Deciphering and disrupting the social, spatial and temporal systems behind transnational human trafficking: a data science approach

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Human trafficking is widely described as one of the world's biggest, fastest growing and most lucrative organised crimes. For all the bold rhetoric, there is woefully little scientific evidence on human trafficking's scale, nature, distribution, organisation and evolution. The number of victims officially identified in the UK grows year-on-year but these cases are just the tip of the iceberg as many victims go unreported or undetected. A recent estimate suggested the UK had around 7,000 to 10,000 trafficking victims in one year. Human trafficking is not only a complex social issue but also a very emotive one: it often involves the sale of vulnerable people and extreme exploitation of their bodies and labour. It causes serious harms, undermining the safety, security and welfare of individual victims, communities and nations. Many millions of pounds are spent each year trying to combat trafficking. Without a strong evidence base, there is a very real risk that myths, stereotypes, assumptions and hidden agendas step in to fill the gaps. Ill-informed measures can be very costly, ineffective and even actively detrimental. It is therefore vital to invest in high-quality research to improve understanding and inform policy and practice. Our ultimate vision is to improve how data are used to analyse and intervene in transnational human trafficking. Our research will support a far more targeted and nuanced approach to counter-trafficking. It focuses on three key dimensions to the complex systems involved in trafficking: social structures, geographical space and time. We will systematically examine the structure of the social networks in which traffickers and victims are embedded, identifying key roles and vulnerabilities. We will determine where major steps in the trafficking process occur, mapping hotspots (places where crime concentrates), profiling key locations and examining supply and demand, risk and resilience and geographical flows. We will analyse patterns and trends in trafficking and their evolution over time. Throughout the research, we will explore the implications of our results for better detecting, deterring and disrupting trafficking, increasing resilience and reducing harms.Our project will be the largest and most comprehensive assessment of transnational human trafficking affecting the UK. It will include adult and child victims trafficked for diverse purposes, including exploitation in the sex trade, the home and numerous other licit and illicit labour markets. Data access is a notorious barrier to trafficking research but we have remarkable access to important national datasets: the UK's central system for victim identification; the Modern Slavery Helpline; and a unique research dataset on trafficking networks. We will also draw widely on publicly available datasets (e.g. Census data) to inform our analyses. We will use methods that vastly advance understanding of human trafficking but have rarely been possible in this field due to shortages of data and skills. Our work is truly interdisciplinary, drawing on geography, crime science, criminology, data science, epidemiology, sociology, computer science and mathematics. Our research will generate vital insights into transnational human trafficking on an unprecedented scale. We have an outstanding team that combines leading academics, non-governmental organisations, law enforcement and government. Our collaborative approach positions us well to translate excellent scientific research into genuine change. As well traditional academic outputs, we will run interactive workshops in the UK and abroad, develop a software solution, training and toolkits, produce policy briefings and deliver an innovative and tightly targeted campaign to counter trafficking. We will also run events and produce outputs designed to stimulate more high-quality research and research-informed interventions not just around human trafficking but other transnational organised crimes.