Crime Victimization During Major Daily Activities

Violence and Victims, Volume 27, Number 5, 2012
© 2012 Springer Publishing Company 635
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 different 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 attention 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 complementary (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 alternative 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 measurement 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.
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 useful 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-
Risk of Violent Crime Victimization 637
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 discarded 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.
Ratcliffe (2010) explains the denominator dilemma as “the problem associated with identifying an appropriate target availability control” (p. 12). In demography and epidemiology, 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 criminologists’ 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 denominators 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 simple 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 during 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 settings 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 private 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
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 victimization 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 comparisons 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 different 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 incident 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 victimizations 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
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 robustness of the findings.
Weights provided in the NCVS incident-level extract file allow us to estimate the incidence 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 (
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 provide 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 population 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 necessary 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 activities 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 average 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 compatibility 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 numerator 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
(C) Average time spent shopping
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
(A) 3 10 million
aNational Crime Victimization Survey (NCVS) Incident-Level Extract File, 2003.
bAmerican Time Use Survey (ATUS) Activity File, 2003.
Risk of Violent Crime Victimization 643
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.
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 category. 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 stability 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 activities 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 leisure 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 opportunities 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 environment 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 destination 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 TABLE 3. Time-Adjusted Violence Rate for Nine Activities, United States, 2003–2008 2003 2004 2005 2006 2007 2008 Mean Standard Deviation Coefficient of Variation 1 Sleeping 1.8 1.2 1.6 2.2 2.1 1.6 1.7 0.4 0.2 2 Other home activities 19.0 14.9 19.4 22.5 17.1 16.7 18.3 2.8 0.2 3 Working 30.6 30.2 28.5 30.0 24.2 22.1 27.6 3.6 0.1 4 Attending school 87.1 49.9 66.4 80.3 91.9 97.8 78.9 17.1 0.2 5 Shopping or errands 34.3 25.2 27.8 35.2 40.1 24.9 31.2 6.0 0.2 6 Leisure away from home 85.9 79.6 90.7 95.9 69.1 74.0 82.5 10.4 0.1 7 To or from work 81.0 92.1 78.3 105.4 90.3 61.0 84.7 10.7 0.1 8 To or from school 319.5 310.5 539.3 509.6 301.9 445.2 404.3 117.8 0.3 9 To or from other activities 50.3 51.7 76.0 56.9 53.8 34.4 53.8 10.5 0.2 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. Numbers in boldface represent the mean risk rates for years 2003-2008 for activity-specific time- adjusted violent crime victimization.
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 certainly 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
To, from
Rank order of risk
without time adjustment
Sleeping Attending
To, from
To, from
Working Leisure
away from
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 different 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 nonetheless 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 conTABLE 4. Mean Time-Adjusted Violence Rates for Different Activities by
Race and Sex, United States, 2003–2008
Violent Victimizations per 10 Million Person-Hours
1 Sleeping 1.2 2.2 1.7 2.1 1.7
2 Other home
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
40.8 24.2 28.3 43.5 31.2
6 Leisure away from
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
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,
*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 activityviolent 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 hourfor-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 categories, 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 victimizaTABLE 5. Mean Time-Adjusted Violence Rate for Different Activities by Age,
United States, 2003–2008
Violent Victimizations
per 10 Million Person-Hoursa
Ages 30
and Older
Age Ratio
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,
aCoefficient of variation for these estimates is $ 0.5.
648 Lemieux and Felson TABLE 6. Mean Time-Adjusted Rate of Violence for Different Activities by Crime Type, United States, 2003–2008 Victimizations per 10 Million Person-Hours Activity Rape or Sexual Assault Robbery Aggravated Assault Simple Assault Threat of Violence Any Type of Violence 1 Sleeping 0.3 0.3 0.2 0.5 0.4a 1.7 2 Other home activities 0.8 1.4 2.2 6.9 6.9 18.3 3 Working 0.3a 1.0 2.7 10.5 13.1 27.6 4 Attending school 1.6a 3.7 4.8 38.3 30.5 78.9 5 Shopping or errands 0.4a 7.1 3.5a 9.7 10.6 31.2 6 Leisure away from home 4.2 9.1 12.8 32.8 23.7 82.5 7 To or from work 5.9a 22.4 9.3a 24.0 27.7 84.7 8 To or from school 14.9a 85.4 28.2a 170.4 105.4 404.3 9 To or from other activities 1.2a 12.5 7.4 15.5 17.3 53.8 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.
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 substantial 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 activities occurring at home, away from home, and travel between these settings.
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 workplace 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 contacts. 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 afterschool 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 substantial 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 supervision 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
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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
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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
(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.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.

What if I don’t like the paper?

There is a very low likelihood that you won’t like the paper.

Reasons being:

  • When assigning your order, we match the paper’s discipline with the writer’s field/specialization. Since all our writers are graduates, we match the paper’s subject with the field the writer studied. For instance, if it’s a nursing paper, only a nursing graduate and writer will handle it. Furthermore, all our writers have academic writing experience and top-notch research skills.
  • We have a quality assurance that reviews the paper before it gets to you. As such, we ensure that you get a paper that meets the required standard and will most definitely make the grade.

In the event that you don’t like your paper:

  • The writer will revise the paper up to your pleasing. You have unlimited revisions. You simply need to highlight what specifically you don’t like about the paper, and the writer will make the amendments. The paper will be revised until you are satisfied. Revisions are free of charge
  • We will have a different writer write the paper from scratch.
  • Last resort, if the above does not work, we will refund your money.

Will the professor find out I didn’t write the paper myself?

Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.

What if the paper is plagiarized?

We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.

When will I get my paper?

You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.

Will anyone find out that I used your services?

We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.

How our Assignment  Help Service Works

1.      Place an order

You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.

2.      Pay for the order

Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.

3.      Track the progress

You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.

4.      Download the paper

The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.

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Basic features
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  • Unlimited revisions
  • Plagiarism-free guarantee
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  • Overnight delivery
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  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

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Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

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Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

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Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

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