Hidden in Plain Sight: Detecting Illicit Massage Businesses from Mobility Data

TL;DR

This paper presents a mobility data-based machine learning approach to detect illicit massage businesses, achieving high accuracy and aiding law enforcement in prioritizing inspections to combat human trafficking.

Contribution

Introduces a novel positive-unlabeled learning model using mobility data features to identify high-risk illicit massage businesses with improved detection performance.

Findings

Model achieves 0.97 AUC and 0.84 Average Precision.

Prioritizing top 10% high-risk venues captures 53% of illicit operations.

Operational signatures are resistant to manipulation.

Abstract

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.