spatial-cluster-helper

A helper package for analyzing and visualizing spatial clusters, designed to facilitate reproducibility and ease of use in academic and applied research contexts. Developed in support of Anselin (2024).

📦 Features

Cluster Analysis & Validation

• Cluster Statistics: Generate labels/cardinality summaries (cluster_stats)
• Fit Metrics: TSS, WSS, BSS, and cluster quality ratios (cluster_fit)
• Stress Evaluation: Raw/normalized stress values (stress_value)
• Optimal Clusters: Elbow plot visualization for K-means (elbow_plot)
• Silhouette Analysis: Observation-level silhouette scores (plot_silhouette)

Spatial Analysis Tools

• Validation Indices:
  • Fragmentation (entropy/Simpson) (cluster_fragmentation)
  • Spatial autocorrelation (Join Count Ratios) (cluster_joincount)
  • Compactness & diameter metrics (cluster_compactness, cluster_diameter)
• Neighborhood Overlap: KNN coverage comparison (common_coverage)

Visualization

• Cluster Maps: Geographic cluster visualization (cluster_map)
• Dendrograms: Hierarchical clustering trees (plot_dendrogram)
• Scatter Plots: 2D cluster visualization (plot_scatter)

Utilities

• Cluster Centers: Mean/median descriptors (cluster_center)
• Data Management: Automated example datasets handling (ensure_datasets)

🚀 Installation

You can install the package from pypi:

pip install spatial-cluster-helper

🗂️ Usage

You can check several usage examples in the lab materials developed for the Spatial Cluster Analysis course taught at the University of Chicago in the Winter of 2025 here.

📄 License

This project is licensed under the MIT License.

Developed at The Center for Spatial Data Science at the University of Chicago by Luc Anselin and Pedro Amaral