This paper uses resilience as a lens through which to analyse disasters and other major threats to patterns of criminal behaviour. A set of indicators and mathematical models are introduced that aim to quantitatively describe changes in crime levels in comparison to what could otherwise be expected, and what might be expected by way of adaptation and subsequent resumption of those patterns. The validity of the proposed resilience assessment tool is demonstrated using commercial theft data from the COVID-19 pandemic period. A 64 per cent reduction in crime was found in the studied city (China) during an 83-day period, before daily crime levels bounced back to higher than expected values. The proposed resilience indicators are recommended as benchmarking instruments for evaluating and comparing the global impact of COVID-19 policies on crime and public safety.

Wildlife crime, including poaching and wildlife trafficking, threaten the existence of particular species. To date, research on wildlife crime has been primarily conducted by those with backgrounds in the biological sciences, however crime scientists, have much to offer in examining wildlife crimes. With this in mind, we aim to highlight general principals of crime science found through an in-depth review of the conservation literature. More specifically, to determine if, and how, different types of wildlife crimes cluster, to identify the existence of interventions for which the mechanisms mirror those found within SCP, and consider their effectiveness. Our review suggests that various types of wildlife crimes concentrate in time and space, among products, along certain routes, and at particular facilities. Further, some overlap exists between mechanisms used to mitigate more traditional crimes and those used to prevent wildlife crimes and protect threatened species. Of note are the attempts by those in the conservation community to increase the risk of crime, remove excuses for non-compliance of rules, and reduce provocations that contribute to particular types of wildlife crime. Given this overlap crime scientists may be able to collaborate with conservationists to draw on the extensive knowledge base of prevention studies to identify potential interventions, analyze their implementation, and evaluate the overall impact of an intervention.

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Abstract:Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime.