Towards a Critical Data Quality Analysis of Open Arrest Record Datasets

Karen M. Wickett, Jarrett Newman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This short paper presents early results from a data quality analysis of an open arrest record dataset from the Los Angeles Police Department. We use data quality metrics from a framework for the evaluation of open government data. We present our results, along with critiques and discussion of the metrics, and describe an ongoing project to analyze the data quality of police arrest datasets and connect data quality to critical accounts of information systems.

Original languageEnglish (US)
Title of host publicationWisdom, Well-Being, Win-Win - 19th International Conference, iConference 2024, Proceedings
EditorsIsaac Sserwanga, Hideo Joho, Jie Ma, Preben Hansen, Dan Wu, Masanori Koizumi, Anne J. Gilliland
PublisherSpringer
Pages311-318
Number of pages8
ISBN (Print)9783031578663
DOIs
StatePublished - 2024
Event19th International Conference on Wisdom, Well-Being, Win-Win, iConference 2024 - Changchun, China
Duration: Apr 15 2024Apr 26 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14598 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Wisdom, Well-Being, Win-Win, iConference 2024
Country/TerritoryChina
CityChangchun
Period4/15/244/26/24

Keywords

  • Critical Information Studies
  • Data Quality
  • Open Government Data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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