The proposed project provides a near real-time evidence base to inform the police approach to the apparent surge in domestic violence and abuse (DA) triggered by the Covid-19 lockdown in the UK. Police case file data from seven diverse police forces are pooled to track the impact of the pandemic on DA, analysing changes in the risk factors, frequency, nature and profile of DA reported to police. These changes are mapped closely to shifts in the restrictions imposed during lockdown, transitional phases and post lockdown, when DA calls to police are expected to spike. The proposed study is the largest and most rigorous analysis of police DA case file data conducted anywhere in the world to date. The statistical analysis is complemented by regular focused semi-structured phone interviews with police officers, to identify emerging challenges and best practice in the frontline response to DA. The mixed-methods study addresses urgent questions on the impact of Covid-19 on DA, which may have significant implications for the complex task of accurate police risk assessment, victim safeguarding, and criminal prosecution as the Covid-19 pandemic evolves. The Home Office, the National Police Chiefs Council (NPCC), and College of Policing (CoP) are project partners and constitute direct links to critical decision-makers and provide direct routes to impact. A timely and evidence-based development of a police strategy is urgently needed to address the emerging DA crisis and its devastating, long-lasting consequences for victims and their children.

Domestic burglary is a high volume crime affecting many households. As well as substantial financial loss and damage to property, it causes high levels of anxiety about the possibility of being burgled. Surveys documenting public priorities about crime place burglary at the top. Burglar alarms and other security devices in principle deter potential burglars. Insurance premiums are discounted when a fully operating burglar alarm exists in the home due to claims about the effectiveness of burglar alarms and other security devices in the marketing literature, but no systematic research studies have been undertaken to assess their effectiveness in different areas, accommodation types and occupants' characteristics. The research proposed is precisely concerned with such an assessment. The primary research question is: Which burglary security devices work for whom and in what context? This study will identify the individual and combined security devices that offer cost-effective burglary protection to (a) the population in England and Wales overall; (b) specific population subgroups according to their socio-economic attributes; (c) the residents of Wales, each of the nine English regions and area types according to population socio-demographic profile and density; and (d) area types and population subgroups plausible combinations. The urgency to gain insights about the cost-effectiveness of burglary devices for tailor-made preventive interventions cannot be exaggerated: at a time of massive public spending cuts and declining disposable incomes the latest Home Office figures show a 14% annual increase in domestic burglary in 2010/11 after an extended (fifteen years) period of falls (Chaplin et al. 2011). The Department for Communities and Local Government (2012) has recently highlighted the need of research evidence on cost-effective burglary security devices to inform the on-going deliberation on national building regulation for minimum standards for security in homes. The proposed research will: -Make a major scientific contribution with immediate and high societal and economic impact. Its theoretical and methodological advancements will inform future research developments in criminology. The current gap in knowledge impedes cost-effective burglary prevention not just in the UK but across the world at a time that wasteful financial decisions are unaffordable. -Engage throughout with high level research users in the public sector and civil society organisations and inform national and international guidelines on burglary prevention. The research results will be regularly conveyed to users in the private sector (the security and insurance industry) who however will not contribute to their development to avoid conflict of interest. -Analyse two decades of a formidable existing data source, the British Crime Survey (BCS). The BCS is a large and complex dataset with currently some 40,000 respondents annually that exists in the public domain, and has been run for three decades. Yet, relative to both data generation cost and its impeccable quality, it has been extremely under-explored. -Employ innovative research techniques for the deeper exploitation of the BCS, including the Security Impact Assessment Tool, pioneered by the co-applicants with ESRC support to assess the effectiveness of car security devices, as well as the multivariate multilevel logit modelling, to investigate the effect of context on trends of related crime types. -Build the national skills base in the analysis of large and complex datasets and expand the limited secondary data analysis capacity in criminology via actively seeking to employ a full time researcher from disciplines (mathematics, statistics, sciences or engineering) beyond traditional BCS users. Therefore the proposed research fits the ESRC-SDAI call specification. The co-applicants' theoretical, methodological and policy contribution to date ensure its successful delivery.

The overall aim of the project is the development of automated tools for automatic spatio-temporal analysis and understanding of human subtle facial behaviour from 4D facial information (i.e. 3D high-quality video recordings of facial behaviour). Two exemplar applications related to security issues will be specifically addressed in this proposal: (a) person verification (i.e. using facial behaviour as a biometric trait), and (b) deception indication. The importance of non-obtrusive person verification and deception indication is undisputable - every day, thousands of people go through airport security checkpoints, border crossing checkpoints, and other security screening points. Automated, unobtrusive monitoring and assessing of deceptive behaviour will form a valuable tool for end users, such as police, justice and prison services. This is in particular important as currently only informal interpretations for detecting deceptive behaviour are used. In addition, the development of alternative methods for person verification that are not based on physical traits only but on behavioural, easily observable traits like facial expressions, would be of great value for the development of multimodal biometric system. Such multi-modal biometric systems will be of great interested to government agencies such as the Home Office or the UK Border agency. For automatic deception indication we propose to develop methodologies for detecting 4D micro-expressions and their dynamics being typical of deceptive behaviour as reported by research in psychology. For automatic person identification we propose to increase the robustness of static face- image-based verification systems by including facial dynamics as an additional biometric trait. The underlying motivation is that the dynamic 4D facial behaviour is very difficult to imitate and , hence, it has natural resilience against spoof attacks. The project focuses on 3D video recordings rather than on 2D video recordings of facial behaviour due to two main reasons: (1) increased robustness to changes in head-pose, and (2) ability to spot subtle changes in the depth of facial surface such as jaw clench and tremor appearance on the cheeks, which are typical of deceptive behaviour and cannot be spotted in 2D images. The research on 3D facial dynamics is now made possible by the tremendous advance of sensors and devices for the acquisition of 3D face video recordings. The core of the project will deal with both the development of 4D-FAB research platform containing tools for human subtle facial behaviour analysis in 4D videos and the development of annotated data repository consisting of two parts: (1) annotated 4D recordings of deceptive and truthful behavior, and (2) annotated 4D recordings of subjects uttering a sentence, deliberately displaying certain facial actions and expressions, and spontaneously displaying certain facial actions and expressions. The work plan is oriented around this central goal of developing 4D-FAB technology and is carried out in 3 work packages described in the proposal. A team of 3 Research Associates (RAs), led by the PIs, and having the background in computer vision and machine learning, will develop 4D-FAB technology. The team will be closely assisted by 6 members of the Advisory Board: Prof. Burgoon, University of Arizona, advising on psychology of deception and credibility Prof. Cohn, Pittsburgh University / Carnegie Mellon University, advising on face perception and facial behaviometrics Prof. Nunamaker, Director of BORDERS, US Nat'l Center for Border Security and Immigration, advising on making 4D-FAB useful for end users in security domain Dr Hampson, Head of Science & Technology, OSCT, Home Office, advising on making 4D-FAB useful for end users Dr Cohen, Director of United Technologies Research Centre Ireland, advising on making 4D-FAB useful for end users Dr Urquhart, CEO of Dimensional Imaging, advising on 4D recording setup design

The COVID-19 crisis is changing the shape of crime. Drawing on crime science, this research will inform evidence-based policy and practice. Lockdown requires people to stay home, leading to domestic violence and child abuse increases. Yet social distancing means police are arresting fewer suspects: reduced services at time of greater need. COVID-19 gives fraudsters a 'conversation starter' to approach people in-person, via text, email and online. Remote working and online leisure activities, furloughs and financial difficulties, provide more potential targets for online crimes of various types. Vulnerable groups including the elderly and disabled are more at risk. Yet a Harvard study (Kissler et al. Science, 14 April) suggests that, absent a vaccine, social distancing may continue into 2022, perhaps 2024. So we will anticipate crime effects of prolonged, graduated or cyclical exit strategies. We will also anticipate post-crisis scenarios, seeking to sustain declines in crimes like burglary, to avoid them returning to 'normal'. We will use (1) national police data, (2) detailed data from three police partners, (3) fraud and e-crime data from industry, and (4) sources from other agencies such as Childline (for unreported crime). Pre/post-change analysis will use a combination of time-series and spatial modelling. Nesting force-level analysis in the national and international context will allow us to gauge scalability. We have police and industry partners, national (Home office, National Police Chief's Council, College of Policing) and international advisors. The aim is to inform policy and practice, producing 16 deliverables including policy and practice briefings and research articles.

This project will develop an operationally and technically viable approach to cargo threat investigation. The main aim of the project is to provide a real-time dynamic passive profiling technique to assist Border Control Agencies and has the potential to improve hit rates; i.e. to improve targeting the people that carry contraband and hence ensure less is entering the UK.To be specific, the real-time dynamic passive profiling technique will be based on the modelling of facial expressions, eye movement and pupil changes in both the visual and thermal domains and link these to malicious intent and physiological processes (such as blood flow, eye movement patterns, and pupil dilation). To facilitate this process, one of the initial aspects of the project will be the collection, analysis and development of the dataset used to model the baseline of facial imagery behaviour of the general population against which physiological behaviours in people with malicious intent would need to be detected. Both the baseline and the dynamic profiling will be based on the response to a series of questions. The developed techniques will be evaluated in operational trails at border control points. The multi-modal facial analysis will provide additional information to the current profiling and the developed techniques will have a wider remit into other domains. It is envisioned that this will be easily integrated into the current process.There are three main challenges:a) to determine the facial/eye features, in combination with psychological profiling, to provide robust baselines that can be linked to malicious intent,b) to develop and combine the various dynamic real-time facial models (visual expression, thermal, eye movement) related to intent, andc) to evaluate the developed system within different environments, ranging from airport to port based border control points.