physical environment and crime and misconduct in kentucky schools

The Journal of Primary Prevention, Vol. 27, No. 3, May 2006 ( C© 2006)DOI: 10.1007/s10935-006-0034-z

Physical Environment and Crime and Misconductin Kentucky Schools

Pamela Wilcox,1,3 Michelle Campbell Augustine,2 and Richard R. Clayton2

Published online: 5 April 2006

Drawing upon ecological theories of crime control, aspects of the physical envi-ronment such as building design, street layout, and land use are thought to indicateterritoriality and natural surveillance, thereby affecting the ability of residents tosupervise and intervene in crime. To date, ecological models have been testedprimarily at community levels of analysis (i.e., neighborhood, block). In contrast,this paper tests the applicability of this theoretical approach to crime in schoolsettings. More specifically, we estimate random-intercept models using surveydata from 3682 7th-grade students and 1351 teachers, nested within 65 Kentuckyschools linked to school-level measures of the physical environment to determinehow they affect various measures of school-based crime and misconduct.

Editors’ Strategic Implications: How one measures school violence may have pro-found effects on what contributing causal factors are identified and strategies forprevention are developed. Student reports of school violence appear linked to morenormative daily behaviors, whereas teacher reports–though limited to officiallyobserved behaviors–are typically more serious. Thus, measurement implicationsabound. Nevertheless, territoriality, surveillance, and a sense of order, particu-larly in the immediate school context more so than the larger community context,appear linked to school violence.

KEY WORDS: violence prevention; school, student, & teacher perceptions; ecological; territoriality;surveillance; pride.

Ecological theories of crime suggest that both social and physical charac-teristics of a community affect crime by altering the administration of resident-based social control. Scholars in the social disorganization tradition purport that

1University of Cincinnati.2University of Kentucky.3Address correspondence to Pamela Wilcox, Department of Criminal Justice, University of Cincinnati,600 Dyer Hall, P.O. Box 210389, Cincinnati, OH, 45221-0389; e-mail: [email protected].

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0278-095X/06/0500-0293/1 C© 2006 Springer Science+Business Media, Inc.

294 Wilcox, Augustine, and Clayton

community structural characteristics such as poverty, ethnic heterogeneity, and res-idential mobility diminish cohesiveness among neighbors thereby affecting theirsupervision and intervention behavior. Others emphasize the role of the physicalenvironment in informal social control, suggesting that facets of physical spacesuch as street layout, building design, lighting, physical decay, and boundarymarkers can affect indicators of informal area-level crime control including terri-toriality, natural surveillance, and image/milieu. In his seminal work, Newman(1972, p. 3) states that aspects of the physical environment serve to create“the physical expression of a social fabric that defends itself” – a notion thatNewman coined as “defensible space” but that has also been referred to as “CrimePrevention through Environmental Design (CPTED)” (see also Brantingham &Brantingham, 1981, 1993; Cisneros, 1995; Newman, 1995, 1996; Samia Mair, &Mair, 2003; Westinghouse Electric, 1977a, 1977b).

Despite strong theoretical justification for expecting a relationship betweenthe physical environment and rates of crime, much of the emphasis in the eco-logical crime literature has focused on testing the extent to which social structureas opposed to physical structure affects area-level informal social control (e.g.,Bellair, 2000; Bursik & Grasmick, 1993; Sampson & Groves, 1989; Sampson,Raudenbush, & Earls, 1997; but also see Sampson & Raudenbush, 1999). Asthe review below will highlight, the smaller body of extant empirical work thatdoes link physical structure and area crime is largely conducted at the com-munity level or sub-community level (e.g., streetblock). School-level analyseshave largely been absent from this literature (Gottfredson & Gottfredson, 2002;Sherman et al., 1997) despite the fact that defensible space and/or CPTED, in the-ory, should apply to schools as well as communities (Crowe, 2000; Toby, 1994).This study addresses a substantial limitation of empirical literature on the linkbetween physical environment and crime by testing this linkage within school en-vironments. As such, this study can help shed light on the potential effectivenessof school-level design changes as a way to reduce school crime.

PHYSICAL ENVIRONMENT AND CRIME

Jacobs (1961) originally posited the idea that the built environment might af-fect informal social control, especially in the form of street surveillance. However,the direction of the relationship hypothesized by Jacobs between, for instance,land use and surveillance, contradicts later suggestions by Newman and others.Jacobs (1961) suggested that non-residential land use was helpful in reducingcrime in that it provided “eyes on the street” in the form of shopkeepers, workersand customers (see also Angel, 1968; Wood, 1961). While Newman also recog-nized the importance of surveillance, he did not necessarily count on “outsiders”(e.g., non-residents using public land) to provide surveillance. In fact, he warnedagainst the juxtaposition of the residential public housing projects he studied to

Physical Environment and School Crime 295

certain non-residential land uses, including high schools and fast-food restau-rants: “. . . commercial and institutional generators of activity do not, in and ofthemselves, necessarily enhance the safety of adjoining areas” (Newman, 1972,p. 112). Newman’s suggestions that non-residential land use might serve to in-crease crime by decreasing informal social control have been addressed by othersas well. Taylor and his colleagues (e.g., Taylor, Gottfredson, & Brower, 1981,1984; Taylor & Brower, 1985; Taylor, 1987, 1988, 2001) have suggested thatshopkeepers and their clients are often transient in a community, thus diminishingtheir ability or desire to exert strong informal control and also decreasing theability of established residents to recognize who belongs in the community (seealso Appleyard, 1981; Baum, Davis, & Aiello, 1978; Brown & Altman, 1981).Indeed, empirical research suggests that non-residential land use such as sec-ondary schools (Roncek & Faggiani, 1985; Roncek & LoBosco, 1983; Wilcox,Quisenberry, Cabrera, & Jones, 2004), bars/taverns and alcohol outlets (Roncek &Maier, 1991; Roncek & Pravatiner, 1989; Smith, Glave Frazee, & Davison, 2000;Speer, Gorman, Labouvie, & Ontkush, 1998), fast-food restaurants (Brantingham& Brantingham, 1982), general businesses (Kurtz, Koons, & Taylor, 1998; Wilcoxet al., 2004), institutional land (Ley & Cybriwsky, 1974), and vacant lots (Smithet al., 2000) are positively associated with neighborhood crime.

Beyond land use, the physical characteristics of street layout and buildingdesign can indicate surveillance and/or territorial functioning. For instance, neigh-borhoods containing a high number of accessible “through streets” are thoughtto invite outside traffic and thus reduce recognizability and control of strangers.Most of the evidence addressing this idea has been based upon analysis of eithersingle neighborhoods as demonstration projects or multiple neighborhoods withinone city. For instance, studies in Atlanta neighborhoods (Greenberg, Rohe, &Williams, 1982), Norfolk neighborhoods (White, 1990) Dayton, Ohio’s Five Oaksneighborhood (Donnelly & Kimble, 1997) and Hartford, Connecticut’s AsylumHill neighborhood (Fowler & Mangione, 1986; Fowler, McCalla, & Mangione,1979) all found that lower crime was associated with neighborhoods having fewermajor arteries, fewer through streets, more two-lane and one-way streets, lessheavily-traveled boundary streets, and less on-street or lot-style parking.

In addition, the placement of buildings, their lobbies, and their windowsvis-a-vis the surrounding environment can be integral in determining whether res-idents provide effective informal social control (Crowe, 1991, 2000; Merry, 1981;Newman, 1972, 1995, 1996). Certain building designs (e.g., high rise build-ings with one or two common entranceways and elevators) can disallow forsurveillance and demarcation of private and semi-private outdoor spaces, thusdiscouraging residents from looking out for one another and taking ownershipof the property. In his analyses of New York City housing projects, Newmanshowed that housing developments with taller buildings were associated withhigher crime (Newman, 1972; Newman & Franck, 1982; but see Mawby, 1977).Newman (1972) presented evidence that the entry design of NYC housing projects


seemed to affect crime as well. Those projects with lobbies facing the street andwith good visibility (e.g., provided by windows) had the lowest rates of crime(see also Brown & Altman, 1983). Similarly, Fisher and Nasar (1992, 1995)illustrated that building designs that allowed for limited prospect, high conceal-ment, and easy escape were positively correlated with fear of crime on a collegecampus.

Physical Environment and School Crime

As the above discussion implies, much of the theoretical and empirical worklinking the physical environment and crime has been done at the neighborhood orsub-neighborhood level (e.g., streetblock). Though secondary schools are clearlya context with potentially important physical/design characteristics that can affectbehavior (Schneider, Marschall, Roch, & Teske, 1999; Toby, 1994), and scholarshave outlined strategies that schools might follow based upon defensible spaceand CPTED theoretical principles (e.g., Crowe, 1990, 2000), relatively few studieshave empirically examined the effects of the physical environment on crime withschools as units of analysis. Noteworthy exceptions include the CPTED schooldemonstration project in Broward County, Florida (Wallis & Ford, 1980) and,more recently, research by Astor and colleagues on several Michigan schools(e.g., Astor & Meyer, 2001; Astor, Meyer, & Behre, 1999; Astor, Meyer, & Pitner,2001).

In 1980, the National Institute of Justice (formerly operating as the NationalInstitute of Law Enforcement and Criminal Justice) released its report on theschool demonstration project in Broward County, Florida (Wallis & Ford, 1980).Four suburban high schools were targeted through the NIJ-sponsored experimentwhich included physical modifications such as adding windows for surveillance,adding signs/markers of school identity (e.g., supergraphics and border definition),limiting access to isolated areas, relocating informal gathering areas (includingconstruction of better-supervised courtyards or “mini-plazas”), and redesigningbus-loading zones and student parking in order to reduce congestion and improvesurveillance (Wallis & Ford, 1980). Before-and-after measures of victimizationspecific to school sub-environments (e.g., loading zones, bathrooms, cafeterias,hallways, parking lots, etc.) revealed that: (1) project schools reported a reduc-tion in theft that exceeded the reduction experienced county-wide (the reductionamong non-project schools was not significant compared to the rest of the schoolsin the county); (2) the demonstration schools saw larger declines in bathroomtheft after the CPTED-related changes; and (3) while all schools but one in thecounty saw declines in assault rates, the largest decline was in the demonstra-tion school receiving the most “effort” in terms of design changes. In contrast,student perceptions of safety did not change significantly, and territorial attitudestowards the school did not appear to improve among students in project schools.


In several cases, fewer students “felt a part of the school” during the post-CPTEDobservational period (Wallis & Ford, 1980).

Colleagues at University of Michigan have been involved recently in a differ-ent type of assessment of the relationship between school physical environmentand school crime. Drawing upon the CPTED literature and using maps, semi-structured interviews, and focus groups, scholars involved in this project havebeen able to identify structural and/or cultural reasons why students identify cer-tain school sub-environments or “sub-contexts” as particularly dangerous and/orfeared (e.g., Astor & Meyer, 2001; Astor et al., 1999; Astor et al., 2001). In sup-port of the work in Broward County, the maps used by Astor and colleagues haverevealed that certain “hot spots” in schools–such as hallways, bathrooms, cafe-terias, and parking lots–are the sights of more incidents and greater fear amongstudents. Interviews with students and teachers revealed that incidents occurred inthese areas when adults were typically not present. The interviews also revealedthat these areas tended to be “unowned” by either students or school staff. Astoret al. (2001) found that sixth-graders in middle schools were more likely thansixth graders in elementary schools to perceive particular school sub-contexts asrisky. The authors suggest that middle schools may have more undefined areasnot only because they are larger, but because teachers in these schools display“isolated” or specific territoriality–a territoriality that does not extend far beyondthe boundaries of their specific classroom or subject area. As such, many physicalspaces in the school are poorly monitored, unkempt, and potentially dangerous.

THE PRESENT STUDY

The literature reviewed above indicates that there are theoretical reasons tosuspect that the physical environment can affect crime in neighborhood and a va-riety of sub-neighborhood contexts, yet there has been surprisingly little empiricalevidence to this effect regarding the school context in particular. Again, the presentstudy addresses this gap in the literature by examining the relationship betweenschool crime and defensible space as indicated by school-specific territoriality,natural surveillance, and image/milieu.

METHOD

The data for the current investigation come from four sources: (1) individual-level data from a Spring 2001 survey of 3682 7th-grade students in Kentucky;(2) individual-level data from a Spring 2001 survey of 1351 Kentucky teach-ers; (3) systematic observation, during the period Fall 2000–Spring 2001, of the65 Kentucky schools housing sampled students and teachers; and (4) school-level “census” data for the 65 sampled schools from the Kentucky Department of


Education, compiled Spring 2000. The student and teacher surveys as well asthe systematic social observation (SSO) are part of a larger, longitudinal researcheffort entitled the “Rural Substance Abuse and Violence Project” (RSVP). Theoriginal sampling design for this project began with disproportionate stratified ran-dom sampling of 30 Kentucky counties. We constructed four strata of Kentucky’s120 total counties based upon population size. Stratum one consisted of countieswith at least 150,000 people. There were only two Kentucky counties in this stra-tum. Stratum two consisted of the 15 counties with populations ranging between40,000 and 150,000. Stratum three consisted of the 48 Kentucky counties withpopulation sizes between 15,000 and 40,000. Finally, stratum four was comprisedof the 55 remaining Kentucky counties, all with population sizes under 15,000.Both metro counties in the state were included in the sample. Beyond that, theremaining sampled counties were selected with probability-proportionate-to-size(stratum size) and systematic sampling techniques. The final sample consisted oftwo counties from stratum one, four counties from stratum two, 11 counties fromstratum three, and 13 counties from stratum four. Within each selected county, wecontacted all public schools containing 7th graders, excluding alternative schools.A total of 74 schools were eligible, 65 of which agreed to participate in the study.Non-response on the part of individual schools does not appear to be systematic.The nine refusals came from large, mid-size, and small districts/schools, rangingin setting from rural to urban and from various regions of the state.

Parents of all 7th graders in the 65 participating schools were sent lettersrequesting permission for their child’s participation in a student survey. FollowingDillman’s (1978) total design method, we first sent a letter explaining the projectwith a mail-back consent form and a postage-paid return envelope. A post-cardreminder was sent 2–3 weeks later, and second and third mailings of the letter,consent form, and postage-paid return envelope were sent to non-respondents 4–5and 6–8 weeks after the initial mailing. Fifty-two percent of parents responded toour inquiry; forty-three percent responded affirmatively. Ninety percent of seventhgraders who were permitted by their parents to participate in the survey did so,leaving a final student sample size of 3692. Once parental consent was obtained,student missing data was largely due to (1) student transferal between the timeof parental consent and survey administrations and (2) student absenteeism onthe day of survey administration. We attempted to obtain both types of missingdata through tracking and follow-up procedures, with such procedures allowingus to obtain what would otherwise be missing data from approximately half thosemissing after the first day of survey administration. Very little of the missingstudent data come from student refusals; fewer than 1 percent of sampled studentswere missing due to their refusal to participate.

A member of the research team group-administered the surveys to all par-ticipating students within each school during one class period on one schoolday, March–May 2001; student assent was obtained at the time of this survey


administration. During the same day that student surveys were administered ateach school, faculty surveys were also administered, resulting in a sample of 1355teachers. Teacher surveys were gathered in one of two ways. In many instances,the surveys were group-administered by a research team member during a before-or after-school faculty meeting. In some instances, however, faculty meetingscould not be scheduled, and the surveys were handed out to faculty (with an ac-companying sealable envelope) and asked to be completed during their planningperiod or during other free time throughout the day. For those teachers who did nothave time to complete the survey during that day, self-addressed return envelopeswere provided. Response rates were comparable across these two data-collectionmethodologies.

In addition to survey administration, systematic social observation tookplace at each of the 65 schools during one school day, with two-member re-search teams visiting each school between September 2000 and May 2001.Each team completed a field instrument intended to guide observation ofaspects of territoriality, natural surveillance, and image for specific sub-contexts throughout school interiors and exterior grounds (e.g., classrooms,hallways, locker areas, gymnasiums, cafeterias, bathrooms, parking lots, load-ing zones, athletic fields, etc.). Each team also completed an additionalfield instrument designed to serve as an inventory of the “two-block” radiussurrounding the school in order to assess how defensible space might be indicatedby the land uses of the surrounding community. These measures of school physicalenvironment, along with school-level census data, were linked by school to thestudents and teachers participating in the Spring 2001 surveys so as to create twomultilevel datasets for analysis purposes.

Measures of Variables

Summary statistics for all variables used in the present study are providedin Table I; more detailed descriptions of these measures are addressed here. Thedependent variables for this analysis include five different measures of schoolmisconduct. First, we examine several dependent variables stemming from stu-dent self-reports. In particular, we measure student victimization as the number ofvictimizations experienced at school or during school-related activities during thecurrent academic year (“current” = year of survey administration), as reported bystudents. More specifically, each student was asked to report the number of timesduring the current school year they had experienced, as a victim, the following:(1) physical attack (kicked, punched, slapped, etc.), (2) force in order to giveup money or property, (3) theft of money or property, not by force, (4) unwel-come sexual remarks, (5) sexual touching/contact, (6) threat by gun, and (7)threat by another weapon being pulled. Each item ranged from 0 = “0 times” to


Tabl

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Des

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Stat

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sfo

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Var

iabl

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ntSa

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Stud

ents

ampl

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d.D

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Dep

ende

ntva

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les

Stud

entv

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ion

7.98

10.8

1.0

0–70

.00

3606

Stud

ent.

perc

eptio

nsof

scho

olcr

ime

1.81

0.77

1.00

–4.0

035

91Te

ache

r-w

itnes

sed

crim

ein

cide

nts

——

——

6.45

6.90

.00–

52.0

013

40Te

ache

rvi

ctim

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ion

——

——

2.41

4.34

.00–

52.0

013

39Te

ache

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tions

ofsc

hool

safe

ty—

——

—2.

35.8

51.

00–5

.00

1333

Scho

olph

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alst

ruct

ure

Mai

n-of

fice

terr

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ality

.91

.28

.00–

1.00

2854

.87

.33

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1.00

1066

Hal

lway

terr

itori

ality

.68

.26

.00–

1.00

2868

.71

.25

.00–

1.00

1081

Ext

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rrito

rial

ity1.

44.7

1.0

0–2.

0028

481.

45.7

4.0

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0010

50M

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60.7

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7.0

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0010

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allw

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llanc

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5.3

0.0

7−

1.00

2868

.55

.31

.07–

1.00

1081

Ext

erio

r-ar

eas

surv

eilla

nce

1.96

1.19

.00–

4.00

2862

1.99

1.13

.00–

4.00

1065

Ext

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ren

trap

men

tare

as.7

2.3

2.0

0–1.

0028

62.7

2.3

2.0

0–1.

0010

65Sc

hool

diso

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−.11

11.6

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3.94

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8028

62.4

311

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9.80

1065

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.29

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5.00

2953

.33

.78

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5.00

1134

You

th-o

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ted

busi

ness

es.0

2.1

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0029

53.0

3.2

3.0

0–2.

0011

34G

ener

albu

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2.32

1.91

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8.00

2953

2.13

1.90

.00–

8.00

1134

Vac

antl

and

.74

.92

.00–

3.00

2953

1.04

1.05

.00–

3.00

1134

Nei

ghbo

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ddi

sord

er.5

5.6

9.0

0–3.

3028

90.7

0.8

0.0

0–3.

3011

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Tota

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8.96

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9310

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3629

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3022

7.67

109.

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0013

25Pe

rcen

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42.6

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2948

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20.7

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1325

Perc

entn

on-w

hite

10.6

513

.14

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52.7

736

928.

2611

.44

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52.7

713

55Pe

rcen

tmal

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.87

2.49

46.1

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.49

3692

52.0

02.

1946

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57.4

913

55


10 = “10 + times,” resulting in an index ranging, potentially, from 0 to 70. Figuresfrom Table I reveal that, on average, students reported 7.98 such victimizationexperiences, though there was considerable variation. We also measure studentperceptions of school crime with a survey item asking students how safe theirschool was from crime. Responses were coded as 1 = “very safe” to 4 = “veryunsafe.” Table I shows that the average value is 1.81 (between “very safe” and“safe”).

We also examine three measures of school misconduct/crime using teachersurvey data. We measure teacher-witnessed misconduct as the number of timesin the current year teachers had witnessed students at their schools engaging in:(1) physical fights, (2) weapon possession, (3) verbal sexual advances, (4) physicalsexual advances, (5) vandalism, and (6) theft. We measure teacher victimizationas the number of past-year, school-specific victimizations experienced by teacherswithin schools, including (1) having been physically attacked, (2) having thingstaken by force, weapons or threats, (3) having had money or property ($10 orless) stolen, (4) having had money or property (more than $10) stolen, (5) havingreceived obscene gestures or remarks, (6) having been threatened, (7) having re-ceived unwelcome verbal sexual advances or propositions, and (8) having receivedunwelcome physical sexual advances or propositions. Both the witnessed miscon-duct and victimization measures were originally coded as 0 = “0 times” to 10 =“10 + times,” with resulting indices ranging potentially from 0–60 and 0–80, re-spectively. As Table I suggests, teachers witnessed, on average, 6.45 incidents ofmisconduct, and they experienced and average of 2.41 incidents of personal vic-timization. Finally, we measure teacher perception of school crime as the averagescore across three survey items asking teachers how safe their school was from:(1) vandalism, (2) personal attacks and (3) theft. Responses reflect an ordinal scalein which 1 = “very safe” and 5 = “very unsafe.” The value for the average crimeperceptions was 2.35, falling between “safe” and “average.” It should be notedthat direct comparison of means for student and teacher perceptions of schoolsafety is difficult due to (1) the use of only one general crime measure on thestudent survey as opposed to three more crime-specific measures on the teachersurvey, and (2) a middle category being included within the response sets for thethree perception measures on the teacher survey, thus resulting in 5-category asopposed to 4-category variables.

In order to examine the effects of physical environment on school crime,we include as explanatory variables measures of school-specific territoriality, nat-ural surveillance, and image/milieu. We also include several characteristics ofthe immediate surrounding neighborhood (the 2-block radius surrounding theschool) in order to get a sense of the opportunity for informal social control of-fered by the neighboring physical environment as well. School-level territorialityis operationalized with three different measures, two tapping interior signs ofownership and another measuring ownership displayed by the school’s exterior.


Interior territoriality is measured, first, with a dichotomous variable–“main-officeterritoriality”–indicating whether (1 = yes, 0 = no) there were indicators ofownership in the main office area of the school (e.g., trophy cases, murals, dis-plays of student achievements, “team” banners, etc.). A second measure of interiorterritoriality, “hallway territoriality,” is the proportion of the school’s hallwaysdisplaying similar signs of ownership. Exterior territoriality is measured by com-bining (summing) two dichotomous survey ite ms, indicating whether there wasa marquee or sign upon entrance onto school grounds and whether there wasdecorative landscaping at the entranceway onto school grounds.

School-level natural surveillance is operationalized by way of four differentmeasures, two based upon observation of the school’s interior and two basedupon exterior observations. Our first measure of interior surveillance taps thesurveillance potential offered by the school’s main-office area. This variable,main-office surveillance, is measured by summing items from the field instrumentwhich tapped (1) whether or not there is full visibility from the main officeto the main entrance (1 = yes, 0 = no) and (2) whether or not there is fullvisibility from the main office to front outside areas (1 = yes, 0 = no). Anothermeasure of interior natural surveillance–hallway surveillance–is measured as theaverage proportion of (1) school hallways that provide a clear view (e.g., norecessed doorways), and (2) school hallways that are free of obstructions thatimpede traffic flow (e.g., free of lockers, drink machines, benches, etc. blockingthe hallway). In order to measure natural surveillance provided to the outsidegrounds from surrounding or juxtaposed areas, we examine items from the surveyinstrument tapping, for each of the school’s four exterior quartiles: (1) whetherthere is available natural surveillance of the school grounds (e.g., from nearbyhouses, streets, etc.); (2) whether there is full visibility of the school groundsfrom these areas of natural surveillance; (3) whether there is available naturalsurveillance of the school building; and (4) whether there is full visibility of theschool building from these areas of natural surveillance. Each of these componentswas measured as a dichotomous variable (1 = yes, 0 = no) for each quartile (thus,sixteen separate measures), but we subsequently summed the values across thefour quartiles for each component and divided by the number of valid responsesin order to create four variables tapping the total extent to which there is availablenatural surveillance of the school grounds, the total extent to which there is fullvisibility of the school grounds from these areas of natural surveillance, the totalextent to which there is available natural surveillance of the school building, andthe total extent to which there is full visibility of the school building from theseareas of natural surveillance. These four measures were then combined (alpha =82) by summing, yielding one measure of the overall available exterior naturalsurveillance. Additionally, we include an additional measure exterior surveillanceby summing four quartile-specific observational assessments (1 = yes; 0 =no) of presence of exterior entrapment areas (e.g., hiding places). We note that


subdividing the school campus/grounds into quartiles for these measures wasdone in order to refine our assessments of the school’s exterior. Pre-testing of ourfield instrument revealed difficulty in achieving cross-rater reliability when theproperty was not subdivided. For instance, there were often instances in whichparts of the school could be observed from areas of natural surveillance while otherareas were not protected by natural surveillance. Making one overall assessmentin such instances was often difficult, but the use of quartiles greatly improvedinter-observer reliability.

We measure school-level image by summing (alpha = 92) z-scores to17 items tapping school disorder, including: presence of exterior litter, presenceof exterior graffiti, presence of exterior disrepair, presence of exterior light fix-tures with broken glass, presence of exterior broken windows, the proportion ofhallways with litter, the proportion of hallways containing graffiti, the proportionof hallways containing defacement, the proportion of hallways containing otherkinds of disrepair, the proportion of bathrooms containing litter, the proportion ofbathrooms containing graffiti, the proportion of bathrooms containing defacement,the proportion of bathrooms with peeling paint, presence of litter in “large” rooms(e.g., library, gymnasium, cafeteria, and locker rooms), presence of graffiti in largerooms, presence of defacement in large rooms, and presence of disrepair in largerooms.

In addition to measures aimed at tapping the physical environment charac-terizing the actual school grounds and building, we also incorporate measurestapping the physical environment of the immediate surrounding neighborhood.As part of the observation of the two-block radius surrounding each school inour sample, we gathered information regarding the land uses within this area.More specifically, a checklist consisting of over fifty specific types of land usewas a part of the neighborhood field instrument (e.g., gas station, bar, cemetery,abandoned house, vacant lot, etc.). For the purposes of this paper, we focus onfour conceptual- and factor-based land-use measures with acceptable reliabilitycoefficients. First, we examine an index tapping neighborhood “adult businesses,”summing five dichotomous measures (alpha = 65) indicating whether or not (1 =yes; 0 = no) the school’s surrounding neighborhood included: (1) bars, (2) gunshops, (3) liquor stores, (4) pawn shops, and (5) tobacco outlets. Secondly, weexamine a summed measure of neighborhood “youth-oriented businesses,” in-cluding pool halls and arcades. Thirdly, we incorporate an index of neighborhood“general businesses,” summing eight dichotomous items (alpha = 71) indicatingpresence (1 = yes; 0 = no) of: (1) convenience stores, (2) fast-food restaurants,(3) other restaurants, (4) service stations, (5) grocery stores, (6) hotels, (7) thriftstores, and (8) video rental stores. We also incorporate a measure of neighbor-hood vacancy, summing three dichotomous measures (alpha = 65), includingpresence of: (1) abandoned cars, (2) vacant buildings, and (3) vacant lots. Inaddition, we measure neighborhood disorder. This measures represents the total


number of streets within the surrounding two-block radius with buildings withmissing or damaged doors, buildings with damaged foundations, buildings withcracked masonry, buildings with peeling paint, buildings with shutters missing,buildings with broken windows, windows with non-conventional window treat-ments (e.g., blankets), buildings with damaged/destroyed wood, graffiti, evidenceof alcohol use (e.g., beer cans/bottles), evidence of drug use (e.g., syringes),evidence of tobacco use (e.g., cigarette butts, wrappers), litter, poorly main-tained lawns, loose pets, poorly maintained open areas, poorly maintained recre-ation areas, damaged fences/walls, damaged sidewalks, and damaged street signs.Summed scores for this index were divided by the total number of items with validdata.

Finally, we include several school-level social structural control variables,all stemming from data obtained from the Kentucky Department of Education.We control for total student enrollment, percentage of students receiving free orreduced-price lunches, school ethnic/racial composition (measured as the per-centage of nonwhite students), and school gender composition (measured as per-cent male students). The number of valid cases for each variable is reported inTable I. Final sample sizes (after listwise deletions) are reported in Tables II andIII. Missing on the dependent measures was due to non-response. Missing dataregarding the physical environment was due to incomplete environmental assess-ments at several schools. Finally, one school had not reported all census data to theKentucky Department of Education, and thus students and teachers have missingdata on several social structural characteristics from that school.

Analysis Plan

We present separate analyses for student and teacher samples. Each anal-ysis is multilevel, employing a two-level data structure (individual and schoollevels). Each dependent variable (student victimization, student perceptions ofschool crime, teacher witnessed misconduct, teacher victimization, and teacherperceptions of school crime) is measured at the individual level, though varianceat both the individual and school levels is estimated, thus appropriately account-ing for the non-random clustering of students within school units. The interceptsin our models are allowed to vary across schools, and we examine the extent towhich mean school levels of these misconduct/crime measures are a function ofschool-level measures of the physical environment through a series of 2-level hier-archical linear models. In short, our two-level modeling strategy is represented asfollows:

Level 1 : Yij = ß0ij + eij

Level 2 : β0ij = γ00j + γ0mjXmj + u0j


Tabl

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elch

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uare

17.6

512

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824

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78.

1632

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Not

e.N

=97

7,∗ p

<.0

5.


where Yij is any of the five dependent measures of school crime/misconductfor individual i in school j, β0ij represents the within-school mean level of thedependent measure, γ 0m represents the coefficient for school-level variable, Xm,and u0j represents the school-level variance associated with the mean level ofthe dependent measure. No student-level characteristics are incorporated into theLevel-1 equations; they are intercept only models since we specifically wantedto address the extent to which variation in these intercepts could be explained, atleast in part, by physical features of the school environment. As such, the effectsof the school environment revealed here are not net of individual-level predictors.

RESULTS

Table II presents results from hierarchical linear regression models predictingvictimization and perceptions of school crime as reported by students. For eachdependent variable, three models are shown. In model 1, the measures of thephysical environment characterizing the school are included as Level-2 covariates.In model 2, the school features are removed and replaced with measures of thephysical environment characterizing the school’s surrounding neighborhood. Inmodel 3, significant effects from the previous two models are included along withsocial structural features of the school environment in order to discern whetherthe physical environmental effects are net of the effects of the social structuralenvironment.

It is clear from results presented in Table II that few features of the school orneighborhood physical environment are associated with student-based measuresof school crime/misconduct. Only vacant land in the surrounding communityappears related to student victimization (Victimization model 2, Table II), andthis effect disappears upon controlling for school social structural characteristics(Victimization Model 3, Table II). In fact, in the final victimization model, onlypercent students receiving free-reduced lunch has a significant effect–indicatinga positive association with student victimization. The lack of substantial effectsof school context on student victimization is not surprising given that a random-intercept null model (with no covariates) showed non-significant cross-schoolvariation in this measure of school crime/misconduct. Accordingly, model chi-squares (based on comparisons of this null model to the models shown here)are non-significant. In terms of student perceptions of school crime (which didexhibit significant cross-school variation in a null model), only school disorderand neighborhood vacant land appear related, and of those effects, only the effectof vacant land holds after controlling for school social structure. Again, none ofthe models represents a significant improvement in fit over the null model.

In Table III, we present a similar set of models for measures of school crimestemming from teacher survey data. In general, it is apparent that features of the


school’s physical environment and the physical characteristics of the surroundingneighborhood are more important in understanding teacher-based measures ofschool crime. Teacher-witnessed misconduct is, after controlling for school socialstructure, negatively associated with hallway territoriality, exterior-area surveil-lance, and presence of both youth-oriented businesses and general businesses inthe neighborhood; it is positively associated with school disorder, and presenceof adult businesses in the neighborhood. In terms of social structure, school per-cent non-white also has a positive effect. The final witnessed-misconduct modelrepresents a significant improvement over a null model. In terms of the analysisof the relationship between the physical environment and teacher victimization,Table III shows a positive association with school disorder only after control-ling for school structural characteristics. In contrast, hallway territoriality andexterior-area surveillance were the only features of the physical environment tobe (negatively) associated with teacher-based perceptions of school crime aftercontrolling for school social structure. Though there are more significant relation-ships between measures of the physical environment and teacher-based measuresof school crime (as opposed to student-based measures), it is noteworthy thatmodel chi-squares for teacher-based measures are generally significant only af-ter including the social characteristics in addition to the physical characteristics(though model 1 for “perceptions of school crime” is an exception).

DISCUSSION AND CONCLUSIONS

The major objective of this paper was to assess the extent to which school-level physical environment affected school crime. The aspects of the physicalenvironment that were of interest included characteristics of territoriality, surveil-lance and order/disorder inside the school buildings as well as outside, on schoolgrounds, in addition to the effects of land use and order/disorder characterizingthe neighborhood in which the schools were located. It is one of the only studiesto date to systematically examine the effects of various aspects of school de-fensible space on school crime using a random sample of schools. In general,few significant effects of the physical environment were found when examiningthe outcome crime measures stemming from student self-reported victimizationand/or perceived safety. On the other hand, models of teacher-reported measuresof crime (including teacher victimization rates, rates of teacher-witnessed actsby students, and teacher-reported school safety perceptions) revealed significanteffects and were typically better-fitting when incorporating aspects of the physicalenvironment.

Variation in ecological findings across different measures of school miscon-duct is not unique to this study, though it is unique in the sense that it is thefirst study to unearth such differences while focusing on school-level defensible


space concepts. Gottfredson and Gottfredson’s (1985) seminal study of schoolmisconduct, for instance, revealed similar disparity across measures when exam-ining the effects of school educational and social climate. For example, they reportthat “school educational and social climate measures are associated with 40 and35% of the variance in teacher victimization for junior and senior high schoolsrespectively; and with 17 and 12% of the variance in student victimizations”(p. 115). The differences uncovered by Gottfredson and Gottfredson as well asthose shown here are suggestive of interesting measurement issues. The variousmeasures of school crime/misconduct may well represent distinct concepts ratherthan uniformly representing some underlying notion of “school crime” or “schoolmisconduct.” It is plausible, for instance, that the teacher victimization measureis indicative of more “serious” school misconduct. Student-reported victimizationmay be more likely to include “everyday,” almost normative, middle school mis-conduct (e.g., pushing and shoving in the hallways, exchanging sexual remarks,stolen property). In contrast, pushing teachers is rarely considered “standard prac-tice.” As such, our measure of teacher victimization may better distinguish “safe”and “unsafe” schools than does our measure of student victimization. If this is thecase, our study highlights the importance of school disorder as a key to under-standing school crime; it was the sole significant predictor of teacher victimization.Much has been made of the role of disorder via the “broken windows thesis” in re-lation to neighborhood crime (e.g., Skogan, 1990; Taylor, 2001; Wilson & Kelling,1982), but the approach appears applicable to understanding school crime as well(see also Toby, 1994), especially with respect to teacher victimization.

While school disorder had fairly consistent effects on the other teacher-reported measures of school crime, two additional covariates–hallway territorialityand exterior-areas surveillance–also emerged as significant in estimating teacher-witnessed student misconduct and teacher perceptions of school crime. Whileour measure of teacher-witnessed incidents can be criticized in that it appears tomeasure only behavior that is observable, it is interesting that similar effects ofschool physical structure emerged in the model for perceptions of school safety.What teachers witness students doing and how teachers perceive crime in theirschools thus seem etiologically similar in terms of effects of school physicalstructure, highlighting the role of certain types of territoriality and surveillancein addition to disorder. In contrast, what teachers actually experience in termsof victimization seems to be related to disorder only. More work is called forin this area in order to validate and better understand distinctions between thesedependent measures.

Despite the many differences across crime measures shown here, overallgeneralizations did emerge. The most consistent effects on teacher-reported mea-sures of misconduct were hallway territoriality, natural surveillance provided byexterior grounds, and school disorder; the effects of land use and disorder charac-terizing the surrounding neighborhood were much less consistent across models.


Concerning the physical environment and school-specific crime linkage, our find-ings therefore indicate that the immediate school context seems more importantthan the community context in which the school is situated (i.e., the 2-block ra-dius surrounding the school). From a policy or prevention standpoint, our resultssupport most strongly efforts to reduce physical disorder in school buildings andschool grounds, increase territoriality in school hallways (e.g., hanging artwork,school banners, etc.), and increase surveillance from outside areas (e.g., makesure landscaping doesn’t obstruct clear views). These characteristics may enhanceinformal social control and thereby diminish serious misconduct.

Further work is necessary to corroborate whether features of the broadercommunity are, overall, less important in understanding school misconduct. Inaddition, more work is needed to understand whether and why certain types ofschool-based territoriality and/or surveillance are more important than others. Ishallway territoriality really more important than territoriality in other areas ofthe school? Is surveillance provided by exterior areas really more important thansurveillance provided by hallway layout or main-office design? Do these effectshold across different measures of school crime/misconduct? If a consistent set offindings can emerge across future studies, then subsequent school-crime reductionefforts can presumably be much more specific in terms of target-hardening andre-design. We are, however, reluctant to dismiss as unimportant at this point anydimension of the physical environment, despite the fact that our study showed weakor inconsistent effects for such concepts. We recognize the regional restrictivenessof our sample, and while our observationally-based measurement of school-leveland neighborhood-level physical environment yielded rich detail, the reliabilitiesof the measures are clearly limited. Furthermore, our findings are limited in that theschool effects are not part of comprehensive multilevel models, with individual-and school-level predictors. Nonetheless, our findings are an important first-steptowards quantitative empirical examination of the role of physical structure inschool crime in a non-experimental setting.

ACKNOWLEDGMENTS

This work was supported by grants (DA-11317 and DA-05312) from theNational Institute of Drug Abuse. An earlier version of this paper was presentedat the annual meeting of the American Society of Criminology, Atlanta, GA,November 2001.

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