Safety in the Suburbs:
Social Disadvantage, Community Mobilisation, and the Prevention of Violence
Rebecca Wickes, Ross Homel and Renee Zahnow
Published as:
Wickes, R., Homel, R. & Zahnow, R. (2015). Safety in the suburbs: Social disadvantage,
community mobilization, and the prevention of violence. In J. Stubbs & S. Tomsen (Eds),
Australian Violence. Sydney: Federation Press
Introduction
There has been a marked decline in violence in most advanced countries in recent
decades. The United States has, for example, experienced its lowest levels of violent crime in
a generation (Goldberger & Rosenfeld, 2008, p.xi). This development could be viewed as a
resumption, after a post second world war disruption, of the downward trend in violence
apparent in Western societies for hundreds of years (Pinker, 2011). Australia too has
witnessed a marked decline in some types of violence (Australian Institute of Criminology,
2013a). For example, homicide victimisation rates have dropped by 29 per cent since the
mid- to late-1990s (Australian Institute of Criminology, 2013a), though over the last century
homicide exhibits essentially a stationary trend. Yet in the new millennium, the pattern for
non-lethal violence is not following the same trajectory. Disturbingly, Australian rates of selfreported violent victimisation are consistently in the upper half of the range reported over
recent decades by countries participating in the International Crime Victimisation Surveys
(van Dijk, van Kesteren & Smit, 2007). Additional evidence suggests that rates of violent
assault have risen by an average of two per cent each year since the early 2000’s, even though
the rates of other crimes have declined (Australian Institute of Criminology, 2010).
Violence is not randomly distributed throughout the community, but instead
concentrates in particular types of places. In Australia, as is the case in many developed,
western countries, higher rates of violence occur in communities characterised by socioeconomic disadvantage. Disadvantage directly impacts violence through increasing one’s
exposure to violence and victimisation (Bingenheimer, Brennan & Earls 2005), but its effects
are also indirect. Poverty sets in motion a cycle that undermines the capacity to come together
to resolve local problems, which in turn sets in play a range of problems that deepen and
reinforce poverty and low levels of social cohesion and trust (Sampson & Morenoff, 2006).
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In Australia, several cross-sectional studies demonstrate the strong relationship
between disadvantage, social cohesion and violence (Mazerolle, Wickes & McBroom, 2010;
Vinson & Homel, 1975; Weatherburn & Lind, 2001). What is less well-understood is the
extent to which these characteristics and community processes influence violence over time.
We argue a longitudinal approach is necessary to, not only identify the key predictors of
violence, but to better inform community crime prevention programs concerned with the
reduction of crime and other social problems. In this chapter we therefore consider the
community-level characteristics and mechanisms associated with changes in violence over
time. Specifically, we explore recent social and economic changes in Brisbane state suburbs,
and the impact of these changes on rates of officially recorded violence. We meld
longitudinal data from the Australian Community Capacity Study (ACCS) with census and
police incident data and identify two critical community-level predictors of reductions in
levels of suburban violence over the past 15 years. These critical factors are: (i) decreases in
levels of social disadvantage, and (ii) increases in levels of social cohesion and trust.
Our analyses suggest that even relatively small changes in these factors over time can
correspond to socially significant changes in levels of officially recorded violence. This is, on
the one hand, very encouraging as evidence suggests that trust can be strengthened and rates
of violence substantially reduced by funding community organisations to develop and
implement local community improvement and prevention initiatives (Cooper, Bumbarger &
Moore, 2013; Ramey & Shrider, 2014). On the other hand, our research indicates that any
reductions in violence achieved through community building strategies might be undermined
if economic events or government policies have the effect of increasing spatial social
inequality, as has indeed been happening for several decades (Australian Social Inclusion
Board, 2011).
Social Disorganisation and Violent Crime
A focus on violence in urban communities is important for several reasons. First, high
levels of violence are associated with decreased social capital; lower property values;
decreases in economic development; low levels of neighbourhood satisfaction; and increased
fear among residents (Rosenberg et al, 2006). Second, in Australia it is mostly in the suburbs
that family violence happens, child abuse is perpetrated, school children are bullied, youth
crime is committed, and alcohol gets abused. A focus on place brings in a large slice of the
violence problem, doubly so if the place is socially disadvantaged (Mazerolle et al, 2010;
Vinson & Homel, 1975).
2
Social disorganisation theorists contend that the concentration of crime and other
problems in socially disorganised areas occurs because community structural characteristics
(particularly poverty, racial/ethnic concentration, and residential instability) break down the
key regulatory processes (formal and informal) associated with maintaining order and that
this, in turn, allows serious crime to flourish (Bursik & Grasmick, 1993; Sampson & Groves,
1989; Sampson, Raudenbush & Earls, 1997; Shaw & McKay, 1969). Traditionally it was
argued that social order was only possible in neighbourhoods where close relationships
between residents were present to foster the realisation of common values and the
enforcement of informal social controls (Bursik & Grasmick, 1993; Kornhauser, 1978). In the
mid-1990’s social disorganisation theory was transformed, shifting in emphasis from the role
of strong ties in preventing crime to the neighbourhood’s capacity to exercise informal
control. Sampson and his colleagues demonstrated that violence and violent victimisation
resulted from the differential ability of neighbourhoods to realise the common values of
residents and maintain effective social controls, which they referred to as collective efficacy.
Even in communities characterised by weak ties, when residents trusted each other and were
willing to work together to solve local problems, violence was lower (Sampson et al., 1997).
In the Australian context the core tenets of social disorganisation theory also help to
explain the spatial concentration of violence. For example, Vinson and Homel (1975)
examined the concordance of social problems (including crime) in communities in
Newcastle, with Braithwaite (1979) later examining the relationship between social status and
crime across Australia. More recently, Weatherburn and Lind (2001) surveyed Sydney-area
suburbs and proposed an epidemic model of growth in the offender population derived from
measures of economic and social stress. In 2010, Mazerolle and her colleagues provided the
most comprehensive Australian test of the contemporary social disorganisation thesis.
Drawing on the first wave of the Australian Community Capacity Study, they examined the
relationship between social structural characteristics (poverty, racial/ethnic concentration and
residential mobility), collective efficacy, and rates of violence and violent victimisation. In
Brisbane, as in Chicago, a significant proportion of variation in collective efficacy and
violence was due to between-suburb differences in structural characteristics. Thus levels of
social cohesion and trust and the willingness of residents to intervene in prosocial ways
differed across the sample neighbourhoods. Although the absolute level of violence in
Brisbane was nearly three times lower than in Chicago, violent crime clustered, as expected,
in neighbourhoods characterised by disadvantage.
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The main finding from this research was that high levels of collective efficacy were
strongly associated with lower rates of violence and the likelihood of household victimisation
(Mazerolle et al., 2010). Moreover, collective efficacy mediated the direct relationship
between disadvantage and violent incidents and rates of victimisation for violence. This
suggests that the spatial clustering of violence in Australia can be explained by the same
socio-demographic characteristics and neighbourhood processes as found in studies in the
United States, the Netherlands, Sweden and the United Kingdom (Markowitz, Bellair, Liska
& Liu, 2001; Sampson et al., 1997; Sampson & Wikstrom, 2008; Steenbeek & Hipp, 2011).
Longitudinal Studies of Social Disorganisation Theory
Inherent in all forms of social disorganisation theory are assumptions about the
temporal associations between social conditions, social processes and violence. To date,
empirical studies concerned with exploring these relationships have relied almost exclusively
on cross-sectional data or have only considered changes over two time points (Markowitz et
al, 2001 and Steenbeek & Hipp, 2011 are exceptions). Because longitudinal survey data on
neighbourhood social processes are so rare, few studies can thoroughly examine the temporal
component of the social disorganisation thesis. Thus it is difficult to ascertain if changes in
neighbourhood crime rates are indeed a function of changes to the neighbourhood sociodemographic structure and the social processes necessary for community regulation, as
argued by many scholars (Hipp, 2011; Kubrin & Herting, 2003; Weisburd, Groff & Yang,
2012).
So what do we know? There is evidence emerging from a handful of longitudinal
studies of places that the structural characteristics associated with violence are stable over
time. In Chicago, the percentage of families in poverty in 1960 is correlated with the
percentage of families in poverty in 2000 at 0.78 (Sampson 2012). Across all census tracts in
the United States the percentages of people receiving public assistance in 1990 and 2000 are
correlated at 0.85. Racial segregation is also profoundly stable in the United States and this
segregation, combined with high levels of poverty has devastating effects on the social
processes necessary to combat violence (Sampson, 2012). Studies also show that the social
processes necessary for crime control, like social cohesion, are stable over time (Markowitz
et al., 2001; Steenbeek & Hipp, 2011). Drawing on six waves of panel and census data from
74 neighbourhoods in the Netherlands, Steenbeek and Hipp’s (2011) research established that
social cohesion was remarkably stable. Although there was considerable variation between
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neighbourhoods, over time areas low in social cohesion at one point displayed low social
cohesion at the next time point. In the U.K. the story is same (Markowitz et al., 2001).
How the stability (or change) in structural characteristics and neighbourhood
processes, like social cohesion, influence crime longitudinally is less clear, though two
studies provide some support for the causal associations depicted in social disorganisation
theory. Using their community longitudinal data, Steenbeek and Hipp (2011) found that
despite the stability in disorder over time, neighbourhood disorder increased residential
instability, which in turn decreased informal social control. Markowitz and colleagues (2001,
p.313) report similar results: “In areas of decline, levels of fear are higher, inhibiting the
degree of neighbourhood cohesion and the capacity for the informal controls necessary to
curb crime.” Thus while we know that social structure and community regulatory processes
are strongly associated with violence, we have a limited understanding of how these
processes influence stability or changes in violence over time.
The Australian Community Capacity Study (ACCS) 1 is one of just four studies in the
world designed to examine neighbourhood processes and crime and disorder prospectively
over time. In this chapter, we draw on three waves of the ACCS, in combination with census
and police incident data, to examine the temporal relationship between socio-structural
characteristics, community cohesion, and violence. Three key questions drive our analyses:
1. How stable is violence over time and across suburbs?
2. How stable are community structures and processes over time?
3. What are the most important predictors of violence over time?
Our final question, which we reflect on in our discussion, is shaped by the answers to the first
three:
4. How can community prevention innovations be developed to address the factors that
predict community-level changes in violence?
The Australian Community Capacity Study
The ACCS is a longitudinal panel study of urban communities in Australia (for
further information please see http://www.uq.edu.au/accs/index.html). The primary goal of
this research is to develop a longitudinal understanding of the socio-structural characteristics
and social processes that influence violence over time. The ACCS can be compared to a
5
handful of international studies that prospectively study the relationship between
neighbourhood processes and social problems, including the Project for Human Development
in Chicago Neighborhoods (PHDCN), the Los Angeles Family and Neighborhoods Study
(LA FANS) and Nieuw Utrechts Piel (NUP) in the Netherlands.
The ACCS has been conducted in Brisbane and Melbourne. Of interest to this study is
the Brisbane site. Brisbane is the state capital of Queensland and at the last census recorded a
population of nearly 2 million people. The current research employs three waves of data
collected in 2008, 2010 and 2012 representing the second, third and fourth waves of the
ACCS conducted in the Brisbane Statistical Division (BSD).2 The ACCS sample comprises
148 randomly selected suburbs with a residential population ranging from 245 to 20,999
(total suburbs in the BSD = 429).
Participants
The survey was administered by the Institute for Social Science Research at the
University of Queensland using computer-assisted telephone interviewing and random digit
dialling. Interviews lasted approximately 20-24 minutes. The sample sizes for Waves 2, 3 and
4 of the ACCS were 4,244; 4,403; and 4,132 participants respectively. Each wave comprised
respondents who had indicated their willingness to continue with the study at the previous
wave, and an additional top up sample. The consent and completion rates for the ACCS were
69.3% for Wave 2; 68.5% for Wave 3 and 46.2% for Wave 4. These rates represent the
number of interviews completed proportional to the number of in-scope contacts. The inscope survey population comprised all people aged 18 years or over with telephones who
were usually resident in private dwellings in the selected communities.
An analysis of attrition rates indicated that older respondents were more likely to take
part in future research compared to younger participants, as were those with dependent
children. Those who owned their home and those who had resided at their current address for
longer were also more likely to participate in future waves. Our results are therefore perhaps
somewhat skewed to the views of older, long-term residents and those with children.
Variables
Dependent variable. Our dependent variable was derived from the Queensland Police
Service (QPS) incident data and is the suburb rate of violent crime per 100,000 residents for
the period 2006 to 2011. Violent crime includes homicide; other homicide; assault; and
robbery. We averaged the data for each ACCS year with the violence rates in the years
6
immediately before and after. For example, for Wave 2 (2008) we averaged the police data
for 2007, 2008 and 2009 to yield the violence score for 2008.3 Independent variable. The
measure of social cohesion and trust employed in the current study is a scale comprising
survey responses to four items. Respondents were asked to state their agreement with the
following statements on a scale of 1-5: People around here are willing to help their
neighbours; This is a close-knit community; People in this community can be trusted; People
in this community do not share the same values. At each wave, the internal reliabilities for
this scale (alpha scores) exceeded 0.68. In our analytic models we estimated fixed-effects
models that included indicator variables for all suburbs in Brisbane, as well as several
individual characteristics that might systematically bias perceptions of social cohesion in the
suburb. The estimated coefficients for each suburb from these analyses were then used as
unbiased estimates of the amount of social cohesion in the suburb. Thus we followed
standard procedures in constructing an ecometrically valid community-level measure of
cohesion and trust (Raudenbush & Sampson, 1999), using part of the widely used 10-item
measure of community collective efficacy (Sampson et al, 1997).
Control Variables. Violent crime rates are influenced by several suburb
characteristics, including socio-economic disadvantage;
population
density;
family
disruption; age structure; racial composition, and residential instability (Baumer, Lauritsen,
Rosenfeld & Wright, 1998; Sampson & Groves, 1989; Sampson et al., 1997). We selected
data from the 2001, 2006 and 2011 censuses to capture these suburb characteristics using the
2006 census boundaries to ensure geographical consistency across all three waves of data.
These census years were selected since they precede the ACCS years (2008, 2010 and 2012)
and allow us to investigate the stability of social structural factors over time.4 The control
variables used in our analyses were:
Disadvantage: Consistent with previous studies, we conducted a principal components
analysis on variables that potentially measured an underlying dimension of ‘disadvantage’
(Land, McCall & Cohen, 1990; Sampson et al, 1997). A single factor comprising four
variables emerged from the results (factor loadings are in parenthesis): percentage of
neighbourhood households renting (0.872); percentage of neighbourhood households that
had an average weekly income less than $799 (0.831); the percentage of unemployed
residents (0.824); and the percentage of neighbourhood residents who identified as
Indigenous (0.751). A general disadvantage factor was clearly identifiable, and a
composite factor score of these four measures was used in all analyses.
7
Ethnic Diversity: Ethnic heterogeneity of the neighbourhood was measured by the Blau
index of language diversity (Blau, 1977). 5 The index represents the likelihood that two
randomly selected residents from the suburb speak a different language. A perfectly
homogenous group would receive a score of 0 while a completely heterogeneous group
would receive a score of 1. While we recognise that ethnic diversity encompasses more
than just language, previous research shows that language diversity is more consequential
for community social processes in the Australian context (Leigh, 2006; Wickes et al.,
2013).
Residential Mobility: Drawing on the social disorganisation perspective we included a
measure of residential mobility in our analyses. This captures the percentage of residents
at a different address five years prior to each census period (2001, 2006 and 2011).
Population Density: Measures of population size or density are frequently included in
studies of neighbourhood violence (Baumer et al., 1998; MacDonald, 2002). Research
tends to find a positive association between population density and violent crime, but
results vary depending on the sample and analytic strategy (Deane et al., 2008). The
measure of population density included in the current study is calculated as the number of
neighbourhood residents per square kilometre. Population density was converted to
logarithms to reduce right skew.
Results
Our first research question was: How stable is violence over time and across suburbs?
The short answer to the first part of this question is that, on average, rates of violence within
the 148 suburbs did not change much between 2006 and 2011. Table 1 shows the
(untransformed) violence rates for the three time points. Although there was a trend
downwards over time, from 896.95 to 814.20 incidents per 100,000 population, this trend is
not statistically significant. Indeed examination of the time series of monthly violence rates
for the 148 suburbs for a longer time period, January 1996 to December 2011 (this graph is
not shown), demonstrates a stationary pattern, reflecting the pattern for the whole of
Queensland.
TABLE 1 ABOUT HERE
The answer to the second part of the first research question is, as expected, there was
considerable variability between suburbs in their violence rates at any given time, and also
considerable variation between suburbs in terms of their levels of within suburb change in
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violence over time. To clarify this last point, although on average the 148 suburbs showed
stability in violence rates, some suburbs became more violent between 2007 and 2012, some
became less violent, and others stayed pretty much the same. Figure 1 shows that the suburbs
that became more violent tended to cluster in the north (e.g., Caboolture, Mango Hill,
Kallangur, Morayfield) or to run in a ragged ribbon across the south (Forest Lakes, Redbank
Plains, Browns Plains, Shailer Park). Suburbs that dropped most in recorded violence
included some surprises, such as Inala, Woodridge, Underwood, and Slacks Creek in the
south, all with reputations for above average rates of crime. Other improving areas included
wealthy near-city suburbs such as Paddington and Ashgrove, as well as Bayside areas such as
Alexandra Hills.
FIGURE 1 ABOUT HERE
Our second research question asked, How stable are neighbourhood structures and
processes over time? As shown in Table 1, living standards improved markedly, particularly
between 2001 and 2006, evidenced by the decline in the percentage of residents with low
incomes or who were unemployed. These trends are reflected in the decline in scores on the
index of disadvantage (from 18.38 to 13.56), a statistically significant drop. There appeared
also to be a drift of Indigenous people to the city, a growth in language diversity, and
increasing residential stability. Population density showed little change.
Notwithstanding this trend to greater affluence and more stability, the index of social
cohesion and trust showed a slight but statistically significant decline, especially between
Wave 2 and Wave 3 (Times 1 and 2 in Table 1). Figure 2, which shows the geographical
distribution of high and low cohesion scores in relation to the distribution of disadvantage,
suggests that lower cohesion suburbs are mostly more disadvantaged, although there is a
cluster of communities on the south side that score in the higher range on cohesion but have
moderate to average levels of disadvantage (e.g., Corinda). The strong negative relationship
between cohesion/trust and disadvantage is confirmed by the correlation of -0.46, shown in
Table 2. Suburbs that are diverse in languages spoken or with high levels of residential
mobility also tended to score lower on cohesion and trust.
FIGURE 2 ABOUT HERE
TABLE 2 ABOUT HERE
Our third research question asked, What are the most important predictors of violence
over time? To examine suburb level predictors of the violent crime rate, specifically the
association between changes in social cohesion and trust and changes in the violent crime rate
9
over time, we adopted a hybrid fixed effects analytic approach. A hybrid fixed effects model
separates the within-neighbourhood, or time variant, effect of social cohesion and trust from
the contextual or between-neighbourhood effect of social cohesion and trust. This is the most
appropriate model because we are interested in examining the influence on violent crime
rates of both within neighbourhood change over time and between neighbourhood variation
in key characteristics (Andreson, 2012).6
TABLE 3 ABOUT HERE
Overall the results conformed to theoretical expectations and the findings of previous
research. As indicated in Table 3, there was a significant association between changes in
suburb social cohesion and trust over time and rates of violent crime, such that suburbs that
reported increases in social cohesion and trust experienced lower rates of violence (β = 0.463, p = 0.05). Changes in residential mobility, population density, and language diversity
did not lead to higher violent crime rates, but increases in disadvantage had a marginally
significant effect (p = 0.06).
Looking to the pooled effects of our variables, on average, across suburbs and across
time, social cohesion and trust had a negative association with rate of violent crime (β= 0.693, p = 0.02) while disadvantage had a positive association with the violent crime rate (β=
0. 094, p<0.001). These were the only variables that influenced violent crime rates in our
model.
Unfortunately the complexity of the model, which is essential in order to incorporate
both within suburb and between suburb variations in rates of violence over time, precludes
simple interpretation of effect sizes. Moreover the suburb level measure of social cohesion
and trust has been modelled as a latent scale score so has no straightforward metric. For these
reasons it is safest to draw conclusions in general terms, without attempting to infer exactly
what reductions in violence would follow improvements in cohesion and trust or increases in
social disadvantage. What the model does allow us to predict is that, at least over a relatively
short time span, only changes in aggregate levels of cohesion and trust and in levels of social
disadvantage will have statistically detectable effects on rates of police-recorded violence.
None of the other variables we included in the model, selected in the light of social
disorganisation research, can be predicted to have such effects.
Future directions for community violence prevention
10
Our research set out to examine the stability of disadvantage, social cohesion and
violence. Working within a social disorganisation framework our findings demonstrate the
stability of these community characteristics and processes. We also find that disadvantage is a
particularly strong predictor of violence. Over time, as disadvantage increases, so too does
the level of violence experienced in the community. Yet we also find that increases in social
cohesion can have a significant influence on violence. So how can community prevention
innovations be developed to address the factors that we have shown to predict communitylevel changes in violence?
The promise of community prevention rests on research that demonstrates the link
between social cohesion and the willingness of residents to work together to solve local
problems and lower rates of crime and delinquency. Even in communities characterised by
poverty, racial/ethnic segregation and residential instability (Mazerolle et al 2010; Morenoff
et al., 2001; Sampson et al., 1997) cohesion can lead to lower crime. For this reason crime
prevention initiatives influenced by social disorganisation theory have historically
emphasised community building strategies to empower residents to participate in decisionmaking, strengthen informal networks, and enhance community organisation (Morgan,
Boxall, Lindeman, & Anderson, 2012; Wickes, in press).
Crime prevention initiatives using the community empowerment approach face
multiple problems, especially when attempted in high crime, socially disadvantaged areas.
One explanation for the consistent failure of community crime prevention initiatives is that
when multiple players and stakeholders attempt to implement their own ideas of what works
the result is “muddled, inconsistent, and untheorised” prevention efforts (Hope, 1995;
Wickes, in press). In particular, many initiatives apparently fail to strengthen the capacity of
community members to do what social disorganisation theory says is important, namely
respond in a timely and preventative way to unwanted behaviour in the neighbourhood.
Proponents of the now dominant research-to-practice model of prevention (Flaspohler et al.,
2008) would argue that the failure to draw on and properly implement evidence-based
interventions is a key reason why community empowerment initiatives do not address
effectively the risk factors for crime.
One response to our findings, and to the disappointing outcomes of eighty years of
attempts to reduce crime through community prevention initiatives, would be to conclude that
suburban violence cannot feasibly be prevented through community development or
mobilisation strategies. It can be argued persuasively that the geographical distribution of
social disadvantage is determined by economic policies, the housing market, and societal
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change, all of which are largely beyond the power of local communities to influence.
Universal services such as preschool, health care, and the welfare safety net, as well as
community-based services such as family support, may do something to buffer the impacts of
markets and large-scale social change on the most vulnerable individuals and communities.
Yet this may have little impact on rates of suburban violence, which can only really be
reduced, if at all, through political and economic actions at the national (or international)
level.
Countering this perspective is one that asserts that in Australia “it is feasible to
substantially reduce violent and other offending by implementing evidence-based prevention
and early intervention strategies” (Toumbourou et al , in press). One particularly promising
such strategy is Communities That Care (CTC), a community mobilisation approach that
arguably synthesises the best of the old tradition of collaborative community action research
with modern prevention science (Greenberg, Feinberg, Gomez & Osgood, 2005; Hawkins et
al., 2009). A second strategy with demonstrated effectiveness for long-term violence
reduction is Washington State’s Neighborhood Matching Fund (NMF), which provides city
funding for neighbourhood improvement activities organised locally (Ramey & Shrider,
2014).
A key link between these contemporary approaches and the older tradition of
community prevention is the creation of community coalitions consisting of engaged
residents (including young people) and representatives from local agencies. These coalitions
frequently grow from grassroots initiatives, but in the CTC model receive extensive technical
assistance to measure risk and protective factors for youth in the area and to implement and
evaluate evidence-based initiatives that have the best chance of effectively addressing the
small number of factors selected by each local coalition as a target for action. The prevention
science elements of the NMF model are less clear, but what is clearly evident from both CTC
and NMF is the critical importance of external funding. Ramey and Shrider found that
funding was particularly beneficial for poorer communities where reductions in violence
were greatest, but so far CTC researchers have struggled to demonstrate effectiveness in
disorganised high crime urban settings (Brown, Feinberg & Greenberg, 2010).
In Australia the CTC model has been trialled in three communities including the
Mornington Peninsula, a moderately disadvantaged region where a prevention-oriented
coalition emerged in 2000 through the initiative of a group of local activists and was then
boosted through state government funding (Toumbourou et al, in press). In 2002, before the
CTC process had begun to be properly initiated, a comprehensive survey of youth showed
12
that 22 percent of 13 year-olds engaged in one or more of a range of violent and antisocial
acts, such as attacking someone with the intent of seriously hurting them. By 2012 this had
dropped to 13 percent, while in the same period in the rest of Victoria the figure was virtually
static at 17 percent in 2002 and 16 percent in 2012.
There is every likelihood that many CTC and related initiatives have increased levels
of community cohesion and trust as a simple function of their effectiveness in building wellfunctioning and comprehensive local coalitions with demonstrable effectiveness, but the
research to test this hypothesis remains to be done. Such research should include, in a
longitudinal design, the measures of community structure and processes that we have
identified in this chapter, alongside the well-established measures of risk and protective
factors, coalition functioning, and impacts on community social cohesion and trust and rates
of violence.
We recognise that experience over many years with community empowerment and
crime prevention initiatives in disadvantaged, socially disorganised communities is not at all
encouraging (Homel & McGee, 2012; Wickes, in press). This long history of failure leads us
to temper our cautious optimism about new community prevention approaches with realism
about the corrosive effects of poverty and increasing social inequality on the capacity of
communities to focus on local problems and to get organised to address them effectively,
even with external funding (Greenberg et al, 2007). Nevertheless on the basis of our analyses
and even more in the light of recent advances in prevention science (Spoth et al., 2013), we
propose that strategies to improve safety must combine the best insights from both
collaborative community action research and prevention science (Weissberg & Greenberg,
1998), using a community mobilisation approach. Specifically, we argue that aligning the
fruits of the many years of social disorganisation research with the emerging science of
prevention, using CTC or a similar model, holds the promise of taking community prevention
research to a new level, especially if experiments are focused on socially disadvantaged
communities. Such endeavours also hold the key to identifying both the critical mechanisms
that allow for the development of social cohesion and the ways in which social cohesion
manifests in activities that lead to lower rates of violence.
Endnotes
1. This work was supported by the Australian Research Council (LP0453763; DP0771785;
RO700002; DP1093960; DP1094589 and DE130100958).
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2. The first wave of the ACCS conducted in 2005 was not employed here because the
geographic boundaries of the sampling unit shifted significantly between Waves 1 and 2.
3. The averaged incident rates were then converted to logarithms to reduce right skew.
4. The census years do however precede the ACCS years by variable periods, with the 2001
and 2006 censuses both preceding the second wave of the ACCS study (which is the first
wave used in this analysis). If there were a stronger theoretical and empirical basis than is
currently available for selecting an appropriate time lag between census and survey year
we could have used data interpolation methods to estimate census characteristics for the
appropriate years, but in the absence of that information the census variables should be
regarded as providing an approximate picture of how suburb characteristics changed
some years before each of the ACCS waves.
5. The Blau index (Blau, 1977) was calculated using the following ABS census language
categories: Northern European; Southern European; Eastern European; Southwest Central
Asian; Southern Asian; South East Asian; Eastern Asian Languages; Australian
Indigenous Languages; English Only. The Blau index is defined as:
1 - Σ𝑝𝑖2
where p is the proportion of group members in a given category and i is the number of
different categories.
6. When using panel data one can use a random or fixed effects estimator. The fixed effects
estimator is based on the time series component of the data while random effects
estimation uses both the cross-section and time-series components (Andreson, 2012).
While the Hausman test suggests the fixed-effects model is most appropriate in this
situation our research question is most fully addressed by the hybrid model (Philips &
Greenberg, 2008). The hybrid model augments the fixed effects model by borrowing
elements of the random effects model to reduce bias associated with the random effects
strategy. It should be noted that the effects of previous violence, often incorporated as an
independent variable into regression models, is factored into the present model as a
function of the time-varying dependent variable. The model was fitted by the maximum
likelihood method and all analyses were conducted in STATA 13.0.
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19
Figure 1. Change in violence rates in ACCS suburbs between Times 1 and 3 (2008 and
2011)
20
Figure 2. Spatial distribution of disadvantage and social cohesion and trust at Time 3
(2011)
21
Table 1: Descriptive statistics for all variables
Time 1
Time 2
Time 3
Mean
SD
Min
Max
Mean
SD
Min
Max
Mean
SD
Min
Max
897
1557
0
16949
854
988
0
7062
814
1440
0
14407
Trust 3.76
0.28
3.08
4.31
3.69
0.27
2.98
4.28
3.71
0.28
3.02
4.28
18.38
6.26
6.54
33.29
13.68
5.84
3.93
28.24
13.56
5.77
3.88
27.42
% low income
39.70
11.77
17.06
66.57
27.30
11.10
8.15
55.24
22.07
9.37
6.96
46.41
% unemployed
4.92
1.82
1.83
10.09
2.80
1.05
0.53
6.04
3.80
1.30
1.34
8.15
% renting
27.39
12.64
4.05
57.76
23.01
12.56
1.75
51.14
26.49
13.70
2.36
57.43
% Indigenous
1.53
1.19
0
6.83
1.58
1.56
0
9.08
1.88
1.66
0
9.09
Language Diversity
0.23
0.12
0.05
0.65
0.26
0.15
0.04
0.78
0.28
0.17
0.06
0.72
Residential Mobility
45.92
9.79
20.72
74.16
42.32
8.97
23.45
79.32
38.19
10.34
14.86
74.09
Population Density
9.75
7.23
0.11
29.30
8.99
8.29
0.08
33.81
10.17
9.02
0.10
36.66
Violent Crime Rate (2008,
2010, 2011)
Social
Cohesion
and
(Wave 2-2008; 3-2010; 4-2012)
Census Variables (2001; 2006;
2011)
Disadvantage
22
Table 2. Correlations of violence, social cohesion and trust, and the independent
variables, averaged across Times 1, 2 and 3
Variables
1- Social Cohesion and Trust Scale
1
2
3
4
5
6
1
2- Disadvantage
-0.46*
1
3- Language Diversity
-0.46*
0.35*
1
4- Residential Mobility
-0.04
0.25*
0.10
1
5- Population Density
-0.32*
0.41*
0.47*
0.23*
1
6- Violence
-0.29*
0.39*
-0.05
0.05
0.27*
1
* Correlation is significant at the 0.01 level (2-tailed)
23
Table 3: Results for hybrid regression analysis examining the effect of suburb social
cohesion and trust on rates of violent crime over time
β
SE
p
Social Cohesion and Trust
-0.463
0.181
0.01
Disadvantage
0.016
0.009
0.06
Residential Mobility
0.004
0.004
0.38
Language Diversity
-0.176
0.450
0.73
Population Density
-0.062
0.053
0.24
Social Cohesion and Trust
-0.693
0.291
0.02
Disadvantage
0.094
0.012
0.00
Residential Mobility
-0.009
0.006
0.14
Language Diversity
0.172
0.415
0.68
Population Density
-0.043
0.047
0.35
Intercept
5.298
0.283
0.00
Variable
Within Neighbourhood Variation
Between Neighbourhood Variation
𝑅2
50.2%
24