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Summary and review
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There are many potential applications for Big Data analytics in criminology research. This chapter has focused on two predictive techniques used in law enforcement and criminal justice – predictive policing and offender risk assess- ment. Big Data techniques allow researchers to gain insights from larger volumes of data than could be analysed without them, possibly discovering pat- terns that might otherwise remain hidden. However, it is important to understand both the limitations of these techniques and their potential, when used as the basis for intervention, to yield unanticipated or undesirable results. They will be more useful in answering some kinds of research questions (exploratory and descriptive research) than others (understanding causal mech- anisms). They should thus be used only with an understanding of their limitations and biases, and their use (particularly where there is an impact on individuals) should be carefully evaluated.
This chapter has detailed in relation to the following:
Defining Big Data:
• The most common definition focuses on data having high volume, high veloc- ity (speed of production and processing) and high variety (of data types and sources), although there is a range of other definitions.
Uses of Big Data in crime prediction:
• Predictive policing is used by police to target particular locations and times, or occasionally individuals, where the likelihood of criminal activity is heightened.
• Offender risk management attempts to quantify risk for the purposes of bail, parole or sentencing decisions. One technique that can be used is the random forest algorithm.
Limitations:
• accuracy and representativeness of data
• challenges in data linking and tracking provenance of data
• based on correlation rather than causation
• inductive bias
• differential impact on subpopulations and the potential for discrimination
• fairness of use, particularly in criminal justice decision making.
Challenges for researchers:
• access to algorithm or analytic technique used
• access to data employed by the algorithm or technique as a basis for future predictions
• evaluation of predictive accuracy distinguished from evaluation of effectiveness.
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SUGGESTIONS FOR FURTHER READING
Various books and articles provide critical commentary on the use of Big Data techniques, either generally or particularly in the context of criminal justice.
While somewhat dated as to particular tools being used, Harcourt, B. E. (2007) Against Prediction: Profiling, Policing and Punishing in an Actuarial Age. Chicago, IL: University of Chicago Press remains an excellent source for a review of concerns around data- driven approaches in this area.
Chan, J. and Bennett Moses, L. (2016) ‘Is Big Data challenging criminology? ’, Theoretical Criminology, 20: 21–39 provides an overview of the use of Big Data in criminology. In the particular context of predictive policing, see Bennett Moses, L. and Chan, J. (2018) ‘Algorithmic prediction in policing: assumptions, evaluation, and accountability’, Policing and Society, 28(7): 806-822. doi: 10. 1080/10439463. 2016. 1253695.
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For a student-friendly technical account of some of the techniques used in offender risk assessment, see Berk, R. (2012) Criminal Justice Forecasts of Risk: A Machine Learning Approach. New York: Springer.
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