geoai2analytics-sdk

Pure-Python Spatial Statistics, Econometrics, and Explainable GeoAI Engine.

📖 Open Interactive Web Manual (GitHub Pages) • 📦 PyPI Package • 🐛 Issue Tracker

🌟 Overview

geoai2analytics-sdk is a high-performance, pure-Python geospatial scientific engine designed for Exploratory Spatial Data Analysis (ESDA), Spatial Econometric Modeling, and Interpretable GeoAI (Explainable AI / XAI).

Built entirely with standard scientific foundations (NumPy, SciPy), it executes advanced spatial autocorrelation, multiscale local regressions, and spatial machine learning attributions completely headless—ideal for Jupyter Notebooks, automated pipelines, GeoPandas workflows, and cloud-native spatial analytics.

🚀 Key Features & Capabilities

1. Spatial Autocorrelation & Clustering (ESDA)

• Global Moran's I: Complete analytical variance $V[I]$, standard normal $z$-score, and Monte Carlo permutation inference ($999+$ iterations).
• Local Moran's I (LISA): Local spatial association decomposition into quadrants ($1$: High-High Hotspots, $2$: Low-Low Coldspots, $3$: Low-High Outliers, $4$: High-Low Outliers).
• Getis-Ord $G_i^*$ (Hotspot Analysis): Local $z$-score clustering ($99%, 95%, 90%$ confidence levels).
• Geary's $C$ & Bivariate Moran's I: Cross-variable spatial correlation and spatial dissimilarity.
• Spatial Gini Inequality: Rey's spatial concentration decomposition.

2. Spatial Econometrics & Local Regressions

• Geographically Weighted Regression (GWR): Golden-section search bandwidth optimization, Gaussian/Bisquare/Exponential spatial kernels, local parameter estimates $\hat{\beta}_i$, local $t$-statistics, local $R_i^2$, and Hurvich AICc.
• Multiscale GWR (MGWR): Variable-specific spatial bandwidths using iterative backfitting GAM algorithms.
• Spatial Autoregressive Models (SAR / Spatial Lag): 2-Stage Least Squares (2SLS) estimation of spatial autoregressive parameter $\rho$.

3. Explainable GeoAI (XAI) & Spatial Machine Learning

• Spatial SHAP (Shapley Additive Explanations): Model-agnostic Shapley feature attributions with continuous 2D geographic coordinate map decomposition.
• Spatial Cross-Validation (Spatial CV): Spatial $K$-Fold coordinate clustering to eliminate spatial autocorrelation data leakage.
• Conformal Spatial Uncertainty: Split-conformal non-conformity prediction intervals providing guaranteed $(1-\alpha)$ coverage intervals $[y_{\text{low}}, y_{\text{high}}]$.

💻 Installation

pip install geoai2analytics-sdk

⚡ Quickstart Example

import numpy as np
import geoai2analytics as geoai

# 1. Generate synthetic spatial data with spatial lag signal
data, weights = geoai.generate_synthetic_spatial_dataset(n=120)

# 2. Test Global Spatial Autocorrelation
moran = geoai.global_moran(data["y"], weights, permutations=999)
print(f"Moran's I: {moran.I:.4f} | z-score: {moran.z_score:.2f} | p-value: {moran.p_sim:.4f}")

# 3. Detect Local Hotspots (LISA)
lisa = geoai.local_moran(data["y"], weights)
print(f"High-High Hotspots: {lisa.high_high_count} | Low-Low Coldspots: {lisa.low_low_count}")

# 4. Geographically Weighted Regression (GWR)
coords = np.column_stack([data["x_coord"], data["y_coord"]])
X = np.column_stack([data["X1"], data["X2"]])

gwr = geoai.GWR(coords, data["y"], X, kernel="bisquare", adaptive=True)
res = gwr.fit()
print(f"Optimal Bandwidth: {res.bandwidth} | Global R²: {res.global_r2:.3f} | AICc: {res.aicc:.1f}")

📄 License & Academic Citation

Distributed under the MIT License.

@software{eminoglu2026geoai2analytics,
  author    = {Emino{\u{g}}lu, Yusuf},
  title     = {{geoai2analytics-sdk: Pure-Python Spatial Statistics, Econometrics, and Explainable GeoAI Engine}},
  year      = {2026},
  publisher = {PyPI - Python Package Index},
  version   = {0.1.0},
  url       = {https://github.com/YusufEminoglu/geoai2analytics-sdk}
}