Why are You
            <i>Here</i>
            ? Modeling Illicit Massage Business Location Characteristics with Machine Learning

White, Anna; Guikema, Seth D.; Carr, Bridgette

doi:10.1080/23322705.2021.1982238

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Keskin et al (Keskin et al 2021) proposed a process of grouping online advertisements to predict the movement of sex trafficking networks based on where related advertisements appear. White et al (White et al 2021) used web scraping to gather data on illicit massage parlors, and determines potential contributing factors for where illicit massage parlors may be located. These works use a visible, forward-facing part of the sex trafficking networks, the advertisements, to perform their analysis.…”

Section: Background On or Modeling Of Sex Trafficking Networkmentioning

confidence: 99%

“…Since these networks are illicit in nature, their operations are hidden to avoid detection (Farrell & De Vries 2020, Fedina 2015, Konrad, Trapp & Maass 2017, Konrad, Trapp, Palmbach & Blom 2017. Current data for analytic approaches often comes from case files, scraped web data on sex advertisements, or data pertaining to massage parlors (Keskin et al 2021, Mayorga et al 2019, White et al 2021. This data can be viewed as the "forwardfacing" aspects of the operations of sex trafficking networks in terms of how they interact with the outside world, thus it is (somewhat) publicly accessible.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Transdisciplinary Approach for Generating Synthetic but Realistic Domestic Sex Trafficking Networks

Daniel¹,

Melander²,

Singerhouse³

et al. 2022

Preprint

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One of the major challenges associated with applying operations research (OR) models to disrupting human trafficking networks is a limited amount of reliable data sources readily available for public use, since operations are intentionally hidden to prevent detection, and data from known operations are often incomplete. To help address this data gap, we propose a network generator for domestic sex trafficking networks by integrating OR concepts and qualitative research. Multiple sources have been triangulated to ensure that networks produced by the generator are realistic, including law enforcement case file analysis, interviews with domain experts, and a survivor-centered advisory group with first-hand knowledge of sex trafficking.The output models the relationships between traffickers, so-called "bottoms", and victims. This generator allows operations researchers to access realistic sex trafficking network structures in a responsible manner that does not disclose identifiable details of the people involved. We demonstrate the use of output networks in exploring policy recommendations from max flow network interdiction with restructuring. To do so, we propose a novel conceptualization of flow as the ability of a trafficker to control their victims. Our results show the importance of understanding

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Section: Background On or Modeling Of Sex Trafficking Networkmentioning

confidence: 99%