Modelling taste heterogeneity regarding offence location choices

Michael Frith

Journal of choice modelling, 2019, vol. 33, issue C

Abstract: One of the central topics in crime research, and one in which discrete choice modelling has been relatively recently introduced, is the study of where offenders choose to commit crime. Since the introduction of this approach in 2003, it has become relatively popular and used in over 25 published studies covering a range of crime types and study areas. However, in most of these analyses the conditional logit has been used which assumes offenders are homogenous in their offence location preferences. This is despite various research finding offenders vary in their decision-making criteria. As such, while three recent studies (Townsley et al., 2016; Frith et al., 2017; Long et al., 2018) used the mixed logit and found some evidence of preference heterogeneity between offenders, there are still open questions regarding its nature. To this end, this study uses the latent class (and mixed and conditional logit) to examine the offence location choices of serious acquisitive crime offenders in York (UK). In particular, to understand how the spatial preferences differ between offenders and if there are any observable sources. Like the previous studies, this analysis identifies the presence of preference heterogeneity. This study also finds that the latent class and mixed logit equally fit the data though there are some differences in the results. These findings and other factors therefore raise questions for future crime location choice research regarding the appropriate model for these types of analyses and the true underlying nature of offender preferences.

Keywords: Crime; Offence locations; Taste heterogeneity; Latent class model; Random parameter logit (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:33:y:2019:i:c:s1755534519300922

DOI: 10.1016/j.jocm.2019.100187

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