geo-l2-bangkok-landuse-intersect
Map (reference ← swipe → agent)
0:00
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.
0:00
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
ls -F
bangkok_landcover.parquet bma_study_area.geojson
0:03
Write
/work/check_data.py
content · 15 lines · py
import geopandas as gpd
# Load the data
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)
# Inspect first few rows
print(landcover.head())
print(study_area.head())
Wrote /work/check_data.py (404 chars).
0:04
Bash
python /work/check_data.py
python /work/check_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
id ... geometry
0 00023927-8760-5550-a47d-48ae7f087574 ... POLYGON ((647454.402 1506832.283, 647738.841 1...
1 0002ad8a-093a-539c-8f6c-0fb0b4f2c3c1 ... POLYGON ((643806.222 1536031.975, 643880.924 1...
2 001206dc-28bb-593e-87bd-72efd2f32c65 ... POLYGON ((695139.782 1532663.1, 695231.345 153...
3 001748f2-f294-53b7-be74-29acd35b3af6 ... POLYGON ((650585.19 1544580.727, 650660.692 15...
4 001ca192-b61e-56b8-a9b1-8f6d429f6fc0 ... POLYGON ((652703.522 1505402.689, 652740.294 1...
[5 rows x 3 columns]
name geometry
0 BMA study area POLYGON ((653609.601 1506149.152, 662313.016 1...
0:15
Write
/work/solve.py
content · 44 lines · py
import geopandas as gpd
# Load the data
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure they are in the same CRS
if landcover.crs != study_area.crs:
landcover = landcover.to_crs(study_area.crs)
# 1. Intersect landcover with study area
# This will clip the polygons to the study area boundary
landcover_intersected = gpd.clip(landcover, study_area)
# 2. Calculate area in square meters
# Since CRS is EPSG:32647 (UTM 47N), units are already in meters.
landcover_intersected['area_m2'] = landcover_intersected.geometry.area
# 3. Simplify geometries to make the file smaller
# We can use the tolerance parameter.
# Since it is in meters, a tolerance of 1-5 meters should be enough for a preview.
landcover_intersected['geometry'] = landcover_intersected.geometry.simplify(tolerance=2.0, preserve_topology=True)
# 4. Ensure everything is stored as MultiPolygon
# Even if a single polygon remains after simplification/clipping, we want MultiPolygon
landcover_intersected['geometry'] = landcover_intersected.geometry.apply(lambda geom:
gpd.GeoSeries([geom]).to_multipolygon().iloc[0] if geom.geom_type == 'Polygon' else geom
)
# 5. Prepare final columns
# We only need 'class', 'area_m2', and 'geometry'
# Let's double check the 'class' column name from the check_data output
final_gdf = landcover_intersected[['class', 'area_m2', 'geometry']]
# 6. Convert to WGS84 (EPSG:4326) for GeoJSON standard/web previewing
final_gdf = final_gdf.to_crs(epsg=4326)
# 7. Write to GeoJSON
final_gdf.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
print(f"Successfully created bma_landcover_intersect.geojson")
print(f"Total features: {len(final_gdf)}")
print(f"Column names: {final_gdf.columns.tolist()}")
Wrote /work/solve.py (1792 chars).
0:16
Bash
python /work/solve.py
python /work/solve.py
Traceback (most recent call last):
File "/work/solve.py", line 13, in <module>
landcover_intersected = gpd.clip(landcover, study_area)
File "/usr/local/lib/python3.14/site-packages/geopandas/tools/clip.py", line 218, in clip
clipped = _clip_gdf_with_mask(gdf, combined_mask, sort=sort)
File "/usr/local/lib/python3.14/site-packages/geopandas/tools/clip.py", line 93, in _clip_gdf_with_mask
gdf_sub.geometry.values[non_point_mask].intersection(mask)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/geopandas/array.py", line 858, in intersection
self._binary_method("intersection", self, other), crs=self.crs
~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/geopandas/array.py", line 795, in _binary_method
return getattr(shapely, op)(left._data, right, **kwargs)
~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/shapely/decorators.py", line 173, in wrapper
result = func(*args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/shapely/decorators.py", line 88, in wrapped
return func(*args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/shapely/set_operations.py", line 168, in intersection
return lib.intersection(a, b, **kwargs)
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
shapely.errors.GEOSException: TopologyException: side location conflict at 671567.85823834711 1501411.7438156258. This can occur if the input geometry is invalid.
0:32
Write
/work/solve.py
content · 80 lines · py
import geopandas as gpd
from shapely.validation import make_valid
# Load the data
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure they are in the same CRS
if landcover.crs != study_area.crs:
landcover = landcover.to_crs(study_area.crs)
# Fix invalid geometries in landcover
print("Fixing landcover geometries...")
landcover['geometry'] = landcover['geometry'].apply(lambda geom: make_valid(geom) if not geom.is_valid else geom)
# Fix invalid geometries in study_area
print("Fixing study_area geometries...")
study_area['geometry'] = study_area['geometry'].apply(lambda geom: make_valid(geom) if not geom.is_valid else geom)
# 1. Intersect landcover with study area
print("Clipping landcover...")
landcover_intersected = gpd.clip(landcover, study_area)
# 2. Calculate area in square meters
# Since CRS is EPSG:32647 (UTM 47N), units are already in meters.
landcover_intersected['area_m2'] = landcover_intersected.geometry.area
# 3. Simplify geometries to make the file smaller
# We can use the tolerance parameter.
# Since it is in meters, a tolerance of 2 meters should be enough for a preview.
print("Simplifying geometries...")
landcover_intersected['geometry'] = landcover_intersected.geometry.simplify(tolerance=2.0, preserve_topology=True)
# 4. Ensure everything is stored as MultiPolygon
# After simplification and clipping, some might be Polygons.
# We convert them to MultiPolygons to satisfy the requirement.
print("Converting to MultiPolygons...")
def ensure_multipolygon(geom):
if geom.is_empty:
return geom
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
elif geom.geom_type == 'MultiPolygon':
return geom
# If it's a GeometryCollection (can happen after make_valid or clip)
elif geom.geom_type == 'GeometryCollection':
# Extract only Polygons/MultiPolygons
polys = [p for p in geom.geoms if p.geom_type in ['Polygon', 'MultiPolygon']]
if not polys:
return None # or some empty geometry
# If we have multiple polygons, we should combine them if possible or return a MultiPolygon
# A simple way is to wrap them into a MultiPolygon structure
from shapely.geometry import MultiPolygon
if len(polys) == 1:
if polys[0].geom_type == 'Polygon':
return MultiPolygon([polys[0]])
return polys[0]
return MultiPolygon(polys)
return geom
from shapely.geometry import MultiPolygon
landcover_intersected['geometry'] = landcover_intersected['geometry'].apply(ensure_multipolygon)
# Remove any None values resulting from the conversion
landcover_intersected = landcover_intersected[landcover_intersected.geometry.notnull()]
# 5. Prepare final columns
# We only need 'class', 'area_m2', and 'geometry'
final_gdf = landcover_intersected[['class', 'area_m2', 'geometry']]
# 6. Convert to WGS84 (EPSG:4326) for GeoJSON standard/web previewing
final_gdf = final_gdf.to_crs(epsg=4326)
# 7. Write to GeoJSON
final_gdf.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
print(f"Successfully created bma_landcover_intersect.geojson")
print(f"Total features: {len(final_gdf)}")
print(f"Column names: {final_gdf.columns.tolist()}")
Wrote /work/solve.py (3294 chars).
0:34
Bash
python /work/solve.py
python /work/solve.py
Fixing landcover geometries... Fixing study_area geometries... Clipping landcover... Simplifying geometries... Converting to MultiPolygons...
Traceback (most recent call last):
File "/work/solve.py", line 62, in <module>
landcover_intersected['geometry'] = landcover_intersected['geometry'].apply(ensure_multipolygon)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/geopandas/geoseries.py", line 800, in apply
result = super().apply(func, args=args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/pandas/core/series.py", line 5084, in apply
).apply()
~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 1520, in apply
return self.apply_standard()
~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 1578, in apply_standard
mapped = obj._map_values(mapper=curried)
File "/usr/local/lib/python3.14/site-packages/pandas/core/base.py", line 1020, in _map_values
return arr.map(mapper, na_action=na_action)
~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/arrays/base.py", line 2745, in map
return map_array(self, mapper, na_action=na_action)
File "/usr/local/lib/python3.14/site-packages/pandas/core/algorithms.py", line 1715, in map_array
return lib.map_infer(values, mapper)
~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
File "pandas/_libs/lib.pyx", line 3071, in pandas._libs.lib.map_infer
File "/work/solve.py", line 42, in ensure_multipolygon
return MultiPolygon([geom])
^^^^^^^^^^^^
UnboundLocalError: cannot access local variable 'MultiPolygon' where it is not associated with a value
0:52
Write
/work/solve.py
content · 86 lines · py
import geopandas as gpd
from shapely.validation import make_valid
from shapely.geometry import MultiPolygon, Polygon
# Load the data
landcover = gpd.read_parquet('bangkok_landcover.parquet')
study_area = gpd.read_file('bma_study_area.geojson')
# Ensure they are in the same CRS
if landcover.crs != study_area.crs:
landcover = landcover.to_crs(study_area.crs)
# Fix invalid geometries in landcover
print("Fixing landcover geometries...")
landcover['geometry'] = landcover['geometry'].apply(lambda geom: make_valid(geom) if not geom.is_valid else geom)
# Fix invalid geometries in study_area
print("Fixing study_area geometries...")
study_area['geometry'] = study_area['geometry'].apply(lambda geom: make_valid(geom) if not geom.is_valid else geom)
# 1. Intersect landcover with study area
print("Clipping landcover...")
landcover_intersected = gpd.clip(landcover, study_area)
# 2. Calculate area in square meters
print("Calculating areas...")
landcover_intersected['area_m2'] = landcover_intersected.geometry.area
# 3. Simplify geometries to make the file smaller
print("Simplifying geometries...")
landcover_intersected['geometry'] = landcover_intersected.geometry.simplify(tolerance=2.0, preserve_topology=True)
# 4. Ensure everything is stored as MultiPolygon
print("Converting to MultiPolygons...")
def ensure_multipolygon(geom):
if geom.is_empty:
return geom
if geom.geom_type == 'Polygon':
return MultiPolygon([geom])
elif geom.geom_type == 'MultiPolygon':
return geom
elif geom.geom_type == 'GeometryCollection':
# Extract only Polygons/MultiPolygons
polys = [p for p in geom.geoms if p.geom_type in ['Polygon', 'MultiPolygon']]
if not polys:
return None
# If we have multiple polygons, combine them.
# If there's only one Polygon, wrap it in MultiPolygon to keep types consistent.
if len(polys) == 1:
if polys[0].geom_type == 'Polygon':
return MultiPolygon([polys[0]])
else:
return polys[0] # it's already a MultiPolygon
# Create a MultiPolygon from the list of polygons/multipolygons
# We need to handle the case where we have a list of MultiPolygons by flattening
combined_geoms = []
for p in polys:
if p.geom_type == 'Polygon':
combined_geoms.append(p)
else:
combined_geoms.extend(list(p.geoms))
return MultiPolygon(combined_geoms)
return geom
landcover_intersected['geometry'] = landcover_intersected['geometry'].apply(ensure_multipolygon)
# Remove any None values resulting from the conversion
landcover_intersected = landcover_intersected[landcover_intersected.geometry.notnull()]
# 5. Prepare final columns
# We only need 'class', 'area_m2', and 'geometry'
final_gdf = landcover_intersected[['class', 'area_m2', 'geometry']]
# 6. Convert to WGS84 (EPSG:4326) for GeoJSON standard/web previewing
final_gdf = final_gdf.to_crs(epsg=4326)
# 7. Write to GeoJSON
print("Writing to GeoJSON...")
final_gdf.to_file('bma_landcover_intersect.geojson', driver='GeoJSON')
print(f"Successfully created bma_landcover_intersect.geojson")
print(f"Total features: {len(final_gdf)}")
print(f"Column names: {final_gdf.columns.tolist()}")
Wrote /work/solve.py (3325 chars).
0:53
Bash
python /work/solve.py
python /work/solve.py
Fixing landcover geometries... Fixing study_area geometries... Clipping landcover... Calculating areas... Simplifying geometries... Converting to MultiPolygons... Writing to GeoJSON... Successfully created bma_landcover_intersect.geojson Total features: 3453 Column names: ['class', 'area_m2', 'geometry']
0:56
Bash
head -n 20 bma_landcover_intersect.geojson
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": [
{ "type": "Feature", "properties": { "class": "mangrove", "area_m2": 4736.4681768837008 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 100.581475394256017, 13.543275311816137 ], [ 100.581539864970097, 13.543571175860935 ], [ 100.581993157301184, 13.543601331637706 ], [ 100.582683465526173, 13.543189518999844 ], [ 100.583010889411213, 13.543176277630829 ], [ 100.583126460999011, 13.54307895980056 ], [ 100.581510035229002, 13.543213807098317 ], [ 100.581475394256017, 13.543275311816137 ] ] ] ] } },
{ "type": "Feature", "properties": { "class": "urban", "area_m2": 23885.211831935558 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 100.581048310503306, 13.543605490767117 ], [ 100.581037352478376, 13.543836510967521 ], [ 100.580803961010616, 13.544044338156169 ], [ 100.580907055369181, 13.544427555411238 ], [ 100.581519592164241, 13.544782250378102 ], [ 100.582033333, 13.543830879113758 ], [ 100.58200961146396, 13.543572444588792 ], [ 100.581740900645812, 13.543194548225822 ], [ 100.580784274593626, 13.543274348904013 ], [ 100.581048310503306, 13.543605490767117 ] ] ], [ [ [ 100.578623197970202, 13.543571175861011 ], [ 100.579005557023308, 13.543633520038769 ], [ 100.579239110588759, 13.544009611463945 ], [ 100.579538993210306, 13.544029372398603 ], [ 100.579850132526175, 13.543689518999843 ], [ 100.580293469029797, 13.543654508861009 ], [ 100.580400679969202, 13.54330634690583 ], [ 100.578585465982499, 13.543457757112806 ], [ 100.578623197970202, 13.543571175861011 ] ] ] ] } },
{ "type": "Feature", "properties": { "class": "forest", "area_m2": 0.5411926562059699 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 100.58190982278272, 13.543184664882933 ], [ 100.581912918588159, 13.54318019829052 ], [ 100.581891439935191, 13.543181990071044 ], [ 100.58190982278272, 13.543184664882933 ] ] ] ] } },
{ "type": "Feature", "properties": { "class": "forest", "area_m2": 528.31582562349195 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 100.578392060672812, 13.543474688593792 ], [ 100.578563741726086, 13.543615583593073 ], [ 100.578851331153587, 13.543493157201739 ], [ 100.578849435681292, 13.543435739757156 ], [ 100.578392060672812, 13.543474688593792 ] ] ] ] } },
{ "type": "Feature", "properties": { "class": "shrub", "area_m2": 19572.676171932188 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 100.610264673047439, 13.541060895705709 ], [ 100.610006842798342, 13.541065335513041 ], [ 100.609595491134129, 13.541373197850771 ], [ 100.60962319785078, 13.541821175532545 ], [ 100.610558021927247, 13.541857939327137 ], [ 100.610781284507809, 13.54144276884456 ], [ 100.611057231155414, 13.541614617841146 ], [ 100.61115577750293, 13.541926277893481 ], [ 100.611499526980865, 13.541861047045 ], [ 100.611592944560172, 13.541155777502974 ], [ 100.611134871033627, 13.540740742118478 ], [ 100.610263478914661, 13.54081353582953 ], [ 100.610264673047439, 13.541060895705709 ] ] ] ] } },
{ "type": "Feature", "properties": { "class": "barren", "area_m2": 7219.8922849964611 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 100.578417139685811, 13.543611047044999 ], [ 100.579072444169597, 13.544176277893484 ], [ 100.579361047045055, 13.544166193647506 ], [ 100.579532250259732, 13.543936258273819 ], [ 100.579434664486925, 13.543423509464926 ], [ 100.579372594969655, 13.543392102920164 ], [ 100.578364228772145, 13.543476209993502 ], [ 100.578417139685811, 13.543611047044999 ] ] ] ] } },
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