spa-l1-capetown-hospital-nn
Map (reference ← swipe → agent)
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I am putting together EMS coverage planning for the Western Cape and need each residential pickup address tagged with its nearest hospital so the ops-research team can join the result against shift schedules. For every address in `addresses.parquet`, find the nearest hospital from `hospitals.parquet` by straight-line distance and record that hospital's name plus the distance in metres. Keep the original `address_id` from the input so we can join back to the address master, and make sure `nearest_hospital_name` comes through as a non-empty string and `distance_m` as a numeric, finite, non-negative value in metres.
Please write the result to `nearest_hospital.gpkg` in GeoPackage format with one feature per input address, keeping the original address geometry and the columns `address_id`, `nearest_hospital_name`, and `distance_m`.
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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
ls -F
addresses.parquet hospitals.parquet
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Write
/work/inspect_data.py
content · 15 lines · py
import geopandas as gpd
import pandas as pd
# Load the datasets
addresses = gpd.read_parquet('addresses.parquet')
hospitals = gpd.read_parquet('hospitals.parquet')
print("Addresses info:")
print(addresses.info())
print(addresses.head())
print("\nHospitals info:")
print(hospitals.info())
print(hospitals.head())
Wrote /work/inspect_data.py (315 chars).
0:04
Bash
python /work/inspect_data.py
python /work/inspect_data.py
Addresses info: <class 'geopandas.geodataframe.GeoDataFrame'> RangeIndex: 120 entries, 0 to 119 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 address_id 120 non-null str 1 geometry 120 non-null geometry dtypes: geometry(1), str(1) memory usage: 2.6 KB None address_id geometry 0 A0001 POINT (18.53441 -33.94184) 1 A0002 POINT (18.50296 -33.93623) 2 A0003 POINT (18.53234 -33.95918) 3 A0004 POINT (18.44857 -33.98103) 4 A0005 POINT (18.49928 -33.99649) Hospitals info: <class 'geopandas.geodataframe.GeoDataFrame'> RangeIndex: 37 entries, 0 to 36 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 hospital_id 37 non-null str 1 name 37 non-null str 2 geometry 37 non-null geometry dtypes: geometry(1), str(2) memory usage: 2.3 KB None hospital_id name geometry 0 H001 Mowbray Maternity Hospital POINT (18.47483 -33.94921) 1 H002 Life Claremont Hospital POINT (18.46637 -33.98695) 2 H003 Old Somerset Hospital POINT (18.42013 -33.9158) 3 H004 Mpheni Block C Elim Hospital POINT (18.43246 -33.92207) 4 H005 Life Kingsbury Hospital POINT (18.46883 -33.98645)
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Write
/work/solve.py
content · 71 lines · py
import geopandas as gpd
import pandas as pd
def solve():
# Load the datasets
addresses = gpd.read_parquet('addresses.parquet')
hospitals = gpd.read_parquet('hospitals.parquet')
# Check CRS
# Both should be in WGS84 (EPSG:4326) based on the inspect output
# To calculate distance in meters, we need to project to a metric CRS.
# For Western Cape, EPSG:32734 (UTM zone 34S) or EPSG:2056 (Lo29) are good.
# Let's use a general projection for South Africa/Western Cape or simply UTM.
# EPSG:32734 is UTM 34S, covering much of the Western Cape.
target_crs = "EPSG:32734"
addresses_metric = addresses.to_crs(target_crs)
hospitals_metric = hospitals.to_crs(target_crs)
# We want to find the nearest hospital for each address.
# Using geopandas sjoin_nearest
# sjoin_nearest requires both to be in the same CRS
# We'll join the metric versions to get distances
nearest_hospitals = gpd.sjoin_nearest(
addresses_metric,
hospitals_metric[['name', 'geometry']],
how='left',
distance_col='distance_m'
)
# The sjoin_nearest will return the columns from the right dataframe.
# We need to ensure the columns are named as requested:
# address_id, nearest_hospital_name, distance_m
# The sjoin_nearest result will have the original address columns + hospital columns.
# 'name' comes from the hospital dataframe.
# Let's map 'name' to 'nearest_hospital_name'
nearest_hospitals = nearest_hospitals.rename(columns={'name': 'nearest_hospital_name'})
# Now we need to make sure we keep the original address geometry (from the original addresses)
# Actually, nearest_hospitals already contains the geometry from addresses_metric.
# Since addresses_metric is just a projection of addresses, the geometry is equivalent.
# However, the user might expect the original CRS (WGS84) in the output if they didn't specify.
# But the requirements say "keeping the original address geometry".
# Usually, this means the geometry of the input. Let's re-project back to original CRS.
original_crs = addresses.crs
result = nearest_hospitals.to_crs(original_crs)
# Select only requested columns
# address_id, nearest_hospital_name, distance_m
# Note: the sjoin_nearest adds 'index_right' which we don't need.
final_df = result[['address_id', 'nearest_hospital_name', 'distance_m']]
# Ensure nearest_hospital_name is non-empty string and distance_m is numeric/finite/non-negative
# Based on sjoin_nearest, if a match is found, it should be fine.
# If no match (though unlikely with hospitals), we handle it.
final_df['nearest_hospital_name'] = final_df['nearest_hospital_name'].fillna('')
final_df['distance_m'] = final_df['distance_m'].fillna(0.0).clip(lower=0.0)
# Save to GeoPackage
final_df.to_file('nearest_hospital.gpkg', driver='GPKG')
print("Successfully wrote nearest_hospital.gpkg")
if __name__ == "__main__":
solve()
Wrote /work/solve.py (3060 chars).
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Bash
python /work/solve.py
python /work/solve.py
Traceback (most recent call last):
File "/work/solve.py", line 70, in <module>
solve()
~~~~~^^
File "/work/solve.py", line 66, in solve
final_df.to_file('nearest_hospital.gpkg', driver='GPKG')
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/generic.py", line 6206, in __getattr__
return object.__getattribute__(self, name)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'to_file'. Did you mean: 'to_pickle'?
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import geopandas as gpd
import pandas as pd
def solve():
# Load the datasets
addresses = gpd.read_parquet('addresses.parquet')
hospitals = gpd.read_parquet('hospitals.parquet')
# Check CRS
# Both should be in WGS84 (EPSG:4326) based on the inspect output
# To calculate distance in meters, we need to project to a metric CRS.
# For Western Cape, EPSG:32734 (UTM zone 34S) or EPSG:2056 (Lo29) are good.
# Let's use a general projection for South Africa/Western Cape or simply UTM.
# EPSG:32734 is UTM 34S, covering much of the Western Cape.
target_crs = "EPSG:32734"
addresses_metric = addresses.to_crs(target_crs)
hospitals_metric = hospitals.to_crs(target_crs)
# We want to find the nearest hospital for each address.
# Using geopandas sjoin_nearest
# sjoin_nearest requires both to be in the same CRS
# We'll join the metric versions to get distances
# nearest_hospitals = gpd.sjoin_nearest(
# addresses_metric,
# hospitals_metric[['name', 'geometry']],
# how='left',
# distance_col='distance_m'
# )
# Wait, the previous error was: AttributeError: 'DataFrame' object has no attribute 'to_file'.
# This happened because I subsetted final_df = result[['address_id', 'nearest_hospital_name', 'distance_m']]
# which returned a Pandas DataFrame instead of a GeoDataFrame because it dropped the geometry column?
# No, the geometry column is usually part of the subset if it's selected,
# but if I subsetted columns and didn't include 'geometry', it's no longer a GeoDataFrame.
# Actually, I should include 'geometry' in the selection.
# Let's fix solve.py
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Write
/work/solve.py
content · 61 lines · py
import geopandas as gpd
import pandas as pd
def solve():
# Load the datasets
addresses = gpd.read_parquet('addresses.parquet')
hospitals = gpd.read_parquet('hospitals.parquet')
# Check CRS
# Both should be in WGS84 (EPSG:4326) based on the inspect output
# To calculate distance in meters, we need to project to a metric CRS.
# For Western Cape, EPSG:32734 (UTM zone 34S) or EPSG:2056 (Lo29) are good.
# Let's use a general projection for South Africa/Western Cape or simply UTM.
# EPSG:32734 is UTM 34S, covering much of the Western Cape.
target_crs = "EPSG:32734"
addresses_metric = addresses.to_crs(target_crs)
hospitals_metric = hospitals.to_crs(target_crs)
# We want to find the nearest hospital for each address.
# Using geopandas sjoin_nearest
# sjoin_nearest requires both to be in the same CRS
# We'll join the metric versions to get distances
nearest_hospitals = gpd.sjoin_nearest(
addresses_metric,
hospitals_metric[['name', 'geometry']],
how='left',
distance_col='distance_m'
)
# The sjoin_nearest result will have the original columns + hospital columns.
# 'name' comes from the hospital dataframe.
# We need to make sure the columns are named as requested:
# address_id, nearest_hospital_name, distance_m
nearest_hospitals = nearest_hospitals.rename(columns={'name': 'nearest_hospital_name'})
# The sjoin_nearest result includes the geometry of the left dataframe (addresses_metric).
# We need to re-project it back to the original CRS.
original_crs = addresses.crs
result = nearest_hospitals.to_crs(original_crs)
# Select only requested columns AND geometry
# address_id, nearest_hospital_name, distance_m, geometry
# We must ensure 'geometry' is included so it remains a GeoDataFrame.
final_df = result[['address_id', 'nearest_hospital_name', 'distance_m', 'geometry']]
# Ensure nearest_hospital_name is non-empty string and distance_m is numeric/finite/non-negative
final_df['nearest_hospital_name'] = final_df['nearest_hospital_name'].fillna('').astype(str)
final_df['distance_m'] = pd.to_numeric(final_df['distance_m'], errors='coerce').fillna(0.0).clip(lower=0.0)
# Save to GeoPackage
final_df.to_file('nearest_hospital.gpkg', driver='GPKG')
print("Successfully wrote nearest_hospital.gpkg")
if __name__ == "__main__":
solve()
Wrote /work/solve.py (2476 chars).
0:48
Bash
python /work/solve.py
python /work/solve.py
Successfully wrote nearest_hospital.gpkg
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Bash
# Let's verify the content of the output file.
# Let's verify the content of the output file.
# We can use a small python script to read it.
python -c "import geopandas as gpd; df = gpd.read_file('nearest_hospital.gpkg'); print(df.head())"
address_id ... geometry 0 A0001 ... POINT (18.53441 -33.94184) 1 A0002 ... POINT (18.50296 -33.93623) 2 A0003 ... POINT (18.53234 -33.95918) 3 A0004 ... POINT (18.44857 -33.98103) 4 A0005 ... POINT (18.49928 -33.99649) [5 rows x 4 columns]
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