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.

This data collection contains information gathered in a two-part survey that was designed to assess institutional conditions in state and federal prisons and in halfway houses. It was one of a series of data-gathering efforts undertaken during the 1970s to assist policymakers in assessing and overcoming deficiencies in the nation's correctional institutions. This particular survey was conducted in response to a mandate set forth in the Crime Control Act of 1976. Data were gathered via self-enumerated questionnaires that were mailed to the administrators of all 558 federal and state prisons and all 405 community-based prerelease facilities in existence in the United States in 1979. Part 1 contains the results of the survey of state and federal adult correctional systems, and Part 2 contains the results of the survey of community-based prerelease facilities. The two files contain similar variables designed to tap certain key aspects of confinement: (1) inmate (or resident) counts by sex and by security class, (2) age of facility and rated capacity, (3) spatial density, occupancy, and hours confined for each inmate's (or resident's) confinement quarters, (4) composition of inmate (or resident) population according to race, age, and offense type, (5) inmate (or resident) labor and earnings, (6) race, age, and sex characteristics of prison (or half-way house) staff, and (7) court orders by type of order and pending litigation. Other data (contained in both files) include case ID number, state ID number, name of facility, and operator of facility (e.g., federal, state, local, or private).

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.

This open-access geospatial dataset (downloadable in csv or shapefile format) contains a total of 11 environmental indicators calculated for 1865 U.S. prisons. This consists of all active state- and federally-operated prisons according to the Homeland Infrastructure Foundation-Level Data (HIFLD), last updated June 2022. This dataset includes both raw values and percentiles for each indicator. Percentiles denote a way to rank prisons among each other, where the number represents the percentage of prisons that are equal to or have a lower ranking than that prison. Higher percentile values indicate higher vulnerability to that specific environmental burden compared to all the other prisons. Full descriptions of how each indicator was calculated and the datasets used can be found here: https://github.com/GeospatialCentroid/NASA-prison-EJ/blob/main/doc/indicator_metadata.md.

From these raw indicator values and percentiles, we also developed three individual component scores to summarize similar indicators, and to then create a single vulnerability index (methods based on other EJ screening tools such as Colorado Enviroscreen, CalEnviroScreen and EPA’s EJ Screen). The three component scores include climate vulnerability, environmental exposures and environmental effects. Climate vulnerability factors reflect climate change risks that have been associated with health impacts and includes flood risk, wildfire risk, heat exposure and canopy cover indicators. Environmental exposures reflect variables of different types of pollution people may come into contact with (but not a real-time exposure to pollution) and includes ozone, particulate matter (PM 2.5), traffic proximity and pesticide use. Environmental effects indicators are based on the proximity of toxic chemical facilities and includes proximity to risk management plan (RMP) facilities, National Priority List (NPL)/Superfund facilities, and hazardous waste facilities. Component scores were calculated by taking the geometric mean of the indicator percentiles. Using the geometric mean was most appropriate for our dataset since many values may be related (e.g., canopy cover and temperature are known to be correlated).

To calculate a final, standardized vulnerability score to compare overall environmental burdens at prisons across the U.S., we took the average of each component score and then converted those values to a percentile rank. While this index only compares environmental burdens among prisons and is not comparable to non-prison sites/communities, it will be able to heighten awareness of prisons most vulnerable to negative environmental impacts at county, state and national scales. As an open-access dataset it also provides new opportunities for other researchers, journalists, activists, government officials and others to further analyze the data for their needs and make comparisons between prisons and other communities. This is made even easier as we produced the methodology for this project as an open-source code base so that others can apply the code to calculate individual indicators for any spatial boundaries of interest. The codebase can be found on GitHub (https://github.com/GeospatialCentroid/NASA-prison-EJ) and is also published via Zenodo (https://zenodo.org/record/8306856).

This data collection provides information about topics and issues of concern in research and policy within the field of corrections. Chief among these are the characteristics of persons confined to state prisons, their current and past offenses, and the circumstances or conditions of their confinement. Also included is extensive information on inmates' drug and alcohol use, program participation, and the victims of the inmates' most recent offenses. This information, which is not available on a national basis from any other source, is intended to assist the criminal justice community and other researchers in analysis and evaluation of correctional issues.

--- ### 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 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.

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.

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.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. This dataset contains correctional facilities run by the Maryland Department of Public Safety and Corrections (DPSCS). Data includes year opened - security level and facility administrators. Last Updated: 07/30/2014 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/PublicSafety/MD_CorrectionalFacilities/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This data collection focused on problems in the women's correctional system over a 135-year period. More specifically, it examined the origins and development of prisoner and sentencing characteristics in three states. Demographic data on female inmates cover age, race, parents' place of birth, prisoner's occupation, religion, and marital status. Other variables include correctional facilities, offenses, minimum and maximum sentences, prior commitments, method of release from prison, and presence of crime partners.

The dataset contains outcome variables, control variables, and policy variables. The outcome variables pertain to the change and growth in state-level incarceration rates between 1975 and 2002. Control variables include violent crime rate, property crime rate, percent population between ages of 18-24, percent population between ages of 25-34, percent population African American, percent population of Hispanic origin, percent population living in urban areas, percent adherents to "fundamentalist" religion, income per capita, unemployment rate, percent population below poverty level, GINI income distribution coefficient, state revenues per 100,000 residents, public welfare per 100,000 residents, police officers per 100,000 residents, drug arrest rate, corrections expenditures per 100,000 residents, citizen political ideology, government political ideology, governor's party affiliation, and region. Policy variables capture information regarding sentencing structure, drug policy, time served requirements, habitual offender laws (HOL), and mandatory sentences. Specifically, sentencing structure variables include information on determinate sentencing, structured sentencing, presumptive sentencing guidelines, voluntary sentencing guidelines, and presumptive sentencing. Drug policy variables include sentencing enhancement score (cocaine, heroin, and marijuana), severity levels for possession and sale (cocaine, heroin, and marijuana), minimum sentence for 28 grams of cocaine (sale), maximum sentence for the lowest quantity of cocaine (possession), minimum sentence for 28 grams of heroin (sale), maximum sentence for the lowest quantity of heroin (possession), minimum sentence for 500 grams of marijuana (sale), and minimum sentence for the lowest quantity of marijuana (possession). Variables regarding time served requirements include both time served (all offenses) and time served (violent offenses). The habitual offender laws variables capture information regarding the two-strikes law, three-strikes law, HOL targeted for violent offenses, and HOL targeted for drug offenses. Lastly, variables pertaining to mandatory sentences include number of mandatory minimums for weapons use, number of mandatory minimums for violent offenses, number of mandatory minimums for offenses against protected individuals, number of mandatory minimums for offenses committed while in state custody, and mandatory score. The study consisted of two phases completed between November 2002 and March 2004. The first phase of the research involved building a framework for understanding the types of state-level sentencing and corrections policies in use between 1975 and 2002. To do this, researchers reviewed prior analyses of policies to construct an initial outline of policies or general areas and their characteristics. Next, members of the Vera Institute of Justice's National Associates Program on State Sentencing and Corrections (SSC) reviewed the outline, suggested minor changes in the characteristics detailed, and constructed an initial data collection instrument (DCI). This initial DCI microdatabase was pilot-tested by collecting data on three states, refined, and then a finalized version of the DCI was developed for use in the second stage of the study. Phase two of the project consisted of state-level data collection for all 50 states for all study years, 1975 to 2002. The year 1975 was chosen as the cut-off year since, according to most criminologists and practitioners, most of the dramatic changes in state-level sentencing and corrections policies have occurred post-1975. The principal investigators and six research assistants began by analyzing microfiche versions of state codes as amended in 1975. Microfiche versions of superseded state codes (including supplements) and state sessions laws were then used to collect data on changes to each state's code for each year between 1975 and 2002. Data collection generally involved reading the entire criminal law and criminal procedure sections of each state's 1975 code, locating the relevant policy, and recording information about the provisions of the policy into the DCI. Annual code supplements were then analyzed to note changes to each state's code. When a revised version of the entire code was published, data collection then involved reviewing the entire criminal law and criminal procedure sections of each state's code again. Where changes to policies were unclear from annual supplements, microfiche versions of state sessions laws were consulted, which provided the actual legislation altering the code. This process continued until data collection reached 2002, and analysis turned to the bound versions of state codes as amended in 2002. In order to assess the impacts of state-level sentencing and corrections policies in the United States implemented between 1975 and 2002 on state incarceration rates during that same time period, researchers conducted a two-phase study between November 2002 a...

Historical Dataset of High Desert State Prison Adult High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2015-2018),Total Classroom Teachers Trends Over Years (2016-2023),Student-Teacher Ratio Comparison Over Years (2017-2018),Hispanic Student Percentage Comparison Over Years (2017-2018),Black Student Percentage Comparison Over Years (2017-2018),Two or More Races Student Percentage Comparison Over Years (2017-2018),Diversity Score Comparison Over Years (2017-2018)

This study assessed the effects of male inmate religiosity on post-release community adjustment and investigated the circumstances under which these effects were most likely to take place. The researcher carried out this study by adding Federal Bureau of Investigation criminal history information to an existing database (Clear et al.) that studied the relationship between an inmate's religiousness and his adjustment to the correctional setting. Four types of information were used in this study. The first three types were obtained by the original research team and included an inmate values and religiousness instrument, a pre-release questionnaire, and a three-month post-release follow-up phone survey. The fourth type of information, official criminal history reports, was later added to the original dataset by the principal investigator for this study. The prisoner values survey collected information on what the respondent would do if a friend sold drugs from the cell or if inmates of his race attacked others. Respondents were also asked if they thought God was revealed in the scriptures, if they shared their faith with others, and if they took active part in religious services. Information collected from the pre-release questionnaire included whether the respondent attended group therapy, religious groups with whom he would live, types of treatment programs he would participate in after prison, employment plans, how often he would go to church, whether he would be angry more in prison or in the free world, and whether he would be more afraid of being attacked in prison or in the free world. Each inmate also described his criminal history and indicated whether he thought he was able to do things as well as most others, whether he was satisfied with himself on the whole or felt that he was a failure, whether religion was talked about in the home, how often he attended religious services, whether he had friends who were religious while growing up, whether he had friends who were religious while in prison, and how often he participated in religious inmate counseling, religious services, in-prison religious seminars, and community service projects. The three-month post-release follow-up phone survey collected information on whether the respondent was involved with a church group, if the respondent was working for pay, if the respondent and his household received public assistance, if he attended religious services since his release, with whom the respondent was living, and types of treatment programs attended. Official post-release criminal records include information on the offenses the respondent was arrested and incarcerated for, prior arrests and incarcerations, rearrests, outcomes of offenses of rearrests, follow-up period to first rearrest, prison adjustment indicator, self-esteem indicator, time served, and measurements of the respondent's level of religious belief and personal identity. Demographic variables include respondent's faith, race, marital status, education, age at first arrest and incarceration, and age at incarceration for rearrest.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. This dataset contains correctional facilities run by the United States Bureau of Prisons (BOP) located within Maryland. The data was obtained from the US BOP (http://www.bop.gov/) Last Updated: 07/30/2014 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/PublicSafety/MD_CorrectionalFacilities/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The Census of Jail Inmates is the eighth in a series of data collection efforts aimed at studying the nation's locally-administered jails. Beginning in 2005, the National Jail Census was broken out into two collections. The 2005 Census of Jail Inmates (CJI) collects data on the facilities' supervised populations, inmate counts and movements, and persons supervised in the community. The forthcoming 2006 Census of Jail Facilities collects information on staffing levels, programming, and facility policies. Previous censuses were conducted in 1970, 1972, 1978, 1983, 1988, 1993, and 1999. The 2005 CJI enumerated 2,960 locally administered confinement facilities that held inmates beyond arraignment and were staffed by municipal or county employees. Among these were 42 privately-operated jails under contract to local governments and 65 regional jails that were operated for two or more jail authorities. In addition, the census identified 12 facilities maintained by the Federal Bureau of Prisons that functioned as jails. These 12 facilities, together with the 2,960 nonfederal facilities, brought the number of jails in operation on June 30, 2005, to a nationwide total of 2,972. The CJI supplies data on characteristics of jails such as admissions and releases, growth in the number of jail facilities, changes in their rated capacities and level of occupancy, crowding issues, growth in the population supervised in the community, and changes in methods of community supervision. The CJI also provides information on changes in the demographics of the jail population, supervision status of persons held, and a count of non-United States citizens in custody. The data are intended for a variety of users, including federal and state agencies, local officials in conjunction with jail administrators, researchers, planners, and the public.

Physical locations were verified from the websites of the Massachusetts Department of Correction (MADOC), Massachusetts Sheriffs' Association (MSA), Federal Bureau of Prisons (BOP) and individual facilities, and verbal communication with many of the facilities. Ancillary support facilities - treatment centers, process divisions, resource centers, etc. - are not included because there were no inmates living at these facilities.This layer was modified from its previous version, which was developed by the Massachusetts Department of Environmental Protection’s (DEP) GIS Program based on database information provided by the Department of Criminal Justice Information Services (DCJIS) (the state agency responsible for maintaining the Commonwealth's criminal justice information system), part of the Massachusetts Executive Office of Public Safety and Security (EOPSS). The EOPSS is also the parent agency of the MADOC, which operates the Commonwealth's state prison system.More details...Map service also available.

This census provides information on county and municipal jails facilities in the United States and their administration. For all jails, the data include number of prisoners and their reason for being held, age and sex of prisoners, maximum sentence that could be served in the facility, facility capacity and age, types of security available, and operating expenditures. For jails in counties and municipalities with populations of 25,000 or more, data are supplied on quarterly jail population, age of cells, and availability of service facilities and programs for inmates.

Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16th degree resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915-2021 at daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the U.S. due to missing station meta-data and lack of local wind speed (WS) measurements. We tested several different post-processing adjustment techniques using regridded WS obtained from NCAR Reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of high-quality reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations, and corrected GHCN stations with in-situ WS, showing good overall agreement. Validation was also done over 40 river basins using comparisons between observed monthly streamflow and VIC model simulations forced by datasets with and without PUC corrections. The new dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (R², Nash Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region.