Modeling and Analysis of Crime Maps in Salahuddin Governorate and Their Prediction Using Geo-Artificial Intelligence Techniques
DOI:
https://doi.org/10.25130/jfa.conf.10.1.2Keywords:
Geo-Artificial Intelligence, Cartographic Prediction, CrimeAbstract
The study aims to develop cartographic models to analyze and interpret the spatiotemporal patterns of crime (homicide, theft) and to predict their occurrence in Salah al-Din Governorate during the period 2015–2024. Spatial analysis tools within a Geographic Information Systems (GIS) environment were employed, including Time Series Clustering (Machine Learning), Emerging Hot Spot Analysis, and Forecast Space-Time Cube. Artificial Intelligence (AI) algorithms were used to construct accurate predictive models for the future distribution of crime by transforming criminal data into spatiotemporal cubes and subsequently forecasting changes in crime hotspots. This study provides a comprehensive scientific perspective for understanding crime dynamics and contributes to supporting security agencies in the study area by providing analytical and predictive maps that can inform the development of effective security policies based on precise scientific methods for the spatiotemporal monitoring and prediction of criminal activities..
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- 2025-12-09 (2)
- 2026-02-14 (1)
