Comparing XAI techniques for interpreting short-term burglary predictions at micro-places

Comparing XAI techniques for interpreting short-term burglary predictions at micro-places

Abstract This study empirically compares multiple eXplainable Artificial Intelligence (XAI) techniques to interpret short-term (weekly) machine learning-based burglary predictions at the micro-place level in Ghent, Belgium. While previous research predominantly relies on SHAP to interpret spatiotemp...

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Bibliographic Details
Main Authors: Robin Khalfa, Naomi Theinert, Wim Hardyns
Format: Article
Language:English
Published: Springer 2025-05-01
Series:Computational Urban Science
Subjects:
Crime prediction
Crime and place
Machine learning
Explainable AI
Micro-place
XGBoost
Online Access:https://doi.org/10.1007/s43762-025-00185-x
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