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Crime
Property Crime
Percent Owner Occupied
Median Income
Population
Glennville, located in Georgia, presents an interesting case study in property crime trends over the past decade. From 2010 to 2020, the total number of property crimes fluctuated, ultimately decreasing by 25% from 120 to 90 incidents. During this same period, the population grew by 8.5%, from 6,195 to 6,722 residents, suggesting a complex relationship between population growth and crime rates.
Burglary trends in the city show a significant decline over time. In 2010, there were 34 burglaries, which decreased to just 6 in 2020, representing an 82.4% reduction. When adjusted for population, the burglary rate fell from 5.49 per 1,000 residents in 2010 to 0.89 per 1,000 in 2020. Interestingly, the city's share of state burglaries fluctuated, dropping from 0.1% in 2010 to 0.09% in 2020, with a peak of 0.27% in 2019. This substantial decrease in burglaries, outpacing population growth, suggests improved security measures or changes in local law enforcement strategies.
Larceny-theft, the most common property crime in the city, showed a less dramatic but still notable decrease. From 82 incidents in 2010, it dropped to 66 in 2020, a 19.5% reduction. The rate per 1,000 residents decreased from 13.24 in 2010 to 9.82 in 2020. However, the city's share of state larceny-theft incidents doubled from 0.08% to 0.16% over this period, indicating that while local rates improved, they did so at a slower pace than the state average.
Motor vehicle theft trends present a more complex picture. The number of incidents increased from 4 in 2010 to 18 in 2020, a 350% increase. The rate per 1,000 residents rose from 0.65 in 2010 to 2.68 in 2020. More strikingly, the city's share of state motor vehicle thefts jumped from 0.03% to 0.37%, a more than tenfold increase. This significant rise in motor vehicle thefts, particularly in contrast to other declining property crime categories, warrants further investigation into local factors that may be contributing to this trend.
Arson data for the city is limited, with only sporadic reports available. In 2011 and 2016-2017, single incidents were reported, representing 0.26% and 0.3% of state arsons respectively. The lack of consistent data makes it difficult to draw meaningful conclusions about arson trends in the city.
Examining correlations between crime trends and demographic factors reveals some interesting patterns. The period of highest property crime rates (2011-2013) coincides with the highest median income levels reported for the city, ranging from $49,052 to $53,790. Conversely, as median income decreased to a low of $44,324 in 2017, property crime rates also declined. This suggests a potential inverse relationship between income levels and property crime rates in the city.
Additionally, there appears to be a correlation between changes in racial demographics and property crime trends. As the white population percentage decreased from 70% in 2014 to 58% in 2022, with a corresponding increase in black and Hispanic populations, property crime rates generally declined. This trend challenges simplistic narratives about race and crime, highlighting the complex interplay of socioeconomic factors in crime patterns.
Applying predictive models to forecast property crime trends for the next seven years (up to 2029, which is five years from now in 2024) suggests a potential stabilization or slight increase in overall property crime rates. Based on the historical data and current trends, burglary rates are expected to remain low, potentially stabilizing around 10-15 incidents per year. Larceny-theft is projected to fluctuate between 60-80 incidents annually. The most concerning projection is for motor vehicle theft, which may continue its upward trend, potentially reaching 25-30 incidents per year by 2029 if current trends persist.
In summary, Glennville has experienced a general decline in property crime over the past decade, with significant reductions in burglary and larceny-theft. However, the sharp increase in motor vehicle thefts presents a notable exception to this trend. The complex relationships between crime rates, economic factors, and demographic changes underscore the need for nuanced, data-driven approaches to crime prevention and community safety in the city.