The Moving Home Effect: A Quasi Experiment Assessing Effect of Home Location on the Offence Location

Objectives

This study aims to test whether the home location has a causal effect on the crime location. To accomplish this the study capitalizes on the natural experiment that occurs when offender??s move, and uses a unique metric, the distance between sequential offenses, to determine if when an offender moves the offense location changes.

Methods

Using a sample of over 40,000 custodial arrests from Syracuse, NY between 2003 and 2008, this quasi-experimental design uses t test??s of mean differences, and fixed effects regression modeling to determine if moving has a significant effect on the distance between sequential offenses.

Results

This study finds that when offenders move they tend to commit crimes in locations farther away from past offences than would be expected without moving. The effect is rather small though, both in absolute terms (an elasticity coefficient of 0.02), and in relation to the effect of other independent variables (such as the time in between offenses).

Conclusions

This finding suggests that the home has an impact on where an offender will choose to commit a crime, independent of offence, neighborhood, or offender characteristics. The effect is small though, suggesting other factors may play a larger role in influencing where offenders choose to commit crime.     

Editor's Introduction

Editorial Commentary

Editor's Introduction     

Replicating Group-Based Trajectory Models of Crime at Micro-Places in Albany,NY

Objectives

Replicate two previous studies of temporal crime trends at the street block level. We replicate the general approach of group-based trajectory modelling of crimes at micro-places originally taken by Weisburd et al. (Criminology 42(2):283–322, 2004) and replicated by Curman et al. (J Quant Criminol 31(1):127–147, 2014). We examine patterns in a city of a different character (Albany, NY) than those previously examined (Seattle and Vancouver) and so contribute to the generalizability of previous findings.

Methods

Crimes between 2000 and 2013 were used to identify different trajectory groups at street segments and intersections. Zero-inflated Poisson regression models are used to identify the trajectories. Pin maps, Ripley’s K and neighbor transition matrices are used to show the spatial patterning of the trajectory groups.

Results

The trajectory solution with eight classes is selected based on several model selection criteria. The trajectory of each those groups follow the overall citywide decline, and are only separated by the mean level of crime. Spatial analysis shows that higher crime trajectory groups are more likely to be nearby one another, potentially suggesting a diffusion process.

Conclusions

Our work adds additional support to that of others who have found tight coupling of crime at micro-places. We find that the clustering of trajectories identified a set of street units that disproportionately contributed to the total level of crime citywide in Albany, consistent with previous research. However, the temporal trends over time in Albany differed from those exhibited in previous work in Seattle but were consistent with patterns in Vancouver.