fio-l2-cairo-mixedgeom-split
Map (reference ← swipe → agent)
0:00
Heritage analyst here. I've put together `heritage_sites.geojson` from my own field work, and every site in it is several features sharing a `site_id`. I need to load the data into our desktop tool, which only ingests typed GPKG layers, no mixed-geometry files, and every feature has to be single-part. Can you give me back `heritage.gpkg` with three layers named `points`, `lines`, and `polygons`, keeping the `site_id` on every feature so I don't lose the cross-layer link? Use Egypt's national grid.
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System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:06
Let me start by understanding what the user needs: 1. Read the input file `heritage_sites.geojson` 2. Create a GeoPackage `heritage.gpkg` with three layers: - `points` - Point geometries - `lines` - LineString geometries - `polygons` - Polygon geometries 3. Each feature should have `site_id` preserved 4. Every feature must be single-part (no Multi* geometries) 5. Use Egypt's national grid (CRS) Let me first find out what Egypt's national grid is. Egypt uses several CRS systems. The most common ones: - EPSG:32635 - WGS 84 / UTM zone 35N (covers most of Egypt) - EPSG:32636 - WGS 84 / UTM zone 36N (covers eastern Egypt) - Egypt's national grid is typically EPSG:22992 or EPSG:7277... Let me think. Actually, Egypt's national grid is often referred to as "Egyptian Red Belt" or "Egypt Blue Belt". The common ones: - EPSG:22992 - Egypt / Red Belt (old Egyptian grid) - EPSG:22993 - Egypt / Purple Belt - EPSG:7277 - Egypt / Red Belt (modern, with different parameters) Wait, let me think more carefully. "Egypt's national grid" - the one most commonly used in Egypt is the "Egyptian Red Belt" which uses a Lambert Conformal Conic projection. Let me check what's commonly used. Actually, the most standard Egyptian national grid is: - EPSG:22992 (Egypt / Red Belt) - Lambert Conic Conformal with two standard parallels - EPSG:7277 is another variant Let me first read the input file to understand the data structure, then determine the appropriate CRS. Let me start by reading the file.
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Bash
ls -la /work/heritage_sites.geojson
-rw-r--r-- 1 agent agent 26832 Jun 16 06:53 /work/heritage_sites.geojson
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Bash
head -80 /work/heritage_sites.geojson
{
"type": "FeatureCollection",
"name": "cairo_heritage_sites",
"crs": {
"type": "name",
"properties": {
"name": "urn:ogc:def:crs:OGC:1.3:CRS84"
}
},
"features": [
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
31.256,
30.0316
],
[
31.2572,
30.0316
],
[
31.2572,
30.0328
],
[
31.256,
30.0328
],
[
31.256,
30.0316
]
]
]
},
"properties": {
"site_id": "EG-CAI-001",
"feature_kind": "enclosure",
"name_en": "Sultan Hassan Mosque-Madrasa",
"name_ar": "مسجد ومدرسة السلطان حسن"
}
},
{
"type": "Feature",
"geometry": {
"type": "LineString",
"coordinates": [
[
31.2556,
30.0321
],
[
31.2566,
30.0322
],
[
31.2576,
30.0323
]
]
},
"properties": {
"site_id": "EG-CAI-001",
"feature_kind": "axial_line_1",
"name_en": "Sultan Hassan Mosque-Madrasa",
"name_ar": "مسجد ومدرسة السلطان حسن"
}
},
{
"type": "Feature",
"geometry": {
"type": "LineString",
"coordinates": [
[
31.2567,
30.0312
0:13
Let me see more of the file to understand the full range of geometry types and site_ids.
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Bash
python3 -c "
python3 -c "
import json
with open('/work/heritage_sites.geojson') as f:
data = json.load(f)
# Count geometry types
from collections import Counter
types = Counter()
site_ids = set()
multi_counts = Counter()
for feat in data['features']:
gtype = feat['geometry']['type']
types[gtype] += 1
site_ids.add(feat['properties']['site_id'])
if gtype.startswith('Multi'):
multi_counts[gtype] += 1
print('Geometry types:', dict(types))
print('Site IDs:', sorted(site_ids))
print('Multi-geometry counts:', dict(multi_counts))
# Show first few features each type
for t in sorted(types):
print(f'\n--- {t} examples ---')
n = 0
for feat in data['features']:
if feat['geometry']['type'] == t:
props = feat['properties']
coords = feat['geometry']['coordinates']
print(f\" site_id={props['site_id']}, kind={props.get('feature_kind','?')}, coords len={len(coords) if isinstance(coords,list) and not isinstance(coords[0],(int,float)) else 'point?'}, props={props}\")
n += 1
if n >= 3:
break
"
Geometry types: {'Polygon': 5, 'LineString': 15, 'Point': 25, 'MultiPolygon': 5}
Site IDs: ['EG-CAI-001', 'EG-CAI-002', 'EG-CAI-003', 'EG-CAI-004', 'EG-CAI-005', 'EG-CAI-006', 'EG-CAI-007', 'EG-CAI-008', 'EG-CAI-009', 'EG-CAI-010']
Multi-geometry counts: {'MultiPolygon': 5}
--- LineString examples ---
site_id=EG-CAI-001, kind=axial_line_1, coords len=3, props={'site_id': 'EG-CAI-001', 'feature_kind': 'axial_line_1', 'name_en': 'Sultan Hassan Mosque-Madrasa', 'name_ar': 'مسجد ومدرسة السلطان حسن'}
site_id=EG-CAI-001, kind=axial_line_2, coords len=3, props={'site_id': 'EG-CAI-001', 'feature_kind': 'axial_line_2', 'name_en': 'Sultan Hassan Mosque-Madrasa', 'name_ar': 'مسجد ومدرسة السلطان حسن'}
site_id=EG-CAI-002, kind=axial_line_1, coords len=3, props={'site_id': 'EG-CAI-002', 'feature_kind': 'axial_line_1', 'name_en': 'Al-Rifai Mosque', 'name_ar': 'مسجد الرفاعي'}
--- MultiPolygon examples ---
site_id=EG-CAI-002, kind=enclosure, coords len=2, props={'site_id': 'EG-CAI-002', 'feature_kind': 'enclosure', 'name_en': 'Al-Rifai Mosque', 'name_ar': 'مسجد الرفاعي'}
site_id=EG-CAI-003, kind=enclosure, coords len=2, props={'site_id': 'EG-CAI-003', 'feature_kind': 'enclosure', 'name_en': 'Ibn Tulun Mosque', 'name_ar': 'مسجد ابن طولون'}
site_id=EG-CAI-006, kind=enclosure, coords len=2, props={'site_id': 'EG-CAI-006', 'feature_kind': 'enclosure', 'name_en': 'Bayt Al-Suhaymi', 'name_ar': 'بيت السحيمي'}
--- Point examples ---
site_id=EG-CAI-001, kind=marker_gateway, coords len=point?, props={'site_id': 'EG-CAI-001', 'feature_kind': 'marker_gateway', 'name_en': 'Sultan Hassan Mosque-Madrasa', 'name_ar': 'مسجد ومدرسة السلطان حسن'}
site_id=EG-CAI-001, kind=marker_mihrab, coords len=point?, props={'site_id': 'EG-CAI-001', 'feature_kind': 'marker_mihrab', 'name_en': 'Sultan Hassan Mosque-Madrasa', 'name_ar': 'مسجد ومدرسة السلطان حسن'}
site_id=EG-CAI-001, kind=marker_minaret, coords len=point?, props={'site_id': 'EG-CAI-001', 'feature_kind': 'marker_minaret', 'name_en': 'Sultan Hassan Mosque-Madrasa', 'name_ar': 'مسجد ومدرسة السلطان حسن'}
--- Polygon examples ---
site_id=EG-CAI-001, kind=enclosure, coords len=1, props={'site_id': 'EG-CAI-001', 'feature_kind': 'enclosure', 'name_en': 'Sultan Hassan Mosque-Madrasa', 'name_ar': 'مسجد ومدرسة السلطان حسن'}
site_id=EG-CAI-004, kind=enclosure, coords len=1, props={'site_id': 'EG-CAI-004', 'feature_kind': 'enclosure', 'name_en': 'Al-Azhar Mosque', 'name_ar': 'الجامع الأزهر'}
site_id=EG-CAI-005, kind=enclosure, coords len=1, props={'site_id': 'EG-CAI-005', 'feature_kind': 'enclosure', 'name_en': 'Al-Hussein Mosque', 'name_ar': 'مسجد الحسين'}
0:26
Now I understand the data: - 25 Points - 15 LineStrings - 5 Polygons - 5 MultiPolygons (need to be exploded into single polygons) The user wants: 1. A GPKG file `heritage.gpkg` with 3 layers: `points`, `lines`, `polygons` 2. All features single-part (so MultiPolygon must be exploded) 3. Egypt's national grid as the CRS 4. Keep `site_id` on every feature Egypt's national grid: This is the "Egyptian Red Belt" or "Egypt TM" — EPSG:22992 (Egypt / Red Belt) or more commonly EPSG:22993 (Egypt / Purple Belt). Actually, the standard national grid for Egypt is often EPSG:22992 (Egypt - Red Belt) or EPSG:22993 (Egypt - Purple Belt). Let me think... Actually, Egypt's national grid system uses: - EPSG:22992 "Egypt / Red Belt" (zone extending 27°E to 31°E) - EPSG:22993 "Egypt / Purple Belt" (zone extending 31°E to 35°E) Since the data is in Cairo (around 31.256°E), Cairo falls within the Purple Belt zone (31°E to 33°E). So EPSG:22993 would be appropriate. However, "Egypt's national grid" might just refer to the common "Egypt / Red Belt" which is the more commonly used one for national mapping. Let me reconsider - "Egypt's national grid" typically refers to the Egyptian Transverse Mercator system. The zones are: - Red Belt: 27°E to 31°E, central meridian 29°E (EPSG:22992) - Purple Belt: 31°E to 35°E, central meridian 33°E (EPSG:22993) Cairo is at ~31.256°E, so it's right on the boundary but technically in Purple Belt (EPSG:22993). Actually, many people refer to "Egypt's national grid" as just "Egypt / Red Belt" (EPSG:22992). Let me use EPSG:22993 since Cairo is in that zone. Or better yet, let me check if there's a single national grid that covers the whole country. Actually, there isn't a single zone that covers all of Egypt - the country uses the "Egyptian Transverse Mercator" (ETM) system with 7 zones (Red, Purple, etc.). I'll go with EPSG:22993 (Egypt / Purple Belt) since Cairo falls in that zone and it's one of the standard national grid codes. Let me write the script.
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0:26
Write
/work/solve.py
content · 83 lines · py
#!/usr/bin/env python3
"""
Split heritage_sites.geojson into a typed, single-part GPKG with three layers
(points, lines, polygons) in Egypt's national grid (EPSG:22993).
"""
import geopandas as gpd
from shapely.geometry import shape
import json
INPUT = "/work/heritage_sites.geojson"
OUTPUT = "/work/heritage.gpkg"
TARGET_CRS = "EPSG:22993" # Egypt / Purple Belt (Cairo ~31.256°E)
# ---------------------------------------------------------------------------
# 1. Read GeoJSON
# ---------------------------------------------------------------------------
with open(INPUT) as f:
raw = json.load(f)
# ---------------------------------------------------------------------------
# 2. Classify by geometry type, exploding multi-geometries
# ---------------------------------------------------------------------------
points = []
lines = []
polygons = []
for feat in raw["features"]:
props = feat["properties"]
site_id = props.get("site_id")
geom_type = feat["geometry"]["type"]
geom = shape(feat["geometry"])
if geom_type == "Point":
points.append({"geometry": geom, "site_id": site_id, **props})
elif geom_type == "LineString":
lines.append({"geometry": geom, "site_id": site_id, **props})
elif geom_type == "Polygon":
polygons.append({"geometry": geom, "site_id": site_id, **props})
elif geom_type == "MultiPolygon":
# Explode into individual Polygon features, keep all properties
for poly in list(geom.geoms):
polygons.append({"geometry": poly, "site_id": site_id, **props})
else:
raise ValueError(f"Unexpected geometry type: {geom_type}")
print(f"Points: {len(points)}")
print(f"Lines: {len(lines)}")
print(f"Polygons: {len(polygons)}")
# ---------------------------------------------------------------------------
# 3. Build GeoDataFrames
# ---------------------------------------------------------------------------
def make_gdf(records):
df = gpd.GeoDataFrame(records, geometry="geometry", crs="EPSG:4326")
# Keep only site_id + original properties; drop geometry from columns
cols = [c for c in df.columns if c != "geometry"]
return df[cols + ["geometry"]]
gdf_points = make_gdf(points)
gdf_lines = make_gdf(lines)
gdf_polygons = make_gdf(polygons)
# ---------------------------------------------------------------------------
# 4. Reproject to Egypt national grid
# ---------------------------------------------------------------------------
gdf_points = gdf_points.to_crs(TARGET_CRS)
gdf_lines = gdf_lines.to_crs(TARGET_CRS)
gdf_polygons = gdf_polygons.to_crs(TARGET_CRS)
# ---------------------------------------------------------------------------
# 5. Write to GPKG — one layer per geometry type
# ---------------------------------------------------------------------------
gdf_points.to_file(OUTPUT, layer="points", driver="GPKG")
gdf_lines.to_file(OUTPUT, layer="lines", driver="GPKG")
gdf_polygons.to_file(OUTPUT, layer="polygons", driver="GPKG")
print("Done →", OUTPUT)
Wrote /work/solve.py (3026 chars).
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Let me run the script and check the output.
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Bash
cd /work && python solve.py
Points: 25 Lines: 15 Polygons: 15 Done → /work/heritage.gpkg
0:37
5 MultiPolygons were exploded into polygons. We had 5 Polygon features + 5 MultiPolygon features (each with 2 polygons? Let me check) → 15 polygons total. That makes sense: 5 original Polygons + 5 MultiPolygons exploded. Let me verify the output.
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Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
# Check each layer
for layer in ['points','lines','polygons']:
df = gpd.read_file('/work/heritage.gpkg', layer=layer)
print(f'--- {layer} ---')
print(f' Features: {len(df)}')
print(f' CRS: {df.crs}')
print(f' Geometry types: {df.geometry.type.value_counts().to_dict()}')
print(f' Columns: {list(df.columns)}')
print(f' site_id values (first 5): {df[\"site_id\"].head(5).tolist()}')
print(f' site_id nulls: {df[\"site_id\"].isna().sum()}')
# Check single-part
multi_mask = df.geometry.type.str.startswith('Multi')
if multi_mask.any():
print(f' WARNING: {multi_mask.sum()} multi-geometries remain!')
else:
print(f' All single-part ✓')
# Check coord magnitudes
print(f' X range: {df.geometry.x.min():.0f} to {df.geometry.x.max():.0f}' if layer=='points' else '')
if layer == 'points':
print(f' Y range: {df.geometry.y.min():.0f} to {df.geometry.y.max():.0f}')
print()
"
--- points ---
Features: 25
CRS: EPSG:22993
Geometry types: {'Point': 25}
Columns: ['site_id', 'feature_kind', 'name_en', 'name_ar', 'geometry']
site_id values (first 5): ['EG-CAI-001', 'EG-CAI-001', 'EG-CAI-001', 'EG-CAI-002', 'EG-CAI-002']
site_id nulls: 0
All single-part ✓
X range: 1109950 to 1111164
Y range: 210733 to 213844
--- lines ---
Features: 15
CRS: EPSG:22993
Geometry types: {'LineString': 15}
Columns: ['site_id', 'feature_kind', 'name_en', 'name_ar', 'geometry']
site_id values (first 5): ['EG-CAI-001', 'EG-CAI-001', 'EG-CAI-002', 'EG-CAI-003', 'EG-CAI-003']
site_id nulls: 0
All single-part ✓
--- polygons ---
Features: 15
CRS: EPSG:22993
Geometry types: {'Polygon': 15}
Columns: ['site_id', 'feature_kind', 'name_en', 'name_ar', 'geometry']
site_id values (first 5): ['EG-CAI-001', 'EG-CAI-002', 'EG-CAI-002', 'EG-CAI-003', 'EG-CAI-003']
site_id nulls: 0
All single-part ✓
0:46
Everything checks out: - **points** layer: 25 Point features, all single-part ✓ - **lines** layer: 15 LineString features, all single-part ✓ - **polygons** layer: 15 Polygon features (5 original + 10 from MultiPolygon explosions), all single-part ✓ - All layers have `site_id` column preserved ✓ - CRS is EPSG:22993 (Egypt / Purple Belt) ✓ - Coordinate magnitudes (~1.1M, ~210k) are typical for Egypt's metric national grid ✓ Let me check the file exists.
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Bash
ls -lh /work/heritage.gpkg
-rw-r--r-- 1 agent agent 136K Jun 16 06:53 /work/heritage.gpkg
0:51
Everything looks good. Let me summarize what was done.
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