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from collections import Counter
import pandas as pd
import numpy as np
# from scipy.spatial import cKDTree
# df_amenities = pd.read_csv("df_indonesia.csv").rename(
# columns={"latitude":"lat", "longitude":"lon"}
# )
# df_banks = pd.read_csv("df_bank_indonesia.csv").rename(
# columns={"latitude":"lat", "longitude":"lon"}
# )
# df_amenities["fsq_category_labels"] = df_amenities["fsq_category_labels"].apply(
# lambda x: eval(x)
# )
# bank_coords = df_banks[['lat','lon']].values
# tree_banks = cKDTree(bank_coords)
# amenity_coords = df_amenities[['lat','lon']].values
# tree_amenities = cKDTree(amenity_coords)
DATASET_COLUMNS = [
'Dining and Drinking', 'Community and Government', 'Retail',
'Business and Professional Services', 'Landmarks and Outdoors',
'Arts and Entertainment', 'Health and Medicine',
'Travel and Transportation', 'Sports and Recreation',
'Event'
]
import os
from google.maps import areainsights_v1
from google.maps.areainsights_v1.types import ComputeInsightsRequest, Filter, LocationFilter, Insight
from google.type import latlng_pb2
import asyncio
async def compute_places_count_with_api_key(api_key, lat, lng, radius, place_type):
try:
client = areainsights_v1.AreaInsightsAsyncClient(
client_options={"api_key": api_key}
)
# 1. Define the geographic filter (a circle)
location_filter = LocationFilter(
circle=LocationFilter.Circle(
lat_lng=latlng_pb2.LatLng(latitude=lat, longitude=lng),
radius=radius
)
)
# 2. Define the place type filter
type_filter = areainsights_v1.TypeFilter(
# included_types=[place_type]
included_types=place
)
# 3. Assemble the main request body
request = ComputeInsightsRequest(
# We want the total count of matching places
insights=[Insight.INSIGHT_COUNT],
filter=Filter(
location_filter=location_filter,
type_filter=type_filter
)
)
response = await client.compute_insights(request=request)
count = int(response.count)
return count
except Exception as e:
print(f"An error occurred: {e}")
return None
def compute_features(candidate_point, api_key, radius=5000):
lat, lon = candidate_point
features = {
'num_banks_in_radius':0,
'total_amenities':0,
'category_diversity':0
}
for i,place in enumerate(GOOGLE_PLACE_TYPE_MAPPING):
total_count = await compute_places_count_with_api_key(
api_key,
lat,
lon,
radius,
place
)
features[f'num_{DATASET_COLUMNS[i]}'] = total_count
n_banks = compute_places_count_with_api_key(
api_key,
lat,
lon,
radius,
['atm']
)
features.update({
'num_banks_in_radius': n_banks,
'total_amenities': sum(v for v in features.values()),
'category_diversity': sum(bool(v) for v in features.values())
})
return features |