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DOI: 10.1177/14407833050483812005 41: 7Journal of Sociology

Rod McCrea, Tung-Kai Shyy, John Western and Robert J. Stimsonperspective

Fear of crime in Brisbane: Individual, social and neighbourhood factors in

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Fear of crime in BrisbaneIndividual, social and neighbourhood

factors in perspectiveRod McCrea1, Tung-Kai Shyy1, John Western2 and

Robert J. Stimson1

1. Centre for Research into Sustainable Urban and Regional Futures, School ofGeography, Planning and Architecture, University of Queensland

2. School of Social Science, University of Queensland

AbstractNumerous theories apply to fear of crime and each are associated with differ-

ent kinds of variables. Most studies use only one theory, though this study

examines the relative importance of different kinds of variables across a num-

ber of theories. The study uses data from a survey of residents in Brisbane,

Australia to examine the relative importance of individual attributes, neigh-

bourhood disorder, social processes and neighbourhood structure in predicting

fear of crime. Individual attributes and neighbourhood disorder were found tobe important predictors of fear of crime, while social processes and neigh-

bourhood structure were found to be far less important. The theoretical impli-

cations are that the vulnerability hypothesis and the incivilities thesis are most

appropriate for investigating fear of crime, though social disorganization theory

does provide conceptual support for the incivilities thesis. Although social pro-

cesses are less important in predicting fear of crime than neighbourhood inci-

vilities, they are still integrally related to fear of crime: they explain how

incivilities arise, they buffer against fear of crime, and they are affected by fear

of crime.

Keywords: fear of crime, incivilities, neighbourhood disorder, neighbourhood

structure, social disorganization, vulnerability

Fear of crime is important, not only for individuals, but also for neigh-bourhoods and the wider society. At the individual level, fear of crime hasdetrimental psychological effects (White et al., 1987), it restricts personalfreedoms by limiting how freely people move about their neighbourhoods

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(Liska et al., 1988), and contributes to dissatisfaction with the neighbour-hood, the community and overall life (Sirgy and Cornwell, 2002). At theneighbourhood level, fear of crime decreases neighbourhood cohesion, par-ticipation in neighbourhood associations and community ties (Riger et al.,1981; Perkins et al., 1990; Markowitz et al., 2001). At the societal level, thefear of crime burden may be unfairly placed on those already socially andeconomically disadvantaged, and without sufficient resources to protectthemselves and their possessions or to move from the high crime areas(Hale, 1996).

This article uses a sample of Brisbane residents to examine the relativeimportance of various factors in predicting fear of crime: individualattributes, neighbourhood disorder, social processes and neighbourhoodstructure. Individual attributes such as age and sex predict fear of crime viaperceptions of vulnerability. Signs of neighbourhood disorder such as

drunken behaviour and vandalism lead to fear of crime via increased per-ceptions of risk. Social processes such as those involving trust, reciprocityand a sense of community are seen as mediating the relationship betweenneighbourhood structure and both neighbourhood disorder and actualcrime rates. However, neighbourhood structural variables such as neigh-bourhood socio-economic status and urbanization are rarely empiricallyrelated to fear of crime. This is despite neighbourhood structure being asso-ciated with neighbourhood disorder, and disorder being associated withfear of crime. Thus an aim in this article is to examine the relative impor-

tance of neighbourhood structural variables in predicting fear of crime ascompared to other factors.

Fear of crimeThe fear of crime construct and its measurement have both been subjectto debate. Regarding the construct, some researchers argue that it bestrelates to, and should be limited to, feelings of fear directed at crime objects(e.g. Hale, 1996). In contrast, others argue that the fear of crime construct

necessarily includes, not only feelings, but also cognitive judgements, suchas the likelihood of victimization, and even behavioural aspects, such asavoiding walking alone at night (e.g. Gabriel and Greve, 2003). Such debateresults in different interpretations of research findings, as well as differentviews about the best ways to measure fear of crime.

There are many issues associated with measuring fear of crime (for areview, see Hale, 1996). One of the most basic issues is whether to useglobal measures or more specific measures. Global measures are single itemindicators that do not refer to any particular crime (Hale, 1996); for exam-

ple, feelings of safety when walking in the neighbourhood alone at night.The underlying problems with global measures are vagueness and over-estimating the prevalence of fear of crime (Farrall et al., 1997). On the other

8 Journal of Sociology 41(1)



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hand, specific measures can distinguish between different dimensions of fearof crime (such as emotional or cognitive judgements; crimes against prop-erty or person; and hypothetical or real situations) as well as exploringimportant time, space and social contexts when measuring fear of crime(Farrall et al., 1997). While specific measures are likely to be better whenused for particular purposes or when combined to form composite mea-sures, most researchers have used global measures (Hale, 1996) presumablybecause of lower collection times and costs.

Theories relating to fear of crimeVulnerability hypothesis

Individual factors are often related to the vulnerability hypothesis where

those perceiving themselves as vulnerable are likely to fear crime. In 1981,Skogan and Maxfield made salient the concept of vulnerability in crimeresearch. Shortly after, Warr and Stafford (1983) tested the idea of fear ofcrime as an interaction between sensitivity to risk and seriousness of conse-quences. Their rationale was that fear of crime related to more than just theseriousness of consequences, otherwise people would be most afraid of seri-ous crimes. Fear is also related to perceptions of risk. They found that sen-sitivity to risk and seriousness of consequences operate in a multiplicativeway in predicting fear of crime, and were of about equal weight. They also

showed that fear is not necessarily highest for the most serious crimes.Later, Killias (1990) developed the idea of vulnerability further by incorp-orating Banduras thesis of lack of control over both exposure to risks andseriousness of consequences (Bandura, 1986). Thus, the vulnerabilityhypothesis incorporates individual characteristics that relate to perceivedrisk, seriousness of consequences and lack of control (see Figure 1).

Sex is consistently the best predictor of fear of crime (Hale, 1996), andis closely linked with vulnerability. The concept of vulnerability helps toexplain why women are more fearful of crime, even when they are less

likely to be victims of crime than men. For example, although women maybe less likely to walk alone around their neighbourhood at night (i.e. lowerrisk exposure), they may feel more vulnerable because of more serious con-sequences (such as being raped) and less control (such as being physicallyweaker). An alternative explanation is that male bravado results in malesreporting lower fear of crime (Stanko and Hobdell, 1993). However, moreevidence exists to support the hypothesis that women feel more vulnerablebecause the risk of sexual assault heightens their fear of victimization (e.g.Ferraro, 1996; Fisher and Sloan, 2003).

Age is a common predictor of fear of crime and is also linked to vulner-ability (Hale, 1996). Like women, older people have lower victimizationrates but tend to have higher fear of crime, which may be from perceptions

McCrea et al.: Fear of crime in Brisbane 9



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of more serious consequences and less situational control. The lower vic-timization rates in older persons (and women) could also be a consequenceof fear of crime as more vulnerable persons take additional precautionsagainst victimization (Hale, 1996).

Despite age and fear of crime being commonly associated, age is not aspowerful in predicting fear of crime as sex, and was insignificant in somestudies (e.g. Ferraro and Lagrange, 1992; Mawby, 2004). However, bothage and sex are included as individual attributes predicting fear of crime inthis study.

Many other individual attributes may be associated with vulnerability.These include weight, physical handicaps, physical shape and self-confi-dence, which Killias and Clerici (2000) use as measures of vulnerability.Housing factors such as living alone or renting accommodation can alsoinfluence levels of perceived control and vulnerability. Despite the impor-

tance of these factors, the main individual attributes predicting fear of crimefound in the literature are sex and age.

Incivilities thesis

Neighbourhood characteristics that predict fear of crime usually relate to theincivilities thesis. The incivilities thesis arose from a need to explain the per-vasiveness of fear of crime despite relatively low chances of victimization,especially in urban settings (Lagrange et al., 1992). Incivilities include bothsocial and physical disorder. Social disorder includes things such as gang

activities, loud parties, homelessness, drunkenness and loitering; whilephys-ical disorder includes things such as vandalism, littering, vacant housing,abandoned cars and buildings, and untidy allotments. These incivilities act assigns of the breaking down of both norms of behaviour and social control inthe local area (Skogan and Maxfield, 1981; Perkins and Taylor, 1996).People associate such signs with increased risk of crime (Skogan, 1990) andin turn experience increased fear of crime (Lagrange et al., 1992), see Figure 1.

Although the incivilities thesis involves social processes via perceptionsof social norms and behaviours, it does not require the relationship between

incivilities and fear of crime to be mediated by social processes. Accordingto the incivilities thesis, people can experience fear of crime simply byobserving incivilities and perceiving a relationship between incivilities andan increased risk of crime. The dotted curved arrow in Figure 1 representsthis perceived relationship. There is evidence for this relatively direct effectof incivilities on fear of crime, mediated by increased perceptions of risk(Lagrange et al., 1992), while there is less support for the relationship beingmediated by social processes. For example, Gibson et al. (2002) found thatthe effect of neighbourhood disorder on fear of crime is not mediated by

collective efficacy, and Kanan and Pruitt (2002) found that neighbourhoodintegration was a relatively unimportant indicator of fear of crime com-pared to incivilities.




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However, while social processes do not mediate the relationship betweenincivilities and fear of crime, they can predict fear of crime directly. Gibsonet al. (2002) found that both social integration and collective efficacy pre-

dicted fear of crime when controlling disorder. Further, Ross and Jang(2000) found that social integration can not only predict fear of crimedirectly, but can moderate the relationship between disorder and fear ofcrime such that the relationship is weaker for those more socially integrated.

Social disorganization theory

Little research has been conducted on the possible influence of neighbour-hood structure on fear of crime. This is surprising given that structuralcharacteristics such as neighbourhood socio-economic status and neigh-

bourhood residential turnover have been consistently associated with actualcrime rates and incivilities in studies investigating social disorganizationtheory. Thus, neighbourhood structure can be associated with fear of crimeby linking social disorganization theory and the incivilities hypothesis (seeFigure 1).

Social disorganization theory focuses on predicting crime rates ratherthan fear of crime, and tries to account for the relationship between neigh-bourhood structure and crime. As such, it focuses on the mediating influ-ence of social processes such as neighbourhood reciprocity, sense of

community, neighbourhood efficacy, and informal social control.The theory was first developed by researchers at the University ofChicago. During the mid- to late 19th century, Chicago experienced rapid


Figure 1: Relationships between theories and fear of crime



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population growth from an influx of foreign migrants and AfricanAmericans from the south. In search of work, these newcomers often set-tled near the city centre, close to factories. Inner city neighbourhoodsbecame social melting pots and were dirty from industrial pollution, withmany residents moving from the neighbourhood when possible. Thus theseneighbourhoods were termed zones in transition by Burgess (1967 [1925]),who theorized that cities expanded from their inner core.

Later, Shaw and McKay (1942) used zones in transition to explain juve-nile delinquency. They theorized that poverty, rapid population growth,ethnic diversity and population turnover associated with transitional zonesdisrupted core social institutions like the family, church and schools so thatthey were less able to exert informal social control on youths and theiractivities. Youths were thus more likely to roam the streets and to come intocontact with older juveniles and criminal elements, thereby linking disorga-

nizedneighbourhoods with incivilities and higher crime rates.Despite losing favour in the 1970s, social disorganization theory re-

emerged in the 1980s partly due to renewed interest in ecological perspec-tives, and also due to Sampson and Groves (1989), who found support forsocial disorganization theory. This study confirmed that neighbourhooddelinquency and crime rates were related to neighbourhood structure suchas ethnic heterogeneity, residential mobility and family disruption.However, it went beyond previous studies by showing that this relationshipwas largely mediated by social processes involving local friendship net-

works, control of street corner teenage peer groups and organizational par-ticipation. These findings have since been replicated (e.g. Lowenkamp et al.,2003).

While social disorganization theory was formulated to explain juveniledelinquency and crime rates, as mentioned the theory can be linked to fearof crime by incorporating the incivilities thesis. An essential part of socialdisorganization theory discourse includes lack of social control over youthactivities resulting in incivilities such as loitering, gang behaviour and van-dalism. This conceptual overlap between social disorganization theory and

the incivilities thesis means that their discourses are intertwined (e.g. Taylor,1996; Kanan and Pruitt, 2002). Thus social disorganization theory can beextended to predict fear of crime by first predicting incivilities or disorder(e.g. Markowitz et al., 2001). However, little research exists relating neigh-bourhood structural characteristics to fear of crime.

Empirical evidenceIndividual characteristics

Individual attributes are generally more important in predicting fear ofcrime than neighbourhood disorder or other neighbourhood characteristics.Killias and Clerici (2000) found that sex, age and self-assessed vulnerabil-




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ity were more important in predicting fear of walking after 10pm in theneighbourhood than were neighbourhood characteristics such as graffiti ordark parks, although they acknowledged that some neighbourhood charac-teristics like street lighting and environmental improvements were notincluded in their study. However, using hierarchical modelling, Robinson etal. (2003) found that individual differences explain more variation in fearof crime than neighbourhood effects.

As mentioned previously, sex is the most important individual attributein predicting fear of crime. Lagrange et al. (1992) found that sex is moreimportant than either physical or social disorder, or whether the respondentlives in urban or rural areas. However, in Perkins and Taylor (1996), sexhad approximately the same correlation with fear of crime as perceptionsof social disorder and physical disorder. Age is also an important predictorof fear of crime, though less important than sex, and also as mentioned, age

may not always be a significant predictor of fear of crime (e.g. Liska et al.,1988; Perkins and Taylor, 1996).

Neighbourhood structural characteristics, social processes anddisorder

As mentioned previously, little research has been undertaken linking neigh-bourhood structure with fear of crime. However, much research has beenundertaken linking it with actual crime. Using social disorganization the-ory, many social processes have been suggested as mediators between

neighbourhood structure and crime (e.g. sense of community, neighbour-hood cohesion and neighbourhood efficacy). However, the general findingis that social processes only partially mediate this relationship. Cantillon etal. (2003) used sense of community as a mediator, which consisted of con-nectedness, trust, exchange, vigilance, participation and interaction.However, only one path between various neighbourhood socio-economicvariables and youth outcome measures was fully mediated by a sense ofcommunity. Markowitz et al. (2001) used neighbourhood cohesion con-sisting of club/committee attendance, helpful neighbours and neighbour-

hood satisfaction. Even after controlling for neighbourhood cohesion,neighbourhood disorder was still predicted by median income, ethnic het-erogeneity and urbanization, while neighbourhood burglary was still pre-dicted by ethnic heterogeneity and urbanization. Sampson andRaudenbush (1999) used neighbourhood efficacy, which combined trustamong residents and willingness to intervene (Sampson et al., 1997).Again, neighbourhood efficacy only partially mediated neighbourhoodstructure, despite neighbourhood efficacy being a useful predictor of crimeand disorder.

Findings of partial mediation by social processes occur in a number ofother studies (e.g. Sampson and Groves, 1989; Veysey and Messner, 1999;Lowenkamp et al., 2003). This implies that while social processes predict




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crime and disorder, the relationship between neighbourhood structure andcrime/disorder is not totally accounted for by social processes.

HypothesesThis article aims to examine the relative importance of individualattributes, neighbourhood disorder, social processes and neighbourhoodstructure in predicting fear of crime. Most often studied are individualattributes relating to vulnerability and neighbourhood disorder. Social pro-cesses are often used in the discourse of explaining the relationship betweenneighbourhood disorder and fear of crime but are less often empiricallytested. Neighbourhood disorder (or incivilities) is often used to directly pre-dict fear of crime. Finally, the relative importance of neighbourhood struc-ture in predicting fear of crime is rarely examined, even though it iscommonly used to predict incivilities and crime rates.

The hypotheses in this article are based on Figure 1. Those factors mostproximate to fear of crime are hypothesized to have greatest predictivepower. Thus, individual attributes relating to vulnerability such as sex andage are hypothesized to explain most variation in fear of crime. Next mostimportant is expected to be neighbourhood disorder, which is connected tofear of crime via perceived risk. Since social processes are theorized to causeneighbourhood disorder, these are expected to be less important, and finallyneighbourhood structure is expected to be least important, being the mostdistal factor from fear of crime in Figure 1.

MethodThe sample

The sample of 140 consists of residents living in the Brisbane City Councilarea aged 18 years and over, and was taken from a larger sample of resi-dents in South East Queensland (SEQ) who participated in a quality of life

survey in 2003. The socio-economic and demographic characteristics ofparticipants in the survey closely matched those in SEQ as at the 2001 pop-ulation census on age, sex, marital status, ethnicity and full-time employ-ment, although survey participants were likely to have higher householdincome, higher level of education and were more likely to be living in ahouse.

The sample size for the SEQ survey was 1610. This sample wasreduced to 140 for this study for two reasons. First, in this study, onlyresidents in the Brisbane City Council area were selected because neigh-

bourhoods in Brisbane align reasonably well with suburbs and StatisticalLocal Areas (SLAs), which allows generation of neighbourhood struc-tural variables as explained later. Second, each SLA often had more than




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one survey respondent (four on average), and to avoid violating theassumption of independent prediction errors in the regression analysis thatfollows, one respondent only was randomly selected from each SLA.

Measures

Fear of crime was measured by asking residents how much they agreed withthe statement I feel safe walking around this neighbourhood after dark ona 5-point scale where 1 = strongly disagree and 5 = strongly agree. This wasreverse coded for ease of interpretation. Thus higher scores reflect higherfear of crime.

The individual attribute variables were age and sex. Age was measuredon an interval scale, while sex was measured as 1 = male and 2 = female.Theneighbourhood disorder variables measured physical disorder only, due todata availability. Two variables were used. Neighbourhood vandalism

asked how much residents agreed with the statement: vandalism is a prob-lem in this neighbourhood, where 1 = strongly disagree and 5 = stronglyagree. Neighbourhood cleanliness asked residents to rate cleanliness ofstreets and public areas, where 1 = not at all good and 5 = very good.

Six social process variables were used. The first three related to socialcapital notions of trust and reciprocity. Neighbourhood trust asked howmuch residents trusted their neighbours (who were not friends or family) toact in their best interests, where 1 = not at all and 5 = to a great extent.Neighbourhood goodwill asked how much residents agreed with the state-

ment that people in this neighbourhood are willing to help each other out,where 1 = strongly disagree and 5 = strongly agree. Neighbourhoodreciprocity asked residents how often they and their neighbours (who werenot friends and family) exchanged practical help or advice, where 1 = neverand 5 = very often.

The other three social process variables were neighbourhood involve-ment, neighbourhood friendliness, and sense of community. Neighbourhoodinvolvementfor each resident was measured by scoring 1 point for each ofthe following activities if they had been undertaken within the last year:

attending a meeting of a neighbourhood association or street committee; serving on a committee or as an officer in a neighbourhood association

or street committee; contacting government officials or city hall to deal with a neighbourhood

problem; meeting informally with neighbours to discuss a neighbourhood prob-

lem.

Using a 5-point scale where 1 = strongly disagree and 5 = strongly agree,

neighbourhood friendliness asked how much residents agreed with myneighbours are friendly people, and sense of community asked how muchthey agreed with there is a strong sense of community here.




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The social process and neighbourhood disorder variables are interpretedas neighbourhood characteristics. However, for any one neighbourhoodthese characteristics were based on the perceptions of only one individual.As Raudenbush and Sampson (1999) have shown, there is substantial vari-ation in individual perceptions of social cohesion, social control and disor-der within any one neighbourhood. Thus, the implications of thismeasurement error for interpreting the results are discussed later.

Neighbourhood structural variables used data for Statistical Local Areas(SLAs) from the 2001 Census of Population and Housing, conducted by theAustralia Bureau of Statistics. They were neighbourhood socio-economicstatus, neighbourhood urbanization, neighbourhood turnover, and neigh-bourhood heterogeneity. SLA boundaries were used as neighbourhoodareas because in Brisbane they closely align with suburbs, and residents inBrisbane often describe their suburb as their neighbourhood (see Table 1).

However, neighbourhoods are also often perceived to be smaller thansuburbs.

Neighbourhood socio-economic status scores were derived from a prin-cipal components analysis of five variables relating to socio-economic sta-tus (see Table 2). These variables all loaded onto one factor, which


Table 1: Description of neighbourhood by Brisbane residents

PercentageTheir neighbourhood of residents

The 5 to 6 houses nearest yours 5.7Your street 14.3The 2 to 5 streets around your address 11.4The 6 to 10 streets around your address 15.7The suburb you live in 34.3An area larger than the suburb you live in 15.7Other 2.9Total 100.0

N=140

Table 2: Variable factor loadings on neighbourhood socio-economic status

FactorVariable (%) loading

One parent families in SLA .929Dwelling in SLA rented from a public housing authority .836Households in SLA with an income of less than $500 per week .871Unemployed in SLA .768Residents in SLA with an education level not beyond Year 10 .567



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explained 64.6 percent of the total variation in the variables and had aneigenvalue of 3.2. Thus, neighbourhood socio-economic status was mea-sured using the factor scores from this single factor. The signs of the factorscores were reversed so that higher scores reflect higher neighbourhoodsocio-economic status.

Neighbourhood heterogeneity was the percentage of SLA residents bornoverseas; neighbourhood turnover was the percentage of SLA residents liv-ing at a different address five years ago; and neighbourhood urbanizationused a geographic information system (GIS) to measure the distance fromeach residents home to the centre (centroid) of the closest area zoned ascommercial or industrial use.

Results

While this article focuses on fear of crime, most residents of Brisbane feelrelatively safe when walking around their neighbourhood at night. In Table3, most residents agreed or strongly agreed that they felt safe. However, areasonable proportion (over 30 %) did not feel safe walking around theirneighbourhood at night.

The correlation table (Table 4) shows that individual attribute variables(age and sex) are significantly correlated with fear of crime in the expecteddirections, as are the disorder variables of neighbourhood vandalism andcleanliness. Generally speaking, the social processes variables were not as

highly correlated to fear of crime as individual attribute and neighbourhooddisorder variables, with two social process variables not significantlyrelated to fear of crime neighbourhood reciprocity and neighbourhoodinvolvement. Three neighbourhood structural variables were significant,although two were significant in an unanticipated direction neighbour-hood turnover and neighbourhood heterogeneity were associated with lessfear of crime. However, these two variables become insignificant in laterregression analysis. Unexpectedly, neighbourhood urbanization was not sig-nificantly correlated with fear of crime.


Table 3: Responses to question: I feel safe walking around this neighbourhoodafter dark

Response Frequency Percent

Strongly disagree 23 16.4Disagree 20 14.3Neither agree nor disagree 13 9.3Agree 54 38.6Strongly agree 30 21.4Total 140 100.0



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Table 4: Pearsons correlation coefficients of variables in the analysis

1 2 3 4 5 6 7 8 9

1. Fear of crimeIndividual attributevariables2. Age .28***3. Sex .36*** .164. Household income .27** .44*** .18*Neighbourhood disordervariables5. Neighbourhood vandalism .33*** .07 .14 .086. Neighbourhood cleanliness .23** .01 .02 .02 .12Social process variables7. Neighbourhood trust .17* .07 .02 .05 .06 .03

8. Neighbourhood goodwill .18* .06 .08 .08 .06 .18* .39***9. Neighbourhood .02 .19* .06 .04 .01 .05 .55*** .36***reciprocity10. Neighbourhood .06 .03 .05 .05 .04 .13 .13 .14 .1involvement11. Neighbourhood .21* .01 .01 .07 .23** .23** .47*** .55*** .3friendliness12. Sense of community .27** .04 .09 .12 .13 .17* .20* .48*** .1Neighbourhood structurevariables13. Neighbourhood .30*** .12 .02 .36*** .23** .09 .07 .15 .0socio-economic status14. Neighbourhood .18* .19* .10 .11 .07 .03 .01 .08 .0heterogeneity15. Neighbourhood turnover .22** .08 .03 .13 .09 .09 .20* .13 .016. Neighbourhood .08 .01 .14 .05 .16 .03 .12 .12 .1urbanization

*p


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Regression analysis

Using multiple regression analysis, the relative importance of each group ofvariables (individual attributes, neighbourhood disorder, social processesand neighbourhood structure) was established by entering each group of

variables at different steps in the analysis (variables that did not signifi-cantly correlate with fear of crime were not included). By entering the vari-ables in groups, estimates of the importance of the variables as a group canbe established without excluding any shared variation between variableswithin a group. This allows for better estimates of the relative importanceof each group of variables.

The regression model explained 42.1 percent of the total variation in fearof crime, as measured by R squared. The results for steps 1 and 4 are mostmeaningful (see Table 5). At step 1, the R squared change statistic for eachgroup is maximized by not controlling for variables in the other groups. Incontrast, at step 4 the R squared change statistic for each group is mini-mized by controlling for the variables in the other groups. Steps 2 and 3 arenot shown because only some variables are controlled.

In Table 5 the groups remain in the same order of importance regardlessof being at step 1 or step 4. Individual attribute variables are most impor-tant for predicting fear of crime, followed by neighbourhood disorder vari-ables. Social processes and neighbourhood structural variables are lessimportant predictors of fear of crime. However, one social process variableand one neighbourhood structural variable were significant at step 1 in the

analysis. They were sense of community ( = .20,p =.04) and neighbour-hood socio-economic status ( =.28,p =.002), respectively.

Neighbourhood socio-economic status was also significant at step 4.However, if individual socio-economic status is also controlled by enteringhousehold income at step 1, neighbourhood socio-economic status is notsignificant at step 4. This suggests that neighbourhood socio-economic sta-tus does not explain significantly more fear of crime than individual socio-economic status.

Visually, the map in Figure 2 shows considerable individual variability in

fear of crime for Brisbane residents, supporting the vulnerability hypothe-sis. The map in Figure 3 illustrates the association between fear of crime


Table 5: Change in R squared when variable groups are entered at steps 1 and 4

Variable groupings Step 1 Step 4

Individual attributes 0.22** 0.15**Neighbourhood disorder 0.15** 0.06**Social process 0.08* 0.03

Neighbourhood structural 0.13** 0.05*

*p


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Figure 2: Spatial distribution of fear of crime in Brisbane city in 2003

Figure 3: Neighbourhood socio-economic status and fear of crime in a segment ofBrisbane city in 2003



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and neighbourhood socio-economic status. For example, the darkest dotsreflecting high fear of crime can only be seen in neighbourhoods with

medium or low socio-economic status.As mentioned, individual attributes and neighbourhood disorder vari-

ables are relatively good predictors of fear of crime, especially consideringthat only two individual attributes (age and sex) and only two indicators ofphysical disorder (vandalism and cleanliness) were used in the analysis. Toexamine the relative importance of these four variables in more detail,another regression analysis was conducted using them to predict fear ofcrime.

The variation in fear of crime explained by these four variables alone

was 30.6 percent (compared with 42.1 % with all variables). Of this, 7 per-cent was from shared variation among these four variables. As Table 6shows, sex uniquely explains 9.1 percent of the variation in fear of crime,age uniquely explains 4.7 percent, neighbourhood vandalism uniquelyexplains 5.7 percent and neighbourhood cleanliness uniquely explains 4.0percent, as reflected in the squared semi-partial statistics (sr2). The tablealso shows that the standardized beta coefficients () were all significant,withp values below .05, and that sex, age and neighbourhood vandalismwere associated with more fear of crime while neighbourhood cleanliness

was associated with less.

DiscussionSummary of results

The hypotheses are generally supported with individual attributes being themost important predictors of fear of crime followed by neighbourhood dis-order. This supports the vulnerability hypothesis and incivilities thesisrespectively in explaining fear of crime. This study also found that social

process and neighbourhood structure variables are less important inexplaining fear of crime. These last two groups of variables are more distalto fear of crime as conceptualized in Figure 1.


Table 6: Regression statistics for predicting crime from individual attribute andneighbourhood disorder variables

sr2

p value (%)

Sex 0.31 0.000 9.1Age 0.22 0.003 4.7Neighbourhood vandalism 0.24 0.001 5.7Neighbourhood cleanliness 0.20 0.006 4.0



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Of the individual attribute variables, sex predicted fear of crime betterthan age, as expected from previous research. Neighbourhood disordervariables did not explain as much variation as individual attribute vari-ables, though only physical disorder was measured. Had social disorderbeen included, the variation in fear explained would probably still havebeen less than that for individual attributes because both social and physi-cal disorder are highly correlated (Lagrange et al., 1992). Both have beenfound to load onto a single factor (Ross and Jang, 2000; Ross andMirowsky, 2001), and both have previously been found to explain aboutthe same or less variation in fear of crime than sex alone (Lagrange et al.,1992; Perkins and Taylor, 1996).

The social process variables are not important predictors of fear of crimein this study. Neighbourhood interaction and neighbourhood reciprocityare not significantly correlated with fear of crime. And while neighbour-

hood willingness, friendliness, trust and sense of community are correlatedwith fear of crime, only sense of community is a unique predictor when allthe social process variables are used to predict fear of crime. However,when controlling for any of the other factors (individual attributes, neigh-bourhood disorder or neighbourhood structure), sense of community is nolonger significant. Thus, the study found social process variables were notrobust predictors of fear of crime.

Regarding neighbourhood structure, only neighbourhood urbanizationis not significantly correlated with fear of crime. However, while neigh-

bourhood turnover and neighbourhood heterogeneity are correlated withfear of crime, they are not significant predictors when included with neigh-bourhood socio-economic status in predicting fear of crime. Only neigh-bourhood socio-economic status is a unique predictor of crime, and thisremains so even when the other factors such as individual attributes andneighbourhood disorder are controlled. However, when household incomeis also entered to control for individual socio-economic status, neighbour-hood socio-economic status is no longer significant. This suggests that theassociation between socio-economic status and fear of crime is an individ-

ual, rather than neighbourhood effect. Thus, the study found that neigh-bourhood structural variables were not robust predictors of fear of crime.

Implications for theory

The results suggest that the vulnerability hypothesis and the incivilities the-sis are complementary explanations for fear of crime rather than competingexplanations. As Figure 1 shows, the two explanations overlap conceptu-ally by both incorporating perceived risk. However, the regression analysisshows that the overlap is not great, with only 7 percent of the variation in

fear of crime being shared between the individual attribute and neighbour-hood disorder variables. This contrasts with 30.6 percent of total variationexplained by these variables. Thus both the vulnerability hypothesis and the




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incivilities thesis are important conceptual frameworks for predicting fearof crime.

In contrast, the neighbourhood structural and social process variablesassociated with social disorganization theory are not as important in pre-dicting fear of crime as incivilities. However, this theory still provides anexplanation about how social processes give rise to incivilities, and also abasis for the perceived association between incivilities and an increased riskof crime, an association that underpins the incivilities thesis. Thus, socialdisorganization theory is important for the discourse supporting the inci-vilities thesis, rather than predicting fear of crimeper se.

While the incivilities thesis is better for predicting fear of crime thansocial disorganization theory, it is important to further investigate the roleof social processes within the incivilities thesis. Two such roles warrantingfurther investigation are social buffering and feedback loops. Ross and Jang

(2000) found that social integration acts as a social buffer, such that thosewho are more socially integrated are not as likely to experience as muchfear of crime from observing neighbourhood disorder. Markowitz et al.(2001) found that fear of crime can also reduce neighbourhood cohesionthrough a feedback loop, leading to more disorder and fear of crime. Futurework on social processes and incivilities would benefit from replicatingthese findings.

Limitations

This study has some limitations. First, the measures of neighbourhood dis-order and social processes for any one neighbourhood were based on the per-ceptions of only one individual. This means that these measures are not asaccurate as they would be if they were, say, based on a sample of individualsin each neighbourhood, and this may result in some loss of predictive power.However, any loss is offset by introducing a same source bias, which inflatespredictive power because the individual is the same source for estimatingboth the neighbourhood variables and the fear of crime variable. For exam-ple, a number of studies have found that individual perceptions of neigh-

bourhood disorder predict fear of crime as well as, if not better than,aggregated measures of neighbourhood disorder (Covington and Taylor,1991; Perkins and Taylor, 1996; Robinson et al., 2003). Given these com-pensating effects, the predictive power of the neighbourhood disorder andsocial process variables in step 1 would be approximately the same as if theyhad been based on an average of individuals in each neighbourhood.However, the predictive power of these neighbourhood variables may havebeen higher in step 4 using an average of individuals, because controlling forthe characteristics of any one individual in the neighbourhood may have had

less impact on measures averaged across a number of individuals.Second, while neighbourhood structure was measured at the neighbour-hood level, the effects of neighbourhood structure may be better tested




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using hierarchical modelling (where possible) because all variables at theindividual unit of analysis can be controlled. However, the influence ofneighbourhood structure was weak in this analysis and became insignificantwhen individual income was controlled. Thus it is unlikely that neighbour-hood effects would have been significant in a hierarchical model where allindividual variables are controlled.

Third, many of the social process measures are based on single items.This can mean that measures are less sensitive and thus the relative impor-tance of any particular social process variable may be underestimated.However, in estimating the relative importance of social processes as awhole, a wide range of measures were relied upon which means that includ-ing any additional measures is not likely to increase the R squared signifi-cantly for this group. Further, the other groups also contained single itemmeasures. Thus, the order of importance of the different factors (individualattributes, neighbourhood disorder, social processes and neighbourhoodstructure) should remain the same.

Finally, fear of crime is also measured as a single item. This raises twoproblems. First, the dependent variable is less sensitive, and while thismeans that weaker associations are found with the independent variables,the relative importance of the variables should remain unchanged. Second,the fear of crime measure used in this study only asked about whether res-idents felt safe walking around their neighbourhood after dark. This relatesto a specific activity, and perhaps the relative importance of the differentfactors changes for other activities such as for intruders in the home, or fornon-specific activities such as fear of crime generally.

ConclusionThis study examined the relative importance of individual attributes, neigh-bourhood disorder, social processes and neighbourhood structure inexplaining fear of crime. As expected, individual attributes and neighbour-hood disorder were most important, supporting the vulnerability and inci-vilities thesis respectively. Additionally, this study found that socialprocesses and neighbourhood structure were less important predictors offear of crime, providing less support for social disorganization theory inpredicting fear of crime. However, social processes still play important rolesin the incivilities thesis and warrant further investigation.

AcknowledgementFunding was provided by the Australian Research Council (ARC Discovery Grant

No. 20010000631) for this article, and for the survey from which the data came(the 2003 Survey on Quality of Life in the BrisbaneSouth East QueenslandRegion).




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Biographical notesRod McCrea is a research officer in the Centre for Research intoSustainable Urban and Regional Futures (CR-SURF) at the University ofQueensland, with research interests in urban quality of life and spatial

behaviour. Address: University of Queensland, Brisbane, 4072, Australia.[email: [email protected]]Tung-Kai Shyy is a research associate in CR-SURF, University ofQueensland, and has research interests in GIS-based spatial modelling andvisualization, and Internet GIS applications. Address: University ofQueensland, Brisbane, 4072, Australia. [email: [email protected]]John Western is an Emeritus professor with the School of Social Science,University of Queensland. His research interests include juveniles in thecriminal justice system, urban growth in the comparative perspective andsocial inequality in Australian society. Address: University of Queensland,Brisbane, 4072, Australia. [email: j.western @uq.edu.au]Robert J. Stimson is a professor with the School of Geography, Planningand Architecture and Director of CR-SURF, University of Queensland. Hisinterests include urban, social, economic and behavioural geography.Address: University of Queensland, Brisbane, 4072, Australia. [email:[email protected]]


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