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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'
]
GOOGLE_PLACE_TYPE_MAPPING = [
# Dining and Drinking
[
'restaurant', 'bar', 'cafe', 'bakery', 'night_club'
],
# Community and Government
[
'government_office', 'local_government_office', 'city_hall',
'courthouse', 'police', 'fire_station', 'post_office', 'library'
],
# Retail
[
'store', 'shopping_mall', 'grocery_store', 'pharmacy',
'supermarket', 'drugstore'
],
# Business and Professional Services
[
'bank', 'atm', 'corporate_office', 'accounting', 'lawyer',
# 'establishment'
],
# Landmarks and Outdoors
[
'park', 'tourist_attraction', 'national_park',
'historical_landmark', 'cultural_landmark'
],
# Arts and Entertainment
[
'movie_theater', 'museum', 'art_gallery',
'performing_arts_theater', 'amusement_park', 'aquarium', 'zoo'
],
# Health and Medicine
[
'hospital', 'doctor', 'dentist', 'pharmacy',
'physiotherapist', 'spa'
],
# Travel and Transportation
[
'airport', 'bus_station', 'train_station', 'transit_station',
'subway_station', 'parking', 'lodging'
],
# Sports and Recreation
[
'gym', 'stadium', 'bowling_alley', 'fitness_center',
'park', 'amusement_center'
],
# Event (Mapped to common event venues)
[
'event_venue', 'convention_center', 'banquet_hall', 'stadium'
]
]
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, places):
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=places
)
# 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,places in enumerate(GOOGLE_PLACE_TYPE_MAPPING):
total_count = asyncio.run(compute_places_count_with_api_key(
api_key,
lat,
lon,
radius,
places
))
features[f'num_{DATASET_COLUMNS[i]}'] = total_count
n_banks = asyncio.run(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())
})
print(features)
return features |