Honour based abuse covers a wide range of family abuse including female genital mutilation and forced marriage. Safeguarding professionals need to identify where abuses are happening in their local community to best support those at risk of these crimes and take preventative action. However, there is little local data about these kinds of crime. To tackle this problem, we ran comparative judgement surveys to map abuses at local level, where participants where shown pairs of wards and asked which had a higher rate of honour based abuse. In previous comparative judgement studies, participants reported fatigue associated with comparisons between areas with similar levels of abuse. Allowing for tied comparisons reduces fatigue, but increase the computational complexity when fitting the model. We designed an efficient Markov Chain Monte Carlo algorithm to fit a model with ties, allowing for a wide range of prior distributions on the model parameters. Working with South Yorkshire Police and Oxford Against Cutting, we mapped the risk of honour based abuse at community level in two counties in the UK.

翻译：基于荣誉的虐待涵盖包括女性生殖器切割和强迫婚姻在内的广泛家庭虐待行为。保护专业人员需要识别其所在社区中此类虐待的发生地点，以便为面临这些犯罪风险的人群提供最佳支持并采取预防措施。然而，关于此类犯罪的本地数据极为匮乏。为解决这一问题，我们开展了比较判断调查以绘制地方层面的虐待分布图，调查中向参与者展示成对的行政区，并询问哪个区域具有更高的基于荣誉的虐待发生率。在以往的对比判断研究中，参与者报告了在虐待水平相近区域间进行比较时产生的疲劳感。允许平局比较虽能减轻疲劳，但会显著增加模型拟合的计算复杂度。我们设计了一种高效的马尔可夫链蒙特卡洛算法来拟合包含平局的模型，该算法支持对模型参数采用广泛的先验分布。通过与南约克郡警察局和牛津反切割组织合作，我们绘制了英国两个郡社区层面的基于荣誉的虐待风险分布图。