TECH Signal 185
Institute for Justice analysis questions causal link between Flock cameras and crime reduction
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A new analysis by the Institute for Justice argues that a recent study claiming Flock Safety's cameras reduce car theft fails to demonstrate a causal effect due to pre-existing trends and unaccounted external factors.
For engineers and policymakers evaluating surveillance infrastructure, this critique highlights the difficulty of isolating the impact of a single technology in complex, real-world environments. It suggests that reported performance metrics for ALPR systems may be inflated by confounding variables rather than reflecting the technology's actual efficacy.
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The Institute for Justice found that theft and arrest rates were already trending favorably in Flock-deploying jurisdictions before the cameras were installed.
The study's reported 11 percent drop in thefts disappears when data are averaged across all agencies or weighted by population rather than by problem size.
External factors such as the pandemic and the viral 'Kia Challenge' were not fully accounted for, complicating the attribution of crime changes to ALPR deployment.
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What the cluster adds up to.
Flock Safety has been promoting a working paper that claims its automatic license plate reader (ALPR) network reduces motor vehicle theft by 11 percent and increases arrests by 15.9 percent. The study analyzed data from 216 agencies that adopted Flock between 2017 and 2023 against 3,108 agencies that did not. However, a new analysis by the Institute for Justice (IJ) challenges the validity of these conclusions, arguing that the data does not support a causal link between the cameras and the observed crime reductions.
The core methodological flaw identified by IJ researcher Matthew West is the lack of comparable control groups. Jurisdictions that chose to deploy Flock cameras were often already experiencing high levels of vehicle theft and were simultaneously implementing other crime-fighting initiatives. Because the study did not compare places on similar trajectories, it is impossible to determine whether the decline in thefts was caused by the ALPRs or by these pre-existing trends and concurrent policy changes.
Furthermore, the statistical significance of the results is highly sensitive to how the data is weighted. The 11 percent drop in thefts only appears when the data is weighted in favor of agencies that had larger initial problems. When the data is averaged across all agencies or weighted by population, the study finds no statistically significant change in theft numbers. This discrepancy suggests that the headline figures may be artifacts of the analytical method rather than robust indicators of the technology's impact.
The analysis also points to unaccounted external variables that distort the data. The COVID-19 pandemic lockdowns affected both crime levels and driving patterns, while the viral 'Kia Challenge' caused specific spikes in thefts of certain vehicle models. These events created noise in the data that the original study did not fully control for, making it difficult to isolate the specific effect of the surveillance network from broader societal shifts.
For engineers and operators, this case illustrates the limits of observational data in validating complex surveillance systems. While the cameras may contribute to crime reduction, the current evidence does not prove they are the primary driver. Relying on such shaky evidence for procurement or policy decisions risks overestimating the return on investment and underestimating the privacy and civil liberties concerns associated with widespread ALPR deployment.
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