good! Let me verify the file was written properly.
1:28
1:28
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
out = gpd.read_parquet('parcels_canonical.geoparquet')
print('Columns:', out.columns.tolist())
print('CRS:', out.crs)
print('Shape:', out.shape)
print()
print('All quality checks:')
print(f' Null geometries: {out.geometry.isna().sum()}')
print(f' Empty geometries: {out.geometry.is_empty.sum()}')
print(f' Invalid geometries: {(~out.geometry.is_valid).sum()}')
print(f' Duplicate geometries: {out.geometry.duplicated().sum()}')
print(f' Min area: {out.geometry.area.min():.2f}')
print(f' All MultiPolygon: {(out.geometry.geom_type == \"MultiPolygon\").all()}')
print()
print('Value counts:')
print(' parcel_class:', out['parcel_class'].value_counts().to_dict())
print(' district:', out['district'].value_counts().to_dict())
print()
print('Sample records:')
print(out.head(15).to_string())
"
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}}
Shape: (210, 5)
All quality checks:
Null geometries: 0
Empty geometries: 0
Invalid geometries: 0
Duplicate geometries: 0
Min area: 600.00
All MultiPolygon: True
Value counts:
parcel_class: {'commercial': 53, 'industrial': 53, 'agricultural': 52, 'residential': 52}
district: {'Giza-East': 70, 'Qalyubia-South': 70, 'Cairo-Central': 70}
Sample records:
parcel_id parcel_class district area_m2 geometry
0 1 commercial Giza-East 1200.0 MULTIPOLYGON (((640000 815000, 640030 815000, 640030 815040, 640000 815040, 640000 815000)))
1 2 industrial Qalyubia-South 1200.0 MULTIPOLYGON (((640050 815000, 640080 815000, 640080 815040, 640050 815040, 640050 815000)))
2 3 agricultural Cairo-Central 1200.0 MULTIPOLYGON (((640100 815000, 640130 815000, 640130 815040, 640100 815040, 640100 815000)))
3 4 residential Giza-East 1200.0 MULTIPOLYGON (((640150 815000, 640180 815000, 640180 815040, 640150 815040, 640150 815000)))
4 5 commercial Qalyubia-South 1200.0 MULTIPOLYGON (((640200 815000, 640230 815000, 640230 815040, 640200 815040, 640200 815000)))
5 6 industrial Cairo-Central 1200.0 MULTIPOLYGON (((640250 815000, 640280 815000, 640280 815040, 640250 815040, 640250 815000)))
6 7 agricultural Giza-East 600.0 MULTIPOLYGON (((640330 815000, 640300 815000, 640315 815020, 640330 815000)), ((640330 815040, 640315 815020, 640300 815040, 640330 815040)))
7 8 residential Qalyubia-South 1200.0 MULTIPOLYGON (((640350 815000, 640380 815000, 640380 815040, 640350 815040, 640350 815000)))
8 9 commercial Cairo-Central 1200.0 MULTIPOLYGON (((640400 815000, 640430 815000, 640430 815040, 640400 815040, 640400 815000)))
9 10 industrial Giza-East 1200.0 MULTIPOLYGON (((640450 815000, 640480 815000, 640480 815040, 640450 815040, 640450 815000)))
10 11 agricultural Qalyubia-South 1280.0 MULTIPOLYGON (((640500 815000, 640530 815000, 640530 815040, 640500 815040, 640500 815000)), ((640535 815005, 640543 815005, 640543 815015, 640535 815015, 640535 815005)))
11 12 residential Cairo-Central 1200.0 MULTIPOLYGON (((640550 815000, 640580 815000, 640580 815040, 640550 815040, 640550 815000)))
12 13 commercial Giza-East 1200.0 MULTIPOLYGON (((640600 815000, 640630 815000, 640630 815040, 640600 815040, 640600 815000)))
13 14 industrial Qalyubia-South 1280.0 MULTIPOLYGON (((640650 815000, 640680 815000, 640680 815040, 640650 815040, 640650 815000)), ((640685 815005, 640693 815005, 640693 815015, 640685 815015, 640685 815005)))
14 15 agricultural Cairo-Central 1200.0 MULTIPOLYGON (((640700 815000, 640730 815000, 640730 815040, 640700 815040, 640700 815000)))
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All checks pass. Let me verify the logic more carefully: 1. **Duplicates**: The data had duplicate geometries (same shape but different parcel_id and record_seq). The task says "When duplicates exist, keep the record with the earliest record_seq." Looking at the data: - e.g., parcel_id=1 (record_seq=1) and parcel_id=900001 (record_seq=211) share the same geometry - Since record_seq=1 < 211, we keep parcel_id=1's record Wait - but the task says "one canonical record per parcel" - and the parcel_ids are different (1 vs 900001). So these are different parcels with identical geometries? The task says "duplicates" - I think this means duplicate geometries. We kept the one with the earliest record_seq (which happens to be parcel_id=1 with record_seq=1). Good. 2. **Polygon fragments smaller than 1 m²**: The 30 features with area = 0.49 m² (parcel_ids 800001-800030) were dropped. The zero-area self-intersecting polygons were fixed by make_valid, producing valid multipolygons with area >= 600. 3. **All MultiPolygon**: Done. 4. **No null/empty geometries**: Checked. 5. **No invalid geometries**: make_valid fixed them all. 6. **No duplicate geometries**: drop_duplicates removed them. 7. **No artifact slivers**: Areas < 1 m² removed. All quality checks pass. The output is in parcels_canonical.geoparquet. Let me also check if there's an issue with the "one canonical record per parcel" - the task says "When duplicates exist, keep the record with the earliest record_seq". The duplicates had different parcel_ids (e.g., parcel_id=1 and parcel_id=900001). So these are different ways of looking at duplicate geometries across the legacy systems. We already handled that by deduplicating geometries, keeping the earliest record_seq. So that logic is correct. Wait, I should double check: does "duplicate" refer to duplicate parcel_id or duplicate geometry? The task says "When duplicates exist" - referring to the parcel. Since we have duplicate geometries with different parcel_ids, I think "duplicates" means records that represent the same parcel (same geometry/location). The approach of deduplicating by geometry and keeping the earliest record_seq is correct.
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