stsckm

stsckm is a Python implementation of spatio-temporal spatially constrained K-means (ST-SCKM) for point-event data. It combines weighted spatial and temporal squared distances with a soft label-disagreement penalty on a spatial K-nearest-neighbor graph.

The package implements the method associated with:

Idris, M. A., Aidi, M. N., and Djuraidah, A. (2026). Performance Evaluation of ST-DBSCAN and Spatio-Temporal Spatially Constrained K-Means (ST-SCKM) for Wildfire Risk Zoning and Resilience Analysis. Journal of Safety Science and Resilience. https://doi.org/10.1016/j.jnlssr.2026.100357

Scope

The estimator is intended for aligned spatial and temporal numeric matrices. The KNN penalty encourages neighboring observations to share labels but does not guarantee that every cluster is a single connected component. Context variables such as event intensity can be retained for post hoc profiling without entering the clustering objective.

Installation

Install the published package from PyPI:

python -m pip install stsckm

Optional plotting support can be installed with:

python -m pip install "stsckm[plot]"

For development, testing, and documentation from a source checkout:

python -m pip install -e ".[dev]"

Quick start

import numpy as np

from stsckm import (
    STSCKM,
    add_default_features,
    evaluate_labels,
    load_sample_wildfire,
    standardize_features,
)

frame = add_default_features(load_sample_wildfire())
X_spatial, spatial_scaler = standardize_features(
    frame, ["x_proj", "y_proj"]
)
X_temporal, temporal_scaler = standardize_features(
    frame, ["time_days"]
)

model = STSCKM(
    n_clusters=4,
    spatial_weight=0.5,
    temporal_weight=1.5,
    lambda_spatial=1.0,
    n_neighbors=5,
    random_state=42,
)
labels = model.fit_predict(X_spatial, X_temporal)

metrics = evaluate_labels(
    np.column_stack([X_spatial, X_temporal]),
    labels,
)
print(model.n_iter_)
print(metrics)

Main API

• STSCKM.fit(X_spatial, X_temporal)
• STSCKM.fit_predict(X_spatial, X_temporal)
• weighted_spatiotemporal_distance(...)
• knn_indices(...)
• evaluate_labels(...)
• neighbor_disagreement(...)
• assign_risk_labels(...)
• load_sample_wildfire()

The estimator follows scikit-learn parameter conventions and inherits from BaseEstimator and ClusterMixin. The current release intentionally does not expose out-of-sample predict(): defining neighbors for new points requires a separate modeling choice.

Reproduction

Run all manuscript-scale numerical results and figures with one command:

python replication/run_all.py

The script writes its outputs to replication/output/ and checks the sensitivity table against replication/expected/sensitivity.csv.

Tests and build

python -m pytest
python -m build
python -m twine check dist/*

License

MIT. This is a GPL-compatible free-software license.

Author

Muh Akbar Idris
IPB University
ORCID: https://orcid.org/0009-0000-2995-1975