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Violence and Victims, Volume 27, Number 5, 2012
© 2012 Springer Publishing Company 635
http://dx.doi.org/10.1891/0886-6708.27.5.635
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 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
C
rime 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-
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 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)
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.
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
(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
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
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.
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
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
(C) Average time spent shopping
(person-hours)
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.

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
T
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B
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fr
om
t
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N
at
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na
l
C
ri
m
e
V

ic
ti
m
iz
at
io
n
S
ur
ve
y,
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nc
id
en
t-
E
xt
ra
ct
F
il
es
, 2

00
3–
20
08
;
de
no
m
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at
or
s
ar
e
fr
om
t
he
A
m
er
ic
an

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im
e
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se
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ur
ve
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ct
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il
es
, 2
00
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. N
um

be
rs
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ld
fa
ce
r
ep
re
se
nt
t
he
m
ea
n
ri
sk
r
at
es
f

or
y
ea
rs
2
00
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20
08
f
or
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ct
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it
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sp
ec
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t
im
e-
ad

ju
st
ed
v
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nt
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ri
m
e
vi
ct
im
iz
at
io
n.
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.
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.
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
(C)
Age Ratio
(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.
T
A
B
L
E
6
.
M
ea
n
T
im
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A
d
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st
ed

R
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of
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f
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y
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yp
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U
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ed
S
ta
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3–
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V
ic
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m
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p
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lt
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T
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1.
2a
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N
o
te
. N
um
er
at
or
s
ar
e
fr
om
t
he
N
at
io
na
l
C

ri
m
e
V
ic
ti
m
iz
at
io
n
S
ur
ve
y,
I
nc
id
en
t-
E
xt
ra
ct

F
il
es
, 2
00
3–
20
08
;
de
no
m
in
at
or
s
ar
e
fr
om
t
he
A
m

er
ic
an
T
im
e
U
se
S
ur
ve
y
A
ct
iv
it
y
F
il
es
,
20
03

–2
00
8.
a C
oe
ff
ic
ie
nt
o
f
va
ri
at
io
n
fo
r
th
es
e
es
ti
m

at
es
i
s
$
0
.5
.
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

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.
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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Correspondence regarding this article should be directed to
Marcus Felson, PhD, 601 University Drive,
Hines-120-Felson, Texas State University, San Marcos, TX
78666. E-mail: [email protected]
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.
Reproduced with permission of the copyright owner. Further

reproduction prohibited without permission.

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