Select Language

AI社区

公开数据集

IPUMS项目是联邦人口普查数据集

IPUMS项目是联邦人口普查数据集

7.97M
298 浏览
0 喜欢
3 次下载
0 条讨论
Social N/A

Data Set Information:The original source for this data set is the IPUMS project (RugglesSobek, 1997). The IPUMS project......

数据结构 ? 7.97M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Data Set Information:

    The original source for this data set is the IPUMS project (RugglesSobek, 1997). The IPUMS project is a large collection of federal census data which has standardized coding schemes to make comparisons across time easy.

    The data is an unweighted 1 in 100 sample of responses from the Los Angeles -- Long Beach area for the years 1970, 1980, and 1990. The household and individual records were flattened into a single table and we used all variables that were available for all three years. When there was more than one version of a variable, such as for race, we used the most general. For occupation and industry we used the 1950 basis.

    Note that PUMS data is based on cluster samples, i.e. samples are made of households or dwellings from which there may be multiple individuals. Individuals from the same household are no longer independent. Ruggles (1995) considers this issue further and discusses its effect (along with the effects of stratification) on standard errors.

    The variable schltype appears to have different coding values across the years 1970, 1980, and 1990.

    There are two versions of this data set:

    1. The Small Data Set

    The small data set contains a 1 in 1000 sample of the Los Angeles and Long Beach area. It was formed by sampling from the large data set.

    2. The Large Data Set

    The large data set contains a 1 in 100 sample of the Los Angeles and Long Beach area.


    Attribute Information:

    Please see ipums.la.names


    Relevant Papers:

    S. Ruggles. (1995). "Sample Designs and Sampling Errors". Historical Methods. Volume 28. Number 1. Pages 40 - 46.
    [Web link]


    Papers That Cite This Data Set1:


    Ke Wang and Shiyu Zhou and Ada Wai-Chee Fu and Jeffrey Xu Yu. Mining Changes of Classification by Correspondence Tracing. SDM. 2003.  [View Context].

    Stephen D. Bay and Michael J. Pazzani. Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl. Discov, 5. 2001.  [View Context].

    Chris Giannella and Bassem Sayrafi. An Information Theoretic Histogram for Single Dimensional Selectivity Estimation. Department of Computer Science, Indiana University Bloomington.  [View Context].


    Citation Request:

    Reproduced here is the original IPUMS citation and use documentation:

    All persons are granted a limited license to use and distribute this documentation and the accompanying data, subject to the following conditions:

       * No fee may be charged for use or distribution.
       * Publications and research reports based on the database must cite it appropriately. The citation should include the following:

         Steven Ruggles and Matthew Sobek et. al.
         Integrated Public Use Microdata Series: Version 2.0
         Minneapolis: Historical Census Projects,
         University of Minnesota, 1997
         
         If possible, citations should also include the URL for the IPUMS site: [Web link].

    In addition, we request that users send us a copy of any publications, research reports, or educational material making use of the data or documentation. Printed matter should be sent to:

    IPUMS
    Historical Census Projects
    University of Minnesota
    614 Social Sciences
    267 19th Avenue South
    Minneapolis, MN 55455
    Send all electronic material to ipums '@' hist.umn.edu


    Original Owner:

    IPUMS
    Historical Census Projects
    University of Minnesota
    614 Social Sciences
    267 19th Avenue South
    Minneapolis, MN 55455
    ipums '@' hist.umn.edu
    http://www.ipums.umn.edu/

    Donor:

    Stephen Bay
    Department of Information and Computer Science,
    University of California, Irvine
    Irvine, CA 92697
    sbay '@' ics.uci.edu

    ×

    帕依提提提温馨提示

    该数据集正在整理中,为您准备了其他渠道,请您使用

    注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。
    暂无相关内容。
    暂无相关内容。
    • 分享你的想法
    去分享你的想法~~

    全部内容

      欢迎交流分享
      开始分享您的观点和意见,和大家一起交流分享.
    所需积分:10 去赚积分?
    • 298浏览
    • 3下载
    • 0点赞
    • 收藏
    • 分享