SPATIAL AND TEMPORAL PATTERNS OF PROPERTY VICTIMIZATION IN
THE CITY OF SACRAMENTO
A Thesis
Presented to the faculty of the Division of Criminal Justice
California State University, Sacramento
Submitted in partial satisfaction of
The requirements for the degree of
MASTER OF SCIENCE
in
Criminal Justice
by
Lance Michael Hachigian
SPRING
2013
© 2013
Lance Michael Hachigian
ALL RIGHTS RESERVED
ii
SPATIAL AND TEMPORAL PATTERNS OF PROPERTY VICTIMIZATION IN
THE CITY OF SACRAMENTO
A Thesis
by
Lance Michael Hachigian
Approved by:
__________________________________, Committee Chair
Timothy Croisdale, Ph.D.
__________________________________, Second Reader
Ricky S. Gutierrez, Ph.D.
____________________________
Date
iii
Student: Lance Michael Hachigian
I certify that this student has met the requirements for format contained in the
University format manual, and that this thesis is suitable for shelving in the Library
and credit is to be awarded for the thesis.
__________________________, Graduate Coordinator
Yvette Farmer, Ph.D.
Division of Criminal Justice
iv
___________________
Date
Abstract
of
SPATIAL AND TEMPORAL PATTERNS OF PROPERTY VICTIMIZATION IN
THE CITY OF SACRAMENTO
by
Lance Michael Hachigian
Citizens in America are nine times more likely to fall victim to a property
crime as compared to personal crimes where there is a physical victimization.
Property crime constitutes a majority of all victimization, placing a research emphasis
on property victimization patterns. This study uses victimized locations (as identified
by address) within the city of Sacramento over a seven-year span. Residential and
commercial burglary are analyzed separately due to the prevalence of burglary. The
data were organized spatially and temporally, allowing for the creation of charts for
analysis of property victimization over an extended period. Property victimization
patterns remained non-random, and signs of clustering were present from year-to-year
by district. Temporally, non-random patterns relating to time of offense were
discovered.
_______________________, Committee Chair
Timothy Croisdale, Ph.D.
_______________________
Date
v
ACKNOWLEDGEMENTS
Although my name is the only one listed on the cover of this thesis, so many
people are responsible for its production. I would like to first thank my family for
always supporting my studies and scholastic effort, as well as guiding me through the
difficult decisions that inevitably occur in life. This thesis would not have been
possible without the guidance of Dr. Tim Croisdale. As my advisor, Dr. Croisdale was
always available and willing to lend a hand or a word of advice. His expertise in the
field of crime analysis proved to be invaluable. Future students who are lucky enough
to work with Dr. Croisdale can be assured their hard work will be focused in the right
direction. Input from Dr. Gutierrez was always insightful, and he offered fresh ideas
and insights to help improve this thesis. Without the help of my advisors, teachers,
and classmates, this thesis would not be the product it is. To my friends, thank you for
making college fun.
vi
TABLE OF CONTENTS
Page
Acknowledgments ........................................................................................................ vi
List of Tables ................................................................................................................ ix
List of Figures................................................................................................................ x
Chapter
1.
INTRODUCTION .................................................................................................. 1
Hypotheses ........................................................................................................ 2
Statement of the Problem .................................................................................. 3
2.
LITERATURE REVIEW ....................................................................................... 7
Theory and Environmental Offending Patterns ................................................. 7
Repeat Victimization and Near-repeat Offending ........................................... 17
Crime Mapping and Hot Spots ........................................................................ 26
Repeat Property Victimization ........................................................................ 36
Conclusion ....................................................................................................... 43
3.
METHODOLOGY ............................................................................................... 45
Participants ...................................................................................................... 45
Data Collection ................................................................................................ 46
Data Analysis................................................................................................... 47
Testable Hypotheses ........................................................................................ 47
Advantages and Limitations of Research Design ............................................ 48
vii
4.
FINDINGS ........................................................................................................... 50
Spatial Patterns of all Property Victimization ................................................. 52
Number of Commercial and Residential Burglary Victimizations ................. 61
Temporal Patterns of all Property Victimization............................................. 70
Temporal Patterns of Commercial and Residential Burglary Victimization... 71
Yearly Trends of Property Victimization ........................................................ 72
Summary of the Findings ................................................................................ 75
5.
SUMMARY AND CONCLUSIONS ................................................................... 81
Summary.......................................................................................................... 81
Implications for Further Research ................................................................... 83
References ................................................................................................................... 88
viii
LIST OF TABLES
Tables
1.
Page
Penal Codes Used in Study.............................................................................46
ix
LIST OF FIGURES
Figures
Page
1.
Sacramento Police Department Beats and Districts ........................................ 51
2.
Victimization by District: 2005 ........................................................................ 52
3.
Victimization by District: 2006 ........................................................................ 53
4.
Victimization by District: 2007 ........................................................................ 54
5.
Victimization by District: 2008 ........................................................................ 55
6.
Victimization by District: 2009 ........................................................................ 56
7.
Victimization by District: 2010 ........................................................................ 57
8.
Victimization by District: 2011 ........................................................................ 58
9.
Victimization by District: 2005-2011 ............................................................... 59
10.
Number of Victimizations by Police Beat: 2005-2011 .................................... 60
11.
Commercial and Residential Burglary Victimization by District: 2005 .......... 61
12.
Commercial and Residential Burglary Victimization by District: 2006 .......... 62
13.
Commercial and Residential Burglary Victimization by District: 2007 .......... 63
14.
Commercial and Residential Burglary Victimization by District: 2008 .......... 64
15.
Commercial and Residential Burglary Victimization by District: 2009 .......... 65
16.
Commercial and Residential Burglary Victimization by District: 2010 .......... 66
17.
Commercial and Residential Burglary Victimization by District: 2011 .......... 67
18.
Commercial and Residential Burglary Victimization by District: 2005-2011 . 68
x
19.
Commercial and Residential Burglary Victimization by Police Beat 20052011................................................................................................ .................. 69
20.
Property Victimization Occurrence Time: 2005-2011 ..................................... 70
21.
Commercial and Residential Burglary Occurrence Time: 2005-2011 ............. 71
22.
Number of Victimizations by Year: 2005-2011 ............................................... 72
23.
Commercial and Residential Burglary Victimization by Year: 2005-2011 ..... 73
24.
Victimization by Month: 2005-2011 ................................................................ 74
xi
1
Chapter 1
Introduction
Understanding the pattern and prevalence of victimization is an important
factor for local authorities in their quest to predict and prevent crime. Victimization is
by no means random or indiscriminate, and does in fact follow a distinct pattern
(Farrell & Pease, 1993; Johnson, 2008; Polvi, Looman, Humphries, & Pease, 1991).
People and properties that have been victimized previously are at an increased risk for
future victimization. Offenders often prefer to revisit previous targets rather than pick
new ones for the simple reason of knowledge and convenience (Bernasco, 2008;
Bowers & Johnson, 2004; Johnson, 2008). With this in mind, local authorities need to
focus resources on people and properties previously victimized. Patterns of
victimization are tracked and analyzed through the creation of maps and charts
revealing crime-ridden areas for authorities to focus their resources (Braga, 2007;
Ratcliffe, 2010; Ratcliffe & McCullagh, 1998). Often what is discovered when
examining these figures closely are that people and places are victimized more than
one time, with the risk of future victimization rising with each previous victimization
(Johnson, 2008; Osborn & Tseloni, 1998; Rey, Mack, & Koschinsky, 2012).
Property crimes tend to display more spatial and temporal patterns when
compared to personal crimes. Offenders will return to a previously victimized
property within a short period to re-offend, often within the first month (Johnson,
Bowers, & Hirschfield, 1997; Osborn, Ellingworth, Hope, & Trickett, 1996; Osborn &
Tseloni, 1998). Not only is the offender comfortable with the neighborhood, but also
2
knows the best means of entry into the property and what goods may have been left
behind during the initial victimization. Additionally, other offenders may perceive an
address as an attractive opportunity and victimize the location: thus, the combination
of different offenders victimizing the same location contributes to repeat victimization
of the same address. The rationality of repeat property victimization makes sense
when comparing the risk and reward of having to victimize a different, less familiar
property (Bernasco, 2008; Bennett & Durie, 1999; Morgan, 2001; Pease & Laycock,
1999). Understanding the phenomenon of repeat property victimization opens the
doorway to understanding offending patterns and effectively preventing property
victimization.
Hypotheses
The finding that property victimization is highly patterned on a small amount
of the population disproportionately has been found multiple times. Criminal justice
scholars have established patterns of property crime over a short period, typically one
year. This study differs from previous research in that the time window of the
analyses allows for the non-random nature of property victimization to be tested over
an extended period. The spatial and temporal patterns of property victimization will
be analyzed over seven years to determine if patterns and clustering remain present.
The purpose of this study is to accept or reject the hypotheses noted below.
Spatial Patterns of Property Crime
H1A: Spatial patterns of property victimization will remain non-random
and clustered by police district over time.
3
H0A: Spatial patterns of property victimization will not remain nonrandom and clustered by police district over time.
Temporal Patterns of Property Crime
H1A: Temporal patterns of property victimization will remain nonrandom and clustered over time.
H0A: Temporal patterns of property victimization will not remain nonrandom and clustered over time.
Statement of the Problem
Previous research regarding victimization, and for that matter repeat
victimization was not a prevalent topic until the 1970s, but since has garnered
increased attention due to the cost-effective advantages of focusing resources on those
who will most likely be victimized (Polvi et al., 1991). Repeat victimization has been
defined differently amongst studies, but encompasses the idea that any place or person
that has experienced more than one victimization within a short period of time is a
repeat victim (Townsley, Homel, & Chaseling, 2000). Numerous studies have
analyzed victimization across many different crime types, and have found similar
results regarding the overall pattern. Crime is often not a random occurrence, but
concentrated on a small portion of the population, and the best indicator of future
victimization is prior victimization (Bernasco, 2008; Farrell & Pease, 1993, Townsley
et al., 2000). While these victims consist of a small percentage of the population, they
account for a large percentage of the crime rate (Farrell, 1995; Johnson et al., 2007;
Polvi, Looman, Humphries, & Pease, 1990; Townsley et al., 2000). Findings such as
these support the need for more research into victimization patterns and the benefits to
law enforcement in their attempt to predict and prevent crime. This research will
4
specifically focus on property crime, with both residential and commercial property
victimization patterns analyzed.
Citizens in America are nine times more likely to fall victim to a property
crime as compared to violent crimes (Cohn & Rotton, 2000), and property crime often
constitutes a majority of all victimization (Canter & Hammond, 2007; Police
Executive Research Forum, 2002), placing a necessary emphasis on property crime
research. According to Ellingworth, Farrell, & Pease (1995), property crime is
defined as “incidents suffered by the household of burglary, attempted burglary, theft
inside and immediately outside the dwelling” (p. 361). When a property is victimized,
the risk of future victimization becomes much greater, often in the following month,
and then lessens as time elapses (Farrell, 1995; Johnson et al., 2007; Polvi et al., 1990;
Polvi et al., 1991; Sagovsky & Johnson, 2007). Two prominent theories have outlined
possible explanations for the increased period of risk for repeat victimization, known
as the boost theory and flag theory (also known as state dependence and risk
heterogeneity). Some properties are at a high risk for repeat victimization because the
prior victimization has boosted the attractiveness of the target to the offender. Other
properties are flagged as attractive targets, and are victimized by multiple offenders
because they stick out as suitable targets (Farrell, Tseloni, Wiersema, & Pease, 2001;
Johnson, 2008; Sagovsky & Johnson, 2007). Often property crime patterns are more
prevalent and consistent in high crime areas, and more random in low-crime areas.
Even with the high percentage of repeat property victims, the majority of the
5
population will rarely experience crime (Johnson, Bowers, & Hirschfield, 1997;
Osborn et al., 1996; Osborn & Tseloni, 1998).
Research has begun to study not only the spatial patterns of property
victimization, but also the long neglected temporal patterns (Felson & Poulsen, 2003;
Polvi et al., 1991; Townsley et al., 2000). Understanding temporal patterns presents
an even more detailed picture of when offenders are most likely to strike, so local
authorities have a better chance to prevent victimization (Felson & Poulsen, 2003;
Johnson et al., 2007; Polvi et al., 1990; Polvi et al., 1991). Further research into
temporal patterns of victimization reveals a need to study property crime patterns over
an extended period (Farrell & Pease, 1993). A large data set spanning multiple years
offers a more consistent look at property crime patterns year to year as well as over the
entire period, and can help support cost-effective and proactive policing.
Research in the field of property crime patterns have left absent two major
themes; spatial patterns of property victimization over an extended period, and
temporal patterns of property victimization over an extended period. This study will
examine whether property victimization patterns remain non-random both spatially
and temporally over an extended period of seven years. By obtaining this knowledge,
police department may better predict future offending patterns. Past victimization is a
solid predictor of future victimization.
The remainder of this study will synthesize relevant knowledge on the topic of
property crime patterns, as well as analyze property crime patterns spatially and
temporally over a period of seven years. Chapter two details current literature in the
6
areas of victimization theory, repeat victimization, crime mapping, hot spot policing,
and repeat property victimization. Chapter three explains the methods used for
collecting the data and creating charts for analysis. Chapter four reveals the findings
of the research, as presented in victimization charts for each year displaying spatial
and temporal patterns. Finally, chapter five offers a discussion of the findings, as well
as future implications of the research.
7
Chapter 2
Literature Review
Theory and Environmental Offending Patterns
Victimization is not a random occurrence, nor does it affect the population
evenly. Victimization follows a distinct pattern that disproportionately targets a small
portion of the population, accounting for a large amount of crime (Brantingham &
Brantingham, 1993a; Brantingham & Brantingham, 1993b; Farrell & Pease, 1993;
Sherman, Gartin, & Buerger, 1989; Townsley, Homel, & Chaseling, 2000).
Victimization is a pattern influenced by many factors of the environment, offender,
and victim. When analyzing patterns of victimization, the similarities become
apparent (Brantingham & Brantingham, 1993b; Rengert, Lockwood, & McCord,
2011; Ratcliffe, 2006; Rossmo, Lu, & Fang, 2011). The predictability of victimization
laid the groundwork for crime pattern theory. Developed in its entirety by
Brantingham and Brantingham (1993a), this theory views crime as an event that is
shaped by many different factors related to the offender, target, and environmental
situation. Environmental crime and offending patterns are explained by three different
theories: rational choice theory, routine activities theory, and crime pattern theory
(Andresen, 2010; Brantingham & Brantingham, 1993a).
The decision to begin a criminal event and the transition into the act of a crime
is a choice made by offenders based on the risk and reward of their actions. This is the
premise behind the rational choice theory (Clarke & Cornish, 1985; Cornish &
Clarke, 1987). Offenders do not randomly choose targets hoping for successful
8
criminal gain. When a potential offender reaches a level of readiness based on
motivation and target suitability, they will begin the criminal event (Clarke & Cornish,
1985; Cornish & Clarke, 1987). With criminals, rationality is of course, a subjective
trait that will differ with each offender. Some offenders are more rational than others
but this does not change the fact there is some type of decision process made by
offenders prior to committing a crime. The term bounded rationality (Andresen,
2010) is used to view rationality relative to the mind of a criminal. It is not
appropriate to assume that offenders practice the same level of rationality as a person
who lives within the boundaries of the law (Andresen, 2010; Clarke & Cornish, 1985;
Cornish & Clarke, 1987).
When an offender decides to commit a crime, they are taking into account
many different factors attached to the actual event (Andresen, 2010; Clarke &
Cornish, 1985; Cornish & Clarke, 1987). Cornish and Clarke (1987) coined the term
choice-structuring properties to describe the factors and characteristics weighed by
offenders when deciding whether to commit a crime. Andresen (2010) lists four
primary choices the potential offender must make prior to committing a crime:
whether to take part in the criminal event, whom to offend based on environmental
cues, how frequently to offend, and if the offender should continue with a life of
crime. These choices are specific to each offender and each crime type. Rational
choice theory is important in understanding crime pattern theory. When and where to
offend is a decision that molds crime patterns, and those decisions come from rational
choices made by the offender (Andresen, 2010; Cornish & Clarke, 1987).
9
Determining where and when an offense will take place is also influenced by the
routine activities of offenders and victims.
The routine activities of offenders often dictate when and where they are most
likely to offend. Throughout their daily activities, offenders become familiar with
their surroundings and learn about the environment, observing potential criminal
opportunities (Brantingham & Brantingham, 1993b; Cohen & Felson, 1979; Felson,
1986). Routine activity theory developed by Cohen and Felson in 1979 states that
both offenders and victims must intersect in time and space along with three factors: a
motivated offender, a suitable target, and the absence of a capable guardian. Any
potential criminal event where one of these elements is not present will often lead the
offender away from the crime (Cohen & Felson, 1979; Sherman et al., 1989).
Research done by Cohen and Felson (1979) also found that prospective offenders find
and analyze suitable targets through their non-criminal routine activities. The analysis
of the target and decision to begin the criminal event blends both the rational choice
theory and the routine activities theory.
Routine activities repeated daily help to form an awareness space, and
offenders will often choose to offend within this space. Present in both location and
time, offenders who are ready and motivated to commit crimes will do so based on
patterns set by their routine activities (Brantingham & Brantingham, 1993a;
Brantingham & Brantingham, 1993b). When analyzing residential property crime,
findings from Kennedy & Forde (1990) determined that young, single adults who are
often mobile and away from their homes are at a higher risk to become victims of
10
property crime. These types of people will frequently be outside of their dwellings,
leaving them vacant and without a guardian. These dwellings are therefore at a higher
risk for victimization when the routine activities of offenders lead them past the vacant
property. A comprehensive view of the path traveled by the offender during criminal
and non-criminal activities is an aspect of the crime pattern theory, as the geometry of
offending between nodes and pathways dictates offender patterns.
Crime pattern theory details the movement and functioning of offenders related
to certain nodes, paths, activity spaces, and awareness spaces. Properties located near
nodes that draw many people throughout the day are exposed to many potential
offenders (Andresen & Malleson, 2010). High volumes of people offer possible
offenders suitable criminal opportunities, eventually leading to increased victimization
(Beavon, Brantingham, & Brantingham, 1994; Brantingham & Brantingham, 1981;
Brantingham & Brantingham, 1993a; Ratcliffe, 2006; Rengert & Wasilchick, 1985).
People and properties located near certain nodes such as sporting arenas,
shopping centers, schools, or liquor stores will draw a high volume of individuals
(Andresen & Malleson, 2010). The paths that potential offenders take on the way to
these types of nodes present vulnerable targets, putting many dwellings at risk.
Frequently visiting or passing these places during routine activities develops the
awareness space for criminals to offend (Brantingham & Brantingham, 1981;
Brantingham & Brantingham, 1993a; Brantingham & Brantingham, 1993b; Rengert &
Wasilchick, 1985). Paths of offenders and victims guided by their routine activities
and rational choices will create a pattern of crime.
11
Offending patterns arise from the rational choices of offenders, their routine
activities, and their movement between on nodes and paths. Crime pattern theory also
adds an element to complete the idea that crime is patterned and not at all random or
spontaneous (Andresen, 2010; Brantingham & Brantingham, 1993a). The added
element incorporating all of the other theories is the crime template. Crime templates
are specific to each offender, and offer a way to understand which cues and
characteristics individual offenders look for when selecting a target. Multiple factors
make up the crime template; environmental characteristics, routine activities of
offenders and victims, rational choices of offenders in target selection, and locations
of nodes and pathways. An experienced crime template will correspond to an offender
who understands a “good” or “bad” target (Andresen, 2010; Beavon, Brantingham, &
Brantingham, 1994; Brantingham & Brantingham, 1993a; Brantingham &
Brantingham, 1993b). Crime pattern theory offers a comprehensive analysis of
victimization by incorporating characteristics of the environment, offender, and
victim.
The environment in which offenders commit crimes will dictate patterns in
their target selection. Environment is a blanket term incorporating several factors,
such as; physical design of buildings and neighborhoods, roadways, types of
buildings, and community institutions (Andresen, 2010; Brantingham & Brantingham,
1993a). These factors will influence the patterns of offenders, and what potential
offenders view as suitable targets. It is important to remember offending is a complex
event, comprised of many different aspects (Beavon, Brantingham, & Brantingham,
12
1994; Brantingham & Brantingham, 1981; Brantingham & Brantingham, 1993a;
Brantingham & Brantingham, 1993b).
Before an offender begins their journey to commit the criminal act, they must
determine what level of target suitability is necessary to carry out the crime. Different
crimes require different characteristics of the environment. For instance, burglary will
have a different template than auto-theft. As mentioned previously, each offender
creates their own crime template, consisting of specific characteristics differentiating
good and bad targets (Beavon, Brantingham, & Brantingham, 1994; Brantingham &
Brantingham, 1993a; Brantingham & Brantingham, 1993b; Clarke & Cornish, 1985;
Harris, 1974; Rossmo, Liu, & Fang, 2011). One of the most commonly found aspects
of crime templates is the possibility of detection. Offenders are often deterred from
criminal opportunities if there is any possibility of being detected (Brantingham &
Brantingham, 1993a; Brantingham & Brantingham, 1993b; Cornish & Clarke, 1987).
The journey to crime for an offender takes place during both their criminal and
non-criminal activities. Some offenders begin their journey to crime with the specific
intent to offend. On the other hand, an offender may be traveling to work or school,
and a criminal opportunity may present itself along the way. Local nodes and
pathways often will influence how offenders travel within their communities
(Brantingham & Brantingham, 1981; Brantingham & Brantingham, 1993b; Rengert &
Wasilchick, 1985; Rossmo, Lu, & Fang, 2011). Offenders usually will not travel very
far from their dwellings when committing a crime.
13
By connecting the residence of the offender, residence of the victim, and the
specific location where the crime occurred, a mobility triangle can be established. An
important study analyzing the journey to crime from offender residences was done by
Felson, Andresen, and Frank (2011). In the dense community of Surrey, British
Columbia, the researchers found that residential burglary often was a very short
journey for offenders. Interestingly, it was found that an increase in co-offenders
lengthened the journey. Ratcliffe (2006) suggests that the temporal constraints of
offenders due to their non-criminal activities limit their crime journey, meaning they
must select targets in an area close to their residence. Research indicates that the
journey to a criminal event is often constructed from nodes and pathways in the
environment.
Nodes and pathways in the community influence offending patterns and target
selection. Nodes are locations within the community that attract a large number of
people throughout the day or are visited often by the offender. Many different
locations can be activity nodes, including; home, work, school, sports arenas, bars, and
stores (Brantingham & Brantingham, 1981; Brantingham & Brantingham, 1993b).
The pathways taken to these nodes also become areas that are vulnerable to
victimization. Locations and pathways combine to bring groups of people together,
some of whom are motivated to offend. As these nodes and pathways become part of
an offender’s routine activities, an awareness space is formed. A small number of
activity nodes and dwellings in the surrounding area can account for a significant
portion of victimized locations (Andresen & Malleson, 2010; Beavon, Brantingham, &
14
Brantingham, 1994; Brantingham & Brantingham, 1981; Brantingham &
Brantingham, 1993a; Brantingham & Brantingham, 1993b; Ratcliffe, 2006).
Multiple studies have found that offenders will habitually offend near activity
nodes and in areas within their awareness space. Not only are they more comfortable
with these areas, but the distance and effort of traveling is much less (Brantingham &
Brantingham, 1981; Brantingham & Brantingham, 1993a; Rengert & Wasilchick,
1985; Rossmo, Lu, & Fang, 2011). Two studies in particular are noted, both of which
support the above statements. A study done by Beavon, Brantingham, and
Brantingham (1994), researched the connection between street accessibility and the
risk of property victimization. Results showed that an increase in street flow and
accessibility correlate positively to an increase in property victimization.
Ratcliffe (2011) studied how alcohol outlets influence violent crime in
Camden, New Jersey. It was discovered that 83.5% of violent crimes took place
within 1,500 feet of an establishment that serves alcohol. As the distance furthered
from the alcohol outlet, the risk decreased. Community nodes such as bars and events
serving alcohol will increase even more the risk of victimization to surrounding areas.
Cases of personal crime are even more prevalent when alcohol is involved. Another
amplifier of crime is properties located on the edge of communities.
The edge of a community is the area on the outskirts of a neighborhood,
bordering another locality. Areas classified as edges will have obvious differences
compared to other regions in the community (Brantingham & Brantingham, 1993b).
When passing through the perimeter of a neighborhood, there will be a noticeable
15
change in the complexion of the area, whether it is race, levels of affluence, or the
mosaic structure (Rengert, Lockwood, & McCord, 2011). Edges often experience
crime at a higher rate than more interior sections of communities where there is more
homogeneity. Studies have found multiple reasons why edges are at a higher risk for
victimization. Areas with heterogeneous housing and populations offer more targets
because offenders can fit in easily and expand their awareness space (Beavon,
Brantingham, & Brantingham, 1994; Brantingham & Brantingham, 1993b; Rengert,
Lockwood, & McCord, 2011).
Categorizing the population of neighborhoods can be divided into two different
classifications of people; insiders and outsiders. Insiders are the people who fit in with
the rest of the neighborhood. Through racial complexion, wealth, appearance, or other
factors, these people will not draw suspicion when traveling through their home area
(Brantingham & Brantingham, 1993b). On the other hand, outsiders are those who do
not fit in with the rest of the community. For the same reasons listed above, outsiders
will draw suspicion when wandering through neighborhoods away from their own
(Brantingham & Brantingham, 1993b). Communities of insiders will recognize
outsiders in their neighborhood, and will take pride in protecting their community
(Beavon, Brantingham, & Brantingham, 1994; Rengert, Lockwood, & McCord, 2011).
At the edge of neighborhoods, there is less of an obligation felt by citizens to
protect their territory. Heterogeneous communities also make it difficult to
differentiate insiders from outsiders, facilitating the transition from routine activities to
a criminal event. A heterogeneous neighborhood not only allows a wider range of
16
criminals to blend, but can make it more difficult to establish a sense of territoriality
due to language and cultural barriers (Brantingham & Brantingham, 1993b; Rengert,
Lockwood, & McCord, 2011). In two different studies, Brantingham & Brantingham,
(1975) and Rengert, Lockwood, and McCord (2011) compared crime rates between
the core and edge of communities. Brantingham and Brantingham (1975) were able to
identify edges of communities using mapping programs. These edges were usually
found around parks, pathways, and changes in land type. In homogeneous
communities, crime rates were higher on the edges, and decreased closer to the core.
In a separate study of drug dealing related to edges, the racial composition of
neighborhood edges in Wilmington, Delaware made it easier for drug dealers to
function. Because no single race dominated the edges, it was difficult to determine
outsiders and insiders. The result was an increase in drug sales both in and around the
edges of Wilmington (Rengert, Lockwood, & McCord, 2011).
The environment surrounding both offenders and victims is an important factor
in the criminal event. The decision to offend is not rash nor absent outside influence,
but a complex and unique blending of factors (Andresen, 2010; Brantingham &
Brantingham, 1993a; Clarke & Cornish, 1985; Cornish & Clarke, 1987). Journeys
taken by offenders to and from local nodes will create an awareness space from which
they will choose to commit crimes. Offenders will construct personal mental
templates, consisting of ideal situations and factors that provide opportunities for
offending. These templates include environmental cues, as the environment shapes
the criminal opportunity and event in multiple ways (Andresen, 2010; Beavon,
17
Brantingham, & Brantingham, 1994; Brantingham & Brantingham, 1993a;
Brantingham & Brantingham, 1993b).
Repeat Victimization and Near-repeat Offending
Offenders and victims include a small percentage of the community, and most
people are not victimized frequently throughout their lives (Johnson, Bowers, &
Hirschfield, 1997; Osborn, Ellingworth, Hope, & Trickett, 1996; Osborn & Tseloni,
1998). Johnson (1973) utilized hospital records to analyze repeat victims of stab
wounds and gunshots. This study was the first major analysis of repeat victimization.
The author’s personal observations were used for the study, and it was found that the
same people frequently returned to the hospital with similar wounds as pervious
hospital visits (Johnson, 1973). Additionally, the victims were often males with
criminal records and usually single (Johnson, 1973). This groundbreaking study
brought to light the phenomenon of repeat victimization and supported the idea that
victims may share similar characteristics.
The importance of repeat victimization was detailed in a study done by
Sherman, Gartin, and Buerger (1989) where the researchers analyzed police calls for
service in Minneapolis over a one-year time window. The premise that a small
percentage of the population accounts for a large percentage of victimization was
confirmed. Of all police calls dispatched with a car, 50.4% came from 3.3% of
addresses. The most victimized 5% of people produced about 24 calls each (Sherman,
Gartin, & Buerger, 1989). This section will synthesize previous literature on repeat
18
and near-repeat victimization, as well as detail certain crimes and their propensity for
repeat victimization.
Repeat victims make attractive targets for multiple reasons, all of which are
amplified in the immediate aftermath of the first offense (Bernasco, 2008; Fagan &
Mazerolle, 2008; Farrell, 1992; Farrell, 1995; Farrell, Phillips, & Pease, 1995;
Johnson, 2008). After an initial victimization, the risk of further victimization
increases greatly with each incident. Offenders choose their targets for distinct
reasons, and when the first offense is successful, they find the target convenient for reoffending. Repeat victims often have individual and environmental characteristics that
increase the risk of being victimized multiple times, such as living with other victims,
living in high crime areas, and spending a large amount of time away from their
dwelling (Farrell, 1992; Farrell, 1995; Farrell, et al., 1995; Johnson, 2008). When the
offender has already proven capable of overpowering a victim, they will return to
commit the crime again. The risk of detection is low due to the awareness of prior
success and lack of changes to harden the target. The path of least resistance is always
the most attractive to offenders, often swiftly after the first victimization (Bernasco,
2008; Farrell, 1995; Farrell et al., 1995; Johnson, 2008).
The time course of repeat victimization is consistent and steady throughout
different crime types. Offenders will often target the same victims shortly after the
first incident, utilizing the same modus operandi. For this reason, changes made to
harden the target must be swift (Farrell, 1997; Farrell & Pease, 1993; Laycock &
Farrell, 2003). Farrell and Pease (1993) outline three reasons why repeat victimization
19
often occurs a short time after the initial victimization. Victims may live in a bad area
where there are many offenders present, sometimes the lifestyles of certain victims
make them vulnerable to repeat victimization, and certain people are victimized
repeatedly in a swift manner because they are constantly around the offender due to
intimate relationships. Researchers use multiple tools of analysis for the time course
of repeat victimization.
When measuring incidents of victimization, it is a mistake to view them
independently. The swift time window of repeat victimization offers multiple
connections between a string of victimizations (Farrell & Pease, 1993). Prevalence,
incidence, and concentration are three units of measurement that apply to the time
pattern of repeat victimization. Prevalence refers to the percentage of the population
at risk for victimization, incidence refers to the average number of victimizations for
each head of household in a population, and concentration is the average number of
victimizations per person in a population (Farrell, 1992; Farrell, 1995; Farrell & Pease,
1993). Incidence rates will always be higher than prevalence rates, because a single
person may be victimized multiple times (Farrell, 1992).
More specifically, victimization patterns can be broken down by daily
activities within a time window. Hawley (1950), defined three levels of time
organization; tempo, rhythms, and timing. Further explored by Felson and Poulsen
(2003), tempo is the number of events within a certain time window, rhythm is the
cycle of a time pattern, and timing refers to the intersection of rhythms between
victims and offenders, leading to a criminal event. Throughout the history of repeat
20
victimization studies, temporal patterns of offending on all levels have been mostly
ignored (Felson & Poulsen, 2003; Polvi et al., 1991; Townsley, Homel, & Chaseling,
2000). The timing measurements listed above become more intense when analyzing
high crime areas.
It can be theorized that prevalence, incidence, and concentration may be high
in certain areas due to a few criminals committing the vast majority of crimes. On the
other hand, high crime areas may consist of many offenders victimizing numerous
people multiple times (Farrell & Pease, 1993; Trickett, Osborn, Seymour, & Pease,
1992). When deciding which unit of measurement is best suited to analyze repeat
victimization, it is necessary to consider the overall crime levels area-by-area (Farrell,
1995). Even in areas that are crime-ridden, many people remain almost unscathed. A
study by Sherman et al. (1989) determined that about 73% of people in Minneapolis
high crime areas were not often victimized, supporting even further the impact of
repeat victimization and the non-random pattern of crime.
Violent crimes, especially ones of a domestic nature, are highly vulnerable to
repeat offending (Farrell, 1992; Farrell, 1995; Farrell et al., 1995). The effort required
by the offender for a domestic violence offense is lower than almost any other crime.
The opportunity is readily available, the offender is familiar with the environment, and
victims often do not seek help from law enforcement due to embarrassment or
personal feelings for the offender (Farrell, 1992; Farrell, 1995; Farrell et al., 1995).
Victims of domestic violence are often females, with a median age of 28, while
offenders are usually males, with a median age of 30 (Mele, 2009).
21
Victims of violent crimes outside of domestic relationships also display similar
characteristics. Typically, repeat victims of violence are young, adolescent males,
from low socioeconomic families and neighborhoods (Fagan & Mazerolle, 2008;
Farrell, 1992). The time elapsed between initial and repeat victimizations of violent
crimes follows a similar pattern as other offenses. A person is at the highest risk for a
repeat victimization in the immediate aftermath of the first incident (Farrell, 1992;
Farrell, 1995; Mele, 2009). Mele (2009) revealed findings regarding the period of
time between victimizations, which was in-line with findings by Farrell and Pease
(1993). As the number of violent victimizations by a domestic partner increased, the
median number of days between the incidents decreased, displaying how target
availability and convenience influence repeat victimization (Farrell & Pease, 1993;
Mele, 2009).
Racially motivated attacks are common repeat crimes because of their ease and
accessibility. The risk is very low for offenders who only have to know where the
victim lives or travels. Often the offender is part of the majority and attacks a
minority individual who happens to be in close proximity. Even if the offender does
not succeed in finding a potential victim, the cost of failing is low and there is no
punishment (Farrell, 1995; Farrell et al., 1995). The prevalence of racial attacks is
high due to the low risk of offending and accessible opportunity (Farrell, 1995; Farrell
et al., 1995; Sampson & Phillips, 1995).
Repeat victimization occurs when an opportunity is paired with the lack of a
capable guardian (Cohen & Felson, 1979; Sherman et al., 1989). Victims of racial
22
attacks are targeted at many different times and locations, often because there is no
capable guardian. Offenders feel confident they will not be interrupted or deterred in
the criminal attempt. Research has revealed that minorities will sometimes not report
the victimization because of a language barrier present between ethnic minorities and
police officers (Farrell, Phillips, & Pease, 1995). A study done by Sampson and
Phillips (1992) examined thirty families who were victims of racially motivated
attacks in East London. The research revealed that 67% of the families were multivictims, usually victimized again within one week of the original attack. Repeat
victimization across all crime types not only puts the same victims at risk, but also
those who are in close proximity of the victim.
Victimization has been explained as a communicable disease because the risk
of falling victim to a crime spreads to targets in the area of the initial victim (Johnson
et al., 2007; Short, D’Orsogna, Brantingham, & Tita, 2009; Youstin, Nobles, Ward, &
Cook, 2011). The term near-repeat victimization refers to offenders victimizing
multiple targets in the same vicinity. When a person or place is victimized, all people
and places in the surrounding area are also at risk (Bowers & Johnson, 2004; Ratcliffe
& Rengert, 2008; Townsley, Homel, & Chaseling, 2003). Similar to multiple
victimizations of the same target, there is a time window associated with the risk; at its
highest in the month following the initial incident (Bernasco, 2010; Bowers &
Johnson, 2004; Morgan, 2001). Near-repeat victimization has not always been at the
forefront of victimization studies, but the phenomenon is quickly becoming a
prominent topic as more victimization patterns are studied.
23
The parallels between repeat and near-repeat victimization are not difficult to
identify. Offenders are more comfortable with neighborhoods and people they have
already victimized once (Johnson et al., 2007; Morgan, 2001; Short et al., 2009).
While committing the initial offense, it is natural for offenders to observe the
surrounding area, adding all targets in the vicinity to their crime template (Townsley et
al., 2003). In addition, homogenous neighborhoods with similar dwelling
characteristics will offer multiple attractive targets to offenders, drawing them away
from a single victim. Neighborhoods that are heterogeneous in housing structure and
layout will be less susceptible to near-repeat victimization because target suitability is
not uniform across all properties (Short et al., 2009; Townsley et al., 2003). In the
analysis of different suburbs in Australia, Townsley et al. (2003) determined that
homogenous neighborhoods facilitated the most near-repeat victims. The similarities
between repeat victimization and near-repeat victimization are also apparent in their
temporal patterns.
In the immediate aftermath of an initial offense, the highest risk for near-repeat
victimization is present. As time elapses, the risk for further victimization lessens.
Spatially, the further from the victimized location, the lower the risk for near-repeat
victimization. Johnson et al. (2007) found that near-repeat incidents followed a
pattern of temporal and spatial decay, 14 days after the initial event presented the
highest risk, but declined beyond that time window. As distance increased from the
initial property victimized, risk also lessened. In a study of Queensland, Australia,
Townsley et al. (2003) reported similar findings; noting that victims were often in
24
close proximity to other victims within a short time window. Similar to patterns of
repeat victimization, near-repeats will cluster in both time and space, across different
crime types (Bernasco, 2008; Johnson et al., 2007; Short et al., 2009; Townsley et al.,
2003; Youstin et al., 2011).
The pattern of near-repeat victimization spreads across all types of crimes.
Studies conducted to analyze personal crime patterns have also found strong patterns
of near-repeat victimization. The first study to focus on near-repeat shootings was
done by Ratcliffe and Rengert (2008) in the city of Philadelphia. Similar to future
findings on the same topic, near-repeat shootings were found to be more prevalent
within two weeks and within 400 feet of the initial incident. Within this time and
space window, there was a 33% greater chance for a repeat shooting. These findings
are typical with near-repeat shooting incidents.
Separate studies done in Houston, Texas and Jacksonville, Florida mapped
near-repeat shooting incidents to analyze spatial and temporal trends. Both studies
found that within 14 days of an initial incident and approximately 400-600 feet, there
was a higher chance for near-repeat victimization (Wells, Wu, & Ye, 2011; Youstin et
al., 2011). It is important to note that any near-repeat study may be skewed due to
what is known as spree crime, meaning a single offender takes part in frequent
offending in a very short period. In addition, a single establishment such as an
apartment complex may constitute a large number of different victimization addresses
(Johnson et al., 2007; Youstin et al., 2011; Wells et al., 2011).
25
Information gained from near-repeat victimization studies is important to
citizens and communities as well as local authorities. What has been discovered
pertaining to the same offender and near-repeat offending is this; the closer in time
and space two incidents are, the more likely the same offender is involved (Bernasco,
2008; Bowers & Johnson, 2004). Bernasco (2008) observed that the more swiftly the
second incident occurs, the more similar the point and method of entry is. Pairs of
burglary victimizations within 200 meters in proximity and within one month of each
other display the strongest similarities. In a similar study of modus operandi in nearrepeats, Bowers and Johnson (2004) found that method of entry was the same for 17 to
20 percent of burglaries in close proximity.
It appears reasonable to assume that a majority of near-repeat victimization
does in fact involve the same offender or sets of offenders (Bernasco, 2008; Bowers &
Johnson, 2004). Considering the idea of offender crime templates, a single offender
can observe an entire neighborhood, analyzing multiple targets in a close proximity.
When that offender decides multiple targets satisfy their template, this provides one
explanation for the near-repeat phenomenon (Andresen, 2010; Beavon, Brantingham,
& Brantingham, 1994; Brantingham & Brantingham, 1993a; Brantingham &
Brantingham, 1993b). One method of defending against this problem is the use of
technology to locate and understand patterns of victimization.
26
Crime Mapping and Hot Spots
By utilizing recent technology and updated geographic information, police
agencies have begun to incorporate useful and informative tools to predict offender
patterns, leading to a reduction in crime and disorder (Braga, 2006; Groff & La Vigne,
2001; Ratcliffe & McCullagh, 1998). Known as crime mapping, crimes are marked
on a digital map so that hot spots can be analyzed (Ratcliffe, 2010). A hot spot is a
cluster of crimes within a given area, informing police departments of a region
experiencing a high rate of criminal activity (Braga, 2006; PERF, 2002; Sherman,
1989). Accurate crime mapping methods open a gateway for hot spot policing
strategies and problem-oriented policing. Data from crime maps and hot spots inform
officers of where and when criminal activity is most prevalent (Braga, 2007; Ratcliffe,
2010; Ratcliffe & McCullagh, 1998).
While crime maps alone are effective in determining offender patterns, the use
of geographic information systems (GIS) paired with crime maps create a more
detailed picture of offense patterns in relation to specific activity nodes (Ratcliffe,
2010; Ratcliffe & McCullagh, 1998). Maps can be layered so that a hot spot is viewed
in relation to community establishments, such as a bar or a bus stop (Ratcliffe &
McCullagh, 1998). The benefits behind GIS systems are very much rooted in theories
such as the rational choice theory, routine activities theory, and crime pattern theory.
The ebb and flow of offender activity and victimization patterns are revealed through
crime maps layered with geographic data. When a hot spot is found, maps can show
which activity nodes are frequented nearby and if any offenders live in the area. Crime
27
mapping technology reflects victimization theories in numerous ways (Braga, 2007;
Groff & La Vigne, 2001; Ratcliffe, 2004; Sherman, 2010).
In a study of Nottingham Shire, Ratcliffe and McCullagh (1998) used GIS to
identify repeat victimization and hot spots related to activity nodes and other locations.
The data contained a combined two years of recorded burglaries in the Trent Police
Division. Locations of one-time incidents were mapped, and then layered with
locations of at least one additional offense. Although 68% of the properties were not
victimized more than once, the GIS maps revealed that the majority of repeatedly
victimized locations were commercial residences, sports arenas, and schools near
major roadways. Similar findings from a GIS crime map of Charlotte, North Carolina
were found by Groff and La Vigne (2001), tracking residential burglaries near
community nodes which offered an opportunity for crime. Hot spots repeatedly were
near major roadways, had poor lighting, and showed signs of disorder. Without GIS
technology, this type of information would be unknown to local authorities.
GIS profiling is an investigative technique used by authorities to track an
offender’s criminal patterns and determine locations where they may live or work
(Bowers, Johnson, & Pease, 2004; Ratcliffe, 2004). The theoretical basis behind the
idea of geographic profiling comes from research done by Brantingham and
Brantingham (1993b). Following the premise that an offender’s search area and target
suitability are affected by the environment they function within, authorities can benefit
greatly from knowledge of where the offender resides and carries out their daily
activities (Brantingham & Brantingham, 1993b; Ratcliffe, 2004). By producing a
28
calculated search area based on prior offenses, officers are not left guessing where an
offender conducts daily activities. A true benefit of GIS profiling is its applicability
across multiple crime types, as the journey to crime is always present (Ratcliffe,
2004).
The importance of crime sites when tracking serial offenders cannot be
understated, and are often studied to determine where an offender resides. Multiple
methods have been applied using crime mapping technology to create a radius
determining where an offender most likely lives based on where they offend
(Hammond & Canter, 2007; Harries & LeBeau, 2007; Rossmo, 1997). Accounting for
the role of distance decay in the hunt for a suitable target, each crime location is
plotted and compared based on distances from likely places where the offender’s
residence is located (Harries & LeBeau, 2007; Ratcliffe, 2004; Rossmo, 1995). While
geographic profiling offers authorities a reasonable search radius for an offender’s
home location, the strategy is best utilized with other proactive policing techniques
and trained staff (Harries & LeBeau, 2007).
Multiple techniques and simulations have been formed to accurately predict an
offender’s residency and create an accurate search area for officials. In a study done
by Canter & Hammond (2007), the five most prevalent methods for creating a search
area were tested for accuracy, these methods included; distance from first offense,
distance from last offense, distance from center of the offense circle, distance from the
center of gravity (mean of x & y coordinates), and distance from the point assigned
highest probability. Data were collected on 92 known burglary series and linked with
29
all known offenders in the area. The coordinates were then processed through each of
the profiling methods to determine which was most accurate. Both the center of
gravity method and distance from the point assigned highest probability method were
the most accurate, capturing actual offenders in 72% of cases. In addition, using first
known offense and last known offense captured over 50% actual offenders, showing
the value of combining methods. These findings coincide with research done by
Harries & LeBeau (2007), which showed that the most successful methods were based
on center of gravity analysis.
Santtila, Zappala, Laukkanen, and Picozzi (2003) applied geographic profiling
to a serial rapist charged with over 50 rapes throughout a 23-year period. A benefit to
this study and something that will always increase the success of geographic profiling
is a consistent modus operandi. Knowing which crime events and locations are
attributed to the same offender will boost the likeness of an accurate search area
(Harries & LeBeau, 2007; Rossmo, 1997; Santtila et al., 2003). Similar to findings
from other research, using the mean distance between each crime site location will
offer the most accurate search radius for authorities (Canter & Hammond, 2007;
Harries & LeBeau, 2003; Santtila et al., 2003).
For geographic profiling to be used in the most effective way, the weakness of
the methods must be realized. Geographic profiling should be used as a compliment
to other tactics and strategies for tracking offenders and their patterns (Breetzke, 2006;
Harries & LeBeau, 2003). The majority of maps do not have topographic
characteristics because city streets are not often symmetrical, which means the path to
30
offending is multi-directional, and may not be accurately captured (Rossmo, 1995;
Santilla et al., 2003). Without a large number of criminal sites, the chosen method for
analysis will be less valid. Tracking a serial offender with a limited number of
incident locations will not produce as accurate of a search area (Rossmo, 1995).
Geographic profiling is the product of theory and technology, and when utilized in the
correct manner can be an effective means to preventing crime.
Hot spot policing entails a variety of different policing techniques and methods
taking advantage of the knowledge provided to officers from crime maps and GIS
profiling. Focusing available resources on a concentrated number of crime locations
saves time and money typically wasted on random patrol (Braga, 2006; Braga, 2007;
PERF, 2002). Researching and understanding how and why crime occurs can be
achieved using crime mapping technology, and offers new ways to approach
community crime issues. This idea was termed problem-oriented policing by
Goldstein (1979), and should be mentioned as an aspect of hot spot policing.
Common problem-oriented policing techniques include counseling, community
meetings, and agency referrals (Braga, 2007). Hot spot and problem-oriented policing
offer new and innovative techniques for long-term crime prevention (Braga, 2006;
Ratcliffe, 2004; Ratcliffe, 2010).
The advantages of hot spot policing and problem-oriented policing strategies
are outlined in numerous studies, with a majority highlighting the effectiveness of
focusing patrol on certain high crime areas (Hope, 1994; Sherman & Rogan, 1995;
Sherman & Weisburd, 1995; Telep & Weisburd, 2011). Two specific cases displayed
31
the largest reductions using hot spot and problem-oriented policing. In Kansas City,
Sherman & Rogan (1995) studied the effects of increased safety frisks during traffic
stops, plain view searches, and searches incident to arrest surrounding drug houses.
The results were a 39% decrease in property crime calls and 24% less violent crimes
in target areas of high offending. A study conducted in Jersey City by Braga,
Weisburd, Waring, Mazerolle, Spelman, and Gajewski (1999), observed the effects of
problem-oriented policing tactics including increased lighting, removing trash from
streets, increased drug enforcement, and restoring abandoned buildings in hot spot
areas. This resulted in a large dip in incidents and calls for service.
Research done by Telep and Weisburd (2011), followed by Telep, Mitchell, &
Weisburd (2012) studied the effects of hot spot policing in Sacramento, California. In
the later study, officers were instructed to spend about 15 minutes within experiment
hot spots, as it was discovered 15 minutes was the optimal time frame for reducing
crime in hot spots. The increase in officer presence created an immediate reduction in
calls for service, dropping about four calls per hot spot. Sergeant Mitchell along with
the researchers found that problem-oriented policing strategies paired with crime
mapping technology was extremely effective in fighting crime (Telep & Weisburd,
2011; Telep, Mitchell, & Weisburd, 2012). Telep, Mitchell, and Weisburd (2012),
suggest that spatial patterns of property crime should be analyzed over a long period to
determine if hot spots remain stable, which is one objective of this study. In addition,
temporal patterns of victimization have not received the necessary attention to benefit
local authorities (Telep & Weisburd, 2011; Telep, Mitchell, & Weisburd, 2012).
32
A study by Poulsen and Kennedy (2004) outlined the effectiveness of merging
crime mapping, problem-oriented policing, and school location. Using crime mapping
technology to identify school proximity to crimes proved to be a successful strategy in
monitoring and preventing criminal activity during routine activities after school
(Kautt & Roncek, 2007; Poulsen & Kennedy, 2004). High amounts of burglary take
place during the day, specifically after school. When law enforcement increases patrol
around schools during afternoon hours, offending becomes more difficult due to the
presence of authority. An increase in officer presence will also cut down on truancy,
which often leads to criminality. The pairing of problem-oriented policing and crime
mapping when correlated with community nodes produces multiple strategies to
prevent victimization (Kautt & Roncek, 2007; Poulsen & Kennedy, 2004).
Even with the proven knowledge regarding the effectiveness of hot spot and
problem-oriented policing, open-minded staff and appropriate training programs must
be in place (Breetzke, 2004; Goldstein, 1979). Crime mapping training and education
is mandatory in guaranteeing law enforcement agencies get the most out of these
technological tools. It is vital to have individuals who are trained to handle the mass
amounts of data and relay that information to officers on patrol. For officers on the
streets, the first step is adapting and adjusting to the new age of technology-based
policing (Breetzke, 2004; Ratcliffe, 2004; Ratcliffe & McCullagh, 2001). By no
means are these changes easy to accept; changing the culture of a police department
requires a changing of the departmental mind set. With much attention toward the
benefits of crime mapping, there also must be a focus on how to correctly brief and
33
train officers to use the technology (Bowers, Johnson & Pease, 2004; Breetzke, 2004;
Ratcliffe, 2004; Ratcliffe & McCullagh, 2001).
Officers do not usually rely on mapping technology to inform them, and are
often untrained in their understanding of technology and crime patterns (Paulsen,
2004; Ratcliffe, 2004; Ratcliffe & McCullagh, 2001). Police culture may promote a
false sense of belief regarding which crimes are most problematic for the community
(Breetzke, 2006). Paulsen (2004) revealed that officers are able to rank only a couple
of the most prevalent crimes in their patrol beat, but after that, are lost. Officers note
that infrastructure and officer buy-in are main roadblocks in the acceptance of crime
mapping (Breetzke, 2006; Paulsen, 2004).
Police culture is a strapping and difficult wall to bring down, and often will
resist new forms of technology or information dissemination (Ratcliffe, 2004). For
instance, proper utilization of crime mapping technology requires a high level of
competence and understanding in the subject matter, frequently requiring new training
(Paulsen, 2004; Ratcliffe, 2004; Ratcliffe, 2010). Members of the department will be
hesitant to rely on a computer instead of their own perceptions and instincts gained
through real life experiences. In the eyes of many officers, data from crime maps do
not help them in their day-to-day operations (Breetzke, 2006; Buerger, Cohn, &
Petrosino, 1995). When changing the culture of a department and implementing new
training regimens, support from the supervisor is very important. Officers will follow
the lead of their supervisor and support them in their decisions for the department
(Ashcroft, Daniels, & Heart, 2000; Engel, 2002; Engel & Worden, 2003).
34
Several studies have analyzed how to best train police staff and implement
crime mapping technology in a way that can be understood and utilized. If the
absence of training on how to operate and interpret crime mapping software persists,
resources will be wasted (Ratcliffe, 2004). Police agencies need leaders who are
capable of adapting crime mapping programs into their departments and finding
reliable operators to run them and interpret the results in a meaningful way. In a bestcase scenario, agency commanders have an idea of which police tactics work best in
different situations and are flexible to trying different ideas (Goldstein, 1979;
Ratcliffe, 2004; Ratcliffe & McCullagh, 2001). Throughout day-to-day operations
within police departments, daily briefings have proven to be a valuable step toward
insuring officers are aware of the information provided by crime maps (Bowers,
Johnson, & Pease, 2004).
While the subject of crime mapping and training is becoming more prominent,
it is not substantial. The International Association of Chiefs of Police (IACP) offers
68 different training programs throughout the U.S. In the area of “Quality
Leadership”, none of the programs are focused on crime prevention. Only one of the
sixty-eight programs addresses problem-oriented or intelligence-led policing
(Ratcliffe, 2004). To summarize the variety of classes offered in the curricula, one
was geared toward crime prevention and three were offered in the area of crime
reduction in specific situations. These programs add up to only 16 days of training.
Managerial training can help increase the understanding and execution of crime
mapping, especially in the area of police management (Ratcliffe, 2004).
35
The effectiveness of crime mapping and hot spot policing has often been
rejected by methodologically flawed studies arguing that crime is not prevented, but
simply displaced to surrounding areas (Andresen & Malleson, 2010; Cornish &
Clarke, 1987; Farrell & Pease, 1993). For example, if an offender observes a
neighborhood with many safeguards, they will move to a different neighborhood more
suitable for criminal opportunity. While this sounds plausible, numerous studies have
determined this is not the case. The offender must be displaced to another suitable
target, and remain motivated to offend (Cornish & Clarke, 1987; Farrell & Pease,
1993; Pease, 1991; Weisburd et al., 2006).
The majority of studies analyzing the effect of repeat victimization and
problem-oriented policing have found similar results; a decrease in crime and no
legitimate signs of displacement. It appears likely that offenders will not decide to
continue their search for a criminal opportunity in areas unfamiliar to them (Chenery,
Holt, & Pease, 1997; Farrell, 1995; Farrell & Pease, 1993; Johnson et al., 1997). In
two separate studies of high crime areas and alternative policing strategies, it was
found that crime was lowered in the neighborhoods bordering the study area (Farrell,
1995; Guerette & Bowers, 2009). In a study of 102 different crime-prevention
situations, displacement only occurred in 26% of instances. In 27% of instances,
surrounding areas displayed lower crime rates (Guerette & Bowers, 2009). As more
studies emerge, displacement will become less of an argument against crime mapping
and problem-oriented policing.
36
Crime analysis studies are valuable and informative, but there are certain focus
areas of the literature that are unknown. Studies have not incorporated long time
windows of data, meaning the seasonal and yearly patterns are not evaluated, nor is
the long-term effectiveness of hot spot policing (Bowers, Johnson, & Pease, 2004;
Braga, 2007; Sherman, 1989; Telep & Weisburd, 2011). Expanding the distance
between the first and last victimization in a study period offers a larger picture of the
ebb and flow of victimization. Obviously, studies such as this require more time,
effort, and money than that of a shortened study such as one year. This study will aim
to address these missing areas of the literature.
Repeat Property Victimization
A majority of victimization rates stem from a small number of people
victimized multiple times in a short period. Even more than crimes of the person,
property crimes are at a very high risk for repeat offending, and offenders will
continue to victimize a property until the owner takes precautions to harden the target
(Johnson, Bowers, & Hirschfield, 1997; Osborn et al., 1996; Osborn & Tseloni, 1998).
Repeat property victimization is categorized as a residential or commercial property
that has been victimized more than once in a given time period, often suffering
burglary, theft inside or outside the immediate area, or damage inside or outside the
immediate area (Ellingworth, Farrell, & Pease, 1995; Johnson et al., 1997). In many
cases, it is easier and more convenient for an offender to target the same property
multiple times. Repeat property crime is non-random to such an extent that studies
have established when a property is most at risk for repeat victimization (Cornish &
37
Clarke, 1987; Farrell, 1992; Farrell, 1995; Farrell et al., 1995). This section will detail
what is known about the pattern of property crime and repeat property victimization.
The prominent theories surrounding repeat property crime offer different
reasons why single properties are targeted multiple times. Known as the Flag Theory
and Boost Theory, each supporting a different explanation behind repeat property
victimization (Farrell, et al., 2001). The flag theory suggests that certain properties
stand out within a neighborhood, and are attractive to offenders, in essence flagging
them. The boost theory provides that previous victimizations dictate future
victimizations, explaining that once a property is victimized, the chances of an
offender revisiting that property to offend again are boosted (Bernasco, 2008; Bowers
& Johnson, 2004; Johnson, 2008).
In reality, a combination of the flag theory and boost theory best explains the
motivation to re-offend. Target attractiveness may lead a potential offender to flag a
property, while the success of an initial offense may boost the chance of a second
offense (Johnson, 2008; Morgan, 2001). A test of the boost theory was done by
Bowers and Johnson (2004) and found that crimes are often committed within a very
tight space and time window. This finding supports the boost theory, determining that
crimes are very much dependent on each other. Bottoms (1994) also rejected the Flag
Theory in his analysis of why offenders choose the same targets within a short spatial
and temporal window. The non-randomness of repeat property crime seems to be
better explained by the boost theory than the flag theory, but both should be accounted
for in the explanation.
38
Neighborhood characteristics also dictate why an offender targets the same
properties multiple times. Offending patterns have been found to change depending
on the type of social and economic factors of a neighborhood (Hope, Bryan, Trickett,
& Osborn, 2001; Johnson et al., 1997; Osborn & Tseloni, 1998; Wells, Wu, & Ye,
2012). Affluent neighborhoods with more cars and higher paying jobs facilitate more
randomized victimization. Deprived neighborhoods have a lower socio-economic
status, with more public housing buildings and less random victimization (Hope et al.,
2001; Osborn & Tseloni, 1998). In most cases, properties in deprived neighborhoods
are at a higher risk for repeat victimization, but do not always receive the most
attention from local authorities (Farrell, 1995; Farrell & Pease, 1993; Hope et al.,
2001; Johnson et al., 1997; Osborn & Tseloni, 1998).
Understanding the phenomenon of repeat property victimization requires an
analysis of two areas; why offenders target the same properties, and the time window
of repeat property victimization. After the initial property victimization, the chances
of a repeat are very high in the following weeks (Farrell & Pease, 1993; Morgan,
2001; Polvi et al., 1991). Not only are offenders more comfortable with the target and
surrounding area, but there is less of a chance the target has been secured in the weeks
immediately after the initial offense. What must be understood is that property crime
is not a random occurrence; previous victimization facilitates future victimization
(Johnson, 2008; Osborn & Tseloni, 1998; Rey, Mack, & Koschinsky, 2012). Several
studies have examined the why and when of repeat property victimization, and offer
an insight to the patterns and prevention of property crime.
39
One of the first studies on repeat property victimization and its effect on the
overall crime rate was done by Forrester, Chatterton, & Pease (1988), and focused on
different preventative efforts taken after an initial victimization. What was found
through the effectiveness of targeted responses after a burglary shed light on the extent
of repeat property victimization. Measures taken to protect houses most at risk due to
a previous incident created a reduction in burglary by 75% in three years. These
measures included turning on more lights, parking cars in the driveway, enforcing
neighborhood watch, securing doors and windows, and increased patrol (Forrester,
Chatterton, & Pease, 1988).
While a majority of the population does not experience frequent property
victimization, an initial incident makes the property extremely vulnerable for further
victimizations. Townsley, Homel, and Chaseling (2000) determined in their study that
after a first property victimization, the chance of a repeat victimization more than
doubles. These findings correlate with research done by Mukherjee & Carcach (1998)
on the National Crime and Safety Survey (Australia), where 28% of the households
victimized accounted for 50% of all property crime. Studies such as those listed above
highlight the importance of initial property victimizations and how to distribute local
resources effectively.
There are multiple reasons why an offender would decide to return to a
previous location to reoffend. Often the offender is more comfortable with a
previously victimized location, and recognizes the ease of access. Quickly targeting a
property previously victimized creates a low risk, high reward scenario (Bennett &
40
Durie, 1999; Bernasco, 2008; Morgan, 2001; Pease & Laycock, 1999). There is also a
chance the offender may have left certain valuables behind during the first offense,
and returned in order to collect them. In some cases word of mouth may tip-off other
potential offenders that a property is vulnerable, leading to repeat victimization by
multiple perpetrators (Bernasco, 2008; Farrell, Phillips, & Pease, 1995; Morgan, 2001;
Polvi, Looman, Humphries, & Pease, 1991). In some cases, the offender and victim
schedules overlap to create the opportunity (Sagovsky & Johnson, 2007).
In most cases of repeat property victimization, the same offender is responsible
for the crime (Laycock & Farrell, 2003; Pease & Laycock, 1999). Bernasco (2008)
found that the less time elapsed between incidents signals a greater chance of the same
offender being responsible, similar to the pattern found with personal crimes. Based
on time of day and modus operandi, studies have been able to determine when the
same offender is to blame. Usually offenders will use the same means of entry into a
property at the same time of day as the initial victimization (Ewart & Oatley, 2005;
Sagovsky & Johnson, 2007). Success with a previous criminal attempt will influence
the offender to offend in the same manner. However, as more time elapses after the
first victimization, the chances lessen that the same offender will return. Time of day
can also be a very telling clue when determining if the same offender is at fault.
Repeat property victimization in residential areas frequently takes place during
the day, most likely due to the routine activities of victims and offenders
corresponding without a capable guardian present (Cohen & Felson, 1979; Rey, Mack,
& Koschinsky, 2011). When an offender successfully victimizes a property, it makes
41
sense to choose the same time of day for continued success, because victim routines
are consistent during the workweek. Similar to other aspects of repeat victimization, a
time-decay does exist; the smaller the time window between first and subsequent
offenses; the greater the chance of the crime occurring at the same time of day
(Johnson & Bowers, 2004; Sagovsky & Johnson, 2007).
The time window of repeat property crime is very predictable. The month
following victimization, the property is at the most risk. As time elapses after the first
victimization, the chances of a repeat attempt lessen, known as risk decay (Farrell,
1995; Johnson et al., 2007; Sagovsky & Johnson, 2007). Polvi et al. (1990) and Polvi
et al. (1991) did one of the first and best-known studies to breakdown the time-course
of repeat property crime. Tracking residential burglary in Saskatoon, Canada, the
authors found an overwhelming pattern of a heightened risk period in the immediate
aftermath of the initial victimization. Within one month of an initial property
victimization, chances of a repeat victimization shot to twelve times the initial rate.
After six months, the risk was still high, but only two times the expected rate (Polvi et
al., 1991). Up until the early 1990’s, the connection between time elapsed from one
victimization to another and risk was not studied.
Morgan (2001) examined repeat burglary patterns in an area consisting of
about 2,000 people in six different designated zones. The study found that an elevated
risk existed within one month of the initial burglary, but declined after that (Morgan,
2001). The same timetable was discovered by Bowers, Hirschfield, and Johnson
(1998) in a study of non-residential property victimization; 43% of repeat incidents
42
occurred within one month of the first crime. The type of neighborhood will also
affect the time window of repeat property victimization. In areas with high crime
rates, repeat property victimization patterns will be more consistent and predictable
(Morgan, 2001; Sagovsky & Johnson, 2007). This finding should not come as a
surprise, considering high crime areas will have more offenders present and fewer
police officers (Farrell, 1995; Hope et al., 2001; Johnson et al., 1997).
Inevitably, repeat victimization studies and observed patterns will suffer the
limitations of underreporting, restrictive time windows, and delayed reporting. While
no crime is reported to complete accuracy, property crime is often underreported more
than other crimes. In addition, repeat victimizations of the same property sometimes
are not linked in police databases, meaning the two incidents are not counted as a
repeat. This may be due to the officer not recording the crime, or the address being
recorded in a different way than the initial victimization (Farrell & Pease, 2003).
Certain victimizations will occur before or after the period of the study. Some
properties that are victimized only once during the study period may have been
victimized prior to the study period, or are counted as the initial victimization for a
repeat that will fall outside the study period (Ellingworth, Farrell, & Pease, 1995;
Farrell & Pease, 2003; Farrell, Sousa, & Weisel, 2002). Data sets consisting of times
and dates of property crimes are diluted by the fact that victimizations may not be
immediately reported. The times and dates sometimes represent when the
victimization was reported, not when it necessarily occurred (Bernasco, 2008; Felson
& Poulsen, 2003). A repeat property victimization study with a longer time window
43
can counteract these issues by creating a buffer period for the data.
Conclusion
An important area missing in the research surrounding property crime patterns
are studies spanning an extended period. Many studies focus on small-time windows
to analyze immediate results of spatial property victimization patterns (Farrell, 1995;
Farrell & Pease, 1993; Hope et al., 2001; Johnson, Bowers, & Hirschfield, 1997).
Taking time to collect data for a multi-year study not only increases the validity of the
study due to a larger data set, but also allows monthly and yearly trends to be
observed. It has been proven property crime displays non-random patterns over a
shorter period of time, but that same claim has not been tested over a period of
multiple years. While repeat victimization undoubtedly plays a role in property crime
patterns, the question of will that hold true over a longer period of time remains
unanswered.
A second topic area not studied within property crime is temporal patterns over
an extended period (Felson & Poulsen, 2003; Polvi et al., 1991; Telep & Weisburd,
2011; Townsley et al., 2000). A study of multiple years offers an opportunity for
researchers to determine if temporal patterns remain clustered and non-random and
also to observe how the temporal patterns ebb and flow over a long period of time.
This knowledge can be paired with police officer shifts, and theoretical knowledge can
be turned into reality (Sagovsky & Johnson, 2007). Studies focusing on the above
areas will help influence police departments to take the next step into a predictive
policing era. This study will not analyze specific addresses over an extended period of
44
seven years, but the overall patterns of property crime over that period both spatially
and temporally.
45
Chapter 3
Methodology
The aim of this study was to analyze data to determine if property crime
displayed non-random patterns over an extended period of seven years both spatially
and temporally in the city of Sacramento. Spatially, if property crime was randomly
distributed, offense patterns would be spontaneous and differ from year to year. If not
randomly distributed, property crime would cluster and display similar trends each
year of the study period. Even if property crime clustered in different sections of the
city from year to year, the patterns would still be considered non-random.
Temporally, the times of offenses were analyzed over the seven-year period to
determine if patterns were present. Creating bar graphs based on the data set obtained
produced a clear picture of property crime patterns during the study period.
Participants
Cases in the study were individual addresses of victimized locations. To be
included in the study, the property must have been victimized between the time
window of January 1, 2005 and August 31, 2011. The following offenses were
regarded as property crimes:
46
Table 1: Penal Codes Used in Study
Penal Code
Description
1212-0
215(A) CARJACKING
2099-10
455 ARSON ATTEMPT
451(B) ARSON OF INHABITED
2099-2
STRUCTURE
2099-3
451(C) ARSON OF STRUCTURE/LAND
2099-4
451(D) ARSON OF PROPERTY
2099-5
455 ARSON (ATTEMPT)
2203-0
459 BURGLARY BUSINESS
2204-0
459 BURGLARY RESIDENCE
2299-01
459 BURGLARY VEHICLE
2299-09
459 BURGLARY (UNSPECIFIED)
2303-3
484 PETTY THEFT-SHOPLIFT
487(A) GRAND THEFT-CLOUT2305-1
UNLOCKED
10851(A)VEHICLE TAKEN WITHOUT
2404-0
OWNER
2404-13
10851 AUTO THEFT LOCATE
2999-02
594(B)(1) VANDALISM +$400
2999-05
594(B)(2)(A) VANDALISM -$400
603 FORCED ENTRY/PROPERTY
2999-08
DAMAGE
7000-12
BURGLARY - I REPEAT
NEIGHBORHOOD DISTURBANCE- I
7000-24
REPEAT
No humans were involved in this study in any way. The persons who inhabit
the dwellings never were contacted, their names were not listed anywhere in the data
set, and the researcher in no way analyzed the properties listed in the data set. No
specific addresses or street names were listed in the study.
Data Collection
The data collected for this study were all reported property crimes between the
dates of January 1, 2005 and August 31, 2011. The data were downloaded to seven
47
spreadsheets and organized in multiple ways, allowing for the creation of graphs for
the analysis of property victimization patterns. The following categories were
included in the spreadsheets: occurrence date, address, apartment, district, beat, grid,
X-coordinate, Y-coordinate, code, description.
The data for all reported property crimes between January 1, 2005 and August
31, 2011 were downloaded and organized by location, year, and time, and then
graphed. Data analysis came from the graphs that were produced. Observing when and
where property crimes clustered for each year determined if the hypotheses were
accepted or rejected.
Data Analysis
The type of analysis is descriptive in nature. Each graph produced presented
different views of property crime patterns for the given year or the overall period.
Through these graphs patterns were observed, such as; spatial clustering or
randomization of property victimization, temporal clustering or randomization of
property victimization, yearly trends of property victimization, and seasonal trends of
property victimization. Numerous types of analysis insured the study was performed
to exhaustion. In addition, commercial and residential property victimization was
specifically analyzed, due to its prevalence in the overall property victimization rate.
Testable Hypotheses
Analyzing all of the graphs at a macro-level and micro-level throughout the
entire period was necessary to test the following hypotheses:
48
H1A: Spatial patterns of property victimization will remain non-random
and clustered by police district over time.
H0A: Spatial patterns of property victimization will not remain nonrandom and clustered by police district over time.
H1A: Temporal patterns of property victimization will remain nonrandom and clustered over time.
H0A: Temporal patterns of property victimization will not remain nonrandom and clustered over time.
Advantages and Limitations of Research Design
Advantages of using a longer time window of examination extend beyond the
ability to analyze monthly and seasonal trends. More study subjects (properties)
increased the validity and reliability of the results. Studies spanning only one year run
the risk of that year being unusually high or low in victimization. This concern was
not valid with data for seven years. Predicting future offending patterns is the goal of
crime analysis, and with multiple years of data, future victimization can be more
accurately predicted.
This study will determine the pattern of property victimization with an
extended data set of seven years. There are limitations present, which are inevitable to
all property crime studies. Any study with a specified time window will be
discounting victimizations that occur directly before or after the designated period.
Fortunately, for this study, seven years of data decreased the importance of those
offenses left outside the study window. In this specific study, victimization in 2011 is
only accounted for through August. While this diminished the total incidents for that
year, patterns were discovered and analysis was conducted.
49
For a data set of recorded property crimes, accuracy will always be an issue.
The information logged by police officers sometimes relies on when the victim
reported the crime, not necessarily when the crime occurred. For instance, a family on
vacation may come home to find their dwelling victimized and report the incident. In
reality, the actual crime may have occurred days earlier. However, officers are usually
able to pinpoint an accurate time of offense. The issue of underreporting is also
present with this study. Victims sometimes have a lack of faith in officers and find
reporting crimes useless. This will decrease victimization rates overall, affecting the
number of property victimizations in the study. Again, with a multi-year study, total
number of victimizations is not a concern.
Police departments and community authorities can utilize the results from this
study. In order to disseminate the knowledge gained from the research, spatial
offending patterns were paired with victimization occurrence times. Findings could
lead police agencies towards a better allocation of resources as well as an
understanding of which crimes most often occurred during certain times of day. The
researcher hypothesizes that property crime patterns will remain non-random both
spatially and temporally over an extended period of seven years.
50
Chapter 4
Findings
The following section will detail the findings from the analysis of property
crime in the City of Sacramento from January 2005 through August 2011. Each chart
is described and compared to other years of the study period to identify patterns that
may be present. Property victimization for each individual year must be understood
before overall patterns can be identified and the hypotheses can be accepted or
rejected. The charts and figures illustrate how property victimization functions over
an extended period of time. Figure 2 below, used by the Sacramento Police
Department, identifies police districts and the beats within those districts. The
numbers on the map represent each district, while the letters represent the police beat.
For the purpose of this study, districts one, two, and three will be categorized as
northern districts. Districts four, five, and six will be categorized as southern districts.
51
Figure 1: Sacramento Police Department Beats and Districts
52
Spatial Patterns of all Property Victimization
Number of Victimizations
Figure 2: Victimization by District
2005
6000
5000
4000
3000
2000
1000
0
1
2
3
4
5
6
Police District
Figure 2 reports the number of victimizations by district in the first year of the
study, 2005. Victimization is heavily concentrated on districts one, two, and three,
with the third district accounting for over 5,000 property crimes in the year. In 2005,
the city of Sacramento experienced approximately 25,000 property victimizations. Of
that total, about 40% of those victimizations were concentrated in districts two and
three, while 55% were from districts one, two, and three. Property victimization
patterns for 2005 indicate higher victimization within districts two and three. Less
property victimization is seen in districts four and five. District six recorded just over
4,000 victimizations, and was a problem area in the southern districts of Sacramento in
2005. District five was the least victimized, with about 1,720 less victimizations than
district three.
53
Figure 3: Victimization by District
2006
Number of Victimizations
6000
5000
4000
3000
2000
1000
0
1
2
3
4
5
6
Police District
Property victimization patterns in 2006 display similar characteristics to the
previous year. Overall, property victimization is slightly higher with an increase of
178 incidents. Between 2005 and 2006, the fact that the difference in total
victimization is less than 200 speaks to the non-random nature of property crime
patterns. If patterns similar to this are discovered, a strong case can be made toward
accepting the hypotheses. Again, districts one, two, and three recorded the highest
victimization rates. Districts two and three alone accounted for 41% of the total in
2006, very similar to the percentage found in 2005. Districts four, five, and six were
again relatively lower than the other districts. District six was the least victimized
district, reporting about 1,650 less incidents than district three.
54
Figure 4: Victimization by District
2007
Number of Victimizations
6000
5000
4000
3000
2000
1000
0
1
2
3
4
5
6
Police District
The northern districts continued their trend of higher numbers of victimization
of property crime. Figure 4 shows Sacramento experienced less property
victimization in 2007 than in 2005 or 2006, but the pattern remains stable. District
three recorded the highest amount of victimization with just under 5,000 reported
cases. While the southern districts overall experienced less victimization, district six
was the least victimized district of all. District six declined in property victimizations.
In 2007, districts two and three accounted for 41% of all property crimes, identical to
2006 and nearly identical to 2005. District six reported about 2,000 less
victimizations than the most victimized district.
55
Figure 5: Victimization by District
2008
Number of Victimizations
6000
5000
4000
3000
2000
1000
0
1
2
3
4
5
6
Police District
Property victimization in 2008 was relatively low compared to the previous
years of the study period. With 19,298 total victimizations, districts two and three
again were the most prevalent, accounting for approximately 40% of the total. Again,
a similar pattern is observed with districts two and three, accounting for an above
average amount of victimization. Similar to the previous years of this study, there is
an incline in victimization from districts one to three. These three districts accounted
for 10,470 victimizations, approximately 54%, with a majority of that total coming
from districts two and three. District three continues to be the most victimized district
in Sacramento during the study period, recording about 1,500 more victimizations than
the lowest district. Analysis from 2005, 2006, 2007, and 2008 display solid patterns
of property victimization.
56
Figure 6: Victimization by District
2009
Number of Victimizations
6000
5000
4000
3000
2000
1000
0
1
2
3
4
5
6
Police District
Figure 6 shows a similar trend in property victimization in the city of
Sacramento. However, district one is slightly lower in victimization as compared to
previous years of the study. Even with this decline in district one, the northern
districts of Sacramento have a heavier concentration of property crime compared to
the southern districts, mostly due to district three’s victimization. Districts two and
three again accounted for 41% of the victimization total, on par with previous years.
The difference in victimization totals between the highest district and lowest district
was approximately 2,000 victimizations. Again, district three was the most victimized
in 2009.
57
Figure 7: Victimization by District
2010
Number of Victimizations
6000
5000
4000
3000
2000
1000
0
1
2
3
4
5
6
Police District
Figure 7 displays the victimization pattern for the year 2010. Continuing the
previous trend, district three was the most victimized overall, and district five was the
most victimized southern district. Districts two and three were again the most
victimized in Sacramento, accounting for 40% of the total 16,508 victimizations.
Districts four, five, and six were more evenly distributed with property victimizations,
as previously found. With the northern districts, a steady incline was again observed
from districts one to three. District 6 was below average as compared to previous
years, with approximately 1,300 less victimization than the most victimized district.
58
Figure 8: Victimization by District
2011
Number of Victimizations
6000
5000
4000
3000
2000
1000
0
1
2
3
4
5
6
Police District
Figure 8 displays the final year of the analysis period, with a noticeable decline
in all districts. This decline is attributed to a shortened data set for 2011, only
capturing victimizations through August. Also, a hot spot policing initiative that was
undertaken in 2011 by the Sacramento Police Department may have had an effect.
Even with lower totals, previous patterns hold true. District three was the most
victimized, with districts two and three accounting for a combined 45% of the total.
The northern districts one, two, and three again display an incline, while the southern
districts four, five, and six are more evenly distributed. The difference between the
highest and lowest victimized districts was approximately 1,000. Figure 7 is an
interesting example of how property victimization patterns can remain non-random
and discriminatory independent of the actual amount of victimization.
59
Number of Victimizations
Figure 9: Victimization by District
2005-2011
35000
30000
25000
20000
15000
10000
5000
0
1
2
3
4
5
6
Police District
Figure 9 displays the distribution of property crimes by district over the entire
study period. The majority of victimization has taken place in districts one, two, and
three, with district three suffering almost 30,000 incidents between 2005 and 2011.
Within district three are the Sacramento midtown and downtown areas, which offer a
very high concentration of dwellings and community nodes. Also, district three offers
a number of establishments serving alcohol, which has been proven to increase
victimization rates across all types of crimes. Numerous entertainment nodes in the
downtown area will also draw people away from their dwellings, making the
properties vulnerable. Districts four, five, and six were less victimized; with district
six suffering approximately 19,000 incidents. The neighborhoods in district six are
more rural and spread out, offering less targets and more distance to travel for
offenders.
60
Number of Victimizations
Figure 10: Number of Victimizations by
Police Beat
2005-2011
12000
10000
8000
6000
4000
2000
0
1A 1B 1C 2A 2B 2C 3A 3B 3C 3M 4A 4B 4C 5A 5B 5C 6A 6B 6C
Police Beat
To understand victimization patterns within districts, police beats also were
analyzed in this study. Figure 10 depicts the victimization levels for every police beat
of the Sacramento Police Department from 2005 through 2011. Clustering is more
clearly illustrated when viewed by police beat. It is no surprise the beats contained in
district three have relatively higher levels of victimization. What is interesting is the
difference in victimization rates between the beats in district three. Beats 3B and 3C
account for a large portion of the district’s victimization. It is also worth noting that
district three does have a small additional beat adding to the total. Even without beat
3M, district three is on average higher than the others. Beat 3C was the most
victimized, with over 10,000 victimizations; this is approximately 6,000 more
victimizations than the least victimized beat, 1C. It is clear to see the increase in
crime for the beats in districts one, two, and three.
61
Number of Commercial and Residential Burglary Victimizations
Number of Victimizations
Figure 11: Commerical and Residential
Burglary Victimization by District
2005
1000
900
800
700
600
500
400
300
200
100
0
Commerical Burglary
Residential Burglary
1
2
3
4
5
6
Police District
Figure 11 illustrates commercial and residential burglary patterns by district
for the year 2005. With district three being the most victimized overall, it would be a
safe assumption the number of burglaries would be higher. However, district six was
the most burglarized district, recording about 500 more victimizations than the lowest
district, district five. With residential burglary, district six is extremely high,
accounting for almost 900 residential burglaries in 2005. Of all burglaries, districts
three and six experienced over 40% of all burglary victimization. Districts with more
dwellings and less commercial properties will obviously offer more targets for
residential burglars than districts high in industrial and commercial buildings.
Commercial burglary was more prevalent in districts two and three, with residential
burglary having more intensity in districts four, five, and six.
62
Number of Victimizations
Figure 12: Commerical and Residential
Burglary Victimization by District
2006
1000
900
800
700
600
500
400
300
200
100
0
Commercial Burglary
Residential Burglary
1
2
3
4
5
6
Police District
In 2006, residential burglary continued to be more prevalent in districts four,
five, and six, but a jump in district two was also observed. Overall, residential
burglary was slightly up from 2005. District four recorded just under 800 residential
burglaries, significantly higher than districts one and three. Commercial burglary on
average was much lower in all districts than residential burglary. District three
experienced the most commercial burglary at approximately 450 incidents. The
northern districts experienced more commercial victimization, while the southern
districts had more residential incidents, similar to 2005. Again, districts two, four, and
six were higher overall than the other districts in burglary victimization. Residential
burglaries accounted for 69% of all burglarized locations in 2006.
63
Number of Victimizations
Figure 13: Commercial and Residential
Burglary Victimization by District
2007
1000
900
800
700
600
500
400
300
200
100
0
Commercial Burglary
Residential Burglary
1
2
3
4
5
6
Police District
While the distribution of property crime in figure 13 seems to be more evenly
distributed in commercial and residential burglary, patterns are still apparent. The
northern districts one, two and three experienced about 300 more commercial
incidents than the southern districts. For residential burglary, districts four, five, and
six accounted for approximately 400 more reported burglaries than the northern
districts. The most burglarized district overall was district five, with over 800
residential burglaries. District three was yet again on the low end of residential
burglary incidents. This is interesting due to the overall number of property
victimization in that district.
64
Figure 14 : Commercial and Residential
Burglary Victimization by District
2008
Number of Victimizations
1000
900
800
700
600
500
Commercial Burglary
400
Residential Burglary
300
200
100
0
1
2
3
4
5
6
Police District
Overall, burglaries dipped in 2008 as compared to previous years. Even with
this, commercial burglary was still at its peak in district three, with residential burglary
being at its lowest point in district three. The trend of northern districts experiencing
more commercial burglary continued in Figure 14, while residential burglary was
higher in the southern districts in 2008. Districts two and six incurred double the
amount of residential burglary as district three. There was a total of 5,156 burglaries
in 2008, with 75% of those being residential.
65
Number of Victimizations
Figure 15 : Commercial and Residential
Burglary Victimization by District
2009
1000
900
800
700
600
500
Commercial Burglary
400
Residential Burglary
300
200
100
0
1
2
3
4
5
6
Police District
Residential burglary continued to be the dominant burglary incident, with
almost 4,000 reported cases. District five experienced about 200 more victimizations
than the previous year, but overall the burglary types remained consistent in the same
districts as previous years. Commercially, district three was again the most
victimized. As previous years have revealed, commercial burglary inclines between
districts one and three. Observing the significant difference in prevalence between
commercial and residential burglary is difficult to ignore. On average in 2009, there
were 3.5 residential burglaries for every one commercial burglary.
66
Figure 16: Commercial and Residential
Burglary Victimization by District
2010
Number of Victimizations
1000
900
800
700
600
500
Commercial Burglary
400
Residential Burglary
300
200
100
0
1
2
3
4
5
6
Police District
Overall, the number of burglaries in 2010 was lower than in previous years.
District five experienced about 650 residential burglaries, the most of all districts,
continuing the pattern from the previous study year. District five had almost double
the amount of residential burglaries as district three. District three again experienced
the most commercial victimizations, about 275. There were a total of 3,968 burglaries
in 2010; of those, 71% were residential burglaries. Districts one, two, and three
gradually inclined in commercial burglary incidents, as noted in previous years.
Districts four, five, and six incurred the majority of residential victimizations yet
again.
67
Number of Victimizations
Figure 17: Commercial and Residential
Burglary Victimization by District
2011
1000
900
800
700
600
500
400
300
200
100
0
Commercial Burglary
Residential Burglary
1
2
3
4
5
6
Police District
Again, the effect a shortened data set are apparent in 2011. Hot spot policing
strategies enforced by the Sacramento Police Department may also be on display in
Figure 17. Interestingly, the only district with more residential than commercial
burglary is district five. Even with the declined statistics, district five is still the most
residentially burglarized, similar to past years. Even though district five reported only
79 incidents, the victimization pattern remains consistent. This decrease in overall
incidents has proven that these patterns are stable and consistent, and most likely will
be in future years, independent of the overall crime rate. In 2011, 42% of all
burglaries were residential.
68
Number of Victimizations
Figure 18: Commercial and Residential
Burglary Victimization by District
2005-2011
5000
4500
4000
3500
3000
2500
Commercial Burglary
2000
Residential Burglary
1500
1000
500
0
1
2
3
4
5
6
Police District
Residential burglary was very prevalent over the entire study period. District
five suffered the most intense residential burglary victimization, with over 4,500
incidents. While district three was the least victimized residentially, commercially
was a different story. District three experienced almost 2,500 commercial burglaries
over the study period, higher than any other district. This chart should look very
similar to the yearly charts previously displayed, as burglary patterns for the most part
remained consistent, even with 2011’s fluctuation. Between the years 2005 and 2011,
there were 31,891 reported burglaries. Of that total, 71% were residential burglaries,
consistent with the yearly findings.
69
Figure 19: Commercial and Residential
Burglary Victimization by Police Beat
2005-2011
2000
Number of Victimizations
1800
1600
1400
1200
Commercial
Burglary
1000
Residential
Burglary
800
600
400
200
1A
1B
1C
2A
2B
2C
3A
3B
3C
3M
4A
4B
4C
5A
5B
5C
6A
6B
6C
0
Police Beat
A more specific view is offered in Figure 19, reporting commercial and
residential burglary by police beat. While district three was relatively low in
residential burglaries, the majority of the incidents fell into beat 3C. In fact, 3C
experienced close to 50% of all residential burglaries in district three. Police beat 4C
was the highest in residential burglary over the entire study period, upwards of 1,800
incidents. Commercially, beat 2C was the highest in victimization, with over 800
incidents. In district six, beat C accounted for over 51% of all commercial burglaries.
Analyzing victimization patterns on a more specific level highlights even more the
non-random patterns of property victimization.
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Temporal Patterns of all Property Victimization
Figure 20: Property Victimization
Occurrence Time
2005-2011
90000
Number of Victimizations
80000
70000
60000
50000
40000
30000
20000
10000
0
Working Hours (7:00AM5:00PM)
Non-Working Hours (5:01PM6:59AM)
The current study accounted for upwards of 135,000 property victimizations
over a seven year period. To best organize and analyze the temporal patterns of those
victimizations, the aim of this study was to clearly and simply display relevant
temporal patterns. This graph clearly does that. Close to 80,000 victimizations took
place during non-working hours. Traditionally, these are times when guardians are at
their homes. For commercial properties, this is when guardians are absent from their
businesses. Close to 60,000 victimizations took place during hours where there is
typically no guardian present at the home, but a greater chance of having commercial
properties occupied by a guardian.
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Temporal Patterns of Commercial and Residential Burglary Victimization
Number of Victimizations
Figure 21: Commercial and Residential
Burglary Occurrence Time
2005-2011
14000
12000
10000
8000
Working Hours
(7:00AM-5:00PM)
6000
4000
Non-Working Hours
(5:01PM-6:59AM)
2000
0
Commercial Burglary
Residential Burglary
Type of Burglary
Figure 21 supports the previous explanation of residential and commercial
victimizations in relation to time and capable guardians. Commercial buildings will
usually be unoccupied during the nighttime, when shops and stores shut down. For
that reason, commercial victimizations more than doubled during non-working hours.
Residential burglars will offend when guardians are at work or carrying out routine
daily activities. Residential victimizations during working hours were near double the
amount during non-working hours. The difference in these statistics between crimes is
by no means spontaneous, but speaks to the non-random nature of property
victimization.
72
Yearly Trends of Property Victimization
Number of Victimizations
Figure 22: Number of Victimizations by
Year
2005-2011
30000
25000
20000
15000
10000
5000
0
2005
2006
2007
2008
2009
2010
2011
Year
Figure 22 is a breakdown of property victimization, taking a broader view of
property crime patterns over an extended period. The years 2005 and 2006 were
almost identical in the total amount of victimization, both above 25,000 incidents.
From 2006, a steady decrease in property crime begins, with a large drop between the
years 2010 and 2011, due to only eight months of data in 2011. As noted earlier, the
Sacramento Police Department underwent a study to identify the benefits of hot spot
policing, which may have also affected the total in 2011. In 2010, Sacramento
experienced approximately 17,000 property victimizations, which is close to 9,000
less than in 2005. While there is a clear decline in overall property victimization, it
has been proven in this study that a decline in overall rates does not correlate to
inconsistency or spontaneity in victimization patterns.
73
Number of Victimizations
Figure 23: Commercial and Residential
Burglary Victimization by Year
2005-2011
4500
4000
3500
3000
2500
2000
1500
1000
500
0
Commerical Burglary
Residential Burglary
2005
2006
2007
2008
2009
2010
2011
Year
Figure 23 compares commercial and residential burglary over the entire study
period. It is clear to see the pattern leans heavily toward residential burglary. In a
sense, this is somewhat surprising due to the fact that certain commercial
establishments such as Arden Fair Mall contain numerous stores. One explanation
often found in other studies is that store owners are not as willing to report crimes
because they do not have insurance and will not be able to regain their lost goods.
Both commercial and residential burglary declined in 2010 and 2011, due to minimal
data for 2011. The years 2005 through 2009 remained consistent and predictable in
burglary victimization totals for each year regarding both types of burglary.
74
Figure 24: Victimization by Month
2005-2011
Number of Incidents
14000
12000
10000
8000
6000
4000
2000
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month
This figure is an interesting display of the distribution of property victimization
by month over the entire study period. It has been discovered in previous studies that
warmer weather creates increased opportunity for property victimization. Nicer
climates bring people away from their dwellings, and leave the property without a
capable guardian. The warmer months of the year display an incline in property
victimizations, with incident rates peaking in August at over 12,000. The colder and
rainier months of October, November, and December, exhibit a lower incident rate.
According to Really Simple Syndication (R.S.S.) Weather, the average precipitation in
Sacramento for the months of April, May, June, July, and August is 0.37 inches. The
average precipitation for the months of October, November, and December is 1.84
75
inches. Nicer weather will inevitably put more dwellings at risk by creating an easier
criminal opportunity.
Summary of the Findings
The findings of this research do accept the alternative spatial and temporal hypotheses:
H1A: Spatial patterns of property victimization will remain non-random
and clustered by police district over time.
H0A: Spatial patterns of property victimization will not remain nonrandom and clustered by police district over time.
H1A: Temporal patterns of property victimization will remain nonrandom and clustered over time.
H0A: Temporal patterns of property victimization will not remain nonrandom and clustered over time.
Spatially, charts by year and for the entire study period of 2005 through 2011 did find
that spatial patterns of property victimization remained non-random and clustered over
an extended period of time. Districts two and three combined consistently accounted
for nearly identical portions of the total property victimization. Specifically; 40% in
2005, 41% in 2006, 41% in 2007, 40% in 2008, 41% in 2009, 40% in 2010, and 45%
in 2011. The clustering and prevalence of victimization in these two districts is
staggering. Increased attention and resources in these bordering districts could
potentially lower the overall victimization rate anywhere up to 40%, maybe more.
These figures alone speak to the non-random nature of property crime patterns.
If property victimization was randomly distributed, it would be expected that
each district would account for approximately 16% of the total, in reality district two
and three accounted for upwards of 20%. District three, the most victimized district
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during the study, reported similar percentages of the total rate each year; 22% in 2005,
21% in 2006, 22% in 2007, 22% in 2008, 23% in 2009, 22% in 2010, and 24% in
2011. On the other end, district six consistently accounted for comparable percentages
of the total; 17% in 2005, 14% in 2006, 13% in 2007, 14% in 2008, 13% in 2009, 14%
in 2010, and 12% in 2011. The likeness found in these numbers points to a significant
importance regarding victimization patterns over an extended period.
Over the entire study period, district three was consistently the most victimized
district, and this did not fluctuate. Although district three did have a small extra beat
adding to the total victimization, analysis of all police beats revealed on average
district three experienced higher victimization rates. Each year of the study, there was
a difference of approximately 1,000 to 2,000 victimizations between the highest and
lowest victimized districts. This remained consistent through each year, specifically;
2,016 in 2005, 1,644 in 2006, 1,969 in 2007, 1,459 in 2008, 1,798 in 2009, 1,304 in
2010, and 1,014 in 2011. When analyzing the northern districts of Sacramento,
districts one, two, and three within each year consistently inclined, peaking in district
three each year.
Overall crime rates were highest in 2005 and 2006, with approximately 25,000
property crimes. After 2006, a clear pattern of decline was present from year to year.
On average from 2007 through 2010, victimization declined by about 1900 crimes per
year. This trend should be viewed as a positive step by the Sacramento Police
Department as well as an opportunity. Continuing to apply hot spot policing tactics
77
but focusing on the most victimized districts and beats will only boost the decline in
overall property victimization.
The source for the dip in victimization in 2011 was due to only eight months of
reported victimizations. Most likely, due to the time it takes to process and create the
spreadsheets, 2011 was not fully entered into the database. While an argument could
be made to simply discard this year, valuable findings were drawn from the chart.
Even in a year with a minimal amount of recorded property victimization, consistent
patterns were still present. Discovering that patterns held true independent of the
number of victimizations is an important finding when analyzing long-term trends.
Within district police beats, clustering and non-random patterns became even
more evident. Over the entire study period, two beats in particular experienced a
disproportionate amount of property victimization. Police beat 3C accounted for 36%
of district three’s victimization, while beat 3A only accounted for 17%, an extremely
unbalanced finding. The non-random patterns of property victimization were also
evident within the beats in district one. Beat 1A incurred 44% of the total district
victimization, while beat 1C only accounted for 23%. An important point in the
findings not specifically noted in the graphs was discovered as the victimization level
decreased. Even in lower victimized years, spatial clustering and patterns remained
consistent.
When analyzing spatial patterns of commercial and residential burglary
patterns, non-random features were evident. Each year, commercial burglary was
more prevalent in districts one, two, and three. Residential burglary was consistently
78
from year to year more prevalent in districts four, five, and six. In every year of the
study period except for 2011, it was observed that district three was the lowest
residentially victimized district, but the highest commercially. The fact that district
three overall was the most victimized district speaks to the prevalence of commercial
victimizations in that district as well as other property crimes.
Similar to the pattern of overall property victimization, commercial and
residential burglary victimization inclines in the northern districts, between district one
and three. This is consistent from year to year. The overwhelming majority of
burglaries stems from residential incidents. Of the total number of burglaries, 67%
were residential in 2005, 69% in 2006, 73% in 2007, 75% in 2008, 77% in 2009, and
71% in 2010. The consistency of these figures speaks to the patterning of property
victimization. Each year of the study found that district five was the most residentially
burglarized district. In district four, beat 4C experienced a disproportionate amount of
residential burglary each year, accounting for 46% of the total amount. Commercial
burglary was heavily concentrated in beat 6C, reporting 51% of the districts total
commercial burglary, while beat 6A only reported 17%.
Temporal patterns of property victimization were also found to be non-random
in this study. Calling upon ideas from the rational choice theory and routine activities
theory, logical explanations can be drawn from the data presented here. In overall
victimization patterns, a majority of the incidents took place during non-working
hours. It is assumed this is due to commercial locations without capable guardians
79
present between the hours of 5:01PM and 6:59AM. Also, vehicle and neighborhood
disturbance offenses often take place at night and in the early morning.
Analyzing temporal patterns of commercial and residential burglary presented
an even more detailed look at when offenders choose to victimize. Commercial
burglary occurred over more than double the amount of time during non-working
hours. Offenders chose commercial targets to offend because the chances of facing a
capable guardian were low during that time window. For residential burglary,
victimization was twice as likely to occur during working hours as compared to nonworking hours. When homes are left empty because inhabitants are working or
carrying out other routine activities, targets become vulnerable. In addition, offenders
who are unemployed will observe potential targets during the day because they have
increased free time.
The monthly and yearly trends of property crime are analyzed on a more
macro-level, but never the less still valuable. Seven years of data would not be
completely utilized without stepping back and analyzing larger trends and patterns.
Findings from this study indicate that overall victimization patterns of property crime
remain stable over an extended period, with incremental differences each year.
Monthly trends are also clearly indicated in this study, and offer valuable insight.
Warmer months of the year will facilitate more property victimization. When people
choose to leave their homes, targets become available to offenders. There is no
question more people leave their dwellings when the weather is warm, especially in
the summer. Specifically, the current study found that over a seven-year period,
80
victimization was higher between the months of March and August, peaking in the
summer.
The findings from this research do accept the alternative hypotheses and reject
the null hypotheses. Over an extended period, property victimization patterns do
remain non-random both spatially and temporally; that is they are highly patterned.
The trends and patterns observed in this research are not random or spontaneous, they
stem from conscious choices by offenders and characteristics of the targets they offend
that create opportunity. Utilizing a large data set as was done in the current study only
strengthens the claims made and findings revealed.
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Chapter 5
Summary and Conclusions
Summary
The purpose of this research was to determine if property crime patterns exist
over an extended period of seven years. Previous research has established property
victimization patterns are not random over a short period. Properties victimized are at
an increased risk of future victimization, as are properties in close proximity. Multiple
theories are applicable to previous and current findings surrounding property
victimization. Routine activities of offenders and victims create opportunities for
victimization. The rational choices of offenders influence their decision to offend
previous targets. Crime patterns are influenced by environmental characteristics.
Combined, the routine activities theory, rational choice theory, and crime pattern
theory offer a lens to view property crime patterns.
Property crime patterns did in fact remain non-random and patterned over an
extended period of time both spatially and temporally. Clustering was present
throughout the entire research period both spatially and temporally, and became more
evident when analyzing police beats. Even in a larger analysis of monthly and yearly
trends, non-random patterns were observed. District three suffered the most intense
property victimization during the study period. Also, overall victimization rates did
not differ greatly from year to year, but did steadily decline.
Seven years of data is a solid stepping-stone for using past victimization as a
predictor of future victimization. Findings from this research, such as the districts and
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police beats that are continually high in victimization, can be paired with the
knowledge of when properties are most often victimized to predict future patterns.
For instance, this study has revealed that 52% of commercial burglary in district one
has taken place in beat A for the entire study period. By correlating this knowledge
with the finding that commercial burglary takes place during non-working hours 65%
of the time, a sound plan can be put into place to combat burglary in district one.
Residential burglary clustered in police beat 4C, reporting 46% of the districts total
residential victimization. Pairing the spatial clustering of residential burglary with the
temporal finding that the majority of residential burglaries take place during the day is
valuable knowledge.
Utilizing monthly victimization patterns in this study can also be beneficial.
The Sacramento Police Department could tailor their officer registration and training
academy to accept more potential officers in the fall and winter so that the influx of
new officers is present in the spring and summer when victimization rates are at their
peak. Increased patrol in these months is also highly advised, and can be directed
toward the districts and beats most in need during the specific time of day.
This research would be at best entertaining, but not important to Sacramento if
it could not be applied to the Sacramento Police Department and their tactical
allocation of officers and resources. The researcher predicts the trends and patterns
found in this study will continue in the future. As crime mapping and analysis become
more advanced and more commonly used, overall property victimization will continue
to decline. District three will most likely continue to be higher in overall property
83
crime, and should receive more attention that other districts. Because districts one,
two, and three are higher in overall crime and also are in the same vicinity, a hot spot
policing study based on those three districts alone targeting only property crime would
make for interesting research.
Residential and commercial burglary has proven to consistently differ
depending on police district, and this trend will most likely continue into the future. It
is reasonable to allocate resources and adjust tactical operations based on the primary
threat within each police beat. Beats that are high in residential burglary should
receive more attention during working hours based on the findings from this study.
Beats that have consistently been high in commercial burglary need to increase officer
presence during non-working hours.
The distinct issue and need behind this study is the understanding that property
crime patterns remain non-random and patterned over an extended period, and that
resources are best allocated to the properties most in need. Police districts and beats
with lower victimization rates during certain times and months should not receive the
same type of attention as those areas that cluster with crime. A majority of property
victimization can be prevented by directing officers and resources toward the areas
most in need.
Implications for Future Research
These findings offer multiple directions for further research to be conducted in
the city of Sacramento and elsewhere. The analysis of specific properties over a
period of five to seven years would be very useful for local authorities. While this
84
research did analyze long-term property crime patterns, specific addresses were not
the focus. Based on the findings of this study, specific addresses from the most
victimized beats should be selected and tracked over a long time period to determine
why they are continually targeted. Increasing the specificity of the findings offers
more detail in the analysis.
To determine which characteristics of dwellings most often entice offenders,
an analysis of selected properties from year to year could be done. This analysis
would be based on security measures such as fences and alarms, as well as
characteristics of the dwelling such as doors, windows, and lighting. Observations
would be conducted on a monthly basis to observe dwelling changes, then compared
with the number of victimizations in that time period to determine the best techniques
to deter offenders from a property. Future studies similar to those listed above would
most likely require agreement from dwelling inhabitants.
Extended study periods also open the door to future studies regarding monthly
and yearly patterns. This study charted victimization by month, finding that summer
months had the highest number of victimizations. A further analysis should be done to
determine which crimes are most prevalent during certain months and weather
conditions. Analysis of specific property crimes would present knowledge of which
crimes are facilitated by warm or cold weather. Knowing which months to expect a
rise or dip in specific property crimes would be very helpful.
Repeating this study in the future to determine if patterns continue would be
useful knowledge. Using 2011 as the first year of the study period and ending in
85
2017, an analysis would either verify or deny the consistency of the patterns observed
between 2005 and 2011. Any new patterns that may arise in the future would be
knowledge for the Sacramento Police Department heading into the next decade. More
data can only increase the validity and reliability of the findings.
Studies utilizing GIS profiling methods would be very beneficial for local
authorities to use in their patrol strategies. Comparing victimized residential and
commercial properties that portray similar points of entry with likely offender
dwellings and community nodes will create a more specific search area for officers
tracking repeat offenders. Specifically in district three, which contains Sacramento’s
midtown and downtown areas, multiple nodes and dwellings are present, influencing
victimization patterns. Nodes located near frequently victimized properties and public
housing structures will produce an accurate mobility triangle predicting offender
residences.
The difference in victimization between edges and interiors of neighborhoods
should be studied over an extended period as well. After the researcher designates the
perimeters and core of each neighborhood or community, a plethora of patterns and
comparisons can be analyzed. Victimization patterns can be compared from year to
year in the edges and interiors to determine which area facilitates more consistent
victimization. In addition, establishing which property crimes are the most consistent
over an extended period would offer a perspective of the most prevalent edge and
interior crimes.
86
Predicting long term trends of victimization would not only be useful to
officers, but also to the budget and finance department of a police agency. The current
budget constraints in California support the need for a more efficient allocation of
police resources to best insure every dollar is used to prevent victimization based on
knowledgeable and predictive policing. If departments could use a study such as this
one to predict the amount of crime in future years, they could also approximate the
necessary budget for those years and save appropriately. For example, summer
months (beginning of the new fiscal year) will most likely see a rise in property crime,
meaning more officers and more money spent. In preparation for this increase,
financial management strategies could be applied during the months of October,
November, and December.
Temporal patterns of property crimes are in need of future research as well.
Understanding which property crimes have the most consistent temporal patterns of
victimization would be quite interesting. Doing this would be accomplished by
charting the average occurrence time of each property crime from year to year, and
then observing which crimes consistently occurred at similar times. Sacramento
authorities would have a clear picture of which crimes could be expected to occur
during certain times of the day over a long period.
Analysis of victimization patterns is useful when placed in the right hands.
Training is vital to understanding and disseminating crime analysis to be used by
patrol officers on the streets. Studies similar to this one will only become more
common and detailed in the future, and police departments worldwide have no reason
87
not to accept this technology and the power it places in knowledge and prediction.
The importance of officer experience and instinct should never be undervalued, nor
should the value of crime analysis and the benefits of predicting future trends based on
the past.
As stated previously, one of the strengths of this research was the extended
time window and number of crimes analyzed. Taking the idea of longitudinal studies
to another level, future research analyzing property victimization trends over decades
would allow police departments to predict even further ahead when and where
victimization will cluster.
88
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