Illicit massage businesses (IMBs) masquerade as legitimate massage parlors while facilitating commercial sex and human trafficking. Law enforcement must identify these businesses within a dense population of lawful establishments, but investigative resources are limited and the illicit status of each location is unknown until inspection. Detection methods based on online reviews offer some insight, yet operators can manipulate these signals, leaving covert establishments undetected. IMBs constitute one of the largest segments of indoor sex trafficking in the United States, with an estimated 9,000 establishments. Mobility data offers an alternative to online signals, covering establishments that avoid digital visibility entirely. We derive features from mobility data spanning temporal visitation patterns, dwell times, visitor catchment areas, and demand stability. Because confirmed labels exist only for establishments identified through advertising platforms, we employ positive-unlabeled learning to address the label asymmetry in ground truth. The model achieves 0.97 AUC and 0.84 Average Precision. Four operational signatures characterize high-risk establishments: demand consistency, evening-concentrated visits, compressed service durations, and locally drawn clientele. The model produces risk scores for each business-week observation. Aggregating to the business level, prioritizing the highest-risk 10% of massage establishments captures 53% of known illicit operations, a 5.3-fold improvement over uninformed inspection. We develop a decision-support system that produces calibrated prioritization scores for law enforcement, enabling investigators to concentrate inspections on the highest-risk venues. The operational signatures may resist strategic manipulation because they reflect actual operations rather than online signals that operators can control.

翻译：非法按摩场所（IMBs）伪装成正规按摩院，实则从事商业性交易和人口贩运活动。执法部门需在大量合法场所中识别此类非法经营点，但调查资源有限，且每个地点的非法状态需经实地核查方能确认。基于在线评论的检测方法虽能提供部分线索，但经营者可操纵这些信号，导致隐蔽场所难以被发现。IMBs构成美国室内性交易的最大组成部分之一，估计涉及9,000家经营场所。移动数据为在线信号提供了替代方案，可覆盖完全规避数字能见度的场所。我们从移动数据中提取特征，涵盖时序访问模式、停留时长、访客辐射范围及需求稳定性。由于仅能通过广告平台获取已确认的标注数据，我们采用正例-未标注学习以处理真实数据中的标签不对称问题。模型达到0.97的AUC值与0.84的平均精确率。高风险场所呈现四大运营特征：需求稳定性、夜间集中访问、压缩的服务时长及本地化客源。模型为每个商业周观测数据生成风险评分。在商业层级进行聚合后，对风险最高的10%按摩场所进行优先排查，可覆盖53%的已知非法经营点，较无目标排查效率提升5.3倍。我们开发了一套决策支持系统，为执法部门提供校准后的优先排查评分，使调查人员能集中资源检查最高风险场所。这些运营特征可能具备抗策略操纵性，因其反映实际运营状况而非经营者可控的在线信号。