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Abstract

The National Crime Victimization Survey (NCVS) has furnished U.S. crime statistics and high quality data for theory development and policy insights for over 50 years. This article highlights few of these achievements drawing on the development of criminological thinking with regard to measurement, and explanation of victimization and its concentration on a small minority of the population. It overviews lifestyle and the more encompassing environmental criminology theory for victimization, and explanations of multiple victimization, and the international crime drop that relied on NCVS data. Selected crime prevention insight and few recommendations for future research conclude the article.

Keywords

crime prevention multiple victimization explanations and statistical models National Crime Victimization Survey victimization measurements victimization theories

Background

Historically, criminology was developed to study offending: Why and arguably under which conditions few people commit crimes—actions, which perceived to threaten the smooth functioning of organized societies, are prohibited and punishable by established laws (Sidebottom & Wortley, 2016, p. 156). This is inherently divisive and offers a partial picture. It juxtaposes law-abiding victims of crime against offenders, who, however, are often victims (Jennings et al., 2010), and assumes that criminal law is generated via democratic processes, which equally represent and serve all society members and communities, although criminal justice may be biased against marginalized communities (Duxbury, 2021a, 2021b).

The cornerstone for crime prevention is measurement—identifying its extent, temporal and spatial variations, and other patterns—via systematically collected data. Such data (un)availability necessitated to some extent the field’s historic focus on offending. Records collected for supporting police departments’ and other criminal justice system (CJS) agencies’ operations constituted the sole source for measuring the extent, and patterns of crime at national level until the 1970s (Addington & Rennison, 2014).

Police-recorded crime statistics have been criticized due to underreporting (by marginalized, lacking trust to, and/or fearful of the police communities) and substantial variations in recording procedures across police departments (Biderman & Lynch, 1991; Kershaw, 2008; Warner, 1931; Xie et al., 2024). In response to these well-documented and widely acknowledged weaknesses in recorded crime data, crime victimization surveys were developed (Aebi & Linde, 2014), to generate robust estimates of crime rates and trends, especially for high volume crimes, and rich supplementary information about: victims’ crime experiences, police reporting, and satisfaction; the general population’s perceptions and attitudes about crime, disorder, the police, and the CJS; and detailed demographic, socioeconomic, and other factual information about survey respondents, their household, and neighborhood. This information has provided the foundations for theory development, especially on victimization; fear of crime; and legitimacy of and trust in the police and the CJS. However, the focus on offending permeated into the first attempts to study victimization, most notably the lifestyle and victim proneness theories.

The National Crime Survey (NCS) is the oldest NCS in the world since 1972. Originally conceived as a means to acquire national level, and most importantly independent of police records, estimates of crime rates in the United States, the NCS and the redesigned National Crime Victimization Survey (NCVS)¹ has furnished criminological, and, in particular, victimization theory development from the outset.

The present article overviews selected NCVS—based on efforts in developing new and/or informing already existing theories, touching upon the following: lifestyle victimization theory which merged with social disorganization theory informs environmental criminology; victim proneness; repeat victimization; and crime drop explanations. These NCVS-informed theoretical insights are presented within victimization measurement that researchers sought to explain, and within this by applied methodology. Organizing the article’s structure around measurement and methodology arguably highlights how criminological thinking to explain victimization evolved, and may support (a) NCVS questionnaire design analysts and (b) researchers in developing additional tools and new theoretical and crime prevention insights in future.² After having overviewed patterns of criminal victimization, the article ends with a discussion of some already suggested crime prevention insights from NCVS studies, and tentative recommendations for future NCVS-based research.

Crime Victimization: Measurements, Methods, and Theoretical Insights

Introduction

The NCVS since the 1970s in the United States (Hindelang et al., 1978) and the other crime victimization surveys of national coverage that followed elsewhere (1981 in the United Kingdom), deviating from police crime reports, have offered an entire new set of crime statistics, and crime rates that measure the extent and patterns of the majority of victim-based crime types, and the impact of crime on victims, their families or households, and communities. Some of these have gradually found their way into national and/or official crime statistics and, of relevance here, have become the basis for developing new theories, as well as testing, and improving existing ones.

Incidence Rates and the Crime Drop

NCVS-based crime incidence rates (number of crimes per 1,000 members of the exposed population)³ provide an alternative and, arguably, more complete picture of national crime levels than police-recorded crime rates for comparable offenses (Biderman & Lynch, 1991). They are produced annually and included in published official crime statistics, sometimes alongside police-recorded crime rates. Furthermore, NCVS-based incidence rates are the most reliable source for investigating crime trends, especially for volume crimes, owing to consistent data collection methodology over time and, when occurring, well-documented and rigorously tested improvements (Lynch & Addington, 2007).

Analyses of the NCVS crime incidence rate trends have illuminated the crime drop of the last 40 years in the United States (Rosenfeld, 2007) and, via cross-national comparisons with data from consistently continual crime surveys elsewhere, what may explain it (van Dijk & Tseloni, 2012; Farrell et al., 2014). Sustained sharp falls of NCVS incidence rates⁴—first, in property crimes, notably burglary and theft since the 1980s, followed by motor-vehicle theft and violence in the early 1990s (Catalano, 2006; Rand et al., 1997)—furnished numerous studies on explaining this novel at the time phenomenon. Early falling crime trends’ analyses focused exclusively on U.S. demographic changes and domestic policies (Barker, 2010). However, the crime drop, after being first manifested in U.S. NCVS rates, occurred in many other industrialized countries about a decade later, since the 1990s (Tseloni, Mailley, et al., 2010). Thus the phenomenon has justifiably been recognized as the international crime drop (van Dijk et al., 2012).

International phenomena call for explanations that spread across individual countries’ jurisdictions, demography, policing, legal systems, and other candidate causes constrained within national borders. A new set of studies looking beyond, while incorporating U.S. data, sought to explain the crime fall and how to sustain it. The international crime drop is arguably the result of widespread societal changes in crime opportunities and exposure (Farrell, 2013): the key assumption of the security hypothesis for the crime drop (Farrell et al., 2011). The security hypothesis has to date been confirmed for vehicle crime falls in studies from a number of different countries, including the United States (Farrell et al., 2014; Fujita & Maxfield, 2012). Relying on NCVS data on recovery rates for stolen cars to distinguish between temporary and permanent thefts, Fujita and Maxfield (2012) concluded the following: Vehicle crime falls were the result of considerable declines in temporary thefts, which are driven by opportunity. In addition, increases in cars with in-built security followed temporary car theft falls over time. The security hypothesis for burglary has been confirmed in several countries and recently been tested in the United States (Farrell, 2022; Farrell et al., 2014).

The timing of these falls across crime types, starting with property crime declines and followed by violence falls, which was observed in NCVS and cross-national data (van Dijk & Tseloni, 2012; Tseloni et al., 2012; Tseloni, Mailley, et al., 2010), led to the debut crime hypothesis which complements the security hypothesis for the crime drop (Farrell et al., 2014, 2015).

Changes in the levels of much-occurring types of property crimes such as car-related thefts and burglaries have had domino effects on other types of crime, for example by . . . not offering learning experiences to new cohorts of habitual offenders or reducing criminogenic opportunities associated with stolen goods markets and transactions. (van Dijk & Tseloni, 2012, p. 33)

Indeed, NCVS car theft and 2-year lagged violence rates over the period 1976 to 2005 portray an incredibly strong correlation (Farrell et al., 2014).⁵

Prevalence: Victims and Non-victims

NCVS (and generally crime victimization surveys) records about whether respondents were victims or non-victims during the reference period have furnished the oldest victimization measurement, which still is extensively analyzed, for informing theory: national crime prevalence rates (number of victims per 1,000 members of the exposed population) and individuals’ victimization risk (averaging likelihood of victimization per person or household). This dichotomy has been the focus of studies that founded the lifestyle victimization theory, and informed social disorganization theory and environmental criminology.

NCVS data provide valuable information about: the circumstances, if known by victims, surrounding distinct or series victimization events; characteristics, routine activities, crime experiences, and perceptions of respondents—who comprise all household members in survey-selected housing units, a unique feature of NCVS—and their households; their homes’ features; and to a small degree their area of residence, notably population density and administrative classification. Furthermore, the last allows linking of NCVS data across administrative data on a wide range of area, sociodemographic, criminal justice, and spatial characteristics to explore individual and contextual risk and protective factors of criminal victimization (Tseloni et al., 2018). The sum of this information speaks to opportunity theory (Felson & Clarke, 1998; Mayhew et al., 1976) and environmental criminology (Andresen, 2010; Wortley & Mazzerole, 2008) originally applied to spatio-temporal and modus operandi crime analyses.

Broadly speaking, the exposure model (the victimization flipside of opportunity regarding offending decision-making) suggests that the probability of victimization is a function of the amount and type of interaction of individuals (or objects) with high risk coordinates. Its origins lie in the two well-rehearsed and compatible assertions: lifestyle theory (Hindelang et al., 1978) and routine activities theory⁶ (Cohen & Felson, 1979). Commonalities between lifestyle and routine activities led from early on to their merging into one (Garofalo, 1987; Gottfredson, 1981). Over the years, routine activities theory has evolved (Felson, 2002, 2006), become the dominant individual-level victimization theory, and tested in numerous studies on various crime types based on, among others, NCVS data.

Lifestyle Theory

The first major theory that originated from NCVS data analysis, which in its guise and consolidation with routine activities theory is still relevant today, is the lifestyle theory. This theory is based on cross-tabulations of respondents’ personal characteristics (gender, age, race, income, marital status, education, and major activity, in other words occupation) with three outcome binary (0/1) variables: experience of personal victimizations; whether they sustained injury; and whether they lost property. Hindelang et al. (1978) proposed that personal characteristics indicate individuals’ lifestyles: the way in which people allocate their time to vocational and leisure activities. These influence their exposure to situations entailing high victimization risk. Exposure is influenced by the amount of time people spend in public places at night or early morning hours, because according to lifestyle theory potential offenders also tend to favor these spatio-temporal coordinates. Personal characteristics influence victimization risk via two avenues: (a) lifestyles, which are the result of attitudes, and adaptations to structural constraints and role expectations⁷ and (b) associations, that is social contacts, which occur among people with similar characteristics and interests.

Sociodemographic characteristics can predict role expectations and behavioral constraints (economic, familial, educational, and legal) imposed by social structure. These factors are interrelated, leading through individual and subcultural adaptations to particular lifestyles. Lifestyle variations influence the probability of being on specific places at specific times and coming into contact with potential offenders. Thus, these variations determine both directly and via associations, exposure to situations with high victimization risk under certain conditions, including offenders’ perceived convenience, target desirability, and vincibility. Therefore, lifestyle theory was modified to incorporate routine activities and reactions to crime (Garofalo, 1987; Gottfredson, 1981).

Despite its limitations,⁸ lifestyle theory recognized and documented communication of victimization risk across household members and multiple victimization—both key themes for theory development and crime prevention. Hindelang et al. (1978) defined multiple victimization as the probability of being a victim of both personal and household crime (see the penultimate section). Individual and contextual predictors of multiple victimization (the above defined joint risks by Hope et al. [2001] and across different periods by Ellingworth et al. [1997]) were investigated some 20 years later with British Crime Survey (BCS) data. Another lasting contribution to wider criminological thinking was the introduction of the role of offenders’ target perceptions. Opportunity theory and situational crime prevention scholars expanded this, and developed a typology of perceived target attractiveness and risk (Cornish & Clarke, 1986).

Lifestyle/Routine Activities and Social Disorganization

The perspective of individuals’ exposure to crime expanded via statistical modeling including multiple predictors that arguably allowed separating causes and mediating factors, and from incorporating area characteristics from other data sources, notably the Census (Osborn et al., 1992). Marrying individuals’ (micro-level) lifestyle/routine activity-related crime exposure and neighborhood (meso-) level social disorganization measurements of local crime rates contributed to further our understanding of victimization from an environmental criminology perspective (Felson, 2006; Miethe & Meier, 1994). Methodologically, this development was facilitated from linking NCVS (notably, Lauritsen [2001] and her ensuing collaborative work) and other national victimization survey data to respective countries’ contextual administrative and Census data (Ellingworth et al., 1997; Kennedy & Forde, 1990). So created multilevel⁹ data and victimization predictors open up possibilities for investigating varying exposure (due to the same activity or characteristic) across contexts via interactions between individual and area effects.

Contextualized and/or studied alongside income individuals’ ethnicity effects on victimization contradict the original lifestyle theory proposition of increased risk (which had been established without considering household and/or community affluence). NCVS statistical modeling controlling for individual characteristics, routine activities, and neighborhood socioeconomic profile evidenced that: Blacks face higher violence victimization risk because they live in disadvantaged central-city neighborhoods, which when controlled for in fact Blacks face lower risk than others (Lauritsen, 2001, p. 14, 19); non-stranger violence risk among ethnic minority males and females is actually lower than that for White and Black females who have similar risk; and stranger violence risk is highest for White males, reversing overall risk estimates, followed by Black males, while all other races by gender groups have (statistically) similar risks (Lauritsen & White, 2001, p. 48).

Jumping ahead to multiple victimization insights (next section), Asian/Pacific Islanders experience significantly fewer personal crimes than Black, Native, and White Americans who effectively face similar numbers (Tseloni, 2000, p. 427; Tseloni & Pease, 2004, p. 939). Lack of increased and even reduced ethnic minority victimization by property and personal crimes has been evidenced outside the United States (Tseloni et al., 2002, 2004; Pease & Tseloni, 2014).

Multiple Victimization: Measurements and Impact

NCVS- and in general crime survey-based crime incidence rates’ exceedance of prevalence rates has exposed the phenomenon of multiple victimization (two or more crimes per victim). The original terminology of “multiple victimization” encompassed the following four distinct ways of crime recurrence against the same victims (Sparks, 1981; Tseloni, 2014). The currently accepted definition of multiple victimization infers that the same victim experienced two or more different crime types on different occasions—for example, a bag-snatching and a burglary. Two or more crimes of the same offense type (e.g., three burglaries) at separate instances are commonly termed repeat victimization. Series, also known as high volume repeat, victimization is when repeat crime incidents are too similar, too many to distinctly remember each one in detail, and/or have a degree of periodicity—for example, vandalism outside residences in a neighborhood near a football stadium after each match (Pease, 1998; Tseloni, 2014). Finally, composite crimes (for need of a better term) are when more than one offenses occur during a single incident (Sparks, 1981; Tseloni, Ntzoufras, et al., 2010).

The phenomenon of multiple victimization was observed and documented by early NCVS studies, but not systematically measured or statistically modeled over potential explanatory factors (until about two decades later). Indeed, Reiss’ (1980) and Sparks’ (1981) early NCVS studies were concerned with a small proportion of victims, and an “insignificant” minority of respondents, who reported two or more crimes of the same (i.e., repeat victimization) or different (currently accepted definition of multiple victimization) offense type at separate instances in the reference period. In a seminal study, Reiss (1980) produced the NCVS matrix of multiple victimization, which portrayed repeat victimization in the diagonal and multiple victimization in the off-diagonal cells. The matrix of multiple victimization demonstrates the connections between crime types and their temporal sequence, albeit only between first and second victimization event within the reference period.

Repeat (which in the case of aggregate crime types may well be multiple when considering crime type specificity) victimization is reflected in the higher crime incidence rates compared to prevalence rates. Repeat crimes contribute disproportionally to overall national or local crime rates: high crime areas have fewer victims, and each victim is more often victimized than chance would predict (Trickett et al., 1992).¹⁰ The extent of crime inequality over the general population and victims (Tseloni & Pease, 2005), and the incredible value of tackling repeat victimization for crime prevention (Farrell & Pease, 1993), was first emphasized during a highly successful burglary prevention initiative in the United Kingdom (the Kirkholt project, Forrester et al., 1990). Farrell and colleagues relying on BCS,¹¹ NCVS, and other crime survey data showed that repeat victimization is a cross-national problem, and not limited to Northern English public housing (council estates; Farrel et al., 2001, 2005; Farrell & Bouloukos, 2000).

Knowing how crime is distributed over the exposed population is immensely important from the point of view of crime inequality, and distributive justice, and offers a more holistic picture of specific causes and impact of crime to inform crime prevention (Hunter et al., 2021; Pease, 1998). This necessitates unpacking the restricting view of multiple and repeat victims as those who experience two or more crimes and examine the entire distribution of crime counts, that is, 0, 1, 2, . . ., up to the maximum number of crimes per member of the exposed population.

Although repeat victimization is entailed within incidence rates, due to its importance for crime prevention it is measured via a new set of crime statistics in the last 40 years: repeat crime rates (number of crimes that were repeats per 1,000 exposed population), repeat victim rates (number of repeat victims per 1,000 exposed population), and crime concentration (mean number of crimes per victim; respective calculation methodology provided by Farrell and Pease [1993] and Tseloni and Pease [2005]). The first two are routinely published crime statistics (additionally, to prevalence and incidence rates) while victimization counts are truncated at the 98th percentile per crime type in the United Kingdom (Office for National Statistics [ONS], 2019).

In the U.S. NCVS-based repeat victimization, measurements have informed justice statistics on an ad hoc basis and usually in relation to series violent victimization. Taking a fresh look, Planty and Strom (2007) revisited NCVS counts of series violent victimization, concluding that their inclusion creates extreme variability on annual crime estimates but their current truncation distorts the patterns, and subsequently potential prevention efforts, of violent victimization. A special Bureau of Justice Statistics (BJS) report on repeat violent victimization trends highlighted their contribution to overall crime rates—repeat violence victimization fell by 62% between 1993 and 2014—and the swift recurrence, within 6 months, after an initial incident (Oudekerk & Truman, 2017). Publication of U.S. official statistics on prevalence and incidence rates in tandem arguably balances between high randomness of annual crime rates incorporating series counts and attention to violence victims (Planty & Strom, 2007), especially when adjusting the BJS counting rule of truncating the number of crimes per series at 10 (rather than 1) incidents per 6-month reference period (Lauritsen et al., 2012).

Explanations of Multiple Victimization

Victim Proneness

Extending Reiss’ work, Sparks (1981) coined the term victim proneness to explain multiple victimization. To this end, he looked at potential characteristics distinguishing respondents who reported 0, single, multiple, or recurrent crime victimization events, and proposed a theoretical model of victim precipitation or vulnerability (Sparks, 1981, 1982). Individuals have certain degrees of proneness at particular times due to their social, psychological, economic, cultural, and spatiotemporal characteristics. Variations in victimization rates may be explained by the victim’s behavior, attributes, or social position via the following mechanisms: precipitation; facilitation; (ecological, status, and/or role) vulnerability; opportunity (exposure); attractiveness; and impunity.

Sparks’ victim proneness approach attributes a sequence of various degrees of culpability to victims (Gottfredson, 1981), which combined with estimation methodological challenges (Nelson, 1980; Sparks, 1982; Ybarra & Lohr, 2002), arguably, interrupted NCVS—based on the study of multiple victimization until the turn of this century. Despite its clear message of victim blame and transplanting theoretical thinking about offending into victimization, Sparks’ (1981, 1982) work highlighted the phenomenon of multiple victimization, recognized that differences in victimization rates (rather than risks) are not random, and that individuals may face victimization spells. All three elements form part of current accepted victimization thinking.

The Epidemiology of (Repeat) Victimization

A body of research, which originated with BCS data in the late 1990s, shifted the focus away from victimization risk to incidence (mean number of crimes per respondent) to investigate risk and protective factors of both single and repeat victimization rather than the victim/nonvictim dichotomy (Osborn & Tseloni, 1998; Tseloni, 1995—the latter with multilevel predictors from BCS and U.K. Census data). Repeat victims have the same only more risk and less protective factors than single victims (Osborn et al., 1996; Sparks, 1982) and, therefore, examining the entire distribution of crime counts per respondent provides a more encompassing picture, and may direct crime prevention resources where most needed (Tseloni et al., 2002).

Repeat victimization may be due to: population heterogeneity, whereby individuals have constant but unequal expected victimization counts; state dependence (or event dependence in U.K. English), whereby individuals have the same initial risks but these change after each subsequent victimization event; and/or spells, namely, periods during which unusually large number of crimes occur (Tseloni, 1995).¹² In this light, victimization risk statistical analyses inform population heterogeneity (see sections on prevalence and victim proneness) but cannot speak to state dependence. Summing up the repeat victimization explanations, the number of crimes experienced (0, 1, 2, 3 . . . up to a maximum) can be predicted from: individual and contextual risk and protective factors, and their interactions, which speak to the theoretical explanation of population heterogeneity; the outcome of each preceding victimization incident, which reflects state dependence; and, via random effects, the conditioning of risk and protective factors by context (Tseloni, 2006) and by victimization history (Tseloni & Pease, 2004).

Modeling of NCVS data toward the turn of the century built upon Nelson’s (1980) work and expanded the original BCS-based statistical modeling of crime counts for informing population heterogeneity and, therefore, environmental criminology (Tseloni, 1999). The NCVS longitudinal component of following respondents for 3.5 years with 6 monthly interviews enabled unique insights about the relative importance of heterogeneity and state dependence explanations of repeat victimization (Lynch et al., 2002; Tseloni & Pease, 2003). Furthermore, NCVS sampling of all household members 12 years old or older¹³ facilitated hierarchical negative binomial modeling of victimization incidence for estimating communication of victimizations between household members (Tseloni, 2000) and conditioning¹⁴ of risk and protective factors across households and repeat victimization state (Tseloni & Pease, 2004).

The key points from this work are the following. Both population heterogeneity and state dependence are important predictors of crime repetition with arguably some household characteristics exerting a stronger effect than previous victimization (Lynch et al., 2002; Tseloni & Pease, 2003). Indeed, the effect of preceding victimization to the next one is moderated from individuals’ characteristics and routine activities, especially during shorter periods when these are likely to be stable (Tseloni & Pease, 2003, p. 207). Therefore, population heterogeneity estimates overlooking state dependence are valid reflections of risk and protective factors for guiding prevention. Furthermore, the models confirmed offender interviews’ results that “[state] dependence is contingent on what is elusive about the individual,” and therefore, it “is triggered by non-obvious characteristics of victims” that become known after an offending attempt (Tseloni & Pease, 2003, p. 209, 210, respectively). State dependence effects in the short term (6–12 months) are accentuated for individuals who already face elevated number of victimizations from routine activities and differ across areas with different population density and urbanization (Tseloni & Pease, 2004).

Discussion, Crime Prevention Insights, and Suggestions

Owing to consistent, transparent, and rigorously tested methodology, NCVS has supplied crime official statistics, which offer a clearer picture of crime problems in the United States than police records. The NCVS entered victims of crime, their profiling, experiences, perceptions, and attitudes in relation to crime and the CJS in this picture. It is the first crime victimization survey of national coverage in the world—other countries followed in establishing and fashioning such surveys after the NCVS, albeit with varying periodicity and methodological consistency (Aebi & Linde, 2014).

The previous sections of this study presented select examples of NCVS contribution to the development of criminological thinking. The unexpected long-term and sustained international crime drop started in the United States, and was first recorded in NCVS rates. Multiple victimization was originally documented in NCVS data. Lifestyle victimization theory (now considered part of the wider environmental criminology perspective) and victim proneness, both attempting to explain victims’ crime experiences, were founded on NCVS data. Furthermore, NCVS linked with Census data contributed to informing environmental perspectives of victimization, and thus “correcting” original lifestyle assertions, especially in relation to race and ethnicity (which were arguably skewed by offending crime perspectives, themselves based on CJS data). The NCVS longitudinal element and inclusion of all household members of selected housing units contributed to informing: communication of victimization rates between household members; the interplay between influence of preceding on subsequent victimization, on one hand, and individual and contextual risk and protective factors, on the other; and conditioning of routine activity effects across different contexts.

What are the most important lessons from this narrow (considering NCVS theory development uses not overviewed here) body of work? The NCVS longitudinal survey design and wealth of fixed and time-varying respondents’ and their households’ characteristics, routine activities, and areas of residence are ideal for studying victimization pathways and their conditioning by contextual factors. Potential insights on “when, for whom, in what context, and with respect to which crime type” may inform cost-effective prevention with lasting benefits. Snippets of the above all-encompassing wish-for evidence have been provided by existing NCVS research overviewed here.

Crime prevention initiatives can turn lifestyle and victim proneness theories’ victim blaming on its head to create the means for preventing initial and subsequent victimizations. For example, guided by victim proneness classifications establish initiatives for awareness campaigns via vulnerable populations’ self-organized support groups, reorganize police units with representation of vulnerable populations, and/or expand public health approaches of criminal victimization. For example, overt or invisible disability is associated with elevated victimization rate (Harrell, 2017) and is the top risk factor in statistical models of crime counts (Hunter et al., 2021) allowing prevention via health services and professionals. Consistently evidenced individual and household victimization risk factors are readily available in national and/or local authority official statistics, and, therefore, can be used for prioritizing crime prevention (Hunter et al., 2021; Tseloni & Pease, 2003).

Victimization risk awareness campaigns should be gender, age, and context specific but there is no evidence that they should target particular ethnicity members. Furthermore, crime prevention initiatives, including victims’ and survivors’ services, should focus on areas of high socioeconomic deprivation and developed in partnership with local residents’ organizations and other voluntary sector agencies (Lauritsen & White, 2001). Since offenders also disproportionally live in high poverty areas, such initiatives may succeed to reduce offending without framing and/or retribution (Jackson et al., 2022).

Repeat victimization measurements should inform the U.S. picture of crime alongside prevalence and incidence rates in regular BJS crime statistics publications. The overall crime-reducing power of investing resources in protecting known victims and their households from subsequent victimizations (and doing so within a short time window) should be included in crime prevention agencies’ philosophy and practitioners’ training and routine operations. Such an example is the (former) Arizona State University (ASU) Center for Problem-Oriented Policing (POP)’s tool on repeat victimization, which consolidated knowledge from across the world, including the NCVS (Weisel, 2005). Police response prioritizing, first, victims and their households immediately after they experienced crime, and, second, those with similar characteristics and routine activities living near them (Lynch et al., 1998; Tseloni & Pease, 2003).

A relatively newly suggested use of NCVS and other reputable crime surveys is estimating crime harm based on victims’ own report of psychological, financial, physical health, and other damage they and members of their family, households, and communities incurred as result of victimization (Ignatans & Pease, 2016; Planty & Langton, 2025). Victim-centered crime harm based on psychological, physical, and financial crime consequences would be an additional avenue to prioritize crime response and prevention and arguably further inform sentencing guidelines and present crime harm indices (Klaus & Kalish, 1984; ONS, 2021; Sherman et al., 2016).

Finally, making use of NCVS longevity to investigate wide societal changes during the crime drop—such as whether routine activity changes may be responsible for reductions in violence and repeat violent victimization, whether homes’ security improvements or uptake brought about domestic burglary falls, and/or computer enabled crime and cybersecurity trends—would complement existing research on national trends. Most important, it will expand knowledge on the international crime drop, which started in the United States a decade before elsewhere, and provide valuable insights to further develop environmental crime prevention.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biography

Andromachi Tseloni is Professor of Quantitative Criminology at Nottingham Trent University, and Co-Editor of Criminology. Her research revolves around five broad themes: risk and protective factors of criminal victimization across population groups and communities, explanations and distributive justice of the crime drop, crime perceptions, social capital, and cross-national comparisons. Since 2020 she is Academic Lead of the ADR UK (Administrative Data Research UK) Ministry of Justice Data First project supporting (ethical, independent, and transparent) multifaceted evidence development for improving justice outcomes.

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Theory Development and Crime Prevention Insights From the NCVS

Abstract

Keywords

Background

Crime Victimization: Measurements, Methods, and Theoretical Insights

Introduction

Incidence Rates and the Crime Drop

Prevalence: Victims and Non-victims

Lifestyle Theory

Lifestyle/Routine Activities and Social Disorganization

Multiple Victimization: Measurements and Impact

Explanations of Multiple Victimization

Victim Proneness

The Epidemiology of (Repeat) Victimization

Discussion, Crime Prevention Insights, and Suggestions

Footnotes

Declaration of Conflicting Interests

Funding

Notes

Author Biography

References