# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 2 Literature Review  
Recent advancements in crime analysis underscore the importance of robust data collection and precise analytical models. The Barcelona Victimization Survey (2015-2020), for instance, highlights the significance of comprehensive data collection in understanding community safety and crime dynamics across neighborhoods [13]. This survey, alongside other studies [14], provides insights that are often overlooked with traditional police records, emphasizing the need for supplementary data sources in crime analysis. The reliability of crime data has been enhanced through the application of models such as the quasi-simplex model (QSM) and its multi-item extension (MI-QSM). These models decompose observed crime data variance into specific components, representing true scores, method effects, and random errors, using Bayesian estimation techniques [15; 16]. Such methodological rigor is crucial for enhancing data validity and adaptability for analyzing regional crime trends [17; 18]. While studies like these focus on survey data, recent research underscores the value of integrating multiple datasets for improved crime prediction [19]. Our approach advances beyond traditional spatiotemporal co-kriging (ST-Cokriging) methods, which combine crime records with high-resolution activity data from police operations, improving short-term forecasting accuracy and enabling the better prediction of hotspots and targeted interventions [20]. Unlike the ST-Cokriging method, which primarily improves short-term forecasting accuracy, our models--utilizing random forest, gradient boosting, and XGBoost--integrate a broader range of temporal and spatial data, enhancing our ability to predict crime hotspots and trends over longer periods and across more diverse settings. [PERSON], [PERSON] and colleagues further advanced predictive methods by introducing spatiotemporal lag variables, which mitigate the spatial and temporal dependencies embedded in crime data. Their study demonstrated that accounting for these dependencies using tree-based machine learning models significantly enhances predictive accuracy, particularly when applied to data from Dallas, Texas. By modeling both environmental and demographic factors, they provided a more accurate representation of crime dynamics and offered practical guidance for proactive urban security strategies [21]. Our study builds on these advancements by not only accounting for spatial and temporal dependencies but also incorporating machine learning algorithms that adapt dynamically to changes in crime patterns due to seasonal and socioeconomic factors. This provides a more detailed and adaptable model than those typically employed in studies like the one conducted in Dallas, Texas. Current research divides temporal forecasting into short-term, medium-term, and long-term categories, employing methods such as LASSO regression and neural networks. Spatial prediction operates across micro-, meso-, and macro-levels using models like kernel density estimation (KDE) and risk terrain modeling (RTM), enabling the efficient management of police resources by forecasting high-crime areas and trends [22]. The review emphasized the importance of integrating reinforcement learning techniques and Shapley additive explanations (SHAPs) to enhance the interpretability and practicality of crime prediction models. This multi-scale approach underscores the complexities in crime dynamics, particularly in regions with diverse urban and rural environments, such as Maryland, where nuanced and adaptable models are required to account for varying crime patterns [23]. In alignment with these studies, subsequent authors proposed an artificial intelligence model for predicting per capita violent crimes in urban areas. This model integratedsocioeconomic and law enforcement data to generate accurate crime forecasts, optimizing resource allocation by leveraging a genetic programming (GP) framework enhanced with local search optimization.