01. A Retrospective Process Monitoring Technique for Sex Trafficking Data using a Flexible Parametric Time-Series Model

Sex trafficking is a pervasive worldwide issue that poses significant challenges for law enforcement agencies, non-profits, and researchers alike. In this study, we present a novel method to monitor counts of sexual service advertisements in over 600 cities across the United States, which have been collected over the past two to three years. The ad volume is believed to be linked to the prevalence of sex trafficking. The data structure poses several difficulties, namely non-stationarity, autocorrelation, and over or under-dispersion. Our method encompasses an approach that models the daily and weekly absolute data differences using a flexible parametric time series model based on the zero-inflated Conway-Maxwell-Poisson distribution with change-points. By effectively capturing the nuanced temporal dynamics within the data, our method overcomes limitations in existing monitoring approaches that may assume independence or equi-dispersion. Leveraging the flexibility of this parametric time-series model, we can better understand key patterns, trends, and anomalies in this data set. Our findings, illustrated with the data, demonstrate the efficacy of the proposed method as a powerful tool for retrospective monitoring of sexual service advertisement data in the fight against sex trafficking.

Process Monitoring

Count Data

Integer-valued Data

Parametric Modeling

Sex Trafficking Data

Discrete Time Series