THE EFFECT OF TEMPERATURE ON DOMESTIC
AND SIMPLE BATTERY
A Thesis Presented to the Faculty
of
California State University, Stanislaus
In Partial Fulfillment
of the Requirements for the Degree
of Master of Arts in Criminal Justice
By
Reyna E. Dominguez
May 2013
CERTIFICATION OF APPROVAL
THE EFFECT OF TEMPERATURE ON DOMESTIC
AND SIMPLE BATTERY
by
Reyna E. Dominguez
Gregory Morris, PhD
Associate Professor of Criminal Justice
Date
Peter Nelligan, PhD
Professor of Criminal Justice
Date
Chau-Pu Chiang, PhD
Professor of Criminal Justice
Date
© 2013
Reyna E. Dominguez
ALL RIGHTS RESERVED
DEDICATION
This thesis is dedicated to my late father, who never experienced the
excitement of learning how to read and to my mother, who never had the opportunity
to attend school beyond first grade.
iv
ACKNOWLEDGEMENTS
I would like to express my gratitude to Dr. Gregory Morris, my advisor, for
his ideas, encouragement and patience. This thesis would have not been possible
without his valuable assistance.
I would like to thank Martin Smale for his guidance and advice regarding data
analysis. I also wish to thank Kevin Larkin for his assistance with SPSS. I am also
grateful to Andy Aldrich for his assistance in helping me understand the results.
Finally, I am eternally grateful for the continued loving support of Julie
Martin in this and my academic pursuit. A master’s degree would have not been
achievable without her financial and moral support. Four years ago, a master’s
degree was only a dream. Thank you for helping me realize my dream.
v
TABLE OF CONTENTS
PAGE
Dedication ...............................................................................................................
iv
Acknowledgements .................................................................................................
v
List of Tables ..........................................................................................................
vii
List of Figures ......................................................................................................... viii
Abstract ...................................................................................................................
ix
CHAPTER
I.
Introduction ...........................................................................................
1
II.
Literature Review..................................................................................
2
Physiological and Social Explanations for Seasonal Changes .
Physiological Explanation ........................................................
Social Explanation ....................................................................
Contrasting Temp/Aggression and Routine Activity Theory ...
Combining Physiological and Social Explanations ..................
Contemporary Research on Domestic Violence .......................
7
8
11
14
15
17
Methodology .........................................................................................
19
Hypothesis.................................................................................
Design .......................................................................................
Definition of Terms...................................................................
Meteorological Data..................................................................
Procedure ..................................................................................
19
19
20
21
22
Results ...................................................................................................
23
Summary of Findings ................................................................
30
Discussion .............................................................................................
31
Limitations and Recommendations...........................................
32
References ...............................................................................................................
35
III.
IV.
V.
vi
LIST OF TABLES
TABLE
PAGE
1. Raw Data Summary for Domestic and Simple Battery ....................................
23
2. Regression of Average Temperature and Domestic Battery.............................
25
3. Significance of the Variability on Domestic Battery (R Square) that can be
Explained by Temperature ................................................................................
25
4. Regression Coefficients for Domestic Battery .................................................
26
5. Regression of Average Temperature and Simple Battery.................................
27
6. Significance of the Variability on Simple Battery (R Square) that can be
Explained by Temperature ................................................................................
27
7. Regression Coefficients for Simple Battery .....................................................
28
8. Regression of Average Temperature and Total Batteries .................................
29
9. Significance of the Variability on Total Batteries (R Square) that can be
Explained by Temperatures ..............................................................................
29
10. Regression Coefficients for Total Batteries ......................................................
30
vii
LIST OF FIGURES
FIGURE
PAGE
1. Comparative regressions of domestic and simple battery.................................
viii
24
ABSTRACT
This study analyzed the daily incidents of domestic and simple battery in the city of
Sacramento with respect to temperature for the years 2005 to 2009. Cases of
domestic and simple battery reported to the Sacramento Police Department were
incorporated into the study. The average temperature reported in Fahrenheit recorded
at Sacramento Airport Weather Station was used in correlation and regression
analyses. Average temperature had a statistically significant weak positive
relationship with the number of domestic and simple batteries committed.
Further
research is needed to identify other social factors as well as other weather influences
that may increase the chance of domestic and simple battery.
ix
CHAPTER I
INTRODUCTION
For centuries, researchers have conducted studies regarding the association of
temperature and crime rates (Quetelet, 1872; Morrison, 1891; Dexter, 1904;
Lombroso, 1911; Falk, 1952; Cohen & Felson, 1979; Anderson et al, 1995; Cohn &
Rotton, 2003). Studies have focused on violent crimes as well as nonviolent crimes
(Anderson, 1984, 1987, 2001; Anderson et al, 1995, 1997; Baron & Bell, 1976;
Baron, 1978; Bell, 1992; Cohen & Felson, 1979; Cohn & Rotton, 1997, 2000, 2003,
2004; Rotton, 1993; Hipp et al, 2004). Although there are numerous studies related
to temperature and crimes including assault, there has not been a study addressing
domestic (non-cohabitant and co-habitant spouse) and simple assault. This paper
aims to examine whether or not there is a consistent increase on domestic and simple
assault due to temperature by day of the year.
The data for this research consisted of all non-duplicate daily domestic (noncohabitant and co-habitant spouse) and simple battery cases recorded by the
Sacramento (California) Police Department between January 1, 2005 and 11:59 p.m.
on December 31, 2009. Average daily temperature was also collected from the
Weather Underground website from 2005 to 2009. Correlation and regression
analyses were conducted on average temperature and number of domestic and simple
battery cases for all days of the five years (2005-2009). Correlation and regression
analyses were performed on domestic and simple battery cases separately.
1
CHAPTER II
LITERATURE REVIEW
Interest in the relationship between temperature and aggressive behavior can
be traced back to the 16th century. Shakespeare centers the whole plot of Romeo and
Juliet on the effects of a hot day. In the third act of the play, scene I, Shakespeare
places the following words into Benvolio’s mouth:
I pray thee, good Mercutio, let’s retire: The day is hot, the Capulets abroad,
And if we meet, we shall not ‘scape a brawl, for now, these hot days, is the
mad blood stirring.
And again in Henry IV, act 1, scene II:
But look you pray, all you that kiss my lady peace at home that our armies
join not in a hot day.
Although Shakespeare did not collect crime statistics to make his point, his
observations regarding hot weather and aggressive behavior are clearly evident in his
works.
The Abbe Du Bos (1719) maintained that high crime rates were associated
with unusually high Roman summer heat. Du Bos believed that increased crime rates
could not be attributed merely to “moral” grounds (i.e. contemporary conditions,
favorable opportunity, and/or patronage). This explanation was insufficient to
explain the increase in criminal behavior; he deduced that it must be due to essential
2
3
differences in climate (Koller 1937, 242-46). Montesquieu (1748), a French social
commentator and political thinker, also presented convincing arguments about the
differences in climate, and how these climate changes had substantial effects on
behavior (James, 1972). However, it was not until the 1800’s that European
researchers started collecting data and conducting empirical research on a variety of
offenses supporting temperature’s effect on aggressive behavior (Anderson, 1989).
M. de Guerry de Champneuf, Director of Criminal Affairs in the French Ministry of
Justice (1821-35), gathered the records of the different types of crimes committed
during the years 1825 to 1830 in the eighty-six departments of France. Regarding his
findings, de Champneuf wrote:
There is the influence of climate, and there is the influence of seasons, for
whereas the crimes against persons are always more numerous in the summer,
the crimes against property are more numerous in the winter—so of the crimes
committed in the South, the crimes against the person are far more numerous
than those against property, while in the North the crimes against property are,
in the same proportion, more numerous than those against the person.
Shortly thereafter, Adolphe Quetelet (1842) collected data from France and
examined seasonal effects on crime rates. Quetelet proposed the temperature/
aggression (T/A) theory. Temperature/aggression theory states that very hot
temperatures raise individuals’ annoyance, leading to violent actions. According to
this theory, violent offenses would be expected to attain the highest levels throughout
the warmest days of summer, whereas high temperature should not have any
4
fluctuation on property offenses. Quetelet collected data on the number of crimes
against persons and crimes against property for the years 1827-1828. He discovered
that the maximum number of crimes against the person occurred in June, which
coincided very close with the minimum in respect to crimes against property. While
crimes against the person reached minimum during winter, crimes against property
peaked. In comparing the two types of crimes, he learned that during January nearly
four crimes took place against property to one against persons, and in the month of
June only two to three. He concluded that violence of the passion prevailed during
the summer (Quetelet, [1842} 1968).
Edwin Grant Dexter (1904) collected records of almost 40,000 cases of assault
and battery by men (36,627) and women (3,134) in New York City during the years
1891 to 1897. Dexter presented a table illustrating the occurrence of assault and
battery; the table showed a steady increase from January, the coldest month, to July,
the hottest month, and a decrease for the remainder of the year. He also presented a
table showing temperature and the occurrence of assault and battery. He argued that
except for the very highest temperatures, the number of assaults increased with the
heat. He concluded, “Temperature more than any other condition affects the
emotional states which are conducive to fighting.” Dexter believed that weather
directly influences the emotional and physiological actions of the individual, thus
creating several inconsistencies of behavior (Dexter, 1904).
William D. Morrison (1891), a guardian of Wandsworth Prison in England,
published Crime and Its Causes. Morrison utilized the annual reports of the British
5
Prison Commissioners to show that prison population declined during cold
temperatures. Morrison showed that there was a decrease of admissions in the prison
from October to February every year. In the annual reports of the Prison
Commissioners, there was an informative diagram illustrating the mean number of
prisoners in the local prisons in England and Wales on the first Tuesday of each
month. The diagram had been published for a significant number of years. Morrison
utilized the sextennial diagram of the Prison Commissioners, which covered six years
(March, 1879 to March, 1884). The mean number of prisoners in the local prisons of
England and Wales on the first Tuesday in February was 17,600; on the first Tuesday
in April it had increased to 18,400; on the first Tuesday in July it had mounted nearly
19,000; on the first Tuesday in October it concluded at 19,200. From this date
forward the numbers declined just as gradually as they had formerly increased,
attaining their lowest number in February.
In one of the principal London prisons the average prison population during
the months of June, July and August for the five years ended August, 1889 was 1,061,
and the daily average number of punishments totaled 9 and a fraction per thousand.
The average population during the winter months of December, January, February,
for the five years ended February, 1890, was 1,009, and the daily average
punishments totaled to 7 and a fraction per thousand. According to these statistics,
there was an increase of 2 punishments per day, or 12 per week (skipping Sundays)
for every thousand prisoners in the three summer months as compared with the three
winter months. According to these findings, there was a greater predisposition among
6
inmates to commit offenses against prison regulations in warmer temperatures than in
cooler ones. Morrison argued that if inmates were free individuals living under a
variety of conditions, and subject to a collection of multifaceted influences, it was
likely to adduce all sorts of causes for the existence of such a phenomenon, and it
would had not been complicated to find reasonable arguments supporting each and
every one of them. However, the nearly absolute resemblance of conditions under
which inmates lived eliminated a vast accumulation of complicating factors.
Morrison argued that inmates were living in the same place under the same rules of
discipline, occupied in the same way, fed the same food, with the same amount of
exercise, the same hours of sleep. In addition, there was no variation that took place
in prisons in summer as compared to winter.
He stated that the only changes to which the prisoners were subjected were
cosmical causes. Of these cosmical causes, temperature was the most evident and
therefore, he concluded that the increase of prison offenses in summer was
attributable to the greater heat. He wrote:
It is the good weather that multiplies occasions for human intercourse; the
multiplication of these facilities augments the volume of crime; and thus it
comes to pass that the conduct of society is, at least, indirectly affected by
changes of season and the oscillations of temperature.
Morrison argued that since temperature affected prisoners, it was only rational to
conclude that temperature created comparable results on the external world.
7
Lombroso (1911) provides a table of crimes by percentage, combining
statistics shown by Guerry on rape crimes in England (1834-56) and France (182960) with those of Curcio who studied the same crimes in Italy (1869). According to
the three studies, rape hits the highest point in the hottest months. Lombroso also
examined political crimes and concluded that the 836 rebellions which took place
between 1791 and 1880 in the world prevailed in the hot months. Hence, in America
and Europe, July is the month with the maximum occurrence of revolution, and in
South America the maximum occurrence takes place in January, which is the hottest
month of the year. In the same study, Lombroso presented a table illustrating a
seasonal synopsis of rebellions in Europe in the course of a century. This table
displays that in nine countries the summer ranks in first place. Finally, Lombroso
quotes Guerry regarding crimes against the person and their seasonal variation; he
concludes that in England and France crimes against the person predominate in the
summer and in the spring (Lombroso, 1911).
Physiological and Social Explanations for Seasonal Changes
Two leading theories explaining the association of seasonal changes and crime
rates are Temperature/Aggression (T/A) theory and Routine Activity (RA) theory.
While T/A theory focuses on the physiological element of individuals, RA theory
centers on the social aspect of individuals. Although both theories propose that
temperature is associated to crime rates, they suggest different approaches in support
of bringing about this connection. Some researchers have conducted studies
employing the heat hypothesis, which is another term for temperature/aggression
8
theory, proposed by Adolph Quetelet in the 1800s. Baron and Bell (1976) proposed
the negative affect escape (NAE) model, which is a combination of
temperature/aggression theory and RA theory. In other words, the NAE model
concentrates on both the physiological and social factors for seasonal oscillation and
an individual’s aggressive behavior. A number of contemporary studies have focused
on both theories as well as the NAE model (Falk, 1952; Anderson, 1984, 1987, 2001;
Anderson et al, 1995, 1997; Baron & Bell, 1976; Baron, 1978; Bell, 1992; Cohen &
Felson, 1979; Cohn & Rotton, 1997, 2000, 2003, 2004; Rotton, 1993; Hipp et al,
2004).
Physiological Explanation
The temperature/aggression theory has properly received extensive
experimental interest for the last 100 years, and a number of studies have been done
concerning this theory (Anderson et al., 1995). This theory has also obtained
different terms such as the “heat hypothesis” and “general aggression.” As originally
framed by Adolphe Quetelet (1842), this theory proposes that warmer temperatures
lead to greater irritation and increases violent actions. A great deal of the support for
the temperature/aggression theory consists of research illustrating association
between temperature and aggressive behavior (Anderson 1989, 2001). To test the
temperature/aggression theory, Anderson and Anderson (1984) conducted a study in
Chicago that was broken down into two parts (study 1 and study 2). The data in the
first study were originally collected by Jones, Terris, and Christensen (1979) about
homicide, rape, battery, and armed robbery. In the second study, the numbers of
9
aggressive (murder and rape) and non-aggressive (robbery and arson) crimes were
recorded from the Houston Chronicle newspaper crime report. The researchers
concluded that the relationship between temperature and crime was linear. These
results are consistent with the temperature aggression theory, which suggest that as
temperature goes up crime goes up.
DeFronzo (1984) examined 142 standard metropolitan statistical areas
(SMSAs) in the U.S. with populations greater than 200,000 in 1970. Most important
about this study was that it discovered that after adding demographic controls, the
number of hot days (temperature greater than 90 degrees Fahrenheit) experienced by
a SMSA had a positive effect only on homicide and burglary rates. Cohn (1990a)
addresses additional studies that have obtained contradictory evidence concerning the
association between temperature and homicide rates.
Cotton (1986) collected data on criminal activity from Des Moines (Study 1)
for the months of July and August of 1979. Data were grouped as violent or
nonviolent crime. Violent crime comprised aggravated assault, assault and battery,
molestation, rape, murder, attempted murder, robbery and terrorism. Nonviolent
crime incorporated all property crime (burglary, theft, vandalism, malicious injury to
motor vehicles, etc.), public intoxication, drunk driving, disorderly conduct, vice and
all misdemeanors. Data on temperature and humidity were gathered from the
national weather service station. Records on criminal activity were also obtained for
the months of June and July, and August of 1978 and 1980 in portions of Indianapolis
(Study 2). Once more, data were grouped as violent or nonviolent crime. Violent
10
crime included robbery, assault, aggravated assault, rape, and murder. Nonviolent
crime included larceny, burglary, and vandalism. Data on temperature and humidity
were also gathered for the three months from the national weather service. Both
studies illustrated a positive relationship between temperature and violent crimes
whereas nonviolent crime showed no significant correlation. Although Cotton does
not specifically mention T/A theory, he states that as temperature goes up, so does
violent crime.
Hipp and colleagues (2004) conducted a study covering the years 1990-92.
Researchers collected data about violent crimes (murder, robbery, and assault) and
property crime (burglary, larceny, and motor vehicle theft) from 10 states with at least
100 communities representing different geographic regions of the United States.
Investigators found some evidence that violent crimes increased during the hottest
days of the summer in warmer areas such as Texas. However, they also observed that
temperature variation in moderate weather areas increased the fluctuation of violent
crimes. Researchers concluded that the T/A theory might possibly have some use in
explaining violent crimes.
The heat hypothesis is the notion that uncomfortably hot temperatures
increase violent actions (Anderson & Anderson, 1996). As mentioned earlier, this
hypothesis is another term for temperature/aggression theory. A few studies have
been performed employing the heat hypothesis. Anderson (1987) collected data for
violent and nonviolent crimes. Data on rates of murder, rape, assault, robbery,
burglary, larceny-theft, and motor vehicle theft were collected from archival source.
11
His findings proved that violent crimes were more dominant in the hotter seasons of
the year and in hotter cities than nonviolent crimes. Other findings also (e.g.
Anderson & Anderson, 1996; Anderson et al., 1997) support the association between
hot temperatures and aggressive behavior.
Social Explanation
In the late twentieth century, a more contemporary routine activity (RA)
theory utilizes a societal explanation, centering on the ongoing actions of persons to
explain seasonal changes in all varieties of crimes (Cohen & Felson, 1979).
According to Routine Activity theory, for an offense to take place there has to be a
motivated offender, a suitable target in the absence of protectors or guardians (Cohen
& Felson, 1979). Routine activity theory proposes that seasonal fluctuation in crime
rates is not necessarily due to increased individual violent behavior, but due to a
variation of activities. In this theory, temperature is viewed as one of several factors
that alter the ordinary activities of persons in a neighborhood. Routine activity theory
argues that when it is extremely cold or hot, people tend to stay indoors, and hence
decreasing the likelihood of becoming victims (targets). By avoiding outdoor
settings, individuals also reduce the probability to come into contact with strangers,
and hence diminishing the likelihood of becoming targets of violent crimes. Cohen
and Felson (1979) in their original research examined female labor force involvement
and observed that women working outside their home increased their probability of
becoming victims. The researchers employed modifications in the proportion of
12
females in the labor force to clarify variations in crime rates in the United States
(Cohen, Felson, & Land, 1980).
After Cohen and Felson’s (1979) original study of RA theory, other
researchers have conducted studies that are consistent with the initial findings.
Routine activity theory has been supported by different studies related to temperature
and violence (Eck, 1995; Goldstein, 1994). Cohn and Rotton (1997) discovered that
association between temperature and assaults depended on time of the day and day of
the week. The analyses were based on 3-hour intervals rather than 24- hour averages.
The data were obtained from Minneapolis, Minnesota based on 1987-1988 records.
The data consisted of all non-duplicate calls for service relating to assaults received
by the Minneapolis Police Department. The category included calls for service for
assaults in progress, fights, and fights with a weapon, shootings, stabbings, threats,
and kidnapping. As the researchers hypothesized, the relations between temperature
and assaults were stronger during the evening hours than during other hours of the
day and more crimes took place on Fridays and Saturdays. Hence, the study’s
examination validated the original hypothesis, which was that the correlation between
temperature and assaults was stronger in the evening hours, consistent with RA
theory.
Three years later, Cohn and Rotton (2000) collected data from Dallas, Texas.
The researchers’ intention was to replicate their study from Minneapolis and find out
if the same results were obtained in a southern city since summers are hotter in Dallas
than Minneapolis. The researchers collected data of all non-duplicate calls for service
13
relating to aggravated assault that were obtained by the Dallas Police Department
between 12:00 a.m., on January 1, 1994, and 11:59 p.m., on December 31, 1995.
During the two-year period, police received a total of 18,687 calls regarding
occurrences that were later categorized as aggravated assault. The data were
combined into three-hour intervals to match them with weather data acquired from
the National Climatic Data Center. Their findings in Dallas corroborated and
expanded their analysis of assaults in Minneapolis. First, as the results in
Minneapolis, the occurrence of aggravated assaults was curvilinear function of
temperature. Second, the relationship between temperature and assault was
controlled by time of day. Finally, the study corroborated results from Minneapolis
by illustrating that associations between temperature and aggression were also
manipulated by season of the year.
In addition, Cohn and Rotton (2003) proposed that crime rates would differ by
the type of crime and the type of holiday with violent crimes taking place more often
and property crimes occurring less frequently on major holidays (i.e. Thanksgiving,
Christmas, New Year’s Eve, Independence Day, etc.). They tested data on calls for
service in 1985, 1987, and 1988 in Minneapolis, Minnesota. Their findings were
consistent with predictions that can be drawn from RA theory, which suggested that
the type of crime and the importance of the holiday shaped the influence of holidays
on criminal activity. Moreover, the researchers concluded that violent crimes were
significantly related to major (official) holidays; violent crimes were considerably
more common than property crimes on major holidays since individuals are more
14
likely to spend time together and therefore reducing the chances of someone breaking
in.
Hipp et al (2004) collected data from 8,460 police departments across the
United States from 1990 to 1992. The researchers argued that while the social
disorganization theory can clarify to a great extent the variation in crime rates among
cities, the theory does not explain seasonal variation in crime rates. The results
suggested that even though demographics in a municipality play a significant role in
the amount of crime occurring in a particular area throughout the year, seasonal
variation influences when that crime occurs. The examiners found that cities with
greater temperature variation have greater seasonal fluctuations in violent crimes
consistent with RA and T/A theories. Property crime rates, on the other hand, are
mainly motivated by pleasurable temperature, coherent with the RA theory.
Contrasting Temperature/Aggression and Routine Activity Theory
While some studies ascribe seasonal changes in crime to increased violent
behavior (e.g. Anderson, 1984, 1989, 2001; Rotton, 1993; DeFronzo, 1984; Hipp et al
2004), other findings attribute similar findings to more time spent outside the home
during pleasant weather. These different conclusions propose that the type of weather
in a region may be significant for recognizing which of the two theories is at work. In
regions where summers are moderately mild studies support RA theory, however,
regions with hot summers may suggest the T/A theory. The two theories offer a
positive association between seasonal temperature variations and fluctuations in
crime rates. The two approaches have slightly different predictions; the T/A
15
approach suggests that hotter summers lead to greater violence and therefore an
increase in violent crimes. In Contrast RA theory suggests that pleasant weather
makes individuals more likely to leave their dwellings. As a result, changed behavior
patterns result in seasonal association for violent offenses (Cohen & Felson, 1979;
Cohen et al, 1980; Eck, 1995; Goldstein, 1994, Cohn & Rotton, 1997, 2003; Hipp et
al, 2004).
Combining Physiological and Social Explanations
Baron and Bell (1976) proposed the negative affect escape (NAE) model, the
most commonly quoted and the most debated model of temperature and crime
(Anderson, 1989). According to this model, violence rises to a certain point “with
increasing discomfort but then declines with further discomfort as motivation to
escape the aversive circumstances becomes more dominant than motivation to
aggress” (Baron & Bell, 1976). The NAE model is based on experiments conducted
by Baron (1978) and Bell (1992); in these studies participants were brought to a
laboratory and were not informed that they were going to be exposed to situations that
people generally evade. As a result, fleeing was the only option to aggression (Bell,
1999). Various studies support the NAE model. Cohn and Rotton (1997) proposed
that relations between weather and assault were greater during late afternoon hours;
the researchers obtained three-hour measures of assault, temperature as well as other
weather variables for a two-year period from the police department in Minneapolis,
Minnesota. The researchers found that assaults decreased after reaching a point at
fairly high temperature as predicted by the negative affect model.
16
Cohn and Rotton (2000) conducted a study on weather and assault, the
variable disorderly conduct was added as a mediator. Their findings suggest that
people not only attempt to get away from extremely uncomfortable climate (hot or
cold situations), but they also engage in activities that allow them to stay away from
severe temperatures. Rotton and Cohn (2004) conducted an additional study in
Dallas, Texas to verify how the distribution between indoor and outdoor activity may
possibly influence correlations between temperature and aggravated assault; they
proposed that temperature and aggravated assault would be controlled by access to air
conditioning. As the researcher hypothesized, more assaults took place in locations
that require climate control than those places that were more likely climate controlled.
Their findings were consistent with results obtained from studies of the police record
in Minneapolis (Cohn & Rotton, 1997) as well as the NAE model predictions.
Cohn and Rotton (1997, 2000, and 2004) have examined the relationship
between temperature and assault in Minneapolis, Minnesota as well as in Dallas.
Their findings have supported the negative affect escape model demonstrating that
the association between temperature and aggravated assaults is influenced by climate
control. Cohn and Rotton studies have also determined that extreme weather
conditions decrease social contact in support of the routine activity theory, however,
they have as well found that geographic variation shapes violent crimes rather than
temperature itself.
17
Contemporary Research on Domestic Violence
Some researchers suggest that domestic violence indicates incompatibilities in
income and educational status and is differentially related with domestic violence
committed by men and women. However, feminist scholars argue that domestic
violence is rooted in gender and power and symbolizes men’s efforts to maintain
supremacy and control over women (Anderson, 1997). Sociological studies of the
sociodemographic associates of domestic violence demonstrates higher rates of
violence among younger, poorer, less educated, unmarried, African American,
Hispanic and urban couples (Loseke et al., 2005; Smith, 1990; Straus et al., 1980).
Yet, the connections between domestic violence and education and non-White
ethnicity are inconsistent (Lupri et al., 1994; Rosenberg & Fenley, 1991). Some
researchers suggest that sociodempgraphic factors manipulate domestic violence
through the greater stress or “social isolation” experienced by people of lower
socioeconomic status or non-White ethnicity (Gelles, 1993; Lockhart, 1987),
however, studies of social isolation find weak empirical support for the isolation
hypothesis (Stets, 1991; Williams, 1992).
Contemporary theoretical work on gender offers the foundation for a
sociological theory of violence that includes feminist, family violence and resource
viewpoints. Some scholars suggest that men and women actively create gender
through social practices intended to distinguish men and women (Connell, 1987;
Segal, 1990; West & Zimmerman, 1987). These social practices create and preserve
the idea that men and women are different and strengthen men’s supremacy in both a
18
real and a symbolic manner (Anderson, 1997). Working and lower class manliness
put emphasis on toughness and aggression (Messerschmidt, 1993). Middle class
concepts of manliness centers on ambition, responsibility and professional
employment (Segal, 1990); lower class men on the other hand, do not have power and
authority in their work atmosphere. As a result, they may create severe aggressive
forms of manliness in the home (Gondolf, 1985; Messerschmid, 1993). Gender
theory proposes that status incompatibility should be less important to women’s
perpetration of violence against male partners. Women have not relied on
“breadwinner status” or on the utilization of aggression to achieve womanliness.
Therefore, lack of income and educational status should not be associated with
women’s violent behavior. Women in lower class and limited resources may be more
likely to engage in violence against male partners as a mean of self-defense since they
are less capable than women with higher resources to leave a violent relationship
(Anderson, 1997).
In conclusion, researches do not agree on one particular cause for violence in
the home, instead they agree domestic violence is rooted on a number of factors such
as gender, race, class, education, and/or social practices.
CHAPTER III
METHODOLOGY
Although a great deal of empirical work has been done regarding domestic
violence and its causes, researchers have not conducted studies with the express
purpose of investigating domestic battery (non-cohabitant and co-habitant spouse)
and simple battery and their association to temperature. This paper aims to
investigate whether there is a steady variation of domestic and simple battery based
on daily temperature.
Hypothesis
The purpose of this study is to determine whether or not there is a correlation
between battery (simple and domestic) and temperature. The hypothesis is that the
number of domestic and simple batteries committed in the course of a day increases
as temperature increases.
Design
The study covered the number of domestic and simple battery (as defined by
the California Penal Code) committed daily for five years (2005-2009) in
Sacramento, California. This study does not include analysis of assault data. Data on
domestic and simple battery were collected from the Sacramento Police Department
website: http://www.sacpd.org/crime/stats/ about domestic (co-habitant and noncohabitant spouses) and simple battery for the years 2005-2009. According to the
Census Bureau, the population in Sacramento was approximately 466,687 in 2009.
19
20
California Penal Codes 273.5 (A) beat spouse/cohabitating; 243 (E) 1 battery
non-cohabitating spouse and 242-PC battery civilian, taken from the official Police
Department of Sacramento website, were used as the basis for this study. The
overarching code 243 has several subcategories ranging from battery against
doctors/nurses/emergency personnel to sexual battery for the purpose of sex. Battery
non-cohabitating spouse is one of the seven subcategories under California Penal
Code 243.
Categories 273.5(A) beat spouse/cohabitating, and 243(E)1 battery noncohabitating were combined in the analysis in an attempt to account for all incidents
of domestic battery. Code 242-PC battery civilian had no subcategories and was
therefore analyzed as a single category for simple (civilian) battery
Definition of terms
The Sacramento Police Department, under the California penal code, defines
battery as “any willful and unlawful use of force or violence upon the person of
another.”
The California Penal Code Section defines “beat spouse/cohabitating” as “any
person who willfully inflicts upon a person who is his or her spouse, former spouse,
cohabitant, former cohabitant, or the mother or father of his or her child, corporal
injury resulting in a traumatic condition.” Battery non-cohabitating spouse is defined
as: “when a battery is committed against a spouse, a person with whom the defendant
is cohabitating, a person who is the parent of the defendant’s child, former spouse,
21
fiancé, or fiancée, or a person with whom the defendant currently has, or has
previously had, a dating or engagement relationship.”
According to the California Penal Code, battery is the actual physical impact
on another person. If the victim has been touched in a painful, harmful, violent or
offensive way by the person committing the crime, this is battery. Assault is defined
as “an unlawful attempt, coupled with a present ability, to commit a violent injury on
the person of another.” Assault is the threat of bodily harm that reasonably causes
fear of harm in the victim.
Meteorological Data
Data on temperature were collected from the Weather Underground website:
www.wunderground.com/weatherstation/US/CA/Sacramento. The Weather
Underground website displays information from the Sacramento Airport Weather
Station (latitude 38 degrees 31 minutes N, longitude 121 degrees 30 minutes W,
elevation 27 feet); this weather station is the closest to the city of Sacramento. The
airport is located west from the city and therefore is more likely to have a better
representation of temperature. This was the best-placed weather station for the city of
Sacramento with a full archive of data for the years 2005 to 2009 available. The
website displays history of daily, weekly, monthly and yearly temperature. It also
records high, low and average daily temperature. For the purpose of this study,
average daily temperature was collected. Temperature was reported in degrees
Fahrenheit.
22
Procedure
The total number of batteries (domestic and simple) on each day for the five
years (2005-2009) was entered in Excel and SPSS along with the average temperature
data. Correlation and regression analyses were conducted on average temperature
and number of batteries (domestic and simple combined) for all days and separately
for domestic and simple.
CHAPTER IV
RESULTS
Table 1 below displays the number of domestic and simple battery and the
combined total of batteries committed daily. There is 68% probability that the
number of domestic batteries committed per day lies between 4.51 and 10.75
occurrences. Likewise, there is 68% probability that the number of simple batteries
committed per day lies between 1.17 and 4.83 occurrences. In addition, Table 1 also
shows that there is 68% probability that the total number of domestic and simple
batteries committed per day lies between 7.01 and 14.23 incidents. The mean
temperature per day was 61.38; the average number of domestic battery committed
was 7.63, simple was 3.00 and the total was 10.62 incidents per day.
Table 1
Raw Data Summary for Domestic and Simple Battery
Total Days
Total
Ave Daily
in Sample
Batteries
Occurrences
Domestic
Battery
1826
13,927
7.63
Simple
Battery
1826
5,473
3.00
Total
1826
19,400
10.62
Standard
Deviation
Mean
Temperature
3.12
61.38
1.83
61.38
3.61
61.38
Figure 1 displays the number of domestic and simple battery as well as the
predicted number. As can be seen, the number of domestic batteries is slightly higher
than the number of simple batteries. However, both domestic and simple show a
similar trend.
23
24
12
10
8
Simple
6
Domestic
4
predicted simple
predicted
domestic
2
0
Figure 1. Comparative regressions of domestic and simple battery
Table 2 shows a weak positive relationship (R = .151) between the number of
domestic batteries and temperature. Nonetheless, there was a well under a 5%
probability that this relationship happened by chance (p = .000). There was a higher
average number of domestic incidents occurred than simple battery. At the mean
temperature of 61.38 there were 7.63 domestic batteries committed. The R Square
value for domestic battery is .023, which means that 2.3% of the variability in the
number of domestic battery can be explained by differences in temperature. The
remaining 97.7% is not explained.
25
Table 2
Regression of Average Temperature and Domestic Battery
Model
R
1
R Square
.151
Adjusted R²
.03
Std. Error
.022
3.084
Table 3 exhibits the significance of R Square at p < .05 (p = .000), stating that
there is nearly 100% probability that the relationship between domestic battery and
temperature did not happen by chance.
Table 3
Significance of the Variability on Domestic Battery (R Square) that can be Explained
by Temperature
Sum of
Degrees of
Mean
Model
F
Sig.
Squares
Freedom
Square
Regression
403.296
1
403.296
Residual
17351.727
1824
9.513
Total
17755.024
1825
42.394
.000
Table 4 shows that for every degree rise in temperature there is an increase in
the number of incidents of domestic battery by .040. It also displays that there is 95%
probability that the true slope of the line that defines the relationship between
domestic battery and temperature lies between .034 and .046. Similarly, there is 95%
probability that the constant of 5.161, which in combination with the slope of the line
defines that the relationship is 95% likely to be within 4.76 and 5.55 domestic
batteries per day.
26
Table 4
Regression Coefficients for Domestic Battery
Model
B
SE(B)
(Constant) 1
5.161
.386
Temp
.040
.006
Beta
.151
T
Sig.
13.388
.000
6.511
.000
The slope intercept form of a linear equation has the following form where the
equation is solved for y in terms of x: y=a+bx. The average low temperature for the
years 2005 to 2009 was 35 degrees and the highest average temperature was 94. If
there were a linear relationship between temperature and domestic battery, there
would be 6.6 batteries per day with the lowest average temperature of 35 degrees and
8.9 batteries with the highest average temperature of 94 degrees.
Y=a+bx
Y = 5.161+.040(35)
Y = 6.6 predicted domestic batteries
Y=a+bx
Y = 5.161+.040(94)
Y = 8.9 predicted domestic batteries
Simple Battery (N=5,473)
Table 5 displays a weak positive relationship (R = .113) between the number
of simple batteries committed and temperature. However, it illustrates statistical
significance at p < .05 (p = .000) that the relationship did not occur by chance. In
addition, there was an average of 3.00 batteries committed at the mean temperature of
61.38. Table 5 also shows the R Square value of .013. The R Square value states
that only 1.3% of the variability in the number of simple batteries can be explained by
differences in temperature. The remaining 98.5% is not explained.
27
Table 5
Regression of Average Temperature and Simple Battery
Model
R
1
R Square
.113
Adjusted R²
.013
Std. Error
.012
1.816
Table 6 shows the significance of R Square at p < .05 (p = .000). There is less
than a 95% probability that the relationship between simple battery and temperature
did not occur by chance.
Table 6
Significance of the Variability on Simple Battery (R Square) that can be Explained by
Temperature
Sum of
Degrees of
Mean
Model
F
Sig.
Squares
Freedom
Square
Regression
77.889
1
77.889
Residual
6015.097
1824
3.298
Total
6092.986
1825
23.619
.000
Table 7 displays that for every degree rise in temperature there is a .018
increase in the total number of simple batteries. Table 7 also shows that there is 95%
probability that the true slope of the line that defines the relationship between
temperature and number of simple batteries lies between .014 and .022. Equally,
there is 95% probability that the constant of 1.914, which in combination with the
slope of the line defines the relationship, is likely within 1.69 and 2.14 number of
simple batteries per day.
28
Table 7
Regression Coefficients for Simple Battery
Model
B
SE(B)
(Constant) 1
1.914
.227
Temp
.018
.004
Beta
.113
T
Sig.
8.431
.000
4.860
.000
Using the slope intercept form of a linear equation y=a+bx, the average low
temperature for the years 2005 to 2009 was 35 degrees and the highest average
temperature was 94. If there were a linear relationship between temperature and
simple battery, there would be 2.5 batteries per day with the lowest average
temperature of 35 degrees and 3.6 batteries with the highest average temperature of
94 degrees.
Y=a+bx
Y = 1.914+.018(35)
Y = 2.5 predicted simple batteries
Y=a+bx
Y = 1.914+.018(94)
Y = 3.6 predicted simple batteries
Domestic and Simple Battery (N=19,400)
Correlation and regression found that there was a weak positive relationship
(R = .188) between the total number of domestic and simple battery committed and
average temperature. As such the null hypothesis is rejected. However, the analyses
found statistical significance at p < .05 (p = .000) that the relationship between
domestic and simple battery did not occur by chance. There is a 95% probability that
there is a relationship between the total number of batteries committed and
temperature.
Furthermore, Table 8 shows the value of R Square, which is .035.
This is telling us that only 3.5% of the variability in the number of domestic and
29
civilian assaults committed can be explained by differences in temperature. The
remaining 96.5% is not explained.
Table 8
Regression of Average Temperature and Total Batteries
Model
R
1
R Square
.188
Adjusted R²
.035
Std. Error
.035
3.543
Table 9 displays the significance of R Square at P< 0.05(p 0.00), meaning that
the relationship between temperature and assault did not occur by chance.
Table 9
Significance of the Variability on Total Batteries (R Square) that can be Explained by
Temperature
Sum of
Degrees of
Mean
Model
F
Sig.
Squares
Freedom
Square
Regression
835.657
1
835.657
Residual
22900.624
1824
12.555
Total
23736.280
1825
66.559
.000
Table 10 shows that for every degree rise in temperature there is a .058
increase in the total number of batteries. Table 10 also shows that there is 95%
probability that the true slope of the line that defines the relationship between
temperature and number of domestic and simple battery lies between .051 and .065.
Likewise, there is 95% probability that the constant of 7.075, which in combination
with the slope of the line defines the relationship, is likely within 6.63 and 7.52
number of batteries per day.
30
Table 10
Regression Coefficients for Total Batteries
Model
B
SE(B)
(Constant) 1
7.075
.443
Temp
.058
.007
Beta
.188
T
Sig.
15.974
.000
8.158
.000
If there were a linear relationship between temperature and total number of
batteries, there would be 9.11 batteries per day with the lowest average temperature
of 35 degrees and 12.53 batteries with the highest average temperature of 94 degrees.
Y=7.075+.058(35)
Y = 7.075 + 2.03
Y = 9.11 expected total batteries
Y=7.075 + .058(94)
Y = 7.075 + 5.452
Y = 12.53 expected total batteries
Summary of Findings
Findings on the influence of temperature on domestic and simple battery did
not support the temperature/aggression hypothesis. Temperature can only explain
2.3% of the variability in the number of domestic batteries and 1.3% of the variability
in the number of simple batteries. In addition, temperature can only explain 3.5% of
the variability in the total number of domestic and simple battery. There are numerous
plausible alternative explanations of both supportive and contradictory results
regarding aggression. Differences in cultures, regions, alcohol, holidays, and
humidity are variables that may produce different aggression rates. These differences
may correlate with temperature differences. It would be informative to carry out
domestic and simple battery analyses in regions with high relative humidity to
compare finding.
CHAPTER V
DISCUSSION
The findings show that temperature is a weak predictor of both domestic and
simple battery; however, the influence that exists is statistically significant. The
results relating to domestic battery show an increased average number of batteries
committed per day compared to the average number of simple batteries. Although
there is no specific cause for domestic violence, individuals who are at the highest
risk of becoming the abusers are those who are male, abuse drugs, are unemployed or
underemployed, do not have a high school diploma, and who grew up in a household
in which domestic violence took place. Individuals, who witness and/or experience
domestic abuse as children, are more likely to become either perpetrators or victims
of intimate partner violence as adults. If temperature only explains 2.3% of domestic
violence, perhaps the factors mentioned above may explain the remaining 97.7%.
In addition, there may be interaction with other calendar effects, such as
holidays that deserve further investigation. Further research is needed to identify
other social factors that may increase the chance of domestic and simple batteries
being committed. It is also worth noting that relative humidity is likely a significant
variable when measuring the effects of temperature on assault. Past studies in
Minneapolis and Dallas have discovered that there is a substantial association
between warm temperatures and assault depending on day of the week and time of
day (Cohn and
31
32
Rotton, 1997, 2004; Anderson et al, 2005; McLean, 2006). These findings do not
apply to Sacramento since the relative humidity is substantially higher in those cities
where the studies have been carried out. Days are hotter in Minneapolis and Texas
than in Sacramento due to relative humidity.
In conclusion, the effect of temperature on domestic and simple battery has a
weak positive relationship. There may be other variables that influence individuals to
commit domestic and simple batteries. Temperature then, is just another variable that
contributes to aggression (as routine activity proposes) rather than the sole variable
causing the effect. Based on these findings, the chief of the Sacramento Police
Department may make the decision to not increase patrol staffing simply based on
predicted temperatures alone.
Limitations and Recommendations
In analyzing data that have been already collected, there are certain
limitations. First, there is the possibility that the reported number of domestic and
simple battery is not an accurate representation of the actual total number of batteries
committed due to lack of reporting to the police. The data on domestic and simple
battery in this study accounts only for offenses reported to the police. Many victims
choose not to report physical attacks (whether domestic or simple) to the police for a
variety of reasons. As a result there may be a gap between the actual number of
batteries committed and the number of batteries reported to the police. Second,
further research is needed to identify the other social and/or environmental factors
that may increase the chance of domestic and simple battery being committed. This
33
will allow us to be aware of the dynamics of domestic and simple battery within our
culture. Finally, data on domestic and simple battery should be collected from a city
with high relative humidity to compare results. Relative humidity may be in part
responsible for promoting aggressive behavior.
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35
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