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.

The U.S. government does not release jail by jail mortality data, keeping the public and policy makers in the dark about facilities with high rates of death. In a first-of-its-kind accounting, Reuters obtained and is releasing that data to the public.

What if the jail in your community had an outsized death rate, but no one knew? For decades, communities across the country have faced that quandary. The Justice Department collects jail death data, but locks the information away, leaving policymakers, investigators and activists unaware of problem facilities.

Reuters journalists filed more than 1,500 public records requests to gain death data from 2008 to 2019 in the nation’s biggest jails. Today, jail by jail and state by state, it is making that information available to the public. Reuters examined every large jail in the United States, those with 750 or more inmates. And, to ensure it examined deaths across the country, it obtained data for the 10 largest jails in each state. The data covers 523 jails or jail systems.

Original Data

Acknowlegement

Foto von Hasan Almasi auf Unsplash

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 DATASET WAS LAST UPDATED AT 2:11 AM EASTERN ON JULY 15

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

A. SUMMARY Please note that the "Data Last Updated" date on this page denotes the most recent DataSF update and does not reflect the most recent update to this dataset. To confirm the completeness of this dataset please contact the Sheriff's Office at sheriff.tech.services@sfgov.org

The dataset provides summary information on individuals booked into the San Francisco City and County Jail since 2012, categorized by ethnicity. The table provides a breakdown of the total number of bookings by month and ethnicity. The unit of measure is the jail booking number. The data is collected by the Sheriff's Office and includes self-report and assigned data. However, some ethnicity categories with small sample sizes are grouped together to reduce the risk of re-identification and protect the privacy of individuals booked into jail.

The booking process refers to the procedure that occurs after an individual has been arrested and is taken into custody. The process begins with the arrest of an individual by law enforcement officers. The arrest can take place on the scene or at a later time if a warrant is issued. Once the individual has been arrested, and statutory law requires incarceration, they would be transported to the jail for booking. The arresting officer will record the reason for the arrest, along with any other relevant information. The sheriff’s deputies will then book the individual into jail, which involves taking their fingerprints, photograph, and recording personal information. The jail will assign a booking number, which is used to identify the individual throughout their time in custody. Once the booking process is complete, the individual will be incarcerated and will remain in custody until they are released per court order.

Disclaimer: The San Francisco Sheriff's Office does not guarantee the accuracy, completeness, or timeliness of the information as the data is subject to change as modifications and updates are completed.

B. HOW THE DATASET IS CREATED

When an arrest is presented to the Sheriff’s Office, relevant data is manually entered into the Sheriff Office's jail management system. Data reports are pulled from this system on a semi-regular basis, and added to Open Data.

C. UPDATE PROCESS This dataset is scheduled to update monthly.

D. HOW TO USE THIS DATASET This data can be used to identify trends and patterns in the jail population over time. The date in this dataset is based on the date the suspect was booked into county jail for the arresting incident. The unit of measurement for this dataset is the booking number. A jail booking number is a unique identifier assigned to each individual who is booked into a jail facility.

E. RELATED DATASETS • Booking by Age • Bookings by Race • Booking by Male/Female

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).

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

The purpose of this study was to examine the crime of identity theft from the offenders' perspectives. The study employed a purposive sampling strategy. Researchers identified potential interview subjects by examining newspapers (using Lexis-Nexis), legal documents (using Lexis-Nexis and Westlaw), and United States Attorneys' Web sites for individuals charged with, indicted, and/or sentenced to prison for identity theft. Once this list was generated, researchers used the Federal Bureau of Prisons (BOP) Inmate Locator to determine if the individuals were currently housed in federal facilities. Researchers visited the facilities that housed the largest number of inmates on the list in each of the six regions in the United States as defined by the BOP (Western, North Central, South Central, North Eastern, Mid-Atlantic, and South Eastern) and solicited the inmates housed in these prisons. A total of 14 correctional facilities were visited and 65 individuals incarcerated for identity theft or identity theft related crimes were interviewed between March 2006 and February 2007. Researchers used semi-structured interviews to explore the offenders' decision-making processes. When possible, interviews were audio recorded and then transcribed verbatim. Part 1 (Quantitative Data) includes the demographic variables age, race, gender, number of children, highest level of education, and socioeconomic class while growing up. Other variables include prior arrests or convictions and offense type, prior drug use and if drug use contributed to identity theft, if employment facilitated identity theft, if they went to trial or plead to charges, and sentence length. Part 2 (Qualitative Data), includes demographic questions such as family situation while growing up, highest level of education, marital status, number of children, and employment status while committing identity theft crimes. Subjects were asked about prior criminal activity and drug use. Questions specific to identity theft include the age at which the person became involved in identity theft, how many identities he or she had stolen, if they had worked with other people to steal identities, why they had become involved in identity theft, the skills necessary to steal identities, and the perceived risks involved in identity theft.

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

Content

Represents return status within three years of release among inmates released to the community during a particular calendar year. Each data record represents a release to the community as a result of completing maximum sentence, reaching conditional release date, or being approved for release by the Board of Parole. The dataset includes data about release year, county of indictment, gender, age at release, and return status.

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 Luo ping on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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

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

Content

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

Context

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

• Update Frequency: This dataset is updated daily.

Acknowledgements

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

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

Each record in this dataset shows information about an arrest executed by the Chicago Police Department (CPD). Source data comes from the CPD Automated Arrest application. This electronic application is part of the CPD CLEAR (Citizen Law Enforcement Analysis and Reporting) system, and is used to process arrests Department-wide.

A more-detailed version of this dataset is available to media by request. To make a request, please email dataportal@cityofchicago.org with the subject line: Arrests Access Request. Access will require an account on this site, which you may create at https://data.cityofchicago.org/signup. New data fields may be added to this public dataset in the future. Requests for individual arrest reports or any other related data other than access to the more-detailed dataset should be directed to CPD, through contact information on that site or a Freedom of Information Act (FOIA) request.

The data is limited to adult arrests, defined as any arrest where the arrestee was 18 years of age or older on the date of arrest. The data excludes arrest records expunged by CPD pursuant to the Illinois Criminal Identification Act (20 ILCS 2630/5.2).

Department members use charges that appear in Illinois Compiled Statutes or Municipal Code of Chicago. Arrestees may be charged with multiple offenses from these sources. Each record in the dataset includes up to four charges, ordered by severity and with CHARGE1 as the most severe charge. Severity is defined based on charge class and charge type, criteria that are routinely used by Illinois court systems to determine penalties for conviction. In case of a tie, charges are presented in the order that the arresting officer listed the charges on the arrest report. By policy, Department members are provided general instructions to emphasize seriousness of the offense when ordering charges on an arrest report.

Each record has an additional set of columns where a charge characteristic (statute, description, type, or class) for all four charges, or fewer if there were not four charges, is concatenated with the | character. These columns can be used with the Filter function's "Contains" operator to find all records where a value appears, without having to search four separate columns.

Users interested in learning more about CPD arrest processes can review current directives, using the CPD Automated Directives system (http://directives.chicagopolice.org/directives/). Relevant directives include:

• Special Order S06-01-11 – CLEAR Automated Arrest System: describes the application used by Department members to enter arrest data. • Special Order S06-01-04 – Arrestee Identification Process: describes processes related to obtaining and using CB numbers. • Special Order S09-03-04 – Assignment and Processing of Records Division Numbers: describes processes related to obtaining and using RD numbers. • Special Order 06-01 – Processing Persons Under Department Control: describes required tasks associated with arrestee processing, include the requirement that Department members order charges based on severity.

This survey of departments of corrections in the United States was undertaken to provide correctional staff with design, implementation, and management strategies to meet the needs of prisoners under the age of 18. The study examined what happens when individuals under age 18 are placed in adult correctional facilities, and explored the ways in which departments of corrections are attempting to deal with the growing population of youthful inmates. The following three objectives were the focus of this study: (1) to describe the number of incarcerated youths (at time of admission) being held in the nation's prison system, (2) to examine the different methods being used to house inmates under 18 years old, and (3) to explore different management approaches used with youthful inmates in terms of the size of the prison system and the area of the country in which they were located. For this study, respondents in 51 departments of corrections (50 states and the District of Columbia) were contacted by telephone regarding survey questions that were mailed prior to the phone interviews. The survey contained five questions concerning current practices for handling offenders under the age of 18 who had been placed in adult correctional institutions. Data were collected on the method used to house underaged inmates and the size of each system's population of inmates under 18 years old. Subsequently, the method and size data were combined to form categories describing four management approaches for dealing with offenders under the age of 18 in adult prisons: (1) separated/big, (2) separated/little, (3) integrated/big, and (4) integrated/little. Demographic variables include the population size and region (Northeast, South, Midwest, or West) of each jurisdiction, as well as the number and proportion of offenders under 18 years old within each state. Also present in the file is the location and name of the facility with the largest under-18 population in each jurisdiction.

A. SUMMARY This dataset contains San Francisco Board of Supervisor district boundaries approved by the San Francisco Redistricting Task Force in April 2022 following redistricting based on the 2020 Decennial Census. B. HOW THE DATASET IS CREATED The dataset was created from the final map submitted by the San Francisco Redistricting Task Force. Boundaries in this map were decided using data from the 2020 Census on the number of people living in each census block in the City and County. This data includes the number of individuals incarcerated in facilities under the control of the Department of Corrections and Rehabilitation based on their last known residential address. This information is made available by the Statewide Database based on U.S. Census Bureau Census Redistricting Data (P.L. 94-171). These map boundaries were trimmed to align with the city and county's physical boundaries for greater usability. This trimming mainly consisted of excluding the water around the City and County from the boundaries. C. UPDATE PROCESS Supervisor District boundaries are updated every 10 years following the federal decennial census. The Supervisor District boundaries reflected in this dataset will remain unchanged. A new dataset will be created after the next decennial census and redistricting process are completed. The dataset is manually updated as new members of the Board of Supervisors take office. The most recent manual update date is reflected in the 'data_as_of' field. Once the redistricting process is completed after the next decennial census and a new dataset is published, this dataset will become static and will no longer be updated. D. HOW TO USE THIS DATASET This dataset can be joined to other datasets for analysis and reporting at the Supervisor District level. If you are building an automated reporting pipeline using Socrata API access, we recommend using this dataset if you'd like your boundaries to remain static. If you would like the boundaries to automatically update after each decennial census to reflect the most recent Supervisor District boundaries, see the Current Supervisor Districts dataset or the Current Supervisor Districts (trimmed to remove water and other non-populated City territories) dataset. E. RELATED DATASETS Supervisor Districts (2012) Current Supervisor Districts Current Supervisor Districts (trimmed to remove water and non-populated areas)

Private Forest Wind Damage Assessment Spatial Database - May 2025. Published by Department of Agriculture, Food and the Marine. Available under the license Licence Not Specified (notspecified).Following Storm Darragh and Storm Éowyn during the winter of 2024/2025, and noting that many forests have been windblown around the country, Minister for Agriculture, Food and the Marine, Martin Heydon, and Minister of State for Forestry, Horticulture and Farm Safety, Michael Healy-Rae, invited key stakeholders to join department officials on a taskforce to ensure that storm-damaged forests were managed safely and appropriately. A Forestry Windblow Taskforce was set up to quantify forest damage and to identify approaches to facilitate the mobilisation of wind damaged timber.

Part of the Taskforce’s work involved initiating a detailed mapping assessment using high-resolution satellite imagery to provide information at a local or forest stand level scale. The detailed assessment of windblow damage was undertaken using high resolution satellite imagery from SkySat, and supplemented with pre and post storm Sentinel-2 and PlanetScope satellite data. In addition, drone imagery was also acquired for a number of specific locations.

The mapping exercise relied on the tasking of SkySat imagery during cloud-free weather conditions to acquire the necessary imagery data. The mapping was conducted largely between early February and the beginning of April 2025. The mapping effort focused on a target area of interest where the damage was deemed most likely to have occurred. These target areas were forests stands that were predominantly coniferous species and at least 15 years of age. These age and species criteria were used to filter both Coillte’s sub-compartment database and DAFM’s private forest dataset to confine the wind damage mapping exercise to the most relevant forests.

The windblow mapping exercise utilised a range of available EO datasets of varying spatial and temporal resolution which included: SkySat: 75% (c. 0.50 m resolution), Sentinel-2/PlanetScope: 20% (c. 10 m resolution/c. 3 m resolution), and drone imagery: 5% ( c. 0.2 m resolution).

The national estimate of private wind damage area (11,414 ha) as included in the private forest wind damage spatial database is within approximately +/- 500 hectares of the actual windblown private forest area. This uncertainty is due in part to the fact that for some parts of the country, SkySat satellite imagery has not yet been acquired. It is expected that there will be an ongoing refinement of the private forest windblow area estimate when new SkySat or other Earth Observation data becomes available over the coming months.

As part of this mapping exercise, “older” windblown areas, i.e. windblown forest areas that are more than 4 years old, were also identified and mapped. It is estimated these damage forest areas represent between 750 and 1,000 hectares of the total national area estimate of private wind damaged forest.

The area of wind damage in broadleaf stands may be greater than identified in the private forest wind damage database given the focus in the mapping exercise on coniferous species that were at least 15 years of age. This is also due in part to the fact that the identification of windblow in broadleaf stands is more challenging, particularly if the damage impacts individual trees.

The output from the mapping assessment is an ESRI Shapefile polygon database of wind damaged, privately owned forest areas greater than or equal to 0.1 hectares. The Shapefile is provided in the Irish Transverse Mercator geographic coordinate system. The main attribute included the spatial database table is area in hectares for each wind damaged forest area delineated.

These data are provisional in that they are a record of DAFM data holding in relation to private wind damaged forest at this time (May 2025). They are not published as legal definitions of the current actuality with regard to their geographic extent. They may contain errors and omissions and it should also be noted that the data cannot be taken as being absolutely current. Therefore they should be treated as indicative of the actual geographic situation. The Department of Agriculture, Food and the Marine will accept no liability for any loss or damage suffered by those using this data for any purpose whatsoever....

NATIONAL JAIL CENSUS, 1988, is the fifth in a series of data collection efforts aimed at studying the nation's locally administered jails. For purposes of this data collection, a jail was defined as a confinement facility intended for holding adults (and in some cases juveniles) pending adjudication or having sentences of a year or less. Jails were further defined as being administered and staffed by municipal or county employees. Also included in this collection were six jails privately operated under contract for local governments. Excluded from the census were federal or state-administered facilities, including the combined jail-prison systems in Alaska, Connecticut, Delaware, Hawaii, Rhode Island, and Vermont. The mailing list used for the census was derived from data gathered from the AMERICAN CORRECTIONAL ASSOCIATION DIRECTORY OF JUSTICE AGENCIES, publications such as AMERICAN JAILS, telephone calls to large metropolitan jail systems (e.g., New York City), state jail inspection bureaus, and newspaper articles. Following the initial mailout to 3,448 facilities, 44 jails were added and 176 deleted according to the criteria for inclusion, leaving a total of 3,316 facilities in 44 states. Variables include information on jail population by legal status, age and sex of prisoners, maximum sentence, admissions and releases, available services, structure and capacity, expenditure, and employment.

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.

A listing, updated nightly, of individuals being held in Department of Correction facilities while awaiting trial. This data is appended on nightly basis reflecting the individual inmates being held in correctional facilities each day beginning July 1, 2016. Field Descriptions: DOWNLOAD DATE: Date in which the data were extracted and reflecting the population for that day. IDENITIFIER: Individual Inmate Identifier LATEST ADMISSION DATE: Most recent date in which the inmate has been admitted. In some instances, this may reflect an original date of admission to a correctional facility. Generally, if a date is more than one year old, an inmate should not be considered to have been held for the entire duration of that time. RACE: Race of inmate AGE: Age of inmate BOND AMOUNT: Amount of bond for which the inmate is being held. In some instances, for particularly low (less than $100), this bond amount may be considered a place holder value OFFENSE: Controlling offense for which the bond amount has been set. FACILITY: Department of Correction facility where the inmate is currently held. DETAINER: Denotes whether inmate is being held at the request of another criminal justice agency, or if another agency is to be notified upon release.