Search results

This research project developed and fully documented a method to estimate the number of females and males trafficked for the purposes of sexual and labor exploitation from eight countries (Colombia, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, Peru, and Venezuela) into the United States at the Southwest border. The model utilizes only open source data. This research represents the first phase of a two-phase project and

• Provides a conceptual framework for identifying potential data sources to estimate the number of victims at different stages in trafficking
• Develops statistical models to estimate the number of males and females at risk of being trafficked for sexual and labor exploitation from the eight countries, and the number of males and females actually trafficked for sex and labor
• Incorporates into the estimation models the transit journey of trafficking victims from the eight countries to the southwest border of the United States
• Designs the estimation models such that they are highly flexible and modular so that they can evolve as the body of data expands
• Utilizes open source data as inputs to the statistical model, making the model accessible to anyone interested in using it
• Presents preliminary estimates that illustrate the use of the statistical methods
• Illuminates gaps in data sources.

The data included in this collection are the open source data which were primarily used in the models to estimate the number of males and females at risk of being trafficked.

This three-year study used quantitative and qualitative data collected between 2003 and 2015 to assess the scale and nature of gang involvement in sex trafficking in San Diego, California. At the time of study, San Diego was ranked as one of the United States' highest areas of commercial sexual exploitation of children (CSEC). This mixed-methods study has generated the following datasets:

• Survivor Services Dataset (DS1 and DS2): A composite of intake surveys conducted by the prostitution diversion program Freedom From Exploitation (FFE) (DS1) and eight organizations that provide direct services to sex trafficking victims/survivors (DS2)
• Law Enforcement Reporting Dataset (DS3 and DS4): Booking records provided by the San Diego Sheriff's Department (DS3), and arrest records provided by the Automated Regional Justice Information System (ARJIS) (DS4)
• Schools Dataset (DS5): Summaries of focus group interviews of staff employed by San Diego County school districts (due to disclosure concerns, full transcripts were not released)
• Facilitator Interview Dataset (DS6): Summaries of interviews of incarcerated and community members with knowledge of trafficking (due to disclosure concerns, full transcripts were not released)
• Social Media Analytics Dataset (DS7): Search criteria and schema of relevant data fields for social network analysis conducted using Twitter data (due to disclosure concerns and dataset size, the social media data themselves were not released)

Survivor services, law enforcement, schools, and facilitator interview data were all collected and analyzed as part of the main NIJ award. The social media analytics data were collected and analyzed under a subaward by the SecDev Cyber Group.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

Product counterfeiting is the fraudulent reproduction of trademark, copyright, or other intellectual property related to tangible products without the authorization of the producer and motivated by the desire for profit. This study create a Product Counterfeiting Database (PCD) by assessing multiple units of analysis associated with counterfeiting crimes from 2000-2015: (1) scheme; (2) offender (individual); (3) offender (business); (4) victim (consumer); and (5) victim (trademark owner). Unique identification numbers link records for each unit of analysis in a relational database.

The collection contains 5 Stata files and 1 Excel spreadsheet file.

• Scheme-Data.dta (n=196, 35 variables)
• Offender-Individual-Data.dta (n=551, 16 variables)
• Offender-Business-Data.dta (n=310, 5 variables)
• Victim-Consumer-Data.dta (n=54, 8 variables)
• Victim-Trademark-Owner-Data.dta (n=146, 5 variables)
• Relational-Data.xlsx (4 spreadsheet tabs)