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Analyzing Repeat Victimization

Tool Guide No. 4 (2005)

by Deborah Lamm Weisel

Analisando a Repetio da Vitimizao (Portuguese) PDF


This guide begins by describing the concept of repeat victimization (RV) and its relationship to other patterns in public safety problems, such as hot spots and repeat offenders. The guide then describes sources of information, and ways to determine the amount and characteristics of repeat victimization in your jurisdiction. Finally the guide reviews responses to repeat victimization from evaluative research and police practice.

Patterns of Repeat Victimization

This guide is intended as a tool to help police identify and understand patterns of repeat victimization for a range of crime and disorder problems. The guide focuses on techniques for determining the amount of RV for specific public safety problems and how analysis of RV generally may be used to develop more effective responses. This publication is not a guide to specific problems, such as burglary, domestic violence, or vehicle theft. You are encouraged to refer to other guides for an in-depth understanding of these problems.

For decades, much effort by police and citizens has been invested in crime prevention-such as marking property, establishing a Neighborhood Watch, conducting crime prevention surveys, hardening targets, increasing lighting, and installing electronic security systems.

While numerous crime prevention efforts are effective, many are adopted by persons, households, and institutions least at risk of being victimized. Crime prevention strategies are most effective when directed at those most likely to be victimized.

Linking crime prevention strategies with likely victims is a challenge because of the difficulty in predicting the most likely victims of crime. Taking steps to prevent that offense from occurring would be easier, if only police knew.

  • What stores will be robbed?
  • Whose homes will be burglarized?
  • Which college students will be sexually assaulted?

It is often painfully obvious that some individuals, households, or businesses are particularly vulnerable to crime. Such vulnerability may be related to factors such as abusing alcohol, failing to secure property, being physically isolated, engaging in risky behaviors, or being in close proximity to pools of likely offenders.

While most people and places do not get victimized by crime, those who are victimized consistently face the highest risk of being victimized again. Previous victimization is the single best predictor of victimization. It is a better predictor of future victimization than any other characteristic of crime.

Lynch, Berbaum, and Planty (1998) disagree. Using data from the NCVS, the authors found that housing location, age, and marital status of the head of household, size, and changes in household composition were stronger predictors of repeat victimization for burglary than initial victimization in the United States. In addition, the authors found that the best predictor of repeat victimization for assault was the reporting of an initial assault to the police.

Not only is repeat victimization predictable, the time period of likely revictimization can be calculated since subsequent offenses are consistently characterized by their rapidity. Much repeat victimization occurs within a week of an initial offense, and some repeat victimization even occurs within 24 hours. Across all crime types, the greatest risk of revictimization is immediately after the initial offense, and this period of heightened risk declines steadily in the following weeks and months.

The predictability of repeat victimization and the short time period of heightened risk after the first victimization provide a very specific opportunity for police to intervene quickly to prevent subsequent offenses. Strategies to reduce revictimization can substantially increase the effectiveness of police. Reducing repeat victimization can result in lower crime, improved efficiency of crime prevention resources, and the apprehension of offenders. It can also conserve both patrol and investigative resources.

Defining Repeat Victimization

In basic terms, repeat victimization is a type of crime pattern. There are several types of well-known crime patterns including hot spots, crime series, and repeat offenders. While repeat victimization is a distinct crime pattern, some offenses feature multiple crime patterns; these patterns are discussed later in this guide.

By most definitions, repeat victimization, or revictimization, occurs when the same type of crime incident is experienced by the same-or virtually the same-victim or target within a specific period of time such as a year. Repeat victimization refers to the total number of offenses experienced by a victim or target including the initial and subsequent offenses. A person's house may be burglarized twice in a year or 10 times, and both examples are considered repeats.

The amount of repeat victimization is usually reported as the percentage of victims (persons or addresses) who are victimized more than once during a time period for a specific crime type, such as burglary or robbery. Repeat victimization is also calculated as the proportion of offenses that are suffered by repeat victims; this figure is usually called repeat offenses. While both figures are important, they are not interchangeable and care should be taken in the reading of such numbers. In this guide, we report both proportions of repeat victims and repeat offenses when the data are available.

For example, the first row in Table 1 would be stated as:

46% of all sexual assaults were experienced by persons suffering two or more victimizations during the data period

Similarly, the second row in Table 2 would read:

11% of assault victims suffered 25% of all assaults over the 25-year period

And the first row in Table 3 would read:

40% of all burglaries were experienced by the 19% of victims who were victimized twice or more during the data period

The term "victimization" usually refers to people, such as a person who has been victimized by domestic violence. But repeat victimization can best be understood as repeat targets since a victim may be an individual, a dwelling unit, a business at a specific address, or even a business chain with multiple locations. Even motor vehicles may be repeat victims. Later in this guide, we discuss how to distinguish repeat victims in police data by address, victim's name, and other identifiers.

The Extent of Repeat Victimization

Repeat victimization is substantial and accounts for a large portion of all crime. While revictimization occurs for virtually all crime problems, the precise amount of crime associated with revictimization varies between crime problems, over time, and across places.

With the exception of Lynch, Berbaum, and Planty (1998), most estimates of repeat victimization are produced outside the United States and are drawn from the British Crime Survey, International Victims Survey, and other surveys. A few American studies in the early 1980s used the National Crime Victimization Survey (NCVS) to examine repeat victimization but the NCVS is not designed to detect RV as it excludes crime series, collects data only for incidents occurring in the preceding six months and uses a sample based on address that cannot control for people moving over time.

These variations reflect the local nature of crime and important differences in the type and amount of data used for computing repeat victimization. Three primary sources of information demonstrate that repeat victimization is prevalent across the world: surveys of victims, interviews with offenders, and crime reports. Although each of these sources has limitations, the prevalence of revictimization is consistent across these different sources.

Table 1

Estimates of Repeat Victimization:

International Victimization Survey1


Repeat offenses

Sexual assault






Vandalism to vehicle


Theft from vehicle


Vehicle theft




Comparison data from international victimization surveys show that repeat victimization is more common for violent crime such as assaults and robbery than for property crime (see Table 1). Assault victims routinely feature a high rate of revictimization (see Table 2), and domestic violence is among the most predictable crimes for which a repeat will occur.

Table 2

Estimates of Repeat Victimization for Assault


Repeat Offenses

Repeat Victims

Data Source and Time Period




Emergency room reports, 25 years, Netherlands2

Sexual assault



Victim surveys, adult experience, Los Angeles, California3

Domestic violence



Victimization survey, one year, Great Britain4

Assaults of youth



National Youth Survey, one year, United States5

Repeat victimization is also common for property crime as evidenced in data from the British Crime Survey (see Table 3).

Table 3

Estimates of Repeat Victimization for Property Crime:

British Crime Survey


Repeat Offenses

Repeat Victims

Residential burglary6



Vehicle crime (thefts of/from)7






Although many studies of repeat victimization are based on surveys of victims, police records also show strong evidence of revictimization for problems ranging from bank robberies to domestic violence and burglaries (see Table 4). As with the victimization surveys, crime reports show the largest amount of repeat victimization for domestic violence.

Table 4

Estimates of Repeat Victimization:

Crime Reports


Repeat Offenses

Repeat Victims


Domestic violence



Merseyside, England9



West Yorkshire, England10

Commercial robbery



Indianapolis, Indiana11

Gas station robbery




Bank robbery




Residential burglary



Nottinghamshire, England14



Merseyside, England15



Beenleigh, Australia16



Enschede, Netherlands17

Commercial burglary



Austin, Texas18



Merseyside, England19

Residential and commercial burglary



Charlotte, North Carolina20

While many repeat victims suffer two victimizations during a reporting period, some repeat offenses are associated with chronic victims who are victimized more often, experiencing three or more offenses during a period of time. The British Crime Survey reveals that 7 percent of burglary and vehicle crime victims are victimized three or more times during a year (see Table 5) while 23 percent of domestic violence victims suffer this concentration of repeat victimization.

The more numerous offenses reported by these chronic victims contribute disproportionately to overall victimization. For example, 7 percent of burglary victims comprise 21 percent of all burglaries (see Table 6).

Table 5:

Concentration of Repeats Among Victims21

Type of Victimization


Vehicle Crime

(Theft of/from)

Domestic Violence

One offense




Two offenses




Three or more




Table 6: Contribution of Repeat Victims to Burglaries22



Proportion of Offenses

One burglary



Two burglaries



Three or more burglaries



Despite strong evidence of repeat victimization, virtually all estimates of repeat victimization are conservative because of data limitations. Victimization surveys show the most repeat victimization, because they capture offenses unreported to police. But longitudinal surveys lose respondents over time, as victims are likely to move, and panel surveys depend on a victim's recall of multiple events. Interviews with offenders support repeat victimization but such studies have been limited and the veracity of offenders is questionable. Unreported crime reduces police estimates of repeat victimization and evidence even suggests that repeat victims are less likely to call the police again.23 Police estimates of repeats may further exclude revictimization of the same individual at different locations, such as offenses reported from hospitals or at police stations while jurisdictional boundaries, recording practices for series offenses, the use of short-time periods such as a single year, and a small number of offenses may also mask repeats that can be identified by police.

When Repeat Victimization Occurs

A critical and consistent feature of repeat victimization is that repeat offenses occur quickly-many repeats occur within a week of the initial offense, and some even occur within 24 hours. An early study of RV showed the highest risk of a repeat burglary was during the first week after an initial burglary.24

After the initial period of heightened risk, the risk of a repeat offense declines rapidly until the victim once again has about the same victimization risk as persons or properties that have never been victimized. This common pattern is displayed in Figure 1 and shows that 60 percent of repeat burglaries occurred within one month of the initial offense; about 10 percent occurred during the second month. After the second month, the likelihood of a repeat offense is quite low.

RV consistently demonstrates a predictable pattern known as time course: a relatively short high-risk period is followed by a rapid decline and then a leveling off of risk. The length of the time period of heightened victimization risk varies based on local crime problems. Determining the time period of heightened risk is critical because any preventive actions must be taken during the high risk period to prevent subsequent offenses. For offenses with a short high-risk time course, the preventive actions must be taken very quickly. The delay of two days or a week may miss the opportunity to prevent a repeat from occurring.

Some research suggests that the predictable time course of repeat victimization may be punctuated by a "bounce"-a slight resurgence in the proportion of revictimization occurring after the risk appears to be steadily declining (see Figure 2). The bounce in the time course may be associated with the replacement of property with insurance money. It seems likely that some repeat offenders may employ a "cool down" period, perceiving victims to be on high alert immediately after an offense but relaxing their vigilance within a few months.

Evidence suggests that the time period between an initial and subsequent offense varies by the type of crime. The time course of domestic violence appears short (see Table 7) with 15% of repeat offenses occurring within a day. The time course of RV may be calculated by hours, days, weeks or months, or even years between offenses, depending upon the temporal distribution of data.

In addition to variation by crime type, it is likely that the time course may also vary by the location of the study. For example, a study in Florida showed 25% of repeat burglaries took place within a week while a study in Merseyside showed 11% of repeats occurred during a similar time period.

Although the time period for reporting repeat victimization varies, the statement of such findings is straightforward. For example, the first row in Table 7 would be stated as:

Of repeat incidents of domestic violence, 15% occurred within 24 hours of the initial incident while 35% of repeat incidents occurred within five weeks.

Table 7: Time Course of Repeat Victimization by Offense Type:

Crime Reports


Proportion of Repeats by Time Period


Domestic violence

15% within 24 hours

35% within five weeks

Merseyside, England25

Bank robbery

33% within three months


Residential burglary

25% within a week

51% within a month

Tallahassee, Florida27

11% within one week

33% within one month

Merseyside, England28

Non-residential burglary

17% within one week

43% within one month

Merseyside, England29

Property crime at schools

70% within a month

Merseyside, England30

Why Repeat Victimization Occurs

There are two primary reasons for repeat victimization: one, known as the "boost" explanation, relates to the role of repeat offenders; the other, known as the "flag" explanation, relates to the vulnerability or attractiveness of certain victims.

In the flag explanation, some targets are unusually attractive to criminals or particularly vulnerable to crime, and these characteristics tend to remain constant over time. In such cases, the victim is repeatedly victimized by different offenders.

  • Some locations, such as corner properties, may have higher victimization because offenders can easily determine if no one is home. Similarly, apartments with sliding glass doors are particularly vulnerable to break-ins.
  • Some businesses, such as convenience stores, are easily accessible and open long hours, which increases exposure to crime.
  • Some jobs, such as taxi driving or delivering pizzas, routinely put employees at higher risk than do other jobs. People who routinely spend time in risky places, such as bars, are at greater risk of victimization.
  • Hot products, such as vehicles desirable for joyriding, are at higher risk of being stolen.

In the boost explanation, repeat victimization reflects the successful outcome of an initial offense. Specific offenders gain important knowledge about a target from their experience and use this information to reoffend.

This knowledge may include easy access to a property, times during which a target is unguarded, or techniques for overcoming security. For example, offenders who steal particular makes of vehicles may have knowledge of ways to defeat their electronic security systems or locking mechanisms. Even fraudulent victimization shows this boost pattern, as insurance fraud may explain some cases of repeat victimization.

  • During initial offenses, offenders may spot butto carry away all the desirable property. These offenders may return for property left behind; or the offenders may tell others about the property, leading different offenders to revictimize the same property. Since many victims will eventually replace stolen property, such as electronics, original offenders may also return after a period of time to steal the replacement property-presumably brand new.31
  • Some victims may be unable to protect themselves from further victimization. An unrepaired window or door may increase vulnerability and make repeat victimization even easier than an initial offense. For example, once victimized, a domestic violence victim faces a high likelihood of revictimization if no protective measures are taken to prevent subsequent offenses.
  • Interviews with offenders suggest that much repeat victimization may be related to boost explanations-experienced offenders can reliably calculate both the risks and rewards of offending. Half to two-thirds of offenders report burglarizing or robbing a specific property twice or more.32 Among domestic violence offenders, as many as two-thirds of incidents are committed by repeat offenders.33

Boost and flag explanations may overlap and vary by offense type. For example, bank robberies are most likely to recur if an initial robbery yielded a large take; when monetary losses were small, banks were less likely to be robbed again.34 Research on repeat victimization-for banks and other targets-suggests that most offenses are highly concentrated on a small number of victims while the majority of targets are never victimized at all.

How Repeat Victimization Relates to Other Crime Patterns

Research has revealed several types of repeat victimization:

  • True repeat victims are the exact same targets that were initially victimized, such as the same house and the same occupants who were burglarized three times within a year.
  • Near victims are victims or targets that are physically close to the original victim and may be similar in important ways. Apartments close to a burglarized unit will tend to contain similar goods, have similar physical vulnerabilities, and a common layout.
  • Virtual repeats are repeat victims that are virtually identical to the original victim in important ways. A chain of convenience stores or fast food restaurants may have identical store layouts and management practices, such as having a single clerk on duty or informal cash handling procedures. The new occupants of a dwelling that had been previously burglarized are another type of virtual repeat.
  • Chronic victims are repeat victims who suffer from different types of victimization over time-such as burglary, domestic violence, and robbery. This phenomenon is also known as multiple victimization.

For some crimes, repeat victimization is related to other common crime patterns:

  • Hot spots are geographic areas in which crime is clustered. Hot spots may be hot because of the frequency of the same type of crime, such as burglaries, or hot spots may include different types of crimes. For many crimes, repeat victimization contributes to hot spots.
  • Hot products are goods that are frequently stolen, and their desirability may underlie repeat victimization. Stores that sell CDs, beer, or gasoline may suffer repeat victimization. Some products, including vehicles, become hot products because of the product's vulnerability-for example, vehicles with locks that are easy to defeat.
  • Repeat offenders are individuals who commit multiple crimes. Some offenders specialize in a single crime type, while others commit complementary offenses-such as breaking into a house and stealing a vehicle to transport the goods, or stealing a license plate to be used in the commission of another crime, such as a commercial robbery.
  • Crime series are offenses of one crime type that appear to be the work of the same offender. The offenses may be clustered in space or time, or reflect a distinctive modus operandi, such as a serial rapist who targets college students. Common series involve property crime at similar targets, such as robberies of convenience stores.
  • Risky facilities are locations such as colleges or shopping areas that routinely attract or generate a disproportionate amount of crime. For example, lots where students routinely park may generate more larcenies from vehicles because the vehicles of students may routinely contain desirable electronic equipment.

These crime patterns are not mutually exclusive and may intersect or overlap; the detection of repeat victimization, however, routinely provides important clues about the reasons for recurrence and permits police to focus on avenues for prevention.

Where Repeat Victimization Occurs

For many crime problems, repeat victimization is most common in high crime areas.

Offenses such as domestic violence and sexual assault do not usually exhibit spatial concentrations, while other targets of repeat victimization, such as convenience stores, budget motels, and banks, may be geographically dispersed.

Persons and places in high crime areas face a greater risk of initial victimization for many crimes, and they may lack the means to block a subsequent offense by improving security measures and doing so quickly.35

In high crime areas, crime is so concentrated among repeat victims that recurring offenses can create hot spots-relatively small geographic areas in which offenses are clustered. As a result, experts have coined the term "hot dots" because incident maps may be dominated by symbols scaled to represent the number of offenses at specific addresses.36 (See Figure 3.)

Figure 3

Repeat Commercial Robberies37

Incident maps are often used to identify hot spots and can be used to detect repeat victimization. Icons or symbols should be used on maps that are scaled in size to reflect the number of incidents, otherwise points that overlap may not be visible, masking RV. Data decisions can also distort the amount of repeat victimization that can be detected on maps. Short time periods, such as a week or month or even a quarter-may mask repeat victimization; imprecise address information, such as a single address for incidents occurring at a large apartment complex, also mask specific locations of RV.

Incident maps may mask RV in densely populated areas because most maps demonstrate the incidence and spatial distribution of offenses, and do not account for the concentration of crimes. In densely populated areas such as those with multi-family dwellings, most maps will not differentiate between apartment units and apartment buildings that may comprise large apartment complexes.

Crime is not always geographically patterned, and this is also true for repeat victimization. For example, victims of domestic violence are unlikely to be geographically concentrated. Even repeat incidents of domestic violence may not occur at a single address; one offense may take place at a residence while the repeat offense may occur at a victim's workplace.

Some crimes, such as burglary, are clustered geographically; repeat burglaries are even more predictably clustered.38 Thus, citywide data on burglaries may mask the proportion of repeat burglaries occurring in smaller geographic areas. This suggests the need to use different geographic levels of analysis to examine RV. In contrast to burglary, offenses such as bank robberies and domestic violence may necessitate the use of data from the entire jurisdiction.

Overlooking Repeat Victimization

Although the phenomenon of repeat victimization is well-established, it is easy to overlook the importance of repeat victimization in crime pattern analysis because most people and properties within a jurisdiction are not victimized by crime, particularly within a period of one or a few years.

Consider a study in which 10,828 burglaries were reported to police in 1990:39

  • 97 percent of the city's estimated 300,000 addresses were not burglarized
  • 3 percent of the jurisdiction's addresses (8,116) were burglarized

At first, repeat victimization appears minimal:

  • 82 percent of the victims (6,616 addresses) suffered only one burglary during the year
  • 18 percent of victims (1,500 addresses) suffered two or more burglaries

Analysis sheds further light on revictimization:

  • 61 percent of all burglaries (6,616) occurred at addresses with only one offense
  • 39 percent of all burglaries (4,212) occurred at addresses with two or more offenses

While repeat victimization may still appear minimal, Figure 4 demonstrates graphically that revictimization accounts for a disproportionately large share of all burglaries: 18 percent of victims accounted for 39 percent of burglaries. If offenses after the initial offense had been prevented, the jurisdiction would have experienced 2,712 fewer burglaries-a 25 percent reduction in burglaries.

Figure 4:

Distribution of Burglaries by Address and Frequency

In addition to its potential for crime reduction, analysis of repeat victimization provides an important analytic and management tool for police organizations by serving the following purposes:

  • Provide a reliable performance measure for evaluating organizational effectiveness (as used by police forces in Great Britain).
  • Serve as a catalyst for developing more effective responses for problems that generate much of the workload for police.
  • Reveal limitations of existing data and police practices, and advance improvements in data quality and victim services. (See Appendix A for ways to easily improve data quality.)
  • Provide insight into patterns underlying recurring crime problems.
  • Prioritize the development and delivery of crime prevention and victim services.

Although recognizing repeat victimization is an important step, working out precisely what to do about revictimization will require additional effort on the part of police.

Special Concerns About Repeat Victimization

Once an agency undertakes analysis of repeat victimization and determines the prevalence and time course of repeats by offense type, the crime pattern can be used as a tool for developing responses to reduce revictimization. Focusing on victims raises a number of special concerns that police should consider:

  • Blaming the victim. Victims may be vulnerable because they are unable, or have failed to secure their property, or have placed themselves in high-risk settings. The behaviors of individuals-such as using poor judgment while under the influence of drugs or alcohol-may contribute to victimization. In most cases, police should provide information to the victim about the increased risk of victimization but must be careful about implying blame.
  • Increasing fearfulness. For offenses such as burglary that are unlikely to be solved, the primary role of police is often to comfort victims. Warning victims about the likelihood of being revictimized may make victims more fearful.
  • Violating privacy of victims. Although victimization increases the risk of revictimization to the original victim, it also increases the risks to persons and properties that are either nearby or virtually identical to the initial victim. While police may be concerned about violating the privacy of an initial victim by warning others, this information may prevent other vulnerable persons or places from being victimized.
  • Displacing crime. It is often believed that thwarting one offense will result in a motivated offender simply picking another target. Unless there are virtual victims, the likelihood of displacement is low.40 For example, preventing repeat domestic violence is unlikely to result in the displacement of violence to another victim. If there are virtual victims available-such as similar nearby houses to be burglarized or similar unsecured parking lots for vehicle theft-police should consider these as candidates for similar crime prevention strategies. Rather than causing displacement, crime prevention efforts focused on victims are just as likely to produce bonus effects. For example, reducing opportunities for vehicle theft may also reduce theft from vehicles.
  • Unintended consequences. Focusing on repeat victimization to reduce offending may have unintended consequences. In a study in New York City, researchers found that follow-up visits and educational services to victims of domestic violence resulted in increased calls for police service,41 and mandatory arrest for some domestic violence offenders increases revictimization.

Understanding Your Local Repeat Victimization Pattern

The information in this guide is only a generalized description of repeat victimization. Because repeat victimization varies in different locations and by the type of crime problem, you must combine the basic concepts of repeat victimization with a more specific understanding of your local problem. Analyzing the local problem carefully will help you design a more effective response strategy.

Estimating the amount and distribution of repeat victimization is necessary to develop an effective response.

Selecting the Problem

Depending upon the crime problem being examined-and its presumed relationship with other related problems-you may wish to examine a cluster of problems. For example, convenience stores victimized by robbery may also be frequent victims of burglary, shoplifting, or larceny. The choice of problems to be examined should be based on practical reasoning.

Although it may be necessary to begin with a broad problem because of the type of data being used, every effort should be made to reduce the problem to one with more similarities than differences and avoid an overly broad problem. For example, vandalism includes graffiti and destruction of property, but each should be examined separately.

For the problem being examined, determine the appropriate "unit of analysis" by deciding if the relevant "victim" is an individual or household, an address, a business, or a group of victims such as convenience stores or an individual chain store.

Selecting Data

Existing police data, such as crime reports, are most often used to document repeat victimization. In selecting data, attention should be given to:

  • Determining the appropriate time frame. For most crime problems, it is best to use at least three calendar years of data. You can then define repeat victims as persons or places victimized more than once during the three years. If you use only a single year of data, an address burglarized in December will not be identified as a repeat if the next burglary at the address occurred in January. Shorter time periods of data will generally underestimate the amount of repeat victimization.
  • Determining data sufficiency. As a general rule, it can be easier to detect repeat victimization for offenses that are common than for offenses that are rare. While the magnitude of problems varies from one jurisdiction to another, it may be useful to have 100 offenses or more per year to detect repeats. While one may find repeats even among 10 bank robberies per year in a small town, analyzing a larger number of robberies will produce more reliable findings. When there are few offenses of one type, you can increase the amount of data by incorporating multiple years, up to ten years or more, or by broadening the geographic scope of analysis, such as incorporating offenses that occur in neighboring jurisdictions.
  • Choosing a denominator. To best calculate repeat victimization, it is useful to know the population or number of possible victims of a similar type. For example, if you want to determine the number of repeat robberies of convenience stores, it is helpful to know the number of convenience stores. For demographic information, such as households, such data can be found in the U.S. Census; for other denominators, sources such as business licenses or tax records can be used to determine the population.
  • Identifying key variables. Data should routinely include victim name, address (including street number, building name or number, apartment or suite number, and direction), and date and time of the offense. If the victim is a business, the business name and the type of business should be examined. Variables examined should also include the outcome such as the type or value of property taken or damaged, whether the offense was attempted or completed, the amount of force used in the offense, the level of harm, and so forth. Additional variables may be useful, and you can identify these by reading the literature on the specific type of problem being considered.
  • Determining data limitations. Police data routinely have limitations, including underreporting, delayed reporting, data errors, and imprecise address information. When selecting data, you should identify the limitations of the data you will use, think through the implications of these limitations, and determine whether the data can be improved with a reasonable amount of effort. Detecting the limitations of different data and techniques for improving data quality are discussed in appendices A and B.

This task may suggest organizational changes that can be made to improve data quality. For example, agencies may modify offense reports, change nature classifications used in recording dispatched calls or standardize recording of victim names.

Analysis Tasks

Basic analysis for repeat victimization is straightforward and rarely requires any complex statistical procedures. Data are typically contained in a spreadsheet with a range of descriptive variables; the data are then sorted based on a particular variable so all related victims are grouped together.

Mapping locations. Many types of revictimization can initially be detected with point maps. These maps place dots or points on the locations of offenses, calls for service, and arrests for crimes such as burglaries, robberies, and assaults. Points that are scaled in size to reflect the number of incidents occurring at individual addresses are useful. Point maps are less useful in areas of high-rise apartments or office buildings where the population is dense. Point maps are also less useful in rural areas or areas with few addresses such as parks, farms, and large parking lots, unless global positioning system (GPS) coordinates have been established for these locations.

Sorting offense data by address. Mapping is essentially a method of putting a chart or table onto a spatial layer. We can accomplish the same task by sorting data by address-by sorting the street names and numbers, we create a table such as Figure 5. This table does not include the victim name, but the data would enable you to sort and then count the number of offenses occurring at each location.

Figure 5

A tool for conducting this analysis is included on the website.

Sample Table for Sorting Addresses

Police Beat

Census Tract

Census Block

Street Number

Street Direction

Street Name

Street Type









































































































Sorting offense data by victim name. Police data can be sorted by victim name as the primary sorting characteristic, using address, and other unique information to verify and resolve any apparent errors in the database. Both calls-for-service and offense data can be analyzed in this way.

Counting victims and offenses. Once data are sorted and matched, most electronic databases such as Microsoft Excel and SPSS contain a procedure for counting the number of unique addresses or names.

The best way to display repeat data is to create a table that includes the number of offenses and the number of victims (see Figure 6.) Additional columns may be used to report the percentage of households or offenses in each row.

Figure 6

Sample Table for Recording Number of Victimizations:

Residential Burglary

Number of burglaries

Number of victims burglarized (address or households)

Total burglaries



(Total population or households - E)







2 x B



3 x C

4 or more


F - (A + 2B + 3C)


E (A +B + C + D)


Once the figures in this table are entered, percentages (as displayed in Table 6) provide an easy way to see the proportions of offenses experienced by repeat victims and to determine the proportion of all victims who have been revictimized.

Cleaning data. Sorting and matching data reveals many errors. These may appear trivial (for example when road type is listed in one record as "avenue" and in another as "street") but such errors may reduce the number of matches and lead to underestimations of repeat victimization. The errors can typically be repaired by using another variable to sort and verify the correct version. For example, in Figure 5, 599 and 591 Telegraph Canyon are both listed as addresses. A check of the victim's name may show that the 599 address was a data entry error that should be corrected to 591.

Calculating time course. The amount of time between an initial offense and a subsequent offense (or between the second and third, or third and fourth) is called the time course. Most software can easily compute the time course in days by subtracting the date of the second offense from the date of the first offense. The procedure is only slightly more complicated when the data set exceeds 12 months, because the calculation of year must be converted so the software interprets Jan. 1, 2001, as 365 days later than Jan. 1, 2000. Descriptive statistics are typically used to report time course-the average number of days between repeat events, the range of least and most time between events, and percentiles such as the proportion of repeat events occurring within one week, 30 days, six months, or 12 months. The choice of the time period for reporting percentiles should be based upon natural or meaningful breaks in the temporal distribution of data.

Calculating rate. For many crime problems, the amount of victimization and repeat victimization will relate to exposure. For example, if there are 118 convenience stores in a city and 75 of these are Handy Andy stores, there are likely to be more robberies of Handy Andy than any other store. Exposure may also be increased by longer operating hours, more residents, more vehicular or foot traffic, and so on.

Planning Further Analysis

The analysis of repeat victimization, its concentration, and time course may clearly point to specific responses that will eliminate or reduce a problem; however, it is likely that further analysis will be necessary. Without further analysis, most police will attempt to conduct surveillance or undercover operations to apprehend one or more offenders at the location of repeat victimization. This response may be appropriate when repeat victimization relates to the repeat offending but such efforts may require extensive resources and will not necessarily change the general conditions of properties or behaviors of persons who face higher risk of victimization.

Collecting additional information. The initial analysis of repeat victimization may shed light on obvious vulnerabilities related to repeat victims. Such vulnerabilities may relate to characteristics for which data will need to be collected: store hours, age of victims (elderly or youth), environmental conditions, management practices, crime prevention devices used, victim behaviors, and so forth. All analysis should relate to factors that could be modified through some action on the part of the police or others. Patrol officers and investigators will often have insight into factors that contribute to high rates of repeat victimization.

Examining victim-suspect relationships. For some offenses, the relationship between victim and suspect will shed light on the nature of revictimization and on the nature of the police response. These relationships may explain many repeats for personal and property victimization of individuals.

Determining the role of boosts. Some offenses such as domestic violence will tend to involve the same offender over time but the role of repeat offenders may not be immediately obvious. For analysis of repeat victimization, efforts should be made to determine the contribution of repeat offenders. If a single prolific offender-an employee, family member, or someone else-underlies much revictimization, this information will guide efforts to determine the most effective response such as a panic or other temporary alarm, or increased short-term surveillance. Repeat victimization that continues after an offender is apprehended reflects flag rather than boost explanations.

Comparing victims and non-victims. Focusing on repeat victimization often highlights the differences between victims and non-victims, or between one-time victims and those who are repeatedly and even chronically victimized. Although convenience store robberies may be numerous, some stores are never robbed; crime in budget motels is not distributed across all such businesses, and factors such as management practices probably explain more variation than does location. Comparisons can reveal the average number of offenses for particular groups of businesses, locations, or victims.

Variables to be examined can be determined through discussions with investigators and patrol officers, as well as a reading of literature on the topic. For property offenses, variables may include method of entry, type of property stolen, security practices employed, proximity to crime generators such as schools or bars, and so forth. For personal victimizations, such as sexual assault or domestic violence, variables such as victim-suspect relationship, and drug or alcohol use may be critical. For commercial offenses, variables such as management practices, security features, demographic characteristics of customers, or type of merchandise may offer insight into distinctions between victims and non-victims.

For specific types of victims, such as schools, bars, budget motels, banks, and convenience stores, information can be collected through public records such as tax and business licenses.42

Establishing correlations between key variables or using cross-tabulations may provide important information about differences in victimization. Depending upon the nature of the problem, however, you may want to seek additional assistance in more complex statistical models. Providing this information may be helpful in implementing responses that require changes in victim behaviors or management practices.

Responding to Repeat Victimization

Since the risk of repeat victimization is highest in the short-term, responses with the greatest potential for being effective have the following characteristics:

Responses emphasize quick action-within 24 hours if possible-to prevent a subsequent offense.

Highest priority is accorded repeat victims with the most victimization, and these victims receive an increased level or amount of the response. This type of graded response deploys the easiest or least expensive measures to first-time victims and increases the intensity of the response if subsequent victimizations occur.

Responses to repeat victimization may be temporary since the increased risk of revictimization is most acute in the short-term.

Types of Responses to Repeat Victimization

There are three primary ways of responding to RV:

  • Protecting victims by blocking future opportunities against these specific persons or places
  • Shifting responsibility for repeat victimization
  • Increasing actual or perceived risks of apprehension for offenders, primarily repeat offenders

These types of responses may be combined, depending on the type of problem.

Protecting Victims

  1. Quickly blocking visual signs of victimization. Obvious signs of property damage should be removed immediately to block visual signs of vulnerability. Needy victims may need assistance in quickly securing properties.
  2. Improving physical security. While it is not practical to provide security surveys to all potential victims, properties that have been victimized once or more should be assessed for vulnerability, and protective actions should be taken rapidly.
  3. Target hardening. Improved security, such as hardware, can be installed; lighting can be improved; security lighting installed; visibility improved such as by unobstructing views of cash registers; and so forth.
  4. Rapidly blocking access to targets. Installing bandit or anti-robbery screens at victimized properties; placing targeted products behind counters, in locked cases, or out of reach; and moving or removing targets such as telephone booths, signs, vending machines, or vehicles.
  5. Removing or protecting targets. Some targets cannot be physically moved but victimization can be reduced by installing driveway barriers where vehicles cannot be garaged; and obstructing access to alleys, walls, signs, or culverts using fences, gates, vegetation, baffles, or sprinklers.
  6. Regulating or controlling access. Access to locations that are repeatedly victimized, such as parks, bathrooms, libraries, and schools can be controlled in different ways including fees, passes, identification cards, or parking permits.

Shifting Responsibility for Repeat Victimization

  1. Educating victims or eliminating excuses for risky behaviors. Victim behaviors, such as failing to secure property or walking alone, may contribute to victimization. Once victimized, victims can be educated about their risk of being victimized again. Services such as escorts for women walking alone on college campuses, access to shelters or protective custody for domestic violence victims, and crime prevention devices such as dead bolt locks can eliminate excuses for risky behavior.
  2. Changing management practices. Management practices, such as increasing the number of employees or adopting security features, may reduce repeat victimization. For example, access controls can be installed in parking lots, retail stores can require receipts for returned merchandise, apartment complexes can screen visitors, gasoline stations can adopt pre-pay policies, stores can limit and secure cash on hand, bars can monitor drinking, the number of customers can be limited, access to hot products can be controlled, or the number of employees can be increased during "hot times."

    Since changes in management practices may be costly and inconvenient, some businesses might prefer to put up with repeat victimization as a "cost" of doing business. In such cases, police should consider steps to encourage the adoption of preventive strategies. Education and informal requests may convince some property owners to adopt protective measures. Since predictable repeat victimization reduces the amount of police service available to unwilling victims, some repeat victims may be persuaded to adopt crime prevention strategies through the application of publicity, user fees, or even civil actions.

Increasing Risks to Offenders

  1. Temporarily increasing surveillance. For victimized locations and people, informal and formal surveillance can be increased temporarily through police patrols, security guards, and employees for a wide range of offenses from vandalism to burglary to domestic violence.

    Temporary surveillance can be increased through "cocoon watch," a type of Neighborhood Watch in which nearby residents are informed of an offense and asked to be particularly vigilant.

    Electronic surveillance, including CCTV and portable burglar alarms, can also be temporarily used in many settings.

    Domestic violence victims may be provided with panic alarms to quickly contact police about repeat offenses.

  2. Reducing rewards. To deter repeat offenders, the rewards associated with offending can be reduced by focusing on victimized locations, persons, or property.
    • Tracking devices, such as units temporarily placed in vehicles, can be used to detect offenders.
    • Marking or etching property with identification makes it difficult to sell property, and ink packs in cash packs limit use.
    • Cash control procedures in retail stores limit the amount to be stolen, and return policies can reduce shoplifting.
  3. Monitoring repeat victimization. It is important to assign clear responsibility for identifying and monitoring repeat victimization. The responsibility for monitoring repeat victimization may vary by the type of offense and the prevalence of revictimization. Call dispatchers, crime analysts, crime prevention personnel, specialists such as victims service personnel, responding officers, and investigators may all take a role in identifying and monitoring repeat victimization; while the assignment of responsibility may vary from one crime problem to another, the locus of responsibility should be clearly articulated.
  4. Cautionary Note

    While some effective responses to repeat victimizations may focus on increasing the risks to offenders, particularly repeat offenders, caution should be exercised in focusing on increasing apprehension of offenders. Efforts to apprehend unknown offenders are resource intensive and may not be successful, particularly for property offenses. In some situations, tactical or short-term police efforts such as baiting, stings, or surveillance to prevent revictimization of individual persons or places may result in the apprehension of an individual offender. While these offenders may be responsible for numerous offenses, police should consider whether the initial characteristics of the vulnerable victim or location are likely to remain unchanged and therefore attract other offenders.

    The most effective and efficient crime reduction strategies will likely consist of longer-term efforts to prevent revictimization by changing the characteristics of types of repeat victims. For example, adopting pre-pay policies at gas stations with repeated gasoline drive-offs will produce longer-term benefits than arresting a single offender or even several offenders.

    Measuring Your Effectiveness

    Measurement allows you to determine to what degree your efforts have succeeded and suggests how you might modify your responses if they are not producing the intended results. You should take measures of your problem before you implement responses to determine how serious the problem is, and after you implement them to determine whether they have been effective. When problems are geographic, measures should be taken for both a target group and the surrounding area to detect any spatial displacement and, if possible, a comparable area to provide a basis of comparison. In many cases, measures should be taken for the problem of interest and any problem to which offenses may be displaced, such as from residential burglaries to commercial burglaries. (For more detailed guidance on measuring effectiveness, see the companion guide to this series, Assessing Responses to Problems: An Introductory Guide for Police Problem-Solvers.)

    The following are potentially useful measures of the effectiveness of responses focused on repeat victimization:

  • reduction in the number or frequency of reported incidents,
  • reduction in the proportion of repeat offenses,
  • reduction in the number or frequency of calls for service,
  • reduction in repeat calls for service,
  • reduction in the proportion of repeat victims,
  • reduction in completed offenses,
  • reduction of value of property taken or harm associated with offenses, and
  • increase in arrests associated with offenses suffered by repeat victims.

Appendix A: Data Sources and Limitations

The type and quality of data used for estimating RV influences the amount of revictimization that can be detected. This, in turn, may mask key elements of revictimization and limit how we can determine the impact of reducing revictimization.

Police Data

Recorded crime data are most often used to detect repeat victimization.

Citizen-initiated calls for service (911 calls) are valuable for identifying repeat victimization, particularly by address. While call data have limited variables and are not subject to the same level of verification as incident reports, they can provide important insight into related problems, such as 911 hang-ups and domestic violence, or groups of problems, such as those occurring at schools or bars.

However, the actual amount of repeat victimization is often masked by common problems, including:

  • Underreporting. Much victimization is unreported but reporting varies from one offense to another. Virtually all bank robberies, vehicle crashes, and most vehicle thefts and commercial robberies are reported. On the other hand, most domestic violence, much theft, and much vandalism are unreported. Underreporting will limit the ability to detect repeat victimization.
  • Incomplete or inaccurate crime reports. Offense reports completed by responding officers are rarely used for analysis, and often contain incomplete or inaccurate information about offenses, including poor address or victim information, or missing information. Analysis of repeat victimization will likely provide an opportunity to involve line personnel in improving the quality of their initial reports; this may contribute to reductions in their workload and provide opportunities to participate in the most effective crime prevention strategies.
  • Time period covered by the data. A single year of offense data is customarily used for crime analysis but provides a weak basis for detecting repeat victimization. Undercounting occurs when offenses straddle calendar years, with an initial offense occurring late in one calendar year and a recurrence early in the next year. The problem is more common when the time course between offenses is longer. While this data may be all that is available to police, truncating data by calendar year substantially masks repeat offenses.
  • Data accuracy. Police data are fraught with errors that reduce the amount of repeat victimization that can be detected. Some errors reflect data entry problems or errors on reports such as misspelled victim or street names, or erroneous street numbers. Basic inconsistencies in data, such as the use of a street suffix or direction (such as north or south), may reduce data quality. Accuracy of data is also affected by victim reporting-victims may delay reporting, may fail to recall key elements such as all types of property taken, may fail to report suspect information, or may provide false information to police such as claiming that properties were secured.
  • Data precision. Even in offense reports, police data often fail to record all the elements of a unique address. Street number, apartment building or building name, floor, and apartment or suite number are often necessary for accurately detecting revictimization at addresses. For large properties, such as parks, parking lots, or sporting venues; or in locations without an address, such as pedestrian or bicycle paths, along creeks or in canyons, or within alleys; very precise address information will be unavailable. Some properties, such as schools or businesses, may have multiple addresses, including different streets. Police data may fail to distinguish between types of residential properties, such as apartments or single-family dwellings, or a type of commercial enterprise.
  • Imprecise victim names. Detecting revictimization of individuals involves matching recorded victim names. But victims may alter their name, move after being victimized, be victimized in different locations, and households may contain individuals with different names who report separate incidents. When a commercial location is victimized, recording victim names should be standardized to consistently distinguish a corporate name from a store name or acronym.
  • Hierarchical crime classification and recording inconsistencies. Hierarchical classification of offenses may mask revictimization for some offense types. For example, the occurrence of a burglary at the address where domestic violence has occurred may result in undercounting burglary or domestic violence revictimization. Some offenses are not consistently classified-an attempted break-in of a motor vehicle may be reported as vandalism.
  • Victim groups. "Virtual" repeat victims may comprise groups, for example, specific restaurant chains, a specific chain of convenience stores or gasoline stations, college students, or surface parking lots.
  • Boundary issues. Repeat victimization may occur across territorial boundaries, particularly for victim groups. For example, a specific chain of fast food restaurants may be robbed but offenses occur in different jurisdictions or police districts, and therefore are not easily detected. In areas where offender mobility is high, RV may be masked.

Victim Surveys

Victim surveys improve upon police data because the data address a common problem in police data-underreporting. Since victimization for any particular offense is usually quite low, random surveys are generally not cost effective. Police, however, can use two basic methods to survey for repeat victimization:

Victim follow-up surveys. Offense reports (or call histories) are used to construct a sampling frame; victims are then surveyed some time after their initial victimization to determine if they have been revictimized, and whether or not they called the police. The time period may be 30 days, or three, six, or 12 months-depending upon the time course for the offense type being examined.

Modified offense reports. A simple way for police to incorporate revictimization surveys into routine police work is to modify offense reports and/or the initial investigation of responding officers to include a few basic questions about victimization such as:

  • Have you previously been victimized?
  • Were you a victim of the same or a different type of offense?
  • Were you victimized at this address or another address?
  • How many times were you victimized?
  • When did the victimization occur?
  • Did you report the victimization to the police?

Questions about victimization experience should be used to identify high-risk victims and may also shed light on the development of the most effective responses. Questions for victims should relate to the specific problem being examined.

In some cases, police may want to carry out larger surveys. An entire population can be surveyed-such as all convenience store managers, budget motel managers, women on small college campuses, public school principals, or homebuilders. Some surveys may be observational, such as crime prevention surveys that assess environmental features such as lighting or building layouts.

In general, methodological issues must be considered in victim surveys:

  • Continuous data. Unlike police data, which are collected continuously over time, data obtained from victim surveys are usually discrete, and data collection occurs at a single point in time. Ideally, surveys of victims should follow victims over time to detect if and when further victimizations occur.
  • Attrition. Victim surveys will be subject to attrition as some victims move or refuse to be surveyed. Attrition may be more likely for individuals who have been revictimized, thus distorting the amount of revictimization detected. Conversely, some studies suggest that victims who are pleased with the police response to an offense are more likely to call the police again.
  • Accuracy. Victims may be unable to recall incidents accurately- particularly dates of incidents-or may be reluctant to discuss victimization, particularly incidents such as domestic violence or sexual assault. Surveys relating to such problems must be sensitive to the presence of an offender or others who may inhibit honesty. Victims may not consider some types of victimization crime or may not consider themselves victims.
  • For guidance on developing surveys, refer to A Police Guide for Surveying Citizens and their Environments, or Conducting Community Surveys. Both are listed in the recommended readings. There are also specialized survey instruments, such as those used for conducting follow-up surveys with women victimized by domestic violence. For example, contains links to validated instruments for such surveys that measure the amount of conflict that a victim has experienced. While many of these survey instruments are copyrighted and too in-depth for police use, they provide ideas about reliable questions can be incorporated into any followup survey.

Other Data Sources and Methods

While there are other existing sources of data about revictimization, data may need to be collected to document repeat victimization.

Important data may be collected through environmental observations. Properties that are vulnerable to graffiti such as vandal-prone walls in urban areas, pedestrian tunnels, or transportation corridors should be monitored to improve the amount and accuracy of information about offenses. Such observations may be daily or weekly, or reflect periods of vulnerability, such as following school holidays. (For more detailed guidance on environmental surveys, see A Police Guide to Surveying Citizens and Their Environment. This publication is listed in Recommended Readings at the end of this monograph.)

Interviews with apprehended or experienced offenders may improve understanding of repeat victimization. Although these interviews won't provide an empirical measure about the amount of repeat victimization, insights from offenders may provide understanding of target selection and an opportunity to prevent recurring victimization. (For more detailed guidance on collecting information from offenders, see the companion guide to this series, Using Offender Interviews to Inform Police Problem Solving.)

Other data sources that have been used to detect repeat victimization include medical records, such as hospital admissions or treatment in emergency rooms, admissions to domestic violence shelters, and inventory systems.

Appendix B: Improving Data Integrity

The extent and the importance of data limitations vary from one problem to another. Data source and quality are seldom an issue for some problems-data on bank robberies are highly accurate and reliable for detecting revictimization. Poor data mask revictimization in other problems, such as burglaries and domestic violence. While data quality can often be improved, a decision to undertake such effort should consider the following factors:

  • Extent of data limitations for problem being examined
  • Presumed value of improving data quality, including short-term and long-term benefits
  • Availability of alternate data or methods to improve data quality
  • Resources necessary to improve data quality

    Depending on the problem being examined, more reliable data may be necessary. For example, if data suggest that private property owners should adopt costly preventive measures, extremely reliable data may be necessary to educate, encourage, convince, or coerce them into doing so.

    An important reason to carefully document the extent of repeat victimization is to provide a foundation for a response-including getting buy-in from others who may help reduce victimization. Depending upon the type of problem being examined, the integrity of data can be easily improved:

  • Collecting additional data. As discussed in Appendix A, offense reports can easily be modified so that victims are asked a short series of questions about prior victimization.

    Offense reports can also be modified to record all victimizations, regardless of whether charges were filed, to identify repeat offenses that may be masked by hierarchy rules. This is consistent with NIBRS (National Incident-Based Reporting System) procedures and will provide more complete information about victimization.

  • Creating data layers. In most jurisdictions, common types of locations- bars and nightclubs, budget motels, schools, banks, movie theatres, convenience stores, gasoline stations-will routinely generate similar types of crimes. For example, assaults will occur at the bars, vandalism at schools, robberies at banks, beer runs at convenience stores and so on. Police should integrate location identifiers into their records management systems so offenses among these location groups can be routinely monitored.
  • Combining data sources. For some problems, victim interviews will provide better information about the impact of responses to problems such as domestic violence than police records. Crime reports may underestimate or overestimate the impact of responses-increased reporting does not demonstrate increased victimization and reduced reporting does not demonstrate reduced victimization. Conducting follow-up surveys with victims who previously reported an offense to the police provides more reliable measures of revictimization. Other data sources-such as calls for service, offense reports, arrests, property recovered, and warrants-can also be combined to create more comprehensive databases, for example, facilitating searches by both name, address and unique identifiers when available.
  • Increasing data precision. Incident reporting forms and police records can be revised to improve specific location information. Whenever possible, police should avoid recording offense locations as intersections or hundred-block addresses. Each unit of an address should be routinely recorded-including building name or number, floor, office suite, or apartment number. For properties that have multiple addresses (such as an apartment complex that has addresses on more than one street), records management systems should be modified to link addresses. An alternative is to use mapping to reveal near-repeat offenses that are recorded on different streets. For offenses that occur in large public or private spaces such as parks or parking lots, police can use global positioning system (GPS) equipment to record precise coordinates for offenses.
  • Improving data quality. Simple procedures, such as mapping offenses, can identify "match rates," or the proportion of offenses that can be linked to valid addresses. Consistent recording of offense types is very important. The distinctions between some offenses may seem trivial but have implications affecting revictimization. For example, if appliances are stolen from a house under construction, this might be classified as larceny or theft from a construction site, residential burglary, or commercial burglary. Offense classifications and recording practices should be monitored to ensure consistent classification and recording methods.
  • Data sharing. To detect revictimization across boundaries and jurisdictions, police agencies should routinely share data about victims. This is practical for counties or neighboring jurisdictions that participate in regional information networks. Individual victims may (unsuccessfully) relocate to avoid revictimization and revictimization of commercial properties. Virtual victims may be especially common across boundaries.


1 Farrell and Bouloukos (2001). [Abstract Only]

2. Kingma (1999).

3. Calculated from data in Sorenson et al. (1991).

4. Simmons and Dodd (2003). [Full Text]

5. Calculated based on one wave of NYS data presented in Lauritsen and Quinet (1995), Table I.

6. Budd (1999). [Full Text]

7. Kinsholt (2001). [Briefing Note]

8. Simmons and Dodd (2003).

9. Calculated from Lloyd, Farrell, and Pease (1994) [Full Text]. The data are referred to as domestic violence calls but it is presumed that these calls are synonymous with domestic violence crime reports.

10. Hanmer, Griffiths, and Jerwood (1999). [Full Text] [Briefing Note]

11. Weisel (2001).

12. Taylor (2004).

13. Matthews, Pease, and Pease (2001). [Full Text]

14. Ratcliffe and McCullagh (1998). [Abstract Only]

15. Calculated based on data from Johnson, Bowers, and Hirschfield (1997). [Abstract Only]

16. Townsley, Homel, and Chaseling (2000). [Full Text]

17. Kleemans (2001). [Abstract Only]

18. Weisel (2001).

19. Calculated based on data in Bowers, Hirschfield, and Johnson (1998). [Abstract Only]

20. Calculated based on data in LeBeau and Vincent (1998). [Abstract Only]

21. Simmons and Dodd (2003).

22. Budd (1999). [Full Text]

23. Mukherjee and Carcach (1998) [Full Text]; Van Dijk (2001) [Abstract Only]; Felson, Messner, and Hoskin (1999); Hotaling and Buzawa (2003) [Full Text].

24. Polvi et al. (1991).

25. Lloyd, Farrell, and Pease (1994). [Full Text]

26. Mathews, Pease, and Pease (2001). [Full Text]

27. Robinson (1998). [Abstract Only]

28. Johnson, Bowers, and Hirschfield (1997). [Abstract Only]

29. Bowers, Hirschfield, and Johnson (1998). [Abstract Only]

30. Burquest, Farrell, and Pease (1992).

31. Clarke, Perkins, and Smith (2001). [Abstract Only]

32. Ashton et al. (1998).

33. Hanmer, Griffiths, and Jerwood (1999). [Full Text] [Briefing Note]

34. Matthews, Pease, and Pease (2001). [Full Text]

35. Kleemans (2001) [Abstract Only]; Trickett et al. (1992); Townsley, Homel, and Chaseling (2000) [Full Text]; Johnson, Bowers, and Hirschfield (1997) [Abstract Only]; Bennett and Durie (1999) [Full Text]; Bennett (1995).

36. Pease and Laycock (1996). [Full Text]

37. Weisel (2001).

38. Trickett et al. (1992); Townsley, Homel, and Chaseling (2000) [Full Text].

39. Calculated from LeBeau and Vincent (1998). [Abstract Only]

40. Boloukos and Farrell (1997).

41. Davis and Maxwell (2003). [Full Text]

42. See Schmerler, Wartell and Weisel (2004) for more guidance on this.


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Van Dijk, J. (2001). "Attitudes of Victims and Repeat Victims Toward the Police: Results of the International Crime Victims Survey." In G. Farrell and K. Pease (eds.), Repeat Victimization. Crime Prevention Studies, Vol. 12. Monsey, New York: Criminal Justice Press. [Abstract Only]

Weisel, D.L. (2001). "Repeat Victimization for Commercial Burglary and Robbery: How Much and Where?" Presentation at U.S. Department of Justice, National Institute of Justice, Crime Mapping Research Conference, Dallas, Texas (December).

Related POP Projects


The quality and focus of these submissions vary considerably. With the exception of those submissions selected as winners or finalists, these documents are unedited and are reproduced in the condition in which they were submitted. They may nevertheless contain useful information or may report innovative projects.

Baker One Domestic Violence Intervention Project [Goldstein Award Finalist], Charlotte-Mecklenburg Police Department, 2002

Bolton BeSafe Reducing Criminal Damage [Tilley Award Winner], Greater Manchester Police (Manchester, UK), 2008

Bulldozing - Construction Site Burglary [Goldstein Award Finalist], Port St. Lucie Police Department (Port St. Lucie, FL, US), 2006

Burglary Reduction Initiative, Lancashire Constabulary, 2003

Choices on Madison Street, Phoenix Police Department, 2001

Diesel Theft at Limerick Road Dormanstown, Cleveland Police Department (Middlesbrough, UK), 2007

Domestic Violence Re-victimization Prevention [Goldstein Award Finalist], Fremont Police Department, 1997

Family Violence Followup Teams [Goldstein Award Finalist], Edmonton Police Service, 1994

Foyle Domestic Violence Protocol, Foyle District Command Police (Londonderry, Northern Ireland, UK)

Jenny's Story: Internet Child Abuse Prevention, Lancashire Constabulary, 2005

Manor Cross Project, South Yorkshire Police, 2001

Molehills from the Mountains [Goldstein Award Finalist], Lancashire Constabulary, 2004

Operation Cobra: Tackling Vehicle Crime in the City of Portsmouth [Goldstein Award Finalist], Hampshire Constabulary, 2004

Operation Focus, South Yorkshire Police, 2006

Operation Larches Storm, Lancashire Constabulary, 2007

Operation Strongbow, Cleveland Police Department (Middlesbrough, UK), 2004

Operation Student Survey, Merseyside Police (Merseyside, UK), 2009

Repeat Victimisation, Domestic Burglary Project, Cleveland Police Department (Middlesbrough, UK), 2003

Return of the Happy Shopper [Tilley Award Finalist], Lancashire Constabulary, 2005

Safer Homes Scheme, Cumbria Constabulary, 2006

Stop Break, Queensland Police (QN, AU), 1999

Wrexham Industrial State, Lancashire Constabulary, 2004