Governments around the world restricted movement of people, using social distancing and lockdowns, to help stem the global coronavirus (COVID-19) pandemic. We examine crime effects for one UK police force area in comparison to 5-year averages. There is variation in the onset of change by crime type, some declining from the WHO 'global pandemic' announcement of 11 March, others later. By 1 week after the 23 March lockdown, all recorded crime had declined 41%, with variation: shoplifting (− 62%), theft (− 52%), domestic abuse (− 45%), theft from vehicle (− 43%), assault (− 36%), burglary dwelling (− 25%) and burglary non-dwelling (− 25%). We use Google Covid-19 Community Mobility Reports to calculate the mobility elasticity of crime for four crime types, finding shoplifting and other theft inelastic but responsive to reduced retail sector mobility (MEC = 0.84, 0.71 respectively), burglary dwelling elastic to increases in residential area mobility (− 1), with assault inelastic but responsive to reduced workplace mobility (0.56). We theorise that crime rate changes were primarily caused by those in mobility, suggesting a mobility theory of crime change in the pandemic. We identify implications for crime theory, policy and future research.

Governments around the world restricted movement of people, using social distancing and lockdowns, to help stem the global coronavirus (COVID-19) pandemic. We examine crime effects for one UK police force area in comparison to 5-year averages. There is variation in the onset of change by crime type, some declining from the WHO ‘global pandemic’ announcement of 11 March, others later. By one week after the 23 March lockdown, all recorded crime had declined 41%, with variation: shoplifting (-62%), theft (-52%), domestic abuse (-45%), theft from vehicle (-43%), assault (-36%), burglary dwelling (-25%) and burglary non-dwelling (-25%). We use Google Covid-19 Consumer Mobility Reports to calculate the mobility elasticity of crime for four crime types, finding shoplifting and other theft inelastic but responsive to reduced retail sector mobility (MEC = 0.84, 0.71 respectively), burglary dwelling elastic to increases in residential area mobility (-1), with assault inelastic but responsive to reduced workplace mobility (0.56). We theorise that crime rate changes were primarily caused by those in mobility, suggesting a mobility theory of crime change in the pandemic. We identify implications for crime theory, policy and future research.

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Crime linkage is a systematic way of assessing behavioural or physical characteristics of crimes and considering the likelihood they are linked to the same offender. This study builds on research in this area by replicating existing studies with a new type of burglar known as optimal foragers, who are offenders whose target selection is conducted in a similar fashion to foraging animals. Using crimes identified by police analysts as being committed by foragers this study examines their crime scene behaviour to assess the level of predictive accuracy for linking crimes based on their offending characteristics. Results support previous studies on randomly selected burglary offence data by identifying inter-crime distance as the highest linking indicator, followed by target selection, entry behaviour, property stolen and offender crime scene behaviour. Results discuss distinctions between this study and previous research findings, outlining the potential that foraging domestic burglary offenders display distinct behaviours to other forms of offender (random/marauder/commuter).

Anti-social behaviour recorded by police more than doubled early in the coronavirus pandemic in England and Wales. This was a stark contrast to the steep falls in most types of recorded crime. Why was ASB so different? Was it changes in ‘traditional’ ASB such as noisy neighbours, or was it ASB records of breaches of COVID-19 regulations? Further, why did police-recorded ASB find much larger early-pandemic increases than the Telephone Crime Survey for England and Wales? This study uses two approaches to address the issues. The first is a survey of police forces, via Freedom of Information requests, to determine whether COVID-regulation breaches were recorded as ASB. The second is natural language processing (NLP) used to interrogate the text details of police ASB records. We find police recording practice varied greatly between areas. We conclude that the early-pandemic increases in recorded ASB were primarily due to breaches of COVID regulations but around half of these also involved traditional forms of ASB. We also suggest that the study offers proof of concept that NLP may have significant general potential to exploit untapped police text records in ways that inform policing and crime policy.

Nationally, small area (LSOAs) were ranked by recorded crime rate and grouped into deciles for May 2020 relative to previous five Mays. Decile rate changes relative to expected from previous five years. Key findings:•Previously high-crime areas saw the largest crime declines. •Previously-low-crime rate areas experienced crime increases. •Urban centres saw the greatest crime drops in absolute (but not necessarily relative) terms. •Public order crime increases likely reflect breaches - or perceived breaches - of lockdown rules. Some crime increases, including drugs and weapon offences, may reflect changes in police activity