# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Data Analysis
#### 3.4.1 Crime Trends over Time: A Comparative Analysis Across Crime Types  
The provided graphs offer an observational view of crime trends across Maryland from 2012 to 2023, highlighting notable changes in various crime types over the years. Figure 1(a) illustrates the trend for auto theft, showing a relatively stable pattern until a sharp increase in 2023, possibly indicating changes in reporting practices or actual incidents. Figure 1(b) presents the arson trend, which remained relatively steady through most of these years, followed by a rise in 2023.  
Figure 1(c), depicting homicide, shows a peak around 2015, with fluctuations in subsequent years and a general decline through 2023. This trend may reflect specific factors or policy changes influencing violent crime in Maryland [51]. Figure 1(d) tracks burglary reporting, which exhibits a continuous decline over the period and a marked decrease after 2016, reaching one of its lowest recorded levels by 2023. However, data covering further years would be necessary to confirm if this trend is sustained.  
Figure 1(e), which depicts the trend regarding rape incidents, shows variability over the years, including peaks around 2017 and 2019, followed by a stabilization period. Figure 1(f) highlights larceny as the most frequent crime type throughout the period, despite a recent decline in incidents, suggesting either an improvement in preventive measures or changes in social conditions. Figure 1(g) illustrates the trend regarding shootings, which remains relatively stable from 2014 onward, with a slight increase in 2023 that warrants closer examination.  
In Figure 1(h), robbery maintains a relatively stable trend until 2016, followed by a gradual decline through 2023. Figure 1(i) shows the trend for assault, which remains consistent for much of the period, but begins a noticeable decline starting in 2023. Finally, Figure 1(j) provides a comprehensive overview of the total crime trends across categories, with larceny leading the figures in terms of frequency over the years. The graphs collectively show declines across most categories by 2023, with significant reductions observed in the reporting of auto theft, burglary, and assault.  
It is essential to interpret these recent declines with caution, as they may reflect variations in reporting practices or data completeness, particularly for more recent years. This observational analysis does not seek to establish causation but instead highlights notable shifts in crime types over time, providing insight into evolving patterns that could inform future research.  
While the findings offer valuable insights into Maryland's crime landscape, they also underscore the need for further analysis, particularly regarding external factors that may have influenced these trends. For instance, research by the Maryland Public Policy Institute [52] highlights a correlation between high poverty rates and elevated crime levels in certain areas, suggesting that socio-economic disparities are crucial to understanding crime trends. Additionally, a report by the Council of State Governments Justice Center  
Figure 2: Crime trends in 23 counties and one city of Maryland, from 2012 to 2023: (**a**) auto theft crime trend over time, (**b**) arson crime trend over time, (**c**) homicide crime trend over time, (**d**) burglary crime trend over time, (**e**) rape crime trend over time, (**f**) larceny crime trend over time, (**g**) shooting crime trend over time, (**h**) robbery crime trend over time, (**i**) assault crime trend over time, and (**j**) total crime trend over time.  
(2024) [51] emphasizes that policy changes and legislative reforms can significantly impact crime patterns, demonstrating the influence of external factors on local crime rates. These limitations underscore the need for a nuanced approach to interpreting trends since data completeness, regional influences, and socio-economic and policy-related factors may significantly impact the observed patterns.