3 datasets found
  1. Data from: Prison Crowding and Forced Releases in Illinois, 1979-1982

    • catalog.data.gov
    • icpsr.umich.edu
    Updated Mar 12, 2025
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    National Institute of Justice (2025). Prison Crowding and Forced Releases in Illinois, 1979-1982 [Dataset]. https://catalog.data.gov/dataset/prison-crowding-and-forced-releases-in-illinois-1979-1982-0db3b
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    Dataset updated
    Mar 12, 2025
    Dataset provided by
    National Institute of Justicehttp://nij.ojp.gov/
    Area covered
    Illinois
    Description

    These data were collected in the Illinois prison system where, in response to a prison overcrowding crisis, approximately two-thirds of the inmates released by the Illinois Department of Corrections (IDOC) were discharged prior to serving their expected sentences. This study was designed to evaluate the effects of an early release program on prisoners, prison populations, offense rates, local criminal justice systems, and the general public. The files contain extensive Federal Bureau of Investigation arrest history information and other personal and social indicators describing inmates released from the state prison system. Data are available for three comparison groups: (1) a sample of prisoners who served their regular sentences prior to the "forced release" program, (2) a group that served regular sentences after implementation of the program, and (3) a group of inmates who were released early under the program (i.e., before serving their full sentences). The "inmate jacket file," which is the comprehensive institutional file maintained for all inmates, contains variables for each inmate on social and personal characteristics, criminal history, risk scales, court decisions for each offense, institutional conduct, prior release and return records, method of release, condition of supervision, and parole violation records. The arrest file includes variables that describe the type and number of charges at arrest, case disposition of each charge, probation length, incarceration length, admission and release dates, and release type.

  2. d

    IDPH Population Projections For Illinois Counties 2010 To 2025.

    • datadiscoverystudio.org
    • data.amerigeoss.org
    • +1more
    csv, json, rdf, xml
    Updated Jun 11, 2016
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    (2016). IDPH Population Projections For Illinois Counties 2010 To 2025. [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/088649dff2874f8bad5e39cde7549522/html
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    csv, json, xml, rdfAvailable download formats
    Dataset updated
    Jun 11, 2016
    Area covered
    Illinois
    Description

    description: Introduction This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends. Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change birth, death and net migration is projected separately for each five-year birth cohort and sex. The cohort component method employs the following basic demographic balancing equation: P1 = P0 + B D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is survived to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years). Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates. Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period. Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health. Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group. Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question Where did you live five years ago? The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods. Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.; abstract: Introduction This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends. Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change birth, death and net migration is projected separately for each five-year birth cohort and sex. The cohort component method employs the following basic demographic balancing equation: P1 = P0 + B D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is survived to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years). Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates. Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The

  3. g

    Recidivism of Prisoners Released in 1994 - Archival Version

    • search.gesis.org
    Updated Feb 28, 2021
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    United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics (2021). Recidivism of Prisoners Released in 1994 - Archival Version [Dataset]. http://doi.org/10.3886/ICPSR03355
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    Dataset updated
    Feb 28, 2021
    Dataset provided by
    GESIS search
    ICPSR - Interuniversity Consortium for Political and Social Research
    Authors
    United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de436460https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de436460

    Description

    Abstract (en): RECIDIVISM OF PRISONERS RELEASED IN 1994 is a database containing information on each of 38,624 sampled prisoners released from prisons in 15 states in 1994 and tracked for three years following their release. The majority of the database consists of information on each released prisoner's entire officially recorded criminal history (before and after the 1994 release). Sources for criminal history information are state and FBI automated RAP ("Records of Arrests and Prosecutions") sheets, which contain records of arrests, adjudications, and sentences. The study is the second major recidivism study conducted by the Bureau of Justice Statistics. The first study, RECIDIVISM AMONG RELEASED PRISONERS, 1983: UNITED STATES, tracked over 16,000 prisoners released in 11 states in 1983 for three years. These two studies are the closest approximation to "national" recidivism studies in the United States. They are distinguished by their large sample size (over 16,000 released prisoners in the first study, 38,624 in the second), geographic breadth of coverage (11 states in the first study, 15 in the second), length of prospective tracking (three years from date of release in both studies), ability to track the movement of released prisoners across state boundaries (both studies), and multiple measures of recidivism (both studies). Demographic data include race, ethnicity, sex, and date of birth. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Prisoners released during 1994 in the 15 states that the study covered. The 15 states account for about two-thirds of releases in the United States in a given year. Smallest Geographic Unit: state The following 15 state Departments of Corrections participated in the study: Arizona, California, Delaware, Florida, Illinois, Maryland, Michigan, Minnesota, New Jersey, New York, North Carolina, Ohio, Oregon, Texas, and Virginia. These departments supplied Bureau of Justice Statistics (BJS) with information on each person released from prison in the state in 1994 (Note: Illinois releases are for fiscal year 1994 rather than calendar year 1994). These 15 states were chosen as a purposive sample, based on willingness to participate, the state's relative contribution to the overall national prison population, and the state's inclusion in the earlier study of recidivism conducted by BJS in 1983 (see ICPSR 8875). The 15 states supplied BJS with release records on 302,309 prisoners released in 1994, approximately two-thirds of all prisoners released in the nation. Using these records, the researchers drew a representative sample from each state, totaling 38,624 out of the 302,309 released prisoners, stratified by most serious conviction offense. More detailed information regarding sampling procedures can be found in the codebook that accompanies this data collection. 2014-12-05 A minor change is made to the codebook.2012-01-12 For variable POTST, values for the state of New York were adjusted per the principal investigator.2011-03-08 All parts are being moved to restricted access and will be available only using the restricted access procedures.2009-02-09 Missing value codes were edited to correct for rounding and data entry errors.2007-03-02 The principal investigator revised the data so that there are 4,834 cases instead of 4,824 for values that are less than or equal to 90 for variable DCDV.2006-12-01 The principal investigator revised the description for variables RPRSD and RPRSITV in the codebook.2003-08-27 The principal investigator recoded some values in variables DCDV, RPRSD, RPRSITV, and RELTYP.2002-10-04 The principal investigator recoded some values (child victim age) in variable DCDV for 89 releases in the state of Virginia. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics.

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National Institute of Justice (2025). Prison Crowding and Forced Releases in Illinois, 1979-1982 [Dataset]. https://catalog.data.gov/dataset/prison-crowding-and-forced-releases-in-illinois-1979-1982-0db3b
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Data from: Prison Crowding and Forced Releases in Illinois, 1979-1982

Related Article
Explore at:
Dataset updated
Mar 12, 2025
Dataset provided by
National Institute of Justicehttp://nij.ojp.gov/
Area covered
Illinois
Description

These data were collected in the Illinois prison system where, in response to a prison overcrowding crisis, approximately two-thirds of the inmates released by the Illinois Department of Corrections (IDOC) were discharged prior to serving their expected sentences. This study was designed to evaluate the effects of an early release program on prisoners, prison populations, offense rates, local criminal justice systems, and the general public. The files contain extensive Federal Bureau of Investigation arrest history information and other personal and social indicators describing inmates released from the state prison system. Data are available for three comparison groups: (1) a sample of prisoners who served their regular sentences prior to the "forced release" program, (2) a group that served regular sentences after implementation of the program, and (3) a group of inmates who were released early under the program (i.e., before serving their full sentences). The "inmate jacket file," which is the comprehensive institutional file maintained for all inmates, contains variables for each inmate on social and personal characteristics, criminal history, risk scales, court decisions for each offense, institutional conduct, prior release and return records, method of release, condition of supervision, and parole violation records. The arrest file includes variables that describe the type and number of charges at arrest, case disposition of each charge, probation length, incarceration length, admission and release dates, and release type.

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