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Violence and Victims, Volume 27, Number 5, 2012

Risk of Violent Crime Victimization During Major Daily Activities

Andrew M. Lemieux, PhD Netherlands Institute for the Study of Crime and Law Enforcement (NSCR)

Marcus Felson, PhD Texas State University

Exposure to risk of violent crime is best understood after considering where people are, what they do, and for how long they do it. This article calculates Americans’ exposure to violent attack per 10 million person-hours spent in differ- ent activities. Numerator data are from the National Crime Victimization Survey (2003-2008) estimates of violent incidents occurring during nine major everyday activities. Comparable denominator data are derived from the American Time Use Survey. The resulting time-based rates give a very different picture of violent crime victimization risk. Hour-for-hour, the greatest risk occurs during travel between activities. This general result holds for demographic subgroups and each type of violent crime victimization.

Keywords: routine activities; lifestyle theory; risk of violence; epidemiology of violence; opportunity for violence

Crime opportunity theories are extremely important for studying how violent crime victimization distributes across time and space. These theories give special atten- tion to how victims and offenders converge. Both lifestyle theory (Hindelang,

Gottfredson, & Garofolo, 1978) and the routine activity approach (Cohen & Felson, 1979) explain this convergence as a function of noncriminal activity patterns. Specifically, the daily movements of individuals and populations through time and space create or diminish opportunities for violent crime to occur. Lifestyle theory focuses mainly on risky personal choices, such as engaging in activities away from home after dark or spending time near youth settings. The routine activity approach gives greater weight to conventional daytime activities, such as work and school, which expose participants to crime opportunities and risks (Roman, 2004). Similar versions of crime opportunity theory were postulated by Dutch and British criminologists around this time indicating the international importance of the link between routine activities and crime (see Mayhew, Clarke, Sturman, & Hough, 1976; van Dijk & Steinmetz, 1980, respectively).

Over time, lifestyle theory and the routine activity approach have been treated as com- plementary (or even synonymous) because they emphasize the impact of everyday activity patterns. Both theories relate victimization risk to the quantity of time people spend in risky settings. Among others, Eck, Chainey, and Cameron (2005) employed these theories

636 Lemieux and Felson

to comprehend how illegal behaviors cluster. Research on “dangerous places” and “hot spots” has repeatedly shown that violent crime concentrates in and around particular places (Block & Block, 1995; Kautt & Roncek, 2007; Roncek & Bell, 1981; Roncek & Faggiani, 1985; Roncek & Lobosco, 1983; Roncek & Maier, 1991; Sherman, 1995; Sherman, Gartin, & Buerger, 1989; Weisburd, 2005). Theoretically, people and populations spending more time in such places should have a higher risk of victimization. Unfortunately, victimization research has been plagued by a limited ability to quantify respondent exposure to risk on a large-scale national basis and instead has been forced to rely on summary measures of risk (Mustaine & Tewksbury, 1998). For example, early research estimated lifestyle exposures from female labor force participation, marital status, age, and sales at eating and drinking establishments (Cohen & Cantor, 1981; Cohen & Felson, 1979; Messner & Blau, 1987).

In this article, we draw from the epidemiology literature to reintroduce an alterna- tive option for measuring and comparing population exposures to risk of violent crime victimization in the United States. This alternative approach adjusts for the time exposed to risk in different major activities. Such adjustment can do more than improve measure- ment precision; it can reverse findings that neglect how much time is spent in settings where risk of violent crime is relatively high. Yet our purpose for writing this article is not methodological, but rather to improve our understanding of violent victimization by taking into account where people are and what they are doing.

EXPOSURE AND VICTIMIZATION

Several victimization studies quantify lifestyles with frequency counts of how respondents use their time. A few questions embedded in a victimization survey can serve this purpose by asking how many nights a week or month respondents spend on certain activities away from home. For example, the British Crime Survey and Canadian General Social Survey victimization supplement have used this approach in the past. The valid ranges of answers for such questions are 0–7 nights (per week) and 0–31 nights (per month). Frequency measures such as these have been used to measure exposure to several types of crime risk, including violent crime victimization (Clarke, Ekblow, Hough, & Mayhew, 1985; Felson, 1997; Gottfredson, 1984; Kennedy & Forde, 1990; Miethe, Stafford, & Long, 1987; Mustaine, 1997; Sampson & Wooldredge, 1987). Counts of nights out are very use- ful for building predictive models, often with logistic regressions, but have unfortunately produced some mixed and confusing results about how victimization relates to lifestyles.

In 1998, Mustaine and Tewksbury expressed doubt about counting nights spent away from home while ignoring what activities occurred while away. They developed a 95-item instrument to collect specific information on the daily activities of college students in eight American states. Although their interest was property crime rather than violence, they demonstrated with a logistic regression model that actual hours out did not predict college student victimization very well. On the other hand, they found that victimization is more a function of which locations and activities students selected. For example, victimization risk increased for those who went out to eat more often but decreased for those who went out to play basketball. Beyond the victimization literature, other studies have also shown specific exposure to risk measures are important and useful predictors of delinquency (Osgood & Anderson, 2004; Osgood, Wilson, O’Malley, Bachman, & Johnston, 1996).

Although measuring what people do when away from home seems obvious after the fact, it is not so easy to accomplish without a substantial questionnaire, and such elabora-

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tion is not currently available from a large-scale national survey. The idea of measuring detailed time use and detailed victimization in the same survey was discussed and dis- carded three decades ago as too long, cumbersome, and expensive (Gottfredson, 1981, pp. 721–722; Skogan, 1981, 1986). Even with the advanced tracking technology of today’s world, this is an enormous task that would produce a vast amount of data. Herein lies the complexity of quantifying “exposure to risk” and the practical rationale for using general time use measures such as demographic proxy variables and frequency counts. To date, no national study has yet collected sufficient lifestyle detail to meet the challenge offered by lifestyle and routine activity theories. Given this roadblock, we seek an alternative approach to disaggregate and comprehend lifestyle exposure to violent crime risk.

THE DENOMINATOR DILEMMA: TIME-ADJUSTED VICTIMIZATION RATES

Ratcliffe (2010) explains the denominator dilemma as “the problem associated with iden- tifying an appropriate target availability control” (p. 12). In demography and epidemi- ology, this is the classic problem of figuring out what population is exposed to risk to make appropriate comparisons. The denominator dilemma has been recognized for more than 40 years in criminal justice research. Indeed, many scholars have argued crimi- nologists’ reliance on population-based rates neglects the actual opportunity structures of many crimes and can produce misleading and even incorrect findings (Harries, 1981; Sparks, 1980; Stipak, 1988). Early attempts to overcome the problem include Leroy Gould’s auto theft work (1969), which calculated rates using the number of automobiles in the denominator, whereas Sarah Boggs (1965) investigated several alternative denomi- nators for exposure to risk.

The general denominator issue was taken into account by Cohen and Felson (1979) and articulated by Ronald V. Clarke (1984). Although there may be different ways to approach the appropriate denominator issue, the larger problem is the uncritical acceptance of sim- ple residential population as the default denominator for crime rate comparisons. As Stipak (1988) wrote, “Exclusive reliance on population-based crime rates stems more from blind tradition than from logic or merit” (p. 258). To illustrate this, we might note that tourist cities have a substantial influx of persons that can be offenders or victims of crime, who are not contained in the traditional denominator such as a census population (Lemieux & Felson, 2011). Using a nontourist example, the movements of a resident population dur- ing the week and on weekends will alter the number of occupied households at any given moment (Harries, 1981)—a topic taken up by Andresen and Jenion (2010) in studying ambient populations. Thus, when describing victimization risk using rates, researchers must select denominators carefully.

In 1984, Stafford and Galle suggested studying unequal exposure to victimization risk by looking beyond population-based rates. They noted that the conventional victimization rate V/Pt (victimizations per 100,000 population during year t) is an inadequate measure because the denominator only controls for population size. Those spending a great deal of time in a dangerous setting are treated no differently from those spending very little time there. That contradicts a central tenet of lifestyle theory and the routine activity approach. Stafford and Galle (p. 174) suggested a more defensible, adjusted rate:

V / (P 3 E)t (where E accounts for the population’s exposure to risk during year t)

638 Lemieux and Felson

This calculation of victimization risk takes into account both population size and a more direct measure of population exposure. Their suggestion reflects epidemiological and demographic thinking that proves useful in this article. The important point is that people spend very unequal amounts of time in different activities, thus distorting estimates of how much risk one activity generates compared to another. Time-adjusted rates take this into account and thus produce a better measure of risk exposure.

The question now is “how do you quantify exposure to enable time-adjusted rate calculations?” The answer is the person-hour. The person-hour is a useful measure for determining how much time individuals or a population spends in a specific place or activity. For example, a person who sleeps at home for 8 hours a night 7 days a week spends 56 person-hours per week in that activity. Aggregating this measure to a population, if 100 persons had the same sleeping pattern, this group would spend 5,600 person-hours per week sleeping. Unlike frequency counts or demographic proxies, the person-hour is a direct measure of time use that enables researchers to calculate time-adjusted rates.

A few examples of time-adjusted rate calculations are already found in the crime literature. Cohen and Felson (1979) combined time use and victimization data from the United States to describe the relative risk of three broad place categories accounting for the unequal durations of time spent in each. The place categories were at home, on the street, and elsewhere. They calculated the number of victimizations per one billion person-hours spent in each location for the American population as a whole. They estimated that the population’s risk of being assaulted by a stranger was 15,684 victimizations per billion person-hours spent on the street, but only 345 for equivalent time spent at home; a ratio of 45:1 (see Cohen & Felson, 1979; Table 1, panel D). A second exception found in the literature is auto crime research by Clarke and Mayhew (1998), which calculated the amount of time cars were parked in different set- tings to compare the relative risk of each. They found that risk increases sharply when cars are in public places; parking in a public lot was more than 200 times more risky than using a pri- vate garage. The rate was reported as the number of car crimes per 100,000 cars per 24 hours parked in a location. A third research exception is found in a series of papers by Andresen and colleagues, who calculated crime rates in British Columbia, Canada, for the ambient population as an alternative to the residential population (Andresen, 2010, 2011; Andresen & Brantingham, 2008; Andresen & Jenion, 2008, 2010). This takes into account the major shift of population as people leave their residential area to go to work, school, or leisure settings. Despite these three exceptions, most studies of the relative risk of violent crime have neglected time adjustment, despite major differences in time spent in various places and activities.

In the field of epidemiology, researchers have long been accustomed to adjusting for time exposed to adverse conditions, including pollution, secondhand smoke, danger in sports, as well as risky consumer products and workplaces (see Barnoya & Glantz, 2005; Cai et al., 2005; Dasgupta, Huq, Khaliquzzaman, Pandey, & Wheeler, 2006; de Löes, 1995; Hayward, 1996; Messina, Farney, & DeLee, 1999; Starr, 1969). In his analysis of consumer product injuries, Hayward (1996) clearly showed that time adjustment makes a difference when describing the relative risk of activities such as riding a bike or using an electric hedge trimmer. Without time adjustment, bicycling appeared to be the most dangerous activity. However, accounting for both the participant population and time spent, bicycling dropped to the seventh most injurious. The most dangerous product per person-hour of use proved to be the electric hedge trimmer, with a time-adjusted injury rate five times higher than bicycles. Put simply, short periods spent using this tool are extremely dangerous compared to other household products. Thus, time-adjusted rates can produce a vastly different picture of risk than incident counts or population-based rates.

Risk of Violent Crime Victimization 639

THE CURRENT STUDY

This study reconsiders how we measure routine exposures to the risk of violent crime in the United States as a whole. Using two national-level data series, we calculate risk for nine broad activity categories, including six destination activities and three transit activities (movement between destination activities). These rates are adjusted for the amount of time people spend participating in each of the nine activities, helping us to compare the exposure to risk. Although this approach is common in epidemiological studies, it was not possible in the past to apply it to violent crime given the limited daily activity data accompanying vic- timization and crime data. A newer data source—the American Time Use Survey—allows us to overcome earlier limitations of denominator data. The purpose of this research is not to compare individuals or families but rather to comprehend the relative exposure to violence in different daily activities, taking into account hours exposed to risk.

This approach is not comparable to the Federal Bureau of Investigation (FBI)’s “crime clock,” which divides the number of crimes by the number of seconds in a year. A crime clock uses the same denominator for every calculation. We use a different denominator for each activity category because unequal amounts of time are spent in each. The ideal approach would use a unified national survey of victimization and time use for both victims and nonvictims. Such a study would enable easy risk calculations for individuals and facilitate logistic regression models of the victimization process (see Mustaine and Tewksbury, 1998). Given that no such survey is found in the United States or elsewhere, we instead follow the lead of epidemiologists, drawing numerator and denominator data from separate sources (see Hayward, 1996).

This multi-dataset approach is not new in criminology where conventional crime rates are usually calculated using two different sources of information. For example, it is common to use Uniform Crime Report data in the numerator and census population data in the denominator even when calculating age-specific arrest rates or comparing one city to another. The main contribution of this study is to draw denominator data from a time use source not usually employed by crime and victimization researchers. Because the American Time Use Survey (ATUS) and National Crime Victimization Survey (NCVS) both use a stratified, multistage sampling strategy and weight estimates to the national level, it was possible to harmonize these data and calculate meaningful rates. Table 1 compares the NCVS and ATUS respondents by dichotomized age, sex, and race, indicating substantial demographic consistency between the two surveys as well as among the six annual samples.

We report rates as the number of violent victimizations per 10 million person-hours. These rates can be used to (a) determine which activity is the most dangerous hour for hour, (b) compare the relative danger of one activity to another, (c) make comparisons among demographic groups, and (d) make future international and longitudinal compari- sons as time use and victim surveys continue to develop. Although we cannot provide a predictive analysis for individuals, we will be able to assess whether the overall findings hold within major demographic subgroups.

In shifting away from an individual analysis, we face at least three limitations: (a) our numerator and denominator data come from different individuals, who are not interviewed simultaneously; (b) we cannot use log-linear analysis or other multivariate methods to predict victimization risk at the individual level; and (c) activity categories are not perfectly matched between our two data sources. Despite these imperfections, we believe this analysis produces results that are important, useful, and robust. We consider a

640 Lemieux and Felson

population’s exposure to risk in different activities even though we lack full details about the individual’s exposure compared to other individuals. The sections that follow describe our data sources and how they were matched to produce time-adjusted victimization rates.

Numerator Data

The NCVS estimates on an annual basis the number of violent victimizations occurring in different everyday activity categories. During an NCVS interview, victims are asked, “What were you doing when the incident (happened/started)?”; NCVS variable V4478. The choices included the following nine broad activity categories including travel to dif- ferent destinations:

1. Sleeping 2. Other activities at home 3. Working 4. Attending school 5. Shopping or errands 6. Leisure activity away from home 7. Going to or from school 8. Going to or from work 9. Going to and from some other place.

During the study period (2003–2008), 93.6% of violent crime victims indicated the inci- dent in question happened during one of these nine activity categories (U.S. Department of Justice’s Bureau of Justice Statistics, 2005, 2006a, 2006b, 2008a, 2010, 2011). The other options available to respondents were “don’t know” or “other”; however, these victimiza- tions are excluded from the present analysis.

Between 2003 and 2008, the NCVS performed 1,273,942 interviews, which captured 9,220 separate violent incidents. Of these, 7,264 incidents are included in this analysis; some data were removed to match the numerator and denominator data, as explained later in this article. Twenty types of violence are included in this analysis, ranging from verbal threats of

TABLE 1. Demographic Composition of National Crime Victimization Survey and American Time Use Survey Samples, 2003–2008

% Male % White % Aged 15–29

NCVS ATUS NCVS ATUS NCVS ATUS

2003 47.6 43.7 82.3 83.5 17.2 18.6

2004 47.6 43.8 82.1 84.1 17.5 18.4

2005 47.8 42.9 82.4 82.9 17.5 19.1

2006 48.0 42.6 83.0 82.0 17.6 19.2

2007 48.1 43.3 82.8 81.6 17.8 18.7

2008 48.1 44.4 82.7 80.8 17.7 18.4

Note. From National Crime Victimization Survey (NCVS) Person Record-Type Files and American Time Use Survey (ATUS) Activity Summary Files.

Risk of Violent Crime Victimization 641

assault to completed rapes. We begin by analyzing all types of violent crime combined and later separate violent crimes into five broad categories (see Appendix) to assess the robust- ness of the findings.

Weights provided in the NCVS incident-level extract file allow us to estimate the inci- dence of violence in the United States for each activity category. Similar estimates were produced for each demographic subgroup. To produce time-adjusted rates, we employ additional data from the ATUS.

Denominator Data

The ATUS officially began collecting data about the routine activities of Americans in 2003. The survey and sample were specifically designed to provide information about time use at the national level; additional information concerning the rationale for and history of the ATUS can be found on the survey’s Website (http://www.bls.gov/tus/overview.htm). The ATUS is a unique survey that uses computer-assisted telephone interviewing (CATI) to create time use diaries for the day before each interview. The ATUS asks respondents to detail where they were, what they were doing, and with whom, over a 24-hour period beginning at 4:00 a.m. the preceding day (Fisher, Gershuny, & Gauthier, 2011). Because the study is spread over the year and has a large sample, these snapshots combine to pro- vide a substantial general picture of time use for the population of the United States.

During the study period (2003–2008), 85,645 individuals were interviewed by the ATUS. Respondents reported 1,971,368 separate activity records that were classified into nearly 400 categories—far more than the nine types of activity delineated in the NCVS. An activity record refers to one activity performed by a single person. For example, sleeping from 8:00 a.m. to 10:00 a.m. constitutes a single activity record. When the respondent gets out of bed and showers from 10 a.m. to 10:15 a.m., this is classified as a separate activity record. The number of activity records reported by each person was not evenly distributed. Some persons reported 10 or fewer records, whereas others reported more than 50. When summed, these activity records produce the total number of hours respondents spent in each activity category. Although a single respondent’s reports are not representative for that one person’s annual experience, the total sample’s reports cover and represent what the American population does in the course of the year.

Like the NCVS, ATUS data files contain weights that enabled us to make national time use estimates. Two component variables were quantified: (a) the daily participant popula- tion for different activities and (b) the mean participation time. Together these produced an estimate of how many person-hours the American population spent in the nine NCVS activity categories each year. To ensure the validity of our time-adjusted rates, it was nec- essary to reconcile the two surveys, taking into account their different levels of detail; this procedure is described in the following section.

Reconciling Discrepancies Between the Two Data Sources

To match these data sources, ATUS activities were recoded to match the nine broad NCVS categories because it was not possible to make the NCVS time use variable more specific. This means the detailed picture of American life the ATUS provides was not captured in this analysis because of NCVS limitations. For example, the numerous home activities detailed by the ATUS were subsumed under two categories: “sleeping” and “other activi- ties at home.” Fortunately, 99.8% of the original ATUS data were amenable to recoding. The final denominator data include 1,967,356 activity records for the 6 years. The average

642 Lemieux and Felson

person-hours per day spent in each of the nine activity categories was sleeping (8.60), other activities at home (8.10), working (8.07), at school (4.90), leisure (2.94), shopping (1.54), to or from other (1.21), to or from work (0.73), and to or from school (0.58). It is important to note here that the participant population of each activity varied; that is, although most Americans slept, only a small proportion attended school. Thus, the total time spent in each activity is dependent on (a) the participant population and (b) the aver- age person-hours spent in the activity per day. This is accounted for in the time-adjusted rates reported in the section that follows (see Table 2).

Demographic features of the samples also needed to be reconciled. The NCVS sample included Americans residing outside the United States, active-duty military personnel, and persons younger than 15 years of age—all of whom were removed to achieve compat- ibility with the ATUS. We also omitted incidents classified as series crimes, which is a standard procedure for making NCVS estimates (see U.S. Department of Justice, Bureau of Justice Statistics, 2008b, p. 459). Future analyses could include these crimes; however, in this analysis, the aggregated, national level approach does not enable us to tease out the individual factors associated with repeat victimization. After these exclusions, the numera- tor data include 7,264 violent incidents for the years 2003–2008.

Table 2 outlines how NCVS and ATUS estimates are used to calculate the time-adjusted rates presented in the sections that follow. These calculations are not as difficult as they may look but do require attention to detail. For example, multiplications by constants are needed to generalize from 1 day to 365 days as well as to arrive at a rate per 10 million person-hours. Activities must be harmonized to make sure numerator and denominator apply as closely as possible to the same activity. Thus, to get the denominator in terms of person-hours shopping (D), we multiply the population of shopping participants (B) by the average time spent shopping per participant per day (C). That product is then multiplied by 365 to cover the time shopping in a year. The numerator data consists of the number of victimizations while shopping (A). However, that fraction is too small to work with, so we

TABLE 2. Example of How Activity-Specific Time-Adjusted Violence Rates Were Calculated: The Risk of Violence While Shopping, United States, 2003

Component Estimated from the Surveys Source National Estimate

(A) Violent victimizations while shopping (incidence count)

NCVS, 2003a 238,530

(B) Average daily population of shoppers (participants)

ATUS, 2003b 133,893,190

ATUS, 2003b 1.42

(D) Total time spent shopping in 2003 (B) 3 (C) 3 365 69,551,975,288

(E) Time-based rate of violence (Victimizations per 10 million person-hours)

(A) 3 10 million (D)

34.3

aNational Crime Victimization Survey (NCVS) Incident-Level Extract File, 2003. bAmerican Time Use Survey (ATUS) Activity File, 2003.

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multiply it by 10 million to produce a smaller index number. For comparison purposes, we use the same standard rate for all activities: the risk of violent victimization per 10 million person-hours engaged in a given activity.

RESULTS

Basic Pattern

We begin with basic violence risk calculations for the American population in general. Table 3 shows the annual time-adjusted violence rate for all nine activities from 2003 to 2008. The mean, standard deviation, and coefficient of variation (CV) are reported for each activity cat- egory. We do not report the standard error of our time-adjusted rates as this calculation would be very complex because the numerator and denominator come from different sources. Yet the coefficient of variation tells us that most statistics in this study display considerable sta- bility from year to year. For this reason, we average the 6 years for subsequent tables.

Compared to every other activity, sleeping (row 1) is the safest activity overall; other activities at home are the second safest activity (row 2). Thus the results strongly uphold a major premise of the routine activity approach and lifestyle theory: being at home is safer than being away from home. Interesting, however, is that by disaggregating at-home activi- ties into two categories, the results indicate that on an hour-for-hour basis, being awake at home is nearly 11 times more risky than being asleep. Although the risk of a violent victimization while sleeping is very low, it is not zero.

On the other hand, activities away from home do not fit a clear and single pattern. The apparent risk of violence during activities away from home differs from one activity to the next (rows 3–6, Table 3). This supports our earlier suggestion and that of Mustaine and Tewksbury (1998) that broad lifestyle measures (such as activities away from home) do not adequately measure risk. Consider that working and shopping are relatively safe among activities away from home, in stark contrast to the higher hour-for-hour risk from both lei- sure activities and school attendance. Indeed, the latter two expose Americans to more than twice the risk as working or shopping. Later in this article (Table 6) we show that students face more low-level violence, whereas those participating in leisure activity have a higher risk of more serious violent victimization, such as rape, robbery, and aggravated assault.

Unlike “at home” and “away from home” activities, rows 7–9 in Table 3 represent a distinct class of activities that we refer to as “in transit.” Many travel locations are subject to less guardianship than work, school, and other settled activities. When moving from one place to another, the opportunity structure for violent victimization can be in constant flux. A person walking home from a bar might traverse both safe and unsafe streets. Thus, movement through the physical environment separates in transit activities from at home and away from home activities. Moving through time and space alters exposure to oppor- tunities created by where you are as well as who you are with. Settled activities such as drinking at a bar are only susceptible to changes in who you are with; the physical environ- ment of the bar is constant. Although this article cannot capture these local processes, we can examine their large-scale manifestation.

The time-adjusted rates in Table 3 indicate the risk of violence while in transit is destina- tion dependent. Going to and from school is by far the most dangerous activity in American life, even though most of the population does not go to school at all. Indeed, in terms of violent crime, transit to and from school is (hour-for-hour) five times more dangerous than

644 Lemieux and Felson

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Risk of Violent Crime Victimization 645

being at school. Like school, this activity concentrates young people in time and space; however, this concentration occurs off school property where guardianship is almost cer- tainly lower if not completely absent. Thus, conflicts that begin at school may spill over into after school hours where students are less likely to be caught and sanctioned.

In closing, this analysis sheds new light on the risk differentials between broad activity categories. We have shown that (a) time-adjusted rates are a useful tool for quantifying and comparing the risk of different activities; (b) activities at home are safer hour for hour than those occurring away from home; (c) the risk of violence while away from home varies greatly between activities; and (d) in transit activities are very dangerous when compared to all other activities. The next section will discuss how these findings compare to a risk assessment based on incident counts—the standard NCVS reporting procedure (see U.S. Department of Justice, Bureau of Justice Statistics, 2011, Table 64).

Incident Counts Versus Time-Adjusted Rates: Different Pictures of Risk?

The next question we ask is: “Are these new risk calculations really necessary?” The NCVS already provides an annual estimate of how many violent incidents occur in nine everyday activities. If those estimates paint a similar picture of risk, the additional data and methodology employed here is unnecessary. We answer this question by creating a relative risk index for the nine everyday activities. The idea is simple, a score of 1 on the scale means that activity is the safest. The most dangerous activity receives a score of 9. These scores greatly reduce the detail presented in Table 3 but enable simple visual comparisons. If measures with and without time adjustment produce the same rank order, this study would be redundant. We find the opposite to be true.

Figure 1 compares the relative risk of each activity using time-adjusted rates as well as estimated incidence counts without time adjustment. Category order was changed to arrange the incident count measure from low to high (following the grey bars from left to right). These incident counts are exactly proportional to rates in which the denominator

MOST Risky

LEAST Risky

To, from school

Time-adjusted Rates

Rank order of risk without time adjustment

Sleeping Attending school

To, from work

Shopping errands

To, from other

Working Leisure away from home

Other activities at home

Figure 1. Risk of violent crime victimization in nine activities, with and without time adjustment. The black bars are proportional to the data in column E of Table 4 as well as the mean in Table 3. The gray bars are rank ordered to illustrate the difference time adjustment makes.

646 Lemieux and Felson

is always the same population number. The comparison shows that incident counts and time-adjusted rates give a completely opposite result. In incidence terms, going to and from school is the safest activity in America, whereas time-adjusted rates show this to be the least safe use of time. Moving up the scale, working, leisure, and other activities at home appear to be the three most dangerous activities in incidence terms. This, of course, is a completely different picture of risk than the findings of the this article, as indicated by the black bars in Figure 1, which show work and other activities at home to be relatively safe hour for hour. To be sure, the two measures do not always give opposite results because by both measures, sleeping is safe, and leisure is risky. Overall, it is evident that time adjustment provides dif- ferent results and offers a unique way to estimate the risk of violence linked to particular categories of activity; this is akin to Hayward’s (1996) work on consumer products. The time-based approach does not replicate the rank order of risk found in incident counts and indeed forces us to think differently about how to quantify risk in the future.

Sensitivity Analysis

Even though these data do not lend themselves to multivariate analysis, we can nonethe- less examine whether the strong results from the total sample also apply within subgroups (Tables 4, 5, and 6). Although this sensitivity analysis does not ascertain the relative con-

TABLE 4. Mean Time-Adjusted Violence Rates for Different Activities by Race and Sex, United States, 2003–2008

Violent Victimizations per 10 Million Person-Hours

Activity (A)

Males (B)

Females (C)

Whites (D)

Nonwhites

(E) All

Americans

1 Sleeping 1.2 2.2 1.7 2.1 1.7

2 Other home activities

16.1 20.2 16.8 25.8 18.3

3 Working 29.2 25.1 27.9 25.5 27.6

4 Attending school 99.1 59.5 81.6 71.2* 78.9

5 Shopping or errands

40.8 24.2 28.3 43.5 31.2

6 Leisure away from home

103.5 60.2 117.4 87.7 82.5

7 To or from work 86.4 82.3 75.0 130.4 84.7

8 To or from school 532.2 292.5 336.2 613.3* 404.3

9 To or from other activities

68.0 41.7 47.2 87.5 53.8

Note. Numerators are from the National Crime Victimization Data, Incident-Extract Files, 2003–2008; denominators are from the American Time Use Survey Activity Files, 2003–2008. *Coefficient of variation for these estimates is $ 0.5.

Risk of Violent Crime Victimization 647

tribution of different independent variables, it can examine whether the general activity- violent crime pattern reported in Table 3 holds within various subgroups.

The first sensitivity analysis compares the activity-violent crime pattern for males and for females (Table 4, columns A and B). We should not be distracted by the higher risk of violence for males in all activities except those occurring at home. Despite this, both population segments display almost the same relative risk pattern, with the highest hour- for-hour risk occurring during transit and leisure activities for males as for females.

The second sensitivity analysis compares White and nonwhite Americans (Table 4, columns C and D). Nonwhites experience more risk than Whites for six of nine catego- ries, whereas for two activities, working and attending school, Whites are slightly more at risk. A clear reversal is only found for leisure activities, where violent victimization risk per 10 million person-hours is 117.4 for Whites and 87.7 for nonwhites. However, these differences should not obscure our basic point: the relative pattern of risk for violent crime across activities persists within each group, with a pronounced risk of violence in transit activities for nonwhites and Whites alike. The transit to and from school appears especially risky for nonwhites but remains consistent with the general American pattern (Table 4, column E).

The third sensitivity analysis considers whether the general pattern applies within two broad age groups. The age ratios (Table 5, column C) show that for eight of nine activities, the risk for those younger than 30 years is at least double the risk for those 30 years and older. Leisure away from home shows the greatest difference with a risk of violent crime victimiza-

TABLE 5. Mean Time-Adjusted Violence Rate for Different Activities by Age, United States, 2003–2008

Violent Victimizations per 10 Million Person-Hoursa

Activity

(A) Ages 15–29

(B) Ages 30

and Older

(A/B)

1 Sleeping 2.8 1.4 2.0

2 Other home activities 34.1 14.2 2.4

3 Working 37.2 24.3 1.5

4 Attending school 84.5 30.5 2.8

5 Shopping or errands 50.2 24.4 2.1

6 Leisure away from home 159.7 40.6 3.9

7 To or from work 145.7 65.0 2.2

8 To or from school 448.4 219.8 2.0

9 To or from other activities 114.1 31.4 3.6

Note. Numerators are from the National Crime Victimization Survey, Incident-Extract Files, 2003–2008; denominators are from the American Time Use Survey Activity Files, 2003–2008. aCoefficient of variation for these estimates is $ 0.5.

648 Lemieux and Felson

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Risk of Violent Crime Victimization 649

tion almost four times higher for the young. The point of Table 5 is that the general pattern of risk over nine activities holds for both younger and older population segments. Analyses with more detailed age categories (Lemieux, 2010) give exactly the same conclusion. In sum, leisure and travel between activities entail the greatest danger of violent crime victimization for males and females, Whites and nonwhites, and younger and older Americans, with sub- stantial and similar risk differentials found within each demographic group.

The general pattern of violent crime risk by activity could conceivably apply only to some types of violence and not others. The fourth and final sensitivity analysis (Table 6) examines patterns for five different types of violence. The lower number of cases in each category because of disaggregation produced more tenuous estimates, especially for the rape-sexual assault category (note where the CV is greater than 0.5); aggravated assault statistics were somewhat unstable from year to year. However, for all five offenses, the trip to and from school is by far the most risky, whereas home activities are quite safe in comparison. Leisure and trips away from home produce much more violence when time is considered than when time is neglected. Most importantly, these five violent offenses show roughly the same relative risk differential among activities. Despite some differences from one crime type to the other that call for future research, the general pattern holds after all four sensitivity analyses. Our results clearly indicate vast risk differentials between activi- ties occurring at home, away from home, and travel between these settings.

CONCLUSION AND DISCUSSION

This article has examined several broad types of daily activity that expose people to the risk of violence. A very strong general pattern is observed, with very high relative risk in transit and leisure activities and low risk in home and work activities. The observation that transit activities are more risky than leisure activities is especially surprising. Perhaps the most important conclusion of this article is that risk differentials among activities are so great in magnitude. Time adjustment brings out that magnitude while reversing many of the observations that appear without it. Although this point was recognized more than 30 years ago by Cohen and Felson (1979), it has never been fully confirmed, least of all validated with modern data.

Yet, the general risk pattern presented here only begins to scratch the surface and surely misses many important details. For example, the low average violence risk in the work- place should not obscure the high risk in some types of work. When Block, Felson, and Block (1985) disaggregated victimization for 246 occupations, they found some with very high crime victimization risk. Lynch (1987) observed extra danger for workers handling money, traveling between worksites, and exposed to a large volume of face-to-face con- tacts. Lynch emphasized risk differentials among domains, taking into account not only what people do but also where they do it. As more detailed data become available, violent crime victimization risk calculations will undoubtedly become more refined. In the near future, it may become possible to calculate time-adjusted rates at a microlevel and to use logistic regression models to disaggregate effects. It may also become practical to take time exposure into account when studying local violence in and around schools, public transportation, barrooms, or local neighborhoods.

Beyond settled activities such as work and school, an important finding of this article is the high risk associated with transit between activities. The essential point we make is that people usually spend much less time in transit than at destinations themselves. We have

650 Lemieux and Felson

accounted for this using time-based rates which show that the risk of violence while commuting is five times higher for students than while at school and three times higher for employees than while working. Thus, it is a mistake to combine an activity, such as attending school, with the travel to and from it because risk could easily derive from the latter process.

Past victimization research has often missed the high risks associated with transit among activities. A noteworthy exception is in the field of school crime, where researchers have long recognized the danger of the period after school (Garofalo, Siegel, & Laub, 1987; Savitz, Lalli, & Rosen, 1977; Toby, 1983) and the policy significance of after- school activities and commutes (see Gottfredson, Gerstenblith, Soulé, Womer, & Lu, 2004; Stokes, Donahue, Caron, & Greene, 1996). Future research on victimization risk might specify how risk varies by mode of transportation during in transit activities. For example, using ATUS and NCVS data, it would be possible to calculate victimization rates during the commute to school for Americans who walk, use public transportation, or travel by private automobile. Although much has been written about in-transit risk on public transportation (see Clarke, 1996), time-adjusted rates could help compare this risk to other commuting methods. At the microlevel, the rates can also be used to monitor the effectiveness of prevention strategies such as those outlined by Smith and Cornish (2006). If the interventions are working, the victimization rate per person-hour of ridership should decline on any mass transit system or individual line. In short, this article has identified in transit activities as an important element of the victimization process that warrants greater attention from both academics and practitioners.

Moving on, this research does not include repeat victimizations, which constitute a sub- stantial component of the victimization problem. Unfortunately, the NCVS does not have sufficient detail about repeat victimizations to allow us to apply the refinements of this article to those incidents. When repeat incidents are reported to the police, it is sometimes possible to study them in greater detail. However, many repeat victims do not report all incidents to the police. As victim surveys improve their attention to repeat victimizations, it may become possible to apply time adjustments to these incidents as well.

The policy significance of hourly risk is a central point of this research. Prevention techniques, which are labor intensive, are likely to be far more effective if focused on short periods that generate the greatest risk hour for hour. For example, policing and supervi- sion of juvenile areas for an hour or two after school will do more to reduce crime than spending the same money protecting far less risky activities. On the other hand, situational prevention measures, including crime prevention through environmental design, might well contribute crime reduction for prolonged periods and hence require less focus on hour-for-hour risk.

We are not suggesting that hours exposed to risk is the sole denominator worth calculating or discussing. But we have learned that the person-hour gives us a more precise way to think about and measure exposure to risk of violence, based on the time people spend in various activities or locations. This approach is far more appealing than frequency counts or demographic proxy variables and can help make possible future comparisons among years, between nations, or across types of violence. The improved understanding of risky activities helps us ask better policy questions. If the trip to and from school is this risky, why doesn’t the community give it more attention? If nonwhite youths suffer most of their risk soon after school lets out, why not focus social and police resources accordingly? We do not have answers to these questions, but we do offer a way to ask them empirically and to calculate risk in a more focused fashion.

Risk of Violent Crime Victimization 651

In conclusion, exposure to risk is a critical element of the violent crime victimization process. Routine activity patterns influence when and where victims of violent crime come into contact with offenders. Violence concentrates in and near certain activities and certain types of trips. Policy and practice needs to take this into account and to employ time adjustment to understand the process. To quantify and comprehend a population’s exposure to risk of violent crime, it is imperative to consider where people are, what they do, and for how long they do it.

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Risk of Violent Crime Victimization 655

APPENDIX. National Crime Victimization Survey (NCVS) Violent Crime Type Categories and Subsequent Aggregation Category Used in This Analysis

(ordered from most to least serious offenses)

NCVS Violence Typea Aggregated Violence

Type

(1) Completed rape Rape or sexual assault

(2) Attempted rape

(3) Sexual attack with serious assault

(4) Sexual attack with minor assault

(5) Completed robbery with injury from serious assault Robbery

(6) Completed robbery with injury from minor assault

(7) Completed robbery without injury from minor assault

(8) Attempted robbery with injury from serious assault

(9) Attempted robbery with injury from minor assault

(10) Attempted robbery without injury

(11) Completed aggravated assault with injury Aggravated assault

(12) Attempted aggravated assault with weapon

(13) Threatened assault with weapon Threat of violence

(14) Simple assault completed with injury Simple assault

(15) Sexual assault without injury Rape or sexual assault

(16) Unwanted sexual contact without force

(17) Assault without weapon without injury Simple assault

(18) Verbal threat of rape Threat of violence

(19) Verbal threat of sexual assault

(20) Verbal threat of assault

aU.S. Department of Justice, Bureau of Justice Statistics. (2008b). National crime victimization survey, 2003. Codebook. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.

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