A snapshot of the incarcerated population sentenced to the Indiana Department of Correction, including race, age, felony type, and most serious offense category. All data reflects December 31st of the selected year. This dataset contains the underlying data for the 'Population' tab of the 'Prison Incarceration' dashboard within the Public Safety domain.

A. SUMMARY Please note that the "Data Last Updated" date on this page denotes the most recent DataSF update and does not reflect the most recent update to this dataset. To confirm the completeness of this dataset please contact the Sheriff's Office at sheriff.tech.services@sfgov.org The dataset provides summary information on individuals booked into the San Francisco City and County Jail since 2012, categorized by ethnicity. The table provides a breakdown of the total number of bookings by month and ethnicity. The unit of measure is the jail booking number. The data is collected by the Sheriff's Office and includes self-report and assigned data. However, some ethnicity categories with small sample sizes are grouped together to reduce the risk of re-identification and protect the privacy of individuals booked into jail. The booking process refers to the procedure that occurs after an individual has been arrested and is taken into custody. The process begins with the arrest of an individual by law enforcement officers. The arrest can take place on the scene or at a later time if a warrant is issued. Once the individual has been arrested, and statutory law requires incarceration, they would be transported to the jail for booking. The arresting officer will record the reason for the arrest, along with any other relevant information. The sheriff’s deputies will then book the individual into jail, which involves taking their fingerprints, photograph, and recording personal information. The jail will assign a booking number, which is used to identify the individual throughout their time in custody. Once the booking process is complete, the individual will be incarcerated and will remain in custody until they are released per court order. Disclaimer: The San Francisco Sheriff's Office does not guarantee the accuracy, completeness, or timeliness of the information as the data is subject to change as modifications and updates are completed. B. HOW THE DATASET IS CREATED When an arrest is presented to the Sheriff’s Office, relevant data is manually entered into the Sheriff Office's jail management system. Data reports are pulled from this system on a semi-regular basis, and added to Open Data. C. UPDATE PROCESS This dataset is scheduled to update monthly. D. HOW TO USE THIS DATASET This data can be used to identify trends and patterns in the jail population over time. The date in this dataset is based on the date the suspect was booked into county jail for the arresting incident. The unit of measurement for this dataset is the booking number. A jail booking number is a unique identifier assigned to each individual who is booked into a jail facility. E. RELATED DATASETS • Booking by Age • Bookings by Race • Booking by Male/Female

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.

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.The data were obtained from one state prison system that was characterized by a diverse and rising prison population. This prison system housed more than 30,000 inmates across 15 institutions (14 men's facilities; 1 women's facility). The data contain information on inmates' placements into different housing units across all 15 state prison complexes, including designated maximum security, restrictive housing units. Inmates placed in restrictive housing were in lockdown the majority of the day, had limited work opportunities, and were closely monitored. These inmates were also escorted in full restraints within the institution. They experienced little recreational time, visitation and phone privileges, and few interactions with other inmates. The data contain information on inmates' housing placements, institutional misconduct, risk factors, demographic characteristics, criminal history, and offense information. These data provide information on every housing placement for each inmate, including the time spent in each placement, and the reasons documented by correctional staff for placing inmates in each housing unit. Demographic information includes inmate sex, race/ethnicity, and age. The collection contains 1 Stata data file "Inmate-Housing-Placements-Data.dta" with 16 variables and 124,942 cases.

The Ministry of the Solicitor General annually releases data on the segregation, restrictive confinement, and deaths in custody of inmates in Ontario’s adult correctional system. Data Source: Offender Tracking Information System (OTIS) Segregation is defined in Ontario Regulation 778 as any type of custody where an inmate is in highly restricted conditions for 22 to 24 hours or does not receive a minimum of two hours of meaningful social interaction each day, excluding circumstances of an unscheduled lockdown. A record is created each time an inmate meets the conditions of segregation and closed when the inmate no longer meets those conditions. A break in a segregation placement is defined as occurring when an individual is out of segregation conditions for 24 or more continuous hours. The Ministry of the Solicitor General defines restrictive confinement as any type of confinement that is more restrictive than the general population but less restrictive than segregation. As a result, the ministry is reporting on any case within the fiscal year reporting period where an individual was held in a unit regularly scheduled to be locked down for 17 hours or more per day. This timeframe is considered more restrictive than that of the general population based on an assessment of provincewide lockdown times. Regularly scheduled lockdowns are daily routine times where movement out of a cell is restricted, such as during meal times and overnight. The Ministry of the Solicitor General is committed to providing greater transparency by releasing data on all custodial-related deaths that occurred within the calendar year reporting period. The datasets in this category include information on gender, race, age, religion or spiritual affiliation, and alerts for mental health concerns and suicide risk. To simplify the provision of data, several data tables include information on both individuals in segregation conditions and individuals in restrictive confinement. Due to the differences in the way that the data on segregation conditions and restrictive confinement have been collected, and the differences in the definitions of these concepts, these numbers should not be compared to each other. Some individuals may have both placements in restrictive confinement and segregation conditions, within the reporting period. Therefore, these numbers should not be added together when calculating proportions out of the total. Please refer to https://www.ontario.ca/page/jahn-settlement-data-inmates-ontario for additional information on the data release, including written overviews of the data and disclosure on data collection methods.

--- ### 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). ---

The Fortune Society, a private not-for-profit organization located in New York City, provides a variety of services that are intended to support former prisoners in becoming stable and productive members of society. The purpose of this evaluation was to explore the extent to which receiving supportive services at the Fortune Society improved clients' prospects for law abiding behavior. More specifically, this study examined the extent to which receipt of these services reduced recidivism and homelessness following release. The research team adopted a quasi-experimental design that compared recidivism outcomes for persons enrolled at Fortune (clients) to persons released from New York State prisons and returning to New York City and, separately, inmates released from the New York City jails, none of whom went to Fortune (non-clients). All -- clients and non-clients alike -- were released after January 1, 2000, and before November 3, 2005 (for state prisoners), and March 3, 2005 (for city jail prisoners). Information about all prisoners released during these time frames was obtained from the New York State Department of Correctional Services for state prisoners and from the New York City Department of Correction for city prisoners. The research team also obtained records from the Fortune Society for its clients and arrest and conviction information for all released prisoners from the New York State Division of Criminal Justice Services' criminal history repository. These records were matched and merged, producing a 72,408 case dataset on 57,349 released state prisoners (Part 1) and a 68,614 case dataset on 64,049 city jail prisoners (Part 2). The research team obtained data from the Fortune Society for 15,685 persons formally registered as clients between 1989 and 2006 (Part 3) and data on 416,943 activities provided to clients at the Fortune Society between September 1999 and March 2006 (Part 4). Additionally, the research team obtained 97,665 records from the New York City Department of Homeless Services of all persons who sought shelter or other homeless services during the period from January 2000 to July 2006 (Part 5). Part 6 contains 96,009 cases and catalogs matches between a New York State criminal record identifier and a Fortune Society client identifier. The New York State Prisons Releases Data (Part 1) contain a total of 124 variables on released prison inmate characteristics including demographic information, criminal history variables, indicator variables, geographic variables, and service variables. The New York City Jails Releases Data (Part 2) contain a total of 92 variables on released jail inmate characteristics including demographic information, criminal history variables, indicator variables, and geographic variables. The Fortune Society Client Data (Part 3) contain 44 variables including demographic, criminal history, needs/issues, and other variables. The Fortune Society Client Activity Data (Part 4) contain seven variables including two identifiers, end date, Fortune service unit, duration in hours, activity type, and activity. The Homelessness Events Data (Part 5) contain four variables including two identifiers, change in homeless status, and date of change. The New York State Criminal Record/Fortune Society Client Match Data (Part 6) contain four variables including three identifiers and a variable that indicates the type of match between a New York State criminal record identifier and a Fortune Society client identifier.

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.

This collection provides annual data on jail populations across the nation and examines the "spillover" effect on local jails resulting from the dramatic growth in federal and state prison populations. These data permit an assessment of the demands placed on correctional resources and provide a comprehensive picture of the adult correctional system and changes that occur within the system. Information is available on the number of inmates by sex, race, adult or juvenile status, reason being held, and cause of death. Also added in the 1992 survey were variables on citizenship, population movement, and total number of inmate deaths for inmates originally confined to the facility in question who died either at that facility or elsewhere. Also, the 1992 version included a more complete survey of jail programs and a supplemental questionnaire (CJ-5S), which dealt with AIDS-related questions. In addition, information was collected for the first time on drug testing, programs that treated or educated inmates, boot camps, work release, and alternatives to incarceration such as electronic monitoring, house arrest, community service, and weekend or day reporting.

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.

These data focus on rates of criminal offending obtained through the use of self-report surveys. Specifically, the study investigates whether two different types of self-report surveys produce different estimates of lambda, an individual's frequency of criminal offending. The surveys, which were administered during personal interviews with inmates in Nebraska prisons, differed in how respondents were asked about their frequency of criminal offending. The more detailed survey asked respondents to indicate their offenses on a month-by-month basis for the reporting period. The less detailed survey only asked respondents to indicate their offending for the entire reporting period. These data also provide information on the relationship between race and offending frequencies, the rates of offending over time and by crime category, and the individual's subjective probability of punishment and offending frequency. The specific crimes targeted in this collection include burglary, business robbery, personal robbery, assault, theft, forgery, fraud, drug dealing, and rape. All respondents were asked questions on criminal history, substance abuse, attitudes about crime and the judicial system, predictions of future criminal behavior, and demographic information, including age, race, education, and marital status.

This dataset lists the total population 18 years and older by census block in Connecticut before and after population adjustments were made pursuant to Public Act 21-13. PA 21-13 creates a process to adjust the U.S. Census Bureau population data to allow for most individuals who are incarcerated to be counted at their address before incarceration. Prior to enactment of the act, these inmates were counted at their correctional facility address. The act requires the CT Office of Policy and Management (OPM) to prepare and publish the adjusted and unadjusted data by July 1 in the year after the U.S. census is taken or 30 days after the U.S. Census Bureau’s publication of the state’s data. A report documenting the population adjustment process was prepared by a team at OPM composed of the Criminal Justice Policy and Planning Division (OPM CJPPD) and the Data and Policy Analytics (DAPA) unit. The report is available here: https://portal.ct.gov/-/media/OPM/CJPPD/CjAbout/SAC-Documents-from-2021-2022/PA21-13_OPM_Summary_Report_20210921.pdf Note: On September 21, 2021, following the initial publication of the report, OPM and DOC revised the count of juveniles, reallocating 65 eighteen-year-old individuals who were incorrectly designated as being under age 18. After the DOC released the updated data to OPM, the report and this dataset were updated to reflect the revision.

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.

The 2019 Census of State and Federal Adult Correctional Facilities (CCF) was the ninth enumeration of state institutions and the sixth enumeration of federal institutions sponsored by the Bureau of Justice Statistics and its predecessors. Earlier censuses were completed in 1979 (ICPSR 7852), 1984 (ICPSR 8444), 1990 (ICPSR 9908), 1995 (ICPSR 6953), 2000 (ICPSR 4021), 2005 (ICPSR 24642), and 2012 (ICPSR 37294). The 2019 CCF consisted of two data collection instruments - one for confinement facilities and one for community-based facilities. For each facility, information was provided on facility operator; sex of prisoners authorized to be housed by facility; facility functions; percentage of prisoners authorized to leave the facility; one-day counts of prisoners by sex, race/ethnicity, special populations, and holding authority; number of walkaways occurring over a one-year period; and educational and other special programs offered to prisoners. Additional information was collected from confinement facilities, including physical security level; housing for special populations; capacity; court orders for specific conditions; one-day count of correctional staff by payroll status and sex; one-day count of security staff by sex and race/ethnicity; assaults and incidents caused by prisoners; number of escapes occurring over a one-year period; and work assignments available to prisoners. Late in the data collection to avoid complete nonresponse from facilities, BJS offered the option of providing critical data elements from the two data collection instruments. These elements included facility operator; sex of prisoners authorized to be housed by facility; facility functions; percentage of prisoners authorized to leave the facility; one-day counts of prisoners by sex, and holding authority. Physical security level was an additional critical data element for confinement facilities. The census counted prisoners held in the facilities, a custody count. Some prisoners who are held in the custody of one jurisdiction may be under the authority of a different jurisdiction. The custody count is distinct from a count of prisoners under a correctional authority's jurisdiction, which includes all prisoners over whom a correctional authority exercises control, regardless of where the prisoner is housed. A jurisdictional count is more inclusive than a prison custody count and includes state and federal prisoners housed in local jails or other non-correctional facilities.

Content

Represents inmates under custody in NYS Department of Corrections and Community Supervision as of March 31 of the snapshot year. Includes data about admission type, county, gender, age, race/ethnicity, crime, and facility.

Context

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

• Update Frequency: This dataset is updated annually.

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 Mitch Lensink on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The 1999 Census of Jails is the seventh in a series of data collection efforts aimed at studying the nation's locally administered jails. Previous censuses were conducted in 1970, 1972, 1978, 1983, 1988, and 1993. The 1999 census enumerated 3,365 locally administered confinement facilities that held inmates beyond arraignment and were staffed by municipal or county employees. Among these were 47 privately operated jails under contract for local governments and 42 regional jails that were operated for two or more jail authorities. In addition, the census identified 11 facilities maintained by the Federal Bureau of Prisons that functioned as jails. The nationwide total of the number of jails in operation on June 30, 1999, was 3,376. For purposes of this data collection, a local jail was defined as a locally operated adult detention facility that receives individuals pending arraignment and holds them awaiting trial, conviction, or sentencing, readmits probation, parole, and bail-bond violators and absconders, temporarily detains juveniles pending transfer to juvenile authorities, holds mentally ill persons pending their movement to appropriate health facilities, holds individuals for the military, for protective custody, for contempt, and for the courts as witnesses, releases convicted inmates to the community upon completion of sentence, transfers inmates to federal, state, or other authorities, houses inmates for federal, state, or other authorities because of crowding of their facilities, relinquishes custody of temporary detainees to juvenile and medical authorities, operates community-based programs with day-reporting, home detention, electronic monitoring, or other types of supervision, and holds inmates sentenced to short terms. Variables include information on jail population by legal status, age and sex of prisoners, maximum sentence, admissions and releases, available services and programs, structure and capacity, facility age and use of space, expenditure, employment, staff information, and health issues, which include statistics on drugs, AIDS, and tuberculosis.

Slice created from parent data set: Full Virginia Girls' Reformatory Admissions Database 1910-1938

Data set of 2,370 individual female reformatory inmates admitted to the Virginia Home and Industrial School for Girls at Bon Air and the Industrial Home School for Colored Girls at Peake’s Turnout between 1910 and 1938. Created out of the unpublished and archived admissions books of these institutions. Due to Virginia’s 75-year privacy restriction, I stopped collecting at December 1938 for each institution. Data from the Home at Bon Air runs 1910 to 1938; from the Home at Peake’s Turnout from 1915 to 1938. Each reformatory kept separate books, which were archived into separate collections. There was enough similarity between the two books to transcribe the data into one large data set.

Reformatory administrators hand wrote basic administrative information about each girl into bound books. For incoming delinquent girls, they recorded: student number, name, birthdate or age at admittance, date of admittance, and committing jurisdiction (by county or city jurisdiction.) The books also recorded the individual’s parole history, including first parole (and up to her third parole on an individual’s performance) and any return dates; administrators recorded the destination of the first parole, but this was inconsistently recorded. The books note when and to whom inmates were married, usually after their official dismissal. Because transferring an inmate officially removed them from the responsibility of the reformatory, administrators recorded transfer information, including where they went and when. Lastly, the books recorded the official dismissal date and reason. Bon Air’s books were more consistent with recording dismissals and neither institution used consistent definitions of “transfer” versus “dismissal.” I manually transcribed these books verbatim into a database. From this core data, I added categories of information to aid my analysis. These include: race, gender, and reformatory; calculations of either age or birthdate (Peake’s recorded birthdates, Bon Air only ages); parole year taken from the first parole date; parole type determined by me based on the parole destination, if recorded. To help me analyze transfer and dismissal information, I determined the “type” and “category” of each transfer or dismissal and added new categories. These allowed me to “rollup” the varieties of recorded data into fewer descriptive types and categories. Because administrators recorded only the institution name or location when girls were transferred and dismissed elsewhere, this allowed me to organize this info into 18 “types”: asylum, colony, court, death, department of public welfare, escape, family, honorable discharge, illegal commitment, maternity, orphanage, other, penal, private, reorganization (only used for Bon Air in 1914), sanitorium/hospital, venereal disease, and wages. These 18 “types” were then further distilled into 9 “categories” to capture the broadest possible categorization of the reasons why girls were transferred or dismissed: administrative, death, escape, mental, penal, physical, private, unknown, and work.

I have removed the names of the individual inmates upon publication. Researchers interested in using this data in their own work can contact me at erin@erinbush.org to request the versions that include full name fields. The Data Dictionary is also available upon request.

The contents of these data sets, as government records, I believe fall under fair use.

Full Collection

Full Virginia Girls' Reformatory Admissions Database 1910-1938: https://zenodo.org/records/3872019

Full Virginia Girls' Reformatory Transfer and Dismissal Data 1910-1938: https://zenodo.org/records/3872110

Full Virginia Girls' Reformatory Parole Data 1910-1938: https://zenodo.org/records/3872100

This table contains details about incarceration rates in youth in King County. It has been developed for the Determinant of Equity - Law and Justice. It includes information about Incarceration Rates in Youth equity indicator. Fields describe the total youth in King County (Denominator), number of youth incarcerated (Numerator), the type of equity indicator being measured (Indicator), and the value that describes this measurement (Indicator Value).The data was compiled by King County Department of Adult & Juvenile Detention (DAJD).For more information about King County's equity efforts, please see:Equity, Racial & Social Justice VisionOrdinance 16948 describing the determinates of equityDeterminants of Equity and Data Tool

Contains national information on prisoners who were in custody on 30 June each year. The statistics are derived from information collected by the ABS from corrective services agencies in each state and territory. Details are provided on the number of people in correctional institutions (including people on remand), imprisonment rates, most serious offence and sentence length. Information is also presented on prisoner characteristics (age, sex, Indigenous status) and on the type of prisoner (all prisoners, sentenced prisoners, and unsentenced prisoners (remandees).

CAPITAL PUNISHMENT IN THE UNITED STATES, 1973-2018 provides annual data on prisoners under a sentence of death, as well as those who had their sentences commuted or vacated and prisoners who were executed. This study examines basic sociodemographic classifications including age, sex, race and ethnicity, marital status at time of imprisonment, level of education, and state and region of incarceration. Criminal history information includes prior felony convictions and prior convictions for criminal homicide and the legal status at the time of the capital offense. Additional information is provided on those inmates removed from death row by yearend 2018. The dataset consists of one part which contains 9,583 cases. The file provides information on inmates whose death sentences were removed in addition to information on those inmates who were executed. The file also gives information about inmates who received a second death sentence by yearend 2018 as well as inmates who were already on death row.