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.

East Asia and the Pacific saw the highest number of convictions related to human trafficking in 2023, just ahead of Europe, reaching *****. Meanwhile, South and Central Asia saw the highest number of victims identified that year.

This study was a response to the Trafficking Victims Protection Reauthorization Act passed by Congress in 2005, which called for a collection of data; a comprehensive statistical review and analysis of human trafficking data; and a biennial report to Congress on sex trafficking and unlawful commercial sex acts. It examined the human trafficking experiences (and to a lesser extent commercial sex acts) among a random sample of 60 counties across the United States. In contrast to prior research that had examined the issue from a federal perspective, this study examined experiences with human trafficking at the local level across the United States. The specific aims of the research were to: Identify victims and potential victims of domestic labor and sex trafficking; Determine whether they have been identified as victims by law enforcement; and Explore differences between sex trafficking and unlawful commercial sex. To achieve these goals the researchers collected data through telephone interviews with local law enforcement, prosecutors, and service providers; a mail-out statistical survey completed by knowledgeable officials in those jurisdictions; and an examination of case files in four local communities. This latter effort consisted of reviewing incident and arrest reports and charging documents for a variety of offenses that might have involved criminal conduct with characteristics of human trafficking. Through this method, the researchers not only gained a sense of how local authorities handled these types of cases but also the ways in which trafficking victims "fall through the cracks" in the interfaces between local and federal judicial systems as well as among local, state, and federal law enforcement and social service systems.

This statistic shows the share of human trafficking defendants charged in U.S. district court in the fiscal year of 2015, by ethnicity. In that year, **** percent of human trafficking defendants were White.

From 2018 to 2023, ***** people were arrested by the police in drug trafficking related operations. In 2018, the most people were arrested, with ***. Since then, the number of arrests has been decreasing, with *** individuals arrested in 2023.

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.

This dataset provides year wise, crime head wise, and gender wise data on the disposal of persons arrested for crimes against children in metropolitan cities. It covers offences such as rape and sexual offences under POCSO, kidnapping and abduction, human trafficking, buying and selling of minors for prostitution, child labour violations, murder, attempt to murder, exposure and abandonment, foeticide/infanticide, assault and hurt, cybercrimes, and offences under the Juvenile Justice Act and Immoral Traffic (Prevention) Act, with separate counts for boys and girls. It includes outcomes such as arrested, chargesheeted, convicted, acquitted, and discharged. Note: Data for 2017 is unavailable, as the source table mistakenly contains disposal data for crimes against women instead of children.

This dataset provides year, crime head, and gender wise details of crime cases against children along with the disposal status of persons arrested. It covers offences such as POCSO sexual offences, kidnapping and abduction, murder, human trafficking, child labour, cyber offences, infanticide, and immoral trafficking, with disposal categories including persons arrested, chargesheeted, convicted, acquitted, and discharged, separately for boys and girls.

This project examined practices and initiatives undertaken by prosecutors across the United States to address trafficking in persons (TIP) in order to learn about TIP case identification and case building; when jurisdictions prosecute utilizing their state's TIP statute or alternative charges; and how prosecutors approach victim identification, serving victims, and increasing convictions and penalties for traffickers and buyers. It also sought to draw lessons learned that other jurisdictions can use to begin this work or increase their capacity and effectiveness, regardless of size or location. This project was a partnership between the Justice Research and Statistics Association (JRSA) and the National District Attorney's Association (NDAA) and consisted of two phases. Phase I was a national survey of prosecutors and Phase II was a series of four case studies in jurisdictions undertaking anti-TIP initiatives. The results of the survey are intended to provide a national snapshot of trends in local TIP prosecutions and the use of state-level TIP statutes by local prosecutors. It serves as a ten-year update to, and expansion of, previous research on local prosecutorial approaches to trafficking that had used data on cases prosecuted through 2008.

This statistic shows the number of investigations, arrests, indictments and convictions in relation to human traffickers in the United States in 2015, by the law enforcement authority responsible for the case. In 2015, The FBI's Violent Crimes Against Children Section made ***** arrests of people engaged in human trafficking.

Number of personal violent and property crimes in Pierce County.

Only specific crimes are highlighted in the crime rates presented here. These numbers represent total numbers of reported crimes in each category (not arrests which may occur over a prolonged period).

The following categories represent the personal violent crimes considered in this data: Murder, Manslaughter, Forcible Sex, Assault, Kidnapping/Abduction, Human Trafficking, and Robbery.

The following categories represent the property crimes considered in this data: Burglary, Theft, Arson, and Destruction of Property.

Each set of crimes is totaled, then the rate per 1,000 people is calculated using the total # of crimes and the current population of each jurisdiction per year as provided in the same report.

This is a voluntary program and as such, some law enforcement agencies do not participate or have only recently participated, which is also reflected in this table.

More than 100 people have been arrested in a crackdown on abuses in Thailand's multi-billion dollar seafood industry, officials say. Last April the European Union threatened to boycott the industry unless it tackled illegal fishing and allegations of human trafficking. On Monday, police said a taskforce set up since had investigated 36 cases and also rescued 130 trafficking victims. Thailand is the world's third largest exporter of seafood. Human rights groups have long highlighted abuses in the Thai industry, saying it is reliant on illegal fishing practices and overfishing, and involves trafficked workers from neighbouring countries who, they say, work in conditions akin to slavery.

The purpose of this data collection was to investigate the possible increase in gang involvement within cocaine and "rock" cocaine trafficking. Investigators also examined the relationships among gangs, cocaine trafficking, and increasing levels of violence. They attempted to determine the effects of increased gang involvement in cocaine distribution in terms of the _location of an incident, the demographic profiles of suspects, and the level of firearm use. They also looked at issues such as whether the connection between gangs and cocaine trafficking yielded more drug-related violence, how the connection between gangs and cocaine trafficking affected police investigative processes such as intra-organizational communication and the use of special enforcement technologies, what kinds of working relationships were established between narcotics units and gang control units, and what the characteristics were of the rock trafficking and rock house technologies of the dealers. Part 1 (Sales Arrest Incident Data File) contains data for the cocaine sales arrest incidents. Part 2 (Single Incident Participant Data File) contains data for participants of the cocaine sales arrest incidents. Part 3 (Single Incident Participant Prior Arrest Data File) contains data for the prior arrests of the participants in the cocaine arrest incidents. Part 4 (Multiple Event Incident Data File) contains data for multiple event incidents. Part 5 (Multiple Event Arrest Incident Data File) contains data for arrest events in the multiple event incidents. Part 6 (Multiple Event Incident Participant Data File) contains data for the participants of the arrest events. Part 7 (Multiple Event Incident Prior Arrest Data File) contains data for the prior arrest history of the multiple event participants. Part 8 (Homicide Incident Data File) contains data for homicide incidents. Part 9 (Homicide Incident Suspect/Victim Data File) contains data for the suspects and victims of the homicide incidents. Major variables characterizing the various units of observation include evidence of gang involvement, presence of drugs, presence of a rock house, presence of firearms or other weapons, presence of violence, amount of cash taken as evidence, prior arrests, and law enforcement techniques.

Abstract (en): The National Incident-Based Reporting System (NIBRS) is a part of the Uniform Crime Reporting Program (UCR), administered by the Federal Bureau of Investigation (FBI). In the late 1970s, the law enforcement community called for a thorough evaluative study of the UCR with the objective of recommending an expanded and enhanced UCR program to meet law enforcement needs into the 21st century. The FBI fully concurred with the need for an updated program to meet contemporary needs and provided its support, formulating a comprehensive redesign effort. Following a multiyear study, a "Blueprint for the Future of the Uniform Crime Reporting Program" was developed. Using the "Blueprint," and in consultation with local and state law enforcement executives, the FBI formulated new guidelines for the Uniform Crime Reports. The National Incident-Based Reporting System (NIBRS) was implemented to meet these guidelines. NIBRS data are archived at ICPSR as 11 separate data files per year, which may be merged by using linkage variables. Prior to 2013 the data were archived and distributed as 13 separate data files, including three separate batch header record files. In 2013 the FBI combined the three batch header files into one file. Consequently, ICPSR instituted new file numbering for the 2013 data. NIBRS data focus on a variety of aspects of a crime incident. Part 2 (formerly Part 4), Administrative Segment, offers data on the incident itself (date and time). Each crime incident is delineated by one administrative segment record. Also provided are Part 3 (formerly Part 5), Offense Segment (offense type, location, weapon use, and bias motivation), Part 4 (formerly Part 6), Property Segment (type of property loss, property description, property value, drug type and quantity), Part 5 (formerly Part 7), Victim Segment (age, sex, race, ethnicity, and injuries), Part 6 (formerly Part 8), Offender Segment (age, sex, and race), and Part 7 (formerly Part 9), Arrestee Segment (arrest date, age, sex, race, and weapon use). The Batch Header Segment (Part 1, formerly Parts 1-3) separates and identifies individual police agencies by Originating Agency Identifier (ORI). Batch Header information, which is contained on three records for each ORI, includes agency name, geographic location, and population of the area. Part 8 (formerly Part 10), Group B Arrest Report Segment, includes arrestee data for Group B crimes. Window Segments files (Parts 9-11, formerly Parts 11-13) pertain to incidents for which the complete Group A Incident Report was not submitted to the FBI. In general, a Window Segment record will be generated if the incident occurred prior to January 1 of the previous year or if the incident occurred prior to when the agency started NIBRS reporting. As with the UCR, participation in NIBRS is voluntary on the part of law enforcement agencies. The data are not a representative sample of crime in the United States. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Created variable labels and/or value labels.; Checked for undocumented or out-of-range codes.. Law enforcement agencies in the United States participating in the National Incident-Based Reporting System. Smallest Geographic Unit: city 2015-06-29 Corrected error in V5011 (Ethnicity of Offender) in the Offender Segment. Funding insitution(s): United States Department of Justice. Federal Bureau of Investigation. United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. Starting with the 2012 data, some offense, location, bias motivation, race, and ethnicity codes have been added or modified to include recent Advisory Policy Board (APB) and Office of Management and Budget (OMB) policy mandates to the UCR Program related to Human Trafficking, Hate Crime, and Race and Ethnicity information.At the recommendation of the CJIS APB and with the approval of the FBI Director, the FBI UCR Program initiated the collection of rape data under a revised definition and removed the term "forcible" from the offense name in 2013. The changes bring uniformity to the offense in both the Summary Reportin...

This dataset includes key details about HSI supporting the U.S. Secret Service with multiple national special security events (NSSE). A key event in 2024 was Super Bowl 58, where HSI supported state, local, and federal partners to seize counterfiet goods, made felony arrests, and identified and assisted victims of human trafficking.

There were a total of ** human trafficking offenses reported in Texas in 2023, the most out of any state. Of the reported offenses, ** were related to commercial sex acts, and ** were related to involuntary servitude.

This hosted feature layer contains three HPD NIBRS crime summary polygon layers- Summary by the City of Houston Super Neighborhood boundaries. - Summary by Houston area 6-character Open Location Code grid- Summary by Houston area 8-character Open Location Code grid- Summary by the City of Houston Council District This Houston area Open Location Code (OLC) grid system is based on latitudes and longitudes in WGS84 coordinates. Each 6-character block has a size of 3 arc-minutes by 3 arc-minutes (approximately 3.41 miles). Each 8-character block has a size of 9 arc-seconds by 9 arc-seconds (approximately 900 feet). This grid system is used for summary statistics. This GIS dataset is based on NIBRS data published by Houston Police Department (HPD). The original source data can be found on HPD's Monthly Crime Data By Street And Police Beat webpage "https://www.houstontx.gov/police/cs/Monthly_Crime_Data_by_Street_and_Police_Beat.htm"This GIS dataset was processed and published by Houston Information Technology Services (HITS). National Incident-Based Reporting System (NIBRS) is an incident-based reporting system used by law enforcement agencies in the United States for collecting and reporting data on crimes. Local, state and federal agencies generate NIBRS data from their records management systems. Data is collected on every incident and arrest in the Group A offense category. These Group A offenses include 52 NIBRS classes in three main categories (Person, Property, and Society.) Specific facts about these offenses are gathered and reported to NIBRS. In addition to the Group A offenses, 10 Group B offenses are reported with only the arrest information. Disclaimer: This GIS dataset is prepared and made available for general reference purposes only and should not be used, or relied upon for specific applications, without independent verification. The City of Houston neither represents, nor warrants COHGIS data accuracy, or completeness, nor will the City of Houston accept liability of any kind in conjunction with its use. COHGIS information is in the public domain and may be copied without permission; citation of the source is appreciated. ***** List of Field Names ***** NIBRS_Class | Description | NIBRS_Group | Group | Field_Name09A | Murder, non-negligent | Group A - Person | AI | HPD_NIBRS_AI_09A_CNT_202009B | Negligent manslaughter | Group A - Person | AI | HPD_NIBRS_AI_09B_CNT_202009C | Justifiable homicide | Not a Crime | N | HPD_NIBRS_N_09C_CNT_2020100 | Kidnapping, abduction | Group A - Person | AI | HPD_NIBRS_AI_100_CNT_202011A | Forcible rape | Group A - Person | AI | HPD_NIBRS_AI_11A_CNT_202011B | Forcible sodomy | Group A - Person | AI | HPD_NIBRS_AI_11B_CNT_202011C | Sexual assault with an object | Group A - Person | AI | HPD_NIBRS_AI_11C_CNT_202011D | Forcible fondling | Group A - Person | AI | HPD_NIBRS_AI_11D_CNT_2020120 | Robbery | Group A - Property | AP | HPD_NIBRS_AP_120_CNT_202013A | Aggravated Assault | Group A - Person | AI | HPD_NIBRS_AI_13A_CNT_202013B | Simple assault | Group A - Person | AI | HPD_NIBRS_AI_13B_CNT_202013C | Intimidation | Group A - Person | AI | HPD_NIBRS_AI_13C_CNT_2020200 | Arson | Group A - Property | AP | HPD_NIBRS_AP_200_CNT_2020210 | Extortion, Blackmail | Group A - Property | AP | HPD_NIBRS_AP_210_CNT_2020220 | Burglary, Breaking and Entering | Group A - Property | AP | HPD_NIBRS_AP_220_CNT_202023A | Pocket-picking | Group A - Property | AP | HPD_NIBRS_AP_23A_CNT_202023B | Purse-snatching | Group A - Property | AP | HPD_NIBRS_AP_23B_CNT_202023C | Shoplifting | Group A - Property | AP | HPD_NIBRS_AP_23C_CNT_202023D | Theft from building | Group A - Property | AP | HPD_NIBRS_AP_23D_CNT_202023E | From coin-operated machine or device | Group A - Property | AP | HPD_NIBRS_AP_23E_CNT_202023F | Theft from motor vehicle | Group A - Property | AP | HPD_NIBRS_AP_23F_CNT_202023G | Theft of motor vehicle parts or accessory | Group A - Property | AP | HPD_NIBRS_AP_23G_CNT_202023H | All other larceny | Group A - Property | AP | HPD_NIBRS_AP_23H_CNT_2020240 | Motor vehicle theft | Group A - Property | AP | HPD_NIBRS_AP_240_CNT_2020250 | Counterfeiting, forgery | Group A - Property | AP | HPD_NIBRS_AP_250_CNT_202026A | False pretenses, swindle | Group A - Property | AP | HPD_NIBRS_AP_26A_CNT_202026B | Credit card, ATM fraud | Group A - Property | AP | HPD_NIBRS_AP_26B_CNT_202026C | Impersonation | Group A - Property | AP | HPD_NIBRS_AP_26C_CNT_202026D | Welfare fraud | Group A - Property | AP | HPD_NIBRS_AP_26D_CNT_202026E | Wire fraud | Group A - Property | AP | HPD_NIBRS_AP_26E_CNT_202026F | Identify theft | Group A - Property | AP | HPD_NIBRS_AP_26F_CNT_202026G | Hacking/Computer Invasion | Group A - Property | AP | HPD_NIBRS_AP_26G_CNT_2020270 | Embezzlement | Group A - Property | AP | HPD_NIBRS_AP_270_CNT_2020280 | Stolen property offenses | Group A - Property | AP | HPD_NIBRS_AP_280_CNT_2020290 | Destruction, damage, vandalism | Group A - Property | AP | HPD_NIBRS_AP_290_CNT_202035A | Drug, narcotic violations | Group A - Society | AS | HPD_NIBRS_AS_35A_CNT_202035B | Drug equipment violations | Group A - Society | AS | HPD_NIBRS_AS_35B_CNT_202036A | Incest | Group A - Person | AI | HPD_NIBRS_AI_36A_CNT_202036B | Statutory rape | Group A - Person | AI | HPD_NIBRS_AI_36B_CNT_2020370 | Pornographs, obscene material | Group A - Society | AS | HPD_NIBRS_AS_370_CNT_202039A | Betting/wagering | Group A - Society | AS | HPD_NIBRS_AS_39A_CNT_202039B | Promoting gambling | Group A - Society | AS | HPD_NIBRS_AS_39B_CNT_202039C | Gambling equipment violations | Group A - Society | AS | HPD_NIBRS_AS_39C_CNT_202040A | Prostitution | Group A - Society | AS | HPD_NIBRS_AS_40A_CNT_202040B | Assisting or promoting prostitution | Group A - Society | AS | HPD_NIBRS_AS_40B_CNT_202040C | Purchasing prostitution | Group A - Society | AS | HPD_NIBRS_AS_40C_CNT_2020510 | Bribery | Group A - Property | AP | HPD_NIBRS_AP_510_CNT_2020520 | Weapon law violations | Group A - Society | AS | HPD_NIBRS_AS_520_CNT_202064A | Human Trafficking/Commercial Sex Act | Group A - Person | AI | HPD_NIBRS_AI_64A_CNT_202064B | Human Trafficking/Involuntary Servitude | Group A - Person | AI | HPD_NIBRS_AI_64B_CNT_2020720 | Animal Cruelty | Group A - Society | AS | HPD_NIBRS_AS_720_CNT_202090A | Bad checks | Group B | B | HPD_NIBRS_B_90A_CNT_202090B | Curfew, loitering, vagrancy violations | Group B | B | HPD_NIBRS_B_90B_CNT_202090C | Disorderly conduct | Group B | B | HPD_NIBRS_B_90C_CNT_202090D | Driving under the influence | Group B | B | HPD_NIBRS_B_90D_CNT_202090E | Drunkenness | Group B | B | HPD_NIBRS_B_90E_CNT_202090F | Family offenses, no violence | Group B | B | HPD_NIBRS_B_90F_CNT_202090G | Liquor law violations | Group B | B | HPD_NIBRS_B_90G_CNT_202090H | Peeping tom | Group B | B | HPD_NIBRS_B_90H_CNT_202090I | Runaway | Group B | B | HPD_NIBRS_B_90I_CNT_202090J | Trespass of real property | Group B | B | HPD_NIBRS_B_90J_CNT_202090Z | All other offenses | Group B | B | HPD_NIBRS_B_90Z_CNT_2020

PurposeCommercial sexual exploitation occurs when anything of value is given in exchange for a sex act. Sex trafficking involves the commercial sexual exploitation of individuals by means of force, fraud, or coercion. Due to the illegal nature of commercial sexual exploitation, there is a profound dearth in the literature. To develop a deeper understanding of the experiences of adult survivors of commercial sexual exploitation, investigators analyzed 1,264 unique case files collected between 2011 and 2021.MethodsKey predictors included mental health diagnoses, childhood sexual abuse, and educational achievement, while relevant outcomes included age of entry into sexual exploitation, length of exploitation, number of arrests, cycling into and out of commercial sexual exploitation, and program placement outcomes. Regression analyses (e.g., linear, binomial, or zero-inflated Poisson) were conducted.ResultsResults suggest that educational achievement is a potential protective factor against exploitation. Higher number of arrest and higher number of children had a bidirectional relationship with longer experiences of exploitation. Further, diagnoses of bipolar disorder and neurodevelopmental disorders were related to higher rates of cycling (i.e., repeated attempts to exit exploitation), and neurodevelopmental disorders and schizophrenia spectrum disorders were related to poorer placement outcomes.ConclusionsThe findings provide a more authentic portrait of contextual influences on commercial sexual exploitation across a lifespan, informing services, interventions, and policy and supporting survivors in their promising futures.

This dataset presents year, crime head and gender wise information on the disposal status of persons arrested for crimes against women in India’s metropolitan cities. It categorises offences under major crime heads such as rape, dowry deaths, acid attacks, abetment to suicide, kidnapping and abduction, assault on women, human trafficking, and offences under laws like the POCSO Act, Dowry Prohibition Act, and Information Technology Act. It also includes data on the number of persons acquitted, arrested, chargesheeted, convicted, and discharged, offering a comprehensive view of law enforcement and judicial response to these crimes.