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

The Duality of Visibility: Does Visibility Increase or Decrease the Fear of Crime in Schools′... 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 Therefore, a more in-depth study with respect to According to a survey of the Seoul metropolitan criminal behavior and visibility in elementary school government (2010), Seoul citizens thought that the areas would be valuable in environmental criminology. first priority of an education policy was 'school safety' To be specific, this paper seeks the effects of visibility (31.8%). However, schools have not received much on fear of crime in elementary school environments. scholarly attention concerning their relationship to In defining the scope of this research, there are crime (Roman, 2002). In addition, previous study several aspects to be considered. To begin with, the (Salmon et al., 1998) suggests that children who are exterior areas of elementary schools were investigated victims of criminal behavior tend to be in their earlier because, exterior areas are different from interior school years and children who experienced criminal or urban areas in terms of morphology, scale, and behavior feel high anxiety and depression. interpretation of visibility (Turner, 2003). Furthermore, Although visibility is considered to be an important school exterior areas show variable visibility, ranging factor that affects fear of crime in environmental from very high (e.g., playgrounds) to very low (e.g., criminology (Andrews and Gatersleben, 2010, the rear of buildings). Fisher and Nasar, 1992, Loewen et al., 1993, Nasar In addition, fear of crime is considered in its and Fisher, 1993, Wang and Taylor, 2006), previous cognitive and emotional dimensions (Ferraro and studies used few quantitative analysis techniques with Grange, 1987). As minor instances of victimization are automated computer routines to analyze visibility. common in schools (Garofalo et al., 1987), the scope Furthermore, they used visibility as one element that of crime was extended to school violence. Therefore, causes fear of crime instead of analyzing visibility only crimes included violent crimes (assault, robbery, in more depth. 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) *Contact Author: Mikyoung Ha, Professor, following Hong (2008). Department of Interior Architecture and Built Environment, Furthermore, to find the spots where fear of crime Yonsei University, 417 Samsung Hall 134 Sinchon-Dong, was actually felt at the children's level, the survey was Seodaemun-Gu, Seoul, 120-749, Korea completed by students in an elementary school and Tel: +82-2-2123-3135 Fax: +82-2-2123-8662 excluded their teachers or other school officials. The E-mail: mkha@yonsei.ac.kr surveys were anonymously completed by children aged ( Received April 8, 2014 ; accepted October 15, 2014 ) Journal of Asian Architecture and Building Engineering/January 2015/152 145 12 (fifth-graders) to 13 (sixth-graders), who experience a given vantage point in space and with respect to an considerable amounts of criminal behavior (Salmon environment" (page 47). In addition, he suggested the et al., 1998) and who were capable of answering the concept of the 'isovist field', which is the resulting map questions in such a survey. of the contours of equal visual areas, suggesting that it may be useful in the study of the relationship between 2. Literature Review humans and environments. 2.1 Fear of Crime in Environments Several methods — e.g., visibility graph analysis Previous studies have supported that cognitive (VGA) (Turner et al., 2001) or visual dynamics (such as risk perception) and emotional (such as analysis (VDA) (Lee and Lee, 2014) — can be used feeling afraid) dimensions of fear of crime need to be to calculate visibility relationships automatically. In distinguished from one another (Ferraro and Grange, this study, VGA was chosen as the method for the 1987, Rountree, 1998). Generally, information about authors' analysis. It presents a grid over the spaces to fear of crime from previous studies (DuBow et al., be analyzed and investigates the visibility relationships 1979, Gray et al., 2008, Miceli et al., 2004) can be of every point in the grid to every other point. The summarized as follows: (a) fear of crime is related to theory of isovist and space syntax (Hillier and Hanson, the environment; (b) fear of crime is more common 1984) —a theory often used in previous studies (Choi than crime itself; (c) fear of crime is related to et al., 2013, Kishimoto and Taguchi, 2014) to validate sociodemographic variables. the relationships among spaces —was combined with Furthermore, a lot of previous studies have VGA, and one important result of the combination mentioned a relationship between criminal behavior was 'connectivity.' Connectivity in VGA is "how many and environmental design (Brown et al., 2003, Jacobs, locations each node can see" ((Turner, 2001), page 10); 1961, Jeffery, 1971, Taylor et al., 1984). Among it is the number of grids that are visible from a point, environmental factors, visibility which was referred and it is related to the primary isovist concept. to as 'prospect' or 'surveillance' in earlier work was Although some studies (Lee et al., 2013) have taken negatively related to criminal behavior. According visual distance into consideration, few have clearly to the theory of Fisher and Nasar (1992), fear of defined visible distances (Desyllas et al., 2003). In an crime was low with high prospect levels. In addition, earlier theory of environmental planning, Spreiregen according to the theory of 'crime prevention through (1965) noted human vision based on the following (page environmental design' (CPTED), natural surveillance is 71): (1) from 3 to 10 feet is in a "close relationship considered to reduce criminal behavior (Jacobs, 1961, to us," (2) up to 40 feet "we can distinguish facial Newman, 1972). Furthermore, several studies (Bennett expression," (3) up to 80 feet "we can recognize a and Wright, 1984, Fujii et al., 2013, MacDonald and friend's face," (4) up to 450 feet "we can discern body Gifford, 1989, Pease, 1991) discussed the relationship gesture," and (5) 4,000 feet is the "maximum distance between natural surveillance and criminal behavior. for seeing people." Desyllas et al. (2003) and Piao et In environmental criminology, studies assert that al. (2012) used a distance of 100m as their restriction there are spaces in which concentrations of crime of the visible distance in criminal studies. arise within a limited area (Block and Block, 1995, Sherman, 1995, Weisburd and Green, 1995). 'Hot 3. Methodology spots' are the areas where these criminal behaviors 3.1 Subjects commonly take place. Therefore, through the analyses To select the elementary schools for the study, of hot spots, the characteristics of the criminals, certain factors concerning school violence (Ministry of victims, and environments which increase the number Education, 2012) — the number of students who were of crimes could be investigated (Maltz et al., 1991). victims of crime and who recognized bully peers — Many previous studies (An and Yoshida, 2011, An and were reviewed. Among 25 districts in Seoul, the most Yoshida, 2013, Takizawa et al., 2010) investigated hot suitable district was the Songpa district. There were spots through GIS (geographic information system), 1,114 students who were victims of crime (M. of the 25 and among the statistical methods that can be used districts = 553.40, S.D = 214.404) and 1,586 students to identify hot spots, one of the most common is the who recognized bully peers (M. of the 25 districts density of crime incidents, which is continuous over an = 785.64, S.D = 334.166). Among 37 schools in the area, being higher in some parts and lower in others. Songpa district, to select the schools for the survey, the 2.2 Visibility in Spaces outlines of the school buildings which would define the The 'isovist' concept was suggested to develop shape of exterior areas were considered. As previous the quantitative visibility analysis method. Tandy studies (Batty, 2001, Turner et al., 2001) mentioned (1967) presented the first isovist concept in the area that the VGA results are related to the morphologies of spatial analysis, and Benedikt (1979) utilized it for of spaces, schools of various outlines were selected. quantitative descriptions of spaces with a set of analytic Table 1. shows the various forms of the exterior spaces measurements (Turner et al., 2001). Benedikt (1979) of selected schools and summarizes the features of proposes an isovist as "the set of all points visible from the schools. In this study, 501 surveys in total were 146 JAABE vol.14 no.1 January 2015 Soyeon Lee Table 1. Major Features of the Selected Schools School name A B C D E F Plan (not to scale) Building form '□' form 'H' form '─' form combined form 'L' form 'T' form Area of the site (m²) 8,000 18,598 13,843 7,146 13,883 15,082 Building to land ratio (%) 22.36 22.69 9.81 41.06 15.93 15.45 Year of construction 1996 2007 1981 2008 1984 1988 Table 2. General Features of the Participants Fifth-graders Sixth-graders Total (12 y) (13y) Males 130 (31.1%) 71 (17.0%) 201 (48.1%) Females 151 (36.1%) 66 (15.8%) 217 (51.9%) Total 281 (67.2%) 137 (32.8%) 418 (100 %) distributed, and among them 418 which were faithfully Fig.1. An Example of Part of a Student's Survey (School D) answered were analyzed. Table 2. presents the features of the participants. crime in two ways: the density (frequency per grid) of 3.2 Spots for Fear of Crime spots for fear of crime (DS), and the sum of the degree As it was occasionally difficult for the elementary of fear of crime per grid (DG). school students to respond to the complex questions, 3.3 Visibility Graph Analysis (VGA) an aerial photograph of their own school exterior areas, For an analysis of the visibility of the exterior and a visual analogue scale (VAS) were provided in the areas of elementary schools, the outer walls of the survey. VAS is used as a measuring tool for phenomena building and the fences of the schools were used in fields where it is difficult to objectively measure (Fig.2.). Depthmap, which is a program made to (such as feeling or pain) (Cox and Davison, 2005), and perform VGA (Turner, 2001), was used in this study. previous studies (Arneill and Devlin, 2002, Devlin, Visibility graphs were created at a resolution of 1m 2008) have used the VAS to analyze feelings associated * 1m for an approximation of a human scale (Pinelo with environments. Fig.1. shows an example of part of and Turner, 2010). This yielded about 58,139 grids in a student's survey answer. exterior areas of all six schools. In addition, graphs A pilot study was performed on a sample of of visual connectivity were created (in this study, the students. To increase the reliability of the survey, the term 'connectivity' in VGA is expressed as 'visual authors provided a survey scenario. The procedure of connectivity' to distinguish it from other methods surveys was as follows: 1) for 15 minutes, the survey based on space syntax). researchers explained the definition and scope of crime. Unfortunately, as there are few related studies, it As studies indicate that severe crimes are relatively was difficult to determine the visible distance for rare whereas minor ones are common in schools, the VGA. For the exterior analysis, referring to previous range of crime was extended to school violence and studies (Desyllas et al., 2003, Piao et al., 2012) which the students were told that even minor issues could be posited the visible distance as 100m and the distance critical information. 2) Students marked spots where at which discerning body gestures as 450 feet (137m) they felt fear of crime on their aerial photograph of (Spreiregen, 1965), this study used a visible distance their own school exterior areas. The number of spots of 100m. To investigate the effects of visibility on fear was limited to three due to space limitations. 3) To of crime, this study analyzed visibility in two ways: define the degree of fear of crime, a 10-cm VAS was visual connectivity with no visible distance restriction presented. Students were asked how fearful they felt at (CON_NO) and visual connectivity with visible the spots, and marked an "X" on a 10-cm line to denote distances restricted to 100m (CON_RES). Fig.2. shows the degree of fear, with 0 cm being the least fearful examples of VGA in these two ways. and 10 cm being the most fearful. 4) Students wrote 3.4 Effects of Visibility on Fear of Crime Analysis the reason for fear of crime at each criminal spot in To compare the levels of the differences between an open-ended answer. They were asked to make their the means of visibility where there was fear of crime 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 Drawing CON_NO CON_RES visualized, after which 'hot spots' for fear of crime became noticeable. To investigate the effects of Fig.2. Examples of Drawing and VGA (Lighter Colors Represent Higher Values, School B) visibility on fear of crime, this study analyzed fear of JAABE vol.14 no.1 January 2015 Soyeon Lee 147 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 Rear area of building Newly built building, Between buildings 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 Curved or recessed Parking area Playground 50. The number of spots for fear of crime, the sum area of the degree of fear, and the number of grids were investigated in the 50 levels. When determining the Fig.4. Hot Spots for Fear of Crime in School Exterior Areas 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 Fig.5. Frequency of Spots for Fear of Crime Presented at Each total of 858 spots for fear of crime. As shown in Fig.3., Level of Visibility 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 Fig.6. The Sum of the Degree for Fear Presented at Each Level degree of fear were calculated at each level of the two of Visibility dimensions of visibility (CON_NO and CON_RES), which were recoded with an ordinal variable ranging 4.2 Visibility Graph Analysis in School Exterior from 1 to 50. Figs.5. and 6. present the sum of values Areas for the six schools, showing that the frequency and sum There were a total of 58,139 grids in the exterior of values for the degree distribution are bimodal for the areas of six schools and the VGA of schools in terms two visibility variables. This means that students felt of CON_NO and CON_RES dimensions are shown fear not only in locations with lower levels of visibility in Fig.7. The visibility analysis values of each school but also in locations with high levels of visibility. were also divided into 50 levels and Fig.8. shows the 148 JAABE vol.14 no.1 January 2015 Soyeon Lee 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 Table 4. R² Values in Linear (L) and Quadratic (Q) Models from of the 50 levels. The results for CON_NO and CON_ the Regression Analyses between Visibility Values and Fear of Crime Values RES were negatively skewed (or minutely bimodal) in Visibility Fear of crime shape. This means that school exteriors consist mainly DS DG of high visibility areas, although there are also some CON_NO L 0.411** 0.405** lower visibility areas. 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 both CON_NO and CON_RES, when 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) Fig.9. Scattergrams Plotting the Values for Adjusted Visibility and the two fear of crime values (DS and DG) are Against DS and DG summarized in Table 4. In all cases, the regression analysis gives a much higher R² value in the quadratic 4.4 Reasons for Fear of Crime models than in the linear models. Therefore, the It was found that about 95% (94.9% for CON_NO, correlations between visibility and fear of crime are 95.2% for CON_RES) of spots where students felt more appropriate in the quadratic models which are fear of crime were in the top and bottom 30% in terms upwards ('U' shaped-graph) compared to the linear of visibility (Table 5.). Therefore, to analyze reasons models (McDonald, 2009) (Fig.9.). Furthermore, for fear of crime, visibility was divided into three when the visible distance is restricted, the R² values categories: the bottom 30%, the middle 40%, and the were higher compared to the non-restricted values. top 30%. As shown in Table 5., in the low visibility Considering DS and DG, DG did not show significant area, human factors (31.2% for CON_NO and 31.4% differences in R² values compared to DS. for CON_RES) and space factors (34.3% for CON_NO JAABE vol.14 no.1 January 2015 Soyeon Lee 149 Table 5. Reasons for Fear of Crime Visibility for CON_NO Total Reasons for fear of crime Low (20%) Medium (40%) High (30%) F % F % F % F (%) 189 268 31.2 11 22 2.6 36 145 16.9 236(27.5) Human Few people factors 66 9 82 157(18.3) Many bully peers 13 2 27 42(4.9) Criminal experience 81 294 34.3 7 18 2.1 14 43 5.0 102(11.9) Space Darkness factors 67 2 1 70(8.2) Refuge 46 4 9 59(6.9) Cul-de-sac 47 3 6 56(6.5) Scary atmosphere 23 1 7 31(3.6) No CCTV 18 1 3 22(2.6) Maintenance Many trees 12 0 3 15(1.7) 53 53 6.2 4 4 0.5 11 11 1.3 68(7.9) Others 615 615 71.7 44 44 5.1 199 199 23.2 858(100.0) Total Visibility for CON_RES Total 189 269 31.4 10 21 2.4 36 145 16.9 235(27.4) Human Few people factors 66 9 82 157(18.3) Many bully peers Criminal experience 14 2 27 43(5.0) 83 297 34.6 6 17 2.0 13 42 4.9 102(11.9) Space Darkness factors 68 2 2 72(8.4) Refuge Cul-de-sac 46 4 8 58(6.8) 47 3 6 56(6.5) Scary atmosphere 23 1 7 31(3.6) No CCTV Maintenance 18 1 3 22(2.6) 12 0 3 15(1.7) Many trees Others 53 53 6.2 3 3 0.3 11 11 1.3 67(7.8) 619 619 72.1 41 41 4.8 198 198 23.1 858(100.0) Total and 34.6% for CON_RES) have a similar distribution. experience fear of crime in high visibility areas and In the high visibility area, on the other hand, human compared these reasons with the causes of fear in low fa c t o rs (1 6. 9% for C ON_NO a nd C ON_R E S) — visibility areas; it was found that human factors (among especially 'many bully peers' factors — have a much human factors, 'peers' were the most frequent reason) higher distribution than space factors (5.0% for CON_ had a much greater proportion in high visibility areas NO and 4.9% for CON_RES). than in low visibility areas. It could be supposed that human factors associated with activities among peers 5. Discussions and Conclusions could be an important clue to explain the rebound Generally, locations for which fear of crime was of fear of crime in the high visibility areas. Previous reported had lower levels of visibility compared to studies have also contended with the problems of open locations not associated with fear of crime, and it was spaces in which people gather. Murota (2009) asserted similar to the results of previous studies (Andrews and that the network in an open space can strengthen Gatersleben, 2010, Fisher and Nasar, 1992, Loewen intercommunication but can also raise safety concerns et al., 1993, Nasar and Fisher, 1993) which asserted due to people passing through it. Kim and Moon the negative relationship between visibility and fear (2013) also claimed that the flow of human traffic of crime. However, this study analyzed visibility with can have a negative side effect. Therefore, such high several precise quantitative analyses and found that visibility areas (e.g., playgrounds) where people tend where there was very high visibility, fear of crime to gather could be criminal hot spots even though these rebounds in what can be shown as a U-shaped graph. environments meet the visibility factors of CPTED. These results support the findings of previous research Although most exterior spaces of elementary schools (Lee et al., 2012) which found that fear of crime can are high-visibility areas, the hottest spots were behind occur not only in places with poor visibility but also the buildings areas which were low in visibility. in those with very good visibility. In other words, Therefore, in order to position school buildings visibility exhibits duality in terms of fear of crime in adequately, narrow paths behind buildings should be environments. Environments with extremely low or avoided, though if these paths are inevitable, as in a high visibility are highly associated with fear of crime. previous study (Kim and Ha, 2011), the fence that This finding is contrary to the conventional theory of follows the path should be possible to see from outside environmental criminology or CPTED, which asserts of the school. Moreover, regarding the exterior areas that areas with good visibility are safe from criminal of buildings to minimize spots related to fear of crime, behavior. This study investigated why students the outer walls of school buildings should be straight 150 JAABE vol.14 no.1 January 2015 Soyeon Lee without curves. In cases where new buildings must be References 1) An, J. & Yoshida, T. (2011) Use of correspondence analysis to added, the creation of places associated with fear of analyze feelings of insecurity among the elderly concerning snatch crime should be avoided. occurrences on roads. 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(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 Soyeon Lee http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Asian Architecture and Building Engineering Taylor & Francis

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

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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 Therefore, a more in-depth study with respect to According to a survey of the Seoul metropolitan criminal behavior and visibility in elementary school government (2010), Seoul citizens thought that the areas would be valuable in environmental criminology. first priority of an education policy was 'school safety' To be specific, this paper seeks the effects of visibility (31.8%). However, schools have not received much on fear of crime in elementary school environments. scholarly attention concerning their relationship to In defining the scope of this research, there are crime (Roman, 2002). In addition, previous study several aspects to be considered. To begin with, the (Salmon et al., 1998) suggests that children who are exterior areas of elementary schools were investigated victims of criminal behavior tend to be in their earlier because, exterior areas are different from interior school years and children who experienced criminal or urban areas in terms of morphology, scale, and behavior feel high anxiety and depression. interpretation of visibility (Turner, 2003). Furthermore, Although visibility is considered to be an important school exterior areas show variable visibility, ranging factor that affects fear of crime in environmental from very high (e.g., playgrounds) to very low (e.g., criminology (Andrews and Gatersleben, 2010, the rear of buildings). Fisher and Nasar, 1992, Loewen et al., 1993, Nasar In addition, fear of crime is considered in its and Fisher, 1993, Wang and Taylor, 2006), previous cognitive and emotional dimensions (Ferraro and studies used few quantitative analysis techniques with Grange, 1987). As minor instances of victimization are automated computer routines to analyze visibility. common in schools (Garofalo et al., 1987), the scope Furthermore, they used visibility as one element that of crime was extended to school violence. Therefore, causes fear of crime instead of analyzing visibility only crimes included violent crimes (assault, robbery, in more depth. 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) *Contact Author: Mikyoung Ha, Professor, following Hong (2008). Department of Interior Architecture and Built Environment, Furthermore, to find the spots where fear of crime Yonsei University, 417 Samsung Hall 134 Sinchon-Dong, was actually felt at the children's level, the survey was Seodaemun-Gu, Seoul, 120-749, Korea completed by students in an elementary school and Tel: +82-2-2123-3135 Fax: +82-2-2123-8662 excluded their teachers or other school officials. The E-mail: mkha@yonsei.ac.kr surveys were anonymously completed by children aged ( Received April 8, 2014 ; accepted October 15, 2014 ) Journal of Asian Architecture and Building Engineering/January 2015/152 145 12 (fifth-graders) to 13 (sixth-graders), who experience a given vantage point in space and with respect to an considerable amounts of criminal behavior (Salmon environment" (page 47). In addition, he suggested the et al., 1998) and who were capable of answering the concept of the 'isovist field', which is the resulting map questions in such a survey. of the contours of equal visual areas, suggesting that it may be useful in the study of the relationship between 2. Literature Review humans and environments. 2.1 Fear of Crime in Environments Several methods — e.g., visibility graph analysis Previous studies have supported that cognitive (VGA) (Turner et al., 2001) or visual dynamics (such as risk perception) and emotional (such as analysis (VDA) (Lee and Lee, 2014) — can be used feeling afraid) dimensions of fear of crime need to be to calculate visibility relationships automatically. In distinguished from one another (Ferraro and Grange, this study, VGA was chosen as the method for the 1987, Rountree, 1998). Generally, information about authors' analysis. It presents a grid over the spaces to fear of crime from previous studies (DuBow et al., be analyzed and investigates the visibility relationships 1979, Gray et al., 2008, Miceli et al., 2004) can be of every point in the grid to every other point. The summarized as follows: (a) fear of crime is related to theory of isovist and space syntax (Hillier and Hanson, the environment; (b) fear of crime is more common 1984) —a theory often used in previous studies (Choi than crime itself; (c) fear of crime is related to et al., 2013, Kishimoto and Taguchi, 2014) to validate sociodemographic variables. the relationships among spaces —was combined with Furthermore, a lot of previous studies have VGA, and one important result of the combination mentioned a relationship between criminal behavior was 'connectivity.' Connectivity in VGA is "how many and environmental design (Brown et al., 2003, Jacobs, locations each node can see" ((Turner, 2001), page 10); 1961, Jeffery, 1971, Taylor et al., 1984). Among it is the number of grids that are visible from a point, environmental factors, visibility which was referred and it is related to the primary isovist concept. to as 'prospect' or 'surveillance' in earlier work was Although some studies (Lee et al., 2013) have taken negatively related to criminal behavior. According visual distance into consideration, few have clearly to the theory of Fisher and Nasar (1992), fear of defined visible distances (Desyllas et al., 2003). In an crime was low with high prospect levels. In addition, earlier theory of environmental planning, Spreiregen according to the theory of 'crime prevention through (1965) noted human vision based on the following (page environmental design' (CPTED), natural surveillance is 71): (1) from 3 to 10 feet is in a "close relationship considered to reduce criminal behavior (Jacobs, 1961, to us," (2) up to 40 feet "we can distinguish facial Newman, 1972). Furthermore, several studies (Bennett expression," (3) up to 80 feet "we can recognize a and Wright, 1984, Fujii et al., 2013, MacDonald and friend's face," (4) up to 450 feet "we can discern body Gifford, 1989, Pease, 1991) discussed the relationship gesture," and (5) 4,000 feet is the "maximum distance between natural surveillance and criminal behavior. for seeing people." Desyllas et al. (2003) and Piao et In environmental criminology, studies assert that al. (2012) used a distance of 100m as their restriction there are spaces in which concentrations of crime of the visible distance in criminal studies. arise within a limited area (Block and Block, 1995, Sherman, 1995, Weisburd and Green, 1995). 'Hot 3. Methodology spots' are the areas where these criminal behaviors 3.1 Subjects commonly take place. Therefore, through the analyses To select the elementary schools for the study, of hot spots, the characteristics of the criminals, certain factors concerning school violence (Ministry of victims, and environments which increase the number Education, 2012) — the number of students who were of crimes could be investigated (Maltz et al., 1991). victims of crime and who recognized bully peers — Many previous studies (An and Yoshida, 2011, An and were reviewed. Among 25 districts in Seoul, the most Yoshida, 2013, Takizawa et al., 2010) investigated hot suitable district was the Songpa district. There were spots through GIS (geographic information system), 1,114 students who were victims of crime (M. of the 25 and among the statistical methods that can be used districts = 553.40, S.D = 214.404) and 1,586 students to identify hot spots, one of the most common is the who recognized bully peers (M. of the 25 districts density of crime incidents, which is continuous over an = 785.64, S.D = 334.166). Among 37 schools in the area, being higher in some parts and lower in others. Songpa district, to select the schools for the survey, the 2.2 Visibility in Spaces outlines of the school buildings which would define the The 'isovist' concept was suggested to develop shape of exterior areas were considered. As previous the quantitative visibility analysis method. Tandy studies (Batty, 2001, Turner et al., 2001) mentioned (1967) presented the first isovist concept in the area that the VGA results are related to the morphologies of spatial analysis, and Benedikt (1979) utilized it for of spaces, schools of various outlines were selected. quantitative descriptions of spaces with a set of analytic Table 1. shows the various forms of the exterior spaces measurements (Turner et al., 2001). Benedikt (1979) of selected schools and summarizes the features of proposes an isovist as "the set of all points visible from the schools. In this study, 501 surveys in total were 146 JAABE vol.14 no.1 January 2015 Soyeon Lee Table 1. Major Features of the Selected Schools School name A B C D E F Plan (not to scale) Building form '□' form 'H' form '─' form combined form 'L' form 'T' form Area of the site (m²) 8,000 18,598 13,843 7,146 13,883 15,082 Building to land ratio (%) 22.36 22.69 9.81 41.06 15.93 15.45 Year of construction 1996 2007 1981 2008 1984 1988 Table 2. General Features of the Participants Fifth-graders Sixth-graders Total (12 y) (13y) Males 130 (31.1%) 71 (17.0%) 201 (48.1%) Females 151 (36.1%) 66 (15.8%) 217 (51.9%) Total 281 (67.2%) 137 (32.8%) 418 (100 %) distributed, and among them 418 which were faithfully Fig.1. An Example of Part of a Student's Survey (School D) answered were analyzed. Table 2. presents the features of the participants. crime in two ways: the density (frequency per grid) of 3.2 Spots for Fear of Crime spots for fear of crime (DS), and the sum of the degree As it was occasionally difficult for the elementary of fear of crime per grid (DG). school students to respond to the complex questions, 3.3 Visibility Graph Analysis (VGA) an aerial photograph of their own school exterior areas, For an analysis of the visibility of the exterior and a visual analogue scale (VAS) were provided in the areas of elementary schools, the outer walls of the survey. VAS is used as a measuring tool for phenomena building and the fences of the schools were used in fields where it is difficult to objectively measure (Fig.2.). Depthmap, which is a program made to (such as feeling or pain) (Cox and Davison, 2005), and perform VGA (Turner, 2001), was used in this study. previous studies (Arneill and Devlin, 2002, Devlin, Visibility graphs were created at a resolution of 1m 2008) have used the VAS to analyze feelings associated * 1m for an approximation of a human scale (Pinelo with environments. Fig.1. shows an example of part of and Turner, 2010). This yielded about 58,139 grids in a student's survey answer. exterior areas of all six schools. In addition, graphs A pilot study was performed on a sample of of visual connectivity were created (in this study, the students. To increase the reliability of the survey, the term 'connectivity' in VGA is expressed as 'visual authors provided a survey scenario. The procedure of connectivity' to distinguish it from other methods surveys was as follows: 1) for 15 minutes, the survey based on space syntax). researchers explained the definition and scope of crime. Unfortunately, as there are few related studies, it As studies indicate that severe crimes are relatively was difficult to determine the visible distance for rare whereas minor ones are common in schools, the VGA. For the exterior analysis, referring to previous range of crime was extended to school violence and studies (Desyllas et al., 2003, Piao et al., 2012) which the students were told that even minor issues could be posited the visible distance as 100m and the distance critical information. 2) Students marked spots where at which discerning body gestures as 450 feet (137m) they felt fear of crime on their aerial photograph of (Spreiregen, 1965), this study used a visible distance their own school exterior areas. The number of spots of 100m. To investigate the effects of visibility on fear was limited to three due to space limitations. 3) To of crime, this study analyzed visibility in two ways: define the degree of fear of crime, a 10-cm VAS was visual connectivity with no visible distance restriction presented. Students were asked how fearful they felt at (CON_NO) and visual connectivity with visible the spots, and marked an "X" on a 10-cm line to denote distances restricted to 100m (CON_RES). Fig.2. shows the degree of fear, with 0 cm being the least fearful examples of VGA in these two ways. and 10 cm being the most fearful. 4) Students wrote 3.4 Effects of Visibility on Fear of Crime Analysis the reason for fear of crime at each criminal spot in To compare the levels of the differences between an open-ended answer. They were asked to make their the means of visibility where there was fear of crime 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 Drawing CON_NO CON_RES visualized, after which 'hot spots' for fear of crime became noticeable. To investigate the effects of Fig.2. Examples of Drawing and VGA (Lighter Colors Represent Higher Values, School B) visibility on fear of crime, this study analyzed fear of JAABE vol.14 no.1 January 2015 Soyeon Lee 147 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 Rear area of building Newly built building, Between buildings 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 Curved or recessed Parking area Playground 50. The number of spots for fear of crime, the sum area of the degree of fear, and the number of grids were investigated in the 50 levels. When determining the Fig.4. Hot Spots for Fear of Crime in School Exterior Areas 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 Fig.5. Frequency of Spots for Fear of Crime Presented at Each total of 858 spots for fear of crime. As shown in Fig.3., Level of Visibility 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 Fig.6. The Sum of the Degree for Fear Presented at Each Level degree of fear were calculated at each level of the two of Visibility dimensions of visibility (CON_NO and CON_RES), which were recoded with an ordinal variable ranging 4.2 Visibility Graph Analysis in School Exterior from 1 to 50. Figs.5. and 6. present the sum of values Areas for the six schools, showing that the frequency and sum There were a total of 58,139 grids in the exterior of values for the degree distribution are bimodal for the areas of six schools and the VGA of schools in terms two visibility variables. This means that students felt of CON_NO and CON_RES dimensions are shown fear not only in locations with lower levels of visibility in Fig.7. The visibility analysis values of each school but also in locations with high levels of visibility. were also divided into 50 levels and Fig.8. shows the 148 JAABE vol.14 no.1 January 2015 Soyeon Lee 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 Table 4. R² Values in Linear (L) and Quadratic (Q) Models from of the 50 levels. The results for CON_NO and CON_ the Regression Analyses between Visibility Values and Fear of Crime Values RES were negatively skewed (or minutely bimodal) in Visibility Fear of crime shape. This means that school exteriors consist mainly DS DG of high visibility areas, although there are also some CON_NO L 0.411** 0.405** lower visibility areas. 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 both CON_NO and CON_RES, when 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) Fig.9. Scattergrams Plotting the Values for Adjusted Visibility and the two fear of crime values (DS and DG) are Against DS and DG summarized in Table 4. In all cases, the regression analysis gives a much higher R² value in the quadratic 4.4 Reasons for Fear of Crime models than in the linear models. Therefore, the It was found that about 95% (94.9% for CON_NO, correlations between visibility and fear of crime are 95.2% for CON_RES) of spots where students felt more appropriate in the quadratic models which are fear of crime were in the top and bottom 30% in terms upwards ('U' shaped-graph) compared to the linear of visibility (Table 5.). Therefore, to analyze reasons models (McDonald, 2009) (Fig.9.). Furthermore, for fear of crime, visibility was divided into three when the visible distance is restricted, the R² values categories: the bottom 30%, the middle 40%, and the were higher compared to the non-restricted values. top 30%. As shown in Table 5., in the low visibility Considering DS and DG, DG did not show significant area, human factors (31.2% for CON_NO and 31.4% differences in R² values compared to DS. for CON_RES) and space factors (34.3% for CON_NO JAABE vol.14 no.1 January 2015 Soyeon Lee 149 Table 5. Reasons for Fear of Crime Visibility for CON_NO Total Reasons for fear of crime Low (20%) Medium (40%) High (30%) F % F % F % F (%) 189 268 31.2 11 22 2.6 36 145 16.9 236(27.5) Human Few people factors 66 9 82 157(18.3) Many bully peers 13 2 27 42(4.9) Criminal experience 81 294 34.3 7 18 2.1 14 43 5.0 102(11.9) Space Darkness factors 67 2 1 70(8.2) Refuge 46 4 9 59(6.9) Cul-de-sac 47 3 6 56(6.5) Scary atmosphere 23 1 7 31(3.6) No CCTV 18 1 3 22(2.6) Maintenance Many trees 12 0 3 15(1.7) 53 53 6.2 4 4 0.5 11 11 1.3 68(7.9) Others 615 615 71.7 44 44 5.1 199 199 23.2 858(100.0) Total Visibility for CON_RES Total 189 269 31.4 10 21 2.4 36 145 16.9 235(27.4) Human Few people factors 66 9 82 157(18.3) Many bully peers Criminal experience 14 2 27 43(5.0) 83 297 34.6 6 17 2.0 13 42 4.9 102(11.9) Space Darkness factors 68 2 2 72(8.4) Refuge Cul-de-sac 46 4 8 58(6.8) 47 3 6 56(6.5) Scary atmosphere 23 1 7 31(3.6) No CCTV Maintenance 18 1 3 22(2.6) 12 0 3 15(1.7) Many trees Others 53 53 6.2 3 3 0.3 11 11 1.3 67(7.8) 619 619 72.1 41 41 4.8 198 198 23.1 858(100.0) Total and 34.6% for CON_RES) have a similar distribution. experience fear of crime in high visibility areas and In the high visibility area, on the other hand, human compared these reasons with the causes of fear in low fa c t o rs (1 6. 9% for C ON_NO a nd C ON_R E S) — visibility areas; it was found that human factors (among especially 'many bully peers' factors — have a much human factors, 'peers' were the most frequent reason) higher distribution than space factors (5.0% for CON_ had a much greater proportion in high visibility areas NO and 4.9% for CON_RES). than in low visibility areas. It could be supposed that human factors associated with activities among peers 5. Discussions and Conclusions could be an important clue to explain the rebound Generally, locations for which fear of crime was of fear of crime in the high visibility areas. Previous reported had lower levels of visibility compared to studies have also contended with the problems of open locations not associated with fear of crime, and it was spaces in which people gather. Murota (2009) asserted similar to the results of previous studies (Andrews and that the network in an open space can strengthen Gatersleben, 2010, Fisher and Nasar, 1992, Loewen intercommunication but can also raise safety concerns et al., 1993, Nasar and Fisher, 1993) which asserted due to people passing through it. Kim and Moon the negative relationship between visibility and fear (2013) also claimed that the flow of human traffic of crime. However, this study analyzed visibility with can have a negative side effect. Therefore, such high several precise quantitative analyses and found that visibility areas (e.g., playgrounds) where people tend where there was very high visibility, fear of crime to gather could be criminal hot spots even though these rebounds in what can be shown as a U-shaped graph. environments meet the visibility factors of CPTED. These results support the findings of previous research Although most exterior spaces of elementary schools (Lee et al., 2012) which found that fear of crime can are high-visibility areas, the hottest spots were behind occur not only in places with poor visibility but also the buildings areas which were low in visibility. in those with very good visibility. In other words, Therefore, in order to position school buildings visibility exhibits duality in terms of fear of crime in adequately, narrow paths behind buildings should be environments. Environments with extremely low or avoided, though if these paths are inevitable, as in a high visibility are highly associated with fear of crime. previous study (Kim and Ha, 2011), the fence that This finding is contrary to the conventional theory of follows the path should be possible to see from outside environmental criminology or CPTED, which asserts of the school. Moreover, regarding the exterior areas that areas with good visibility are safe from criminal of buildings to minimize spots related to fear of crime, behavior. This study investigated why students the outer walls of school buildings should be straight 150 JAABE vol.14 no.1 January 2015 Soyeon Lee without curves. In cases where new buildings must be References 1) An, J. & Yoshida, T. (2011) Use of correspondence analysis to added, the creation of places associated with fear of analyze feelings of insecurity among the elderly concerning snatch crime should be avoided. occurrences on roads. Journal of Asian Architecture and Building A restriction on visible distance improves the Engineering, 10 (1), pp.179-186. coefficient of determination of visibility and fear of 2) An, J. & Yoshida, T. (2013) Use of omnidirectional images crime models. Unfortunately, there are few studies to analyze elderly people's feelings of insecurity about snatch occurrences on roads. Journal of Asian Architecture and Building of visible distances, especially with regard to the Engineering, 12 (2), pp.301-308. relationship between visible distances and fear of 3) Andrews, M. & Gatersleben, B. (2010) Variations in perceptions crime. If more in-depth studies about visible distances of danger, fear and preference in a simulated natural environment. are suggested, more valuable data related to visibility Journal of Environmental Psychology, 30 (4), pp.473-481. and fear of crime can be gained. To be specific, lighting 4) Arneill, A. B. & Devlin, A. S. (2002) Perceived quality of care: The influence of the waiting room environment. Journal of and natural daylight can influence the visible distance. Environmental Psychology, 22 (4), pp.345-360. Visibility was only slightly related to the degree 5) Batty, M. (2001) Exploring isovist fields: space and shape in of fear of crime. In other words, visibility does not architectural and urban morphology. Environment and Planning B, heighten or lower the degree of fear of crime. It could 28 (1), pp.123-150. be supposed that the multiple and combined elements 6) Benedikt, M. L. (1979) To take hold of space: isovists and isovist fields. Environment and Planning B, 6 (1), pp.47-65. suggested in the previous studies (Lee et al., 2012, 7) Bennett, T. & Wright, R. (1984) Burglars on burglary: Prevention Park and Ha, 2012, Yoo and Ha, 2011) — including and the offender, Gower Aldershot. human factors, vegetation, and darkness — all heighten 8) Block, R. L. & Block, C. R. 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(2005) The visual analogue scale as a this element to a visibility graph analysis cannot be tool for self-reporting of subjective phenomena in the medical done accurately, and there were no clear criteria for radiation sciences. The Radiographer, 52 (1), pp.22-4. determining whether certain vegetation interferes with 12) Desyllas, J., Connoly, P., & Hebbert, F. (2003) Modelling natural visibility. In addition, as the children in the survey surveillance. Environment and Planning B, 30 (5), pp.643-656. were too young to pinpoint the exact locations in which 13) Devlin, A. S. (2008) Judging a Book by Its Cover Medical Building Facades and Judgments of Care. Environment and they feared crime, using a technological method such behavior, 40 (3), pp.307-329. as a global positioning system (GPS) could improve 14) Donovan, G. H. & Prestemon, J. P. (2012) The effect of trees on the validity of the study (Sugihara et al., 2010). crime in Portland, Oregon. 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(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 Soyeon Lee

Journal

Journal of Asian Architecture and Building Engineering – Taylor & Francis

Published: Jan 1, 2015

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

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