Abstract

This thesis presents a systematic literature review and an empirical demonstration focused on predicting violent-crime hotspots. Drawing on 50 studies published between 2010 and 2025, the review synthesises methodological developments across hotspot mapping, spatio-temporal modelling, risk terrain analysis, and machine-learning approaches. The review highlights a clear evolution from retrospective density maps to more dynamic, data-driven techniques, while also identifying persistent challenges related to data bias, temporal granularity, environmental context, fairness, and operational implementation. To complement the review, the thesis applies kernel density estimation (KDE) and three ensemble machine-learning models—Random Forest, Gradient Boosting, and XGBoost—to 769,680 geocoded violent-crime incidents recorded in Chicago between 2015 and 2024. Using a strictly temporal hold-out design, KDE achieved strong baseline performance with a Predictive Accuracy Index (PAI) of 2.64 at 5% coverage, while the machine-learning models achieved area-under-curve (AUC) values exceeding 0.93, with Random Forest yielding the highest score (AUC = 0.9365). These results confirm findings from the literature that machine-learning techniques offer improved discrimination over traditional density-based methods, particularly when forecasting short-term micro-scale variation in violent crime. The study concludes that although predictive models have advanced considerably, their practical deployment remains constrained by issues of data quality, spatial-temporal reso- lution, interpretability, and fairness. Effective implementation therefore requires rigorous governance, transparent modelling pipelines, and safeguards to prevent reinforcing historical disparities. Future research should integrate real-time data streams, environmental context, and community-informed evaluation frameworks to ensure more accurate, equitable, and accountable hotspot prediction.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Khalid Ezzeldeen

Campus

RIT Dubai

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