The National Prisoner Statistics (NPS) data collection began in 1926 in response to a congressional mandate to gather information on persons incarcerated in state and federal prisons. Originally under the auspices of the U.S. Census Bureau, the collection moved to the Bureau of Prisons in 1950, and then in 1971 to the National Criminal Justice Information and Statistics Service, the precursor to the Bureau of Justice Statistics (BJS) which was established in 1979. From 1979 to 2013, the Census Bureau was the NPS data collection agent. In 2014, the collection was competitively bid in conjunction with the National Corrections Reporting Program (NCRP), since many of the respondents for NPS and NCRP are the same. The contract was awarded to Abt Associates, Inc. The NPS is administered to 51 respondents. Before 2001, the District of Columbia was also a respondent, but responsibility for housing the District of Columbia's sentenced prisoners was transferred to the Federal Bureau of Prisons, and by yearend 2001 the District of Columbia no longer operated a prison system. The NPS provides an enumeration of persons in state and federal prisons and collects data on key characteristics of the nation's prison population. NPS has been adapted over time to keep pace with the changing information needs of the public, researchers, and federal, state, and local governments.

Overview

The Marshall Project, the nonprofit investigative newsroom dedicated to the U.S. criminal justice system, has partnered with The Associated Press to compile data on the prevalence of COVID-19 infection in prisons across the country. The Associated Press is sharing this data as the most comprehensive current national source of COVID-19 outbreaks in state and federal prisons.

Lawyers, criminal justice reform advocates and families of the incarcerated have worried about what was happening in prisons across the nation as coronavirus began to take hold in the communities outside. Data collected by The Marshall Project and AP shows that hundreds of thousands of prisoners, workers, correctional officers and staff have caught the illness as prisons became the center of some of the country’s largest outbreaks. And thousands of people — most of them incarcerated — have died.

In December, as COVID-19 cases spiked across the U.S., the news organizations also shared cumulative rates of infection among prison populations, to better gauge the total effects of the pandemic on prison populations. The analysis found that by mid-December, one in five state and federal prisoners in the United States had tested positive for the coronavirus -- a rate more than four times higher than the general population.

This data, which is updated weekly, is an effort to track how those people have been affected and where the crisis has hit the hardest.

Methodology and Caveats

The data tracks the number of COVID-19 tests administered to people incarcerated in all state and federal prisons, as well as the staff in those facilities. It is collected on a weekly basis by Marshall Project and AP reporters who contact each prison agency directly and verify published figures with officials.

Each week, the reporters ask every prison agency for the total number of coronavirus tests administered to its staff members and prisoners, the cumulative number who tested positive among staff and prisoners, and the numbers of deaths for each group.

The time series data is aggregated to the system level; there is one record for each prison agency on each date of collection. Not all departments could provide data for the exact date requested, and the data indicates the date for the figures.

To estimate the rate of infection among prisoners, we collected population data for each prison system before the pandemic, roughly in mid-March, in April, June, July, August, September and October. Beginning the week of July 28, we updated all prisoner population numbers, reflecting the number of incarcerated adults in state or federal prisons. Prior to that, population figures may have included additional populations, such as prisoners housed in other facilities, which were not captured in our COVID-19 data. In states with unified prison and jail systems, we include both detainees awaiting trial and sentenced prisoners.

To estimate the rate of infection among prison employees, we collected staffing numbers for each system. Where current data was not publicly available, we acquired other numbers through our reporting, including calling agencies or from state budget documents. In six states, we were unable to find recent staffing figures: Alaska, Hawaii, Kentucky, Maryland, Montana, Utah.

To calculate the cumulative COVID-19 impact on prisoner and prison worker populations, we aggregated prisoner and staff COVID case and death data up through Dec. 15. Because population snapshots do not account for movement in and out of prisons since March, and because many systems have significantly slowed the number of new people being sent to prison, it’s difficult to estimate the total number of people who have been held in a state system since March. To be conservative, we calculated our rates of infection using the largest prisoner population snapshots we had during this time period.

As with all COVID-19 data, our understanding of the spread and impact of the virus is limited by the availability of testing. Epidemiology and public health experts say that aside from a few states that have recently begun aggressively testing in prisons, it is likely that there are more cases of COVID-19 circulating undetected in facilities. Sixteen prison systems, including the Federal Bureau of Prisons, would not release information about how many prisoners they are testing.

Corrections departments in Indiana, Kansas, Montana, North Dakota and Wisconsin report coronavirus testing and case data for juvenile facilities; West Virginia reports figures for juvenile facilities and jails. For consistency of comparison with other state prison systems, we removed those facilities from our data that had been included prior to July 28. For these states we have also removed staff data. Similarly, Pennsylvania’s coronavirus data includes testing and cases for those who have been released on parole. We removed these tests and cases for prisoners from the data prior to July 28. The staff cases remain.

About the Data

There are four tables in this data:

• covid_prison_cases.csv contains weekly time series data on tests, infections and deaths in prisons. The first dates in the table are on March 26. Any questions that a prison agency could not or would not answer are left blank.

• prison_populations.csv contains snapshots of the population of people incarcerated in each of these prison systems for whom data on COVID testing and cases are available. This varies by state and may not always be the entire number of people incarcerated in each system. In some states, it may include other populations, such as those on parole or held in state-run jails. This data is primarily for use in calculating rates of testing and infection, and we would not recommend using these numbers to compare the change in how many people are being held in each prison system.

• staff_populations.csv contains a one-time, recent snapshot of the headcount of workers for each prison agency, collected as close to April 15 as possible.

• covid_prison_rates.csv contains the rates of cases and deaths for prisoners. There is one row for every state and federal prison system and an additional row with the National totals.

Queries

The Associated Press and The Marshall Project have created several queries to help you use this data:

Get your state's prison COVID data: Provides each week's data from just your state and calculates a cases-per-100000-prisoners rate, a deaths-per-100000-prisoners rate, a cases-per-100000-workers rate and a deaths-per-100000-workers rate here

Rank all systems' most recent data by cases per 100,000 prisoners here

Find what percentage of your state's total cases and deaths -- as reported by Johns Hopkins University -- occurred within the prison system here

Attribution

In stories, attribute this data to: “According to an analysis of state prison cases by The Marshall Project, a nonprofit investigative newsroom dedicated to the U.S. criminal justice system, and The Associated Press.”

Contributors

Many reporters and editors at The Marshall Project and The Associated Press contributed to this data, including: Katie Park, Tom Meagher, Weihua Li, Gabe Isman, Cary Aspinwall, Keri Blakinger, Jake Bleiberg, Andrew R. Calderón, Maurice Chammah, Andrew DeMillo, Eli Hager, Jamiles Lartey, Claudia Lauer, Nicole Lewis, Humera Lodhi, Colleen Long, Joseph Neff, Michelle Pitcher, Alysia Santo, Beth Schwartzapfel, Damini Sharma, Colleen Slevin, Christie Thompson, Abbie VanSickle, Adria Watson, Andrew Welsh-Huggins.

Questions

If you have questions about the data, please email The Marshall Project at info+covidtracker@themarshallproject.org or file a Github issue.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

--- ### Context This dataset contains the complete detail about the Prison and various characteristics of inmates. This will help to understand better about prison system in India. ### Content 1. Details of Jail wise population of prison inmates 1. Details about the list of jails in India at the end of year 2015. 1. Jail category wise population of inmates. 1. Capacity of jails by inmate population. 1. Age group, nationality and gender wise population of inmates. 1. Religion and gender wise population of inmates. 1. Caste and gender wise population of inmates. 1. Education standards of inmates. 1. Domicile of inmates. 1. Incidence of recidivism. 1. Rehabilitation of prisoners. 1. Distribution of sentence periods of convicts in various jails by sex and age-groups. 1. Details of under trial prisoners by the type of IPC (Indian Penal Code) offences. 1. Details of convicts by the type of IPC (Indian Penal Code) offences. 1. Details of SLL (special & local law) Crime headwise distribution of inmates who convicted 1. Details of SLL (special & local law) Crime head wise distribution of inmates under trial 1. Details of educational facilities provided to prisoners. 1. Details of Jail breaks, group clashes and firing in jail (Tranquility). 1. Details of wages per day to convicts. 1. Details of Prison inmates trained under different vocational training. 1. Details of capital punishment (death sentence) and life imprisonment. 1. Details of prison inmates escaped. 1. Details of prison inmates released. 1. Details of Strength of officials 1. Details of Total Budget and Actual Expenditure during the year 2015-16. 1. Details of Budget 1. Details of Expenditure 1. Details of Expenditure on inmates 1. Details of Inmates suffering from mental ilness 1. Details of Period of detention of undertrials 1. Details of Number of women prisoners with children 1. Details of Details of inmates parole during the year 1. Details of Value of goods produced by inmates 1. Details of Number of vehicles available 1. Details of Training of Jail Officers 1. Details of Movements outside jail premises 1. Details of Details of electronic equipment used in prison ### Inspiration There are many questions about Indian prison with this dataset. Some of the interesting questions are 1. Percentage of jails over crowded. Is there any change in percentage over time? 1. How many percentage of inmates re-arrested? 1. Which state/u.t pay more wages to the inmates? 1. Which state/u.t has more capital punishment/life imprisonment inmates? 1. Inmates gender ratio per state ### Acknowledgements National Crime Records Bureau (NCRB), Govt of India has shared this [dataset](https://data.gov.in/dataset-group-name/prison-statistics) under [Govt. Open Data License - India](https://data.gov.in/government-open-data-license-india). NCRB has also shared prison data on their [website](http://ncrb.nic.in/StatPublications/PSI/PrevPublications.htm). ---

This dataset includes people released to Philadelphia from the Philadelphia Department of Prisons (PDP) and the Pennsylvania Department of Correction (PA DOC). Individual-level data for releases from Federal (BOP) incarceration was not available, and makes up less than 2% of people released to Philadelphia in the year analyzed. The dataset also only includes people released to Philadelphia who have been charged with a criminal non-summary type offense in the Philadelphia adult criminal justice system.

Trouble downloading or have questions about this City dataset? Visit the OpenDataPhilly Discussion Group

Content

Daily inmates in custody with attributes (custody level, mental health designation, race, gender, age, leagal status, sealed status, security risk group membership, top charge, and infraction flag). This data set excludes Sealed Cases. Resulting summaries may differ slightly from other published statistics.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Fredrik Öhlander on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Adult correctional services, custodial and community supervision, average counts of offenders in federal programs, Canada and regions, five years of data.

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 is a secondary analysis of data from ICPSR Study Number 27101, Serious and Violent Offender Reentry Initiative (SVORI) Multi-site Impact Evaluation, 2004-2011 [United States]- specifically the adult male dataset -to examine the associations among child support obligations, employment and reentry outcomes. The study addressed the following research questions: Are the demographic, criminal justice and employment-related characteristics of incarcerated men with child support orders significantly different in any important way from incarcerated males without child support orders? Did SVORI clients receive more support and services related to child support orders and modification of debt after release from prison compared to non-SVORI participants? Does having legal child support obligations decrease the likelihood of employment in later waves, net of key demographic and criminal justice history factors? How does employment influence the relationship between child support debt and recidivism? and Is family instrumental support a significant predictor of reduced recidivism or increased employment in models assessing the relationship between child support obligations, employment and recidivism? The study includes one document (Syntax_ChildSupport_Reentry_forICPSR_2012-IJ-CX-0012.docx) which contains SPSS and Stata syntax used to create research variables.

Summary: This is a collection of publicly reported data relevant to the COVID-19 pandemic scraped from state and federal prisons in the United States. Data are collected each night from every state and federal correctional agency’s site that has data available. Data from Massachusetts come directly from the ACLU Massachusetts COVID-19 website (https://data.aclum.org/sjc-12926-tracker/), not the Massachusetts DOC website. Data from a small number of states come from Recidiviz (https://www.recidiviz.org/) whose team manually collects data from these states. Not all dates are available for some states due to websites being down or changes to the website that cause some data to be missed by the scraper.The data primarily cover the number of people incarcerated in these facilities who have tested positive, negative, recovered, and have died from COVID-19. Many - but not all - states also provide this information for staff members. This dataset includes every variable that any state makes available. While there are dozens of variables in the data, most apply to only a small number of states or a single state.The data is primarily at the facility-date unit, meaning that each row represents a single prison facility on a single date. The date is the date we scraped the data (we do so each night between 9pm-3am EST) and not necessarily the date the data was updated. While many states update daily, some do so less frequently. As such, you may see some dates for certain states contain the same values. A small number of states do not provide facility-level data, or do so for only a subset of all the variables they make available. In these cases we have also collected state-level data and made that available separately. Please note: When facility data is available, the state-level file combines the aggregated facility-level data with any state-level data that is available. You should therefore use this file when doing a state-level analysis instead of aggregating the facility-level data, as some states report values only at the state level (these states may still have some data at the facility-level), and some states report cumulative numbers at the state level but do not report them at the facility level. As a result, when we identify this, we typically add the cumulative information to the state level file. The state level file is still undergoing quality checks and will be released soon.These data were scraped from nearly all state and federal prison websites that make their data available each night for several months, and we continue to collect data. Over time some states have changed what variables are available, both adding and removing some variables, as well as the definition of variables. For all states and time periods you are using this data for, please carefully examine the data to detect these kinds of issues. We have spent extensive time doing a careful check of the data to remove any issues we find, primarily ones that could be caused by a scraper not working properly. However, please check all data for issues before using it. Contact us at covidprisondata@gmail.com to let us know if you find any issues, have questions, or if you would like to collaborate on research.

Research shows that prison visitation is integral to the success of incarcerated people, reducing recidivism, facilitating reentry into the community, and promoting positive parent-child relationships. However, people are often incarcerated long distances from their home communities in areas that are difficult to reach by public transport, creating significant barriers to in-person visitation. Departments of corrections are exploring the use of computer-based video visits as a means to address some of the visitation needs of those in custody in a cost-effective way while continuing to encourage in-person visits. To learn more about this practice, the study team conducted the following research activities: A survey of incarcerated people: The study team surveyed 211 people incarcerated in Washington State prisons about their use of video visits, their perceptions of the service, and their experiences of in-person visits more generally. This was a self-administered, pen-and-paper survey. An impact evaluation of video visits: The study team analyzed individual-level administrative data from the Washington Department of Corrections (WADOC) and the private video visit vendor (JPay) to understand whether use of the service affected four outcomes: 1) the number of in-person visits people received, 2) the number of rule violations (of any severity) people committed in prison, 3) the number of general (ie. non-serious) rule violations they committed, and 4) the number of serious (as defined by WADOC) rule violations that were committed. The researchers used two analytic techniques: 1) a difference-in-difference test, using inverse probability of treatment weighting, and 2) Bayesian additive regression trees. An analysis of in-person visit rates: The study team analyzed administrative data relating to all people who were incarcerated for the 12 month period ending November 2015 (n=11,524). The study team produced descriptive statistics and conducted negative binomial regressions to understand the rates of in-person visits and how these related to the characteristics of the incarcerated people.

Adult correctional services, custodial and community supervision, average counts of adults in provincial and territorial programs, five years of data.

This project sought to investigate a possible relationship between sentencing guidelines and family structure in the United States. The research team developed three research modules that employed a variety of data sources and approaches to understand family destabilization and community distress, which cannot be observed directly. These three research modules were used to discover causal relationships between male withdrawal from productive spheres of the economy and resulting changes in the community and families. The research modules approached the issue of sentencing guidelines and family structure by studying: (1) the flow of inmates into prison (Module A), (2) the role of and issues related to sentencing reform (Module B), and family disruption in a single state (Module C). Module A utilized the Uniform Crime Reporting (UCR) Program data for 1984 and 1993 (Parts 1 and 2), the 1984 and 1993 National Correctional Reporting Program (NCRP) data (Parts 3-6), the Urban Institute's 1980 and 1990 Underclass Database (UDB) (Part 7), the 1985 and 1994 National Longitudinal Survey on Youth (NLSY) (Parts 8 and 9), and county population, social, and economic data from the Current Population Survey, County Business Patterns, and United States Vital Statistics (Parts 10-12). The focus of this module was the relationship between family instability, as measured by female-headed families, and three societal characteristics, namely underclass measures in county of residence, individual characteristics, and flows of inmates. Module B examined the effects of statewide incarceration and sentencing changes on marriage markets and family structure. Module B utilized data from the Current Population Survey for 1985 and 1994 (Part 12) and the United States Statistical Abstracts (Part 13), as well as state-level data (Parts 14 and 15) to measure the Darity-Myers sex ratio and expected welfare income. The relationship between these two factors and family structure, sentencing guidelines, and minimum sentences for drug-related crimes was then measured. Module C used data collected from inmates entering the Minnesota prison system in 1997 and 1998 (Part 16), information from the 1990 Census (Part 17), and the Minnesota Crime Survey (Part 18) to assess any connections between incarceration and family structure. Module C focused on a single state with sentencing guidelines with the goal of understanding how sentencing reforms and the impacts of the local community factors affect inmate family structure. The researchers wanted to know if the aspects of locations that lose marriageable males to prison were more important than individual inmate characteristics with respect to the probability that someone will be imprisoned and leave behind dependent children. Variables in Parts 1 and 2 document arrests by race for arson, assault, auto theft, burglary, drugs, homicide, larceny, manslaughter, rape, robbery, sexual assault, and weapons. Variables in Parts 3 and 4 document prison admissions, while variables in Parts 5 and 6 document prison releases. Variables in Part 7 include the number of households on public assistance, education and income levels of residents by race, labor force participation by race, unemployment by race, percentage of population of different races, poverty rate by race, men in the military by race, and marriage pool by race. Variables in Parts 8 and 9 include age, county, education, employment status, family income, marital status, race, residence type, sex, and state. Part 10 provides county population data. Part 11 contains two different state identifiers. Variables in Part 12 describe mortality data and welfare data. Part 13 contains data from the United States Statistical Abstracts, including welfare and poverty variables. Variables in Parts 14 and 15 include number of children, age, education, family type, gender, head of household, marital status, race, religion, and state. Variables in Part 16 cover admission date, admission type, age, county, education, language, length of sentence, marital status, military status, sentence, sex, state, and ZIP code. Part 17 contains demographic data by Minnesota ZIP code, such as age categories, race, divorces, number of children, home ownership, and unemployment. Part 18 includes Minnesota crime data as well as some demographic variables, such as race, education, and poverty ratio.

This dataset documents the records of mainly Black people incarcerated in the Tennessee State Penitentiary in the period directly before, during, and after the Civil War, from 1850-1870. It includes a staggering amount of formerly enslaved Civil War soldiers and veterans who had enlisted in the segregated regiments of the United States Military, the U.S.C.T. This demographic information of over 1,400 inmates incarcerated in an occupied border state allows us to examine trends, patterns, and relationships that speak to the historic ties between the US military and the TN State Penitentiary, and more broadly, the role of enslavement’s legacies in the development of punitive federal systems. Further analysis of this dataset reveals the genesis of many modern trends in incarceration and law. The dataset of this article and its historiographical implications will be of interest to scholars who study the regional dynamics of antebellum and post-Civil War prison systems, convict leasing and the development of the modern carceral state, Black resistance in the forms of fugitivity and participation in the Civil War, and pre-war era incarceration of free Black men and women and non-Black people convicted of crimes related to enslavement.

COVID-19 testing and outcomes data in local adult detention facilities in California. The data includes the date reported, reporting county, incarcerated people who tested positive for COVID-19 (at intake, in custody, in first 14 days), incarcerated people hospitalized due to COVID-19, resolved cases of incarcerated people, incarcerated people's deaths, staff tested, staff positive cases, staff resolved cases reported to the California Board of State and Community Corrections.

This dataset contains:

1. Key services measures - Custody

2. Average daily number of young people in custody

3. Average weekly number of young people in custody by Legal Status

4. Average daily number of young people in custody by month

5. Admissions to Juvenile Justice Centres

6. Length of stay for young people in custody on remand

7. Length of stay for young people in custody on control

8. Proportion of young people with a remand episode who receive, or do not receive a Control order within 12 months

Daily counts for Department of Corrections (DOC) facilities from FY 2011 through June 30, 2016 (FY 2016). Title XVI youth, i.e., juveniles charged as adults, are supervised by the Central Detention Facility (CDF) staff but housed at the Correctional Treatment Facility (CTF). Note that the CDF is also known as DC Jail.DOC phased out use of two halfway houses for men, Efforts for Ex-Convicts in FY 2014 and Extended House in FY 2015.These counts do not include inmates who are short term sentenced felons (STSF) housed for FBOP at CTF, or USMS Greenbelt Inmates housed by CCA for the USMS Greenbelt at CTF.

Number, rate and percentage changes in rates of homicide victims, Canada, provinces and territories, 1961 to 2023.

Dataset - Kenya in the news

Estimates of COVID-19 mortality for people in prison relative to the general population, controlling for age and sex, in studies included in a systematic review on the health impacts of the COVID-19 pandemic on people who experience imprisonmenta.

Genetic factors contribute to antisocial and criminal behavior. Dopamine transporter DAT-1 (SLC6A3) and DRD2 gene for the dopamine-2 receptor are dopaminergic system genes that regulate dopamine reuptake and signaling, and may be part of the pathogenesis of psychiatric disorders including antisocial behaviors and traits. No previous studies have analyzed DAT-1 and DRD2 polymorphisms in convicted murderers, particularly from Indian subcontinent. In this study we investigated the association of 40 bp VNTR polymorphism of DAT-1 and Taq1 variant of DRD2 gene (rs1800479) with criminal behavior and self-reported aggression in 729 subjects, including 370 men in Pakistani prisons convicted of first degree murder(s) and 359 control men without any history of violence or criminal tendency. The 9R allele of DAT-1 VNTR polymorphism was more prevalent in convicted murderers compared with control samples, for either one or two risk alleles (OR = 1.49 and 3.99 respectively, P = 0.003). This potential association of DAT-1 9R allele polymorphism with murderer phenotype was confirmed assuming different genetic models of inheritance. However, no genetic association was found for DRD2 Taq1 polymorphism. In addition, a combined haplotype (9R-A2) of DAT-1 and DRD2 genes was associated with this murderer phenotype. Further, 9R allele of DAT-1 was also associated with response to verbal abuse and parental marital complications, but not with other measures pertinent to self-reported aggression. These results suggest that 9R allele, which may influence levels of intra-synaptic dopamine in the brain, may contribute to criminal tendency in this sample of violent murderers of Pakistani origin. Future studies are needed to replicate this finding in other populations of murderers and see if this finding extends to other forms of violence and lesser degrees of aggression.