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services/external/app/core/poi_config.py
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services/external/app/core/poi_config.py
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"""
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POI Detection Configuration
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Defines POI categories, search parameters, and relevance thresholds
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for automated Point of Interest detection and feature engineering.
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"""
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from dataclasses import dataclass
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from typing import Dict
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@dataclass
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class POICategory:
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"""POI category definition with OSM query and ML parameters"""
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name: str
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osm_query: str
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search_radius_m: int
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weight: float # Importance weight for ML model (positive or negative)
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description: str
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# POI Category Definitions based on OpenStreetMap tags
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# Research-based search radii and weights for bakery demand forecasting
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POI_CATEGORIES: Dict[str, POICategory] = {
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"schools": POICategory(
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name="schools",
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osm_query='["amenity"~"school|kindergarten|university|college"]',
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search_radius_m=500,
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weight=1.5, # High positive impact - morning drop-off rush
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description="Educational institutions causing morning/afternoon rush patterns"
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),
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"offices": POICategory(
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name="offices",
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osm_query='["office"]',
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search_radius_m=800,
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weight=1.3, # Positive impact - weekday lunch/breakfast demand
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description="Office buildings and business centers"
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),
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"gyms_sports": POICategory(
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name="gyms_sports",
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osm_query='["leisure"~"fitness_centre|sports_centre|stadium"]',
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search_radius_m=600,
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weight=0.8, # Moderate impact - morning/evening activity
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description="Fitness centers and sports facilities"
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),
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"residential": POICategory(
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name="residential",
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osm_query='["building"~"residential|apartments|house"]',
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search_radius_m=400,
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weight=1.0, # Base demand from residents
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description="Residential buildings and housing"
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),
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"tourism": POICategory(
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name="tourism",
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osm_query='["tourism"~"attraction|museum|hotel|hostel|guest_house"]',
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search_radius_m=1000,
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weight=1.2, # Positive impact - tourist foot traffic
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description="Tourist attractions, hotels, and points of interest"
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),
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"competitors": POICategory(
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name="competitors",
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osm_query='["shop"~"bakery|pastry|confectionery"]',
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search_radius_m=1000,
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weight=-0.5, # Negative impact - competition pressure
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description="Competing bakeries and pastry shops"
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),
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"transport_hubs": POICategory(
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name="transport_hubs",
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osm_query='["public_transport"~"station|stop"]["railway"~"station|subway_entrance|tram_stop"]',
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search_radius_m=800,
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weight=1.4, # High impact - commuter foot traffic
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description="Public transport stations and hubs"
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),
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"coworking": POICategory(
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name="coworking",
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osm_query='["amenity"="coworking_space"]',
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search_radius_m=600,
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weight=1.1, # Moderate-high impact - flexible workers
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description="Coworking spaces and shared offices"
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),
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"retail": POICategory(
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name="retail",
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osm_query='["shop"]',
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search_radius_m=500,
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weight=0.9, # Moderate impact - general foot traffic
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description="Retail shops and commercial areas"
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)
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}
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# Feature Relevance Thresholds
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# Determines which POI features are significant enough to include in ML models
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# Based on retail gravity model research and distance decay patterns
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RELEVANCE_THRESHOLDS: Dict[str, Dict[str, float]] = {
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"schools": {
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"min_proximity_score": 0.5, # At least moderate proximity required
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"max_distance_to_nearest_m": 500, # Must be within 500m
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"min_count": 1 # At least 1 school
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},
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"offices": {
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"min_proximity_score": 0.3,
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"max_distance_to_nearest_m": 800,
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"min_count": 2 # Offices are common; need multiple for impact
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},
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"gyms_sports": {
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"min_proximity_score": 0.4,
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"max_distance_to_nearest_m": 600,
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"min_count": 1
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},
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"residential": {
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"min_proximity_score": 1.0, # High threshold; residential is everywhere in cities
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"max_distance_to_nearest_m": 400,
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"min_count": 5 # Need significant residential density
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},
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"tourism": {
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"min_proximity_score": 0.2, # Lower threshold; tourism is high-impact even at distance
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"max_distance_to_nearest_m": 1000,
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"min_count": 1
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},
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"competitors": {
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"min_proximity_score": 0.1, # Any competition is relevant (even distant)
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"max_distance_to_nearest_m": 1000,
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"min_count": 1
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},
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"transport_hubs": {
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"min_proximity_score": 0.4,
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"max_distance_to_nearest_m": 800,
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"min_count": 1
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},
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"coworking": {
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"min_proximity_score": 0.3,
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"max_distance_to_nearest_m": 600,
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"min_count": 1
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},
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"retail": {
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"min_proximity_score": 0.8, # Retail is common; higher bar for relevance
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"max_distance_to_nearest_m": 500,
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"min_count": 3
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}
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}
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# Overpass API Configuration
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OVERPASS_API_URL = "https://overpass-api.de/api/interpreter"
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OVERPASS_TIMEOUT_SECONDS = 30
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OVERPASS_MAX_RETRIES = 4 # Increased from 3 to 4 for better resilience
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OVERPASS_RETRY_DELAY_SECONDS = 2 # Base delay (will use exponential backoff)
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# POI Cache Configuration
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POI_CACHE_TTL_DAYS = 90 # Cache POI results for 90 days
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POI_REFRESH_INTERVAL_DAYS = 180 # Refresh every 6 months
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POI_COORDINATE_PRECISION = 4 # Decimal places for cache key (≈10m precision)
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# Distance Bands for Feature Engineering (meters)
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DISTANCE_BANDS = [
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(0, 100), # Immediate proximity
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(100, 300), # Primary catchment (walking distance)
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(300, 500), # Secondary catchment
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(500, 1000) # Tertiary catchment
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]
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# Competitive Pressure Zones
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COMPETITOR_ZONES = {
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"direct": {
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"max_distance_m": 100,
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"pressure_multiplier": -1.0 # Strong negative impact
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},
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"nearby": {
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"max_distance_m": 500,
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"pressure_multiplier": -0.5 # Moderate negative impact
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},
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"market": {
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"max_distance_m": 1000,
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"min_count_for_district": 5, # If 5+ bakeries = bakery district
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"district_multiplier": 0.3, # Positive impact (destination area)
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"normal_multiplier": -0.2 # Slight negative (competitive market)
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}
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}
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