py-geodetector 0.2.1

A simple Python package for the geodetector

py-geodetector

A simple and efficient Python package for the Geographical Detector (GeoDetector).

Features

• Four Detectors: Factor, Interaction, Risk, and Ecological detectors.
• High Performance: Vectorized calculations using Pandas and NumPy for large-scale datasets.
• Flexible API: Supports both batch processing and interactive exploratory analysis.
• Auto Detection: Automatically identifies discrete/categorical variables as factors.
• Visualization: Built-in heatmap for interaction results with statistical significance markers.

Install

pip install py-geodetector

Usage

1. Data Preparation

• Format: pandas DataFrame.
• Y (Dependent Variable): Numerical/Continuous.
• X (Independent Variable): Categorical/Discrete. If your $X$ is continuous, you must discretize it first (e.g., using pd.qcut or Jenks natural breaks).

2. Quick Start

from geodetector import load_example_data, GeoDetector

# Load example disease dataset
df = load_example_data()

# Initialize: Automatically detects discrete columns as factors if 'factors' is not provided
gd = GeoDetector(df, y='incidence')
print(f"Detected factors: {gd.factors}")

# 1. Factor Detector
# Batch detection for all factors
factor_df = gd.factor_detector()
# Single factor detection: returns (q_value, p_value)
q, p = gd.factor_detector('type')

# 2. Interaction Detector
# Full matrix calculation
interaction_df = gd.interaction_detector()
# Pairwise detection with relationship description
q_inter, relationship = gd.interaction_detector('type', 'region', relationship=True)

# 3. Ecological Detector
# Determine if the impact of two factors are significantly different
eco_df = gd.ecological_detector()

# 4. Risk Detector
# Compare average Y between sub-groups of a factor
risk_result = gd.risk_detector('type')
print(risk_result['risk']) # Mean values for each stratum

# 5. Visualization
# Plot interaction heatmap (red markers indicate significant ecological difference)
gd.plot(factors=['type', 'region', 'level'])

3. Visualization Result

The plot() method generates a heatmap of the interaction $q$-statistics. Red text indicates that the ecological detector shows a significant difference ($p < 0.05$) between those factors.

References

@article{wang2010geographical,
  title={Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China},
  author={Wang, Jin-Feng and Li, Xin-Hu and Christakos, George and Liao, Yi-Lan and Zhang, Tin and Gu, Xue and Zheng, Xiao-Ying},
  journal={International Journal of Geographical Information Science},
  volume={24},
  number={1},
  pages={107-127},
  year={2010},
  publisher={Taylor \& Francis}
}