Repeat Victimization Analysis
Also known as: Repeat Victimisation Analysis, Re-Victimization Risk Analysis, Multiple Victimization Analysis, Time-Course of Repeat Victimization
Repeat victimization analysis studies the sharply elevated short-term risk that the same target — a household, person, or business — is victimized again soon after an initial offense. Established as a crime-prevention priority by Ken Pease, Graham Farrell, and colleagues in the early 1990s, it models the time-course of re-victimization, quantifies how the hazard of a repeat decays as time passes since the first event, and asks whether repeats arise because an event 'boosts' future risk or because stable target features 'flag' that risk.
Key highlights
- Pinpoints the short, intense window after an offense when re-victimization risk is highest, enabling timely targeted prevention.
- Concentrates scarce prevention resources on the small set of targets that account for a disproportionate share of crime.
- Frames repeats as survival times, bringing well-developed hazard-modeling tools and interpretable risk decay to crime analysis.
- Separates event-dependent 'boost' from stable 'flag' risk, clarifying why repeats occur and what intervention should address.
- Applies across many offense types — burglary, domestic abuse, hate crime, robbery — wherever targets can be re-victimized.
Intuition
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How it works
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When to use it
Use repeat victimization analysis when you can link crime events to identifiable targets over time and want to characterize and exploit the elevated short-term risk that follows an initial offense. It is well suited to burglary, domestic abuse, hate crime, commercial robbery, and other offenses where the same target is plausibly re-attacked, and it underpins prevention strategies that protect recent victims during their high-risk window. It is less appropriate when targets cannot be reliably re-identified across events, when recording systems miss many repeats, or when the inter-event times are too coarse to estimate a hazard. Distinguishing boost from flag mechanisms requires data on both prior events and stable target characteristics.
Strengths & limitations
- Pinpoints the short, intense window after an offense when re-victimization risk is highest, enabling timely targeted prevention.
- Concentrates scarce prevention resources on the small set of targets that account for a disproportionate share of crime.
- Frames repeats as survival times, bringing well-developed hazard-modeling tools and interpretable risk decay to crime analysis.
- Separates event-dependent 'boost' from stable 'flag' risk, clarifying why repeats occur and what intervention should address.
- Applies across many offense types — burglary, domestic abuse, hate crime, robbery — wherever targets can be re-victimized.
- Requires that successive victimizations of the same target be reliably linked; identification errors hide or invent repeats.
- Under-recording of repeat events, especially in survey or police data, biases hazard estimates and understates true repeat rates.
- Distinguishing boost from flag mechanisms is difficult and often under-identified without rich covariates and event histories.
- Coarse or censored inter-event times limit the resolution of the estimated hazard and the timing of the risk window.
- Observed repeat clustering describes risk but does not by itself prove the causal mechanism driving re-victimization.
Common pitfalls
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Applications
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Frequently asked
What is the difference between the 'boost' and 'flag' explanations of repeat victimization?
The flag explanation says some targets are simply more attractive or vulnerable due to stable characteristics, so they are repeatedly victimized for the same enduring reasons. The boost explanation says the first event itself raises future risk — the offender learns the target is rewarding and reachable and returns. Both produce clustered repeats, but they imply different prevention responses: flags call for addressing persistent vulnerabilities, while boosts call for protecting victims immediately after an event before risk decays.
How does repeat victimization differ from near-repeat victimization?
Repeat victimization is the elevated risk that the same target is victimized again. Near-repeat victimization extends this in space: targets near a recently victimized one also face elevated short-term risk. Repeat analysis tracks the time-course of re-attacks on one target, while near-repeat analysis tests space-time clustering across neighboring targets. They share the same intuition of contagious, front-loaded risk and are often studied together.
Why does focusing on repeat victims improve crime prevention?
Because crime is highly concentrated on a small set of targets and re-victimization risk spikes right after an event, recently victimized targets are an efficient, self-identifying group to protect. Acting during the brief high-risk window — hardening a burgled home, escalating safeguarding after a domestic incident — yields large reductions for limited resources, compared with spreading prevention thinly across all potential targets.
Sources
- 1.Tseloni, A., & Pease, K. (2003). Repeat personal victimization: 'Boosts' or 'flags'? British Journal of Criminology, 43(1), 196–212.
- 2.Farrell, G., & Pease, K. (1993). Once Bitten, Twice Bitten: Repeat Victimisation and its Implications for Crime Prevention. Home Office Crime Prevention Unit Paper 46. London: Home Office.
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Cite this page
ScholarGate. (2026, June 22). Repeat Victimization Analysis. ScholarGate. https://scholargate.app/criminology/repeat-victimization-analysis