A comparative analysis of Deep Learning Models for Spatio-Temporal Crime Prediction in Chicago

Kathiriya, Yug Alpesh (2024) A comparative analysis of Deep Learning Models for Spatio-Temporal Crime Prediction in Chicago. Masters thesis, Dublin, National College of Ireland.

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Abstract

Chicago’s complicated crime patterns provide a serious public challenge. While crime is a widespread issue, Chicago’s high rates have received special attention. The city’s varied neighbourhoods experience varying degrees of crime, emphasising the importance of localised crime prevention initiatives. This study tries to address this difficulty by studying the efficacy of deep learning models. ATTNBILSTM, LSTM, and CNN-LSTM for predicting crime patterns. Additionally, geospatial clustering approaches, such as HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) and PCA-aided clustering, are used to detect and visualise crime hotspots. Analysing crime datasets and using these models can reveal trends in crime patterns, identify high-crime areas, and influence data-driven crime prevention efforts.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Basilio, Jorge UNSPECIFIED
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare > Criminology > Crimes and Offences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	20 Aug 2025 09:23
Last Modified:	20 Aug 2025 09:23
URI:	https://norma.ncirl.ie/id/eprint/8579

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