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MAPPING CRIME, OFFENDERS AND SOCIO-DEMOGRAPHIC FACTORS Crime Research Centre University of Western Australia For the Ministry of Justice Contract No. 297/98 December, 1999 crime centre R E S E A R C H

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MAPPING CRIME,

OFFENDERS

AND

SOCIO-DEMOGRAPHIC FACTORS

Crime Research Centre

University of Western Australia

For the Ministry of Justice

Contract No. 297/98

December, 1999

crime

centre R E S E A R C H

i

TABLE OF CONTENTS

1 INTRODUCTION AND OVERVIEW ...................................................................................................................1

2 BACKGROUND...........................................................................................................................................................2

3 DATA ON CRIME, OFFENDING AND SOCIO-DEMOGRAPHIC FACTORS .....................................3

OFFENCE DATA................................................................................................................................................................3POLICE-OFFENDER CONTACT DATA..............................................................................................................................3ARRESTS...........................................................................................................................................................................4INTEGRATED POLICE AND JUSTICE DATA.....................................................................................................................4COURTS DATA..................................................................................................................................................................5SOCIO-DEMOGRAPHIC DATA FROM THE 1996 AUSTRALIAN CENSUS.......................................................................5THE INTERPRETATION OF CRIME AND OFFENDER DATA.............................................................................................5

4 MAPPING CRIME AND OFFENDING................................................................................................................7

GEO-CODING.....................................................................................................................................................................7LEVELS OF GEOGRAPHIC ANALYSIS..............................................................................................................................8

5 THE CALCULATION OF AREA RATES AND INDICATORS - AN EXPLANATION ANDWARNING................................................................................................................................................................................8

WHY CALCULATE RATES AND INDICATORS?...............................................................................................................8PROBLEMS ARISING IN RATE CALCULATIONS AND THEIR IMPLICATIONS?..............................................................9REPLICATION 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...............................................................................17PROPENSITY OF OFFENDERS TO OFFEND WITHIN THEIR OWN REGION. ..................................................................18DISTINCTIVE SUBURBS..................................................................................................................................................19

11 FURTHER RESEARCH AND DEVELOPMENT REQUIRED.................................................................. 20

12 CONCLUSION........................................................................................................................................................... 21

EXPLANATORY NOTES..........................................................................................................................................22REFERENCES..............................................................................................................................................................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

1 INTRODUCTION AND OVERVIEW

• This study describes the patterns in crime rates across regions in Western Australia, aswell as corresponding patterns in police-offender contact and socio-demographicconditions.

• Property offences comprise over eighty percent of all recorded offences in WesternAustralia and rates of property crime are higher in the Perth metropolitan area than theyare in any Regional Development Commission region. However, rates for offencesagainst the person are higher for the regions of Kimberley, Gascoyne, Goldfields-Esperance, Pilbara and the Mid-West than they are for Perth. Similar patterns apply tooffences against good order1. Furthermore, regional drug offence rates are generallyhigher than those in Perth.

• The study confirms the significance of earlier research conducted by the Crime ResearchCentre in 1998, in that crime rates in regional towns have a major impact on overallregional crime rates. Crime rates vary substantially among towns just as rate differencesbetween towns and more rural parts of the region may be substantial. The impact of townsis not restricted to personal offences: a number of regional towns have property crimerates higher than those recorded in the Perth metropolitan area.

• Just as within-region differences are important, so are differences in crime rates withinthe metropolitan area. Local Government areas vary in their rates of crime. Crime ratesas well as the mix of crimes in each area are influenced by the varying opportunities forcrime provided by their mix of social and economic activity, but also by the differentialrates 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 surveysdesigned to provide regional crime data are of great potential importance. They couldilluminate the extent to which regional differences in crime and police-offender contactrepresent differences in levels of victimisation; differences in the propensity of citizens toreport 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 importantsocial indicators. They represent the extent to which citizens have found it necessary tocall for public assistance in dealing with crime and the extent to which offenders havebeen publicly identified. They also indicate the mobilisation of considerable publicresources and, however imperfectly, reflect the consequences of crime for victims andoffenders. They provide a starting point2 for a systematic consideration of relative needfor crime prevention resources on a geographic basis.

1 For a description of the good order category of offences see the Explanatory Notes.2 Other systematically collected indicators of need should be considered, along with crime data. The range ofother 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.

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2 BACKGROUND

This project was conducted in response to the Ministry of Justice’s requirement for researchto map the following:

a) where crimes currently occur, broken down by type of crime, and to a level ofgeographic detail to be decided;

b) where known offenders, i.e. those arrested for and/or convicted of offences, comefrom 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 factorswere to include those shown by research to be predictive, and needed to include forconsideration those listed in the Action Plan to Address the Cycle of AboriginalJuvenile Offending (the Action Plan)3.

The Ministry required the mapping techniques to be repeatable so that the maps, charts andtables presented in the report could be updated on a regular basis. This requirement has beenaddressed by several strategies discussed in the body of this report.

The purpose of this consultancy was to inform crime prevention policy by locating the areaswhere specific types of prevention action may work best. Specifically the report was toinform 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 andrelevant social factors in a way that could inform policy and debate on primary, secondaryprevention interventions with offenders or potential offenders, as well as informingtraditional physical and environmental crime prevention.

From the beginning of the study it was clear to the researchers and the project SteeringCommittee that social and demographic factors4 could only be obtained or constructed frominformation in the 1996 Australian census. Other important information is available from arange of State and Commonwealth agencies, but not readily broken down by the geographicboundaries required for the current study. This does not mean that they could not begenerated in useable form in the context of longer-term planning and consultation with therelevant 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 ofcrime in communities6. The list includes demographic (total population, age, sex andAboriginality) as well as social and economic (educational background, employment andsocio-economic indexes) which are often found to correlate with recorded crime and police-offender contact. The demographic factors allow the calculation of relevant rates of crimeand police-offender contact and provide a picture of the social location of recorded crime toaccompany 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)4 These include both the factors connected with crime and offending and also those essential for the calculationof 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.

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the residential mobility of individuals in areas, and overcrowded housing may be used bycrime theories as indicators of key concepts (of social disorganisation7 for example), but donot in-themselves encapsulate causal processes.

3 DATA ON CRIME, OFFENDING AND SOCIO-DEMOGRAPHICFACTORS

Since one purpose of this study was to develop a methodology that could be replicated, it isimportant 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 theappendices is included in the Explanatory Notes below. The following paragraphs focus onbroader 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 theWestern Australian Police Service in gaining access to key data for the research conducted inthis 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 generatedwhen police complete an offence report form (P49), which is entered into the computer inelectronic form. The OIS is the major source of offence data and is triggered in most casesby a complaint from a member of the public.

Reported offence data were available at sub-regional level8 for the years 1996-1998. At thelevel 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 allegedoffenders10. However, there is not a simple relationship between offences and police-offender contacts and the following comments capture the main features, if not the fullcomplexity of the relationship.

• Not all offences reported to police lead to a ‘clear-up’, or more specifically, to theapprehension of an offender. For example approximately ten percent of burglaries leadto the apprehension of an offender, whereas over ninety percent of murders lead to anapprehension.

• Some offenders are involved in group-offences; that is to say there may be more than oneoffender involved in a single offence. Where offenders are detected in such situationsthere will be more police-offender contacts listed than there are offences.

7 See Miethe and Meier (1994).8 The research for this project enables offences to be mapped to the level of towns within region or localgovernment areas within the metropolitan area.9 This study adopted for its regional analysis the administrative regions outlined in Schedule 1 of the RegionalDevelopment Commissions Act 1993 which provided meaningful boundaries for previous research conducted bythe Crime Research Centre (1998a). The Perth metropolitan area, excluding any part of Peel, was selected asanother ‘region’ for comparison with the RDC regions.10 This is generally referred to by police as the ‘processed person’ database.

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• Alternatively, a single offender may be involved in many offences. When such asituation is detected, it will lead to the recording of as many police-offender contacts asthere are offences.

It should be evident from the above comments that counting police-offender contacts is notthe same as counting individual offenders. For example, a group of five police-offendercontacts may result from a single offender committing five offences or five offenderscommitting a single offence. However, it is not possible to disentangle these differentscenarios from the data used for this study.

The major advantage for this study in the use of police-offender contact information is that itenables the offender’s residence to be allocated to an appropriate geographic area (see thesection on geo-coding below). Police-offender contact data were analysed only for the years1997 and 1998 because locality information was most reliable for these years.

ARRESTS

A separate arrest database deriving from a different (P18) data collection form suppliesinformation on individuals whom police have charged with offences. In many cases theseoffenders will be charged for offences reported by members of the public and recorded in theOIS system. Hence the arrest database can be regarded as representing information‘downstream’ from the offence database11. However, the arrest database also includesinformation arising from police, as opposed to public, initiative. Many charges (relating forexample to drink-driving and street offences) arise from situations where there is no publiccomplainant. Arrest data are a valuable source of information about offenders who appearbefore court. Furthermore, they do support an analysis of the frequency of offending ofindividual offenders. However, geographic information about arrests is more limited than itis for police-offender contacts and it provides incomplete data about offender residence. Forthis reason, arrest information is used only in a limited way in this study. It is used to provideinformation about ‘street offences’ at the regional level in the time series analysis for theperiod 1991-199812.

INTEGRATED POLICE AND JUSTICE DATA

In an ideal world it would be possible to pursue the progress of an offence report from thetime it is logged by police, through the investigation and apprehension process, to thedecision to caution or charge an offender. It would also be possible to track the subsequentcourt outcome. Ideally, the relevant databases would be capable of identifying individualoffenders and linking them with all of their offences. Unfortunately, such a flow ofinformation can be assembled (in Western Australia and in most police jurisdictions) only ona case by case basis. There is no straightforward way of linking aggregate OIS data onoffences and police-offender contacts with data on arrests, where an individual identifier isavailable. The Ministry of Justice experiences similar information problems. Data aboutcourt appearances, prison episodes, community supervision, juvenile custody and juvenilesupervision are held in separate systems. These systems do not currently have the capacity togenerate an integrated view of the progress of an offender through the various stages of thecriminal justice process. 11 The arrest database only records information about offenders appearing before a court. It does not coveryoung offenders who receive a caution or who are referred by police to a Juvenile Justice Team.12 See Appendix E.

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The Western Australian Police Service is developing plans to implement such integratedsystems in the near future through its DCAT projects, but they do not exist at present. TheMinistry of Justice also has a means of integrating its separate systems within its datawarehouse, but the necessary mechanisms and procedures for integration are not yetoperational.

COURTS DATA

An exploratory analysis was undertaken of data from the Ministry of Justice courts database,CHIPS. This database contains information on lower court13 and juvenile court appearancesin 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 allowsdata from the 1996 and earlier censuses to be extracted at a refined geographic level. At thebroadest geographical level of analysis, data in this report were required at the level ofregions defined in the Regional Development Commissions Act 1993. These are defined asaggregations of Local Government Areas (LGAs). At a more refined geographic level thedata were extracted at LGA level and Police District level (in the metropolitan area) and werealso tabulated for selected country towns. The country town data were extracted asaggregations of Collection Districts (CDs) using urban boundaries defined in the CDATA96package.

THE INTERPRETATION OF CRIME AND OFFENDER DATA

There is a temptation to interpret data about crime and offending in a way that frames itsolely as offender behaviour and which hides from view the reaction to crime by the publicand police. Furthermore, public policy initiatives and new legislation may intervene toensure that greater priority is given to previously neglected areas such as domestic violenceand child abuse. The text below outlines the basic processes involved in the officialrecording of offences. In particular, the following (simplified) steps are required before anoffence 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 appropriateaction, such as laying charges, administering a caution, or referring the case to a juvenilejustice team14,15.

13 Note that Police Courts cover more remote areas of Western Australia and adult appearances in these courtsare 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 beignored for present purposes.

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In some cases steps 1-4 are collapsed when a police officer intervenes directly to make anarrest, most commonly in situations involving breaches of public order, drink driving or drugoffences16.

Each step in the above process ensures that the offences recorded by police and the contactsbetween police and offenders are shaped not simply by offending behaviour but also bypublic and police reaction that behaviour. This process of reaction has been documentedthrough the use of crime surveys and other types of research. The pattern of recorded crimeand police offender contact is shaped by some well-known factors and by others which areless well understood. Rather than attempting a comprehensive summary of this issue, weprovide some examples particularly relevant to the current study.

• Some studies (Weisheit, Falcone and Wells, 1994) have found that individuals in ruralareas prefer to rely on informal measures of dealing with criminal behaviour and are lesslikely to report offences to police. However, there is dearth of Australian research on thisissue.

• Australian crime surveys (Australian Bureau of Statistics 1999a) show that almost thirtypercent of assaults are reported to police, compared with one third of sexual assaults, fiftypercent of robberies, almost eighty percent of completed household burglaries and overninety percent of motor vehicle thefts.

• Other sources (Australian Bureau of Statistics, 1999b) show that when assaults arereported to and recorded by police they lead to proceedings against an offender in overfifty 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 juvenileoffenders 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 withadults and (b) juveniles are more likely than adults to be detected. This over-representation of juveniles in police-offender contact statistics is also relevant toAustralia17.

• New Zealand research indicates that young Maori offenders were overrepresented inpolice contact records compared with non-Maori offenders, after controlling for self-reported offending (Fergusson, Horwood and Lynskey, 1993). Australian research hasalso pointed to high rates of Aboriginal contact with police (Harding et al., 1995) and theextent to which this over-representation results from differential practices of police andother agencies has been vigorously debated.

These examples merely scratch the surface of what is known about the differential reaction tooffending 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 manycountries and time-periods, using varied measures of involvement. However, the proposition that officialrecords exaggerate the picture has been consistently raised (Marvell and Moody, 1991)

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to the fact that official records of crime and police-offender contact18 do not reflect patternsof offending behaviour in a simple way. A useful image used by Maguire (1998) to describethe process of crime measurement (using varied sources of data) was that of artists paintingand re-painting a canvas - arguably a more accurate analogy than that of a photographertaking a high-resolution snapshot.

4 MAPPING CRIME AND OFFENDING

Efforts to map and correlate crime with social and economic factors have a long if episodichistory, beginning with the cartographic work of Guerry in France around 1830. Of interestwith regard to this current exercise in Western Australia is Guerry's analysis of the impact ofpopulation density on crime. Guerry noticed that relatively less property crime occurred inthe administrative regions containing large cities, than in regions containing smaller cities,although there were exceptions19. He concluded that the generalisation associating crimeand population density (or large cities) was premature, a mistake derived from attributing toomuch explanatory power to population density and too little to a variety of factors whichoften but not always accompany population density (Beirne, 1993: 121).

Guerry's conclusion is highly applicable to the Australian context over the past twenty fiveyears. Funding bodies such as the Grants Commission have been presented with argumentsby New South Wales and Victoria claiming that the crime-producing environments of theirlarge capital cities justify additional law and order funding for those States. These argumentsare contradicted by the evidence of both crime surveys and police data. States such asWestern Australia and South Australia have consistently experienced higher crime rates thanNew South Wales and Victoria, and their capital cities have crime rates at least comparablewith Australia's two largest cities. The complexity of the relationship between city size andcrime was emphasised by research conducted by the Crime Research Centre (1998a). Thisindicated that the regional cities of Kalgoorlie and Geraldton had both personal and propertyrates comparable with those in Perth. In Western Australia, prior regional analyses of crimepatterns have been published by the Western Australian Police Service and WesternAustralian Ministry of Justice (1999) and the Crime Research Centre20.

GEO-CODING

Police data were geo-coded for this project by Excalibur Consultancies and aggregated fromthe level of CD to other relevant boundaries. The CD is the smallest geographical unit atwhich 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 levelsallows comparable maps and tabulations to be prepared and also for appropriate crime andoffender rates to be calculated. More detail about geo-coding is provided in the ExplanatoryNotes.

18 The problems of measurement are not restricted to official records. Similar problems confront the use ofcrime surveys and self-reports of offending. However, the use of more than one measuring tool can helpovercome 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 ofthe Crime Research Centre (1998) and in various specialised reports and books published by the Centre.

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LEVELS OF GEOGRAPHIC ANALYSIS

Any study with the ambitions of the present one requires important judgements to be madeabout the most suitable scale(s) of geographic analysis. Furthermore, there are trade-offs tobe made about the levels of analysis most suitable for the nature of the data (crime, offendersor 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 Perthmetropolitan area; local government areas within Perth; and larger towns and the ruralremnants within RDC regions.

Additionally, at the request of the Steering Committee, information is supplied at a fourthlevel of analysis: Police Districts within the metropolitan area. Note also that an initialanalysis was prepared for Perth suburbs, a suitable level of analysis for crime data, and thebasis 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, difficultiesdue to small numbers were evident in the analysis of police-offender contacts at the suburblevel so a decision was made to focus the main metropolitan analysis on local governmentareas. The nature of the difficulties associated with rate calculations in small areas isdiscussed below and care needs to be exercised in interpreting the rates of crime and police-offender contact for some of the smaller regional towns.

5 THE CALCULATION OF AREA RATES AND INDICATORS - ANEXPLANATION AND WARNING

WHY CALCULATE RATES AND INDICATORS?

The areas of interest in this study do not contain populations of the same size andcharacteristics. If they did, the task of comparison would be greatly simplified but far lessinteresting and the calculation of rates would be unnecessary. The presentation of counts ofcrime and police offender contact would be sufficient for comparison. However, rates arerequired to enable comparisons between areas of different size and composition. Areas showvariation in their percentage of juveniles, males, Aboriginal citizens, income levels, socio-economic status and so on. These differences can be displayed through the calculation ofrates, percentages or other constructs. With regard to crime, the simplest approach is tocalculate a rate based on total population. The number of offences per 1,000 populationprovides a seemingly obvious way of comparing areas of different size. This is the standardapproach adopted in this report.

At the regional level, however it is possible to calculate some special rates for specificoffences. For example, regional domestic burglary rates are also calculated as a rate per1,000 dwellings and motor vehicle theft rates are calculated as a rate per 1,000 registeredmotor vehicles. These rates provide a different means of comparing areas, based not on totalpopulation, but on the number of potential targets for each specific offence. The calculationof other rates is conceivable and subject only to data availability. For example, commercialburglary should ideally be calculated as a rate per 1,000 commercial premises, but systematicenumeration of these premises is not available across the State.

With regard to police-offender contact, total population rates can be calculated as well asrates for special populations. For example, it is possible to separate police-offender contacts

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by age, sex and Aboriginality, and to separate the area population in the same way. Thisallows the calculation of age-, sex- and race-specific rates of police-offender contact21.

PROBLEMS ARISING IN RATE CALCULATIONS AND THEIR IMPLICATIONS?

The principal issues of data reliability relate to the calculation of rates of crime and offenderresidence rates where both the numerator and denominator required for rate calculation aresubject to relatively large measurement error. A detailed discussion of the problems isincluded in the Explanatory Notes, however some guidelines for interpreting the Appendicesare offered below. These technical problems in the calculation of rates operate at a differentconceptual level from the issues concerning the interpretation of crime and offender data asdiscussed earlier. Furthermore, the details of rate calculations are also included in theExplanatory 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 indicators22

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 bereplicated. Apart from providing a clear documentation of the methods applied, the studyteam has developed a set of boundaries which can easily be re-used and has implementedthese 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 ofAboriginal identification this is not exactly true. The census uses a self-identification methodology, while policeoften use physical appearance as a basis for classification. However, a 1991 investigation by the CrimeResearch Centre of offenders sentenced to prison (Crime Research Centre, 1991) found a high correlationbetween the racial classification applied by prison authorities (using a self-report method) and that arrived at bypolice. This research is now in need of updating.22 Note that no data, including census data, should be considered completely reliable. The Australian Bureau ofStatistics has documented its problems in obtaining accurate enumeration of Australia’s Aboriginal populationat census time. Data about employment status and other social variables may be particularly difficult tocompare with the variables labelled similarly in non-aboriginal populations because of different socialarrangements (eg. of working for unemployment benefits), or of different interpretations (eg. of what is meantby a household or family). Issues relating to the interpretation of social indicators for Aboriginal people arediscussed 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 usualresidence. Nevertheless, indicators derived solely from census data have the advantage of using a consistentmeasurement methodology.

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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 CDas were the socio-demographic and base population figures. Each CD was mapped intolarger 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 presentedin this report through a series of linked spreadsheets containing summary data. Updatedcharts 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 CRIMINOLOGICALTHEORY

The application of a mapping methodology and the ready availability of census data focusattention on the correlations between crime and selected social-structural factors. Although itis becoming increasingly common to rally crime prevention efforts around the slogan that thecauses and prevention of crime are local, the structure of this study draws attention to broadersocial factors. It is clear that there is a case for an integrated assessment of local andstructural influences.

Agnew (1999) provides a useful assessment of the current state of knowledge about thetheoretical significance of community-level differences in crime particularly as they relate tosocial disorganisation theory, subcultural deviance theory and relative deprivation (strain)theory.

While noting that research results have been somewhat contradictory, Agnew points out therepeated findings that high crime communities are low in economic status, as measured byincome, poverty, unemployment, welfare, occupation, education, inequality, owner-occupieddwellings, and sub-standard housing 23. Family disruption is a mediating factor betweencrime and other variables. He identifies social disorganisation theory, with its key concept ofsocial 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 theydo weaken the ability of local residents to directly control crime in their communities andindirectly allow the development of delinquent peer groups.

Farrington (quoted in National Crime Prevention, 1999) favours a developmental approach tocrime prevention but has presented a view that communities are simply settings for individualbehaviour and potential intervention programs, with community level variables having littleor no causal effects. Nevertheless, other researchers have identified some community levelvariables 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 ofviolence, 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.

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Protective factors: access to support services, community networking, attachment to thecommunity, participation in church or community group, community/cultural norms againstviolence, cultural identity and ethnic pride.

The developmental perspective has been advocated in a recent report on crime preventioncommissioned by National Crime Prevention (1999). Furthermore, Homel and others discussrisk 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 arisingfrom a ‘common folk tradition’ characteristic of rural areas in many countries. Wirth andothers 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 discussionof 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 DevelopmentCouncil (1999) reveals that all regions of Western Australia are characterised by indicators ofwhat Wirth would describe as ‘urbanism’, namely, high levels of mobility motivated byemployment opportunities; population concentration in towns; and only a minority ofresidents having long-term connections with the area. However, the survey did indicate ahigh level of perceived community safety24 for most regional inhabitants.

A final word on theory must include some discussion of rational choice (Cornish and Clarke1986) and routine activity (Cohen and Felson, 1979) theories of crime. These theories givefar greater emphasis to crime opportunities and the situations in which they arise. Thesetheories and their crime prevention counterpart - situational crime prevention - emphasisehighly focused crime contexts. These contexts are very specific with regard to the type ofcrime under consideration and its spatial and temporal distribution. The examination of largegeographic areas, and aggregated crime counts would not be considered adequate by theproponents of these theories since they could not address the issues of required scale andfocus. Furthermore, the community level indicators relevant to routine activity theory aredifferent in nature to those measured in more traditional theory. For example, indicatorsrelevant to weekend street assault and robbery in Northbridge bear little relation to theresident population since the number of visitors to the area on Friday and Saturday nights is alarge multiple of the resident population. Routine activity theory points to the need forindicators 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 ofoffences 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 limitedsuccess. An obvious candidate as lead indicator for crime is population growth in the agegroups most prone to crime - for example those aged between 15 and 29. However, attemptsto use this methodology have not been particularly successful (Marvell and Moody, 1991). Apossible reason for this lack of success is that the age composition of a population maychange slowly and be swamped by more important social changes. Another reason issuggested 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.

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and other apparently important offender characteristics may not be as significant as theyappear 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 isthat future crime rates will preserve current relativities. This result would mirror thatexperienced in Chicago, which experienced very stable community relativities in crime ratesfor many years. Bursik and Grasmick (1994) provide a balanced assessment of thisproposition. Other insights are provided by Bottoms (1998) who discusses research intocrime rate changes in the light of general neighbourhood decline in U.S. cities and into theimpact of housing policy changes on U.K. housing estates. The demolition andredevelopment of some sub-standard housing concentrations in Perth will provide a test forsuch impacts in an Australian context. A most important recent study was published byRalph Taylor (1999). Taylor’s conclusions provide a strong challenge to those who support apolice 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 removalby other authorities). In Baltimore, Taylor’s research covered an 18-year time span andindicated that the major factors predicting urban area increases in crime rates were not‘incivilities’ but ‘neighbourhood basics’, which included the enhancement of neighbourhoodstability, maintenance of house prices relative to other areas and improvements in localeconomic development.

8 TIME SERIES ANALYSIS

One of the interests of the project steering group was in the temporal patterns of offending inWestern Australia and its regions. Temporal patterns may be present at various levels ofanalysis, for example there may be strong daily, weekly, monthly or seasonal patternssuperimposed on longer term trends and cycles. Of greatest interest to the SteeringCommittee were patterns that could be of relevance to the timing of crime preventionprograms, rather than day to day policing. To investigate the relevant patterns, eight years ofdata on recorded offences - the limits of available data from the OIS system - were analysedat the regional level. A time-series of this length should be considered to be a minimalrequirement for such an analysis. However, major temporal influences on crime should beevident.

The data on good order25 offences from the OIS were supplemented by data on ‘streetoffences’ from the arrest database over the same time period. These data were includedbecause the OIS data on good order do not completely cover all offences resulting from theexercise of police initiative to immediately arrest a citizen. However, OIS data do appear tohave good coverage of offences arising from complaints by a member of the public. Thereported 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 offirearms and other weapons, child pornography offences, liquor licensing offences, andbetting 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 disorderlyconduct.

The reported offence data required extreme care in interpretation given that they are based onthe 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 ..

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Initial analysis appeared to provide strong evidence for a monthly concentration of offencesin January. More detailed examination revealed that this effect was largely an artefact ofrecording practices (see page A134). These affected the recording of sexual offences andfraud in particular. For example, many sexual offences are reported in the context of abusethat may have been part of a chronic pattern over long time periods, making the exact dateand time of offences difficult to pinpoint. Police recording practice is evidently toapproximate the date of these offences as closely as possible, and this results in aconcentration of offences on January 1 of the relevant year or on the first day of the relevantmonth. Furthermore, the time stamp of these offences is concentrated at hour zero, minutezero 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 thefraud may be difficult to determine and a similar time concentration of recorded fraudemerges which again is an artefact of recording practices. It would not be possible to identifythe artefactual nature of these patterns if data were available or analysed only at the monthlylevel. 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 theState during the changeover period to the new system in 1991. It is evident from the data thatthe 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 theregional charts from pages A125 to A133.

The charts in Appendix E indicate the patterns of offending in each of the regions. Thesepatterns vary somewhat from region to region but it is fair to say that there appear to be nostrong seasonal effects in the State as a whole or in any region. A good way of examiningthis at the State level for offences against the person and fraud is to examine the day of yearanalysis on page A134. The daily ‘spikes’ due to the first day of the year and the first day ofeach month are evident but, apart from these, there is an overwhelming impression of flatnessin the reported crime data when examined on a daily basis. Even when sexual offences areremoved from the analysis this flatness by day of year predominates. This pattern appears togive little encouragement to the notion that interventions targeted at particular days of theyear could prevent crime. In order to test a more specific patterning of crime an investigationwas conducted, but is not displayed graphically in Appendix E, of the daily average offencecount for weekends and public and school holidays. There are variations by holiday and non-holiday 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 streetoffence arrests (page A136). At the State level there is evidence of higher offence rates in themonths of January and February, with the lowest rates being evident for November. The highrates 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 peaksuperimposed on a very flat monthly pattern), Peel (small general decline over the calendaryear), Pilbara (October peak followed by November trough) and Great Southern (Novemberpeak).

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 theState there are approximately 50% more street offences on weekend days than on non-

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weekend days. The ‘weekend effect’ is weakest in Goldfields-Esperance and in Pilbara, andstrongest 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 andseven times more street offence arrests on New Year’s day (they concentrate in the earlyhours of the morning) than on other days in the month. This single day focus is a majorfactor in - but does not completely explain - the January peak in street offence arrests acrossthe whole state. In the case of arrests the effect seems to be ‘real’ rather than being anartefact of data entry as was the case for reported offences against the person and for fraud.

9 AREAS WHICH COULD BE TARGETED FOR SPECIALASSISTANCE

The detailed area analysis in this report draws attention to a number of locations withrelatively high rates of crime, offenders and varied economic and social stressors. A numberof these areas were identified as being at ‘high risk’ in the Needs Analysis conducted byMacWilliam and Moore (1998), but the present study places these in the context ofcomprehensive crime, offender and census data. The study has confirmed the relativerankings 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, andvery high rates of both violent and property crime were registered in the Kimberley towns ofHalls Creek, Fitzroy Crossing and Derby. Crime rates high by comparison with towns inother regions were also registered in Wyndham and Kununurra. Similar patterns wereexperienced for processed person data. In the Kimberley there is a contrast between highrates of crime in these towns and lower rates of crime and police contact in rural areas. Theissue arising in this context is that some of the highest rates of poverty in the State are evidentoutside of the towns and it is impossible to put aside the difficulties faced by police processedperson data in accurately identifying the origins of offenders when they may also have pointsof contact in the towns. The mobilisation of police resources is inevitably more difficult inmore remote locations.

Issues which go to the heart of understanding and reacting constructively to regional crimepatterns are raised by the AJC (Aboriginal Justice Council, 1999). The case study of HallsCreek (p 82) refers to the need to understand and plan for the influx of large groups of desertpeople into Halls Creek from time to time. At a practical level the AJC identifies a numberof key planning issues. However, the case study vividly illustrates issues involved in themapping process. It is uncertain how accurately the official records of crime and police-offender contact can map complex interactions between people and places such as thoseexemplified in Halls Creek26. Detailed local information is required for a more completeunderstanding 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 andPort Hedland. While Wickham ranks higher than other towns in the region on measures ofsocial stress, its crime and processed person ranking is high only for drug and good orderoffences.

26 For example, offences will be correctly mapped to Halls Creek. However, police-offender contact may alsobe mapped to Halls Creek because of a temporary abode, even though the offender's usual residence is outsidethe town.

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In the Gascoyne, Carnarvon stands out from other towns in terms of crime rates andprocessed person rates and, because of its large regional population share it has a heavyinfluence on the regional average figures.

The 1998 research conducted by the Crime Research Centre identified the significantinfluence of Geraldton on Mid-West crime rates. This project confirms this influence over athree-year time period, but also identifies the smaller town of Meekatharra as having highercrime 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-Esperanceregion was also evident in the 1998 research. With the more detailed analysis possible in thisstudy the high rates of recorded crime and police contact in the smaller towns of Leonora andCoolgardie are also evident. Crime rate rankings in the region depend very much on theoffence type. Only Kalgoorlie ranks with other towns in the State as exceeding propertycrime rates over 150 per thousand persons, although Esperance approaches this level.However, both Leonora and the rural areas experience personal offence rates exceeding 20per thousand, and Norseman, Leonora and, to a lesser extent the rural parts of the region havehigh rates of drug and good order offences.

The 1998 CRC report identified the regions of Great Southern, the South West, Wheatbeltand 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 1996-1998. The one reversal for 1996-1998 is that Peel exhibits a higher rate for the high-volumeproperty offences than Pilbara. Post-census population increases for Peel27 are not taken intoaccount by our rate calculations so the increase in crime rates may be partly artificial. Theparticular towns with higher than average crime rates in these regions are as follows:

Great Southern: Katanning, particularly for its property crime rates and moderately highprocessed person rates and to a lesser extent Mount Barker and Kojonup. The region’slargest city, Albany, records low crime rates.

Wheatbelt: The towns of Northam, Toodyay and Narrogin record relatively high rates ofproperty crime - all over 160 per thousand population. It is clear that the Wheatbelt’s crimerate advantage derives from the high proportion of its population (70%) resident outside themajor 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) recordrelatively 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 inthe highest categories (set arbitrarily at over 160 per thousand for property and 20 for violentcrime). Bunbury has the peak property crime rate for the region at 130 per thousand whileHarvey ranks first for offences against the person.

Metropolitan area

The analysis of crime, police contact and socio-economic factors in the Perth metropolitanarea is illustrated by way of local government area maps. The change of the scale of analysisfrom 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 populationgrowth.

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tend to cut through clusters of high crime rate suburbs. Some indication of this is to be seenfrom 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 analysisof cities or towns as a whole because a clear conceptual distinction needs to be made betweenoffence and offender rates for the area. A more detailed exploratory analysis of thedifference between offence and offender patterns is based on courts data from the Ministry ofJustice CHIPS database.

However, beginning with the LGA maps it is clear that Perth, Victoria Park, Fremantle andEast Fremantle have high rates of recorded offences in almost all offence categories. Theinsights of routine activity theory help explain these patterns. The daily and weekly patternsof city life attract thousands of non-residents to these LGAs for work, business andentertainment. Opportunities for crime (and, of course, for legitimate activity) exist in fargreater abundance than the resident populations of these areas indicate. Unfortunately,adequate estimates of the population of central city and Fremantle populations at differenttimes of the day are not available even though they would provide more meaningfuldenominators 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 andStirling 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) databasewhich contains records of appearances in the Children’s Court and Courts of Petty Sessionsin Western Australia. Our analysis of CHIPS data focuses on juvenile appearances foroffences in the calendar year 1998. This restrictive focus is based on the assumption that thecoverage of the Children’s Court is State-wide, whereas there is no complete andcomprehensive State coverage of the Courts of Petty Sessions. CHIPS is designed to have aunique identification number for an offender who has court appearances both as a juvenileand as an adult. There is some duplication of identification numbers in CHIPS but the levelof this duplication has not been accurately assessed.

Further data issues with the CHIPS database concern its geographic indicators. It was notpossible to geocode the offender and offence locations to points, therefore the allocation ofoffenders and offences to regions and localities was achieved initially by mapping postcodesto RDC regions. However, not all offence and offender locations were assigned postcodes inCHIPS. 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% ofthe 42,730 cases in 1998) which had missing locality information and could not be assignedto an RDC region. A final issue with data coding in CHIPS concerns the identification ofoffender age. A total of 3,144 cases (7.4%) had no information about offender date of birthso that age could not be calculated. Cases with missing locality information were highlylikely to contain missing age information as well, and the approach taken is to ignore themissing data completely. For offenders with known age, there were few with missinggeographic indicators (less than 0.1%).

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The analysis which follows is based on offender prevalence in the calendar year 1998. Thismeans that an offender will appear only once in the analysis regardless of the number of courtappearances or offences charged during the year. Only one offence - the first offencerecorded for the year - is listed against each offender when the relationship between offencelocation and offender residence is investigated below. Consequently, the patterns discussedcould be quite different from those to be found if every offence or charge committed by eachoffender were to be taken into account.

An internal Ministry of Justice report (MacWilliam and Moore, 1999) made use of CHIPSdata 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 indecreasing order.

Table 1: Juvenile offender court appearance rate by RDC region

Region Rate per 1000 ofrelevant population

Juvenile CourtRanking

Police contactranking

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, somediscrepancies occur. The two main reasons for this are that Table 1 counts each offenderonly once regardless of the number of offences, charges or appearances in the calendar year1998, whereas the police contact statistics count an offender as many times as they are linkedto offences in 1997 and 199828. Second, the police contact figures include all contactsregardless of the way they are dealt with. Some young offenders, for example, will be dealtwith by way of a caution or a referral to a Juvenile Justice Team. These individuals willappear in the police contact statistics but will not appear in the Juvenile Court datasummarised in Table 1. Table 2.9 of the report Crime and Justice Statistics for WesternAustralia: 1997 (Crime Research Centre, 1998b) indicates that there were over 7,000 distinctpersons cautioned in 1997 and almost 2,000 distinct persons referred by police to JuvenileJustice Teams 29. This may be compared with the 3,923 individual offenders appearing in

28 Note that the rate calculated for police contact is an annual figure averaged over the two relevant years.29 Note that a further 1,258 distinct persons were referred by courts to Juvenile Justice Teams.

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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 AppendixA and the Juvenile Court appearance rates in Table 1 and explain why both absolute rates andalso 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 notidentical - under both measures of offender contact. While this is the case for juvenileoffending, the ranking of regions is very different for adult offenders under the alternativemeasures30. The principal difference in adult ranking affects the Kimberley region which isranked highest in terms of police contact for each of the three age groups aged 18 or over31,but is ranked lowest for court appearance prevalence for each of these age-groups. Thismajor discrepancy in ranking is no doubt due to inadequate coverage of adult courtappearances in the Kimberley, but other factors may contribute. These may include thefrequency of contact amongst offenders in the Kimberley relative to other regions. Thecontribution of each factor to this discrepancy cannot be stated with confidence, but it seemsunlikely 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 whooffend within the Perth region (95.5%). Column 3 indicates how many offences committedwithin the Perth region were known to be committed by Perth offenders (96.0%). It is clearthat the bulk of offending occurs in the region of residence of the offender. However, someoffenders living outside Perth are more likely to offend outside their region than offenders inPerth. Where offending outside the region does occur this is overwhelmingly concentrated inPerth as shown in column 2. Furthermore, offences committed in any region by offendersliving 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 inPerth, 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 thePerth region is that both of these regions are adjacent to Perth and offending in the Perthregion need not take an offender too far from home. The examination of these and otherregions reinforced the importance of the major towns in each region as sites for bothoffenders and offences. Thus, Mandurah dominates the Peel region as a site for offendersand offending. In a similar way, Northam is the major town in the Wheatbelt.

30 A detailed table of comparative adult rankings is not included here, but is discussed below.31 The age groups listed in Appendix A are 18-29, 30-39, and 40 and over.

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Table 2: Regional analysis: Juvenile Offenders

Region Offender percentages Offence percentages

Offending inown region

Offending inPerth1

By offendersfrom that region

By Perthoffenders2

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 0.71 The balance of offending is spread over other regions.2 The remaining offenders come from all other regions.3 By definition, this figure is identical to the percentage in the adjacent column. Perth offenders whooffend in their own region must offend in Perth.

DISTINCTIVE SUBURBS

A final use of the CHIPS database concerns the categorising of suburbs in Perth as having arelatively high degree of local or non-local offending. This analysis is limited to thosesuburbs having at least 15 juvenile offences in 1998. The suburbs which have over 40% oftheir 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 wherefewer than 10% of offences are committed by local offenders32 are Cannington, Fremantle,Northbridge, Perth, South Perth, Victoria Park and Warwick. These suburbs provide criminalopportunities that attract offenders from a large geographic base.

This suburb-based analysis is far from ideal. Size of suburb is an uncontrolled variable andthe greater the suburb size, the greater is the likelihood that offences will be committed bylocal offenders33. A better approach would examine the distance between offence location 32 These suburbs are also restricted to those with over 15 offences.33 In large suburbs offenders are able to commit offences at some distance from home yet still remain in theirown suburb.

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and offender residence, independently of suburb size. However, it was not possible toperform such an analysis in the time available for this project. Remember also that theoffence location of only a single offence per offender was used for the analysis. It is possiblethat 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 ofadult 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 systemswithin the Police Service, a need being addressed in current database planning processes.However, there would be considerable value in retrospectively linking, for the purpose offurther research, the police-offender contact ('processed person') database with the arrestdatabase. This linkage would then allow the strengths of both databases to be combined. Thedetailed geographic information in OIS could be combined with the ability of the arrestdatabase to identify individual offenders. Information would then be obtained concerning thenumber of individuals who have police contact in each area, their frequency of offending, andthe 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 CHIPSdatabase maintained by the Ministry of Justice. The design of this database is capable ofproducing extremely useful information about juvenile and adult offenders. It has thepotential to map across the State both the prevalence of offenders and frequency of offendingfor 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 diminishedby a relatively high level of missing data (including missing data about age, sex andAboriginality) and by locality indicators that could be improved through the application ofconsistent rules about data entry.

Even given the limitation of current systems it appears that extensions to the current studyshould be considered. There would be value, for example, in investigating the relationshipbetween offender residence and offence location in a more comprehensive way. This couldbe pursued through the analysis of either the CHIPS database, suitably geo-coded, or of theOIS database. The suggested research would probably become more manageable and usefulwith a focus on a particular offence or offences of interest and with a narrower geographicscope. For example, burglary prevention initiatives in selected metropolitan areas andcountry towns could be greatly assisted by research which maps the 'journey to crime' ofoffenders and relates this to crime prevention initiatives focused on offenders or victims34.

The analysis of socio-demographic factors collected for this study and their relationship tocrime and offending could be extended. This could be achieved through factor analysis,regression techniques or the development of meaningful theoretical area typologies relevantto the prediction of levels and patterns of crime. Furthermore, the range of factors includedin future study should be extended to include welfare, health, housing and educationalindicators.

A final but most important initiative would be the planning of cost-effective crime surveyscapable 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 linessuggested here.

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enhanced by developing new sampling strategies for the conduct of State surveys, thegrouping of survey data collected over several time periods, and discussions with variedagencies and researchers with an interest in improving regional information. The aim wouldbe 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 crimeacross Western Australia seems slim given the heterogeneity of ‘types of places’ across theState. These vary from Aboriginal communities, fishing communities, more traditionaltowns, through to modern ‘fly-in fly-out’ private mining towns where the recreational, familyand working lives of individuals are separated by thousands of kilometres and the temporalrhythm of ‘on’ and ‘off’ weeks. This research (following on from its 1998 precursor) hasconfirmed the existence of highly variable crime rates attaching themselves to cities andtowns of varying sizes and categories.

One reaction to the current study is to use the recorded crime and police-offender contactrates as indicators of the need for crime prevention resources. There is certainly a prima faciecase to be made - under a straightforward risk assessment basis - that State governmentfunding for crime prevention should be differentially targeted at communities with high crimeand offender rates. The relative position of communities should be balanced by a carefulanalysis 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 regionalcrime surveys which could provide any small area validation of the official statistics used inthis report. Surveys of community crime perceptions could provide other indicators of theextent of crime problems but systematic data are unavailable at the regional or small-arealevel and would not necessarily correspond with views expressed through State and localpolitical 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 tangledmix of crimes committed together with the reaction (of the public in reporting and the policein investigating) to those crimes.

The distribution of resources for crime prevention also assumes that there are proven methodsof reducing crime. Here also there is a degree of controversy, about the favoured forms ofcrime 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 recordedcrime and disadvantage, that both affluence and poverty need to be considered together, andthat both social and situational crime prevention must belong to the mix of strategies. Notethat the data for implementing and evaluating situational crime prevention measures will needto be collected in a more local, offence specific and disaggregated way than could beachieved with the current study.

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EXPLANATORY NOTES

This section provides explanatory notes regarding methodology, terminology and someimportant issues in the appendices. Appendices A, B, C and D provide information at fourdifferent levels of geographical focus in Western Australia: namely, regions; towns withinregions; and both Police Districts and local government areas within Perth. Each of theseappendices is broken into sections which present information about socio-demographicfactors, 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 incoloured thematic maps. A table of denominators used to calculate rates is also included ineach appendix. Appendix E presents time series of offence rates in each region for differenttemporal levels: year, month and day.

Socio-demographic factors

The socio-demographic factors in this study were obtained from 1996 Australian Census ofPopulation and Housing data via the Australian Bureau of Statistics CDATA96 softwarepackage. Appendices A to D show the prevalence of these factors for each of the fourgeographic levels. Some factors, though not included in the charts of Appendices A to D, areincluded 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 beenaggregated. Each CD was placed into each larger geographic area in which the CD's centroidlay.

The information on socio-demographic factors is expressed as a percentage of the relevantdenominator in the area. For example, in Appendix A: Summary of Gascoyne, the value ofthe factor "Population 10 - 17 years" is 7.6%, which is obtained by dividing the number ofpersons aged 10 to 17 years in the Gascoyne region (1134) by the total number of persons inthe Gascoyne (14836), and then multiplying by 100.

The Socio-Economic index is included as a factor in this study because it is a compositeindicator of economic and social characteristics. It is calculated by ABS using principal-components procedures on variables in the census. Some of these underlying variables areincome, educational attainment, and unemployment. It is included as the Index of RelativeSocio-Economic Disadvantage in the ABS Socio-Economic Indexes for Areas (SEIFA). Fordetails 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

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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 inEnglish

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 agedunder 15 years

Total number of families, groupsor 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 geo-coded so that each offence was assigned a particular ABS CD as far as is possible. Thisprocess allowed information on offences to be aggregated into larger geographical areas ofinterest, 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 anAustralian National Classification of Offences (ANCO) code, which was then allocated intoone 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 Sheetsin Appendix A.

There is some value in describing the offences in the category good order and its sub-category of street offences. Offences in this category sometimes cause confusion and thegrouping of offences is very disparate. The group includes offences against governmentoperations, attempts to pervert the course of justice, breaches of court orders such asprobation, parole and so on, conspiracy and other offences against justice, resisting orhindering police, trespassing and vagrancy, disorderly conduct offences and (when stillcriminalised) offences of public drunkenness. The last four groups constitute the streetoffence category and offences such as these are better represented in the arrest database thanin 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. Forexample, in Appendix A: Summary of Gascoyne, the rate for Against the Person offences is20.4, which is obtained by dividing the average yearly number of Against the Person offencesin the Gascoyne region (303) by the total number of persons in the Gascoyne (14836), andthen multiplying by 1000. Note that, although the offence period is 1996 to 1998, the

24

population figure is for 1996 only. This would overestimate offence rates if the populationhad increased over the period.

Police-Offender Contacts

The information on offenders is also obtained from the WA Police OIS. It covers all police-offender contacts (also known as "processed persons") in the OIS for the years 1997 and1998.

A police-offender contact is a person listed on a police OIS report. Because there is nounique numerical identifier recorded against an offender on a report, there is no ability toidentify unique individuals on different reports. Hence, one individual may account formultiple police-offender contacts (ie, a person arrested on more than one occasion is countedeach time they are arrested). Thus, the information presented on offenders is event-based andis not so much prevalence of offenders but prevalence of offending.

The data contain information relating to the location of offender residence. This informationwas geo-coded so that each offence was assigned to a particular ABS collector's district as faras is possible. This process allowed information on offender addresses to be aggregated intolarger geographical areas of interest, however, not all offender addresses were able to be geo-coded into a CD.

The offender information is also presented by demographic classifications, such as sex, agegroup and race. However, demographic information could not be ascribed with certainly tomultiple offenders who appeared on the same police report. Accordingly, about 15% of therecords were removed from the analysis for this reason. The remaining 85% of the recordsrepresent 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 wouldalmost certainly vary across different offender demographic and geographic characteristics.

The offender information is presented as average yearly rates per 1000 persons in an area. Forexample, in Appendix A: Summary of Gascoyne, the rate for male police-offender contacts is41.4, which is obtained by dividing the average yearly number of male offenders in theGascoyne region (323) by the total number of male persons in the Gascoyne (7802, see thetable 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. Thiswould 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 theWA 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 seriesanalysis is only on a regional level.

The first section presents the time series of offences recorded in the OIS, and is presented bythe four major offence categories within each region. Sexual and fraud offences are notincluded in the charts for the regions. This is because for these offences the precise date andtime of occurrence is often unknown. This is highlighted on page A134, which shows theaverage number of reported sexual and fraud offences in WA for each day of the year from

25

1991 to 1998. It is marked by the spikes at the beginning of each month and particularly onJanuary 1. This is an artefact of the recording procedures of offences where the date ofoccurrence is not known. Also shown on page A134 are two time series for offences againstthe person; one includes sexual offences and the other does not. The time series withoutsexual 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 asdisorderly 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 detailedissues are listed below.

The first kind of problem occurs when crime and population are measured over different timeperiods. The primary denominator used for the calculation of rates is the population of thearea of interest. Population data are available at the small area level from the 1996 censuswhereas crime data are available over varying time periods as described above. Two areaswhich may have the same rate of recorded crime may appear to have different rates simplybecause one has grown more rapidly in population since July 1996. The area with fastergrowth will appear to have a higher crime rate simply because its correct population basewill be underestimated relative to the other. For example, crime rates for fast growing areason 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 localgovernment areas for example). Here there may be sources of error in measuring both thepopulation, the amount of crime or the number of offenders. When errors appear in both thenumerator and denominator used to calculate a rate, the relative errors are additive. Thesources 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. Thegeo-coding process described above may not be able to accurately assign a crime or anoffender residence to the correct area. This is likely to be a more significant issue in ruralareas rather than in Perth and regional towns but the full dimension of the problem is not wellcharted.

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 incrime rates than there are between areas. An examination of suburb crime rates providessupport 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 ofthree principal geographic levels in this study.

35 Note that there are deliberate errors introduced into small-area census data in order to protect theconfidentiality of citizens.

26

REFERENCES

Aboriginal Justice Council (1999). Our mob our justice: keeping the vision alive. Perth:Perth: Aboriginal Justice Council Secretariat.

Agnew, R. (1999) ‘A general strain theory of community differences in crime rates’. Journalof Research in Crime and Delinquency 36(2): 123-155.

Australian Bureau of Statistics (1999a). Crime and Safety Australia, April 1998. Canberra: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-economicIndexes for Areas. Canberra: Australian Bureau of Statistics (cat number 2039.0.)

Beirne, P. (1993). Reinventing criminology. Albany: SUNY Press.

Bottoms, A. (1997). 'Environmental criminology', in Maguire, M., Morgan, R. and Reiner, M.The Oxford handbook of criminology (2nd edition). Oxford: Clarendon Press.

Bursik, R. and Grasmick, H. (1993). Neighbourhoods and crime: the dimensions of effectivecommunity control. New York: Lexington Books.

Cohen, L. and Felson, M. (1979). 'Social change and crime rate trends: a routine activityapproach'. 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 : theoreticalfoundations. Brookfield, USA : Ashgate.

Cornish, D and Clarke, R. (Eds.), (1986). The reasoning criminal. New York: Springer-Verlag.

Fergusson, D., Horwood, L. and Lynskey, M. (1993). ‘Ethnicity, social background andyoung offending: a fourteen year longitudinal study’. Australian and New Zealand Journalof Criminology, 26: 155-170.

Harding, R., Broadhurst, R., Ferrante, A. and Loh, N. (1995). Aboriginal contact with thecriminal justice system and the impact of the Royal Commission into Aboriginal Deaths inCustody. Sydney; Hawkins Press.

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Homel, R., Lincoln, R. and Herd, B. (1999). ‘Risk and resilience: crime and violenceprevention in Aboriginal communities’. Australian and New Zealand Journal ofCriminology, 32(2): 182-196.

Hough, M. and Tilley, N. (1998). Auditing crime and disorder: guidance for localpartnerships. 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 Ministryof Justice, Juvenile Justice Community Funding Program. Perth: Ministry of Justice.

Maguire, M. (1997 ). 'Crime statistics, patterns and trends', in Maguire, M., Morgan, R. andReiner, 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 earlyintervention approaches to crime in Australia. Canberra: Attorney-General’s Department.

Regional Development Council (1999). Living in the regions. Perth: Department ofCommerce 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: WesternAustralian Police Service.

Snyder, R. (1999). ‘The overrepresentation of juvenile crime proportions in robberyclearance 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 inAction 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 AustralianGovernment.

Wirth, L. ‘Urbanism as a way of life’. American Journal of Sociology, 44: 1-24.

INDEX OF APPENDICES

Page A1

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

Peel

Perth

Wheatbelt

Great SouthernSouth West

Pilbara

Goldfields-Esperance

Gascoyne

Mid West

Kimberley

APPENDIX A - RDC REGIONS OF WESTERN AUSTRALIA

Page A2

APPENDIX A - REGIONS IN WA SUMMARYOF GASCOYNE

Page A3

SOCIO-DEMOGRAPHIC FACTORS N %Population 14836Population 10 -17 years 1134 7.6Population 18 -29 years 2092 14.1Population 15 years or more who are unemployed 433 3.5Population 15 years or more who left school aged 15 or less 6036 49.1Population who are Aboriginal or Torres Strait Islander 1461 9.8Persons enumerated at a different address five years earlier 6262 42.2OPD's with no motor vehicle 499 9.2Socio-economic Index 946Population 15 years or more with no qualification 7384 60.1Population born overseas and have limited proficiency in English 104 0.7Families in OPD's with weekly income less than $500 633 27.8Households in OPD's with one parent and children aged under 15 years 185 6.1Private dwellings that are flats, units or apartments 284 4.9OPD's that are rented 1937 35.6OPD's with more than five residents 172 3.2OPD's with more than one family 47 0.9

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 2 0.11Assault 246 16.56Sexual offences 38 2.56Robbery 3 0.22Other 14 0.94Sub-total 303 20.40

Property offencesBurglary - dwellings 275 18.51Burglary - commercial 118 7.95Burglary - other 4 0.27Motor vehicle theft 70 4.70Other theft 909 61.25Property damage 402 27.10Sub-total 1777 119.78

Drug offencesPossession/use 76 5.15Deal/manufacture 18 1.21Other 56 3.80Sub-total 151 10.16

Good order offences 95 6.38Miscellaneous other offences 50 3.35Total offences reported 2375 160.06Special rates:

Burglary - dwellings (per 1000 dwellings) 47.32Motor vehicle theft (per 1000 vehicles) 11.19

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 393 26.46Male 323 41.40Female 55 7.75ATSI 221 151.27Non-ATSI 156 11.66Aged 10 - 17 109 96.12Aged 18 - 29 174 82.93Aged 30 - 39 61 30.44Aged 40 or more 32 4.03

Note: In 1997 to 1998 there were 30 cases of unknown sex, 30 of unknown age and 31 of unknown race.

APPENDIX A - REGIONS IN WA SUMMARYOF GOLDFIELDS-ESPERANCE

Page A4

SOCIO-DEMOGRAPHIC FACTORS N %Population 57368Population 10 -17 years 6030 10.5Population 18 -29 years 13645 23.8Population 15 years or more who are unemployed 1766 4.1Population 15 years or more who left school aged 15 or less 17630 40.6Population who are Aboriginal or Torres Strait Islander 4702 8.2Persons enumerated at a different address five years earlier 30967 54.0OPD's with no motor vehicle 1584 8.8Socio-economic Index 981Population 15 years or more with no qualification 25973 59.8Population born overseas and have limited proficiency in English 127 0.2Families in OPD's with weekly income less than $500 2520 19.5Households in OPD's with one parent and children aged under 15 years 896 5.3Private dwellings that are flats, units or apartments 987 4.9OPD's that are rented 7391 41.0OPD's with more than five residents 877 4.9OPD's with more than one family 269 1.5

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 6 0.10Assault 713 12.43Sexual offences 107 1.87Robbery 42 0.73Other 75 1.31Sub-total 943 16.43

Property offencesBurglary - dwellings 1180 20.56Burglary - commercial 694 12.10Burglary - other 35 0.62Motor vehicle theft 612 10.66Other theft 4100 71.47Property damage 1429 24.92Sub-total 8051 140.33

Drug offencesPossession/use 428 7.47Deal/manufacture 86 1.50Other 242 4.22Sub-total 756 13.18

Good order offences 175 3.06Miscellaneous other offences 321 5.60Total offences reported 10246 178.61Special rates:

Burglary - dwellings (per 1000 dwellings) 58.46Motor vehicle theft (per 1000 vehicles) 21.97

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 1509 26.30Male 1167 36.88Female 288 11.19ATSI 694 147.74Non-ATSI 755 14.32Aged 10 - 17 335 55.47Aged 18 - 29 701 51.37Aged 30 - 39 301 27.63Aged 40 or more 111 6.57

Note: In 1997 to 1998 there were 108 cases of unknown sex, 108 of unknown age and 121 of unknown race.

APPENDIX A - REGIONS IN WA SUMMARYOF GREAT SOUTHERN

Page A5

SOCIO-DEMOGRAPHIC FACTORS N %Population 48223Population 10 -17 years 6309 13.1Population 18 -29 years 6582 13.6Population 15 years or more who are unemployed 1738 4.8Population 15 years or more who left school aged 15 or less 15803 43.9Population who are Aboriginal or Torres Strait Islander 1547 3.2Persons enumerated at a different address five years earlier 20723 43.0OPD's with no motor vehicle 1176 6.7Socio-economic Index 982Population 15 years or more with no qualification 23118 64.2Population born overseas and have limited proficiency in English 125 0.3Families in OPD's with weekly income less than $500 4597 35.6Households in OPD's with one parent and children aged under 15 years 1001 5.9Private dwellings that are flats, units or apartments 626 3.0OPD's that are rented 4889 27.8OPD's with more than five residents 724 4.1OPD's with more than one family 102 0.6

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 3 0.06Assault 264 5.48Sexual offences 63 1.31Robbery 16 0.32Other 48 0.99Sub-total 394 8.16

Property offencesBurglary - dwellings 537 11.13Burglary - commercial 429 8.90Burglary - other 33 0.68Motor vehicle theft 143 2.97Other theft 1937 40.16Property damage 761 15.79Sub-total 3840 79.62

Drug offencesPossession/use 213 4.42Deal/manufacture 83 1.72Other 135 2.80Sub-total 431 8.94

Good order offences 116 2.41Miscellaneous other offences 134 2.79Total offences reported 4915 101.93Special rates:

Burglary - dwellings (per 1000 dwellings) 25.90Motor vehicle theft (per 1000 vehicles) 4.99

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 866 17.95Male 677 27.87Female 158 6.58ATSI 292 188.75Non-ATSI 539 11.54Aged 10 - 17 252 39.94Aged 18 - 29 355 53.93Aged 30 - 39 164 21.85Aged 40 or more 58 2.92

Note: In 1997 to 1998 there were 63 cases of unknown sex, 63 of unknown age and 70 of unknown race.

APPENDIX A - REGIONS IN WA SUMMARYOF KIMBERLEY

Page A6

SOCIO-DEMOGRAPHIC FACTORS N %Population 32911Population 10 -17 years 3350 10.2Population 18 -29 years 6331 19.2Population 15 years or more who are unemployed 790 3.1Population 15 years or more who left school aged 15 or less 8801 35.0Population who are Aboriginal or Torres Strait Islander 11459 34.8Persons enumerated at a different address five years earlier 13159 40.0OPD's with no motor vehicle 1708 17.8Socio-economic Index 914Population 15 years or more with no qualification 14162 56.3Population born overseas and have limited proficiency in English 32 0.1Families in OPD's with weekly income less than $500 1549 30.2Households in OPD's with one parent and children aged under 15 years 601 9.4Private dwellings that are flats, units or apartments 264 2.6OPD's that are rented 4490 46.9OPD's with more than five residents 856 8.9OPD's with more than one family 382 4.0

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 6 0.18Assault 821 24.95Sexual offences 98 2.97Robbery 13 0.41Other 61 1.86Sub-total 999 30.36

Property offencesBurglary - dwellings 764 23.22Burglary - commercial 400 12.16Burglary - other 13 0.38Motor vehicle theft 296 8.98Other theft 2250 68.38Property damage 846 25.70Sub-total 4569 138.83

Drug offencesPossession/use 164 4.97Deal/manufacture 30 0.90Other 111 3.36Sub-total 304 9.24

Good order offences 210 6.39Miscellaneous other offences 127 3.87Total offences reported 6210 188.69Special rates:

Burglary - dwellings (per 1000 dwellings) 76.04Motor vehicle theft (per 1000 vehicles) 30.67

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 1422 43.19Male 1150 66.98Female 242 15.37ATSI 1112 97.04Non-ATSI 278 12.96Aged 10 - 17 283 84.48Aged 18 - 29 638 100.77Aged 30 - 39 327 60.48Aged 40 or more 133 10.72

Note: In 1997 to 1998 there were 60 cases of unknown sex, 60 of unknown age and 63 of unknown race.

APPENDIX A - REGIONS IN WA SUMMARYOF MID WEST

Page A7

SOCIO-DEMOGRAPHIC FACTORS N %Population 51100Population 10 -17 years 6012 11.8Population 18 -29 years 9078 17.8Population 15 years or more who are unemployed 2290 5.9Population 15 years or more who left school aged 15 or less 16573 42.6Population who are Aboriginal or Torres Strait Islander 3853 7.5Persons enumerated at a different address five years earlier 23929 46.8OPD's with no motor vehicle 1384 8.2Socio-economic Index 964Population 15 years or more with no qualification 24119 62.0Population born overseas and have limited proficiency in English 216 0.4Families in OPD's with weekly income less than $500 3470 29.5Households in OPD's with one parent and children aged under 15 years 938 6.0Private dwellings that are flats, units or apartments 970 4.8OPD's that are rented 5640 33.3OPD's with more than five residents 675 4.0OPD's with more than one family 122 0.7

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 6 0.11Assault 568 11.12Sexual offences 81 1.58Robbery 24 0.46Other 51 1.00Sub-total 729 14.27

Property offencesBurglary - dwellings 1358 26.58Burglary - commercial 690 13.50Burglary - other 41 0.80Motor vehicle theft 284 5.56Other theft 3545 69.37Property damage 1284 25.12Sub-total 7201 140.93

Drug offencesPossession/use 260 5.08Deal/manufacture 98 1.92Other 165 3.24Sub-total 523 10.23

Good order offences 209 4.08Miscellaneous other offences 145 2.83Total offences reported 8807 172.34Special rates:

Burglary - dwellings (per 1000 dwellings) 67.74Motor vehicle theft (per 1000 vehicles) 11.09

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 1134 22.18Male 884 32.10Female 217 9.19ATSI 628 162.86Non-ATSI 461 9.76Aged 10 - 17 307 51.06Aged 18 - 29 498 54.86Aged 30 - 39 215 24.44Aged 40 or more 72 3.80

Note: In 1997 to 1998 there were 66 cases of unknown sex, 66 of unknown age and 90 of unknown race.

APPENDIX A - REGIONS IN WA SUMMARYOF PEEL

Page A8

SOCIO-DEMOGRAPHIC FACTORS N %Population 61754Population 10 -17 years 8023 13.0Population 18 -29 years 7871 12.7Population 15 years or more who are unemployed 3049 6.6Population 15 years or more who left school aged 15 or less 23989 51.8Population who are Aboriginal or Torres Strait Islander 872 1.4Persons enumerated at a different address five years earlier 32608 52.8OPD's with no motor vehicle 1559 6.8Socio-economic Index 948Population 15 years or more with no qualification 29381 63.5Population born overseas and have limited proficiency in English 128 0.2Families in OPD's with weekly income less than $500 6393 36.4Households in OPD's with one parent and children aged under 15 years 1394 6.2Private dwellings that are flats, units or apartments 894 3.0OPD's that are rented 5366 23.4OPD's with more than five residents 752 3.3OPD's with more than one family 157 0.7

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 4 0.06Assault 357 5.78Sexual offences 86 1.39Robbery 36 0.58Other 51 0.83Sub-total 534 8.64

Property offencesBurglary - dwellings 1221 19.77Burglary - commercial 484 7.83Burglary - other 36 0.58Motor vehicle theft 474 7.68Other theft 3597 58.25Property damage 1134 18.36Sub-total 6945 112.47

Drug offencesPossession/use 201 3.25Deal/manufacture 104 1.69Other 86 1.40Sub-total 392 6.34

Good order offences 133 2.15Miscellaneous other offences 181 2.94Total offences reported 8185 132.54Special rates:

Burglary - dwellings (per 1000 dwellings) 41.48Motor vehicle theft (per 1000 vehicles) 13.41

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 877 14.20Male 696 22.69Female 151 4.84ATSI 87 99.20Non-ATSI 754 12.39Aged 10 - 17 208 25.86Aged 18 - 29 370 47.01Aged 30 - 39 177 18.80Aged 40 or more 89 3.34

Note: In 1997 to 1998 there were 62 cases of unknown sex, 62 of unknown age and 73 of unknown race.

APPENDIX A - REGIONS IN WA SUMMARYOF PERTH

Page A9

SOCIO-DEMOGRAPHIC FACTORS N %Population 1234939Population 10 -17 years 148442 12.0Population 18 -29 years 231038 18.7Population 15 years or more who are unemployed 49814 5.1Population 15 years or more who left school aged 15 or less 352582 36.3Population who are Aboriginal or Torres Strait Islander 17068 1.4Persons enumerated at a different address five years earlier 555687 45.0OPD's with no motor vehicle 43914 9.5Socio-economic Index 1020Population 15 years or more with no qualification 552369 56.9Population born overseas and have limited proficiency in English 23642 1.9Families in OPD's with weekly income less than $500 82588 25.3Households in OPD's with one parent and children aged under 15 years 25863 5.8Private dwellings that are flats, units or apartments 43010 8.6OPD's that are rented 123852 26.9OPD's with more than five residents 13806 3.0OPD's with more than one family 3230 0.7

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 50 0.04Assault 8822 7.14Sexual offences 2218 1.80Robbery 2042 1.65Other 1696 1.37Sub-total 14827 12.01

Property offencesBurglary - dwellings 32624 26.42Burglary - commercial 11526 9.33Burglary - other 443 0.36Motor vehicle theft 15248 12.35Other theft 90969 73.66Property damage 35655 28.87Sub-total 186464 150.99

Drug offencesPossession/use 5099 4.13Deal/manufacture 1376 1.11Other 2876 2.33Sub-total 9351 7.57

Good order offences 4091 3.31Miscellaneous other offences 7851 6.36Total offences reported 222585 180.24Special rates:

Burglary - dwellings (per 1000 dwellings) 65.41Motor vehicle theft (per 1000 vehicles) 21.98

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 22276 18.04Male 16975 28.07Female 4456 7.07ATSI 3180 186.26Non-ATSI 17977 14.76Aged 10 - 17 6267 42.22Aged 18 - 29 9614 41.61Aged 30 - 39 3584 18.68Aged 40 or more 1895 3.85

Note: In 1997 to 1998 there were 1692 cases of unknown sex, 1692 of unknown age and 2240 of unknown race.

APPENDIX A - REGIONS IN WA SUMMARYOF PILBARA

Page A10

SOCIO-DEMOGRAPHIC FACTORS N %Population 44826Population 10 -17 years 4726 10.5Population 18 -29 years 9061 20.2Population 15 years or more who are unemployed 1201 3.6Population 15 years or more who left school aged 15 or less 11837 35.4Population who are Aboriginal or Torres Strait Islander 5163 11.5Persons enumerated at a different address five years earlier 22993 51.3OPD's with no motor vehicle 1035 7.4Socio-economic Index 995Population 15 years or more with no qualification 17320 51.8Population born overseas and have limited proficiency in English 188 0.4Families in OPD's with weekly income less than $500 906 9.9Households in OPD's with one parent and children aged under 15 years 582 5.0Private dwellings that are flats, units or apartments 1408 8.7OPD's that are rented 6706 48.1OPD's with more than five residents 691 5.0OPD's with more than one family 183 1.3

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 5 0.10Assault 529 11.79Sexual offences 72 1.61Robbery 10 0.22Other 41 0.91Sub-total 656 14.63

Property offencesBurglary - dwellings 726 16.20Burglary - commercial 361 8.05Burglary - other 18 0.39Motor vehicle theft 274 6.11Other theft 2342 52.24Property damage 772 17.21Sub-total 4492 100.20

Drug offencesPossession/use 224 5.00Deal/manufacture 51 1.13Other 164 3.67Sub-total 439 9.80

Good order offences 153 3.42Miscellaneous other offences 113 2.52Total offences reported 5853 130.58Special rates:

Burglary - dwellings (per 1000 dwellings) 45.02Motor vehicle theft (per 1000 vehicles) 14.54

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 1072 23.90Male 836 33.20Female 202 10.27ATSI 593 114.76Non-ATSI 439 11.06Aged 10 - 17 310 65.49Aged 18 - 29 452 49.83Aged 30 - 39 201 21.83Aged 40 or more 71 5.11

Note: In 1997 to 1998 there were 68 cases of unknown sex, 68 of unknown age and 81 of unknown race.

APPENDIX A - REGIONS IN WA SUMMARYOF SOUTH WEST

Page A11

SOCIO-DEMOGRAPHIC FACTORS N %Population 108342Population 10 -17 years 14651 13.5Population 18 -29 years 16150 14.9Population 15 years or more who are unemployed 3886 4.8Population 15 years or more who left school aged 15 or less 37163 46.0Population who are Aboriginal or Torres Strait Islander 1998 1.8Persons enumerated at a different address five years earlier 49374 45.6OPD's with no motor vehicle 2600 6.6Socio-economic Index 979Population 15 years or more with no qualification 50244 62.2Population born overseas and have limited proficiency in English 466 0.4Families in OPD's with weekly income less than $500 8779 30.3Households in OPD's with one parent and children aged under 15 years 2396 6.4Private dwellings that are flats, units or apartments 1469 3.2OPD's that are rented 11731 30.0OPD's with more than five residents 1379 3.5OPD's with more than one family 196 0.5

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 4 0.04Assault 503 4.64Sexual offences 176 1.63Robbery 28 0.26Other 66 0.61Sub-total 777 7.17

yProperty offencesBurglary - dwellings 1036 9.57Burglary - commercial 863 7.96Burglary - other 71 0.66Motor vehicle theft 350 3.23Other theft 4493 41.47Property damage 1454 13.42Sub-total 8266 76.30

yDrug offencesPossession/use 539 4.97Deal/manufacture 207 1.91Other 300 2.77Sub-total 1046 9.65

yGood order offences 318 2.94yMiscellaneous other offences 278 2.56yTotal offences reported 10685 98.62ySpecial rates:

Burglary - dwellings (per 1000 dwellings) 22.54Motor vehicle theft (per 1000 vehicles) 5.61

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 1410 13.01Male 1107 20.26Female 251 4.66ATSI 186 93.09Non-ATSI 1166 10.96Aged 10 - 17 345 23.55Aged 18 - 29 622 38.51Aged 30 - 39 256 14.51Aged 40 or more 130 3.08

Note: In 1997 to 1998 there were 106 cases of unknown sex, 106 of unknown age and 116 of unknown race.

APPENDIX A - REGIONS IN WA SUMMARYOF WHEATBELT

Page A12

SOCIO-DEMOGRAPHIC FACTORS N %Population 69120Population 10 -17 years 8155 11.8Population 18 -29 years 10118 14.6Population 15 years or more who are unemployed 2039 3.9Population 15 years or more who left school aged 15 or less 23334 45.2Population who are Aboriginal or Torres Strait Islander 2564 3.7Persons enumerated at a different address five years earlier 27594 39.9OPD's with no motor vehicle 1523 6.1Socio-economic Index 987Population 15 years or more with no qualification 35002 67.8Population born overseas and have limited proficiency in English 127 0.2Families in OPD's with weekly income less than $500 5476 29.9Households in OPD's with one parent and children aged under 15 years 1097 4.6Private dwellings that are flats, units or apartments 475 1.5OPD's that are rented 6751 27.0OPD's with more than five residents 987 4.0OPD's with more than one family 100 0.4

OFFENCES REPORTED Annual average (1996-98) Annual rate per 1000 personsAgainst the person offences

Homicide 7 0.10Assault 459 6.64Sexual offences 155 2.25Robbery 11 0.15Other 61 0.88Sub-total 692 10.02

yProperty offencesBurglary - dwellings 744 10.77Burglary - commercial 608 8.79Burglary - other 49 0.71Motor vehicle theft 196 2.83Other theft 2547 36.85Property damage 1143 16.54Sub-total 5287 76.49

yDrug offencesPossession/use 365 5.29Deal/manufacture 105 1.51Other 280 4.06Sub-total 750 10.86

yGood order offences 248 3.58yMiscellaneous other offences 173 2.50yTotal offences reported 7151 103.45ySpecial rates:

Burglary - dwellings (per 1000 dwellings) 23.95Motor vehicle theft (per 1000 vehicles) 4.43

Note: Sums of numbers may not equal totals owing to rounding.

POLICE-OFFENDER CONTACTS Annual Average (1997-98) Annual rate per 1000 personsPolice-offender contacts 1180 17.06Male 921 25.32Female 218 6.66ATSI 420 163.61Non-ATSI 712 10.69Aged 10 - 17 296 36.24Aged 18 - 29 518 51.20Aged 30 - 39 198 17.43Aged 40 or more 111 4.01

Note: In 1997 to 1998 there were 81 cases of unknown sex, 81 of unknown age and 97 of unknown race.

APPENDIX A - REGIONS IN WA SOCIO-DEMOGRAPHIC FACTORS

Page A13

POPULATION AGED 10 - 17 YEARS

0

3

6

9

12

15

Gas

coyn

eG

oldfie

lds-

Esp.

Gre

at S

outh

ern

Kim

berle

yM

id W

est

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Per

cen

tPOPULATION AGED 18 - 29 YEARS

0

5

10

15

20

25

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Per

cen

t

UNEMPLOYED

0

2

4

6

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Per

cen

t

APPENDIX A - REGIONS IN WA SOCIO-DEMOGRAPHIC FACTORS

Page A14

ATSI34.8

0

3

6

9

12

15

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Per

cen

t

OPD'S WITH NO VEHICLE

0

4

8

12

16

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Per

cen

t

SOCIO-ECONOMIC INDEX

900

950

1000

1050

1100

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

APPENDIX A - REGIONS IN WA OFFENCES REPORTED

Page A15

OFFENCES AGAINST THE PERSON

0

5

10

15

20

25

30

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

3

6

9

12

15

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

30

60

90

120

150

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

1

2

3

4

5

6

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

APPENDIX A - REGIONS IN WA POLICE-OFFENDER CONTACTS

Page A16

ALL PERSONS

0

10

20

30

40G

asco

yne

Gol

dfie

lds-

Esp.

Gre

at S

outh

ern

Kim

berle

yM

id W

est

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

APPENDIX A - REGIONS IN WA POLICE-OFFENDER CONTACTS

Page A17

AGED 10 - 17 YEARS

0

20

40

60

80

100

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

AGED 18 - 29 YEARS

0

20

40

60

80

100

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

10

20

30

40

50

60

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

2

4

6

8

10

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

APPENDIX A - REGIONS IN WA POLICE-OFFENDER CONTACTS

Page A18

ATSI

0

50

100

150

200

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

3

6

9

12

15

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

MALE

0

10

20

30

40

50

60

70

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

FEMALE

0

4

8

12

16

Gas

coyn

eG

oldf

ield

s-Es

p.G

reat

Sou

ther

nKi

mbe

rley

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atbe

lt

WA

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN GASCOYNE

Page A19

POPULATION AGED 10 - 17 YEARS

0

2

4

6

8

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Per

cen

tPOPULATION AGED 18 - 29 YEARS

0

4

8

12

16

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Per

cen

t

UNEMPLOYED

0

1

2

3

4

5

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Per

cen

t

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN GASCOYNE

Page A20

ATSI

0

3

6

9

12

15Ca

rnar

von

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Per

cen

t

OPD'S WITH NO VEHICLE

0

3

6

9

12

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Per

cen

t

SOCIO-ECONOMIC INDEX

900

950

1000

1050

1100

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

APPENDIX B - TOWNS IN REGIONS OFFENCES REPORTED IN GASCOYNE

Page A21

OFFENCES AGAINST THE PERSON

0

10

20

30

40Ca

rnar

von

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

50

100

150

200

250

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

5

10

15

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

3

6

9

12

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN GASCOYNE

Page A22

ALL PERSONS

0

10

20

30

40

50

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN GASCOYNE

Page A23

AGED 10 - 17 YEARS

0

30

60

90

120

150

180Ca

rnar

von

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

sAGED 18 - 29 YEARS

0

30

60

90

120

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

10

20

30

40

50

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

2

4

6

8

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN GASCOYNE

Page A24

MALE

0

15

30

45

60

75Ca

rnar

von

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

FEMALE

0

3

6

9

12

15

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

ATSI

0

50

100

150

200

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

3

6

9

12

15

Carn

arvo

n

Denh

am

Exm

outh

Rura

l

Gas

coyn

e

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN GOLDFIELDS-ESPERANCE

Page A25

POPULATION AGED 18 - 29 YEARS

0

5

10

15

20

25

30

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

balda

(Eas

t)Ka

mba

lda W

est

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Per

cen

t

UNEMPLOYED

0

2

4

6

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

balda

(Eas

t)Ka

mba

lda W

est

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Per

cen

t

POPULATION AGED 10 - 17 YEARS

0

3

6

9

12

15

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

balda

(Eas

t)Ka

mba

lda W

est

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Per

cen

t

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN GOLDFIELDS-ESPERANCE

Page A26

ATSI

0

3

6

9

12

15

18

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

15

30

45

60

75

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Per

cen

t

OPD'S WITH NO VEHICLE

0

3

6

9

12

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Per

cen

t

SOCIO-ECONOMIC INDEX

900

950

1000

1050

1100

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

balda

(Eas

t)Ka

mba

lda W

est

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

APPENDIX B - TOWNS IN REGIONS OFFENCES IN GOLDFIELDS-ESPERANCE

Page A27

OFFENCES AGAINST THE PERSON

0

5

10

15

20

25

Cool

gard

ieEs

pera

nce

Kal.-

Boul

der

Kam

balda

Eas

tKa

mba

lda W

est

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

30

60

90

120

150

180

Cool

gard

ieEs

pera

nce

Kal.-

Boul

der

Kam

bald

a Ea

stKa

mba

lda W

est

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

10

20

30

Cool

gard

ieEs

pera

nce

Kal.-

Boul

der

Kam

balda

Eas

tKa

mba

lda W

est

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

1

2

3

4

5

Cool

gard

ieEs

pera

nce

Kal.-

Boul

der

Kam

balda

Eas

tKa

mba

lda W

est

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN GOLDFIELDS-ESPERANCE

Page A28

ALL PERSONS73

0

10

20

30

40

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN GOLDFIELDS-ESPERANCE

Page A29

AGED 10 - 17 YEARS284

0

20

40

60

80

100

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

sAGED 18 - 29 YEARS

0

30

60

90

120

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

15

30

45

60

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

3

6

9

12

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN GOLDFIELDS-ESPERANCE

Page A30

MALE97

0

15

30

45

60

75

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

sFEMALE

29

0

5

10

15

20

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

ATSI333

0

50

100

150

200

250

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

4

8

12

16

20

Cool

gard

ieEs

pera

nce

Kalg

oorlie

-Bou

lder

Kam

bald

a (E

ast)

Kam

balda

Wes

t

Lein

ster

Leon

ora

Nors

eman

Rura

lG

oldf

ield

s-Es

p.

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN GREAT SOUTHERN

Page A31

POPULATION AGED 10 - 17 YEARS

0

3

6

9

12

15Al

bany

Denm

ark

Kata

nnin

g

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Per

cen

t

POPULATION AGED 18 - 29 YEARS

0

4

8

12

16

20

Alba

ny

Denm

ark

Kata

nnin

g

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Per

cen

t

UNEMPLOYED

0

2

4

6

Alba

ny

Denm

ark

Kata

nnin

g

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Alba

ny

Denm

ark

Kata

nnin

g

Kojon

up

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Per

cen

t

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN GREAT SOUTHERN

Page A32

ATSI

0

2

4

6

8

Alba

ny

Denm

ark

Kata

nning

Kojo

nup

Mou

nt B

arke

r

Rura

l

Gre

at S

outh

ern

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

Alba

ny

Denm

ark

Kata

nnin

g

Kojon

up

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Per

cen

t

OPD'S WITH NO VEHICLE

0

3

6

9

12

Alba

ny

Denm

ark

Kata

nning

Kojon

up

Mou

nt B

arke

r

Rura

l

Gre

at S

outh

ern

Per

cen

t

SOCIO-ECONOMIC INDEX

900

950

1000

1050

1100

Alba

ny

Denm

ark

Kata

nnin

g

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

APPENDIX B - TOWNS IN REGIONS OFFENCES REPORTED IN GREAT SOUTHERN

Page A33

OFFENCES AGAINST THE PERSON

0

4

8

12

16

20Al

bany

Denm

ark

Kata

nnin

g

Kojon

up

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

30

60

90

120

150

180

Alba

ny

Denm

ark

Kata

nnin

g

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

5

10

15

20

25

Alba

ny

Denm

ark

Kata

nnin

g

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

1

2

3

4

Alba

ny

Denm

ark

Kata

nnin

g

Kojon

up

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN GREAT SOUTHERN

Page A34

ALL PERSONS

0

10

20

30

40

Alba

ny

Denm

ark

Kata

nnin

g

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN GREAT SOUTHERN

Page A35

AGED 10 - 17 YEARS

0

20

40

60

80Al

bany

Denm

ark

Kata

nning

Kojon

up

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

AGED 18 - 29 YEARS144

0

20

40

60

80

100

Alba

ny

Denm

ark

Kata

nnin

g

Kojon

up

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

10

20

30

40

50

Alba

ny

Denm

ark

Kata

nning

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

1

2

3

4

5

Alba

ny

Denm

ark

Kata

nning

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN GREAT SOUTHERN

Page A36

MALE

0

10

20

30

40

50Al

bany

Denm

ark

Kata

nning

Kojon

up

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

FEMALE

0

4

8

12

16

20

24

Alba

ny

Denm

ark

Kata

nnin

g

Kojon

up

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

ATSI

0

50

100

150

200

250

Alba

ny

Denm

ark

Kata

nning

Kojo

nup

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

5

10

15

20

Alba

ny

Denm

ark

Kata

nnin

g

Kojon

up

Mou

nt B

arke

r

Rura

lG

reat

Sou

ther

n

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN KIMBERLEY

Page A37

POPULATION AGED 10 - 17 YEARS

0

3

6

9

12

15

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Per

cen

t

POPULATION AGED 18 - 29 YEARS

0

3

6

9

12

15

18

21

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Per

cen

t

UNEMPLOYED

0

1

2

3

4

5

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Per

cen

t

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN KIMBERLEY

Page A38

ATSI

0

10

20

30

40

50

60Br

oom

e

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Per

cen

t

OPD'S WITH NO VEHICLE

0

6

12

18

24

30

36

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Per

cen

t

SOCIO-ECONOMIC INDEX

750

800850

900

950

1000

1050

11001150

1200

1250

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

APPENDIX B - TOWNS IN REGIONS OFFENCES REPORTED IN KIMBERLEY

Page A39

OFFENCES AGAINST THE PERSON

139

0

10

20

30

40

50

60

70Br

oom

e

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Wyn

dham Rura

l

Kim

berle

y

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

50

100

150

200

250

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Wyn

dham

Rura

l

Kim

berle

y

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

3

6

9

12

15

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Wyn

dham

Rura

l

Kim

berle

y

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

2

4

6

8

10

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Wyn

dham

Rura

l

Kim

berle

y

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN KIMBERLEY

Page A40

ALL PERSONS

0

30

60

90

120

150

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN KIMBERLEY

Page A41

AGED 10 - 17 YEARS

0

40

80

120

160

200

240Br

oom

e

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Rat

e p

er 1

000

per

son

s

AGED 18 - 29 YEARS

0

50

100

150

200

250

300

350

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

332

0

40

80

120

160

200

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

10

20

30

40

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN KIMBERLEY

Page A42

MALE

0

50

100

150

200

250Br

oom

e

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Rat

e p

er 1

000

per

son

s

FEMALE

0

10

20

30

40

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Rat

e p

er 1

000

per

son

s

ATSI

409

0

50

100

150

200

250

300

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

4

8

12

16

Broo

me

Derb

yFi

tzro

y Cr

ossin

g

Halls

Cre

ek

Kunu

nurra

Rura

l

Wyn

dham

Kim

berle

y

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN MID WEST

Page A43

POPULATION AGED 10 - 17 YEARS

0

3

6

9

12

15G

eral

dton

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Per

cen

t

POPULATION AGED 18 - 29 YEARS

0

5

10

15

20

Ger

aldto

n

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Per

cen

t

UNEMPLOYED

0

2

4

6

8

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Per

cen

t

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN MID WEST

Page A44

ATSI30

0

3

6

9

12

15G

eral

dton

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

Ger

aldto

n

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Per

cen

t

OPD'S WITH NO VEHICLE

0

3

6

9

12

15

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Per

cen

t

SOCIO-ECONOMIC INDEX

900

950

1000

1050

1100

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

APPENDIX B - TOWNS IN REGIONS OFFENCES REPORTED IN MID WEST

Page A45

OFFENCES AGAINST THE PERSON

0

5

10

15

20

25

30G

eral

dton

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

30

60

90

120

150

180

210

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

3

6

9

12

Ger

aldto

n

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

1

2

3

4

5

6

7

8

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN MID WEST

Page A46

ALL PERSONS68

0

6

12

18

24

30

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN MID WEST

Page A47

AGED 10 - 17 YEARS

257

0

20

40

60

80

100G

eral

dton

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

AGED 18 - 29 YEARS

169

0

20

40

60

80

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

10

20

30

40

50

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

3

6

9

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN MID WEST

Page A48

MALE107

0

15

30

45

60G

eral

dton

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

FEMALE

0

4

8

12

16

20

24

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

ATSI

0

50

100

150

200

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

3

6

9

12

15

Ger

aldt

on

Kalb

arri

Mee

kath

arra

North

ampt

on

Rura

l

Mid

Wes

t

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN PEEL

Page A49

POPULATION AGED 10 - 17 YEARS

0

3

6

9

12

15

18M

andu

rah

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Per

cen

t

POPULATION AGED 18 - 29 YEARS

0

3

6

9

12

15

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Per

cen

t

UNEMPLOYED

0

2

4

6

8

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

60

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Per

cen

t

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN PEEL

Page A50

ATSI

0

1

2

3

4

5M

andu

rah

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

60

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Per

cen

t

OPD'S WITH NO VEHICLE

0

2

4

6

8

10

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Per

cen

t

SOCIO-ECONOMIC INDEX

800

850

900

950

1000

1050

1100

1150

1200

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

APPENDIX B - TOWNS IN REGIONS OFFENCES REPORTED IN PEEL

Page A51

OFFENCES AGAINST THE PERSON

0

3

6

9

12

15M

andu

rah

North

Pin

jarra

Pinj

arra

War

oona

Rura

l

Peel

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

30

60

90

120

150

180

Man

dura

h

North

Pin

jarra

Pinj

arra

War

oona

Rura

l

Peel

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

3

6

9

12

15

Man

dura

h

North

Pin

jarra

Pinj

arra

War

oona

Rura

l

Peel

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

1

2

3

4

Man

dura

h

North

Pin

jarra

Pinj

arra

War

oona

Rura

l

Peel

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN PEEL

Page A52

ALL PERSONS

0

5

10

15

20

25

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN PEEL

Page A53

AGED 10 - 17 YEARS

0

10

20

30

40

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Rat

e p

er 1

000

per

son

s

AGED 18 - 29 YEARS

0

15

30

45

60

75

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

5

10

15

20

25

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

3

6

9

12

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN PEEL

Page A54

MALE

0

10

20

30

40

50M

andu

rah

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Rat

e p

er 1

000

per

son

s

FEMALE

0

1

2

3

4

5

6

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Rat

e p

er 1

000

per

son

s

ATSI

0

30

60

90

120

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

4

8

12

16

20

24

Man

dura

h

North

Pin

jarra

Pinj

arra

Rura

l

War

oona Peel

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN PILBARA

Page A55

POPULATION AGED 10 - 17 YEARS

0

3

6

9

12

15

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Pa

rabu

rdoo

Port

Hedl

and

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Per

cen

t

POPULATION AGED 18 - 29 YEARS

0

4

8

12

16

20

24

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Pa

rabu

rdoo

Port

Hedl

and

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Per

cen

t

UNEMPLOYED

0

2

4

6

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Pa

rabu

rdoo

Port

Hedl

and

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Pa

rabu

rdoo

Port

Hedl

and

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Per

cen

t

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN PILBARA

Page A56

ATSI2958

0

3

6

9

12

15

18

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Pa

rabu

rdoo

Port

Hedla

ndRo

ebou

rne

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

60

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Pa

rabu

rdoo

Port

Hedl

and

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Per

cen

t

OPD'S WITH NO VEHICLE

0

3

6

9

12

15

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Pa

rabu

rdoo

Port

Hedla

ndRo

ebou

rne

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Per

cen

t

SOCIO-ECONOMIC INDEX

800

850

900

950

1000

1050

1100

1150

1200

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Pa

rabu

rdoo

Port

Hedl

and

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

APPENDIX B - TOWNS IN REGIONS OFFENCES REPORTED IN PILBARA

Page A57

OFFENCES AGAINST THE PERSON61

0

5

10

15

20

25

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dlan

dRo

ebou

rne

Tom

Pric

eW

ickha

m

Rura

l

Pilb

ara

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

30

60

90

120

150

180

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dlan

dRo

ebou

rne

Tom

Pric

eW

ickha

m

Rura

lPi

lbar

a

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

4

8

12

16

20

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dlan

dRo

ebou

rne

Tom

Pric

eW

ickha

m

Rura

l

Pilb

ara

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

1

2

3

4

5

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dlan

dRo

ebou

rne

Tom

Pric

eW

ickha

m

Rura

l

Pilb

ara

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN PILBARA

Page A58

ALL PERSONS97

0

8

16

24

32

40

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dlan

dRo

ebou

rne

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN PILBARA

Page A59

AGED 10 - 17 YEARS217

0

20

40

60

80

100

120Da

mpie

rKa

rrath

aNe

wman

Pann

awon

icaPa

rabu

rdoo

Port

Hedl

and

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Rat

e p

er 1

000

per

son

sAGED 18 - 29 YEARS

197

0

15

30

45

60

75

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dlan

dRo

ebou

rne

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS156

0

10

20

30

40

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dland

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

15

0

2

4

6

8

10

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dland

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN PILBARA

Page A60

MALE150

0

10

20

30

40

50

60

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dlan

dRo

ebou

rne

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Rat

e p

er 1

000

per

son

s

FEMALE39

0

3

6

9

12

15

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dlan

dRo

ebou

rne

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Rat

e p

er 1

000

per

son

s

ATSI

0

30

60

90

120

150

180

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dland

Roeb

ourn

e

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

3

6

9

12

15

Dam

pier

Karra

tha

Newm

anPa

nnaw

onica

Para

burd

ooPo

rt He

dlan

dRo

ebou

rne

Rura

lTo

m P

rice

Wick

ham

Pilb

ara

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN SOUTH WEST

Page A61

POPULATION AGED 10 - 17 YEARS

0

4

8

12

16

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Per

cent

POPULATION AGED 18 - 29 YEARS

0

4

8

12

16

20

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n Ru

ral

Sout

h W

est

Per

cent

UNEMPLOYED

0

2

4

6

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Per

cent

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n Ru

ral

Sout

h W

est

Per

cent

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN SOUTH WEST

Page A62

ATSI

0

1

2

3

4

Augu

sta

Austr

alind

Bridg

etow

nBu

nbur

yBu

sselt

on

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Per

cent

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

60

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Per

cent

OPD'S WITH NO VEHICLE

0

3

6

9

12

15

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n Ru

ral

Sout

h W

est

Per

cent

SOCIO-ECONOMIC INDEX

900

950

1000

1050

1100

Augu

staAu

stra

lind

Bridg

etow

nBu

nbur

yBu

sselt

on

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n Ru

ral

Sout

h W

est

APPENDIX B - TOWNS IN REGIONS OFFENCES REPORTED IN SOUTH WEST

Page A63

OFFENCES AGAINST THE PERSON

0

3

6

9

12

15

18

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e pe

r 10

00 p

erso

ns

PROPERTY OFFENCES

0

25

50

75

100

125

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e pe

r 10

00 p

erso

ns

DRUG OFFENCES

0

5

10

15

20

25

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e pe

r 10

00 p

erso

ns

GOOD ORDER OFFENCES

0

1

2

3

4

5

6

7

8

Augu

sta

Austr

alind

Bridg

etow

nBu

nbur

yBu

sselt

on

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e pe

r 10

00 p

erso

ns

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN SOUTH WEST

Page A64

ALL PERSONS

0

4

8

12

16

20

Augu

sta

Aust

ralin

dBr

idget

own

Bunb

ury

Buss

elto

n

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN SOUTH WEST

Page A65

AGED 10 - 17 YEARS

0

8

16

24

32

40

Augu

staAu

stra

lind

Bridg

etow

nBu

nbur

yBu

sselt

on

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e p

er 1

000

per

son

sAGED 18 - 29 YEARS

88

0

15

30

45

60

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

5

10

15

20

25

30

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

2

4

6

8

Augu

staAu

stra

lind

Bridg

etow

nBu

nbur

yBu

sselt

on

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN SOUTH WEST

Page A66

MALE

0

5

10

15

20

25

30

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e p

er 1

000

per

son

s

FEMALE

0

2

4

6

8

10

12

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e p

er 1

000

per

son

s

ATSI

237

0

30

60

90

120

Augu

staAu

strali

ndBr

idget

own

Bunb

ury

Buss

elton

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

4

8

12

16

Augu

staAu

stra

lind

Bridg

etow

nBu

nbur

yBu

sselt

on

Collie

Duns

boro

ugh

Harv

eyM

anjim

upM

arga

ret R

iver

Pem

berto

n

Rura

lSo

uth

Wes

t

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN WHEATBELT

Page A67

POPULATION AGED 10 - 17 YEARS

0

3

6

9

12

15

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Per

cen

t

POPULATION AGED 18 - 29 YEARS

0

5

10

15

20

25

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Per

cen

t

UNEMPLOYED

0

1

2

3

4

5

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Per

cen

t

APPENDIX B - TOWNS IN REGIONS SOCIO-DEMOGRAPHIC FACTORS IN WHEATBELT

Page A68

ATSI

0

3

6

9

12

15

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Per

cen

t

OPD'S WITH NO VEHICLE

0

4

8

12

16

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Per

cen

t

SOCIO-ECONOMIC INDEX

900

950

1000

1050

1100

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

APPENDIX B - TOWNS IN REGIONS OFFENCES REPORTED IN WHEATBELT

Page A69

OFFENCES AGAINST THE PERSON

0

5

10

15

20

25

Mer

redi

n

Moo

ra

Narro

gin

North

amSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Rura

lW

heat

belt

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

40

80

120

160

200

Mer

redi

n

Moo

ra

Narro

gin

North

amSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Rura

lW

heat

belt

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

39

0

5

10

15

20

25

Mer

redi

n

Moo

ra

Narro

gin

North

amSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Rura

lW

heat

belt

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

2

4

6

8

10

Mer

redi

n

Moo

ra

Narro

gin

North

amSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Rura

lW

heat

belt

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN WHEATBELT

Page A70

ALL PERSONS

0

8

16

24

32

40

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN WHEATBELT

Page A71

AGED 10 - 17 YEARS

140

0

20

40

60

80

100

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Rat

e p

er 1

000

per

son

s

AGED 18 - 29 YEARS

0

25

50

75

100

125

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

10

20

30

40

50

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

2

4

6

8

10

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS POLICE-OFFENDER CONTACTS IN WHEATBELT

Page A72

MALE

0

10

20

30

40

50

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Rat

e p

er 1

000

per

son

s

FEMALE

0

4

8

12

16

20

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Rat

e p

er 1

000

per

son

s

ATSI

0

50

100

150

200

250

300

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

5

10

15

20

25

Mer

redi

n

Moo

ra

Narro

gin

North

am

Rura

lSo

uthe

rn C

ross

Tood

yay

Wag

in

York

Whe

atbe

lt

Rat

e p

er 1

000

per

son

s

APPENDIX B - TOWNS IN REGIONS DENOMINATORS IN TOWNS IN REGIONS

Region Town Po

pu

lati

on

Po

pu

lati

on

10-

17

Po

pu

lati

on

18-

29

Po

pu

lati

on

30-

39

Po

pu

lati

on

40+

Po

pu

lati

on

15+

Mal

e

Fem

ale

AT

SI

No

n-A

TS

I

Fam

ilies

in O

PD

s

Fam

/Gp

/Lo

in O

PD

s

Veh

icle

s in

OP

Ds

OP

Ds

Pri

vate

Dw

ellin

gs

Gascoyne Carnarvon 6377 567 991 895 3030 5107 3277 3100 949 5428 1217 1596 2697 2339 2491Denham 1130 71 101 123 762 1004 587 543 79 1051 142 215 515 456 517Exmouth 3079 222 329 463 1700 2545 1621 1458 20 3059 474 647 1357 1177 1301Rural 4250 274 671 523 2326 3636 2317 1933 413 3837 446 560 1658 1476 1496

Gascoyne Total 14836 1134 2092 2004 7818 12292 7802 7034 1461 13375 2279 3018 6227 5448 5805Goldfields-Esp. Coolgardie 1253 141 274 254 328 909 682 571 224 1240 292 399 596 424 459

Esperance 8596 1117 1368 1400 3177 6352 4139 4457 379 8300 2224 3014 4572 3198 3632Kalgoorlie-Boulder 28033 2951 7360 5257 7630 21210 15164 12869 1367 26654 6433 8677 14446 9250 10005Kambalda (East) 1200 119 290 243 268 836 643 557 14 1190 329 404 609 397 477Kambalda West 2404 293 520 533 517 1648 1286 1118 22 2380 633 715 1346 748 844Leinster 1437 61 414 374 364 1163 971 466 7 1254 236 242 373 264 277Leonora 1138 79 338 235 308 902 700 438 183 1138 188 229 330 262 298Norseman 1488 130 316 284 482 1105 825 663 141 1384 355 476 726 518 622Rural 11819 1139 2765 2295 3811 9290 7223 4596 2365 9134 2204 2657 4847 2958 3565

Goldfields-Esperance Total 57368 6030 13645 10875 16885 43415 31633 25735 4702 52674 12894 16813 27845 18019 20179Great Southern Albany 20564 2685 2915 2859 8959 15682 9788 10776 528 20036 5497 7539 11163 7836 8944

Denmark 1949 238 155 326 858 1400 953 996 13 1936 557 791 1121 778 980Katanning 4069 568 718 669 1380 2973 2030 2039 372 3697 998 1372 2047 1399 1519Kojonup 1049 124 177 162 403 780 535 514 64 985 262 356 546 380 437Mount Barker 1706 229 225 251 733 1296 829 877 151 1555 440 596 876 625 714Rural 18886 2465 2392 3217 7552 13901 10139 8747 419 18443 5151 6206 12952 6578 8127

Great Southern Total 48223 6309 6582 7484 19885 36032 24274 23949 1547 46652 12905 16860 28705 17596 20721Kimberley Broome 11382 999 2011 1935 4874 9119 5746 5636 1937 9445 1744 2335 4287 3666 3864

Derby 3490 356 676 603 1197 2566 1788 1702 1458 2032 720 909 1284 1104 1225Fitzroy Crossing 1159 95 210 170 492 909 568 591 519 640 175 211 275 310 308Halls Creek 1229 106 258 169 507 976 640 589 387 842 152 204 286 361 398Kununurra 4850 376 911 800 2067 3875 2508 2342 594 4256 696 1028 1705 1694 1780Wyndham 886 1304 2107 1558 2976 606 5478 4437 357 3708 165 209 1589 286 311Rural 9915 114 158 172 242 7096 434 452 6207 529 1483 1504 215 2151 2166

Kimberley Total 32911 3350 6331 5407 12355 25147 17162 15749 11459 21452 5135 6400 9641 9572 10052

Page A73

APPENDIX B - TOWNS IN REGIONS DENOMINATORS IN TOWNS IN REGIONS

Region Town Po

pu

lati

on

Po

pu

lati

on

10-

17

Po

pu

lati

on

18-

29

Po

pu

lati

on

30-

39

Po

pu

lati

on

40+

Po

pu

lati

on

15+

Mal

e

Fem

ale

AT

SI

No

n-A

TS

I

Fam

ilies

in O

PD

s

Fam

/Gp

/Lo

in O

PD

s

Veh

icle

s in

OP

Ds

OP

Ds

Pri

vate

Dw

ellin

gs

Mid West Geraldton 25245 3492 4429 3934 9056 18780 12502 12743 1812 23433 6380 8770 13040 9105 10235Kalbarri 1798 76 199 269 1038 1515 933 865 9 1789 263 346 783 705 907Meekatharra 1280 128 213 303 355 907 676 604 393 887 247 314 435 384 417Northampton 846 108 100 119 377 628 414 432 91 755 224 298 457 320 356Rural 21931 2208 4137 4150 8133 17098 13015 8916 1548 20383 4652 5817 10922 6427 8132

Mid West Total 51100 6012 9078 8775 18959 38928 27540 23560 3853 47247 11766 15545 25637 16941 20047Peel Mandurah 35998 4493 4663 5276 15851 27280 17419 18579 576 35422 10504 13755 19592 13977 18586

North Pinjarra 1018 173 135 141 390 719 492 526 8 1008 266 372 551 352 393Pinjarra 1920 271 193 269 862 1426 919 1001 84 1917 490 672 992 689 752Waroona 1828 2863 2632 3428 8634 1308 10927 10063 29 20694 496 649 13289 665 725Rural 20990 223 248 274 729 15555 894 934 175 1811 5825 7030 957 7255 8973

Peel Total 61754 8023 7871 9388 26466 46288 30651 31103 872 60852 17581 22478 35381 22938 29429Pilbara Dampier 1405 177 172 287 537 1039 764 641 49 1356 336 410 763 457 553

Karratha 10053 1284 2020 2031 2898 7364 5368 4685 404 9649 2380 2933 5044 3402 3987Newman 4779 530 980 1088 1214 3431 2631 2148 230 4549 1129 1507 2240 1708 2031Pannawonica 769 45 162 188 184 546 438 331 12 757 170 215 320 246 259Paraburdoo 2020 175 427 494 420 1372 1131 889 41 1979 463 580 729 590 728Port Hedland 12840 1413 2645 2504 4005 9576 7153 5687 1661 11179 2542 3469 5619 4176 4700Roebourne 980 124 182 134 360 720 476 504 572 408 180 230 262 270 320Tom Price 3919 346 1443 1218 2793 2714 4200 2225 106 4514 890 1066 1446 1173 1272Wickham 1636 410 756 938 931 1108 2122 1797 177 3813 392 499 1621 559 830Rural 6425 222 274 302 465 5577 879 757 1911 1459 636 684 778 1364 1445

Pilbara Total 44826 4726 9061 9184 13807 33447 25162 19664 5163 39663 9118 11593 18822 13945 16125South West Augusta 1079 66 122 146 651 941 567 512 6 1073 268 386 615 454 759

Australind 5684 955 745 1051 1892 3974 2815 2869 59 5647 1579 1876 3206 1928 2094Bridgetown 2105 267 254 342 872 1561 1023 1082 25 2080 618 763 1210 784 918Bunbury 24914 3165 4506 3659 10012 19379 12368 12546 796 24118 6606 9269 13723 9518 10458Busselton 10620 1270 1438 1524 4771 8183 5038 5582 177 10443 2893 4047 5694 4204 4939Collie 7272 1001 1166 1089 2838 5459 3778 3494 230 7053 1882 2563 4930 2681 2985Dunsborough 1112 101 173 200 434 831 543 569 9 1103 303 398 662 453 927

Page A74

APPENDIX B - TOWNS IN REGIONS DENOMINATORS IN TOWNS IN REGIONS

Region Town Po

pu

lati

on

Po

pu

lati

on

10-

17

Po

pu

lati

on

18-

29

Po

pu

lati

on

30-

39

Po

pu

lati

on

40+

Po

pu

lati

on

15+

Mal

e

Fem

ale

AT

SI

No

n-A

TS

I

Fam

ilies

in O

PD

s

Fam

/Gp

/Lo

in O

PD

s

Veh

icle

s in

OP

Ds

OP

Ds

Pri

vate

Dw

ellin

gs

Harvey 2525 356 421 360 987 1924 1233 1292 76 2502 672 893 1334 909 1005Manjimup 4361 506 753 730 1591 3262 2208 2153 106 4255 1158 1550 2384 1576 1775Margaret River 2864 357 480 572 917 2089 1459 1405 24 2840 666 938 1452 961 1039Pemberton 990 138 162 140 403 745 516 474 0 990 223 301 477 315 362Rural 43374 6252 5762 7544 16195 31423 22303 21071 487 42833 11691 14183 26606 14859 18152

South West Total 108342 14651 16150 17641 42084 80814 54608 53734 1998 106371 28926 37650 62293 39122 45970Wheatbelt Merredin 2887 403 529 451 1027 2165 1549 1338 138 2795 702 1013 1443 1060 1220

Moora 1615 230 244 275 553 1147 801 814 208 1407 381 545 819 559 641Narrogin 4513 744 719 626 1690 3350 2205 2308 273 4240 1110 1557 2326 1596 1781Northam 6295 871 977 909 2517 4709 3137 3158 492 5803 1636 2221 3222 2285 2542Southern Cross 1134 5366 6853 8263 19787 873 26063 22670 37 47427 246 346 33603 346 392Toodyay 659 72 294 228 336 492 659 475 40 1125 191 273 518 274 296Wagin 1300 82 81 110 273 977 308 351 88 619 341 497 414 526 614York 1984 158 197 210 521 1523 646 654 59 1212 538 728 725 758 885Rural 48733 229 224 287 948 36387 1000 984 1229 1925 13179 16770 1116 17579 22707

Wheatbelt Total 69120 8155 10118 11359 27652 51623 36368 32752 2564 66553 18324 23950 44186 24983 31078WA Offshore Areas & Migratory 3045 29 1489 732 774 3021 2745 300 33 3036 0 0 0 0 0

Non-Perth Total 491525 58419 82417 82849 186685 371007 257945 233580 33652 457875 118928 154307 258737 168564 199406Perth Total 1234939 148442 231038 191785 491645 970829 604645 630294 17068 1217869 326891 449715 693741 460459 498771

WA Grand Total 1726464 206861 313455 274634 678330 1341836 862590 863874 50720 1675744 445819 604022 952478 629023 698177

Notes:"OPDs" is occupied private dwellings."Fam/Gp/Lo in OPDs" is families,groups or lone persons in OPDs.

Page A75

PERTH

MIRRABOOKA

FREMANTLE

CANNINGTON

MIDLAND

JOONDALUP

WA POLICE DISTRICTS IN PERTH

Page A76

APPENDIX C

APPENDIX C - POLICE DISTRICTS IN PERTH SOCIO-DEMOGRAPHIC FACTORS

Page A77

POPULATION AGED 10 - 17 YEARS

0

3

6

9

12

15

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Per

cen

tPOPULATION AGED 18 - 29 YEARS

0

5

10

15

20

25

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Per

cen

t

UNEMPLOYED

0

1

2

3

4

5

6

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Per

cen

t

APPENDIX C - POLICE DISTRICTS IN PERTH SOCIO-DEMOGRAPHIC FACTORS

Page A78

ATSI

0.0

0.5

1.0

1.5

2.0

2.5

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Per

cen

t

OPD'S WITH NO VEHICLE

0

3

6

9

12

15

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Per

cen

t

SOCIO-ECONOMIC INDEX

900

950

1000

1050

1100

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

APPENDIX C - POLICE DISTRICTS IN PERTH OFFENCES REPORTED

Page A79

OFFENCES AGAINST THE PERSON

0

4

8

12

16

20

24

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

50

100

150

200

250

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

5

10

15

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

1

2

3

4

5

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

APPENDIX C - POLICE DISTRICTS IN PERTH POLICE-OFFENDER CONTACTS

Page A80

ALL PERSONS

0

5

10

15

20

25

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

APPENDIX C - POLICE DISTRICTS IN PERTH POLICE-OFFENDER CONTACTS

Page A81

AGED 10 - 17 YEARS

0

15

30

45

60

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

AGED 18 - 29 YEARS

0

15

30

45

60

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

5

10

15

20

25

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

1

2

3

4

5

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

APPENDIX C - POLICE DISTRICTS IN PERTH POLICE-OFFENDER CONTACTS

Page A82

MALE

0

10

20

30

40

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

FEMALE

0

2

4

6

8

10

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

ATSI

0

50

100

150

200

250

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

4

8

12

16

20

CANN

ING

TON

FREM

ANTL

E

JOO

NDAL

UP

MID

LAND

MIR

RABO

OKA

PERT

H

Rat

e p

er 1

000

per

son

s

APPENDIX C - POLICE DISTRICTS IN PERTH DENOMINATORS IN PERTH POLICE DISTRICTS

DISTRICT Po

pu

lati

on

Po

pu

lati

on

10-

17

Po

pu

lati

on

18-

29

Po

pu

lati

on

30-

39

Po

pu

lati

on

40+

Po

pu

lati

on

15+

Mal

e

Fem

ale

AT

SI

No

n-A

TS

I

Fam

ilies

in O

PD

s

Fam

/Gp

/Lo

in O

PD

s

Veh

icle

s in

OP

Ds

OP

Ds

Pri

vate

Dw

ellin

gs

CANNINGTON 261341 30074 52968 40320 101604 206458 128734 132607 5408 255954 68494 96960 144753 99585 107700FREMANTLE 283874 35161 49558 43356 114650 220665 138628 145246 3461 280420 77397 102220 160709 104264 113762JOONDALUP 234242 34323 38814 38985 82614 172810 115764 118478 1963 232295 65141 76280 132680 77482 82009MIDLAND 138768 19381 22567 21355 53939 105179 68530 70238 2786 135979 38000 47347 80985 48442 51890MIRRABOOKA 213256 19670 44840 33786 90584 176891 102955 110301 2956 210306 55986 87595 121539 89538 97565PERTH 113200 11142 23582 15652 52018 96011 55018 58182 559 112592 24555 42381 59336 44296 49271

Notes:"OPDs" is occupied private dwellings."Fam/Gp/Lo in OPDs" is families,groups or lone persons in OPDs. `

Page A83

Peppermint Grove (S)

Claremont (T)

Nedlands (C)Cottesloe (T)

Mosman Park (T)

Armadale (C)

South Perth (C)

Fremantle (C)

Melville (C)

Perth (C)Subiaco (C)

Victoria Park (T)

Belmont (C)

Canning (C)

Kalamunda (S)

Mundaring (S)

Bassendean (T)

Vincent (T)

Bayswater (C)Stirling (C)

Swan (S)

Kwinana (T)

Wanneroo (C)

Rockingham (C)

Cambridge (T)

Cockburn (C)

East Fremantle (T)

Gosnells (C)

PERCENT OF POPULATION AGED 10 TO 17 YEARS

Percentage15 to 23 (3)13 to 15 (7)10 to 13 (8)8 to 10 (6)2 to 8 (4)

Page A84

SOCIO-DEMOGRAPHIC FACTORSAPPENDIX D - LGA's IN PERTH

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Peppermint Grove (S)

Claremont (T)

Nedlands (C)Cottesloe (T)

Mosman Park (T)

Armadale (C)

South Perth (C)

Fremantle (C)Melville (C)

Perth (C)Subiaco (C)

Victoria Park (T)

Belmont (C)

Canning (C)

Kalamunda (S)

Mundaring (S)

Bassendean (T)

Vincent (T)

Bayswater (C)Stirling (C)

Swan (S)

Kwinana (T)

Wanneroo (C)

Rockingham (C)

Cambridge (T)

Cockburn (C)

East Fremantle (T)

Gosnells (C)

PERCENT OF POPULATION AGED 18 TO 29 YEARS

Percentage25 to 28 (3)19.3 to 25 (7)18.2 to 19.3 (6)16 to 18.2 (8)13.9 to 16 (4)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A85

SOCIO-DEMOGRAPHIC FACTORSAPPENDIX D - LGA's IN PERTH

Peppermint Grove (S)

Claremont (T)

Nedlands (C)Cottesloe (T)

Mosman Park (T)

Armadale (C)

South Perth (C)

Fremantle (C)

Melville (C)

Perth (C)Subiaco (C)

Victoria Park (T)

Belmont (C)

Canning (C)

Kalamunda (S)

Mundaring (S)

Bassendean (T)

Vincent (T)

Bayswater (C)Stirling (C)

Swan (S)

Kwinana (T)

Wanneroo (C)

Rockingham (C)

Cambridge (T)

Cockburn (C)

East Fremantle (T)

Gosnells (C)

PERCENT OF POPULATION UNEMPLOYED

Percentage6.1 to 7.5 (4)5.5 to 6.1 (6)4.8 to 5.5 (7)3.7 to 4.8 (7)1.8 to 3.7 (4)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A86

SOCIO-DEMOGRAPHIC FACTORSAPPENDIX D - LGA's IN PERTH

Armadale (C)

South Perth (C)

Claremont (T)

pPeppermint Grove (S)Nedlands (C)

Cottesloe (T)

Mosman Park (T)

Fremantle (C)Melville (C)

Perth (C)Subiaco (C)

Victoria Park (T)

Belmont (C)

Canning (C)

Kalamunda (S)

Mundaring (S)

Bassendean (T)

Vincent (T)

Bayswater (C)Stirling (C)

Swan (S)

Kwinana (T)

Wanneroo (C)

Rockingham (C)

Cambridge (T)

Cockburn (C)

East Fremantle (T)

Gosnells (C)

PERCENT OF POP'N LEFT SCHOOL AGED 15 YEARS OR LESS

Percentage45 to 51 (3)40 to 44 (5)33 to 49 (8)19 to 32 (8)10 to 18 (4)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A87

APPENDIX D - LGA's IN PERTH SOCIO-DEMOGRAPHIC FACTORS

Peppermint Grove (S)

Claremont (T)

Nedlands (C)Cottesloe (T)

Mosman Park (T)

Armadale (C)

South Perth (C)

Fremantle (C)Melville (C)

Perth (C)Subiaco (C)

Victoria Park (T)

Belmont (C)

Canning (C)

Kalamunda (S)

Mundaring (S)

Bassendean (T)

Vincent (T)

Bayswater (C)Stirling (C)

Swan (S)

Kwinana (T)

Wanneroo (C)

Rockingham (C)

Cambridge (T)

Cockburn (C)

East Fremantle (T)

Gosnells (C)

PERCENT OF POPULATION WHO ARE ABORIGINAL OR TSI

APPENDIX D - LGA's IN PERTH

Percentage2.6 to 4.6 (4)1.4 to 2.5 (5)0.9 to 1.3 (8)0.6 to 0.8 (6)0 to 0.5 (5)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A88

SOCIO-DEMOGRAPHIC FACTORS

Armadale (C)

South Perth (C)

Claremont (T)

Peppermint Grove (S)Nedlands (C)

Cottesloe (T)

Mosman Park (T)

Fremantle (C)Melville (C)

Perth (C)Subiaco (C)

Victoria Park (T)

Belmont (C)

Canning (C)

Kalamunda (S)

Mundaring (S)

Bassendean (T)

Vincent (T)

Bayswater (C)Stirling (C)

Swan (S)

Kwinana (T)

Wanneroo (C)

Rockingham (C)

Cambridge (T)

Cockburn (C)

East Fremantle (T)

Gosnells (C)

PERCENT OF POP'N AT DIFFERENT ADDRESS 5 YEARS EARLIER

Percentage50.8 to 54.1 (5)47 to 50.8 (5)43.1 to 47 (8)40.3 to 43.1 (5)36.8 to 40.3 (5)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A89

APPENDIX D - LGA's IN PERTH SOCIO-DEMOGRAPHIC FACTORS

Peppermint Grove (S)

Claremont (T)

Nedlands (C)Cottesloe (T)

Mosman Park (T)

Armadale (C)

South Perth (C)

Fremantle (C)Melville (C)

Perth (C)Subiaco (C)

Victoria Park (T)

Belmont (C)

Canning (C)

Kalamunda (S)

Mundaring (S)

Bassendean (T)

Vincent (T)

Bayswater (C)Stirling (C)

Swan (S)

Kwinana (T)

Wanneroo (C)

Rockingham (C)

Cambridge (T)

Cockburn (C)

East Fremantle (T)

Gosnells (C)

PERCENT OF OPD'S WITH NO VEHICLE

Percentage19 to 37.7 (3)13 to 18.9 (5)8 to 12.9 (9)6 to 7.9 (7)4 to 5.9 (4)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A90

APPENDIX D - LGA's IN PERTH SOCIO-DEMOGRAPHIC FACTORS

Peppermint Grove (S)

Claremont (T)

Nedlands (C)Cottesloe (T)

Mosman Park (T)

Armadale (C)

South Perth (C)

Fremantle (C)Melville (C)

Perth (C)Subiaco (C)

Victoria Park (T)

Belmont (C)

Canning (C)

Kalamunda (S)

Mundaring (S)

Bassendean (T)

Vincent (T)

Bayswater (C)Stirling (C)

Swan (S)

Kwinana (T)

Wanneroo (C)

Rockingham (C)

Cambridge (T)

Cockburn (C)

East Fremantle (T)

Gosnells (C)

SOCIO-ECONOMIC INDEX

Index907 to 974 (4)975 to 999 (7)

1,000 to 1,049 (6)1,050 to 1,099 (6)1,100 to 1,174 (5)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A91

APPENDIX D - LGA's IN PERTH SOCIO-DEMOGRAPHIC FACTORS

Mosman Park (T)

Peppermint Grove (S)

East Fremantle (T)

Cottesloe (T)

Claremont (T)

Nedlands (C)

Perth (C)

Mundaring (S)

Fremantle (C)

Victoria Park (T)

Canning (C)

Armadale (C)

Kalamunda (S)

Bayswater (C)

Bassendean (T)

Swan (S)

Rockingham (C)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Subiaco (C)

Vincent (T)

OFFENCES AGAINST THE PERSON PER 1000 PERSONS

1996-98 average annual rate20 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

APPENDIX D - LGA's IN PERTH OFFENCES REPORTED

Mosman Park (T)

Peppermint Grove (S)

East Fremantle (T)

Cottesloe (T)

Claremont (T)

Nedlands (C)

Mundaring (S)

Fremantle (C)

Perth (C)

Victoria Park (T)

Canning (C)

Armadale (C)

Kalamunda (S)

Bayswater (C)

Bassendean (T)

Swan (S)

Rockingham (C)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Subiaco (C)

Vincent (T)

PROPERTY OFFENCES PER 1000 PERSONS

1996-98 average annual rate240 or more (4)160 to 239 (6)135 to 159 (8)110 to 134 (5)82 to 109 (5)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A93

APPENDIX D - LGA's IN PERTH OFFENCES REPORTED

Mosman Park (T)

Peppermint Grove (S)

East Fremantle (T)

Cottesloe (T)

Claremont (T)

Nedlands (C)

Mundaring (S)

Fremantle (C)

Perth (C)

Victoria Park (T)

Canning (C)

Armadale (C)

Kalamunda (S)

Bayswater (C)

Bassendean (T)

Swan (S)

Rockingham (C)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Subiaco (C)

Vincent (T)

DRUG OFFENCES PER 1000 PERSONS

1996-98 average annual rate10 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

APPENDIX D - LGA's IN PERTH OFFENCES REPORTED

Mosman Park (T)

Peppermint Grove (S)

Cottesloe (T)

East Fremantle (T)

Claremont (T)

Nedlands (C)

Mundaring (S)

Fremantle (C)

Perth (C)

Victoria Park (T)

Canning (C)

Armadale (C)

Kalamunda (S)

Bayswater (C)

Bassendean (T)

Swan (S)

Rockingham (C)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Subiaco (C)

Vincent (T)

GOOD ORDER OFFENCES PER 1000 PERSONS

1996-98 average annual rate5 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

APPENDIX D - LGA's IN PERTH OFFENCES REPORTED

Mosman Park (T)

Peppermint Grove (S)

East Fremantle (T)

Cottesloe (T)

Claremont (T)

Nedlands (C)

Perth (C)

Mundaring (S)

Fremantle (C)

Victoria Park (T)

Canning (C)

Armadale (C)

Kalamunda (S)

Bayswater (C)

Bassendean (T)

Swan (S)

Rockingham (C)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Subiaco (C)

Vincent (T)

ALL OFFENCES, PER 1000 PERSONS

1996-98 average annual rate275 or more (4)200 to 275 (4)170 to 200 (7)130 to 170 (8)90 to 130 (5)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A96

APPENDIX D - LGA's IN PERTH OFFENCES REPORTED

Peppermint Grove (S)

Claremont (T)

Mosman Park (T)

Cottesloe (T)

Subiaco (C)

Mundaring (S)

Canning (C)

Victoria Park (T)

Perth (C)

Bassendean (T)

Bayswater (C)

Nedlands (C)

East Fremantle (T)

Fremantle (C)

Kalamunda (S)

Armadale (C)

Rockingham (C)

Swan (S)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Vincent (T)

POLICE-OFFENDER CONTACTS, PER 1000 PERSONS

Rate per 1000 persons24 to 29.1 (5)20 to 24 (6)14.5 to 20 (7)8 to 14.5 (5)5.7 to 8 (5)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A97

APPENDIX D - LGA's IN PERTH POLICE-OFFENDER CONTACTS

Mundaring (S)

Canning (C)

Victoria Park (T)

Perth (C)

Bassendean (T)

Bayswater (C)

Subiaco (C)

Claremont (T)

Nedlands (C)

East Fremantle (T)

Fremantle (C)

Mosman Park (T)

Peppermint Grove (S)

Cottesloe (T)

Kalamunda (S)

Armadale (C)

Rockingham (C)

Swan (S)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Vincent (T)

AGED 10 TO 17, PER 1000 PERSONS

Rate per 1000 persons75 or more (4)50 to 74 (5)30 to 49 (9)20 to 29 (6)5 to 19 (4)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A98

APPENDIX D - LGA's IN PERTH POLICE-OFFENDER CONTACTS

Mundaring (S)

Canning (C)

Victoria Park (T)

Perth (C)

Bassendean (T)

Bayswater (C)

Subiaco (C)

Claremont (T)

Nedlands (C)

East Fremantle (T)

Fremantle (C)

Mosman Park (T)

Peppermint Grove (S)

Cottesloe (T)

Kalamunda (S)

Armadale (C)

Rockingham (C)

Swan (S)Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Vincent (T)

AGED 18 TO 29, PER 1000 PERSONS

Rate per 1000 persons58.6 to 63.5 (2)49.2 to 58.6 (8)37.2 to 49.2 (7)24.2 to 37.2 (4)13.4 to 24.2 (7)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A99

APPENDIX D - LGA's IN PERTH POLICE-OFFENDER CONTACTS

Mundaring (S)

Canning (C)

Victoria Park (T)

Perth (C)

Bassendean (T)

Bayswater (C)

Subiaco (C)

Claremont (T)

Nedlands (C)

East Fremantle (T)

Fremantle (C)

Mosman Park (T)

Peppermint Grove (S)

Cottesloe (T)

Kalamunda (S)

Armadale (C)

Rockingham (C)

Swan (S)Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Vincent (T)

AGED 30 TO 39, PER 1000 PERSONS

Rate per 1000 persons32.7 to 59.5 (5)22.5 to 32.7 (6)18 to 22.5 (4)12.6 to 18 (5)0 to 12.6 (8)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A100

APPENDIX D - LGA's IN PERTH POLICE-OFFENDER CONTACTS

Mundaring (S)

Canning (C)

Victoria Park (T)

Perth (C)

Bassendean (T)

Bayswater (C)

Subiaco (C)

Claremont (T)

Nedlands (C)

East Fremantle (T)

Fremantle (C)

Mosman Park (T)

Peppermint Grove (S)

Cottesloe (T)

Kalamunda (S)

Armadale (C)

Rockingham (C)

Swan (S)Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Vincent (T)

AGED 40 OR MORE, PER 1000 PERSONS

Rate per 1000 persons32.7 to 59.5 (5)22.5 to 32.7 (6)18 to 22.5 (4)12.6 to 18 (5)0 to 12.6 (8)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A101

APPENDIX D - LGA's IN PERTH POLICE-OFFENDER CONTACTS

Mundaring (S)

Canning (C)

Victoria Park (T)

Perth (C)

Bassendean (T)

Bayswater (C)

Subiaco (C)

Claremont (T)

Nedlands (C)

East Fremantle (T)

Fremantle (C)

Mosman Park (T)

Peppermint Grove (S)

Cottesloe (T)

Kalamunda (S)

Armadale (C)

Rockingham (C)

Swan (S)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Vincent (T)

MALE, PER 1000 PERSONS

Rate per 1000 persons41.8 to 44.2 (3)34.4 to 41.8 (6)31.3 to 34.4 (3)18.8 to 31.3 (8)9 to 18.8 (8)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A102

APPENDIX D - LGA's IN PERTH POLICE-OFFENDER CONTACTS

Mundaring (S)

Canning (C)

Victoria Park (T)

Perth (C)

Bassendean (T)

Bayswater (C)

Subiaco (C)

Claremont (T)

Nedlands (C)

East Fremantle (T)

Fremantle (C)

Mosman Park (T)

Peppermint Grove (S)

Cottesloe (T)

Kalamunda (S)

Armadale (C)

Rockingham (C)

Swan (S)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Vincent (T)

FEMALE, PER 1000 PERSONS

Rate per 1000 persons10 to 12.5 (5)8.5 to 10 (6)6.2 to 8.5 (6)4 to 6.2 (4)1.1 to 4 (7)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A103

APPENDIX D - LGA's IN PERTH POLICE-OFFENDER CONTACTS

Mundaring (S)

Canning (C)

Victoria Park (T)

Perth (C)

Bassendean (T)

Bayswater (C)

Subiaco (C)

Claremont (T)

Nedlands (C)

East Fremantle (T)

Fremantle (C)

Mosman Park (T)

Peppermint Grove (S)

Cottesloe (T)

Kalamunda (S)

Armadale (C)

Rockingham (C)

Swan (S)

Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Vincent (T)

ABORIGINAL OR TSI, PER 1000 PERSONS

Rate per 1000 persons481 to 582 (2)214 to 481 (6)164 to 214 (9)71 to 164 (7)0 to 71 (4)

Numerals in parentheses indicate numbers of LGA's.Boundaries are ABS 1996 Census LGA boundaries.

Page A104

APPENDIX D - LGA's IN PERTH POLICE-OFFENDER CONTACTS

Mundaring (S)

Canning (C)

Victoria Park (T)

Perth (C)

Bassendean (T)

Bayswater (C)

Subiaco (C)

Claremont (T)

Nedlands (C)

East Fremantle (T)

Fremantle (C)

Mosman Park (T)

Peppermint Grove (S)

Cottesloe (T)

Kalamunda (S)

Armadale (C)

Rockingham (C)

Swan (S)Wanneroo (C)

Belmont (C)

Cambridge (T)

Cockburn (C)

Gosnells (C)

Kwinana (T)

Melville (C)

South Perth (C)

Stirling (C)

Vincent (T)

NON-ABORIGINAL AND NON-TSI, PER 1000 PERSONS

Rate per 1000 persons22.8 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

APPENDIX D - LGA's IN PERTH POLICE-OFFENDER CONTACTS

APPENDIX D - LGA's IN PERTH DENOMINATORS IN PERTH LGA's

LGA Po

pu

lati

on

Po

pu

lati

on

10-

17

Po

pu

lati

on

18-

29

Po

pu

lati

on

30-

39

Po

pu

lati

on

40+

Po

pu

lati

on

15+

Mal

e

Fem

ale

AT

SI

No

n-A

TS

I

Fam

ilies

in O

PD

s

Fam

/Gp

/Lo

in O

PD

s

Veh

icle

s in

OP

Ds

OP

Ds

Pri

vate

Dw

ellin

gs

Armadale (C) 49634 7239 8512 7352 18185 36748 24382 25252 1234 48399 13846 16965 27586 17264 18328Bassendean (T) 13199 1408 2314 2133 5569 10537 6478 6721 354 12857 3568 5169 7323 5234 5597Bayswater (C) 43798 4791 8124 6711 18842 35529 21550 22248 525 43273 12194 16332 25556 16713 17757Belmont (C) 26779 2222 5169 4074 12036 22156 13252 13527 886 25896 7228 10847 14360 11154 12134Cambridge (T) 23040 2256 3614 3421 10781 18650 10926 12114 34 22982 5820 8602 13743 8788 9533Canning (C) 68404 8882 12545 10116 27386 53431 33550 34854 788 67644 18744 24429 39567 24814 26577Claremont (T) 8830 1354 1476 927 4333 7412 4050 4780 17 8810 2018 3468 4965 3665 4042Cockburn (C) 57334 7192 10789 9822 20140 43288 28692 28642 1009 56297 15784 19893 32162 20272 21678Cottesloe (T) 7115 750 1405 987 3273 5974 3354 3761 42 7073 1677 2900 4529 3025 3401East Fremantle (T) 6265 524 1051 1159 2758 5169 3035 3230 33 6238 1543 2454 3645 2548 2805Fremantle (C) 24417 1995 4465 4087 11312 20688 12118 12299 298 24116 5898 9801 12118 10123 11120Gosnells (C) 73728 9959 14301 11548 25966 55460 36841 36887 1696 72005 20047 24913 41089 25303 26805Kalamunda (S) 46420 6983 7246 6498 18980 35484 22780 23640 429 45985 12891 15441 28447 15705 16554Kwinana (T) 19188 2333 3708 3138 6434 14063 9811 9377 891 18340 5256 6555 9728 6668 7412Melville (C) 89318 11211 15485 11889 40118 71916 42236 47082 514 88807 24303 32700 54023 33319 35829Mosman Park (T) 7425 968 1386 1010 3348 6195 3340 4085 73 7349 1663 3047 3875 3181 3582Mundaring (S) 31646 4620 4400 4734 12923 23707 15579 16067 350 31312 8850 10669 19758 10918 11585Nedlands (C) 20891 2440 3375 2307 10419 17029 9996 10895 99 20789 5018 6979 11325 7134 7736Peppermint Grove (S) 1587 360 232 140 677 1211 688 899 9 1578 381 491 921 531 604Perth (C) 10105 281 2495 1345 5737 9765 5829 4276 79 10026 713 2244 1812 2543 3123Rockingham (C) 58174 7423 9333 9381 21601 42893 28576 29598 592 57576 16539 20848 31208 21210 24214South Perth (C) 35336 3250 8668 5128 15171 30421 16904 18432 423 34938 8200 15121 20461 15949 17962Stirling (C) 174169 16869 37121 26285 74510 144615 84044 90125 2397 171772 45128 72457 100244 73940 80935Subiaco (C) 15130 1026 4232 2544 5902 13107 7092 8038 107 15023 3042 6506 7671 6795 7564Swan (S) 69113 9059 12559 12663 22015 50427 34352 34761 1948 67137 18645 22880 37114 23477 25324Victoria Park (T) 26316 1635 7121 4241 11089 23228 12694 13622 413 25900 5659 11375 12914 11861 12952Vincent (T) 24692 1433 6581 4879 9514 21588 12247 12445 126 24563 5800 10538 12849 11116 12335Wanneroo (C) 202886 29979 33331 33266 72626 150138 100249 102637 1702 201184 56436 66091 114748 67209 71283

Notes:"OPDs" is occupied private dwellings."Fam/Gp/Lo in OPDs" is families,groups or lone persons in OPDs.

Page A106

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH SOCIO-DEMOGRAPHIC FACTORSCENTRAL METROPOLITAN SUBDIVISION

Page A107

POPULATION AGED 10 - 17 YEARS

0

5

10

15

20

25

Cam

bridg

eCl

arem

ont

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Per

cen

tPOPULATION AGED 18 - 29 YEARS

0

5

10

15

20

25

30

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Cam

brid

ge

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedla

nds

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Per

cen

t

UNEMPLOYED

0

1

2

3

4

5

6

7

8

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Per

cen

t

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH SOCIO-DEMOGRAPHIC FACTORSCENTRAL METROPOLITAN SUBDIVISION

Page A108

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

60

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Per

cen

t

SOCIO-ECONOMIC INDEX

850

900

950

1000

1050

1100

1150

1200

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

ATSI

0.00.5

1.0

1.5

2.02.5

3.03.5

4.04.5

Cam

brid

geCl

arem

ont

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Per

cen

t

OPD'S WITH NO VEHICLE

0

5

10

15

20

25

30

35

40

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Per

cen

t

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH REPORTED OFFENCESCENTRAL METROPOLITAN SUBDIVISION

Page A109

OFFENCES AGAINST THE PERSON137

0

5

10

15

20

25

30

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedla

nds

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES1037

0

50

100

150

200

250

300

350

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES103

0

2

4

6

8

10

12

14

16

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedla

nds

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES16

0

1

2

3

4

5

6

7

8

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subia

co

Vinc

ent

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH POLICE-OFFENDER CONTACTSCENTRAL METROPOLITAN SUBDIVISION

Page A110

ALL PERSONS

0

5

10

15

20

25

30Ca

mbr

idge

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedla

nds

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH POLICE-OFFENDER CONTACTSCENTRAL METROPOLITAN SUBDIVISION

Page A111

AGED 10 - 17 YEARS

0102030405060708090

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subia

co

Vinc

ent

Rat

e p

er 1

000

per

son

s

AGED 18 - 29 YEARS

0

10

20

30

40

50

60

Cam

bridg

eCl

arem

ont

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

10

20

30

40

50

60

70

Cam

brid

ge

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subia

co

Vinc

ent

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

1

2

3

4

5

6

7

8

Cam

brid

ge

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedl

ands

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH POLICE-OFFENDER CONTACTSCENTRAL METROPOLITAN SUBDIVISION

Page A112

MALE

0

5

10

15

20

25

30

35

40

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedla

nds

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Rat

e p

er 1

000

per

son

s

FEMALE

0

2

4

6

8

10

12

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedla

nds

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Rat

e p

er 1

000

per

son

s

ATSI

0

100

200

300

400

500

600

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedla

nds

Pepp

erm

int G

rove

Perth

Subia

co

Vinc

ent

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

5

10

15

20

25

Cam

bridg

e

Clar

emon

t

Cotte

sloe

Mos

man

Par

k

Nedla

nds

Pepp

erm

int G

rove

Perth

Subi

aco

Vinc

ent

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH SOCIO-DEMOGRAPHIC FACTORSNORTH AND EAST METROPOLITAN SUBDIVISIONS

Page A113

POPULATION AGED 18 - 29 YEARS

0

5

10

15

20

25

30

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Per

cen

t

POPULATION AGED 10 - 17 YEARS

0

5

10

15

20

25

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Per

cen

t

UNEMPLOYED

0

1

2

3

4

5

6

7

8

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Per

cen

t

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH SOCIO-DEMOGRAPHIC FACTORSNORTH AND EAST METROPOLITAN SUBDIVISIONS

Page A114

ATSI

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

60

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Per

cen

t

OPD'S WITH NO VEHICLE

0

5

10

15

20

25

30

35

40

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Per

cen

t

SOCIO-ECONOMIC INDEX

850

900

950

1000

1050

1100

1150

1200

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH REPORTED OFFENCESNORTH AND EAST METROPOLITAN SUBDIVISIONS

Page A115

OFFENCES AGAINST THE PERSON

0

5

10

15

20

25

30

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

50

100

150

200

250

300

350

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

2

4

6

8

10

12

14

16

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

1

2

3

4

5

6

7

8

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirl

ing

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH POLICE-OFFENDER CONTACTSNORTH AND EAST METROPOLITAN SUBDIVISIONS

Page A116

ALL PERSONS

0

5

10

15

20

25

30

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH POLICE-OFFENDER CONTACTSNORTH AND EAST METROPOLITAN SUBDIVISIONS

Page A117

AGED 10 - 17 YEARS

0

10

20

30

40

50

60

70

80

90

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

10

20

30

40

50

60

70

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

AGED 18 - 29 YEARS

0

10

20

30

40

50

60

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

1

2

3

4

5

6

7

8

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH POLICE-OFFENDER CONTACTSNORTH AND EAST METROPOLITAN SUBDIVISIONS

Page A118

MALE

0

5

10

15

20

25

30

35

40

45

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

ATSI

0

100

200

300

400

500

600

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

5

10

15

20

25

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

FEMALE

0

2

4

6

8

10

12

14

Bass

ende

an

Bays

wate

r

Kalam

unda

Mun

darin

g

Stirli

ng

Swan

Wan

nero

o

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH SOCIO-DEMOGRAPHIC FACTORSSOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS

Page A119

POPULATION AGED 10 - 17 YEARS

0

5

10

15

20

25

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwin

ana

Mel

ville

Rock

ingh

amSo

uth

Perth

Vict

oria

Par

k

Per

cen

tPOPULATION AGED 18 - 29 YEARS

0

5

10

15

20

25

30

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwin

ana

Mel

ville

Rock

ingh

amSo

uth

Perth

Vict

oria

Par

k

Per

cen

t

UNEMPLOYED

0

1

2

3

4

5

6

7

8

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwina

naM

elvil

leRo

ckin

gham

Sout

h Pe

rthVi

ctor

ia P

ark

Per

cen

t

LEFT SCHOOL 15 YEARS OR LESS

0

10

20

30

40

50

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwina

naM

elvil

leRo

ckin

gham

Sout

h Pe

rthVi

ctor

ia P

ark

Per

cen

t

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH SOCIO-DEMOGRAPHIC FACTORSSOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS

Page A120

ATSI

0.0

0.51.01.5

2.02.5

3.03.54.04.5

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwin

ana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

Per

cen

t

AT DIFFERENT ADDRESS FIVE YEARS EARLIER

0

10

20

30

40

50

60

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwin

ana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

Per

cen

t

OPD'S WITH NO VEHICLE

0

5

10

15

20

25

30

35

40

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwin

ana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

Per

cen

t

SOCIO-ECONOMIC INDEX

850

900

950

1000

1050

1100

1150

1200

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwin

ana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH REPORTED OFFENCESSOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS

Page A121

OFFENCES AGAINST THE PERSON

0

5

10

15

20

25

30

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwina

naM

elvil

leRo

ckin

gham

Sout

h Pe

rthVi

ctor

ia P

ark

Rat

e p

er 1

000

per

son

s

PROPERTY OFFENCES

0

50

100

150

200

250

300

350

Arm

adal

eBe

lmon

tCa

nning

Cock

burn

East

Fre

man

tleFr

eman

tleG

osne

llsKw

inana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

Rat

e p

er 1

000

per

son

s

DRUG OFFENCES

0

2

4

6

8

10

12

14

16

Arm

adal

eBe

lmon

tCa

nning

Cock

burn

East

Fre

man

tleFr

eman

tleG

osne

llsKw

inana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

Rat

e p

er 1

000

per

son

s

GOOD ORDER OFFENCES

0

1

2

3

4

5

6

7

8

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwina

naM

elvil

leRo

ckin

gham

Sout

h Pe

rthVi

ctor

ia P

ark

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH POLICE-OFFENDER CONTACTSSOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS

Page A122

ALL PERSONS

0

5

10

15

20

25

30Ar

mad

ale

Belm

ont

Cann

ing

Cock

burn

East

Fre

man

tleFr

eman

tleG

osne

llsKw

inan

a

Mel

ville

Rock

ingh

amSo

uth

Perth

Vict

oria

Par

k

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH POLICE-OFFENDER CONTACTSSOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS

Page A123

AGED 10 - 17 YEARS

0102030405060708090

Arm

adal

e

Belm

ont

Cann

ing

Cock

burn

East

Fre

man

tle

Frem

antle

Gos

nells

Kwina

na

Mel

ville

Rat

e p

er 1

000

per

son

sAGED 18 - 29 YEARS

0

10

20

30

40

50

60

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwina

naM

elvil

leRo

cking

ham

Sout

h Pe

rthVi

ctor

ia P

ark

Rat

e p

er 1

000

per

son

s

AGED 40 YEARS OR MORE

0

1

2

3

4

5

6

7

8

Arm

adal

eBe

lmon

tCa

nning

Cock

burn

East

Fre

man

tleFr

eman

tleG

osne

llsKw

inana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

Rat

e p

er 1

000

per

son

s

AGED 30 - 39 YEARS

0

10

20

30

40

50

60

70

Arm

adal

eBe

lmon

tCa

nning

Cock

burn

East

Fre

man

tleFr

eman

tleG

osne

llsKw

inana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

Rat

e p

er 1

000

per

son

s

APPENDIX D - LOCAL GOVERNMENT AREAS IN PERTH POLICE-OFFENDER CONTACTSSOUTH-WEST AND SOUTH-EAST METROPOLITAN SUBDIVISIONS

Page A124

MALE

0

5

10

15

20

25

30

35

40

Arm

adal

e

Belm

ont

Cann

ing

Cock

burn

East

Fre

man

tle

Frem

antle

Gos

nells

Kwina

na

Mel

ville

Rat

e p

er 1

000

per

son

sFEMALE

0

2

4

6

8

10

12

14

Arm

adal

eBe

lmon

tCa

nning

Cock

burn

East

Fre

man

tleFr

eman

tleG

osne

llsKw

inana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

Rat

e p

er 1

000

per

son

s

NON-ATSI

0

5

10

15

20

25

Arm

adal

eBe

lmon

tCa

nning

Cock

burn

East

Fre

man

tleFr

eman

tleG

osne

llsKw

inana

Mel

ville

Rock

ingha

mSo

uth

Perth

Vict

oria

Par

k

Rat

e p

er 1

000

per

son

s

ATSI

0

100

200

300

400

500

600

Arm

adal

eBe

lmon

tCa

nnin

gCo

ckbu

rnEa

st F

rem

antle

Frem

antle

Gos

nells

Kwina

naM

elvil

leRo

cking

ham

Sout

h Pe

rthVi

ctor

ia P

ark

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS GASCOYNE

Page A125

Against the Person

0

10

20

30

40

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

sProperty

0

50

100

150

200

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Drugs

0

5

10

15

20

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Good Order

0

3

6

9

12

15

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS GOLDFIELDS-ESPERANCE

Page A126

Against the Person

0

4

8

12

16

20

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

sProperty

0

25

50

75

100

125

150

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Drugs

0

5

10

15

20

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Good Order

0

1

2

3

4

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS GREAT SOUTHERN

Page A127

Against the Person

0

2

4

6

8

10

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

sProperty

0

20

40

60

80

100

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Drugs

0

2

4

6

8

10

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Good Order

0

1

2

3

4

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS KIMBERLEY

Page A128

Against the Person

0

10

20

30

40

50

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

sProperty

0

50

100

150

200

250

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Drugs

0

3

6

9

12

15

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Good Order

0

3

6

9

12

15

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS MID WEST

Page A129

Against the Person

0

3

6

9

12

15

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e pe

r 10

00 p

erso

nsProperty

0

30

60

90

120

150

180

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Drugs

0

3

6

9

12

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Good Order

0

1

2

3

4

5

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS PEEL

Page A130

Against the Person

0

2

4

6

8

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Drugs

0

1

2

3

4

5

6

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

sProperty

0

20

40

60

80

100

120

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Good Order

0.0

0.5

1.0

1.5

2.0

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS PILBARA

Page A131

Against the Person

0

3

6

9

12

15

18

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

sProperty

0

30

60

90

120

150

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Drugs

0

3

6

9

12

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Good Order

0

1

2

3

4

5

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS SOUTH WEST

Page A132

Against the Person

0

2

4

6

8

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

sProperty

0

20

40

60

80

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Drugs

0

2

4

6

8

10

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Good Order

0

1

2

3

4

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS WHEATBELT

Page A133

Against the Person

0

2

4

6

8

10

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

sProperty

0

20

40

60

80

100

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Drugs

0

2

4

6

8

10

12

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

Good Order

0

1

2

3

4

5

1991 1992 1993 1994 1995 1996 1997 1998

Rat

e p

er 1

000

per

son

s

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS SEXUAL AND FRAUD OFFENCES

Page A134

Reported Against the Person OffencesDaily Average, 1991 to 1998

873

0

50

100

150

200

1-Ja

n

1-F

eb

1-M

ar

1-A

pr

1-M

ay

1-Ju

n

1-Ju

l

1-A

ug

1-S

ep

1-O

ct

1-N

ov

1-D

ecReported Against the Person Offences (non-sexual)

Daily Average, 1991 to 1998

100

0

20

40

60

1-Ja

n

1-F

eb

1-M

ar

1-A

pr

1-M

ay

1-Ju

n

1-Ju

l

1-A

ug

1-S

ep

1-O

ct

1-N

ov

1-D

ec

Reported Sexual OffencesDaily Average, 1991 to 1998

773

0

50

100

150

1-Ja

n

1-F

eb

1-M

ar

1-A

pr

1-M

ay

1-Ju

n

1-Ju

l

1-A

ug

1-S

ep

1-O

ct

1-N

ov

1-D

ec

Reported Fraud OffencesDaily Average, 1991 to 1998

282

0

50

100

150

200

1-Ja

n

1-F

eb

1-M

ar

1-A

pr

1-M

ay

1-Ju

n

1-Ju

l

1-A

ug

1-S

ep

1-O

ct

1-N

ov

1-D

ec

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS DAILY STREET OFFENCE ARRESTS

Page A135

Street Offence Arrests in WA - daily average, 1991 to 1998

110

0

20

40

60

80

100

120

1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-DecDay of year

Nu

mb

er

Ratio of weekend day arrests to non-weekend day arrests, by Region

0

0.5

1

1.5

2

2.5

Gas

coyn

e

Gol

df.-E

sp.

Grt

Sout

hern

Kim

berle

y

Mid

Wes

t

Peel

Perth

Pilb

ara

Sout

h W

est

Whe

atBe

lt

Rat

io

APPENDIX E - TIME SERIES OF OFFENCES IN REGIONS MONTHLY STREET OFFENCE ARRESTS

Page A136

Perth Street Offence ArrestsMonthly average, 1991 to 1998

0

100

200

300

400

500

600

700

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Nu

mb

erStreet Offence Arrests

Monthly average, 1991 to 1998

0

10

20

30

40

50

60

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Nu

mb

er

Peel Gascoyne WheatBelt

Street Offence ArrestsMonthly average, 1991 to 1998

0

20

40

60

80

100

120

140

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Nu

mb

er

South West Mid West Kimberley

Street Offence ArrestsMonthly average, 1991 to 1998

0

20

40

60

80

100

120

140

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Nu

mb

er

Pilbara Goldf.-Esp. Grt Southern