Methods for understanding and analyzing NIBRS data

被引:32
|
作者
Akiyama, Y
Nolan, J
机构
[1] Fed Bur Invest, Criminal Justice Informat Sci Div, Washington, DC 20535 USA
[2] Fed Bur Invest, Criminal Justice Informat Sci Div, Clarksburg, WV 26306 USA
关键词
NIBRS; UCR; crime data; unit of count;
D O I
10.1023/A:1007531023247
中图分类号
DF [法律]; D9 [法律];
学科分类号
0301 ;
摘要
The National Incident-Based Reporting System (NIBRS) is an incident-based crime reporting program for local, state, and federal law enforcement agencies; Within each criminal incident, NIBRS captures information on offenses, victims, offenders, property, and persons arrested, as well as information about the incident itself. The ability to link and analyze this detailed information is a significant improvement to the existing Uniform Crime Reporting (UCR) summary reporting system. As one might expect, however, this increase in crime data significantly complicates the life of the data analyst, particularly when cross tabulating the NIBRS data. To deal with the complexity of NIBRS data, one must understand its structure. This article provides an overview of the NIBRS structure and methods for maneuvering within it to present and interpret correctly cross tabulations of the NIBRS data.
引用
收藏
页码:225 / 238
页数:14
相关论文
共 50 条
  • [1] Methods for Understanding and Analyzing NIBRS Data
    Yoshio Akiyama
    James Nolan
    [J]. Journal of Quantitative Criminology, 1999, 15 : 225 - 238
  • [2] Analyzing the NIBRS Data: the Impact of the Number of Records Used per Segment
    Brendan Lantz
    Marin R. Wenger
    [J]. American Journal of Criminal Justice, 2020, 45 : 379 - 409
  • [3] Analyzing the NIBRS Data: the Impact of the Number of Records Used per Segment
    Lantz, Brendan
    Wenger, Marin R.
    [J]. AMERICAN JOURNAL OF CRIMINAL JUSTICE, 2020, 45 (03) : 379 - 409
  • [4] NIBRS Data Available for Secondary Analysis
    Christopher S. Dunn
    Thomas J. Zelenock
    [J]. Journal of Quantitative Criminology, 1999, 15 : 239 - 248
  • [5] Assessing the Representativeness of NIBRS Arrest Data
    Pattavina, April
    Carkin, Danielle Marie
    Tracy, Paul E.
    [J]. CRIME & DELINQUENCY, 2017, 63 (12) : 1626 - 1652
  • [6] NIBRS data available for secondary analysis
    Dunn, CS
    Zelenock, TJ
    [J]. JOURNAL OF QUANTITATIVE CRIMINOLOGY, 1999, 15 (02) : 239 - 248
  • [7] Analyzing genomic data: understanding the genome
    Fernandez-Suarez, Xose M.
    [J]. WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, 2012, 2 (02) : 116 - 137
  • [8] Understanding and Analyzing Methods of Character Identification in Movies
    Shambharkar, Prashant Giridhar
    Gupta, Anubhav
    Gupta, Anirudh
    Anshuman
    Doja, M. N.
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS 2020), 2020, : 788 - 795
  • [9] Why More Agencies and Researchers Should Embrace the Upcoming NIBRS Transition: Contributions and Promise of the NIBRS Data
    Brendan Lantz
    [J]. American Journal of Criminal Justice, 2022, 47 : 462 - 484
  • [10] Computational Methods for Analyzing Patient Data
    Crane, Alexander B.
    Crane, Elliot
    Shum, May
    Kim, Jason S.
    Kim, Eliott
    Chu, David S.
    [J]. INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2016, 57 (12)