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1# -----------------------------------------------------------------------------2# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)3# Auteur : Simon-Pierre Boucher — contact@spboucher.ai4# quartier.py : statistiques de quartier par annonce (à la Centris, en libre)5# Base statique data/quartier.db construite par scripts/build_*.py :6# - da_poly / da_stats : aires de diffusion 2021 + profil du recensement7# - da_pmd : mesures de proximité StatCan (scores 0..1)8# - da_defav : défavorisation matérielle/sociale INSPQ (quintiles)9# - heat : classe d'îlot de chaleur/fraîcheur INSPQ par immeuble10# - crime_mtl / igc : actes criminels SPVM (points) + indice de gravité11# Jointure : lat/lng -> DAUID par point-dans-polygone local (préfiltre bbox),12# mémorisée dans listings.dauid à l'enrichissement (boucle watch).13# -----------------------------------------------------------------------------14from __future__ import annotations1516import json17import math18import sqlite319import time20from pathlib import Path2122from . import db2324QUARTIER_DB = Path(__file__).resolve().parent.parent / "data" / "quartier.db"2526# villes couvertes par les points SPVM (agglomération de Montréal)27_VILLES_SPVM = {"montreal", "montreal-est", "montreal-ouest", "westmount",28 "cote saint-luc", "cote-saint-luc", "hampstead", "mont-royal",29 "outremont", "verdun", "lasalle", "lachine", "anjou",30 "saint-leonard", "saint-laurent", "ahuntsic", "dorval",31 "pointe-claire", "kirkland", "beaconsfield", "dollard-des-ormeaux"}3233# correspondance ville -> fragment du nom de service dans la table igc34_IGC_SERVICE = {35 "quebec": "SPVQ", "levis": "Lévis", "montreal": "SPVM",36 "laval": "Laval", "longueuil": "Longueuil",37}383940def disponible() -> bool:41 return QUARTIER_DB.exists()424344def _connect() -> sqlite3.Connection:45 con = sqlite3.connect(f"file:{QUARTIER_DB}?mode=ro", uri=True)46 con.row_factory = sqlite3.Row47 return con484950# ---------------------------------------------------------------------------51# lat/lng -> DAUID (point dans polygone, préfiltre bbox)52# ---------------------------------------------------------------------------5354def _dans_anneau(lat: float, lng: float, anneau: list) -> bool:55 """Lancer de rayon (even-odd). anneau = [[lng, lat], ...]."""56 dedans = False57 n = len(anneau)58 j = n - 159 for i in range(n):60 xi, yi = anneau[i][0], anneau[i][1]61 xj, yj = anneau[j][0], anneau[j][1]62 if (yi > lat) != (yj > lat) and \63 lng < (xj - xi) * (lat - yi) / (yj - yi + 1e-12) + xi:64 dedans = not dedans65 j = i66 return dedans676869def dauid_for(qcon: sqlite3.Connection, lat: float, lng: float) -> str | None:70 rows = qcon.execute(71 "SELECT dauid, poly FROM da_poly WHERE lat_min<=? AND lat_max>=?"72 " AND lng_min<=? AND lng_max>=?", (lat, lat, lng, lng)).fetchall()73 for r in rows:74 anneaux = json.loads(r["poly"])75 # even-odd sur tous les anneaux (les trous annulent)76 compte = sum(1 for a in anneaux if _dans_anneau(lat, lng, a))77 if compte % 2 == 1:78 return r["dauid"]79 return None808182# ---------------------------------------------------------------------------83# Assemblage pour la fiche84# ---------------------------------------------------------------------------8586def _cle_ville(city: str) -> str:87 import unicodedata88 s = "".join(c for c in unicodedata.normalize("NFD", city or "")89 if unicodedata.category(c) != "Mn")90 return s.strip().lower()919293def _crime_mtl(qcon: sqlite3.Connection, lat: float, lng: float) -> dict | None:94 """Comptage des actes criminels SPVM à < 500 m : 12 mois vs 12 précédents."""95 dlat = 500 / 111000.096 dlng = 500 / (111000.0 * max(0.2, math.cos(math.radians(lat))))97 now = time.time()98 rows = qcon.execute(99 "SELECT lat, lng, ts, categorie FROM crime_mtl WHERE lat BETWEEN ? AND ?"100 " AND lng BETWEEN ? AND ? AND ts >= ?",101 (lat - dlat, lat + dlat, lng - dlng, lng + dlng, now - 730 * 86400)).fetchall()102 recent = avant = 0103 cats: dict[str, list[int]] = {} # categorie -> [12 mois, 12 prec.]104 for r in rows:105 # distance exacte (le bbox est un carré)106 d = math.hypot((r["lat"] - lat) * 111000.0,107 (r["lng"] - lng) * 111000.0 * math.cos(math.radians(lat)))108 if d > 500:109 continue110 c = cats.setdefault(r["categorie"] or "Autre", [0, 0])111 if r["ts"] >= now - 365 * 86400:112 recent += 1113 c[0] += 1114 else:115 avant += 1116 c[1] += 1117 if recent == 0 and avant == 0:118 return None119 categories = [{"nom": k, "n": v[0], "n_prec": v[1]}120 for k, v in sorted(cats.items(),121 key=lambda kv: -(kv[1][0] + kv[1][1]))]122 return {"type": "points", "rayon_m": 500, "douze_mois": recent,123 "douze_mois_precedents": avant, "categories": categories}124125126def _crime_igc(qcon: sqlite3.Connection, city: str) -> dict | None:127 service = _IGC_SERVICE.get(_cle_ville(city))128 if not service:129 return None130 row = qcon.execute(131 "SELECT annee, indice FROM igc WHERE service LIKE '%' || ? || '%'"132 " ORDER BY annee DESC LIMIT 1", (service,)).fetchone()133 if row is None or row["indice"] is None:134 return None135 ref = qcon.execute(136 "SELECT indice FROM igc WHERE service LIKE '%canada%' AND annee=?",137 (row["annee"],)).fetchone()138 return {"type": "igc", "ville": city, "annee": row["annee"],139 "indice": round(row["indice"], 1),140 "indice_canada": round(ref["indice"], 1) if ref and ref["indice"] else None}141142143def fiche_quartier(lat: float | None, lng: float | None, city: str,144 dauid: str | None = None) -> dict | None:145 """Bloc « Le quartier » d'une fiche. None si données indisponibles."""146 if not disponible() or lat is None or lng is None:147 return None148 qcon = _connect()149 try:150 if not dauid:151 dauid = dauid_for(qcon, lat, lng)152 out: dict = {"dauid": dauid}153154 if dauid:155 r = qcon.execute("SELECT * FROM da_stats WHERE dauid=?", (dauid,)).fetchone()156 if r:157 out["demographie"] = {k: r[k] for k in158 ("population", "densite", "age_median",159 "revenu_median", "pct_locataires",160 "loyer_moyen", "pct_francais", "pct_univ")}161 # rangs centiles québécois (0-100) — voir scripts/merge_quartier.py162 r = qcon.execute("SELECT * FROM da_pmd_pct WHERE dauid=?", (dauid,)).fetchone()163 if r:164 out["proximite"] = {k: r[k] / 100.0 for k in r.keys()165 if k != "dauid" and r[k] is not None}166 r = qcon.execute("SELECT quintile_materiel, quintile_social FROM da_defav"167 " WHERE dauid=?", (dauid,)).fetchone()168 if r:169 out["defavorisation"] = dict(r)170171 # îlot de chaleur : coordonnée exacte, sinon la plus proche (~120 m)172 key = f"{round(lat, 4)},{round(lng, 4)}"173 r = qcon.execute("SELECT classe, ecart FROM heat WHERE coord_key=?",174 (key,)).fetchone()175 if r is None:176 r = qcon.execute(177 "SELECT classe, ecart FROM heat WHERE coord_key LIKE ?"178 " AND classe IS NOT NULL LIMIT 1",179 (f"{round(lat, 3)}%",)).fetchone()180 if r and r["classe"] is not None:181 out["chaleur"] = {"classe": r["classe"], "ecart": r["ecart"]}182183 # criminalité : points SPVM sur l'île, indice IGC ailleurs184 crime = None185 if _cle_ville(city) in _VILLES_SPVM:186 crime = _crime_mtl(qcon, lat, lng)187 if crime is None:188 crime = _crime_igc(qcon, city)189 if crime:190 out["crime"] = crime191192 return out if len(out) > 1 else None193 except sqlite3.Error:194 return None195 finally:196 qcon.close()197198199# ---------------------------------------------------------------------------200# Enrichissement : mémoriser le DAUID de chaque annonce (boucle watch)201# ---------------------------------------------------------------------------202203def enrich(limit: int | None = None) -> dict:204 """Remplit listings.dauid pour les annonces géolocalisées qui ne l'ont pas."""205 if not disponible():206 print("[immo-ka] quartier: data/quartier.db absent — étape sautée")207 return {"enriched": 0, "missing_db": True}208 con = db.connect()209 qcon = _connect()210 rows = con.execute(211 "SELECT uid, lat, lng FROM listings WHERE active=1 AND lat IS NOT NULL"212 " AND (dauid IS NULL OR dauid='')").fetchall()213 if limit is not None:214 rows = rows[:limit]215 done = introuvable = 0216 for r in rows:217 d = dauid_for(qcon, r["lat"], r["lng"])218 con.execute("UPDATE listings SET dauid=? WHERE uid=?",219 (d or "hors-zone", r["uid"]))220 if d:221 done += 1222 else:223 introuvable += 1224 con.commit()225 qcon.close()226 con.close()227 stats = {"enriched": done, "hors_zone": introuvable, "candidats": len(rows)}228 print(f"[immo-ka] quartier {stats}")229 return stats230