This study explores preference variation in location choice strategies of residential burglars. Applying a model of offender target selection that is grounded in assertions of the routine activity approach, rational choice perspective, crime pattern and social disorganization theories, it seeks to address the as yet untested assumption that crime location choice preferences are the same for all offenders.
Analyzing detected residential burglaries from Brisbane, Australia, we apply a random effects variant of the discrete spatial choice model to estimate preference variation between offenders across six location choice characteristics. Furthermore, in attempting to understand the causes of this variation we estimate how offenders’ spatial target preferences might be affected by where they live and by their age.
Findings of this analysis demonstrate that while in the aggregate the characteristics of location choice are consistent with the findings from previous studies, considerable preference variation is found between offenders.
This research highlights that current understanding of choice outcomes is relatively poor and that existing applications of the discrete spatial choice approach may underestimate preference variation between offenders.
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The terminology used here is different to that used in other fields. Economists are interested in how taste varies among decision makers, and what individual characteristics are associated with, and therefore can predict, taste. For our purposes, the term preference is considered more germane to the variable of interest.
Instead of the well known Red Bus/Blue Bus example often used to highlight the restrictive property of the IIA assumption, we present an alternative example better suited to spatial choices, where adjacent areas often have similar characteristics and their boundaries may be somewhat arbitrary. Suppose offenders can choose from a choice set of two target areas: A and B. If both are equally preferred, the probability of choosing each is .5 and the odds ratio is 1. Say area B is divided, for administrative reasons unrelated to crime, into B-North and B-South. Assuming offenders do not know or care about areal names, the preference between A and B should remain equal, with A at .5 and .25 for both B-North and B-South. But the IIA property states that the odds ratio of A and B is fixed at one, so the probabilities need to change to .33 A, .33 B-North and .33 B-South.
In addition to the results reported later in this paper, we estimated a series of models on subsets of the sample. Removing low rate and prolific offenders did not yield different estimates.
A closed form solution is any formula that can be computed in a finite number of steps. Integral functions often require an infinite number of steps to compute, because they represent the area under a curve. The accuracy of the estimate is related to the number of the estimation points used. More accurate estimates can be obtained by using more points, but there is no upper limit on how many points could be used. In practical terms, calculating an integral function is performed through approximation.
Simulated probabilities involve Monte Carlo integration, which requires drawing a series of values from an uniform distribution on a unit interval. Halton sequences are a popular method of drawing and are considered superior to random draws because they provide better coverage of the sampling space.
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Townsley, M., Birks, D., Ruiter, S. et al. Target Selection Models with Preference Variation Between Offenders. J Quant Criminol 32, 283–304 (2016). https://doi.org/10.1007/s10940-015-9264-7
- Offender mobility
- Residential burglary
- Discrete spatial choice
- Mixed logit