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.

The Jails and Prisons sub-layer is part of the Emergency Law Enforcement Sector and the Critical Infrastructure Category. A Jail or Prison consists of any facility or location where individuals are regularly and lawfully detained against their will. This includes Federal and State prisons, local jails, and juvenile detention facilities, as well as law enforcement temporary holding facilities. Work camps, including camps operated seasonally, are included if they otherwise meet the definition. A Federal Prison is a facility operated by the Federal Bureau of Prisons for the incarceration of individuals. A State Prison is a facility operated by a state, commonwealth, or territory of the US for the incarceration of individuals for a term usually longer than 1 year. A Juvenile Detention Facility is a facility for the incarceration of those who have not yet reached the age of majority (usually 18 years). A Local Jail is a locally administered facility that holds inmates beyond arraignment (usually 72 hours) and is staffed by municipal or county employees. A temporary holding facility, sometimes referred to as a "police lock up" or "drunk tank", is a facility used to detain people prior to arraignment. Locations that are administrative offices only are excluded from the dataset. This definition of Jails is consistent with that used by the Department of Justice (DOJ) in their "National Jail Census", with the exception of "temporary holding facilities", which the DOJ excludes. Locations which function primarily as law enforcement offices are included in this dataset if they have holding cells. If the facility is enclosed with a fence, wall, or structure with a gate around the buildings only, the locations were depicted as "on entity" at the center of the facility. If the facility's buildings are not enclosed, the locations were depicted as "on entity" on the main building or "block face" on the correct street segment. Personal homes, administrative offices, and temporary locations are intended to be excluded from this dataset; however, some personal homes of constables are included due to the fact that many constables work out of their homes. TGS has made a concerted effort to include all correctional institutions. This dataset includes non license restricted data from the following federal agencies: Bureau of Indian Affairs; Bureau of Reclamation; U.S. Park Police; Federal Bureau of Prisons; Bureau of Alcohol, Tobacco, Firearms and Explosives; U.S. Marshals Service; U.S. Fish and Wildlife Service; National Park Service; U.S. Immigration and Customs Enforcement; and U.S. Customs and Border Protection. The Law Enforcement dataset and the Correctional Institutions dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. With the merge of the Law Enforcement and the Correctional Institutions datasets, NAICS Codes & Descriptions were assigned based on the facility's main function which was determined by the entity's name, facility type, web research, and state supplied data. In instances where the entity's primary function is both law enforcement and corrections, the NAICS Codes and Descriptions are assigned based on the dataset in which the record is located (i.e., a facility that serves as both a Sheriff's Office and as a jail is designated as [NAICSDESCR]="SHERIFFS' OFFICES (EXCEPT COURT FUNCTIONS ONLY)" in the Law Enforcement layer and as [NAICSDESCR]="JAILS (EXCEPT PRIVATE OPERATION OF)" in the Correctional Institutions layer). Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard fields that TGS populated. Double spaces were replaced by single spaces in these same fields. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on the values in this field, the oldest record dates from 05/03/2006 and the newest record dates from 10/19/2009.

Homeland Security Use Cases: Use cases describe how the data may be used and help to define and clarify requirements. 1. A threat to cause the mass release of prisoners by an outside terrorist group has been identified. Steps need to be taken to provide extra security at the targeted prisons. 2. Massive civil unrest has resulted in a large number of arrests. Appropriate space is needed outside of the immediate area to house the arrested individuals. 3. Massive civil unrest has resulted in a large number of arrests. A "holding camp" has been established to hold those arrested. Trained security guards are needed to staff the holding camp. 4. A disaster has caused the need for an emergency labor force (e.g., sandbagging during a flood) and prisoners may fill that need. 5. Inmates may need to be evacuated, or appropriate steps may need to be taken at a prison to protect the inmates and to ensure that a disaster does not present an opportunity for escape.

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 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 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.

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 paro...

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.

This study is a follow-up to the National Jail Census, 1970. In addition to gathering more information concerning jails, the survey also collected demographic data, incarceration history, and legal status data from a sample of the inmates of local jails. The study includes basic demographic data, income and employment data, reason for incarceration, bail status, dates of admission and sentencing, length and type of sentence and previous incarceration history.

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.

This is a dataset of prisoner mugshots and associated data (height, weight, etc). The copyright status is public domain, since it's produced by the government, the photographs do not have sufficient artistic merit, and a mere collection of facts aren't copyrightable.

The source is the Illinois Dept. of Corrections. In total, there are 68149 entries, of which a few hundred have shoddy data.

It's useful for neural network training, since it has pictures from both front and side, and they're (manually) labeled with date of birth, name (useful for clustering), weight, height, hair color, eye color, sex, race, and some various goodies such as sentence duration and whether they're sex offenders.

Here is the readme file:

---BEGIN README---
Scraped from the Illinois DOC.

https://www.idoc.state.il.us/subsections/search/inms_print.asp?idoc=
https://www.idoc.state.il.us/subsections/search/pub_showfront.asp?idoc=
https://www.idoc.state.il.us/subsections/search/pub_showside.asp?idoc=

paste <(cat ids.txt | sed 's/^/http://www.idoc.state.il.us/subsections/search/pub_showside.asp?idoc=/g') <(cat ids.txt| sed 's/^/ out=/g' | sed 's/$/.jpg/g') -d ' ' > showside.txt
paste <(cat ids.txt | sed 's/^/http://www.idoc.state.il.us/subsections/search/pub_showfront.asp?idoc=/g') <(cat ids.txt| sed 's/^/ out=/g' | sed 's/$/.jpg/g') -d ' ' > showfront.txt
paste <(cat ids.txt | sed 's/^/http://www.idoc.state.il.us/subsections/search/inms_print.asp?idoc=/g') <(cat ids.txt| sed 's/^/ out=/g' | sed 's/$/.html/g') -d ' ' > inmates_print.txt

aria2c -i ../inmates_print.txt -j4 -x4 -l ../log-$(pwd|rev|cut -d/ -f 1|rev)-$(date +%s).txt

Then use htmltocsv.py to get the csv. Note that the script is very poorly written and may have errors. It also doesn't do anything with the warrant-related info, although there are some commented-out lines which may be relevant.
Also note that it assumes all the HTML files are located in the inmates directory., and overwrites any csv files in csv if there are any.

front.7z contains mugshots from the front
side.7z contains mugshots from the side
inmates.7z contains all the html files
csv contains the html files converted to CSV

The reason for packaging the images is that many torrent clients would otherwise crash if attempting to load the torrent.

All CSV files contain headers describing the nature of the columns. For person.csv, the id is unique. For marks.csv and sentencing.csv, it is not.
Note that the CSV files use semicolons as delimiters and also end with a trailing semicolon. If this is unsuitable, edit the arr2csvR function in htmltocsv.py.

There are 68149 inmates in total, although some (a few hundred) are marked as "Unknown"/"N/A"/"" in one or more fields.

The "height" column has been processed to contain the height in inches, rather than the height in feet and inches expressed as "X ft YY in."
Some inmates were marked "Not Available", this has been replaced with "N/A".
Likewise, the "weight" column has been altered "XXX lbs." -> "XXX". Again, some are marked "N/A".

The "date of birth" column has some inmates marked as "Not Available" and others as "". There doesn't appear to be any pattern. It may be related to the institution they are kept in. Otherwise, the format is MM/DD/YYYY.

The "weight" column is often rounded to the nearest 5 lbs.

Statistics for hair:
43305 Black
17371 Brown
2887 Blonde or Strawberry
2539 Gray or Partially Gray
740 Red or Auburn
624 Bald
396 Not Available
209 Salt and Pepper
70 White
7 Sandy
1 Unknown

Statistics for sex:
63409 Male
4740 Female

Statistics for race:
37991 Black
20992 White
8637 Hispanic
235 Asian
104 Amer Indian
94 Unknown
92 Bi-Racial
4

Statistics for eyes:
51714 Brown
7808 Blue
4259 Hazel
2469 Green
1382 Black
420 Not Available
87 Gray
9 Maroon
1 Unknown
---END README---

Here is a formal summary:

---BEGIN SUMMARY---
Documentation:

1. Title: Illinois DOC dataset

2. Source Information
   -- Creators: Illinois DOC
   -- Illinois Department of Corrections
   1301 Concordia Court
   P.O. Box 19277
   Springfield, IL 62794-9277
   (217) 558-2200 x 2008
   -- Donor: Anonymous
   -- Date: 2019

3. Past Usage:
   -- None

4. Relevant Information:
   -- All CSV files contain headers describing the nature of the columns. For person.csv, the id is unique. For marks.csv and sentencing.csv, it is not.
   -- Note that the CSV files use semicolons as delimiters and also end with a trailing semicolon. If this is unsuitable, edit the arr2csvR function in htmltocsv...

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: http://geodata.md.gov/imap/rest/services/PublicSafety/MD_CorrectionalFacilities/FeatureServer/1 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.

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.

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 international, interdisciplinary project provides new perspectives on the experience of prison for a range of end users. Using innovative mixed methods its aim is to critically analyze current developments in penal architecture, the design of carceral spaces and the impact of advanced technologies of communication, surveillance and monitoring of movement, with a view to providing a theoretically informed, empirically grounded and comparative account of prison architecture, design and technology (ADT) and their effects on prisoners, staff and prison visitors. Tracing the commission, design and construction of two UK prisons, the project explores: (i) the intentions behind the architecture, design and technologies of spatial management and control that characterize the recent penal estate, paying attention to external and internal spaces, and incorporating consideration of the introduction of Building Information Modelling (BIM) to the UK custodial sector; (ii) the impacts of the architecture, design and technologies of spatial management and control that characterize both the recent UK and northwest European penal estate.This research investigates developments in the design of prisons, exploring the propositions that punishment is manifested architecturally, that 'good' prison design need not cost any more than 'bad' design, that architecture, design and technology (ADT) may impact on prisoners' emotional and psychological reactions to incarceration, including their behaviour, their willingness to engage with regimes and their capacity to build positive relations with other prisoners and staff, and that ADT may significantly influence prisoners' prospects of rehabilitation and reintegration into society on release. One 'lifer' notes that many of the crises facing penal systems in the developed world (including overcrowding, violence, mental and physical illness, drug use, high levels of suicide, self-harm etc.) are intrinsically related to the 'fear-suffused environments' created by prison architects (Hassine, 2008: 8). This research critically interrogates this statement. Against that backdrop, a few new penal experiments in parts of northern and western Europe might be welcomed as 'humane' alternatives to the traditional architecture of incarceration. Equipped with state-of-the-art lighting imitating natural daylight, extensive use of glass, no bars on windows, different colour palettes creating varied atmospheres in each 'zone', displays of artwork, curved lines, rounded walls and uneven horizons, the design features being incorporated into some new prisons might be assumed to mitigate against the harms caused by imprisonment. But can aesthetic considerations make a difference to behaviour? If, as 19th century prison commissioners and designers believed, architecture can be used as a means of inflicting punishment, is it equally true that architecture can deliver rehabilitation? Should the briefs issued to those who design and plan new prisons include a requirement to build into their construction features that normalize carceral space and have potential to ease offenders' reintegration back into society? Or is it simply that 'a prison is a prison', regardless of the enlightened humanism that may underpin its design? Could it even be that these prisons have unintended outcomes and perverse consequences, or represent an extension of power and control orientated towards docile compliance and bring their own distinctive pains of imprisonment? Moreover, if the general public are as punitive in their attitudes to offenders, as is commonly thought, how do communities feel when prisons are built in their midst? How do architects of prisons balance the requirements that prisons should pass the 'public acceptability' test (which may include an expectation that they should 'look' and 'feel' like places of punishment) with the 'NIMBYism' which frequently greets the announcement of a new prison? This project will empirically investigate these issues and inform future debates about how prisons might be designed differently in order to fulfill the goal of rehabilitation as well as those of security, deterrence, retribution and punishment. Challenging conventional wisdom and taken-for-granted assumptions concerning the purposes and 'effectiveness' of prisons, the proposed project is innovative, significant and timely. No research currently exists on the impact and effects - on prison staff as well as on inmates - of penal architecture, spatial design and the implementation of advanced monitoring, surveillance and communication technologies. The study's intent is to move beyond the traditional, historical focus on penal architecture e.g. the legacy of Bentham's Panopticon and the 19th century 'separate' and 'silent' systems (in which the goals of discipline and reform were built in to the fabric of the carceral environment), and to inform knowledge and debates from a contemporary and future-oriented perspective. In doing this, the proposed project promises to deliver significant advances on previous research and extant knowledge. Interviews with male and female prisoners (semi-structured; walked interviews); interviews with prison staff (semi-structured; walked); focus groups; surveys; photo project.

This study sought to identify and examine current policies and strategies for controlling prison gangs and to determine the ways in which correctional facilities were dealing with gangs in their institutions. Respondents to the mail survey included 55 local jail systems and 52 state prison systems (the 50 state Departments of Corrections, the District of Columbia, and the Federal Bureau of Prisons). The survey question text used the term "security threat group" (STG), which was defined as "two or more inmates, acting together, who pose a threat to the security or safety of staff/inmates and/or are disruptive to programs and/or to the orderly management of the facility/system," rather than the generic term "gang." Data contain information on total inmate population, number of STGs, number of inmates identified as confirmed, suspected, associate, and drop-out members of STGs, total incidents of violence, number of violent incidents by STG members, management strategies to deal with gangs, and names of STGs known to be present within the system.

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.

Abstract (en): This data collection provides annual data on prisoners under a sentence of death and on those whose offense sentences were commuted or vacated. Information is available on basic sociodemographic characteristics such as age, sex, race and ethnicity, marital status at time of imprisonment, level of education, and state of incarceration. Criminal history data include prior felony convictions for criminal homicide and legal status at the time of the capital offense. Additional information is provided on those inmates removed from death row by yearend 1988 and those inmates who were executed. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Checked for undocumented or out-of-range codes.. Inmates in state prisons under the sentence of death. 2008-11-12 Minor changes have been made to the metadata.2008-10-30 All parts have been moved to restricted access and are available only using the restricted access procedures.2006-01-12 All files were removed from dataset 3 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 3 and flagged as study-level files, so that they will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.1997-05-30 SAS data definition statements are now available for this collection, and the SPSS data definition statements were updated. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. (1) Information collected prior to 1972 is in many cases incomplete and reflects vestiges in the reporting process. (2) The inmate identification numbers were assigned by the Bureau of Census and have no purpose outside this dataset.

This study analyzes shock incarceration (boot camp) programs in Florida, Georgia, Illinois, Louisiana, Oklahoma, South Carolina, and Texas. In each state, offenders who participated in boot camps were compared with demographically similar offenders who were sentenced to prison, probation, or parole. The impact of shock incarceration on offenders was assessed in two major areas: (1) changes in offenders' attitudes, expectations, and outlook during incarceration (self-report/attitude data), and (2) performance during and adjustment to community supervision after incarceration (community supervision data). The self-report/attitude data include variables measuring criminal history, drinking and drug abuse, and attitudes toward the shock incarceration program, as well as demographic variables, such as age, race, employment, income, education, and military experience. The community supervision data contain information on offenders' behaviors during community supervision, such as arrests, absconding incidents, jail time, drug use, education and employment experiences, financial and residential stability, and contacts with community supervision officers, along with demographic variables, such as age and race. In addition to these key areas, more detailed data were collected in Louisiana, including a psychological assessment, a risk and needs assessment, and a community supervision follow-up at two different time periods (Parts 11-18). For most states, the subjects sampled in the self-report/attitude survey were different from those who were surveyed in the community supervision phase of data collection. Data collection practices and sample structures differed by state, and therefore the data files are organized to explore the impact of shock incarceration at the state level. For each state, the unit of analysis is the offender.

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).

The purpose of this collection was to measure the extent to which the Prisoner Management Classification (PMC) system in Washington state improved overall operations of prison facilities and reduced safety risks to inmates and staff. Four primary issues were addressed: (1) To what extent the PMC reduces rates of assaults on staff and inmates, (2) To what extent the PMC reduces rates of other serious misconduct, (3) To what extent the PMC increases rates of inmate participation in work or vocational programs, and (4) To what extent the PMC enhances staff job satisfaction, morale, and staff performance. Information is included on outcome variables against which comparisons between the experimental and control groups can be made. For each correctional facility, figures were collected for the number of staff-inmate assaults, number of inmate-inmate assaults, number of suicides and suicide attempts, number of escapes and escape attempts, number of "serious" disciplinary incidents, number of total staff, number of inmates, number of security staff vacancies, rated capacity of the facility, number of staff transfers and reasons, and number of inmates involved in educational, vocational, and work programs. Demographic variables include date of birth, sex, and race. Additional information concerns the family structure of the inmates and conditions surrounding the inmates' lives prior to entering prison.