geo-l2-bangkok-landuse-intersect
Map (reference ← swipe → agent)
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Working on a flood-mitigation green-cover briefing. `landcover` has land-cover polygons for the Bangkok metro; `study_area` defines the BMA boundary. I need the land-cover within the study area, simplified enough that the file is small for the policy lead to preview in a browser, with the original `class` string and a per-feature `area_m2` in square metres.
Write the result to `bma_landcover_intersect.geojson` as a GeoJSON FeatureCollection with every feature stored as a MultiPolygon.
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System info
{
"adapter": "openrouter",
"model": "google/gemma-4-26b-a4b-it",
"base_url": "https://openrouter.ai/api/v1"
}
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Bash
ls -F
bangkok_landcover.parquet bma_study_area.geojson
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Bash
head -n 5 bangkok_landcover.parquet
PAR1��i��[L�� ��4�$ 00023927-8760-5550-a47d-48ae7f087574$ (|ad8a-093a-539c-8f6c-0fb0b4f2c3c1 P�1206dc-28bb-593e-87bd-72efd2f32c65$P�1748f2-f294-53b7-be74-29acd35b3af6$ (Lca192-b61e-56b8-a9b1} d429f6fc0 x�e31cc-6a5f-5c6e-84e4-2f5cba3966bd (Dff542-0ee6-58e9-b6x(08f6a5e294b (�21f6dd-50e9-53cc-835d-a24c79606daf$��250870-2aa2-5e91-94fb-73b80cf6b05(|80538-2318-501c-8a51-147ea783041 � 295bda-30�P5bf-bf5d-d495c5f15a92 �ac31b-1(P37d-a3b4-0667280f5fc3 (P361883-b14e-5003-b2e3� 2e51b658-��367866-63e4-5b8e-a5eb-04f2fc94c7da$��37e0e3-e4bd-52e4-89c6-b4748671e55M0�448ffc-86b2-59d1-b099-1f523b9d303-h�458581-0c65-5601-8552-c457223d99fe$x�4709eb-d772-5da4-b008-333639afed5 ��4e3263-0f86-5fd3-ad04-54589294687c$ Pf6!;dba29-5258-892b-e721d7bb870 (�582830-4580-52d1-a59b-ca6827d6b6aM�45a9d35-f8a1-55A�<831-58f15df21647)�(5bdb0f-26cbA�H0-be82-903a89e253c8 ( c3da0-90!�Le92-80fb-c7f71d44982 ��5f7f38-f23c-5c21-9b35-c5c2e8a1b8d9$�$62d98d-6c5�H87-bfa3-39aab2ba168 x464132d-c7a5-54!�86c4-9120c61c384M0�64c1e1-7a01-5c56-89ae-6d7f56927ceM0|66c2f0-15f4-59e3-82a6-c022a4547eQ$68d566-a21A�L2d-9d58-b0701cbe1611) 691174-baMP5fed-b5f8-7426cf5c5b9-� 6b3975-16AX 2�`8589-ce7fc34fb98MX�6c99e7-d66f-5d2f-a834-1de62d8a037M�(6d419d-df20A�D4-8c0a-135185ab78a��46f3f1e-9fdb-5aA�824-fc6�c609d �h73303a-35ce-57f5-bd11-2a345��87 ��85f2be-40b7-53f8-b9c3-38620e104ae��864dcb-6b98-5ddf-95bc-7b902a47652��8692f6-!-53�8e51-45c49aba806MX 876d5d-bePD29-93f6-9cf94f1362� 89d3a6-05�16-acd��� cf66M�(91ff17-0303�PDb-bc9f-b66f1745633mp(963c2d-faf7i�89a83-c3ce2d9f92QX|98a444-34bf-5769-b856-e0b9b261e8Q�98ea� 4615-5bbd�� eA? 1261182a �$9ce8b7-f85� e0-ae54-b�M48ed67M�9e5�[ 0A�P5b9a-9fa4-d2f8c36e1a4-�(a0e13b-568f�D7-b1d4-e01b230ab0e�a39da2AT4-5a14-8ee6-4abf090580q�$a59a7a-deb�0 4a-9��(3c26f517761M0�a86ec0-44c3-5153-b34b-db0cc1db884(Hed27b-a516-559c-b49�� 6f7da82af�� b0ac7b-19AL562-bc5e-fc7d6440182 � b34257-ec��38a�,b-a9ed3e1968�P b39044-25apae9-9f�$2ac481c4cbM<b3d5b8-e0f6-5e75A�,7-24ba909f6cx0f0971-e164-5c�K4181-6aed665b9e���c0c95b-cf87-551a-bc83-4ada000661f�8c63902-5236-533p01b-84cd0eaba21h(cf70da-8584�D3-9a4e-242bc8fd053-�Ld2ed28-a71c-55c0-af4!� 614d561ca x$d93548-d0dHHf7-acc3-a706a76dc36�` e074bb-95�L2da-b686-a45e31f2cd9MЀe227c6-654f-5bee-9ef9-8864bd520c9P 26d27-95�h��<9bfa-c57587eb4d0�P�e36de6-ba06-5d37-a15a-d0c209069c6�@ e48540-f��50aA�470-c9640cebfbc- e55ef6-7b��L729-b7be-345a765140dP 7e10b-b17��f3-bf98��e588c7b1�(e8466f-9627aHD3-926f-b286f8e4028�$cb876-2d32��5-affe_ 6cf7a7f1M�(eeefba-ff25� b�0d-d6f774af637-h(f0ff52-23e7P 7��02-4f7f7e6128f� f415��T4d9-582d-8f30-548d9495v�$fa9608-98cA0H36-838c-841b8256c98͐ fd7d46-f2pLc0d-98e1-65a83a75f51xf2) 29e2-5aA�8906-a6708c11e0fm�Lff2ce8-edd5-5861-827A 5c9f3bc37),100b1ec-a811)h 87d3x 1be6� �`(1017a7c-66a�ca-90A�6db26d �@1068919-240b-5d59�08-e870f273023I�,1069909-f982�@Dd-8033-677c5654850�� 1�!610A�az4388-f45c09cbef� 10a1A��P-5a25-a267-969ad2fd89x a4b0e-8d �<a0a6-78e23cc26b4I�10d8f6� cd-51E�4d2-e94964d1138 Ȁ102f2c-fb96-5467-a847-8ba911f512f8 1139��2a�.L10e-9498-64e574c9bf8I0T1140c4e-592c-517b-9a22e�16cc1ce �(15ffa1-3f61��a8�� e400be8beM��116776c-e334-50db-88f0-82864032fb � 8b4-4bd�50A,7-2cdcf482c11�17cfd0�^Xd-570a-9e0f-36566a66ba5I� 11a3!c93a He50-b58a-735c2777ca��$12220a1-20�L754-ac08-d278c756342M�<278188-b317-5973Z 06-1db0b1a709b-h 2cb259-06�a3d-90�= a65ab8e3cm ,12fc203-f339� <bcf3-7adbe35c857iH$133f0bd-fea�Lb55-89b6-447d433a642M�459f7�X2d-58d5-806d-dadbabf5a9X 1483c24-0�PL710-bc47-cdbb7d0e8b5�� 14a1 7aa�H99f-9194-eaa662fb76�x14a70!6\9d3-5617-b705-d579a41e191�$5010c9-729apH2d-aca8-e653dfe6258ɸ150837c��X2-55cd-99ea-0462088e624�(,151d616-c35eA�@5-9ea4-935bbbdabcpA�tf09-8afc-5918-ac1b-b62fefbb368m�(628b62-031a�@c-af0a-6d99745951p 16cd90d-7. 5��<a788-7c74c21d03c �743 ec 5b66�0f-fc0f33c91bb-� 79cb61-63��cf7-bae� 4c35d5d0c- 7a80f0-6a�hb5c 0e-468a8013e99M�,7beca5-2c2a-A�@-90b4-8a2a14b9dc5Pe0�a9A�7c9�
0:13
Write
/work/inspect_data.py
content · 12 lines · py
import geopandas as gpd
import pandas as pd
# Load the datasets
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
print("Landcover columns:", landcover.columns)
print("Study area columns:", study_area.columns)
print("Landcover CRS:", landcover.crs)
print("Study area CRS:", study_area.crs)
Wrote /work/inspect_data.py (353 chars).
0:14
Bash
python /work/inspect_data.py
Landcover columns: Index(['id', 'class', 'geometry'], dtype='str')
Study area columns: Index(['name', 'geometry'], dtype='str')
Landcover CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / UTM zone 47N", "base_crs": {"name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}, {"name": "World Geodetic System 1984 (G2296)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "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": 4326}}, "conversion": {"name": "UTM zone 47N", "method": {"name": "Transverse Mercator", "id": {"authority": "EPSG", "code": 9807}}, "parameters": [{"name": "Latitude of natural origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8801}}, {"name": "Longitude of natural origin", "value": 99, "unit": "degree", "id": {"authority": "EPSG", "code": 8802}}, {"name": "Scale factor at natural origin", "value": 0.9996, "unit": "unity", "id": {"authority": "EPSG", "code": 8805}}, {"name": "False easting", "value": 500000, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 0, "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": "Navigation and medium accuracy spatial referencing.", "area": "Between 96\u00b0E and 102\u00b0E, northern hemisphere between equator and 84\u00b0N, onshore and offshore. China. Indonesia. Laos. Malaysia - West Malaysia. Mongolia. Myanmar (Burma). Russian Federation. Thailand.", "bbox": {"south_latitude": 0, "west_longitude": 96, "north_latitude": 84, "east_longitude": 102}, "id": {"authority": "EPSG", "code": 32647}}
Study area CRS: EPSG:32647
1:03
Write
/work/solve.py
content · 65 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon
def solve():
# Load the datasets
# Landcover is in UTM zone 47N (EPSG:32647) based on inspection
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure CRS match
if landcover.crs != study_area.crs:
study_area = study_area.to_crs(landcover.crs)
# 1. Intersect landcover with study area
# We use overlay to get the intersection geometries
# Note: 'intersection' in overlay returns geometries within the study area
print("Performing intersection...")
intersected = gpd.overlay(landcover, study_area, how='intersection')
# 2. Calculate area in square meters
# Since CRS is EPSG:32647 (metric), area is in m2
print("Calculating areas...")
intersected['area_m2'] = intersected.geometry.area
# 3. Simplify geometries for smaller file size
# We'll use a small tolerance. Since it's UTM (meters), 1-5 meters is reasonable.
# However, we must be careful not to simplify away too much.
# Let's try 1 meter first.
print("Simplifying geometries...")
intersected['geometry'] = intersected.simplify(tolerance=1, preserve_topology=True)
# 4. Convert all geometries to MultiPolygon as requested
print("Converting to MultiPolygon...")
def ensure_multipolygon(geom):
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
return geom
intersected['geometry'] = intersected.geometry.apply(ensure_multipolygon)
# 5. Keep only required columns: class, area_m2, and geometry
# The prompt specifies: original 'class' string and per-feature 'area_m2'
result = intersected[['class', 'area_m2', 'geometry']]
# 6. Write to GeoJSON
# Note: GeoJSON usually implies WGS84 (EPSG:4326) for web browsers.
# The user wants it for a policy lead to preview in a browser.
# I should convert to EPSG:4326 for the final output.
print("Converting to WGS84 for web compatibility...")
result = result.to_crs(epsg=4326)
# Re-calculate area in m2?
# The prompt says: "a per-feature area_m2 in square metres".
# If I convert to 4326, the geometry changes.
# I should calculate area in the metric CRS *before* converting to 4326.
# I already did that in step 2. I must ensure I don't overwrite it.
# I'll make sure area_m2 is kept.
print(f"Writing to bma_landcover_intersect.geojson. Number of features: {len(result)}")
result.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
if __name__ == "__main__":
solve()
Wrote /work/solve.py (2662 chars).
1:06
Bash
python /work/solve.py
Performing intersection... Calculating areas... Simplifying geometries... Converting to MultiPolygon... Converting to WGS84 for web compatibility... Writing to bma_landcover_intersect.geojson. Number of features: 3453
1:09
Bash
ls -lh bma_landcover_intersect.geojson
-rw-r--r-- 1 agent agent 4.9M Jun 18 11:16 bma_landcover_intersect.geojson
1:20
Write
/work/solve.py
content · 54 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon
def solve():
# Load the datasets
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure CRS match
if landcover.crs != study_area.crs:
study_area = study_area.to_crs(landcover.crs)
# 1. Intersect landcover with study area
print("Performing intersection...")
intersected = gpd.overlay(landcover, study_area, how='intersection')
# 2. Calculate area in square meters (using metric CRS)
print("Calculating areas...")
intersected['area_m2'] = intersected.geometry.area
# 3. Simplify geometries for smaller file size
# Let's try a slightly more aggressive simplification if needed,
# but let's start with a reasonable one.
# A tolerance of 2-5 meters might be better for "small file" requirement.
print("Simplifying geometries...")
intersected['geometry'] = intersected.simplify(tolerance=5, preserve_topology=True)
# 4. Convert all geometries to MultiPolygon as requested
print("Converting to MultiPolygon...")
def ensure_multipolygon(geom):
if geom.is_empty:
return geom
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
return geom
intersected['geometry'] = intersected.geometry.apply(ensure_multipolygon)
# 5. Keep only required columns: class, area_m2, and geometry
# Check if 'class' exists (it should from inspection)
result = intersected[['class', 'area_m2', 'geometry']].copy()
# 6. Write to GeoJSON
# Convert to WGS84 for web compatibility
print("Converting to WGS84 for web compatibility...")
result = result.to_crs(epsg=4326)
print(f"Writing to bma_landcover_intersect.geojson. Number of features: {len(result)}")
result.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
if __name__ == "__main__":
solve()
Wrote /work/solve.py (1980 chars).
1:21
Bash
python /work/solve.py
Performing intersection... Calculating areas... Simplifying geometries... Converting to MultiPolygon... Converting to WGS84 for web compatibility... Writing to bma_landcover_intersect.geojson. Number of features: 3453
1:26
Bash
ls -lh bma_landcover_intersect.geojson
-rw-r--r-- 1 agent agent 4.9M Jun 18 11:16 bma_landcover_intersect.geojson
1:36
Write
/work/solve.py
content · 52 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon
def solve():
# Load the datasets
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure CRS match
if landcover.crs != study_area.crs:
study_area = study_area.to_crs(landcover.crs)
# 1. Intersect landcover with study area
print("Performing intersection...")
intersected = gpd.overlay(landcover, study_area, how='intersection')
# 2. Calculate area in square meters (using metric CRS)
print("Calculating areas...")
intersected['area_m2'] = intersected.geometry.area
# 3. Simplify geometries for smaller file size
# Let's try a more aggressive simplification.
# 10 meters tolerance for a metropolitan area should still be fine for a preview.
print("Simplifying geometries...")
intersected['geometry'] = intersected.simplify(tolerance=10, preserve_topology=True)
# 4. Convert all geometries to MultiPolygon as requested
print("Converting to MultiPolygon...")
def ensure_multipolygon(geom):
if geom.is_empty:
return geom
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
return geom
intersected['geometry'] = intersected.geometry.apply(ensure_multipolygon)
# 5. Keep only required columns: class, area_m2, and geometry
result = intersected[['class', 'area_m2', 'geometry']].copy()
# 6. Write to GeoJSON
# Convert to WGS84 for web compatibility
print("Converting to WGS84 for web compatibility...")
result = result.to_crs(epsg=4326)
print(f"Writing to bma_landcover_intersect.geojson. Number of features: {len(result)}")
result.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
if __name__ == "__main__":
solve()
Wrote /work/solve.py (1866 chars).
1:39
Bash
python /work/solve.py
Performing intersection... Calculating areas... Simplifying geometries... Converting to MultiPolygon... Converting to WGS84 for web compatibility... Writing to bma_landcover_intersect.geojson. Number of features: 3453
1:42
Bash
ls -lh bma_landcover_intersect.geojson
-rw-r--r-- 1 agent agent 4.8M Jun 18 11:16 bma_landcover_intersect.geojson
1:52
Write
/work/solve.py
content · 57 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon
def solve():
# Load the datasets
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure CRS match
if landcover.crs != study_area.crs:
study_area = study_area.to_crs(landcover.crs)
# 1. Intersect landcover with study area
print("Performing intersection...")
intersected = gpd.overlay(landcover, study_area, how='intersection')
# 2. Calculate area in square meters (using metric CRS)
print("Calculating areas...")
intersected['area_m2'] = intersected.geometry.area
# 3. Simplify geometries for smaller file size
# The goal is a small file. Let's use a higher tolerance or more aggressive approach.
# But first, let's group by class to reduce the number of features?
# The prompt says "per-feature area_m2". This implies we shouldn't dissolve.
# However, if many small polygons of the same class are adjacent, dissolving them
# would drastically reduce feature count and file size.
# BUT the prompt asks for "per-feature area_m2", which usually implies individual
# polygons from the input.
# Let's try to simplify and see.
print("Simplifying geometries...")
intersected['geometry'] = intersected.simplify(tolerance=15, preserve_topology=True)
# 4. Convert all geometries to MultiPolygon as requested
print("Converting to MultiPolygon...")
def ensure_multipolygon(geom):
if geom.is_empty:
return geom
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
return geom
intersected['geometry'] = intersected.geometry.apply(ensure_multipolygon)
# 5. Keep only required columns
result = intersected[['class', 'area_m2', 'geometry']].copy()
# 6. Write to GeoJSON
print("Converting to WGS84 for web compatibility...")
result = result.to_crs(epsg=4326)
print(f"Writing to bma_landcover_intersect.geojson. Number of features: {len(result)}")
result.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
if __name__ == "__main__":
solve()
Wrote /work/solve.py (2200 chars).
1:54
Bash
python /work/solve.py
Performing intersection... Calculating areas... Simplifying geometries... Converting to MultiPolygon... Converting to WGS84 for web compatibility... Writing to bma_landcover_intersect.geojson. Number of features: 3453
2:07
Write
/work/solve.py
content · 62 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon
def solve():
# Load the datasets
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure CRS match
if landcover.crs != study_area.crs:
study_area = study_area.to_crs(landcover.crs)
# 1. Intersect landcover with study area
print("Performing intersection...")
intersected = gpd.overlay(landcover, study_area, how='intersection')
# 2. Calculate area in square meters (using metric CRS)
print("Calculating areas...")
intersected['area_m2'] = intersected.geometry.area
# 3. Dissolve by class to reduce feature count significantly
# This is a common way to make "preview" files small.
# Even though the prompt says "per-feature area_m2", in a dissolved context,
# "per-feature" would mean the area of the resulting dissolved feature.
# However, if the user wants the individual polygons, we can't dissolve.
# Let's try to dissolve first to see the size, then decide.
# Actually, let's check the current size. 4.8MB is not huge but for a "browser preview"
# it might be a bit much if it's meant for a quick load.
# Let's stick to the requirement: "original 'class' string and a per-feature 'area_m2'".
# If we dissolve, we lose the "per-feature" identity of the original polygons.
# Let's instead simplify the geometries more aggressively.
print("Simplifying geometries...")
# A very high tolerance to ensure small file size.
intersected['geometry'] = intersected.simplify(tolerance=30, preserve_topology=True)
# 4. Convert all geometries to MultiPolygon
print("Converting to MultiPolygon...")
def ensure_multipolygon(geom):
if geom.is_empty:
return geom
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
return geom
intersected['geometry'] = intersected.geometry.apply(ensure_multipolygon)
# 5. Keep only required columns
result = intersected[['class', 'area_m2', 'geometry']].copy()
# 6. Write to GeoJSON (WGS84)
print("Converting to WGS84 for web compatibility...")
result = result.to_crs(epsg=4326)
print(f"Writing to bma_landcover_intersect.geojson. Number of features: {len(result)}")
result.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
if __name__ == "__main__":
solve()
Wrote /work/solve.py (2477 chars).
2:08
Bash
python /work/solve.py
Performing intersection... Calculating areas... Simplifying geometries... Converting to MultiPolygon... Converting to WGS84 for web compatibility... Writing to bma_landcover_intersect.geojson. Number of features: 3453
2:12
Bash
ls -lh bma_landcover_intersect.geojson
-rw-r--r-- 1 agent agent 2.7M Jun 18 11:17 bma_landcover_intersect.geojson
2:19
Write
/work/solve.py
content · 51 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon
def solve():
# Load the datasets
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure CRS match
if landcover.crs != study_area.crs:
study_area = study_area.to_crs(landcover.crs)
# 1. Intersect landcover with study area
print("Performing intersection...")
intersected = gpd.overlay(landcover, study_area, how='intersection')
# 2. Calculate area in square meters (using metric CRS)
print("Calculating areas...")
intersected['area_m2'] = intersected.geometry.area
# 3. Simplify geometries for smaller file size
# A tolerance of 20 meters is a good balance for a "preview" file in a browser.
print("Simplifying geometries...")
intersected['geometry'] = intersected.simplify(tolerance=20, preserve_topology=True)
# 4. Convert all geometries to MultiPolygon
print("Converting to MultiPolygon...")
def ensure_multipolygon(geom):
if geom.is_empty:
return geom
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
return geom
intersected['geometry'] = intersected.geometry.apply(ensure_multipolygon)
# 5. Keep only required columns
# Using .copy() to avoid SettingWithCopyWarning
result = intersected[['class', 'area_m2', 'geometry']].copy()
# 6. Write to GeoJSON (WGS84)
print("Converting to WGS84 for web compatibility...")
result = result.to_crs(epsg=4326)
print(f"Writing to bma_landcover_intersect.geojson. Number of features: {len(result)}")
result.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
if __name__ == "__main__":
solve()
Wrote /work/solve.py (1785 chars).
2:21
Bash
python /work/solve.py
Performing intersection... Calculating areas... Simplifying geometries... Converting to MultiPolygon... Converting to WGS84 for web compatibility... Writing to bma_landcover_intersect.geojson. Number of features: 3453
2:25
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ls -lh bma_landcover_intersect.geojson
-rw-r--r-- 1 agent agent 3.4M Jun 18 11:17 bma_landcover_intersect.geojson
2:27
Bash
head -n 20 bma_landcover_intersect.geojson
{
"type": "FeatureCollection",
"name": "bma_landcover_intersect",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
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2:38
Write
/work/solve.py
content · 50 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon
def solve():
# Load the datasets
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure CRS match (Landcover is EPSG:32647, Study area is also EPSG:32647)
if landcover.crs != study_area.crs:
study_area = study_area.to_crs(landcover.crs)
# 1. Intersect landcover with study area
print("Performing intersection...")
intersected = gpd.overlay(landcover, study_area, how='intersection')
# 2. Calculate area in square meters (using the metric CRS)
print("Calculating areas...")
intersected['area_m2'] = intersected.geometry.area
# 3. Simplify geometries for smaller file size
# Using a tolerance of 20m to balance detail and file size.
print("Simplifying geometries...")
intersected['geometry'] = intersected.simplify(tolerance=20, preserve_topology=True)
# 4. Convert all geometries to MultiPolygon
print("Converting to MultiPolygon...")
def ensure_multipolygon(geom):
if geom.is_empty:
return geom
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
return geom
intersected['geometry'] = intersected.geometry.apply(ensure_multipolygon)
# 5. Keep only required columns: class, area_m2, and geometry
result = intersected[['class', 'area_m2', 'geometry']].copy()
# 6. Convert to WGS84 for web-friendly GeoJSON and write
print("Converting to WGS84 for web compatibility...")
result = result.to_crs(epsg=4326)
print(f"Writing to bma_landcover_intersect.geojson. Number of features: {len(result)}")
result.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
if __name__ == "__main__":
solve()
Wrote /work/solve.py (1831 chars).
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Bash
python /work/solve.py
Performing intersection... Calculating areas... Simplifying geometries... Converting to MultiPolygon... Converting to WGS84 for web compatibility... Writing to bma_landcover_intersect.geojson. Number of features: 3453
2:49
Bash
ls -lh bma_landcover_intersect.geojson
-rw-r--r-- 1 agent agent 3.4M Jun 18 11:17 bma_landcover_intersect.geojson
2:59