Uncovering Hidden Crimes: Using Math to Spot Honour-Based Abuse

Honour-based abuse is a serious issue that includes things like forced marriage and female genital mutilation. It's hard to track because local data is scarce. To tackle this, experts used a clever method called comparative judgement. They asked people to compare different areas and guess which had more abuse. But comparing similar areas was tiring. So, they added an option for ties, making it easier. However, this made the math more complex. They solved this by creating a smart algorithm. This allowed them to use different assumptions in their model. By working with South Yorkshire Police and Oxford Against Cutting, they mapped out the risk in two UK counties. This helps professionals protect those at risk. Honour-based abuse is not just one thing. It's a range of harmful practices. These include forced marriage, female genital mutilation, and other forms of control. Victims are often isolated and afraid. They may not report the abuse due to fear or shame. This makes it hard for authorities to know where to focus their efforts. Local data is often lacking. This is where comparative judgement comes in. It's a way to gather information without direct reports. People compare areas based on their knowledge or perceptions. This gives a rough idea of where abuse might be happening. Comparative judgement is not new. It's been used in education to assess student work. But applying it to honour-based abuse is different. The challenge is the nature of the abuse. It's hidden and underreported. Areas with similar levels of abuse can be hard to compare. This leads to fatigue and less accurate results. Allowing for ties helps. It acknowledges that some areas may be equal. But it complicates the math. The solution was an efficient algorithm. This algorithm fits the model with ties. It also allows for different assumptions about the data. This flexibility is key. It means the model can adapt to new information. The project involved collaboration. South Yorkshire Police and Oxford Against Cutting worked together. They aimed to map the risk in two counties. The results help professionals. They can see where abuse is more likely. This guides their efforts to protect victims. It also helps them prevent future abuse. The model is a tool. It's not perfect, but it's a start. It shows how math can be used to tackle real-world problems. It also highlights the importance of collaboration. Different groups bring different skills. Together, they can make a bigger impact.