Datasets:
File size: 3,300 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 | """Validate synthetic noise pollution & urban health dataset."""
from __future__ import annotations
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
SCENARIO_FILES = {
"megacity_traffic": "noise_megacity.csv",
"secondary_city_mixed": "noise_secondary_city.csv",
"periurban_emerging": "noise_periurban.csv",
}
COLORS = {"megacity_traffic": "#e6550d", "secondary_city_mixed": "#756bb1", "periurban_emerging": "#31a354"}
def load_data() -> pd.DataFrame:
frames = []
for scenario, filename in SCENARIO_FILES.items():
df = pd.read_csv(Path("data") / filename)
frames.append(df)
return pd.concat(frames, ignore_index=True)
def plot_validation(df: pd.DataFrame, output_path: Path) -> None:
fig, axes = plt.subplots(4, 2, figsize=(14, 16))
axes = axes.flatten()
for s in SCENARIO_FILES:
subset = df[df["scenario"] == s]
axes[0].hist(subset["lden_db"], bins=40, alpha=0.5, color=COLORS[s], label=s)
axes[0].axvline(53, color="red", ls="--", lw=1, label="WHO 53 dB")
axes[0].set_title("Lden Noise Distribution (dB)")
axes[0].legend(fontsize=6)
exc_cols = ["exceeds_who_lden_53", "exceeds_who_lnight_45", "exceeds_85db"]
exc = df.groupby("scenario")[exc_cols].mean() * 100
exc.plot(kind="bar", ax=axes[1])
axes[1].set_title("WHO Guideline Exceedance (%)")
axes[1].legend(fontsize=7)
health_cols = ["hearing_loss", "tinnitus", "hypertension", "cardiovascular"]
health = df.groupby("scenario")[health_cols].mean() * 100
health.plot(kind="bar", ax=axes[2])
axes[2].set_title("Physical Health Outcomes (%)")
axes[2].legend(fontsize=7)
mental_cols = ["sleep_disturbance", "annoyance", "stress_anxiety", "concentration_difficulty"]
mental = df.groupby("scenario")[mental_cols].mean() * 100
mental.plot(kind="bar", ax=axes[3])
axes[3].set_title("Mental Health & Wellbeing (%)")
axes[3].legend(fontsize=6)
src = df.groupby(["scenario", "noise_source"]).size().groupby(level=0).apply(lambda s: s / s.sum())
src.unstack().plot(kind="bar", stacked=True, ax=axes[4])
axes[4].set_title("Noise Source Distribution")
axes[4].legend(fontsize=5)
for s in SCENARIO_FILES:
subset = df[df["scenario"] == s]
axes[5].scatter(subset["lden_db"], subset["hearing_loss"],
s=4, alpha=0.05, color=COLORS[s], label=s)
axes[5].set_title("Lden vs Hearing Loss")
axes[5].legend(fontsize=7)
reg_cols = ["noise_regulation", "noise_monitoring", "uses_hearing_protection", "noise_complaint"]
reg = df.groupby("scenario")[reg_cols].mean() * 100
reg.plot(kind="bar", ax=axes[6])
axes[6].set_title("Regulation & Protection (%)")
axes[6].legend(fontsize=6)
child = df[df["is_child"] == 1]
if len(child) > 0:
cl = child.groupby("scenario")["child_learning"].mean() * 100
cl.plot(kind="bar", ax=axes[7], color=[COLORS[s] for s in cl.index])
axes[7].set_title("Child Learning Impairment (%)")
plt.tight_layout()
fig.savefig(output_path, dpi=200)
plt.close(fig)
def main() -> None:
df = load_data()
plot_validation(df, Path("validation_report.png"))
print("Saved validation_report.png")
if __name__ == "__main__":
main()
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