The Evidence-Based Investigative Tool (EBIT): a Legitimacy-Conscious Statistical Triage Process for High-Volume Crimes
Featherstone, Andrew M.
Phillips, John M.
Cambridge Journal of Evidence-Based Policing
Springer International Publishing
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McFadzien, K., Pughsley, A., Featherstone, A. M., & Phillips, J. M. (2020). The Evidence-Based Investigative Tool (EBIT): a Legitimacy-Conscious Statistical Triage Process for High-Volume Crimes. Cambridge Journal of Evidence-Based Policing, 4 (3-4), 218-232. https://doi.org/10.1007/s41887-020-00050-3
Funder: University of Cambridge
Abstract: Research Question: Based on the evidence at the end of a preliminary investigation of minor, non-domestic assault and public order cases, how accurately can the likelihood of a sanctioned detection be predicted for triage decisions while maintaining high awareness of legitimacy issues? Data: Investigative records on assault and public order offences recorded by Kent Police, with a case-control sample of 522 randomly selected detected cases and 482 randomly selected undetected cases, a test sample of 931 cases, and an additional 7947 cases for testing the model on all eligible cases in the force area for the initial six months of its use. Methods: A case control comparison between solved and unsolved cases produced a logistic regression model that was used to predict investigative outcomes in both the test sample and the complete tracking of its use in investigative operations. Findings: Eight elements of evidence available by the end of the preliminary investigation were found to predict whether a sanctioned detection would result from further investigation: (1) victim supports police prosecution, and evidence includes (2) a named suspect, (3) a cooperative witness, (4) CCTV evidence, (5) confirming police testimony, (6) forensic evidence, (7) a connection to other cases and (8) a report of the crime to police less than 28 days after the incident occurred. When the EBIT was calibrated to identify only the 31% of cases most likely to yield a detection from further investigation, the model correctly forecast 97% of cases that would not be solved, producing only 3% false negatives. It also reduced the false-positive rate from 73 to 22% in cases that did not lead to a sanctioned detection. Conclusions: A case control analysis of solvability factors at the end of a preliminary investigation can identify almost all of the cases that are likely to be solved, even while the model predicts that the majority of all cases at that point will not be solved.
Research Article, Solvability, Investigative triage, Volume crime, Police decision-making, Resource allocation, Case clearance, False negatives, Non-domestic assault
External DOI: https://doi.org/10.1007/s41887-020-00050-3
This record's URL: https://www.repository.cam.ac.uk/handle/1810/315596
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/