"""Generate synthetic noise pollution & urban health dataset for SSA. Research-based parameterization: - WHO (2024): Environmental noise causes cardiovascular, mental health, sleep disturbance; updated disability weights for noise exposure. - WHO guidelines: 53 dB Lden road traffic; 45 dB Lnight for sleep. - Nature (2022): Traffic noise quantified in South African cities. - PMC11221953: Nigerian noise levels and health perceptions - annoyance, mental stress, sleep disturbance, hearing loss, cardiovascular effects. - IntechOpen (2022): Noise policy challenges in developing countries; most lack enforceable legislation. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd SEED = 42 N_PER_SCENARIO = 10_000 YEAR_RANGE = np.arange(2010, 2025) YEAR_WEIGHTS = np.linspace(0.85, 1.3, len(YEAR_RANGE)) YEAR_WEIGHTS = YEAR_WEIGHTS / YEAR_WEIGHTS.sum() SCENARIOS = { "megacity_traffic": { "setting_probs": {"commercial_road": 0.35, "residential_road": 0.30, "industrial": 0.15, "market": 0.20}, "noise_source_probs": {"road_traffic": 0.40, "generators": 0.20, "industry": 0.15, "entertainment": 0.10, "construction": 0.10, "religious": 0.05}, "lden_mean": 72, "lden_sd": 10, "lnight_mean": 58, "lnight_sd": 8, "hearing_loss_prev": 0.12, "hypertension_noise_pct": 0.08, "sleep_disturbance_pct": 0.35, "noise_regulation": 0.15, }, "secondary_city_mixed": { "setting_probs": {"commercial_road": 0.30, "residential": 0.35, "market": 0.20, "industrial": 0.15}, "noise_source_probs": {"road_traffic": 0.30, "generators": 0.25, "entertainment": 0.15, "religious": 0.10, "construction": 0.10, "industry": 0.10}, "lden_mean": 65, "lden_sd": 10, "lnight_mean": 52, "lnight_sd": 8, "hearing_loss_prev": 0.08, "hypertension_noise_pct": 0.05, "sleep_disturbance_pct": 0.25, "noise_regulation": 0.08, }, "periurban_emerging": { "setting_probs": {"residential": 0.40, "market": 0.25, "peri_urban_road": 0.20, "industrial_fringe": 0.15}, "noise_source_probs": {"road_traffic": 0.25, "generators": 0.20, "entertainment": 0.15, "religious": 0.15, "agriculture": 0.10, "construction": 0.15}, "lden_mean": 58, "lden_sd": 10, "lnight_mean": 45, "lnight_sd": 8, "hearing_loss_prev": 0.05, "hypertension_noise_pct": 0.03, "sleep_disturbance_pct": 0.15, "noise_regulation": 0.05, }, } SCENARIO_FILES = { "megacity_traffic": "noise_megacity.csv", "secondary_city_mixed": "noise_secondary_city.csv", "periurban_emerging": "noise_periurban.csv", } def _choice(rng, prob_map): keys = list(prob_map.keys()) weights = np.array(list(prob_map.values()), dtype=float) weights = weights / weights.sum() return rng.choice(keys, p=weights) def _simulate_scenario(name, params, seed): rng = np.random.default_rng(seed) records = [] for idx in range(N_PER_SCENARIO): year = int(rng.choice(YEAR_RANGE, p=YEAR_WEIGHTS)) setting = _choice(rng, params["setting_probs"]) age = int(np.clip(rng.normal(32, 15), 5, 75)) sex = rng.choice(["male", "female"], p=[0.50, 0.50]) is_child = int(age < 15) occupation = rng.choice(["outdoor_worker", "indoor_worker", "student", "homemaker", "vendor"], p=[0.25, 0.20, 0.20, 0.20, 0.15]) noise_source = _choice(rng, params["noise_source_probs"]) distance_to_source_m = float(np.clip(rng.exponential(50), 5, 500)) proximity_factor = max(0.5, 1.0 - distance_to_source_m / 200) lden = float(np.clip( rng.normal(params["lden_mean"] * proximity_factor, params["lden_sd"]), 35, 110)) lnight = float(np.clip( rng.normal(params["lnight_mean"] * proximity_factor, params["lnight_sd"]), 25, 90)) exceeds_who_lden = int(lden > 53) exceeds_who_lnight = int(lnight > 45) exceeds_85db = int(lden > 85) exposure_years = int(np.clip(rng.normal(8, 5), 0, 30)) daily_exposure_hours = float(np.clip(rng.normal(8, 3), 1, 16)) uses_hearing_protection = int(rng.random() < 0.05) risk_mult = lden / 53 hearing_loss = int(rng.random() < np.clip( params["hearing_loss_prev"] * risk_mult * (exposure_years / 10), 0, 0.35)) tinnitus = int(rng.random() < np.clip(0.05 + risk_mult * 0.04, 0, 0.25)) hypertension = int(age >= 25 and rng.random() < np.clip( params["hypertension_noise_pct"] * risk_mult, 0, 0.20)) cardiovascular = int(age >= 35 and hypertension and rng.random() < 0.15) sleep_disturbance = int(rng.random() < np.clip( params["sleep_disturbance_pct"] * (lnight / 45), 0, 0.60)) annoyance = int(rng.random() < np.clip(0.20 + risk_mult * 0.15, 0, 0.70)) stress_anxiety = int(rng.random() < np.clip(0.10 + risk_mult * 0.08, 0, 0.35)) concentration_difficulty = int(rng.random() < np.clip(0.08 + risk_mult * 0.06, 0, 0.30)) child_learning = int(is_child and rng.random() < np.clip(risk_mult * 0.08, 0, 0.20)) noise_complaint = int(annoyance and rng.random() < 0.10) noise_regulation = int(rng.random() < params["noise_regulation"]) noise_monitoring = int(rng.random() < 0.05) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "setting": setting, "age": age, "sex": sex, "is_child": is_child, "occupation": occupation, "noise_source": noise_source, "distance_to_source_m": round(distance_to_source_m, 0), "lden_db": round(lden, 1), "lnight_db": round(lnight, 1), "exceeds_who_lden_53": exceeds_who_lden, "exceeds_who_lnight_45": exceeds_who_lnight, "exceeds_85db": exceeds_85db, "exposure_years": exposure_years, "daily_exposure_hours": round(daily_exposure_hours, 1), "uses_hearing_protection": uses_hearing_protection, "hearing_loss": hearing_loss, "tinnitus": tinnitus, "hypertension": hypertension, "cardiovascular": cardiovascular, "sleep_disturbance": sleep_disturbance, "annoyance": annoyance, "stress_anxiety": stress_anxiety, "concentration_difficulty": concentration_difficulty, "child_learning": child_learning, "noise_complaint": noise_complaint, "noise_regulation": noise_regulation, "noise_monitoring": noise_monitoring, } records.append(record) return pd.DataFrame(records) def main(): output_dir = Path("data") output_dir.mkdir(parents=True, exist_ok=True) for idx, (name, params) in enumerate(SCENARIOS.items()): df = _simulate_scenario(name, params, SEED + idx * 211) df.to_csv(output_dir / SCENARIO_FILES[name], index=False) print(f"Saved {name} -> {SCENARIO_FILES[name]}") if __name__ == "__main__": main()