CRIM402H Honours Thesis-signed

i

AN ANALYSIS OF YOUTH MISUSE OF FIRE

IN NEW SOUTH WALES

By

Kamarah Pooley

Student Number: 220128520

Being a thesis submitted as a partial requirement for the Degree of Bachelor of Criminology with Honours

Department of Criminology School of Behavioural, Cognitive and Social Sciences

University of New England Armidale NSW 2351

Supervisor: Dr. Jenny Wise

Date: 21st January 2015

Word Count: 22084

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Declaration of Originality

I hereby declare that this submission is my own work and to the best of my knowledge

contains no materials previously published or written by another person, nor materials

which to a substantial extent has been accepted for the award of any other degree or

diploma, except where due acknowledgement is made in the thesis. I also declare that

the intellectual content of this thesis is the product of my own work, except to the extent

that the assistance from others in the project’s design and conception or in style,

presentation and linguistic expression is acknowledged.

Signed: .............................................

Date: ................................................

21st January 2015

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Acknowledgments

I would like to thank both Fire and Rescue New South Wales (FRNSW) and the New

South Wales Rural Fire Service (NSWRFS) for providing access to data for this thesis.

Namely, I would like to thank The Office of the Commissioner (FRNSW), Mymy Murphy

(FRNSW), the Station Officers of Regentville Fire Station (FRNSW), Christine Roach

(NSWRFS) and Stephen Burgoine (NSWRFS) for approving and enabling access. I

would especially like to thank Graeme Last (FRNSW) who, promptly and tirelessly,

sought any information requested for this research.

I would also like to recognise the help and support provided to me by my supervisors,

Dr. Claire Ferguson and Dr. Jenny Wise. Dr. Ferguson and Dr. Wise were unwavering

in their provision of advice and encouragement, ensuring this research was an

enjoyable and educational process.

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Table of Contents

Declaration of Originality ii Acknowledgements iii Table of Contents iv List of Tables v List of Charts vi List of Graphs vi List of Abbreviations vii Abstract viii

Chapter 1: Introduction

1

Chapter 2: Literature Review Definition 4 The Scope and Magnitude of YMF 5 Theoretical Analysis 8 Responses to YMF 21 Chapter 3: Methodology

Methodological Review of Past Studies 25 Aims, Research Questions and Hypotheses 30 Research Design 31 Participants 32 Measures 35 Procedure 40 Ethical Considerations 43 Limitations 44

Chapter 4: Results and discussion: The scope and magnitude of YMF within NSW

48

Chapter 5: Results and discussion: The applicability of existing literature to the YMF population of NSW

53

Chapter 6: Results and discussion: The availability of IFAP to the YMF population of NSW

97

Chapter 7: Conclusion

106

References

110

Appendix 120

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List of Tables

Table Number Table Name Page

Table 1.1. Incidence Rates of YMF in NSW 49

Table 1.2. YMF Prevalence Rates for Top 10 NSW Suburbs 50

Table 1.3. YMF and Arson Incidence Rates in NSW 50

Table 1.4. AIRS Cost Analysis 51

Table 2.1. Chi Square r x c Tests for Independence 55

Table 3.1. Population Descriptive Statistics 57

Table 3.2. Population/YMF Correlation 57

Table 4.1. YMF x Area of Origin 60

Table 4.2. YMF x Alarm Source 61

Table 5.1. Familial Structure Descriptive Statistics 63

Table 5.2. Familial Structure/YMF Correlation 63

Table 5.3. Family Type Descriptive Statistics 63

Table 5.4. Family Type/YMF Correlation 64

Table 5.5. Child Type Descriptive Statistics 64

Table 5.6. Child Type/YMF Correlation 65

Table 6.1. YMF x Type of Fire 67

Table 6.2. YMF x Type of Property 68

Table 6.3. YMF x Type of Owner 70

Table 6.4. YMF x Form of Heat Ignition 71

Table 6.5. YMF x Form of Material Ignited First 72

Table 7.1. YMF x Incident Outcome 82

Table 7.2. YMF x Dollar Loss 84

Table 8.1. SEIFA value/YMF Correlation 85

Table 8.2. Tenure Type Descriptive Statistics 86

Table 8.3. Tenure Type/YMF Correlation 86

Table 8.4. Landlord Type Descriptive Statistics 86

Table 8.5. Landlord Type/YMF Correlation 87

Table 9.1.

Table 9.2.

Table 9.3.

Indigeneity Descriptive Statistics

Indigeneity/YMF Correlation

Birthplace of Persons Descriptive Statistics

88

89

90

Table 9.4. Birthplace of Persons/YMF Correlation 90

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Table 9.5. Birthplace of Parents Descriptive Statistics 90

Table 9.6. Birthplace of Parents/YMF Correlation 91

Table 9.7. Citizenship Descriptive Statistics 91

Table 9.8. Citizenship/YMF Correlation 91

Table 9.9. Ancestry Descriptive Statistics 92

Table 9.10 Ancestry/YMF Correlation 92

Table 10.1. Residential Mobility Descriptive Statistics 94

Table 10.2. Residential Mobility/YMF Correlation 94

Table 11.1. Societal Level Variables/IFAP Correlation 98

List of Charts

Chart Number Chart Name Page

Chart 1.1. Area of Origin 59

Chart 1.2. Alarm Source 60

Chart 2.1. Type of Fire 66

Chart 2.2. Type of Property 68

Chart 2.3. Type of Owner 69

Chart 2.4. Form of Heat Ignition 70

Chart 2.5. Form of Material Ignited First 72

List of Graphs

Graph Number Graph Name Page

Graph 1.1. YMF by Day of the Week 73

Graph 1.2. YMF by Time of Day 74

Graph 1.3. 0-5 years YMF by Time of Day 74

Graph 1.4. 0-5 years YMF by Weekday 75

Graph 1.5. 0-5 years YMF by Weekend 75



Graph 1.8. 6-12 years YMF by Week end 77


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Graph 1.11. 13-16 years YMF by Week end 78

Graph 2.1 YMF Incident Outcome 82

Graph 2.2. Cost of YMF 83

Graph 3.1. IFAP 9-year Longitudinal Analysis 100

Graph 3.2. YMF 10-year Longitudinal Analysis 100

Graph 3.3. IFAP By Financial Year 101

Graph 3.4. YMF by Financial Year 102

Graph 3.5. IFAP by Month 102

Graph 3.6. YMF by Month 103

Graph 3.7. YMF/IFAP Ratio 104

List of Abbreviations

Abbreviation Name/Phrase

ABS Australian Bureau of Statistics

AIRS Australian Incident Reporting System

CARS Community Activity Reporting System

FIRS Fire Incident Reporting System

FRNSW Fire and Rescue New South Wales

IFAP Juvenile Intervention and Fire Awareness Program

NSWRFS New South Wales Rural Fire Service

RAT Routine Activities Theory

RO Reporting Officer

SDT Social Disorganisation Theory

SRS Strategic Reporting System

YMF Youth Misuse of Fire

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Abstract

Youth misuse of fire (YMF) is a substantive community concern which has attracted

the attention of authorities and researchers throughout the world. Despite the

existence of a substantial body of international YMF literature, a lack of theoretical

and empirical consensus means such research remains ungeneralisable. This

problem is compounded by the fact only a few Australian based studies exist. In

order to partially fill these theoretical and empirical voids, this research has produced

empirical evidence specific to the YMF population of New South Wales (NSW). The

aim of the research was to analyse the scope and magnitude of YMF within NSW, to

determine the applicability of existing literature to the YMF population of NSW, and to

evaluate the availability of YMF intervention within NSW. In order to conduct such

research, quantitative secondary data analysis of NSW fire brigade data was

performed. Results suggest that YMF is highly prevalent within spatial clusters of

NSW, and although it is more prevalent within adolescents than children, the younger

the youth, the higher the level of severity and risk. While the majority of existing

literature was found to be generalisable to the YMF population of NSW, there were

some notable exceptions. Such findings suggest that YMF literature must be critically

analysed to determine its contextual applicability before being applied to different

populations of interest. Furthermore, evaluative evidence reveals that YMF

intervention within NSW is not applied in proportion to current demand, and may not

be available to those youths who are most at risk. It is recommended that further

critical evaluation of YMF intervention within NSW be performed to determine its

applicability and effectiveness in reaching its target population.

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CHAPTER ONE: INTRODUCTION

Youth misuse of fire (YMF) is a substantive community concern which has

attracted the attention of authorities and researchers throughout the world. Although

there exists a substantial body of research pertaining to the study of YMF, a lack of

theoretical and empirical consensus means such research remains ungeneralisable

(Williams 2005, 150). There is also a resounding lack of YMF research which

specifically relates to an Australian context. Consequently, there is an urgent need

for epistemological study of YMF which presents data on incidence and prevalence

rates, and individual, situational, and societal level correlates within an Australian

context. Furthermore, there exists a need to evaluate YMF intervention in terms of

program availability and applicability (Kolko 2002; Stanley 2010).

Such research is imperative where YMF remains one of the least understood

forms of youth behaviour (Attorney General’s Department, 2009; Brett, 2004; Prins,

1994; Stanley, 2010). According to the Attorney General’s Department (2009, iii) this

lack of understanding arises from difficulties in detection, prevention and

apprehension; issues compounded by a lack of observational analysis, parental

reluctance to involve professional services, and an absence of multi-agency

cooperation (Kolko et al. 2002, 178). A lack of understanding is further perpetuated

by differing definitions of YMF utilised amongst medical, legal, sociological,

criminological, and psychiatric literature (Williams 2005, 5), and an absence of

theoretical consensus regarding classification and operationalisation of YMF groups

(Brett 2004; Kolko 2002; Stadolnik 2000). Empirical research into YMF is also limited

by inadequate funding (Stanley, 2010, 14); small, homogenous and non-

representative sample populations produced by restricted access to youths; and an

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absence of valid and reliable measurement tools (Stadolnik 2000, 3-4). As a result,

YMF is not congruous to empirical research, and is conducive to the propagation of

myths amongst professionals and the community (Brett 2004; Doley 2003; Stadolnik

2000; Stanley 2010; Williams 2005). Ultimately, this proliferation of myths impairs the

development of prevention and intervention programs (Williams 2005, 148).

The aim of this research was to investigate YMF within New South Wales

(NSW) in order to partially fill the theoretical and empirical voids which currently exist

within YMF literature. Specifically, the objectives of this analysis were to ascertain

the scope and magnitude of YMF within NSW; to determine if existing literature is

applicable to the YMF population of NSW; and to evaluate the availability of YMF

intervention within NSW. This study thus aimed to answer the following research

questions;

1. What is the scope and magnitude of YMF within NSW?

2. Are the individual, situational and societal level variables, and temporal

patterns, identified within the recorded YMF population of NSW

representative of the theoretical propositions presented within existing

literature?

3. Do the societal level variables and temporal patterns associated with YMF

intervention reflect the societal level variables and temporal patterns

associated with YMF within NSW?

To answer these questions, quantitative secondary data analysis of YMF

behaviour recorded by Fire and Rescue New South Wales (FRNSW) and the New

South Wales Rural Fire Service (NSWRFS) has been performed.

In order to appreciate this study within the broader context of YMF research, a

summary of existing YMF literature has been presented. This literature review and

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summary of theoretical perspectives is detailed in Chapter Two. Specifically, the

literature review has focused upon seven specific variables; age, supervision,

opportunity, familial disruption, socioeconomic status, ethnic heterogeneity, and

residential mobility. These variables are discussed from a criminological perspective,

within the frameworks of Routine Activity Theory and Social Disorganisation Theory.

In Chapter Three, the methodology employed to undertake this research is

presented. This chapter provides a summary of existing methodological approaches

to the study of YMF, before outlining the research aims, questions, and hypotheses.

Thereafter, details pertaining to the research design, participants, measures,

procedures and ethical considerations employed within this research have been

provided. The chapter concludes with a discussion of the limitations of this study.

The subsequent three chapters present the results and discussion for each of

the research questions prescribed. Chapter Four presents the empirical findings and

implications for the scope and magnitude of YMF within NSW. Chapter Five presents

empirical evidence and theoretical implications pertaining to the applicability of

existing literature to the YMF population of NSW. In Chapter Six, empirical evidence

relating to the availability of YMF intervention to the YMF population of NSW is

discussed.

Finally, Chapter Seven draws these findings together to address the overall

aims of the study. The implications of the findings along with directions for future

research are also presented.

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CHAPTER TWO: LITERATURE REVIEW

The following chapter presents a review of existing YMF literature. Firstly, the

term YMF has been defined and justified. Thereafter, research which presents

findings on the scope and magnitude of YMF are summarised. This is proceeded by

a theoretical analysis of YMF, where individual, situational, and societal level

variables are analysed within the frameworks of Routine Activity Theory and Social

Disorganisation Theory. Finally, formal responses to YMF have been presented.

Definition

The term ‘youth misuse of fire’ (YMF), coined by Johnson, Beckenbach and

Kilbourne (2013), has been employed to refer to all fire incidents reportedly caused

by a youth. For the purposes of this study, a youth is any person between the ages

of 0 and 16 years inclusive. This definition was determined by the databases made

available for this research, where fire incident data relating to youths was compiled

into the age category 0 - 16 years.

The term YMF has been specifically selected because its broad definition

encompasses all fire incidents which exist along a continuum from fire interest to

arson. Although most researchers distinguish between fire interest attributed to 3 – 5

year olds, fire-play attributed to 6 – 9 year olds, firesetting attributed to youths 10

years and over, and arson, the criminalisation of the aforementioned (Gaynor 2002;

NSWFB 2009; Putnam and Kirkpatrick 2005), such categorisations are problematic.

These theoretical divisions assume that fire interest is natural and inquisitive, that

fire-play is experimental, prevalent, yet less harmful, and that firesetting and arson

are defined by a higher level of intent, frequency, severity, and complexity (Britt

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2011; Gaynor 2002; NSWFB, 2009; Putnam and Kirkpatrick 2005). These theoretical

divisions are founded upon the premise that, by the age of 10 years, youths have a

reasonable knowledge of fire safety (Dolan et al. 2011, 379). Research thus

suggests that YMF over the age of 10 years is intentional and malicious, or the

product of low education, poor parenting, conduct disorder or mental health issues

(Britt 2011; DJST 2011; Dolan and Stanley 2010; Drabsch 2003; NSWFB 2009).

Although the assumption that the age of 10 years marks an immediate transition

between experimentation and maliciousness persists throughout YMF literature, it is

not upheld by empirical consensus (Lambie and Randell 2011, 309).

Consequently, all references to fire interest, fire-play, firesetting and juvenile

arson within existing literature, will hereafter be classified as a form of YMF. This

broad definition provides an avenue through which YMF can be investigated without

having to conform to complex and changeable divisions of misuse of fire based on

theoretical explanations.

The Scope and Magnitude of YMF

The majority of empirical literature which exists utilises official arson data to

provide an insight, albeit limited, into the scope and magnitude of YMF. Although

Kocsis (2002, 1) states that arson rates in Australia have increased at 40 times the

rate of the population over the past 30 years, the Bureau of Crime Statistics and

Research (2014) reveals arson rates within NSW have remained relatively stable

over the past 10 years. Economic cost analyses reveal that arson costs Australians

hundreds of millions of dollars annually (Kocsis 2002, 1), while Stanley (2010, 13)

defines arson as the most costly crime in Australia. A cost analysis conducted by

Rollings (2008, 36) revealed that the estimated cost of arson in 2005 was $812

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million, accounting for 8.0% of the total cost of crime in Australia during that period.

However, when analysed in comparison to the costs associated with fire damage,

Drabsch (2003, vii) advances Café and Stern’s proposition that arson accounts for

30.0% of all fire-related costs. Nevertheless, it is important to note that arson rates

account for a very small proportion of YMF incidents (Corcoran et al. 2007; DJST

2011; Hardesty and Gayton 2002; Jayaraman and Frazer 2006). As a result, such

indications provide only limited insight into the magnitude of the YMF problem.

For the most part, existing literature suggests that youths are over-

represented within misuse of fire statistics. This over-representation has been

theoretically linked with the natural inquisitiveness of youth and the ease of

performing and concealing the behaviour. However, prevalence rates of YMF vary

within international literature depending on the source. Research conducted within

the United States of America (USA) on community samples over a 20 year period

revealed that, by the age of 12, over 50.0% of all children had committed YMF (Cole,

Crandall and Kourofsky 2002, 92). Yet, prevalence rates within other community

samples vary from 5.0% to 67.0% (Lambie et al. 2013, 1295). Lower rates of YMF

are reported when drawn from official agency records, where estimates derived from

the USA suggest the ratio of unreported fires to reported fires is 3:1 (Hardesty and

Gayton 2002, 2). Official agency records may also be skewed where the Survey of

English Housing suggests that fires which occur outdoors are reported 71.0% of the

time, while fires which occur indoors are only reported 15.0% of the time (Corcoran

et al. 2007, 632). Furthermore, MacKay et al. (2012, 843) suggest that when

caregivers are the informants of YMF, reported rates are much lower. This may be

due to parental reluctance to involve professional services (Kolko et al. 2002, 178),

or a lack of parental awareness of a child’s behaviour (Britt 2011, 40). Although self-

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report data may reveal higher rates of YMF given its relevance to the study of covert

behaviour (MacKay et al. 2012, 843), ethical constraints surrounding the study of

youths limit the application of this methodological approach.

Within Australia, research suggests that youths account for three quarters of

deliberately lit fires (AIC 2005, para. 1). Bryant (2008) conducted an Australian

based study of deliberately lit fires, focussing upon vegetation fires only. Findings

revealed that, between 1997/1998 and 2001/2002, youths accounted for 0.4% of all

rural fires and 16.0% of all urban fires (Bryant 2008, 134). However, Bryant’s study

did not include incidents of suspicious fires or those involving structures such as

residential dwellings. When considered in light of evidence which indicates around

half of all structure fires occur as a result of YMF (Lowenstein, 2003, 193), such

findings still provide only limited insight into the magnitude of the YMF problem.

This limited insight is further complicated by inconsistencies within recidivism

literature where, depending on the population of study, YMF recidivism rates vary

from 4.0% to 60.0% (Brett 2004, 424). Repo and Verkkunen (1997) conducted a

study of 45 Finnish adolescent arsonists and found, while 62.0% of their sample

committed some form of crime within 6 years, only 15.0% were re-convicted for

arson within the same period. Edwards and Grace (2013, 7) discerned between

types of recidivism and found that, of their sample of 1,250 persons convicted of

arson in New Zealand over a 10 year period, only 6.2% were re-convicted for arson,

while 48.5% were convicted of a new violent offence and 79.3% were convicted of a

new non-violent offence. Muller (2008, 5) similarly found that, of the 555 persons

who had a prior conviction for arson in NSW, only 1.3% were exclusively arsonists.

However, such research only provides an insight into the arson population, and does

not shed light on the recurring nature YMF.

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Drawing inferences from arson data to understand YMF is problematic where

YMF is only defined as arson once the behaviour is identified and classified as an

offence by police. In addition to the usual problems associated with inconsistencies

in the application of police discretion and recording and classification systems, the

most problematic factor is that arson is under-reported and under-counted (Williams,

2005, 2). In a study of self-report data, Gannon and Barrowcliffe (2012, 7) found that,

of the 18 participants who self-reported YMF, only five (28.0%) reported being

caught, and none were formally apprehended. Furthermore, Australian research

suggests that between 2001 and 2005, NSW and Victoria collectively convicted an

average of 40.5 arsonists per year (DJST 2011, 4). However, during this time, these

two states experienced 27, 000 fires, an estimated 50.0% of which were caused by

arson. This, according to the Department of Justice State of Tasmania (2011, 4),

indicates that the conviction rate of arson within Australia is around 4 in 1,000. Such

difficulties regarding detection and apprehension mean that the study of arson is not

congruous to the study of YMF. When such analyses are relied upon, professionals

and the community alike remain unaware of, and unable to effectively manage, the

magnitude and complexity of YMF within NSW.

Theoretical Analysis

The incidence, prevalence and recidivism rates of YMF, particularly within an

Australian context, remain unknown largely due to the fact that YMF is a highly

variable and complex behaviour. Although existing literature has correlated YMF with

a multitude of factors, the scope of this research has limited analysis to seven of

these variables. The following section will present a theoretical analysis of the

individual level variable of age, the situational level variables of supervision,

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opportunity, and familial disruption, and the societal level variables of socioeconomic

status, ethnic heterogeneity, and residential mobility. The variables age, supervision,

opportunity, and familial disruption will be analysed in accordance with Routine

Activity Theory. This theoretical framework has not been applied to YMF within

existing literature, yet it is deemed, from a criminological perspective, to have the

explanatory power required to analyse delinquency in everyday life (Felson 2008,

75). The variables socioeconomic status, ethnic heterogeneity, and residential

mobility will be considered within the framework of Social Disorganisation Theory.

Although this theory has not been explicitly applied to YMF, its three core elements

have been utilised within existing literature to explain the occurrence of YMF.

Individual Level of Analysis

At an individual level of analysis, YMF can be best explained through the

theoretical framework of Routine Activity Theory (RAT). Cohen and Felson’s (1979)

RAT proposes that it is the routine activities of everyday life which present criminal

opportunities. These criminal opportunities emerge from the convergence in time and

space of a motivated offender, a suitable target, and the absence of a capable

guardian (Cohen and Felson 1979, 588). Although this framework has not been

applied to the study YMF within existing literature, it is deemed relevant where

suspicious fires have been empirically correlated with everyday patterns of activity

influenced by guardian movement and opportunities for misuse of fire. RAT is

therefore a suitable framework which can explain why only some youths engage in

YMF, and why youths misuse fire in only some situations.

RAT can be applied to the individual variable of age, which has been

consistently utilised within existing literature to differentiate between types of YMF. In

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1940, Helen Yarnell became one of the first known researchers to examine misuse

of fire within youth populations in the USA (Stadolnik 2000, 9). Since this time, a

growing international body of research has attempted to distinguish between child

and adolescent misuse of fire. However, given the lack of consensus regarding age

divisions, differentiation within this study will not be based on existing literature.

Instead, age groups will be analysed as they are presented within the datasets made

available for this research. Where Fire and Rescue New South Wales (FRNSW) and

the New South Wales Rural Fire Service (NSWRFS) discern YMF according to the

age groups: 0-5 years, 6-12 years, and 13-16 years, the following analysis will

conform to these categorisations.

Firstly, research suggests an interest in fire typically emerges by the time a

child is 5 years of age (Bowling, Merrick and Omar 2013; Dolan and Stanley 2010;

Lyons, McClelland, and Jordan 2010). Zero to five year olds are thought to be over-

represented due to increased levels of cognitive curiosity and natural childhood

inquisitiveness (Pinsonneault 2002; Stadolnik 2000). Dolan et al. (2011) and Bahr

(2000) propose that 0 – 5 year olds are more likely to set fires in the home, a

proposition supported by Corry (2002, 90) who suggests young children are more

likely to set fires in areas where they sleep or play. Although fire interest at this age

is often portrayed as low risk (NSWFB 2009, 15), recent empirically derived evidence

suggests otherwise. Pinsonneault (2002, 16) proposes that young children are five

times more likely than other age groups to die in fires, one third of which are set by

themselves. Harpur, Boyce, and McConnell (2013, 73) similarly found that children 5

years and under have not yet developed a sense of danger and consequently, are

more likely to become a dwelling fire fatality by fires set by themselves. Furthermore,

the earlier the onset of YMF, the more likely YMF will become persistent, frequent

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and more severe (MacKay et al. 2012, 845). In a pattern which mirrors general

criminological findings, the earlier the onset, the higher the likelihood of recidivism

(Schoonover 2013, 67).

Youths between the ages of 6 and 12 years may still be intrigued by fire and

although their capacity to understand the world expands and cause-and-effect

reasoning develops, it is not sufficient to comprehend the consequences of YMF

(Pinsonneault 2002, 21). YMF may still occur at this age out of curiosity however,

may also arise in response to rejection or disordered coping (Mehregany 1993, 20).

Similarly, Pinsonneault (2002, 24) theorises that YMF at this age may result from a

lack of emotional and cognitive maturity to cope with change or traumatic events.

Regardless of motivation, Corry (2002) and Talbot and Harris (2008) suggest that

older children are more likely to use matches, lighters, or a stove top to ignite

combustible material, either in the home, or in a nearby location. Such YMF,

according to Dolan et al. (2011, 383), occurs predominantly between 1300 and 1900

hours.

According to Stadolnik (2000) and Pinsonneault (2002), 13 – 16 year olds are

over-represented within the YMF population due to increased levels of

experimentation, increased peer influence, a need to be independent and to test

limits and structure. YMF is often portrayed as developmentally appealing to

adolescents due to the delayed maturation of the prefrontal cortex which is

responsible for decision making and risk assessment (Britt, 2011, 16). Studies

conducted by Dolan et al. (2011), Corry (2002) and Schoonover (2013) collectively

suggest that adolescents are more likely to set fires away from home while in

groups, between 2200 and 0100 hours. This research aligns with general

criminological findings which suggest that adolescents who engage in unstructured

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socialising in semi-public or public areas are more likely to commit delinquent acts

than those who engage in unstructured socialising in private areas (Hoeben and

Weerman 2014).

This age differentiation can be explained within the framework of RAT. Firstly,

RAT requires the presence of a motivated offender. According to Cohen and Felson

(1979, 590), a motivated offender must have natural inclination and capacity to

commit crime. Although the concept of natural criminal inclination has been the topic

of considerable debate within criminological literature, it is especially applicable to

the study of YMF. This is because the majority of literature suggests that YMF

occurs due to the natural inquisitiveness of children or the need for adolescents to

experiment. Furthermore, it is not motivation which ultimately determines whether

YMF is committed, but a youth’s perception of the environment and their decision-

making processes (Cozens 2010, 49). Where YMF, by its very definition, refers to all

types of misuse of fire regardless of motivation, it requires a theoretical framework

which assumes motivation exists, but does not require motivational differentiation for

explanation. RAT is therefore a valid framework for the study of YMF.

Furthermore, as described above, existing YMF literature suggests youths

exhibit patterns of misuse of fire particular to their age. According to RAT, these

patterns are facilitated by the physical and social environment (Hollis, Felson and

Welsh 2013, 65). Where opportunity to commit YMF differs according to routine

activity, children are more likely to light fires in the home during the day, while

adolescents are more likely to light fires outside of the home during the evening or

on weekends (Dolan et al 2011, 383). Research performed by Pollack-Nelson et al.,

(2006), Britt (2011), and Harpur, Boyce, and McConnell (2013) similarly reveal that

youths carry out YMF in environments where routine activities facilitate opportunity.

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Pollack-Nelson et al. (2006) conducted a study of 60 American parents of 57 children

aged 6 years and younger, who misuse fire. The study revealed that 44.6% of fires

were lit in a child’s bedroom, while 22.8% were lit in their parent’s bedroom (Pollack-

Nelson 2006, 173). A study of 187 American youths who misuse fire conducted by

Britt (2011, 38) revealed that 5 – 9 year olds lit the majority (53.0%) of their fires

outside. However, of those fires which occurred inside the home, 83.0% were lit in a

bedroom (Britt 2011, 38). Such research suggests that opportunity to commit YMF

may differ according to the routine activities of youths. Where the everyday routines

of a young child differ from those of an older child or adolescent, RAT gives this

phenomenon explanatory power.

Situational Level of Analysis

RAT’s assumption that offender motivation subsists means that the study of

behaviour can move away from an individual level of analysis towards a situational

and environmental level (Hollis, Felson, and Welsh 2013, 66). Situational level

variables can therefore also be explained from a RAT perspective.

RAT posits that a motivated offender must converge in time and space with a

suitable target. For YMF to occur, access to a suitable target refers to access to both

combustible materials and incendiary devices. Putnam and Kirkpatrick (2005, 2)

state that incendiary materials are readily accessible, more so than other tools or

weapons of desire. Kolko (2002, 39) suggests that the greater the degree of access

and exposure to incendiary materials, the higher the likelihood that a youth will

engage in YMF. From a RAT perspective, any accessible and available combustible

material and incendiary device which a youth encounters during their daily activities

can be defined as a suitable target. Harpur, Boyce, and McConnell (2013, 78) found

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that, of the 9 fatal dwelling fires lit by children 5 years and under, 8 (89.0%) occurred

in a household where at least one member smoked. Bahr (2000, 34) similarly

suggests most young children will find ignition sources in easily accessible locations.

As a result, suitable targets will differ according to routine activities. Evidence

supports this proposition where young children are more likely to conduct YMF in or

around the home, against combustible materials such as clothing or toys when

indoors, and leaves or paper when outdoors (Dolan et al 2011, 383). Meanwhile,

older children and adolescents are more likely to commit YMF away from the home,

against leaves or bushes when outdoors, and to vandalise or damage property when

indoors (Dolan et al. 2011, 383). Mehregany (1993, 20) purports that this age

differentiation results from an interactionist effect between individual development

and environmental influences; a proposition supported by RAT. Suitable targets for

YMF are therefore those combustible materials and incendiary devices which

converge in time and space with a motivated offender.

The final core element of RAT is the absence of capable guardianship. A

capable guardian refers to a human element who, through mere presence, makes

crime less likely (Hollis, Felson and Welsh 2013, 66). Capable guardianship is often

referred to within YMF and juvenile delinquency literature as supervision, or direct

guardian monitoring and control of youth behaviour (Jang and Smith 1997, 307).

From a criminological perspective, supervision is a form of familial influence which

acts as a protective factor against antisocial behaviour (Aseltine 1995; Britt, 2011;

Dolan et al. 2011). Accordingly, it has been consistently negatively correlated with

YMF (Barreto et al. 2004; Doherty 2002; Muller and Stebbins 2007; NSWFB 2009).

Although RAT states that effective guardianship requires a controller who is

available and able to monitor the situation (Hollis, Felson and Welsh 2013, 72),

15

researchers such as Pollack-Nelson et al. (2006) suggest mere supervision may not

be sufficient to deter YMF. Pollack-Nelson et al.’s (2006) study revealed that parental

presence did not inhibit YMF and that children actively seek incendiary materials and

covert locations in which to engage in YMF. Of the 60 parents studied, 84.2%

reported being inside the home at the time their child lit a fire, while parents deemed

the home to be the safest place to leave a child without direct supervision (Pollack-

Nelson et al. 2006, 173). Similarly, Harpur, Boyce, and McConnell (2013, 78) found

that, of the 9 fatal dwelling fires lit by children 5 years and under, 8 (88.9%) incidents

occurred when a parent was home, and 3 (33.3%) occurred when the parent was in

the same room. Such research suggests the mere presence of an adult may not be

sufficient to deter YMF.

The study of YMF may therefore require a more comprehensive analysis of

RAT’s guardian-offender relationship. According to Smith (1970), the mere presence

of a parental figure is not sufficient to deter delinquency. Instead, parents must

possess power perceived by their child as legitimate, referent, expert, and able to

reward and punish behaviour appropriately (Smith 1970, 862). This practice of

parental power, according to Ary et al. (1999, 226), relies on adequate parental

monitoring. While a large body of literature suggests parental monitoring is

negatively correlated with antisocial behaviour, Fletcher, Steinberg, and Williams-

Wheeler (2004) suggest it is parental knowledge which is most effective. Where

parental monitoring involves direct supervision, parental knowledge involves active

solicitation of information regarding child activities, a high level of control over

behaviour, and high child disclosure (Fletcher, Steinberg, and Williams-Wheeler

2004, 786). Collectively, these studies suggest that the mere presence of an adult

may not be sufficient to deter YMF. Instead, parental power and child dependency

16

on that power, coupled with parental knowledge, may be central to the prevention of

YMF.

RAT, specifically the absence of capable guardianship, can also be applied to

explain the link existing literature draws between familial disruption and YMF.

Familial disruption refers to any familial structure which is non-traditional and non-

nuclear, where a youth does not reside with both biological parents (Kierkus and

Hewitt 2009, 124). A large body of research suggests that familial disruption is

criminogenic (Kierkus and Baer 2003, 406). Ward (2005, 104) conducted a

qualitative study on three adults who committed acts of YMF in childhood, and found

all three felt disconnected or isolated from their families. A study of 111 randomly

selected 2009 NSW Children’s Court criminal cases revealed that more than a third

involved children in out-of-home care, where the most common offence was

malicious damage to property belonging to the care home in which they were

residing (Cashmore 2011, 35). Despite this evidence, there are very few empirical

studies of YMF within child welfare populations. One such study, conducted by

Lyons, McClelland, and Jordan (2010, 723) in the USA, found that, of the 4,155

youths who had been taken into state care, only 1.4% had a history of YMF. Other

researchers postulate that the correlation between familial disruption and YMF may

be spurious. Lambie and Randell (2011, 311) state that familial disruption is

correlated with antisocial behaviour in general and may not be a risk factor specific

to YMF.

Nevertheless, RAT sheds light on the link which may exist between familial

disruption and YMF. Specifically, familial disruption may be correlated with an

absence of capable guardianship, where protective factors such as supervision,

parental attachment, and communication, are less likely to exist when children reside

17

with only one parent, a step-parent, or in state care (Kierkus and Hewitt 2009, 124).

Additionally, familial disruption has been correlated with increased contact with

delinquent peers, antisocial behaviour, and delinquent opportunities (Kierkus and

Hewitt 2009, 124). Where familial disruption impedes the development of parent-

child relationships and parental knowledge, this lack of parental influence may

generate correlations between poor parental supervision and YMF. RAT may

therefore be applied to explain how a lack of supervision generated by inadequate

parental power, a lack of child dependency on that power, and a lack of parental

knowledge may lead to YMF within intact families. Similarly, familial disruption may

impede parental attachment, supervision, and communication, producing conditions

conducive to YMF within families which are not intact.

Finally, RAT may also explain delinquency at an aggregate level. Both Ary et

al. (1999, 226) and Osgood and Anderson (2004, 525) propose that a lack of

parental supervision, or an absence of capable guardianship, may lead to

unstructured socialising. Cozens and Christensen (2011, 124) suggest that

motivated offenders who are not engaged in structured socialising are more likely to

discover vulnerable targets for YMF during their daily activities. Where geographical

areas experience a lack of capable guardianship and high rates of unstructured

socialising, opportunities for delinquency, and thus YMF, are increased (Osgood and

Anderson 2004, 525). RAT therefore provides a theoretical framework which can

explain the relationship between routine activities and YMF, while providing evidence

which aligns with the following societal level analysis.

18

Societal Level of Analysis

From a societal level of analysis, existing literature frequently associates three

particular variables with higher rates of YMF and delinquency in general. These

include low socioeconomic status (SES), high levels of ethnic heterogeneity, and

high levels of residential mobility. These three variables are central to the theoretical

framework of Social Disorganisation Theory (SDT). SDT proposes that environments

characterised by low SES, ethnic heterogeneity and residential mobility are more

favourable to delinquency due to conditions which generate the destruction of

community social organisation (Bernard, Snipes, and Gerould 2010, 136). Over time,

this social disorganisation generates the development of delinquent subcultures

which are eventually supported by shared values and norms within the

neighbourhood (Bernard, Snipes, and Gerould 2010, 141). These shared values and

norms are transmitted from adolescents to younger children during unstructured

socialising, generating stable delinquency rates despite population turnover

(Bernard, Snipes, and Gerould 2010, 138).

Existing literature provides support for SDT’s proposition where empirical

evidence links YMF with the three main explanatory variables (Corcoran et al. 2007;

Corcoran et al. 2012; Law and Quick 2013). The first, SES, is a relative measure of

the economic and social conditions of people (ABS 2013a). Areas characterised by

low SES have been correlated with a higher incidence of fire (Corcoran et al. 2007;

Corcoran et al. 2012; Corcoran et al. 2011; Drabsch 2003; Gannon 2010: Law and

Quick 2013). Corcoran et al. (2012) state that youths who misuse fire are more likely

to experience disadvantage than those who do not, a pattern indicative of the

relationship between SES and offending generally. Furthermore, low SES has been

found to impact upon identification of fire risk (Harpur, Boyce, and McConnell 2013,

19

80), and the implementation of informal social controls such as parental supervision

and familial stability (Cunneen and White 2011, 140). However, not all research

supports this correlation. Britt (2011, 43) found no significant correlation between

household income, employment status or housing type and YMF.

Research also suggests that YMF is spatially clustered within geographical

locations characterised by high ethnic heterogeneity (Corcoran et al. 2007; Corcoran

et al. 2012; Law and Quick 2013). Ethnic heterogeneity refers to the level of ethnic

diversity within a given area (Law and Quick 2013, 90). An Australian study

conducted by Corcoran et al. (2012, 55) found a positive correlation between

suspicious fire incidents and a high proportion of immigrants within given

geographical areas. A high level of ethnic heterogeneity is not, however, reflected

within all YMF research. Williams (2005, 13) and Prins (1994, 80) state that YMF

within the USA is predominantly practiced by Caucasian males. In a study of 187

American youths who misused fire, Britt (2011, 36) found 47.6% were white, a higher

proportion than that within the general population (43.2%). Edwards and Grace

(2013, 4) similarly found 805 (64.4%) persons within their sample of 1,250 New

Zealand arsonists to be Caucasian.

Existing research also reveals some support for SDT where correlations have

been drawn between YMF and residential mobility (Corcoran et al. 2007; Corcoran et

al. 2012; Law and Quick 2013). Residential mobility is a measure of the percentage

of people within a given area who have moved within a specified timeframe (Law and

Quick 2013, 94). Law and Quick (2013) conducted an analysis of youth offending in

specific geographical areas within Canada, and found that the spatial distribution of

youth offenders was significantly correlated with residential mobility measured at one

year intervals. Similarly, Porter and Vogel (2014, 188) propose that USA based

20

adolescents who are residentially mobile are more likely to commit delinquent acts

than those whose residency is stable. However, when controlling for other

background factors, Porter and Vogel (2014, 188) found no differences in

delinquency between mobile and non-mobile adolescents. Instead, the researchers

suggest that observed differences in delinquency between mobile and non-mobile

adolescents may be attributed to background characteristics which increase both the

likelihood of delinquency and the likelihood of moving (Porter and Vogel 2014, 188).

This proposition aligns with SDT which suggests areas characterised by rapid

population shifts become impaired due to an absence of natural organisation and are

thus more likely to experience delinquency and other social problems (Bernard,

Snipes, and Gerould 2010, 138-139).

The relationship between SES, ethnic heterogeneity, residential mobility and

YMF may therefore be far more complex than YMF literature suggests. SDT

proposes that there is an interrelationship between its three explanatory variables.

Specifically, SDT theorises that delinquency levels in an area characterised by low

SES would remain stable despite ethnic changes in the population (Bernard, Snipes,

and Gerould 2010, 139). Xie and McDowell (2010, 885-886) found support for this

theory where a direct correlation between location of crime and housing transitions

was identified. Xie and McDowell (2010) found that racial inequality in access to

housing led to a high population of ethnic minorities within high crime

neighbourhoods. According to SDT, these structural inequalities negatively impact

upon social networks and informal social controls, leading to the intergenerational

propagation of delinquency (Cantillon, Davidson & Schweitzer 2003, 322).

Despite the prominent application of SDT within criminological literature, the

empirical association between delinquency and social disorganisation remains

21

unclear. Williams (2005, 32) purports that the evidence which links delinquency with

lower SES may reflect the higher rates of police contact, apprehension and

subsequent intervention within disadvantaged communities. Furthermore, most

empirical studies correlate social disorganisation with offence data rather than

offender data, providing an insight into the environment in which crime occurs, rather

than the environment in which offenders live (Law and Quick 2013, 91). The link

between delinquency rates and social disorganisation may therefore be spurious.

Responses to YMF

Due to the complex and multivariate nature of YMF, there does not exist a

widely accepted or empirically valid diagnostic tool, or form of management, which

has proven effective and all-encompassing (Prins 1994, 67; Stadolnik 2000, 56).

Where intervention programs do exist, they largely target youths at an individual

level, focussing on education and cognitive behavioural therapy (Corcoran et al.

2012, 13). Most of these intervention programs respond to the offence rather than to

the offender (Caudill et al. 2012, 310), meaning that many of the criminogenic needs

of youths who misuse fire are overlooked. In a study of juvenile justice system

responses to YMF individuals, Caudill et al. (2012, 310) found that YMF individuals

received less treatment-oriented programs and less supervision than non-YMF

individuals. Yet, an analysis of existing research suggests YMF individuals require

therapeutic treatment in order to address the multitude of criminogenic needs

correlated with their behaviour (Caudill et al. 2012, 318).

Within Australia, the only type of formal YMF intervention available is fire

education (Fritzon et al. 2011, 396-397). Fire education programs do not require

referral from the criminal justice system (Muller and Stebbins, 2007, 3), and as a

22

result, have the potential to target all forms of YMF. Although available in each state

and territory, Fritzon et al. (2011, 405) describes this as a one size fits all approach

which fails to respond to individual level differences. Nevertheless, educational

interventions are employed nationally and internationally to reduce the fire-specific

risk factors which arise due to basic curiosity, low levels of fire-specific education,

and low levels of fear associated with fire (Lambie and Randell 2011, 320).

The main YMF intervention program operated within NSW is the Juvenile

Intervention and Fire Awareness Program (IFAP). IFAP has been established in

accordance with s6(1) Fire Brigades Act 1989 (NSW) which states that it is the duty

of the Commissioner to take all practical measures for the prevention of fire. Its aim

is to “reduce the tragedy and trauma caused by child and youth fire related activities”

(FRNSW 2014, 42). IFAP provides YMF intervention services to clients referred by

both FRNSW and the NSWRFS. It involves a three-tiered system consisting of

indirect intervention via telephone interview and resource kit, direct intervention via a

face-to-face interview, and referral to specialist agencies where required (FRNSW

2014, 22). Primarily, IFAP relies upon firefighter-parent/guardian communication,

where the onus is placed on educating both the parent/guardian and the youth about

the importance of fire safety (NSWFB 2009, 24). Such mechanisms are designed to

target fire-specific risk factors such as access to incendiary materials and low levels

of parental supervision. FRNSW predicted that IFAP would receive around 400

referrals per year, which would increase as the program became more widely

branded (FRNSW 2014, 22).

Although the theoretical underpinnings of IFAP were derived from literature

which existed at the time of its inception in 1990, it is somewhat limited when

analysed in light of contemporary literature. Recent research suggests that only

23

28.0% of parents are aware of their child’s YMF behaviour (MacKay et al. 2012,

843). Furthermore, parents tend to underestimate the likelihood that their child will

misuse fire. In a study conducted by Pollack-Nelson et al. (2006, 175), 61.4% of the

60 parents questioned thought that their child did not know how to ignite matches or

a lighter, while 70.2% thought that their child knew of the dangers of playing with fire.

Similarly, in a study of 187 youths who misuse fire, Britt (2011, 40) revealed that

77.0% of parents believed their children knew of the risks associated with fire, 82.0%

of parents assumed their child had acquired fire knowledge at school, and 90.0%

were surprised their child had played with fire. The problems associated with

fallacious assumptions regarding fire-specific knowledge and education are

exacerbated by the fact that YMF requires little strength, few resources, and modest

forethought (Prins 1994, 57). IFAP is therefore limited to those parents/guardians

who are aware of their child’s lack of fire education, the risk of YMF occurring, and

the means through which these risks can be addressed.

Furthermore, recent evidence suggests low SES and familial disruption are

correlated with YMF (Corcoran et al. 2012; Ward 2005). Where IFAP relies upon

referral by, and subsequent participation of, a parent/guardian, it is limited to those

parents/guardians who have a vested interest in their child, an awareness of their

child’s behaviour, and the knowledge and resources through which to seek

assistance. However, as Cunneen and White (2011, 140) state, social and economic

differences in resources impact upon the ability of parents/guardians to not only

regulate their child’s behaviour, but to recognise bad behaviour as a serious risk

which needs addressing. In their study of fatal dwelling fires lit by young children,

Harpur, Boyce, and McConnell (2013, 80) found that risk posed by fire interest was

ignored more often when living conditions were poor. Although IFAP has recognised

24

the need to address familial disruption and disadvantage in order to reduce YMF, the

program relies upon the dissemination of information to educate parents about the

effect parental behaviour can have on YMF. Where disadvantaged

parents/guardians do not have the resources to respond to YMF through

conventional avenues, IFAP may be inaccessible to this subset of the population.

Finally, despite the fact FRNSW generates IFAP data for internal review, no

independent empirical evaluation of the program has been published to date. Kolko

(2002, 43) suggests this lack of program evaluation is common, and that critical

content and skills must be reviewed, consistency of program delivery must be

analysed, and that outcome evaluation is required. There is thus an urgent need to

empirically evaluate IFAP.

Conclusion

An analysis of existing literature pertaining to the scope and magnitude of

YMF, theoretical perspectives of YMF, and responses to YMF, has revealed a large

body of research. However, this research is limited in scope, meaning the incidence,

prevalence and recidivism rates of YMF within NSW remain unknown. Furthermore,

much of the research produced lacks generalisability, and therefore may not be

contextually applicable to the YMF population of NSW. Moreover, YMF intervention

within NSW is yet to undergo independent empirical evaluation, and as a result, the

effectiveness and suitability of IFAP is undetermined. In order to partially fill these

voids, secondary data analysis of NSW fire brigade data has been performed. The

methodological approach taken has been outlined in the following chapter.

25

CHAPTER THREE:METHODOLOGY

This chapter details the methodological approach taken to partially fill the

gaps within existing literature. Commencing with a methodological review of past

studies, this chapter goes on to describe the aims, research questions, and

hypotheses employed within this research. Thereafter, the research design,

participants, measures, and procedures utilised within this study are reported. The

ethical considerations which have governed this research are then described, as are

the limitations of this study.

Methodological Review of Past Studies

The methodology engaged within this research has been governed by the

same factors which have shaped YMF research historically. This is because much of

the difficulty pertaining to YMF research arises from the challenge associated with

accessing youth populations. Ethical constraints limit the ability to collect primary

data by way of interviews, questionnaires and observations, due to the obtrusive

nature of these approaches. Consequently, most research must rely upon literature

reviews, secondary data analysis, retrospective studies, or primary data collection

from third parties or special populations. Despite the efforts of researchers to date,

such constraints mean existing literature remains ungeneralisable to the YMF

population (Williams 2005, 150).

YMF has been explored predominantly from a theoretical perspective (Brett

2004; Dolan et al. 2011; Doley 2003; Flynn 2009; Horley and Bowlby 2011; Johnson,

Beckenbach, and Kilbourne 2013; Kocsis 2002; Lowenstein 2003; MacKay et al.

2012; Mehregany 1993; Willis 2004), where literature reviews form the foundation

26

upon which inferences regarding YMF are drawn. Although such research has

produced a number of explanatory theories of YMF, little empirical consensus exists

in support of these theoretical propositions (Merrick, Bowling, and Omar 2013).

Without empirical evaluation, the replication of such theories risks the propagation of

faulty generalisations, impeding the effectiveness of prevention and intervention

programs (Brett 2004; Doley 2003; Stadolnik 2000; Stanley 2010; Williams 2005).

There also exists a large body of literature which utilises secondary data

analysis for the study of YMF. This research method appears the most prevalent due

to the inherent limitations involved in gaining direct access to youth populations.

Secondary data analysis within YMF research has involved analysis of data derived

from the following sources: coronial courts in Northern Ireland (Harpur, Boyce, and

McConnell 2013), criminal courts in the USA, Australia and the UK (Caudill et al.

2012; Ducat, McEwan, and Ogloff 2013; Jayaraman and Frazer 2006), New Zealand

and Canadian police forces (Lambie et al. 2013; Law and Quick 2013), family

services in the USA (Lyons, McClelland, and Jordan 2010), Australian and Finnish

psychiatric registers (Ducat, Ogloff, and McEwan 2013; Repo and Virkkunen, 1997),

fire brigades in Australia, South Wales and Northern Ireland (Bryant 2008; Corcoran

et al. 2012; Corcoran et al. 2011; Corcoran et al. 2007; Harpur, Boyce, and

McConnell 2013), fire brigade intervention programs in Australia, the USA and New

Zealand (Bahr 2000; Britt 2011; Lambie et al. 2013), and Australian parks and

wildlife services (Bryant 2008). Despite the sizeable contribution such research has

made to the study of YMF, secondary data is usually derived from databases

pertaining to special populations, such as youths who have been apprehended

and/or referred for intervention for YMF. Where evidence suggests only a small

proportion of youths who misuse fire come to the attention of authorities (Corcoran et

27

al. 2007; Hardesty and Gayton 2002; Kolko et al. 2002; MacKay et al. 2012), such

research may not be generalisable to the broader YMF population. Furthermore,

databases are often compiled for non-research purposes and consequently, limit the

statistical analyses which can be performed.

To avoid the problems associated with secondary data analysis, several YMF

researchers have conducted primary data analysis. Where secondary data collection

relies upon research or data collected for other purposes, primary data collection

methods are specifically designed by the researcher to elucidate particular

information (Alder and Clarke 2011, 328). However, where access to youths is

restricted, researchers have collected primary data from third parties or retrospective

surveys. One Australian based study, conducted by Bahr (2000), utilised secondary

and primary data collection by reviewing historic youth fire intervention interview

transcripts while also conducting interviews with fire intervention officers. Other

researchers, such as Harpur, Boyce, and McConnell (2013) and Pollack-Nelson et

al. (2006), relied upon third party primary data collection by interviewing parents of

youths who had misused fire. Although these studies provide an insight into youth

behaviour without breaching ethical considerations, inferences can only be drawn

about the populations studied, rather than the YMF population itself (Bahr 2000, 31).

Another commonly employed methodology is the study of adults who retrospectively

report YMF in childhood (Gannon and Barrowcliffe 2012; Ward 2005). Although self-

report data obtained via retrospective studies provides an important insight into the

dark figure of YMF, such studies are limited by an inability to rely heavily on data

derived retrospectively due to biases caused by memory distortion (Gannon and

Barrowcliffe 2012; Ward 2005).

28

Many of these problems have been avoided, and some of the greatest

contributions to YMF literature have been made, from the collection of primary data

from various sub-samples of youths who misuse fire. Del Bove (2005, 70) conducted

a study of 240 youths between the ages of 4 and 17 years who had been referred to

The Arson Prevention Program for Children (TAPP-C) in Canada. Primary data was

retrieved from semi-structured, comprehensive and follow up interviews with both the

youths and their parents/carers. While this study was the first to empirically classify

YMF sub-types, it was limited to those youths who had been referred to formal

intervention. Del Bove’s findings may therefore not be generalisable to the YMF

population more broadly (Del Bove 2005, 132). Nevertheless, Root et al. (2008)

similarly collected primary data from the Canadian TAPP-C population which, in

conjunction with Del Bove’s findings, have significantly enhanced YMF classification

efforts.

Collectively, these different methodological approaches have generated

substantive empirical evidence pertaining to YMF. However, differences lie not only

in the data collection process, but also in the way in which data is analysed.

Empirical evidence has been gathered via a multitude of statistical analyses

performed to ascertain whether significant relationships or associations exist

between variables. One of the most readily employed forms of statistical analyses

within YMF research is descriptive statistics. Utilised by researchers such Bryant

(2008), Harpur, Boyce, and McConnell (2013), Jayaraman and Frazer (2006), and

Pollack-Nelson et al. (2006), descriptive statistics provide a description of the

research sample by utilising frequency data to conduct univariate analyses. Although

powerful, such analyses are restricted to examining measures of central tendency

and frequency distributions (Alder and Clark 2011, 416-421). Consequently, these

29

studies present findings which are only significant to the samples themselves, and

cannot be employed to draw inferences about the broader population.

While most studies incorporate descriptive statistics within their research,

many also utilise inferential statistics. Such techniques allow researchers to draw

conclusions about the population of interest from the sample studied (Gray 2009,

139). While some researchers have analysed the differences between samples

(Ducat, McEwan, and Ogloff 2013; Ducat, Ogloff, and McEwan 2013; Gannon and

Barrowcliffe 2012; Lambie et al. 2013), others have measured the association

between variables (Ducat, McEwan, and Ogloff 2013; Ducat, Ogloff, and McEwan

2013; Gannon and Barrowcliffe 2012; Repo and Virkkunen 1997; Root et al. 2008).

Predictive models have also been employed (Caudill et al. 2012; Lambie et al. 2013;

Root et al. 2008), while Del Bove (2005) notably employed two step cluster analysis

to empirically discern between sub-groups of youths who misuse fire. Such statistical

analyses have provided a means through which inferences can be drawn about the

broader YMF population. However, many of these inferences are limited to special

populations, such as youths referred to formal intervention, the criminal justice

system or psychiatric assessment.

Despite the empirical evidence which has arisen from these many and varied

studies, the inherent limitations within the research designs and/or the statistical

analyses employed mean most lack generalisability. Furthermore, only two studies,

conducted by Bryant (2008) and Muller (2008), specifically relate to the YMF

population of NSW. Despite providing valuable information regarding the scope of

YMF within NSW, Bryant (2008) studied vegetation fires lit between 1995 and 2004,

while Muller’s (2008) study utilised official arson data collected between 2001 and

2006. Consequently, these studies do not provide an up-to-date analysis nor data

30

pertaining to YMF more broadly. Given the lack of generalisability of existing studies

and the absence of contemporary contextually-specific research, a study into the

YMF population of NSW is timely.

Aims, Research Questions and Hypotheses

This research thus aimed to partially fill the theoretical and empirical voids

which exist within YMF literature. The purpose of this research was to answer the

following research questions;

1. What is the scope and magnitude of YMF within NSW?

2. Are the individual, situational and societal level variables, and temporal

patterns, identified within the recorded YMF population of NSW

representative of the theoretical propositions presented within existing

literature?

3. Do the societal level variables and temporal patterns associated with

YMF intervention reflect the societal level variables and temporal

patterns associated with YMF within NSW?

Based on the findings presented within the literature review, the following

hypotheses have been devised. Firstly, it was predicted that the prevalence and

incidence rates of YMF within NSW would be significant, exceeding rates of arson.

Furthermore, costs associated with YMF were predicted to account for a significant

proportion of all fire-related costs, while the youngest group was hypothesised to

generate the greatest proportion of all costs. It was also hypothesised that the

individual, situational and societal level variables, and temporal patterns, identified

within the recorded YMF population of NSW would empirically support the theoretical

propositions made within existing literature. Finally, it was predicted that the societal

31

level variables and temporal patterns associated with YMF intervention would reflect

the societal level variables and temporal patterns associated with YMF within NSW.

Research Design

Accordingly, the research design involved hypothesis testing, where research

questions and hypotheses were derived from existing literature. In order to test these

hypotheses, quantitative secondary data analysis of YMF behaviour recorded by Fire

and Rescue New South Wales (FRNSW) and the New South Wales Rural Fire

Service (NSWRFS) has been performed. FRNSW and NSWRFS datasets were

utilised as these agencies are the primary combat agencies for fire in NSW, as

legislated by the State Emergency and Rescue Management Act 1989 (NSW). This

quantitative approach was deemed the most suitable because, as previously

examined, there is a substantial body of YMF literature which presents theoretical

propositions, and therefore hypotheses, which can be empirically evaluated by

analysing available data.

The research design derives from a post-positivist perspective, a paradigm

which increasingly underpins empirical inquiry (Clarke 1998, 1245). Post-positivism

does not assume the same level of objectivity and generalisability of results as

positivism (Charney 1996, 578). Instead, it suggests that empirically derived results

are context-dependent and aid the development of intersubjectivity (Charney 1996,

578-588). This study therefore conducts context-specific research to garner context-

dependent results, an approach which permits generalisability issues to be

overcome. Although conclusions drawn within this context have been compared to

results published within existing literature, all results are considered significant within

NSW only. Such reasoning aims to achieve intersubjectivity, where authority of

32

findings are obtained through the replication of research conducted in similar

situations in the aim of producing similar results (Charney 1996, 588).

In order for context-specific research to be conducted, this research requires

empirical inquiry into the YMF population of NSW. Despite the limitations identified

within existing YMF studies, secondary data analysis was deemed the most

appropriate research design through which to conduct such an inquiry. This

suitability derives primarily from the fact that secondary data analysis is unobtrusive

(Gray 2009, 497), meaning that data could be collected on youths who misuse fire

with negligible risk. The nature of this study also required research which was

minimal in cost, time, and other resources, meaning secondary data analysis was

the most feasible option (Gray 2009, 497). Finally, the available data provided

information on all fire brigade reports of youths who misuse fire, regardless of the

need to define severity or intent. It was therefore ideal for the study of YMF.

Secondary data analysis involves a reliance upon available datasets, and it

was these datasets which provided the foundation upon which the research design

was devised. Where the datasets restricted access to intact groups, the research

design was non-experimental. There were two intact groups prescribed within the

datasets, one which clustered participants according to Ignition Factor, and the other

which clustered participants according to Suburb. Consequently, the research design

involved two levels of analyses, as described below.

Participants

Ignition Factor Unit of Analysis

The unit of analysis within the first part of this study is ignition factor. Ignition

factor refers to “the circumstances which permitted the heat source and combustible

33

material to combine to start the fire” (NSWFB 1998, sec. 9, 9). Subjects, grouped by

ignition factor, were drawn from the official incident reporting systems of FRNSW’s

Australian Incident Reporting System (AIRS) and the NSWRFS’s Fire Incident

Reporting System (FIRS).

Both AIRS and FIRS are Windows-based computer programs designed to

capture incident related information for the creation of incident reports (NSWFB

2007, 1; NSWRFS n.d., sec. 1, 1). Both databases contain information automatically

captured from emergency triple zero calls and that which is submitted by the

Reporting Officer (RO) at the completion of the incident. The RO for AIRS can be

any firefighter or Officer who first arrived at the incident scene, however is usually

the Station Officer of the first arriving crew (NSWFB 2007, 2). The FIRS RO is the

Officer in Charge of the incident (NSWRFS n.d., sec. 1, 1). Although it is the RO’s

duty to ensure that the information entered into AIRS and FIRS is correct, both

reporting tools have inbuilt validation processes which ensure that all mandatory

fields have been completed (NSWFB 1997, 37; NSWRFS n.d., sec. 3, 4).

Ignition factor participants have been accessed via convenience sampling,

where all reported fire incidents were accessible within the AIRS and FIRS datasets.

Participants within this sample include every fire incident within NSW caused by a

youth between the ages of 0 - 16 years between July 2004 and June 2014, where

this fire was attended to, and recorded by, FRNSW or the NSWRFS. This timeframe

was chosen to provide for both a large sample size and a 10 year analysis of YMF,

backdated from the end of the most recent financial year. AIRS contained 25,369

participants while FIRS contained 1,011 participants, creating a sample size of

26,380 participants.

34

Suburb Unit of Analysis

The unit of analysis within the second part of this study is Suburb. The

Geographical Names Board of New South Wales (2013, 1) defines a suburb as a

geographical division which has defined limits. The Australian Bureau of Statistics

(ABS) 2011 Census of Population and Housing aggregates data at the suburb level

(ABS 2011a, para. 22). AIRS, FIRS and IFAP datasets have also aggregated data at

the suburb level.

IFAP data was drawn from the FRNSW Community Activity Reporting System

(CARS). CARS is designed to collect information relating to FRNSW community

activities and programs which address specific community risks (NSWFB n.d., 2).

Each CARS report relates to one specific activity carried out by operational

personnel at one specific location (NSWFB n.d., 35). IFAP activities are recorded

within CARS.

Suburb participants were drawn from the ABS 2011 Census via convenience

sampling. This non-probability sampling method included all suburbs within NSW

and all relevant societal level variables as recorded by the Census on the 9th August,

2011. Although the Census aims to include all people within Australia on Census

night, the non-response rate for NSW was 3.6% (ABS 2013). Nevertheless, all 2,626

suburbs within NSW as at 9th August, 2011 have been included in the sample.

IFAP participants were also selected via convenience sampling, a non-

probability sampling method which utilised the entire population of IFAP activities as

recorded within the CARS database. IFAP activities involve youths between the ages

of 0 - 16 years, who have been referred to, completed, and had their participation

recorded by CARS between May 2005 and August 2014, inclusive. This timeframe

reflects the earliest recorded IFAP activity and the time at which the data was

35

collected. Given the scope of this study was to investigate the YMF population of

NSW, the IFAP activities counted within this sample included activities conducted

within NSW only. The IFAP sample included 395 participants.

Measures

The following variables are both mutually exclusive (where each variable can

only be classified within one category) and exhaustive (where the levels within each

variable provide for the classification of every case) (Alder and Clarke 2011, 145).

AIRS, FIRS and CARS datasets are devised so that RO’s must complete each report

by selecting variables from drop-down menus. Where only one level within each

variable can be selected, each variable and each level relates to a different

phenomenon. This process ensured the variables and their levels were mutually

exclusive. Furthermore, although there were many levels of variables to choose

from, data collection involved collation of these levels according to broad

categorisations as defined within the FRNSW AIRS Reference Manual (NSWFB

1998). All levels which existed as single events, or listed as unknown, undetermined,

or other, were collated into an ‘other’ category. Consequently, all variables and their

levels were exhaustive. These variables and their levels include the following:

Individual Level Variables

The individual level variable of age, as identified within YMF literature, was

operationalised by the categorical variable ignition factor, on a nominal scale. Ignition

factor refers to the cause of the fire. This variable includes the following levels: 0-5

years, 6-12 years, 13-16 years, and youth, age undetermined. Age was analysed at

the ignition factor unit of analysis, and was the focus variable of the research.

36

Situational Level Variables

The situational level variables identified within the datasets were all categorical and

nominal in nature, representing the contingency variables of the study. The variables

and their associated levels were analysed at the ignition factor unit of analysis, and

included the following: The variable day refers to the day of the week when the fire

brigade was first notified of the incident (NSWFB 1998, sec. 1, 9). The variable time

refers to the time when the brigade was first notified of the incident, analysed at hourly

intervals (NSWFB 1998, sec. 1, 10). The variable type of fire refers to the type of

incident as reported by the RO after arrival at the scene (NSWFB 1998, sec. 1, 13).

This variable has seven levels; building, special structure, mobile property, rubbish,

storage, vegetation, other. The variable type of property refers to the main function of

the property at the time of the incident (NSWFB 1998, sec. 1, 59). This variable has

seven levels; residential, recreational, institutional, commercial, public, storage, other.

The variable type of owner refers to the type of owner of the property, and includes

seven levels; private, Local Government, State Government, Commonwealth

Government, Department of Health, Housing and Community Services, Indigenous,

other. The variable area of origin refers to the area within a property where the fire

originated as defined by its use at the time of the fire (NSWFB 1998, sec. 9, 2). This

variable has eight levels; interior living, exterior living, sleeping, rubbish,

transportation, commercial, public, other. The variable form of heat ignition refers to

the form of heat energy which caused the ignition (NSWFB 1998, sec. 9, 6). The seven

levels include; matches/lighters, smoker’s materials, open flame, heat/hot object,

electrical equipment, fuelled equipment, other. The variable form of material ignited

first refers to the form of the material ignited first by the heat source (NSWFB 1998,

sec. 9, 17). The seven levels include; structural, furniture/wares, apparel/linen,

37

recreational, rubbish, vegetation, other. The variable alarm source refers to the person

or agency responsible for notifying the brigade of the incident (NSWFB 1998, sec. 1,

33). This variable has seven levels; occupier, passer-by, fire, police, ambulance,

automatic, other. The variable incident outcome refers to the human costs associated

with the incident as well as the actions taken to render the incident site safe. This

variable has four levels; injuries, fatalities, rescues, and evacuations. Injuries and

fatalities include non-firefighting injuries and fatalities which are attributed to, or due to

the handling of, the incident (NSWFB 1998, sec. 7, 2). Rescue includes the number of

people who were trapped and who required extrication, release or removal as a result

of the incident (NSWFB 1998, sec. 7, 5). Evacuations include the number of people

removed from an area due to the hazards presented by the incident (NSWFB 1998,

sec. 7, 6). Finally, the variable dollar loss refers to the estimated monetary value of the

damage caused to property and contents due to the incident and firefighting operations

(NSWFB 1998, sec. 12, 1). This variable has five levels; below $999, $1,000 - $9,999,

$10,000 - $99,999, above $100,000, unknown.

Societal Level Variables

Societal level variables were drawn from the ABS 2011 Census at the suburb

level, and are discrete in nature. Their definitions and levels include the following:

The variable population refers to the usual resident population of the suburb (ABS

2011a). Data has been collated into five levels; total population, 0-5 year population,

6-12 year population, 13-16 year population, total youth (0-16 years) population. The

variable SEIFA refers to the socioeconomic index for areas, a relative measure of

the economic and social conditions of people (ABS 2013a). It is recorded at a

continuous level. Indigeneity refers to the Indigenous origins of the respondent (ABS

38

2011a). Data has been collated into four levels; non-Indigenous, Aboriginal, Torres

Strait Islander, Aboriginal and Torres Strait Islander. Birthplace of person refers to

the country of birth of the respondent (ABS, 2011a). Data has been collated into two

levels; Australian, not Australian. Birthplace of parents refers to the country of birth of

both the respondent’s male parent and female parent (ABS 2011a). Data has been

collated into three levels; both Australia, one Australia, both overseas. The variable

citizenship records the country of citizenship of the respondent (ABS 2011a) and has

been collated into two levels; Australian, not-Australian. Ancestry refers to the ethnic

background of the respondent, based on the respondent’s first and second

responses (ABS 2011a). Data has been collated into four levels; first response

Australian, first response not Australian, second response Australia, second

response not Australian. Residential mobility refers to the usual address of the

respondent both one year ago and five years ago, compared to the time of the 2011

Census (ABS 2011a). Data has been collated into four levels; same one year ago,

different one year ago, same five years ago, different five years ago. Tenure type

indicates whether the respondent owns or rents the dwelling in which they were

enumerated (ABS 2011a). Data has been collated into three levels; owned, rented,

other. Landlord type refers to the type of landlord responsible for rented dwellings

(ABS 2011a). Data has been collated into four levels; real estate, housing

commission, housing co-operative, other. Familial structure refers to the structure of

the family and has been collated into three levels; one parent, two parent, other.

Family type refers to the type of family based on the child-parent relationship (ABS

2011a). Data has been collated into four levels; intact, step, blended, other. Child

type identifies children according to their child-parent relationship (ABS 2011a). Data

has been collated into four levels; natural/adopted, step, foster, other.

39

The Operationalisation of Theoretical Concepts

According to Gray (2009, 14), deductive research requires the

operationalisation of concepts in order to empirically measure theoretical notions. In

order to empirically evaluate those concepts identified within YMF literature, the

variables correlated with YMF within existing literature have been operationalised by

indicators within the available datasets. This method of operationalisation was based

on theoretical reasoning, and has not been empirically validated. Although the

degree to which the indicators measure the concepts is unknown, such measures do

have good face validity, where the indicators appear to measure what is intended

(Alder and Clarke 2011, 148-149). They also have concurrent criterion validity,

where the criterion (the concept identified within the literature) should be associated

with the indicators within the datasets used to operationalise that criterion (Alder and

Clark 2011, 148-149). Furthermore, it is possible to replicate this method of

operationalisation to produce consistent results. The method of operationalisation of

the theoretical propositions was therefore deemed valid and reliable.

The theoretical concepts and indicators of operationalisation include the

following: Supervision was operationalised at the ignition factor level by the

indicators area of origin and alarm source. Familial disruption was operationalised at

the suburb level by the indicators familial structure, family type, and child type.

Opportunity was operationalised at the ignition factor level by the indicators type of

fire, type of property, type of owner, form of heat ignition, form of material ignited

first, and alarm source, along with a temporal analysis. The cost of YMF was

operationalised at the ignition factor level by the variables incident outcome and

dollar loss. The variable socioeconomic status (SES) was operationalised at the

suburb level of analysis by the SEIFA index, tenure type, landlord type, and at the

40

ignition factor level by type of owner. Ethnic heterogeneity was operationalised at the

suburb level by the indicators Indigeneity, birthplace of person, birthplace of parents,

citizenship, and ancestry. Finally, residential mobility was operationalised at the

suburb level by the indicator residential mobility at one and five years.

Procedure

Data Collection

Ethical approval was obtained in September 2014. Data collection

commenced thereafter, involving access to FRNSW, NSWRFS and ABS datasets.

Official access to FRNSW and NSWRFS data required formal application to the

respective organisations in accordance with the Government Information (Public

Access) Act 2009 (NSW) (GIPA). Once approval was obtained, data collection

methods were implemented in accordance with the privacy policies of each

organisation, as outlined by FRNSW Standing Orders and the NSWRFS Service

Standards. ABS data is publicly available and did not require formal application.

FRNSW AIRS and CARS data were accessed via FRNSW Strategic

Reporting System (SRS) database. SRS collates all data submitted within AIRS and

CARS at the aggregate level. The FRNSW SRS database was accessed via secure

FRNSW computers located at Regentville Fire Station. Access to SRS is restricted,

however the FRNSW Business Intelligence Unit granted access to SRS via

password.

The NSWRFS FIRS database collates all information collected within fire

incident reports. The FIRS database was accessed by NSWRFS personnel within

the Operations Services Directorate. Data was drawn out of the FIRS database by

41

NSWRFS personnel, aggregated, uploaded onto a CD, and made available to the

researcher.

Data Recording

All data was collated and recorded in SPSS version 22, saved onto a hard

drive, protected by password and locked in a filing cabinet when not in use. Given all

datasets contained aggregated data which was non-identifiable, data was accessed

and recorded without undue constraints. The only consideration was to ensure that

data collection and recording procedures complied with the GIPA and the privacy

policies of each organisation, as specified by ethics approval.

All data was cleaned as it was uploaded into SPSS. Data cleaning was

conducted by undertaking frequency analyses of variables as they were input into

SPSS to ensure data frequencies aligned with those produced within the original

datasets. Data was then recoded and collated. The collation process was necessary

in order to marry data obtained from two different organisations. Although the

variables available within both datasets were very similar, NSWRFS data was

recoded into categories defined by FRNSW to ensure smooth collation. Finally,

missing data was recoded as ‘unknown’ where appropriate. The only missing data

handled was that which arose from the non-response error within the 2011 Census

and from non-completion of CARS reports for IFAP activities. The fire-incident

datasets, AIRS and FIRS, require reports to be completed before submission, and

consequently, did not contain missing data.

Data Analysis

To address the first research question, a descriptive study was performed on

the data to determine the incidence and prevalence rates of YMF within NSW. A

42

normative study was then performed to compare rates of YMF with rates of adult and

juvenile arson within NSW. Finally a cost analysis was completed.

To address the second research question, an exploratory study was

completed at the ignition factor and suburb unit of analyses. The variables collected

from AIRS and FIRS datasets were aggregated at the ignition factor unit of analysis.

These individual and situational level variables were categorical in nature, lending

themselves to measures of association. As a result, bivariate analyses was

performed using Chi Square tests to determine if there were any statistically

significant measures of association between individual and situational level

variables. The chi square test was deemed the most appropriate statistical tool for

this part of the study where ignition factor data was categorical in nature and where

chi square tests are designed to measure associations between two categorical

variables (Streiner and Lin 1998, 837). Chi square tests determine whether there is a

difference between observed frequencies and those frequencies expected by

chance. The greater the observed frequency differs from the expected frequency, the

greater the chi square value, the more likely the observed differences are due to

differences identified within the sample (Streiner and Lin 1998, 837).

The second research question has also been investigated at the suburb unit

of analysis. Incidents of YMF and all societal level variables were aggregated at the

suburb level. To determine whether any statistically significant relationships existed

between these variables, bivariate analysis was required. Where these variables

were discrete in nature, they lent themselves to correlational analysis. Correlational

analyses are employed to determine if there is a meaningful relationship between

two continuous or discrete variables, which is unlikely to have occurred due to

sampling error alone (Dancey and Reidy 2011, 170). Correlational analysis also

43

enables the determination of the strength and magnitude of any relationship

identified, the direction of the relationship, and how much variance in one variable

can be explained by the variance in the other (Dancey and Reidy 2011, 170).

Spearman’s rank correlational coefficient was performed to identify any significant

relationships which may exist between YMF and all societal level variables.

Finally, where archival data lends itself well to temporal scrutiny (Hoeppner

and Proeschold-Bell 2012, 131), a temporal analysis of YMF within NSW was

conducted. The variables time and day were employed to conduct a temporal

analysis of YMF at the daily, weekly, and yearly level. Longitudinal analysis was also

performed to conduct a 10 year trend analysis.

To address the third research question, IFAP data was analysed at the suburb

unit of analysis. The variable IFAP was discrete in nature. To investigate whether

any correlations existed between IFAP, YMF and societal level variables,

Spearman’s rank correlational coefficient was again utilised. This was deemed the

most appropriate statistical tool given that IFAP was discrete in nature and

aggregated at the suburb level. To further address the third research question, IFAP

variables have also undergone temporal analysis at the monthly, yearly, and 9 year

longitudinal level. All temporal patterns identified have been compared with patterns

of YMF to determine whether the temporal application of IFAP fluctuates with

incidents of YMF.

Ethical Considerations

Secondary data analysis presents a negligible risk to participants (National

Health and Medical Research Council [NHMRC] 2007, 15). As a result, this research

has been specifically designed to remove any risk of harm to children and young

44

people in accordance with 4.2.1 National Statement on Ethical Conduct in Human

Research (NHMRC 2007, 50-51). Additionally, the aggregated, non-identifiable

nature of the data means that there is sufficient protection of the privacy of

participants to not warrant consent (NHMRC 2007, 21). Consequently, the primary

ethical concern within this study was to ensure that data collection, recording and

analysis complied with the conditions specified by FRNSW and the NSWRFS

(NHMRC 2007, 28). These procedures were approved by, and employed in

accordance with, FRNSW, the NSWRFS, and the University of New England Human

Research Ethics Committee (No. HE14-236).

Limitations

Although the methodology applied within this research was deemed the most

appropriate means through which to address the research questions, there are some

inherent limitations. Firstly, the research design was shaped by ethical constraints.

Where access to youths was restricted and unobtrusive methods of data collection

were required, this research relied upon secondary data collection. In turn, this

available data restricted access to intact groups only, meaning that experimental or

quasi-experimental designs could not be utilised. Consequently, the data collection

method employed governed the scientific validity of the research design. To improve

scientific validity, future research should involve the random allocation of participants

and a control group to warrant experimental design.

Furthermore, incident reporting systems only record data of interest to

FRNSW and the NSWRFS. As a result, this study was limited by the information

collected therein, and could not produce empirical evidence relevant to all YMF

variables identified within the literature. Consequently, only those variables identified

45

within existing literature which could be operationalised by available indicators within

the datasets were included in this study. Future research which utilises primary data

collection will be better positioned to analyse YMF more comprehensively.

Thirdly, it was difficult to determine the reliability of the FRNSW and NSWRFS

data where the variables and their levels were selected by the RO completing the

incident report. The RO is obligated to make a reasonable, educated judgement to

complete a report as accurately as possible without requiring irrefutable evidence

before making a determination (NSWFB 1998, sec. 9, 9). As a result, fire incident

records are reliant upon the discretion of the RO, their experience, expertise, and

perceptions at the time of the incident. Consequently, fire incident reports may differ

by RO, meaning data input may not be consistent. This may hamper the consistency

and internal validity of the study.

The fourth limitation of this research design surrounds ignition factor

determination. The AIRS Reference Manual (NSWFB 1998, sec. 9, 10) informs RO’s

to indicate whether a fire has an ignition factor pertaining to a youth. If, however, this

fire is deemed deliberately lit, regardless of whether the age of the person

responsible is known, the ignition factor is recorded as either ‘suspicious’ or

‘incendiary’. This distinction is problematic given that ROs make value judgements

regarding ignition factors without requiring irrefutable evidence, and that intent

cannot always be inferred from immediate observation. As a result, some incidents

recorded as suspicious or incendiary may have resulted from the unintended actions

of a youth. Furthermore, those incidents correctly identified as suspicious or

incendiary, but which were also lit by a youth, will not have an ignition factor

pertaining to a youth. Consequently, the sample may not include all incidents of

YMF, and may not represent YMF which occurs at the more severe end of the

46

spectrum. For a deeper understanding of YMF, future research will require access to

data pertaining to all fires attributed to youths, regardless of perception of motivation.

The fifth limitation arises from literature which suggests small-area analysis is

superior to census tract analysis (Law and Quick 2013). However, this study does

not lend itself to such analysis. Due to ethical considerations, the need to protect the

individual locations of each fire incident, and the manner in which data has been

aggregated within the available datasets, societal level data was only available at the

suburb level. As a result, any measures of correlation identified within societal level

variables must be considered significant at the suburb level only. Future research

pertaining to societal influences on YMF should conduct inquiry at a smaller-area, or

individual level, analysis.

Finally, data analysis involved measures of association and correlation only,

meaning results cannot be employed to infer causation. Current research also

suggests that many of the correlations historically identified may in fact be spurious.

For example, low SES has been correlated with YMF, yet recent studies suggest that

low SES may produce a stressful familial environment, which increases likelihood of

child maltreatment, limbic system dysfunction, incapacity for emotional regulation,

and thus externalisation of behaviour such as YMF (Stewart, Livingston and

Dennison 2008, 61). Accordingly, findings must be considered within context.

Conclusion

Despite these limitations, quantitative analysis of fire brigade data was

deemed the most appropriate means through which empirical evidence could be

derived from the YMF population of NSW. The research design employed has also

resulted in the collection of rich and unique data which has proved invaluable to the

47

study of YMF within NSW. The empirical findings, and their implications, are

discussed in detail in the subsequent three chapters.

48

CHAPTER FOUR: RESULTS AND DISCUSSION

THE SCOPE AND MAGNITUDE OF YMF WITHIN NSW

Between July 2004 and June 2014, FRNSW and NSWRFS collectively

responded to 419,736 fires, 26,380 (6.3%) of which were attributed to a youth.

Prevalence and incidence rates provide an estimation of the scope of this

phenomenon. Prevalence rates measure the ratio of the number of cases of YMF

and the number of individuals within the population, at a specific time (Crooks 2008,

530). In 2013, the ABS (2014) estimated that there were 1,579,347 youths residing

within NSW. During this same year, 1,956 cases of YMF were recorded. Prevalence

rates for YMF in 2013 therefore indicate that YMF occurred in 0.12% of the total

youth population. However, where prevalence rates measure cases as individuals,

and where rates of recidivism of YMF within NSW are unknown, incidence rates offer

a far more accurate measure. Incidence rates refer to the number of times YMF

occurs within a given population within a given timeframe (Popp 2008, 353). For

example, in 2013, with 1,956 cases of YMF and 1,579,347 youths, the incidence rate

of YMF within NSW was 123.8 cases per 100,000 youths. As shown in table 1.1,

incidence rates for all age groups have decreased over time. The 13-16 year group

has maintained the highest incidence rate while the 0-5 year group consistently

accounts for the lowest.

49

Table 1.1. Incidence rates of YMF in NSW Year Group n = 2005 2006 2007 2008 2009 2010 2011 2012 2013

0-5 Popα 515803 518209 528279 540915 553269 563935 566435 573791 582283 years YMFβ 74 68 85 80 66 49 45 62 44 Rateγ 14.3 13.1 16.1 14.8 11.9 8.7 7.9 10.8 7.6 6-12 Popα 620764 618660 617583 615106 616123 617488 621878 628891 636069 years YMFβ 716 768 480 470 362 304 307 336 302 Rateγ 115.3 124.1 77.7 76.4 58.8 49.2 49.4 53.4 47.5 13-16 Popα 363448 365122 363749 363063 361994 362161 361170 359829 360995 years YMFβ 1683 2202 1442 1455 1349 1001 1119 1345 988 Rateγ 463.1 603.1 396.4 400.8 372.7 276.4 309.8 373.8 273.7 Total Popα 1500015 1501991 1509611 1519084 1531386 1543584 1549483 1562511 1579347 Youth YMFβ 3286 3976 2757 2834 2676 1947 2062 2537 1956 Rateγ 219.1 264.7 182.6 186.6 174.7 126.1 133.1 162.4 123.8

Source: Statistics derived from raw data collected from FRNSW, NSWRFS and ABS (2014).

α Usual youth population of NSW, as recorded by the ABS (2014).

β Cases of YMF as recorded by FRNSW and the NSWRFS. γ Incidence rate per 100,000 youths.

Based on existing literature, it was hypothesised that the prevalence and

incidence rates of YMF would be significant. However, prevalence rates indicate that

YMF is not particularly prevalent within NSW, providing evidence against the

hypothesis. Incidence rates similarly illustrate that, in 2005, YMF occurred only 219.1

times per 100,000 youths, a rate which has declined over time. This pattern mirrors

the general downward trend in youth offending within NSW (BOSCAR 2014a; Goh

and Holmes 2014, 3). When analysed by age, YMF committed by 0-5 year olds

occurred least often, while YMF committed by 13-16 year olds occurred at a higher

rate than any other group. These figures align with existing literature which proposes

that YMF is more prevalent in adolescents than children (Pinsonneault 2002;

Stadolnik 2000).

At the suburb level, YMF recorded between July 2004 and June 2014 ranged

from 0 - 2,016 cases per suburb. Fifty one percent of suburbs recorded nil incidents

of YMF, while 11.7% recorded 1 case, and 6.2% recorded 2 cases. Fifty two suburbs

recorded over 100 cases of YMF each, while one suburb recorded 2,016 cases.

50

When prevalence rates were analysed at the suburb level for 2011, results suggest

that ten suburbs within NSW experienced YMF within 7.7 – 121.4% of their youth

populations. These figures, presented in table 1.2, suggest that there is a high

degree of spatial variability in YMF.

Table 1.2. YMF Prevalence rates for top 10 NSW Suburbs (2011) Suburb Rank

Suburb Typeα Total Youth Populationβ n =

YMF casesγ n =

Prevalence rate %

1 Inner Regional 28 34 1.21 121.4 2 Major City 10 2 0.20 20.0 3 Outer Regional 2345 408 0.17 17.4 4 Inner Regional 31 4 0.13 12.9 5 Remote 740 92 0.12 12.4 6 Inner Regional 398 40 0.10 10.1 7 Major City 838 78 0.09 9.3 8 Major City 1099 94 0.09 8.6 9 Inner Regional 24 2 0.08 8.3 10 Major City 26 2 0.08 7.7

Source: Statistics derived from raw data collected from FRNSW, NSWRFS and the ABS (2011).

α As defined by the Australian Statistical Geography Standard Remoteness Structure (ABS 2014) β Usual youth population as recorded by the 2011 Census (ABS 2011). γ Cases of YMF as recorded by FRNSW and the NSWRFS for 2011.

Although evidence at the state level suggests YMF is not prevalent within

NSW overall, analysis at the suburb level reveals that it is markedly more prevalent

within some areas. Further investigation is required to elucidate the nature of this

phenomenon.

To gain a greater understanding of the magnitude of YMF, incidence rates

were compared with rates of arson within NSW, as displayed in table 1.3.

Table 1.3. Comparison of YMF and Arson Incidence* rates in NSW

Financial Year YMF Incidence rateα Arson Incidence rateβ

2007/08 188.6 105.8 2008/09 173.9 103.0 2009/10 162.7 94.9 2010/11 129.4 81.5 2011/12 132.1 89.2 2012/13 158.3 95.8

2013/14 101.1 85.8 Source: Statistics derived from raw data collected from FRNSW, NSWRFS and BOSCAR (2014).

* Incidence rates calculated per 100,000 youths. α YMF incidence rate as recorded by FRNSW and NSWRFS. β Arson incidence rate as recorded by BOSCAR (2014).

51

Based on existing literature, it was hypothesised that official rates of YMF

would be markedly higher than official rates of arson. Incidence rate comparisons

reveal that, in NSW, recorded incidents of YMF were consistently higher than

recorded incidents of arson. These findings not only lend support for the hypothesis,

but validate concerns regarding the incongruity between the study of arson and that

of YMF.

Finally, the magnitude of YMF within NSW has been illustrated via a cost

analysis. Table 1.4 displays costs associated with YMF recorded by AIRS between

June 2004 and July 2014.

Table 1.4. AIRS Cost Analysis Group Total Costs ($) % of total Median ($) Range ($)

0-5 years 14,272,082 39.2 1,000.00 0 – 1,170, 000 6-12 years 7,060,922 19.4 0.00 0 – 350,000 13-16 years 10,360,735 28.4 0.00 0 – 1,000,000 Age Undetermined 4,744,072 13.0 0.00 0 – 400,000 Total 36,437,811 100 0.00 0 – 1,170, 000

Source: Statistics derived from raw data collected from FRNSW.

Analysis of existing literature led to the hypothesis that costs associated with

YMF would account for a significant proportion of costs associated with fires

generally, while the youngest age group would be responsible for the highest

proportion of costs. Cost analysis revealed that between June 2004 and July 2014,

YMF recorded by AIRS cost property owners $36,437,811. Where all fires recorded

by AIRS during this period cost $4,071,474,678, YMF contributed to only 0.9% of

these costs. Further calculations specify that costs for all fires were, on average,

$13,387.20 per fire, while YMF cost on average $1,436.30 per fire. The cost-related

magnitude of YMF is therefore much lower than hypothesised. Nevertheless, there is

strong evidence to support the hypothesis that the youngest age group contributes to

the highest proportion of all YMF-related costs. Analysis by age group revealed that

52

although the 0-5 year old group accounted for only 2.4% of all recorded cases of

YMF, they were responsible for 39.2% of all costs, the greatest range of costs ($0 –

$1,170,000), and the highest median cost ($1,000.00). This evidence supports

existing literature which advocates YMF committed by young children as higher in

risk and severity than that committed by older youths (Harpur, Boyce, and McConnell

2013; Pinsonneault 2002). As discussed in detail in the following chapter, this may

be because younger children are more likely to commit YMF within residential

dwellings when residents are home.

Conclusion

Collectively, these results suggest that YMF is highly prevalent within spatial

clusters of NSW, and although it is more prevalent within adolescents than children,

the younger the youth, the higher the level of severity and risk. In order to explain

these findings contextually, they should be compared with existing literature.

However, concerns regarding the generalisability of existing literature mean that

further empirical inquiry is required. Consequently, the applicability of existing

literature to the YMF population of NSW will be examined within the following

chapter.

53

CHAPTER FIVE: RESULTS AND DISCUSSION

THE APPLICABILITY OF EXISTING LITERATURE TO THE YMF POPULATION

OF NSW

It was hypothesised that the individual, situational and societal level variables,

and temporal patterns, identified within the recorded YMF population of NSW would

empirically support the theoretical propositions made within existing literature. To

test this hypothesis, each of the situational and societal level variables, as well as

temporal patterns, were analysed at each level of the individual variable. Where

situational level variables were categorical, and societal level variables were

discrete, relationships were identified using measures of association and correlation

respectively. For both situational and societal level variables, an alpha level of .05

was adopted to determine statistical significance. A statistical significance of .05

proposes that the probability of the results occurring due to sampling error, rather

than real relationships or differences observed within the population, will occur less

than 5.0% of the time (Dancey and Reidy 2011, 141). Empirical evidence suggests

that while existing literature pertaining to population, supervision, familial disruption,

opportunity and socioeconomic status have been supported by this research, no

evidence could be found in support of literature which correlates YMF with ethnic

heterogeneity or residential mobility.

Situational Level Analysis

Situational level variables were analysed from an ignition factor unit of

analysis. The sample contained 26,380 cases of YMF, unless otherwise specified.

The categorical variables included type of fire, type of property, type of owner, area

54

of origin, form of heat ignition, form of material ignited first, alarm source, incident

outcome, and dollar loss. For definitions of each variable, and examples of fires

classified within each level, see Appendix A.

Chi square r x c tests for independence were performed to determine if there

were any statistically significant associations between YMF and each of the

situational level variables. Chi square tests have underlying assumptions which must

be met in order to ensure validity of results. Firstly, chi square necessitates the use

of categorical variables (Dancey and Reidy 2011, 277). Where the situational

variables utilised within this research involved the allocation of participants to

categories, this fundamental requirement was upheld. Chi square tests also require

mutually exclusive variables (Dancey and Reidy 2011, 277). Where each of the

variables, and associated levels, could only be classified within one category, the

assumption of independence was upheld. Furthermore, chi square tests assume that

no cell within a contingency table contains a frequency of less than one, while no

more than 25.0% of cells contain an expected frequency of less than five (Dancey

and Reidy 2011, 285). Finally, the total number of cell frequencies must equal the

total number of participants (Dancey and Reidy 2011, 285). Frequency analysis, and

the consolidation of categories where necessary, ensured the minimum cell and total

cell frequency assumptions were upheld.

Although all YMF variable levels have been analysed descriptively, chi square

calculations did not include the variable, youth, age undetermined. This level was

removed prior to analysis because its inclusion reduced the ability to discern

measures of association between the three distinct YMF age groups and each of the

categorical variables.

55

Table 2.1. Chi square r x c tests for independence

r x c

Cross Tabulation Variables

Minimum Cell Frequency

df

2

Asymp. Sig (2-sided)

Cramer’s V

3 x 7 YMF Type of Fire 2.73 12 5315.81 <.001 .38 3 x 7 YMF Type of Property 2.87 12 2749.77 <.001 .27 3 x 8 YMF Type of Ownerα 2.70 14 1136.03 <.001 .18 3 x 8 YMF Area of Origin 2.80 14 14973.63 <.001 .63 3 x 7 YMF Heat Ignition 3.11 12 2443.33 <.001 .26 3 x 7 YMF Material Ignited First 8.05 12 2391.56 <.001 .25 3 x 7 YMF Alarm Sourceα 0.67 12 1164.71 <.001 .18 3 x 3 YMF Incident Outcomeβ 8.84 4 335.03 <.001 .20

3 x 5 YMF Dollar Lossα 3.33 8 3168.90 <.001 .29 Source: Statistics derived from raw data collected from FRNSW and NSWRFS.

N = 18,815 for all variables except type of owner, alarm source, incident outcome and dollar loss. α N = 18,454 for type of owner, alarm source and dollar loss. β N = 4,242 for incident outcome.

Chi square r x c tests for independence revealed statistically significant

relationships between YMF and all ignition factor level variables (see table 2.1).

Large chi square values suggest there exists a discrepancy between the observed

data and that expected under the null hypothesis. Here, the null hypothesis assumes

that all levels of the YMF variable would maintain proportionate frequencies at all

levels of the situational variables. A deviation from the null suggests that there are

associations between YMF and all situational level variables, the nature of which

have been analysed throughout the remainder of this chapter.

The effect sizes (Cramer’s V) range from 3.2% to 40.0%, suggesting

significant variation in the relationships identified. While the variance in type of

owner, alarm source and incident outcome attribute to very small degrees of

variance in YMF, other variables have higher proportions of shared variance. While

8.4% of variance in dollar loss can be attributed to variance in YMF, 14.4% of

variance in type of fire can be attributed to variance in YMF. The highest percentage

of shared variance arises from area of origin, where 40.0% of variance in area of

origin can be attributed to variance in YMF. These results indicate that variance

observed in the YMF variable is more likely to be attributed to variance in area of

56

origin, type of fire, and dollar loss, than any other situational level variable.

Consequently, these situational variables, particularly area of origin, possess

significant explanatory power.

Societal Level Analysis

Societal level variables were analysed from a suburb unit of analysis. Given

there were 2,626 suburbs within NSW at the time of the 2011 Census (ABS 2011),

each variable contained a sample size of 2,626, unless otherwise stated. All

variables analysed at the societal level were positively skewed, characterised by

leptokurtic (peaked) distributions. While the variables were ratio-level, having equal

intervals between adjacent scores and an absolute zero (Dancey and Reidy 2011,

8), the data was characterised by non-normal distributions, heterogeneity of

variances and extreme scores. Consequently, the data violated three out of the four

assumptions underlying parametric tests, necessitating the use of non-parametric

tests. Spearman’s rank correlational coefficient was deemed the most suitable

statistical test to employ. Spearman’s rho transforms original data into ranks,

negating the need for normally distributed data, homogeneity of variances, or an

absence of extreme scores (Dancey and Reidy 2011, 529). Instead, Spearman’s rho

requires data which is interval or ratio in nature, and which maintains monotonicity. A

monotonic relationship was identified between all variables, which were measured at

the ratio-level. The assumptions of Spearman’s rho were therefore upheld, meaning

that the results presented hereafter can be deemed valid.

All YMF by societal variable correlations were statistically significant (α < .01),

suggesting the relationships identified would occur in the YMF population of NSW

less than 1.0% of the time due to sampling error alone. Where measures of

57

correlation are statistically significant, results can be generalised to the broader YMF

population of NSW.

In order to operationalise and statistically analyse the variables of interest,

both measures of association and correlation were performed, as were temporal

analyses. Results from these three tests are employed collectively to provide

empirical evidence pertaining to; population, supervision, familial disruption,

opportunity, cost, socioeconomic status, ethnic heterogeneity, and residential

mobility.

Population

The variable population has been operationalised at the suburb unit of

analysis by population data collected by the ABS Census (2011).

Table 3.1. Population Descriptive Statistics

Variable Level Median Min Max Range

Population Total Resident 763.50 0 43,367 43,367 Total Youth 168.00 0 10,355 10,355 0-5 years 54.00 0 4,208 4,208 6-12 years 71.00 0 4,491 4,491 13-16 years 43.00 0 2,250 2,250

Source: Statistics derived from raw data collected from the ABS (2011).

The recorded usual resident population within each suburb was correlated

with YMF, as presented in table 3.2. Spearman’s rho revealed moderate-strong

positive correlations between both total resident and total youth population and total

YMF. Moderate positive correlations were identified between all other levels.

Table 3.2. Population/YMF Correlation

Source: Statistics derived from raw data collected from FRNSW, NSWRFS, and the ABS (2011).

Population YMF df 𝜌 p 𝜌2

Total Resident Total 2,624 .70 <.001 .49 Total Youth Total 2,624 .70 <.001 .49 0-5 years 0-5 years 2,624 .40 <.001 .16 6-12 years 6-12 years 2,624 .55 <.001 .30 13-16 years 13-16 years 2,624 .64 <.001 .41

58

Deduction

Correlational analysis suggests that there is a strong positive correlation

between total YMF and total youth population (𝜌 (2,624) =.70, p<.001). This indicates

that incidents of YMF increase as the youth population increases. Measures of

shared variance suggest that 49.0% of the variance in total YMF can be attributed to

variance in total youth population. Such evidence supports existing literature which

suggests that YMF is a normal, developmental behaviour (Britt 2011; Pinsonneault

2002; Stadolnik 2000), where rates will increase as youth population increases. Such

findings may also support the notion that motivation for YMF subsists within the

youth population, a concept which forms the foundation of RAT. Furthermore,

analysis suggests that there is a stronger correlation between 13-16 years

population and 13-16 year YMF (𝜌 (2,624) =.64, p<.001), than that identified within

the other age groups. This provides additional evidence in support of the notion that

YMF occurs more often in adolescent populations than child populations

(Pinsonneault 2002; Stadolnik 2000).

Supervision

Supervision has been operationalised at the ignition factor unit of analysis by

the variables area of origin and alarm source.

Area of Origin

Chart 1.1 illustrates that, of the 26,380 cases of YMF committed in NSW,

73.0% were lit in a public area, while a further 13.2% were lit in an exterior living

area.

59

Chart 1.1. Area of Origin

Source: Statistics derived from raw data collected from FRNSW and NSWRFS.

Table 4.1 illustrates that the vast majority of fires were lit by 13-16 year olds in

public areas (44.7%). This was followed by fires lit by youths, age undetermined, in

public areas (24.3%), and those lit by 6-12 year olds in exterior living areas (10.9%).

Analysis of the 0-5 year group reveals that the majority of fires were lit in interior

living areas, followed very closely by sleeping areas. Despite the 0-5 year group

accounting for the least amount of fires overall, they committed the highest rate of

YMF in sleeping areas and a high proportion of interior living area fires. The 6-12

year group lit the majority of their fires in exterior living areas. When compared with

all age groups, the 6-12 year group was responsible for the vast majority of exterior

living area fires and a high proportion of sleeping area fires. Thirteen to sixteen year

olds committed YMF most often in public places, a finding replicated for the youth,

age undetermined, group.

172

3474961

944

19255

651440 483

Commercial

Exterior Living Area

Interior Living Area

Other

Public Place

Rubbish Area

Sleeping Area

Transportation

60

Table 4.1. YMF x Area of Origin

YMF Area of Origin

Sleeping Area

Interior Living

Exterior Living

Rubbish Area

Transport Area

Commerce Area

Public Area

Other

Total

0-5 years count 224 228 44 3 12 5 102 24 642

within age 34.9% 35.5% 6.9% 0.5% 1.9% 0.8% 3.7% 3.7% 100% within type 50.9% 23.7% 1.3% 0.5% 2.5% 2.9% 2.5% 2.5% 2.4%

6-12 years

count 126 200 2887 63 23 26 965 174 4464 within age 2.8% 4.5% 64.7% 1.4% 0.5% 0.6% 21.6% 3.9% 100% within type 28.6% 20.8% 83.1% 9.7% 4.8% 15.1% 5.0% 18.4% 16.9%

13-16 years


Undetermd.



Alarm Source

FIRS data did not contain the variable alarm source. Chart 1.2 reveals that, of

the 25,369 cases of YMF recorded by AIRS, 76.7% were called in by a passer-by,

15.6% by an occupier, and 4.5% by police.

Chart 1.2.Alarm Source


26 96 299 756

3947

19467

1131Ambulance

Automatic

Fire

Other

Occupier

Passer-by

Police

61

Bivariate analysis, as illustrated in table 4.2, suggests that the majority of YMF

incidents committed by 0-5 year olds were called in by the occupier of the property

targeted (65.5%). For all other age groups, passers-by were more likely to raise the

alarm than any other source. Police were more likely to call in a fire lit by a 13-16

year old (60.2%) than any other age group, as were automatic fire alarms (59.4%),

fire brigades (49.5%), and ambulance personnel (50.0%).

Table 4.2. YMF x Alarm Source

YMF Alarm Source

Occupier Passer-by Fire Police Ambulance Automatic Other Total

0-5 years count 402 191 4 6 2 6 3 614

within age 65.5% 31.1% 0.7% 1.0% 0.3% 1.0% 0.7% 100% within type 10.2% 1.0% 1.3% 0.5% 7.7% 6.3% 1.3% 2.4%

6-12 years

count 862 3174 54 147 5 10 89 4341 within age 19.9% 73.1% 1.2% 3.4% 0.1% 0.2% 2.1% 100% within type 21.8% 16.3% 18.1% 13.0% 19.2% 10.4% 22.1% 17.1%

13-16 years


Undetermined



Deduction

Collectively, these results provide empirical support for the premise that

supervision is negatively correlated with YMF. Evidence suggests that 0-5 year olds

commit the majority of YMF in interior living areas (35.5%) and sleeping areas

(34.9%), while accounting for the majority (50.9%) of all sleeping area fires. Where

these findings possess strong explanatory power, they support the theory that 0-5

year olds are more likely to commit YMF where they sleep or play (Bahr 2000; Corey

2005; Dolan et al. 2011). Furthermore, fires lit by 0-5 year olds were called in by the

62

occupier of the property 65.5% of the time. This evidence supports the findings of

Pollack-Nelson et al. (2006) who suggest that parents are usually home when YMF

takes place. Where parents deem the home the safest place to leave a child without

direct supervision (Pollack-Nelson et al. 2006), 0-5 year olds commit YMF most often

in this environment.

Further evidence which associates a lack of supervision with YMF can also be

found at the 6-12 and 13-16 year levels. This research found that 6-12 year olds

were responsible for the vast majority (83.1%) of exterior living area fires, and a

substantial proportion (28.6%) of sleeping area fires. This evidence supports the

theory that 6-12 year olds are more likely to set fires in the home or a near-by

location (Corry 2002; Talbot and Harris 2008). However, where the majority of these

YMF cases (74.1%) were called in by a passer-by, evidence suggests direct

supervision may have been absent. Furthermore, the 13-16 year group lit the

majority (86.0%) of their fires in public places, and had the majority (77.8%) of their

fires called in by a passer-by. Such evidence supports the theory that 13-16 year

olds are more likely to set fires away from home, where direct supervision is minimal

(Dolan et al. 2011; Schoonover 2013).

When considered within the RAT framework, this evidence suggests that YMF

occurs most often when capable guardianship is absent. However, as identified at

the 0-5 year level, the mere presence of a capable guardian is not sufficient to deter

YMF. Instead, the prevention of YMF may require direct supervision, or, as

suggested within existing literature, parental power, child dependency on that power,

and parental knowledge (Ary et al. 1999; Fletcher, Steinberg, and Williams-Wheeler

2004; Smith 1970).

63

Familial Disruption

The variable familial disruption has been operationalised at the suburb level

by the indicators familial structure, family type and child type.

Familial Structure

Table 5.1. Familial Structure Descriptive Statistics


Spearman’s rho revealed a stronger positive correlation between total YMF

and one parent families than two parent families or other familial structures. Results

displayed in table 5.2 suggest this pattern is replicated within all levels of the YMF

variable.

Table 5.2. Familial Structure/YMF Correlation

Familial Structure YMF df 𝜌 p 𝜌2

One Parent Total YMF 2,624 .72 <.001 .52 0-5 years YMF 2,624 .42 <.001 .15 6-12 years YMF 2,624 .57 <.001 .32 13-16 years YMF 2,624 .67 <.001 .45 Two Parent Total YMF 2,624 .68 <.001 .46 0-5 years YMF 2,624 .38 <.001 .14 6-12 years YMF 2,624 .53 <.001 .28 13-16 years YMF 2,624 .64 <.001 .41 Other Total YMF 2,624 .68 <.001 .46 0-5 years YMF 2,624 .39 <.001 .15 6-12 years YMF 2,624 .53 <.001 .28 13-16 years YMF 2,624 .63 <.001 .40


Family Type Table 5.3. Family Type Descriptive Statistics



Familial Structure One Parent 28.00 0 2,172 2,172 Two Parents 87.00 0 5,983 5,983 Other 180.00 0 12,974 12,974


Family Type Intact 76.00 0 5,583 5,583 Step 7.00 0 355 355 Blended 5.00 0 215 215 Other 212.50 0 50,002 50,002

64

Correlational analysis performed on the variables YMF and family type

revealed moderate correlations between total YMF and both step and blended

families. Slightly weaker correlations were identified between total YMF and intact

and other families. As presented in table 5.4, this pattern is consistent across levels

of YMF, suggesting that there is a slightly weaker correlation between YMF and

intact family types, than step or blended family types.

Table 5.4. Family Type/YMF Correlation

Family Type YMF df 𝜌 p 𝜌2

Intact Total YMF 2,624 .67 <.001 .45 0-5 years YMF 2,624 .38 <.001 .14 6-12 years YMF 2,624 .52 <.001 .27 13-16 years YMF 2,624 .63 <.001 .40 Step Total YMF 2,624 .68 <.001 .46 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .53 <.001 .28 13-16 years YMF 2,624 .64 <.001 .41 Blended Total YMF 2,624 .68 <.001 .46 0-5 years YMF 2,624 .41 <.001 .17 6-12 years YMF 2,624 .55 <.001 .30 13-16 years YMF 2,624 .64 <.001 .41 Other Total YMF 2,624 .69 <.001 .48 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .54 <.001 .29 13-16 years YMF 2,624 .64 <.001 .41


Child Type

Table 5.5. Child Type Descriptive Statistics


Finally, Spearman’s rho revealed equivalent moderate positive correlations

between total YMF and natural/adopted child and step child. When child type was


Child Type Adopted/Natural 195.00 0 13,607 13,607 Step 15.00 0 798 798 Foster 0.00 0 52 52 Other 546.50 0 31,556 31,556

65

classified as foster, positive correlations were much weaker. Table 5.6 displays

results for all levels of the YMF variable, suggesting this pattern is consistent.

Table 5.6. Child Type/YMF Correlation

Child Type YMF df 𝜌 p 𝜌2

Adopted/Natural Total YMF 2,624 .69 <.001 .48 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .54 <.001 .29 13-16 years YMF 2,624 .65 <.001 .42 Step Total YMF 2,624 .69 <.001 .48 0-5 years YMF 2,624 .41 <.001 .17 6-12 years YMF 2,624 .55 <.001 .30 13-16 years YMF 2,624 .65 <.001 .42 Foster Total YMF 2,624 .44 <.001 .19 0-5 years YMF 2,624 .33 <.001 .11 6-12 years YMF 2,624 .41 <.001 .17 13-16 years YMF 2,624 .42 <.001 .18 Other Total YMF 2,624 .69 <.001 .48 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .54 <.001 .29 13-16 years YMF 2,624 .65 <.001 .42


Deduction

These results provide empirical evidence supporting the proposition made

within existing literature that familial disruption is positively correlated with YMF

(Ward 2005). The above results indicate that YMF at all levels displayed a stronger

correlation with one parent families, than two or other parent families. YMF at all

levels also held a stronger correlation with blended family types and step,

adopted/natural children, than other family or child types. The only deviation from

this pattern occurred in the 13-16 year group where step, blended and other family

types, and adopted/natural, step, and other child types, all correlated with YMF at

equal magnitudes.

While this study provides empirical evidence to support the positive

correlation between familial disruption and YMF, it is the application of RAT which

gives this relationship explanatory power. Existing literature suggests that factors

66

such as supervision, parental attachment, and communication, are less likely to

occur when children reside with only one parent or a step-parent (Kierkus and Hewitt

2009, 124). Where RAT suggests that effective guardianship requires a guardian

who is available and able to monitor the situation, familial disruption may impede

capable guardianship, generating conditions conducive to YMF.

Opportunity

The situational variable opportunity has been operationalised at the ignition

factor level by the indicators type of fire, type of property, type of owner, form of heat

ignition, and form of material ignited first. Opportunity has also been operationalised

by conducting a temporal analysis of YMF.

Type of Fire

Of the 26,380 cases of YMF recorded, there were seven distinct types of fire.

Chart 2.1 reveals that 58.7% were vegetation fires, 30.0% were outside rubbish fires,

and 6.2% were building fires.

Chart 2.1. Type of Fire


1631

479 104

7907

171

610

15478

Building

Mobile Property

Other

Outside Rubbish

Storage

Special Structure

Vegetation

67

Further analysis by age group revealed that the majority of cases of YMF

were vegetation fires lit by 13-16 year olds (27.9%), followed by outside rubbish fires

lit by 13-16 year olds (19.1%), and vegetation fires, attributed to youths, age

undetermined (18.8%). Bivariate analysis revealed that, although 0-5 year olds

accounted for the smallest proportion of YMF cases, they were responsible for the

majority of building fires. Thirteen to sixteen year olds were responsible for the

majority of all other fire types as displayed in table 6.1.

Table 6.1. YMF x Type of Fire

YMF Type of Fire

Building

Special Structure

Storage

Mobile Property

Rubbish

Vegetation

Other

Total

0-5 years count 485 8 8 18 38 78 7 642


6-12 years


13-16 years


Undetermined



Type of Property

Of the 26,380 fires attributed to a youth, 68.8% were lit on public property,

14.9% were committed on residential property, and 9.2% were committed in

recreational areas (see chart 2.2).

68

Chart 2.2. Type of Property


Table 6.2 provides cross tabulations by age group. These figures suggest that

the majority of cases of YMF were committed in public places by 13-16 year olds

(36.9%), followed by youths, age undetermined (20.5%), and 6-12 year olds (11.1%).

While these age groups lit the majority of their fires in public places, the 0-5 year

group committed the majority of YMF in residential areas (87.4%). The 13-16 year

group was responsible for the majority of all fires within each property type.

Table 6.2. YMF x Type of Property

YMF Type of Property

Residential Recreational Institutional Commercial Public Storage Other Total

0-5 years count 561 13 1 4 54 4 5 642


6-12 years


13-16 years


Undetermd.



3928529

680

114

18146

2432

551Residential

Institutional

Commercial

Storage

Public

Recreational

Other

69

Type of Owner

FIRS data did not contain the variable Type of Owner. As a result, the

following analysis was performed on AIRS data only. Chart 2.3 reveals that, of the

25,369 fires attributed to a youth, 53.8% were lit on Local Government property,

23.9% on private property, and 16.4% on State Government property.

Chart 2.3. Type of Owner


Table 6.3 indicates that the majority of fires were lit by a 13-16 year old on

Local Government property (29.4%), followed by a youth, age undetermined, on

Local Government property (15.0%). While 0-5 year olds accounted for the least

amount of fires, they were responsible for a significant amount of YMF committed on

Department of Health, Housing and Community Service (DHHCS) property (23.4%).

Similarly, 6-12 year olds accounted for a similar proportion of fires lit on DHHCS

property (27.0%), while only attributing to a small proportion of YMF. Again, 13-16

year olds were responsible for the majority of fires within each level with the

exception of Commonwealth Government property, where 52.8% of fires were

attributed to youths, age undetermined.

229 222102

136556086

4149

926Commonwealth Govt.

DHHCS

Indigenous

Local Govt.

Private

State Govt.

Other

70

Table 6.3. YMF x Type of Owner

YMF Type of Owner

Private

Local Govt.

State Govt.

Common. Govt.

Indigenous

DHHCS

Other

Total

0-5 years count 385 40 119 3 10 52 5 614


6-12 years


13-16 years


Undetermined



Form of Heat Ignition

Chart 2.4 reveals that, of the 26,380 cases of YMF, the vast majority (75.6%)

were lit with matches or a lighter. A further 10.4% were lit with other forms of heat

ignition, such as fireworks, explosives and means unknown, while 9.8% were lit

using an open flame.

Chart 2.4. Form of Heat Ignition

Source: Statistics derived from raw data collected from FRNSW and NSWRFS

19948

652

2551

121 184

179

2745

Matches/Lighters

Smokers Materials

Open Flame

Heat/Hot Objects

Electrical Equipment

Fuel Powered Object

Other

71

Table 6.4 illustrates that all age groups committed the majority of YMF with

matches or a lighter. The 0-5 year group accounted for the majority of all fires ignited

with electrical equipment (59.2%) and a high proportion of those ignited with fuelled

equipment (21.2%). Consistent with most other ignition factor variables, 13-16 year

olds were responsible for the majority of YMF committed by each form of heat

ignition. Although the ‘other’ level appears to account for a significant number of fires

across all age groups, this level contained data primarily pertaining to forms of heat

ignition which could not be determined.

Table 6.4. YMF x Form of Heat Ignition

YMF Form of Heat Ignition

Matches/ Lighter

Smoker’s Materials

Open Flame

Heat/Hot Objects

Electrical Equipment

Fuel Powered

Other

Total

0-5 years count 390 19 37 15 109 38 34 642


6-12 years


13-16 years


Undetermined



Form of Material Ignited First

Chart 2.5 shows that, of the 26,380 fires attributed to youths, 55.6% were

committed against vegetation, 16.6% against rubbish, and 13.1% against other

materials, such as fuel, bales, supplies, and other unknown forms of material.

72

Chart 2.5. Form of Material Ignited First


Bivariate analysis indicated that while the 0-5 year group committed the

majority of YMF against apparel and linen (32.4%), the other three age groups

committed the majority of YMF against vegetation, as illustrated in table 6.5. Once

again, 13-16 year olds committed the majority of YMF within each level. The high

number of YMF attributed to the ‘other’ category occurred primarily due to the high

number of cases classified as undetermined.

Table 6.5. YMF x Form of Material Ignited First

YMF Form of Material Ignited First

Apparel/ Linen

Furniture/ Wares

Recreational

Structural

Rubbish

Vegetation

Other

Total

0-5 years count 208 120 57 16 44 67 130 642


6-12 years


13-16 years


Undetermined

count 93 145 285 76 1198 4655 1113 7565 within age 1.2% 1.9% 3.8% 1.0% 15.8% 61.5% 14.7% 100% within type 10.3% 21.3% 14.4% 24.4% 27.3% 31.7% 32.1% 28.7% Source: Statistics derived from raw data collected from FRNSW and NSWRFS.

900

682

3462

1975

4382

312

14667

Apparel and Linen

Furniture and Wares

Other

Recreational

Rubbish

Structural

Vegetation

73

Temporal Analysis

To determine if the temporal patterns of YMF in NSW reflect findings within

existing literature, a temporal analysis of YMF by age was performed. Day of the

week analysis, as presented in graph 1.1, suggests that YMF occurs most readily on

Saturdays (18.0%), followed closely by Sundays (17.7%). This pattern reflects the

temporal trends of bushfire arson identified by the Australian Institute of Criminology

(AIC) (Beale and Jones 2010, 513).

Graph 1.1. YMF by Day of the Week

Source: Statistics derived from raw data collected from FRNSW and the NSWRFS.

Graph 1.2 displays temporal analysis at the hourly level, where results

suggest that YMF is committed most often between 1600 and 1659 hours, and least

often between 0600 and 0759 hours. Overall, there is a focused temporal hotspot,

where there is significantly more YMF activity between the hours of 1400 and 1959,

than at any other time. This hourly temporal pattern also mirrors bushfire arson

trends identified by the AIC (Beale and Jones 2010, 513).

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M O N T U E S W E D T H U R S F R I S A T S U N

0-5 years 6-12 years 13-16 years Age Undetermined

74

Graph 1.2. YMF by Time of Day


However, when broken down into age groups, temporal analysis at the hourly

level reveals different patterns. Graph 1.3 presents temporal analysis of the 0-5 year

group. This group commits the highest rate of YMF between 1500 and 1659 hours,

while the timeframe between 2300 to 0559 hours experiences very little incidents.

There is therefore a focused temporal hotspot between the hours of 0800 and 1959,

meaning that there are significantly more incidents of YMF during this period than

any other time (Ratcliffe 2004, 12).

Graph 1.3. 0-5 years YMF by Time of Day


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75

This focused hotspot is similarly discernible when data is broken up into both

weekdays and weekends, suggesting young children have a broader temporal

opportunity to commit YMF (see graphs 1.4 and 1.5).

Graph 1.4. 0-5 years YMF by Week Day


Graph 1.5. 0-5 years YMF by Weekend


Graph 1.6 displays analysis of the 6-12 year group at the hourly level. Results

suggest that YMF is temporally clustered between 1300 and 1959 hours. Although

focused, this pattern tends towards an acute temporal hotspot, meaning that few

events occur outside this timeframe (Ratcliffe 2004, 12).

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Saturday Sunday

76

Graph 1.6. 6-12 years YMF by Time of Day


When analysis is broken down into weekdays and weekends, the temporal

dimensions alter slightly. As illustrated by graph 1.7, YMF committed by 6-12 year

olds on weekdays is acutely temporally clustered between the hours of 1400 and

1859.



However, as graph 1.8 demonstrates, YMF committed by 6-12 year olds on

weekends is less clustered in nature, occurring across a broader timeframe than

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77

during the week. These results indicate that weekends offer 6-12 year olds a broader

temporal opportunity to commit YMF.



Graph 1.9 reveals that YMF committed by 13-16 year olds occurs most

frequently between 1700 and 1759 hours, and least frequently between 0700 and

0759 hours. The focused temporal hotspot begins at around midday, rising, and

remaining relatively elevated until 0059 hours.

Graph 1.9. 13-16 years YMF by Time of day


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78

This focused temporal pattern is reflected when analysis is differentiated by

weekday and weekend. Graph 1.10 illustrates one notable diversion which occurs on

Friday, when YMF increases after 1959 hours, reaching its peak between 2300 and

2359 hours.



Although hourly trends on the weekend reflect those experienced during the

week, there is one noticeable difference. Results displayed in graph 1.11 reveal that

while Saturday’s hourly trends mirror those of Friday, YMF committed on Sundays

declines after 2059 hours.



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79

These slight variations in temporal trends may indicate differences in

opportunity provided by routine activities and levels of supervision. Consequently,

the situational and temporal level findings presented above provide vital information

for the study of the relationship between opportunity and YMF. This relationship can

be best explained from a RAT perspective, where Cohen and Felson (1979) suggest

that it is the routine activities of everyday life which present opportunities for

delinquency or crime.

Deduction

This research has produced evidence in support of the theory that

opportunity, generated through routine activities, facilitates YMF. Within NSW, 0-5

year olds were responsible for only 2.4% of all cases of YMF, yet accounted for the

majority (29.7%) of building fires, and committed 87.4% of their fires on residential

property. This group lit the majority (60.7%) of their fires using matches or a lighter,

and were more likely to set alight apparel and linen (32.4%) or furniture and wares

(18.7%) than any other form of material. Temporal analysis suggests 0-5 year olds

commit YMF more often on weekends than during the week, while hourly analysis

reveals a focused temporal hotspot between the hours of 0800 and 1959. Where 0-5

year olds are more likely to be active and less likely to be directly supervised during

this time, they have more opportunity to commit YMF in environments typified by

their routine activities.

Six to twelve year olds were found to be responsible for 16.9% of all cases of

YMF in NSW, the majority of which were vegetation fires (68.5%) lit on public

(65.6%) or residential (19.0%) property owned by the Local Government (53.7%) or

private owners (23.5%). This group lit the majority of their fires using matches or a

80

lighter (75.3%) and vegetation (63.9%), in exterior living areas (64.7%). Temporal

analysis suggests that 6-12 year olds are more likely to commit YMF on weekends

rather than weekdays, while analysis at the hourly level indicates 6-12 year olds light

fires in a pattern which tends towards an acute temporal hotspot between 1300 and

1959 hours. This evidence mirrors Dolan et al.’s (2011) findings that children under

12 years are more likely to light fires between 1300 and 1900 hours. It also supports

the notion that 6-12 year olds are more likely to commit YMF when unsupervised

routine activities occur within the home or at a nearby location.

Finally, 13-16 year olds accounted for the majority (52.0%) of all cases of

YMF, and were more likely to light vegetation fires (53.8%), on public property

(71.1%), owned by the Local Government (55.3%). This age group set the majority

of their fires using matches or a lighter (79.3%), against vegetation material (51.7%),

in a public area (86.0%). Temporal analysis of the 13-16 year group revealed that

YMF is more likely to occur on weekends than weekdays. Analysis at the hourly level

suggests YMF is temporally focused, occurring most often between 1200 and 0059

hours. On Fridays and Saturdays YMF increases to its weekly peak between 2300

and 0159 hours. Although the temporal hotspot for 13-16 year olds is not as acute as

Dolan et al. (2011) found, the peak of YMF committed by this age group falls within

the predicted 2200 and 0100 hours. These figures provide support for the theory that

adolescents are more likely to commit delinquent acts during times of unstructured

socialising when this socialising occurs in semi-public or public places (Hoeben and

Weerman 2014, 494).

Collectively, these figures suggest that each age group commits the majority

of YMF in environments where they spend the majority of their unstructured time,

and do so with resources naturally affiliated with those environments. Where

81

matches or lighters are common household items, all age groups were more likely to

use these than any other form of heat ignition. This evidence supports the notion that

access to incendiary devices that are readily available increases the likelihood that

YMF will occur (Kolko 2002). Furthermore, suitable targets like apparel, linen,

furniture and wares for 0-5 year olds, and vegetation for 6-12 and 13-16 year olds,

are accessible and available combustible materials which each group would

encounter during their usual daily activities. Temporal patterns are also consistent

with routine activities where children are more likely to light fires in the home during

the day, while adolescents are more likely to light fires outside of the home during

the evening (Dolan et al. 2011, 383). These findings provide evidence for

Mehregany’s (1993, 20) proposition that age differentiation in YMF results from the

interaction between individual development and environmental influences. Empirical

evidence therefore provides support for the notion that youths carry out YMF in

environments where routine activities facilitate access to resources and opportunity

(Britt 2011; Harpur, Boyce, and McConnell 2013; Pollack-Nelson et al., 2006).

Cost

The situational variable cost has been operationalised at the ignition factor

level by the indicators incident outcome and dollar loss.

Incident Outcome

Graph 2.1 illustrates that, as a result of the 26,380 instances of YMF

committed between July 2004 and June 2014, 4,097 persons were evacuated, 414

persons suffered injury, 43 persons required rescue, and 10 fatalities occurred.

82

Graph 2.1. YMF Incident Outcome

Source: Statistics derived from raw data collected from FRNSW and NSWRFS

Table 7.1 reveals that, despite the 0-5 year group accounting for the least

amount of fires, this group was responsible for the majority of persons rescued,

persons injured, and 40.0% of all fatalities. Thirteen to sixteen year olds were

responsible for the majority of persons evacuated, while the youth, age

undetermined, group also accounted for 40.0% of all fatalities.

Table 7.1. YMF x Incident Outcome

YMF Incident Outcome

Fatalities Persons Injured

Persons Rescued

Persons Evacuated

Total

0-5 years count 4 207 29 697 937

within age 0.4% 22.1% 3.1% 74.4% 100% within type 40.0% 50.0% 67.4% 17.0% 20.5%

6-12 years

count 0 113 7 1546 1666 within age 0.0% 6.8% 0.4% 92.8% 100% within type 0.0% 27.3% 16.3% 37.7% 36.5%

13-16 years


Undetermined



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F A T A L I T I E S P E R S O N S I N J U R E D P E R S O N S R E S C U E D P E R S O N S E V A C U A T E D


83

Dollar Loss

FIRS data did not contain the variable dollar loss. As a result, the following

analysis has been based on AIRS data only. Graph 2.2 illustrates that, of the 25,369

cases of YMF recorded by AIRS, the majority were lit by 13-16 year olds, costing

property owners less than $999 (31.4%). This was followed by YMF committed by

youths, age undetermined, costing property owners less than $999 (18.9%), and that

committed by 6-12 year olds, costing property owners less than $999 (12.9%).

Graph 2.2. Dollar Loss associated with YMF


Table 7.2 illustrates that despite these low figures, 0-5 year olds accounted for

the majority of cases of YMF where costs exceeded $100,000 (46.4%), and the

second highest proportion of fires where costs fell between $10,000 and $99,999

(29.8%). Thirteen to sixteen year olds were responsible for almost half of all cases of

YMF which cost less than $999 (48.9%), and the highest proportion of fires where

costs fell between $10,000 and $99,999 (32.3%), and $1,000 and $9,999 (44.6%). A

substantial proportion of YMF cases were classified as costs unknown (30.6%).

26

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93

< $ 9 9 9 $ 1 , 0 0 0 - $ 9 , 9 9 9 $ 1 0 , 0 0 0 - $ 9 9 , 9 9 9 > $ 1 0 0 , 0 0 0 U N K N O W N


84

Table 7.2. YMF x Dollar Loss

YMF Dollar Loss

<$999 $1,000- $9,999

$10,000- $99,999

>$100,000 Unknown Total

0-5 years count 262 167 96 52 37 614

within age 42.7% 27.2% 15.6% 8.5% 6.0% 100% within type 1.6% 19.1% 29.8% 46.4% 0.5% 2.4%

6-12 years

count 3262 156 73 25 825 4341 within age 75.1% 3.6% 1.7% 0.6% 19.0% 100% within type 20.0% 17.8% 22.7% 22.3% 10.6% 17.1%

13-16 years


Undetermined



Deduction

Results pertaining to incident outcome and dollar loss provide information on

the costs associated with YMF. This empirical evidence aligns with existing research

which suggests the youngest group generates the highest degree of risk to life and

property (Harpur, Boyce, and McConnell 2013; Pinsonneault 2002). This research

has revealed that although 0-5 year olds were responsible for the least amount of

fires (2.4%), they caused the greatest number of rescues (67.4%), injuries (50%),

and 40.0% of all fatalities. This group was also responsible for the greatest number

of fires which cost over $100,000 (46.4%) and the second highest proportion of fires

where costs fell between $10,000 and $99,999 (29.8%). Such figures suggest that

YMF committed by 0-5 year olds is more likely to cause significant harm than that

committed by 6-12 or 13-16 year olds. The theoretical framework of RAT can also be

applied to explain this phenomenon where the routine activities of children means

YMF committed by this age group occurs predominantly in residential dwellings

85

when residents are home. In these situations, YMF produces the greatest degree of

harm.

Socioeconomic status

Socioeconomic status has been operationalised at the suburb unit of analysis

by socioeconomic index for areas (SEIFA) data, tenure type, landlord type, and at

the ignition factor unit of analysis by property type data.

Socioeconomic Index for Areas (SEIFA)

SEIFA data contained values for 2,620 suburbs only due to limitations

associated with the 2011 Census (ABS 2013a). The data was continuous and

normally distributed (M = 993.91, SD = 85.51), ranging from 493.74 to 1191.20 on

the SEIFA index. Spearman’s rho was performed to determine if there was a

relationship between incidents of YMF and SEIFA index at the Suburb level. Table

8.1 presents results which indicate weak negative correlations.

Table 8.1. SEIFA/YMF Correlation


These results suggest that as incidents of total YMF increase, SEIFA values

decrease (𝜌 (2,618) =-.14, p<.001). Although there is less than 1% chance of these

negative correlations occurring due to sampling error, the relationships are weak.

Only 1-4% of variance in YMF can be attributed to variance in SEIFA value.

SEIFA YMF df 𝜌 p 𝜌2

Total YMF 2,618 -.14 <.001 .02 0-5 years YMF 2,618 -.19 <.001 .04 6-12 years YMF 2,618 -.18 <.001 .03 13-16 years YMF 2,618 -.11 <.001 .01

86

Housing Tenure Table 8.2. Housing Tenure Descriptive Statistics


There exists a strong positive correlation between total YMF and rented

residence, which is slightly stronger than that found between total YMF and owned

residence. Results published in table 8.3 illustrate that this pattern is evident across

all levels of the YMF variable.

Table 8.3. Housing Tenure/YMF Correlation

Housing Tenure YMF df 𝜌 p 𝜌2

Owned Total YMF 2,624 .70 <.001 .49 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .55 <.001 .30 13-16 years YMF 2,624 .65 <.001 .42 Rented Total YMF 2,624 .72 <.001 .52 0-5 years YMF 2,624 .44 <.001 .19 6-12 years YMF 2,624 .58 <.001 .34 13-16 years YMF 2,624 .66 <.001 .44 Other Total YMF 2,624 .60 <.001 .36 0-5 years YMF 2,624 .39 <.001 .15 6-12 years YMF 2,624 .50 <.001 .25 13-16 years YMF 2,624 .56 <.001 .31


Landlord Type Table 8.4. Landlord Type Descriptive Statistics


When Spearman’s rho was performed on YMF and landlord type, the

strongest positive correlation was found between total YMF and housing

commission, followed closely by real estates. Moderate positive correlations were


Housing Tenure Owned 204.50 0 11,195 11,195 Rented 45.00 0 5,280 5,280 Other 73.50 0 3,611 3,611


Landlord Type Real Estate 21.00 0 4,008 4,008 Housing Commission 0.00 0 1,718 1,718 Housing Co-operative 0.00 0 175 175 Other 24.00 0 1,221 1,221

87

identified between YMF and housing co-operatives and other landlord types. As

displayed in table 8.5, this pattern holds true for all YMF variable levels and suggests

that there is a stronger relationship between YMF and housing commission landlords

than any other landlord type.

Table 8.5. Landlord Type/YMF Correlation

Landlord Type YMF df 𝜌 p 𝜌2

Real Estate Total YMF 2,624 .71 <.001 .50 0-5 years YMF 2,624 .42 <.001 .17 6-12 years YMF 2,624 .56 <.001 .31 13-16 years YMF 2,624 .65 <.001 .42 Housing Commission Total YMF 2,624 .73 <.001 .53 0-5 years YMF 2,624 .50 <.001 .25 6-12 years YMF 2,624 .62 <.001 .38 13-16 years YMF 2,624 .68 <.001 .46 Housing Co-operative Total YMF 2,624 .62 <.001 .38 0-5 years YMF 2,624 .43 <.001 .19 6-12 years YMF 2,624 .53 <.001 .28 13-16 years YMF 2,624 .58 <.001 .34 Other Total YMF 2,624 .65 <.001 .42 0-5 years YMF 2,624 .41 <.001 .17 6-12 years YMF 2,624 .53 <.001 .28 13-16 years YMF 2,624 .60 <.001 .36


Deduction

Collectively, these results suggest that YMF is associated and correlated with

socioeconomic disadvantage. The relationship between socioeconomic

disadvantage and juvenile delinquency is a core component of SDT. Comparably,

low SES has also been consistently negatively correlated with YMF within existing

literature. This study found that YMF at all levels increased when scores on the

SEIFA index decreased. Furthermore, at the suburb level, YMF at all levels

displayed a stronger correlation with rented residence than owned residence, and

housing commission landlords than landlords of any other type. These results

indicate that the lower the level of SES, the more likely YMF is to occur. However,

the negative correlation between SEIFA and YMF appears strongest within the 0-5

88

year group (ρ (2,618) =-.19, p<.001), followed by the 6-12 year group (ρ (2,618) =-

.18, p<.001). Type of owner cross tabulations also revealed that a disproportionate

number of cases of YMF attributed to 0-5 year olds and 6-12 year olds were

committed on DHHCS property. Such findings provide support for the notion that

youths who misuse fire are likely to experience some degree of socioeconomic

disadvantage (Corcoran et al. 2012), where this relationship appears stronger within

child populations.

When these findings are analysed within the framework of SDT, they provide

empirical support for the theory that environments characterised by low SES are

more favourable to delinquency (Bernard, Snipes, and Gerould 2010, 136). More

specifically, SDT suggests low SES impedes the implementation of informal social

controls such as parental supervision and familial stability (Cunneen and White

2011, 140). As a result, areas characterised by low SES may provide environments

conducive to YMF, particularly within child populations.

Ethnic Heterogeneity

Ethnic heterogeneity has been operationalised at the suburb unit of analysis

by the indicators Indigeneity, birthplace of person, birthplace of parents, citizenship,

and ancestry.

Indigeneity Table 9.1. Indigeneity Descriptive Statistics



Indigeneity Non-Indigenous 690.50 0 40,822 40,822 Aboriginal 17.00 0 4,557 4,557 Torres Strait Islander 0.00 0 61 61 ATSI 0.00 0 100 100

89

As illustrated in table 9.2, Spearman’s rho revealed a strong-moderate

positive correlation between total YMF and non-Indigenous status (𝜌 (2,624) =.69,

p<.001).

Table 9.2. Indigeneity/YMF Correlation

Indigeneity YMF df 𝜌 p 𝜌2

Aboriginal Total YMF 2,624 .65 <.001 .42 0-5 years YMF 2,624 .41 <.001 .17 6-12 years YMF 2,624 .55 <.001 .30 13-16 years YMF 2,624 .60 <.001 .36 Torres Strait Total YMF 2,624 .51 <.001 .26 Islander 0-5 years YMF 2,624 .38 <.001 .14 6-12 years YMF 2,624 .47 <.001 .22 13-16 years YMF 2,624 .50 <.001 .25 ATSI Total YMF 2,624 .38 <.001 .14 0-5 years YMF 2,624 .31 <.001 .10 6-12 years YMF 2,624 .37 <.001 .14 13-16 years YMF 2,624 .38 <.001 .14 Non-Indigenous Total YMF 2,624 .69 <.001 .48 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .53 <.001 .28 13-16 years YMF 2,624 .65 <.001 .42


These results suggest that non-Indigenous status has a stronger correlation

with total YMF than Aboriginal, Torres Strait Islander or ATSI status. This pattern

also holds true for the 13-16 year group. The 0-5 and 6-12 year groups both

displayed a slightly stronger moderate correlation with Aboriginal status than non-

Indigenous status. These results indicate that the correlation between YMF and

Aboriginality may only be significant within child populations. Without access to data

pertaining to the number of cases of YMF attributed to Aboriginal or non-Indigenous

youths however, these correlations are considered significant at the suburb level

only.

90

Birthplace of Person Table 9.3. Birthplace of Person Descriptive Statistics


Results presented in table 9.4 reveal a strong positive correlation between

total YMF and persons born in Australia, while there is only a moderate positive

correlation between total YMF and persons not born in Australia. This pattern is

apparent for all YMF levels.

Table 9.4. Birthplace of Person/YMF Correlation

Birthplace of Person YMF df 𝜌 p 𝜌2

Australia Total YMF 2,624 .70 <.001 .49 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .55 <.001 .30 13-16 years YMF 2,624 .65 <.001 .42 Not Australia Total YMF 2,624 .65 <.001 .42 0-5 years YMF 2,624 .36 <.001 .13 6-12 years YMF 2,624 .49 <.001 .24 13-16 years YMF 2,624 .61 <.001 .37


Birthplace of Parents

Table 9.5. Birthplace of Parents Descriptive Statistics


Spearman’s rho revealed a stronger positive correlation between total YMF

and both parents born in Australia, than one or both parents born overseas. Table

9.6 illustrates that this pattern is discernible for all YMF levels.


Birthplace of Australia 614.00 0 34,297 34,297 Person Not Australia 80.00 0 19,753 19,753


Birthplace of Both in Australia 482.50 0 28,320 28,320 Parents One in Australia 78.00 0 4,423 4,423 Both Overseas 103.00 0 26,208 26,208

91

Table 9.6. Birthplace of Parents/YMF Correlation

Birthplace of Parents YMF df 𝜌 p 𝜌2

Both in Australia Total YMF 2,624 .69 <.001 .48 0-5 years YMF 2,624 .39 <.001 .15 6-12 years YMF 2,624 .54 <.001 .29 13-16 years YMF 2,624 .63 <.001 .40 One in Australia Total YMF 2,624 .67 <.001 .45 0-5 years YMF 2,624 .38 <.001 .14 6-12 years YMF 2,624 .51 <.001 .26 13-16 years YMF 2,624 .63 <.001 .40 Both Overseas Total YMF 2,624 .65 <.001 .42 0-5 years YMF 2,624 .36 <.001 .13 6-12 years YMF 2,624 .49 <.001 .24 13-16 years YMF 2,624 .62 <.001 .38


Citizenship

Table 9.7. Citizenship Descriptive Statistics


At the suburb level, there appears to be a slightly stronger correlation

between total YMF and Australian citizenship than total YMF and non-Australian

citizenship. Results presented in table 9.8 suggest this pattern is replicated for all

age groups.

Table 9.8. Citizenship/YMF Correlation

Citizenship YMF df 𝜌 p 𝜌2

Australian Total YMF 2,624 .70 <.001 .49 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .54 <.001 .29 13-16 years YMF 2,624 .65 <.001 .42 Not Australian Total YMF 2,624 .66 <.001 .44 0-5 years YMF 2,624 .37 <.001 .14 6-12 years YMF 2,624 .49 <.001 .24 13-16 years YMF 2,624 .62 <.001 .38



Citizenship Australia 681.50 0 37,804 37,804 Not Australia 25.00 0 7,756 7,756

92

Ancestry Table 9.9. Ancestry Descriptive Statistics


Results presented in table 9.10 suggest correlations between YMF and

ancestry first and second responses produce relationships of similar magnitudes.

Table 9.10. Ancestry/YMF Correlation

Ancestry YMF df 𝜌 p 𝜌2

1st Response Total YMF 2,624 .70 <.001 .48 Australian 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .55 <.001 .30 13-16 years YMF 2,624 .65 <.001 .42 1st Response Total YMF 2,624 .69 <.001 .48 Not Australian 0-5 years YMF 2,624 .39 <.001 .15 6-12 years YMF 2,624 .53 <.001 .28 13-16 years YMF 2,624 .65 <.001 .42 2nd Response Total YMF 2,624 .69 <.001 .48 Australian 0-5 years YMF 2,624 .39 <.001 .15 6-12 years YMF 2,624 .53 <.001 .28 13-16 years YMF 2,624 .64 <.001 .41 2nd Response Total YMF 2,624 .69 <.001 .48 Not Australian 0-5 years YMF 2,624 .40 <.001 .16 6-12 years YMF 2,624 .54 <.001 .29 13-16 years YMF 2,624 .65 <.001 .42


Although there are stronger positive correlations between all levels of YMF

and Australian ancestry first response, there are only slightly weaker positive

correlations between YMF and non-Australian ancestry first response. Interestingly,

the 0-5, 6-12 and 13-16 year groups also maintained a stronger correlation with non-

Australian ancestry second response than Australian ancestry second response.

Although differences in magnitude are very small, these figures suggest there may

be a similar correlation between YMF and first and second generation Australians.


Ancestry 1st Response

Australia Not Australian

218.00 522.50

0 0

12,362 36.689

12,362 36,689

Ancestry 2nd Response

Australia Not Australian

90.00 650.00

0 0

5,306 40,731

5,306 40,731

93

Deduction

Collectively, these results provide empirical evidence pertaining to YMF and

ethnic heterogeneity, another core component of SDT. SDT proposes that ethnic

heterogeneity generates social disorganisation, leading to the development of

shared values and norms which propagate a lack of social cohesion and

unstructured socialising (Bernard, Snipes, and Gerould 2010). Although existing

research has found some evidence to support the correlation between ethnic

heterogeneity and delinquency, this research found no such evidence. At the suburb

level, YMF increased at the greatest magnitude when the following variables

increased; non-Indigenous status, persons born in Australia, both parents born in

Australia, Australian citizenship, and Australian ancestry first response. Deviations

from this pattern occurred within the YMF and Indigeneity and ancestry correlations.

Here, YMF committed by 0-5 year olds and 6-12 year olds had a slightly stronger

correlation with Aboriginal status, suggesting YMF correlations with Aboriginality may

be significant within child populations only. Furthermore, while all YMF levels

displayed a stronger correlation with Australian ancestry first response, the 0-5, 6-12,

and 13-16 year groups displayed a stronger correlation with non-Australian ancestry

second response. These correlations suggest that YMF may occur more often in

suburbs characterised by both Australian and second generation Australian

populations. Nevertheless, collectively these findings provide evidence to support the

notion that YMF is correlated more so with ethnic homogeneity (being Caucasian-

Australian) than ethnic heterogeneity (being non-Caucasian-Australian).

94

Residential Mobility

The variable residential mobility has been operationalised by the indicator

residential mobility at one and five years.

Residential Mobility at one and five years

Table 10.1. Residential Mobility Descriptive Statistics


Table 10.2 reveals that there exists a stronger positive correlation between

total YMF and the levels same residence one year ago and same residence five

years ago. This pattern is also observed within the 6-12 and 13-16 year groups. For

0-5 year olds, a weaker positive correlation was identified between YMF and

different residence five years ago, than any other residential mobility level.

Table 10.2. Residential Mobility/YMF Correlation

Residential Mobility YMF df 𝜌 p 𝜌2

Same Residence Total YMF 2,624 .72 <.001 .52 1 year ago 0-5 years YMF 2,624 .43 <.001 .19 6-12 years YMF 2,624 .58 <.001 .34 13-16 years YMF 2,624 .70 <.001 .49 Different Residence Total YMF 2,624 .71 <.001 .50 1 year ago 0-5 years YMF 2,624 .43 <.001 .18 6-12 years YMF 2,624 .56 <.001 .31 13-16 years YMF 2,624 .66 <.001 .44 Same Residence Total YMF 2,624 .72 <.001 .52 5 years ago 0-5 years YMF 2,624 .43 <.001 .19 6-12 years YMF 2,624 .57 <.001 .32 13-16 years YMF 2,624 .67 <.001 .45 Different Residence Total YMF 2,624 .71 <.001 .50 5 years ago 0-5 years YMF 2,624 .42 <.001 .18 6-12 years YMF 2,624 .56 <.001 .31 13-16 years YMF 2,624 .66 <.001 .44



Residential Mobility 1 year

Same Different

622.50 89.00

0 0

34,941 7,032

34,941 7,032

Residential Mobility 5 years

Same Different

443.00 227.50

0 0

22,315 17,516

22,315 17,516

95

Deduction

These results suggest that there is a stronger correlation between YMF and

residential stability than mobility. This relationships can be best understood within

the framework of SDT where residential mobility is the third and final societal level

variable correlated with delinquency. Although SDT proposes that areas

characterised by residential mobility are more favourable to delinquency, evidence

was not found to support this notion. When YMF was correlated with residential

mobility at the suburb level, the 6-12, 13-16, and total YMF groups displayed a

stronger correlation with the variables same residence at one and five years, than

different residence at one and five years. The 0-5 year group displayed a stronger

correlation with same residence at one and five years, and different residence at one

year, than different residence at five years. Overall, these results indicate that

residential stability is correlated with YMF more so than residential mobility.

Conclusion

Collectively, this research provides strong empirical evidence in support of

RAT. Specifically, empirical evidence suggests that YMF within NSW is more likely

to occur when offender motivation subsists, capable guardianship is absent, suitable

targets are available, and opportunity is generated by the convergence of these

elements in time and space. Consequently, RAT literature is deemed contextually

applicable to the YMF population of NSW. Such findings do not however, provide

similar support for SDT.

Although results published within existing literature provide some support for

SDT, empirical evidence pertaining to YMF within NSW does not. There is a

negative correlation between YMF and SES that is consistent across all YMF levels.

96

This evidence supports the premise that areas characterised by low SES maintain a

stable level of delinquency (Bernard, Snipes, and Gerould 2010, 139). However

where SDT states that this level of delinquency will persist despite changes in the

population, NSW specific research does not support this proposition. Instead, YMF

within NSW is correlated more so with ethnic homogeneity than heterogeneity, and

residential stability than mobility. Such findings suggest that SDT may not be an

appropriate theoretical framework through which to analyse YMF.

97

CHAPTER SIX: RESULTS AND DISCUSSION

THE AVAILABILITY OF IFAP TO THE YMF POPULATION OF NSW

Based on existing literature, it was hypothesised that the societal level

variables and temporal patterns associated with IFAP would reflect the societal level

variables and temporal patterns associated with YMF within NSW. To test this

hypothesis, a societal level analysis and temporal analysis was conducted on IFAP

to enable direct comparison with YMF results.

Societal Variable Analysis

To determine if the societal level variables associated with IFAP reflect the

societal level variables associated with YMF, IFAP variables were analysed at the

suburb unit of analysis. The sample of IFAP subjects (N = 395) included all recorded

IFAP activities conducted within NSW from May 2005 to August 2014. IFAP

application (median = .00) ranged from 0 to 50 activities per suburb.

Spearman’s rank correlational coefficient was employed to determine if any

significant relationships existed between IFAP application and the societal level

variables. All correlations were statistically significant (α ≤ .01) suggesting the

relationships identified would occur in the IFAP population less than 1.0% of the time

due to sampling error alone. Results revealed a weak-moderate positive correlation

between incidents of YMF and IFAP activities, as presented in table 11.1. The

measure of shared variance suggests that 12.0% of variance in IFAP can be

attributed to variance in total YMF. Further correlational analysis reveals very similar

patterns as those identified between YMF and societal level variables.

98

Table 11.1. Societal Level Variables/IFAP Correlation

IFAP Variable Level df 𝜌 p 𝜌2

YMF Total YMF 2,624 .35 <.001 .12 0-5 years 2,624 .29 <.001 .08 6-12 years 2,624 .35 <.001 .12 13-16 years 2,624 .36 <.001 .13 SES SEIFA 2,618 -.07 .001 <.00 Indigeneity Aboriginal 2,624 .29 <.001 .08 Torres Strait Islander 2,624 .29 <.001 .08 ATSI

Non-ATSI 2,624 2,624

.24

.31 <.001 <.001

.06

.10 Birthplace of Australia 2,624 .31 <.001 .10 Person Not Australia 2,624 .28 <.001 .08 Birthplace of Both in Australia 2,624 .31 <.001 .10 Parents One in Australia 2,624 .30 <.001 .09 Both Overseas 2,624 .28 <.001 .08 Citizenship Australian 2,624 .31 <.001 .10 Not Australian 2,624 .29 <.001 .08 Ancestry Australian 2,624 .31 <.001 .10 1st Response Not Australian 2,624 .30 <.001 .09 Ancestry Australian 2,624 .31 <.001 .10 2nd Response Not Australian 2,624 .31 <.001 .10 Residential Mobility Same 1 Year 2,624 .59 <.001 .35 Different 1 Year 2,624 .58 <.001 .34 Same 5 Years 2,624 .59 <.001 .35 Different 5 Years 2,624 .58 <.001 .34 Housing Tenure Owned 2,624 .31 <.001 .10 Rented 2,624 .30 <.001 .09 Other 2,624 .28 <.001 .08 Landlord Type Real Estate 2,624 .30 <.001 .09 Housing Commission 2,624 .32 <.001 .10 Housing Cooperative 2,624 .30 <.001 .09 Other 2,624 .29 <.001 .08 Familial Structure One Parent 2,624 .31 <.001 .10 Two Parent 2,624 .30 <.001 .09 Other 2,624 .30 <.001 .09 Family Type Intact 2,624 .30 <.001 .10 Step 2,624 .31 <.001 .10 Blended 2,624 .32 <.001 .10 Other 2,624 .30 <.001 .10 Child Type Adopted/Natural 2,624 .31 <.001 .10 Step 2,624 .31 <.001 .10 Foster 2,624 .26 <.001 .07 Other 2,624 .31 <.001 .10

Source: Statistics derived from raw data collected from FRNSW, NSWRFS and the ABS (2011).

99

Initial analysis revealed that the strongest correlations between IFAP and

societal level variables mirrored those correlations identified within the YMF analysis.

IFAP activities increased at the greatest magnitude when the following variables

increased; non-Indigenous status, persons born in Australia, parents born in

Australia, Australian citizenship, Australian ancestry first response, same residence

at one and five years, housing commission landlords, one parent families, step or

blended family types, and adopted/natural or step child types. Neither ancestry

second response levels displayed a distinct correlation with IFAP activities. Although

these findings provide support for the hypothesis that societal level variables

associated with IFAP reflect those associated with YMF, two notable deviations are

apparent.

These two distinct differences provide evidence against the hypothesis.

Where a negative correlation was identified between YMF and SES, an almost

negligible correlation was found between IFAP and SES (ρ (2,618) =-.07, p<.001).

This evidence suggests that although the demand for IFAP is higher in suburbs

characterised by low SES, its application may not meet such demand. Furthermore,

YMF is more strongly correlated with rental properties, where high numbers of rental

properties may indicate an area of lower SES. However, where IFAP is more

strongly correlated with owned properties, which may indicate higher levels of SES,

its application does not appear to meet demand. These findings are given

explanatory power by Cunneen and White (2011, 140) who state that SES impacts

upon a parent/guardian’s ability to regulate their child’s behaviour, recognition of the

risk certain behaviour presents, and the need and means through which to address

this risk. Where IFAP relies upon referral by, and participation of, parents/guardians,

it may not be available to those who need it the most.

100

Temporal Analysis

To determine whether the temporal patterns of IFAP reflect the temporal

patterns of YMF, temporal analysis was performed on both IFAP and YMF variables.

Between July 2005 and June 2014, there were 393 IFAP activities carried out by

FRNSW in NSW. Graph 3.1 displays a 9 year longitudinal analysis which reveals a

downward trend in IFAP utilisation.

Graph 3.1. IFAP 9-year Longitudinal Analysis


Graph 3.2 similarly illustrates a downward trend evident in recorded cases of

YMF within NSW.

Graph 3.2. YMF 10-year Longitudinal Analysis


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Further analysis, as illustrated in graph 3.3, reveals that the application of

IFAP was relatively stable for the first four financial years. Thereafter, IFAP

application sharply declined, reaching a historic low in 2013/2014. Where 2005/2006

saw an average of 5.0 IFAP activities carried out per month, by 2011/2012 there

were only 2.5 per month, while the most recent figures for 2013/2014 reveal an

average of 0.83 per month.

Graph 3.3. IFAP by Financial Year

\


YMF incidents within the same period are presented in graph 3.4. When

compared with the application of IFAP, YMF intervention appears to reflect the

general downward trend in YMF incidents overall.

6058

60 60

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102

Graph 3.4. YMF by Financial Year


Graph 3.5 charts an analysis of IFAP, aggregated at the monthly level.

Results suggest IFAP is utilised most often in August (15.6%), September (13.5%)

and July (9.7%), while utilised least often in January (4.1%), April (5.3%) and March

(5.9%).

Graph 3.5. IFAP by Month


3843

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28572653

2501

2001 2055

2486

1588

05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14

16

29

23 21

3530

38

61

53

30 3227

103

Similarly, YMF incidents were aggregated at the monthly level, as presented

in graph 3.6. Results suggest YMF occurs most often in August (12.1%), September

(11.1%), and October (10.3%), while occurring least often in February (5.2%), June

(5.3%), and March (7.0%).

Graph 3.6. YMF by Month


Although IFAP is yet to reach its target of 400 referrals per year (FRNSW

2014, 22), general temporal analysis reveals that IFAP is applied in proportion to the

incidence of YMF. Longitudinal analysis suggests that both IFAP and YMF have

experienced a general downward trend over the past 9 and 10 years respectively.

Given the downward trend in youth offending overall, initial analysis suggests that

both YMF, and the need for YMF intervention, are declining in accordance with this

trend. Analysis at the monthly level suggests that both IFAP and YMF peak during

the winter-spring transition (August, September) with troughs occurring during

summer (January, February), and autumn-winter (April, June). This seasonal

phenomenon has not been explained within existing research, and further inquiry is

required in order to identify explanatory factors.

2055

1375

1842

2263 2224

1403

2426

3188

29302723

1929 2022

104

Nevertheless, despite the congruence of IFAP and YMF temporal trends,

further investigation reveals otherwise. YMF incidents were compared with IFAP

activities at the financial year level to determine the ratio of YMF incidents to IFAP

activities. Results are presented in graph 3.7. Analysis revealed that during

2005/2006 there were 64 YMF incidents for every one IFAP activity. This ratio

declined to 44.2 YMF incidents for every one IFAP activity by 2008/2009. Although

rising and dropping slightly in 2009/2010 and 2010/2011, the ratio of YMF incidents

to IFAP activities sharply increased thereafter. By 2013/2014, there were 158.8 YMF

incidents for every one IFAP activity.

Graph 3.7. YMF/IFAP Ratio


These results suggest that the temporal application of IFAP does not mirror

the temporal trends of YMF. Analysis of the ratio of YMF to IFAP reveals that, over a

9 year period, the difference between YMF incidents and IFAP activities increased

by almost 250.0%. These figures suggest that the gap between YMF and IFAP is

increasing dramatically. Although both have declined overall, the application of IFAP

6455.4

47.6 44.2

58.2

43.5

68.5

95.6

158.8

05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14

105

has declined at a greater rate, and subsequently, is decreasingly meeting demand.

These results provide evidence against the hypothesis.

Conclusion

Overall, analysis of IFAP suggests that although it is utilised within suburbs

characterised by the same societal level variables as YMF, there are some

concerns. From a suburb level of analysis, IFAP is not applied within those areas

characterised by socioeconomic disadvantage or high numbers of rental properties.

According to existing literature, this means YMF intervention may be not be available

to those youths who are most at risk. Furthermore, evaluative evidence reveals that

IFAP is not applied in proportion to current demand. The implications of these

findings, along with directions for future research, are presented in the following

chapter.

106

CHAPTER SEVEN: CONCLUSION

By critically analysing FRNSW and NSWRFS data, this research has enabled

the production of empirical evidence which is specific to the YMF population of NSW.

This study has also provided evidence which delineates between existing literature

which is generalisable to the YMF population of NSW, and that which is not.

Analysis of IFAP at the societal and temporal level has also provided the first step

towards empirical evaluation of YMF intervention within NSW.

The implications of the findings within this study are therefore three-fold.

Firstly, this research has provided an empirically derived snap-shot of YMF within

NSW. Findings suggest that while YMF is not particularly prevalent state-wide, it is

highly spatially clustered and extremely problematic within some areas. Incident

rates also suggest that YMF occurs more often than arson. However, the costs

associated with YMF are much lower than costs associated with fires generally,

suggesting that although the problem may be prevalent in some areas, risk to life

and property is, for the most part, minor. Nevertheless, the greatest threat to life and

property arises from the youngest age group, where fires attributed to 0-5 year olds

cost property owners the highest proportion of all YMF-related costs. Although this

evidence elucidates the scope and magnitude of YMF within NSW, further inquiry at

a smaller-area analysis will allow for a more thorough investigation into the nature of,

and factors associated with, spatial clustering of YMF.

This research has also provided empirical evidence to discern between

existing literature which is generalisable to the YMF population of NSW, and that

which is not. For example, empirical evidence derived from the YMF population of

NSW does not support the notion that YMF attributed to youths 10 years and over

107

presents a higher level of severity and risk (Gaynor 2002; NSWFB 2009; Putnam

and Kirkpatrick 2005), or that the earlier the onset of YMF, the more likely YMF will

become more severe (MacKay et al. 2012, 845). As noted, findings indicate that 0-5

year olds commit YMF which is higher in severity, and presents a greater risk to life

and property, than older youths. Although this research supports the notion that the

older a youth becomes, the more frequent their involvement in YMF (Britt 2011;

Gaynor 2002; MacKay et al. 2012, 845; NSW Fire Brigade, 2009; Putnam and

Kirkpatrick 2005), evidence also suggests that with age comes reduced levels of

severity and risk. Consequently, existing literature which promotes the findings that

as youths mature they engage in more severe forms of YMF, may not be applicable

to the YMF population of NSW. Furthermore, there is no evidence to support the

theory that ethnic heterogeneity or residential mobility are correlated with YMF. In

contrast, ethnic homogeneity has a stronger correlation with YMF, suggesting that

YMF may be a Caucasian-Australian problem. Furthermore, residential stability

maintained a stronger correlation with YMF than residential mobility, providing

evidence against the notion that areas characterised by high levels of population

turnover are more susceptible to YMF.

Collectively, the results present strong evidence in support for RAT. Empirical

evidence suggests that YMF within NSW is a behaviour shaped by routine activities

and the opportunities presented by guardianship movement and access to suitable

targets. Where YMF is deemed a product of natural childhood inquisitiveness and

adolescent experimentation, offender motivation subsists. Consequently, literature

pertaining to RAT is deemed contextually applicable to the YMF population of NSW,

and may be employed to inform the development of prevention and intervention

programs. Conversely, empirical evidence does not provide the same level of

108

support for SDT. Although YMF is negatively correlated with SES, it maintains a

stronger correlation with ethnic homogeneity than heterogeneity, and residential

stability than mobility. In contrast to SDT, such findings suggest that those youths

most at risk of committing YMF reside in areas characterised by residentially stable,

yet socioeconomically disadvantaged, Caucasian-Australian populations.

Finally, this research indicates that the application of IFAP does not currently

reflect demand for YMF intervention within NSW. Although the application of IFAP is

correlated with most of the societal variable levels identified within the YMF analysis,

two notable deviations are apparent. These signify that IFAP does not have the

same relationship with SES or housing tenure as YMF, suggesting the program may

not be available to those youths who are most at risk. In addition, despite temporal

analyses superficially portraying a reflection between YMF and IFAP application,

deeper analysis suggests otherwise. The gap between YMF incidents and IFAP

application has increased by 250.0% over the past 9 years. IFAP is therefore not

temporally applied in proportion to incidents of YMF, denoting that this program is

not meeting current demand. This is especially concerning given YMF incidents

included those fires recorded by FRNSW and the NSWRFS, and that IFAP is a

program devised to provide services to clients of FRNSW and the NSWRFS. It is

recommended that further empirical inquiry be conducted into IFAP to determine its

applicability and effectiveness in reaching its target population.

Although these conclusions are founded upon empirical evidence, the

methodological limitations inherent within this study mean any conclusions drawn

must be considered within context. The scope and magnitude of YMF within NSW

relates only to those incidents of YMF which are recorded by FRNSW and the

NSWRFS. Furthermore, empirical evidence which delineates existing literature

109

based on its applicability to the YMF population of NSW has been derived from the

ignition factor and suburb unit of analyses. All relationships identified are those of

association or correlation, rather than causation, and are significant for each

respective unit of analysis only. Finally, the availability of IFAP could only be

analysed at the societal and temporal levels, meaning further investigation is

required in order to conduct a thorough program evaluation.

Nevertheless, this research has partially filled the empirical and theoretical

voids which exist within YMF literature by presenting an empirically-derived analysis

of YMF within NSW. The findings have also reduced the problems associated with a

lack of generalisability by providing context-dependant results which can be

employed to determine the applicability of existing literature. Finally, this research as

provided the first step towards independent empirical evaluation of YMF intervention

within NSW. Directions for future research highlight the need for further inquiry into

the YMF population of NSW at a smaller-area analysis and further evaluation of YMF

intervention within NSW.

110

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APPENDIX A.

Situational Variable Level Categories

Variable Level Categories Included

Type of Fire Building - Building fire - Fire damaging structure and contents - Fire damaging structure only - Fire damaging contents only - Foodstuffs burnt, confined to cooking

equipment - Fire in building confined to container, bin,

chimney or flue Special Structure - Pier, quay or piling fire

- Tunnel, pipeline, underground fire - Bridge, trestle, overhead elevated

structure fire - Transformer, power or utility vault, utility

equipment fire and power pole - Fence fire - Air-supported structure fire or tent fire - Oil refinery fire - Special structure or outside equipment fire

not otherwise classified Outside Storage - Outside storage fire, not rubbish

- Storage yards including timber yards, tyres etc.

- Outside storage fire not otherwise classified

Mobile Property - Passenger vehicle fire - Road transport vehicle fire - Rail vehicle fire - Water vessel fire - Aircraft fire - Camper, caravan or recreational vehicle

fire - Off-road vehicles or mobile equipment fire - Vehicle fire not otherwise classified

Outside Rubbish - Abandoned outside rubbish, refuse or waste fire

- Garbage dump or sanitary landfill fire - Construction or demolition landfill fire - Dumpster or other outside trash

receptacle fire - Outside stationary compactor or

compacted trash fire - Outside refuse fire not otherwise

classified Vegetation - Forest or wood fire (more than I hectare)

- Scrub or bush and grass mixture fire - Grass fire

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- Cultivated grain or crop fire - Cultivated orchard or vineyard fire

Cultivated trees or nursery stock fire - Small vegetation fire less than one

hectare - Small vegetation fire not otherwise

classified - Vegetation or other outside fire not

otherwise classified Other - Munitions or bomb explosion

- Blasting agent explosion - Fireworks explosion - Incendiary device explosion - Gas or vapour explosion - Explosion with ensuing fire - Explosion not as a result of fire and

without after-fire not classified above - Explosion not as a result of fire and

without after-fire; insufficient information to classify further

- Fire or explosion not otherwise classified Type of Property

Residential Any place designed primarily for residential purposes, including;

- Dwellings - Units - Apartments - Boarding houses - Dormitories - Granny flats - Motels/hotels/lodges - Residential tool sheds and garages.

Recreational Any place designed primarily for recreational activities such as;

- Clubs - Centres - Swimming pools - Bowling alleys - Golf courses - Theatres - Exhibition halls.

Institutional Any place primarily designed for institutional purposes such as;

- Religious services - Education - Care of the aged, young, sick, physically

disabled, or mentally handicapped - Juvenile detention centres - Prisons - Rehabilitation centres.

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Commercial Any place designed primarily for commercial purposes such as;

- Shops - Supermarkets - Restaurants - Sales - Service - Manufacturing - Production

Storage Any place designed primarily for bulk storage, such as;

- Grain silos - Agricultural sheds - Public garages - Heavy equipment storage.

Public Any place designed primarily for public use including

- Libraries and museums - Railway/bus stations - Roadways/bridges/tunnels - Parks/beaches - Bushland/forests

Other - Defence facilities - Communication facilities - Rubbish disposal site - Demolition or construction of building - Fixed use not applicable - Other type not otherwise classified

Type of Owner

Private Property owned by a private party or organisation including;

- Residential property - Commercial property

Local Government

Property owned by the Local Government (Council) including;

- Parks - Recreation centres

State Government

Property owned by the State Government including;

- Bushland/forests - Institutional properties

Commonwealth Government

Property owned by the Commonwealth Government including;

- Bushland - Defence establishments

DHHCS Property owned by the Department of Health Housing and Community Services including;

- Residential properties - Institutional properties

Indigenous Property owned by the following;

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- Department of Aboriginal Development Commission

- Aboriginal and Torres Strait Islander Commission

- Aboriginal Hostels Other Property owned by;

- Type of owner undetermined - Type of owner not classified elsewhere

Area of Origin

Interior Living - Lounge room - Kitchen - Dining - Laundry - Hallway/corridor - Entrance/Lobby - Closet/small storage space - Crawl space - Ceiling/wall assembly

Exterior Living - Exterior stairway - Exterior balcony, open porch or veranda - Exterior wall or roof surface - Awning - Court, terrace, patio - Garage, car-port, vehicle storage area

Sleeping Area - Bedroom - Patient room/wards - Dormitories - Barracks - Other sleeping area

Transportation - Passenger areas of transportation - Luggage compartment, load-carrying area

of transportation - Engine area, running gear, wheel area of

transportation - Fuel tank, fuel line area of transportation - Operating control area of transportation - Exterior exposed surface of transportation - Transportation, vehicle areas not

otherwise classified Commercial - Maintenance shop/area

- Product storage room or area, storage tanks, storage bin

- Supply storage room or area - Shipping, receiving, loading area, loading

dock - Office - Personal service area - Laboratory - Printing or photographic room - First aid, treatment room - Electronic equipment room/area

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- Projection room, area - Process, manufacturing area - Other commercial areas not otherwise

classified Rubbish - Waste or rubbish area Public - On or near railroad

- On or near highway, roadway, street, public way, parking lot

- Lawn, field, open area including crops - Scrub or bush area, woods, forest - Library. Included are galleries and exhibit

spaces - Swimming pools - Large assembly areas with fixed seats

(100 or more persons - Large open room without fixed seats - Small assembly area with or without fixed

seats - Vacant structural area with no current use - Other area accessible to the public

undetermined - Other area accessible to the public not

otherwise classified Other - Area of fire origin undetermined

- Area of fire origin not otherwise classified Form of Heat

Matches/Lighters - Matches - Lighters

Ignition Smoker’s Materials

- Cigarettes - Cigars - Pipe - Smoker’s materials not otherwise

classified Heat/Hot Object - Heat, spark from friction

- Molten, hot material - Hot ember, ash. - Electric lamp - Re-kindle, re-ignition - Radiated heat - Heat from flying brand, ember, spark - Conducted heat - Heat spreading from another hostile fire - Heat from hot objects or friction

undetermined - Heat from hot objects not classified above

Open Flame - Candle - Camp fire - Rubbish fire - Bonfire - Burn-off fire - Welding torch

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- Torch operation - Incinerator - Heat from open flame undetermined - Heat from open flame not otherwise

classified Fuelled

Equipment - Spark, ember, flame, heat escaping from

gas-fuelled equipment - Spark, ember, flame, heat escaping from

liquid-fuelled equipment - Spark, ember, flame, heat escaping from

solid-fuelled equipment - Heat from fuel-fired, fuel-powered object

undetermined - Heat from fuel-fired, fuel-powered object

not otherwise classified Electrical

Equipment - Arcing - Heat from overloaded equipment - Fluorescent light ballast - Microwaves - Heat from properly operating electrical

equipment - Heat from improperly operating electrical

equipment - Heat from electrical equipment

undetermined - Heat from electrical equipment not

otherwise classified Other - Munitions

- Blasting agent, primer cord, black powder fuse

- Fireworks - Paper cap, party popper - Model rocket, and amateur rocketry - Incendiary device such as Molotov

cocktails - Sun’s heat, usually concentrated - Static discharge - Multiple forms of heat of ignition - Form of heat ignition undetermined - Other forms of heat of ignition not

otherwise classified Material Ignited First

Apparel/Linen - Mattress, pillow - Bedding, blanket, sheet, comforter - Linen, other than bedding - Wearing apparel not on a person - Wearing apparel on a person - Curtain, blind, drapery, tapestry - Goods not made up including fabrics and

bolts of cloth - Luggage

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- Basket, barrel - Apparel/Linen undetermined - Apparel/Linen not otherwise classified

Furniture/Wares - Upholstered sofa, chair, vehicle seats - Non-upholstered chair, bench - Cabinetry including filing cabinets, pianos,

dressers, chests of drawers, desks, tables and bookcases

- Ironing board - Appliance housing or casing - Kitchen household utensils, tableware - Cleaning supplies. Included are brooms,

brushes, mops and cleaning cloths - Cooking materials. Included are edible

materials for man or animals - Furniture/wares undetermined - Furniture/wares not otherwise classified

Structural - Structural component, finish - Exterior roof covering, surface, finish - Exterior side wall covering, surface, finish - Exterior trim, appurtenances - Floor covering, surface - Interior wall covering, surface items

permanently affixed to wall and door surface

- Ceiling covering, surface - Structural member, framing - Thermal, acoustical insulation within wall,

partition or floor/ceiling space - pole - Awning, canopy - Tarpaulin, tent - Structural component undetermined - Structural component not otherwise

classified Recreational - Christmas tree

- Decoration for special event - Book - Magazine, newspaper, writing paper - Toy, game - Rope, cord, twine, yarn - Packing, wrapping material - Rolled material. Included is rolled paper - Adhesive - Recreational material undetermined - Recreational materials not otherwise

classified above Rubbish - Box, carton, bag

- Pallet, skid - Rubbish, trash, waste

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Vegetation - Grass, bush and forests, whether growing or dead

Other - Supplies, stock - In bales - In bulk - Tyres - Fuel/Fertiliser - Palletised material - Agricultural products - Electrical equipment - Multiple forms of material ignited first - Form of material undetermined - Form of material not otherwise classified

Alarm Source

Occupier - Resident - Occupier - Employee

Passer-by - Passer-by - Neighbour - Traveller

Fire - Fire and Rescue New South Wales - Rural Fire Service of New South Wales Police - New South Wales Police Service Ambulance - Ambulance Service of New South Wales Automatic - Automatic Sprinkler System

- Automatic Detection System - Automatic alarm system undetermined - Automatic alarm system not otherwise

classified Other - Fire Look-out

- Aircraft spotting, observation - Air Traffic Control, airport management - Other agency/persons raising alarm

undetermined - Other agency/persons raising alarm not

otherwise classified

CRIM402H Honours Thesis-signed

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