If we are to meet the United Nation's Sustainable Development Goals by their target of 2030, we need to develop better statistical methods to map the prevalence of vulnerable populations. In this fellowship, I will A. carry out foundational research into effective computational statistics methods for hidden populations, B. use the methods to map modern slavery at local, national and international levels, and C. work with my project partners to change policy based on our evidence-based research. To meet the Sustainable Development Goals, we need to measure how close we are to meeting them, quantify who is most in need of support and evaluate how successful interventions are in creating sustainable development. Take, for example, victims of modern slavery. Victims are often marginalised and hidden, with abuses going unreported and unmonitored. Estimating how many victims there are, where the abuses are happening and evaluating the effectiveness of interventions to support victims remain a challenge to the field of modern slavery and sustainable development more broadly. Data about victims and abuses is often noisy, poor quality or simply not collected. Developments in computational statistics can be really powerful here. They will provide a framework to deal with poor quality and missing data, while simultaneously avoiding specific and arbitrary assumptions about how the abuses are happening. Current methods require researchers to make specific assumptions about the abuses they are modelling which are difficult to justify from the data. The methods I develop will move away from this, instead making more general, mathematical assumptions. This will allow the data to speak for itself and can provide better counterfactual evidence and more realistic conclusions. To meet this aim, I bring a strong track record of developing these methods for epidemics, where my methods have been shown to reduce the need for specific assumptions when the data is poor quality. However, this flexibility comes at the cost of a larger computational burden, increased uncertainty in the results, and a requirement for technical expertise when using the methods. To speed up progress to meeting the Sustainable Development Goals, researchers need methods that can be used in practice. I will lead the development of effective computational statistical methods. By reducing the computational burden, providing mechanisms to deal with the uncertainty in the results, and making methods easy to implement, they will become much more attractive to non-statisticians. I have already shown how my developments can considerably reduce the data collection burden when mapping poverty, making these methods more attractive to research and organisations working in poverty reduction. A key part of this fellowship is collaboration with a research software engineer who can develop data systems and software that other researchers and organisations can use to implement my methods. I will use my methods to solve pressing problems in modern slavery and advance the field to meet the UN's goal to end slavery by 2030. I will work with my project partners to map modern slavery at local, national and international levels. This fellowship has the potential to save lives and show how computational statistics can advance progress towards the Sustainable Development Goals. By leveraging support from my project partners, I will influence politicians and policy makers to use my results to safeguard victims and prevent potential victims from suffering from modern slavery abuses.

In times of crisis, we know that offenders continue to commit crimes, and do so in a manner afforded by the new context (Thornton & Voigt, 2012). Sexual offenders are versatile (Lovell et al., 2019), and change their offending behaviour in accordance with the opportunity to offend (Woodhams & Komarzynska, 2014). The Covid-19 outbreak is a crisis that will alter offender behaviour, as well as who is vulnerable to sexual violence and under what circumstances. Our research is highly urgent because, in the UK and internationally, the police and other stakeholders need to know now how to protect people from increased and new vulnerabilities to sexual violence created by Covid-19, and how to best support those victimised. We will address this research gap, documenting the 'who, what, when, where and how' of stranger sexual offending (Leclerc et al. 2016), pre-, peri- (and potentially, post-) Covid-19, and across shorter time-periods defined by differing local/national restrictions. Our project partner, the Serious Crime Analysis Section (SCAS) of the National Crime Agency, has a unique, large dataset of serious stranger sexual offences. Using this, we will document how offender modus operandi (MO) and victim vulnerability changes from March 2020 to September 2021, and compare this to one year of pre-Covid-19 data (March 2019 to February 2020). These data will be subject to repeated, multi-level analyses using our complementary expertise in analytical techniques from the social and engineering sciences. For example, relative frequencies for behaviours will be compared for periods of differing restrictions, and trends will be compared to macro-level findings regarding crime rates being produced by other research groups. The Covid-19 crisis is unlikely to impact on specific behaviours in isolation; therefore, we will also study patterns of co-occurrence between behaviours using clustering techniques. As well as being of urgent relevance to stakeholders, our research will bring new insights to the sparse literature on situational crime prevention and sexual offending (Chiu et al., 2020). Dissemination of our methodology will assist other countries where, during the Covid-19 outbreak, the proportion of stranger sexual offences is high (e.g., Kenya; Flowe et al., 2020). Our findings will be relevant for preparations and responses to future pandemics and events where a population's routine activities (locally/nationally) are changed or disrupted (e.g., pandemics, natural disasters, humanitarian emergencies).

Footwear impressions provide a source of evidence within a range of criminal investigations including gathering of criminal intelligence. A potential suspect will leave foot or footwear impressions en route to, at and while exiting, a crime scene. This not only allows a sequence of events to be determined but may also link a suspect to a scene if their footwear is distinctive, for example as a consequence of damage or wear. Indoors crime scene officers deal mainly with two-dimensional traces; impressions left by a foot tracking mud, blood or other bodily fluids. Three-dimensional tracks, the proverbial footprint in the flower bed, are common at outdoor scenes. Traditional methods for the collection of three-dimensional traces consist of photography and casting, supported by two-dimensional pattern recognition that can type a footwear sole to a particular make or model of shoe. Three-dimensional imaging is now available as an alternative or complementary option, particularly as algorithms for digital photogrammetry have improved dramatically in recent years allowing easy operational deployment. No expensive three-dimensional scanners are required, only that a crime scene photographer take a few moments to collect additional oblique photographs of footwear impression. Consequently, three-dimensional analyses of footwear impressions are now already possible at a routine operational level, but remain the exception rather than the norm. As part of previous NERC grant NE/H004246/1 into ancient footprints we developed a range of methods and freeware to facilitate the three-dimensional capture and analysis of footprints. Engaging with police as part of the Impact Plan demonstrated the interest and potential of such methods to enhance the analysis of three-dimensional trace evidence especially by allowing statistical analysis of differences between tracks and/or footwear. Currently comparison is done primarily via visual inspection rather than by quantitative and statistical comparison. Working with our Project Partners we propose to employ a software engineer to draw on this research, practice and existing code to create a single integrated software application for the capture, analysis and presentation of three-dimensional footwear evidence which will allow routine operational deployment by police and forensic agencies both in the UK and overseas. This will change the fundamental cost-benefit ratio associated with the collection of this type of evidence, such that three-dimensional imaging can become the norm rather than the exception. Software of this sort needs to be available to all parties involved in forensic jurisprudence - the defence as well as the prosecution - without handicap of cost. As such our proposed software will be made available as freeware rather than commercialised, which will also assist with user adoption. This knowledge translation has the potential to contribute to criminal investigations and in the safeguarding of society.

The landscape of where modern slavery and sexual exploitation takes place has changed over the past decade with the onset of digital technologies dominating the organisation of the commercial sex industry. Adult Service Websites, where most sexual services are advertised, negotiated and facilitated in the UK, have been identified as a space where offenders and traffickers can manipulate, entrap, coerce and force individuals into selling sexual services. Whilst the majority of commercial sex interactions are amongst consenting adults and legal, the role of ASWs in facilitating offending behaviour is complicated and least regulated. There are many agencies trying to understand this relationship, with national intelligence services understanding routes to trafficking and the police working to identify victims and target offenders. In addition there are first responders who deliver interventions to victims to assist with treating their crimes seriously and helping individuals move away from exploiters. This project, will for the first time, bring together a range of organisations who are working to prevent modern slavery in ASWs, in an effort to understand, share new knowledge and learning, and work towards strategies and actions plans that can reduce crimes of human trafficking and sexual exploitation. The core partners in the project are the National Crime Agency, National Police Chief's Council, and the NGO Unseen. Together we will investigate how ASWs can be at the forefront of preventing modern slavery, what those who use ASWs to sell and buy sex think about the platforms, legislation to govern them and strategies to prevent harm. The design of the project includes survivors who will inform the research process, data analysis and knowledge transfer activities. Capacity building activities are built into the design by implementing a training programme for survivors around research skills, upskilling, employability skills and access to higher education. We plan to develop training for third party businesses around sexual exploitation as well as engage the ASW operators in developing transparent and robust mechanisms to prevent their websites harbouring exploitation. Our work will feed directly into government discussions, APPGs and other forums to bring this contemporary data to the places where modern slavery, sex work and policing are discussed at strategic and operational levels.

The project considers the economical, psychological and social effects of ransomware. Ransomware is a particular type of malware, and a new crime of extortion committed online. Malicious software gets installed through a phishing email or a drive-by download on a website. When it runs, it performs an action such as the encryption of the user's files, and asks a ransom for this action to be undone. The victim is coerced into paying through psychological manipulations which sometimes masquerade as advice. Due to the subtle ways that the technological aspects of the crime blend with - and are exploited through - various human dimensions, it has profound economic, psychological and societal impacts upon its victims, which makes its eradication all the more complicated. Law Enforcement Agencies have estimated that losses to criminals using ransomware are many millions of pounds, but the true costs may never be known because victims have shown to be particularly reluctant to report. This project sets out to answer the following questions: Why is ransomware so effective as a crime and why are so many people falling victim to it? Who is carrying out ransomware attacks? How can police agencies be assisted? What interventions are required to mitigate the impacts of ransomware? In order to do so, the project gathers data from Law Enforcement Agencies (which have agreed to closely collaborate with the project), through surveys of the general public and SMEs, and through interviews with stakeholders. The data will be analysed using script analysis, behavioural analysis, and other profiling techniques, leading to narratives regarding the criminals, the victims, and the typical ransomware scenario. Economical and behavioural models of ransomware will then be constructed and used to improve ransomware mitigation and advice, as well as support for law enforcement.