Thinking about police data: Analysts’ perceptions of data quality in Canadian policing
| Author | Christopher D O’Connor,John Ng,Dallas Hill,Tyler Frederick |
| DOI | http://doi.org/10.1177/0032258X211021461 |
| Published date | 01 December 2022 |
| Date | 01 December 2022 |
| Subject Matter | Articles |
Article
Thinking about police data:
Analysts’ perceptions of data
quality in Canadian policing
Christopher D O’Connor
Faculty of Social Science and Humanities, University of Ontario Institute
of Technology, Oshawa, ON, Canada
John Ng
Crime Analyst with a Canadian Police Agency, Saskatoon, SK, Canada
Dallas Hill
Criminology and Social Justice, Faculty of Social Science and Humanities,
University of Ontario Institute of Technology, Oshawa, ON, Canada
Tyler Frederick
Faculty of Social Science and Humanities, University of Ontario Institute
of Technology, Oshawa, ON, Canada
Abstract
Policing is increasingly being shaped by data collection and analysis. However, we still
know little about the quality of the data police services acquire and utilize. Drawing on a
survey of analysts from across Canada, this article examines several data collection,
analysis, and quality issues. We argue that as we move towards an era of big data policing
it is imperative that police services pay more attention to the quality of the data they
collect. We conclude by discussing the implications of ignoring data quality issues and the
need to develop a more robust research culture in policing.
Keywords
Data quality, crime analysts, police data, police research culture
Corresponding author:
Christopher D O’Connor, Faculty of Social Science and Humanities, University of Ontario Institute of
Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada.
Emails: Christopher.O’Connor@ontariotechu.ca; christopher.oconnor1@ontariotechu.net
The Police Journal:
Theory, Practice and Principles
ªThe Author(s) 2021
Article reuse guidelines:
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DOI: 10.1177/0032258X211021461
journals.sagepub.com/home/pjx
2022, Vol. 95(4) 637–656
638 The Police Journal: Theory, Practice and Principles 95(4)
Introduction
The collection of various types of data by police services has increasingly become a key
aspect of everyday police work. Canadian police agencies have been moving towards
data and intelligence driven approaches, including storing and using big data. This has
partly stemmed from discussions around developing more effective and efficient police
services in order to tackle increasingly complex crime and safety issues (Sanders et al.,
2018). As a result, police officers are spending large amounts of their time documenting
their work (e.g., report writing) and entering data into computer databases (Chan, 2001;
Ericson and Haggerty, 1997). In addition, new technologies have broadened police data
collection beyond crime and arrest data to include data from videos, pictures, social
media posts, automated license-plate re aders, biometrics, phone records, and faci al-
recognition software (Chan and Bennett Moses, 2016; Ferguson, 2017; Mackenzie,
2015; Sanders and Sheptycki, 2017; Walsh and O’Connor, 2018). Likewise, the rapid
growth of the volume of this data has overtaken the ability of analysts to meaningfully
process, organize, and analyse it (Burcher and Whelan, 2018; Sheptycki, 2004).
While the police continue to collect vast amounts of data, we know little about the
quality of the data coming into police data management systems. Therefore, the purpose
of this paper is to examine police data quality from the perspectives of the analysts
working regularly with police data in Canada. To that end, this paper first examines the
literature pertaining to policing approaches that are increasingly more data-driven. Next,
we examine the existing literatu re on data quality in policing befo re describing the
survey methods used to collect analysts’ perspectives. Then, the findings highlight the
main data quality themes emerging from the survey. Finally, in our discussion we pay
particular attention to the implications of ignoring data quality in policing. We argue that
as police services become more committed to the use of data to inform operations, it is
imperative that they also consider the quality of their data. Without doing so questions
the validity and reliability of the analyses that drive police decision-making.
Data-driven policing
Advances in information technologies have substantially changed policing (Chan, 2001;
Lum et al., 2017). Ericson and Haggerty (1997) go as far as to argue that the primary role
of police in contemporary times has changed from being principally law enforcers and
maintainers of social order to that of being information managers, communicators, and
collectors of risk knowledge. This shift also comes at a time when the new harms police
are asked to manage are ever-increasing (e.g., identity theft, cybercrime) (CCA, 2014).
Additionally, police officers are increasingly spending more of their time dealing with
non-criminal matters (e.g., mental health calls), engaging in proactive policing (e.g.,
community meetings), and documenting their work (CACP, 2015). Thus, much of poli-
cing work is unrelated to crime statistics/rates (Bass et al., 2014).
Despite this increasing complexity and demand, police are expected to do more with
fewer resources as the costs of policing have become a growing concern for the public
and governments (AMO, 2015; CACP, 2015; Huey, 2017; PSC, 2013; Taylor Griffiths
et al., 2015). While tapering off since 2010/2011 after nearly a decade of steady
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