Research on the prevalence of human trafficking (HT) is relatively scarce, even though more attention has been brought to this human rights issue in the past couple of decades. Widely known as a form of modern day slavery, trafficking of persons for sexual exploitative reasons to earn a profit for the trafficker occurs in every major city across the country, despite common misconceptions that it only thrives in foreign countries. To expand on limited existing literature on human trafficking, this research study explores possible correlations among areas of high violent crime rates, drug arrests, the presence of demand reduction strategies, sociodemographic variables, and tourism measures among the Florida counties to determine if they can act as predictive measures to locate areas where a human trafficking arrest is the most likely to occur. These relationships were investigated through the Offender Based Transaction Systems (OBTS), documented court actions filed by prosecutors between 2012-2016 of human trafficking arrests, and comparing it to violent crime rates and drug arrest rates for the Florida counties using data from the Florida Department of Law Enforcement, in conjunction with demand reduction efforts. The results from this study did not support the hypothesis that the higher rate of violent crime and drug arrest rates would significantly increase to the presence of a human trafficking arrest. Instead, demand reduction efforts, e.g. street and web sting operations, neighborhood action, and public awareness, emerged as the only significant variable that predicted the likelihood of a human trafficking arrest occurring in a county. These findings stress the importance of reduction efforts targeting the leading consumers in this lucrative market; the demand for sex from sex buyers.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-6849 |
| Date | 01 January 2018 |
| Creators | Diaz, Madelyn |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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