Measuring the dark figure of crime
This research, undertaken by researchers at the University of Manchester, used data made available via the Office for National Statistics (ONS) Secure Research Service (SRS), which is being expanded and improved with ADR UK funding.
The ‘dark figure’ of crime (the number of offences which go unrecorded in crime statistics) has ‘severe consequences’ on crime prevention strategies. This research, using the Office for National Statistics’ (ONS) Crime Survey for England and Wales, explores the geographical inequalities between the dark figure and official police statistics. By using small area estimations, this research presents the first dark figure of crime map for England and Wales.
The dark figure is compared across local authority districts and middle layer super output areas – small areas designed to improve reporting of statistics. The results show the dark figure is larger in deprived and wealthy municipalities than in middle-class areas. The dark figure is also larger in low-cost housing areas with greater concentrations of unqualified citizens, immigrants, and non-Asian minorities. Identifying the areas where crimes are being unreported can inform policy makers and police forces to help design better interventions aimed at improving crime reporting rates.
The ONS Crime Survey for England and Wales (CSEW) has measured crime since 1981. Alongside police recorded crime data (collected by the Home Office), the survey is a valuable source of information for the government about the extent and nature of crime. The nationally representative survey measures crime by asking members of the public about their experiences of crime over the previous 12 months. In this way, the survey records all types of crimes experienced, including those that may not have been reported to the police. It is important that the voices of those people who have and have not experienced crime are recorded so that an accurate picture can be captured.
The aim of this research was to trial several model-based small area estimation methods and evaluate the reliability of their dark figure of crime estimates for local authority districts and middle layer super output areas. The small area estimation methods evaluated include direct estimators, and traditional, spatial, temporal and spatial-temporal model-based approaches.
The research used the auxiliary variables available for all years of the CSEW. These were: ratio of males or females, average age, unemployment, house prices, income, population density, urban/rural classification (urban conurbations, small urban areas and rural areas), ethnic groups, qualifications, occupations and workday population density. A parametric bootstrap approach was used to assess the uncertainty of each SAE approach.
Two small area estimation models were found to best estimate the dark figure of crime – the first for local authority districts and the second for middle layer super output areas. For local authority district estimates this was a spatial-temporal empirical best linear unbiased predictor (STEBLUP). For middle layer super output areas estimates this was a spatial empirical best linear unbiased predictor (SEBLUP). In both cases, the predictors were chosen for their ability to handle small and large data variance and to compute estimates of adequate precision. The chosen small area estimation models were used to create dark figure of crime maps of England and Wales for 2011 to 2017.
Estimates of the dark figure of crime by local authority district for 2011-12 to 2016–17.
The small area estimations showed that the dark figure of crime increased in 180 out of 348 local authority districts between 2011 and 2017. Nine out of ten most populated local authority districts saw decreases in the figure, with the only exception of Liverpool, where the observed percentage of crimes unknown to the police increased by 4.3 per cent between 2011 and 2017. In Greater London, the dark figure of crime increased in 23 out of 33 local area districts.
This research has reached international policy makers at both local and national levels. Two ‘agendas of best practices’ from the EU funded Horizon 2020 programme of research and innovation European projects have cited the outcomes. These are:
- the MARGIN project, with contributions to sustainable modes of cooperation between stakeholders dealing with security issues.
- the Cutting Crime Impact Project – a consortium of UK and EU Universities, Police Forces, Government departments and think-tanks aiming to support law enforcement agencies reduce and prevent crime.
In the UK, the research has been included on the College of Policing research map, which enables police forces to engage directly with researchers. The methodology has been promoted by the UK Data Service, to introduce researchers to analytical methods using the CSEW.
At an international level, the research has been presented to the Catalan, Dutch and Mexican governments, with the Barcelona Victimization Survey now applying small area estimation techniques to calculate crime estimates.
Publications and reports
- European Journal of Criminology paper, May 2019: Worry about crime in Europe: A model-based small area estimation from the European Social Survey
- Journal of Applied Geography paper, August 2019: The geographies of perceived neighbourhood disorder. A small area estimation approach
- Journal of Applied Spatial Analysis and Policy paper, March 2020: Applying the Spatial EBLUP to Place-Based Policing. Simulation Study and Application to Confidence in Police Work
- Big Data Meets Survey Science: A Collection of Innovative Methods book chapter, September 2020: Nonparametric Bootstrap and Small Area Estimation to Mitigate Bias in Crowdsourced Data
- British Journal of Criminology paper, October 2020: Measuring the dark figure of crime in geographic areas: Small area estimation from the Crime Survey for England and Wales
- Journal of Experimental Criminology paper, March 2021: The accuracy of crime statistics: assessing the impact of police data bias on geographic crime analysis
- David Buil-Gil, ORCID: https://orcid.org/0000-0002-7549-6317
Blogs, news posts, and videos
- Catalan Government Security blog, February 2017: A research project calculates variables of crime surveys in small areas
- UK Data Service Workshop: Introduction to analysing data about crime using R, February 2020
- Cutting Crime Impact factsheet, 2021: Measuring & Mitigating feelings of insecurity
Presentations and awards
- Early Career Award, ONS Research Excellence Awards 2021
- EFUS Conference 2017 Security, Democracy & Cities - The Urban Thinkers Campus: The Barcelona City Lab on Safer Cities presentation, November 2017
- Second Prize at the 2018 “Rafael Bonet” Award for Policing Studies of the Society of Chief Police Officers from Alicante
- University of Leeds School of Law Event, December 2020: Applying the small area estimation techniques to criminological data: methodological foundations and applications
- INEGI New data and methods for generating official information presentation, November 2021
About the ONS Secure Research Service
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