Datasets:
File size: 7,409 Bytes
8d77690 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | """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()
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