Some people are repeatedly victimized and, in conformity with the 80-20 rule (Step 18), a small proportion of victims account for a large proportion of all victimizations. Ken Pease and Graham Farrell carefully documented this fact in a seminal Home Office publication called "Once Bitten, Twice Bitten". Using British Crime Survey data (see the table), they showed that about 4% of people experience about 40% of all victimizations in one year. They showed that repeat victimization occurs for a variety of crimes including domestic violence, sexual assault, burglary and car-related thefts. They also showed that repeats occur quite quickly, often within a week of the first victimization, though this varies with the offense.

Research has shown that it is easy to miss the extent of repeat victimization for several reasons:

• Many victims do not report crimes to the police, which means that repeat victimization is undercounted in official police records. This is why researchers have tried to use surveys, in which people can be asked about crimes they did not report to the police. Unfortunately, the National Crime Victimization Survey, the United States equivalent to the British Crime Survey, undercounts repeat victimization because it uses only a 6-month recall period and does not count all the crimes committed in a series against a particular victim.
• Crime analysts often look for repeat victimization by counting the number of crimes at addresses, but police data often contains incomplete address information, especially for apartment units. This leads to higher estimates of one-time only victimizations than is actually the case. This difficulty is being reduced by the greater availability of GIS systems and through the use of address matching in mapping software (i.e., geocoding).
• Repeat victimization can be underestimated because of the "time-window effect". If only victimizations during a specific time period are counted - a time window of January 2002 through June 2002, for example - then someone who had been victimized in December 2001 and once during the six-month window would not be counted as a repeat victim. If they had the misfortune to be victimized in July 2002, we would not know that this person had three victimizations. Ideally, a moving window should be used where each new victim is followed for a year after the first reported event.

In explaining repeat victimization, Ken Pease distinguishes two kinds of accounts:

1. Boost accounts explain repetitions in terms of positive experiences at the initial offense. A burglar, for example, learns a great deal about a home during a break-in. This knowledge may encourage him to come back for another break-in. A burglar may also tell others about goods he left behind, leading to subsequent break-ins by other burglars.
2. Flag accounts explain repetitions in terms of the unusual attractiveness or vulnerability of particular targets that result in their victimization by a variety of offenders. Some occupations have much higher victimization rates than others (taxi drivers, for example) and people who spend time in risky facilities (such as convenience store clerks) are also more prone to repeated victimization. Finally, the ownership of hot products, such as cars attractive to joyriders (Step 31), will also increase the probability of repeat victimization.

"Virtual" or "near" repeats involve victims with characteristics similar to the original victim or target. After successfully attacking the first target, offenders generalize to targets with similar characteristics. Houses with the same lay-out and in the same neighborhood as the first burglary, for example, can be expected to have higher risks because the offender has learned something about them from breaking in before.

Knowledge of repeat victimization is useful for predicting who is most at risk and when they are at risk. This means that crime prevention resources can be focused on these people, rather than spreading resources over a number of people, most of whom have a very low risk of crime.

Many police agencies now also use a "graded response" when dealing with repeat victims. This means that the more often someone has been victimized the more intensive the preventive action taken by the police. Knowing the time period between repeats also makes it possible to temporarily deploy crime prevention for short periods when the risk of crime is the greatest. For example, some police agencies will install temporary burglar alarms where the risk is high of a repeat burglary occurring soon.

Next Step

Neighbor Beware!

Repeat victimization tells of an elevated risk that the same victim will suffer again, most often in the immediate days or weeks following the preceding crime. But risk can be communicated to nearby places. Kate Bowers and Shane Johnson of the Jill Dando Institute of Crime Science have shown how burglary risk is communicated down a street. This is illustrated in the graph. A home is burgled, which we will call the reference burglary. The numbers at the bottom are a measure of distance from the reference burglary. A distance of one tells of a home next to a burglary location on the same side of the street, or the home immediately opposite. A distance of two refers to homes two doors down on the same side of the street, or diagonally opposite, and so on. The ordinate shows the number of burglaries following reference burglaries. The data come from Merseyside Police in the UK. You can see that the risk of another burglary declines the further the distance from the reference burglary. For any given distance, the risk is greater for homes on the same side of the street. This shows which homes one should seek to protect in the wake of a burglary. Priority should be given to homes close to the burgled home and especially on the same side of the street.