MAPPING CRIME,
OFFENDERS
AND
SOCIO-DEMOGRAPHIC FACTORS
Crime Research Centre
University of Western Australia
crime
RESEARCH
centre
For the Ministry of Justice
Contract No. 297/98
December, 1999
TABLE OF CONTENTS
1
INTRODUCTION AND OVERVIEW ...................................................................................................................1
2
BACKGROUND...........................................................................................................................................................2
3
DATA ON CRIME, OFFENDING AND SOCIO-DEMOGRAPHIC FACTORS .....................................3
OFFENCE DATA................................................................................................................................................................3
POLICE -OFFENDER CONTACT DATA ..............................................................................................................................3
A RRESTS...........................................................................................................................................................................4
INTEGRATED POLICE AND JUSTICE DATA .....................................................................................................................4
COURTS DATA ..................................................................................................................................................................5
SOCIO-DEMOGRAPHIC DATA FROM THE 1996 A USTRALIAN CENSUS.......................................................................5
THE INTERPRETATION OF CRIME AND OFFENDER DATA.............................................................................................5
4
MAPPING CRIME AND OFFENDING................................................................................................................7
GEO-CODING.....................................................................................................................................................................7
LEVELS OF GEOGRAPHIC ANALYSIS ..............................................................................................................................8
5
THE CALCULATION OF AREA RATES AND INDICATORS - AN EXPLANATION AND
WARNING................................................................................................................................................................................8
W HY CALCULATE RATES AND INDICATORS?...............................................................................................................8
PROBLEMS ARISING IN RATE CALCULATIONS AND THEIR IMPLICATIONS?..............................................................9
REPLICATION OF THE RESEARCH METHODOLOGY ......................................................................................................9
6
SOME RELEVANT ISSUES ARISING FROM CRIMINOLOGICAL THEORY................................ 10
7
PREDICTING FUTURE CRIME RATES FROM THE PRESENT.......................................................... 11
8
TIME SERIES ANALYSIS .................................................................................................................................... 12
9
AREAS WHICH COULD BE TARGETED FOR SPECIAL ASSISTANCE........................................... 14
10
AN EXPLORATORY ANALYSIS OF JUVENILE COURT DATA.......................................................... 16
PREVALENCE OF JUVENILE COURT APPEARANCES BY REGION ...............................................................................17
PROPENSITY OF OFFENDERS TO OFFEND WITHIN THEIR OWN REGION . ..................................................................18
DISTINCTIVE SUBURBS..................................................................................................................................................19
11
FURTHER RESEARCH AND DEVELOPMENT REQUIRED.................................................................. 20
12
CONCLUSION........................................................................................................................................................... 21
EXPLANATORY NOTES..........................................................................................................................................22
REFERENCES..............................................................................................................................................................26
APPENDIX A - REGIONS IN WA .....................................................................................................................................A2
APPENDIX B - TOWNS AND RURAL REMNANTS IN EACH REGION........................................................................ A19
APPENDIX C - WA POLICE DISTRICTS IN PERTH ................................................................................................... A76
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH ......................................................................................... A84
APPENDIX E - TIME SERIES OF OFFENCES IN EACH REGION ..............................................................................A125
i
1 INTRODUCTION AND OVERVIEW
•
This study describes the patterns in crime rates across regions in Western Australia, as
well as corresponding patterns in police-offender contact and socio-demographic
conditions.
•
Property offences comprise over eighty percent of all recorded offences in Western
Australia and rates of property crime are higher in the Perth metropolitan area than they
are in any Regional Development Commission region. However, rates for offences
against the person are higher for the regions of Kimberley, Gascoyne, GoldfieldsEsperance, Pilbara and the Mid-West than they are for Perth. Similar patterns apply to
offences against good order 1 . Furthermore, regional drug offence rates are generally
higher than those in Perth.
•
The study confirms the significance of earlier research conducted by the Crime Research
Centre in 1998, in that crime rates in regional towns have a major impact on overall
regional crime rates. Crime rates vary substantially among towns just as rate differences
between towns and more rural parts of the region may be substantial. The impact of towns
is not restricted to personal offences: a number of regional towns have property crime
rates higher than those recorded in the Perth metropolitan area.
•
Just as within-region differences are important, so are differences in crime rates within
the metropolitan area. Local Government areas vary in their rates of crime. Crime rates
as well as the mix of crimes in each area are influenced by the varying opportunities for
crime provided by their mix of social and economic activity, but also by the differential
rates of police-offender contact recorded in different residential areas.
•
Rates of recorded crime and recorded contact with police do not necessarily represent
‘true’ rates of crime or ‘true’ patterns of offending. Because of this, crime surveys
designed to provide regional crime data are of great potential importance. They could
illuminate the extent to which regional differences in crime and police-offender contact
represent differences in levels of victimisation; differences in the propensity of citizens to
report crime to police; or regional differences in the ability of police to ‘clear-up’
offences.
•
Nevertheless, recorded levels of crime and police contact with offenders are important
social indicators. They represent the extent to which citizens have found it necessary to
call for public assistance in dealing with crime and the extent to which offenders have
been publicly identified. They also indicate the mobilisation of considerable public
resources and, however imperfectly, reflect the consequences of crime for victims and
offenders. They provide a starting point 2 for a systematic consideration of relative need
for crime prevention resources on a geographic basis.
1
For a description of the good order category of offences see the Explanatory Notes.
Other systematically collected indicators of need should be considered, along with crime data. The range of
other desirable data for local crime audits is discussed in Safer WA (1998), and Hough and Tilley (1998).
Examples include health, welfare, education and housing data on drug use, intentionally inflicted injuries,
domestic violence, child abuse, truancy levels and vandalism.
2
2 BACKGROUND
This project was conducted in response to the Ministry of Justice’s requirement for research
to map the following:
a)
where crimes currently occur, broken down by type of crime, and to a level of
geographic detail to be decided;
b)
where known offenders, i.e. those arrested for and/or convicted of offences, come
from in terms of the location of their usual residence at the time of the offence;
c)
where factors known to be predictive of offending are most prevalent. These factors
were to include those shown by research to be predictive, and needed to include for
consideration those listed in the Action Plan to Address the Cycle of Aboriginal
Juvenile Offending (the Action Plan) 3 .
The Ministry required the mapping techniques to be repeatable so that the maps, charts and
tables presented in the report could be updated on a regular basis. This requirement has been
addressed by several strategies discussed in the body of this report.
The purpose of this consultancy was to inform crime prevention policy by locating the areas
where specific types of prevention action may work best. Specifically the report was to
inform the choice of and debate over areas suitable for the mounting of specific initiatives,
including sites for the Action Plan and other inter-agency initiatives.
Specific objectives were to establish a methodology for mapping crime, offenders and
relevant social factors in a way that could inform policy and debate on primary, secondary
prevention interventions with offenders or potential offenders, as well as informing
traditional physical and environmental crime prevention.
From the beginning of the study it was clear to the researchers and the project Steering
Committee that social and demographic factors4 could only be obtained or constructed from
information in the 1996 Australian census. Other important information is available from a
range of State and Commonwealth agencies, but not readily broken down by the geographic
boundaries required for the current study. This does not mean that they could not be
generated in useable form in the context of longer-term planning and consultation with the
relevant agencies.
The list of social factors included in the study5 should not be thought of as ‘causes’ of crime.
However, they may be regarded as contextual influences that increase or decrease the risk of
crime in communities6 . The list includes demographic (total population, age, sex and
Aboriginality) as well as social and economic (educational background, employment and
socio-economic indexes) which are often found to correlate with recorded crime and policeoffender contact. The demographic factors allow the calculation of relevant rates of crime
and police-offender contact and provide a picture of the social location of recorded crime to
accompany the geographical picture which emerges from the study. Other factors, such as
3
The Action Plan risk factor approach is described in Appendix four of MacWilliam and Moore (1999)
These include both the factors connected with crime and offending and also those essential for the calculation
of rates.
5
See the explanatory notes for a detailed listing.
6
Note that these factors are constructed at the area- rather than at the individual-level.
4
2
the residential mobility of individuals in areas, and overcrowded housing may be used by
crime theories as indicators of key concepts (of social disorganisation7 for example), but do
not in-themselves encapsulate causal processes.
3 DATA ON CRIME, OFFENDING AND SOCIO-DEMOGRAPHIC
FACTORS
Since one purpose of this study was to develop a methodology that could be replicated, it is
important to describe in some detail the process through which this mapping of crime,
offenders and social factors was achieved. A detailed explanation of the data included in the
appendices is included in the Explanatory Notes below. The following paragraphs focus on
broader issues surrounding the nature of the data used in this report and their interpretation.
OFFENCE DATA
This project could not have been completed without the cooperation and assistance of the
Western Australian Police Service in gaining access to key data for the research conducted in
this project. The major source of both crime and offender information was the Service’s OIS
(Offence Information System) database. Information in this system is initially generated
when police complete an offence report form (P49), which is entered into the computer in
electronic form. The OIS is the major source of offence data and is triggered in most cases
by a complaint from a member of the public.
Reported offence data were available at sub-regional level8 for the years 1996-1998. At the
level of RDC region9 , offence data for longitudinal analysis were available from 1991-1998.
POLICE-OFFENDER CONTACT DATA
The OIS also contains basic information about contacts between police and alleged
offenders 10 . However, there is not a simple relationship between offences and policeoffender contacts and the following comments capture the main features, if not the full
complexity of the relationship.
•
Not all offences reported to police lead to a ‘clear-up’, or more specifically, to the
apprehension of an offender. For example approximately ten percent of burglaries lead
to the apprehension of an offender, whereas over ninety percent of murders lead to an
apprehension.
•
Some offenders are involved in group-offences; that is to say there may be more than one
offender involved in a single offence. Where offenders are detected in such situations
there will be more police-offender contacts listed than there are offences.
7
See Miethe and Meier (1994).
The research for this project enables offences to be mapped to the level of towns within region or local
government areas within the metropolitan area.
9
This study adopted for its regional analysis the administrative regions outlined in Schedule 1 of the Regional
Development Commissions Act 1993 which provided meaningful boundaries for previous research conducted by
the Crime Research Centre (1998a). The Perth metropolitan area, excluding any part of Peel, was selected as
another ‘region’ for comparison with the RDC regions.
10
This is generally referred to by police as the ‘processed person’ database.
8
3
•
Alternatively, a single offender may be involved in many offences. When such a
situation is detected, it will lead to the recording of as many police-offender contacts as
there are offences.
It should be evident from the above comments that counting police-offender contacts is not
the same as counting individual offenders. For example, a group of five police-offender
contacts may result from a single offender committing five offences or five offenders
committing a single offence. However, it is not possible to disentangle these different
scenarios from the data used for this study.
The major advantage for this study in the use of police-offender contact information is that it
enables the offender’s residence to be allocated to an appropriate geographic area (see the
section on geo-coding below). Police-offender contact data were analysed only for the years
1997 and 1998 because locality information was most reliable for these years.
ARRESTS
A separate arrest database deriving from a different (P18) data collection form supplies
information on individuals whom police have charged with offences. In many cases these
offenders will be charged for offences reported by members of the public and recorded in the
OIS system. Hence the arrest database can be regarded as representing information
‘downstream’ from the offence database 11 . However, the arrest database also includes
information arising from police, as opposed to public, initiative. Many charges (relating for
example to drink-driving and street offences) arise from situations where there is no public
complainant. Arrest data are a valuable source of information about offenders who appear
before court. Furthermore, they do support an analysis of the frequency of offending of
individual offenders. However, geographic information about arrests is more limited than it
is for police-offender contacts and it provides incomplete data about offender residence. For
this reason, arrest information is used only in a limited way in this study. It is used to provide
information about ‘street offences’ at the regional level in the time series analysis for the
period 1991-199812 .
INTEGRATED POLICE AND JUSTICE DATA
In an ideal world it would be possible to pursue the progress of an offence report from the
time it is logged by police, through the investigation and apprehension process, to the
decision to caution or charge an offender. It would also be possible to track the subsequent
court outcome. Ideally, the relevant databases would be capable of identifying individual
offenders and linking them with all of their offences. Unfortunately, such a flow of
information can be assembled (in Western Australia and in most police jurisdictions) only on
a case by case basis. There is no straightforward way of linking aggregate OIS data on
offences and police-offender contacts with data on arrests, where an individual identifier is
available. The Ministry of Justice experiences similar information problems. Data about
court appearances, prison episodes, community supervision, juvenile custody and juvenile
supervision are held in separate systems. These systems do not currently have the capacity to
generate an integrated view of the progress of an offender through the various stages of the
criminal justice process.
11
The arrest database only records information about offenders appearing before a court. It does not cover
young offenders who receive a caution or who are referred by police to a Juvenile Justice Team.
12
See Appendix E.
4
The Western Australian Police Service is developing plans to implement such integrated
systems in the near future through its DCAT projects, but they do not exist at present. The
Ministry of Justice also has a means of integrating its separate systems within its data
warehouse, but the necessary mechanisms and procedures for integration are not yet
operational.
COURTS DATA
An exploratory analysis was undertaken of data from the Ministry of Justice courts database,
CHIPS. This database contains information on lower court 13 and juvenile court appearances
in Western Australia. Some comments are provided on these data in the text of this report.
CHIPS data were examined for the 1998 calendar year.
SOCIO-DEMOGRAPHIC DATA FROM THE 1996 AUSTRALIAN CENSUS
Census data were extracted from the ABS census product CDATA96. This system allows
data from the 1996 and earlier censuses to be extracted at a refined geographic level. At the
broadest geographical level of analysis, data in this report were required at the level of
regions defined in the Regional Development Commissions Act 1993. These are defined as
aggregations of Local Government Areas (LGAs). At a more refined geographic level the
data were extracted at LGA level and Police District level (in the metropolitan area) and were
also tabulated for selected country towns. The country town data were extracted as
aggregations of Collection Districts (CDs) using urban boundaries defined in the CDATA96
package.
THE INTERPRETATION OF CRIME AND OFFENDER DATA
There is a temptation to interpret data about crime and offending in a way that frames it
solely as offender behaviour and which hides from view the reaction to crime by the public
and police. Furthermore, public policy initiatives and new legislation may intervene to
ensure that greater priority is given to previously neglected areas such as domestic violence
and child abuse. The text below outlines the basic processes involved in the official
recording of offences. In particular, the following (simplified) steps are required before an
offence comes to police notice and an offender is detected.
1. A victim is subject to behaviour he or she judges to be a criminal offence.
2. The victim reports the offence to police.
3. Police accept the report as a criminal offence and record that fact.
4. Police investigation leads to the detection of an alleged offender and other appropriate
action, such as laying charges, administering a caution, or referring the case to a juvenile
justice team14,15 .
13
Note that Police Courts cover more remote areas of Western Australia and adult appearances in these courts
are not yet recorded in CHIPS.
14
Cautions and referrals apply only to juvenile offenders who admit an offence.
15
Other court procedures follow so that guilt may be determined and punishment handed down, but these can be
ignored for present purposes.
5
In some cases steps 1-4 are collapsed when a police officer intervenes directly to make an
arrest, most commonly in situations involving breaches of public order, drink driving or drug
offences 16 .
Each step in the above process ensures that the offences recorded by police and the contacts
between police and offenders are shaped not simply by offending behaviour but also by
public and police reaction that behaviour. This process of reaction has been documented
through the use of crime surveys and other types of research. The pattern of recorded crime
and police offender contact is shaped by some well-known factors and by others which are
less well understood. Rather than attempting a comprehensive summary of this issue, we
provide some examples particularly relevant to the current study.
•
Some studies (Weisheit, Falcone and Wells, 1994) have found that individuals in rural
areas prefer to rely on informal measures of dealing with criminal behaviour and are less
likely to report offences to police. However, there is dearth of Australian research on this
issue.
•
Australian crime surveys (Australian Bureau of Statistics 1999a) show that almost thirty
percent of assaults are reported to police, compared with one third of sexual assaults, fifty
percent of robberies, almost eighty percent of completed household burglaries and over
ninety percent of motor vehicle thefts.
•
Other sources (Australian Bureau of Statistics, 1999b) show that when assaults are
reported to and recorded by police they lead to proceedings against an offender in over
fifty percent of cases. This figure compares with almost thirty percent for sexual assault,
twenty percent for robbery, and less than ten percent for motor vehicle theft and burglary.
•
Recently published research in the United States (Snyder, 1999) indicates that juvenile
offenders are responsible for fewer robbery offences than are indicated by arrest statistics,
because (a) there are more juveniles involved with each offence than is the situation with
adults and (b) juveniles are more likely than adults to be detected. This overrepresentation of juveniles in police-offender contact statistics is also relevant to
Australia 17 .
•
New Zealand research indicates that young Maori offenders were overrepresented in
police contact records compared with non-Maori offenders, after controlling for selfreported offending (Fergusson, Horwood and Lynskey, 1993). Australian research has
also pointed to high rates of Aboriginal contact with police (Harding et al., 1995) and the
extent to which this over-representation results from differential practices of police and
other agencies has been vigorously debated.
These examples merely scratch the surface of what is known about the differential reaction to
offending by victims, witnesses and police. However, they are sufficient to draw our attention
16
Police may occasionally be in a position to intervene directly for offences such as assault, burglary and others,
which generally come to notice via reports from members of the public.
17
High rates of offending concentrated in the late juvenile and early adult years are found in studies in many
countries and time-periods, using varied measures of involvement. However, the proposition that official
records exaggerate the picture has been consistently raised (Marvell and Moody, 1991)
6
to the fact that official records of crime and police-offender contact 18 do not reflect patterns
of offending behaviour in a simple way. A useful image used by Maguire (1998) to describe
the process of crime measurement (using varied sources of data) was that of artists painting
and re-painting a canvas - arguably a more accurate analogy than that of a photographer
taking a high-resolution snapshot.
4 MAPPING CRIME AND OFFENDING
Efforts to map and correlate crime with social and economic factors have a long if episodic
history, beginning with the cartographic work of Guerry in France around 1830. Of interest
with regard to this current exercise in Western Australia is Guerry's analysis of the impact of
population density on crime. Guerry noticed that relatively less property crime occurred in
the administrative regions containing large cities, than in regions containing smaller cities,
although there were exceptions 19 . He concluded that the generalisation associating crime
and population density (or large cities) was premature, a mistake derived from attributing too
much explanatory power to population density and too little to a variety of factors which
often but not always accompany population density (Beirne, 1993: 121).
Guerry's conclusion is highly applicable to the Australian context over the past twenty five
years. Funding bodies such as the Grants Commission have been presented with arguments
by New South Wales and Victoria claiming that the crime-producing environments of their
large capital cities justify additional law and order funding for those States. These arguments
are contradicted by the evidence of both crime surveys and police data. States such as
Western Australia and South Australia have consistently experienced higher crime rates than
New South Wales and Victoria, and their capital cities have crime rates at least comparable
with Australia's two largest cities. The complexity of the relationship between city size and
crime was emphasised by research conducted by the Crime Research Centre (1998a). This
indicated that the regional cities of Kalgoorlie and Geraldton had both personal and property
rates comparable with those in Perth. In Western Australia, prior regional analyses of crime
patterns have been published by the Western Australian Police Service and Western
Australian Ministry of Justice (1999) and the Crime Research Centre 20 .
GEO-CODING
Police data were geo-coded for this project by Excalibur Consultancies and aggregated from
the level of CD to other relevant boundaries. The CD is the smallest geographical unit at
which ABS census data are published. Data at CD level may be aggregated into towns,
LGAs, regions and so on. The availability of both police and census data at these levels
allows comparable maps and tabulations to be prepared and also for appropriate crime and
offender rates to be calculated. More detail about geo-coding is provided in the Explanatory
Notes.
18
The problems of measurement are not restricted to official records. Similar problems confront the use of
crime surveys and self-reports of offending. However, the use of more than one measuring tool can help
overcome the weaknesses of a single method.
19
Paris had high rates of offences against both property and the person.
20
Regional crime breakdowns and a Perth suburb-based analysis are available in the annual statistical report of
the Crime Research Centre (1998) and in various specialised reports and books published by the Centre.
7
LEVELS OF GEOGRAPHIC ANALYSIS
Any study with the ambitions of the present one requires important judgements to be made
about the most suitable scale(s) of geographic analysis. Furthermore, there are trade-offs to
be made about the levels of analysis most suitable for the nature of the data (crime, offenders
or socio-demographic data) and the number of years of data required for adequate analysis.
Three major levels of analysis were selected in this study: RDC regions and the Perth
metropolitan area; local government areas within Perth; and larger towns and the rural
remnants within RDC regions.
Additionally, at the request of the Steering Committee, information is supplied at a fourth
level of analysis: Police Districts within the metropolitan area. Note also that an initial
analysis was prepared for Perth suburbs, a suitable level of analysis for crime data, and the
basis for maps prepared in the annual statistical reports of the Crime Research Centre.
Suburbs are also suitable for the display of socio-demographic data. However, difficulties
due to small numbers were evident in the analysis of police-offender contacts at the suburb
level so a decision was made to focus the main metropolitan analysis on local government
areas. The nature of the difficulties associated with rate calculations in small areas is
discussed below and care needs to be exercised in interpreting the rates of crime and policeoffender contact for some of the smaller regional towns.
5 THE CALCULATION OF AREA RATES AND INDICATORS - AN
EXPLANATION AND WARNING
WHY CALCULATE RATES AND INDICATORS?
The areas of interest in this study do not contain populations of the same size and
characteristics. If they did, the task of comparison would be greatly simplified but far less
interesting and the calculation of rates would be unnecessary. The presentation of counts of
crime and police offender contact would be sufficient for comparison. However, rates are
required to enable comparisons between areas of different size and composition. Areas show
variation in their percentage of juveniles, males, Aboriginal citizens, income levels, socioeconomic status and so on. These differences can be displayed through the calculation of
rates, percentages or other constructs. With regard to crime, the simplest approach is to
calculate a rate based on total population. The number of offences per 1,000 population
provides a seemingly obvious way of comparing areas of different size. This is the standard
approach adopted in this report.
At the regional level, however it is possible to calculate some special rates for specific
offences. For example, regional domestic burglary rates are also calculated as a rate per
1,000 dwellings and motor vehicle theft rates are calculated as a rate per 1,000 registered
motor vehicles. These rates provide a different means of comparing areas, based not on total
population, but on the number of potential targets for each specific offence. The calculation
of other rates is conceivable and subject only to data availability. For example, commercial
burglary should ideally be calculated as a rate per 1,000 commercial premises, but systematic
enumeration of these premises is not available across the State.
With regard to police-offender contact, total population rates can be calculated as well as
rates for special populations. For example, it is possible to separate police-offender contacts
8
by age, sex and Aboriginality, and to separate the area population in the same way. This
allows the calculation of age-, sex- and race-specific rates of police-offender contact 21 .
PROBLEMS ARISING IN RATE CALCULATIONS AND THEIR IMPLICATIONS?
The principal issues of data reliability relate to the calculation of rates of crime and offender
residence rates where both the numerator and denominator required for rate calculation are
subject to relatively large measurement error. A detailed discussion of the problems is
included in the Explanatory Notes, however some guidelines for interpreting the Appendices
are offered below. These technical problems in the calculation of rates operate at a different
conceptual level from the issues concerning the interpretation of crime and offender data as
discussed earlier. Furthermore, the details of rate calculations are also included in the
Explanatory Notes.
Two simple guidelines for assessing the rates calculated in the Appendices are suggested.
First, the order of (decreasing) reliability of the calculated rates is as follows:
1. Socio-demographic indicators 22
2. Crime rates
3. Police-offender contact rates
Second, the levels of geographic analysis appear in the following order of reliability:
1. Regional analyses
2. LGA and Police district analysis in Perth
3. Towns within regions and the rural remainder
REPLICATION OF THE RESEARCH METHODOLOGY
The brief for this research required the development of a methodology that could be
replicated. Apart from providing a clear documentation of the methods applied, the study
team has developed a set of boundaries which can easily be re-used and has implemented
these boundaries in a form suitable for use with the CDATA96 product developed by the
21
A basic assumption is that police and census collectors use the same counting methods. In the case of
Aboriginal identification this is not exactly true. The census uses a self-identification methodology, while police
often use physical appearance as a basis for classification. However, a 1991 investigation by the Crime
Research Centre of offenders sentenced to prison (Crime Research Centre, 1991) found a high correlation
between the racial classification applied by prison authorities (using a self-report method) and that arrived at by
police. This research is now in need of updating.
22
Note that no data, including census data, should be considered completely reliable. The Australian Bureau of
Statistics has documented its problems in obtaining accurate enumeration of Australia’s Aboriginal population
at census time. Data about employment status and other social variables may be particularly difficult to
compare with the variables labelled similarly in non-aboriginal populations because of different social
arrangements (eg. of working for unemployment benefits), or of different interpretations (eg. of what is meant
by a household or family). Issues relating to the interpretation of social indicators for Aboriginal people are
discussed fully in the final report of the Royal Commission into Aboriginal Deaths and Custody (1991, Ch. 11).
Census data about the location of individuals are also based on place of enumeration rather than place of usual
residence. Nevertheless, indicators derived solely from census data have the advantage of using a consistent
measurement methodology.
9
Australian Bureau of Statistics. This software is used by many individuals and organisations,
including the Ministry of Justice. Each offence and offender location was mapped into a CD
as were the socio-demographic and base population figures. Each CD was mapped into
larger geographical levels, allowing rates to be calculated for the aggregated boundaries.
Another procedure at the technical level is generation of the maps, tables and charts presented
in this report through a series of linked spreadsheets containing summary data. Updated
charts and tables can be produced simply by refreshing the database.
Just as important as pure replication is the ability to gain new insights from the study itself.
The methodology lays the groundwork for a learning process to inform further work.
6 SOME RELEVANT ISSUES ARISING FROM CRIMINOLOGICAL
THEORY
The application of a mapping methodology and the ready availability of census data focus
attention on the correlations between crime and selected social-structural factors. Although it
is becoming increasingly common to rally crime prevention efforts around the slogan that the
causes and prevention of crime are local, the structure of this study draws attention to broader
social factors. It is clear that there is a case for an integrated assessment of local and
structural influences.
Agnew (1999) provides a useful assessment of the current state of knowledge about the
theoretical significance of community-level differences in crime particularly as they relate to
social disorganisation theory, subcultural deviance theory and relative deprivation (strain)
theory.
While noting that research results have been somewhat contradictory, Agnew points out the
repeated findings that high crime communities are low in economic status, as measured by
income, poverty, unemployment, welfare, occupation, education, inequality, owner-occupied
dwellings, and sub-standard housing 23 . Family disruption is a mediating factor between
crime and other variables. He identifies social disorganisation theory, with its key concept of
social control, as providing the dominant explanation for the influence of the above factors.
In this theory the structural factors identified above do not directly influence crime, but they
do weaken the ability of local residents to directly control crime in their communities and
indirectly allow the development of delinquent peer groups.
Farrington (quoted in National Crime Prevention, 1999) favours a developmental approach to
crime prevention but has presented a view that communities are simply settings for individual
behaviour and potential intervention programs, with community level variables having little
or no causal effects. Nevertheless, other researchers have identified some community level
variables and these include (National Crime Prevention, 1999: 136-138):
Risk factors: disadvantage, population density, housing conditions, urban areas,
neighbourhood violence and crime, cultural norms concerning violence, media portrayal of
violence, lack of support services, and social and cultural discrimination.
23
Community size, population density, overcrowding, residential mobility and percentage non-white population,
in the USA context, are other correlates of crime.
10
Protective factors: access to support services, community networking, attachment to the
community, participation in church or community group, community/cultural norms against
violence, cultural identity and ethnic pride.
The developmental perspective has been advocated in a recent report on crime prevention
commissioned by National Crime Prevention (1999). Furthermore, Homel and others discuss
risk and resilience (protective) factors in the context of Aboriginal communities (Homel,
Lincoln and Herd, 1999).
Theories more specific to urban-rural differences are provided by Wirth (1933) and others.
These focus on the impact of the bonds of kinship, neighbourliness and sentiment arising
from a ‘common folk tradition’ characteristic of rural areas in many countries. Wirth and
others contrast this tradition with an urban way of life based on a spirit of competition,
aggrandisement and mutual exploitation. It is not appropriate to provide a detailed discussion
of such theories, which, in any case, do not have a strong influence in criminology.
However, it is of interest that a recent survey commissioned by the Regional Development
Council (1999) reveals that all regions of Western Australia are characterised by indicators of
what Wirth would describe as ‘urbanism’, namely, high levels of mobility motivated by
employment opportunities; population concentration in towns; and only a minority of
residents having long-term connections with the area. However, the survey did indicate a
high level of perceived community safety24 for most regional inhabitants.
A final word on theory must include some discussion of rational choice (Cornish and Clarke
1986) and routine activity (Cohen and Felson, 1979) theories of crime. These theories give
far greater emphasis to crime opportunities and the situations in which they arise. These
theories and their crime prevention counterpart - situational crime prevention - emphasise
highly focused crime contexts. These contexts are very specific with regard to the type of
crime under consideration and its spatial and temporal distribution. The examination of large
geographic areas, and aggregated crime counts would not be considered adequate by the
proponents of these theories since they could not address the issues of required scale and
focus. Furthermore, the community level indicators relevant to routine activity theory are
different in nature to those measured in more traditional theory. For example, indicators
relevant to weekend street assault and robbery in Northbridge bear little relation to the
resident population since the number of visitors to the area on Friday and Saturday nights is a
large multiple of the resident population. Routine activity theory points to the need for
indicators which measure, in an offence specific and local way, motivated offenders,
potential targets and capable guardians. Studies which cover large areas and a wide range of
offences will have great difficulty in obtaining the relevant data.
7 PREDICTING FUTURE CRIME RATES FROM THE PRESENT
Various methods have been used to project current crime rates into the future, with limited
success. An obvious candidate as lead indicator for crime is population growth in the age
groups most prone to crime - for example those aged between 15 and 29. However, attempts
to use this methodology have not been particularly successful (Marvell and Moody, 1991). A
possible reason for this lack of success is that the age composition of a population may
change slowly and be swamped by more important social changes. Another reason is
suggested by the interpretation of crime and offender data discussed above. In particular, age
24
This questionnaire assessed perceptions of community safety relative to the city of Perth.
11
and other apparently important offender characteristics may not be as significant as they
appear due to distortions inherent in recorded crime and police-offender contact patterns.
A useful starting point for predicting the relative importance of regions and smaller areas is
that future crime rates will preserve current relativities. This result would mirror that
experienced in Chicago, which experienced very stable community relativities in crime rates
for many years. Bursik and Grasmick (1994) provide a balanced assessment of this
proposition. Other insights are provided by Bottoms (1998) who discusses research into
crime rate changes in the light of general neighbourhood decline in U.S. cities and into the
impact of housing policy changes on U.K. housing estates. The demolition and
redevelopment of some sub-standard housing concentrations in Perth will provide a test for
such impacts in an Australian context. A most important recent study was published by
Ralph Taylor (1999). Taylor’s conclusions provide a strong challenge to those who support a
police or local authority focus on social or physical ‘incivilities’ (the ‘broken windows’
approach involving zero-tolerance policing on the police side and a focus on graffiti removal
by other authorities). In Baltimore, Taylor’s research covered an 18-year time span and
indicated that the major factors predicting urban area increases in crime rates were not
‘incivilities’ but ‘neighbourhood basics’, which included the enhancement of neighbourhood
stability, maintenance of house prices relative to other areas and improvements in local
economic development.
8 TIME SERIES ANALYSIS
One of the interests of the project steering group was in the temporal patterns of offending in
Western Australia and its regions. Temporal patterns may be present at various levels of
analysis, for example there may be strong daily, weekly, monthly or seasonal patterns
superimposed on longer term trends and cycles. Of greatest interest to the Steering
Committee were patterns that could be of relevance to the timing of crime prevention
programs, rather than day to day policing. To investigate the relevant patterns, eight years of
data on recorded offences - the limits of available data from the OIS system - were analysed
at the regional level. A time-series of this length should be considered to be a minimal
requirement for such an analysis. However, major temporal influences on crime should be
evident.
The data on good order 25 offences from the OIS were supplemented by data on ‘street
offences’ from the arrest database over the same time period. These data were included
because the OIS data on good order do not completely cover all offences resulting from the
exercise of police initiative to immediately arrest a citizen. However, OIS data do appear to
have good coverage of offences arising from complaints by a member of the public. The
reported offences covered by OIS in the good order category include some ‘street offences’
as listed below, but the focus is mainly on breaches of court orders, possession and use of
firearms and other weapons, child pornography offences, liquor licensing offences, and
betting and gaming offences. The supplementary arrest data included as ‘street offences’
were in the major categories ‘resist or hinder police’ (37%), ‘trespassing and vagrancy’
(10%) and ‘other good order’ offences (53%) consisting mainly of offensive and disorderly
conduct.
The reported offence data required extreme care in interpretation given that they are based on
the recorded date and time of offence, rather than date and time of its reporting to police.
25
See the Explanatory Notes for a description of this category and the sub-group of street offences ..
12
Initial analysis appeared to provide strong evidence for a monthly concentration of offences
in January. More detailed examination revealed that this effect was largely an artefact of
recording practices (see page A134). These affected the recording of sexual offences and
fraud in particular. For example, many sexual offences are reported in the context of abuse
that may have been part of a chronic pattern over long time periods, making the exact date
and time of offences difficult to pinpoint. Police recording practice is evidently to
approximate the date of these offences as closely as possible, and this results in a
concentration of offences on January 1 of the relevant year or on the first day of the relevant
month. Furthermore, the time stamp of these offences is concentrated at hour zero, minute
zero or at hour zero minute 1 of the relevant day.
A similar timing issue confronts offences of fraud. Here also the exact date and time of the
fraud may be difficult to determine and a similar time concentration of recorded fraud
emerges which again is an artefact of recording practices. It would not be possible to identify
the artefactual nature of these patterns if data were available or analysed only at the monthly
level. Nevertheless, a careful analysis using a more refined time scale reveals the problem.
A further issue affecting the offence data is the time taken to fully implement OIS across the
State during the changeover period to the new system in 1991. It is evident from the data that
the system was not fully implemented in some country regions until mid-1991. Furthermore,
the recording of drug offences on OIS was not implemented until 1994, as is clear from the
regional charts from pages A125 to A133.
The charts in Appendix E indicate the patterns of offending in each of the regions. These
patterns vary somewhat from region to region but it is fair to say that there appear to be no
strong seasonal effects in the State as a whole or in any region. A good way of examining
this at the State level for offences against the person and fraud is to examine the day of year
analysis on page A134. The daily ‘spikes’ due to the first day of the year and the first day of
each month are evident but, apart from these, there is an overwhelming impression of flatness
in the reported crime data when examined on a daily basis. Even when sexual offences are
removed from the analysis this flatness by day of year predominates. This pattern appears to
give little encouragement to the notion that interventions targeted at particular days of the
year could prevent crime. In order to test a more specific patterning of crime an investigation
was conducted, but is not displayed graphically in Appendix E, of the daily average offence
count for weekends and public and school holidays. There are variations by holiday and nonholiday daily offence counts by region and offence type, but these differences are small.
The only offence types for which significant temporal differences are evident are street
offence arrests (page A136). At the State level there is evidence of higher offence rates in the
months of January and February, with the lowest rates being evident for November. The high
rates in January and February are particularly evident in the following regions: South West
(the region with the largest monthly fluctuations), Goldfields-Esperance, Perth, the Mid-West
(moderate effect) and the Wheatbelt and Kimberley (February peak only for the latter two).
Few monthly differences are evident in the other regions: Gascoyne (modest March peak
superimposed on a very flat monthly pattern), Peel (small general decline over the calendar
year), Pilbara (October peak followed by November trough) and Great Southern (November
peak).
There is some interest in the daily and weekly rhythms of street offence arrests (page A135).
In all regions there are more street offences from 6pm Friday to 5.59 p.m. on Sunday. For the
State there are approximately 50% more street offences on weekend days than on non13
weekend days. The ‘weekend effect’ is weakest in Goldfields-Esperance and in Pilbara, and
strongest in the Great Southern region. A final comment refers to the ‘one day of the year’
for street offences - New Years’ Day (page A135). In all regions there are between four and
seven times more street offence arrests on New Year’s day (they concentrate in the early
hours of the morning) than on other days in the month. This single day focus is a major
factor in - but does not completely explain - the January peak in street offence arrests across
the whole state. In the case of arrests the effect seems to be ‘real’ rather than being an
artefact of data entry as was the case for reported offences against the person and for fraud.
9 AREAS WHICH COULD BE TARGETED FOR SPECIAL
ASSISTANCE
The detailed area analysis in this report draws attention to a number of locations with
relatively high rates of crime, offenders and varied economic and social stressors. A number
of these areas were identified as being at ‘high risk’ in the Needs Analysis conducted by
MacWilliam and Moore (1998), but the present study places these in the context of
comprehensive crime, offender and census data. The study has confirmed the relative
rankings of regional crime rates evident in the CRC’s (1998) study Rural Crime and Safety:
A Preliminary Study, which was based on 1996 crime data.
In the Kimberley region, high rates of violent crime were evident throughout the region, and
very high rates of both violent and property crime were registered in the Kimberley towns of
Halls Creek, Fitzroy Crossing and Derby. Crime rates high by comparison with towns in
other regions were also registered in Wyndham and Kununurra. Similar patterns were
experienced for processed person data. In the Kimberley there is a contrast between high
rates of crime in these towns and lower rates of crime and police contact in rural areas. The
issue arising in this context is that some of the highest rates of poverty in the State are evident
outside of the towns and it is impossible to put aside the difficulties faced by police processed
person data in accurately identifying the origins of offenders when they may also have points
of contact in the towns. The mobilisation of police resources is inevitably more difficult in
more remote locations.
Issues which go to the heart of understanding and reacting constructively to regional crime
patterns are raised by the AJC (Aboriginal Justice Council, 1999). The case study of Halls
Creek (p 82) refers to the need to understand and plan for the influx of large groups of desert
people into Halls Creek from time to time. At a practical level the AJC identifies a number
of key planning issues. However, the case study vividly illustrates issues involved in the
mapping process. It is uncertain how accurately the official records of crime and policeoffender contact can map complex interactions between people and places such as those
exemplified in Halls Creek 26 . Detailed local information is required for a more complete
understanding and Halls Creek provides but a single example of the issues which can arise.
In the Pilbara the highest rates of crime and police contact are recorded in Roebourne and
Port Hedland. While Wickham ranks higher than other towns in the region on measures of
social stress, its crime and processed person ranking is high only for drug and good order
offences.
26
For example, offences will be correctly mapped to Halls Creek. However, police-offender contact may also
be mapped to Halls Creek because of a temporary abode, even though the offender's usual residence is outside
the town.
14
In the Gascoyne, Carnarvon stands out from other towns in terms of crime rates and
processed person rates and, because of its large regional population share it has a heavy
influence on the regional average figures.
The 1998 research conducted by the Crime Research Centre identified the significant
influence of Geraldton on Mid-West crime rates. This project confirms this influence over a
three-year time period, but also identifies the smaller town of Meekatharra as having higher
crime rates than Geraldton for both offences against property and against the person.
The importance of Kalgoorlie-Boulder in shaping the crime rate of the Goldfields-Esperance
region was also evident in the 1998 research. With the more detailed analysis possible in this
study the high rates of recorded crime and police contact in the smaller towns of Leonora and
Coolgardie are also evident. Crime rate rankings in the region depend very much on the
offence type. Only Kalgoorlie ranks with other towns in the State as exceeding property
crime rates over 150 per thousand persons, although Esperance approaches this level.
However, both Leonora and the rural areas experience personal offence rates exceeding 20
per thousand, and Norseman, Leonora and, to a lesser extent the rural parts of the region have
high rates of drug and good order offences.
The 1998 CRC report identified the regions of Great Southern, the South West, Wheatbelt
and Peel as having relatively low crime rates in comparison with Perth and the RDC regions.
These patterns remain valid for offences against the person for the three-year analysis 19961998. The one reversal for 1996-1998 is that Peel exhibits a higher rate for the high-volume
property offences than Pilbara. Post-census population increases for Peel27 are not taken into
account by our rate calculations so the increase in crime rates may be partly artificial. The
particular towns with higher than average crime rates in these regions are as follows:
Great Southern: Katanning, particularly for its property crime rates and moderately high
processed person rates and to a lesser extent Mount Barker and Kojonup. The region’s
largest city, Albany, records low crime rates.
Wheatbelt: The towns of Northam, Toodyay and Narrogin record relatively high rates of
property crime - all over 160 per thousand population. It is clear that the Wheatbelt’s crime
rate advantage derives from the high proportion of its population (70%) resident outside the
major regional towns rather than low crime rates within towns.
Peel: The urban area of Mandurah (36,000) and the small town of Pinjarra (2,000) record
relatively high and comparable rates of property (but not violent) offences.
South West: None of the South West towns experience rates of property or violent crime in
the highest categories (set arbitrarily at over 160 per thousand for property and 20 for violent
crime). Bunbury has the peak property crime rate for the region at 130 per thousand while
Harvey ranks first for offences against the person.
Metropolitan area
The analysis of crime, police contact and socio-economic factors in the Perth metropolitan
area is illustrated by way of local government area maps. The change of the scale of analysis
from suburb to LGA leads to some smoothing out of crime rate peaks, as LGA boundaries
27
The same issue affects all regions but Peel may be particularly affected because of its high rate of population
growth.
15
tend to cut through clusters of high crime rate suburbs. Some indication of this is to be seen
from the maps published in the annual statistical reports of the Crime Research Centre
(1998).
The analysis of metropolitan areas within a city is potentially more complex than the analysis
of cities or towns as a whole because a clear conceptual distinction needs to be made between
offence and offender rates for the area. A more detailed exploratory analysis of the
difference between offence and offender patterns is based on courts data from the Ministry of
Justice CHIPS database.
However, beginning with the LGA maps it is clear that Perth, Victoria Park, Fremantle and
East Fremantle have high rates of recorded offences in almost all offence categories. The
insights of routine activity theory help explain these patterns. The daily and weekly patterns
of city life attract thousands of non-residents to these LGAs for work, business and
entertainment. Opportunities for crime (and, of course, for legitimate activity) exist in far
greater abundance than the resident populations of these areas indicate. Unfortunately,
adequate estimates of the population of central city and Fremantle populations at different
times of the day are not available even though they would provide more meaningful
denominators to estimate the risk of crime in business and entertainment areas.
In terms of processed persons, the picture changes significantly. The LGAs of Swan,
Belmont and Kwinana exhibit high rates while Perth, Gosnells, Fremantle, Vincent and
Stirling occupy the second rank.
10 AN EXPLORATORY ANALYSIS OF JUVENILE COURT DATA
The Ministry of Justice maintains its CHIPS (Children’s Court and Petty Sessions) database
which contains records of appearances in the Children’s Court and Courts of Petty Sessions
in Western Australia. Our analysis of CHIPS data focuses on juvenile appearances for
offences in the calendar year 1998. This restrictive focus is based on the assumption that the
coverage of the Children’s Court is State-wide, whereas there is no complete and
comprehensive State coverage of the Courts of Petty Sessions. CHIPS is designed to have a
unique identification number for an offender who has court appearances both as a juvenile
and as an adult. There is some duplication of identification numbers in CHIPS but the level
of this duplication has not been accurately assessed.
Further data issues with the CHIPS database concern its geographic indicators. It was not
possible to geocode the offender and offence locations to points, therefore the allocation of
offenders and offences to regions and localities was achieved initially by mapping postcodes
to RDC regions. However, not all offence and offender locations were assigned postcodes in
CHIPS. Consequently, a second pass at geocoding made use of locality information alone.
However, at the completion of the geocoding there remained 606 cases in CHIPS (1.4% of
the 42,730 cases in 1998) which had missing locality information and could not be assigned
to an RDC region. A final issue with data coding in CHIPS concerns the identification of
offender age. A total of 3,144 cases (7.4%) had no information about offender date of birth
so that age could not be calculated. Cases with missing locality information were highly
likely to contain missing age information as well, and the approach taken is to ignore the
missing data completely. For offenders with known age, there were few with missing
geographic indicators (less than 0.1%).
16
The analysis which follows is based on offender prevalence in the calendar year 1998. This
means that an offender will appear only once in the analysis regardless of the number of court
appearances or offences charged during the year. Only one offence - the first offence
recorded for the year - is listed against each offender when the relationship between offence
location and offender residence is investigated below. Consequently, the patterns discussed
could be quite different from those to be found if every offence or charge committed by each
offender were to be taken into account.
An internal Ministry of Justice report (MacWilliam and Moore, 1999) made use of CHIPS
data to assess the need for juvenile offender programs across the State.
PREVALENCE OF JUVENILE COURT APPEARANCES BY REGION
Table 1 displays the rates of juvenile court appearance by region as well as their ranking in
decreasing order.
Table 1: Juvenile offender court appearance rate by RDC region
Region
Rate per 1000 of
relevant population
Juvenile Court
Ranking
Police contact
ranking
Perth
16.1
8
6
Peel
15.5
10
9
South West
15.7
9
10
Great Southern
23.8
6
7
Wheatbelt
16.2
7
8
Mid-west
30.1
5
5
Goldfields-Esperance
40.6
3
4
Gascoyne
90.8
1
1
Pilbara
36.6
4
3
Kimberley
46.3
2
2
When this table is compared with the rates of police contact evident in Appendix A, some
discrepancies occur. The two main reasons for this are that Table 1 counts each offender
only once regardless of the number of offences, charges or appearances in the calendar year
1998, whereas the police contact statistics count an offender as many times as they are linked
to offences in 1997 and 1998 28 . Second, the police contact figures include all contacts
regardless of the way they are dealt with. Some young offenders, for example, will be dealt
with by way of a caution or a referral to a Juvenile Justice Team. These individuals will
appear in the police contact statistics but will not appear in the Juvenile Court data
summarised in Table 1. Table 2.9 of the report Crime and Justice Statistics for Western
Australia: 1997 (Crime Research Centre, 1998b) indicates that there were over 7,000 distinct
persons cautioned in 1997 and almost 2,000 distinct persons referred by police to Juvenile
Justice Teams 29 . This may be compared with the 3,923 individual offenders appearing in
28
29
Note that the rate calculated for police contact is an annual figure averaged over the two relevant years.
Note that a further 1,258 distinct persons were referred by courts to Juvenile Justice Teams.
17
Juvenile Courts who offended in 1998 and formed the basis for the rates displayed in Table 1.
These figures illustrate differences in the construction of the police contact rates in Appendix
A and the Juvenile Court appearance rates in Table 1 and explain why both absolute rates and
also the relative ranking of regions may vary depending on the data source used.
Despite the differences in absolute rates, the ranking of regions is similar - though not
identical - under both measures of offender contact. While this is the case for juvenile
offending, the ranking of regions is very different for adult offenders under the alternative
measures30 . The principal difference in adult ranking affects the Kimberley region which is
ranked highest in terms of police contact for each of the three age groups aged 18 or over 31 ,
but is ranked lowest for court appearance prevalence for each of these age-groups. This
major discrepancy in ranking is no doubt due to inadequate coverage of adult court
appearances in the Kimberley, but other factors may contribute. These may include the
frequency of contact amongst offenders in the Kimberley relative to other regions. The
contribution of each factor to this discrepancy cannot be stated with confidence, but it seems
unlikely that frequency of contact alone could explain such a major discrepancy.
PROPENSITY OF OFFENDERS TO OFFEND WITHIN THEIR OWN REGION.
Table 2 presents information about
•
the propensity of juvenile offenders to offend within their own region and
•
the propensity of offences in the region to be committed by local offenders.
For example, Column 1 of the first row indicates the percentage of offenders from Perth who
offend within the Perth region (95.5%). Column 3 indicates how many offences committed
within the Perth region were known to be committed by Perth offenders (96.0%). It is clear
that the bulk of offending occurs in the region of residence of the offender. However, some
offenders living outside Perth are more likely to offend outside their region than offenders in
Perth. Where offending outside the region does occur this is overwhelmingly concentrated in
Perth as shown in column 2. Furthermore, offences committed in any region by offenders
living outside it are likely to be committed by Perth offenders.
Because of the relatively large numbers of offenders in the Wheatbelt and Peel who offend in
Perth, a more detailed examination of offending patterns in these regions was undertaken.
There appeared to be no clear patterns of preference for offence location outside the region.
The simplest explanation for the propensity of Wheatbelt and Peel offenders to offend in the
Perth region is that both of these regions are adjacent to Perth and offending in the Perth
region need not take an offender too far from home. The examination of these and other
regions reinforced the importance of the major towns in each region as sites for both
offenders and offences. Thus, Mandurah dominates the Peel region as a site for offenders
and offending. In a similar way, Northam is the major town in the Wheatbelt.
30
31
A detailed table of comparative adult rankings is not included here, but is discussed below.
The age groups listed in Appendix A are 18-29, 30-39, and 40 and over.
18
Table 2: Regional analysis: Juvenile Offenders
Region
Offender percentages
Offending in
own region
Offence percentages
Offending in
Perth1
By offenders
from that region
By Perth
offenders 2
Perth
95.5
95.53
96.0
96.03
Peel
83.2
10.1
79.8
12.9
South West
85.8
5.9
89.1
7.4
Great Southern
86.4
7.8
88.7
6.0
Wheatbelt
75.7
17.7
78.0
15.9
Mid-west
84.5
4.7
90.1
7.2
Goldfields-Esperance
89.8
6.7
93.1
5.7
Gascoyne
97.8
1.1
86.4
3.9
Pilbara
93.1
0.6
93.6
3.5
Kimberley
93.6
2.6
97.3
0.7
1
The balance of offending is spread over other regions.
The remaining offenders come from all other regions.
3
By definition, this figure is identical to the percentage in the adjacent column. Perth offenders who
offend in their own region must offend in Perth.
2
DISTINCTIVE SUBURBS
A final use of the CHIPS database concerns the categorising of suburbs in Perth as having a
relatively high degree of local or non-local offending. This analysis is limited to those
suburbs having at least 15 juvenile offences in 1998. The suburbs which have over 40% of
their offences committed by local offenders (living in the same suburb) are Balga, Ballajura,
Bassendean, Beechboro, Forrestfield, Girrawheen, Gosnells, Greenwood, South Lake,
Thornlie and Wanneroo.
At the other extreme are certain suburbs which have very few local offenders. Those where
fewer than 10% of offences are committed by local offenders 32 are Cannington, Fremantle,
Northbridge, Perth, South Perth, Victoria Park and Warwick. These suburbs provide criminal
opportunities that attract offenders from a large geographic base.
This suburb-based analysis is far from ideal. Size of suburb is an uncontrolled variable and
the greater the suburb size, the greater is the likelihood that offences will be committed by
local offenders33 . A better approach would examine the distance between offence location
32
These suburbs are also restricted to those with over 15 offences.
In large suburbs offenders are able to commit offences at some distance from home yet still remain in their
own suburb.
33
19
and offender residence, independently of suburb size. However, it was not possible to
perform such an analysis in the time available for this project. Remember also that the
offence location of only a single offence per offender was used for the analysis. It is possible
that a multiple offence analysis could produce different results from those discussed above.
As previously discussed, this type of analysis would be inappropriate for the investigation of
adult offending patterns, because adult court coverage in CHIPS is not complete.
11 FURTHER RESEARCH AND DEVELOPMENT REQUIRED
Earlier comment has drawn attention to the need for greater integration of data systems
within the Police Service, a need being addressed in current database planning processes.
However, there would be considerable value in retrospectively linking, for the purpose of
further research, the police-offender contact ('processed person') database with the arrest
database. This linkage would then allow the strengths of both databases to be combined. The
detailed geographic information in OIS could be combined with the ability of the arrest
database to identify individual offenders. Information would then be obtained concerning the
number of individuals who have police contact in each area, their frequency of offending, and
the movement of both high- and low-frequency offenders in the course of their offending.
Furthermore, similar value would accrue to improving the quality of data in the CHIPS
database maintained by the Ministry of Justice. The design of this database is capable of
producing extremely useful information about juvenile and adult offenders. It has the
potential to map across the State both the prevalence of offenders and frequency of offending
for both adults and juveniles with court appearances. Also, it has the potential to track the
‘progress’ of juvenile offenders into adult courts. However, its value is severely diminished
by a relatively high level of missing data (including missing data about age, sex and
Aboriginality) and by locality indicators that could be improved through the application of
consistent rules about data entry.
Even given the limitation of current systems it appears that extensions to the current study
should be considered. There would be value, for example, in investigating the relationship
between offender residence and offence location in a more comprehensive way. This could
be pursued through the analysis of either the CHIPS database, suitably geo-coded, or of the
OIS database. The suggested research would probably become more manageable and useful
with a focus on a particular offence or offences of interest and with a narrower geographic
scope. For example, burglary prevention initiatives in selected metropolitan areas and
country towns could be greatly assisted by research which maps the 'journey to crime' of
offenders and relates this to crime prevention initiatives focused on offenders or victims 34 .
The analysis of socio-demographic factors collected for this study and their relationship to
crime and offending could be extended. This could be achieved through factor analysis,
regression techniques or the development of meaningful theoretical area typologies relevant
to the prediction of levels and patterns of crime. Furthermore, the range of factors included
in future study should be extended to include welfare, health, housing and educational
indicators.
A final but most important initiative would be the planning of cost-effective crime surveys
capable of supporting regional analyses. It is possible that cost effectiveness could be
34
Note that the Crime Research Centre has applied for research funding to undertake research along the lines
suggested here.
20
enhanced by developing new sampling strategies for the conduct of State surveys, the
grouping of survey data collected over several time periods, and discussions with varied
agencies and researchers with an interest in improving regional information. The aim would
be to pool resources to collect information on crime in conjunction with other types of data for example on drug use and other health areas.
12 CONCLUSION
The possibility of traditional theories being successful in explaining the distribution of crime
across Western Australia seems slim given the heterogeneity of ‘types of places’ across the
State. These vary from Aboriginal communities, fishing communities, more traditional
towns, through to modern ‘fly-in fly-out’ private mining towns where the recreational, family
and working lives of individuals are separated by thousands of kilometres and the temporal
rhythm of ‘on’ and ‘off’ weeks. This research (following on from its 1998 precursor) has
confirmed the existence of highly variable crime rates attaching themselves to cities and
towns of varying sizes and categories.
One reaction to the current study is to use the recorded crime and police-offender contact
rates as indicators of the need for crime prevention resources. There is certainly a prima facie
case to be made - under a straightforward risk assessment basis - that State government
funding for crime prevention should be differentially targeted at communities with high crime
and offender rates. The relative position of communities should be balanced by a careful
analysis of the absolute differences from one community to the next.
It is recognised that police recorded crime rates provide only a single indicator of crime.
Crime surveys provide an important alternative measure of crime but there are no regional
crime surveys which could provide any small area validation of the official statistics used in
this report. Surveys of community crime perceptions could provide other indicators of the
extent of crime problems but systematic data are unavailable at the regional or small-area
level and would not necessarily correspond with views expressed through State and local
political processes. It seems arguable therefore that recorded crime levels are currently the
‘best’ data source available, as long as there is recognition that police data represent a tangled
mix of crimes committed together with the reaction (of the public in reporting and the police
in investigating) to those crimes.
The distribution of resources for crime prevention also assumes that there are proven methods
of reducing crime. Here also there is a degree of controversy, about the favoured forms of
crime prevention - broadly speaking, situational crime prevention or social crime prevention.
It is clear from the current study that there is a high spatial correlation between recorded
crime and disadvantage, that both affluence and poverty need to be considered together, and
that both social and situational crime prevention must belong to the mix of strategies. Note
that the data for implementing and evaluating situational crime prevention measures will need
to be collected in a more local, offence specific and disaggregated way than could be
achieved with the current study.
21
EXPLANATORY NOTES
This section provides explanatory notes regarding methodology, terminology and some
important issues in the appendices. Appendices A, B, C and D provide information at four
different levels of geographical focus in Western Australia: namely, regions; towns within
regions; and both Police Districts and local government areas within Perth. Each of these
appendices is broken into sections which present information about socio-demographic
factors, offences and police-offender contacts. The information is presented in bar charts,
except for Appendix D, where the Perth local government area analysis is presented in
coloured thematic maps. A table of denominators used to calculate rates is also included in
each appendix. Appendix E presents time series of offence rates in each region for different
temporal levels: year, month and day.
Socio-demographic factors
The socio-demographic factors in this study were obtained from 1996 Australian Census of
Population and Housing data via the Australian Bureau of Statistics CDATA96 software
package. Appendices A to D show the prevalence of these factors for each of the four
geographic levels. Some factors, though not included in the charts of Appendices A to D, are
included in the Summary Sheets in Appendix A.
The factors have been derived form the Basic Community Profile table in CDATA96.
CDATA96 provided data at CD level, from which the larger geographic areas have been
aggregated. Each CD was placed into each larger geographic area in which the CD's centroid
lay.
The information on socio-demographic factors is expressed as a percentage of the relevant
denominator in the area. For example, in Appendix A: Summary of Gascoyne, the value of
the factor "Population 10 - 17 years" is 7.6%, which is obtained by dividing the number of
persons aged 10 to 17 years in the Gascoyne region (1134) by the total number of persons in
the Gascoyne (14836), and then multiplying by 100.
The Socio-Economic index is included as a factor in this study because it is a composite
indicator of economic and social characteristics. It is calculated by ABS using principalcomponents procedures on variables in the census. Some of these underlying variables are
income, educational attainment, and unemployment. It is included as the Index of Relative
Socio-Economic Disadvantage in the ABS Socio-Economic Indexes for Areas (SEIFA). For
details about its derivation see Australian Bureau of Statistics (1997).
The relevant denominator for each of the factors is given below.
SOCIO-DEMOGRAPHIC FACTORS
DENOMINATOR
Population 10 -17 years
Total population
Total population
—
Population 15 years or more who are unemployed
Population aged 15 or over
Population 15 years or more who left school aged 15 or less
Population aged 15 or over
Population who are Aboriginal or Torres Strait Islander
Total population
22
Persons enumerated at a different address five years earlier
Total population
OPD's with no motor vehicle
Total OPD's
Socio-economic Index
—
Population 15 years or more with no qualification
Population aged 15 or over
Population born overseas and have limited proficiency in
English
Total population
Families in OPD's with weekly income less than $500
Total number of families in OPD's
Households in OPD's with one parent and children aged
under 15 years
Total number of families, groups
or lone persons in OPD's
Private dwellings that are flats, units or apartments
Total private dwellings
OPD's that are rented
Total OPD's
OPD's with more than five residents
Total OPD's
OPD's with more than one family
Total OPD's
OPD is an abbreviation for 'occupied private dwelling'.
Offences
The information on offences was obtained from the WA Police Offence Information System
(OIS). It covers all offences in the OIS for the years 1996 to 1998.
The OIS includes information relating to the location of offences. This information was geocoded so that each offence was assigned a particular ABS CD as far as is possible. This
process allowed information on offences to be aggregated into larger geographical areas of
interest, however, not all offences were able to be geo-coded into a CD.
The offences were classified according to five major offence categories: against the person,
property, drugs, good order, and other offences. Each record in the OIS data was assigned an
Australian National Classification of Offences (ANCO) code, which was then allocated into
one of the major offence categories. Information about the rates of sub-categories of offences,
though not included in the charts of Appendices A to D, are included in the Summary Sheets
in Appendix A.
There is some value in describing the offences in the category good order and its subcategory of street offences. Offences in this category sometimes cause confusion and the
grouping of offences is very disparate. The group includes offences against government
operations, attempts to pervert the course of justice, breaches of court orders such as
probation, parole and so on, conspiracy and other offences against justice, resisting or
hindering police, trespassing and vagrancy, disorderly conduct offences and (when still
criminalised) offences of public drunkenness. The last four groups constitute the street
offence category and offences such as these are better represented in the arrest database than
in the OIS, although trespassing and vagrancy offences also appear in OIS.
Offence information is presented as average yearly rates per 1000 persons in an area. For
example, in Appendix A: Summary of Gascoyne, the rate for Against the Person offences is
20.4, which is obtained by dividing the average yearly number of Against the Person offences
in the Gascoyne region (303) by the total number of persons in the Gascoyne (14836), and
then multiplying by 1000. Note that, although the offence period is 1996 to 1998, the
23
population figure is for 1996 only. This would overestimate offence rates if the population
had increased over the period.
Police-Offender Contacts
The information on offenders is also obtained from the WA Police OIS. It covers all policeoffender contacts (also known as "processed persons") in the OIS for the years 1997 and
1998.
A police-offender contact is a person listed on a police OIS report. Because there is no
unique numerical identifier recorded against an offender on a report, there is no ability to
identify unique individuals on different reports. Hence, one individual may account for
multiple police-offender contacts (ie, a person arrested on more than one occasion is counted
each time they are arrested). Thus, the information presented on offenders is event-based and
is not so much prevalence of offenders but prevalence of offending.
The data contain information relating to the location of offender residence. This information
was geo-coded so that each offence was assigned to a particular ABS collector's district as far
as is possible. This process allowed information on offender addresses to be aggregated into
larger geographical areas of interest, however, not all offender addresses were able to be geocoded into a CD.
The offender information is also presented by demographic classifications, such as sex, age
group and race. However, demographic information could not be ascribed with certainly to
multiple offenders who appeared on the same police report. Accordingly, about 15% of the
records were removed from the analysis for this reason. The remaining 85% of the records
represent offender addresses and demography for single-offender reports. It should be noted,
however, that this is an overall figure for the state, and patterns of multiple offending would
almost certainly vary across different offender demographic and geographic characteristics.
The offender information is presented as average yearly rates per 1000 persons in an area. For
example, in Appendix A: Summary of Gascoyne, the rate for male police-offender contacts is
41.4, which is obtained by dividing the average yearly number of male offenders in the
Gascoyne region (323) by the total number of male persons in the Gascoyne (7802, see the
table Denominators in Towns and Regions on page A72), and then multiplying by 1000.
Note that, although the period is 1997 to 1998, the population figure is for 1996 only. This
would overestimate the rates if the male population had increased over the period.
Time Series
The information on offences was obtained from two sources: the WA Police OIS, and the
WA Police arrest database. They cover the years 1991 to 1998.
The data from both sources did not need to be geo-coded to CD level because the time series
analysis is only on a regional level.
The first section presents the time series of offences recorded in the OIS, and is presented by
the four major offence categories within each region. Sexual and fraud offences are not
included in the charts for the regions. This is because for these offences the precise date and
time of occurrence is often unknown. This is highlighted on page A134, which shows the
average number of reported sexual and fraud offences in WA for each day of the year from
24
1991 to 1998. It is marked by the spikes at the beginning of each month and particularly on
January 1. This is an artefact of the recording procedures of offences where the date of
occurrence is not known. Also shown on page A134 are two time series for offences against
the person; one includes sexual offences and the other does not. The time series without
sexual offences does not display the spikes at the beginning of each month.
The OIS does not have satisfactory coverage of the level of "street" offences, such as
disorderly conduct. The section on street offence arrests is presented to fill this need.
Potential problems in calculating rates
A general discussion of rate problems was included earlier. However some of the detailed
issues are listed below.
The first kind of problem occurs when crime and population are measured over different time
periods. The primary denominator used for the calculation of rates is the population of the
area of interest. Population data are available at the small area level from the 1996 census
whereas crime data are available over varying time periods as described above. Two areas
which may have the same rate of recorded crime may appear to have different rates simply
because one has grown more rapidly in population since July 1996. The area with faster
growth will appear to have a higher crime rate simply because its correct population base
will be underestimated relative to the other. For example, crime rates for fast growing areas
on the fringe of the metropolitan area need to be scrutinised carefully.
A different issue arises in the calculation of rates for small areas (some towns and local
government areas for example). Here there may be sources of error in measuring both the
population, the amount of crime or the number of offenders. When errors appear in both the
numerator and denominator used to calculate a rate, the relative errors are additive. The
sources of error may be in the measurement of crime and offending and also in the census 35 .
One source of error important at the small-area level is the problem of area boundary. The
geo-coding process described above may not be able to accurately assign a crime or an
offender residence to the correct area. This is likely to be a more significant issue in rural
areas rather than in Perth and regional towns but the full dimension of the problem is not well
charted.
Data issues of a different kind arise when the level of aggregation is too broad. For example,
for LGAs within Perth, or within RDC regions, there are greater within-area variations in
crime rates than there are between areas. An examination of suburb crime rates provides
support for this assertion, as do the patterns of crime between towns within the same region.
The selection of an appropriate scale of analysis is not straightforward, hence the selection of
three principal geographic levels in this study.
35
Note that there are deliberate errors introduced into small-area census data in order to protect the
confidentiality of citizens.
25
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of Research in Crime and Delinquency 36(2): 123-155.
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Australian Bureau of Statistics (cat no. 4509.0)
Australian Bureau of Statistics (1999b). Recorded Crime Australia, 1998. Canberra:
Australian Bureau of Statistics (cat no. 4510.0)
Australian Bureau of Statistics (1997). Census of Population and Housing: Socio-economic
Indexes for Areas. Canberra: Australian Bureau of Statistics (cat number 2039.0.)
Beirne, P. (1993). Reinventing criminology. Albany: SUNY Press.
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The Oxford handbook of criminology (2nd edition). Oxford: Clarendon Press.
Bursik, R. and Grasmick, H. (1993). Neighbourhoods and crime: the dimensions of effective
community control. New York: Lexington Books.
Cohen, L. and Felson, M. (1979). 'Social change and crime rate trends: a routine activity
approach'. American Sociological Review, 44:588-608.
Crime Research Centre (1998a). Rural crime and safety: a preliminary study. Perth:
Department of Commerce and Trade.
(available at http://www.wa.gov.au/regional/rcrime/index.htm).
Crime Research Centre (1998b). Crime and Justice Statistics for Western Australia, 1997.
Perth: Crime Research Centre.
Crime Research Centre (1991). Crime and Justice Statistics for Western Australia, 1990.
Perth: Crime Research Centre.
Clarke, R. (1997). Rational choice and situational crime prevention : theoretical
foundations. Brookfield, USA : Ashgate.
Cornish, D and Clarke, R. (Eds.), (1986). The reasoning criminal. New York: SpringerVerlag.
Fergusson, D., Horwood, L. and Lynskey, M. (1993). ‘Ethnicity, social background and
young offending: a fourteen year longitudinal study’. Australian and New Zealand Journal
of Criminology, 26: 155-170.
Harding, R., Broadhurst, R., Ferrante, A. and Loh, N. (1995). Aboriginal contact with the
criminal justice system and the impact of the Royal Commission into Aboriginal Deaths in
Custody. Sydney; Hawkins Press.
26
Homel, R., Lincoln, R. and Herd, B. (1999). ‘Risk and resilience: crime and violence
prevention in Aboriginal communities’. Australian and New Zealand Journal of
Criminology, 32(2): 182-196.
Hough, M. and Tilley, N. (1998). Auditing crime and disorder: guidance for local
partnerships. London: Home Office, Crime Detection and Prevention Series, Paper 91
(available at http://www.homeoffice.gov.uk/prgpubs.htm).
MacWilliam, H. and Moore, A. (1999). Needs analysis for the Western Australian Ministry
of Justice, Juvenile Justice Community Funding Program. Perth: Ministry of Justice.
Maguire, M. (1997 ). 'Crime statistics, patterns and trends', in Maguire, M., Morgan, R. and
Reiner, M. The Oxford handbook of criminology. Oxford: Clarendon Press.
Marvell, T. and Moody, C. (1991). ‘Age structure and crime rates: the conflicting evidence’.
Journal of Quantitative Criminology, 7(3): 237-273.
Miethe, T. and Meier, R. (1994). Crime and its social context. Albany: SUNY Press.
Morrison, W. (1995). Theoretical criminology. London: Cavendish publishing.
National Crime Prevention (1999). Pathways to prevention: Developmental and early
intervention approaches to crime in Australia. Canberra: Attorney-General’s Department.
Regional Development Council (1999). Living in the regions. Perth: Department of
Commerce and Trade. (State report and individual regional reports)
Royal Commission into Aboriginal Deaths in Custody (1991). National Report: Volume 2.
Canberra: Australian Government Publishing Service.
Safer WA (1998). Safer WA crime audit guidelines for local governments. Perth: Western
Australian Police Service.
Snyder, R. (1999). ‘The overrepresentation of juvenile crime proportions in robbery
clearance statistics’. Journal of Quantitative Criminology, 15(2): 151-161.
Taylor, R. Crime, grime, fear, and decline: a longitudinal look. Research in Brief series.
Washington: National Institute of Justice.
Weisheit, R., Falcone, D. and Wells, L. (1994). Rural crime and rural policing. Research in
Action series. Washington: National Institute of Justice.
Western Australian Police Service and Western Australian Ministry of Justice (1999.
Regional Crime and Safety Statistics for Safer WA, 1997-98. Perth: Western Australian
Government.
Wirth, L. ‘Urbanism as a way of life’. American Journal of Sociology, 44: 1-24.
27
INDEX OF APPENDICES
Page
APPENDIX A - Regions in WA ......................................................................................A2
APPENDIX B - Towns and rural remnants in each region ............................................A19
APPENDIX C - WA Police districts in Perth.................................................................A76
APPENDIX D - Local government areas in Perth .........................................................A84
APPENDIX E - Time series of offences in each region...............................................A125
Page A1
APPENDIX A - RDC REGIONS OF WESTERN AUSTRALIA
Kimberley
Pilbara
Gascoyne
Mid West
Goldfields-Esperance
Wheatbelt
Perth
Peel
South West
Great Southern
Page A2
SUMMARYOF
APPENDIX A - REGIONS IN WA
SOCIO-DEMOGRAPHIC FACTORS
%
N
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
OFFENCES REPORTED
GASCOYNE
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
Good order offences
Miscellaneous other offences
Total offences reported
Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
14836
1134
2092
433
6036
1461
6262
499
946
7384
104
633
185
284
1937
172
47
7.6
14.1
3.5
49.1
9.8
42.2
9.2
60.1
0.7
27.8
6.1
4.9
35.6
3.2
0.9
Annual rate per 1000 persons
2
246
38
3
14
303
0.11
16.56
2.56
0.22
0.94
20.40
275
118
4
70
909
402
1777
18.51
7.95
0.27
4.70
61.25
27.10
119.78
76
18
56
151
95
50
2375
5.15
1.21
3.80
10.16
6.38
3.35
160.06
47.32
11.19
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
Annual rate per 1000 persons
393
323
55
221
156
109
174
61
32
26.46
41.40
7.75
151.27
11.66
96.12
82.93
30.44
4.03
Note: In 1997 to 1998 there were 30 cases of unknown sex, 30 of unknown age and 31 of unknown race.
Page A3
APPENDIX A - REGIONS IN WA SUMMARYOF
GOLDFIELDS-ESPERANCE
SOCIO-DEMOGRAPHIC FACTORS
OFFENCES REPORTED
%
N
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
Good order offences
Miscellaneous other offences
Total offences reported
Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
57368
6030
13645
1766
17630
4702
30967
1584
981
25973
127
2520
896
987
7391
877
269
10.5
23.8
4.1
40.6
8.2
54.0
8.8
59.8
0.2
19.5
5.3
4.9
41.0
4.9
1.5
Annual rate per 1000 persons
6
713
107
42
75
943
0.10
12.43
1.87
0.73
1.31
16.43
1180
694
35
612
4100
1429
8051
20.56
12.10
0.62
10.66
71.47
24.92
140.33
428
86
242
756
175
321
10246
7.47
1.50
4.22
13.18
3.06
5.60
178.61
58.46
21.97
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
1509
1167
288
694
755
335
701
301
111
Annual rate per 1000 persons
26.30
36.88
11.19
147.74
14.32
55.47
51.37
27.63
6.57
Note: In 1997 to 1998 there were 108 cases of unknown sex, 108 of unknown age and 121 of unknown race.
Page A4
APPENDIX A - REGIONS IN WA
SUMMARYOF
GREAT SOUTHERN
SOCIO-DEMOGRAPHIC FACTORS
OFFENCES REPORTED
%
N
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
Good order offences
Miscellaneous other offences
Total offences reported
Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
48223
6309
6582
1738
15803
1547
20723
1176
982
23118
125
4597
1001
626
4889
724
102
13.1
13.6
4.8
43.9
3.2
43.0
6.7
64.2
0.3
35.6
5.9
3.0
27.8
4.1
0.6
Annual rate per 1000 persons
3
264
63
16
48
394
0.06
5.48
1.31
0.32
0.99
8.16
537
429
33
143
1937
761
3840
11.13
8.90
0.68
2.97
40.16
15.79
79.62
213
83
135
431
116
134
4915
4.42
1.72
2.80
8.94
2.41
2.79
101.93
25.90
4.99
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
Annual rate per 1000 persons
866
677
158
292
539
252
355
164
58
17.95
27.87
6.58
188.75
11.54
39.94
53.93
21.85
2.92
Note: In 1997 to 1998 there were 63 cases of unknown sex, 63 of unknown age and 70 of unknown race.
Page A5
APPENDIX A - REGIONS IN WA
SUMMARYOF
SOCIO-DEMOGRAPHIC FACTORS
%
N
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
OFFENCES REPORTED
KIMBERLEY
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
Good order offences
Miscellaneous other offences
Total offences reported
Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
32911
3350
6331
790
8801
11459
13159
1708
914
14162
32
1549
601
264
4490
856
382
10.2
19.2
3.1
35.0
34.8
40.0
17.8
56.3
0.1
30.2
9.4
2.6
46.9
8.9
4.0
Annual rate per 1000 persons
6
821
98
13
61
999
0.18
24.95
2.97
0.41
1.86
30.36
764
400
13
296
2250
846
4569
23.22
12.16
0.38
8.98
68.38
25.70
138.83
164
30
111
304
210
127
6210
4.97
0.90
3.36
9.24
6.39
3.87
188.69
76.04
30.67
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
Annual rate per 1000 persons
1422
1150
242
1112
278
283
638
327
133
43.19
66.98
15.37
97.04
12.96
84.48
100.77
60.48
10.72
Note: In 1997 to 1998 there were 60 cases of unknown sex, 60 of unknown age and 63 of unknown race.
Page A6
APPENDIX A - REGIONS IN WA
SUMMARYOF
SOCIO-DEMOGRAPHIC FACTORS
%
N
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
OFFENCES REPORTED
MID WEST
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
Good order offences
Miscellaneous other offences
Total offences reported
Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
51100
6012
9078
2290
16573
3853
23929
1384
964
24119
216
3470
938
970
5640
675
122
11.8
17.8
5.9
42.6
7.5
46.8
8.2
62.0
0.4
29.5
6.0
4.8
33.3
4.0
0.7
Annual rate per 1000 persons
6
568
81
24
51
729
0.11
11.12
1.58
0.46
1.00
14.27
1358
690
41
284
3545
1284
7201
26.58
13.50
0.80
5.56
69.37
25.12
140.93
260
98
165
523
209
145
8807
5.08
1.92
3.24
10.23
4.08
2.83
172.34
67.74
11.09
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
Annual rate per 1000 persons
1134
884
217
628
461
307
498
215
72
22.18
32.10
9.19
162.86
9.76
51.06
54.86
24.44
3.80
Note: In 1997 to 1998 there were 66 cases of unknown sex, 66 of unknown age and 90 of unknown race.
Page A7
APPENDIX A - REGIONS IN WA
SOCIO-DEMOGRAPHIC FACTORS
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
OFFENCES REPORTED
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
Good order offences
Miscellaneous other offences
Total offences reported
Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
SUMMARYOF
PEEL
N
%
61754
8023
7871
3049
23989
872
32608
1559
948
29381
128
6393
1394
894
5366
752
157
13.0
12.7
6.6
51.8
1.4
52.8
6.8
63.5
0.2
36.4
6.2
3.0
23.4
3.3
0.7
Annual rate per 1000 persons
4
357
86
36
51
534
0.06
5.78
1.39
0.58
0.83
8.64
1221
484
36
474
3597
1134
6945
19.77
7.83
0.58
7.68
58.25
18.36
112.47
201
104
86
392
133
181
8185
3.25
1.69
1.40
6.34
2.15
2.94
132.54
41.48
13.41
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
Annual rate per 1000 persons
877
696
151
87
754
208
370
177
89
14.20
22.69
4.84
99.20
12.39
25.86
47.01
18.80
3.34
Note: In 1997 to 1998 there were 62 cases of unknown sex, 62 of unknown age and 73 of unknown race.
Page A8
APPENDIX A - REGIONS IN WA
SUMMARYOF
SOCIO-DEMOGRAPHIC FACTORS
%
N
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
OFFENCES REPORTED
PERTH
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
Good order offences
Miscellaneous other offences
Total offences reported
Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
1234939
148442
231038
49814
352582
17068
555687
43914
1020
552369
23642
82588
25863
43010
123852
13806
3230
12.0
18.7
5.1
36.3
1.4
45.0
9.5
56.9
1.9
25.3
5.8
8.6
26.9
3.0
0.7
Annual rate per 1000 persons
50
8822
2218
2042
1696
14827
0.04
7.14
1.80
1.65
1.37
12.01
32624
11526
443
15248
90969
35655
186464
26.42
9.33
0.36
12.35
73.66
28.87
150.99
5099
1376
2876
9351
4091
7851
222585
4.13
1.11
2.33
7.57
3.31
6.36
180.24
65.41
21.98
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
22276
16975
4456
3180
17977
6267
9614
3584
1895
Annual rate per 1000 persons
18.04
28.07
7.07
186.26
14.76
42.22
41.61
18.68
3.85
Note: In 1997 to 1998 there were 1692 cases of unknown sex, 1692 of unknown age and 2240 of unknown race.
Page A9
APPENDIX A - REGIONS IN WA
SUMMARYOF
SOCIO-DEMOGRAPHIC FACTORS
%
N
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
OFFENCES REPORTED
PILBARA
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
Good order offences
Miscellaneous other offences
Total offences reported
Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
44826
4726
9061
1201
11837
5163
22993
1035
995
17320
188
906
582
1408
6706
691
183
10.5
20.2
3.6
35.4
11.5
51.3
7.4
51.8
0.4
9.9
5.0
8.7
48.1
5.0
1.3
Annual rate per 1000 persons
5
529
72
10
41
656
0.10
11.79
1.61
0.22
0.91
14.63
726
361
18
274
2342
772
4492
16.20
8.05
0.39
6.11
52.24
17.21
100.20
224
51
164
439
153
113
5853
5.00
1.13
3.67
9.80
3.42
2.52
130.58
45.02
14.54
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
Annual rate per 1000 persons
1072
836
202
593
439
310
452
201
71
23.90
33.20
10.27
114.76
11.06
65.49
49.83
21.83
5.11
Note: In 1997 to 1998 there were 68 cases of unknown sex, 68 of unknown age and 81 of unknown race.
Page A10
APPENDIX A - REGIONS IN WA
SUMMARYOF
SOCIO-DEMOGRAPHIC FACTORS
%
N
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
OFFENCES REPORTED
SOUTH WEST
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
y Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
y Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
y Good order offences
y Miscellaneous other offences
y Total offences reported
y Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
108342
14651
16150
3886
37163
1998
49374
2600
979
50244
466
8779
2396
1469
11731
1379
196
13.5
14.9
4.8
46.0
1.8
45.6
6.6
62.2
0.4
30.3
6.4
3.2
30.0
3.5
0.5
Annual rate per 1000 persons
4
503
176
28
66
777
0.04
4.64
1.63
0.26
0.61
7.17
1036
863
71
350
4493
1454
8266
9.57
7.96
0.66
3.23
41.47
13.42
76.30
539
207
300
1046
318
278
10685
4.97
1.91
2.77
9.65
2.94
2.56
98.62
22.54
5.61
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
Annual rate per 1000 persons
1410
1107
251
186
1166
345
622
256
130
13.01
20.26
4.66
93.09
10.96
23.55
38.51
14.51
3.08
Note: In 1997 to 1998 there were 106 cases of unknown sex, 106 of unknown age and 116 of unknown race.
Page A11
APPENDIX A - REGIONS IN WA
SUMMARYOF
SOCIO-DEMOGRAPHIC FACTORS
%
N
Population
Population 10 -17 years
Population 18 -29 years
Population 15 years or more who are unemployed
Population 15 years or more who left school aged 15 or less
Population who are Aboriginal or Torres Strait Islander
Persons enumerated at a different address five years earlier
OPD's with no motor vehicle
Socio-economic Index
Population 15 years or more with no qualification
Population born overseas and have limited proficiency in English
Families in OPD's with weekly income less than $500
Households in OPD's with one parent and children aged under 15 years
Private dwellings that are flats, units or apartments
OPD's that are rented
OPD's with more than five residents
OPD's with more than one family
OFFENCES REPORTED
WHEATBELT
Annual average (1996-98)
Against the person offences
Homicide
Assault
Sexual offences
Robbery
Other
Sub-total
y Property offences
Burglary - dwellings
Burglary - commercial
Burglary - other
Motor vehicle theft
Other theft
Property damage
Sub-total
y Drug offences
Possession/use
Deal/manufacture
Other
Sub-total
y Good order offences
y Miscellaneous other offences
y Total offences reported
y Special rates:
Burglary - dwellings (per 1000 dwellings)
Motor vehicle theft (per 1000 vehicles)
69120
8155
10118
2039
23334
2564
27594
1523
987
35002
127
5476
1097
475
6751
987
100
11.8
14.6
3.9
45.2
3.7
39.9
6.1
67.8
0.2
29.9
4.6
1.5
27.0
4.0
0.4
Annual rate per 1000 persons
7
459
155
11
61
692
0.10
6.64
2.25
0.15
0.88
10.02
744
608
49
196
2547
1143
5287
10.77
8.79
0.71
2.83
36.85
16.54
76.49
365
105
280
750
248
173
7151
5.29
1.51
4.06
10.86
3.58
2.50
103.45
23.95
4.43
Note: Sums of numbers may not equal totals owing to rounding.
POLICE-OFFENDER CONTACTS
Police-offender contacts
Male
Female
ATSI
Non-ATSI
Aged 10 - 17
Aged 18 - 29
Aged 30 - 39
Aged 40 or more
Annual Average (1997-98)
Annual rate per 1000 persons
1180
921
218
420
712
296
518
198
111
17.06
25.32
6.66
163.61
10.69
36.24
51.20
17.43
4.01
Note: In 1997 to 1998 there were 81 cases of unknown sex, 81 of unknown age and 97 of unknown race.
Page A12
Page A13
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
6
Pe
rth
UNEMPLOYED
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
0
Pe
el
5
Mid
We
st
3
Kim
be
rle
y
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
10
Percent
6
Mid
We
st
0
Ga
sco
yne
POPULATION AGED 10 - 17 YEARS
Kim
be
rle
y
4
Percent
WA
Pil
ba
ra
So
uth
We
st
Wh
ea
tbe
lt
Pe
rth
Pe
el
Ga
sco
yne
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rley
Mid
We
st
Percent
9
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Percent
12
Ga
sco
yne
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Kim
be
rley
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Ga
sco
yne
APPENDIX A - REGIONS IN WA
SOCIO-DEMOGRAPHIC FACTORS
POPULATION AGED 18 - 29 YEARS
25
15
20
15
0
LEFT SCHOOL 15 YEARS OR LESS
50
40
30
2
20
10
0
Page A14
0
900
WA
950
So
uth
We
st
Wh
ea
tbe
lt
4
Pil
ba
ra
OPD'S WITH NO VEHICLE
Pe
rth
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
0
Pe
el
0
Pe
el
10
Mid
We
st
3
Kim
be
rle
y
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Percent
Percent
ATSI
Mid
We
st
8
Ga
sco
yne
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Kim
be
rley
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Ga
sco
yne
6
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rle
y
Percent
9
Ga
sco
yne
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Kim
be
rley
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Ga
sco
yne
APPENDIX A - REGIONS IN WA
SOCIO-DEMOGRAPHIC FACTORS
34.8
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
15
12
50
40
30
20
SOCIO-ECONOMIC INDEX
16
1100
12
1050
1000
Page A15
3
0
WA
6
So
uth
We
st
Wh
ea
tbe
lt
9
Pil
ba
ra
12
Pe
rth
DRUG OFFENCES
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
0
Pe
el
5
Mid
We
st
OFFENCES AGAINST THE PERSON
Mid
We
st
10
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rle
y
15
Rate per 1000 persons
20
Ga
sco
yne
Rate per 1000 persons
25
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rle
y
15
Rate per 1000 persons
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Kim
be
rle
y
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Ga
sco
yne
30
Ga
sco
yne
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rley
Ga
sco
yne
Rate per 1000 persons
APPENDIX A - REGIONS IN WA
OFFENCES REPORTED
PROPERTY OFFENCES
150
120
90
60
30
0
GOOD ORDER OFFENCES
6
5
4
3
2
1
0
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Kim
be
rle
y
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Ga
sco
yne
Rate per 1000 persons
APPENDIX A - REGIONS IN WA
POLICE-OFFENDER CONTACTS
ALL PERSONS
40
30
20
10
0
Page A16
Page A17
20
10
0
WA
30
So
uth
We
st
Wh
ea
tbe
lt
40
Pil
ba
ra
50
Pe
rth
AGED 30 - 39 YEARS
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
0
Mid
We
st
20
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rle
y
40
Ga
sco
yne
60
Rate per 1000 persons
80
Pe
el
60
Rate per 1000 persons
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Kim
be
rley
Ga
sco
yne
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Rate per 1000 persons
AGED 10 - 17 YEARS
Mid
We
st
Rate per 1000 persons
100
Ga
sco
yne
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rley
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rley
Ga
sco
yne
APPENDIX A - REGIONS IN WA
POLICE-OFFENDER CONTACTS
AGED 18 - 29 YEARS
100
80
60
40
20
0
AGED 40 YEARS OR MORE
10
8
6
4
2
0
Page A18
ATSI
150
100
50
0
9
6
3
0
So
uth
We
st
Wh
ea
tbe
lt
So
uth
We
st
Wh
ea
tbe
lt
WA
12
WA
15
Pil
ba
ra
NON-ATSI
Pil
ba
ra
0
Pe
rth
10
Pe
rth
20
Pe
el
30
Pe
el
40
Mid
We
st
50
Rate per 1000 persons
60
Ga
sco
yne
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rley
Rate per 1000 persons
MALE
Mid
We
st
200
Rate per 1000 persons
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rley
Ga
sco
yne
70
Ga
sco
yne
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Kim
be
rle
y
WA
So
uth
We
st
Wh
ea
tbe
lt
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Kim
be
rle
y
Ga
sco
yne
Go
ldfi
eld
s-E
sp.
Gr
ea
tS
ou
the
rn
Rate per 1000 persons
APPENDIX A - REGIONS IN WA
POLICE-OFFENDER CONTACTS
FEMALE
16
12
8
4
0
SOCIO-DEMOGRAPHIC FACTORS IN GASCOYNE
APPENDIX B - TOWNS IN REGIONS
POPULATION AGED 10 - 17 YEARS
POPULATION AGED 18 - 29 YEARS
16
12
UNEMPLOYED
Ga
sco
yne
Ca
rna
rvo
n
Ga
sco
yne
0
Ru
ral
0
Ex
mo
uth
4
De
nh
am
2
Ru
ral
8
Ex
mo
uth
4
De
nh
am
Percent
6
Ca
rna
rvo
n
Percent
8
LEFT SCHOOL 15 YEARS OR LESS
5
50
Percent
3
2
40
30
Ga
sco
yne
Ru
ral
Ex
mo
uth
Ga
sco
yne
Page A19
Ru
ral
0
Ex
mo
uth
0
De
nh
am
10
Ca
rna
rvo
n
1
De
nh
am
20
Ca
rna
rvo
n
Percent
4
SOCIO-DEMOGRAPHIC FACTORS IN GASCOYNE
APPENDIX B - TOWNS IN REGIONS
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
50
12
40
0
Ca
rna
rvo
n
OPD'S WITH NO VEHICLE
SOCIO-ECONOMIC INDEX
12
1100
9
1050
6
1000
3
950
0
Ga
sco
yne
Ru
ral
Ex
mo
uth
De
nh
am
Ga
sco
yne
Ru
ral
Ex
mo
uth
De
nh
am
Page A20
Ca
rna
rvo
n
900
Ca
rna
rvo
n
Percent
Ga
sco
yne
0
Ga
sco
yne
10
Ru
ral
3
Ex
mo
uth
20
De
nh
am
6
Ru
ral
30
Ex
mo
uth
9
De
nh
am
Percent
15
Ca
rna
rvo
n
Percent
ATSI
Page A21
5
0
Ga
sco
yne
15
Ru
ral
DRUG OFFENCES
Ga
sco
yne
Ru
ral
Ex
mo
uth
0
Ex
mo
uth
10
De
nh
am
20
Ca
rna
rvo
n
30
Rate per 1000 persons
OFFENCES AGAINST THE PERSON
De
nh
am
10
Rate per 1000 persons
Ga
sco
yne
Ru
ral
Ex
mo
uth
De
nh
am
Ca
rna
rvo
n
Rate per 1000 persons
40
Ca
rna
rvo
n
Ga
sco
yne
Ru
ral
Ex
mo
uth
De
nh
am
Ca
rna
rvo
n
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
OFFENCES REPORTED IN GASCOYNE
PROPERTY OFFENCES
250
200
150
100
50
0
GOOD ORDER OFFENCES
12
9
6
3
0
POLICE-OFFENDER CONTACTS IN GASCOYNE
APPENDIX B - TOWNS IN REGIONS
ALL PERSONS
Rate per 1000 persons
50
40
30
20
10
Ga
sco
yne
Ru
ral
Ex
mo
uth
De
nh
am
Ca
rna
rvo
n
0
Page A22
Page A23
40
30
20
10
0
Ga
sco
yne
AGED 30 - 39 YEARS
Ru
ral
Ga
sco
yne
Ru
ral
0
Ex
mo
uth
AGED 10 - 17 YEARS
Ex
mo
uth
30
De
nh
am
60
Ca
rna
rvo
n
90
Rate per 1000 persons
120
De
nh
am
50
Rate per 1000 persons
Ga
sco
yne
Ru
ral
Ex
mo
uth
De
nh
am
Ca
rna
rvo
n
Rate per 1000 persons
150
Ca
rna
rvo
n
Ga
sco
yne
Ru
ral
Ex
mo
uth
De
nh
am
Ca
rna
rvo
n
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN GASCOYNE
AGED 18 - 29 YEARS
180
120
90
60
30
0
AGED 40 YEARS OR MORE
8
6
4
2
0
Page A24
ATSI
150
100
50
0
9
6
3
0
Ga
sco
yne
12
Ga
sco
yne
15
Ru
ral
NON-ATSI
Ru
ral
0
Ex
mo
uth
MALE
Ex
mo
uth
15
De
nh
am
30
Ca
rna
rvo
n
45
Rate per 1000 persons
60
De
nh
am
200
Rate per 1000 persons
Ga
sco
yne
Ru
ral
Ex
mo
uth
De
nh
am
Ca
rna
rvo
n
Rate per 1000 persons
75
Ca
rna
rvo
n
Ga
sco
yne
Ru
ral
Ex
mo
uth
De
nh
am
Ca
rna
rvo
n
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN GASCOYNE
FEMALE
15
12
9
6
3
0
Page A25
0
UNEMPLOYED
6
Ru
ral
Go
ldfi
eld
s-E
sp.
0
Ru
ral
Go
ldfi
eld
s-E
sp.
5
No
rse
ma
n
3
No
rse
ma
n
25
Le
on
ora
12
Le
ins
ter
30
Le
on
ora
0
Ka
lgo
orli
e-B
ou
lde
r
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Percent
6
Co
olg
ard
ie
Es
pe
ran
ce
Percent
POPULATION AGED 10 - 17 YEARS
Le
ins
ter
4
Percent
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
Le
ins
ter
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Co
olg
ard
ie
Es
pe
ran
ce
15
Es
pe
ran
Ka
ce
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Percent
9
Co
olg
ard
ie
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
Le
ins
ter
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Es
pe
ran
ce
Co
olg
ard
ie
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN GOLDFIELDS-ESPERANCE
POPULATION AGED 18 - 29 YEARS
20
15
10
LEFT SCHOOL 15 YEARS OR LESS
50
40
30
2
20
10
0
Page A26
1100
9
1050
6
1000
3
950
0
900
Ru
ral
Go
ldfi
eld
s-E
sp.
OPD'S WITH NO VEHICLE
No
rse
ma
n
12
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
0
Le
on
ora
3
Le
ins
ter
9
Le
ins
ter
12
Percent
15
Es
pe
ran
Ka
ce
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Percent
18
Co
olg
ard
ie
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
Le
ins
ter
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Co
olg
ard
ie
Es
pe
ran
ce
75
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Percent
ATSI
Co
olg
ard
ie
Es
pe
ran
ce
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
Le
ins
ter
Es
pe
ran
Ka
ce
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Co
olg
ard
ie
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN GOLDFIELDS-ESPERANCE
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
60
45
30
6
15
0
SOCIO-ECONOMIC INDEX
Page A27
10
0
Ru
ral
Go
ldfi
eld
s-E
sp.
20
No
rse
ma
n
DRUG OFFENCES
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
0
Le
on
ora
5
Le
ins
ter
10
Ka
l.-B
ou
lde
r
Ka
mb
ald
aE
ast
Ka
mb
ald
aW
est
15
Co
olg
ard
ie
Es
pe
ran
ce
20
Rate per 1000 persons
OFFENCES AGAINST THE PERSON
Le
ins
ter
30
Rate per 1000 persons
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
Le
ins
ter
Ka
l.-B
ou
lde
r
Ka
mb
ald
aE
ast
Ka
mb
ald
aW
est
Co
olg
ard
ie
Es
pe
ran
ce
Rate per 1000 persons
25
Co
olg
ard
ie
Es
pe
ran
ce
Ka
l.-B
ou
lde
r
Ka
mb
ald
aE
ast
Ka
mb
ald
aW
est
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
Le
ins
ter
Co
olg
ard
ie
Es
pe
ran
ce
Ka
l.-B
ou
lde
r
Ka
mb
ald
aE
ast
Ka
mb
ald
aW
est
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
OFFENCES IN GOLDFIELDS-ESPERANCE
PROPERTY OFFENCES
180
150
120
90
60
30
0
GOOD ORDER OFFENCES
5
4
3
2
1
0
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
40
Le
on
ora
Le
ins
ter
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Co
olg
ard
ie
Es
pe
ran
ce
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER
CONTACTS IN GOLDFIELDS-ESPERANCE
ALL PERSONS
73
30
20
10
0
Page A28
Page A29
30
15
0
Ru
ral
Go
ldfi
eld
s-E
sp.
45
No
rse
ma
n
AGED 30 - 39 YEARS
Le
on
ora
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
0
Le
ins
ter
AGED 10 - 17 YEARS
Le
ins
ter
20
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
40
Co
olg
ard
ie
Es
pe
ran
ce
60
Rate per 1000 persons
80
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
60
Rate per 1000 persons
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
Le
ins
ter
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Co
olg
ard
ie
Es
pe
ran
ce
Rate per 1000 persons
284
Co
olg
ard
ie
Es
pe
ran
ce
Ru
ral
Go
ldfi
eld
s-E
sp.
Rate per 1000 persons
100
No
rse
ma
n
Le
on
ora
Le
ins
ter
Ka
lgo
orli
e-B
ou
lde
r
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Co
olg
ard
ie
Es
pe
ran
ce
APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER
CONTACTS IN GOLDFIELDS-ESPERANCE
AGED 18 - 29 YEARS
120
90
60
30
0
AGED 40 YEARS OR MORE
12
9
6
3
0
Page A30
ATSI
200
150
100
50
0
8
4
0
Ru
ral
Go
ldfi
eld
s-E
sp.
12
Ru
ral
Go
ldfi
eld
s-E
sp.
16
No
rse
ma
n
20
No
rse
ma
n
NON-ATSI
Le
on
ora
0
Le
on
ora
15
Le
ins
ter
MALE
Le
ins
ter
30
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
45
Rate per 1000 persons
60
Co
olg
ard
ie
Es
pe
ran
ce
Rate per 1000 persons
75
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
333
Rate per 1000 persons
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
Le
on
ora
Le
ins
ter
Ka
lgo
orli
e-B
ou
lde
r
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Co
olg
ard
ie
Es
pe
ran
ce
97
Co
olg
ard
ie
Es
pe
ran
ce
Ru
ral
Go
ldfi
eld
s-E
sp.
No
rse
ma
n
250
Le
on
ora
Le
ins
ter
Ka
lgo
orl
ieBo
uld
er
Ka
mb
ald
a(
Ea
st)
Ka
mb
ald
aW
est
Co
olg
ard
ie
Es
pe
ran
ce
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER
CONTACTS IN GOLDFIELDS-ESPERANCE
FEMALE
29
20
15
10
5
0
Page A31
6
0
Gr
ea
tS
ou
the
rn
UNEMPLOYED
Ru
ral
Gr
ea
tS
ou
the
rn
Ru
ral
0
Mo
un
tB
ark
er
0
Mo
un
tB
ark
er
4
Ko
jon
up
3
Ko
jon
up
16
Ka
tan
nin
g
12
Ka
tan
nin
g
20
De
nm
ark
6
Alb
an
y
9
Percent
POPULATION AGED 10 - 17 YEARS
De
nm
ark
4
Percent
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Percent
15
Alb
an
y
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Percent
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN GREAT SOUTHERN
POPULATION AGED 18 - 29 YEARS
12
8
LEFT SCHOOL 15 YEARS OR LESS
50
40
30
2
20
10
0
Page A32
6
1000
3
950
0
900
Mo
un
tB
ark
er
Ko
jon
up
Gr
ea
tS
ou
the
rn
1050
Gr
ea
tS
ou
the
rn
9
Ru
ral
SOCIO-ECONOMIC INDEX
Ru
ral
1100
Mo
un
tB
ark
er
OPD'S WITH NO VEHICLE
Ko
jon
up
12
Ka
tan
nin
g
0
Ka
tan
nin
g
2
De
nm
ark
ATSI
De
nm
ark
4
Percent
6
Alb
an
y
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Percent
8
Alb
an
y
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Percent
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN GREAT SOUTHERN
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
50
40
30
20
10
0
Page A33
25
15
10
5
0
2
1
0
Mo
un
tB
ark
er
Ko
jon
up
Gr
ea
tS
ou
the
rn
3
Gr
ea
tS
ou
the
rn
4
Ru
ral
GOOD ORDER OFFENCES
Ru
ral
Mo
un
tB
ark
er
DRUG OFFENCES
Ko
jon
up
0
Ka
tan
nin
g
OFFENCES AGAINST THE PERSON
Ka
tan
nin
g
4
De
nm
ark
8
Alb
an
y
12
Rate per 1000 persons
16
De
nm
ark
20
Rate per 1000 persons
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Rate per 1000 persons
20
Alb
an
y
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
OFFENCES REPORTED IN GREAT SOUTHERN
PROPERTY OFFENCES
180
150
120
90
60
30
0
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN GREAT SOUTHERN
Rate per 1000 persons
ALL PERSONS
40
30
20
10
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
0
Page A34
Page A35
30
20
10
0
3
2
1
0
Gr
ea
tS
ou
the
rn
4
Gr
ea
tS
ou
the
rn
5
Ru
ral
AGED 40 YEARS OR MORE
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
100
Mo
un
tB
ark
er
40
Ko
jon
up
AGED 30 - 39 YEARS
Ka
tan
nin
g
AGED 10 - 17 YEARS
Ka
tan
nin
g
0
De
nm
ark
20
Alb
an
y
40
Rate per 1000 persons
60
De
nm
ark
50
Rate per 1000 persons
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Rate per 1000 persons
80
Alb
an
y
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN GREAT SOUTHERN
AGED 18 - 29 YEARS
144
80
60
40
20
0
Page A36
150
100
50
0
10
5
0
Mo
un
tB
ark
er
Gr
ea
tS
ou
the
rn
15
Gr
ea
tS
ou
the
rn
20
Ru
ral
NON-ATSI
Ru
ral
Mo
un
tB
ark
er
ATSI
Ko
jon
up
MALE
Ko
jon
up
200
Ka
tan
nin
g
0
Ka
tan
nin
g
10
De
nm
ark
20
Alb
an
y
30
Rate per 1000 persons
40
De
nm
ark
250
Rate per 1000 persons
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Rate per 1000 persons
50
Alb
an
y
Gr
ea
tS
ou
the
rn
Ru
ral
Mo
un
tB
ark
er
Ko
jon
up
Ka
tan
nin
g
De
nm
ark
Alb
an
y
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN GREAT SOUTHERN
FEMALE
24
20
16
12
8
4
0
Page A37
1
0
Kim
be
rle
y
2
Wy
nd
ha
m
4
Ru
ral
5
Ku
nu
nu
rra
UNEMPLOYED
Ha
lls
Cre
ek
Kim
be
rle
y
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
Ha
lls
Cre
ek
0
Fitz
roy
Cro
ssi
ng
3
Fit
zro
yC
ros
sin
g
12
De
rby
6
Bro
om
e
9
Percent
POPULATION AGED 10 - 17 YEARS
De
rby
3
Percent
Kim
be
rley
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
Ha
lls
Cre
ek
Fitz
roy
Cro
ssi
ng
De
rby
Bro
om
e
Percent
15
Bro
om
e
Kim
be
rle
y
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
Ha
lls
Cre
ek
Fitz
roy
Cro
ssi
ng
De
rby
Bro
om
e
Percent
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN KIMBERLEY
POPULATION AGED 18 - 29 YEARS
21
18
15
12
9
6
3
0
LEFT SCHOOL 15 YEARS OR LESS
40
30
20
10
0
Page A38
OPD'S WITH NO VEHICLE
30
24
18
12
6
0
1150
1100
1050
1000
950
900
850
800
750
Ru
ral
Kim
be
rle
y
1200
Kim
be
rle
y
SOCIO-ECONOMIC INDEX
Wy
nd
ha
m
1250
Wy
nd
ha
m
Ru
ral
36
Ku
nu
nu
rra
0
Ku
nu
nu
rra
0
Ha
lls
Cre
ek
10
Ha
lls
Cre
ek
10
Fit
zro
yC
ros
sin
g
20
Fit
zro
yC
ros
sin
g
50
De
rby
ATSI
De
rby
30
Percent
40
Bro
om
e
Kim
be
rley
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
Ha
lls
Cre
ek
Fit
zro
yC
ros
sin
g
De
rby
Bro
om
e
Percent
60
Bro
om
e
Kim
be
rley
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
Ha
lls
Cre
ek
Fitz
roy
Cro
ssi
ng
De
rby
Bro
om
e
Percent
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN KIMBERLEY
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
50
40
30
20
Page A39
3
0
Kim
be
rle
y
6
Ru
ral
9
Wy
nd
ha
m
12
Ku
nu
nu
rra
DRUG OFFENCES
Kim
be
rle
y
Ru
ral
Wy
nd
ha
m
Ku
nu
nu
rra
Ha
lls
Cre
ek
OFFENCES AGAINST THE PERSON
Ha
lls
Cre
ek
0
Fit
zro
yC
ros
sin
g
10
Fit
zro
yC
ros
sin
g
20
De
rby
30
Bro
om
e
40
Rate per 1000 persons
50
De
rby
15
Rate per 1000 persons
Kim
be
rle
y
Ru
ral
Wy
nd
ha
m
Ku
nu
nu
rra
60
Bro
om
e
Kim
be
rley
Ru
ral
Wy
nd
ha
m
Ku
nu
nu
rra
Ha
lls
Cre
ek
Fit
zro
yC
ros
sin
g
De
rby
Bro
om
e
Rate per 1000 persons
70
Ha
lls
Cre
ek
Fit
zro
yC
ros
sin
g
De
rby
Bro
om
e
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
OFFENCES REPORTED IN KIMBERLEY
PROPERTY OFFENCES
139
250
200
150
100
50
0
GOOD ORDER OFFENCES
10
8
6
4
2
0
POLICE-OFFENDER CONTACTS IN KIMBERLEY
APPENDIX B - TOWNS IN REGIONS
Rate per 1000 persons
ALL PERSONS
150
120
90
60
30
Kim
be
rle
y
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
Ha
lls
Cre
ek
Fit
zro
yC
ros
sin
g
De
rby
Bro
om
e
0
Page A40
Page A41
120
80
40
0
30
20
10
0
Ru
ral
Kim
be
rley
40
Kim
be
rley
332
Wy
nd
ha
m
AGED 40 YEARS OR MORE
Wy
nd
ha
m
Ru
ral
AGED 30 - 39 YEARS
Ku
nu
nu
rra
0
Ku
nu
nu
rra
40
Ha
lls
Cre
ek
80
Ha
lls
Cre
ek
120
De
rby
Fit
zro
yC
ros
sin
g
160
Bro
om
e
200
Rate per 1000 persons
AGED 10 - 17 YEARS
De
rby
Fit
zro
yC
ros
sin
g
160
Rate per 1000 persons
Kim
be
rley
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
Ha
lls
Cre
ek
Fit
zro
yC
ros
sin
g
De
rby
Bro
om
e
Rate per 1000 persons
240
Bro
om
e
Kim
be
rley
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
200
Ha
lls
Cre
ek
Fit
zro
yC
ros
sin
g
De
rby
Bro
om
e
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN KIMBERLEY
AGED 18 - 29 YEARS
350
300
250
200
150
100
50
0
Page A42
ATSI
200
150
100
50
0
4
0
Ku
nu
nu
rra
Kim
be
rle
y
8
Kim
be
rley
12
Wy
nd
ha
m
16
Wy
nd
ha
m
NON-ATSI
Ru
ral
409
Ru
ral
Ku
nu
nu
rra
0
Ha
lls
Cre
ek
MALE
Ha
lls
Cre
ek
50
De
rby
Fit
zro
yC
ros
sin
g
100
Bro
om
e
150
Rate per 1000 persons
200
De
rby
Fit
zro
yC
ros
sin
g
250
Rate per 1000 persons
Kim
be
rley
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
Ha
lls
Cre
ek
Fit
zro
yC
ros
sin
g
De
rby
Bro
om
e
Rate per 1000 persons
250
Bro
om
e
Kim
be
rle
y
Wy
nd
ha
m
Ru
ral
Ku
nu
nu
rra
300
Ha
lls
Cre
ek
Fit
zro
yC
ros
sin
g
De
rby
Bro
om
e
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN KIMBERLEY
FEMALE
40
30
20
10
0
Page A43
Mid
We
st
6
Ru
ral
UNEMPLOYED
No
rth
am
pto
n
Mid
We
st
Ru
ral
No
rth
am
pto
n
0
Me
eka
tha
rra
3
Ka
lba
rri
12
Me
eka
tha
rra
0
Ge
rald
ton
9
Percent
POPULATION AGED 10 - 17 YEARS
Ka
lba
rri
4
Percent
Mid
We
st
Ru
ral
No
rth
am
pto
n
Me
eka
tha
rra
Ka
lba
rri
Ge
rald
ton
Percent
15
Ge
rald
ton
Mid
We
st
Ru
ral
No
rth
am
pto
n
Me
eka
tha
rra
Ka
lba
rri
Ge
rald
ton
Percent
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN MID WEST
POPULATION AGED 18 - 29 YEARS
20
15
6
10
5
0
LEFT SCHOOL 15 YEARS OR LESS
8
50
40
30
2
20
10
0
Page A44
15
OPD'S WITH NO VEHICLE
12
1050
9
6
1000
3
950
0
900
Mid
We
st
SOCIO-ECONOMIC INDEX
Mid
We
st
1100
Ru
ral
0
Ru
ral
0
No
rth
am
pto
n
10
No
rth
am
pto
n
3
Me
eka
tha
rra
20
Me
eka
tha
rra
6
Ka
lba
rri
12
Ka
lba
rri
Percent
9
Ge
rald
ton
Mid
We
st
Ru
ral
ATSI
30
Ge
rald
ton
Mid
We
st
Ru
ral
No
rth
am
pto
n
Me
eka
tha
rra
Ka
lba
rri
Ge
ral
dto
n
Percent
15
No
rth
am
pto
n
Me
eka
tha
rra
Ka
lba
rri
Ge
rald
ton
Percent
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN MID WEST
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
50
40
30
Page A45
6
3
0
Mid
We
st
9
Ru
ral
DRUG OFFENCES
Mid
We
st
Ru
ral
No
rth
am
pto
n
0
No
rth
am
pto
n
5
Me
eka
tha
rra
OFFENCES AGAINST THE PERSON
Me
eka
tha
rra
10
Ka
lba
rri
15
Ge
ral
dto
n
20
Rate per 1000 persons
25
Ka
lba
rri
12
Rate per 1000 persons
Mid
We
st
Ru
ral
No
rth
am
pto
n
Me
eka
tha
rra
Ka
lba
rri
Ge
ral
dto
n
Rate per 1000 persons
30
Ge
rald
ton
Mid
We
st
Ru
ral
No
rth
am
pto
n
Me
eka
tha
rra
Ka
lba
rri
Ge
rald
ton
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
OFFENCES REPORTED IN MID WEST
PROPERTY OFFENCES
210
180
150
120
90
60
30
0
GOOD ORDER OFFENCES
8
7
6
5
4
3
2
1
0
POLICE-OFFENDER CONTACTS IN MID WEST
APPENDIX B - TOWNS IN REGIONS
ALL PERSONS
68
Rate per 1000 persons
30
24
18
12
6
Mid
We
st
Ru
ral
No
rth
am
pto
n
Me
eka
tha
rra
Ka
lba
rri
Ge
ral
dto
n
0
Page A46
Page A47
AGED 30 - 39 YEARS
40
30
20
10
0
3
0
No
rth
am
pto
n
Mid
We
st
6
Mid
We
st
9
Ru
ral
AGED 40 YEARS OR MORE
Ru
ral
No
rth
am
pto
n
0
80
Me
eka
tha
rra
AGED 10 - 17 YEARS
Me
eka
tha
rra
20
Ka
lba
rri
40
Ge
rald
ton
60
Rate per 1000 persons
80
Ka
lba
rri
50
Rate per 1000 persons
Mid
We
st
Ru
ral
No
rth
am
pto
n
257
Ge
rald
ton
Mid
We
st
Ru
ral
No
rth
am
pto
n
Me
eka
tha
rra
Ka
lba
rri
Ge
ral
dto
n
Rate per 1000 persons
100
Me
eka
tha
rra
Ka
lba
rri
Ge
rald
ton
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN MID WEST
AGED 18 - 29 YEARS
169
60
40
20
0
Page A48
100
50
0
9
6
3
0
No
rth
am
pto
n
Mid
We
st
12
Mid
We
st
15
Ru
ral
NON-ATSI
Ru
ral
No
rth
am
pto
n
ATSI
Me
eka
tha
rra
0
Ka
lba
rri
MALE
Me
eka
tha
rra
150
Ge
rald
ton
15
Rate per 1000 persons
30
Ka
lba
rri
200
Rate per 1000 persons
Mid
We
st
Ru
ral
No
rth
am
pto
n
45
Ge
ral
dto
n
Mid
We
st
Ru
ral
No
rth
am
pto
n
Me
eka
tha
rra
Ka
lba
rri
Ge
ral
dto
n
Rate per 1000 persons
60
Me
eka
tha
rra
Ka
lba
rri
Ge
ral
dto
n
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN MID WEST
FEMALE
107
24
20
16
12
8
4
0
Page A49
4
2
0
Pe
el
8
Wa
roo
na
UNEMPLOYED
Ru
ral
Pe
el
Wa
roo
na
Ru
ral
0
Pin
jar
ra
3
Pin
jar
ra
15
No
rth
Pin
jar
ra
9
Ma
nd
ura
h
12
Percent
POPULATION AGED 10 - 17 YEARS
No
rth
Pin
jar
ra
6
Percent
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Percent
18
Ma
nd
ura
h
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Percent
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN PEEL
POPULATION AGED 18 - 29 YEARS
15
12
9
6
6
3
0
LEFT SCHOOL 15 YEARS OR LESS
60
50
40
30
20
10
0
SOCIO-DEMOGRAPHIC FACTORS IN PEEL
APPENDIX B - TOWNS IN REGIONS
ATSI
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
60
5
50
Percent
Percent
4
3
40
30
2
20
1
10
0
OPD'S WITH NO VEHICLE
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
0
SOCIO-ECONOMIC INDEX
10
1200
1150
Percent
8
1100
1050
6
1000
4
950
900
2
850
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
Page A50
No
rth
Pin
jar
ra
800
Ma
nd
ura
h
0
Page A51
9
6
3
0
Pe
el
15
Ru
ral
DRUG OFFENCES
Wa
roo
na
Pe
el
Ru
ral
Wa
roo
na
0
Pin
jar
ra
OFFENCES AGAINST THE PERSON
Pin
jar
ra
3
No
rth
Pin
jar
ra
6
Ma
nd
ura
h
9
Rate per 1000 persons
12
No
rth
Pin
jar
ra
12
Rate per 1000 persons
Pe
el
Ru
ral
Wa
roo
na
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Rate per 1000 persons
15
Ma
nd
ura
h
Pe
el
Ru
ral
Wa
roo
na
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
OFFENCES REPORTED IN PEEL
PROPERTY OFFENCES
180
150
120
90
60
30
0
GOOD ORDER OFFENCES
4
3
2
1
0
POLICE-OFFENDER CONTACTS IN PEEL
APPENDIX B - TOWNS IN REGIONS
Rate per 1000 persons
ALL PERSONS
25
20
15
10
5
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
0
Page A52
Page A53
10
5
0
Pe
el
15
Wa
roo
na
20
Ru
ral
AGED 30 - 39 YEARS
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
AGED 10 - 17 YEARS
Pin
jar
ra
0
No
rth
Pin
jar
ra
10
Ma
nd
ura
h
20
Rate per 1000 persons
30
No
rth
Pin
jar
ra
25
Rate per 1000 persons
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Rate per 1000 persons
40
Ma
nd
ura
h
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN PEEL
AGED 18 - 29 YEARS
75
60
45
30
15
0
AGED 40 YEARS OR MORE
12
9
6
3
0
Page A54
ATSI
90
60
30
0
16
12
8
4
0
Ru
ral
Pe
el
20
Pe
el
24
Wa
roo
na
NON-ATSI
Wa
roo
na
Ru
ral
0
Pin
jar
ra
MALE
Pin
jar
ra
10
No
rth
Pin
jar
ra
20
Ma
nd
ura
h
30
Rate per 1000 persons
40
No
rth
Pin
jar
ra
120
Rate per 1000 persons
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Rate per 1000 persons
50
Ma
nd
ura
h
Pe
el
Wa
roo
na
Ru
ral
Pin
jar
ra
No
rth
Pin
jar
ra
Ma
nd
ura
h
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN PEEL
FEMALE
6
5
4
3
2
1
0
Page A55
Pri
ce
UNEMPLOYED
2
0
30
20
10
0
To
m
Pri
ce
Pil
ba
ra
6
Pil
ba
ra
40
Wi
ckh
am
LEFT SCHOOL 15 YEARS OR LESS
Wi
ckh
am
Pri
ce
0
To
m
3
Ru
ral
20
Ru
ral
12
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
24
Ka
rra
tha
Percent
6
Da
mp
ier
Pil
ba
ra
POPULATION AGED 10 - 17 YEARS
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
4
Percent
Pri
ce
Wi
ckh
am
To
m
Ru
ral
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
Ka
rra
tha
Da
mp
ier
Percent
15
Ka
rra
tha
Percent
9
Da
mp
ier
Pil
ba
ra
Wi
ckh
am
To
m
Ru
ral
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
Ne
wm
an
Ka
rra
tha
Da
mp
ier
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN PILBARA
POPULATION AGED 18 - 29 YEARS
16
12
8
4
0
Page A56
12
6
3
0
1100
9
1050
1000
950
900
850
800
To
m
Pri
ce
Pil
ba
ra
1150
Pil
ba
ra
SOCIO-ECONOMIC INDEX
Wi
ckh
am
1200
Wi
ckh
am
Pri
ce
15
To
m
OPD'S WITH NO VEHICLE
Ru
ral
0
Ru
ral
3
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
9
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
15
Ka
rra
tha
12
Percent
18
Da
mp
ier
Pil
ba
ra
60
Ka
rra
tha
Pri
ce
Wi
ckh
am
To
m
Percent
29
Da
mp
ier
Pil
ba
ra
Wi
ckh
am
Pri
ce
Ru
ral
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
Ne
wm
an
Ka
rra
tha
Da
mp
ier
58
To
m
Percent
ATSI
Ru
ral
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
Ne
wm
an
Ka
rra
tha
Da
mp
ier
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN PILBARA
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
50
40
30
6
20
10
0
Page A57
DRUG OFFENCES
12
8
4
0
4
3
2
1
0
Pil
ba
ra
5
Pil
ba
ra
20
Ru
ral
GOOD ORDER OFFENCES
Ru
ral
Wi
ckh
am
OFFENCES AGAINST THE PERSON
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
To
m
Pri
ce
Wi
ckh
am
0
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
To
m
Pri
ce
5
Ka
rra
tha
10
Da
mp
ier
15
Rate per 1000 persons
20
Ka
rra
tha
16
Rate per 1000 persons
Pil
ba
ra
Ru
ral
Wi
ckh
am
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
To
m
Pri
ce
Ka
rra
tha
Da
mp
ier
Rate per 1000 persons
25
Da
mp
ier
Pil
ba
ra
Ru
ral
Wi
ckh
am
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
To
m
Pri
ce
Ka
rra
tha
Da
mp
ier
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
OFFENCES REPORTED IN PILBARA
PROPERTY OFFENCES
61
180
150
120
90
60
30
0
Pri
ce
Pil
ba
ra
Wi
ckh
am
To
m
Ru
ral
40
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
Ka
rra
tha
Da
mp
ier
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN PILBARA
ALL PERSONS
97
32
24
16
8
0
Page A58
Page A59
Pri
ce
20
10
0
10
6
4
2
0
To
m
Pri
ce
Pil
ba
ra
8
Pil
ba
ra
15
Wi
ckh
am
AGED 40 YEARS OR MORE
Wi
ckh
am
Pri
ce
156
To
m
AGED 30 - 39 YEARS
Ru
ral
0
Ru
ral
217
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
AGED 10 - 17 YEARS
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
20
Ka
rra
tha
40
Rate per 1000 persons
60
Da
mp
ier
Pil
ba
ra
80
Ka
rra
tha
30
Rate per 1000 persons
Pri
ce
Ru
ral
Wi
ckh
am
To
m
100
Da
mp
ier
Pil
ba
ra
Wi
ckh
am
To
m
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
Ka
rra
tha
Da
mp
ier
Rate per 1000 persons
120
Ru
ral
40
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
Ka
rra
tha
Da
mp
ier
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN PILBARA
AGED 18 - 29 YEARS
197
75
60
45
30
15
0
Page A60
Pri
ce
180
120
90
60
30
0
9
6
3
0
To
m
Pri
ce
Pil
ba
ra
12
Pil
ba
ra
15
Wi
ckh
am
NON-ATSI
Wi
ckh
am
Pri
ce
ATSI
To
m
0
Ru
ral
15
Ru
ral
10
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
MALE
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
20
Ka
rra
tha
30
Rate per 1000 persons
40
Da
mp
ier
Pil
ba
ra
50
Ka
rra
tha
150
Rate per 1000 persons
Pri
ce
Wi
ckh
am
To
m
150
Da
mp
ier
Pil
ba
ra
Wi
ckh
am
To
m
Ru
ral
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
Ka
rra
tha
Da
mp
ier
Rate per 1000 persons
60
Ru
ral
Ne
wm
an
Pa
nn
aw
on
ica
Pa
rab
urd
oo
Po
rt H
ed
lan
d
Ro
eb
ou
rne
Ka
rra
tha
Da
mp
ier
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN PILBARA
FEMALE
39
12
9
6
3
0
Page A61
0
UNEMPLOYED
6
0
Ru
ral
So
uth
We
st
4
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Percent
8
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Percent
12
Ru
ral
So
uth
We
st
4
Percent
Ru
ral
So
uth
We
st
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Co
llie
Du
nsb
oro
ug
h
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
POPULATION AGED 10 - 17 YEARS
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Percent
16
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Ru
ral
So
uth
We
st
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN SOUTH WEST
POPULATION AGED 18 - 29 YEARS
20
16
12
8
4
0
LEFT SCHOOL 15 YEARS OR LESS
50
40
30
2
20
10
0
Page A62
15
OPD'S WITH NO VEHICLE
12
3
0
Ru
ral
So
uth
We
st
0
Ru
ral
So
uth
We
st
Percent
2
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Percent
3
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Ru
ral
So
uth
We
st
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
ATSI
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Percent
4
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Ru
ral
So
uth
We
st
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN SOUTH WEST
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
60
50
40
30
1
20
10
0
1100
SOCIO-ECONOMIC INDEX
1050
9
6
1000
950
900
Page A63
3
0
DRUG OFFENCES
25
15
10
5
0
Ru
ral
So
uth
We
st
6
Ru
ral
So
uth
We
st
9
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
12
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
15
Rate per 1000 persons
OFFENCES AGAINST THE PERSON
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
20
Rate per 1000 persons
Ru
ral
So
uth
We
st
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Rate per 1000 persons
18
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Ru
ral
So
uth
We
st
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
up
Ma
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
OFFENCES REPORTED IN SOUTH WEST
PROPERTY OFFENCES
125
100
75
50
25
0
GOOD ORDER OFFENCES
8
7
6
5
4
3
2
1
0
Ru
ral
So
uth
We
st
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
Ma
up
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
str
alin
d
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN SOUTH WEST
ALL PERSONS
20
16
12
8
4
0
Page A64
Page A65
0
AGED 30 - 39 YEARS
25
20
15
10
5
0
Ru
ral
So
uth
We
st
8
Ru
ral
So
uth
We
st
16
60
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
Ma
up
rga
ret
Riv
er
Pe
mb
ert
on
24
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
32
Rate per 1000 persons
AGED 10 - 17 YEARS
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
Ma
up
rga
ret
Riv
er
Pe
mb
ert
on
30
Rate per 1000 persons
Ru
ral
So
uth
We
st
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
Ma
up
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Rate per 1000 persons
40
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Ru
ral
So
uth
We
st
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
Ma
up
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN SOUTH WEST
AGED 18 - 29 YEARS
88
45
30
15
0
AGED 40 YEARS OR MORE
8
6
4
2
0
Page A66
5
0
ATSI
60
30
0
Ru
ral
So
uth
We
st
10
Ru
ral
So
uth
We
st
15
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
Ma
up
rga
ret
Riv
er
Pe
mb
ert
on
20
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
25
Rate per 1000 persons
MALE
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
Ma
up
rga
ret
Riv
er
Pe
mb
ert
on
90
Rate per 1000 persons
Ru
ral
So
uth
We
st
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
Ma
up
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Rate per 1000 persons
30
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Ru
ral
So
uth
We
st
120
Co
llie
Du
nsb
oro
ug
h
Ha
rve
y
Ma
njim
Ma
up
rga
ret
Riv
er
Pe
mb
ert
on
Au
gu
sta
Au
stra
lind
Bri
dg
eto
wn
Bu
nb
ury
Bu
sse
lton
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN SOUTH WEST
FEMALE
12
10
8
6
4
2
0
NON-ATSI
237
16
12
8
4
0
Page A67
Cro
ss
Ru
ral
50
40
30
20
0
Wa
gin
To
od
yay
Cro
ss
Ru
ral
Wh
ea
tbe
lt
4
Wh
ea
tbe
lt
5
Yo
rk
LEFT SCHOOL 15 YEARS OR LESS
Yo
rk
0
Wa
gin
10
To
od
yay
1
Cro
ss
UNEMPLOYED
So
uth
ern
0
Ru
ral
0
So
uth
ern
5
No
rth
am
3
Na
rro
gin
10
Mo
ora
6
No
rth
am
2
Me
rre
din
Wh
ea
tbe
lt
Yo
rk
Wa
gin
Percent
POPULATION AGED 10 - 17 YEARS
Na
rro
gin
3
Percent
Cro
ss
Ru
ral
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
Percent
9
Mo
ora
Percent
12
Me
rre
din
Wh
ea
tbe
lt
Yo
rk
Wa
gin
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN WHEATBELT
POPULATION AGED 18 - 29 YEARS
15
25
20
15
Page A68
Cro
ss
Ru
ral
1000
950
0
900
To
od
yay
Cro
ss
Ru
ral
Wh
ea
tbe
lt
1050
Wh
ea
tbe
lt
12
Yo
rk
16
Yo
rk
SOCIO-ECONOMIC INDEX
Wa
gin
1100
Wa
gin
4
To
od
yay
8
Cro
ss
OPD'S WITH NO VEHICLE
So
uth
ern
0
Ru
ral
0
So
uth
ern
10
No
rth
am
3
No
rth
am
20
Na
rro
gin
6
Mo
ora
Percent
9
Me
rre
din
Wh
ea
tbe
lt
Yo
rk
Wa
gin
Percent
12
Na
rro
gin
Cro
ss
Ru
ral
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
ATSI
Mo
ora
Percent
15
Me
rre
din
Wh
ea
tbe
lt
Yo
rk
Wa
gin
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
APPENDIX B - TOWNS IN REGIONS
SOCIO-DEMOGRAPHIC FACTORS IN WHEATBELT
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
50
40
30
Page A69
Cro
ss
5
0
2
0
Wa
gin
To
od
yay
Cro
ss
No
rth
am
So
uth
ern
Wh
ea
tbe
lt
4
Wh
ea
tbe
lt
6
Ru
ral
8
Ru
ral
10
Yo
rk
GOOD ORDER OFFENCES
Yo
rk
Wa
gin
10
To
od
yay
15
Cro
ss
39
So
uth
ern
OFFENCES AGAINST THE PERSON
No
rth
am
DRUG OFFENCES
Na
rro
gin
0
Na
rro
gin
5
Mo
ora
10
Rate per 1000 persons
15
Me
rre
din
Wh
ea
tbe
lt
Ru
ral
Yo
rk
Wa
gin
20
Mo
ora
20
Rate per 1000 persons
Cro
ss
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
Rate per 1000 persons
25
Me
rre
din
Wh
ea
tbe
lt
Ru
ral
Yo
rk
Wa
gin
25
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
OFFENCES REPORTED IN WHEATBELT
PROPERTY OFFENCES
200
160
120
80
40
0
Cro
ss
Ru
ral
Wh
ea
tbe
lt
Yo
rk
Wa
gin
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN WHEATBELT
ALL PERSONS
40
32
24
16
8
0
Page A70
Page A71
Cro
ss
Ru
ral
0
2
0
Cro
ss
Ru
ral
To
od
yay
So
uth
ern
No
rth
am
Wh
ea
tbe
lt
4
Wh
ea
tbe
lt
6
Yo
rk
8
Yo
rk
10
Wa
gin
AGED 40 YEARS OR MORE
Wa
gin
To
od
yay
10
Cro
ss
20
Ru
ral
30
So
uth
ern
40
Na
rro
gin
AGED 10 - 17 YEARS
No
rth
am
AGED 30 - 39 YEARS
Na
rro
gin
0
Mo
ora
20
Me
rre
din
40
Rate per 1000 persons
60
Mo
ora
50
Rate per 1000 persons
Wh
ea
tbe
lt
Yo
rk
Wa
gin
80
Me
rre
din
Wh
ea
tbe
lt
Yo
rk
Cro
ss
Ru
ral
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
Rate per 1000 persons
100
Wa
gin
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN WHEATBELT
AGED 18 - 29 YEARS
140
125
100
75
50
25
0
Page A72
Cro
ss
Ru
ral
100
50
0
So
uth
ern
Cro
ss
Ru
ral
Wh
ea
tbe
lt
0
Wh
ea
tbe
lt
5
Yo
rk
10
Yo
rk
15
Wa
gin
20
Wa
gin
25
To
od
yay
NON-ATSI
To
od
yay
Cro
ss
ATSI
Ru
ral
150
No
rth
am
MALE
So
uth
ern
200
No
rth
am
250
Na
rro
gin
0
Na
rro
gin
10
Mo
ora
20
Rate per 1000 persons
30
Me
rre
din
Wh
ea
tbe
lt
Yo
rk
Wa
gin
40
Mo
ora
300
Rate per 1000 persons
Cro
ss
Ru
ral
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
Rate per 1000 persons
50
Me
rre
din
Wh
ea
tbe
lt
Yo
rk
Wa
gin
To
od
yay
So
uth
ern
No
rth
am
Na
rro
gin
Mo
ora
Me
rre
din
Rate per 1000 persons
APPENDIX B - TOWNS IN REGIONS
POLICE-OFFENDER CONTACTS IN WHEATBELT
FEMALE
20
16
12
8
4
0
Page A73
Gascoyne Total
Goldfields-Esp. Coolgardie
Esperance
Kalgoorlie-Boulder
Kambalda (East)
Kambalda West
Leinster
Leonora
Norseman
Rural
Goldfields-Esperance Total
Great Southern Albany
Denmark
Katanning
Kojonup
Mount Barker
Rural
Great Southern Total
Kimberley
Broome
Derby
Fitzroy Crossing
Halls Creek
Kununurra
Wyndham
Rural
Kimberley Total
6377
1130
3079
4250
14836
1253
8596
28033
1200
2404
1437
1138
1488
11819
57368
20564
1949
4069
1049
1706
18886
48223
11382
3490
1159
1229
4850
886
9915
32911
567
71
222
274
1134
141
1117
2951
119
293
61
79
130
1139
6030
2685
238
568
124
229
2465
6309
999
356
95
106
376
1304
114
3350
991
101
329
671
2092
274
1368
7360
290
520
414
338
316
2765
13645
2915
155
718
177
225
2392
6582
2011
676
210
258
911
2107
158
6331
895
123
463
523
2004
254
1400
5257
243
533
374
235
284
2295
10875
2859
326
669
162
251
3217
7484
1935
603
170
169
800
1558
172
5407
3030
762
1700
2326
7818
328
3177
7630
268
517
364
308
482
3811
16885
8959
858
1380
403
733
7552
19885
4874
1197
492
507
2067
2976
242
12355
5107
1004
2545
3636
12292
909
6352
21210
836
1648
1163
902
1105
9290
43415
15682
1400
2973
780
1296
13901
36032
9119
2566
909
976
3875
606
7096
25147
3277
587
1621
2317
7802
682
4139
15164
643
1286
971
700
825
7223
31633
9788
953
2030
535
829
10139
24274
5746
1788
568
640
2508
5478
434
17162
3100
543
1458
1933
7034
571
4457
12869
557
1118
466
438
663
4596
25735
10776
996
2039
514
877
8747
23949
5636
1702
591
589
2342
4437
452
15749
949
79
20
413
1461
224
379
1367
14
22
7
183
141
2365
4702
528
13
372
64
151
419
1547
1937
1458
519
387
594
357
6207
11459
5428
1051
3059
3837
13375
1240
8300
26654
1190
2380
1254
1138
1384
9134
52674
20036
1936
3697
985
1555
18443
46652
9445
2032
640
842
4256
3708
529
21452
1217
142
474
446
2279
292
2224
6433
329
633
236
188
355
2204
12894
5497
557
998
262
440
5151
12905
1744
720
175
152
696
165
1483
5135
1596
215
647
560
3018
399
3014
8677
404
715
242
229
476
2657
16813
7539
791
1372
356
596
6206
16860
2335
909
211
204
1028
209
1504
6400
2697
515
1357
1658
6227
596
4572
14446
609
1346
373
330
726
4847
27845
11163
1121
2047
546
876
12952
28705
4287
1284
275
286
1705
1589
215
9641
2339
456
1177
1476
5448
424
3198
9250
397
748
264
262
518
2958
18019
7836
778
1399
380
625
6578
17596
3666
1104
310
361
1694
286
2151
9572
Private Dwellings
OPDs
Vehicles in OPDs
Fam/Gp/Lo in OPDs
Families in OPDs
Non-ATSI
ATSI
Female
Male
Population 40+
Population 30-39
Population 18-29
Population 10-17
Town
Carnarvon
Denham
Exmouth
Rural
Population
Region
Gascoyne
Population 15+
DENOMINATORS IN TOWNS IN REGIONS
APPENDIX B - TOWNS IN REGIONS
2491
517
1301
1496
5805
459
3632
10005
477
844
277
298
622
3565
20179
8944
980
1519
437
714
8127
20721
3864
1225
308
398
1780
311
2166
10052
Page A74
Augusta
Australind
Bridgetown
Bunbury
Busselton
Collie
Dunsborough
9056
1038
355
377
8133
18959
15851
390
862
8634
729
26466
537
2898
1214
184
420
4005
360
2793
931
465
13807
651
1892
872
10012
4771
2838
434
18780
1515
907
628
17098
38928
27280
719
1426
1308
15555
46288
1039
7364
3431
546
1372
9576
720
2714
1108
5577
33447
941
3974
1561
19379
8183
5459
831
12502
933
676
414
13015
27540
17419
492
919
10927
894
30651
764
5368
2631
438
1131
7153
476
4200
2122
879
25162
567
2815
1023
12368
5038
3778
543
12743
865
604
432
8916
23560
18579
526
1001
10063
934
31103
641
4685
2148
331
889
5687
504
2225
1797
757
19664
512
2869
1082
12546
5582
3494
569
1812
9
393
91
1548
3853
576
8
84
29
175
872
49
404
230
12
41
1661
572
106
177
1911
5163
6
59
25
796
177
230
9
23433
1789
887
755
20383
47247
35422
1008
1917
20694
1811
60852
1356
9649
4549
757
1979
11179
408
4514
3813
1459
39663
1073
5647
2080
24118
10443
7053
1103
6380
263
247
224
4652
11766
10504
266
490
496
5825
17581
336
2380
1129
170
463
2542
180
890
392
636
9118
268
1579
618
6606
2893
1882
303
8770
346
314
298
5817
15545
13755
372
672
649
7030
22478
410
2933
1507
215
580
3469
230
1066
499
684
11593
386
1876
763
9269
4047
2563
398
13040
783
435
457
10922
25637
19592
551
992
13289
957
35381
763
5044
2240
320
729
5619
262
1446
1621
778
18822
615
3206
1210
13723
5694
4930
662
9105
705
384
320
6427
16941
13977
352
689
665
7255
22938
457
3402
1708
246
590
4176
270
1173
559
1364
13945
454
1928
784
9518
4204
2681
453
Private Dwellings
OPDs
Vehicles in OPDs
Fam/Gp/Lo in OPDs
Families in OPDs
Non-ATSI
ATSI
3934
269
303
119
4150
8775
5276
141
269
3428
274
9388
287
2031
1088
188
494
2504
134
1218
938
302
9184
146
1051
342
3659
1524
1089
200
Female
4429
199
213
100
4137
9078
4663
135
193
2632
248
7871
172
2020
980
162
427
2645
182
1443
756
274
9061
122
745
254
4506
1438
1166
173
Male
3492
76
128
108
2208
6012
4493
173
271
2863
223
8023
177
1284
530
45
175
1413
124
346
410
222
4726
66
955
267
3165
1270
1001
101
Population 40+
Population 30-39
Pilbara Total
South West
Dampier
Karratha
Newman
Pannawonica
Paraburdoo
Port Hedland
Roebourne
Tom Price
Wickham
Rural
Population 18-29
Peel Total
Pilbara
Mandurah
North Pinjarra
Pinjarra
Waroona
Rural
25245
1798
1280
846
21931
51100
35998
1018
1920
1828
20990
61754
1405
10053
4779
769
2020
12840
980
3919
1636
6425
44826
1079
5684
2105
24914
10620
7272
1112
Population 10-17
Mid West Total
Peel
Town
Geraldton
Kalbarri
Meekatharra
Northampton
Rural
Population
Region
Mid West
Population 15+
DENOMINATORS IN TOWNS IN REGIONS
APPENDIX B - TOWNS IN REGIONS
10235
907
417
356
8132
20047
18586
393
752
725
8973
29429
553
3987
2031
259
728
4700
320
1272
830
1445
16125
759
2094
918
10458
4939
2985
927
Private Dwellings
OPDs
Vehicles in OPDs
Fam/Gp/Lo in OPDs
Families in OPDs
Non-ATSI
ATSI
Female
Male
Population 40+
Population 30-39
Population 18-29
Population 10-17
Town
Harvey
Manjimup
Margaret River
Pemberton
Rural
Population
Region
Population 15+
DENOMINATORS IN TOWNS IN REGIONS
APPENDIX B - TOWNS IN REGIONS
2525
4361
2864
990
43374
108342
2887
1615
4513
6295
1134
659
1300
1984
48733
69120
3045
356
506
357
138
6252
14651
403
230
744
871
5366
72
82
158
229
8155
29
421
753
480
162
5762
16150
529
244
719
977
6853
294
81
197
224
10118
1489
360
730
572
140
7544
17641
451
275
626
909
8263
228
110
210
287
11359
732
987
1591
917
403
16195
42084
1027
553
1690
2517
19787
336
273
521
948
27652
774
1924
3262
2089
745
31423
80814
2165
1147
3350
4709
873
492
977
1523
36387
51623
3021
1233
2208
1459
516
22303
54608
1549
801
2205
3137
26063
659
308
646
1000
36368
2745
1292
2153
1405
474
21071
53734
1338
814
2308
3158
22670
475
351
654
984
32752
300
76
2502
106
4255
24
2840
0
990
487
42833
1998 106371
138
2795
208
1407
273
4240
492
5803
37
47427
40
1125
88
619
59
1212
1229
1925
2564
66553
33
3036
672
1158
666
223
11691
28926
702
381
1110
1636
246
191
341
538
13179
18324
0
893
1550
938
301
14183
37650
1013
545
1557
2221
346
273
497
728
16770
23950
0
1334
2384
1452
477
26606
62293
1443
819
2326
3222
33603
518
414
725
1116
44186
0
909
1576
961
315
14859
39122
1060
559
1596
2285
346
274
526
758
17579
24983
0
1005
1775
1039
362
18152
45970
1220
641
1781
2542
392
296
614
885
22707
31078
0
Non-Perth Total
Perth Total
491525
1234939
58419
148442
82417
231038
82849
191785
186685
491645
371007
970829
257945
604645
233580
630294
33652 457875
17068 1217869
118928
326891
154307
449715
258737
693741
168564
460459
199406
498771
WA Grand Total
1726464
206861
313455
274634
678330 1341836
862590
863874
50720 1675744
445819
604022
952478
629023
698177
South West Total
Wheatbelt
Merredin
Moora
Narrogin
Northam
Southern Cross
Toodyay
Wagin
York
Rural
Wheatbelt Total
WA Offshore Areas & Migratory
Notes:
"OPDs" is occupied private dwellings.
"Fam/Gp/Lo in OPDs" is families,groups or lone persons in OPDs.
Page A75
WA POLICE DISTRICTS IN PERTH
APPENDIX C
JOONDALUP
MIDLAND
MIRRABOOKA
PERTH
CANNINGTON
FREMANTLE
Page A76
Page A77
5
1
0
PE
RT
H
UNEMPLOYED
MI
RR
AB
OO
KA
PE
RT
H
MI
RR
AB
OO
KA
0
MI
DL
AN
D
5
JO
ON
DA
LU
P
3
FR
EM
AN
TL
E
10
MI
DL
AN
D
3
CA
NN
ING
TO
N
6
Percent
9
JO
ON
DA
LU
P
4
Percent
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
Percent
POPULATION AGED 10 - 17 YEARS
FR
EM
AN
TL
E
Percent
12
CA
NN
ING
TO
N
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
APPENDIX C - POLICE DISTRICTS IN PERTH
SOCIO-DEMOGRAPHIC FACTORS
POPULATION AGED 18 - 29 YEARS
25
15
20
15
0
LEFT SCHOOL 15 YEARS OR LESS
6
40
30
2
20
10
0
Page A78
OPD'S WITH NO VEHICLE
15
12
3
0
PE
RT
H
PE
RT
H
0
MI
RR
AB
OO
KA
0.0
MI
RR
AB
OO
KA
10
MI
DL
AN
D
0.5
MI
DL
AN
D
40
JO
ON
DA
LU
P
2.0
JO
ON
DA
LU
P
50
FR
EM
AN
TL
E
1.0
Percent
1.5
CA
NN
ING
TO
N
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
Percent
ATSI
FR
EM
AN
TL
E
Percent
2.5
CA
NN
ING
TO
N
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
APPENDIX C - POLICE DISTRICTS IN PERTH
SOCIO-DEMOGRAPHIC FACTORS
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
30
20
SOCIO-ECONOMIC INDEX
1100
1050
9
6
1000
950
900
Page A79
5
0
PE
RT
H
15
MI
RR
AB
OO
KA
DRUG OFFENCES
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
0
MI
DL
AN
D
4
JO
ON
DA
LU
P
OFFENCES AGAINST THE PERSON
JO
ON
DA
LU
P
8
FR
EM
AN
TL
E
12
CA
NN
ING
TO
N
16
Rate per 1000 persons
20
FR
EM
AN
TL
E
10
Rate per 1000 persons
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
Rate per 1000 persons
24
CA
NN
ING
TO
N
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
Rate per 1000 persons
APPENDIX C - POLICE DISTRICTS IN PERTH
OFFENCES REPORTED
PROPERTY OFFENCES
250
200
150
100
50
0
GOOD ORDER OFFENCES
5
4
3
2
1
0
POLICE-OFFENDER CONTACTS
APPENDIX C - POLICE DISTRICTS IN PERTH
Rate per 1000 persons
ALL PERSONS
25
20
15
10
5
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
0
Page A80
Page A81
10
5
0
PE
RT
H
15
MI
RR
AB
OO
KA
20
MI
DL
AN
D
AGED 30 - 39 YEARS
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
AGED 10 - 17 YEARS
JO
ON
DA
LU
P
0
FR
EM
AN
TL
E
15
CA
NN
ING
TO
N
30
Rate per 1000 persons
45
FR
EM
AN
TL
E
25
Rate per 1000 persons
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
Rate per 1000 persons
60
CA
NN
ING
TO
N
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
Rate per 1000 persons
APPENDIX C - POLICE DISTRICTS IN PERTH
POLICE-OFFENDER CONTACTS
AGED 18 - 29 YEARS
60
45
30
15
0
AGED 40 YEARS OR MORE
5
4
3
2
1
0
Page A82
200
150
100
50
0
12
8
4
0
MI
DL
AN
D
PE
RT
H
16
PE
RT
H
20
MI
RR
AB
OO
KA
NON-ATSI
MI
RR
AB
OO
KA
MI
DL
AN
D
ATSI
JO
ON
DA
LU
P
MALE
JO
ON
DA
LU
P
0
FR
EM
AN
TL
E
10
CA
NN
ING
TO
N
20
Rate per 1000 persons
30
FR
EM
AN
TL
E
250
Rate per 1000 persons
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
Rate per 1000 persons
40
CA
NN
ING
TO
N
PE
RT
H
MI
RR
AB
OO
KA
MI
DL
AN
D
JO
ON
DA
LU
P
FR
EM
AN
TL
E
CA
NN
ING
TO
N
Rate per 1000 persons
APPENDIX C - POLICE DISTRICTS IN PERTH
POLICE-OFFENDER CONTACTS
FEMALE
10
8
6
4
2
0
Notes:
"OPDs" is occupied private dwellings.
"Fam/Gp/Lo in OPDs" is families,groups or lone persons in OPDs.
101604
114650
82614
53939
90584
52018
206458
220665
172810
105179
176891
96011
128734
138628
115764
68530
102955
55018
132607
145246
118478
70238
110301
58182
5408
3461
1963
2786
2956
559
255954
280420
232295
135979
210306
112592
68494
77397
65141
38000
55986
24555
96960
102220
76280
47347
87595
42381
144753
160709
132680
80985
121539
59336
Private Dwellings
OPDs
Vehicles in OPDs
Fam/Gp/Lo in OPDs
Families in OPDs
Non-ATSI
ATSI
40320
43356
38985
21355
33786
15652
Female
52968
49558
38814
22567
44840
23582
Male
Population 30-39
30074
35161
34323
19381
19670
11142
Population 40+
Population 18-29
261341
283874
234242
138768
213256
113200
Population 10-17
CANNINGTON
FREMANTLE
JOONDALUP
MIDLAND
MIRRABOOKA
PERTH
Population
DISTRICT
Population 15+
DENOMINATORS IN PERTH POLICE DISTRICTS
APPENDIX C - POLICE DISTRICTS IN PERTH
99585
104264
77482
48442
89538
44296
107700
113762
82009
51890
97565
49271
`
Page A83
SOCIO-DEMOGRAPHIC FACTORS
APPENDIX D - LGA's IN PERTH
PERCENT OF POPULATION AGED 10 TO 17 YEARS
Wanneroo (C)
Swan (S)
Stirling (C)
Bayswater (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Belmont (C)
Perth (C)
Victoria Park (T)
Claremont (T)
Kalamunda (S)
Cottesloe (T)
Nedlands (C)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Percentage
Kwinana (T)
15 to 23
13 to 15
10 to 13
8 to 10
2 to 8
(3)
(7)
(8)
(6)
(4)
Rockingham (C)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A84
SOCIO-DEMOGRAPHIC FACTORS
PERCENT OF POPULATION AGED 18 TO 29 YEARS
APPENDIX D - LGA's IN PERTH
Wanneroo (C)
Swan (S)
Stirling (C)
Bayswater (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Belmont (C)
Perth (C)
Victoria Park (T)
Claremont (T)
Kalamunda (S)
Cottesloe (T)
Nedlands (C)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Percentage
Kwinana (T)
25 to 28
19.3 to 25
18.2 to 19.3
16 to 18.2
13.9 to 16
(3)
(7)
(6)
(8)
(4)
Rockingham (C)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A85
SOCIO-DEMOGRAPHIC FACTORS
APPENDIX D - LGA's IN PERTH
PERCENT OF POPULATION UNEMPLOYED
Wanneroo (C)
Swan (S)
Stirling (C)
Bayswater (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Belmont (C)
Perth (C)
Victoria Park (T)
Claremont (T)
Kalamunda (S)
Cottesloe (T)
Nedlands (C)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Percentage
Kwinana (T)
6.1 to 7.5
5.5 to 6.1
4.8 to 5.5
3.7 to 4.8
1.8 to 3.7
(4)
(6)
(7)
(7)
(4)
Rockingham (C)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A86
SOCIO-DEMOGRAPHIC FACTORS
APPENDIX D - LGA's IN PERTH
PERCENT OF POP'N LEFT SCHOOL AGED 15 YEARS OR LESS
Wanneroo (C)
Swan (S)
Stirling (C)
Bayswater (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Belmont (C)
Perth (C)
Victoria Park (T)
Claremont (T)
Kalamunda (S)
Cottesloe (T)
Nedlands (C)
South Perth (C)
pPeppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Percentage
Kwinana (T)
45 to 51
40 to 44
33 to 49
19 to 32
10 to 18
(3)
(5)
(8)
(8)
(4)
Rockingham (C)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A87
SOCIO-DEMOGRAPHIC FACTORS
APPENDIX D - LGA's IN PERTH
PERCENT OF POPULATION WHO ARE ABORIGINAL OR TSI
Wanneroo (C)
Swan (S)
Stirling (C)
Bayswater (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Belmont (C)
Perth (C)
Victoria Park (T)
Claremont (T)
Kalamunda (S)
Cottesloe (T)
Nedlands (C)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Percentage
Kwinana (T)
2.6 to 4.6
1.4 to 2.5
0.9 to 1.3
0.6 to 0.8
0 to 0.5
(4)
(5)
(8)
(6)
(5)
Rockingham (C)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A88
SOCIO-DEMOGRAPHIC FACTORS
APPENDIX D - LGA's IN PERTH
PERCENT OF POP'N AT DIFFERENT ADDRESS 5 YEARS EARLIER
Wanneroo (C)
Swan (S)
Stirling (C)
Bayswater (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Belmont (C)
Perth (C)
Victoria Park (T)
Claremont (T)
Kalamunda (S)
Cottesloe (T)
Nedlands (C)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Percentage
Kwinana (T)
50.8 to 54.1
47 to 50.8
43.1 to 47
40.3 to 43.1
36.8 to 40.3
(5)
(5)
(8)
(5)
(5)
Rockingham (C)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A89
SOCIO-DEMOGRAPHIC FACTORS
APPENDIX D - LGA's IN PERTH
PERCENT OF OPD'S WITH NO VEHICLE
Wanneroo (C)
Swan (S)
Stirling (C)
Bayswater (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Belmont (C)
Perth (C)
Victoria Park (T)
Claremont (T)
Kalamunda (S)
Cottesloe (T)
Nedlands (C)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Percentage
Kwinana (T)
19 to 37.7
13 to 18.9
8 to 12.9
6 to 7.9
4 to 5.9
(3)
(5)
(9)
(7)
(4)
Rockingham (C)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A90
SOCIO-DEMOGRAPHIC FACTORS
APPENDIX D - LGA's IN PERTH
SOCIO-ECONOMIC INDEX
Wanneroo (C)
Swan (S)
Stirling (C)
Bayswater (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Belmont (C)
Perth (C)
Victoria Park (T)
Claremont (T)
Kalamunda (S)
Cottesloe (T)
Nedlands (C)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Index
Kwinana (T)
907 to 974
975 to 999
1,000 to 1,049
1,050 to 1,099
1,100 to 1,174
(4)
(7)
(6)
(6)
(5)
Rockingham (C)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A91
OFFENCES REPORTED
APPENDIX D - LGA's IN PERTH
OFFENCES AGAINST THE PERSON PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Kalamunda (S)
Victoria Park (T)
Claremont (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
1996-98 average annual rate
20 or more (3)
12 to 19.9
(6)
9 to 11.9 (10)
6 to 8.9
(5)
4 to 5.9
(4)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A92
OFFENCES REPORTED
APPENDIX D - LGA's IN PERTH
PROPERTY OFFENCES PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Belmont (C)
Perth (C)
Nedlands (C)
Kalamunda (S)
Victoria Park (T)
Claremont (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
Canning (C)
East Fremantle (T)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
1996-98 average annual rate
240 or more
160 to 239
135 to 159
110 to 134
82 to 109
(4)
(6)
(8)
(5)
(5)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A93
OFFENCES REPORTED
APPENDIX D - LGA's IN PERTH
DRUG OFFENCES PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Kalamunda (S)
Victoria Park (T)
Claremont (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
Canning (C)
East Fremantle (T)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
1996-98 average annual rate
10 or more (4)
8 to 9.9
(4)
6 to 7.9
(10)
4 to 5.9
(5)
2.6 to 3.9
(5)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A94
OFFENCES REPORTED
APPENDIX D - LGA's IN PERTH
GOOD ORDER OFFENCES PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Kalamunda (S)
Victoria Park (T)
Claremont (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
Canning (C)
East Fremantle (T)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
1996-98 average annual rate
5 or more (3)
4 to 4.9
(3)
3 to 3.9
(10)
2.4 to 2.9
(7)
1.3 to 2.3
(5)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A95
OFFENCES REPORTED
APPENDIX D - LGA's IN PERTH
ALL OFFENCES, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Kalamunda (S)
Victoria Park (T)
Claremont (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
Canning (C)
East Fremantle (T)
Melville (C)
Gosnells (C)
Fremantle (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
1996-98 average annual rate
275 or more
200 to 275
170 to 200
130 to 170
90 to 130
(4)
(4)
(7)
(8)
(5)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A96
POLICE-OFFENDER CONTACTS
APPENDIX D - LGA's IN PERTH
POLICE-OFFENDER CONTACTS, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Claremont (T)
Kalamunda (S)
Victoria Park (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Fremantle (C)
Gosnells (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
Rate per 1000 persons
24 to 29.1
20 to 24
14.5 to 20
8 to 14.5
5.7 to 8
(5)
(6)
(7)
(5)
(5)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A97
POLICE-OFFENDER CONTACTS
APPENDIX D - LGA's IN PERTH
AGED 10 TO 17, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Claremont (T)
Kalamunda (S)
Victoria Park (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Fremantle (C)
Gosnells (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
Rate per 1000 persons
75 or more
50 to 74
30 to 49
20 to 29
5 to 19
(4)
(5)
(9)
(6)
(4)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A98
POLICE-OFFENDER CONTACTS
APPENDIX D - LGA's IN PERTH
AGED 18 TO 29, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Claremont (T)
Kalamunda (S)
Victoria Park (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Fremantle (C)
Gosnells (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
Rate per 1000 persons
58.6 to 63.5
49.2 to 58.6
37.2 to 49.2
24.2 to 37.2
13.4 to 24.2
(2)
(8)
(7)
(4)
(7)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A99
POLICE-OFFENDER CONTACTS
APPENDIX D - LGA's IN PERTH
AGED 30 TO 39, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Claremont (T)
Kalamunda (S)
Victoria Park (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Fremantle (C)
Gosnells (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
Rate per 1000 persons
32.7 to 59.5
22.5 to 32.7
18 to 22.5
12.6 to 18
0 to 12.6
(5)
(6)
(4)
(5)
(8)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A100
POLICE-OFFENDER CONTACTS
APPENDIX D - LGA's IN PERTH
AGED 40 OR MORE, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Claremont (T)
Kalamunda (S)
Victoria Park (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Fremantle (C)
Gosnells (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
Rate per 1000 persons
32.7 to 59.5
22.5 to 32.7
18 to 22.5
12.6 to 18
0 to 12.6
(5)
(6)
(4)
(5)
(8)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A101
POLICE-OFFENDER CONTACTS
APPENDIX D - LGA's IN PERTH
MALE, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Claremont (T)
Kalamunda (S)
Victoria Park (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Fremantle (C)
Gosnells (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
Rate per 1000 persons
41.8 to 44.2
34.4 to 41.8
31.3 to 34.4
18.8 to 31.3
9 to 18.8
(3)
(6)
(3)
(8)
(8)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A102
POLICE-OFFENDER CONTACTS
APPENDIX D - LGA's IN PERTH
FEMALE, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Claremont (T)
Kalamunda (S)
Victoria Park (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Fremantle (C)
Gosnells (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
Rate per 1000 persons
10 to 12.5
8.5 to 10
6.2 to 8.5
4 to 6.2
1.1 to 4
(5)
(6)
(6)
(4)
(7)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A103
POLICE-OFFENDER CONTACTS
APPENDIX D - LGA's IN PERTH
ABORIGINAL OR TSI, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Claremont (T)
Kalamunda (S)
Victoria Park (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Fremantle (C)
Gosnells (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
Rate per 1000 persons
481 to 582
214 to 481
164 to 214
71 to 164
0 to 71
(2)
(6)
(9)
(7)
(4)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A104
POLICE-OFFENDER CONTACTS
APPENDIX D - LGA's IN PERTH
NON-ABORIGINAL AND NON-TSI, PER 1000 PERSONS
Wanneroo (C)
Swan (S)
Bayswater (C)
Stirling (C)
Bassendean (T)
Mundaring (S)
Vincent (T)
Cambridge (T)
Subiaco (C)
Perth (C)
Belmont (C)
Nedlands (C)
Claremont (T)
Kalamunda (S)
Victoria Park (T)
Cottesloe (T)
South Perth (C)
Peppermint Grove (S)
Mosman Park (T)
East Fremantle (T)
Canning (C)
Melville (C)
Fremantle (C)
Gosnells (C)
Cockburn (C)
Armadale (C)
Kwinana (T)
Rockingham (C)
Rate per 1000 persons
22.8 to 23.7 (3)
18.8 to 22.8 (4)
13.4 to 18.8 (10)
8.7 to 13.4 (5)
4.5 to 8.7 (6)
Numerals in parentheses indicate numbers of LGA's.
Boundaries are ABS 1996 Census LGA boundaries.
Page A105
Page A106
Notes:
"OPDs" is occupied private dwellings.
"Fam/Gp/Lo in OPDs" is families,groups or lone persons in OPDs.
1234
354
525
886
34
788
17
1009
42
33
298
1696
429
891
514
73
350
99
9
79
592
423
2397
107
1948
413
126
1702
48399
12857
43273
25896
22982
67644
8810
56297
7073
6238
24116
72005
45985
18340
88807
7349
31312
20789
1578
10026
57576
34938
171772
15023
67137
25900
24563
201184
16965
5169
16332
10847
8602
24429
3468
19893
2900
2454
9801
24913
15441
6555
32700
3047
10669
6979
491
2244
20848
15121
72457
6506
22880
11375
10538
66091
Vehicles in OPDs
13846
3568
12194
7228
5820
18744
2018
15784
1677
1543
5898
20047
12891
5256
24303
1663
8850
5018
381
713
16539
8200
45128
3042
18645
5659
5800
56436
27586
7323
25556
14360
13743
39567
4965
32162
4529
3645
12118
41089
28447
9728
54023
3875
19758
11325
921
1812
31208
20461
100244
7671
37114
12914
12849
114748
Private Dwellings
25252
6721
22248
13527
12114
34854
4780
28642
3761
3230
12299
36887
23640
9377
47082
4085
16067
10895
899
4276
29598
18432
90125
8038
34761
13622
12445
102637
OPDs
24382
6478
21550
13252
10926
33550
4050
28692
3354
3035
12118
36841
22780
9811
42236
3340
15579
9996
688
5829
28576
16904
84044
7092
34352
12694
12247
100249
Fam/Gp/Lo in OPDs
36748
10537
35529
22156
18650
53431
7412
43288
5974
5169
20688
55460
35484
14063
71916
6195
23707
17029
1211
9765
42893
30421
144615
13107
50427
23228
21588
150138
Families in OPDs
18185
5569
18842
12036
10781
27386
4333
20140
3273
2758
11312
25966
18980
6434
40118
3348
12923
10419
677
5737
21601
15171
74510
5902
22015
11089
9514
72626
Non-ATSI
7352
2133
6711
4074
3421
10116
927
9822
987
1159
4087
11548
6498
3138
11889
1010
4734
2307
140
1345
9381
5128
26285
2544
12663
4241
4879
33266
Female
Population 40+
8512
2314
8124
5169
3614
12545
1476
10789
1405
1051
4465
14301
7246
3708
15485
1386
4400
3375
232
2495
9333
8668
37121
4232
12559
7121
6581
33331
Male
Population 30-39
7239
1408
4791
2222
2256
8882
1354
7192
750
524
1995
9959
6983
2333
11211
968
4620
2440
360
281
7423
3250
16869
1026
9059
1635
1433
29979
Population 15+
Population 18-29
49634
13199
43798
26779
23040
68404
8830
57334
7115
6265
24417
73728
46420
19188
89318
7425
31646
20891
1587
10105
58174
35336
174169
15130
69113
26316
24692
202886
Population 10-17
Armadale (C)
Bassendean (T)
Bayswater (C)
Belmont (C)
Cambridge (T)
Canning (C)
Claremont (T)
Cockburn (C)
Cottesloe (T)
East Fremantle (T)
Fremantle (C)
Gosnells (C)
Kalamunda (S)
Kwinana (T)
Melville (C)
Mosman Park (T)
Mundaring (S)
Nedlands (C)
Peppermint Grove (S)
Perth (C)
Rockingham (C)
South Perth (C)
Stirling (C)
Subiaco (C)
Swan (S)
Victoria Park (T)
Vincent (T)
Wanneroo (C)
Population
LGA
ATSI
DENOMINATORS IN PERTH LGA's
APPENDIX D - LGA's IN PERTH
17264
5234
16713
11154
8788
24814
3665
20272
3025
2548
10123
25303
15705
6668
33319
3181
10918
7134
531
2543
21210
15949
73940
6795
23477
11861
11116
67209
18328
5597
17757
12134
9533
26577
4042
21678
3401
2805
11120
26805
16554
7412
35829
3582
11585
7736
604
3123
24214
17962
80935
7564
25324
12952
12335
71283
Page A107
Pa
rk
4
3
1
0
Vin
cen
t
40
Su
bia
co
50
6
Pe
rth
UNEMPLOYED
Vin
cen
t
Su
bia
co
Pe
rth
Ne
dla
nd
Pe
s
pp
erm
int
Gr
ove
0
Ne
dla
nd
Pe
s
pp
erm
int
Gr
ove
0
Pa
rk
5
Pa
rk
5
Mo
sm
an
10
Co
tte
slo
e
25
Cla
rem
on
t
20
Percent
30
Mo
sm
an
5
Ca
mb
ridg
e
POPULATION AGED 10 - 17 YEARS
Co
tte
slo
e
7
Percent
Vin
cen
t
Su
bia
co
Pe
rth
Ne
dla
nd
Pe
s
pp
erm
int
Gr
ove
Ca
mb
ridg
e
Cla
rem
on
t
Co
tte
slo
e
Mo
sm
an
Pa
rk
Percent
25
Cla
rem
on
t
Percent
15
Ca
mb
ridg
e
Vin
cen
t
Su
bia
co
Pe
rth
Pe
pp
erm
int
Gr
ove
Ne
dla
nd
s
Mo
sm
an
Co
ttes
loe
Cla
rem
on
t
Ca
mb
ridg
e
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
SOCIO-DEMOGRAPHIC FACTORS
CENTRAL METROPOLITAN SUBDIVISION
POPULATION AGED 18 - 29 YEARS
20
15
10
LEFT SCHOOL 15 YEARS OR LESS
8
30
2
20
10
0
Page A108
Pa
rk
25
20
1050
15
1000
10
950
5
900
0
850
Pe
rth
Pe
pp
erm
int
Gr
ove
Ne
dla
nd
s
Vin
cen
t
1100
Vin
cen
t
30
Su
bia
co
SOCIO-ECONOMIC INDEX
Su
bia
co
1150
Pe
rth
35
Pe
pp
erm
int
Gr
ove
40
1200
Ne
dla
nd
s
OPD'S WITH NO VEHICLE
Pa
rk
0.0
Pa
rk
1.0
Mo
sm
an
1.5
Mo
sm
an
2.0
Co
tte
slo
e
2.5
Co
tte
slo
e
3.0
Cla
rem
on
t
3.5
Percent
4.0
Ca
mb
ridg
e
Vin
cen
t
Su
bia
co
Pe
rth
ATSI
Cla
rem
on
t
Pa
rk
Ne
dla
nd
Pe
s
pp
erm
int
Gr
ove
Mo
sm
an
Co
tte
slo
e
Ca
mb
rid
ge
Cla
rem
on
t
Percent
4.5
Ca
mb
ridg
e
Vin
cen
t
Su
bia
co
Pe
rth
Ne
dla
nd
Pe
s
pp
erm
int
Gr
ove
Mo
sm
an
Co
tte
slo
e
Cla
rem
on
t
Ca
mb
ridg
e
Percent
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
SOCIO-DEMOGRAPHIC FACTORS
CENTRAL METROPOLITAN SUBDIVISION
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
60
50
40
30
20
0.5
10
0
Page A109
Pa
rk
10
8
6
4
2
0
7
6
5
4
3
2
1
0
Pe
rth
Ne
dla
nd
Pe
s
pp
erm
int
Gr
ove
Pa
rk
Vin
cen
t
16
Vin
cen
t
8
Su
bia
co
GOOD ORDER OFFENCES
Su
bia
co
Pe
rth
12
Pe
pp
erm
int
Gr
ove
14
Ne
dla
nd
s
103
Pa
rk
DRUG OFFENCES
Mo
sm
an
0
Mo
sm
an
5
Co
tte
slo
e
OFFENCES AGAINST THE PERSON
Co
tte
slo
e
10
Cla
rem
on
t
15
Ca
mb
ridg
e
20
Rate per 1000 persons
25
Cla
rem
on
t
16
Rate per 1000 persons
Vin
cen
t
Su
bia
co
137
Ca
mb
ridg
e
Vin
cen
t
Su
bia
co
Pe
rth
Pe
pp
erm
int
Gr
ove
Pa
rk
Ne
dla
nd
s
Mo
sm
an
Co
tte
slo
e
Cla
rem
on
t
Ca
mb
ridg
e
Rate per 1000 persons
30
Pe
rth
Pe
pp
erm
int
Gr
ove
Ne
dla
nd
s
Mo
sm
an
Co
tte
slo
e
Cla
rem
on
t
Ca
mb
ridg
e
Rate per 1000 persons
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
REPORTED OFFENCES
CENTRAL METROPOLITAN SUBDIVISION
PROPERTY OFFENCES
350
1037
300
250
200
150
100
50
0
POLICE-OFFENDER CONTACTS
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
CENTRAL METROPOLITAN SUBDIVISION
Rate per 1000 persons
ALL PERSONS
30
25
20
15
10
5
Vin
cen
t
Su
bia
co
Pe
rth
Pe
pp
erm
int
Gr
ove
Ne
dla
nd
s
Pa
rk
Mo
sm
an
Co
tte
slo
e
Cla
rem
on
t
Ca
mb
ridg
e
0
Page A110
Page A111
Pa
rk
10
0
6
5
4
3
2
1
0
Pa
rk
Pe
rth
Ne
dla
nd
Pe
s
pp
erm
int
Gr
ove
Mo
sm
an
Co
ttes
loe
Vin
cen
t
7
Vin
cen
t
8
Su
bia
co
AGED 40 YEARS OR MORE
Su
bia
co
Pe
rth
20
Pe
pp
erm
int
Gr
ove
30
Ne
dla
nd
s
40
Ca
mb
ridg
e
Cla
rem
on
t
Rate per 1000 persons
Rate per 1000 persons
AGED 10 - 17 YEARS
Pa
rk
50
Mo
sm
an
AGED 30 - 39 YEARS
Co
tte
slo
e
60
Cla
rem
on
t
70
Rate per 1000 persons
Vin
cen
t
Su
bia
co
Pe
rth
Pe
pp
erm
int
Gr
ove
Pa
rk
Ne
dla
nd
s
Mo
sm
an
Co
tte
slo
e
Cla
rem
on
t
Ca
mb
ridg
e
90
80
70
60
50
40
30
20
10
0
Ca
mb
ridg
e
Vin
cen
t
Su
bia
co
Pe
rth
Pe
pp
erm
int
Gr
ove
Ne
dla
nd
s
Mo
sm
an
Co
tte
slo
e
Cla
rem
on
t
Ca
mb
ridg
e
Rate per 1000 persons
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
CENTRAL METROPOLITAN SUBDIVISION
POLICE-OFFENDER CONTACTS
AGED 18 - 29 YEARS
60
50
40
30
20
10
0
Page A112
Pa
rk
ATSI
300
200
100
0
5
0
Pe
pp
erm
int
Gr
ove
Ne
dla
nd
s
Vin
cen
t
10
Vin
cen
t
15
Su
bia
co
20
Su
bia
co
25
Pe
rth
NON-ATSI
Pe
rth
Pe
pp
erm
int
Gr
ove
400
Pa
rk
MALE
Ne
dla
nd
s
500
Pa
rk
0
Mo
sm
an
5
Mo
sm
an
10
Co
tte
slo
e
15
Co
tte
slo
e
20
Cla
rem
on
t
25
Rate per 1000 persons
30
Ca
mb
ridg
e
Vin
cen
t
Su
bia
co
Pe
rth
Pe
pp
erm
int
Gr
ove
35
Cla
rem
on
t
600
Rate per 1000 persons
Pa
rk
Ne
dla
nd
s
Mo
sm
an
Co
ttes
loe
Cla
rem
on
t
Ca
mb
ridg
e
Rate per 1000 persons
40
Ca
mb
ridg
e
Vin
cen
t
Su
bia
co
Pe
rth
Pe
pp
erm
int
Gr
ove
Ne
dla
nd
s
Mo
sm
an
Co
tte
slo
e
Cla
rem
on
t
Ca
mb
ridg
e
Rate per 1000 persons
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
CENTRAL METROPOLITAN SUBDIVISION
POLICE-OFFENDER CONTACTS
FEMALE
12
10
8
6
4
2
0
Page A113
3
1
0
Wa
nn
ero
o
6
Sw
an
7
Sti
rlin
g
UNEMPLOYED
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
ari
ng
Ka
lam
un
da
5
Ba
ysw
ate
r
10
Mu
nd
arin
g
4
Ba
sse
nd
ea
n
15
Percent
20
Ka
lam
un
da
5
Percent
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
arin
g
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
Percent
POPULATION AGED 10 - 17 YEARS
Ba
ysw
ate
r
Percent
25
Ba
sse
nd
ea
n
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
arin
g
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
NORTH AND EAST METROPOLITAN SUBDIVISIONS
SOCIO-DEMOGRAPHIC FACTORS
POPULATION AGED 18 - 29 YEARS
30
25
20
15
10
5
0
0
LEFT SCHOOL 15 YEARS OR LESS
8
50
40
30
2
20
10
0
SOCIO-DEMOGRAPHIC FACTORS
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
NORTH AND EAST METROPOLITAN SUBDIVISIONS
ATSI
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
60
4.5
4.0
50
Percent
Percent
3.5
3.0
2.5
2.0
40
30
20
1.5
1.0
10
0.5
Sw
an
Wa
nn
ero
o
Wa
nn
ero
o
Sti
rlin
g
Mu
nd
arin
g
Sw
an
Percent
OPD'S WITH NO VEHICLE
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
arin
g
Ka
lam
un
da
0
Ba
ysw
ate
r
Ba
sse
nd
ea
n
0.0
SOCIO-ECONOMIC INDEX
40
1200
35
1150
30
1100
25
1050
20
1000
15
Sti
rlin
g
Mu
nd
arin
g
Ka
lam
un
da
Ba
sse
nd
ea
n
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Page A114
Mu
nd
arin
g
850
Ka
lam
un
da
0
Ba
ysw
ate
r
900
Ba
sse
nd
ea
n
5
Ba
ysw
ate
r
950
10
Page A115
DRUG OFFENCES
14
12
10
8
6
4
6
5
4
3
2
1
0
0
Wa
nn
ero
o
7
Wa
nn
ero
o
8
Sw
an
GOOD ORDER OFFENCES
Sw
an
2
Sti
rlin
g
0
Sti
rlin
g
0
Mu
nd
arin
g
50
Mu
nd
arin
g
5
Ka
lam
un
da
OFFENCES AGAINST THE PERSON
Ka
lam
un
da
10
Ba
ysw
ate
r
15
Ba
sse
nd
ea
n
20
Rate per 1000 persons
25
Ba
ysw
ate
r
16
Rate per 1000 persons
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
ari
ng
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
Rate per 1000 persons
30
Ba
sse
nd
ea
n
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
ari
ng
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
Rate per 1000 persons
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
REPORTED OFFENCES
NORTH AND EAST METROPOLITAN SUBDIVISIONS
PROPERTY OFFENCES
350
300
250
200
150
100
POLICE-OFFENDER CONTACTS
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
NORTH AND EAST METROPOLITAN SUBDIVISIONS
Rate per 1000 persons
ALL PERSONS
30
25
20
15
10
5
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
arin
g
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
0
Page A116
Page A117
AGED 30 - 39 YEARS
60
50
40
30
20
10
0
6
5
4
3
2
1
0
Wa
nn
ero
o
7
Wa
nn
ero
o
8
Sw
an
AGED 40 YEARS OR MORE
Sw
an
0
Sti
rlin
g
10
Sti
rlin
g
20
Mu
nd
arin
g
30
Mu
nd
arin
g
40
Ka
lam
un
da
AGED 10 - 17 YEARS
Ka
lam
un
da
50
Ba
ysw
ate
r
60
Ba
sse
nd
ea
n
70
Rate per 1000 persons
80
Ba
ysw
ate
r
70
Rate per 1000 persons
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
arin
g
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
Rate per 1000 persons
90
Ba
sse
nd
ea
n
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
ari
ng
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
Rate per 1000 persons
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
NORTH AND EAST METROPOLITAN SUBDIVISIONS
POLICE-OFFENDER CONTACTS
AGED 18 - 29 YEARS
60
50
40
30
20
10
0
Page A118
ATSI
500
400
300
200
100
0
15
10
5
0
Wa
nn
ero
o
20
Wa
nn
ero
o
25
Sw
an
NON-ATSI
Sw
an
0
Sti
rlin
g
5
Sti
rlin
g
10
Mu
nd
arin
g
MALE
Mu
nd
arin
g
15
Ka
lam
un
da
20
Ka
lam
un
da
25
Ba
ysw
ate
r
30
Ba
sse
nd
ea
n
35
Rate per 1000 persons
40
Ba
ysw
ate
r
600
Rate per 1000 persons
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
arin
g
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
Rate per 1000 persons
45
Ba
sse
nd
ea
n
Wa
nn
ero
o
Sw
an
Sti
rlin
g
Mu
nd
arin
g
Ka
lam
un
da
Ba
ysw
ate
r
Ba
sse
nd
ea
n
Rate per 1000 persons
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
NORTH AND EAST METROPOLITAN SUBDIVISIONS
POLICE-OFFENDER CONTACTS
FEMALE
14
12
10
8
6
4
2
0
Page A119
Arm
ad
ale
Be
lmo
nt
Ca
nn
ing
Co
ckb
Ea
urn
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
10
Percent
25
30
20
25
5
5
0
0
50
6
5
40
4
3
1
0
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
7
Percent
Arm
ad
ale
Be
lmo
nt
Ca
nn
ing
Co
ckb
Ea
urn
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Percent
POPULATION AGED 10 - 17 YEARS
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Percent
15
Arm
ad
ale
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Arm
ad
ale
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
SOCIO-DEMOGRAPHIC FACTORS
SOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS
POPULATION AGED 18 - 29 YEARS
20
15
10
UNEMPLOYED
LEFT SCHOOL 15 YEARS OR LESS
8
30
2
20
10
0
Page A120
1.5
20
1.0
0.5
10
0.0
0
OPD'S WITH NO VEHICLE
40
1200
35
1150
30
25
1100
20
15
10
5
900
0
850
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
2.5
2.0
Percent
3.0
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Percent
4.0
3.5
Arm
ad
ale
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Arm
ad
ale
ATSI
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Percent
4.5
Arm
ad
ale
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Arm
ad
ale
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
SOCIO-DEMOGRAPHIC FACTORS
SOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS
AT DIFFERENT ADDRESS FIVE YEARS EARLIER
60
50
40
30
SOCIO-ECONOMIC INDEX
1050
1000
950
Page A121
5
0
DRUG OFFENCES
14
12
10
8
6
4
2
1
0
0
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
10
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
15
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
20
Arm
ad
ale
25
Rate per 1000 persons
OFFENCES AGAINST THE PERSON
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
16
Rate per 1000 persons
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Arm
ad
ale
Rate per 1000 persons
30
Arm
ad
ale
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Arm
ad
ale
Rate per 1000 persons
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
REPORTED OFFENCES
SOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS
PROPERTY OFFENCES
350
300
250
200
150
100
50
0
GOOD ORDER OFFENCES
8
7
6
5
4
3
2
POLICE-OFFENDER CONTACTS
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
SOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS
Rate per 1000 persons
ALL PERSONS
30
25
20
15
10
5
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Me
lvil
le
Kw
ina
na
Go
sne
lls
Ea
st F
rem
an
tle
Fre
ma
ntle
Ca
nn
ing
Co
ckb
urn
Be
lmo
nt
Arm
ad
ale
0
Page A122
Page A123
Rate per 1000 persons
70
60
50
40
30
20
10
Rate per 1000 persons
0
Arm
ad
ale
Be
lmo
nt
Ca
nn
ing
Co
ckb
Ea
urn
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Me
lvil
le
Kw
ina
na
Go
sne
lls
Fre
ma
ntle
Ea
st F
rem
an
tle
Co
ckb
urn
Ca
nn
ing
Be
lmo
nt
Arm
ad
ale
Rate per 1000 persons
Rate per 1000 persons
90
80
70
60
50
40
30
20
10
0
Arm
ad
ale
Be
lmo
nt
Ca
nn
ing
Co
ckb
Ea
urn
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Arm
ad
ale
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
SOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS
POLICE-OFFENDER CONTACTS
AGED 10 - 17 YEARS
AGED 18 - 29 YEARS
60
50
40
30
20
10
0
AGED 30 - 39 YEARS
AGED 40 YEARS OR MORE
8
7
6
5
4
3
2
1
0
25
20
15
10
5
0
ATSI
600
500
400
300
200
100
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
30
Rate per 1000 persons
35
Arm
ad
ale
40
Rate per 1000 persons
Me
lvil
le
Kw
ina
na
Go
sne
lls
Fre
ma
ntle
Ea
st F
rem
an
tle
Co
ckb
urn
Ca
nn
ing
Be
lmo
nt
Arm
ad
ale
Rate per 1000 persons
MALE
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
0
Arm
ad
ale
Page A124
0
Arm
ad
ale
Be
lmo
nt
Ca
nn
ing
Co
ckb
urn
Ea
st F
rem
an
tle
Fre
ma
ntle
Go
sne
lls
Kw
ina
na
Me
lvil
le
Ro
cki
ng
ha
m
So
uth
Pe
rth
Vic
tor
ia P
ark
Rate per 1000 persons
APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH
SOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS
POLICE-OFFENDER CONTACTS
FEMALE
14
12
10
8
6
4
2
0
NON-ATSI
25
20
15
10
5
GASCOYNE
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Against the Person
200
Rate per 1000 persons
Rate per 1000 persons
40
30
20
10
0
150
100
50
0
1991 1992 1993 1994 1995 1996 1997 1998
1991 1992 1993 1994 1995 1996 1997 1998
Drugs
15
Rate per 1000 persons
Rate per 1000 persons
20
Property
15
10
5
Page A125
0
Good Order
12
9
6
3
0
1991 1992 1993 1994 1995 1996 1997 1998
1991 1992 1993 1994 1995 1996 1997 1998
GOLDFIELDS-ESPERANCE
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Against the Person
Property
150
Rate per 1000 persons
Rate per 1000 persons
20
16
12
8
4
125
100
75
50
25
0
0
1991 1992 1993 1994 1995 1996 1997 1998
Drugs
15
10
5
Page A126
0
Good Order
4
Rate per 1000 persons
Rate per 1000 persons
20
1991 1992 1993 1994 1995 1996 1997 1998
3
2
1
0
1991 1992 1993 1994 1995 1996 1997 1998
1991
1992
1993
1994
1995
1996
1997
1998
GREAT SOUTHERN
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Against the Person
100
Property
Rate per 1000 persons
Rate per 1000 persons
10
8
6
4
2
80
60
40
20
0
0
1991 1992 1993 1994 1995 1996 1997 1998
1991 1992 1993 1994 1995 1996 1997 1998
Drugs
Good Order
4
Rate per 1000 persons
Rate per 1000 persons
10
8
6
4
2
Page A127
0
3
2
1
0
1991 1992 1993 1994 1995 1996 1997 1998
1991 1992 1993 1994 1995 1996 1997 1998
KIMBERLEY
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Against the Person
250
Rate per 1000 persons
Rate per 1000 persons
50
40
30
20
10
0
200
150
100
50
0
1991 1992 1993 1994 1995 1996 1997 1998
1991 1992 1993 1994 1995 1996 1997 1998
Drugs
15
Rate per 1000 persons
Rate per 1000 persons
15
Property
12
9
6
3
Page A128
0
Good Order
12
9
6
3
0
1991 1992 1993 1994 1995 1996 1997 1998
1991 1992 1993 1994 1995 1996 1997 1998
MID WEST
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Against the Person
Rate per 1000 persons
Rate per 1000 persons
12
9
6
3
150
120
90
60
30
0
0
1991 1992 1993 1994 1995 1996 1997 1998
1991 1992 1993 1994 1995 1996 1997 1998
Drugs
9
6
3
Page A129
0
Good Order
5
Rate per 1000 persons
Rate per 1000 persons
12
Property
180
15
4
3
2
1
0
1991 1992 1993 1994 1995 1996 1997 1998
1991
1992
1993
1994
1995
1996
1997
1998
PEEL
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Against the Person
120
Rate per 1000 persons
Rate per 1000 persons
8
6
4
2
0
100
80
60
40
20
0
1991
1992
1993
1994
1995
1996
1997
1998
1991 1992 1993 1994 1995 1996 1997 1998
Drugs
2.0
Rate per 1000 persons
6
Rate per 1000 persons
Property
5
4
3
2
1
Page A130
0
Good Order
1.5
1.0
0.5
0.0
1991
1992
1993
1994
1995
1996
1997
1998
1991 1992 1993 1994 1995 1996 1997 1998
PILBARA
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Against the Person
Rate per 1000 persons
Rate per 1000 persons
15
12
9
6
3
120
90
60
30
0
0
1991 1992 1993 1994 1995 1996 1997 1998
1991 1992 1993 1994 1995 1996 1997 1998
Drugs
Rate per 1000 persons
9
6
3
Page A131
0
Good Order
5
12
Rate per 1000 persons
Property
150
18
4
3
2
1
0
1991 1992 1993 1994 1995 1996 1997 1998
1991
1992
1993
1994
1995
1996
1997
1998
SOUTH WEST
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Against the Person
Rate per 1000 persons
Rate per 1000 persons
6
4
2
0
60
40
20
0
1991
1992
1993
1994
1995
1996
1997
1998
1991 1992 1993 1994 1995 1996 1997 1998
Drugs
Rate per 1000 persons
8
6
4
2
Page A132
0
Good Order
4
10
Rate per 1000 persons
Property
80
8
3
2
1
0
1991 1992 1993 1994 1995 1996 1997 1998
1991
1992
1993
1994
1995
1996
1997
1998
WHEATBELT
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Against the Person
Rate per 1000 persons
Rate per 1000 persons
8
6
4
2
80
60
40
20
0
0
1991 1992 1993 1994 1995 1996 1997 1998
1991 1992 1993 1994 1995 1996 1997 1998
Drugs
Rate per 1000 persons
10
8
6
4
2
Page A133
0
Good Order
5
12
Rate per 1000 persons
Property
100
10
4
3
2
1
0
1991 1992 1993 1994 1995 1996 1997 1998
1991
1992
1993
1994
1995
1996
1997
1998
SEXUAL AND FRAUD OFFENCES
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Reported Sexual Offences
Daily Average, 1991 to 1998
150
Reported Fraud Offences
Daily Average, 1991 to 1998
200
773
282
150
100
100
50
50
1-Dec
1-Nov
1-Oct
1-Sep
1-Aug
1-Jul
1-Jun
1-May
1-Apr
1-Mar
1-Jan
1-Dec
1-Nov
1-Oct
1-Sep
1-Aug
1-Jul
1-Jun
1-May
1-Apr
1-Mar
1-Feb
1-Jan
Reported Against the Person Offences
Daily Average, 1991 to 1998
200
1-Feb
0
0
Reported Against the Person Offences (non-sexual)
Daily Average, 1991 to 1998
60
873
100
150
40
100
20
50
1-Dec
1-Nov
1-Oct
1-Sep
1-Aug
1-Jul
1-Jun
1-May
1-Apr
1-Mar
1-Feb
1-Jan
1-Dec
1-Nov
1-Oct
1-Sep
1-Aug
1-Jul
1-Jun
1-May
1-Apr
1-Mar
1-Feb
0
1-Jan
Page A134
0
DAILY STREET OFFENCE ARRESTS
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
Street Offence Arrests in WA - daily average, 1991 to 1998
120
110
100
Number
80
60
40
20
0
1-Jan
1-Feb
1-Mar
1-Apr
1-May
1-Jun
1-Jul
1-Aug
1-Sep
1-Oct
1-Nov
1-Dec
Day of year
Ratio of weekend day arrests to non-weekend day arrests, by Region
2.5
Ratio
2
1.5
1
0.5
Wh
ea
tBe
lt
So
uth
We
st
Pil
ba
ra
Pe
rth
Pe
el
Mid
We
st
Kim
be
rley
Gr
tS
ou
the
rn
Go
ldf.
-Es
p.
Page A135
Ga
sco
yne
0
APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS
MONTHLY STREET OFFENCE ARRESTS
Street Offence Arrests
Monthly average, 1991 to 1998
Perth Street Offence Arrests
Monthly average, 1991 to 1998
60
600
50
500
40
Number
Number
700
400
300
200
30
20
10
100
0
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Peel
140
140
120
120
100
100
Number
Number
Street Offence Arrests
Monthly average, 1991 to 1998
80
60
Gascoyne
WheatBelt
Street Offence Arrests
Monthly average, 1991 to 1998
80
60
40
40
20
20
Page A136
0
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
South West
Mid West
Kimberley
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Pilbara
Goldf.-Esp.
Grt Southern