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

Research paper by Andrew P. Wheeler, Robert E. Worden; Sarah J. McLean

Indexed on: 02 Dec '16Published on: 01 Dec '16Published in: Journal of Quantitative Criminology



Abstract

Abstract 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. Abstract 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. ObjectivesReplicate 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.20042014 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. MethodsCrimes 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. ResultsThe 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. ConclusionsOur 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.