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International Journal of
Offender Therapy and
Comparative Criminology
1 –26
© The Author(s) 2016
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DOI: 10.1177/0306624X16632259
ijo.sagepub.com
Article
Crime Seasonality: Examining
the Temporal Fluctuations
of Property Crime in Cities
With Varying Climates
Shannon J. Linning
1,2
, Martin A. Andresen
1
,
and Paul J. Brantingham
1
Abstract
This study investigates whether crime patterns fluctuate periodically throughout the year
using data containing different property crime types in two Canadian cities with differing
climates. Using police report data, a series of ordinary least squares (OLS; Vancouver,
British Columbia) and negative binomial (Ottawa, Ontario) regressions were employed
to examine the corresponding temporal patterns of property crime in Vancouver (2003-
2013) and Ottawa (2006-2008). Moreover, both aggregate and disaggregate models
were run to examine whether different weather and temporal variables had a distinctive
impact on particular offences. Overall, results suggest that cities that experience greater
variations in weather throughout the year have more distinct increases of property
offences in the summer months and that different climate variables affect certain crime
types, thus advocating for disaggregate analysis in the future.
Keywords
crime seasonality, climate, weather, property crime, routine activities theory
Introduction
The study of seasonal fluctuations in crime patterns dates back to the mid-1800s and
continues to be a prominent topic in the criminological literature (see Baron & Bell,
1976; Block, 1983; Cohn & Rotton, 2000; Hipp, Bauer, Curran, & Bollen, 2004;
1
Institute for Canadian Urban Research Studies, Simon Fraser University, Burnaby, BC, Canada
2
University of Cincinnati, OH, USA.
Corresponding Author:
Shannon J. Linning, School of Criminal Justice, University of Cincinnati, PO Box 210389, 665 Dyer Hall,
Cincinnati, Ohio, USA, 45221-0389
Email: linninsj@mail.uc.edu
632259IJOXXX10.1177/0306624X16632259International Journal of Offender Therapy and Comparative CriminologyLinning et al.
research-article2016
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2 International Journal of Offender Therapy and Comparative Criminology
Landau & Fridman, 1993; Linning, 2015; McDowall, Loftin, & Pate, 2012; Quetelet,
1842; Tompson & Bowers, 2015). A better understanding of how the frequency of
various crime types change throughout the year can lead to more efficient policy
implementations. More specifically, research into this field can assist enforcement
personnel in knowing when to implement crime prevention initiatives as well as when
to evaluate them (Andresen & Malleson, 2013). Although it is widely accepted that
“seasonal fluctuation in crime is an established fact” (Block, 1984, p. 1), debate con-
tinues to exist regarding which crime types experience annual changes, when these
changes occur, and why (McDowall et al., 2012; Uittenbogaard & Ceccato, 2012).
While much of the earlier literature applied temperature aggression theory to
explain annual variations in offending (see Anderson & Anderson, 1984; Baron &
Bell, 1976), L. E. Cohen and Felson’s (1979) routine activities theory has become
instructive in recent years. The former contends that because higher temperatures act
as “psychological causal mechanisms” in persons, increased temperatures lead to
greater aggression increasing crime rates particularly in the summer months (Hipp
et al., 2004, p. 1338). Furthermore, research has shown that a relationship exists
between aggression and temperature (see Anderson & Anderson, 1984; Baron & Bell,
1976). However, based on the principles of temperature aggression theory, it is only
able to account for the increase in violent crime. Research has since found that sea-
sonal peaks exist for other crime types, such as property crime, and that routine activi-
ties theory can account for these changes (Hipp et al., 2004). In the context of
seasonality, the latter theory suggests that fluctuations in crime rates are the result of
changes in peoples’ activities over the course of the year (Cohen & Felson, 1979). For
instance, in the summer months, warmer temperatures encourage people to engage in
leisure activities outside of the home, leaving their homes unguarded for motivated
offenders—this leads to the increased convergence of offenders and targets that leads
to more criminal activity (see Andresen & Malleson, 2013). It is important to note that,
differing from temperature aggression theory, an increase in crime (both violent and
property) is not expected because of increased motivation but because of increases in
the number of convergences of (already motivated) offenders and suitable targets. As
such, routine activities theory is capable of predicting the seasonal occurrence of many
crime types, not just those involving aggressive behavior.
Although the empirical literature on crime seasonality has been giving more atten-
tion to property offences, there still exists a great deal of unanswered questions. For
instance, in some cases, studies employ an aggregate property crime variable that
often combines burglary, mischief, larceny, and/or motor vehicle thefts into one depen-
dent variable (for example, see Hipp et al., 2004). Even though this was appropriate
for testing the merits of multiple theories, the next logical step would be to conduct
analyses of each of the individual property crime types. These disaggregate analyses
are becoming more prominent in the literature (Cohen, Gorr, & Durso, 2003; Yan,
2004) but consist primarily of research conducted in the United States or Europe
(Breetzke & Cohn, 2012) and would benefit from further investigation in a Canadian
context (for exception, see Andresen & Mallson, 2013). As such, this study seeks to
explore the above ideas using property crime data from two major Canadian cities,
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Linning et al. 3
namely, Vancouver, British Columbia and Ottawa, Ontario, regarding the following
research questions:
Research Question 1: What temporal seasonal property crime patterns are gener-
ated in cities with differing climates?
Research Question 2: Do different property crime types exhibit distinct seasonal
patterns throughout the year?
Based on our research questions and study design, we contribute to the literature in
a number of ways. First, though some research has investigated many more cities in
the same analysis (Hipp et al., 2004; McDowall et al., 2012), we contribute to the lit-
erature comparing multiple locations with different climatic conditions. Second, one
of our cities (Vancouver, British Columbia) has a relatively long data series available
for analysis (11 years), contributing to studies that analyze seasonal variations in crime
over extended time periods. Third, though research has investigated climatic variables
(Hipp et al., 2004) and individual crime types (McDowall et al., 2012), we contribute
to the literature using both to show the nuances for the effects of climate on individual
crime types. Moreover, we include a number of different climate variables not always
present in the previous empirical literature on crime seasonality.
The Previous Empirical Literature on Crime Seasonality
Crime peaks, particularly in the summer months, have been frequently exhibited in the
literature. However, disagreement continues over which specific crime types experi-
ence seasonal fluctuations and when (McDowall et al., 2012; Uittenbogaard &
Ceccato, 2012). Research on this topic dates back to the work of Quetelet (1842) who
observed that property offences were higher in the winter months, whereas crimes
against persons peaked in June. Quetelet (1842) argued that this pattern was likely due
to the fact that winter months brought about more difficult conditions, thus evoking
criminal activity of necessity (i.e., stealing goods). In the summer months, however, he
believed that crimes against persons were precipitated by higher temperatures that lead
to increased discomfort and aggression as well as increased “collisions” with other
people (Quetelet, 1842, p. 56). During the summer, the former implied that higher
temperatures brought on violent behavior due to physical discomfort, whereas the lat-
ter involved the principles of environmental criminology, namely, that the more people
come into contact with one another, the more likely they are to cross paths with a
potential offender and/or victim.
Empirical testing of routine activities theory with respect to crime seasonality has
predominantly focused on the patterns of assault (see Block, 1984; Breetzke & Cohn,
2012; Cohn & Rotton, 1997; Harries, Stadler, & Zdorkowski, 1984; Michael & Zumpe,
1983). As discussed above, however, more recent research has begun to emerge
regarding various crime types, including property crime (see Cohn & Rotton, 2000;
Farrell & Pease, 1994; Hipp et al., 2004; McDowall et al., 2012), and the majority of
these articles employ routine activities as the theoretical framework for the research.
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4 International Journal of Offender Therapy and Comparative Criminology
Violent Crime
Although much of the research on seasonality has not come to a firm consensus on the
temporal trends of various crime types, most of the empirical literature has consis-
tently found statistically significant peaks for assault in the summer months (Cohn,
1990; Harries et al., 1984; Michael & Zumpe, 1983). Due to the violent nature of these
offences, researchers often attribute the trends to both heightened aggression levels,
due to elevated temperatures, as well as increased engagement in leisure activities
outside of the home (Breetzke & Cohn, 2012; Cohn & Rotton, 1997; Uittenbogaard &
Ceccato, 2012). In other words, having large groups of frustrated and/or aggressive
people congregating in specific places during these times increases the occurrence of
offending and subsequent victimization. Moreover, these effects can also be exacer-
bated when alcohol is a factor in the interaction (Harries et al., 1984).
Research regarding the seasonality of sexual assault is limited, and of the literature
that exists, there seems to be no firm consensus on any distinct annual rhythms for the
offence. Some studies have found significant peaks for rapes in the summer months
(Michael & Zumpe, 1983; Perry & Simpson, 1987). Others, however, have not found
statistically significant relationships between rape and higher temperatures (DeFronzo,
1984). These empirical inconsistencies may be present due to the specificity of victim
selection preferred by sexual offenders and whether they choose to offend against
strangers or acquaintances as well as whether or not they assault the same victims on
multiple occasions (Leclerc, Wortley, & Smallbone, 2010; Maguire & Brookman,
2009).
After assault, homicide is arguably the second most studied crime type in the sea-
sonality literature. Unlike assault, however, little agreement exists regarding universal
offending patterns (Cohn, 1990). Some research has found distinct summer increases
of homicides in Dallas, Texas (Harries et al., 1984); Sao Paulo, Brazil (Ceccato, 2005);
and Tshwane, South Africa (Breetzke & Cohn, 2012), whereas other studies in major
American cities (McDowall et al., 2012), Canada (Dagum, Huot, & Morry, 1988), and
Israel (Landau & Fridman, 1993) were unable to identify concrete statistical evidence
for a single seasonal peak. Similar to the conclusions made in the sexual offending
literature, researchers have argued that these seasonal inconsistencies could be due to
individually motivating factors and that the victims were often known to the offenders
(see Landau & Fridman, 1993).
Property Crime
In a test designed to contrast the explanatory capacities of temperature aggression and
routine activities theory, Hipp et al. (2004) generated a hypothesis based on tempera-
ture aggression theory stating that because seasonal variation is due to higher tempera-
tures that lead to violent behavior, “there [would] not be a seasonal effect for property
crime rates” because the offences are not aggressive in nature (Hipp et al., 2004,
p. 1338). After applying a latent curve model to their 3 years’ worth of uniform crime
report data, they found statistically significant oscillations for both violent and
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Linning et al. 5
property crimes (Hipp et al., 2004). Such results provided further support for routine
activities theory as well as evidence for the existence of crime seasonality for property
crimes. Other studies have also found that burglaries tend to peak during the summer
months (Chimbos, 1973; Cohn & Rotton, 2000; McDowall et al., 2012). Such tempo-
ral patterns have often been attributed to the fact that summertime leisure activities of
people remove them from their homes, thus eliminating capable guardianship of their
property. This provides ideal target selection for motivated offenders (Chimbos, 1973).
Furthermore, one of the most effective crime prevention techniques for burglary is to
lock the doors and windows of one’s home, but in the summer months, these are more
often left open due to higher temperatures (Hamilton-Smith & Kent, 2009). Such
actions provide offenders with easier access to homes. Despite these findings, other
research in England has indicated that residential burglary drops to the lowest annual
levels in the summer months and is instead at its highest in February and March
(Farrell & Pease, 1994). What should be noted, however, is that Farrell and Pease’s
(1994) work focused solely on residential burglaries as opposed to a more routinely
used aggregate burglary variable. Unfortunately, little work has been done with disag-
gregated burglary data. As such, less is known as to whether residential burglaries
experience the same seasonal trends as commercial burglaries. Contrary to the issue of
unlocked doors and windows of houses in the hotter months, many businesses operate
consistently throughout the year, thus eliminating a particular prime time to commit
such an offence (see also Sherman, Gartin, & Buerger, 1989).
Robbery has also been characterized by an inconsistency in the empirical literature.
In their analysis of 3 years’ worth of data for Minneapolis, Minnesota, Cohn and
Rotton (2000) found that robberies peaked during the summer months. Conversely,
McDowall et al. (2012) found that all crimes except for robbery peaked in the summer
months. More specifically, they observed that robberies were highest in December but
that there was also a peak in the summer (McDowall et al., 2012). Michael and Zumpe
(1983) found that only five of their 16 American locations had statistically significant
rhythms for robbery and that they peaked in November-December. Finally, Landau
and Fridman (1993) found that in Israel, robberies peaked in the winter months and
attributed these findings to monetary motives based on increased unemployment and
necessities such as food, clothing, and shelter. Moreover, they explained that the colder
weather present in winter months reduced the number of people in the streets (i.e.,
capable guardians), thus increasing the suitability of targets who were present. They
also believed that shortened daylight hours put potential victims at an increased likeli-
hood of victimization (Landau & Fridman, 1993). However, some contradictory
results have argued that the winter months provide increased opportunities due to peak
shopping times for the Christmas holidays (Andresen & Malleson, 2013). Therefore,
robberies may also be largely attributed to “the intensity of activity on the street”
(Loukaitou-Sideris, 1999, p. 397).
Of the existing seasonality literature concerning motor vehicle theft, mixed results
have been found. Farrell and Pease (1994) found no distinct seasonal trend for motor
vehicle theft. Instead, they only observed a drop in calls for service to police in 1988
that they attributed to a “change in recording practices” (p. 492). Likewise, E. G. Cohen
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6 International Journal of Offender Therapy and Comparative Criminology
et al. (2003, p. 21) discovered a “cancelling-out effect” in their assessment of vehicle
thefts. More specifically, the statistically significant predictor variables
1
demonstrated
opposing impacts on the dependent variable, thus annulling any annual peaks (Cohen
at al., 2003). More recently, McDowall et al. (2012) found distinct seasonal peaks for
motor vehicle theft over a 24-year period across 88 American cities. The months that
the crime count spiked, however, differed once they controlled for average monthly
temperatures. When solely using the raw crime data, offences peaked in August and
reached their lowest in February. However, when temperature was accounted for in the
model, motor vehicle thefts were highest in December-January and lowest “in the
spring and early summer” (McDowall et al., 2012, p. 399). Finally, when exploring
trends within each of the 88 American cities, McDowall et al. (2012) found much larger
magnitude changes in the percentage difference between peaks and troughs in cities
with generally colder climates. In other words, cities that experience large annual tem-
perature changes (e.g., Minneapolis, Minnesota) exhibited more distinct seasonal trends
for motor vehicle theft than cities with more consistent ambient temperatures (e.g.,
Honolulu, Hawaii; McDowall et al., 2012). This provides strong support for the need
for additional comparisons of cities with differing annual climates.
The Present Study
Based on the above literature, there is still a need for further research on property crime
patterns, particularly when two cities have divergent climates. As such, this study seeks
to explore the relationship between property crime and weather using Canadian data
from Vancouver, British Columbia and Ottawa, Ontario. Table 1 shows the temperature
differences, by season, for Vancouver and Ottawa.
2
Overall, the seasonal differences
are statistically significant at the 1% level, but there are variations for the different cit-
ies. Ottawa has greater differences while comparing seasons than Vancouver, and con-
sequently, all of the seasonal temperature differences are statistically significant at the
1% level. The temperature differences in Vancouver are statistically significant at the
1% level for all comparisons except for fall and winter, p = .15. Given the proposed
research questions and principles guided by routine activities theory, the following
hypotheses were generated regarding the temporal aspects of crime seasonality:
Hypothesis 1: Temporal peaks in property crime will be more distinct in cities
with more variable weather seasons.
Hypothesis 2: Different weather variables will have greater impacts on particu-
lar property crime types due to the situation-specific characteristics needed to
carry out the offences.
Method
Characteristics of the Two Cities
Vancouver, British Columbia and Ottawa, Ontario are the third and fourth most popu-
lated census metropolitan areas (CMAs), respectively, in Canada, following only
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Linning et al. 7
Toronto, Ontario and Montreal, Québec. The former is a coastal city located on the
Pacific Ocean that is famous for its frequent rainfall in all seasons except the summer
and very little snowfall in the winter. Generally, there is very little variability in the
overall temperature throughout the calendar year. In fact, based on 29 years’ worth of
historical climate data, Environment Canada (2015b) lists average daily temperatures
in Vancouver that range between 3.6 °C (38 °F) in December to 18.0 °C (64 °F) in July
and August. Average rainfall levels are more drastic and range from 35.6 mm (1.4
inches) in July to 185.8 mm (7.3 inches) in November. Conversely, average snowfall
amounts remain relatively low and range from 0 cm in April through October and 15
cm (5.9 inches) in December (Environment Canada, 2015b). Ottawa, Ontario is the
capital city of Canada and is located between Toronto, Ontario and Montreal, Québec.
The city’s climate is very humid and experiences more drastic changes in temperature
throughout the year and less rainfall than Vancouver. Based on historical climate data
from 1981 to 2010, Environment Canada lists Ottawa’s average daily temperatures as
ranging from −10.3 °C (13 °F) in January to 21.0 °C (70 °F) in July. Extreme tempera-
tures have historically ranged between −36.1 °C (−33 °F) and 37.8 °C (100 °F).
Average rainfall amounts range from 18.7 mm (0.7 inches) in February to 92.8 mm
(3.7 inches) in June, and average snowfall ranges from 0 cm in May through September
and can reach as high as 54 cm (21 inches) in January (Environment Canada, 2015a).
In addition to these differences in regard to weather, there are notable differences
between these cities. Ottawa is the federal capital of Canada with a population of
812,000 in 2006 according to the census, an increase in population of 5% from 2001.
This population makes Ottawa the fourth largest city in Canada. The CMA of Ottawa
had a population of approximately 875,000 the same year, making it the sixth largest
CMA in Canada (Dauvergne & Turner, 2010; Gannon, 2006; Statistics Canada, 2007).
In 2006, the Ottawa CMA had a crime rate of 5,775 per 100,000 persons, nearly one
half of the crime rate in the Vancouver CMA, 10,609 per 100,000 persons (Silver,
2007). In 2006, the City of Vancouver had a population of 578,000 persons, whereas
the Vancouver CMA had a population of approximately two million persons. This also
corresponds to far greater population density in Vancouver than Ottawa. Because of
the longer time frame for data from Vancouver, discussed below, this city experienced
substantial events and change during this time. Most notable during the study period
Table 1. Temperature Differences by Season, ANOVA.
Vancouver Ottawa
Spring and summer 4.75*** 5.38***
Spring and fall 5.75*** 11.66***
Spring and winter 7.18*** 19.35***
Summer and fall 10.50*** 17.14***
Summer and winter 11.93*** 24.73***
Fall and winter 1.43 7.69***
Overall F = 154.54*** F = 829.84***
*p < .10. **p < .05. ***p < .01.
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8 International Journal of Offender Therapy and Comparative Criminology
were the 2010 Winter Olympic Games and the establishment of a new light rail rapid
transit line the previous year. And finally, with regard to crime, research has shown
that crime is far more concentrated in Ottawa than in Vancouver (Andresen & Linning,
2012). Needless to say, weather does not represent the only set of differences between
Ottawa and Vancouver.
Data
The data for both Vancouver and Ottawa were employed to test the proposed hypoth-
eses. The dependent variables for Vancouver are the natural logarithms of crime rates
per 10,000 calculated using police incident data and annual city population data. (We
used a linear interpolation between the annual figures to obtain monthly population
estimates—natural logarithms ease the interpretation of estimated parameters across
crime types because the estimated parameters represent the percentage change in the
dependent variable, given a one unit change in the independent variable.) The police
incident data were retrieved from the Vancouver Open Data Catalogue website
3
and
contained information pertaining to location and month of occurrence on four crime
types over an 11-year period (2003-2013), whereas the Vancouver population data
were retrieved from BC Stats.
4
The crime classifications include commercial break
and enter, mischief,
5
theft from vehicle, and theft of vehicle.
Similarly, dependent variables for the city of Ottawa were taken from police inci-
dent data posted on the Ottawa Police Service website.
6
Those data contained informa-
tion regarding the specific dates and times as well as locations of offences that occurred
between 2006 and 2008. Because these data were available for each day, crime rate
calculations would prove to be problematic. With the range of the crime counts for
these crime types ranging from 0 to 22 (0 to 41 for total crime), a count-based regres-
sion model is appropriate, discussed further below. The crime classifications that
included data throughout this entire 3-year period include commercial break and enter,
residential break and enter, robbery,
7
and stolen vehicle. Records of mischief incidents
began consistently being reported in April 2007 until December 2008. As such, the use
of mischief will be discussed further when its inclusion could be justified in certain
analyses. Finally, it should be noted that these data apply exclusively to the cities of
Vancouver and Ottawa, not their CMAs.
The independent variables concerning climatic conditions were obtained from two
sources. All data representing weather variables including temperature, rain, snow,
and precipitation were retrieved from the Environment Canada historical climate data
webpage.
8
Similarly, all information regarding the times and hours of illumination
were obtained from the National Research Council of Canada website.
9
For both cit-
ies, we included the average and maximum temperature: average and maximum for
the month in Vancouver and for the day in Ottawa. For rain and snow, we included the
maximum, average, and total measures for Vancouver because we use monthly data;
for Ottawa, we only use total rain and total snow because the data are measured daily.
Information regarding the descriptive statistics and frequencies of the independent and
dependent variables are available upon request.
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Linning et al. 9
Month and month-squared trend variables were included in the analyses to account
for the quadratic seasonal trend found in some of the literature, discussed above:
Month is expected to have a positive sign, and month-squared is expected to have a
negative sign. The month variable was assigned sequential values (1, 2, 3, . . ., 12) for
each month, and each crime type and month squared is simply the square of month.
Because the Vancouver data were identified only by month, variables for the number
of days in the month as well as the number of weekend days in a month were created.
We also included a set of dummy variables. Dummy variables were created to control
for the annual seasons, and the seasons themselves were defined as the following:
winter (January-March), spring (April-June), summer (July-September), and fall
(October-December). In the case of Ottawa, however, information regarding the spe-
cific day was available, and thus, trend variables for day of the year and day of the
entire data set were included. Moreover, dummy variables for major and minor holi-
days
10
were created because they could be identified in the data set. The dummy vari-
ables for the seasons remained the same as those of Vancouver for consistency. The
descriptive statistics for the dependent variables and the climate-related independent
variables are all shown in Table 2.
Analytical Strategy
To operationalize and test the aforementioned temporal hypotheses, a series of descrip-
tive evaluations, ordinary least squares (OLS) regressions (Vancouver), and negative
binomial regressions (Ottawa) were conducted on the Vancouver and Ottawa data
using R: The Project for Statistical Computing (http://www.R-project.org). As stated
above, the dependent variables for Vancouver are calculated rates making OLS appro-
priate, and the dependent variables for Ottawa overdispersed counts making negative
binomial regression appropriate. Eleven distinct models were run on each of the inde-
pendent variables (both aggregate and disaggregate) using the Vancouver and Ottawa
data. Initial tests checking for OLS assumptions were conducted and revealed that
error terms in all models were not independently or identically distributed. In fact,
both autocorrelation and heteroscedasticity were present in the data. As such, robust
covariance matrix estimators were incorporated into the regression analyses using the
vcovHAC function in R—feasible generalized least squares. The use of robust stan-
dard errors most often lead to a smaller number of statistically significant variables in
all of the models; this shows the importance of correcting for nonspherical errors that
can lead to the erroneous inclusion of variables in a model when they are, in fact, sta-
tistically insignificant.
Results
Descriptive Patterns
Graphical representations of crime frequencies in both cities can be found in Figures 1
through 3. Figure 1 contains the aggregate total crime trends over the 11 years in
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10 International Journal of Offender Therapy and Comparative Criminology
Vancouver and 3 years in Ottawa. With the exception of the graph for mischief,
11
each
figure shows the percent variation from the annual mean to display a more standard-
ized view of the data for easier comparison. Given the more distinct changes in weather
patterns mentioned above that occur in Ottawa, it is not surprising that more definite
peaks in crime are visible, particularly in the summer months. In Vancouver, however,
consistent and reoccurring surges in crime are not as apparent. Moving to Figures 2
and 3, trends for each of the individual crime types can be seen in both cities. These
graphs demonstrate the importance of disaggregating data. For instance, in Figure 3c,
the monthly frequencies of robbery in Ottawa do not match the overall trends seen in
the total crime graph (Figure 1a). Instead, the latter may be largely driven by crime
types that occurred more frequently, such as theft of vehicle or residential break and
Table 2. Descriptive Statistics, Crime Rates and Counts, and Climate Variables, Vancouver
and Ottawa.
Minimum Maximum M SD
Ottawa
Commercial burglary (count) 0 15 3.91 2.46
Residence burglary (count) 0 22 6.03 3.28
Robbery (count) 0 10 1.95 1.58
Theft of vehicle (count) 0 20 6.46 3.31
Total crime (count) 0 41 18.35 6.44
Maximum temperature, °C (°F) −18.1 (−0.6) 36.3 (97) 11.86 (53) 12.36 (54)
Average temperature, °C (°F) −21.6 (−7) 29.3 (84) 6.84 (44) 11.62 (53)
Total rain, mm (inches) 0 68 (2.7) 2.18 (0.1) 5.57 (0.2)
Total snow, mm (inches) 0 35.6 (1.4) 0.754 (0.03) 2.89 (0.1)
Total illumination, hours 9.87 16.91 13.31 2.41
Twilight, hours 0.98 1.26 1.08 0.09
Vancouver
Commercial burglary (rate) 1.45 6.40 3.21 1.04
Mischief (rate) 4.55 11.09 6.90 1.37
Theft from vehicle (rate) 7.61 30.96 16.55 5.86
Theft of vehicle (rate) 0.91 11.02 4.24 2.83
Total crime (rate) 16.09 55.51 30.91 10.50
Maximum temperature, °C (°F) 8.5 (47) 34.4 (94) 19.48 (67) 6.40 (44)
Average temperature, °C (°F) 0.9 (34) 19.7 (67) 10.50 (51) 5.37 (42)
Maximum rain, mm (inches) 0 91.6 (3.6) 21.71 (0.9) 14.33 (0.6)
Average rain, mm (inches) 0 10.27 (0.4) 3.04 (0.1) 2.06 (0.1)
Total rain, mm (inches) 0 308 (12.1) 92.37 (3.6) 63.20 (2.5)
Maximum snow, mm (inches) 0 45 (1.8) 1.60 (0.1) 5.29 (0.2)
Average snow, mm (inches) 0 3 (0.1) 0.09 (0.003) 0.31 (0.01)
Total snow, mm (inches) 0 89 (3.5) 2.63 (0.1) 9.41 (0.4)
Total illumination, hours 10 18 13.44 2.79
Twilight, hours 1.1 1.4 1.20 0.11
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Linning et al. 11
enter (frequency tables are available upon request). As such, the underlying trends of
other crime types are likely masked by the overall aggregate trends.
Regression Results
The first regression models that were run for this study merely involved each of the
available crime types and the month and month-squared variables. Seeing as a sea-
sonal/quadratic relationship was anticipated, a standard OLS regression would not
Figure 1. Monthly crime trends: (a) Total crime, Ottawa (2006-2008) and (b) total crime,
Vancouver (2003-2013).
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Linning et al. 13
provide an accurate reflection of the data because it assumes that a linear relationship
exists between the independent and dependent variables. As such, month and month-
squared variables were computed and incorporated to test/control for the quadratic
(inverted U-shaped) distribution that was likely present in the data. Tables 3 and 4,
show the 11 models that were run with the trend variables to test for the presence of
crime seasonality using all 14 years of data. Not surprisingly, quadratic relationships
were present across all crime types in Ottawa and were statistically significant at the
p < .001 level. Conversely, mischief was the only crime classification that exhibited a
significant curvilinear distribution in Vancouver between 2003 and 2013. Given the
differences in climate, it is not surprising that crime in Ottawa yielded differing results.
Each of the aggregate and disaggregate crime variables generated statistically signifi-
cant results when tested with the aforementioned predictor variables indicating the
existence of quadratic relationships. More specifically, the positive beta values for the
month variables and reciprocal negative values for the month-squared variable imply
that a curvilinear relationship exists. That is, crime levels increase early in the year and
subsequently decline over time.
Vancouver, British Columbia. Results from the OLS regression models for Vancouver
can be seen in Table 5. With the addition of weather, dummy, trend, and illumination
variables, month and month squared are no longer statistically significant for any
crime type but do retain their positive and negative estimated parameters, as expected,
Figure 2. Monthly crime trends, disaggregate crime types: (a) Commercial break and
enter, Vancouver (2003-2013), (b) mischief, Vancouver (2003-2013), (c) theft from vehicle,
Vancouver (2003-2013), and (d) theft of vehicle, Vancouver (2003-2013).
Figure 2. (continued)
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Linning et al. 15
except for commercial break and enter. The trend variable that was created to represent
each of the months over the 11-year time period and number of days in the month were
all statistically significant and possessed the same directional relationships. In other
words, as time went on between 2003 and 2013, crime decreased, and when there were
more days in a given month, more crime was committed. These two trends are consis-
tent with previous literature that has been conducted chronicling and examining the
Figure 3. Monthly crime trends, disaggregate crime types: (a) commercial break and enter,
Ottawa (2006-2008), (b) residential break and enter, Ottawa (2006-2008), (c) robbery,
Ottawa (2006-2008), (d) theft of vehicle, Ottawa (2006-2008), and (e) mischief, Ottawa
(2006-2008).
Note. Data for mischief were only available from April 2007 to December 2008 and the graph denotes
the frequency of crime, not the percent variation from annual mean.
Figure 3. (continued)
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16 International Journal of Offender Therapy and Comparative Criminology
well-documented crime drop that has been occurring in North America and Europe
(see Blumstein & Wallman, 2006; Farrell, Tilley, Tseloni, & Mailley, 2011). Moreover,
it is more than expected that crime will occur more frequently in months that contain
more days due to the increased time (and thus opportunity) that it allows for criminal
activity (see also Harries et al., 1984). Other than those two variables, however, no
additional variables possess significant relationships across all crime types. As such, a
disaggregate evaluation of each offence classification is considered.
Commercial break and enter in Vancouver had a significant relationship with the
three seasonal dummy variables (all negative) as well as the maximum snow variable
(positive). Collectively, these statistically significant results indicate that commercial
break and enter is most frequent during the winter months. This is also consistent with
the signs on month and month squared being opposite of expectations. Although mis-
chief possessed the expected signs for month and month squared, and had a statisti-
cally significant relationship with the number of weekend days in the month, none of
the seasonal or climate-related or illumination variables were statistically significant.
The results for theft from and theft of vehicle have the most potential to provide
invaluable insights into the seasonality literature regarding cities with different cli-
mates. Once again, the month and month-squared variables indicate that there is no
statistically significant quadratic relationship present in the data. Moreover, none of
Table 3. Temporal Regression Results, Seasonal/Quadratic Relationship, Vancouver,
2003-2013.
Commercial
break and enter Mischief
Theft from
vehicle
Theft of
vehicle
Total
crime
Month −0.419 25.739*** 15.703 −1.185 39.839
Month squared −0.027 −2.016*** −0.917 −0.076 −3.036
Adjusted R
2
−.013 .0688 −.013 −.013 −.012
F-statistic 0.136 5.839*** 0.16 0.140 0.197
*p < .10. **p < .05. ***p < .01.
Table 4. Temporal Regression Results, Seasonal/Quadratic Relationship, Ottawa,
2006-2008; Mischief (2007-2008).
Commercial
break and
enter
Residential
break and
enter Robbery
Theft of
vehicle
Total crime
(without
mischief)
Mischief
(2007-2008)
Month 0.580**** 1.255**** 0.227**** 0.686**** 2.826**** 6.323****
Month
squared
−0.042**** −0.094**** −0.015*** −0.044*** −0.201**** −0.465****
Adjusted R
2
.033 .092 .013 .033 .123 .283
F-statistic 19.770**** 56.510**** 8.233**** 19.280**** 77.720**** 127.800****
*
p < .10. **p < .05. ***p < .01. ****p < .001.
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Linning et al. 17
the seasonal dummy variables were able to elicit specific temporal concentrations to a
given time of the year for theft from vehicle or theft of vehicle. However, when the
maximum temperature for each month increased, there was an increase in the theft
from vehicle crime rate of 1.3% (β = 1.3%; p < .01). It is possible that warmer tem-
peratures make activities outside of the home more attractive, thus increasing the
exposure to potential targets. However, even when temperatures are at their peak,
offenders may still have more activities outside of the home because of the greater
volume of rain in Vancouver and the fact that maximum temperatures in Vancouver are
often considered relatively comfortable because of the temperate climate.
Ottawa, Ontario. Results from the negative binomial regression models are displayed
in Table 6. These models show that the addition of additional weather, illumination,
and dummy variables renders the month and month-squared relationship insignificant
Table 5. Temporal Regression Results, Vancouver, 2003-2013.
Commercial
break and
enter Mischief
Theft from
vehicle
Theft of
vehicle Total crime
Month −0.075 0.087 0.071 0.074 0.058
Month squared 0.007 −0.007 −0.005 −0.005 −0.004
Month total (trend) −0.007*** −0.004*** −0.009*** −0.018*** −0.008***
No. of days in
month
0.049** 0.054*** 0.062*** 0.061** 0.060***
No. of weekend
days in month
0.009 0.018** 0.000 −0.007 0.003
Spring (dummy) −0.132* 0.011 0.013 −0.022 −0.008
Summer (dummy) −0.235** −0.088 −0.093 −0.118 −0.124**
Fall (dummy) −0.166* −0.008 −0.110 −0.103 −0.100*
Maximum
temperature
−0.004 −0.009 0.013* 0.000 0.003
Average
temperature
0.012 0.014 0.003 0.017 0.011**
Maximum rain 0.002 0.001 0.001 0.001 0.001
Average rain 0.111 0.097 0.329 0.275 0.256*
Total rain −0.004 −0.004 −0.011 −0.009 −0.009*
Maximum snow 0.006* −0.001 −0.001 0.000 0.001
Average snow 0.061 0.091 0.967 −1.079 0.401
Total snow −0.005 −0.004 −0.030 0.034 −0.013
Total hours of
illumination
0.036 −0.017 −0.048 −0.046 −0.033
No. of twilight hours −0.299 0.064 −0.003 0.069 −0.001
Adjusted R
2
.7215 .676 .859 .939 .918
F-statistic 19.86*** 16.17*** 45.24*** 112.7*** 82.83***
*p < .10. **p < .05. ***p < .01.
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18 International Journal of Offender Therapy and Comparative Criminology
in all models except for commercial break and enter and mischief. These outcomes
suggest that the additional climate-related variables are accounting for further varia-
tion in crime that was initially captured by the month and month-squared variables.
This implies that with the exception of these two offences, factors other than time and/
or calendar months contribute to the seasonal fluctuations of crime in the city.
Contrary to the results found in Vancouver, nearly all models indicated that weather
had an impact on crime in Ottawa. These are not surprising given the more distinct
weather variations that are characteristic of the city. Curiously, only total crime has a
statistically significant and positive relationship with the summer variable. However,
many other variables revealed results that were consistent with summer crime peaks.
On average, as temperatures rose so did property crime, and as snowfall amounts
increased in the winter months, property crime fell in all cases but robbery and theft of
Table 6. Temporal Negative Binomial Regression Results, Ottawa, 2006-2008.
Commercial
break and
enter
Residential
break and
enter Robbery
Theft of
vehicle
Total crime
(without
mischief)
Mischief
(2008)
Month 0.223* 0.071 0.130 −0.088 0.053 0.475***
Month
squared
−0.017* −0.008 −0.011 0.008 −0.004 −0.034***
Day of data
set (trend)
−0.000 −0.000*** −0.000* −0.001*** −0.000*** −0.001
Major holiday
(dummy)
0.150 −0.074 −0.244 −0.208** −0.084 0.064
Minor holiday
(dummy)
0.028 −0.037 −0.224 −0.261* −0.119 −0.289
Spring
(dummy)
0.060 −0.067 0.127 0.011 0.010 −0.225
Summer
(dummy)
0.172 0.195 0.073 0.141 0.167** −0.066
Fall (dummy) −0.058 0.218 0.151 0.127 0.129 −0.003
Maximum
temperature
−0.027** −0.012 0.015 0.003 −0.007 0.005
Average
temperature
0.027** 0.024*** −0.001 −0.001 0.013** 0.009
Total rain −0.071 −0.050 0.078 −0.043 −0.039* 0.001
Total snow −0.069* −0.052* 0.061 −0.044 −0.041** −0.016**
Total hours of
illumination
−0.053 −0.037 −0.086 0.062* −0.002* −0.114*
No. of twilight
hours
0.387 1.105*** 0.547 −0.492 0.345 0.999*
AIC 4,857.3 5,340.0 3,850.3 5,459.0 6,855.1 2,590.4
Note. AIC = Akaike information criterion.
*p < .10. **p < .05. ***p < .01.
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Linning et al. 19
vehicle. Variables that measured the number of daylight hours and/or twilight also
revealed that longer days experienced more residential break and enter and theft of
vehicle. This is not surprising given that daylight hours increase in the summer months.
Overall, these results suggest that property crime tends to peak in the summer months
in cities that experience greater fluctuations in weather and temperature patterns.
Discussion
Although a number of studies have been conducted to assess crime seasonality, works
that have focused on property crime trends are less common. Moreover, in some cases
when property offences were evaluated, the investigators often aggregated the crime
types into an aggregate property crime variable (see Hipp et al., 2004). Fortunately, the
data sets employed in this study contained sufficient information to assess distinct prop-
erty crime types separately. For instance, both the commercial and residential break and
enter offence variables in Ottawa and the theft of and theft from vehicle data from
Vancouver allowed for some very interesting comparisons. Given the frequently cited
offence-specific characteristics sought out by offenders in the search of suitable targets
regardless of crime type (see Beauregard, Rebocho, & Rossmo, 2010; Clarke, 1980;
Kennedy, 1990; Neel & Taylor, 2000; Wright, Logie, & Decker, 1995), it is unsurpris-
ing that different offences yielded distinct seasonal patterns. Moreover, the direct exam-
ination of two cities with differing climates also generated some valuable insights into
the existence (or lack thereof) of seasonal crime trends within the same country.
Before proceeding to further discussion, it should be noted that no research is con-
ducted without limitations, and this study is no exception. First, although these results
are certainly informative, they can only provide insights into the various property
crime types included in the datasets. As previously mentioned, the total crime vari-
ables are simply an aggregate of each of the crime types included in the data. Therefore,
no conclusions can be made regarding other crime classifications such as assault,
homicide, or drug offences. Second, the crime classifications were not explicitly
defined in the data and should thus be factored into all interpretations particularly
when comparing results from the two cities. Although crime types such as theft of
vehicle and/or commercial break and enter are relatively straightforward, a variable
such as mischief could be defined differently by each police department.
The above issue also coincides with the concern regarding the accuracy of police
data. While discrepancies in reporting practices of different police agencies are a very
common occurrence, one must also consider the variation between the individual
police officers who file the reports. For an in-depth discussion of the accuracy of
police report data, see Sherman et al. (1989). Finally, the differing temporal units of
analysis should be discussed. Although the analyses incorporated Vancouver crime
data over an 11-year period, the data were aggregated to the month of occurrence as
opposed to the day. Conversely, the Ottawa Police Department released data contain-
ing the specific date of offence (i.e., day) but only for a 3-year period. Despite these
discrepancies between cities/data, however, comparisons are still warranted but must
be taken with caution.
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20 International Journal of Offender Therapy and Comparative Criminology
By comparing the temporal property crime trends in two cities with different cli-
mates, it is no surprise that divergent trends emerged. In fact, these results are consis-
tent with past research that had concluded that cities with warmer climates had flatter
seasonal crime cycles and that, conversely, seasonality was strongest in colder climate
cities (Hipp et al., 2004; McDowall et al., 2012). As such, because Vancouver experi-
ences relatively minor climatic fluctuations throughout the year, it was expected that
annual variations would likely be limited. Instead, factors such as frequent rainfall,
which is famously characteristic of the city, and warmer temperatures appeared to
have more of an impact. Given the scarcity of snowfall, it was also predictable to find
that snow had no effect on crime in Vancouver.
Conversely, Ottawa’s more distinct seasons exhibited readily apparent impacts on
crime in the regression models. In fact, with the exception of robbery, each of the
property crimes generated multiple significant relationships with many of the climate
variables. A prime example would be the consistently significant negative relation-
ships between snowfall and crime across nearly every crime type. Such findings rein-
force the results of Hipp et al. (2004) who found “considerable evidence of a seasonal
effect for property crime,” thus providing support for the explanatory use of routine
activities theory as opposed to temperature aggression theory (Hipp et al., 2004,
p. 1363). Therefore, the hypothesis that posited that seasonality would exist in cities
with more distinct weather seasons appears to be substantiated by these data.
The disaggregation of crime types has also proven useful in understanding some of
the underlying property crime trends. The most obvious example demonstrating the
advantages of individual analyses is robbery in Ottawa. Although the initial month and
month squared only models indicated that quadratic relationship existed, the models
with climatic variables showed virtually no seasonal pattern. However, the aggregate
variable for Ottawa indicated that temporal-, illumination-, and weather-related influ-
ences were all present and that they could account for much of the occurrence of
crime. Therefore, had the disaggregation of crime types been omitted from these anal-
yses, the underlying robbery patterns would have been masked within the overall total
crime variable. As such, these results provide further support for the situational crime
prevention literature and the importance of studying crime-specific trends (see Clarke,
1980; L. E. Cohen & Felson, 1979).
At yet another level, different climate-related variables demonstrated varying
impacts on each crime type in both cities. There were, however, weather variables that
possessed more common impacts on various crime types in Ottawa than Vancouver.
More specifically, in Vancouver, rainfall variables mattered for the occurrence of total
crime, but only one temperature variable affected theft from vehicle. Furthermore,
there were no set variables that were consistently significant across all crime types.
This is likely due to the relative lack of seasonal variations in climate in Vancouver;
rain is common all year and temperatures are most often mild. In Ottawa, however,
very different trends emerged. Instead, climatic variables such as temperature and
snowfall generated consistent relationships across most crime types incorporated into
the models. For example, with the exception of robbery and theft of vehicle, there was
a statistically significant decrease in crime as snowfall levels increased. Simultaneously,
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Linning et al. 21
increasing rainfall amounts had no significant impact on any of the individual crime
types included, only their aggregate. Interestingly, other variables such as hours of
illumination and temperature provided significant impacts on crime but only with cer-
tain types of offences. This provides some support for the second hypothesis that dif-
ferent weather variables will only affect certain crime types. Therefore, these results
indicate that not only was the disaggregation of crime types important to parse out
individual offence patterns but these disaggregate patterns can also vary across cities
with differing climates. As such, replication of such temporal analyses in different cit-
ies would not be redundant to our knowledge of seasonal crime trends.
Within the above discussion, we have alluded to implications from these results that
are relevant for policing and communities in the context of crime prevention. However,
some more explicit discussion is warranted. Of course, climate and illumination vari-
ables cannot be modified with the goal of preventing or reducing crime, but under-
standing their impact may be useful for police and communities to be able to affect the
volume of criminal events. For example, in the context of Vancouver, climate and
illumination variables have very little impact on temporal crime patterns. However,
increases in the maximum temperature lead to modest increases in theft from vehicle.
Knowing this in conjunction with knowledge of where theft from vehicle occurs may
be instructive for those who wish to prevent such crimes. In the context of commercial
break and enter, winter is the season most affected by this crime; knowing this may
influence the behavior of the business community with specific crime prevention
efforts. In the context of Ottawa, weather can have more of a predictive effect for
police and communities. For example, increases in average temperature lead to
increases in commercial and residential break and enter with (albeit statistically insig-
nificant) decreases in robbery and theft of vehicle. Conversely, increases in snow
decrease the volume of commercial and residential break and enter. Such knowledge
can help inform police of the appropriate crime prevention initiatives, or proactive
policing strategies, given the nature of the weather.
This also leads to the importance of understanding theory in the context of temporal
variations in crime. Within the seasonality of, or temporal variations in, crime litera-
ture, routine activity theory is often cited as being instructive for both property crime
and violent crime. The general assertion is that warmer temperatures, and correspond-
ing less precipitation, leads to more people being outside of the relatively protective
environment of the home. This leads to an increase in both property and violent crime.
Although general support for this hypothesis is found in the current article, we also
find more specific relationships between routine activities and criminal activity. We
argue that this is centered on the suitability of targets and capable guardianship.
In the results for Vancouver, as stated above, winter and snow are associated with
increases in commercial break and enter. Presumably, this is related to the shopping
season after and immediately prior to the Christmas and Boxing Day holidays.
Although some new commercial outlets may emerge during this time of the year, it is
unlikely that the change would be enough to alter citywide crime rates. Rather, because
of the increased volume of people in any given commercial outlet, capable guardian-
ship decreases. Even with modern security practices and increased security personnel
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22 International Journal of Offender Therapy and Comparative Criminology
at this time of the year, the volume of people out in commercial areas dominates
increases in security at this time. Perhaps more important is the role of commercial
burglary that occurs when commercial outlets are closed and there are no extra capable
guardians in the area. Because of the huge volume of sales during this time of year,
commercial outlets must necessarily increase their stock of goods. This increases the
payoffs for any given commercial break and enter. Consequently, the suitability of a
target has a temporal component and is not simply placed on the suitability of the
actual items being stolen: target suitability has multiple dimensions. For Ottawa,
though this manifests itself through temperature and snowfall, the opposite relation-
ship exists: decreases in temperature and increases in snow decrease the volume of
both commercial and residential break and enter. In this particular case, though com-
mercial outlets and residences will have more items to steal this time of the year, the
weather decreases target suitability because it would be more difficult to leave a crime
scene in this more extreme weather. Again, this shows that target suitability has mul-
tiple dimensions, not simply related to the presence of lightweight and valuable items;
the volume of these items available and the environment within which they are placed
also affects the suitability of these targets and shows that routine activity theory pro-
vides deeper insight into the temporal variations of crime literature than simple
changes in the volume of convergences.
Future research on these trends is still warranted and any similar replications should
strongly consider some of the aforementioned methodological choices. The use of
longitudinal data, particularly of daily intervals, over many years in multiple cities
would also be beneficial to the literature. Although the Vancouver data used in this
analysis were ideal for the study of a trend over time, data containing which day each
offence occurred as opposed to simply which month would have strengthened the
study. The analysis of data that contain an even greater variety of criminal offences
would be even more valuable. Moreover, the examination of data from multiple juris-
dictions, ideally recorded by the same provincial and/or federal police agency, would
also be invaluable to the seasonality literature. As previously discussed, it is very
likely that data sets that include other crime types, such as violent crimes that involved
mobile targets (i.e., people), will generate very different spatial results in the study of
crime seasonality. Access to and use of such data would provide invaluable insights
into the true seasonal variations of crime. The use of temporal statistical methods such
as autoregressive integrated moving averages (ARIMA) and/or the Box-Jenkins
method, seasonal adjustments, and spectral and Fourier analyses could also provide a
better understanding of seasonal crime patterns (Block, 1983; Breetzke, 2015;
McPheters & Stronge, 1974; Rotton & Frey, 1985). Moreover, when possible, other
predictors that are relevant to each crime type should be included; however, the avail-
ability of such variables will be limited.
Running the above analyses across multiple cities and countries is imperative for
our understanding of annual crime patterns and the application of environment-spe-
cific prevention policies (see Block, 1983). It is apparent from these data that temporal
seasonality analyses in a given city may not translate effectively into another.
Moreover, law enforcement management should be aware of annual fluctuations in
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Linning et al. 23
crime to delegate available resources to the most effective units (Block, 1983). Because
each city will have its own weather patterns and general routine activities, the monitor-
ing of each city’s respective trends must be conducted individually. From a crime
prevention standpoint, knowledge of when these surges occur will assist in more effec-
tively applying the initiatives as well as when to evaluate their effectiveness (Andresen
& Malleson, 2013). As such, research into the seasonal patterns of both property and
violent offences is warranted and should prove useful in the continuing attempts to
reduce crime.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship,
and/or publication of this article: This research was supported by the Social Sciences and
Humanities Research Council of Canada (SSHRC).
Notes
1. The two variables deemed statistically significant from the authors’ principal components
analysis were the presence of retail establishments (e.g., department stores, check cash-
ing, etc.) and higher presence of young persons and transient populations (Cohen, Gorr, &
Durso, 2003, p.21).
2. There is also the issue of temperature comfort that includes humidity. However, each of
the two cities is analyzed independently such that the differences between Vancouver and
Ottawa do not need to be controlled for in the analysis.
3. http://data.vancouver.ca/datacatalogue/index.htm
4. http://www.bcstats.gov.bc.ca/StatisticsBySubject/Demography/PopulationEstimates.aspx
5. Mischief is defined as acts such as vandalism and property damage.
6. www.ottawapolice.ca
7. Although robbery is considered a violent crime, some have argued that it is best considered
a violent property crime because of its monetary nature (Indermaur, 1995).
8. http://climate.weather.gc.ca/
9. http://www.nrc-cnrc.gc.ca/eng/services/sunrise/advanced.html
10. Some literature has shown that the changes in routine activities brought about on holidays
can decrease the occurrence of crime because offenders are otherwise engaged in holiday
activities (see Cohn & Rotton, 2003).
11. Because only one full year of data were present for Ottawa (2008), an annual mean could
not be calculated for 2007. Therefore, counts were used in the mischief graph.
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