Year: 2025
Type: preprint
Abstract: Defense innovations in sensing, autonomy, and information fusion have matured rapidly over the last two decades. As cities face complex crime problems, several of these capabilities—automated license plate recognition, drone-as-first-responder, AI-enabled video analytics, counter-uncrewed aircraft systems, and structured open-source intelligence—are migrating from the battlefield to public safety. This paper develops an applied evaluation and implementation blueprint to determine whether and under what conditions these defense-derived systems reduce crime harm rather than merely shifting offending in space or time; which components of the sensor→fusion→analytics→command-and-control→responder stack deliver measurable operational leverage; what safeguards convert algorithmic detections into lawful stops and durable courtroom outcomes; and how agencies can scale without vendor lock-in, data silos, or brittle operations. We propose a mixed methods design that combines staggered-adoption difference-in-differences and synthetic control with time-to-event models, paired with qualitative inquiry into chain of- custody, discovery practice, and suppression-hearing survivability. A focal case study examines automated license plate recognition (ALPR) deployments from Flock Safety as a natural experiment due to high-resolution deployment and audit logs. Companion analyses outline performance metrics for drone-as-first-responder (DFR) architectures exemplified by Aerodome, autonomous low-light operations illustrated by Skydio, structured OSINT pipelines, and counter-UAS sensing for stadiums and critical infrastructure. We specify a capability maturity model, procurement and data governance controls, equity monitoring, and a practitioner dashboard to ensure that sensor-to-responder workflows translate to measurable reductions in harm-weighted crime, faster response, higher clearance, and stronger prosecutorial outcomes.
Author: Joshua Luberisse
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4414931766
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