moveq-core
Pure NumPy algorithms for measuring transport equity.
- Population-weighted Gini coefficient via numerical Lorenz curve integration
- Palma ratio (top 10% highest-service vs bottom 40% lowest-service population)
- Wagstaff Concentration Index via fractional-rank weighted covariance
- Weighted composite scoring with dynamic missing-term renormalization
No I/O, no database, no framework lock-in: pass NumPy arrays, get numbers back.
Most applications should install the umbrella package instead:
pip install moveq
Installation
Requires Python 3.10+.
pip install moveq-core
Pandas DataFrame helpers:
pip install "moveq-core[frames]"
Quickstart
import numpy as np
from moveq_core import (
compute_gini,
compute_palma_ratio,
compute_concentration_index,
compute_score,
)
service = np.array([10.0, 20.0, 5.0, 50.0, 8.0])
population = np.array([1000, 800, 1200, 300, 900])
deprivation_rank = np.array([1, 3, 2, 5, 4]) # 1 = most deprived
gini = compute_gini(service, population)
palma = compute_palma_ratio(service, population)
ci = compute_concentration_index(service, deprivation_rank, population)
score_res = compute_score(
terms={"coverage": 0.75, "evening": 0.50, "night": None},
weights={"coverage": 0.50, "evening": 0.30, "night": 0.20},
)
print(f"Score: {score_res.score:.1f} ({score_res.note})")
DataFrame helpers (moveq_core.frames)
import pandas as pd
from moveq_core.frames import compute_vulnerability_index, identify_multiply_deprived
df = pd.DataFrame({
"unemployment_pct": [5.0, 12.0, 20.0],
"no_car_pct": [10.0, 30.0, 60.0],
"elderly_pct": [15.0, 25.0, 35.0],
})
vulnerability = compute_vulnerability_index(
df, ["unemployment_pct", "no_car_pct", "elderly_pct"]
)
flagged = identify_multiply_deprived(
df, ["unemployment_pct", "no_car_pct", "elderly_pct"], min_factors=2
)
Documentation
License
1 maintainer
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