Crime Science最新文献

This study introduces an algorithmic strategy for measuring dimensions of police presence at microgeographic units using GPS data from police patrol units. The proposed strategy builds upon the integrated theory of hot spots patrol strategy from Sherman et al. (Journal of Contemporary Criminal Justice 30:95-122, 2014), focusing on three key dimensions: the frequency, duration, and intermittency of police presence. This study provides pseudocodes for the algorithm, facilitating the pre-processing of GPS-derived data sequences to generate measures of these three central concepts. The measures presented in this article offer a framework for investigating the impact of police presence on crime and other relevant crime-related outcomes at microgeographic units, using GPS data. This algorithmic strategy may further contribute to the development of evidence-based strategies in place-based policing initiatives.

Background setting: Punitive approaches to deter offending remain popular despite limited evidence of their effectiveness. This study investigated what effect presenting empirical criminological findings about the effectiveness of deterrence to a general public has on their punishment preferences. It builds on earlier research showing that such presentation reduces the public's inclination towards strict punishment. The present study extended this research by exploring whether the impact of scientific evidence on public punishment preferences is affected by crime severity and by exploring cognitive and psychological factors that may underpin this relationship.

Methods: Using a vignette study paradigm, a general public sample of 330 participants were asked to make hypothetical punishment decisions to reduce crime (whether or not to double sentences) for one of three crime types that varied in severity. For each crime type, half of participants were additionally provided with a summary of research on the deterrent effect of punitive policy measures.

Results: Presenting scientific evidence reduced participants' preferences for stronger punishment and that this effect remained consistent regardless of crime severity-ranging from burglary to homicide. In addition, we did not find evidence that difference in individuals' cognitive style, negative emotional reactions, perceptions about seriousness, or beliefs about redeemability moderated or mediated this relationship.

Conclusions: This study provides compelling findings that further clarify the circumstances required for scientific evidence to be successfully disseminated to a general public to bring their punishment preferences more in line with the state of empirical science.

Supplementary information: The online version contains supplementary material available at 10.1186/s40163-024-00240-8.

Background setting: Traditional spatial or non-spatial regression techniques require individual variables to be defined as dependent and independent variables, often assuming a unidirectional and (global) linear relationship between the variables under study. This research studies the Bayesian shared component spatial (BSCS) modeling as an alternative approach to identifying local associations between two or more variables and their spatial patterns.

Methods: The variables to be studied, young offenders (YO) and violent crimes (VC), are treated as (multiple) outcomes in the BSCS model. Separate non-BSCS models that treat YO as the outcome variable and VC as the independent variable have also been developed. Results are compared in terms of model fit, risk estimates, and identification of hotspot areas.

Results: Compared to the traditional non-BSCS models, the BSCS models fitted the data better and identified a strong spatial association between YO and VC. Using the BSCS technique allowed both the YO and VC to be modeled as outcome variables, assuming common data-generating processes that are influenced by a set of socioeconomic covariates. The BSCS technique offered smooth and easy mapping of the identified association, with the maps displaying the common (shared) and separate (individual) hotspots of YO and VC.

Conclusions: The proposed method can transform existing association analyses from methods requiring inputs as dependent and independent variables to outcome variables only and shift the reliance on regression coefficients to probability risk maps for characterizing (local) associations between the outcomes.

Supplementary information: The online version contains supplementary material available at 10.1186/s40163-024-00235-5.

Abstract

Analyses of emergency calls for service data in the United States suggest that around 50% of dispatched police deployment time is spent on crime-related incidents. The remainder of time is spent in a social service capacity: attending well-being checks and resolving disturbances, for instance. These findings have made a considerable contribution to the discourse around public perceptions of the police and the distribution of public funds towards (or away) from law enforcement. Yet, an outstanding issue remains. No investigation has been undertaken into whether findings are robust to the different ways in which ‘time spent’ is operationalized in these studies. Using dispatch data for Amsterdam during 2019, this study compares three operationalizations of ‘time spent’. Additionally, in order to provide some context on the potential mechanisms through which these different operationalizations might yield different results, we report on dispatch numbers per incident category and provide an initial exploration into ‘multi-dispatch’ incident types. We find that general proportional breakdowns are fairly robust to the time measure used. However, for some incident categories (e.g. Health) and incident types (e.g. Shootings), analyzed in isolation, the results are not robust to the different operationalizations. We propose that the mechanism explaining this lack of robustness can be traced to the high dispatch numbers for specific incident categories and types, particularly those with an imminent threat to life.

Preregistration: This study has been preregistered under the title: Scale and composition of emergency reactive police demand in Amsterdam, Netherlands (https://osf.io/qgwv6/).