855,340 research outputs found

    Patterns in Tennessee's Black Population 2000-2010

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    The recent increase in the rate of growth in the black population has important implications for the state's population mix.Tennessee, population, black, black population, census, 2010, census 2010, population growth, population, growth, change, county, counties, data, grand divisions, east Tennessee, middle Tennessee, west Tennessee, regions, regional

    Tennessee Population Growth 2000-2010

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    Tennessee's growth rate during the relevant 10-year-period was almost 20 percent higher than the comparable national growth rate of 9.7 percent.census, 2010, Tennessee, census 2010, population growth, population, growth, change, county, counties, data, regions, regional, grand divisions, east Tennessee, middle Tennessee, west Tennessee

    Automated census record linking: a machine learning approach

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    Thanks to the availability of new historical census sources and advances in record linking technology, economic historians are becoming big data genealogists. Linking individuals over time and between databases has opened up new avenues for research into intergenerational mobility, assimilation, discrimination, and the returns to education. To take advantage of these new research opportunities, scholars need to be able to accurately and efficiently match historical records and produce an unbiased dataset of links for downstream analysis. I detail a standard and transparent census matching technique for constructing linked samples that can be replicated across a variety of cases. The procedure applies insights from machine learning classification and text comparison to the well known problem of record linkage, but with a focus on the sorts of costs and benefits of working with historical data. I begin by extracting a subset of possible matches for each record, and then use training data to tune a matching algorithm that attempts to minimize both false positives and false negatives, taking into account the inherent noise in historical records. To make the procedure precise, I trace its application to an example from my own work, linking children from the 1915 Iowa State Census to their adult-selves in the 1940 Federal Census. In addition, I provide guidance on a number of practical questions, including how large the training data needs to be relative to the sample.This research has been supported by the NSF-IGERT Multidisciplinary Program in Inequality & Social Policy at Harvard University (Grant No. 0333403)

    What\u27s in a Name? Racial and Ethnic Classifications and the Meaning of Hispanic/Latino in the United States

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    The first national census was conducted in 1790, and has been repeated at ten year intervals ever since. While census taking has been consistent, the way individuals have been counted and categorized on the basis of race and ethnicity has varied over time. This paper examines how the official census definition of Latinos has changed over the twenty-two census periods. The modifications of the official definition of this group are discussed in relation to changes in national borders, variations in methodology used for census data gathering, and shifting political contexts

    Quantitative Analysis of Disparities in Juvenile Delinquency Referrals

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    Minority youths in Anchorage are referred to the Alaska Division of Juvenile Justice (DJJ) for delinquent behavior at rates much higher than white youths. This report, presenting the first findings from an extended examination of extended examination of race, ethnicity, and juvenile justice in Anchorage, provides a broad overview of the level of disproportionate minority contact in the Alaska juvenile justice system and examines whether disproportionate minority contact occurs (1) for all minority youth, (2) for both males and females, (3) for both youth referred for new crimes and youth referred for conduct or probation violations, and (4) throughout the Municipality of Anchorage or in specific geographical areas within the Municipality of Anchorage. By developing a detailed understanding of the scope of disproportionate minority contact, we become much better prepared to identify its causes and to develop promising evidence-based solutions. The sample in this analysis includes 1,936 youths who resided in Anchorage and were referred to DJJ in Anchorage during fiscal year 2005 for new crimes, probation violations, or conduct violations.National Institute of Justice Grant No. 2005-IJ-CX-0013Table and Figures / Acknowledgments / Executive Summary / Quantitative Analysis of Disparities in Juvenile Delinquency Referrals / Sample and Data / Geographic Data / Census Data / Juvenile Justice Data / Analysis / Results / Racial, Ethnic, and Gender Composition of Referred Youth / Disproportionate Minority Contact in Anchorage / Rates of Referral by Census Tract / Disproportionate Minority Contact by Census Tract / Disproportionate Minority Contact by Census Tract, for All Minority Youth / Disproportionate Minority Contact by Census Tract, for Black Youth / Disproportionate Minority Contact by Census Tract, for Native Youth / Disproportionate Minority Contact by Census Tract, for Asian Youth / Disproportionate Minority Contact by Census Tract, for Pacific Youth / Disproportionate Minority Contact by Census Tract, for Other Minority Youth / Disproportionate Minority Contact by Census Tract, for Multiracial Youth / Disproportionate Minority Contact by Census Tract, for Hispanic Youth / Summary of DMC Analyses by Census Tract / Summary and Conclusion / Appendices A. Technical Notes on Relative Rate Indices B. Technical Notes on Relative EB Rate Indices C. Type of Analysis by Census Trac

    Measuring Confidentiality Risks in Census Data

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    Two trends have been on a collision course over the recent past. The first is the increasing demand by researchers for greater detail and flexibility in outputs from the decennial Census of Population. The second is the need felt by the Census Offices to demonstrate more clearly that Census data have been explicitly protected from the risk of disclosure of information about individuals. To reconcile these competing trends the authors propose a statistical measure of risks of disclosure implicit in the release of aggregate census data. The ideas of risk measurement are first developed for microdata where there is prior experience and then modified to measure risk in tables of counts. To make sure that the theoretical ideas are fully expounded, the authors develop small worked example. The risk measure purposed here is currently being tested out with synthetic and a real Census microdata. It is hoped that this approach will both refocus the census confidentiality debate and contribute to the safe use of user defined flexible census output geographies

    How Many Hispanics? Comparing New Census Counts With the Latest Census Estimates

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    Examines differences between 2010 census data on the Latino/Hispanic population nationwide as well as in each state with April 2010 estimates by the Census Bureau. Compares results with the 1990 and 2000 census counts

    Anchorage Community Indicators: Public Use Data Files

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    The four SPSS datasets described and included here include: ACSPUBFILE.SAV: The data collected in the course of the 2005 Anchorage Community Survey in SPSS format. / ACSCT.SAV: a merged dataset consisting of composite measures extracted from the 2005 Anchorage Community Survey, 2000 U.S. Census, and Anchorage Police Department Dispatches 2003–2005. / BLOCKGROUPMEASURES.SAV: A description of the 214 census block groups within the city of Anchorage, including composite measures derived from the 2000 Census of Population and Housing (documented in ACI Technical Report Initial Measures derived from Census) and dispatch measures derived from APD dispatch data files (documented in Anchorage Police Department Dispatch Data). / CTRACTMEASURES.SAV: A census tract level of aggregation of the 214 census blocks into the 55 census tracts that compose the city of Anchorage. Also includes composite measures derived from the 2000 Census of Population and Housing (documented in ACI Technical Report Initial Measures derived from Census) and dispatch measures derived from APD dispatch data files (documented in Anchorage Police Department Dispatch Data).The Anchorage Community Survey is a biannual study conducted by the Justice Center at the University of Alaska Anchorage as a principal component of the Community Indicators Project at UAA. As the premier source of data on Anchorage Community Indicators, the ACS also provides insight into the communities of Anchorage, Girdwood and Eagle River. This document explains the various SPSS datasets, collection methods, and variables of the 2005 Anchorage Community Survey (https://scholarworks.alaska.edu/handle/11122/3729).[Introduction] / 2005 Anchorage Community Survey / 2000 U.S. Census Extracts / Appendices — Variables / 1: ACSPubFile.sav / 2: ACSCT.sav / 3: CTractMeasures.sav / 4: BlockGroupMeasures.sa

    Measuring Confidentiality Risks in Census Data

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    Two trends have been on a collision course over the recent past. The first is the increasing demand by researchers for greater detail and flexibility in outputs from the decennial Census of Population. The second is the need felt by the Census Offices to demonstrate more clearly that Census data have been explicitly protected from the risk of disclosure of information about individuals. To reconcile these competing trends the authors propose a statistical measure of risks of disclosure implicit in the release of aggregate census data. The ideas of risk measurement are first developed for microdata where there is prior experience and then modified to measure risk in tables of counts. To make sure that the theoretical ideas are fully expounded, the authors develop small worked example. The risk measure purposed here is currently being tested out with synthetic and a real Census microdata. It is hoped that this approach will both refocus the census confidentiality debate and contribute to the safe use of user defined flexible census output geographies
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