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 traffickingDevelops 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 laborIncorporates into the estimation models the transit journey of trafficking victims from the eight countries to the southwest border of the United StatesDesigns 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 itPresents preliminary estimates that illustrate the use of the statistical methodsIlluminates 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.

What is the Counter-Trafficking Data Collaborative?

The Counter-Trafficking Data Collaborative is the first global data hub on human trafficking, publishing harmonized data from counter-trafficking organizations around the world. Launched in November 2017, the goal of CTDC is to break down information-sharing barriers and equip the counter-trafficking community with up to date, reliable data on human trafficking.

The global victim of trafficking dataset

The CTDC global victim of trafficking dataset is the largest of its kind in the world, and currently exists in two forms. The data are based on case management data, gathered from identified cases of human trafficking, disaggregated at the level of the individual. The cases are recorded in a case management system during the provision of protection and assistance services, or are logged when individuals contact a counter-trafficking hotline. The number of observations in the dataset increases as new records are added by the contributing organizations. The global victim of trafficking dataset that is available to download from the website in csv format has been mathematically anonymized, and the complete, non k-anonymized version of the dataset is displayed throughout the website through visualizations and charts showing detailed analysis.

Where do the data come from?

The data come from a variety of sources. The data featured in the global victim of trafficking dataset come from the assistance activities of the contributing organizations, including from case management services and from counter-trafficking hotline logs.

How are the global datasets created?

Each dataset has been created through a process of comparing and harmonizing existing data models of contributing partners and data classification systems. Initial areas of compatibility were identified to create a unified system for organizing and mapping data to a single standard. Each contributing organization transforms its data to this shared standard and any identifying information is removed before the datasets are made available.

How is the individual-level data protected?

Step 1

Counter-trafficking case data contains highly sensitive information, and maintaining privacy and confidentiality is of paramount importance for CTDC. For example, all explicit identifiers, such as names, were removed from the global victim dataset and some data such as age has been transformed into age ranges. No personally identifying information is transferred to or hosted by CTDC, and organizations that want to contribute are asked to anonymize in accordance to the standards set by CTDC.

Step 2

In addition to the safeguard measures outlined in step 1 the global victim dataset has been anonymized to a higher level, through a mathematical approach called k-anonymization. For a full description of k-anonymization, please refer to the definitions page.

IOM collects and processes data in accordance to its own Data Protection Policy. The other contributors adhere to relevant national and international standards through their policies for collecting and processing personal data.

How to interpret the data?

These data reflect the victims assisted/identified/referred/reported to the contributing organizations, which may not represent all victims identified within a country. Nevertheless, the larger the sample size for a given country (or, the more victims displayed on the map for a given country), the more representative the data are likely to be of the identified victim of trafficking population.

A larger number of identified victims of trafficking does not imply that there is a larger number of undetected victims of trafficking (i.e. a higher prevalence of trafficking).

In addition, samples of identified victims of trafficking cannot be considered random samples of the wider population of victims of trafficking (which includes unidentified victims), since counter-trafficking agencies may be more likely to identify some trafficking cases rather than others. However, with this caveat in mind, the profile of identified victims of trafficking tends to be considered as indicative of the profile of the wider population, given that the availability of other data sources is close to zero.

How does human trafficking case data relate to prevalence data?

There are currently no global or regional estimates of the prevalence of human trafficking. National estimates have been conducted in a few countries but they are also based on modelling of existing administrative data from identified cases and should therefore only be considered as basic baseline estimates. Historically, producing estimates of the prevalence of trafficking based on the collection of new primary data through surveys, for example, has been difficult. This is due to trafficking’s complicated legal definition and the challenges of a...

Dataset

This dataset was created by Shubham

Released under CC0: Public Domain

Contents

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. This study examined life histories and experiences of individuals involved in the sex trade in New York City. Also interviewed were twenty-eight criminal justice policymakers, practitioners, and community representatives affiliated with New York City's Human Trafficking Intervention Courts (HTICs). The collection contains 1 SPSS data file (Final-Quantitative-Data-resubmission.sav (n=304; 218 variables)). Demographic variables include gender, age, race, ethnicity, education level, citizenship status, current housing, family size, sexual orientation, and respondent's place of birth.

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. This study was a process evaluation of three programs funded by the U.S. Department of Justice (DOJ) Office for Victims of Crime (OVC) to identify and provide services to victims of sex and labor trafficking who are U.S citizens and lawful permanent residents (LPR) under the age of 18. The three programs evaluated in this study were: The Standing Against Global Exploitation Everywhere (SAGE) Project The Salvation Army Trafficking Outreach Program and Intervention Techniques (STOP-IT) program The Streetwork Project at Safe Horizon The goals of the evaluation were to document program implementation in the three programs, identify promising practices for service delivery programs, and inform delivery of current and future efforts by the programs to serve this population. The evaluation examined young people served by the programs, their service needs and services delivered by the programs, the experiences of young people and staff with the programs, and programs' efforts to strengthen community response to trafficked youth.

Content:

This dataset contains information about total number of human trafficking cases reported per State/Union Territories in India, number of victims trafficked/rescued, nationality of the victims, age-group, purpose of trafficking, police and court disposal of cases, and number of culprits arrested/acquitted.

To know more about the Indian states and Union Territories, you may refer Know India

Till 2019, India had 29 states and 7 Union Territories. But in 2020, there were changes in the demographics and now, there are 28 states and 8 union territories.

2018 - (29 states and 7 Union Territories)

• Rate of Cognizable Crimes- this column refers to cases reported per 1 lakh population.
• Due to non-receipt of data from Assam & Jharkhand for 2018, data furnished for 2017 has been used.
  ###### 2019 - (29 states and 7 Union Territories)
• Due to non-receipt of data from West Bengal in time for 2019, data furnished for 2018 has been used.
  ###### 2020 - (28 states and 8 Union territories).
• The earlier two UTs of D & N Haveli and Daman & Diu were combined into 1.
• State of Jammu and Kashmir was changed to Union Territories of:
  1. Jammu & Kashmir
  2. Ladhak
• The number of cases reported in 2018, 2019 for Jammu & Kashmir includes data for Ladhak too.

Here is a short description about few terms present in the dataset. For further reading, you may refer this site.

1. Charge Sheet- is the complaint of a private individual on which criminal proceedings are initiated. When the charge sheet is sent by police to Magistrate, the preliminary stage of investigation and preparation is over.
2. Final report- The charge sheet is followed by the Final Report. It records the conclusion arrived at by the Police after the investigation process.

So, if Final Report column contains 0, it implies that the investigation is not yet complete.

Acknowledgement

The data has been taken from the National Crime Records Bureau portal of India.

Inspiration

I recently watched some movies/documentaries on Human Trafficking which prompted me to compile this dataset.

BackgroundThere is very limited evidence on the health consequences of human trafficking. This systematic review reports on studies investigating the prevalence and risk of violence while trafficked and the prevalence and risk of physical, mental, and sexual health problems, including HIV, among trafficked people. Methods and FindingsWe conducted a systematic review comprising a search of Medline, PubMed, PsycINFO, EMBASE, and Web of Science, hand searches of reference lists of included articles, citation tracking, and expert recommendations. We included peer-reviewed papers reporting on the prevalence or risk of violence while trafficked and/or on the prevalence or risk of any measure of physical, mental, or sexual health among trafficked people. Two reviewers independently screened papers for eligibility and appraised the quality of included studies. The search identified 19 eligible studies, all of which reported on trafficked women and girls only and focused primarily on trafficking for sexual exploitation. The review suggests a high prevalence of violence and of mental distress among women and girls trafficked for sexual exploitation. The random effects pooled prevalence of diagnosed HIV was 31.9% (95% CI 21.3%–42.4%) in studies of women accessing post-trafficking support in India and Nepal, but the estimate was associated with high heterogeneity (I2 = 83.7%). Infection prevalence may be related as much to prevalence rates in women's areas of origin or exploitation as to the characteristics of their experience. Findings are limited by the methodological weaknesses of primary studies and their poor comparability and generalisability. ConclusionsAlthough limited, existing evidence suggests that trafficking for sexual exploitation is associated with violence and a range of serious health problems. Further research is needed on the health of trafficked men, individuals trafficked for other forms of exploitation, and effective health intervention approaches. Please see later in the article for the Editors' Summary

The National Referral Mechanism (NRM) is a framework for identifying victims of human trafficking and ensuring they receive the appropriate protection and support. The NRM is also the mechanism through which the UKHTC collects data about victims. This information contributes to building a clearer picture about the scope of human trafficking in the UK. The NRM was introduced in 2009 to meet the UK’s obligations under the Council of European Convention on Action against Trafficking in Human Beings. At the core of every country’s NRM is the process of locating and identifying “potential victims of trafficking” (PVoT). The NRM grants a minimum 45-day reflection and recovery period for victims of human trafficking. Trained case owners decide whether individuals referred to them should be considered to be victims of trafficking according to the definition in the Council of Europe Convention.

An estimated 30 million people are subject to modernday slavery, including forced labor and sexual exploitation. Moreover, the trafficking of human beings is one of the fastest growing transnational criminal activities. Not only is it an abuse of the human rights of the victims involved, but it also incurs social, political, and economic costs for the countries it most impacts. Providing first an overview of the global phenomenon of modern-day slavery, this paper proceeds to study the Greater Mekong Subregion for whose states human trafficking represents a serious challengeone which requires a well-coordinated response to, among other measures, scrutinize labor contracts in risk economic sectors, enhance interstate cooperation, and more effectively identify and prosecute human traffickers.

Context

Human trafficking is thought to be one of the fastest-growing activities of trans-national criminal organizations. It is defined as the trade of humans for the purpose of forced labour, sexual slavery, or commercial sexual exploitation for the trafficker or others. Human trafficking is condemned as a violation of human rights by international conventions. (Source: Wikipedia)

The National Referral Mechanism (NRM) is a framework for identifying victims of human trafficking and ensuring they receive the appropriate protection and support. The NRM is also the mechanism through which the UKHTC collects data about victims. This information contributes to building a clearer picture about the scope of human trafficking in the UK.

The NRM was introduced in 2009 to meet the UK’s obligations under the Council of European Convention on Action against Trafficking in Human Beings. At the core of every country’s NRM is the process of locating and identifying potential victims of trafficking.

Content

For years 2013 to 2016, the following tables are available:

• YEAR_competent_authority.csv

• YEAR_country_of_referral.csv

• YEAR_exploitation_type.csv

• YEAR_referrals_adult.csv

• YEAR_referrals_all.csv

• YEAR_referrals_minor.csv

• YEAR_referring_agency.csv

For 2015 to 2016, there is an additional table:

• YEAR_decision_data.csv

Acknowledgements

Data obtained from UK National Crime Agency, National Referral Mechanism Statistics end of year summary reports (2013 - 2016). Human Trafficking data reports are provided under Publications section of the UK National Crime Agency website. Data tables were extracted from pdf reports with Tabula.

Licensed under Open Government License

Photo by Pedro Gabriel Miziara on Unsplash

In 2023, a total of ***** human traffickers were convicted worldwide, an increase of approximately ***** compared to the previous year. However, the number of convictions remains lower than levels recorded prior to the COVID-19 pandemic.

This article analyzes the images of human trafficking presented by the principal anti-trafficking campaigns created by the Brazilian state and some of its most important allies in civil society. We seek to understand how these campaigns represent victims, perpetrators and trafficking itself in their attempts to inculcate in Brazilian society a culture of “resistance to modern slavery”. Such a culture has as its most important component the anonymous denunciation of “suspicious people”. We present an ideal typography of these images, comparing them to similar images produced in the Western European and North American contexts. We then look at the changes and continuities that appear in the Brazilian images over the years, and analyze some of the specific characteristics of Brazilian campaigns. We conclude with a brief discussion of the possible side effects of campaigns that are based on these kinds of iconography.

Human trafficking (HT) is a pervasive global issue affecting vulnerable individuals, violating their fundamental human rights. Investigations reveal that a significant number of HT cases are associated with online advertisements (ads), particularly in escort markets. Consequently, identifying and connecting HT vendors has become increasingly challenging for Law Enforcement Agencies (LEAs). To address this issue, we introduce IDTraffickers, an extensive dataset consisting of 87,595 text ads and 5,244 vendor labels to enable the verification and identification of potential HT vendors on online escort markets. To establish a benchmark for authorship identification, we train a DeCLUTR-small model, achieving a macro-F1 score of 0.8656 in a closed-set classification environment. Next, we leverage the style representations extracted from the trained classifier to conduct authorship verification, resulting in a mean r-precision score of 0.8852 in an open-set ranking environment. Finally, to encourage further research and ensure responsible data sharing, we plan to release IDTraffickers for the authorship attribution task to researchers under specific conditions, considering the sensitive nature of the data. We believe that the availability of our dataset and benchmarks will empower future researchers to utilize our findings, thereby facilitating the effective linkage of escort ads and the development of more robust approaches for identifying HT indicators. The dataset contains text sequences as inputs and labels as the arbitrary vendor IDs obtained by linking the phone numbers mentioned in Backpage escort market advertisements. To protect privacy, all personal information, except for the pseudonyms used by the escorts and the post locations, has been redacted so that it cannot be retrieved. For more details, kindly refer to our research attached to the submission. It is important to emphasize that this dataset should only be used for its intended purpose, research on authorship attribution of vendors on escort markets, and not other commercial/non-commercial purposes.

The MATCHED dataset is a novel multimodal collection of escort advertisements curated to support research in Authorship Attribution (AA) and related tasks. It comprises 27,619 unique text descriptions and 55,115 images (in jpg format) sourced from Backpage escort ads across seven major U.S. cities–Atlanta, Dallas, Detroit, Houston, Chicago, San Fransisco, and New York. These cities are further categorized into four geographical regions—South, Midwest, West, and Northeast—offering a structured dataset that enables both in-distribution and out-of-distribution (OOD) evaluations. Each ad in the dataset contains metadata that links text and visual components, providing a rich resource for studying multimodal patterns, vendor identification, and verification tasks. The dataset is uniquely suited for multimodal authorship attribution, vendor linking, stylometric analysis, and understanding the interplay between textual and visual patterns in advertisements. All text descriptions are carefully processed to redact any explicit references to phone numbers, email addresses, advertisement IDs, age-related information, or other contact details that could be used to identify individuals or vendors. The structured metadata allows researchers to explore how multimodal features contribute to uncovering latent patterns in stylometry and vendor behaviors. A demi-data file showcasing the format and structure of our MATCHED dataset is attached with the entry. Given the sensitivity of the subject matter, the actual dataset resides securely on Maastricht University's servers. Only the metadata will be publicly released on Dataverse to ensure ethical use. Researchers interested in accessing the full dataset must sign a Non-Disclosure Agreement (NDA) and a Data Transfer Agreement with Prof. Dr. Gijs Van Dijck from Maastricht University. Access will only be granted under strict restrictions, and recipients must adhere to the ethical guidelines established by the university's committee. These guidelines emphasize the responsible use of the dataset to prevent misuse and to safeguard the privacy and dignity of all individuals involved.

Dataset

This dataset was created by Zamora McBride

Contents

Dataset

This dataset was created by Husnaa Anwar

Contents

The set contains a directory of phones "hotlines" for people affected by human trafficking

this graph was created in OurDataWorld:

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Definition of the SDG indicator: Indicator 16.1.1 is the “number of victims of intentional homicide per 100,000 population, by sex and age” in the UN SDG framework.

Intentional homicides are unlawful deaths inflicted upon a person with the intent to cause death or serious injury.

Data for this indicator is shown in the interactive visualization.

Target: “Significantly reduce all forms of violence and related death rates” across all countries by 2030.

More research: Further data and research can be found at the Our World in Data topic page on Homicides.

Definition of the SDG indicator: Indicator 16.1.2 is “conflict-related deaths per 100,000 population, by sex, age and cause” in the UN SDG framework.

Data for this indicator is shown in the interactive visualization, using data from the Uppsala Conflict Data Program. It includes both deaths from conflicts within countries and between them.

Target: “Significantly reduce all forms of violence and related death rates” across all countries by 2030.

More research: Further data and research can be found at the Our World in Data topic pages on War and Peace and Terrorism.

This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.

This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.

The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.

For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: All businesses identified as victims in CPD data have been removed from this dataset.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”

Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

We're Zero Trafficking: a data company bringing innovative solutions to anti-trafficking. We’re the only data company to fight human trafficking networks with digital intelligence, proactively detecting and tracking sophisticated criminal organizations who exploit people for profit through force, fraud, and coercion.

Human trafficking organizations are closely bound to other high-priority threats (drug trafficking, organized theft rings, terrorists, money launderers and more) making human trafficking network data relevant to law enforcement, military, and intelligence. Network-centric investigations enable more effective dismantling of sex trafficking enterprises, while alleviating substantial testimonial burdens traditionally placed on survivors. That’s where we come in.

We proactively discover never-before-seen criminal networks and make data on their members and whereabouts available to you, so you can shrink your investigation time, mitigate corporate risk, begin transformative counter-threat workflows, and more.

We’re proud to make our threat data available on Datarade, offering location and entity information to provide risk analysts, investigators, and decisionmakers with accurate information about the trafficking threats which may impact their operations. With a wide range of partners from retailers to government agencies, our unique solutions to the human trafficking problem are designed to support a network-centric anti-trafficking approach.

Zero Trafficking has produced and maintained a dataset of more than 30,000 active and closed illicit massage businesses (IMBs) in the United States. These are storefront locations where commercial sex is purportedly available and, as a result, may be of interest to law enforcement personnel or private investigation units tasked with monitoring public corruption, money laundering, organized crime, human trafficking, drug trafficking, and more.

For those not directly involved in criminal investigations, this dataset displays concentrations of criminal businesses currently thriving in the cities that are most permissive of them, and may prove useful in assessing future business placement, likely trends in property value, and more.

Zero Trafficking’s IMB data feed tracks month over month changes in IMB distribution and activity, allowing for more accurate assessments of:

• Business risk to community
• Likelihood of ongoing human trafficking activity
• Effectiveness of state and local policies to curb IMB activity
• IMB responses to law enforcement raids
• Developing crime trends which may impact property value
• Relational intersection between human trafficking and other crime trends