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Isolating Target And Neighbourhood Vulnerabilities In Crime Forecasting

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Abstract

Risk terrain modelling (RTM) is emerging as an effective approach for predicting how and where crimes concentrate within cities and regions. However, in its previous applications there is a tendency to overestimate the influence of external environmental risks and preventive factors. Most studies applying RTM have investigated factors associated with the characteristics of the urban setting, whilst only a limited number have focused on identifying the risks associated with the availability and the characteristics of potential targets for criminals. This study uses RTM to identify the spatial risk and protective factors related to residential burglaries in the city of Milan, Italy. Factors considered are the neighbourhood- and target-related contextual factors, the exposure to crime and potential mitigating strategies. The results show that when the place and target of the offence are intrinsically related a target-oriented approach to select factors is useful for increasing the understanding of why some locations are most likely to experience future crimes. Indeed, the peculiarities of the target itself are integral to understan both the decision-making of criminals and the overall level of crime risk. Related policy implications are discussed.

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Notes

  1. It is worth noting that the distinction between target and neighbourhood characteristics is not always clear-cut. As an example, in analysing residential burglaries a high rate of unemployed inhabitants in a block could be a protective endogenous factor (i.e. the houses are less likely to be vacant during the day), but the same variable considered for surrounding blocks can be seen as a risk factor (i.e. due to the presence of potential offenders). Hence, the decisions should be driven by the aims of the analysis, specific assumptions, particular knowledge of the researchers or the characteristics of the data available.

  2. “The approach is rooted in the micro-economic theory of random utility maximisation (RUM) and enables studying the choice behaviour of decision-makers selecting one alternative from a larger set of exhaustive and mutually exclusive alternatives” (Vandeviver et al. 2015).

  3. The definition of residential burglary is the one specified in Art.624-bis of the Italian Criminal Code.

  4. A total of 20,921 burglaries are successfully geocoded as point data (i.e. 6166 in 2012, 7356 in 2013, 7399 in 2014).

  5. Polizia di Stato: https://questure.poliziadistato.it; Arma dei Carabinieri: http://www.carabinieri.it

  6. People moving in and out of the census tract during the day for either work or study. This measure is computed, for each census tract, as the ratio: (resident population + people coming in - people going out)/(resident population).

  7. Milano Geoportale: https://geoportale.comune.milano.it/sit

  8. This method differs slightly from the one applied by the RTMDx software (Caplan and Kennedy 2016), although the basic approach of empirically selecting the relevant factors is the same. The decision of not using the RTMDx software was mainly driven by its current limitation in considering only risk or protective factors geocoded as point data.

  9. To assess the potential effect of spatial dependence, we calculated the Moran’s I to test the spatial autocorrelation of our dependent variable and for the residuals of the models. The result are very close to 0 in all the cases suggesting that the autocorrelation of the dependent variable is negligible and that the residuals are spatially uncorrelated. These tests suggest that spatial dependence is not a relevant issue in our dataset and does not seriously affect the results obtained.

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Dugato, M., Favarin, S. & Bosisio, A. Isolating Target And Neighbourhood Vulnerabilities In Crime Forecasting. Eur J Crim Policy Res 24, 393–415 (2018). https://doi.org/10.1007/s10610-018-9385-2

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