Duality of visibility

The Duality of Visibility: Does Visibility Increase or Decrease the Fear of Crime in Schools' Exterior Environments?

Soyeon Lee1 and Mikyoung Ha*2 1

Research Professor, Department of Interior Architecture and Built Environment, Yonsei University, Korea 2 Professor, Department of Interior Architecture and Built Environment, Yonsei University, Korea

Abstract Visibility is an important factor that affects fear of crime in environments. This study suggests empirical evidence of the relationship between visibility and fear of crime in environments using an automated quantitative analysis method. Spots where children felt fear in exterior areas of elementary schools were aggregated, and their characteristics were analyzed with two visibility dimensions of visual connectivity with no visible distance restrictions and with visible distances restricted. In general, locations with lower levels of visibility reported higher levels of fear of crime. However, where there was very high visibility, fear of crime also increased. In other words, visibility has a dual nature, both increasing and decreasing fear of crime in environments. In addition, the visible distance was a significant element that affects fear of crime. With the assistance of methods and results of this study, designers can plan safer environments and improve existing environments by considering visibility in a systematic and significant manner.

Keywords: visibility; fear of crime; elementary school; space syntax; GIS (geographic information system)

1. Introduction According to a survey of the Seoul metropolitan government (2010), Seoul citizens thought that the first priority of an education policy was 'school safety' (31.8%). However, schools have not received much scholarly attention concerning their relationship to crime (Roman, 2002). In addition, previous study (Salmon et al., 1998) suggests that children who are victims of criminal behavior tend to be in their earlier school years and children who experienced criminal behavior feel high anxiety and depression.

Although visibility is considered to be an important factor that affects fear of crime in environmental criminolog y (Andrew s and Gaters leben, 2010, Fisher and Nasar, 1992, Loewen et al., 1993, Nasar and Fisher, 1993, Wang and Taylor, 2006), previous studies used few quantitative analysis techniques with automated computer routines to analyze visibility.

Furthermore, they used visibility as one element that causes fear of crime instead of analyzing visibility only in more depth.

*Contact Author: Mikyoung Ha, Professor, Department of Interior Architecture and Built Environment, Yonsei University, 417 Samsung Hall 134 Sinchon-Dong, Seodaemun-Gu, Seoul, 120-749, Korea Tel: +82-2-2123-3135 Fax: +82-2-2123-8662 E-mail: mkha@yonsei.ac.kr ( Received April 8, 2014 ; accepted October 15, 2014 )

Therefore, a more in-depth study with respect to criminal behavior and visibility in elementary school areas would be valuable in environmental criminology.

To be specific, this paper seeks the effects of visibility on fear of crime in elementary school environments.

In defining the scope of this research, there are several aspects to be considered. To begin with, the exterior areas of elementary schools were investigated because, exterior areas are different from interior or urban areas in terms of morphology, scale, and interpretation of visibility (Turner, 2003). Furthermore, school exterior areas show variable visibility, ranging from very high (e.g., playgrounds) to very low (e.g., the rear of buildings).

In addition, fear of crime is considered in its cognitive and emotional dimensions (Ferraro and Grange, 1987). As minor instances of victimization are common in schools (Garofalo et al., 1987), the scope of crime was extended to school violence. Therefore, crimes included violent crimes (assault, robbery, threats), property crimes (theft, fraud, pick-pocketing), peer violence (bullying, emotional bullying, gang assault), and sexual incidents (attempted and/or completed rape, sexual harassment, sexual molestation) following Hong (2008).

Furthermore, to find the spots where fear of crime was actually felt at the children's level, the survey was completed by students in an elementary school and excluded their teachers or other school officials. The surveys were anonymously completed by children aged

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12 (fifth-graders) to 13 (sixth-graders), who experience considerable amounts of criminal behavior (Salmon et al., 1998) and who were capable of answering the questions in such a survey.

2. Literature Review 2.1 Fear of Crime in Environments Previous studies have supported that cognitive (such as risk perception) and emotional (such as feeling afraid) dimensions of fear of crime need to be distinguished from one another (Ferraro and Grange, 1987, Rountree, 1998). Generally, information about fear of crime from previous studies (DuBow et al., 1979, Gray et al., 2008, Miceli et al., 2004) can be summarized as follows: (a) fear of crime is related to the environment; (b) fear of crime is more common than crime itself; (c) fear of crime is related to sociodemographic variables.

Fu rthermo re , a lo t of pre vio us stud ies h a ve mentioned a relationship between criminal behavior and environmental design (Brown et al., 2003, Jacobs, 1961, Jeffery, 1971, Taylor et al., 1984). Among environmental factors, visibility which was referred to as 'prospect' or 'surveillance' in earlier work was negatively related to criminal behavior. According to the theory of Fisher and Nasar (1992), fear of crime was low with high prospect levels. In addition, according to the theory of 'crime prevention through environmental design' (CPTED), natural surveillance is considered to reduce criminal behavior (Jacobs, 1961, Newman, 1972). Furthermore, several studies (Bennett and Wright, 1984, Fujii et al., 2013, MacDonald and Gifford, 1989, Pease, 1991) discussed the relationship between natural surveillance and criminal behavior.

In environmental criminology, studies assert that there are spaces in which concentrations of crime arise within a limited area (Block and Block, 1995, Sherman, 1995, Weisburd and Green, 1995). 'Hot spots' are the areas where these criminal behaviors commonly take place. Therefore, through the analyses of hot spots, the characteristics of the criminals, victims, and environments which increase the number of crimes could be investigated (Maltz et al., 1991).

Many previous studies (An and Yoshida, 2011, An and Yoshida, 2013, Takizawa et al., 2010) investigated hot spots through GIS (geographic information system), and among the statistical methods that can be used to identify hot spots, one of the most common is the density of crime incidents, which is continuous over an area, being higher in some parts and lower in others.

2.2 Visibility in Spaces The 'isovist' concept was suggested to develop the quantitative visibility analysis method. Tandy (1967) presented the first isovist concept in the area of spatial analysis, and Benedikt (1979) utilized it for quantitative descriptions of spaces with a set of analytic measurements (Turner et al., 2001). Benedikt (1979) proposes an isovist as "the set of all points visible from

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a given vantage point in space and with respect to an environment" (page 47). In addition, he suggested the concept of the 'isovist field', which is the resulting map of the contours of equal visual areas, suggesting that it may be useful in the study of the relationship between humans and environments.

Several methods — e.g., visibility graph analysis (VGA) (Turner et al. , 2001) or visual dynamics analysis (VDA) (Lee and Lee, 2014) — can be used to calculate visibility relationships automatically. In this study, VGA was chosen as the method for the authors' analysis. It presents a grid over the spaces to be analyzed and investigates the visibility relationships of every point in the grid to every other point. The theory of isovist and space syntax (Hillier and Hanson, 1984) —a theory often used in previous studies (Choi et al., 2013, Kishimoto and Taguchi, 2014) to validate the relationships among spaces —was combined with VGA, and one important result of the combination was 'connectivity.' Connectivity in VGA is "how many locations each node can see" ((Turner, 2001), page 10); it is the number of grids that are visible from a point, and it is related to the primary isovist concept.

Although some studies (Lee et al., 2013) have taken visual distance into consideration, few have clearly defined visible distances (Desyllas et al., 2003). In an earlier theory of environmental planning, Spreiregen (1965) noted human vision based on the following (page 71): (1) from 3 to 10 feet is in a "close relationship to us," (2) up to 40 feet "we can distinguish facial expression," (3) up to 80 feet "we can recognize a friend's face," (4) up to 450 feet "we can discern body gesture," and (5) 4,000 feet is the "maximum distance for seeing people." Desyllas et al. (2003) and Piao et al. (2012) used a distance of 100m as their restriction of the visible distance in criminal studies.

3. Methodology 3.1 Subjects To select the elementary schools for the study, certain factors concerning school violence (Ministry of Education, 2012) — the number of students who were victims of crime and who recognized bully peers — were reviewed. Among 25 districts in Seoul, the most suitable district was the Songpa district. There were 1,114 students who were victims of crime (M. of the 25 districts = 553.40, S.D = 214.404) and 1,586 students who recognized bully peers (M. of the 25 districts = 785.64, S.D = 334.166). Among 37 schools in the Songpa district, to select the schools for the survey, the outlines of the school buildings which would define the shape of exterior areas were considered. As previous studies (Batty, 2001, Turner et al., 2001) mentioned that the VGA results are related to the morphologies of spaces, schools of various outlines were selected.

Table 1. shows the various forms of the exterior spaces of selected schools and summarizes the features of the schools. In this study, 501 surveys in total were

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Table 1. Major Features of the Selected Schools School name A Plan (not to scale)

Building form Area of the site (m²) Building to land ratio (%) Year of construction

'□' form 8,000 22.36 1996

B

C

'H' form 18,598 22.69 2007

'─' form 13,843 9.81 1981

Table 2. General Features of the Participants Fifth-graders Sixth-graders (12 y) (13y) Males 130 (31.1%) 71 (17.0%) Females 151 (36.1%) 66 (15.8%) Total 281 (67.2%) 137 (32.8%)

combined form 7,146 41.06 2008

E

'L' form 13,883 15.93 1984

F

'T' form 15,082 15.45 1988

Total 201 (48.1%) 217 (51.9%) 418 (100 %)

distributed, and among them 418 which were faithfully answered were analyzed. Table 2. presents the features of the participants.

3.2 Spots for Fear of Crime As it was occasionally difficult for the elementary school students to respond to the complex questions, an aerial photograph of their own school exterior areas, and a visual analogue scale (VAS) were provided in the survey. VAS is used as a measuring tool for phenomena in fields where it is difficult to objectively measure (such as feeling or pain) (Cox and Davison, 2005), and previous studies (Arneill and Devlin, 2002, Devlin, 2008) have used the VAS to analyze feelings associated with environments. Fig.1. shows an example of part of a student's survey answer.

A pilot study was performe d on a sample of students. To increase the reliability of the survey, the authors provided a survey scenario. The procedure of surveys was as follows: 1) for 15 minutes, the survey researchers explained the definition and scope of crime.

As studies indicate that severe crimes are relatively rare whereas minor ones are common in schools, the range of crime was extended to school violence and the students were told that even minor issues could be critical information. 2) Students marked spots where they felt fear of crime on their aerial photograph of their own school exterior areas. The number of spots was limited to three due to space limitations. 3) To define the degree of fear of crime, a 10-cm VAS was presented. Students were asked how fearful they felt at the spots, and marked an "X" on a 10-cm line to denote the degree of fear, with 0 cm being the least fearful and 10 cm being the most fearful. 4) Students wrote the reason for fear of crime at each criminal spot in an open-ended answer. They were asked to make their reasons as specific as possible.

In the 418 surveys, a total of 858 spots for fear of crime were noted. All of the information was input into the ArcGIS program for analysis. Through ArcGIS, the density and degree of fear of crime results were visualized, after which 'hot spots' for fear of crime became noticeable. To inves tigate the effects of visibility on fear of crime, this study analyzed fear of

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D

Fig.1. An Example of Part of a Student's Survey (School D)

crime in two ways: the density (frequency per grid) of spots for fear of crime (DS), and the sum of the degree of fear of crime per grid (DG).

3.3 Visibility Graph Analysis (VGA) For an analysis of the visibility of the exterior areas of elementary schools, the outer walls of the building and the fences of the schools were used (Fig.2.). Depthmap, which is a program made to perform VGA (Turner, 2001), was used in this study.

Visibility graphs were created at a resolution of 1m * 1m for an approximation of a human scale (Pinelo and Turner, 2010). This yielded about 58,139 grids in exterior areas of all six schools. In addition, graphs of visual connectivity were created (in this study, the term 'connectivity' in VGA is expressed as 'visual connectivity' to distinguish it from other methods based on space syntax).

Unfortunately, as there are few related studies, it was difficult to determine the visible distance for VGA. For the exterior analysis, referring to previous studies (Desyllas et al., 2003, Piao et al., 2012) which posited the visible distance as 100m and the distance at which discerning body gestures as 450 feet (137m) (Spreiregen, 1965), this study used a visible distance of 100m. To investigate the effects of visibility on fear of crime, this study analyzed visibility in two ways: visual connectivity with no visible distance restriction (CON_NO) and visual connectivity with visible distances restricted to 100m (CON_RES). Fig.2. shows examples of VGA in these two ways.

3.4 Effects of Visibility on Fear of Crime Analysis To compare the levels of the differences between the means of visibility where there was fear of crime

Drawing

CON_NO

CON_RES

Fig.2. Exa m ples of D raw ing and V G A (Lighter Colors Represent Higher Values, School B)

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Fig.3. The Exterior Plans of the Six Schools and the Spots for Fear of Crime (R: Rear Areas of Buildings, N: Newly Built Buildings, B: between Buildings, CR: Curved or Recessed Areas, PK: Parking Areas, and PG: Playgrounds)

and where there was not, an independent-samples t-test was used. To use statistical methods, the 58,139 grids were separately coded according to whether or not fear of crime was felt.

In addition, it was necessary to adjust the various levels of visibility of the six schools to investigate the effects of visibility on fear of crime with a regression analysis. Therefore, the visibility value of each school was divided into 50 levels with equal intervals and recoded as an ordinal variable that ranges from 1 to 50. The number of spots for fear of crime, the sum of the degree of fear, and the number of grids were investigated in the 50 levels. When determining the number of levels which would be the sample size in the regression analysis, a previous similar study which used 50 rooms of the Tate Gallery in London (Turner et al., 2001, Turner and Penn, 1999) was referred to.

4. Results 4.1 Hot Spots for Fear of Crime in School Exterior Areas The aggregated data for school exterior areas show a total of 858 spots for fear of crime. As shown in Fig.3., there were some hot spots of fear in school exterior areas. The rear areas of buildings; areas located near newly built buildings; areas between buildings; curved or recessed walls; parking areas; and playgrounds were hot spots. The figure also shows that the density and degree of fear do not necessarily mach. Fig.4. shows examples of hot spots for fear of crime in school exterior areas. The frequency of fear and the sum of the degree of fear were calculated at each level of the two dimensions of visibility (CON_NO and CON_RES), which were recoded with an ordinal variable ranging from 1 to 50. Figs.5. and 6. present the sum of values for the six schools, showing that the frequency and sum of values for the degree distribution are bimodal for the two visibility variables. This means that students felt fear not only in locations with lower levels of visibility but also in locations with high levels of visibility.

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Rear area of building Newly built building, Between buildings

Curved or recessed area

Parking area

Playground

Fig.4. Hot Spots for Fear of Crime in School Exterior Areas

Fig.5. Frequency of Spots for Fear of Crime Presented at Each Level of Visibility

Fig.6. The Sum of the Degree for Fear Presented at Each Level of Visibility

4.2 Visibility Graph Analysis in School Exterior Areas There were a total of 58,139 grids in the exterior areas of six schools and the VGA of schools in terms of CON_NO and CON_RES dimensions are shown in Fig.7. The visibility analysis values of each school were also divided into 50 levels and Fig.8. shows the

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CON_NO

CON_RES Fig.7. VGA of Six Schools in Terms of CON_NO and CON_RES (Lighter Colors Represent Higher Values) Table 3. Means of Visibility Analysis Values for Non-Fear-Related Grids (N_F) and for Fear-Related Grids (F) with Independent Samples Tests N Mean Sd df t CON_NO N_F 57,404 5,784.38 3,250.667 755.581 29.476** F 735 2,450.04 3,044.632 CON_RES N_F 57,404 5,490.03 3,126.481 773.247 26.105** F 735 2,340.70 2,897.976 * p < 0.05, ** p < 0.01.

sum of the grid frequency of the six schools at each of the 50 levels. The results for CON_NO and CON_ RES were negatively skewed (or minutely bimodal) in shape. This means that school exteriors consist mainly of high visibility areas, although there are also some lower visibility areas.

Table 4. R²Values in Linear (L) and Quadratic (Q) Models from the Regression Analyses between Visibility Values and Fear of Crime Values Visibility Fear of crime DS DG CON_NO L 0.411** 0.405** Q 0.694** 0.689** CON_RES L 0.435** 0.431** Q 0.761** 0.766** * p < 0.05, ** p < 0.01.

Fig.8. Grid Frequency Presented at Each Level of Visibility

4.3 Effects of Visibility on Fear of Crime in School Exterior Areas In b ot h CON _N O an d CON _RES , whe n the visibility values of the grids where fear of crime appears and does not appear are compared, the mean of the former is lower than that of the latter (p < 0.01) (Table 3.).

The results from the regression analysis of the two visibility analysis values (CON_NO and CON_RES) and the two fear of crime values (DS and DG) are summarized in Table 4. In all cases, the regression analysis gives a much higher R² value in the quadratic models than in the linear models. Therefore, the correlations between visibility and fear of crime are more appropriate in the quadratic models which are upwards ('U' shaped-graph) compared to the linear models (McDonald, 2009) (Fig.9.). Furthermore, when the visible distance is restricted, the R² values were higher compared to the non-restricted values.

Considering DS and DG, DG did not show significant differences in R² values compared to DS.

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Fig.9. Scattergrams Plotting the Values for Adjusted Visibility Against DS and DG

4.4 Reasons for Fear of Crime It was found that about 95% (94.9% for CON_NO, 95.2% for CON_RES) of spots where students felt fear of crime were in the top and bottom 30% in terms of visibility (Table 5.). Therefore, to analyze reasons for fear of crime, visibility was divided into three categories: the bottom 30%, the middle 40%, and the top 30%. As shown in Table 5., in the low visibility area, human factors (31.2% for CON_NO and 31.4% for CON_RES) and space factors (34.3% for CON_NO

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Table 5. Reasons for Fear of Crime Reasons for fear of crime

Visibility for CON_NO

Low (20%) F

Human factors Space factors

Few people Many bully peers Criminal experience Darkness Refuge Cul-de-sac Scary atmosphere No CCTV Maintenance Many trees

Others Total Human factors Space factors

Others Total

Few people Many bully peers Criminal experience Darkness Refuge Cul-de-sac Scary atmosphere No CCTV Maintenance Many trees

Total

Medium (40%) %

High (30%) %

F (%)

189 66 13

268

31.2

11 9 2

22

2.6

36 82 27

145

16.9

236(27.5) 157(18.3) 42(4.9)

81 67 46 47 23 18 12

294

34.3

7 2 4 3 1 1 0

18

2.1

14 1 9 6 7 3 3

43

5.0

102(11.9) 70(8.2) 59(6.9) 56(6.5) 31(3.6) 22(2.6) 15(1.7)

53 615

53 615

6.2 71.7

11 199

11 199

1.3 23.2

189 66 14

269

31.4

4 4 0.5 44 44 5.1 Visibility for CON_RES 10 21 2.4 9 2

36 82 27

145

16.9

68(7.9) 858(100.0) Total 235(27.4) 157(18.3) 43(5.0)

83 68 46 47 23 18 12

297

34.6

6 2 4 3 1 1 0

17

2.0

13 2 8 6 7 3 3

42

4.9

102(11.9) 72(8.4) 58(6.8) 56(6.5) 31(3.6) 22(2.6) 15(1.7)

53 619

53 619

6.2 72.1

3 41

3 41

0.3 4.8

11 198

11 198

1.3 23.1

67(7.8) 858(100.0)

and 34.6% for CON_RES) have a similar distribution.

In the high visibility area, on the other hand, human fa c t o rs (1 6. 9% for C ON_NO a nd C ON_R E S) â&#x20AC;&#x201D; especially 'many bully peers' factors â&#x20AC;&#x201D; have a much higher distribution than space factors (5.0% for CON_ NO and 4.9% for CON_RES).

5. Discussions and Conclusions Generally, locations for which fear of crime was reported had lower levels of visibility compared to locations not associated with fear of crime, and it was similar to the results of previous studies (Andrews and Gatersleben, 2010, Fisher and Nasar, 1992, Loewen et al., 1993, Nasar and Fisher, 1993) which asserted the negative relationship between visibility and fear of crime. However, this study analyzed visibility with several precise quantitative analyses and found that where there was very high visibility, fear of crime rebounds in what can be shown as a U-shaped graph.

These results support the findings of previous research (Lee et al., 2012) which found that fear of crime can occur not only in places with poor visibility but also in those with very good visibility. In other words, visibility exhibits duality in terms of fear of crime in environments. Environments with extremely low or high visibility are highly associated with fear of crime.

This finding is contrary to the conventional theory of environmental criminology or CPTED, which asserts that areas with good visibility are safe from criminal behavior. This study investigate d why students

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F

%

F

experience fear of crime in high visibility areas and compared these reasons with the causes of fear in low visibility areas; it was found that human factors (among human factors, 'peers' were the most frequent reason) had a much greater proportion in high visibility areas than in low visibility areas. It could be supposed that human factors associated with activities among peers could be an important clue to explain the rebound of fear of crime in the high visibility areas. Previous studies have also contended with the problems of open spaces in which people gather. Murota (2009) asserted that the network in an open space can strengthen intercommunication but can also raise safety concerns due to people passing through it. Kim and Moon (2013) also claimed that the flow of human traffic can have a negative side effect. Therefore, such high visibility areas (e.g., playgrounds) where people tend to gather could be criminal hot spots even though these environments meet the visibility factors of CPTED. Although most exterior spaces of elementary schools are high-visibility areas, the hottest spots were behind the buildings areas which were low in visibility.

T he re fore , i n orde r t o posi t i on sc hool bui l di ngs adequately, narrow paths behind buildings should be avoided, though if these paths are inevitable, as in a previous study (Kim and Ha, 2011), the fence that follows the path should be possible to see from outside of the school. Moreover, regarding the exterior areas of buildings to minimize spots related to fear of crime, the outer walls of school buildings should be straight

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without curves. In cases where new buildings must be added, the creation of places associated with fear of crime should be avoided.

A res triction on vis ible distance improves the coefficient of determination of visibility and fear of crime models. Unfortunately, there are few studies of visible distances, especially with regard to the relationship between visible distances and fear of crime. If more in-depth studies about visible distances are suggested, more valuable data related to visibility and fear of crime can be gained. To be specific, lighting and natural daylight can influence the visible distance.

Visibility was only slightly related to the degree of fear of crime. In other words, visibility does not heighten or lower the degree of fear of crime. It could be supposed that the multiple and combined elements suggested in the previous studies (Lee et al., 2012, Park and Ha, 2012, Yoo and Ha, 2011) â&#x20AC;&#x201D; including human factors, vegetation, and darkness â&#x20AC;&#x201D; all heighten the degree of fear as compared to visibility alone.

This s tudy has several limitations. In exterior e n v i r o n m e n t s, a l t h o u g h m a ny st u d i e s ( Do n o v a n and Prestemon, 2012, Kuo and Sullivan, 2001, Lee et al., 2012, Troy and Grove, 2008) suggested that v e ge t a t i o n i s re l a t e d t o c ri m e , t h i s st u dy d i d n ot consider vegetation in the analysis because applying this element to a visibility graph analysis cannot be done accurately, and there were no clear criteria for determining whether certain vegetation interferes with visibility. In addition, as the children in the survey were too young to pinpoint the exact locations in which they feared crime, using a technological method such as a global positioning system (GPS) could improve the validity of the study (Sugihara et al., 2010).

This study provides the evidence of effects of visibility on fear of crime in environments, and builds on previous research about fear of crime and environments in a more precise manner through its use of automated quantitative analysis techniques.

In addition, through its use of surveys completed by students, this study suggests substantial spots for fear of crime which teachers, designers and other school officials may not notice. In the long run, a reduced level of stress from fear of crime would improve the quality of the students' life (Li and Yao, 2013). Furthermore, the results of this study would help school environment designers to plan safer environments and improve existing environments by considering visibility conditions in an automated and systematic way.

Acknowledgements This work was supported in part by the Yonsei University Research Fund of 2013 and the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2012S1A5A2A01019737).

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References 1)

An, J. & Yoshida, T. (2011) Use of correspondence analysis to analyze feelings of insecurity among the elderly concerning snatch occurrences on roads. Journal of Asian Architecture and Building Engineering, 10 (1), pp.179-186.

2) An, J. & Yoshida, T. (2013) Use of omnidirectional images to analyze elderly people's feelings of insecurity about snatch occurrences on roads. Journal of Asian Architecture and Building Engineering, 12 (2), pp.301-308.

3) Andrews, M. & Gatersleben, B. (2010) Variations in perceptions of danger, fear and preference in a simulated natural environment.

Journal of Environmental Psychology, 30 (4), pp.473-481.

4) Arneill, A. B. & Devlin, A. S. (2002) Perceived quality of care: The influence of the waiting room environment. Journal of Environmental Psychology, 22 (4), pp.345-360.

5) Batty, M. (2001) Exploring isovist fields: space and shape in architectural and urban morphology. Environment and Planning B, 28 (1), pp.123-150.

6) Benedikt, M. L. (1979) To take hold of space: isovists and isovist fields. Environment and Planning B, 6 (1), pp.47-65.

7) Bennett, T. & Wright, R. (1984) Burglars on burglary: Prevention and the offender, Gower Aldershot.

8) Block, R. L. & Block, C. R. (1995) Space, place and crime: Hot spot areas and hot places of liquor-related crime. Crime and place, 4 pp.145-183.

9) Brown, B., Perkins, D. D., & Brown, G. (2003) Place attachment in a revitalizing neighborhood: Individual and block levels of analysis. Journal of environmental psychology, 23 (3), pp.259-271.

10) Choi, J., Kim, M., & Byun, N. (2013) Quantitative Analysis on the Spatial Configuration of Korean Apartment Complexes. Journal of Asian Architecture and Building Engineering, 12 (2), pp.277-284.

11) Cox, J. M. & Davison, A. (2005) The visual analogue scale as a tool for self-reporting of subjective phenomena in the medical radiation sciences. The Radiographer, 52 (1), pp.22-4.

12) Desyllas, J., Connoly, P., & Hebbert, F. (2003) Modelling natural surveillance. Environment and Planning B, 30 (5), pp.643-656.

13) Devlin, A. S. (2008) Judging a Book by Its Cover Medical Building Facades and Judgments of Care. Environment and behavior, 40 (3), pp.307-329.

14) Donovan, G. H. & Prestemon, J. P. (2012) The effect of trees on crime in Portland, Oregon. Environment and Behavior, 44 (1), pp.3-30.

15) DuBow, F., McCabe, E., & Kaplan, G. (1979) Reactions to crime: a critical review of the literature: executive summary, Department of Justice, Law Enforcement Assistance Administration, National Institute of Law Enforcement and Criminal Justice.

16) Ferraro, K. F. & Grange, R. L. (1987) The measurement of fear of crime. Sociological inquiry, 57 (1), pp.70-97.

17) Fisher, B. S. & Nasar, J. L. (1992) Fear of crime in relation to three exterior site features. Environment and Behavior, 24 (1), pp.35-65.

18) Fujii, T., Fujikawa, Y., & Oikawa, K. (2013) A quantitative analysis of natural surveillance at elementary schools-evaluation method based on perspectives from both outside visibility and visibility from inside buildings. Journal of Asian Architecture and Building Engineering, 12 (1), pp.17-23.

19) Garofalo, J., Siegel , L., & Laub, J. (1987) School-related victimizations among adolescents: An analysis of National Crime Survey (NCS) narratives. Journal of Quantitative Criminology, 3 (4), pp.321-338.

20) Gray, E., Jackson, J., & Farrall, S. (2008) Reassessing the fear of crime. European Journal of Criminology, 5 (3), pp.363-380.

21) Hillier, B. & Hanson, J. (1984) The social logic of space, Cambridge University Press Cambridge.

22) Hong, Y. (2008) Juvenile victimization in Korea, Seoul, Korean Institute of Criminology.

23) Jacobs, J. (1961) The death and life of great American cities, Vintage.

24) Jeffery, C. R. (1971) Crime prevention through environmental design, Sage Publications Beverly Hills.

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151

25) Kim, J. & Ha, M. (2011) A Study on the Natural Surveillance of External Environment Influencing School Crime Journal of Korea Design Knowledge, 18, pp.42-51.

26) Kim, M. & Moon, J. (2013) A Study on the Correlation between Viewing Behavior and Exhibiting Methods in Museums - Focusing on Viewing Behavior on Weekdays and Weekends in Medium Sized History Museums in Korea. Journal of Asian Architecture and Building Engineering, 12 (2), pp.173-180.

27) Kishimoto, T. & Taguchi, M. (2014) Spatial Configuration of Japanese Elementary Schools: Analyses by the Space Syntax and Evaluation by School Teachers. Journal of Asian Architecture and Building Engineering, 13 (2), 373-380.

28) Kuo, F. E. & Sullivan, W. C. (2001) Environment and Crime in the Inner City Does Vegetation Reduce Crime? Environment and behavior, 33 (3), pp.343-367.

29) Lee, S., Ryu, H., & Ha, M. (2012) Criminal Spots on the Way Home from School A Case Study of Middle Schools in the Gangseo District. Journal of Asian Architecture and Building Engineering, 11 (1), pp.63-70.

30) Lee, S. J. & Lee, K. H. (2014) Suggestion for a Visual Dynamics Analysis Model Using a Natural Movement Model. Journal of Asian Architecture and Building Engineering, 13 (2), pp.381-388.

31) Lee, S. J., Lee, K. H., & Kang, S. J. (2013) Study on a pedestrian simulation model of natural movement. Journal of Asian Architecture and Building Engineering, 12 (1), pp.41-48.

32) Li, W. Y. & Yao, C. C. (2013) Trends of Livability in the Capital Region of Taiwan. Journal of Asian Architecture and Building Engineering, 12 (2), pp.293-300.

33) Loewen, L. J., Steel, G. D., & Suedfeld, P. (1993) Perceived safety from crime in the urban environment. Journal of environmental psychology, 13 (4), pp.323-331.

34) MacDonald, J. E. & Gifford, R. (1989) Territorial cues and defensible space theory: The burglar's point of view. Journal of Environmental Psychology, 9 (3), pp.193-205.

35) Maltz, M. D., Gordon, A. C., & Friedman, W. (1991) Mapping crime in its community setting: Event geography analysis, Springer.

36) McDonald, J. H. (2009) Handbook of biological statistics, Sparky House Publishing Baltimore.

37) Miceli, R., Roccato, M., & Rosato, R. (2004) Fear of crime in Italy Spread and determinants. Environment and Behavior, 36 (6), pp.776-789.

38) Murota, M. (2009) Study on the Use of Parks in the Green Matrix System of Kohoku New Town, Japan -Focusing on Parks Combined with Pedestrian Roads. Journal of Asian Architecture and Building Engineering, 8 (1), pp.73-79.

39) Nasar, J. L. & Fisher, B. (1993) 'Hot spots' of fear and crime: a multi-method investigation. Journal of environmental psychology, 13 (3), pp.187-206.

40) Newman, O. (1972) Defensible space, Macmillan New York.

41) Park, H. & Ha, M. (2012) A Study on the Classroom Design in Middle School for Preventing School Violence. Journal of Korean Institute of Interior Design, 21 (3), pp.163-173.

42) Pease, K. (1991) The Kirkholt project: Preventing burglary on a British public housing estate. Security Journal, 2 (2), pp.73-77.

43) Piao, G., Lee, S., & Park, I. (2012) Interpretation of CrimeProne Locations through Visual Exposure Model Considered for 'Visit Frequency'. Journal of the Architectural Institute of Korea Planning & Design, 28 (4), pp.111-118.

44) Pinelo, J. & Turner, A. (2010) Introduction to UCL Depthmap 10.

45) Roman, C. G. (2002) Schools as generators of crime: routine activities and the sociology of place. Villanova University.

46) Rountree, P. W. (1998) A reexamination of the crime-fear linkage.

Journal of Research in Crime and Delinquency, 35 (3), pp.341372.

47) Salmon, G., James, A., & Smith, D. (1998) Bullying in schools: self reported anxiety, depression, and self esteem in secondary school children. BMj, 317 (7163), pp.924-925.

48) Sherman, L. W. (1995) Hot spots of crime and criminal careers of places. Crime and place, 4, pp.35-52.

152 JAABE vol.14 no.1 January 2015

49) Spreiregen, P. D. (1965) Urban Design: The Architecture of Towns and Cities, McGraw-Hill.

50) Sugihara, S., Matsushita, D., & Munemoto, J. (2010) Observations on Primary School Children's Behavior after School by Using the Global Positioning System. Journal of Asian Architecture and Building Engineering, 9 (1), pp.171-176.

51) Takizawa, A., Koo, W., & Katoh, N. (2010) Discovering Distinctive Spatial Patterns of Snatch Theft in Kyoto City with CAEP. Journal of Asian Architecture and Building Engineering, 9 (1), pp.103-110.

52) Tandy, C. (1967) The isovist method of landscape survey. In: Symposium: Methods of Landscape Analysis, 1967. Landscape Research Group London, pp.9-10.

53) Taylor, R. B., Gottfredson, S. D., & Brower, S. (1984) Block crime and fear: Defensible space, local social ties, and territorial functioning. Journal of Research in Crime and Delinquency, 21 (4), pp.303-331.

54) Troy, A. & Grove, J. M. (2008) Property values, parks, and crime: A hedonic analysis in Baltimore, MD. Landscape and Urban Planning, 87 (3), pp.233-245.

55) Turner, A. (2001) Depthmap: A program to perform visibility graph analysis . In: Proceedings of the 3rd Space Syntax Symposium, 2001.

56) Turner, A. (2003) Analysing the visual dynamics of spatial morphology. Environment and Planning B, 30 (5), pp.657-676.

57) Turner, A., Doxa, M., O'sullivan, D., & Penn, A. (2001) From isovists to visibility graphs: a methodology for the analysis of architectural space. Environment Planning B, 28 (1), pp.103-121.

58) Turner, A. & Penn, A. (1999) Making isovists syntactic: isovist integration analysis. In: 2nd International Symposium on Space Syntax, Brasilia, 1999. Citeseer.

59) Wang, K. & Taylor, R. B. (2006) Simulated walks through dangerous alleys: Impacts of features and progress on fear. Journal of environmental psychology, 26 (4), pp.269-283.

60) Weisburd, D. & Green, L. (1995) Policing drug hot spots: The Jersey City drug market analysis experiment. Justice Quarterly, 12 (4), pp.711-735.

61) Yoo, S. & Ha, M. (2011) A Study on Middle School Environmental Planning for Preventing School Violence - Based on Students' Perceptions of Violence Experience and Fear for Violence in Schools. Journal of Korea Design Knowledge 20, pp.327-336.

Soyeon Lee