dd-l2-tokyo-overture-schools
Map (reference ← swipe → agent)
0:00
Pulling together a summer briefing on school-density disparities for children aged 8–14 across the 23 special wards. Out of `tokyo_places` I need every school relevant to that age range whose point sits inside the wards rectangle in `tokyo_23wards_bbox`. A colleague will visualise the result in R, so it has to land as `tokyo_schools.geojson`.
Each feature's properties must use these exact keys: `id`, `name`, `confidence`, `address_freeform`, `address_locality`, and `address_postcode`.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:07
Let me start by exploring the data to understand what we're working with. I need to find the files `tokyo_places` and `tokyo_23wards_bbox` in the working directory.
0:07
Bash
ls -la /work/
total 1820 drwxr-xr-x 1 agent agent 92 Jun 16 00:55 . drwxr-xr-x 1 root root 14 Jun 16 00:55 .. -rw-r--r-- 1 agent agent 779 Jun 16 00:55 tokyo_23wards_bbox.geojson -rw-r--r-- 1 agent agent 1858237 Jun 16 00:55 tokyo_places.parquet
0:12
Let me first examine the input files to understand their structure.
0:12
Read
/work/tokyo_23wards_bbox.geojson
1 {
2 "type": "FeatureCollection",
3 "name": "tokyo_23wards_bbox",
4 "crs": {
5 "type": "name",
6 "properties": {
7 "name": "urn:ogc:def:crs:OGC:1.3:CRS84"
8 }
9 },
10 "features": [
11 {
12 "type": "Feature",
13 "properties": {
14 "name": "Tokyo 23 Special Wards (bbox)"
15 },
16 "geometry": {
17 "type": "Polygon",
18 "coordinates": [
19 [
20 [
21 139.56,
22 35.52
23 ],
24 [
25 139.91,
26 35.52
27 ],
28 [
29 139.91,
30 35.82
31 ],
32 [
33 139.56,
34 35.82
35 ],
36 [
37 139.56,
38 35.52
39 ]
40 ]
41 ]
42 }
43 }
44 ]
45 }
0:12
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
df = pd.read_parquet('tokyo_places.parquet')
print('Columns:', list(df.columns))
print('Shape:', df.shape)
print()
print(df.head(5).to_string())
print()
print('dtypes:')
print(df.dtypes)
"
Columns: ['id', 'geometry', 'bbox', 'confidence', 'categories', 'names', 'addresses', 'bucket']
Shape: (13402, 8)
id geometry bbox confidence categories names addresses bucket
0 0036772c-0c6a-4a84-a168-95f8f2097d0b b'\x01\x01\x00\x00\x00;\xdfO\x8d\x97wa@\xa8W\xca2\xc4\xd1A@' {'xmax': 139.73727416992188, 'xmin': 139.73724365234375, 'ymax': 35.63880157470703, 'ymin': 35.6387939453125} 0.733686 {'alternate': ['education', 'topic_concert_venue'], 'primary': 'school'} {'common': None, 'primary': 'ちいさなピカソ（親と子の絵画・造形）', 'rules': None} [{'country': 'JP', 'freeform': '高輪1-16-25', 'locality': '港区', 'postcode': '108-0074', 'region': None}] 0
1 005b475a-c465-4fdc-b2b7-863f5b63cf5b b'\x01\x01\x00\x00\x00\x90f,\x9a\xcexa@6\xce\xa6#\x80\xd9A@' {'xmax': 139.7752227783203, 'xmin': 139.77520751953125, 'ymax': 35.69922637939453, 'ymin': 35.699222564697266} 0.770000 {'alternate': None, 'primary': 'cafe'} {'common': None, 'primary': '推しは推せる時に推せっ! AKIBA', 'rules': None} [{'country': 'JP', 'freeform': '神田松永町1-1', 'locality': '千代田区', 'postcode': '101-0023', 'region': '東京都'}] 0
2 00e41387-1ceb-4dee-80a4-29705b5a09fd b'\x01\x01\x00\x00\x00ky3\x94\x99wa@\xef\xa7\x19\xe0\xb2\xd6A@' {'xmax': 139.7375030517578, 'xmin': 139.73748779296875, 'ymax': 35.677337646484375, 'ymin': 35.67733383178711} 0.770000 None {'common': None, 'primary': '中国東北農家菜福', 'rules': None} [{'country': 'JP', 'freeform': '永田町2-14-3', 'locality': '千代田区', 'postcode': '100-0014', 'region': '東京都'}] 0
3 01067b37-4a19-4791-90c0-6c2e99a8867a b'\x01\x01\x00\x00\x00/\x82\xce\xbd\xcaua@\x19d`Wr\xcfA@' {'xmax': 139.68099975585938, 'xmin': 139.6809844970703, 'ymax': 35.62067794799805, 'ymin': 35.62067413330078} 0.885675 {'alternate': ['european_restaurant', 'restaurant'], 'primary': 'french_restaurant'} {'common': None, 'primary': 'ビストロ アルブル', 'rules': None} [{'country': 'JP', 'freeform': '東京都目黒区碑文谷４丁目２４−１６', 'locality': '目黒区', 'postcode': '152-0003', 'region': None}] 0
4 01139e42-34cf-4306-a3e8-9f3b58bc769d b'\x01\x01\x00\x00\x00\xb1\xeb\x84?\x81xa@\x8f\x08Z-%\xd6A@' {'xmax': 139.76577758789062, 'xmin': 139.76576232910156, 'ymax': 35.67301559448242, 'ymin': 35.67300796508789} 0.770000 {'alternate': None, 'primary': 'italian_restaurant'} {'common': None, 'primary': 'ristorante KURODINO', 'rules': None} [{'country': 'JP', 'freeform': '銀座3-4-17', 'locality': '中央区', 'postcode': '104-0061', 'region': 'Tokyo'}] 0
dtypes:
id str
geometry object
bbox object
confidence float64
categories object
names object
addresses object
bucket int32
dtype: object
0:31
Let me understand the data: 1. **tokyo_23wards_bbox.geojson** - A bounding box polygon covering Tokyo's 23 special wards: lat 35.52-35.82, lon 139.56-139.91. 2. **tokyo_places.parquet** - 13,402 points of interest. Key columns: - `id` - unique ID - `geometry` - WKB binary (point geometries) - `confidence` - confidence score - `categories` - dict with `primary` and `alternate` categories - `names` - dict with `primary` name - `addresses` - list of dicts with `freeform`, `locality`, `postcode`, etc. I need to: 1. Filter for schools relevant to children aged 8-14. This means finding places whose category is "school" (primary or alternate). But "school" might be too broad - I need schools relevant to ages 8-14, which would be elementary schools and junior high schools in Japan. Let me look at what school categories exist. 2. Filter to points inside the bbox polygon. 3. Output as GeoJSON with properties: `id`, `name`, `confidence`, `address_freeform`, `address_locality`, `address_postcode`. Let me explore the data to understand the categories available.
0:31
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
df = pd.read_parquet('tokyo_places.parquet')
# Look at all unique primary categories
print('=== Primary categories ===')
cats = df['categories'].dropna().apply(lambda x: x.get('primary')).unique()
for c in sorted(cats):
print(c)
print()
print('=== Categories with school in name ===')
for idx, row in df.iterrows():
cat = row['categories']
if cat:
primary = cat.get('primary', '')
alt = cat.get('alternate')
if 'school' in str(primary).lower():
print(f'primary={primary}, alt={alt}')
elif alt and any('school' in str(a).lower() for a in alt):
print(f'primary={primary}, alt={alt}')
"
=== Primary categories === accommodation accountant active_life acupuncture adult_education adult_entertainment adult_store advertising_agency airport airport_lounge airport_terminal alternative_medicine amateur_sports_league amateur_sports_team american_restaurant amusement_park animal_rescue_service antique_store appliance_manufacturer appliance_repair_service appliance_store appraisal_services aquatic_pet_store arcade architect architectural_designer aromatherapy art_gallery art_museum art_school arts_and_crafts arts_and_entertainment asian_restaurant assisted_living_facility atms attractions_and_activities audio_visual_equipment_store auditorium auto_body_shop auto_company auto_customization auto_detailing auto_manufacturers_and_distributors automation_services automotive automotive_dealer automotive_parts_and_accessories automotive_repair automotive_services_and_repair b2b_equipment_maintenance_and_repair b2b_jewelers b2b_science_and_technology b2b_textiles baby_gear_and_furniture bagel_shop bakery bank_credit_union banks baptist_church bar bar_and_grill_restaurant barbecue_restaurant barber baseball_field baseball_stadium beach beauty_and_spa beauty_product_supplier beauty_salon bed_and_breakfast beer_bar beer_garden beer_wine_and_spirits belgian_restaurant beverage_store beverage_supplier bicycle_shop bike_rentals biotechnology_company bistro book_magazine_distribution bookstore botanical_garden boutique bowling_alley boxing_class boxing_gym brasserie brazilian_restaurant breakfast_and_brunch_restaurant brewery bridal_shop bridge broadcasting_media_production brokers bubble_tea buddhist_temple buffet_restaurant builders building_supply_store burger_restaurant bus_station business business_advertising business_consulting business_management_services business_manufacturing_and_supply business_office_supplies_and_stationery business_to_business butcher_shop cafe cafeteria campground campus_building canal candy_store car_dealer car_rental_agency car_stereo_store car_wash car_window_tinting cardiologist carpenter carpet_store casino caterer catholic_church central_government_office check_cashing_payday_loans cheese_shop chemical_plant chicken_restaurant child_care_and_day_care child_protection_service childrens_clothing_store childrens_hospital chinese_restaurant chiropractor chocolatier church_cathedral cinema cleaning_services clothing_company clothing_store cocktail_bar coffee_shop college_university comedy_club comfort_food_restaurant commercial_industrial commercial_printer commercial_real_estate community_center community_services_non_profits computer_coaching computer_hardware_company computer_store condominium construction_services contractor convenience_store cooking_school corporate_office cosmetic_and_beauty_supplies cosmetic_dentist cosmetic_surgeon cosmetology_school costume_museum costume_store counseling_and_mental_health coworking_space credit_and_debt_counseling credit_union cuban_restaurant cultural_center currency_exchange custom_clothing cycling_classes damage_restoration dance_club dance_school day_care_preschool day_spa delicatessen dentist department_store dermatologist desserts diagnostic_services dialysis_clinic dim_sum_restaurant diner disability_services_and_support_organization discount_store display_home_center distribution_services doctor dog_park dog_trainer doner_kebab donuts driving_range driving_school drugstore dry_cleaning dumpling_restaurant ear_nose_and_throat eastern_european_restaurant eat_and_drink education educational_services educational_supply_store electrician electronics elementary_school embassy employment_agencies employment_law engineering_services environmental_conservation_organization european_restaurant ev_charging_station event_photography event_planning event_technology_service eye_care_clinic eyewear_and_optician fabric_store fair family_practice family_service_center farm farmers_market fashion fashion_accessories_store fast_food_restaurant fencing_club ferry_service fertility filipino_restaurant financial_advising financial_service fire_department fish_and_chips_restaurant fishmonger fitness_trainer flea_market flowers_and_gifts_shop food food_beverage_service_distribution food_consultant food_court food_delivery_service food_stand food_truck football_stadium forestry_service formal_wear_store framing_store freight_and_cargo_service french_restaurant fruits_and_vegetables funeral_services_and_cemeteries furniture_store futsal_field game_publisher garbage_collection_service gardener gas_station gastroenterologist gastropub gay_bar gelato general_dentistry german_restaurant gift_shop glass_and_mirror_sales_service glass_blowing glass_manufacturer golf_course golf_equipment golf_instructor government_services graphic_designer greek_restaurant grocery_store gym hair_removal hair_salon hair_supply_stores halal_restaurant hardware_store hawaiian_restaurant health_and_medical health_and_wellness_club health_food_store health_spa heliports high_school hiking_trail himalayan_nepalese_restaurant hindu_temple history_museum hobby_shop hockey_field home_and_garden home_cleaning home_developer home_goods_store home_health_care home_improvement_store home_service hookah_bar horse_boarding horse_riding hospital hostel hotel hotel_bar hungarian_restaurant hunting_and_fishing_supplies hvac_services ice_cream_and_frozen_yoghurt ice_cream_shop image_consultant imported_food indian_restaurant indoor_playcenter industrial_company industrial_equipment information_technology_company inn insurance_agency interior_design internal_medicine international_restaurant internet_cafe internet_marketing_service internet_service_provider investing ip_and_internet_law irish_pub iron_and_steel_industry it_service_and_computer_repair italian_restaurant jamaican_restaurant janitorial_services japanese_confectionery_shop japanese_restaurant jazz_and_blues jewelry_and_watches_manufacturer jewelry_store karaoke key_and_locksmith kitchen_supply_store korean_restaurant laboratory land_surveying landmark_and_historical_building landscaping language_school laser_hair_removal latin_american_restaurant laundromat laundry_services lawyer legal_services library lighting_store lingerie_store liquor_store lodge lottery_ticket lounge luggage_store lumber_store machine_and_tool_rentals machine_shop makeup_artist malaysian_restaurant marina marketing_agency marketing_consultant martial_arts_club massage massage_therapy maternity_centers mattress_store media_agency media_news_company media_news_website medical_center medical_school medical_service_organizations medical_spa memorial_park mens_clothing_store metal_supplier metro_station mexican_restaurant middle_eastern_restaurant middle_school military_surplus_store mobile_phone_store modern_art_museum monument motel motorcycle_dealer motorcycle_repair movers movie_television_studio museum music_and_dvd_store music_production music_school music_venue musical_instrument_store nail_salon naturopathic_holistic newspaper_and_magazines_store non_governmental_association noodles_restaurant nurse_practitioner nursery_and_gardening observatory obstetrician_and_gynecologist office_equipment onsen ophthalmologist optometrist organic_grocery_store organization orthodontist orthopedist osteopathic_physician outdoor_gear outlet_store package_locker paintball pancake_house park parking passport_and_visa_services pawn_shop pediatrician perfume_store peruvian_restaurant pet_boarding pet_groomer pet_services pet_sitting pet_store pets pharmaceutical_companies pharmacy photo_booth_rental photographer photography_store_and_services physical_therapy piano_bar pilates_studio pizza_restaurant planetarium plastic_fabrication_company plastic_surgeon playground plaza police_department political_party_office pool_billiards portuguese_restaurant post_office prenatal_perinatal_care preschool print_media printing_equipment_and_supply printing_services private_association private_school professional_services property_management prosthetics psychiatrist psychic pub public_and_government_association public_bath_houses public_health_clinic public_plaza public_relations public_school public_service_and_government public_utility_company pulmonologist radio_station railroad_freight real_estate real_estate_agent real_estate_investment real_estate_service recording_and_rehearsal_studio recycling_center rehabilitation_center religious_organization rental_kiosks rental_service reptile_shop resort restaurant retail retirement_home river rock_climbing_spot russian_restaurant sake_bar salad_bar sandwich_shop sauna scale_supplier school science_museum scuba_diving_center sculpture_statue seafood_market seafood_restaurant self_storage_facility senior_citizen_services session_photography sewing_and_alterations shared_office_space shaved_ice_shop shipping_center shoe_repair shoe_store shopping shopping_center sign_making singaporean_restaurant skate_shop ski_and_snowboard_shop skilled_nursing skin_care smoothie_juice_bar soccer_field social_and_human_services social_club social_service_organizations software_development solar_installation soup_restaurant souvenir_shop spanish_restaurant spas speakeasy specialty_grocery_store specialty_school sporting_goods sports_and_fitness_instruction sports_and_recreation_venue sports_bar sports_club_and_league sports_wear stadium_arena steakhouse storage_facility structure_and_geography sunglasses_store supermarket superstore surf_shop surgeon surgical_appliances_and_supplies sushi_restaurant swimming_instructor swimming_pool taco_restaurant tai_chi_studio taiwanese_restaurant tanning_salon tapas_bar tattoo_and_piercing tax_law taxi_service tea_room teeth_whitening telecommunications_company television_station tennis_court test_preparation texmex_restaurant thai_restaurant theatre theatrical_productions theme_restaurant thrift_store ticket_sales tire_dealer_and_repair tire_repair_shop tobacco_shop topic_concert_venue topic_publisher tours town_hall toy_store train_station translating_and_interpreting_services translation_services transportation travel travel_agents travel_company travel_services truck_dealer trusts turkish_restaurant tutoring_center urban_farm urologist used_bookstore used_car_dealer used_vintage_and_consignment vegan_restaurant vegetarian_restaurant venue_and_event_space veterinarian vietnamese_restaurant vinyl_record_store vocational_and_technical_school walk_in_clinic warehouses watch_store water_supplier web_designer wedding_chapel wedding_planning whiskey_bar wholesale_store wholesaler wine_bar wine_wholesaler womens_clothing_store womens_health_clinic yoga_studio youth_organizations zoo === Categories with school in name === primary=school, alt=['education' 'topic_concert_venue']
Traceback (most recent call last):
File "<string>", line 20, in <module>
elif alt and any('school' in str(a).lower() for a in alt):
^^^
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
0:51
The error at the end is not important - I already found what I need. Let me look at the relevant school categories: - `school` (primary category) - `elementary_school` - `middle_school` - `high_school` - `preschool` - `private_school` - `public_school` - `specialty_school` - `vocational_and_technical_school` - `art_school` - `language_school` - `music_school` - `cooking_school` - `dance_school` - `driving_school` - `cosmetology_school` - `adult_education` - `educational_services` - `tutoring_center` - `test_preparation` - `child_care_and_day_care` - `day_care_preschool` For children aged 8-14, the relevant school types would be: - `elementary_school` (elementary in Japan is ages 6-12) - `middle_school` (middle school in Japan is ages 12-15) - `school` (general school category, could be any) - `private_school` - `public_school` But the question says "every school relevant to that age range" - ages 8-14. In Japan: - Elementary school: ages 6-12 (covers 8-12) - Middle school (junior high): ages 12-15 (covers 12-14) So I should look for schools that serve this age range: - `elementary_school` - `middle_school` - `school` (generic) - `private_school` (could be private elementary or middle) - `public_school` (could be public elementary or middle) But also `primary` category `school` has alternate `['education', 'topic_concert_venue']`. Let me also check what categories are available with 'school' in the name more carefully. Actually, let me be more systematic. Let me look at all unique primary categories that contain 'school', and also look at the data for the 'school' primary category.
0:51
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
df = pd.read_parquet('tokyo_places.parquet')
# Unique primary categories containing 'school'
cats = df['categories'].dropna().apply(lambda x: x.get('primary')).unique()
school_cats = [c for c in sorted(cats) if 'school' in str(c).lower()]
print('School-related primary categories:')
for c in school_cats:
print(c)
print()
# Also look at the 'school' category records specifically
school_mask = df['categories'].apply(lambda x: x is not None and x.get('primary') == 'school')
print(f'Records with primary=school: {school_mask.sum()}')
# Look at the names for a few
school_df = df[school_mask]
print()
print('Sample school entries:')
for idx, row in school_df.head(10).iterrows():
names = row['names']
addr = row['addresses']
print(f' id={row[\"id\"]}, name={names.get(\"primary\", \"?\")}, addr_freeform={addr[0][\"freeform\"] if addr else \"?\"}, locality={addr[0][\"locality\"] if addr else \"?\"}')"
School-related primary categories: art_school cooking_school cosmetology_school dance_school day_care_preschool driving_school elementary_school high_school language_school medical_school middle_school music_school preschool private_school public_school school specialty_school vocational_and_technical_school Records with primary=school: 1742 Sample school entries: id=0036772c-0c6a-4a84-a168-95f8f2097d0b, name=ちいさなピカソ（親と子の絵画・造形）, addr_freeform=高輪1-16-25, locality=港区 id=014f234d-3bd8-49dc-b5c5-ddaa294accfa, name=speek, addr_freeform=銀座6-13-16, locality=中央区 id=024f6273-2a30-4f8e-85fc-1c0bb5338894, name=大田区大森町の個別指導塾 学習塾｜英才個別学院 大森町校, addr_freeform=大森西5-29-10, locality=大田区 id=02eb2153-e773-4f8e-a837-8eed7c04e12d, name=Anabuki College Japanese Tokushima, addr_freeform=2-20, locality=台東区 id=0310d475-4ce2-4ce5-993e-6a8f7a36b1ca, name=奥田 開業実践塾, addr_freeform=神田須田町1-8-3, locality=千代田区 id=0323c2d7-cae1-440e-96ab-e161d14d5045, name=橋口孝司・燻製料理とお酒の教室, addr_freeform=港区西麻布1-2-3 アクティブ六本木203, locality=港区 id=04cf8f56-b70a-4172-ba72-0a6229eae2da, name=Hakubikyotokimonogakuin Ikebukuro School, addr_freeform=Higashiikebukuro, 1 Chome−41−6 菊邑91ビル 6F, locality=豊島区 id=04dbc83d-c0e9-4ae8-b1e5-8ac13b99ce7c, name=渋谷の彫金教室 studio crucible | スタジオ クルーシブル, addr_freeform=東京都渋谷区渋谷１丁目１０−６, locality=渋谷区 id=05ad0db9-8086-43f3-93c6-6d115bb67698, name=スクールPTI, addr_freeform=吉祥寺南町1丁目27-1, locality=武蔵野市 id=05b1d280-23ee-45f2-92f2-6d6f0b029936, name=セルモ 東十条教室, addr_freeform=1 Chome-18-1 Higashijujo, locality=北区
1:08
Now I need to identify which categories correspond to schools relevant for children aged 8–14. This age range is roughly elementary school (ages 6-12 in Japan) and middle school (ages 12-15). So the relevant categories would be: - `elementary_school` (ages 6-12) - `middle_school` (ages 12-15) - `school` - this is a general category that could include various school types - `private_school` - could be private elementary/middle schools - `public_school` - could be public elementary/middle schools I should also consider `day_care_preschool` and `preschool` - those are for younger children (0-5 or 0-6), not relevant for 8-14. `high_school` is for ages 15-18, so not relevant. `vocational_and_technical_school`, `language_school`, `music_school`, `art_school`, `cooking_school`, `dance_school`, `driving_school`, `cosmetology_school`, `medical_school`, `specialty_school` - these are specialized schools, possibly for older students. I think the main ones for 8-14 would be: - `elementary_school` - `middle_school` - `school` (general) - `private_school` - `public_school` Let me look at the data more carefully to see what categories exist and what makes sense for 8-14 year olds. Actually, let me think more carefully. In Japan: - Elementary school (小学校) = ages 6-12 → category `elementary_school` - Middle school (中学校) = ages 12-15 → category `middle_school` - Both are compulsory education. So schools relevant for ages 8-14: elementary schools and middle schools. But `school`, `private_school`, `public_school` could also include elementary/middle schools. Let me check what kind of names/descriptions these have. Actually, the instruction says "every school relevant to that age range." So I should be inclusive. Let me include: - `elementary_school` - `middle_school` - `school` - `private_school` - `public_school` But I'll also need to intersect with the bbox geometry. Let me write the Python script.
1:08
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
df = pd.read_parquet('tokyo_places.parquet')
# Check counts for each school-related category
cats_list = ['elementary_school', 'middle_school', 'school', 'private_school', 'public_school',
'high_school', 'preschool', 'day_care_preschool', 'vocational_and_technical_school',
'language_school', 'music_school', 'art_school', 'specialty_school']
for c in cats_list:
mask = df['categories'].apply(lambda x: x is not None and x.get('primary') == c)
print(f'{c}: {mask.sum()}')
# Check some 'school' entries to see if they're typically for 8-14 age range
print()
print('=== Sample \"school\" entries (names) ===')
school_mask = df['categories'].apply(lambda x: x is not None and x.get('primary') == 'school')
for idx, row in df[school_mask].head(20).iterrows():
names = row['names']
addr = row['addresses']
print(f' name={names.get(\"primary\", \"?\")}')
print()
print('=== Sample \"private_school\" entries ===')
priv_mask = df['categories'].apply(lambda x: x is not None and x.get('primary') == 'private_school')
for idx, row in df[priv_mask].head(20).iterrows():
names = row['names']
print(f' name={names.get(\"primary\", \"?\")}')
print()
print('=== Sample \"public_school\" entries ===')
pub_mask = df['categories'].apply(lambda x: x is not None and x.get('primary') == 'public_school')
for idx, row in df[pub_mask].head(20).iterrows():
names = row['names']
print(f' name={names.get(\"primary\", \"?\")}')
"
elementary_school: 48 middle_school: 9 school: 1742 private_school: 8 public_school: 1 high_school: 27 preschool: 48 day_care_preschool: 7 vocational_and_technical_school: 4 language_school: 28 music_school: 19 art_school: 7 specialty_school: 26 === Sample "school" entries (names) === name=ちいさなピカソ（親と子の絵画・造形） name=speek name=大田区大森町の個別指導塾 学習塾｜英才個別学院 大森町校 name=Anabuki College Japanese Tokushima name=奥田 開業実践塾 name=橋口孝司・燻製料理とお酒の教室 name=Hakubikyotokimonogakuin Ikebukuro School name=渋谷の彫金教室 studio crucible | スタジオ クルーシブル name=スクールPTI name=セルモ 東十条教室 name=スタジオアロイ タイ料理教室 name=OES Academy 横浜校 name=桜坂トーストマスターズクラブ〜話し方で四季を彩る〜 name=元気日本語文化学校東京校 name=Yoji Sansuu School Spica name=日本大学 國學院大學 発祥記念碑 name=不動岡高校 name=誠心 name=学校法人日本女子大学 name=東京都立田園調布特別支援学校 === Sample "private_school" entries === name=St. Mary's International School name=田園調布学園中等部・高等部 name=横浜シュタイナー学園 name=佐鳴予備校東京総 name=ジーニアス name=The Montessori School of Tokyo name=PAL International School Hiroo name=桜蔭学園 === Sample "public_school" entries === name=小平三中
1:27
Let me think about what schools are relevant for children aged 8-14. This age range typically covers: - Elementary school (ages 6-12 in Japan, but age 8+ would be grades 3-6) - Middle school / junior high (ages 12-15, so ages 12-14 would be grades 1-3 of middle school) So the relevant school categories would be: - `elementary_school` (48 records) - relevant for ages 6-12, so 8-12 fits - `middle_school` (9 records) - relevant for ages 12-15, so 12-14 fits - `school` (1742 records) - this is a generic category, could include many types - `private_school` (8 records) - could be any level - `public_school` (1 record) - could be any level Also possibly: - `high_school` - not relevant (ages 15-18) - `preschool` / `day_care_preschool` - not relevant (too young) Let me look more carefully at the "school" category entries to see what kinds of schools they are. Some might be tutoring centers (塾, juku), language schools, etc. The age range 8-14 would cover elementary and middle school. I think the best approach is to filter for: 1. Primary categories that are clearly for this age range: `elementary_school`, `middle_school` 2. Also include `school` (generic) and `private_school`, `public_school` - since these could be schools for this age range 3. Exclude categories that are clearly not for 8-14: `preschool`, `day_care_preschool`, `high_school`, `vocational_and_technical_school`, `language_school`, `music_school`, `art_school`, `specialty_school`, `driving_school`, `cosmetology_school`, `cooking_school`, `medical_school`, `dance_school` Actually, let me think more carefully. The user says "every school relevant to that age range". The key school types for ages 8-14 in Japan: - Elementary schools (小学校) - ages 6-12, so includes 8-12 - Middle schools / junior high schools (中学校) - ages 12-15, so includes 12-14 Categories that clearly map: - `elementary_school` -> yes - `middle_school` -> yes - `school` -> could be (need to inspect) - `private_school` -> could be (need to inspect) - `public_school` -> could be (need to inspect) Let me look at more names to understand. Actually, I think the safest and most comprehensive approach is: 1. Include `elementary_school` and `middle_school` 2. Include `school` (generic) - many of these are juku/cram schools or other educational facilities that serve this age range 3. Include `private_school` and `public_school` as they could serve this age range 4. Exclude categories that are clearly for other age ranges or specializations Now let me also check what the geometry looks like and do the spatial filter.
1:27
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
df = pd.read_parquet('tokyo_places.parquet')
# Look at 'school' entries more - check for clues about age range
school_mask = df['categories'].apply(lambda x: x is not None and x.get('primary') == 'school')
school_df = df[school_mask]
# Check names for common patterns
names_list = school_df['names'].apply(lambda x: x.get('primary', '')).tolist()
# Check for keywords related to elementary/middle school
for n in names_list:
n_lower = n.lower()
if any(k in n_lower for k in ['小学', '中学', '小学生', '中学生', '小学校', '中学校', 'elementary', 'middle', 'junior']):
print(n)
"
都田小学校放課後キッズクラブ 彦成小学校 中学受験グノーブル 白金高輪校 法政大学中学高等学校 若杉小学校 西六郷小学校 大森東小学校 東京女学館中学校・高等学校 Waseda University Junior & Senior High School サピックス小学部用賀校 六木小学校 八成小学校 三輪田学園中学校・高等学校情報 桜丘中学校 EccJunior上宗岡2丁目教室 芝園中学校 中山中学校 学研 成増小学校前教室 蒲田中学校 最上町立満沢小学校 第二大島中学校 谷中小学校バス停 世田谷区立松沢中学校同窓会fbページ 大田区役所中学校大森東 菅生中学校 辰巳中学校 旧坂本小学校 第三小学校 練馬区光が丘第一中学校 豊島区率池袋第一小学校仮校舎 恩田小学校 布田小学校体育館 法政大学中学高等学校ブラスバンド会 (旧)区立第一日野小学校 池袋中学校グランド 芝中学校 上白根中学校 秦野市立鶴巻中学校 旧 今川中学校 跡地 板橋区立紅梅小学校 市場小学校放課後キッズクラブ 福栄中学校 東京都市大学 付属中学校・高等学校 笹野台小学校 三芳中学校 中央大学附属横浜中学校・高等学校 Siodina elementary school 中学受験算数教室＜アイデム＞（イデムリン先生の教室） 六戸中学校 安行中学校 代々木八幡・代々木公園駅徒歩3分 東京都渋谷区にある小学生対象のプログラミング教室 スモールトレイン 小学館アカデミー・パソコン倶楽部神保町 旧竜泉中学校 フィジー中学・高校留学のフリーバード 開智日本橋学園中学・高等学校 別館 中学受験 Logic 仙川 南小岩第二小学校 Teiko Middle School 公文書写喜沢中学校北教室 小岩第三中学校 多摩川小学校
2:00
2:00
Write
/work/solve.py
content · 87 lines · py
import geopandas as gpd
import pandas as pd
import json
from shapely.geometry import shape, Point, box
from shapely import wkb
# 1. Load the bbox polygon
bbox_gdf = gpd.read_file('/work/tokyo_23wards_bbox.geojson')
bbox_poly = bbox_gdf.geometry.iloc[0]
# 2. Load the parquet file
df = pd.read_parquet('/work/tokyo_places.parquet')
# 3. Filter to school-related categories relevant for ages 8-14
# In Japan: elementary school (ages 6-12), middle school (ages 12-15)
# So relevant: elementary_school, middle_school, school, private_school, public_school
# Also include high_school since 14-year-olds may be in 9th grade
relevant_categories = {
'elementary_school', 'middle_school', 'school',
'private_school', 'public_school', 'high_school'
}
def is_school_for_age(categories):
if categories is None:
return False
primary = categories.get('primary')
return primary in relevant_categories
school_mask = df['categories'].apply(is_school_for_age)
school_df = df[school_mask].copy()
print(f"Total school records (all categories): {len(school_df)}")
# 4. Decode WKB geometry and filter by bbox
def decode_wkb(geom_bytes):
return wkb.loads(geom_bytes)
geoms = school_df['geometry'].apply(decode_wkb)
points_within = geoms.apply(lambda pt: bbox_poly.contains(pt))
school_df = school_df[points_within].copy()
geoms = geoms[points_within]
print(f"Schools within bbox: {len(school_df)}")
# 5. Build the output features
features = []
for idx, row in school_df.iterrows():
pt = geoms.loc[idx]
names = row['names']
addrs = row['addresses']
# Get address info
if addrs and len(addrs) > 0:
addr = addrs[0]
freeform = addr.get('freeform', '')
locality = addr.get('locality', '')
postcode = addr.get('postcode', '')
else:
freeform = ''
locality = ''
postcode = ''
feature = {
'type': 'Feature',
'geometry': {
'type': 'Point',
'coordinates': [pt.x, pt.y]
},
'properties': {
'id': row['id'],
'name': names.get('primary', '') if names else '',
'confidence': row['confidence'],
'address_freeform': freeform,
'address_locality': locality,
'address_postcode': postcode
}
}
features.append(feature)
# 6. Write GeoJSON
fc = {
'type': 'FeatureCollection',
'features': features
}
with open('/work/tokyo_schools.geojson', 'w', encoding='utf-8') as f:
json.dump(fc, f, ensure_ascii=False, indent=2)
print(f"Written {len(features)} features to /work/tokyo_schools.geojson")Wrote /work/solve.py (2644 chars).
2:21
2:21
Bash
cd /work && python solve.py
Total school records (all categories): 1835 Schools within bbox: 1523 Written 1523 features to /work/tokyo_schools.geojson
2:54
Let me check the output file to make sure it's valid GeoJSON and looks correct.
2:54
2:54
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('tokyo_schools.geojson', 'r') as f:
data = json.load(f)
print('Type:', data['type'])
print('Number of features:', len(data['features']))
print()
# Show first 3 features
for feat in data['features'][:3]:
print('---')
print('Geometry:', json.dumps(feat['geometry']))
print('Properties:', json.dumps(feat['properties'], ensure_ascii=False))
print()
# Check property key names on the last one
print('Property keys:', list(data['features'][0]['properties'].keys()))
# Check a few categories show up
import pandas as pd
from shapely import wkb
df = pd.read_parquet('tokyo_places.parquet')
ids_in_geojson = {f['properties']['id'] for f in data['features']}
print('Unique IDs in output:', len(ids_in_geojson))
# Check that school categories are represented
relevant_categories = {'elementary_school', 'middle_school', 'school', 'private_school', 'public_school', 'high_school'}
def cat_primary(c):
return c.get('primary') if c else None
df_in = df[df['id'].isin(ids_in_geojson)]
cat_counts = df_in['categories'].apply(cat_primary).value_counts()
print()
print('Category breakdown:')
for cat, cnt in cat_counts.items():
if cat in relevant_categories:
print(f' {cat}: {cnt}')
"
Type: FeatureCollection
Number of features: 1523
---
Geometry: {"type": "Point", "coordinates": [139.73725, 35.6388]}
Properties: {"id": "0036772c-0c6a-4a84-a168-95f8f2097d0b", "name": "ちいさなピカソ（親と子の絵画・造形）", "confidence": 0.7336856722831726, "address_freeform": "高輪1-16-25", "address_locality": "港区", "address_postcode": "108-0074"}
---
Geometry: {"type": "Point", "coordinates": [139.76742580881, 35.675630313049]}
Properties: {"id": "014f234d-3bd8-49dc-b5c5-ddaa294accfa", "name": "speek", "confidence": 0.6116926670074463, "address_freeform": "銀座6-13-16", "address_locality": "中央区", "address_postcode": "104-0061"}
---
Geometry: {"type": "Point", "coordinates": [139.73486, 35.56782]}
Properties: {"id": "024f6273-2a30-4f8e-85fc-1c0bb5338894", "name": "大田区大森町の個別指導塾 学習塾｜英才個別学院 大森町校", "confidence": 0.7147371768951416, "address_freeform": "大森西5-29-10", "address_locality": "大田区", "address_postcode": "143-0015"}
Property keys: ['id', 'name', 'confidence', 'address_freeform', 'address_locality', 'address_postcode']
Unique IDs in output: 1523
Category breakdown:
school: 1456
elementary_school: 37
high_school: 17
private_school: 7
middle_school: 6
3:07
The output looks good. Let me verify the geometry was properly decoded - the first entry had coordinates [139.73725, 35.6388] which matches the bbox of that record, and the second had [139.76742580881, 35.675630313049] which looks like it came from the WKB decoded properly. Let me also double-check that all points are indeed within the bbox polygon.
3:07
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
import geopandas as gpd
from shapely.geometry import shape, Point
# Verify all points are inside the bbox
bbox_gdf = gpd.read_file('tokyo_23wards_bbox.geojson')
bbox_poly = bbox_gdf.geometry.iloc[0]
with open('tokyo_schools.geojson', 'r') as f:
data = json.load(f)
all_inside = True
for feat in data['features']:
pt = Point(feat['geometry']['coordinates'])
if not bbox_poly.contains(pt):
print(f'Point outside: {feat[\"properties\"][\"id\"]} at {feat[\"geometry\"][\"coordinates\"]}')
all_inside = False
print(f'All points inside bbox: {all_inside}')
print(f'Total features: {len(data[\"features\"])}')
"
All points inside bbox: True Total features: 1523
3:15
The output looks good. All 1523 school features are inside the bbox, have the correct property keys, and are written as GeoJSON. Let me verify once more by looking at the file.
3:15