dc-l2-cairo-invalid-dedup

Status: done
Score: 1.000
Duration: 3:18
Cost: 0.70¢
Model: deepseek/deepseek-v4-flash

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"
}
0:09 
Let me start by reading the GeoJSON file to understand its structure and content.
0:09
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>
0:21 
Let me get a better understanding of the data. Let me first check the full structure and see what we're dealing with.
0:21
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
import json

# Read the GeoJSON
gdf = gpd.read_file('cairo_parcels_legacy.geojson')
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print('Columns:', gdf.columns.tolist())
print('Geometry types:', gdf.geometry.type.value_counts())
print()
print('dtypes:')
print(gdf.dtypes)
print()
print('Null geometries:', gdf.geometry.isna().sum())
print()
print('parcel_id stats: nunique', gdf['parcel_id'].nunique(), 'total', len(gdf))
print('record_seq range:', gdf['record_seq'].min(), '-', gdf['record_seq'].max())
print('parcel_class nunique:', gdf['parcel_class'].nunique(), 'vals:', gdf['parcel_class'].unique().tolist())
print('district nunique:', gdf['district'].nunique(), 'vals:', gdf['district'].unique().tolist())
print()
print('area_m2 range:', gdf['area_m2'].min(), '-', gdf['area_m2'].max())
print('area_m2 dtype:', gdf['area_m2'].dtype)
print()
# Check for duplicates by parcel_id
dup_count = gdf['parcel_id'].value_counts()
print('parcel_id with >1 record:', (dup_count > 1).sum())
print('Max duplicates:', dup_count.max())
"
CRS: EPSG:22992
Shape: (290, 6)
Columns: ['parcel_id', 'record_seq', 'parcel_class', 'district', 'area_m2', 'geometry']
Geometry types: Polygon         260
MultiPolygon     30
Name: count, dtype: int64

dtypes:
parcel_id          int32
record_seq         int32
parcel_class         str
district             str
area_m2          float64
geometry        geometry
dtype: object

Null geometries: 0

parcel_id stats: nunique 290 total 290
record_seq range: 1 - 290
parcel_class nunique: 5 vals: ['commercial', 'industrial', 'agricultural', 'residential', 'unknown']
district nunique: 4 vals: ['Giza-East', 'Qalyubia-South', 'Cairo-Central', 'border-sliver']

area_m2 range: 0.49 - 1200.0
area_m2 dtype: float64

parcel_id with >1 record: 0
Max duplicates: 1
0:40 
Interesting. So every parcel_id is unique - no duplicates by parcel_id. But the task says "When duplicates exist, keep the record with the earliest record_seq". Let me look more carefully - maybe there are duplicate geometries even if parcel_id is unique? Or maybe there are parcels that should be considered the same by geometry or something else.

Let me check: is there any parcel_id with >1 record? No, each parcel_id appears once. So maybe the "duplicates" are duplicate geometries (same shape, different parcel_id)? Or maybe I need to look at this differently.

Wait, the CRS is EPSG:22992. The area_m2 range has values as low as 0.49 m². I need to discard fragments smaller than 1 m².

Let me examine the data more closely to understand the duplicate situation.
0:40
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
import numpy as np

gdf = gpd.read_file('cairo_parcels_legacy.geojson')

# Check for duplicate geometries
from shapely import wkt
geoms = gdf.geometry.values
# Check for exact duplicates
seen = {}
dup_geom_indices = []
for i, geom in enumerate(geoms):
    wkt_str = wkt.dumps(geom, rounding_precision=6)
    if wkt_str in seen:
        dup_geom_indices.append((seen[wkt_str], i))
    else:
        seen[wkt_str] = i

print(f'Number of duplicate geometries: {len(dup_geom_indices)}')
for first, second in dup_geom_indices[:10]:
    print(f'  Index {first} (parcel_id={gdf.iloc[first][\"parcel_id\"]}) and index {second} (parcel_id={gdf.iloc[second][\"parcel_id\"]})')
print()

# Also check if there are grouped parcel_ids - maybe by some other logic?
# Let's see the smallest areas
small = gdf[gdf['area_m2'] < 1.0]
print(f'Features with area < 1 m²: {len(small)}')
print(small[['parcel_id', 'area_m2', 'district']])
print()

# Check district 'border-sliver'
border = gdf[gdf['district'] == 'border-sliver']
print(f'border-sliver features: {len(border)}')
print(border[['parcel_id', 'area_m2', 'record_seq', 'parcel_class']])
print()

# Let's check for parcels with same geometry (not exact WKT, but spatially equal)
# Or maybe there are overlapping parcels?
"
Number of duplicate geometries: 50
  Index 0 (parcel_id=1) and index 210 (parcel_id=900001)
  Index 1 (parcel_id=2) and index 211 (parcel_id=900002)
  Index 2 (parcel_id=3) and index 212 (parcel_id=900003)
  Index 3 (parcel_id=4) and index 213 (parcel_id=900004)
  Index 4 (parcel_id=5) and index 214 (parcel_id=900005)
  Index 5 (parcel_id=6) and index 215 (parcel_id=900006)
  Index 7 (parcel_id=8) and index 216 (parcel_id=900007)
  Index 8 (parcel_id=9) and index 217 (parcel_id=900008)
  Index 9 (parcel_id=10) and index 218 (parcel_id=900009)
  Index 11 (parcel_id=12) and index 219 (parcel_id=900010)

Features with area < 1 m²: 30
     parcel_id  area_m2       district
260     800001     0.49  border-sliver
261     800002     0.49  border-sliver
262     800003     0.49  border-sliver
263     800004     0.49  border-sliver
264     800005     0.49  border-sliver
265     800006     0.49  border-sliver
266     800007     0.49  border-sliver
267     800008     0.49  border-sliver
268     800009     0.49  border-sliver
269     800010     0.49  border-sliver
270     800011     0.49  border-sliver
271     800012     0.49  border-sliver
272     800013     0.49  border-sliver
273     800014     0.49  border-sliver
274     800015     0.49  border-sliver
275     800016     0.49  border-sliver
276     800017     0.49  border-sliver
277     800018     0.49  border-sliver
278     800019     0.49  border-sliver
279     800020     0.49  border-sliver
280     800021     0.49  border-sliver
281     800022     0.49  border-sliver
282     800023     0.49  border-sliver
283     800024     0.49  border-sliver
284     800025     0.49  border-sliver
285     800026     0.49  border-sliver
286     800027     0.49  border-sliver
287     800028     0.49  border-sliver
288     800029     0.49  border-sliver
289     800030     0.49  border-sliver

border-sliver features: 30
     parcel_id  area_m2  record_seq parcel_class
260     800001     0.49         261      unknown
261     800002     0.49         262      unknown
262     800003     0.49         263      unknown
263     800004     0.49         264      unknown
264     800005     0.49         265      unknown
265     800006     0.49         266      unknown
266     800007     0.49         267      unknown
267     800008     0.49         268      unknown
268     800009     0.49         269      unknown
269     800010     0.49         270      unknown
270     800011     0.49         271      unknown
271     800012     0.49         272      unknown
272     800013     0.49         273      unknown
273     800014     0.49         274      unknown
274     800015     0.49         275      unknown
275     800016     0.49         276      unknown
276     800017     0.49         277      unknown
277     800018     0.49         278      unknown
278     800019     0.49         279      unknown
279     800020     0.49         280      unknown
280     800021     0.49         281      unknown
281     800022     0.49         282      unknown
282     800023     0.49         283      unknown
283     800024     0.49         284      unknown
284     800025     0.49         285      unknown
285     800026     0.49         286      unknown
286     800027     0.49         287      unknown
287     800028     0.49         288      unknown
288     800029     0.49         289      unknown
289     800030     0.49         290      unknown
1:12 
Let me understand the data:

1. **Duplicate geometries**: There are 50 duplicate geometries. Looking at the pattern, it seems like parcel_ids 1-? have duplicates with parcel_ids 900001-9000??. These are identical geometries with different parcel_ids, different record_seq values. I need to keep the one with the earliest record_seq per parcel... wait, the instructions say "parcel_id is the join key" - so duplicates are defined by parcel_id? But parcel_id is unique already (nunique=290, total=290).

Wait, let me re-read: "When duplicates exist, keep the record with the earliest record_seq, and discard any polygon fragments smaller than 1 m²."

So the duplicates are by geometry - duplicate geometries should be resolved by keeping the record with the earliest record_seq. But there are also 30 slivers (area < 1 m²) to discard.

Let me look more carefully at the duplicate geometry situation. The 50 duplicates mean there are 50 pairs of identical geometries. So indices 0 and 210 share the same geometry, 1 and 211 share the same geometry, etc.

So the plan is:
1. Remove geometries smaller than 1 m² (30 features with area 0.49 m² - the border slivers)
2. For duplicate geometries, keep the one with the earliest record_seq
3. Make sure all geometries are MultiPolygon
4. Validate geometries (no null, no invalid)
5. Write to geoparquet with columns: parcel_id, parcel_class, district, area_m2

Let me also check for invalid geometries.

Actually, wait - the parcel_id is unique already (290 unique IDs for 290 records). So "duplicates" must refer to duplicate geometries (same shape). The 50 duplicate geometries are pairs - so there are 50 pairs = 100 features involved. But I see pairs like (0, 210), (1, 211), ... Let me check - indices 0-... 

Let me examine more carefully.
1:12
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
from shapely import wkt

gdf = gpd.read_file('cairo_parcels_legacy.geojson')

# Let's examine the duplicate pairs
seen = {}
dup_pairs = []
for i, geom in enumerate(gdf.geometry.values):
    wkt_str = wkt.dumps(geom, rounding_precision=6)
    if wkt_str in seen:
        dup_pairs.append((seen[wkt_str], i))
    else:
        seen[wkt_str] = i

print('First 15 duplicate pairs:')
for first, second in dup_pairs[:15]:
    r1 = gdf.iloc[first]
    r2 = gdf.iloc[second]
    print(f'  [{first}] pid={r1[\"parcel_id\"]}, seq={r1[\"record_seq\"]}, cls={r1[\"parcel_class\"]}, dist={r1[\"district\"]}')
    print(f'  [{second}] pid={r2[\"parcel_id\"]}, seq={r2[\"record_seq\"]}, cls={r2[\"parcel_class\"]}, dist={r2[\"district\"]}')
    print()

# Check which record_seq is earlier for each pair
print('\\nWhich record_seq is earlier in each pair?')
for first, second in dup_pairs[:15]:
    r1 = gdf.iloc[first]
    r2 = gdf.iloc[second]
    earlier_idx = first if r1['record_seq'] < r2['record_seq'] else second
    later_idx = second if r1['record_seq'] < r2['record_seq'] else first
    print(f'  Earlier: [{earlier_idx}] (pid={gdf.iloc[earlier_idx][\"parcel_id\"]}, seq={gdf.iloc[earlier_idx][\"record_seq\"]})')
    print(f'  Later:   [{later_idx}] (pid={gdf.iloc[later_idx][\"parcel_id\"]}, seq={gdf.iloc[later_idx][\"record_seq\"]})')
    print()
"
First 15 duplicate pairs:
  [0] pid=1, seq=1, cls=commercial, dist=Giza-East
  [210] pid=900001, seq=211, cls=industrial, dist=Giza-East

  [1] pid=2, seq=2, cls=industrial, dist=Qalyubia-South
  [211] pid=900002, seq=212, cls=agricultural, dist=Qalyubia-South

  [2] pid=3, seq=3, cls=agricultural, dist=Cairo-Central
  [212] pid=900003, seq=213, cls=residential, dist=Cairo-Central

  [3] pid=4, seq=4, cls=residential, dist=Giza-East
  [213] pid=900004, seq=214, cls=commercial, dist=Giza-East

  [4] pid=5, seq=5, cls=commercial, dist=Qalyubia-South
  [214] pid=900005, seq=215, cls=industrial, dist=Qalyubia-South

  [5] pid=6, seq=6, cls=industrial, dist=Cairo-Central
  [215] pid=900006, seq=216, cls=agricultural, dist=Cairo-Central

  [7] pid=8, seq=8, cls=residential, dist=Qalyubia-South
  [216] pid=900007, seq=217, cls=commercial, dist=Giza-East

  [8] pid=9, seq=9, cls=commercial, dist=Cairo-Central
  [217] pid=900008, seq=218, cls=industrial, dist=Qalyubia-South

  [9] pid=10, seq=10, cls=industrial, dist=Giza-East
  [218] pid=900009, seq=219, cls=agricultural, dist=Cairo-Central

  [11] pid=12, seq=12, cls=residential, dist=Cairo-Central
  [219] pid=900010, seq=220, cls=commercial, dist=Giza-East

  [12] pid=13, seq=13, cls=commercial, dist=Giza-East
  [220] pid=900011, seq=221, cls=industrial, dist=Qalyubia-South

  [14] pid=15, seq=15, cls=agricultural, dist=Cairo-Central
  [221] pid=900012, seq=222, cls=residential, dist=Cairo-Central

  [15] pid=16, seq=16, cls=residential, dist=Giza-East
  [222] pid=900013, seq=223, cls=commercial, dist=Giza-East

  [17] pid=18, seq=18, cls=industrial, dist=Cairo-Central
  [223] pid=900014, seq=224, cls=agricultural, dist=Qalyubia-South

  [19] pid=20, seq=20, cls=residential, dist=Qalyubia-South
  [224] pid=900015, seq=225, cls=commercial, dist=Cairo-Central


Which record_seq is earlier in each pair?
  Earlier: [0] (pid=1, seq=1)
  Later:   [210] (pid=900001, seq=211)

  Earlier: [1] (pid=2, seq=2)
  Later:   [211] (pid=900002, seq=212)

  Earlier: [2] (pid=3, seq=3)
  Later:   [212] (pid=900003, seq=213)

  Earlier: [3] (pid=4, seq=4)
  Later:   [213] (pid=900004, seq=214)

  Earlier: [4] (pid=5, seq=5)
  Later:   [214] (pid=900005, seq=215)

  Earlier: [5] (pid=6, seq=6)
  Later:   [215] (pid=900006, seq=216)

  Earlier: [7] (pid=8, seq=8)
  Later:   [216] (pid=900007, seq=217)

  Earlier: [8] (pid=9, seq=9)
  Later:   [217] (pid=900008, seq=218)

  Earlier: [9] (pid=10, seq=10)
  Later:   [218] (pid=900009, seq=219)

  Earlier: [11] (pid=12, seq=12)
  Later:   [219] (pid=900010, seq=220)

  Earlier: [12] (pid=13, seq=13)
  Later:   [220] (pid=900011, seq=221)

  Earlier: [14] (pid=15, seq=15)
  Later:   [221] (pid=900012, seq=222)

  Earlier: [15] (pid=16, seq=16)
  Later:   [222] (pid=900013, seq=223)

  Earlier: [17] (pid=18, seq=18)
  Later:   [223] (pid=900014, seq=224)

  Earlier: [19] (pid=20, seq=20)
  Later:   [224] (pid=900015, seq=225)
1:26 
Now I understand the data:

1. There are 290 features total
2. Some are duplicate geometries (50 pairs, meaning 100 features) - duplicates have parcel_id 9xxxxx and a later record_seq
3. 30 features have area < 1 m² (parcel_ids 800001-800030, all "border-sliver" district)
4. No duplicate parcel_ids, but duplicate geometries exist across different parcel_ids

The task says:
- One canonical record per parcel - "When duplicates exist, keep the record with the earliest record_seq"
- Discard polygon fragments smaller than 1 m²
- Output to parcels_canonical.geoparquet with columns: parcel_id, parcel_class, district, area_m2
- Every feature must be a MultiPolygon
- No null/empty geometries, no invalid geometries, no duplicate geometries, no artifact slivers

Now, "one canonical record per parcel" - but the duplicate geometries have different parcel_ids (1-xxx and 900xxx). So how do we define "per parcel"? 

Looking at the data: the duplicates are exact geometric duplicates but have different parcel_ids and different properties. The instruction says "When duplicates exist, keep the record with the earliest record_seq." So for each set of duplicate geometries, keep the one with the lowest record_seq.

But also, "one canonical record per parcel" - parcel_id is the join key. The duplicate geometries have different parcel_ids but the same geometry. So they represent the same parcel with different records from different legacy systems.

I think the approach is:
1. Identify features with area < 1 m² and remove them
2. For duplicate geometries, keep only the one with the earliest record_seq
3. Ensure all geometries are MultiPolygon
4. Validate geometries (fix invalids)
5. Remove any remaining null/empty geometries
6. Output

Let me think about what "parcel_id" column should be in the output. The instruction says "parcel_id (the join key)". Since we're de-duplicating by geometry, and each duplicate pair has two different parcel_ids, we should keep the parcel_id of the record we kept (the one with the earliest record_seq).

Let me also check for any invalid geometries.
1:26
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
from shapely.validation import explain_validity

gdf = gpd.read_file('cairo_parcels_legacy.geojson')

# Check for invalid geometries
invalid = []
for i, geom in enumerate(gdf.geometry):
    if geom is None or geom.is_empty:
        invalid.append((i, 'null or empty'))
    elif not geom.is_valid:
        invalid.append((i, explain_validity(geom)))

print(f'Invalid/null/empty geometries: {len(invalid)}')
for idx, reason in invalid:
    print(f'  [{idx}] pid={gdf.iloc[idx][\"parcel_id\"]}: {reason}')
print()

# Check area calculations vs stored area_m2
gdf['calc_area'] = gdf.geometry.area
diff = (gdf['calc_area'] - gdf['area_m2']).abs()
print(f'Max area diff: {diff.max()}')
print(f'Features with area diff > 0.01: {(diff > 0.01).sum()}')
"
Invalid/null/empty geometries: 20
  [6] pid=7: Self-intersection[640315 815020]
  [16] pid=17: Self-intersection[640065 815080]
  [22] pid=23: Self-intersection[640365 815080]
  [34] pid=35: Self-intersection[640215 815140]
  [40] pid=41: Self-intersection[640515 815140]
  [52] pid=53: Self-intersection[640365 815200]
  [57] pid=58: Self-intersection[640615 815200]
  [70] pid=71: Self-intersection[640515 815260]
  [75] pid=76: Self-intersection[640015 815320]
  [88] pid=89: Self-intersection[640665 815320]
  [93] pid=94: Self-intersection[640165 815380]
  [106] pid=107: Self-intersection[640065 815440]
  [111] pid=112: Self-intersection[640315 815440]
  [124] pid=125: Self-intersection[640215 815500]
  [129] pid=130: Self-intersection[640465 815500]
  [142] pid=143: Self-intersection[640365 815560]
  [147] pid=148: Self-intersection[640615 815560]
  [160] pid=161: Self-intersection[640515 815620]
  [165] pid=166: Self-intersection[640015 815680]
  [178] pid=179: Self-intersection[640665 815680]

Max area diff: 1200.0
Features with area diff > 0.01: 50
1:42 
Let me analyze what needs to be done:

1. **Duplicate geometries**: There are 50 duplicate geometries (25 pairs). For each pair, the records have the same geometry but different parcel_id, record_seq, etc. The rule says: "keep the record with the earliest record_seq" when duplicates exist. So for each duplicate pair, we keep the one with the lower record_seq.

2. **Fragments smaller than 1 m²**: There are 30 features with area 0.49 m² (all in "border-sliver" district). These should be discarded.

3. **Invalid geometries**: There are 20 self-intersecting geometries that need to be fixed (e.g., with `buffer(0)` or `make_valid()`).

4. **Every feature must be a MultiPolygon**: Need to convert all Polygons to MultiPolygons.

5. **No null/empty geometries**: Already none, but need to ensure after processing.

6. **No duplicate geometries**: Need to remove duplicates.

7. **No artifact slivers**: The small fragments are removed.

Let me write a comprehensive script to do all this.

The plan:
1. Read the GeoJSON
2. Filter out features with area < 1 m²
3. For duplicate geometries, keep only the one with the earliest record_seq
4. Fix invalid geometries (self-intersections) using `buffer(0)` or `make_valid()`
5. Convert all geometries to MultiPolygon
6. Ensure no null/empty geometries, no duplicates
7. Recalculate area_m2
8. Select the 4 required columns: parcel_id, parcel_class, district, area_m2
9. Write to Parquet

Wait, I need to think about the "duplicates" more carefully. The problem says "When duplicates exist" - this likely refers to duplicate geometries (same parcel in multiple legacy systems). The instruction says "keep the record with the earliest record_seq". 

But there are also the parcel_id duplicates? No, parcel_id values are all unique (290 unique out of 290). So "duplicates" must refer to duplicate geometries.

Let me think about the approach step by step:

1. Remove features with area < 1 m² (the 30 border-sliver features)
2. Group by geometry (or use spatial equality) to find duplicates. For each unique geometry, keep only the record with the smallest record_seq.
3. Fix invalid geometries
4. Ensure MultiPolygon
5. Recalculate area_m2
6. Ensure no duplicate geometries after fixing

Let me write the script.

Actually, I need to be careful about which "parcel_id" to keep. The problem says "the join key" is parcel_id, and we keep the record with earliest record_seq. So for each duplicate geometry, we keep the row with the minimum record_seq, and that row's parcel_id becomes the canonical ID.

Let me check: in the pairs, the earlier record_seq corresponds to the smaller parcel_id (1-290 range), while the later ones are 900001+ range. So keeping earliest record_seq means keeping the original 1-290 IDs and discarding the 900000+ ones.

Let me also think about recalculating area_m2 vs keeping the stored one. The problem says area_m2 should be a column. I think I should recalculate from the geometry to be safe, especially since some geometries might be invalid/fixed.

Let me check what the area differences are about for the 50 features with diff > 0.01.

Actually, looking at the duplicate pairs - indices 0 and 210 are duplicates. Index 0 has area_m2=1200, index 210 has area_m2=0.0. So the area_m2 values differ between duplicates! The stored area_m2 in the earlier records is correct (1200, matching the geometry area), while the later duplicates have 0.0.

So I should recalculate area from geometry.

Let me now write the full processing script.
1:42
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
from shapely import wkt

gdf = gpd.read_file('cairo_parcels_legacy.geojson')

# Check the area_m2 values on the duplicate pairs
seen = {}
dup_pairs = []
for i, geom in enumerate(gdf.geometry.values):
    wkt_str = wkt.dumps(geom, rounding_precision=6)
    if wkt_str in seen:
        dup_pairs.append((seen[wkt_str], i))
    else:
        seen[wkt_str] = i

print('Comparing stored area_m2 vs computed area for duplicate pairs:')
for first, second in dup_pairs[:5]:
    r1 = gdf.iloc[first]
    r2 = gdf.iloc[second]
    a1 = r1.geometry.area
    a2 = r2.geometry.area
    print(f'  [{first}] pid={r1[\"parcel_id\"]}, stored_area={r1[\"area_m2\"]}, geom_area={a1}')
    print(f'  [{second}] pid={r2[\"parcel_id\"]}, stored_area={r2[\"area_m2\"]}, geom_area={a2}')
    print()

# Check all 50 features with area diff > 0.01
gdf['calc_area'] = gdf.geometry.area
diff = (gdf['calc_area'] - gdf['area_m2']).abs()
print('Features with area diff > 0.01:')
print(gdf[diff > 0.01][['parcel_id', 'record_seq', 'area_m2', 'calc_area']])
"
Comparing stored area_m2 vs computed area for duplicate pairs:
  [0] pid=1, stored_area=1200.0, geom_area=1200.0
  [210] pid=900001, stored_area=1200.0, geom_area=1200.0

  [1] pid=2, stored_area=1200.0, geom_area=1200.0
  [211] pid=900002, stored_area=1200.0, geom_area=1200.0

  [2] pid=3, stored_area=1200.0, geom_area=1200.0
  [212] pid=900003, stored_area=1200.0, geom_area=1200.0

  [3] pid=4, stored_area=1200.0, geom_area=1200.0
  [213] pid=900004, stored_area=1200.0, geom_area=1200.0

  [4] pid=5, stored_area=1200.0, geom_area=1200.0
  [214] pid=900005, stored_area=1200.0, geom_area=1200.0

Features with area diff > 0.01:
     parcel_id  record_seq  area_m2  calc_area
6            7           7   1200.0        0.0
10          11          11   1200.0     1280.0
13          14          14   1200.0     1280.0
16          17          17   1200.0        0.0
18          19          19   1200.0     1280.0
22          23          23   1200.0        0.0
28          29          29   1200.0     1280.0
31          32          32   1200.0     1280.0
34          35          35   1200.0        0.0
36          37          37   1200.0     1280.0
40          41          41   1200.0        0.0
46          47          47   1200.0     1280.0
49          50          50   1200.0     1280.0
52          53          53   1200.0        0.0
54          55          55   1200.0     1280.0
57          58          58   1200.0        0.0
64          65          65   1200.0     1280.0
67          68          68   1200.0     1280.0
70          71          71   1200.0        0.0
72          73          73   1200.0     1280.0
75          76          76   1200.0        0.0
82          83          83   1200.0     1280.0
85          86          86   1200.0     1280.0
88          89          89   1200.0        0.0
90          91          91   1200.0     1280.0
93          94          94   1200.0        0.0
100        101         101   1200.0     1280.0
103        104         104   1200.0     1280.0
106        107         107   1200.0        0.0
108        109         109   1200.0     1280.0
111        112         112   1200.0        0.0
118        119         119   1200.0     1280.0
121        122         122   1200.0     1280.0
124        125         125   1200.0        0.0
126        127         127   1200.0     1280.0
129        130         130   1200.0        0.0
136        137         137   1200.0     1280.0
139        140         140   1200.0     1280.0
142        143         143   1200.0        0.0
144        145         145   1200.0     1280.0
147        148         148   1200.0        0.0
154        155         155   1200.0     1280.0
157        158         158   1200.0     1280.0
160        161         161   1200.0        0.0
162        163         163   1200.0     1280.0
165        166         166   1200.0        0.0
172        173         173   1200.0     1280.0
175        176         176   1200.0     1280.0
178        179         179   1200.0        0.0
180        181         181   1200.0     1280.0
1:52 
Let me understand the data structure:

1. **290 features** total
2. **50 duplicate geometries** - pairs of features sharing exact same shape (one from the "early" set with parcel_id 1-... and one from the "late" set with parcel_id 900000+)
3. **30 sliver features** with area < 1 m² (all in "border-sliver" district, area=0.49)
4. **20 invalid geometries** (self-intersections) - these have calc_area=0.0 but stored area_m2=1200.0
5. **Some geometries** with calc_area=1280.0 but stored area_m2=1200.0

So the plan:
1. Fix invalid geometries (self-intersections) by using `buffer(0)` to clean them up, then compute proper area
2. Remove sliver polygons (< 1 m²)
3. For duplicate geometries, keep the one with the earliest record_seq
4. Ensure all geometries are MultiPolygon
5. Ensure no null/empty geometries, no invalid geometries, no duplicate geometries
6. Recompute area_m2 from the geometry (since stored values are sometimes wrong)
7. Write to parcels_canonical.geoparquet

Let me also check what's happening with those features that have calc_area=0.0 (the self-intersecting ones) and those with 1280 (maybe they're just polygons with slightly different shape than expected).

Actually, looking more closely:
- Features with self-intersection have calc_area=0.0 which makes sense - invalid geometries have 0 area
- Features with 1280.0 m² have valid geometries but the stored area_m2 is 1200.0 (wrong)

Let me check what those 1280 m² features look like.
1:52
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd

gdf = gpd.read_file('cairo_parcels_legacy.geojson')

# Check the features with calc_area=1280
mask = (gdf.geometry.area - gdf['area_m2']).abs() > 0.01
diff = gdf[mask]
print('Features with area discrepancy:')
for i, row in diff.iterrows():
    print(f'  [{i}] pid={row[\"parcel_id\"]}, seq={row[\"record_seq\"]}, cls={row[\"parcel_class\"]}, dist={row[\"district\"]}, stored={row[\"area_m2\"]}, calc={row.geometry.area:.2f}, valid={row.geometry.is_valid}, geom_type={row.geometry.geom_type}')
print()

# Check the self-intersecting ones more closely
for idx in [6, 16]:
    print(f'Index {idx}:')
    print(gdf.iloc[idx].geometry)
    print()
"
Features with area discrepancy:
  [6] pid=7, seq=7, cls=agricultural, dist=Giza-East, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [10] pid=11, seq=11, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [13] pid=14, seq=14, cls=industrial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [16] pid=17, seq=17, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [18] pid=19, seq=19, cls=agricultural, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [22] pid=23, seq=23, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [28] pid=29, seq=29, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [31] pid=32, seq=32, cls=residential, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [34] pid=35, seq=35, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [36] pid=37, seq=37, cls=commercial, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [40] pid=41, seq=41, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [46] pid=47, seq=47, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [49] pid=50, seq=50, cls=industrial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [52] pid=53, seq=53, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [54] pid=55, seq=55, cls=agricultural, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [57] pid=58, seq=58, cls=industrial, dist=Giza-East, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [64] pid=65, seq=65, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [67] pid=68, seq=68, cls=residential, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [70] pid=71, seq=71, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [72] pid=73, seq=73, cls=commercial, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [75] pid=76, seq=76, cls=residential, dist=Giza-East, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [82] pid=83, seq=83, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [85] pid=86, seq=86, cls=industrial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [88] pid=89, seq=89, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [90] pid=91, seq=91, cls=agricultural, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [93] pid=94, seq=94, cls=industrial, dist=Giza-East, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [100] pid=101, seq=101, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [103] pid=104, seq=104, cls=residential, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [106] pid=107, seq=107, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [108] pid=109, seq=109, cls=commercial, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [111] pid=112, seq=112, cls=residential, dist=Giza-East, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [118] pid=119, seq=119, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [121] pid=122, seq=122, cls=industrial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [124] pid=125, seq=125, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [126] pid=127, seq=127, cls=agricultural, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [129] pid=130, seq=130, cls=industrial, dist=Giza-East, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [136] pid=137, seq=137, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [139] pid=140, seq=140, cls=residential, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [142] pid=143, seq=143, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [144] pid=145, seq=145, cls=commercial, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [147] pid=148, seq=148, cls=residential, dist=Giza-East, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [154] pid=155, seq=155, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [157] pid=158, seq=158, cls=industrial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [160] pid=161, seq=161, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [162] pid=163, seq=163, cls=agricultural, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [165] pid=166, seq=166, cls=industrial, dist=Giza-East, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [172] pid=173, seq=173, cls=commercial, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [175] pid=176, seq=176, cls=residential, dist=Qalyubia-South, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon
  [178] pid=179, seq=179, cls=agricultural, dist=Qalyubia-South, stored=1200.0, calc=0.00, valid=False, geom_type=Polygon
  [180] pid=181, seq=181, cls=commercial, dist=Giza-East, stored=1200.0, calc=1280.00, valid=True, geom_type=MultiPolygon

Index 6:
POLYGON ((640300 815000, 640330 815040, 640300 815040, 640330 815000, 640300 815000))

Index 16:
POLYGON ((640050 815060, 640080 815100, 640050 815100, 640080 815060, 640050 815060))
2:10 
Let me understand the data:

1. **Duplicate geometries**: There are 50 duplicate geometries (25 pairs). Each pair consists of an earlier record (lower `record_seq`) with parcel_id 1-290 and a later record with parcel_id 900001-900025. The instruction says to keep the record with the earliest `record_seq`.

2. **Small geometries**: 30 features with area < 1 m² (all in district "border-sliver", parcel_ids 800001-800030). These should be discarded.

3. **Invalid geometries**: 20 features with self-intersection (calc_area=0). These need to be fixed.

4. **Area discrepancy**: Some features have stored area_m2=1200 but calculated area=1280 (MultiPolygons). These are valid.

5. **"No duplicate geometries"** - besides the duplicate records (same geometry for different parcel_ids), we also need to ensure no two remaining features have identical geometries.

Let me plan the processing steps:

1. Read the GeoJSON
2. Fix invalid geometries (buffer(0) to handle self-intersections)
3. Discard polygon fragments smaller than 1 m²
4. For duplicate geometries, keep only the one with earliest record_seq
5. Ensure all geometries are MultiPolygon
6. Recalculate area_m2 from the actual geometry
7. Write to GeoParquet

Wait, the instruction says "keep the record with the earliest record_seq" when duplicates exist. But the duplicates here have different parcel_ids (1 vs 900001). So which parcel_id do we keep? The one with the earlier record_seq.

Let me think about this more carefully:

The user says "one canonical record per parcel" and "parcel_id (the join key)". So parcel_id is the unique identifier. But in the data, duplicate geometries have different parcel_ids. So the "duplicates" concept here might mean "duplicate geometries" - when two records have the same geometry, we keep the one with earliest record_seq.

Actually, looking at the data more carefully - each pair has the same geometry but different parcel_id. The task says "keep the record with the earliest record_seq" - so for each duplicate geometry, we keep the record with lower record_seq.

Also need to ensure "no duplicate geometries" - so after processing, there should be no two features with identical geometries.

Let me also consider: the invalid geometries (self-intersecting) produce area=0 when computed. After fixing with buffer(0), they should have proper area.

Let me write the comprehensive script.

Actually, let me re-read: "parcel_id: the join key" - so parcel_id is how we identify parcels. But we have duplicate geometries with different parcel_ids. 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 lower record_seq.

But also: "one canonical record per parcel" - does this mean per parcel_id? Since parcel_id values are all unique (290 unique out of 290), there's no duplicate parcel_ids. So "duplicates" must mean "duplicate geometries" (same shape).

Let me think about what to do with the stored area_m2 column. The user says the output should include "area_m2" but doesn't say whether to use the original or recalculate. Since we're fixing geometries, it makes sense to recalculate.

Let me write a comprehensive script.

Also: "no null or empty geometries" - need to check and remove any.
"no invalid geometries" - need to fix.
"no duplicate geometries" - need to deduplicate.
"no artifact slivers" - fragments < 1 m² removed.
"Every feature must be a MultiPolygon" - convert all to MultiPolygon.

Let me look at the data once more to understand the invalid geometry pattern:

Index 6: POLYGON ((640300 815000, 640330 815040, 640300 815040, 640330 815000, 640300 815000))

This is a bow-tie (self-intersecting) polygon. buffer(0) should fix it.

Let me write the script now.
2:10
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
from shapely import wkt, make_valid, MultiPolygon, Polygon

gdf = gpd.read_file('cairo_parcels_legacy.geojson')

# Check the self-intersecting geometry at index 6
geom = gdf.iloc[6].geometry
print('Original:', geom)
print('Is valid:', geom.is_valid)
fixed = make_valid(geom)
print('Fixed:', fixed)
print('Fixed is_valid:', fixed.is_valid)
print('Fixed area:', fixed.area)
print('Fixed type:', fixed.geom_type)

# Also test buffer(0)
fixed2 = geom.buffer(0)
print('buffer(0):', fixed2)
print('buffer(0) area:', fixed2.area)
print('buffer(0) type:', fixed2.geom_type)
"
Original: POLYGON ((640300 815000, 640330 815040, 640300 815040, 640330 815000, 640300 815000))
Is valid: False
Fixed: MULTIPOLYGON (((640330 815000, 640300 815000, 640315 815020, 640330 815000)), ((640330 815040, 640315 815020, 640300 815040, 640330 815040)))
Fixed is_valid: True
Fixed area: 600.0
Fixed type: MultiPolygon
buffer(0): POLYGON ((640300 815000, 640315 815020, 640330 815000, 640300 815000))
buffer(0) area: 300.0
buffer(0) type: Polygon
2:46 
Let me understand the data:

1. **Duplicate geometries**: 25 pairs (50 features) where two features have identical geometries. The duplicates are index `i` paired with index `i+210` (roughly). The earlier record_seq should be kept.

2. **Small fragment slivers**: 30 features with area < 1 m² (all from district "border-sliver", area 0.49 m²). These should be discarded.

3. **Invalid geometries**: 20 features with self-intersections (area 0.0 in the stored area_m2 but calc_area 0.0). These need to be made valid.

4. **Features with area 1280 vs stored 1200**: These are MultiPolygons that have correct computed area of 1280 (likely the actual area). We should compute the actual area from the geometry.

5. **No null or empty geometries**: Already none.

Let me write a comprehensive script:

Steps:
1. Read the GeoJSON
2. Convert all geometries to valid geometries using `make_valid`
3. Discard any polygon fragments smaller than 1 m² after making valid
4. Discard any features where the entire geometry area < 1 m²
5. For duplicate geometries, keep the one with the earliest record_seq
6. Ensure all features are MultiPolygon
7. Compute area_m2 from the geometry
8. Write to geoparquet with the specified columns

Wait, let me think about this more carefully.

The instruction says:
- "When duplicates exist, keep the record with the earliest record_seq" - so for each unique geometry, keep the row with smallest record_seq.
- "discard any polygon fragments smaller than 1 m²" - this could mean discarding entire features whose geometry area < 1m², OR within a multipolygon, removing individual polygon parts smaller than 1 m². I think it's the latter - within a multipolygon, remove fragment polygons smaller than 1 m². But also the sliver features (30 features with 0.49 m²) would be discarded entirely.

Let me re-read: "discard any polygon fragments smaller than 1 m²" - "polygon fragments" suggests individual polygon parts within a geometry, not entire features. But the border-sliver features are entire features with area < 1m², so they'd be dropped too.

Let me think about the proper approach:

1. Load data
2. Make all geometries valid (fix self-intersections)
3. Ensure all are MultiPolygon
4. For each MultiPolygon, filter out individual polygon parts with area < 1 m²
5. Drop any features that end up with empty geometry
6. For duplicate geometries (same WKT after cleanup), keep the one with smallest record_seq
7. Compute area_m2 from the cleaned geometry
8. Output with required columns

Let me also check what the duplicate situation looks like - the duplicates are exact geometric duplicates between the original and the 900000-series parcel_ids. So after fixing invalid geometries, some geometries might not be duplicates anymore.

Actually, looking more carefully:
- Index 0 (pid=1, seq=1) and index 210 (pid=900001, seq=211) have identical geometry (a simple rectangle).
- Index 6 (pid=7, seq=7) has a self-intersecting geometry, but index 216 (pid=900007, seq=217) - let me check if index 216 is one of the duplicates.

Let me check: dup_pairs showed index 7 (pid=8) paired with index 216 (pid=900007). So index 6 is NOT a duplicate pair - it's a standalone self-intersecting geometry.

So the 25 duplicate pairs involve indices 0-5, 7-9, 11-12, 14-15, 17, 19-20, etc. (the original records with their 900000-series counterparts).

The 20 self-intersecting geometries are indices 6, 16, 22, 34, 40, 52, 57, 70, 75, 88, 93, 106, 111, 124, 129, 142, 147, 160, 165, 178 - these are standalone (not duplicated).

OK so the approach should be:

1. Make all geometries valid (using `make_valid`)
2. For MultiPolygons, filter out polygon fragments < 1 m²
3. Drop features that become empty or have total area < 1 m²
4. For duplicate geometries (same shape after cleanup), keep only the one with earliest record_seq
5. Ensure all remaining features are MultiPolygon types
6. Recompute area_m2 from the geometry
7. Output the columns: parcel_id, parcel_class, district, area_m2

Wait, I need to think about what "duplicate" means here. The instruction says "When duplicates exist" - I think it means duplicate geometries (same shape). Let me use spatial equality.

Actually, the duplicate pairs are exact geometric duplicates already. After fixing invalid geometries, the self-intersecting ones won't match any others, so the duplicates will remain as exact copies.

Let me write the script.

Actually, one more thing to think about: "discard any polygon fragments smaller than 1 m²" - this could mean fragment-level removal from multipolygons, or entire feature removal. Let me do both: inside each geometry, remove sub-polygons < 1 m², then drop any features whose total area < 1 m².

Also, I need to compute area_m2 from the geometry rather than using the stored value.

Let me write the solution.
2:46
2:46
Write /work/solve.py
content · 107 lines · py 
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely import make_valid, MultiPolygon, Polygon, wkt
from shapely.validation import make_valid

# 1. Read source data
gdf = gpd.read_file('/work/cairo_parcels_legacy.geojson')
print(f"Original features: {len(gdf)}")

# 2. Make all geometries valid (fix self-intersections etc.)
gdf['geometry'] = gdf['geometry'].apply(lambda g: make_valid(g) if g is not None and not g.is_empty else g)
print(f"After make_valid: {len(gdf)}")

# 3. Ensure all geometries are MultiPolygon, and filter out polygon fragments < 1 m²
def clean_geometry(geom):
    """Convert to MultiPolygon and remove sub-polygons smaller than 1 m²."""
    if geom is None or geom.is_empty:
        return None
    
    # Make sure we have a MultiPolygon
    if geom.geom_type == 'Polygon':
        geom = MultiPolygon([geom])
    elif geom.geom_type == 'MultiPolygon':
        pass  # already a multipolygon
    elif geom.geom_type == 'GeometryCollection':
        # Extract only polygons from collection
        polys = [g for g in geom.geoms if g.geom_type in ('Polygon', 'MultiPolygon')]
        if not polys:
            return None
        # Flatten MultiPolygons
        flat_polys = []
        for p in polys:
            if p.geom_type == 'Polygon':
                flat_polys.append(p)
            else:
                flat_polys.extend(list(p.geoms))
        geom = MultiPolygon(flat_polys) if len(flat_polys) > 1 else flat_polys[0]
        if geom.geom_type == 'Polygon':
            geom = MultiPolygon([geom])
    else:
        # Unknown type - try to make work
        return None
    
    # Filter out polygon fragments smaller than 1 m²
    valid_polys = [p for p in geom.geoms if p.area >= 1.0]
    
    if len(valid_polys) == 0:
        return None
    elif len(valid_polys) == 1:
        return MultiPolygon(valid_polys)  # Always return MultiPolygon
    else:
        return MultiPolygon(valid_polys)

gdf['geometry'] = gdf['geometry'].apply(clean_geometry)

# 4. Drop features with null/empty geometry
before = len(gdf)
gdf = gdf.dropna(subset=['geometry'])
gdf = gdf[~gdf['geometry'].is_empty]
print(f"After dropping null/empty: {len(gdf)} (dropped {before - len(gdf)})")

# 5. Also drop features where total geometry area < 1 m²
before = len(gdf)
gdf['_area'] = gdf.geometry.area
gdf = gdf[gdf['_area'] >= 1.0].copy()
print(f"After dropping area < 1 m²: {len(gdf)} (dropped {before - len(gdf)})")

# 6. Deduplicate by geometry: for duplicate geometries, keep earliest record_seq
# Sort by record_seq so the first occurrence of each geometry is the one we keep
gdf = gdf.sort_values('record_seq')

# Use spatial equality via WKT to identify duplicates
seen_geoms = set()
keep_mask = []
for geom in gdf.geometry:
    wkt_str = wkt.dumps(geom, rounding_precision=6)
    if wkt_str in seen_geoms:
        keep_mask.append(False)
    else:
        seen_geoms.add(wkt_str)
        keep_mask.append(True)

before = len(gdf)
gdf = gdf[keep_mask].copy()
print(f"After deduplication: {len(gdf)} (removed {before - len(gdf)} duplicates)")

# 7. Compute area_m2 from the cleaned geometry (no longer using stored area_m2)
gdf['area_m2'] = gdf.geometry.area.round(2)

# 8. Verify all geometries are valid and MultiPolygon
assert all(g.is_valid for g in gdf.geometry), "Some geometries are still invalid!"
assert all(g.geom_type == 'MultiPolygon' for g in gdf.geometry), "Some geometries are not MultiPolygon!"

# 9. Select output columns
out = gdf[['parcel_id', 'parcel_class', 'district', 'area_m2', 'geometry']].copy()
out = out.reset_index(drop=True)

# 10. Write to GeoParquet
out.to_parquet('/work/parcels_canonical.geoparquet', index=False)
print(f"\nWritten {len(out)} features to /work/parcels_canonical.geoparquet")
print(f"Columns: {out.columns.tolist()}")
print(f"Geometry types: {out.geometry.geom_type.value_counts().to_dict()}")
print(f"All valid: {out.geometry.is_valid.all()}")
print(f"Area range: {out['area_m2'].min():.2f} - {out['area_m2'].max():.2f}")
print(f"Districts: {out['district'].unique().tolist()}")
print(f"Classes: {out['parcel_class'].unique().tolist()}")
Wrote /work/solve.py (4128 chars).
2:48 
Let me run this script and check the results.
2:48
Bash cd /work && python solve.py 
Original features: 290
After make_valid: 290
After dropping null/empty: 260 (dropped 30)
After dropping area < 1 m²: 260 (dropped 0)
After deduplication: 210 (removed 50 duplicates)

Written 210 features to /work/parcels_canonical.geoparquet
Columns: ['parcel_id', 'parcel_class', 'district', 'area_m2', 'geometry']
Geometry types: {'MultiPolygon': 210}
All valid: True
Area range: 600.00 - 1280.00
Districts: ['Giza-East', 'Qalyubia-South', 'Cairo-Central']
Classes: ['commercial', 'industrial', 'agricultural', 'residential']
3:04 
Let me verify the output carefully:

1. 30 features were null/empty geometries - these were the 30 "border-sliver" artifacts with area 0.49 m². They got dropped because after make_valid and clean_geometry, they probably ended up as None. Let me check...

Actually wait - those border-sliver features had district='border-sliver', area_m2=0.49. They had valid Polygon geometries (tiny triangles presumably). Let me check what happened to them more carefully.

The output says:
- After make_valid: 290
- After dropping null/empty: 260 (dropped 30)
- After dropping area < 1 m²: 260 (dropped 0)
- After deduplication: 210 (removed 50 duplicates)

So 30 features got dropped as null/empty - those are the 30 border-sliver features. Let me check if they had valid geometry but just tiny area...

Actually wait, the border-sliver features (parcel_id 800001-800030) had area 0.49 m². In my clean_geometry function, I'm filtering out sub-polygons with area < 1.0. If a single Polygon has area 0.49, it would be removed from the MultiPolygon, leaving 0 valid polygons, so it returns None. That's correct - they get dropped.

But I also have a check `gdf = gdf[gdf['_area'] >= 1.0]` which says 0 dropped. That's because the null/empty ones were already dropped before that step.

Let me verify the output more thoroughly.
3:04
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd

gdf = gpd.read_parquet('/work/parcels_canonical.geoparquet')
print(f'Total features: {len(gdf)}')
print(f'CRS: {gdf.crs}')
print(f'All valid: {gdf.geometry.is_valid.all()}')
print(f'All MultiPolygon: {(gdf.geometry.geom_type == \"MultiPolygon\").all()}')
print(f'No null geom: {gdf.geometry.notna().all()}')
print(f'No empty geom: {(~gdf.geometry.is_empty).all()}')
print()

# Check for duplicate parcel_ids
dup = gdf['parcel_id'].value_counts()
print(f'Duplicate parcel_ids: {(dup > 1).sum()}')
print(f'Unique parcel_ids: {gdf[\"parcel_id\"].nunique()}')
print()

# Check for any remaining duplicate geometries
from shapely import wkt
seen = {}
dups = 0
for i, geom in enumerate(gdf.geometry):
    wkt_str = wkt.dumps(geom, rounding_precision=6)
    if wkt_str in seen:
        dups += 1
    seen[wkt_str] = i
print(f'Duplicate geometries remaining: {dups}')
print()

# Verify the earliest record_seq was kept
orig = gpd.read_file('/work/cairo_parcels_legacy.geojson')
duped_pids = []
geom_index = {}
for i, geom in enumerate(orig.geometry):
    wkt_str = wkt.dumps(geom, rounding_precision=6)
    if wkt_str not in geom_index:
        geom_index[wkt_str] = []
    geom_index[wkt_str].append(i)

# For each geometry appearing multiple times in original, check we kept the right one
for wkt_str, indices in geom_index.items():
    if len(indices) > 1:
        rows = orig.iloc[indices]
        min_seq = rows['record_seq'].min()
        kept_idx = rows[rows['record_seq'] == min_seq].index[0]
        kept_pid = orig.iloc[kept_idx]['parcel_id']
        # Check if this pid is in the output
        if kept_pid not in gdf['parcel_id'].values:
            print(f'WARNING: Lost parcel_id={kept_pid} (record_seq={min_seq}) for duplicate geometry')
        else:
            pass  # kept correctly

print('Verification complete.')
print()
print('Sample output:')
print(gdf.head(10).to_string())
"
Total features: 210
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}}
All valid: True
All MultiPolygon: True
No null geom: True
No empty geom: True

Duplicate parcel_ids: 0
Unique parcel_ids: 210

Duplicate geometries remaining: 0

Verification complete.

Sample output:
   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)))
3:15 
Everything looks good. Let me verify a few more things:

1. 290 original features
2. 30 border-sliver features (area 0.49 m²) were dropped after make_valid + area filter (they're < 1 m²)
3. 50 duplicate geometries removed (keeping earliest record_seq)
4. 210 features remain
5. All are MultiPolygon, all valid, no null/empty
6. No duplicate geometries, no duplicate parcel_ids
7. Self-intersecting geometries were fixed with make_valid (e.g. parcel 7 shows correct area of 600 m² after fix)

The output file is at `/work/parcels_canonical.geoparquet`.
3:15