# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Data Analysis
#### 3.4.5 Clustering Analysis of Crime Trends Across Maryland Counties

Table 2: Summary of clustering results. Figure 6: _Cont._  
4.6 Contextual Analysis of Crime Rates Across Maryland Counties: Total and Average Crime Comparisons  
This section explores the variations in crime rates across Maryland counties, with a focus on specific crime types such as aggravated assault, robbery, rape, murder, motor vehicle theft, and larceny theft. The analysis uses normalized crime rates for each county, providing a comprehensive overview of crime distribution patterns. Choropleth maps are utilized to visualize these rates, highlighting regional disparities and offering insights into crime dynamics across the state. General findings shown in Table 3 include:  
* Urban centers: Higher rates of assault, robbery, and motor vehicle theft are concentrated in more urbanized regions such as Baltimore City and Prince George's County. * Rural counties: Generally, these have lower rates across most crime types, with some exceptions like motor vehicle theft in specific counties. * Variability: Notable variability in crime rates like rape is seen across different counties, reflecting the diverse regional dynamics. Figure 7 offers a series of maps that provide a comprehensive visual representation of the variation in crime rates across Maryland counties. Each map highlights significant geographic disparities in specific crime types, offering insights into their distribution throughout the state. The first map (Figure 7a) focuses on aggregated assault rates, revealing how this crime is more prevalent in certain counties, illustrating the uneven spread across Maryland. Following this, the second map (Figure 7b) details the variation in robbery rates, showing those areas that are disproportionately affected by this type of crime. Similarly, the third map (Figure 7c) portrays the disparities in rape rates across the state, highlighting  
Figure 6: Clustering results. (**a**) Robbery rate trends (Cluster 0): gradual decline over time. (**b**) Robbery rate trends (Cluster 1): high variability across counties. (**c**) Robbery rate trends (Cluster 2): persistently high rates in Baltimore City. (**d**) Robbery rate trends (Cluster 3): steady declines across semi-urban regions. (**e**) Robbery rate trends (Cluster 4): sporadic spikes in low-population areas. (**f**) Rape rate trends (Cluster 0): gradual increase, with some recent declines. (**g**) Rape rate trends (Cluster 1): high variability across counties. (**h**) Rape rate trends (Cluster 2): persistently high rates in Baltimore City. (**i**) Rape rate trends (Cluster 3): steady declines in semi-urban regions. (**j**) Rape rate trends (Cluster 4): sporadic spikes in low-population areas. (**k**) Murder rate trends (Cluster 0): gradual increase with variability. (**l**) Murder rate trends (Cluster 1): high variability across rural counties. (**m**) Murder rate trends (Cluster 2): persistently high rates in Baltimore City. (**n**) Murder rate trends (Cluster 3): steady declines in semi-urban areas. (**o**) Murder rate trends (Cluster 4): sporadic spikes in low-population areas. regions with particularly high incidences and underscoring the varied landscape of this serious offense. The fourth map (Figure 7d) displays the distribution of murder rates, indicating areas with higher occurrences, which emphasizes the critical nature of targeted interventions in these regions. The fifth map (Figure 7e) shows motor vehicle theft rates, providing a clear view of where this crime is more or less common and reflecting the challenges faced by different counties. Lastly, the sixth map (Figure 7f) examines the rates of larceny theft, further illustrating the notable differences in crime rates from one county to another. Together, these maps serve as a powerful tool for visualizing the geographic spread of crime in Maryland, reflecting the unique challenges faced by different counties and supporting the development of targeted interventions and policies to address specific regional needs.