A LONGITUDINAL ANALYSIS OF NEIGHBORHOOD CRIME RATES USING LATENT GROWTH CURVE MODELING

Although social disorganization theory provides a framework for understanding how changes in neighborhoods can influence crime rates over time, research on neighborhood characteristics and crime has relied primarily on cross-sectional data. Using a latent growth curve model and longitudinal data on residential burglary and vehicle theft in Indianapolis, measured annually between 1992 and 2006 at the census block group level, the authors analyzed the relationships between neighborhood characteristics and crime trends. For both residential burglary and vehicle theft, baseline models revealed that changes in crime rates were best captured by a quadratic function with an initial linear decrease and subsequent deceleration ( slowing) of the decrease. When neighborhood characteristics were included as predictors, change in neighborhood disadvantage was significantly related to changes in both residential burglary and vehicle theft over time, while change in residential stability did not have a significant effect on changes in residential burglary or vehicle theft.