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Abstract of thesis entitled
Use of GIS in Campus Crime Analysis:
A Case Study of the University of Hong Kong Submitted by
Chi Pun Chung, Edward
for the Degree of Master of Geographic Information Systems at the University of Hong Kong
in June 2005
Accurate forecasting of crime can have immense benefits to crime prevention,
and if acted upon appropriately, may lead to apprehension of criminals. From the
perspective of campus security of the University of Hong Kong, this paper studies
the crime situation of the university campus by adopting Geographic Information
Systems (GIS) to manage, visualise and analyse the spatial data of crimes. A special
technique of GIS, hot-spot analysis, will be employed to reveal the trend, spreading
pattern and temporal changes of crime and also help forecast potential future crime
spots. GIS, if developed appropriately, can help stipulate effective crime prevention
measures and patrol strategies in the campus.
Use of GIS in Campus Crime Analysis:
A Case Study of the University of Hong Kong
By
Chi Pun Chung, Edward
A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Geographic Information Systems
at the University of Hong Kong
June 2005
Declaration
I declare that this thesis represents my own work, except where due acknowledge is
made, and that it has not been previously included in a thesis, dissertation or report
submitted to this University or to any other institution for a degree, diploma or other
qualification.
Signed Chi Pun Chung, Edward
June 2005
i
Acknowledgements
I would like to express my sincere gratitude to Dr. Ann Mak for her
wholeheartedly guidance, knowledge and assistance in this research, particularly her profound influence and helpful comments in the preparation of the study.
I am indebted to Miss Kawin Chan for her frankly technical support throughout the research period. Her generous encouragement helped overcome the crucial moment in this research.
I would also like to extend my appreciation to Mr. Teddy Wong who provided
the crime data of the campus and generously shared his knowledge, expertise and experience to help shape my understanding of the security of the campus.
ii
TABLE OF CONTENTS
DECLARATION i
ACKNOWLEDGEMENTS ii
TABLE OF CONTENTS iii
LIST OF FIGURES vii
LIST OF TABLES ix
CHAPTER ONE INTRODUCTION 1
1.1 Research Background 2
1.2 Scope and Objectives of the Research 3
1.3 Chapter Organisation 4
CHAPTER TWO LITERATURE REVIEW 6
2.1 Introduction 6
2.2 Crime Mapping 7
2.2.1 Crime Analysis 8
2.2.2 Crime Place Research 9
2.2.3 Crime Analysis by Manual Pin Maps 13
2.2.4 Crime Analysis by Geographic Information Systems (GIS) 15
2.3 Crime Analysis Process 19
2.3.1 Analysis Process 20
2.3.1.1 Tactical Crime Analysis 21
2.3.1.2 Strategic Crime Analysis 22
2.3.1.3 Administrative Crime Analysis 23
iii
2.3.1.4 Criminal Intelligence Analysis 23
2.3.1.5 Police Operations Analysis 24
2.3.2 Crime Analysis Model 24
2.4 Problem Solving Philosophy in Crime Analysis 25
2.4.1 Problem-oriented Policing 26
2.4.2 SARA Model 29
2.5 CompStat 32
2.6 Literatures on Campus Crime 37
2.7 Summary 38
CHAPTER THREE METHODOLOGY 40
3.1 Introduction 40
3.2 The Research Data 40
3.2.1 Study Area – the University of Hong Kong 41
3.2.2 Campus Security 45
3.2.3 Research Tools 48
3.3 Methodological Framework 49
3.3.1 Data Management 52
3.3.1.1 Data Collection 52
3.3.1.2 Data Set in this Research 54
3.3.1.3 Data Classification 56
3.3.1.4 Temporal Data Conversion 59
3.3.1.5 Geocoding 59
3.3.1.6 Data Re-classification 60
3.3.4 Data Analysis 61
iv
3.3.4.1 Visual Inspection 61
3.3.4.2 Hot Spot Identification 62
3.3.4.3 Hot Spot Analysis Techniques 63
3.4 Summary 65
CHAPTER FOUR CAMPUS CRIME ANALYSIS 66
4.1 Introduction 66
4.2 Campus Crime – the University of Hong Kong 66
4.3 Spatial Analysis 69
4.3.1 Spatial Analysis – Theft 73
4.3.2 Spatial Analysis – Loitering & Peeping Tom 81
4.3.3 Spatial Analysis - Burglary 83
4.4 Temporal Analysis 84
4.4.1 Temporal Identification – Changes in Months 85
4.4.2 Temporal Identification – Changes in Weekdays 87
4.4.3 Temporal Identification – Changes in Hours 88
4.4.4 Temporal Analysis – Theft 95
4.4.5 Temporal Analysis – Loitering & Peeping Tom 97
4.4.6 Temporal Analysis - Burglary 98
4.5 Campus Crime outside Hong Kong 100
4.6 Summary 102
CHAPTER FIVE CONCLUSION 103
5.1 Introduction 103
5.2 Limitations of Study 103
v
5.3 Analysis Findings 104
5.3.1 Crime in General 104
5.3.2 Prevailing Crimes 105
5.3.3 Forecast of Prevailing Crimes 107
5.4 Recommendations 110
5.5 Suggestions for Future Research 114
5.6 Conclusion 114
REFERENCES 116
vi
LIST OF FIGURES
Figure 2.1 Interaction of Crime Pattern Theory 11
Figure 2.2 Problem Analysis Triangle 12
Figure 2.3 Crime Analysis Model 25
Figure 2.4 Incident-driven Policing 27
Figure 2.5 Problem-oriented Policing 29
Figure 2.6 SARA Model 31
Figure 2.7 Principles of CompStat Model 36
Figure 3.1 Sub-division of the study area of HKU 42
Figure 3.2 Population Density around HKU Campus 44
Figure 3.3 Income Groups around HKU Campus 44
Figure 3.4 Sub-division of the campus security of HKU 47
Figure 3.5 Sample of the monthly crime report of HKU 48
Figure 3.6 Methodological framework adopted for this research 52
Figure 3.7 Re-classification process of crime event data 60
Figure 4.1 Crime Statistics of HKU between 2002 and 2004 68
Figure 4.2 Spatial distribution of crimes of HKU between 2002 and 2004 71
Figure 4.3 Hot spots of theft in the Main Campus in 2002 78
Figure 4.4 Hot spots of theft in the Main Campus and the Western Region
in 2003
79
Figure 4.5 Hot spots of theft in the Main Campus and the Western Region
in 2004
81
Figure 4.6 Hot spots of loitering and suspected peeping tom activities in
the Main Campus in 2004
83
vii
Figure 4.7 Spatial distribution of burglary in the Main Campus between
2003 and 2004
84
Figure 4.8 Comparison of frequency of crime by months between 2002
and 2004
86
Figure 4.9 Comparison of accumulative frequency of crime by month
between 2002 and 2004
87
Figure 4.10 Frequency of crime by weekday between 2002 and 2004 88
Figure 4.11 Frequency of crime by hours between 2002 and 2004 91
Figure 4.12 Temporal distribution of crime in the Main Campus between
0800 and 1800hrs in 2002
92
Figure 4.13 Temporal distribution of crime in the Main Campus between
1800 and 2400hrs in 2002
92
Figure 4.14 Temporal distribution of crime in the Main Campus between
0800 and 1800hrs in 2003
93
Figure 4.15 Temporal distribution of crime in the Main Campus between
1800 and 2400hrs in 2003
93
Figure 4.16 Temporal distribution of crime in the Main Campus between
0800 and 1800hrs in 2004
94
Figure 4.17 Temporal distribution of crime in the Main Campus between
1800 and 2400hrs in 2004
94
Figure 4.18 Temporal variation of theft between 2002 and 2004 97
Figure 5.1 Forecast of crimes in the Main Campus and the Western
Region
108
Figure 5.2 Forecast of crimes in the Southern Region 108
Figure 5.3 Crime Statistics of campus crime between 2002 and 2004 109
viii
LIST OF TABLES
Table 2.1 Historical Statistics of CompStat between 1993 and 2003 33
Table 3.1 Extract of raw crime data of HKU between 2002 and 2004 53
Table 3.2 Statistics of crime data with reported and non-reported time
between 2002 and 2004
55
Table 4.1 Crime statistics of HKU between 2002 and 2004 67
Table 4.2 Comparison of Overall Crime Rate between HK and HKU
between 2002 and 2004
68
Table 4.3 Comparison of Overall Violent Crime Rate between HK and
HKU between 2002 and 2004
69
Table 4.4 Geographical distribution of crimes of HKU between the year
2002 and 2004
70
Table 4.5 Crime statistics of Main Campus (M), Western Region (W),
and Southern Region (S) of HKU between 2002 and 2004
71
Table 4.6 Victimisation rate of theft of notebook computer between 2002
and 2004
72
Table 4.7 Victimisation rate of theft of wallet and cash between 2002
and 2004
73
Table 4.8 Prominent locations of theft between 2002 and 2004 73
Table 4.9 Prominent locations of theft of notebook computer between
2002 and 2004
74
Table 4.10 Prominent locations of theft of wallet and cash between 2002
and 2004
75
Table 4.11 Prominent locations of theft of projector between 2002 and 76
ix
2004
Table 4.12 Classroom facilities in the Main Campus buildings 77
Table 4.13 Statistics of known and unknown crime records between 2002
and 2004
89
Table 4.14 Statistics of known crime records by hours between 2002 and
2004
90
Table 4.15 Temporal distribution of theft between 2002 and 2004 95
Table 4.16 Temporal distribution of loitering, peeping tom and indecent
exposure between 2002 and 2004
98
Table 4.17 Temporal distribution of burglary between 2002 and 2004 98
Table 4.18 Statistics of theft of notebook computer and projector between
2002 and 2004
100
Table 4.19 Comparison of campus crime statistics between HKU and US
Universities in 2003
101
x
CHAPTER ONE
INTRODUCTION
In the recent decades, computerised mapping through the use of Geographic
Information Systems (GIS) has become a valuable and popular tool for spatial
analysis. Law enforcement agencies in Western countries have utilised this advanced
technology to its full extent in identifying spatial pattern as well as exploring
temporal relationship as an aid in problem-solving and decision-making processes to
reduce and control crimes, and improve efficiency in the allocation of police
resources.
Criminologists have long been interested in the study of criminal behaviour
and its motivation. Some have studied the environmental influences of criminals and
have found that their offending behaviour often appears to form discernible patterns
and is not randomly distributed. This spatial characteristic has been carefully
examined and studied to suggest that crime occurrences can be very informative.
Schools of environmental criminologists later developed the crime place theory with
various supporting observations and arguments that crime can be profiled from a
different perspective. Many of the analytical techniques developed together with
well-structured crime databases allow the practitioners to test and experiment
various spatiotemporal hypotheses. The provision of information generated by
computerised mapping has been extremely valuable in real situations in providing a
better picture of patterns and trends in crime. The success of its application in crime
control and crime reduction increases the confidence of the public and secures better
co-operation between law enforcement agencies and the public.
1
1.1 Research Background
In the eyes of the public, the campus of the University of Hong Kong has
always presented a peaceful and harmonic impression of an elegant tertiary
institution educating elites of the society of Hong Kong. Crimes occurring on the
campus have never caught much attention from the members of the public based on
a simple perception that well educated people do not commit crime, or even any
misdemeanour act. This belief understates the seriousness of crime and it can be
quite threatening.
In actual fact, the crime rate of the campus is really low in terms of crime per
population of Hong Kong and almost all of the crimes reported were minor crimes.
Yet minor crime can always turn into serious crime if it is left unchecked, especially
as the campus is not usually patrolled by the police but by the campus security
guards. It is for this reason the study of campus crime necessitates the management
have a genuine appreciation of the overall crime situation to secure the long-enjoyed
reputation and to ensure that the frontline security personnel have a tactical approach
to prevent crimes from prevailing.
Indeed, there are many ways of studying crime trends and crime pattern.
Through the history of past incidents, it is possible to see the trend of crime and
suggest ways to control its spread. Through the records of previous incidents, it is
also possible to identify the pattern of crime and increase the chance of detection
and apprehension. Cartographic presentation of crime clusters is always the most
direct and simplest way to promote one’s knowledge and understanding, it
collaborates with an old saying “a picture is better than a thousand words”.
2
Nevertheless, this is not to suggest GIS computerised mapping is a panacea that can
cure all problems of crime, it is merely an analytical tool, which is able to assist the
management and frontline security personnel in reviewing crimes away from their
traditional angle.
1.2 Scope and Objectives of the Research
This research represents the first attempt of its kind in adopting GIS crime
mapping techniques to visualise crime incidents which occurred on the campus of
the University of Hong Kong between the year of 2002 and 2004. The primary
concern of this research is to introduce the techniques of GIS crime mapping to the
estate management and frontline patrols of the University of Hong Kong who are
mostly unfamiliar with GIS. The demonstration of the capability of GIS in
identifying spatial and temporal distribution of crimes and the methodology of
analytical functions and crime prevention strategies employed in this study will be
explicable to them. Subsequently, they would be able to appreciate the crime
situation by visualising the aggregating concentration of crime clusters in space and
time and react immediately to allocate sufficient resources effectively in the area
required for their attention.
The research will attempt to present a balanced view from a technical and
management perspective with a view to proposing some feasible real world
solutions. In summary, the focus of the current study is to:
(1) explore data management and data visualisation techniques in
presenting crime data;
(2) examine the feasibility of applying spatial and temporal analysis in
3
the campus environment; and
(3) recommend practical crime control and crime reduction strategies in
the campus environment.
1.3 Chapter Organisation
Chapter one provides an introduction to the current research by discussing
the background to the study and outlining the research foci. The significance of the
current study is highlighted through a discussion of the needs to appreciate the past
in order to understand the crimes in the future.
Chapter two presents a review of the literature. This chapter highlights the
development of computerised mapping and the centrality of work in crime place
theory strengthening the relationship between them in the application of crime
mapping. In addition the functionalities of various crime analysis are discussed, and
a considerable body of empirical research draws the attention to the problem-solving
technique and the citation of a successful example on crime mapping. Toward the
end, it also touches on the lack of literature on the use of GIS in campus crime
analysis.
Chapter three describes the methodology adopted for the current research.
Information pertaining to the validity and reliability in the data collection stage is
presented. The benefits of using various techniques in data management, such as
data integrity and data reclassification, and the procedures are also described. The
chapter also describes the visual identification method to locate the hot spots of
crime.
4
Chapter four presents the findings of this research from spatial analysis and
temporal analysis. The analysis focuses on some of the prevailing crimes and intends
to predict the future crime trend as well.
Chapter five presents a summary of the research and discusses some of the
limitations raised in the study. Finally, it provides some recommendations to
improve crime control and crime prevention measures, and future areas for research
are also discussed.
5
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter will first review the classical studies of crime mapping from the
perspective of sociology and criminology in section 2.2. This spatial analysis of
crime shows how a close association between people and place and crime location
soon becomes a focus of studying crimes occurrence. In the early days, crime
analysis by means of pin mapping did produce fruitful results but technology and
philosophy at that time restricted crime mapping from further development. The
crime place research in Section 2.2.3 will summarise the principle of crime mapping
stemming from the major criminological theories.
In Section 2.2.4 will show that the introduction of Geographic Information
Systems (GIS) has evolved from a traditional crime analysis to a new era in crime
mapping. The automation of crime mapping is not only capable of visualising instant
high quality geographic distribution of crime incidents, it is also capable of making
enquires from a series of complicated variables to perform spatiotemporal crime
analysis. Section 2.3 will discuss various crime analysis techniques developed in
support of the growth of GIS, which in turn enable law enforcement agencies at
various levels to devise effective tactics and strategies in crime control and crime
reduction programmes.
Section 2.4 will illustrate the transformation of conventional policing
6
philosophy to Problem-Oriented Policing (POP) philosophy, which when later
supplemented by the SARA model gives a further boost to the development of
qualitative and quantitative crime analysis. Research has proved that a proactive
approach in policing together with detailed crime analysis can eradicate the root
problem of a crime and may even assist in predicting future crime. Finally, Section
2.5 will examine the structure of crime analysis conducted by tertiary institutions in
the US as part of their current crime prevention strategy.
2.2 Crime Mapping
Mapping has a long history. Maps have served for a wide range of purposes
in human civilisation but it was not until two centuries ago that maps were used for
showing crimes occurrence (Weisburd and McEven 1998; Boba 2001). According to
the book “Mapping Out Crime: Providing 21st Century Tools for Safer
Communities” published by the United States Department of Justice in 1999 reveals
that it could be traced back to 19th century in France where cartographers first
analysed national patterns of crime (Quetlet 1835 as reported in Weisburd and
McEven 1998). Another source cites that spatial studies of crime and delinquency
were written by sociologists and criminologists dating back to about 1830 (Harries
1999). A classical example by Shaw and McKay (1942) who conducted spatial
analysis on juvenile delinquency in Chicago showed that there was a substantial
correlation between delinquency and various social conditions in Chicago (Weisburd
and McEwen 1998).
Maps are static and compiled for designated purposes and users. In the early
days of crime mapping, it encountered a great deal of limitations, as it could neither
7
be manipulated or queried, nor could it be produced or amended in a short period of
time. However, cartographers pioneering in the field of crime mapping already
recognised it as an integral part of the process of crime analysis (Harries 1999).
Crime mapping is not a new term coined, however, until the emergence of
Geographic Information Systems (GIS) – a computerised spatial analysis of crime
incidents becomes widely accepted and through its continuously development GIS
has gradually obtained more weight than before in crime analysis.
2.2.1 Crime Analysis
Crime happens everywhere and anytime – both space and time. Crime has
never been continuously distributed and the distribution of criminal activities can be
clustered, dispersed or sparse and some activities can occur more in daytime than in
night time, or more in summer than in winter. Spatial analysis allows patterns to be
measured by means of simple reference systems and temporal analysis can be
measured in many different ways, such as time interval: day, week, month and
season.
Many psychologists and criminologists have tried to study these criminal
acts by focusing closely upon the offender’s internal motivation. As early as in
1830’s, French social ecologists Guerry and Quetelet were already interested in
examining the concentration of crime in distinct types of communities ((Weisburd
and McEven 1998; Anselin 2000). Over the years psychologists and criminologists
have studied various criminal behaviours, which were attributed to many causes,
such as genetic theories, anti-social theories and personality dimensions theories
8
(Ainsworth 2001). Recent researches have shown that environment tends to suggest
attractiveness of a place for crime and disorder to take place (Harries 1990).
Nevertheless, analysing crime from the perspectives of sociologist, psychologists
and criminologists requires long-term observation and study, but this can assist the
law enforcement agencies in gaining a more comprehensive understanding of
criminal intent.
For crimes to occur (except today’s electronic crimes), offenders and victims,
and perhaps their properties must exist at the same location. The act committed by
an offender against a victim, a criminal act, constitutes a criminal offence. In order
to study the occurrence of a specific crime and before establishing a hypothesis or
identifying a crime pattern, it is necessary to understand the attributes of a crime.
Indeed, there are many factors which influence offenders in committing crimes, such
as mens rea (guilt intent), modus operandi (methods of operation) and properties
involved to develop a correlation of a crime incident. Interestingly enough, crime
patterns are not limited to offender and victim attributes. The location and time of a
crime may have a unique characteristic explaining why people choose to break the
law. Therefore, it is important to understand where and why crimes take place.
2.2.2 Crime Place Research
There are many other criminological theories providing a basic explanation
for constructing a theory of crime places. Three recent and prominent theories: crime
pattern theory, rational choice theory and routine activity theory have influenced the
basic understanding of the relationships between crime and place.
9
Brantingham P.L. and Brantingham P.J., who are the founders of
environmental criminology, developed the Crime Pattern Theory. They hypothesise
that crime is the result of people’s (both offenders and potential victims) interaction
and movement in the urban landscape in space or time (Brantingham and
Brantingham 1984). So, for a crime to take place it must consist of four essential
elements: a law, an offender, a target and a place (Brantingham and Brantingham
1981). The theory they developed suggests that criminal opportunities at a location
open to the attention of offenders have an increased risk to become targets (Eck and
Weisburd 1995). Their further research on location quotients and crime hot spots
reiterated that crime occurs in spatial patterns and therefore some parts of a city
experience more crimes than others, and that some crimes seem to congregate near
certain types of locations. They also affirm that crime has never occurred randomly
in time and in space and there are temporal patterns, for example, bar assaults are
evening events (Brantingham and Brantingham, 1995). From their observation, there
is always a cognitive connection or potential link between certain crimes and
designated places. Likewise, certain crimes occur at a particular time.
10
Figure 2.1: Interaction of Crime Pattern Theory Source: Compiled by author based on Crime Pattern Theory, Brantingham and Brantingham, 1995
Rational choice theory constructed by Cornish and Clarke (1986) suggests
that offenders will weigh the risk involved in committing a criminal act before
selecting target and the basic rationale for selecting a place is important to achieve
their goals. In other words, successful crime prevention measures will increase the
cost of offending and at the same time reduce the likelihood of rewards. However,
this theory may well be able to explain target selection for some types of crime and
for some types of offenders but is less helpful in explaining target selection in other
forms of crime (Ainsworth 2001). For example, some burglars, the steal-to-order
type burglars in particular, may ignore any preventive measures, such as CCTV, or
any other forms of risk and still attack the targeted premises which appears to offer
them fruitful results.
11
Another well-known theory is routine activity theory (or crime triangle)
formulated by Cohen and Felson (1979) in which they explain that predatory crime
occurs when a likely offender and potential target come together in time and place
without the presence or effectiveness of other types of controllers. An intimate
handler, such as parents or friends; or a capable guardian which includes human
actors, such as police or security guards, or physical devices, such as CCTV
monitors; or a place manager, such as bartenders or shop managers, who is capable
of protecting their belongings and security devices (Eck and Weisburd 1995). That is
to say, in order for a crime to develop, there must be a motivated offender and a
desirable target at the same place and at the same time while the controller is absent
or ineffective (Felson 1995).
Figure 2.2: Problem Analysis Triangle Source: The Problem Analysis Triangle, Goldstein, H (2001) at http://www.popcenter.org/about-triangle.htm accessed in Jan 2005
12
Obviously, all of these three theories can be valid in their contexts and
situations. But of greater importance is that all three theories conclude that place has
a direct connection to crime. Only through comprehension of these theories will
analyst be able to investigate the interaction of the physical and social environment
with the choice of targeting.
2.2.3 Crime Analysis by Manual Pin Maps
Traditional crime analysis was always conducted by deduction method and
investigation of crime with emphasis on who committed the crime and why the
crime was committed, leading to the end purpose of apprehending the criminal and
bring closure to the criminal case (Vellani and Nahoun, 2001). This philosophy of
policing before 1980’s or before the development of Problem-Oriented Policing
(POP) by Goldstein in 1979 was considered to be reactive and ineffective (Goldstein
1979, http://popcenter.org/about-whatisPOP.htm).
All along, police tend to incline heavily on criminal intelligence to detect
crimes and even today the reality remains much the same. That was particularly true
before 1980’s or even much earlier because the police perception of crime analysis
was mostly limited by their abilities, knowledge and skills, particularly computer
skills (Spelman 1988). Mindset of many officers remained focused on their
short-term problems rather than long-term problems (Grinder 2000). No wonder
many of them relied very much on their police instinct and very often their common
sense too, and put less emphasis on crime analysis. Nevertheless, there were
conventional techniques developed to assist them in understanding crimes and
13
mapping crimes. One of the most common techniques used in analysing crimes was
to examine the frequency of specific crimes, the temporal analysis, in order to derive
a time sequence model for preventing any future occurrence of crime in their crime
prevention strategy. Calculation could be done easily with simple formulae and
manual mathematics. Yet, this approach only solved part of the puzzles, the
prediction of a crime as to where it was going to happen was far beyond the
technology capabilities at that time.
Another way of analysing crime is by way of plotting crime incidents on a
map. This manual approach of pin mapping was useful for visualising where crime
occurred but there were certain limitations: (1) when updating the maps the previous
details were lost; (2) the maps could not be archived except by photographing them;
(3) maps were static and therefore they could not be manipulated and queried; (4)
maps could be difficult to read when several types of crime were mixed, especially
when coloured pins were used (Harries 1999); and (5) locations of crime were
approximate and not exact (Vann and Garson, 2003). Having said that, pin mapping
is still regarded as a simple method for analysing and presenting tactical crime
pattern data though limited from combining other data for an in-depth analysis
(Velasco and Boba 2000). It sometimes serves its purpose adequately and
successfully, particularly in smaller communities with no major crimes. For instance,
an example quoted by Vann and Garson (2003) about mapping crime in a small
tourist town in western North Carolina with approximately 13,000 residents and the
most serious incidents ever reported were vandalism, auto accidents and
alcohol-related incidents. That is to say, using manual pin maps to visualise crime
patterns remains practical but rather preliminary, rough-cut at problem identification,
and very much dictated by the size of an area and the amount of crime.
14
Even though pin mapping has been practiced for some time and up till now
in many law enforcement agencies it is more commonly used for informative
purpose than for analytical purpose. In fact, before GIS came into operation not
much spatial analysis could be done by pin mapping because of its aforementioned
limitations. There was also little sense of organising theories or perspectives
developed to integrate criminal activity maps into police operations (Weisburd and
McEven 1998).
No doubt, mapping crime is capable of assisting law enforcers in uncovering
spatial relationships of a crime. The definition of crime mapping is a study which
involves manipulation and processing of spatially referenced crime data in order to
have it visually displayed in an output that is informative to the particular user
(Bowers and Hirschfield 2001). The obvious distinction between crime mapping and
GIS can be described that the former is a software while the latter is a hardware. A
combination of the two allows crime analysts to understand the occurrence of crime
more effectively from the perspective of spatial and temporal analysis.
2.2.4 Crime Analysis by Geographic Information Systems (GIS)
The earliest applications of using GIS in crime analysis conducted by Pauly,
Finch and McEwen happened in 1967 (Wiesburd and McEwen 1998). They used
mainframe computers and punch cards to produce black and white shaded
choropleth maps from a line printer outlining the distribution of a type of crime in St.
Louis. This pioneering work conducted by St. Louis Police Department was done
with the intention of establishing a Resource Allocation Research Unit to improve
15
the efficiency of patrol operations (Harries 1999). Certainly, these maps proved to be
pragmatic in their use and soon they were observed to possess a great deal of
potential for understanding spatial distribution of criminal activities and for assisting
the management in better deployment of police resources in problematic areas.
Research eventually expanded by plotting the boundaries on these maps and since
then maps could be used for crime mapping purpose. All in all, the system
developed in this way was basically an informative crime mapping system that
produced maps of crime distribution and yet it was still lacking analytical capability.
The development of micro-computers in the late 1970’s, faster speed of
processing power of micro-computer in the 1980’s (Boba 2001), larger storage and
networking capability of micro-computers in the 1990’s (Rich 1999) together with
sophisticated mapping software (Rich 1995), has meant that adopting GIS in crime
mapping on desktop computer has created a new age of the history of crime
mapping. Not only does it become an affordable analytical device, it also provides
user-friendly operations in handling complicated queries, higher compatibility in
data exchanging (Weisburd and McEven 1998) and better connectivity in sharing of
crime information with other agencies (Vann and Garson, 2003).
Mapping crime data is a scientific process and without explicit theory in
crime analysis, the value of crime assessment can only rely on the proficiency of the
analyst’s personal understanding of relations between crime and space (Eck 1998).
However, criminological theories of crime and places have been developed along
with the growth of computer technology and practical experiences have been shared
and integrated crime mapping into law enforcement operations. Crime mapping has
become a well-established discipline of science in crime prevention.
16
The versatility of computer technology and the advance of criminological
theory in the past two decades brought the automated crime mapping into being -
Geographic Information Systems (GIS). GIS is a computerised mapping system that
permits stacking of information layers to produce detailed descriptions of conditions
and analysis of relationships among variables (Harries 1999). It also provides a
digital representation that enables the user to map crimes analytically, not just
descriptively (La Vigne 1999). Mapping crime incidents on GIS turns out to be no
more a time-consuming and tedious task as it is often confused by a series of
possible variables in solving a crime. Its output provides instant analysis for
immediate police action that improves the overall efficiency of modern policing.
Moreover, GIS is capable of combining data, including temporal data, from various
databases, which share common geographic features, and many others from sources
outside the police, to perform layering operations for complicated crime analysis. In
this way, spatiotemporal study of a crime analysis becomes more viable than before.
Like many computer systems, GIS is a combination of hardware and
software. The configuration of GIS basically consists of four major sub-systems: (1)
the data input sub-system for creating, importing and accessing data; (2) the data
storage and retrieval sub-system for storing and retrieving data; (3) the manipulation
and analysis sub-system for performing database management function and
analysing geographical data; and (4) the reporting sub-system for producing visual
representation of the data on a computer screen or a map printout. When comparing
the advantages of a GIS map with the limitations of a pin map, it is not difficult to
find out that a GIS map can overcome all the shortcomings of a pin map and that is
the main reason for technological replacement.
17
The momentum of the use of automated crime mapping continues to grow. In
1997, National Institute of Justice (NIJ) determined to create its Crime Mapping
Research Centre (CMRC) in United States to advance the use of analytic mapping in
research and practice (Travis 1998).
In 1998, Vice President of the United States of America, Al Gore established
a Task Force on Crime Mapping and Data-Driven Management to further the efforts
of the Clinton-Gore Administration to reduce and prevent crime (Rich 1999).
Quoted from a book “Mapping Out Crime” published by the United States
Department of Justice in 1999, Gore appraised the use of crime mapping that “Maps
can represent every dimension of a community …. They can show how healthy a
community’s children are, where social services are most needed and most effective,
and ways to protect the safety of each citizen …. Innovative communities are using
maps to mobilise resources to solve their toughest problem.” Utilising GIS in crime
mapping once again has been proven to provide the communities with an innovative
approach in targeting crime and supporting decision-making in crime management.
Many police forces, particularly in Western countries, have quickly adopted
GIS in a variety of police operational situations and crime prevention initiatives.
CMRC conducted the nationwide Crime Mapping Survey in 1997 indicating that
over 94% of the surveyed police departments with crime mapping capability used it
to inform officers and investigators of crime incident locations; 56% to make
resource allocation decisions; 49% to evaluate interventions (Mamalian et al 1999).
Griffin (2001) quoted from the Police Foundation survey of American police
18
departments showed that in 1998 almost 70% of large departments (100+ sworn
personnel), and 40% of smaller departments (between 55 – 99 sworn personnel)
have engaged in some form of crime mapping (Weisburd, Greenspan and Mastrofski
1998). In 1999, 44% of police forces in the United Kingdom have a crime mapping
facility (Ratcliffe 1999). Canada, Australia and other nations have embarked on
similar programme in the field as well.
That the concept of crime mapping has been widely accepted by many law
enforcement agencies nowadays can be evidenced by a massive number of
successful analysis reports published in Crime Mapping Case Studies: Successes in
the Field (La Vigne and Wartell (Ed) (1998 and 2000). The strengthening of
computer literacy among police officers also boosts their proficiency in handling
spatial analysis (Griffin 2001). More and more officers begin to realise the
significant advantages of crime mapping in police deployment to tackle prevailing
crimes. The growth of these practices and efforts promote the availability of spatial
data from diverse sources and the accumulation of information and knowledge
strengthen the crime mapping capability for future prediction of crimes. Apparently,
over the past few years after the said surveys, all these favourable factors encourage
the utilisation of GIS in crime mapping worldwide. GIS has become a universal and
effective tool in analysing and preventing crime. More importantly, GIS contributes
greatly in decision support management where law enforcement resources are
allocated more efficiently.
2.3 Crime Analysis Process
Crime analysis is an important process to law enforcement in understanding
19
the occurrence of a crime. It involves the collection and analysis of data relating to a
criminal incident, offender and victim, and develops information of use for crime
prevention and detection activities. Crime analysis is defined as a set of systematic,
analytical processes directed at providing timely and pertinent information relative
to crime patterns and trend correlations to assist operational and administrative
personnel in planning the deployment of resources for the prevention and
suppression of criminal activities, aiding the investigative process, and increasing
apprehension of offenders and the clearance of outstanding investigation (Gottlieb
1994). Therefore, the ultimate goal of crime analysis is to identify and generate the
information required for making appropriate decisions in deploying a suitable
amount of resources to prevent and control crimes. In addition, crime analysis can
be used to evaluate the effectiveness of crime prevention programmes, develop
policy through research and help identify or define a problem (Canter 2000). It can
also inform policy and decision makers about the actual or anticipated impact of
interventions, polices, or operational procedures (Boba 2000). Different from
intelligence analysis, crime analysis aims at identifying patterns and trends of a
crime while the former aims at examining the association and identification of
criminals with any criminal activity.
2.3.1 Analysis Process
The book Exploring Crime Analysis published by the International
Association of Crime Analysts (2005) presents a more precise definition for a crime
analyst in conducting crime analysis which "focuses on the study of crime incidents,
the identification of patterns, trends, and problems; and the dissemination of
information to develop tactics and strategies to solve patterns, trends, and
20
problems.” In the same book, crime pattern can be defined as when two or more
incidents are related by a common casual factor, usually to do with an offender.
Trend represents long-term increases, decreases or changes in crime. The concepts
of pattern and trend provide an overarching framework to identify relationships of
crimes.
By and large, there are five major types of crime analysis conducted on a
regular basis by law enforcement agencies: tactical crime analysis, strategic crime
analysis, administrative crime analysis, criminal investigative analysis, and police
operations analysis.
2.3.1.1 Tactical crime analysis
Tactical analysis provides information to assist operations personnel (patrol
investigative officers) in the identification of specific and immediate crime problems
and the arrest of criminal offenders. Analysis data is used to promote response to
field situations (Gottlieb 1994). The goal of tactical analysis is to: (1) identify
emerging crime patterns as soon as possible; (2) complete comprehensive analysis of
any patterns; (3) notify the agency of the pattern’ existence; and (4) work with the
agency to develop the best strategies to address the pattern (Bruce 2004). In
comparison with all the related crime incidents, it is always possible to identify
some commonalities among them. The most significant element in tactical crime
analysis is to identify the pattern and that can be achieved in a number of ways, from
tabulation comparison to statistical analysis and from simple pin map to automated
GIS mapping. This timely qualitative analysis enables the frontline officers to make
good use of the available resources for interdicting recent criminal and potential
21
criminal activity and leading to the apprehension of offenders. After all, tactical
crime analysis relies totally on the methods however refined and scientific, flexible
and intuitive that the analyst can utilise.
GIS has an important role to play in the compilation of tactical crime
analysis mapping – a common process of using GIS in combination with crime
analysis techniques to focus on the spatial context of criminal and law enforcement
activity (Boba 2001).
2.3.1.2 Strategic crime analysis
Strategic analysis is concerned with long-range problems and projections of
long-term increases or decreases in crime (crime trends). Strategic analysis also
includes the preparation of crime statistical summaries, which are generally referred
to as exception reports (Gottlieb 1994). Since there are always changes in the
operations of criminal activity, such as spatial and temporal modification, targeting
properties and modus operandi. From time to time, resources allocated for tackling
will vary according to the crime situation. Exception reports are an effective means
to deliver the information for better communication. Strategic crime analysis
incorporates two primary functions: (1) to assist in the identification and analysis of
long-term problems; and (2) to conduct studies to investigate or evaluate responses
and procedures (Boba 2001). After a long-term study, trends can be mapped and
tested by repeated hypothesis and subsequently provide a findings of the correlation
of a specific crime. From the perspective of the management, these crime
projections are useful in the anticipation of crime trends and future challenges and
the assessment can assist them to have an insight to initiate strategies, priorities,
22
resource deployment and organisational and planning needs (Baker 2005). Crime
trend is the focal point of compiling a sound strategic crime analysis.
2.3.1.3 Administrative crime analysis
This is different from the previous types of analysis in that administrative
crime analysis helps facilitate strategic goals (Baker 2005) and focuses on the
provision of economic, geographic, or social information to administrators (Gottlieb
1994). It refers more to presentation of findings rather than to statistical analysis or
research (Boba 2001) and it is a broad category including an eclectic selection of
administrative and statistical reports, research and other projects not focused on the
immediate or long-term reduction or elimination of a pattern or trend (Bruce 2004).
Administrative crime analysis supports law enforcement agencies to initiate special
research projects, feasibility studies and questionnaire surveys which ultimately
provide additional information to the administration for better crime prevention
responses. Its subsidiary outcome may improve and promote public relationships by
making available a picture of the overall crime situation for publication to the
public.
2.3.1.4 Criminal investigative analysis
Criminal investigative analysis is the study of criminal personality behaviour,
especially in violent crimes. It involves profiling of offenders, victims and
geographical features with a view to linking and solving current serial criminal
activity. This is a very specific type of crime analysis that is primarily carried out by
law enforcement agencies (Boba 2001) as it requires professional skills and a high
23
degree of expertise.
2.3.1.5 Police operations analysis
Police operations analysis depicts the review of the organisation and
operations of a police department. Generally speaking, it involves the measurement
of effective allocation of police resources in deterrence of crime and disorder (Bruce
2004).
2.3.2 Crime Analysis Model
From the perspective of information requirements, the crime analysis model
introduced by Boba (2001) explains the relations between the aforementioned first
four types of crime analysis and the levels of aggregation. In short, types with low
levels of aggregation aim at individual cases and use qualitative data and analysis
techniques and those with high levels of aggregation aim at a limited scope of larger
amounts of data and information. Tactical crime analysis relies heavily on latest and
immediate crime information with the aim of putting together a timely and
qualitative analysis of crime patterns. Strategic crime analysis depends greatly on
massive quantified crime data for statistical operations. Administrative crime
analysis when compared with the former two analyses depends on additional
information, perhaps the inclusion of economic, geographic, or social information,
for the compilation of an overall crime summary. Criminal investigative analysis,
however, deals primarily with profiling information.
24
Figure 2.3: Crime Analysis Model Source: Boba 2001
From the perspective of crime analysis outcome, these five categories of
analysis produce different outcomes. The ultimate aim of tactical crime analysis is to
deter reoccurrence of a crime with an intention to apprehend the offender. Strategic
crime analysis serves to inform the concerned stakeholders of the prevailing crime
situation. Administrative crime analysis fosters closer public relationship and
garners community support. Police operations analysis helps with the preparation of
annual budgeting and resources allocation. The ultimate expectation of criminal
investigative analysis is no more than to bring the serial offender to justice.
2.4 Problem Solving Philosophy in Crime Analysis
As GIS is capable of handling large volume of spatial information and
performing high-speed analysis. The use of GIS in policing has two broad
applications in crime analysis: tactical crime analysis and strategic crime analysis
25
(Canter 2001). In tactical crime analysis, GIS can be used to map recent criminal
incidents and related information for the purpose of identifying a crime problem for
interdiction or prevention. Visualisation of GIS allows effective display of incident
locations over time or case attributes suggesting commonalities in target, offender,
or victim. Together with the integration of relevant information, GIS can improve an
analyst’s ability to associate crime with other factors. As such, the use of GIS in
tactical crime analysis boosts the accuracy of a crime assessment. In strategic crime
analysis, since most information has a geographic component, GIS can review these
data and information collected for qualitative or quantitative analysis and determine
whether a crime problem was displaced, reduced or unchanged within a target area.
GIS can, therefore, be deployed to assist in formulating crime prevention
programmes, planning for resources allocation, and even improving problem solving
capability.
2.4.1 Problem-Oriented Policing (POP)
Reinforced by crime analysis, conventional policing, or traditional
incident-driven policing, relies heavily on patrolling, rapid response, and follow-up
investigations (Eck and Spelman 1987). After reviewing considerable research, they
find that conventional policing effectiveness depends largely on how effective are
the tactics employed. For example, focused patrolling on hot spots can have a large
impact only for a short period of time and these patrols cannot be maintained for
long. Similarly, rapid response to crime reports may not always be applicable and
the delay in reporting gives the offender time to escape. In addition, follow-up
investigation may not always lead to detection of crime.
26
Figure 2.4: Incident-driven Policing Source: Eck and Spelman 1987
The deficiency in conventional policing encouraged Goldstein to explore a
new way of policing. In 1979, he developed the concept of Problem-Oriented
Policing (POP). In his book, Problem-Oriented Policing, Goldstein (1990) states that
“in a narrow sense, [Problem-Oriented Policing] focuses directly on the substance of
policing – on the problems that constitute the business of the police and on how they
handle them. This focus establishes a better balance between the reactive and
prospective aspects of policing…. In the broadest context, Problem-Oriented
Policing is a comprehensive plan for improving policing in which the high priority
attached to addressing substantive problems shapes the police agency, influencing
all changes in personnel, organisation, and procedures.” He further states that “the
Problem-Oriented approach calls developing – preferably within the police agency –
the skills, procedures, and research techniques to analyse problems and evaluate
police effectiveness as an integral continuing part of management.” (Goldstein 1990).
27
This new approach to policing has changed the usual response in crime control and
crime prevention and has shifted the traditional problem-solving concept in crime
analysis too.
The concept of POP is quite simple and it can be summarised in four steps as
follows: (1) scan data to identify patterns in the incidents being routinely handled; (2)
conduct an in-depth analysis of cause from the patterns identified; (3) employ
appropriate tactics to intervene and prevent any future reoccurrence; and (4) assess
the impact of the interventions and if they have not worked, repeat the process all
over again. This approach involves a well-defined problem, a root cause analysis, a
tailor-made strategy to prevent future crime from developing. The strategy so
employed relies less on arresting offenders and more on developing long-term
methods to deter crime by preventing potential offenders, protecting likely victims
and reducing hazards of a potential crime location. In essence, POP transforms the
concept of policing from a reactive response to a proactive response.
28
Figure 2.5: Problem-oriented Policing Source: Eck and Spelman 1987
The philosophy of POP has seen rapidly adopted by many law enforcement
agencies worldwide and a tremendous number of successful case studies have been
recorded, such as those reported in the book Problem-Oriented Policing: Success in
the Field, Volume 1 to 3 published by Police Executive Research Forum (Shelley
and Grant (Ed) 1998, Brito and Allen 1999 (Ed) 1999, Brito and Gratto (Ed) 2000).
There have openly recognised the effectiveness of the problem solving approach,
and some of the essential ingredients of which are also incorporated into community
policing.
2.4.2 SARA Model
To cope with the introduction of POP and its implementation, a common and
29
widely accepted problem solving technique in crime analysis has also been
developed – SARA Model, a simplified process of POP. SARA is the acronym
formulated by Eck and Spelman to refer to the four phases of problem solving –
Scanning, Analysis, Response and Assessment (Center for Problem-Oriented
Policing, http://www.popcenter.org/about-SARA.htm)
Problem solving is crucial to crime analysis because underlying factors
leading to crime and disorder problems for which effective responses can be
developed and through which assessment can be conducted may affect the relevance
and success of the responses (Boba 2003).
The SARA model employed in crime analysis proposes that the first step is a
Scanning phase to identify a problem, such as a cluster, related or reoccurring crime
incident, and to accord priority to the crime problem for future examination.
Following that, the second step is an Analysis phase to conduct a comprehensive
review, both qualitative and quantitative, of the crime problem from various sources
of information to determine the root cause of it. After that, the next step is a
Response phase to formulate a tailored set of intervention and enforcement actions
to be implemented and if necessary modify the implementation. Finally, the last step
is an Assessment phase to evaluate the effectiveness of the response phase, both
before and after the responses have been implemented (Boba 2001, Braga 2002,
Vann and Garson 2003).
Overall, the Scanning phase is crucial in determining the direction of the
effort in that the problem should be clearly defined and validated. Thereafter, the
Analysis phase should focus on the particular elements of crime, such as offender,
30
victim and venue of location. During the Response phase, the emphasis is placed on
the actions to be taken by key personnel and time scales. When it comes to the
Assessment phase, the whole operation should be reviewed and assessed to the
degree of effectiveness and if it was not, why particular actions were not successful
and what other alternatives might be able to achieve the same goal (Patrick 2002).
Figure 2.6: SARA Model Source: Clarke and Eck 2003
Still, the SARA model can be misleading in suggesting that these four phases
should follow one another in a strictly linear manner (Clarke and Eck 2003). In the
problem solving process, it is always possible to receive and collate new information,
especially during scanning and analysis phases, and the process can be revisited at
any phase. SARA model is a repetitive process in thinking and it allows the analyst
to refocus on the problem and continue to examine the best possible solution to the
problem.
31
Crime analysis and crime mapping can be interacting with each other in all
phases of the problem solving process. It is imperative to assess the problem
accurately during the scanning, analysis and the assessment phases (Boba 2001). In
the scanning phase, crime maps can show the analyst where the problems are located
and that can help test the hypothesis about the problem in the subsequent analysis
phase. Analyst can also monitor the situation in the response phase by advising the
effective allocation of resources of that operation requires, based on the time span of
occurrence and the locations where offences are mainly occurring. After the
enforcement action, it is very likely to see changes in the maps, be it a spatial or
temporal change or both. After all, it is vital for crime analysts to have a
comprehensive understanding of the model in support of the mapping process while
it is equally important for members of a law enforcement agency to understand the
role of crime analysis in problem solving even though it always relies very much on
their experiences and understandings of a problem.
2.5 CompStat
The most effective crime control techniques and later with the aid of GIS
crime mapping witnessed in the success of the CompStat process pioneered by the
former commissioner William Bratton of the New York Police Department (NYPD)
and his management team in 1994 (Shane 2004). CompStat is short for “computer
comparison statistics” or “computerised statistics”. The implementation of CompStat
has remarkably caused 65.99% reduction in the total number of reported crimes for
the seven major crime categories (murder, robbery, first degree rape, felony assault,
burglary, larceny and grand larceny) in New York from 430,460 in 1993 to 146,397
32
in 2003 (CompStat Report 2004 Vol.11 No.50 at
http//www.nyc.gov/html/nypd/pdf/chfdept/cscity.pdf). The ultimate goal of
CompStat is to reduce crime and to improve quality of live.
Major Category 1993 1997 % chg vs. 1993 2003 % chg vs. 1993
Murder 1,927 767 -60.2 598 -68.9
Rape 3,225 2,783 -13.7 1,875 -41.8
Robbery 85,892 44,335 -48.3 25,919 -69.8
Fel. Assault 41,121 30,259 -26.4 18,774 -54.3
Burglary 100,936 54,866 -45.6 29,215 -71.0
Gr. Larceny 85,737 55,686 -35.0 46,877 -45.3
G.L.A. 111,622 51,312 -54.0 23,139 -79.2
TOTAL 430,460 240,008 -44.24 146,397 -65.99
Table 2.1: Historical Statistics of CompStat between 1993 and 2003 Source: NYPD at http//www.nyc.gov/html/nypd/pdf/chfdept/cscity.pdf (accessed on 2005.01.15)
The CompStat process is a hybrid management style that combines the best
and most effective elements of various organisational models and policing
philosophies (Henry 2003). Not only does it retain the best practices of traditional
policing, it also incorporates the philosophy of Problem-oriented policing (POP),
including the SARA model, to formulate crime control strategy. One of the most
important theory resulting from this process is the “Broken Windows” theory which
was introduced by George Kelling and James Wilson in 1982, which state that if
minor offences are left unchecked they will lead to more serious crime (Kelling
1995). The dominant role of police has long focused on serious crime, such as
murder and robbery etc., and rarely geared up to focus on minor crime, such as petty
cash theft. Broken Windows theory raises the level of awareness of the police and
the public that if minor problems are not taken care of they will develop into serious
33
problems. Kelling and Sousa (2001) later revealed in their research on the impact of
Broken Window policing to violent crime in New York that the adoption of Broken
Window policing had successfully prevented 60,000 violent crimes between 1989
and 1998.
Another significant policy encompassed in the CompStat process is the
“zero-tolerance” policy which connotes a complete lack of responsiveness on the
part of police officers in the manner they enforce the law (Henry 2003). The
promotion of zero-tolerance policy transforms law enforcement agencies into
focused, efficient and accountable organisations by motivating the officers who are
guided by a focused mission, disciplined and stimulated by a rigorous concern of
direct and personal accountability.
The advocacy of Broken Windows theory and zero-tolerance policy as well
as other management philosophies meant that new police cultures then evolved to a
four-crime reduction principle model, namely the CompStat model. These changes
in the management and practices of law enforcement agencies have transformed the
traditional police culture in devising crime reduction strategy.
The first step in the CompStat model is the collection of accurate and timely
intelligence so that police have to respond to crime effectively and immediately by
having accurate knowledge of particular types of crime occurrence. The
effectiveness of police response to crime will increase proportionately as the
accuracy of criminal intelligence increases. The second step is to employ effective
tactics designed to reduce the number of crimes. The tactics should be
comprehensive, flexible and adaptable to the shifting crime trends to avoid any
34
possible displacement of crime. The third step requires rapid deployment of
personnel and resources. Immediate task forces or “split-forces” should be formed
and tasked to tackle and even eradicate the specific crimes, which are accorded with
high priority or assigned as a primary responsibility. The fourth step is to adopt an
ongoing process of relentless follow-up and assessment to ensure the desired goals
are actually achieved. The evaluation process allows the agency to assess the
feasibility of a particular response and to incorporate the knowledge acquired for
future tactical options (Shane 2004).
One of the vital facilities in CompStat, especially for the purpose of data
analysis and data presentation, is the weekly CompStat Report which meticulously
captures all the details of crime statistics, ranging from summaries of weekly crime
complaints, arrest and summons activity, crime patterns to specific times and
locations at which the crimes and enforcement actions took place. These reports will
be presented by the personnel from each of the 76 precincts and other departments in
the weekly Crime Control Strategy Meeting where the responsible precinct
commander will be challenged on the skills and effectiveness of their staff in the
capability of crime reduction and police performance. Crime maps obviously have a
significant role to play in this regard.
35
Figure 2.7: Principles of CompStat Model Source: Reconstructed based on Shane 2004
In another research conducted by Willis, Mastrofski and Weisburd (2003),
they commented that the characteristics of CompStat can be generalised using six
core elements: (1) mission clarification; (2) internal accountability; (3) devolution of
decision making authority; (4) organisational flexibility; (5) data-driven analysis of
problems and assessment of department’s problem solving; and (6) innovative
problem solving tactics. CompStat has not been so successfully implemented in the
three study cities, Lowell, Minneapolis and Newark, as its pioneer in the New York
City. One striking effect on key strategic decision-making process was noticeably
placed on new information technology and the emphasis of the implementation of
CompStat process. The data-driven analysis process in CompStat relies heavily on
the accuracy and timeliness of intelligence of crime information and this has caused
the middle managers to be highly sensitive to the need of what detail of information
they were looking for. Without the support of GIS crime mapping techniques,
36
particularly the visual display of aggregate data explaining the relationships among
criminal offences in time and space, immediate spatial and temporal analysis would
not be possible.
2.6 Literatures on Campus Crime
Campus crime appears to be a sensitive topic to discuss. Any search for basic
information, even locally, has always been fruitless, and no mention of any form of
utilisation of GIS in analysing crime in tertiary institutions can be found. Perhaps,
any disclosure of crime situations may cause an adverse effect on the reputation of
an institution. This research has made repeated attempts to locate any updated
articles and reports from the Western countries through internet and publication, and
through email subscription from the Crime Mapping Association and the Law
Enforcement Analysts Network in the United States but almost all has been in vain.
In spite of this, some respondents replied and gave several useful websites on
campus crime. The website of the Office of Post-secondary Education
(http://ope.ed.gov/security/index.asp) is supplemented by the United States
Department of Education offering a wealth of campus crime information of over six
thousand tertiary institutions the United States. The search engine allows the user to
make one enquiry at a time and yet it does not yield very useful results. The
enactment of the Higher Education Act of 1965 (amended in 1998) requires tertiary
institutions to report criminal offences to the Department of Education and the
Department of Education has an obligation to inform the students and their parents
of the safety of the environment in which they are studying (Campus Crime and
Security at Post-Secondary Education Institutions,
37
http://ope.ed.gov/security/index.asp). No similar law seems to be enacted in Hong
Kong to keep the members of the public abreast of the campus safety policies.
An email reply from Sean Bair, ([email protected]) the programme manager of
the Crime Mapping Analysis Program (CMAP) of the National Law Enforcement
and Corrections Technology Centre under the administration of University of
Denver affirmed that they have just begun to get involved in analysing crime activity
on campus and agreed that they have not come across too much literature on the
captioned research topic. He further suggested discrete analysis would suit the
purpose for a confined campus.
2.7 Summary
The development of crime mapping over the past few decades can be
generalised by four contributing factors: (1) the technological evolution from
manual mapping to automated GIS mapping has improved the mapping process
greatly in quality and in quantity; (2) the conceptual transformation of crime
prevention from criminal behaviour based to crime and place theory assists law
enforcement in formulating effective strategies focusing more on the geographical
effects; (3) the philosophical advance from traditional policing to POP by adopting
the SARA problem solving model forces law enforcement to take a proactive
approach to tackling crime; and (4) the expansion of scope of crime analysis from
investigative-centred to a multifaceted dimension allows the management a better
understanding of the impact of crime to resources allocation and may even
strengthen public confidence in fighting crimes. CompStat model creates a
management structure that helps law enforcement agencies to control crime and
38
disorder effectively in the communities. CompStat is an information-driven process
relying on accurate and timely intelligence, partly based on the effective
performance of GIS crime mapping to identify and plot the occurrences of crime.
The visual display of aggregate data enables crime analysts to explain the
relationships among criminal offences in time and space makes immediate spatial
and temporal analysis possible. The world of policing therefore is able to benefit
from these theoretical development and refinement.
In the absence of any useful literature on the subject of campus crime, this
research will evaluate two dimensions of the campus crime situation from the
available data. From the analytical perspective, this research will utilise crime
mapping techniques to identify crime hotspots and their spatiotemporal correlations
in the campus environment. From the management perspective, it will examine the
current organisation structure and public safety procedures of the campus security
and derive an effective crime reduction strategy. The methodology used will be
discussed in the following chapter.
39
CHAPTER THREE
METHODOLGY
3.1 Introduction
From the previous chapter, it seems no one would argue that the distribution
of crime incidents is spatially random. Pioneer environmental criminologists have
concluded that the types of built environments have a direct link with many types of
crime.
The University of Hong Kong was selected as the study area. Section 3.2
will give a brief introduction of the study area and the security system of the campus
as well as the source of research data and the tools to be used in this study. All these
serve to provide some basic information of the study area.
Section 3.3 will explain the design schema of this research. Since the study is
intended to assist the management and frontline personnel to formulate effective
crime prevention measures and patrol strategies in the campus, a practical approach
was adopted for this research. The methodological framework will gear up the
operation of data management and data analysis processes. Section 3.1.1 will
explain the functions of data management how data can fully be optimised for
analysis purpose. Section 3.3.2 will outline the design of analytical flow and
determine the appropriate techniques in visual identification of crime hot spots.
3.2 The Research Data
40
3.2.1 Study Area – The University of Hong Kong
Due to the sparse distribution of buildings in the campus of the University of
Hong Kong, this research sub-divides the campus into three study areas, namely the
Main Campus, Western Region and South Region and these are the terms later
referred to in this research. The first study area is the Main Campus which is situated
at the north side between Pokfulam Road and Bonham Road in the Mid-levels. It
covers most of the academic buildings, administration departments and student
facilities in the whole university. The second study area is the Western Region where
a dense convergence of student dormitories can be found along the west bank of
Pokfulam Road. The third study area is the Southern Region in Sassoon Road where
the medical school and staff residences are located. The size of the study area covers
about 39.5 hectares (Sustainability Report 2004 available at http://www.hku.edu.hk).
The map of the campus of the University of Hong Kong can be found at Figure 3.1.
Special off-campus facilities, such as the Swire Institute of Marine Science
in Stanley and Kadoorie Agricultural Research Centre in Taipo, however, will not be
included in this research simply because there was no residences and no crime
reported and they are distanced from the study area. These facilities occupy an area
of about 9.8 hectares (Sustainability Report 2004 available at
http://www.hku.edu.hk).
41
Figure 3.1: Sub-division of the study area of HKU
The ownership of the campus of the University of Hong Kong is under the
Authority of the University of Hong Kong (Sustainability Report 2004). Members of
the public may have a wrong perception that they have access rights to the open
campus. It is actually a private property and therefore police do not patrol the
campus on a regular basis. Indeed, there are many blocks of buildings in the campus
restricted to authorised access only and controlled by campus security guards.
42
The total number of regular students of the University of Hong Kong
between the year of 2002 and 2003 was 19,000, including 11,700 undergraduate
students and 7,300 postgraduate students of which more than 1,000 were
international students (University at a Glance available at http://www.hku.edu.hk).
The number of regular students between 2003 and 2004 was reported as 19,562
(Review 2004 available at http://www.hku.edu.hk). This figure, however, does not
take into account the staff population which was reported as 6,724 in December
2003, including 4,462 regular staff and 2,262 temporary staff (Quick Statistics
available at http://www.hku.edu.hk). In standard calculations in campus crime
statistics in American universities staff have never been counted. Neither does it take
into account of the headcount enrolment on School of Professional and Continuing
Education (SPACE) programme and the out-reach programme which amounted to
105,427 and 730 respectively in the year 2003 and 2004 (Quick Statistics available
at http://www.hku.edu.hk) as these programmes are operating outside the university
campus or outside of Hong Kong.
The geographic location of the campus of the University of Hong Kong is
very unique. The Main Campus and the Western Region are enclosed by medium
density and middle to high-income residential blocks while the Southern Region is
quite remote from the urban area and surrounded by low density and high-income
residential blocks as at Figures 3.2 and 3.3. There is neither any entertainment centre,
such as cinema, nor any liquor premises, such as bar, closely found in these three
areas. Residents nearby have seldom entered the campus probably because of the
topography and because academic institutions rarely attract any unnecessary visitors,
and therefore the access to the campus is almost confined to the students and staff
only.
43
Figure 3.2: Population Density around HKU Campus Source: Lands Dept, 2001 B1000, Digital Topographic Map
Figure 3.3: Income Groups around HKU Campus Source: Lands Dept, 2001 B1000, Digital Topographic Map
44
That is to say, the campus area has rarely been influenced by any potential
crime prone factors, such as high population density, high poverty level and high
accessibility in an area. This corresponds to two observations made by crime
analysts that low-socioeconomic-status areas have more crime and high-status areas
have low crime; and high pedestrian flow has accelerated higher rate of crime
(Kamber, Mollenkopf, and Ross 2000).
Unlike many other crime analyses conducted by police that frequently focus
their targeting efforts on mixed socio-economic areas within their jurisdiction.
Campus crime mapping, however, is a discrete analysis in that the targeting area is
an isolated area where activities are confined to academic affairs and have little
interaction with socio-economic events.
3.2.2 Campus Security
An interview was conducted with Mr. Teddy Wong, manager of the Security
Section of the University of Hong Kong on 26th Jan 2004. Responsibility of campus
security falls on the shoulder of the security manager under the management arm of
the Estates Office. He directly supervises the security of the Main Campus with a
strength of ninety five guards and co-ordinates security issues with security
management of the Western region and Southern Region. The staff turnover rate is
low and about one third of his personnel are pension staff. Most of his staff are
experienced and trained guards but none of them are capable of performing crime
analysis. Surprisingly, upon the interview with the security manager, it was revealed
that the primary duty of campus security guards is to maintain a good degree of
public relationship with the staff and the students. Their secondary duty is to
45
respond to incident calls including reporting building defects, and the tertiary duty is
to patrol the campus. The Main Campus is divided into nine patrol zones and there is
no static post so that they have to patrol their zone and record their attendance with
an electronic device, very much like an electronic visiting book, at all the check
points. Unlike most of the security guards employed in the tertiary institutions, they
work on a three shifts system instead of a two shifts system and therefore their
turnover rate is very low.
The situation outside the Main Campus is slightly different. Security of the
Western Region is carried out by dormitory attendants whose primary responsibility
is for caretaking duties. Security of the Southern Region is outsourced to three
private security companies, which were awarded by the lowest bids, for building
management purpose, such as the staff quarters and medical blocks. This explains
the diversified management style of security within the campus. Each of the
attendants and guards is responsible for the security of their own buildings and the
rotation of staff of the private security companies is quite frequent. The map at
Figure 3.4 shows the sub-division of the campus security.
Reports of crime can be made directly to the Security Section and a security
officer will scrutinise each incident. Cases requiring police attention will be referred
to the police immediately for action. Minor cases, such as loss or theft, will be
recorded for documentation purpose and it is up to the victim to make a report to the
police personally. There is no designated computer system to store the crime
incident records, instead they were kept in a word processor, Microsoft Word, for
easy retrieval and report writing. On a monthly basis, the Security Section emails to
all staff and students a crime report keeping them abreast of the current crime
46
situation of the campus and an extract is at Figure 3.5. Around the middle of each
year, the Security Section stages a one-day security campaign advising participants
of the importance of protection of personal belongings.
Figure 3.4: Sub-division of the campus security of HKU
Since the campus falls within the boundary of the Western Police District,
the Security Section maintains a close liaison with the police for exchanging crime
information. Police undertake a proactive role in crime prevention initiatives by
47
providing District Intelligence Section (DIS) to monitor the crime situation of the
campus and Regional Crime Prevention Office (RCPO) to hold crime prevention
seminars to the fresher at the beginning of each academic year.
Figure 3.5: Sample of the monthly crime report of HKU
3.2.3 Research Tools
Software for mapping crime has made great strides in crime analysis. There
are many GIS products specialised in crime analysis available on today’s market and
some are even free software, such as the CrimeStat III developed by Ned Levine.
CrimeStat is a spatial programme for the analysis of crime incident locations with
six common tools, such as journey-to-crime estimate, distance analysis and hot spot
analysis etc., made available from National Archive of Criminal Justice Data at
http://www.icpsr.umich.edu/CRIMESTAT. This application promotes crime
48
mapping practice to individual researchers and smaller scale law enforcement
agencies.
One of the most common GIS programmes used is ArcGIS, an advanced
version of ArcView developed by Environmental Systems Research Institute (ESRI).
This is the software to be used in this research and more importantly it is available at
the University of Hong Kong. This software application allows crime analysts to
capture and create an integrated picture of information in the form of interactive
maps and reports and in a Graphical User Interface (GUI) environment. Different
from ArcGIS, CrimeStat is standalone software for spatial analysis and its output can
be visualised using GIS software such as ArcGIS.
Along with the software package, a course workbook on crime mapping and
analysis programme, “An Introduction to Crime Analysis using ArcGIS”, can be
obtained from the National Law Enforcement and Corrections Technology Centre
(available at http://www.nlectc.org) as a reference book for conducting the analysis
in this research.
3.3 Methodological Framework
Establishing a systematic framework for this research is the foundation to
produce an accurate and meaningful picture of the crime situation of the campus at
Figure 3.6. On this basis, there are two structures to be constructed and they are the
data management and spatial analysis. Proper data management helps eliminate the
chances of errors and appropriate spatial analysis helps increase the validity of the
data presented.
49
Crime data provided by the Security Section may not be always useful for
crime analysis or even further for spatial analysis function, particularly when most
of the data was collected based on their subjective purposes and their own data
standards. The first step is therefore to evaluate the usefulness of data and examine
the data in order to fit in for further spatial analysis purpose.
Data integrity and data structure are crucial in assessing the crime analysis
and the maps produced. Data integrity affects the quality of data, such as consistency
and accuracy, in the outcome of an analysis. The design of data structure determines
the entities of data to be collected. Most of the crime data are not always spatially
referenced and neither are they properly captured as it depends on the needs of
individual agency, so they need to be processed and transformed. Geocoding or
address matching is a prerequisite in plotting the crime incidents on a map. Data
classification will assist in re-arranging the data by putting it into the designed
arrays for precise measurement.
Spatial analysis brings a closer focus to explore the cause of a crime at a
specific crime spot and leads to a holistic crime picture. Spatial distribution of crime
clearly demonstrates that certain areas attract more crime than the others as
mentioned in Chapter 2. This is the hot spot identification revealing the
co-relationship between a specific geographic location and other potential variables,
such as land uses and population characteristics (Anselin et. la. 2000). This research
will make use of single layer hot-spot operation to identify the concentration of
crimes in the campus and examine the spatial and temporal changes of these crimes
to determine an appropriate strategy to prevent and tackle the crime problems.
50
The adoption of the SARA model in crime analysis has always helped
uncover the underlying causes of crime problems. Strategic crime analysis and
tactical crime analysis will be useful for devising effective strategy and
implementing specific enforcement efforts to reduce and prevent crimes. Details of
this analysis hierarchy will be discussed in later sections.
51
Figure 3.6: Methodological framework adopted for this research
3.3.1 Data Management
3.3.3.1 Data collection
52
Collecting data from the police on campus crime is nearly impossible due to
Personal Data (Privacy) Ordinance in which it stipulates that data collected should
not be used other than for its originated purpose, except that it is used for the
prevention and detection of crime specified in Data Protection Principle 3.
Nonetheless, the Security Section provided some useful crime statistics of the
campus between the years of 2002 and 2004. This is the database used for the
construction of this research and Table 3.1 shows part of the tabular data given.
When crime complaints are made to police, detailed information can be
obtained by questioning the victims and witnesses, interrogating suspects, and
collecting evidence from the scene by exercising their power under the laws for
crime detection and crime prevention. Valuable information, such as victim and
suspect description and property loss description etc. are crucial for immediate
police action and investigation. However, due to concerns over confidentiality,
Estates Office does not enjoy this privilege for data collection and data collected are
very much depersonalised. In most of the situations, only limited information or
even aggregated data can be obtained, such as type of crime, time or location of a
crime etc. This limits the variety of analysis techniques to be conducted for crime
analysis. One of the alternative techniques which can be used is the hot spots
analysis.
Offence Date/time
of offence
Location Victim Property Summary of
Facts
Police
Involvement
Remarks
1 Lost / Stolen
BET 2015 – 2130 on 02-JAN-2002
3/F female toilet, Main Library
A female student
A personal bag containing cash 800, mobile phone and IDs
VTM went to the toilet with the bag. She forgot to fetch back the bag after the toilet. When she returned to the toilet, the bag
53
went missing 2 Car
found damaged
BET 25-DEC-2001 and 06-JAN-2002
Space No. 89, Tam Tower, Sha Wan 25
A resident of Tam Tower
A motor cycle VTM parked his m/c at AA for a week. When he returned to the bay, he found the m/c body scratched and suspected that his m/c had been tampered with
3 Criminal Damage
1550 hrs on 21-JAN-2002
Inside HK Bank, RunRun Shaw Bldg
HK Bank The leaflet holders
A male Chinese student whilst doing transaction inside the bank suddenly turned violent and threw all the leaflet holders on the ground. The student was identified to have mental problem.
4 Lost / Stolen
1435 hrs on 29-JAN-2002
4/F Main Library
A female Student
A handbag with cash, mobile phone and ID
VTM left the handbag at the reading desk and went to toilet. On her return, the handbag was found missing
5 Lost/Stolen
BET 1530 and 1730 on 29-JAN-2002
1/F Old Library
A female student
A cloth bag with cash and and IDs
VTM studied at Library at the material time. During the period, she had left her desk for an hour. And she subsequently found the bag missing
VTM would report to police direct
Table 3.1: Extract of raw crime data of HKU between 2002 and 2004 Source: Estates Office (Security Section) of the University of Hong Kong in January 2005.
3.3.3.2 Data set in this research
The crime statistics extracted and shown in Table 3.1 are the records stored
in a table form of Microsoft Word format along with some information about the
crime incident. The format of tabular crime data is still commonly adopted by the
Hong Kong Police for their daily crime analysis due to the fact most of the crime
information was not captured electronically nor from any non-spatial databases,
except the Incident Mapping System (IMS) which is the only spatial database
system capable of providing a visual display of geographic location of police
incidents.
54
To an extent, the crime statistics kept by the Estate Office consist of some
types of geographic variable, such as address. Yet, many of these crime locations
reported were not precisely maintained in the tabular records. For example, the
location of a crime occurred in the library only provides the setting of crime but it
does not yield an exact location where crime took place. Data, which carries no
inherent geographic information, renders little value in spatial analysis. With the aim
of producing an effective measurement of crime situation, quantitative geographic
data is required by means of geocoding or address matching to map the location
from these textual geographic variables or non-spatial attributes to a geographic
location recognised in a digital map.
The incompleteness of temporal records in the data set poses a serious
problem in assessing the accuracy of temporal findings. Of the 225 crime records,
only 164 records contain information of time in hours when the offences were
committed, which represents about 73% of the total crime data. That means the
accuracy of temporal analysis of this study comes is approximately 73% accuracy.
Table 3.2 presents the percentage of the known and unknown hours of the temporal
analysis in this research. There was no reason given as to absence of this valuable
information. This valuable temporal information was concatenated in one data field
called date / time which squeeze the date and time information in one single column
Known / Unknown 2002 2003 2004 Total
Known 66 42 56 164
Known % 84.62% 65.63% 67.47% 72.89%
Unknown 12 22 27 61
Unknown % 15.38% 34.38% 32.53% 27.11%
55
Total 78 64 83 225
Table 3.2: Statistics of crime data with reported and non-reported time between 2002 and 2004.
Apart from the lack of spatial and temporal attributes in the data set for
sustaining possible crime mapping performance, there are other data which require
further verification. For instance, a lengthy narrative illustrating the criminal act of a
suspect in a single data field - the summary of facts field makes them difficult to
differentiate from each other for data matching and data comparison. Obviously, the
data structure needs to be revised to cater for more precise measurement.
In addition to that, other deficiencies were also detected from the data set
where arrays of information were maintained inconsistently and inaccurately. For
instance, floor numbers may be missing and building names are not standardised.
Main Library sometimes refers to the New Wing of Main Library but it happens to
be the Old Wing occasionally. These data, which cannot be matched against the
campus map and the too simplified victim information in some records, create a
great deal of problem in data integrity.
3.3.3.3 Data classification
The original data structure and the deficiencies spotted in the data set
prohibit further automation of crime analysis. It was at this stage the rectification
process started to re-examine the value of each entity and to collate them into
expanded common attributes, which were ignored in the original design.
In fact, each type of crime carries some significant attributes to establish the
56
hypothesis of a modus operandi (M.O.). For example, robbery analysis aims at
identifying the properties robbed and the method employed to succeed; burglary
analysis also aims at not only identifying the stolen properties but also on the
method the burglar entered the premises; Analysing theft of conveyance without
authority is aimed at identifying the types of stolen vehicle, the method employed to
prise open the vehicle and the way to dispose of it. Furthermore, police is able to
construct offenders profiling by means of utilising their criminal records system,
criminal intelligence system and MO system to compile a comprehensive targeting
report. Yet, all these go beyond the scope of this study.
Having said that, there are some common elements determined from all
crimes; such as geographic factors, time factors, victim descriptors, property loss
descriptors, physical evidence descriptors, specific MO factors, suspect descriptors,
and suspect vehicle descriptors (Gottlieb, Arenberg and Singh 1998). They are the
basic data fields considered to supplement the original data set. The date and time
file expanded to include start date, end date, weekday (Monday etc.), start time and
end time. Some other files were also created to include type of stolen properties and
MO descriptors derived from the original data fields of property and the summary of
brief facts.
In some of the lost or stolen incidents, especially those which were reported
in 2002, it was up to the victim’s preference to decide whether or not he or she
wished to report it to the police, and hence data reliability becomes doubtful. Along
the same lines, most of these cases, if they were carefully examined, should be
classified as theft since none of the stolen properties were recovered at a later time.
It is hard for anyone to imagine a loss of notebook computer in the library be
57
classified as lost or stolen. At least, there is a prima facie case to establish that
offender has no intention to return the properties to the owner. It is for this reason
that all the lost or stolen cases were reclassified to theft cases. Nonetheless, the
situation improved in 2003 when the Estates Office changed the handling procedure
and maintained a closer liaison with the police in crime reporting. This shift of
administrating crime complaints indicates an apparent strategic change in handling
crime reports by the management probably due to the upsurge of theft offences.
Classified theft offence will be investigated by the Criminal Investigation
Department (CID) of the police, while making a lost or stolen report will be
processed merely for documentation purpose.
There are other deficiencies found in the data classification stage. For
example, there are non-crime incidents recorded in the original crime data classified
as crime incidents. Report of peeping tom in itself is not a crime and it is only a
police report of suspicious behaviour due to probably, one’s sexual instinct. Yet, if
one is found performing a suspicious act in a place where he could not give a
reasonable explanation to his existence, he may be arrested for a loitering offence
according to the Crimes Ordinance. Another example is incidents of trespassing
which should not be counted as a crime, except in the case of trespassing with armed
weapons or explosives stipulated in the statutory law of Hong Kong. Trespassing is
merely a civic action against violation of intruding one’s property under the
regulations or by-laws. However, it might give rise to the suspicions of other
potential criminal activities, such as loitering. Therefore, peeping tom and
trespassing are also included in the data set of this study to reveal fully the overall
crime situation of the campus.
58
3.3.3.4 Temporal data conversion
Monitoring temporal change of crime would always yield a good prediction
of time of offences because of the offenders’ temporal behaviour but it depends
largely on the data accuracy and data availability. In order to able to allow the
existing data to be measurable and comparable with other temporal variables,
particularly the hours variable, all the available data was transformed in a spread
sheet of an Excel format by means of manual input and converted into an expanded
data table, including fields of date, day in week, length of crime, automatically.
3.3.3.5 Geocoding
Geocoding or address matching is the process of transforming tabular and
geographic data, some non-spatial attributes or addresses in this study, to
georeferencing spatial data. The process involves matching the addresses against a
geographically referenced base map constructed from address data in the Centamap
(http://www.centamap.com/). Since nearly all the crime incidents, except one street
crime snatching, took place inside buildings and the geographic variables kept in the
records did not indicate the exact whereabouts the crime occurred, the centre point
of a building block inevitably becomes the georeference point or the geocode of a
crime location. Regarding the street crimes, the geocodes were manually converted
direct from the referenced base map. This is the analysis unit to be used in the
research.
The geocodes collected were stored with the crime data in a tabular Excel
format and subsequently plotted in ArcGIS against the topology of the base map
59
obtained from the Lands Department. All the crime locations were charted
accordingly.
3.3.3.6 Data re-classification
Data classification is deemed necessary to uphold the data integrity
requirements and to explore the extent of parameters and the value of crime
information for future data collection. Finally, data cleaning was initiated to
eliminate the manual input errors after which data classification was completed.
Figure 3.7 depicts the process of data classification.
Figure 3.7: Re-classification process of crime event data
60
3.3.4 Data Analysis
3.3.4.1 Visual inspection
Boba (2000) stated clearly that there are six general areas to be included in
making a crime analysis and mapping needs assessment: (1) current state of affairs;
(2) strategic goals; (3) data sources; (4) technology; (5) current crime analysis and
mapping products and their uses; and (6) crime analysis and mapping needs. In the
area of crime analysis and mapping needs, she argues that information presented in a
map are subjective information but more important is how this information is
perceived by the audience.
In data analysis, as well as in crime analysis, one needs to apply visual
thinking to generate ideas and make a hypothesis of a problem (Harries 1999). This
visual identification allows map users to see some relationships between different
phenomena. Map production, therefore, depends not only on the availability of data
and the objective goal but more importantly the expectations of the readers. Since,
campus administration and front line personnel are the targeted readers of crime
maps in this research, crime maps to be produced have to gauge their level of
thinking.
To ease the understanding of map users, single layer hot spots identification
is used in this study that is similar to a digital pin map displaying points of crime
incident and forming an overall picture of spatial distribution of crime over a
specific period of time. Graduated size point which serves to distinguish the
locations with multiple occurrences will be applied and since most of the crime
61
locations are represented by building blocks it also overcomes the problem of
overlapping.
3.3.4.2 Hot spots identification
In crime analysis, hot spots are always referred to as clusters of crimes. Even
though there is no fixed definition for hot spot, a common interpretation recognised
by most is that hot spot is a small place where crime occurred repeatedly, such that it
becomes highly predictable over a period of one year at least (Sherman 1995). Some
criminologists and analysts generally define hot spot as a somewhat larger area in
size that even the extended surroundings of a building are included (Vann and
Garson 2003). These two definitions will be employed in this research, especially
the latter because of the limitation of geographic variables obtained from the original
data set, where a large number of points are geocoded from the centre points of
buildings.
There are other arguments in formulating the definition of a hot spot by
focusing on the issues of public space and private property, nature of boundaries and
some variants in calculation of a linear block (Buerger, Cohn and Petrosino 1995).
These quickly become confusing as the location of a crime can be measured to the
accuracy of which side of a street, including “street cartilage” – an American term to
describe a large open area in front of the property lines where police can park their
patrol car. Obviously, there are geographical differences in the design and layout of
urban land use between the United States and Hong Kong and high precision of
calculating hot spots is not required in this research. Besides, the characteristics of
the campus of the University of Hong Kong can be generalised as a single
62
ownership campus and a discrete campus with less influence from its surroundings.
Boundary problems and demarcation of public space and private properties do not
exist in this research.
In order to determine whether there is a hot spot or no hot spot, the crime
data has to satisfy that crimes occurred frequently in an area at least once during a
one year period. Initial review of the data set apparently indicated that there are
clusters of crime incidents more contagious than other areas in a long span of time.
In short, hot spots are detected where the density of crimes is high. The crime data
gathered for this study fulfils this basic requirement for crime analysis.
Eck, Gersh and Taylor (2000) suggest that if any one of the following
hypotheses is established, mapping hot spots will be made possible: (1) a location
where crimes are spilling into surrounding areas – central place hypothesis; (2) a
location which appears to have a weak attraction to offenders but there are
interesting crime target in the nearby places – side effect hypothesis; and (3) some
locations (hot spots) are disproportionately vulnerable than other locations (non-hot
spots) but not in crime-resistant areas (area effect). The first and last hypotheses
seem to fit into the spatial distribution of crime in this study and these reasons
suffice to sustain the adoption of hot spot analysis.
3.3.4.3 Hot spots analysis techniques
Identifying a crime hot spot has an element of subjectivity resulting from
individual’s interpretation of a crime hot spot and the analysis techniques applied
accordingly may produce different results (Harries 1999, Vann and Garson 2003).
63
In general, there are five hot spot analysis techniques to help visualise spatial
distribution and they are: (1) visual interpretation which is the most basic method of
presenting crime patterns similar to that of a digitised pin map but the major
problems of it are stacking points and over-lapping points; (2) choropleth mapping
which is very much like a thematic map in which areas are shaded according to the
data values and the fallacy of it may be resulted from generalisation within a
boundary; (3) grid cell analysis, which is based upon artificial boundaries, uses a
uniform-sized grid cell to overlay an area of interest, yet error may occur when a
point falls closely within a neighbouring grid forcing it to give an aggregated value
in the neighbouring cell; (4) point pattern or cluster analysis involves identifying an
arbitrary starting point or a “seed” and thereafter calculating the following seeds
statistically in order to identify the clusters. A statistical algorithm is required in this
employment; (5) spatial autocorrelation which is designed to establish spatial
relationships among clusters of points based on spatial similarities and traits among
the points of interest, yet there is a chance of generating a negative autocorrelation
where no cluster may be located (Harries 1999, Vann and Garson 2003). Although
there are a variety of techniques for detecting hot spots in crime data, there is no
single approach superior to the others (Grubesic and Murray 2001). The argument of
adopting the appropriate visualisation technique really depends on the targeted
audience.
As mentioned above the data provided in this research has its own limitation
and taking into consideration that the primary objective is to produce a single layer
hot spot analysis and that the analysis is to be explicable to the management and
frontline security personnel, visual interpretation technique is chosen to perform the
64
analysis because of its simplicity in visual presentation. The stacking point and
over-lapping point problems resulting from this technique can be overcome by
increasing the scale of a map. That is, the use of smaller scale map will prevent the
occurrence of these problems.
3.4 Summary
The methodological framework of this research was discussed in full detail
in this chapter. Though most of the theoretical frameworks are designed for a
different setting they are purported to have a more universal applicability. One
commonality they all share is that a well-structured data management and data
analysis framework is essential.
The operation of data management process was explained by maintaining a
high degree of data integrity and data quality control as well as data validity and
data reliability. The importance of it cannot be emphasised any higher. The logical
design of analytical workflow was also outlined by examining each and every
consideration required for making data visualisation possible. These are determining
factors in the success of the following spatial and temporal analysis.
The methodological framework selected in this study serves as the springboard
to research into the crime situation within a University of Hong Kong sample. The
hot spot identification and visual interpretation techniques employed proved to be
able to present crime data accurately and meaningfully.
65
CHAPTER FOUR
CAMPUS CRIME ANALYSIS
4.1 Introduction
From reading the literature review in chapter two, it seems no one would
argue that the distribution of crime incidents is spatially random. Pioneer
environmental criminologists have concluded that the types of built environments
have a direct link with many types of crimes. These are the spatial crime patterns
expecting that certain types of crimes will follow.
Section 4.2 will present the overall crime situation of the campus from the
year 2002 to 2004. The spatial and temporal analysis in section 4.3 and section 4.4
will aim at examining the available data to uncover the spatiotemporal relationship
between the crime problems. Serial crimes are detectable through a “signature”, that
is unique behaviours on the part of the offender, and the commonalities they share,
such as timing, location, victim similarities, and modus operandi will be discussed.
Regarding the overall crime distributions, this study will focus on the severe hit
areas and the prevailing crimes that may affect the law and order of the campus.
Finally, section 4.5 will make a comparison between the crime situation of the
campus and that of the universities and colleges in the United States.
4.2 Campus Crime - the University of Hong Kong
Overall, the total number of crimes which occurred on the campus of the
66
University of Hong Kong between the year 2002 and 2004 was not considered
serious at all. The statistics at Table 4.1 shows that there were altogether 225 crime
incidents reported within fourteen categories of crimes, and the total numbers of
crime cases reported in these three years were 78, 64 and 83 respectively.
Criminal Offence 2002 % 2003 % 2004 % Total %
Burglary 0 0.00% 4 6.25% 4 4.82% 8 3.56%
Criminal Damage 2 2.56% 1 1.56% 1 1.20% 4 1.78%
Criminal Intimidation 0 0.00% 0 0.00% 1 1.20% 1 0.44%
Deception 0 0.00% 0 0.00% 1 1.20% 1 0.44%
Indecent Exposure 0 0.00% 0 0.00% 1 1.20% 1 0.44%
Loitering 0 0.00% 1 1.56% 1 1.20% 2 0.89%
Object Fell From Height 0 0.00% 0 0.00% 1 1.20% 1 0.44%
Peeping Tom # 0 0.00% 0 0.00% 7 8.43% 7 3.11%
Robbery 0 0.00% 1 1.56% 0 0.00% 1 0.44%
Snatching 0 0.00% 0 0.00% 1 1.20% 1 0.44%
TCWA 0 0.00% 2 3.13% 0 0.00% 2 0.89%
Theft 73 93.59% 55 85.94% 63 75.90% 191 84.89%
Theft From Vehicle 3 3.85% 0 0.00% 1 1.20% 4 1.78%
Trespassing # 0 0.00% 0 0.00% 1 1.20% 1 0.44%
Total 78 100.00% 64 100.00% 83 100.00% 225 100.00%
Table 4.1: Crime statistics of HKU between 2002 and 2004 Source: Estates Office (Security Section) of the HKU in January 2005. # Peeping tom and trespassing are not crimes under the statutory law.
Further observations from Table 4.1 indicate that more than 90% of the total
number of crimes was property crime, including burglary, taking conveyance
without authority (TCWA), theft and theft from vehicle. Of concern are the number
of peeping tom activities reported, which rose from 0 in the years 2002 and 2003 to
7 cases in the year 2004, which must surely catch the attention of the management.
The number of burglaries remained steady with 4 in each year from 2003 to 2004.
When examining the bar chart at Figure 4.1 it can be seen that loitering and related
67
activity, such as peeping tom, and burglary figures have become alarming. For these
reasons, this study will focus on the following crimes: theft, loitering and peeping
tom and burglary, which pose a greater threat to the law and order in the campus
environment. Targeting offenders of these prevailing crimes will become the primary
objective in formulating an effective crime control and crime reduction initiative.
Statistics of Crime (2002 - 2004)
1
10
100
1000
Burglar
y
Crimina
l Dam
age
Crimina
l Intim
idatio
n
Decepti
on
Indece
nt Exp
osure
Loiteri
ng
Object
Fell From
Heig
ht
Peeping
Tom
Robbe
ry
Snatch
ing
TCWA
Theft
Theft F
rom V
ehicl
e
Trepass
er
Offences
No.
of C
rimes
200420032002
Figure 4.1: Crime Statistics of HKU between 2002 and 2004 Source: Estates Office (Security Section) of the University of Hong Kong in January 2005.
Tables 4.2 and 4.3 also suggest that the overall crime situation and violent
crime rate of HKU are quite low when compared with the figures for the whole of
Hong Kong between the years of 2002 and 2004.
Year Hong Kong
Population
Total no. of
Crime
Overall Crime
Rate *
HKU Student
Population
Total no. of
Crime
Overall Crime
Rate *
2002 6,787,000 75,877 111.80 19,000 78 41.05
68
2003 6,803,000 88,377 129.91 19,562 64 32.72
2004 6,845,000 81,315 118.79 19,562 83 42.43
Table 4.2: Comparison of Overall Crime Rate between HK and HKU between 2002 and 2004 Sources of Information: Hong Kong Police (http://info.gov.hk/police) and Census and Statistics Department (http://info.gov.hk/censtatd) * represents crime rate per 10,000 population
Year Hong Kong
Population
Total no. of
Violent Crime
Overall Crime
Rate *
HKU Student
Population
Total no. of
Crime
Overall Crime
Rate *
2002 6,787,000 14,140 20.83 19,000 0 0
2003 6,803,000 14,542 21.38 19,562 5 # 2.56
2004 6,845,000 13,890 20.29 19,562 4 # 2.04
Table 4.3: Comparison of Overall Violent Crime Rate between HK and HKU between 2002 and 2004 Sources of Information: Hong Kong Police (http://info.gov.hk/police) and Census and Statistics Department (http://info.gov.hk/censtatd) * represents crime rate per 10,000 population # burglary and robbery are the only violent crimes took place in HKU
On average, it takes nearly five days for one crime to occur on the campus as
opposed to two hundred and twenty four crimes reported in Hong Kong each day. It
collaborates with the early presumption that the campus of the University of Hong
Kong University is a peaceful and harmonious institution.
In the following spatial and temporal analysis, it will be examining the crime
data from the approach of tactical crime analysis to identify crime patterns by means
of visual identification as well as from the approach of strategic crime analysis to
evaluate the effectiveness of crime intervention initiatives. Alongside this, it will be
aided with elementary statistical analysis to confirm its validity.
4.3 Spatial Analysis
69
Spatial analysis is the study of location. Spatial distribution of crime events
helps crime analysts to understand what has happened and what may happen in the
future. Computerised mapping speeds up the process of spatial analysis in locating
crime clusters of an area and identification of hot spots becomes visible.
Before going into the analysis stage, it is always good to review the spatial
distribution of crimes from the statistical data. Table 4.4 portrays the geographical
distribution of crimes in the campus. Apparently, the Main Campus was heavily
attacked and suffered slightly over 80% of the total number of crimes from the year
2002 to 2004. Following that was the Southern Region with 14% and thereafter the
Western Region with 5%. Interestingly, there was a drop of crime in the Main
Campus in the year 2003 and no crime was reported in the year 2002. Cartographic
distribution of crimes at Figure 4.2 also signifies the changes of crime locations
between the year 2002 and 2004.
Study Area 2002 2003 2004 Total Percentage
Main Campus 68 47 66 181 80.44%
Western Region 0 7 5 12 5.33%
Southern Region 10 10 12 32 14.22%
Total 78 64 83 225 100.00%
Table 4.4: Geographical distribution of crimes of HKU between the year 2002 and 2004
70
Year 2002 Year 2003 Year 2004 Figure 4.2: Spatial distribution of crimes of HKU between 2002 and 2004
The following crime statistics in Table 4.5 provide a better presentation of
crime distribution in the Main Campus, Western Region, and Southern Region
within the campus between the years 2002 and 2004. Most of targeted offences, theft,
loitering and peeping tom and burglary, were committed in the Main Campus which
is the most congregated region amongst the three study areas.
Criminal 2002 2003 2004 Total
Offences M W S M W S M W S
Burglary - - - 3 - 1 3 - 1 8
Criminal Damage 1 - 1 - - 1 1 - 4
Criminal Intimidation - - - - - - 1 - - 1
Deception - - - - - - 1 - - 1
Indecent Exposure - - - - - - 1 - - 1
Loitering - - - 1 - - 1 - - 2
Object Fell From H - - - - - - 1 - - 1
Peeping Tom - - - - - - 6 - 1 7
Robbery - - - 1 - - - - - 1
Snatching - - - - - - 1 - - 1
TCWA - - - - - 1 - - - 1
Theft 66 - 7 42 7 6 48 5 10 191
Theft From Vehicle 1 - 2 - - 1 1 - 5
71
Trespassing - - - - - - 1 - - 1
Total 68 0 10 47 7 10 66 5 12 225
Table 4.5: Crime statistics of Main Campus (M), Western Region (W), and Southern Region (S) of HKU between 2002 and 2004
One of the characteristics of crime in the University of Hong Kong is that
over 90% of crime was property crime. Property crime usually has a direct
relationship with the types of victims that the offenders normally profiled. The
statistics of victimisation briefly gives a hint as to who was usually being targeted.
In the case of theft of notebook computers, nearly half of them belonged to
academic departments and the thefts took place in administration blocks, offices and
classrooms. Thirty seven notebook computers belonged to the students of which
only a small portion of victims was female. Perhaps, the staff were more cautious
about their personal notebook computer and their victimisation rate was the lowest.
The situation seems slightly different in the case of theft of wallet and cash, where
some 60% of the victims were female students and nearly 83% of the victims were
students. In the case of burglary, 5 out of 8 cases were involved in a loss of 28
computers and another 3 cases in a loss of 5 projectors, and 7 out of 8 victims were
academic departments. In the case of loitering and peeping tom, all victims were
noticeably female students. Tables 4.6 and 4.7 present the victimisation rates of the
former two situations.
Office / Gender of Victim
of Theft
No. of
Victim
Percentage of
Victim
No. of stolen
computer
Percentage of stolen
computer
Office 12 21.82% 41 48.81%
Female student 5 9.09% 5 5.95%
Male student 11 20.00% 11 13.10%
72
Student (gender unknown) 21 38.18% 21 25.00%
Female staff 1 1.82% 1 1.19%
Male staff 3 5.45% 3 3.57%
Staff (gender unknown) 2 3.64% 2 2.38%
Total 55 100.00% 84 100.00%
Table 4.6: Victimisation rate of theft of notebook computer between 2002 and 2004
Victim of Theft of Wallet & Cash Total Percentage
Female student 53 61.63%
Male student 4 4.65%
Student (gender unknown) 14 16.28%
Female staff 5 5.81%
Male staff 0 0.00%
Staff (gender unknown) 10 11.63%
Total 86 100.00%
Table 4.7: Victimisation rate of theft of wallet and cash between 2002 and 2004
4.3.1 Spatial Analysis - Theft
The statistics at Table 4.8 show the locations which suffered severely from
theft between the year 2002 and 2004. The library area was severely hit by thieves
with 60 theft cases and ranks on the top of the list. Following that the Knowles
Building was attacked 24 times while K.K. Leung Building ranks the third with 9
reports. The statistics also table the top ten hit locations of theft.
Location of Theft No. of Crime No. of Theft Percentage
Main Library 36 36 100.00%
Knowles Building 24 17 70.83%
Old Library 17 17 100.00%
K.K. Leung Building 9 7 77.78%
Composite Building 7 3 42.86%
Haking Wong Building 7 6 85.71%
73
Library Extension 7 5 71.43%
Swire Hall 7 4 57.14%
Faculty of Medicine Building 6 6 100.00%
Chow Yei Ching Building 6 6 100.00%
Simon K.Y. Lee Hall 6 6 100.00%
Total 132 113 85.61%
Table 4.8: Prominent locations of theft between 2002 and 2004
Theft is a prevailing crime and a property crime. Understanding the
properties of the offenders targeted helps unearth the relationship of its spatial and
temporal characteristics. The most popular items being stolen were: (1) notebook
computer because of its mobility (83 were stolen); (2) wallet and cash which
involved 46 cases because of easy disposal; (3) projectors because of its high re-sell
value (33 were stolen which added up to nearly a million Hong Kong dollars); (4)
mobile phone because of its trendy attraction (15 were stolen); (5) camera because
of its compactness (13 were stolen of which 11 were digital cameras).
Hence, studying statistics of stolen properties helps to identify the black
spots. The prominent black spots of theft of computers at Table 4.9 show that the
Knowles Building and Chong Yuet Ming Complex were primarily targeted.
Location of Theft of Computer No. of Crime No. of Stolen Computer
Knowles Building 11 12
Chong Yuet Ming Physics Building 4 4
Chong Yuet Ming Amenities Centre 2 2
Chong Yuet Ming Chemistry Building 3 3
Main Library and Library Extension 4 4
St. John's College 4 4
James Lee Hall 3 3
Table 4.9 Prominent locations of theft of notebook computer between 2002 and 2004
74
The prominent black spots of theft of wallet and cash at Table 4.10 indicate
that in the library area, the classrooms of the Faculty of Medicine Building and the
dormitory at the Starr Hall a total of 53 students were victimised.
Location of Theft of Wallet & Cash No. of Crime Victim Gender
New Library 28 25 female students and 3 male students
Old Library 15 all female students
Library Extension 2 all female students
Faculty of Medicine Building 4 all female students
Starr Hall 4 all student, gender unknown
Table 4.10 Prominent locations of theft of wallet and cash between 2002 and 2004
In the total of 191 cases of theft, 88 cases, representing 46% of the total
thefts, were related to theft of wallet and cash. 45 of which took place in the library
area, especially in the year of 2002, and a few at the Faculty of Medicine Building
and the Starr Hall, classroom and dormitory were also targeted.
Statistics at Table 4.11 indicate that the well-known black spots of theft of
projectors were at the Knowles Building and the Ming Wah Complex and a total of
11 projectors were stolen. Between the year 2002 and 2004, there were 33 projectors
stolen. 5 cases reported in the year 2002 with a loss of 6 projectors, 13 cases in the
year after with 20 projectors and finally 5 cases in 2002 with 7 projectors being
stolen. The upsurge of this trend, particularly in the year 2003, was a result of the
legalising soccer gambling when the demand became high.
75
Location of Theft of Projector No. of Crime No. of Stolen Projector
Knowles Building 5 7
Ming Wah Complex 3 4
Total 8 11
Table 4.11 Prominent locations of theft of projector between 2002 and 2004
When compared with the statistics at Table 4.12 which shows computer
projection facilities in the buildings of the Main Campus, it tends to suggest the
block with more classrooms attracts more thefts (theft of computers and projectors
in particular) from the blocks with less classrooms. However, the Main Building,
which is one of the oldest buildings in the Main Campus and ranks the third in the
number of most classrooms blocks, was rarely attacked with only 3 theft cases
reported and they all happened in the year of 2004. When examining the security
devices installed in the building, such as surveillance camera and electronic door
locks, they are comparatively weaker than other targeted blocks. One of the
premises drawn from this observation is that the top 4 buildings with most
classrooms, except the Main Building, are situated in the centre of the Main Campus
or in the radius of communal area, such as cafeteria and student associations. The
centrality of its geographic location with accesses to almost all the blocks in the
campus allows students to muster and socialise, and it becomes a common assembly
point. This provides potential offender an excellent setting to conceal himself or
even make good of his escape in case something went wrong. The bus stops and
mini-bus stops along Boham Road which are within close proximity of the Knowles
Building and K.K. Leung Building offer good escape route to culprits. In contrast,
the Main Building does not provide offenders with many of these advantages.
76
Building No. Classroom /
Theatre
Seating Capacity No. of Computer
Projection
Meng Wah Complex 16 classrooms 1,529 16
K.K. Leung Building 12 classrooms 755 12
Main Building 11 classrooms 953 11
Library Extension 9 classrooms 1,227 9
Knowles Building 5 classrooms 793 5
Chong Yuet Ching Building 4 classrooms 720 4
James Lee Building 4 classrooms 269 4
Run Run Shaw Building 4 classrooms 186 4
Chow Yei Ching Building 3 theatres 395 3
T.T Tsui Building. 3 classrooms 330 3
Eliot Hall 2 classrooms 82 2
Rayson Huang Theatre Shaw Bldg 1 theatre 298 1
Table 4.12: Classroom facilities in the Main Campus buildings Source: HKU Examination Unit (http://www.hku.hk/exam)
In the year 2002, over 93% of the total crimes were theft of which 66 cases
occurred in the Main Campus and 7 in the Southern Region. Of the 66 cases of theft
which cropped up in the Main Campus, the commonly attacked areas were the Main
Library and the Old Library with 47 reports. Of the 7 theft reports made in the
Southern Region, four took place in the Faculty of Medicine Building and three
were in the nearby buildings. Figure 4.3 shows that the hot spots of theft in the Main
Campus are centred around the library blocks. Seemingly, the library is one of the
most popular spots with the university for staff and students, and targeting victims
for petty cash and personal belongings at the library always yields good rewards for
the criminals.
77
Figure 4.3: Hot spots of theft in the Main Campus in 2002
In the year 2003, theft dropped to 55 cases from 77 cases in the preceding
year. The Main Campus was still the targeted area with 42 thefts reported.
Meanwhile, the Western Region was also attacked with 7 cases and Southern Region
remained very much similar to last year with 6 cases. Surprisingly, only one report
of theft took place in the Main Library and another two in the Library Extension.
This was as a result of a thief being arrested red-handed in the Main Library in
October 2002 as well as the strengthening of consciousness of crime prevention and
the tactical option employed by the Security Section. There were signs and notices
affixed to the studying desks warning the library users to be cautious about their
belongings. At the same time, plainclothes guards were deployed to identify any
suspicious or potential offenders mingling in the vicinity. Figure 4.4 portrays the hot
spots of theft in the Main Campus and the Western Region.
78
Equally astonishingly, 13 cases of theft were recorded in the Knowles
Building and 5 cases in the Chong Yuet Ming Physics and Chemistry Building
blocks. It seems the trend of theft has shifted away from the library area eastward
and westward due to displacement factor and a small number of thefts started to
spread around from the centre of the library to all the classroom blocks.
Dormitory theft became one of the characteristics of crime in the year 2003.
All 7 reports of theft which occurred in the Western Region were located in the
dormitories with 5 reports at the St. John’s College. 6 reports of theft took place in
the Southern Region where 3 were found in the Wei Lun Hall and one in the Lee
Hysan Hall. In the case of Wei Lun Hall, 2 thefts were reported in the neighbouring
flats.
Figure 4.4: Hot spots of theft in the Main Campus and the Western Region in 2003
79
In the year 2004, theft experienced a slightly upward trend and increased
from 55 in the previous year to 63. 48 reports of theft were made in the Main
Campus, 5 in the Western Region and 10 in the Southern Region. Figure 4.5 shows
the hot spots of theft of theft in the Main Campus and the Western Region.
The spatial distribution of theft of the year 2004 has changed in the Main
Campus. It has expanded from congregating around the Knowles Building and the
Chong Yuet Ming Physics and Chemistry Building blocks northward to the Swire
Hall and westward to the Simon K.Y. Lee Hall. A two-fold increase of theft with 7
reports was made in the Main Library and the Library Extension but there was a
reduction by nearly a half with 6 reports in the Knowles Building. Dormitories were
also heavily attacked with 4 cases of theft occurring in the Swire Hall where one of
the thefts took place in the amenities centre and 4 in the Simon K.Y. Lee Hall.
A similar situation happened in the Western Region where 4 out of 5 theft
reports were made in the Starr Hall shifted northward from the St. John’s College,
which was the targeted dormitory block in the previous year. The situation in the
Southern Region remained not much different from last year except that 4 out of 10
theft reports were made in the R.C. Lee Hall moved southward from the Wei Lun
Hall, which was the badly hit dormitory block in the preceding year.
80
Figure 4.5: Hot spots of theft in the Main Campus and the Western Region in 2004
4.3.2 Spatial Analysis – Loitering and Peeping Tom
Of deep concerned was that number of loitering and related peeping tom and
indecent exposure complaints rose to 10 cases between the latter part of the year of
2003 and the end of the year 2004 which appeared outrageous. There was no history
of complaints of this nature made to the Security Section before that and the upsurge
of this activity requires further attention.
Spatially, both loitering offences, the 7 reports of peeping tom and one report
of indecent exposure in the female toilets all occurred in the Main Campus except
for one in the Southern Region. One loitering and one peeping tom case were
located in the Library Extension, one peeping tom and one indecent exposure took
81
place in the same disabled toilet in the Fong Shu Chuen Amenities Building, two
peeping tom cases were reported in the same toilet complex in the K.K. Leung
Building, one loitering case on the upper floor and one peeping tom case on the
lower floor were located in the Knowles Building, one peeping tom case occurred in
the Hui Oi Chow Building; and last peeping tom case occurred in the dormitory of
the Madam S.H. Ho Residence in the Southern Region.
In connection with this peculiar activity, a case of theft was reported in July
2004 where some woman’s clothing were stolen from a laundry room at the R.C.
Lee Hall in the small hours. Yet, there is no evidence to link this case with peeping
tom activity.
In one of the loitering cases a suspect was found acting suspiciously in a
female toilet by a female student and later charged with the offence but he was
acquitted in court. In the rest of the cases, no one was arrested. There were three
cases reported in connection with weird behaviour in the use of camera. Security
Section has been actively investigating this unusual behaviour and has advised the
management to heighten the cubicle partition in order to avoid any re-occurrence of
“sneak a look” activity in the female toilet around the black spots as a preventive
measure.
All the spatial evidence suggests that the female toilets in the communal
blocks and classroom blocks, especially at the lower floors where 8 out of 10 cases
were reported, were seriously targeted. It is easy to understand that the lower floors
always foster a better chance of escape upon discovery. As such, it is strongly
believed that the suspect has a good local knowledge of the area and probably even
82
resides not far away from the scenes as a hideaway. Figure 4.6 plots out the hot spots
of this activity.
Figure 4.6: Hot spots of loitering and suspected peeping tom activities in the Main Campus in 2004
4.3.3 Spatial Analysis – Burglary
Between the year 2003 and 2004 there were 8 cases of burglary reported
and 4 in each year of which 3 were in the Main Campus and one in the Southern
region. The spatial pattern appeared to be the same in these two study areas. In
general, two locations were burgled twice where the Run Run Shaw Building and
the New Clinical Building was hit once in each year and another six cases were
single incident.
Figure 4.7 shows an even amount of spatial distribution of burglary in the
83
Main Campus. Due to small number of burglary reports and light density of its
distribution, no hot spot can be identified. Nonetheless, one significant observation
can be generalised that the burglars tended to target the administration blocks or
office blocks.
Figure 4.7: Spatial distribution of burglary in the Main Campus between 2003 and 2004
4.4 Temporal Analysis
Temporal analysis is the study of pattern changes in time. Temporal
distribution of crime events helps crime analysts to understand what has happened
and what will happen in the future. Measuring time can be done in many ways, such
as annually, seasonally, monthly, weekly, daily and even hourly. When considering
the measurement interval, one has take into account the characteristics of an area,
clusters and serial occurrence of crime events. The University of Hong Kong is a
tertiary institution where activities of staff and students are worked on regular
84
monthly, weekly and daily schedules. This is the framework of measurement interval
to be applied in temporal analysis.
As stated in Section 4.2 for examining crime distribution this study only
aims at studying temporal changes of the three specific crimes affecting the law and
order of the campus which are theft, loitering and peeping tom, and burglary. If other
crimes are prevailing, the application of temporal analysis will be equally
applicable.
4.4.1 Temporal Identification – Changes in Months
The comparative frequency of crime per month at Figure 4.8 demonstrates
that the number of crime events in the year 2002 were highest in the months of
February, May, September and October with an average of 9 or more crimes.
Summer vacation seems to provide a favourable buffer period when crime dropped
dramatically.
The overall crime situation in the year 2003 started to decline as the total
number of crimes decreased 19% from 78 in the year 2002 down to 64, and the peak
months were shifted to March, May, July, September and December with a month
interval in between. The upsurge of nine cases of crime in July seems unusual where
2 burglaries and 3 thefts were purposely targeted at the administration and office
blocks. One of the possible explanations for this escalation of crime is believed to be
that a good number of offices were either left unoccupied or closed during summer
holidays turning them into a more vulnerable situation thus attracting the attention of
opportunity offenders.
85
In the year 2004, the trend of crime was changed totally in the second half of
the year that the average crime rate rose to slightly more than 8 cases each month. It
may be conclusive to say that the rise was resulted from the emergence of all crimes,
particularly peeping tom, indecent exposure, snatching and other minor offences.
March was the worst month in which 10 cases of theft and 2 cases of burglary took
place.
Frequency of Crime by Month (2002 - 2004)
02468
10121416
Jan Feb Mar Apri May Jun Jul Aug Sept Oct Nov Dec
No.
of C
rime
200220032004
Figure 4.8: Comparison of frequency of crime by month between 2002 and 2004
The following accumulative bar chart at Figure 4.9 depicts the monthly
distribution of crimes in the campus over the duration of the research period. It can
be concluded that February and March, and September and October are the peak
periods of criminal activities. It is highly suggested that these high crime seasons
always come around after the winter and summer holidays.
86
Frequency of Crime by Month (2002 - 2004)
05
1015
2025
3035
Jan Feb Mar Apri May Jun Jul Aug Sept Oct Nov Dec
No.
of C
rime
200420032002
Figure 4.9: Accumulative frequency of crime by month between 2002 and 2004
4.4.2 Temporal Identification - Changes in Weekdays
Figure 4.10 displays the changes of temporal pattern of campus crime in
weekdays and shows that Monday, Tuesday, Thursday and Friday are more
vulnerable than the other days in the rest of the week. This is probably having a
direct link with the school days as many students have no classes in the low risk
days. This is quite true until the year 2004 when almost every day was targeted and
there is no apparent trend identified.
87
Frequency of Crime by Weekday (2002 - 2004)
02468
1012141618
Mon Tue Wed Thur Fri Sat Sun
No.
of C
rime
200220032004
Figure 4.10: Frequency of crime by weekday between 2002 and 2004
4.4.3 Temporal Identification - Changes in Hours
The deficiency of some missing temporal records found in the data
classification stage certainly has an adverse effect in presenting the findings of
temporal analysis.
Table 4.13 shows that the extent of missing temporal data of hours is quite
scattered around in the three study areas. 42 crime events in the Main Campus where
time was not recorded represents about 23% of the total number of crime data in this
study. It becomes worse in the Western Region and Southern Region where one third
and nearly half of the respective records were absent. Therefore, the temporal study
will focus on the Main Campus. In order to not to cause any confusion with the
missing hours records in this section, those records containing hours information
will be referred as known records whereas those which do not will be the unknown
records.
88
Main Campus 2002 2003 2004 Total
Unknown 6 15 21 42
Known 62 32 45 139
Total 68 47 66 181
Unknown % 8.82% 31.91% 31.82% 23.20%
Western Region 2002 2003 2004 Total
Unknown 0 3 1 4
Known 0 4 4 8
Total 0 7 5 12
Unknown % 0.00% 42.86% 20.00% 33.33%
Southern Region 2002 2003 2004 Total
Unknown 6 4 5 15
Known 4 6 7 17
Total 10 10 12 32
Unknown % 60.00% 40.00% 41.67% 46.88%
Table 4.13: Statistics of known and unknown crime records between 2002 and 2004
The incompleteness of temporal data records in this research has been
explained in Chapter three and how it affects the measurement on the frequency of
crime committed in a 24-hour period. As mentioned previously, the accuracy of
hours assessment in this study is about 73%.
Table 4.14 outlines the temporal distribution of the known records of crimes.
This study will assess the temporal distribution of crime in three sessions. The
tuition hours of the University of Hong Kong start from eight o’clock (0800hrs) in
the morning to six o’clock (1800hrs) in the evening. The time before and after the
school hours will be sub-divided into two time segments using the mid-night line
(2400hrs). Between six o’clock (1800hrs) in the evening and before mid-night is the
89
evening session; and from mid-night to eight o’clock (0800hrs) in the morning is the
morning session. The delineation of three separate periods will explain the temporal
distribution of crimes as some crimes occur at a specific time.
Time 2002 2003 2004 Total Percentage
0:00 0 0 1 1 0.61%
1:00 1 0 0 1 0.61%
2:00 0 2 0 2 1.22%
3:00 0 0 1 1 0.61%
4:00 0 0 1 1 0.61%
5:00 0 0 2 2 1.22%
6:00 0 0 1 1 0.61%
7:00 0 0 1 1 0.61%
8:00 0 3 1 4 2.44%
9:00 3 0 1 4 2.44%
10:00 5 3 2 10 6.10%
11:00 3 1 1 5 3.05%
12:00 6 2 2 10 6.10%
13:00 5 5 4 14 8.54%
14:00 8 2 7 17 10.37%
15:00 8 4 3 15 9.15%
16:00 5 3 7 15 9.15%
17:00 7 6 2 15 9.15%
18:00 8 4 5 17 10.37%
19:00 2 5 4 11 6.71%
20:00 3 0 4 7 4.27%
21:00 2 0 3 5 3.05%
22:00 0 0 0 0 0.00%
23:00 0 2 3 5 3.05%
Known 66 42 56 164 100.00%
Unknown 12 22 27 61 ---
Total 78 64 83 225 ---
Table 4.14: Statistics of known crime records by hours between 2002 and 2004
Again from Table 4.14, it can be seen that between the year 2002 and 2004,
90
the occurrence of crime in the morning session took up slightly over 6% of the total
of 104 crime cases. Nearly 77% of the total known cases occurred in the school
session while another 27% took place in the evening session. Figure 4.8 shows the
variation of time of the campus crime.
Frequency of Crime by Hours (2002 - 2004)
0
2
4
6
8
10
0:00
2:00
4:00
6:00
8:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
Time Interval
No.
of C
rime
200220032004
Known cases:164
Unknowncases:6112 in 200222 in 200327 in 2004
Figure 4.11: Frequency of crime by hours between 2002 and 2004.
In the following cartographic presentations, the temporal changes of crime
between the year 2002 and 2004 are shown to be quite significant. Figure 4.12 and
Figure 4.13 indicate s that in the year 2002 the temporal pattern is centred on the
library area, especially during the class session. Figure 4.14 and Figure 4.15 reveal
the situation in the year 2003 was changed that the pattern extended eastward and
westward again from the library area with a much lower crime rate and that is
similar to its spatial distribution. Figure 4.16 and Figure 4.17 shows that in the year
2004 the pattern shift to northeast during the class session while the pattern in the
evening session remained much the same as that of the year 2003.
91
Figure 4.12: Temporal distribution of crime in the Main Campus between 0800 and 1800hrs in 2002
Figure 4.13: Temporal distribution of crime in the Main Campus between 1800 and 2400hrs in 2002
92
Figure 4.14: Temporal distribution of crime in the Main Campus between 0800 and 1800hrs in 2003
Figure 4.15: Temporal distribution of crime in the Main Campus between 1800 and 2400hrs in 2003
93
Figure 4.16: Temporal distribution of crime in the Main Campus between 0800 and 1800hrs in 2004
Figure 4.17: Temporal distribution of crime in the Main Campus between 1800 and 2400hrs in 2004
94
4.4.4 Temporal Analysis - Theft
Table 4.15 presents the temporal distribution of theft between the year 2002
and 2004. The upsurge of theft cases in the year 2002 was an exceptional
phenomenon. An observation of this trend between the year 2003 and 2004 indicates
that on average it is most active in February and March during the first half of the
year and same is true in September, November and December in the second half.
The reason is believed to be connected with the opening of school after the winter
and summer holidays when the students returned to school and the opportunity for
being targeted becomes higher. Yet, there is no pattern of weekday identified in this
analysis.
Year 2400-0800 hrs 0800-1800 hrs 1800-2400 hrs Total
2002 1 52 11 64
2003 1 27 7 35
2004 5 20 14 39
Total 7 99 32 138
Percentage 5.07% 71.74% 23.19% 100.00%
Table 4.15: Temporal distribution of theft between 2002 and 2004
Figure 4.15, however, suggests that theft varies in different time segments. It
seldom occurred in the morning session which only takes a small portion of 5% of
all the known records. In the year 2004 there were 5 cases reported in which 4 took
place in the dormitory where some petty cash, clothing and notebook computers
were stolen, and one in the computer laboratory.
Crime become active during the class session. In the year 2002, 24 cases of
95
theft took place in the library area falling into this time segment. The year after,
there were 3 each found in the Chong Yuet Ming Chemistry Building, Knowles
Building and the library area. In these 9 cases of theft, 5 took place in the office
where notebook computers were targeted and one in the registry where 3 projectors
worth HK$130,000 were found missing. Probably due to the frequent flow of staff
and student in these areas over this period of time, offices and classroom became
targeted. By the year 2004, no significant temporal pattern of theft of more than two
occurrences could be detected.
Stealing activity does not diminish in the evening course hours. The only
temporal pattern of theft was reported at the Knowles Building where 3 offices and
one workshop were attacked with 3 stolen notebook computers and some cash taken,
while the remaining one was an attempted theft of a notebook computer. In two of
these cases, a male was found acting suspiciously in the building and another male
was found to be at his age of early fifty’s. Again, it is believed that many staff and
students remained in their offices and classrooms after normal working hours or
studying hours, and their alertness was lowered so their negligence increased.
96
Temporal Pattern of Theft (2002 - 2004)
0
10
2030
40
50
60
2400-0800 hrs 0800-1800 hrs 1800-2400 hrs
Time Interval
No.
of C
rime
200220032004
Figure 4.18: Temporal variation of theft between 2002 and 2004
4.4.5 Temporal Analysis – Loitering and Peeping Tom
As mentioned before, loitering, peeping tom and indecent exposure did not
occur prior to the end of the year 2003. 4 reports of peeping tom were made between
May and July, and 2 in October in the year 2004. 3 occurrences were found on
Wednesdays.
Of the nine known cases as shown at Table 4.16, one report of peeping tom
was made at eleven o’clock (1100hrs) in the morning and 4 between half-past one
(1330hrs) and four o’clock (1600hrs) in the afternoon. During the class session,
female toilets probably have fewer visitors and therefore give the potential offenders
an opportunity. The sole indecent exposure also falls into this time frame. Another
two reports of peeing tom and one loitering took place between seven o’clock
(1900hrs) and eleven o’clock (2300hrs) in the evening and there was not conclusive
explanation to this phenomenon as the data sample is rather small.
97
Loitering 2400-0800 0800-1800 1800-2400 Total
2002 0 0 0 0
2003 0 0 0 0
2004 0 6 3 9
Total 0 6 3 9
Percentage 0.00% 66.67% 33.33% 100.00%
Table 4.16: Temporal distribution of loitering, peeping tom and indecent exposure between 2002 and 2004
4.4.6 Temporal Analysis – Burglary
Estimating the actual time of burglary is no easy task except with the
assistance of technology. As a result, therefore, the temporal distribution at Table
4.17 only shows the starting time when the victim had last seen the stolen properties.
Burglary reports are often received in the morning and it is usually a few hours
before the victim discovered the loss.
Burglary 2400-0800 0800-1800 1800-2400 Total
2002 0 0 0 0
2003 0 2 2 4
2004 1 2 0 3
Total 1 4 2 7
Percentage 14.29% 57.14% 28.57% 100.00%
Table 4.17: Temporal distribution of burglary between 2002 and 2004
There was no report of burglary made in the year 2002 but 4 were made in
each of the following two years. Of the 7 known records, there was one which was
responded to immediately after the offenders triggered the burglar alarm and another
one reacted to within 2 hours, while in the rest of the 5 cases the average time span
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for making a report was more than 15 hours. This explains the complexity in
measuring the exact time a burglary was committed. One significant observation is
revealed that in the 4 known cases of burglary in the year 2003, 2 were made in
March and July and in both cases they were just one day apart. That is not to suggest
they were connected but the frequency of burglary in these two months was high.
Further examination of the 7 burglary records shows that 6 burglaries took place
between Tuesday and Thursday and burglars tend to aim at the office blocks and
commit their act in the late evening or in the small hours.
In 4 burglary cases offenders smashed the window panel in order to gain the
entry and stole the notebook computer therein, and in two of these cases 12
computers were stolen each time. In another case the offender prised opened the
door to the theatre and two projectors worth HK$280,000 were stolen. Theft of a
single notebook computer might be committed by a student who could hide it away
easily and swiftly but the appropriation of 12 notebook computers and 2 theatre
projectors might not. It is highly suspected that these serious burglaries could only
be committed by professionals on a steal-to-order motive. They also required a
vehicle to move away these heavy load of stolen properties.
Security Section also noticed the seriousness of theft of notebook computers
and projectors and started to launch a series of crime prevention campaigns to
control the situation by the end of the year 2003. They carried out risk assessment to
the buildings at high risk of theft and burglary and advised the administration
personnel to install closed circuit television (CCTV) in the area likely to be exposed
to theft and burglary. They also revised their deployment strategy to task the patrols
in the high-risk area and tighten up the exit control of the campus. More and more
99
CCTV and electronic door locks were installed in the classrooms equipped with
expensive equipment. The crime statistics at Table 4.18 seem promising that in terms
of crime cases, theft of projector was down significantly from 13 cases in the year
2003 to 5 in the following year and there was no report made between March and
October in that year. Theft of notebook computer also witnessed a slight drop from
26 in the year 2003 to 20 in the year after but the actual number of loss of notebook
computers went up due to the two incidents where 12 computers were stolen each
time in July and September respectively.
Property Crime 2002 2003 2004
Theft of projector 5 13 5
No. of stolen projector 6 20 17
Theft of notebook computer 16 26 20
No. of stolen computer 17 24 43
Table 4.18: Statistics of theft of notebook computer and projector between 2002 and 2004
4.5 Campus Crime outside Hong Kong
Subject to the Student Right-to-Know and Campus Security Act 1990 and
upon request from the Department of Education, an institute, the National Center for
Education Statistics (NCES) was appointed to conduct surveys annually on campus
crime and security at tertiary institutions. The Act also demands tertiary institutions
publish and distribute a security report containing information about campus policies
and crime situation for all students and staff. The purpose of conducting the survey
and compiling security report is intended to encourage institutions to put more
emphasis on promoting campus safety and launching crime prevention campaign
(National Center for Education Statistics.
100
http://nces.gov/surveys/peqis/publications/97402/3.asp).
The website of Crime On Campus (Crime On Campus, Inc.,
http://securityoncampus.org/schools/cleryact/index.html) provides promising result
on campus crime statistics of five hundred tertiary institutions in the year 2003. Of
the 500 institutions, sixteen with an enrolment between 18,000 and 21,000 students
were selected to compare with the University of Hong Kong. Table 4.19 shows the
sharp contrast in crime situation between the United States institutions and the
University of Hong Kong. One may argue the difference in social cultures,
education systems and security measures may vary the result. Still, it demonstrates
that the University of Hong Kong is comparatively safer than those in the United
States.
University / College Enrolment Mur Rape Rob Assault Bur Theft M Theft Arson
University of Delaware 20949 0 4 8 14 46 395 7 2
New York State
University, Stony Brook
20855 0 2 5 5 43 638 14 *
California State
University, Los Angeles
20675 0 1 3 3 38 229 17 0
University of California
Santa Barbara
20373 0 4 1 4 58 358 4 3
University of Memphis 20332 0 2 4 0 50 225 34 0
University of Toledo 20313 0 1 2 2 61 315 12 3
Middle Tennessee State
University
20073 0 0 5 6 31 197 5 2
California State
University, Fresno
20007 0 0 0 5 30 342 40 1
Fresno Community
College
19888 0 1 0 0 8 187 26 0
Grand Valley State
University
19762 0 4 0 4 4 133 1 0
101
Northern Arizona
University
19728 0 7 1 18 61 416 7 7
Columbus State
Community University
19642 0 0 0 5 6 263 3 0
East Carolina University 19412 0 2 9 2 22 290 4 0
University of Alabama,
Tuscaloosa
19130 0 4 1 6 14 400 9 *
California State Polytec
University, Pomona
19041 0 1 3 0 40 252 30 0
Hong Kong University 19000 0 0 1 0 4 55 2 0
Bowling Green State
University
18739 0 2 1 0 8 255 3 2
Table 4.19: Comparison of campus crime statistics between HKU and US Universities in 2003 Source of Statistics: Security on Campus. www.securityoncampus.org/crimestats/ucr03.pdfMur – Murder & Non-negligent Manslaughter; Rape – Forcible; Rob – Robbery; Assault – Aggravated
Assault; Bur – Burglary; Theft – Larceny / Theft; M theft – Motor Vehicle Theft Arson - Arson
* - FBI did not receive 12 months of arson data from either the agency or the state.
4.6 SUMMARY
This chapter has demonstrated the capability of data visualisation in spatial
and temporal analysis for crime mapping. In comparison with the statistical analysis
operated side by side in the study, hot spot area crime analysis can identify the
hotspots of the prevailing crimes in the campus visually with the use of graduated
size symbol, and presents far better result than the textual description of crime
distribution. It further substantiates the feasibility of applying GIS computerised
mapping techniques in the campus environment.
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CHAPTER FIVE
CONCLUSION
5.1 Introduction
The data visualisation method of crime mapping and its practicality in spatial
and temporal crime analysis were discussed in the previous chapter. This chapter
will discuss some of the limitations encountered in this research and summarise the
major findings from the current analysis. To echo to the last focus stated in chapter
one, it will also make some recommendations to improve the current crime control
and crime prevention strategy and finally some suggestions for the future research.
5.2 Limitations of the Study
The first and perhaps most obvious limitation of the study lies in the data
availability. Like most of the crime studies, comprehensive crime information from
the police is anticipated to foster an accurate assessment on the development of
crime. Due to personal confidentiality and data privacy control, some vital and
useful information were not recorded or made available from the Security Section.
Realistically, information on victims and potential offenders will assist in
constructing profiling analysis and some interesting findings may be stemmed out
from this analysis.
Another limitation of the current study is the data structure of the raw crime
data. Not only was it presented in a textual descriptive format, it was not structured
103
nor suitably categorised for any possible comparative analysis. Not to mention the
manual input errors, missing temporal data records and low data standardisation
found in the data set. It took quite a long while to overcome these problems by
verifying and rectifying each and every single crime incident record before the data
could be graphically presented.
A final key limitation in this research lies in the small data sample. There
were only 225 crime incidents reported between the year 2002 and 2004, including a
few non-crime incidents explained in Chapter three. With an average of only 75
crime cases a year and given the disparity of crime distribution in some areas, the
outcome of this analysis tends towards generalisation and therefore the inferences
could only be tentatively drawn.
5.3 Analysis Findings
The findings of this research are summarised in the context of the
effectiveness of spatial and temporal analysis in crime mapping and the prediction of
future crime trend using counter-factual forecast method.
5.3.1 Crime in General
The application of GIS in mapping campus crime has been proved to be
effective in plotting all the crime black spots on a two-dimension platform for spatial
analysis. Some hot spots of prevailing crimes were more identifiable than the others
because the occurrence of the former were more frequent in volume and more
concentrated in an area than that of the latter. This reveals one fact that in the setting
104
of the University of Hong Kong due to low rate of some rare crimes, such as
snatching and robbery, they do not produce any hot spot. Analysis into these rarely
occurred offences require further justification by means of other analysis techniques,
such as temporal and modus operandi analysis used in this study.
When comparing the crime rate between the year 2002 and 2004 with the
annual average of 75, the crime rate of the year 2004 climbed to the highest point at
83, a recorded high in a three-year period. An alarming signal is the rising number of
different crime types, which went from 3 in the year 2002 to 6 in the following year
and finally reached to 14 in the year 2004, which poses a challenge to the security of
the campus. The emergence of other crimes into the campus, even if the rate of
which was low, if they are not properly controlled or enforced the opportunity for
crimes will be promoted, such as snatching and robbery. The deployment of frequent
patrols to the high-risk areas will initiate a displacement effect, which, in turns,
might provide an opportunity to potential offenders to commit crime in the
less-attention or low-risk areas.
5.3.2 Prevailing Crimes
The apprehension of an active thief in the year 2002 has neutralised theft
activities in the library area, but theft is still prevailing throughout the whole campus
and continues to spread out from the conventional targets, such as classrooms and
office areas, to the dormitories. More importantly, theft of petty cash, notebook
computers and some trendy appliances, such as mobile phones and digital cameras,
are higher. Spatial analysis of theft is very effective in locating all the black spots as
well as monitoring the annual changes of it. Since there was no one arrested or
105
confronted and victim kept on neglecting on their personal belongings, the identified
target locations would still be vulnerable to opportunity thieves. If the economy of
Hong Kong in the coming year stays very much like the year 2004, the current crime
situation will not seem to be changed very much.
The disquieting burglary also remains problematic. There was no arrest made
nor any stolen properties recovered in the past providing few clues to the analysts
for further study. Spatial analysis in this research does not yield any useful
information in identifying any hot spot due to small data samples and its sparse
spatial distribution. Yet, temporal analysis and modus operandi (MO) analysis
provide an important lead that most of these “bespoken” burglars target on the high
value items, such as projectors and notebook computers, or perhaps some other
expensive equipment. As long as these properties are well protected and kept
confidential, it will render less chances to the burglars. The step up of preventive
measures against burglary, such as installation of CCTV and strengthening of door
locks, in the high-risk blocks recently will serve its purpose. However, spatial
analysis reveals one significance phenomenon that they never take place twice in the
same building block at least this is the situation so far. If the hypothesis is correct,
the attention should be drawn to other blocks in the campus.
Of concern are the increasing numbers of the activities of peeping tom and
indecent exposure in the year 2004. Not only have they caused anxiety to the
females, the worst scenario is that they would turn into some serious sexual offences,
such as indecent assault and rape, if “Broken Windows” theory is correct. Spatial
analysis has precisely identified a hot spot of 4 building blocks in the Main Campus
where the suspects usually frequent. Temporal analysis has also traced the time span
106
when the suspects become more active. This spatiotemporal analysis narrows down
the parameter of the suspects’ peculiar behaviour in time and space and serves to
provide the Security Section sufficient information and justification as well to
allocate resources to keep the areas under close surveillance. That is to say,
spatiotemporal analysis can be a handy tool for problem-solving and
decision-making process without the assistance of modus operandi analysis.
5.3.3 Forecast of Prevailing Crimes
Based on hypothesis made in Section 5.3.2, the forecast of two prevailing
crimes, theft and peeping tom activities would continue in the three study areas as
shown in Figure 5.1 and 5.2. Theft will stay prevailing in the communal areas, such
as library, classrooms, amenities hall and dormitories. Peeping tom activities will
shift to the building blocks east and west from the Knowles Building, especially at
the lower floor female toilets, which have not been renovated with heightened
partition.
107
Figure 5.1: Forecast of crimes in the Main Campus and the Western Region
Figure 5.2: Forecast of crimes in the Southern Region
108
Figure 5.3 exhibits the crime trend between the year 2002 and 2004.
Seemingly, it has been varied in its patterns from a seasonal variation pattern in the
year 2002 to a cyclical fluctuation pattern in the year after, and finally to an irregular
fluctuation pattern which was cyclical high in the first half and became steady in the
second half. The changes of these patterns tend to suggest that the pattern in the
coming year will be on a more stable development with a small level of fluctuation
if there is no change in intervention or in other variables. In other words, the number
of crime will be evenly spread out over the said period.
Statistics of Campus Crime (2002 - 2004)
02468
10121416
Jan Feb MarApri May Jun Jul Aug Sep
tOct
Nov Dec
No.
of C
rime
200220032004
Figure 5.3: Statistics of campus crime between 2002 and 2004
In a nutshell, GIS computerised mapping is found to be a feasible solution in
mapping crime hot spots but not considered to be fully successful in the campus
environment. In the case of burglary and rarely occurred offences, no hot spot was
produced because of the thin distribution of burglary cases and low crime rate of the
minor offences. In the case of theft, hot spots were identified spatially but not quite
apparent temporally. In the case of peeping tom activities, hot spots were shown in
109
both spatial analysis and temporal analysis.
Hence, spatial analysis should not be operated independently and has to be
supplemented by temporal analysis in order to reveal the spatiotemporal relationship
of crimes. The four scenarios discussed in sections 5.3.1 and 5.3.2 summarise the
current situation in this study. To unearth the root cause of a complex crime problem,
other analysis techniques, particularly the modus operandi (MO) analysis, are
required to operate together.
5.4 Recommendations
Conventional policing, or traditional incident-drive policing, is a reactive
approach aiming at resolving each and individual crime incidents (Clarke and Eck
2003). This is the approach the Security Section adopting in the current situation. It
is considered to be successful in reducing the crime rate in some of the hot spot
areas but it does not control them from spreading out. Problem-oriented policing
(POP) is a proactive approach aimed at assessing the underlying causes of problems
behind a string of crime incidents (Goldstein 1990). This is a practical approach
recommended to control the distribution of crimes and at the same time to eradiate
the crime problems.
CompStat paradigm incorporates the best practices of traditional policing and
the philosophy of Problem-oriented Policing (POP) in formulating crime control
strategy. The four principles of CompStat are equally applicable in targeting crimes
in a campus environment for better security protection, though it was originally
designed for law enforcement environment and for high crime rate communities.
110
Though the subjects of protection between police and private security are similar,
police aim at protecting the safety of the general public while private security aims
at protecting corporate or individual assets. There are some commonalities in the
nature of duties of the two professional which are overlapping, such as the methods
and mandates of policing, and the organisational and operational structure (Henry
2003). The following recommendations in this study are made to improve the
current situation.
The cultural change from conventional policing to Problem-oriented policing
(POP) requires a clear and well-defined mission statement and scope of vision. It
will provide all the personnel a better understanding of the direction an organisation
is heading and a straightforward guideline to perform their designated task. It is,
therefore, recommended that the Security Section under the Estates Office take on
an active approach of Problem-oriented policing in controlling and reducing crime.
Once the philosophy is instilled and anchored in their minds, personal accountability
and job satisfaction will be raised accordingly.
The structure of the management should be revised as well to cope with the
introduction of new technology and techniques, and to suite the changes of the new
culture, particularly one of the essential ingredients of CompStat is to stress on the
fast flow of information. Thus, exchanging timely information from the crime
database and the crime map system makes immediate assessment possible and can
devise functional tactics readily for operation. CompStat meeting is another way to
facilitate the flow of communication, it allows all the stakeholders to evaluate the
effectiveness of enforcement action and share their experience on fighting crime.
111
The complexity of today’s society means that no one can fight against crime
on his own. Partnership with other organisations always garners better support,
gathers more resources and trades new skills from each other. Resources in the
campus are huge. The Geography Department can provide the Security Section a
GIS platform for crime mapping analysis. The Sociology Department can conduct
survey on the awareness of crime prevention from the students and provide Security
Section detailed analysis to devise effective crime control and crime reduction
strategies. Psychologists and criminologists may also be helpful in profiling specific
offenders, such as those involved in peeping tom activities, so that frontline
personnel can look for some characteristics of the potential offender. Liaison with
the police surely is important, as they can provide professional advice on crime
reduction strategy and detail analysis for targeting specific offenders.
Estates Office should take up the leading role in this regard to co-ordinate
with the aforementioned departments and faculties for administration, research and
strategic analysis functions. Annual assessment on campus crime should be put
forward to the authority for endorsement of crime prevention policy as well as
resources allocation for the coming year. Security Section should liaise with the
police for intelligence purposes and the administration unit of all departments for
routine security checks and risk evaluation functions. On the ground level, the
section should conduct regular tactical analysis based on the outcome of spatial and
temporal distribution of crime and other forms of intelligence, and task frontline
security teams to mount targeting operation and tactical patrol duties. Besides, the
section should always evaluate the effectiveness of crime maps and the result of
tactical operations, and from time to time to fine-tune the operational plan.
112
In order to enhance the professional capability of the staff, training is
definitely a must for all to acquire the required knowledge and skills for a specific
function. Some indoor officers should be trained to perform crime analysis together
with some basic understanding of GIS and crime mapping. Their analysis will be
vital for the supervisors and the management to allocate resources to handle the
problems at the right time and at the right place. Patrol guards should have a good
grasp of local knowledge and always be briefed of the development of the prevailing
crime for tactical deployment.
Education to the students is equally important in crime reduction. Crime
prevention seminars should continue and be held more frequently to raise their
awareness. Posting precaution notices and distributing anti-crime leaflets will be
more effective than disseminating email crime information to individual students
each month. Perhaps, an eye-catching presentation of crime information could be
shown on the front page of the web before they logged on the university computer
system or received their emails.
Crime analysis relies greatly on the clarity of data set and structure of the
database. Data collected from the current crime incident reports needs to be
expanded and cover more information for analysis, such as standardising keyword
for modus operandi (MO) search which is handy for an advanced and multifaceted
crime analysis. Also, special event data, such as dates of important school events,
opening days and public holidays, are indicators to link some crime incidents with
festive events for more precise temporal analysis. Other tabular data should be
considered for future inclusion, such as victim’s particulars for victimisation or
repeat victimisation analysis, yet issues on personal privacy should not be
113
overlooked.
5.5. Suggestions for Future Research
Since this research represents the first attempt in studying the spatial and
temporal distribution of crime on the campus of the University of Hong Kong, there
are two areas that would benefit from further research.
There is a need for further research into the crime distribution from a
multifaceted perspective. Acquiring more information on the demography and land
use of the campus, such as census distribution on the campus, functions and
categories of buildings etc., would enable future researchers to construct a
multilayer spatial analysis. It will present a clearer correlation between crime and
place and certainly will verify and extend the results of this study.
Future research should, where possible, address the issue of crime prediction.
A longitudinal research using statistical analysis techniques, such as time-span
analysis, regression analysis and correlation analysis, would enable researchers to
forecast the crime trend and crime pattern in a specific time span. It will show the
changes of crime over time period and also show the effects of displacement after
intervention. Again, it will help researchers to develop and test their hypothesis for
initiating a better crime prevention strategy.
5.6 Conclusion
GIS computerised mapping technology cannot in itself stand against crime,
114
and neither can spatial analysis nor temporal analysis. It is the joint efforts of the
management and the frontline personnel. Crime mapping is a good means of
communication providing management and frontline staff visual information of
crimes so that they can easily explore relationships between crime, time and place to
identify hot spots for targeting.
In this research, crime mapping techniques have proven to be an effective
tool in mapping crime hot spots, and its potential for further development should not
be ignored. Integrating computerised mapping technology in law enforcement is the
way forward for the future of police and private security. In order to assist the
management in formulating effective crime control and crime reduction strategies,
and the frontline personnel in deciding appropriate tactical options at ground level, a
reform in the current management system and policing strategy is deemed necessary.
This is not a technical question but rather a management decision.
115
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