21 datasets found
  1. ⛓️ US Prisons Dataset

    • kaggle.com
    zip
    Updated Aug 16, 2023
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    mexwell (2023). ⛓️ US Prisons Dataset [Dataset]. https://www.kaggle.com/datasets/mexwell/us-prisons-dataset
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    zip(963685 bytes)Available download formats
    Dataset updated
    Aug 16, 2023
    Authors
    mexwell
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    United States
    Description

    The prison boundary feature class contains secure detention facilities. These facilities range in jurisdiction from federal (excluding military) to local governments. Polygon geometry is used to describe the extent of where the incarcerated population is located (fence lines or building footprints). This feature class’s attribution describes many physical and social characteristics of detention facilities in the United States and some of its territories.

    Original Data

    Acknowlegement

    Foto von Milad Fakurian auf Unsplash

  2. Crime and Incarceration in the United States

    • kaggle.com
    zip
    Updated Oct 12, 2018
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    ChrisC (2018). Crime and Incarceration in the United States [Dataset]. https://www.kaggle.com/christophercorrea/prisoners-and-crime-in-united-states
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    zip(70191 bytes)Available download formats
    Dataset updated
    Oct 12, 2018
    Authors
    ChrisC
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    United States
    Description

    Context

    In 1975, the United States set a new record with 240,593 prisoners incarcerated by state or federal agencies. The United States achieved new record totals during each of the next 34 years. Today, there are over 1,500,000 prisoners in the United States. Over one quarter of the world's entire population of prisoners is located in the United States.

    The U.S. Education deparment reports state and local government expenditures on prisons (and jails - not reflected in this dataset) have increased about three times as fast as spending on elementary and secondary education during this time period. Does this significant investment into imprisonment improve public safety? This dataset brings together crime and incarceration statistics to help researchers explore this relationship.

    Content

    The Bureau of Justice Statistics administers the National Prisoners Statistics Program (NPS), an annual data collection effort that began in response to a 1926 congressional mandate. The population statistics reflect each state's prisoner population as of December 31 for the recorded year. Prisoners listed under federal jurisdiction are incarcerated by the U.S. Bureau of Prisons.

    The Uniform Crime Report (UCR) has served as the FBI's primary national data collection tool since a 1930 congressional mandate directed the Attorney General to "acquire, collect, classify, and preserve identification, criminal identification, crime, and other records." The FBI collects this information voluntarily submitted by local, state, and fedral law enforcement agencies. Some U.S. municipalities choose not to participate fully in the program. The crimes_estimated field indicates cases where the FBI estimated state totals due to lack of participation by some municipalities within a state. The crime_reporting_change field reflects instances when states' reporting standards change. For more information on the responsible use of this dataset, please see Uniform Crime Reporting Statistics: Their Proper Use

    Acknowledgements

    State and Federal prisoner population figures published by Bureau of Justice Statistics.

    State crime and population statistics published by the FBI Uniform Crime Reporting (UCR) Program. https://www.ucrdatatool.gov/Search/Crime/State/RunCrimeStatebyState.cfm

    Banner Photo by Oscar Söderlund on Unsplash

    Inspiration

    What is the relationship between incarceration rates and crime rates? Does mass incarceration improve public safety? See below for some recent statements from U.S. politicians related to the relationship between crime and incarceration. Are the data consistent with any of these statements?

    "There is no better way to reduce crime than to identify, target, and incapacitate those hardened criminals... we cannot incapacitate these criminals unless we build sufficient prison and jail space to house them. " - Nominee for 85th U.S. Attorney General William Barr, [October 28, 1992][13]

    "Violent crime has declined since the 1980s because mandatory minimums adopted then locked up violent criminals." - Senator Tom Cotton, August 15, 2018

    "You may assume mass incarceration exists because people are committing more crimes. But that is not true... The incredibly costly reality is that prisons in our nation continue to grow irrespective of crime rates. It is a bureaucracy that has been expanding independent of our security or safety." - Senator Cory Booker, Apr 28, 2015

    "It is far from clear whether this dramatic increase in incarceration for drug crimes has had enough of an effect on property and violent crime rates to justify the human toll of more incarceration." - Senator Ted Cruz, Apr 27, 2015

    "For several decades, tough laws and long sentences have created the illusion that public safety is best served when we treat all offenders the same way: arrest, convict, incarcerate..." - Senator Kamala Harris, [Apr 27, 2015][11]

    "We've got some space to put some people! We need to reverse a trend that suggested that criminals won't be confronted seriously with their crimes" - 84th U.S. Attorney General Jeff Sessions, [March 15, 2018][12]

    ...

  3. d

    Correctional Institutions

    • catalog.data.gov
    • data.oregon.gov
    • +2more
    Updated Jan 31, 2025
    + more versions
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    TechniGraphics, Inc. (2025). Correctional Institutions [Dataset]. https://catalog.data.gov/dataset/correctional-institutions
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    Dataset updated
    Jan 31, 2025
    Dataset provided by
    TechniGraphics, Inc.
    Description

    Jails and Prisons (Correctional Institutions). 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. This dataset is comprised completely of license free data. 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 sin

  4. National Prisoner Statistics, [United States], 1978-2022

    • icpsr.umich.edu
    ascii, delimited, r +3
    Updated Jan 10, 2024
    + more versions
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    United States. Bureau of Justice Statistics (2024). National Prisoner Statistics, [United States], 1978-2022 [Dataset]. http://doi.org/10.3886/ICPSR38871.v1
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    r, delimited, spss, sas, stata, asciiAvailable download formats
    Dataset updated
    Jan 10, 2024
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States. Bureau of Justice Statistics
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/38871/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/38871/terms

    Time period covered
    1978 - 2022
    Area covered
    United States
    Description

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

  5. Prison population in the US

    • kaggle.com
    zip
    Updated May 10, 2023
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    Konrad Banachewicz (2023). Prison population in the US [Dataset]. https://www.kaggle.com/datasets/konradb/prison-population-in-the-us
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    zip(244630 bytes)Available download formats
    Dataset updated
    May 10, 2023
    Authors
    Konrad Banachewicz
    Area covered
    United States
    Description

    From the project page: https://github.com/jkbren/incarcerated-populations-data/

    The United States has the highest incarceration rate in the world. Through combinations of structural biases in the criminal justice and police systems, we see even higher incarceration rates among Black and Hispanic people. During the first year of the COVID-19 pandemic, the number of incarcerated people in the United States decreased by at least 17%---the largest, fastest reduction in prison population in American history. Using an original dataset curated from public sources on prison demographics across all 50 states and the District of Columbia, we show that incarcerated white people benefited disproportionately from this decrease in the U.S. prison population, and the fraction of incarcerated Black and Latino people sharply increased. This pattern persists across prison systems in nearly every state and deviates from a decade-long trend before 2020 and the onset of COVID-19, when the proportion of incarcerated white people was increasing amid declining numbers of Black people in prison. While a variety of mechanisms underlie these alarming trends, we explore why racial inequities in average sentence length are a likely major contributor. Ultimately, this study reveals how disruptions caused by COVID-19 exacerbated racial inequalities in the criminal legal system, and highlights key forces that drive mass incarceration.

    Released under MIT license

  6. c

    HSIP Correctional Institutions in New Mexico

    • s.cnmilf.com
    • gstore.unm.edu
    • +1more
    Updated Dec 2, 2020
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    (Point of Contact) (2020). HSIP Correctional Institutions in New Mexico [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/hsip-correctional-institutions-in-new-mexico
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    Dataset updated
    Dec 2, 2020
    Dataset provided by
    (Point of Contact)
    Area covered
    New Mexico
    Description

    Jails and Prisons (Correctional Institutions). 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. 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. This dataset is comprised completely of license free data. 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 12/27/2004 and the newest record dates from 09/08/2009

  7. National Prisoner Statistics, [United States], 1978-2019

    • icpsr.umich.edu
    • catalog.data.gov
    ascii, delimited, r +3
    Updated Dec 16, 2021
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    United States. Bureau of Justice Statistics (2021). National Prisoner Statistics, [United States], 1978-2019 [Dataset]. http://doi.org/10.3886/ICPSR37986.v1
    Explore at:
    ascii, stata, spss, sas, delimited, rAvailable download formats
    Dataset updated
    Dec 16, 2021
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States. Bureau of Justice Statistics
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/37986/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/37986/terms

    Time period covered
    1978 - 2019
    Area covered
    United States
    Description

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

  8. u

    New Hampshire Correctional Institutions

    • nhgeodata.unh.edu
    • granit.unh.edu
    • +3more
    Updated Dec 30, 2009
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    New Hampshire GRANIT GIS Clearinghouse (2009). New Hampshire Correctional Institutions [Dataset]. https://www.nhgeodata.unh.edu/datasets/new-hampshire-correctional-institutions
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    Dataset updated
    Dec 30, 2009
    Dataset authored and provided by
    New Hampshire GRANIT GIS Clearinghouse
    Area covered
    Description

    Jails and Prisons (Correctional Institutions). 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. This dataset is comprised completely of license free data. 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 04/26/2006 and the newest record dates from 10/19/2009

  9. Survey of the Incarcerated

    • datacatalog.library.wayne.edu
    Updated Apr 2, 2020
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    The Marshall Project (2020). Survey of the Incarcerated [Dataset]. https://datacatalog.library.wayne.edu/dataset/survey-of-the-incarcerated
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    Dataset updated
    Apr 2, 2020
    Dataset provided by
    The Marshall Projecthttps://www.themarshallproject.org/
    Slate
    Description

    Beginning in 2019, The Marshall Project/Slate began a large ongoing reporting project to survey the political views of incarcerated people in American prisons and jails. Responses are being collected on a rolling basis over several months; by March of 2020 over 8,000 had arrived from incarcerated people across the country. The raw survey data are cleaned, formatted, and anonymized.

  10. d

    County Buddy: A Companion Dataset for Socioeconomic Data Analysis and...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Oct 29, 2025
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    Vu, Colin; Andris, Clio; Baniassad, Leila (2025). County Buddy: A Companion Dataset for Socioeconomic Data Analysis and Exploration of U.S. Datasets [Dataset]. http://doi.org/10.7910/DVN/V7LNJK
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    Dataset updated
    Oct 29, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    Vu, Colin; Andris, Clio; Baniassad, Leila
    Time period covered
    Jan 1, 2017 - Dec 31, 2020
    Area covered
    United States
    Description

    County Buddy is a dataset detailing the presence, count, and institutions of special populations (incarcerated individuals, college students, military personnel, and Native Americans) at the U.S. county and census tract levels. It offers geographic and demographic context to help explain variation in socio-economic indicators like life expectancy, income, and education.

  11. d

    2020 U.S. Census Block Adjustments

    • catalog.data.gov
    • data.ct.gov
    • +1more
    Updated Sep 14, 2025
    + more versions
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    data.ct.gov (2025). 2020 U.S. Census Block Adjustments [Dataset]. https://catalog.data.gov/dataset/2020-u-s-census-block-adjustments
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    Dataset updated
    Sep 14, 2025
    Dataset provided by
    data.ct.gov
    Description

    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.

  12. d

    GEO - data and analysis

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Do, Tuan (2023). GEO - data and analysis [Dataset]. http://doi.org/10.7910/DVN/ELHH1Q
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Do, Tuan
    Description

    Summary Since 2017, GEO shares have fallen sharply from $30 to ~$8.50 per share, at one point below even the book value of $8.19 per share. President Biden recently signed an executive order that banned the renewal of Department of Justice contracts with private prisons, but the effect on GEO is way way less than the market thinks. The border crisis renders ICE dependent on GEO for capacity, making it near impossible for ICE to cut ties in the near future. With a market cap of just $1.02 Billion, GEO has the potential to increase 2-3x in the next 6-12 months. cropped image of african american prisoner reading book LightFieldStudios/iStock via Getty Images Thesis GEO Group (GEO) is a deeply mispriced provider of privately-owned prisons, falling from a price of $30+ in early 2017 to the current price of $8.50 per share. GEO has fallen primarily as a result of concerns about legislation regarding private prisons, a canceled dividend, the likely shift away from a REIT structure, and high levels of debt. These overblown concerns have created a pretty solid structural opportunity. kmosby1992@gmail.com password kmosby1992@gmail.com Subscribe Company overview GEO operates in several segments, such as GEO care, International services, and U.S. Secure Services. Source: Annual report 1 - U.S. Secure Services U.S. Secure services account for the majority of their revenue, 67%, and includes their correctional facilities and processing centers. Secure services manage 74,000 beds across 58 facilities as of the 2020 annual report. GEO transport is included in U.S. secure services, but we felt it warranted its own paragraph. GEO transport provides secure transportation services to government agencies. With 400 customized, U.S. Department of Transportation compliant vehicles, GEO transport drove more than 14 million miles in 2020. 2 - GEO Care GEO care is a series of programs designed to reintegrate inmates and troubled youth into society. They operate through reentry centers, non-residential reentry programs, and youth treatment programs. GEO care operates approximately 4-dozen reentry centers, which provide housing, employment assistance, rehabilitation, substance abuse counseling, and vocational and education programs to current and former inmates. Through their reentry segment, they operate more than 70 non-residential reentry programs that provide behavioral assessments, treatment, supervision, and education. GEO care made up 23% of total 2020 revenue. Geo monitoring is included in GEO care. Through a wholly-owned subsidiary, BI Inc., GEO offers monitoring technology for parolees, probationers, pretrial defendants, and individuals involved in the immigration process. As of the 2020 annual report, BI helps monitor ~155,000 individuals across all 50 states. 3 - International operations International operations made up only 10% of revenue in 2020, but it is showing signs of growth. GEO recently landed a 10-year contract with the United kingdom, which they expect to total $760 million in revenue over the course of the contract. They also landed an 8-year contract with the Scottish Prison Service, which grants an annualized revenue of $39 million and has a 4-year renewal period. Why is GEO Mispriced? While there are several reasons for the dramatic reduction in share price over the last 4 years, the main reason was the looming fear of legislation destroying privately owned prisons. To a degree, this fear materialized on January 26th, 2021, when President Biden signed an Executive Order ordering the Attorney General not to renew any Department of Justice contracts with "privately operated criminal detention facilities." At face value, this order seems as though it would have a devastating impact on GEO. However, only ~25% of total revenue is impacted in any form by this order. The executive order only concerns branches of the Department of Justice. Only 2 DOJ branches have business connections with GEO, the US Marshals (USMS), and the Bureau of Prisons (BOP). Source: Annual report It is imperative to note that Immigration and Customs Enforcement (ICE), is not a branch of the DOJ and is therefore unaffected by this order. Individual states, as well as other countries, are unaffected by this order Bureau of Prisons GEO currently holds several agreements with the BOP relating to operations of prisons across the country. As of year-end 2020, agreements involving the BOP accounted for 14% of total revenue. All revenue from the BOP will not disappear, as the executive order does not impact reentry facilities. In 2Q21, after the executive order was made, GEO renewed 5 BOP reentry contracts. GEO even scored a new contract with the BOP, regarding the construction and operation of a new facility in Tampa. United States Marshal Service The United States Marshal Service does not own o... Visit https://dataone.org/datasets/sha256%3A900514e651e0d2c774ad90f358c9db90884c2baf98c068f470b290b3c4b3103a for complete metadata about this dataset.

  13. Average counts of adults in provincial and territorial correctional programs...

    • www150.statcan.gc.ca
    • open.canada.ca
    Updated Sep 23, 2025
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    Government of Canada, Statistics Canada (2025). Average counts of adults in provincial and territorial correctional programs [Dataset]. http://doi.org/10.25318/3510015401-eng
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    Dataset updated
    Sep 23, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

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

  14. IDOC Mugshots

    • kaggle.com
    zip
    Updated Sep 13, 2019
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    Elliot (2019). IDOC Mugshots [Dataset]. https://www.kaggle.com/elliotp/idoc-mugshots
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    zip(6606095589 bytes)Available download formats
    Dataset updated
    Sep 13, 2019
    Authors
    Elliot
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    This dataset contains 70k pairs of front-facing and side-facing images of unique prisoners from the USA. Some demographic information and description of the offense is provided.

    Content

    70,008 front-facing images and 70,008 corresponding side-facing images. Each image pair is of a unique prisoner. 69,827 of the prisoners have labels: sex, height, weight, hair color, eye color, race and type of offense. For an example of loading the data, see https://www.kaggle.com/elliotp/sample-data-loader

    Inspiration

    Can we predict a person's sex, height, weight, hair color, eye color, race and type of criminal offense from only a picture of their face?

    Can we do so from only a side-facing picture?

    Can we generate a side-facing image from a front-facing image? Can we generate a front-facing image from a side-facing image?

    Can we create a conditional generative model and manipulate a person's perceived sex, height, weight, hair color, eye color and race?

    Acknowledgements

    We would like to thank the law enforcement agencies of the United States of America for collecting the data and making it public, and of course for fighting crime and protecting the people.

  15. US Prisons

    • kaggle.com
    zip
    Updated Aug 15, 2023
    + more versions
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    Joakim Arvidsson (2023). US Prisons [Dataset]. https://www.kaggle.com/datasets/joebeachcapital/us-prisons/discussion
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    zip(5249271 bytes)Available download formats
    Dataset updated
    Aug 15, 2023
    Authors
    Joakim Arvidsson
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    United States
    Description

    The prison boundary feature class contains secure detention facilities. These facilities range in jurisdiction from federal (excluding military) to local governments. Polygon geometry is used to describe the extent of where the incarcerated population is located (fence lines or building footprints). This feature class’s attribution describes many physical and social characteristics of detention facilities in the United States and some of its territories. The attribution for this feature class was populated by open source search methodologies of authoritative sources. Changes from the previous version include 70 records added, 72 closed, and 37 removed.

  16. Q

    Data for: Making Sense of Human Rights Diplomacy: Evidence from a US...

    • data.qdr.syr.edu
    Updated Jan 19, 2022
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    Rachel Myrick; Rachel Myrick; Jeremy Weinstein; Jeremy Weinstein (2022). Data for: Making Sense of Human Rights Diplomacy: Evidence from a US Campaign to Free Political Prisoners [Dataset]. http://doi.org/10.5064/F6OYTNPQ
    Explore at:
    html(540862), tsv(75913), pdf(59163), csv(2501971), html(13930), tsv(32621), pdf(188951), html(46348), tsv(55446), html(346559), html(13460), html(31844), html(316215), txt(13794), html(560983), pdf(38951), pdf(1443358), pdf(46777), application/x-json-hypothesis(53447), html(553116), tsv(91817), html(34035), pdf(1392575), html(107999), html(47068), jpeg(233024), pdf(734497), html(526918)Available download formats
    Dataset updated
    Jan 19, 2022
    Dataset provided by
    Qualitative Data Repository
    Authors
    Rachel Myrick; Rachel Myrick; Jeremy Weinstein; Jeremy Weinstein
    License

    https://qdr.syr.edu/policies/qdr-standard-access-conditionshttps://qdr.syr.edu/policies/qdr-standard-access-conditions

    Time period covered
    Jan 1, 2000 - Dec 31, 2015
    Area covered
    United States
    Description

    This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the publisher's website here. Project Summary Scholarship on human rights diplomacy (HRD)—efforts by government officials to engage publicly and privately with their foreign counterparts—often focuses on actions taken to “name and shame” target countries, because private diplomatic activities are unobservable. To understand how HRD works in practice, we explore a campaign coordinated by the US government to free twenty female political prisoners. We compare release rates of the featured women to two comparable groups: a longer list of women considered by the State Department for the campaign; and other women imprisoned simultaneously in countries targeted by the campaign. Both approaches suggest that the campaign was highly effective. We consider two possible mechanisms through which expressive public HRD works: by imposing reputational costs and by mobilizing foreign actors. However, in-depth interviews with US officials and an analysis of media coverage find little evidence of these mechanisms. Instead, we argue that public pressure resolved deadlock within the foreign policy bureaucracy, enabling private diplomacy and specific inducements to secure the release of political prisoners. Entrepreneurial bureaucrats leveraged the spotlight on human rights abuses to overcome competing equities that prevent government-led coercive diplomacy on these issues. Our research highlights the importance of understanding the intersection of public and private diplomacy before drawing inferences about the effectiveness of HRD. Data Generation We generated four sources of data for this project: 1. A dataset of political prisoners from 13 countries based on Amnesty International Urgent Action reports between 2000 and 2015. 2. Arrest and release information for a dataset of female political prisoners 3. A dataset on media attention based on both news articles from LexisNexis and online search trends from Google Trends 4. Interviews conducted with U.S. government officials and other human rights advocates involved in the #Freethe20 campaign to free political prisoners launched in September 2015 We used two sources of data for each of our two research questions. Our first research question was: Did the #Freethe20 campaign have an impact on the release rate of political prisoners? In an ideal world, we would have a comprehensive set of female political prisoners to compare with #Freethe20 prisoners. However, as we explain in the manuscript, in countries with more dire human rights situations, arrests often go unreported. In some cases, the sheer volume of political prisoners makes chronicling information about them challenging, if not impossible. Therefore, in order to construct a comparable set of cases, one strategy we used was to collect information from Amnesty International’s “Urgent Action” campaigns. To our knowledge, Amnesty International has the most comprehensive, publicly available list of contemporary political prisoners globally. Their records are public and searchable, which allowed us to construct a population of political prisoners from the countries targeted by the #Freethe20 campaign. We began our data collection with a base set of Urgent Actions metadata generated by Judith Kelley and Dan Nielson via webscraping from the Amnesty International website. Using a list of URLs that linked to each Urgent Action Report, we coded the name and sex of individuals featured in each Urgent Action Report from 2000 through September 2015 (the start of the #Freethe20 campaign) in the 13 countries featured in the campaign (Azerbaijan, Burma, China, Egypt, Ethiopia, Eritrea, Iran, North Korea, Russia, Syria, Uzbekistan, Venezuela, and Vietnam). Instructions about how we coded this information and sample documents are available in the QDR repository (QDR: MyrickWeinstein_codebook_urgentaction.pdf). After compiling a base dataset of individuals featured in Urgent Action reports, we identified the women in the dataset (~17% of entries) and conducted additional research about (1) whether these women could be classified as political prisoners, and (2) whether and when these women were released from prison, detention, or house arrest. Here, we relied on both follow-up reporting from Amnesty International as well as a variety of online news sources. We deposited the coding instructions for this process (MyrickWeinstein_codebook_releaseinfo.pdf) and also include documentation on additional online news sources that we used to make a judgment on a particular case. Our second question was: How and under what conditions did #Freethe20 affect the release rate of female political prisoners? To answer this question, we look at strategies of both public pressure and private, coercive diplomacy. For the former, we collected data on media attention and online search trends. We searched for newspapers and news articles that featured...

  17. Illinois DOC labeled faces dataset

    • kaggle.com
    zip
    Updated Dec 6, 2019
    + more versions
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    David J. Fisher (2019). Illinois DOC labeled faces dataset [Dataset]. https://www.kaggle.com/datasets/davidjfisher/illinois-doc-labeled-faces-dataset/code
    Explore at:
    zip(6556377362 bytes)Available download formats
    Dataset updated
    Dec 6, 2019
    Authors
    David J. Fisher
    License

    https://www.usa.gov/government-works/https://www.usa.gov/government-works/

    Area covered
    Illinois
    Description

    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...

  18. f

    Data from: Retention in HIV care during the 3 years following release from...

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Oct 9, 2018
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    Desai, Mayur M.; Loeliger, Kelsey B.; Meyer, Jaimie P.; Gallagher, Colleen; Altice, Frederick L.; Ciarleglio, Maria M. (2018). Retention in HIV care during the 3 years following release from incarceration: A cohort study [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000685892
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    Dataset updated
    Oct 9, 2018
    Authors
    Desai, Mayur M.; Loeliger, Kelsey B.; Meyer, Jaimie P.; Gallagher, Colleen; Altice, Frederick L.; Ciarleglio, Maria M.
    Description

    BackgroundSustained retention in HIV care (RIC) and viral suppression (VS) are central to US national HIV prevention strategies, but have not been comprehensively assessed in criminal justice (CJ) populations with known health disparities. The purpose of this study is to identify predictors of RIC and VS following release from prison or jail.Methods and findingsThis is a retrospective cohort study of all adult people living with HIV (PLWH) incarcerated in Connecticut, US, during the period January 1, 2007, to December 31, 2011, and observed through December 31, 2014 (n = 1,094). Most cohort participants were unmarried (83.7%) men (77.0%) who were black or Hispanic (78.1%) and acquired HIV from injection drug use (72.6%). Prison-based pharmacy and custody databases were linked with community HIV surveillance monitoring and case management databases. Post-release RIC declined steadily over 3 years of follow-up (67.2% retained for year 1, 51.3% retained for years 1–2, and 42.5% retained for years 1–3). Compared with individuals who were not re-incarcerated, individuals who were re-incarcerated were more likely to meet RIC criteria (48% versus 34%; p < 0.001) but less likely to have VS (72% versus 81%; p = 0.048). Using multivariable logistic regression models (individual-level analysis for 1,001 individuals after excluding 93 deaths), both sustained RIC and VS at 3 years post-release were independently associated with older age (RIC: adjusted odds ratio [AOR] = 1.61, 95% CI = 1.22–2.12; VS: AOR = 1.37, 95% CI = 1.06–1.78), having health insurance (RIC: AOR = 2.15, 95% CI = 1.60–2.89; VS: AOR = 2.01, 95% CI = 1.53–2.64), and receiving an increased number of transitional case management visits. The same factors were significant when we assessed RIC and VS outcomes in each 6-month period using generalized estimating equations (for 1,094 individuals contributing 6,227 6-month periods prior to death or censoring). Additionally, receipt of antiretroviral therapy during incarceration (RIC: AOR = 1.33, 95% CI 1.07–1.65; VS: AOR = 1.91, 95% CI = 1.56–2.34), early linkage to care post-release (RIC: AOR = 2.64, 95% CI = 2.03–3.43; VS: AOR = 1.79; 95% CI = 1.45–2.21), and absolute time and proportion of follow-up time spent re-incarcerated were highly correlated with better treatment outcomes. Limited data were available on changes over time in injection drug use or other substance use disorders, psychiatric disorders, or housing status.ConclusionsIn a large cohort of CJ-involved PLWH with a 3-year post-release evaluation, RIC diminished significantly over time, but was associated with HIV care during incarceration, health insurance, case management services, and early linkage to care post-release. While re-incarceration and conditional release provide opportunities to engage in care, reducing recidivism and supporting community-based RIC efforts are key to improving longitudinal treatment outcomes among CJ-involved PLWH.

  19. Trial and Terror

    • kaggle.com
    zip
    Updated Aug 16, 2017
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    Jacob Boysen (2017). Trial and Terror [Dataset]. https://www.kaggle.com/jboysen/trial-and-terror
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    zip(153092 bytes)Available download formats
    Dataset updated
    Aug 16, 2017
    Authors
    Jacob Boysen
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    Context:

    This database of terrorism prosecutions and sentencing information was created using public records including three lists of prosecutions from the U.S. Department of Justice (from 2010, 2014, and 2015), court files available through the federal judiciary’s case management system, DOJ press releases, and inmate data from the Bureau of Prisons.

    Content:

    Trevor Aaronson created the first iteration of this database as part of a project funded by the Investigative Reporting Program at the University of California, Berkeley. Mother Jones magazine published that data in 2011, along with accompanying articles, in a package that is still available online. Beginning in 2016, Aaronson and Margot Williams collaborated to update and expand the database, with a new emphasis to include Bureau of Prisons data because so many post-9/11 terrorism defendants had been released. The cases include any prosecutions after September 11, 2001, that the U.S. government labeled as related to international terrorism. The Intercept first published this database on April 20, 2017. For each defendant in the database, U.S. criminal code data related to charges has been categorized according to this legend

    Acknowledgements:

    This database is licensed under Creative Commons for noncommercial uses with appropriate attribution. If you publish this database, in part or whole, you must credit Trevor Aaronson and Margot Williams.

    Inspiration:

    • What are the most common charges?
    • Are the sentence lengths similar?
  20. Ethnic Albanian Organized Crime in New York City, 1975-2014

    • icpsr.umich.edu
    • datasets.ai
    • +1more
    Updated Mar 31, 2017
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    Arsovska, Jana (2017). Ethnic Albanian Organized Crime in New York City, 1975-2014 [Dataset]. http://doi.org/10.3886/ICPSR35487.v1
    Explore at:
    Dataset updated
    Mar 31, 2017
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Arsovska, Jana
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/35487/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/35487/terms

    Time period covered
    1975 - 2013
    Area covered
    New York, United States, New Jersey, New York (state)
    Description

    These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The main aim of this research is to study the criminal mobility of ethnic-based organized crime groups. The project examines whether organized crime groups are able to move abroad easily and to reproduce their territorial control in a foreign country, or whether these groups, and/or individual members, start a life of crime only after their arrival in the new territories, potentially as a result of social exclusion, economic strain, culture conflict and labeling. More specifically, the aim is to examine the criminal mobility of ethnic Albanian organized crime groups involved in a range of criminal markets and operating in and around New York City, area and to study the relevance of the importation/alien conspiracy model versus the deprivation model of organized crime in relation to Albanian organized crime. There are several analytical dimensions in this study: (1) reasons for going abroad; (2) the nature of the presence abroad; (3) level of support from ethnic constituencies in the new territories; (4) importance of cultural codes; (5) organizational structure; (6) selection of criminal activities; (7) economic incentives and political infiltration. This study utilizes a mixed-methods approach with a sequential exploratory design, in which qualitative data and documents are collected and analyzed first, followed by quantitative data. Demographic variables in this collection include age, gender, birth place, immigration status, nationality, ethnicity, education, religion, and employment status. Two main data sources were employed: (1) court documents, including indictments and court transcripts related to select organized crime cases (84 court documents on 29 groups, 254 offenders); (2) in-depth, face-to-face interviews with 9 ethnic Albanian offenders currently serving prison sentences in U.S. Federal Prisons for organized crime related activities, and with 79 adult ethnic Albanian immigrants in New York, including common people, undocumented migrants, offenders, and people with good knowledge of Albanian organized crime modus operandi. Sampling for these data were conducted in five phases, the first of which involved researchers examining court documents and identifying members of 29 major ethnic Albanian organized crime groups operating in the New York area between 1975 and 2013 who were or had served sentences in the U.S. Federal Prisons for organized crime related activities. In phase two researchers conducted eight in-depth interviews with law enforcement experts working in New York or New Jersey. Phase three involved interviews with members of the Albanian diaspora and filed observations from an ethnographic study. Researchers utilized snowball and respondent driven (RDS) recruitment methods to create the sample for the diaspora dataset. The self-reported criteria for recruitment to participate in the diaspora interviews were: (1) age 18 or over; (2) of ethnic Albanian origin (foreign-born or 1st/2nd generation); and (3) living in NYC area for at least 1 year. They also visited neighborhoods identified as high concentrations of ethnic Albanian individuals and conducted an ethnographic study to locate the target population. In phase four, data for the cultural advisors able to help with the project data was collected. In the fifth and final phase, researchers gathered data for the second wave of the diaspora data, and conducted interviews with offenders with ethnic Albanian immigrants with knowledge of the organized crime situation in New York City area. Researchers also approached about twenty organized crime figures currently serving a prison sentence, and were able to conduct 9 in-depth interviews.

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mexwell (2023). ⛓️ US Prisons Dataset [Dataset]. https://www.kaggle.com/datasets/mexwell/us-prisons-dataset
Organization logo

⛓️ US Prisons Dataset

Prisons in the US with metadata

Explore at:
zip(963685 bytes)Available download formats
Dataset updated
Aug 16, 2023
Authors
mexwell
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Area covered
United States
Description

The prison boundary feature class contains secure detention facilities. These facilities range in jurisdiction from federal (excluding military) to local governments. Polygon geometry is used to describe the extent of where the incarcerated population is located (fence lines or building footprints). This feature class’s attribution describes many physical and social characteristics of detention facilities in the United States and some of its territories.

Original Data

Acknowlegement

Foto von Milad Fakurian auf Unsplash

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