Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/multidisciplinary-digital-publishing-institute/perceived-safety-in-the-neighborhood-exploring-the-role-of-built-26Eu0ZgAIg
References
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
- Publisher
- Multidisciplinary Digital Publishing Institute
- Copyright
- © 1996-2022 MDPI (Basel, Switzerland) unless otherwise stated Disclaimer Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Terms and Conditions Privacy Policy
- ISSN
- 2075-5309
- DOI
- 10.3390/buildings13010002
- Publisher site
- See Article on Publisher Site
Abstract
Article Perceived Safety in the Neighborhood: Exploring the Role of Built Environment, Social Factors, Physical Activity and Multiple Pathways of Influence 1,2 2, 1 2,3 Erli Zeng , Yu Dong *, Li Yan and Alin Lin School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621000, China School of Architecture, Harbin Institute of Technology, Harbin 150001, China School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China * Correspondence: dongyu@hit.edu.cn; Tel.: +86-0451-8641-2114 Abstract: Considering the sensitivity to environmental safety is rooted in human genes, the external variables that affect the perception of environmental safety and their influence mechanisms have become a point of concern. The existing literature has proven that elements of the built environment are vital influencing factors; however, little is known about the mechanism by which the built envi- ronment affects perceived safety and multiple influence pathways have been ignored. To define the concept of perceived safety, this article applies a structural equation model to study the relationship between the built environment and residents’ safety perception with the social environment and physical activity as potential mediators. The statistical results suggest that the variables of the built environment, social factors, and physical activity all significantly influence perceived safety. This finding also reveals that the social environment and group physical activities slightly mediate the relationship, proving that the built environment exerts both direct and indirect effects on perceived Citation: Zeng, E.; Dong, Y.; Yan, L.; safety. This study provides evidence that built environment design is more important than previ- Lin, A. Perceived Safety in the ously thought because it contributes positively to the social atmosphere and encourages the passion Neighborhood: Exploring the Role for physical activities, which are also beneficial to safety perception. of Built Environment, Social Factors, Physical Activity and Multiple Keywords: perceived safety; built environment; social environment; physical activity; Pathways of Influence. Buildings mediation effect; structural equation model 2023, 13, 2. https://doi.org/ 10.3390/buildings13010002 Academic Editors: Baojie He, Deo Prasad, Ali Cheshmehzangi, Wu 1. Introduction Deng, Samad Sepasgozar Safety is considered a basic requirement, inferior only to physiological drive (food, and Xiao Liu shelter); thus, the safe sentiment could be treated as the premise for realizing higher emo- Received: 2 November 2022 tional needs as a part of well-being [1]. In addition, numerous studies have shown that Revised: 15 December 2022 the positive perception of safety positively contributes to health outcomes, especially in Accepted: 15 December 2022 the neighborhood [2–4]. Conversely, perceiving the neighborhood as unsafe appears to Published: 20 December 2022 have a negative effect on residents, including increased anxiety and decreased life satis- faction [5], which further results in poor self-rated health [3] and undesirable health out- comes [2,6]. Copyright: © 2022 by the authors. Despite its importance, the loss of safety is increasingly identified as a critical social Licensee MDPI, Basel, Switzerland. problem [7–9]. In this case, determining the factors and their mechanisms through which This article is an open access article they contribute to the growing unsafe feeling is among the most pressing concerns for distributed under the terms and urban planners [9]. In 1971, Clarence Ray Jeffery first mentioned the theory of Crime Pre- conditions of the Creative Commons vention through Environmental Design (CPTED), which mainly studies how to prevent Attribution (CC BY) license crime and decrease fear through environment design. With the efforts of various scholars (https://creativecommons.org/license (Newman, Crowe, Moffat, etc.), relative environmental principles were summarized into s/by/4.0/). four strategies: (1) surveillance; (2) access control; (3) territoriality; and (4) maintenance Buildings 2023, 13, 2. https://doi.org/10.3390/buildings13010002 www.mdpi.com/journal/buildings Buildings 2023, 13, 2 2 of 19 [10,11]. After further expansion, Greg Saville and Gerry Cleveland proposed the second generation of CPTED, highlighting the “community view“ as the theory’s essence [12]. Favorable physical characteristics and attractive public space could enhance residents’ communication and cultivate a sense of community to improve safe feelings. In addition, the Routine Activity Theory (RAT), which indicates that routine activities decrease the likelihood of crime, could also be applied to explore safety perception after environmental interventions [13]. That is, perceived safety may be affected by daily activities which fre- quently occur in the surroundings. When it comes to communities, residents’ regular ex- ercises serve as indispensable events which take place within the neighborhood. Rela- tively speaking, the development of CPTED theory has changed from statically empha- sizing the physical space from architectural perspectives to dynamically focusing on social atmosphere, and the RAT enlightens us to take essential activities into account. Although research to date has verified that the degree of perceived safety varies with different environmental variables [14], most researchers merely concentrate on one or one particular group of elements. Specifically, some studies only focus on the fine-scale built environmental features’ influence on safety perception, while others explore the factors of social networks, social support and social cohesion, all of which are constrained in social environment perspectives. However, the comprehensive influence framework, including elements of the built environment, social factors and other frequently discussed variables, such as physical activity, is generally overlooked. In addition, it is critical to clearly define the research object, since a recurring issue is that the inconsistent concepts of perceived safety are frequently found in relevant studies. Among them, some even simply substitute “perceived safety” with “fear of crime”, and the conceptual difference may produce bias in correlative research. To address the aforementioned knowledge gap, after the connotation of perceived safety is clearly defined, this study aims to explore the relationship between built envi- ronment and residents’ safety perception and clarify the possible mediating role of social factors and group physical activities. Based on the data collected from 13 neighborhoods in Mianyang, China, a quantitative model was built to reveal the possible direct influence, which we used to validate the effect of the built environment characterized by certain attributes, social factors and physical activity upon perceived safety. In addition, indirect influence pathways were examined to explore whether social environment and physical activity could explain the relationship between the built environment and perceived safety by acting as mediating variables. By discussing these issues, we aim to provide a clearer understanding of how perceived safety is affected in communities. 2. Theoretical Framework 2.1. Perceived Safety: Defining the Concept and Content Despite being a topic of concern, there is a lack of a clear explanation for the concept of perceived safety (as well as its synonyms of perception/feeling of safety). Some re- searchers treat it as a conventional term and make no reference to a definition, which may lead to different interpretations in various cultural contexts [9]. Although some studies do explicitly define what the word means, these scholars roughly equate perceived safety to the fear of crime [1,8,15–18], which has long been an area of interest in the criminology field and Crime Prevention Through Environmental Design (CPTED) theory [16]. In these studies, perceived safety is generally quantified by the reverse-coded value of fear of crime [9], while the understanding is limited within the crime-threatening aspect. However, there is increasing acknowledgment that perceived safety comprises vari- ous factors and emotions [19,20], which is supposed to reflect the level of general anxiety about external threats. Specifically, Won [21] has clearly pointed out that this idea con- cerns more than the narrow dimension of crime-related fear. Therefore, in our paper, per- ceived safety is conceptualized as psychological emotion towards external stimuli in a particular environment; higher values represent individual perception of small Buildings 2023, 13, 2 3 of 19 extraneous dangers, and the lower values are used to express broader concerns towards outside threats. Considering the research context is located within the neighborhood, the conceptual dimensions can be further refined. First, although it is believed that the conceptual confu- sion between fear of crime and perceived safety is inaccurate, it is still important to note that the existence of various crimes is perceived as a great menace to residential safety. Therefore, fear of crime could be seen as a distinct construct of safety perception [9,16,22]. Moreover, studies concerning different dimensions of perceived neighborhood safety also generally include traffic-related [8,21] and activity-related safety [9], representing op- pressed feelings derived from undesirable transportation flow and environmental behav- ior. Furthermore, empirical research has shown that levels of interpersonal communica- tion and trust could partially be seen as predictors of safety feeling [23]. The neighborhood is supposed to encourage interaction between people, while seclusion is also preferred [24]. With regard to this, private anxiety is also seen as a manifestation of the external environment that fails to protect the life and property of its citizens, especially in most developing countries [25]. 2.2. Built Environment Previous research has explored the associations between the built environment and the feeling of safety, and empirical evidence supporting a significant influence is fairly conclusive. Through a comprehensive literature review, the influencing factors of the built environment could be classified into four groups, consisting of seven independent varia- bles. 2.2.1. Detailed Elements This category involves the core structure of the residence zone, mainly referring to the characteristics of plants and architecture. Greening is the most frequently discussed environmental element connected with safety feelings. Empirical cases indicate strong as- sociations between landscape attributes and perceived safety [26,27], which consistently applies to urban centers, residence areas or more natural parks. In addition, the literature supports that the feeling of safety is closely associated with domain awareness, and build- ings are of paramount importance to construct territorial definitions between private, public or semi-public areas [10,28]. Specifically, large-scale building is an effective predic- tor for lower “territoriality“, as well as higher fear levels [29]. 2.2.2. Mobility Arrangement The close relationship between the “walkable“ environment and positive perception is well-supported by empirical studies [30]. Similarly, as a crucial element in building a walking-friendly environment, street design plays a significant role in affecting safety per- ception. Optimizing road design, such as decreasing road barriers and broadening walk- ing paths, positively promotes traffic fluency, thereby improving users’ perceived safety [31]. On the contrary, the defense system focuses on controlling threatening persons or objects with the application of entrance guards, closed-circuit television and lighting. As a consequence, users’ sense of safety would be improved on awareness that the mobility of various dangerous sources is restricted and opportunities for self-defense are increased [10,18]. 2.2.3. Supportive Aspects Supportive elements, referring to the factors which could influence personal daily routines, play a subsidiary role in safety perception, either positively or negatively. For example, environmental amenities attract citizens to participate in necessary or recrea- tional activities or provide them with a place for a short break. These service facilities could motivate positive behavior and facilitate the interaction between the environment Buildings 2023, 13, 2 4 of 19 and inhabitants and further increase their feeling of safety [23]. Instead, being alone may arouse fearful emotions for certain individuals [20], thus the evaluation of whether a space is actively used is frequently discussed in relative research [10]. Branas [13] has empha- sized the harm of negative space and demonstrated that structural dilapidation might act as the primary threat to residents’ safety feelings. Additionally, researchers point out that the environmental factor of blind angles or vacant houses may trigger a vicious cycle, where avoidance behavior for a negative spot is generated with lower safety perception [17] and leads to further dilapidated disorder and increased fear [3]. 2.2.4. Maintenance Researchers have long pointed out the deleterious influence that disorder could have on perceived safety [13,17]. Broken window theory suggests that disordered characters, such as obsolete objects, garbage and vandalism [17], would deliver a message that little attention is given to this place and discourage objective as well as subjective safety. Rela- tive research has focused on the topic of fear of crime [29] or perceived safety [7,9,20,23,31], and it has have been found in either theme that a high level of maintenance is associated with lower fear of crime or higher feelings of general safety. 2.3. Social Environment and Physical Activity Social environment also plays a vital role in encouraging or discouraging perceived safety among residents, although this topic has not been widely studied compared with the built environment. It has been claimed that feeling unsafe is partly the direct product of an undesirable social environment [32]. The indirect explanation is that social condition is the primary driver or inhibitor of deviant behavior and illegal events, and these phe- nomena would further cause the decline or increase in safety perception [17,33]. Relevant studies may focus on particular elements of social environment, such as social capital [23], social cohesion [4], sense of community [34] and collective efficacy [35]. In most research, specific social factors remain significant predictors of perceived safety. In addition, the literature has proven the strong connection between the perception of safety and physical activity [21,31]. In this paper, physical activity emphasizes the per- ception of residents’ activity participation instead of the physical activity level of the in- terviewees. Several studies have pointed out that residents, whether consciously or not, are inclined to be in the presence of others as an implication that might raise or lower fear [26]. The existence of suspicious figures may serve as a sign of disorder and would reduce safety feeling. On the contrary, residents may feel safe in situations where they see posi- tive people, especially those who exercise. Therefore, group physical activities could be considered an important factor that influences our fear emotions [36]. 2.4. Hypothesized Model Based on the existing literature, we focus on three external factors mostly correlated in explaining perceived safety, including the built environment, social environment and physical activity. The hypothesized model was initially established as shown in Figure 1 and illustrates the unidirectional relationship between possible influencing factors and perceived safety. Although these factors were always studied independently in most explorations, it must be noted that the intricate relationship between these independent variables could not be ignored. Otherwise, the influence effect would be underestimated, and some influ- ence pathways are unlikely to be detectable. Initially, the CPTED theory only paid close attention to physical characteristics of the environment. Gradually, the environmental de- sign, which could facilitate social communication and improve a sense of community, has gained some attention, suggesting that the built environment may change residents’ safety perception by shaping the social climate. Additionally, the RAT implies that routine activities, influenced by the environment, would also contribute significantly to perceived Buildings 2023, 13, 2 5 of 19 safety. These relevant theories not only indicate the importance of built environment, but also reveal the possible influence paths from physical features to safety perception with social environment and physical activity as possible mediating variables. Figure 1. Elementary framework: the independent relationship between determinants and per- ceived safety. Some empirical research has also offered evidence for the influence path. For exam- ple, both the built and social environments may independently influence the level of per- ceived safety. At the same time, empirical studies from the 1990s have proven that these two elements are also potentially related [23]. Proper environment design could encour- age informal interactions and strengthen social ties among residents [37]. Moreover, re- search on a worldwide scale has verified that environmental attributes could promote res- idents’ physical activity [38], as favorable surrounding offers opportunities for inexpen- sive and unstructured forms of exercise [28]. Similarly, physical activity is also closely connected with social environment. As Ingram [39] has explained, social features in the neighborhoods may increase positive perceptions of the surroundings, thus a place with greater social connections would increase the likelihood for residents to complete the sug- gested amount of exercise [38]. In addition, different social structures may influence the perception of the same behavior [28], suggesting that the impression of group activities participation may vary depending on the social context. Considering the possible existence of relative relationships, a complete theoretical framework was constructed and is shown in Figure 2, depicting how the built environ- ment impacts the perceived safety directly and indirectly through specific influencing pathways through social attributes and the level of group physical activities. Buildings 2023, 13, 2 6 of 19 Figure 2. Complete framework: the integrated relationship between determinants and perceived safety with mediators. 3. Materials and Methods 3.1. Study Context and Data Collection Environment-psychological research from China was frequently conducted in meg- acities such as Beijing and Guangzhou, while the data for this study was collected in Mian- yang, an ordinary third-tier city in Sichuan province. Considering that cities of similar size to Mianyang largely exist in China, this research could bring some universal conclusions for medium-sized cities, which tend to be overlooked in urban studies, and raise more awareness towards this common city type. Mianyang is located in southwest China, with an urban area of 20,250 square kilome- ters and a permanent population of 4.87 million in 2020. As shown in Figure 3, 13 neigh- borhoods in the main urban area were selected as specific research subjects scattered in different regions to avoid typical errors. Moreover, they all share similar planning pat- terns, close completion time and housing price ranges to control for macroscopic urban discrepancies. Although not intentionally, it should be noted that our targets are all gated communities that are fenced or walled off from their surroundings, which is the predom- inant dwelling pattern in Chinese urban areas [40]. Figure 3. Distribution of surveyed communities in Mianyang. Note: The 13 dots in the figure repre- sent the location of the selected communities in the main urban area of Mianyang. Paper-based questionnaires were randomly distributed among the selected commu- nities from April to June 2020, throughout the day on both weekdays and weekends to avoid leaving out office workers. In addition, participants were required to age between 18 and 75 years old to guarantee a clear perceptual reflection. All of them were clearly informed that the questionnaires would be anonymously analyzed for scientific purposes. Although the survey was conducted after the outbreak of the COVID-19 pandemic, the investigated areas were not influenced by the infectious disease during that period, and did not bias our research. To encourage the residents’ willingness to finish the question- naires, the research team prepared some small gifts for participants (WeChat bonus, ta- bleware, USB light, etc.). Finally, a total of 620 questionnaires were sent out and 573 were Buildings 2023, 13, 2 7 of 19 recovered (92% response rate). After eliminating the questionnaires with missing answers and conflicting information, 535 pieces of valid data were finally obtained with an effec- tive rate of 86.3%. 3.2. General Characteristics of the Respondents Among those effectively surveyed, there were slightly more females (51.0%) than their male counterparts, and the majority of them were married (72.3%). Most interview- ees were middle-aged between 31 to 45 years old (42.1%), followed by residents aged 18– 30 and 45–60 composing 30% and 20% of respondents, respectively, with only a few older than 61 years old. As for family income, those who earned CNY 80–120 thousand a year accounted for the largest percentage of 24.5%, with other income data largely conforming to a normal distribution. In addition, some research has emphasized that those who expe- rienced victims’ memory may have lower perceived safety compared with others [9], and the data showed that only 35 (6.5%) residents had experienced dangerous situations. 3.3. Measures and Analysis 3.3.1. Perceived Safety Considering the conceptual gap in perceived safety, items from previous survey questionnaires cannot be referred to directly, especially those that equate feelings of safety to fear of crime and mainly inquire about the emotional reactions to the possibility of be- ing the victim of certain types of crimes [16,35]. Still, relevant research also provides some references. For example, the frequently cited evaluation for both the feeling of safety and fear of crime, “How safe would you feel walking alone during day/ night in your neigh- borhood?” [16,29,38] was included in our study. In addition, some items of the Neighbor- hood Environment Walkability Scale (NEWS) [41], especially the part for traffic and safety survey, were revised into this study. This study emphasized perceived safety to reflect the general anxiety within the neighborhood. Thus, most measured items were specifically developed in line with this definition in Section 2.1, including different questions on the residents’ perception relating to criminal safety, traffic safety, activity safety, communication safety, and privacy safety. For example, people were asked to rate their psychological feelings about transportation safety within the neighborhood through questions such as “I am worried about being knocked down by the crowd or vehicle”. The responses were calculated on a 7-point scale ranging from 1 = extremely not worried to 7 = extremely worried to effectively distinguish the subtle perceptual difference. Before analysis, the detailed statistics of perceived safety were reverse-coded to guarantee greater values corresponding to higher safety feeling. 3.3.2. Neighborhood Built Environment The data on neighborhood characteristics were obtained by aggregating community perception rather than objective measures. As the literature review has mentioned, the environmental factors that may affect perceived safety were comprehensively summa- rized into four categories and seven variables, including greening, buildings, roads, de- fense systems, service facilities, negative spaces and maintenance, and a total of 24 ques- tions were set concerning these elements. Participants were asked to evaluate these envi- ronmental features on a 5-section scale, and specific response options varied slightly de- pending on the question settings. For example, the answer for “How do you feel about the walkway width within the community” ranges from “extremely narrow” to “ex- tremely wide”, and the options for “How do you feel about the quality of community facilities” include a score of 1 as representing “extremely poor” and 5 representing “ex- tremely good”. In general, higher points illustrate the perception of more desirable elements. Because the statements for negative space are inverted in nature, these answers were reverse- Buildings 2023, 13, 2 8 of 19 coded to guarantee higher scores imply better environmental awareness of fewer unused areas. 3.3.3. Neighborhood Social Environment and Physical Activity Social environment is a complex system including various sub-group elements. In- dicative questions were screened from the previous studies to simplify the quantification process. In relevant research, social networks were a commonly explored section of the social environment [4], and were broadly defined as an individual’s connection among groups. This idea was frequently measured by the Social Networks Index (BSSNI) using four items [42], which could be simplified as “the familiarity degree between residents”. Another vital factor of social environment is social cohesion [4,17,38], the higher of which represents residents’ willingness to help as well as shared values and trust within com- munities [35]. Therefore, the most representative question, “neighbors’ willingness to help each other” [4], was extracted to roughly conceptualize this construct. Moreover, some researchers have claimed that social support, known as resources provided by others ei- ther in the way of emotional or financial support [4], could increase the likelihood of civic community engagement [43]. Thus, specific behaviors and attitudes relating to civic en- gagement were summarized to examine social support [43], which is “neighbors’ concerns degree towards communal environment and problems”. The responses connected with social environment were all measured on a 5-point scale. If this study emphatically explored the intensity of individual physical activity, it is undisputed that this variable would be elaborately measured through mature scales such as International Physical Activity Questionnaire (IPAQ) [44], collecting the duration and frequency data of various types of physical activity in the last seven days or longer. How- ever, this paper aims to preliminarily understand the possible role of “group physical ac- tivities participation” during the influencing process. Therefore, physical activity was es- timated by a single question of “The number of people participate in physical activity within communities”, revealing interviewees’ general perception of dwellers’ physical ac- tivity status within their communities. The answers to this question fell into 5-point scale from 1 = very few to 5 = a great many. 4. Results 4.1. Structural Equation Model Assumptions of normality were verified using the Skew and Kurtosis values, all of which were within the acceptable threshold of ±2 (Appendix A Table A1) [45]. Following this, a structural equation model (SEM) with maximum likelihood estimation was con- structed to test our hypotheses using AMOS 23.0 (see Figure 4). In order to quantify vari- ables more accurately, perceived safety was included in the SEM as a second-order meas- urement model, influenced by its five first-order dimensions defined as criminal safety, traffic safety, activity safety, communication safety and privacy safety. Similarly, the built environment was established as another second-order latent variable determined by seven first-order environmental elements: greening, buildings, roads, defense systems, service facilities, negative spaces and maintenance. While social environment was simpli- fied as a first-order construct measured through three items, social network, social cohe- sion, social support, and physical activity was quantified as an observable variable by a single indicator. Buildings 2023, 13, 2 9 of 19 Figure 4. Structural equation model of built environment, social environment, physical activity and perceived safety (N = 535). Note: Full line paths show the statistically significant associations be- tween factors at 95% significant level and the standardized coefficients. Table 1 shows the full names of variables. 4.2. Data Analysis 4.2.1. Preliminary Analysis As an initial step before formal analysis, the Cronbach’s alpha and Kaiser–Meyer– Olkin (KMO) values were calculated to verify the internal reliability and sample adequacy of the data. These two indices were regarded as perfect with all Cronbach’s alpha values greater than 0.8 and KMO values higher than 0.9 [46]. Next, Confirmatory Factor Analysis (CFA) was performed for measurement model modification and assessment. First, the standardized factor loadings were analyzed, and elements with factor loadings lower than the recommended threshold of 0.5 [47] were eventually eliminated, which can be found in Appendix A Table A2. In the final measure- ment model, twenty-two built environment elements (twenty-four were initially consid- ered) and fifteen observable items of perceived safety (twenty-three were initially consid- ered) were retained (see Table 1). Furthermore, the indices of composite reliability (CR) and the convergent validity of average variance extracted (AVE) were tested. Table 1 also suggests that the value of CR coefficients for each latent variable exceeded the threshold criterion of 0.7. The majority of AVE scores were higher than 0.5 [46], indicating that the results satisfy the reasonable criterion, which also provides evidence that the five per- ceived safety variables significantly explained the construct of Perceived Safety. In addi- tion, the goodness of fit of both the measurement models of perceived safety and built environment were assessed, and the results reveal good adjustment (Appendix A Tables A3 and A4). Buildings 2023, 13, 2 10 of 19 Table 1. Factor loadings of selective items in CFA and reliability-validity analyses. Standardized Second-Order First-Order Re- Path Formative Con- flective Con- Items/Questions SMC CR AVE Coefficients struct structs Std. CS1:Feel afraid to go out alone af- 0.608 0.78 ter 10:30 p.m. Criminal CS3:Feel worried about property 0.815 0.916 0.786 0.903 Safety safety. CS4: Feel worried about personal 0.966 0.933 security. TS1: Fear of being hit by other ob- jects (people/vehicles) while walk- 0.752 0.566 ing. TS3: It is difficult to feel com- Traffic pletely relaxed on the way home 0.898 0.806 0.879 0.710 Safety from the communal border. TS4: Need to pay attention to the surroundings on the way home 0.872 0.760 from the communal border. AS1: It is difficult to feel com- pletely relaxed during activities 0.77 0.593 (such as exercise or walking). Activity AS2: Feel a lack of control over 0.923 0.852 0.848 0.653 Safety yourself during activities. Perceived AS3: Fear of slipping, tripping, Safety and falling objects during activi- 0.717 0.514 ties. CoS2: Feel nervous when unfa- miliar people get closer to you 0.84 0.706 within communities. CoS3: Feel uneasy when unfamil- Communication iar people ask you questions 0.943 0.889 0.928 0.812 Safety within communities. CoS4: Feel uneasy when chatting with unfamiliar people within 0.916 0.839 communities. PS1: Feel nervous about being no- 0.82 0.672 ticed. PS3: Feel difficult to find personal Privacy space thus feel reluctant to stay in 0.674 0.454 0.809 0.587 Safety the community. PS4: Always rush through the communal spaces with heads 0.798 0.637 down. G1:Area 0.859 0.738 Greening G2:Shadow 0.596 0.355 0.792 0.564 Built Environ- G3:Flourishing degree 0.774 0.599 ment B1:Interval 0.74 0.548 Buildings 0.709 0.463 B2:Volume 0.429 0.184 Buildings 2023, 13, 2 11 of 19 B3:Enclosed space 0.811 0.658 R1:Footpath width 0.795 0.632 Roads R2:Road patency 0.787 0.619 0.822 0.606 R3:Road zoning 0.752 0.566 D1:Night lighting 0.546 0.298 D2:Electronic monitoring equip- 0.608 0.370 Defense ment 0.784 0.483 Systems D3:Access control of the commu- 0.813 0.661 nity D4:Access control of the cell gate 0.776 0.602 F1:Number of facilities for service 0.447 0.200 Service F2:Area of facilities 0.826 0.682 0.742 0.505 Facilities F3:Number of facilities for activi- 0.795 0.632 ties N1:Dead angle 0.694 0.482 Negative N2:Vacant houses 0.67 0.449 0.719 0.460 Spaces N3:Empty public area 0.671 0.450 M1:Public facilities 0.797 0.635 Maintenance M2:Ground pavement 0.801 0.642 0.841 0.638 M3:Cleanliness 0.798 0.637 When fitting the collected dataset to the established model, the values of chi-square to the degree of freedom ratio (χ2/DF), comparative fit index (CFI), Tucker–Lewis index (TLI) and incremental fit index (IFI) were 2.56, 0.906, 0.901 and 0.907, respectively, satis- fying the ideal criterion. Other indices, including goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI) and root mean square error of approximation (RMSEA), also reached the acceptable standard. In general, as shown in Table 2, the structural equation model provides satisfactory goodness of fit to the dataset and indicates a good basis to explain the relationship between perceived safety and other variables. Table 2. Model fit indices. Model Fit Index Acceptable Criterion Ideal Criterion Model Fit Statistics Model Evaluation χ2/DF <5 <3 2.567 Ideal IFI >0.8 >0.9 0.907 Ideal TLI >0.8 >0.9 0.901 Ideal CFI >0.8 >0.9 0.906 Ideal GFI >0.8 >0.9 0.841 Acceptable AGFI >0.8 >0.9 0.821 Acceptable RMSEA <0.08 <0.05 0.054 Acceptable 4.2.2. Mediation Analyses The SEM was established as shown in Figure 4. Table 3 displays the standardized coefficients of the path analysis, and positive associations were found between most of the variables. Specifically, the effect of the built environment on perceived safety revealed that favorable built-up environment could be a supporting factor in improving safety feel- ings. This influence path is highly significant considering the standardized coefficient is as high as 0.631 (SE = 0.096, p = ***). Similarly, favorable physical environment is also ob- served to influence the social environment, which implies that the social environment would be perceived to be better as the built-up environment condition improves within the research area. Meanwhile, the effect of the social environment on both the feeling of safety (β = 0.109, SE = 0.084, p = 0.041) and activity (β = 0.422, SE = 0.071, p = ***) are Buildings 2023, 13, 2 12 of 19 significant, suggesting that the interviewees with positive evaluations of their social envi- ronment may express greater safety feelings and raise the possibility for outdoor activities among groups. In addition, the results also indicate a positive statistical effect of activity perception on perceived safety; however, the influence effect is relatively weak with a standard coefficient of 0.082. No direct association was observed between selected ele- ments of the built environment and residents’ activity att endance (β = −0.075, SE = 0.089, p = 0.395). Table 3. Standardized coefficients of SEM. Standardized Coef- Critical Ratio Path Standard Error (S.E.) p ficients (C.R.) Built Environment → Perceived Safety 0.631 0.096 10.444 *** Built Environment → Social Environment 0.44 0.055 8.073 *** Social Environment → Perceived Safety 0.109 0.084 2.044 0.041(*) Social Environment → Physical Activity 0.422 0.071 8.387 *** Physical Activity → Perceived Safety 0.082 0.046 2.001 0.045(*) Note: *** p < 0.001, ** p < 0.01, * p < 0.05. The statistical results support most of our expectations and demonstrate that the built environment’s influence on perceived safety is more than direct. The bootstrap method was further applied to verify whether the supposed mediated path is statistically signifi- cant based on 95% confidence intervals with a random sample of n = 5000 (Table 4). This analysis illustrated that all the confidence intervals do not cover the value of zero, proving that both the direct and mediated effects are significant. The established model has proven to be partial mediation. Table 4. Bootstrap analysis of mediated effects. 95% Confidence Intervals Standardized Influence Path Effect (Bootstrap n = 5000) Type Effect Lower Level Upper Level Total 0.694 0.622 0.761 Built Environment → Social Environment Partial-medi- Direct 0.631 0.529 0.722 → Physical Activity → Perceived Safety ation Indirect 0.063 0.015 0.138 5. Discussion 5.1. Idea of Perceived Safety According to the literature review in Section 2.1, it was concluded that the definition of perceived safety is either vaguely articulated or sometimes directly replaced by the criminological idea of fear of crime. Although the idea necessarily encompasses judgment of crime [8], we clearly argue that perceived safety contains more subjective emotions than narrow crime-related attitudes, which express the state of being free from proximate threats. This paper takes notice of the conceptual difference and mixed definition of per- ceived safety to remind future researchers that the gaps in existing studies weaken the findings and decrease the generalizability of the conclusions [20], leading to the definition of perceived safety serving as the research basis for relevant study [16,20]. We do not claim that the exploration of feeling of general safety is more valuable than crime-related emo- tions. However, it must be noted that perceived safety and fear of crime are closely con- nected while also mutually independent. Future research should distinguish the study subjects clearly, otherwise the confusion may also muddy the waters of crime research [16]. Buildings 2023, 13, 2 13 of 19 5.2. Direct Impact of Built and Social Environment, Physical Activity As previous research has identified, our study also finds that built environmental factors play an important role in increasing or decreasing residents’ perceived safety, and the influence effect is highly significant. Discussing which specific physical features influ- ence perceived safety and in which direction or to what extent are not the research goals of this paper. The combined findings still reveal that a neighborhood without comfortable, detailed design (including architecture and greenery), reasonable mobility (traffic fluency of insiders and limited access for outsiders) [9], as well as extended maintenance, is less likely to make inhabitants feel free from unclear menaces. Conversely, a place with lower uncertainty, such as wider walking paths, less negative spaces and better environmental quality, where residents could extensively observe the surroundings from a protected lo- cation, would provide higher levels of perceived safety to dwellers [48]. Existing research concentrates on more specific aspects of the social environment, such as social cohesion or social capital [4]. This study proves that generally desirable social environments are linked with higher perceived neighborhood safety. Riger [49] pro- posed that healthy social environments could prevent emotional stress from developing, the increase of which would pose a strong negative effect on perceived safety from a psy- chological level. Additionally, as Eduardo [19] has explained, favorable social environ- ments may be viewed as a buffer to compensate for the lack of control over the environ- ment. Residents with a positive sense of social environment may have adequate material and affective energy to support themselves, and the interchange process could provide ample sources and information to counteract the feelings of insecurity [32]. However, other studies mentioned that excessive information delivery may exacerbate the unsafe feeling, as frequent social interactions may increase fear by promoting communication about accidents [29]. The possibility that the influence of the social environment upon perceived safety may be non-linear, and negative effects may arise beyond a certain threshold. Therefore, the precise influence of the social environment and the proper level of information exchange is worth further in-depth study. The positive role of group physical activities could be interpreted from different per- spectives. On the one hand, the presence of people engaged in physical activity is a posi- tive signal, serving as an effective way of Natural Surveillance that facilitates observation of intruders [1]. Residents may obtain more security in areas with confidence that there are more chances for people to intervene or report dangers, incivilities or accidents [50,51]. On the other hand, although groups rather than individuals were assessed in this study, it appears conclusive that sufficient exercise is conducive to personal psychological health, improving residents’ well-being [52]. Therefore, it is reasonable to estimate that daily ac- tivities may increase personal safety consciousness, as the safe sentiment is the premise for realizing higher emotional needs. 5.3. Indirect Role of Built Environment from Mediators This paper also reveals the mediating role that the social environment and physical activity have played during the influencing process. The built environment exerts both direct and indirect effects upon perceived safety, suggesting that the environmental im- pact is more complicated than we thought. Favorable built environment of urban space is conducive to creating healthy social structural settings [17], and a place that encourages routine activities motivated by stronger social dynamic would be perceived as safer [1]. Therefore, separate elements of the built environment may also influence perceived safety in various paths. For example, high-quality pedestrian space could increase perceived safety directly [53], and an indirect influence, considering previous literature, may be that desirable road design will enhance a sense of community [54]. Similarly, well-maintained vegetation is observed to increase perceived safety as it brings a strong indication of pro- fessional management [17]. In addition, the positive effect of green space on providing an improved social atmosphere has also been extensively verified [1,55]. Buildings 2023, 13, 2 14 of 19 Nevertheless, the insignificant relationship between the built environment and phys- ical activity does not imply contradiction to previous significant evidence. The model here suggests that heterogeneous environmental factors which contribute positively to emo- tional safety may not significantly influence the level of group physical activities. While other specific environment variables, such as aesthetics [56] and accessibility [57], which were widely documented in previous studies, may still appear as strong influencing fac- tors on physical activity. This situation also reminds us that some unexpected environ- mental measures that do not directly influence perceived safety, may change it indirectly through increasing social ties or promoting exercise. 5.4. Mutual-Contradictory Causal Relationships In some studies, safety has been regarded as the “reason” instead of the “result”. It was found that residents who perceive the environment to be safer tend to have a higher sense of belonging to the community [32], as well as a satisfactory assessment of the over- all social environment [54]. Moreover, other research has shown that emotional responses to safe states have a modest effect on health behaviors [58], especially physical activity [32,59]. Relevant contexts suggest that when perceived safety is theoretically considered an independent variable, it is shown to be an essential factor in changing the level of social environment evaluation and influencing physical activity. In contrast, our paper reveals that perceived safety relates to the evaluation of the social environment as well as group physical activity. By comparing existing conclusions and the results of this paper, as well as related supporting evidence, it is apparent that the results from various studies speak mainly in two mixed and contradictory streams. That is to say, the relationship between the built and social environment, physical activity, and perceived safety may be more complicated than we thought. While the cross-sectional data of this research, similar to most relevant experiments [18], limits the possibility of exploring the explicit two-way causality. Therefore, further studies may consider the pos- sible bidirectional relationship, adopt more scientific methods, and propose more reason- able research designs. 6. Conclusions This article expands the content of previous studies by including various safety do- mains to define the concept of perceived safety, which contains more information than reversed crime fear. On this basis, the relationship between perceived safety, the built en- vironment, social environment and physical activity was investigated through a structural equation model. The results demonstrated that perceived safety is highly correlated with the built environment, and also significantly related to the social environment as well as physical activity to a lesser degree. Moreover, the built environment was observed to change perceived safety indirectly through the social environment and the level of group physical activities, revealing that the environmental impact could be slightly amplified via mediating variables, which also suggests that the influence of the built environment upon perceived safety may be underestimated in some existing studies. The structural equation model assisted in understanding the significant moderation effect, and contributed to our knowledge on the potential pathways through which built environmental factors influence perceived safety, serving as a valuable complement to current literature to answer the ‘‘how’’ question. The findings reported here effectively acknowledge the necessity of environment optimization for designing a more reassuring place to contribute positively to a harmonious social atmosphere and encourage physical activity, which are beneficial to safety perception. Author Contributions: conceptualization, E.Z and Y.D; methodology, Y.D.; software, E.Z.; valida- tion, E.Z., L.Y. and A.L.; formal analysis, E.Z.; investigation, L.Y. and A.L.; writing—original draft preparation, E.Z.; writing—review and editing, L.Y. and A.L.; visualization, E.Z.; funding acquisi- tion, Y.D. All authors have read and agreed to the published version of the manuscript. Buildings 2023, 13, 2 15 of 19 Funding: This project was funded by National Natural Science Foundation of China (Grant No. 52278057). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data displayed in this research are obtainable upon consent from the corresponding and the first author. Acknowledgments: The authors greatly appreciate anonymous reviewers and editors for their in- sightful suggestions. Conflicts of Interest: Authors declare no conflicts of interest. Appendix A Table A1. Assessment of normality. Variable Skew Kurtosis Group Physical Activities −0.205 −0.157 Social Support −0.380 −0.101 Social Network −0.069 −0.482 Social Cohesion 0.080 −0.213 PS 4 −0.864 −0.068 PS 3 −0.667 −0.647 PS 1 −0.760 −0.126 CS 4 −0.888 0.014 CS 3 −0.797 −0.179 CS 1 −0.730 −0.286 TS 4 −0.456 −0.704 TS 3 −0.514 −0.812 TS 1 −0.378 −0.948 AS 3 −0.410 −0.712 AS 2 −0.411 −0.710 AS 1 −0.339 −1.012 CS 4 −0.851 0.075 CS 3 −0.728 −0.166 CS 2 −0.593 −0.413 D4 −0.332 −0.519 D3 −0.512 −0.124 D2 0.005 −0.162 D1 0.072 −0.558 N3 −0.382 −0.040 N2 −0.395 −0.115 N1 −0.376 0.033 M3 −0.574 0.388 M2 −0.336 −0.360 M1 −0.188 −0.083 F3 0.169 −0.415 F2 0.234 −0.357 F1 −0.259 −0.701 Buildings 2023, 13, 2 16 of 19 G3 −0.652 0.311 G2 −0.126 −0.770 G1 −0.437 −0.609 B3 −0.186 −0.277 B2 −0.003 0.308 B1 −0.165 −0.270 R3 −0.273 −0.988 R2 −0.529 −0.121 R1 −0.383 −0.121 Table A2. Deleted Items and Possible Justification. Constructs Items/Questions Reasoning for Low Factor Loading The vast majority of residents will not do activities in CS2: Feel afraid to do activity alone after the public space within communities after 10:30 p.m.; 10:30 p.m. therefore, it would be difficult for them to answer this Criminal question. Safety Although some residents may feel a little scared when CS5: The security environment needs to be walking outside after 10:30 p.m., they are still satisfied improved. with the security environment because they have low environmental expectations. Residents usually park their cars in the underground parking at the community entrance instead of along Traffic TS2: Feel worried about the car being the road or in the courtyard within the community. Safety scratched while driving or parked. Therefore, residents are rarely worried about the con- dition of the car. Activity AS4: Feel worried about your young or old Some interviewees live alone, therefore they have Safety relatives when they do activity alone. lower anxiety for relatives. China has high population density; therefore, resi- CoS1: Feel uneasy when make eye contacts dents are quite accustomed to coming across with unfamiliar people. strangers. Communication Sometimes the neighborhood committee will visit the Safety CoS5: Feel afraid when unfamiliar people family; therefore, residents generally will not feel knocks on the door. scared when someone they do not know knocks on the door. Most residents in China do not care about this issue PS2: Deliveries to the community will not (the exposure of real name) compared with people have real names written on them. from other cultural backgrounds. Privacy The vast majority of residents prefer to open the cur- Safety PS5: Always keep the curtains closed at tains because they would like to open the windows for home during the day. better ventilation, which is a common practice in Chi- nese families. The installation of anti-theft fences is strictly forbid- D5: Anti-theft fences den by some management offices; therefore, the situa- Defense tion could not offer enough information. System Some interviewees could not accurately understand D6: Sight permeability the term “permeability”. Buildings 2023, 13, 2 17 of 19 Table A3. CFA fit indices of perceived safety measurement model. Fit Index Acceptable Criterion Ideal Criterion Model Fit Statistics Model Evaluation CMIN/DF <5 <3 3.479 Acceptable GFI >0.8 >0.9 0.936 Ideal AGFI >0.8 >0.9 0.904 Ideal NFI >0.8 >0.9 0.956 Ideal IFI >0.8 >0.9 0.968 Ideal TLI >0.8 >0.9 0.958 Ideal CFI >0.8 >0.9 0.968 Ideal RMSEA <0.08 <0.05 0.068 Acceptable Table A4. CFA fit indices of built environment measurement model. Fit Index Acceptable Criterion Ideal Criterion Model Fit Statistics Model Evaluation CMIN/DF <5 <3 2.956 Ideal GFI >0.8 >0.9 0.913 Ideal AGFI >0.8 >0.9 0.883 Acceptable NFI >0.8 >0.9 0.899 Acceptable IFI >0.8 >0.9 0.931 Ideal TLI >0.8 >0.9 0.930 Ideal CFI >0.8 >0.9 0.930 Ideal RMSEA <0.08 <0.05 0.061 Acceptable References 1. Mouratidis, K. The impact of urban tree cover on perceived safety. Urban For. Urban Green. 2019, 44, 126434. https://doi.org/10.1016/j.ufug.2019.126434. 2. Macintyre, S.; Ellaway, A. Ecological Approaches: Rediscovering the Role of the Physical and Social Environment. Soc. Epidemiol. 2000, 9, 332–348. 3. Chandola, T. The fear of crime and area differences in health. Health Place 2001, 7, 105–116. https://doi.org/10.1016/S1353- 8292(01)00002-8. 4. Baum, F.E.; Ziersch, A.M.; Zhang, G.; Osborne, K. Do perceived neighbourhood cohesion and safety contribute to neighbourhood differences in health? Health Place 2009, 15, 925–934. https://doi.org/10.1016/j.healthplace.2009.02.013. 5. Møller, V. Resilient or Resigned? Criminal victimisation and quality of life in South Africa. Soc. Indic. Res. 2005, 72, 263–317. https://doi.org/10.1007/s11205-004-5584-y. 6. He, B.J.; Zhao, D.; Dong, X.; Zhao, Z.; Li, L.; Duo, L.; Li, J. Will individuals visit hospitals when suffering heat-related illnesses? Yes, but… Build. Environ. 2022, 208, 108587. https://doi.org/10.1016/j.buildenv.2021.108587. 7. Innes, M.; Jones, V. Neighbourhood Security and Urban Change: Risk, resilience and recovery. Neighbourhood Security and Urban Change: Risk, Resilience and Recovery; Joseph Rowntree Foundation: York, UK, 2006; p. 70. Available online: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=a5a4bb832c9ec06a3ac93f16292d360d9a355b98. (accessed on 1 November 2022). 8. Wang, R.; Yuan, Y.; Liu, Y.; Zhang, J.; Liu, P.; Lu, Y.; Yao, Y. Using street view data and machine learning to assess how perception of neighborhood safety influences urban residents’ mental health. Health Place 2019, 59, 102186. https://doi.org/10.1016/j.healthplace.2019.102186. 9. Makinde, O.O. The correlates of residents’ perception of safety in gated communities in Nigeria. Soc. Sci. Humanit. Open 2020, 2, 100018. https://doi.org/10.1016/j.ssaho.2020.100018. 10. Shach-Pinsly, D. Measuring security in the built environment: Evaluating urban vulnerability in a human-scale urban form. Landsc. Urban Plan. 2019, 191, 103412. https://doi.org/10.1016/j.landurbplan.2018.08.022. 11. Cozens, P.M.; Saville, G.; Hillier, D. Crime prevention through environmental design (CPTED): A review and modern bibliography. Prop. Manag. 2005, 23, 328–356. 12. Saville, G.; Cleveland, G. 2nd Generation CPTED: An Antidote to the Social Y2K Virus of Urban Design. In Proceedings of the International CPTED Association Conference, Washington, DC, USA, December 1998; pp. 3–5. 13. Branas, C.C.; South, E.; Kondo, M.C.; Hohl, B.C.; Bourgois, P.; Wiebe, D.J.; MacDonald, J.M. Citywide cluster randomized trial to restore blighted vacant land and its effects on violence, crime, and fear. Proc. Natl. Acad. Sci. USA 2018, 115, 2946–2951. https://doi.org/10.1073/pnas.1718503115. 14. Rišová, K.; Sládeková Madajová, M. Gender differences in a walking environment safety perception: A case study in a small town of Banská Bystrica (Slovakia). J. Transp. Geogr. 2020, 85, 102723. https://doi.org/10.1016/j.jtrangeo.2020.102723. Buildings 2023, 13, 2 18 of 19 15. Jansson, M.; Fors, H.; Lindgren, T.; Wiström, B. Perceived personal safety in relation to urban woodland vegetation–A review. Urban For. Urban Green 2013, 12, 127–133. https://doi.org/10.1016/j.ufug.2013.01.005. 16. Hinkle, J.C. Emotional Fear of Crime vs. Perceived Safety and Risk: Implications for Measuring “Fear” and Testing the Broken Windows Thesis. Am. J. Crim. Justice 2015, 40, 147–168. https://doi.org/10.1007/s12103-014-9243-9. 17. Jiang, B.; Mak, C.N.S.; Zhong, H.; Larsen, L.; Webster, C.J. From broken windows to perceived routine activities: Examining impacts of environmental interventions on perceived safety of urban alleys. Front. Psychol. 2018, 9, 2450. https://doi.org/10.3389/fpsyg.2018.02450. 18. Rees-Punia, E.; Hathaway, E.D.; Gay, J.L. Crime, Perceived Safety, and Physical Activity: A Meta-Analysis. Preventive Medicine. Academic Press Inc: Cambridge, MA, USA, 2018. https://doi.org/10.1016/j.ypmed.2017.11.017. 19. Wills, E. Encyclopedia of Quality of Life and Wellbeing Research; Springer: Berlin/Heidelberg, Germany, 2014; pp. 2233–2235. 20. Odufuwa, B.; Badiora, A.I.; Olaleye, D.O.; Akinlotan, P.A.; Adebara, T.M. Perceived personal safety in built environment facilities: A Nigerian case study of urban recreation sites. J. Outdoor Recreat. Tour. 2019, 25, 24–35. https://doi.org/10.1016/j.jort.2018.11.002. 21. Won, J.; Lee, C.; Forjuoh, S.N.; Ory, M.G. Neighborhood safety factors associated with older adults’ health-related outcomes: A systematic literature review. Social Science and Medicine; Elsevier Ltd: Amsterdam, The Netherlands, 2016. https://doi.org/10.1016/j.socscimed.2016.07.024. 22. Warr, M. Fear of crime in the United States: Avenues for policy and research. In Measurement and Analysis of Crime and Justice; US Department of Justice, Office of Justice Programs: Washington, DC, USA, 2000; Volume 4, pp. 451–489. 23. Wood, L.; Shannon, T.; Bulsara, M.; Pikora, T.; McCormack, G.; Giles-Corti, B. The anatomy of the safe and social suburb: An exploratory study of the built environment, social capital and residents’ perceptions of safety. Health Place 2008, 14, 15–31. https://doi.org/10.1016/j.healthplace.2007.04.004. 24. Lis, A.; Anwajler, K. Privacy in public places. Landsc. Archit. 2014, 42, 4–19. Available online: http://architekturakrajobrazu.up.wroc.pl/2014/23-2014/15 1-1-2014a. (accessed on 1 November 2022 ) 25. Atkinson, R.; Flint, J. Fortress UK? Gated communities, the spatial revolt of the elites and time-space trajectories of segregation. Hous. Stud. 2004, 19, 875–892. https://doi.org/10.1080/0267303042000293982. 26. Jorgensen, L.J.; Ellis, G.D.; Ruddell, E. Fear perceptions in public parks: Interactions of environmental concealment, the presence of people recreating, and gender. Environ. Behav. 2013, 45, 803–820. https://doi.org/10.1177/0013916512446334. 27. Baran, P.K.; Tabrizian, P.; Zhai, Y.; Smith, J.W.; Floyd, M.F. An exploratory study of perceived safety in a neighborhood park using immersive virtual environments. Urban For. Urban Green. 2018, 35, 72–81. https://doi.org/10.1016/j.ufug.2018.08.009. 28. Timperio, A.; Veitch, J.; Carver, A. Safety in numbers: Does perceived safety mediate associations between the neighborhood social environment and physical activity among women living in disadvantaged neighborhoods? Prev. Med. 2015, 74, 49–54. 29. Lorenc, T.; Clayton, S.; Neary, D.; Whitehead, M.; Petticrew, M.; Thomson, H.; Renton, A. Crime, fear of crime, environment, and mental health and wellbeing: Mapping review of theories and causal pathways. Health Place 2012, 18, 757–765. https://doi.org/10.1016/j.healthplace.2012.04.00. 30. Koohsari, M.J.; Nakaya, T.; McCormack, G.R.; Shibata, A.; Ishii, K.; Yasunaga, A.; Oka, K. Traditional and novel walkable built environment metrics and social capital. Landsc. Urban Plan. 2021, 214, 104184. https://doi.org/10.1016/j.landurbplan.2021.104184. 31. Hong, J.; Chen, C. The role of the built environment on perceived safety from crime and walking: Examining direct and indirect impacts. Transportation 2014, 41, 1171–1185. https://doi.org/10.1007/s11116-014-9535-4. 32. Allik, M.; Kearns, A. “There goes the fear”: Feelings of safety at home and in the neighborhood: The role of personal, social, and service factors. J. Community Psychol. 2017, 45, 543–563. https://doi.org/10.1002/jcop.21875. 33. Furr-Holden, C.D.M.; Lee, M.H.; Milam, A.J.; Johnson, R.M.; Lee, K.S.; Ialongo, N.S. The growth of neighborhood disorder and marijuana use among urban adolescents: A case for policy and environmental interventions. J. Stud. Alcohol Drugs 2011, 72, 371– 379. https://doi.org/10.15288/jsad.2011.72.371. 34. Wilson-Doenges, G. An exploration of sense of community and fear of crime in gated communities. Environ. Behav. 2020, 32, 597–611. https://doi.org/10.1177/00139160021972694. 35. Abdullah, A.; Marzbali, M.H.; Bahauddin, A.; Tilaki, M.J.M. Broken windows and collective efficacy: Do they affect fear of crime? SAGE Open 2015, 5, 1–11. Available online: https://journals.sagepub.com/doi/pdf/10.1177/2158244014564361 (accessed on 1 November 2022). 36. Lis, A.; Krzeminska, A. Social control as an indicator of safety in residential neighborhoods in western societies and China. Landsc. Archit. 2013, 3, 4–15. Available online: http://architekturakrajobrazu.up.wroc.pl/ (accessed on 1 November 2022). 37. Baum, F.; Palmer, C. “Opportunity structures”: Urban landscape, social capital and health promotion in Australia. Health Promot. Int. 2002, 17, 351–361. https://doi.org/10.1093/heapro/17.4.351. 38. Aliyasa, Z. Does social environment mediate the association between perceived safety and physical activity among adults living in low socioeconomic neighborhoods? J. Transp. Health 2019, 14, 100578–100578. 39. Ingram, M.; Adkins, A.; Hansen, K.; Cascio, V.; Somnez, E. Sociocultural perceptions of walkability in Mexican American neighborhoods: Impications for policy and practice. J. Transport. Health 2017, 7 172–180. http://doi.org/10.1016/j.jth.2017.10.001. 40. Hamama, B.; Liu, J. What is beyond the edges? Gated communities and their role in China’s desire for harmonious cities. City Territ. Archit. 2020, 7, 13. https://doi.org/10.1186/s40410-020-00122-x. Buildings 2023, 13, 2 19 of 19 41. Cerin, E.; Saelens, B.E.; Sallis, J.F.; Frank, L.D. Neighborhood environment walkability scale: Validity and development of a short form. Med. Sci. Sport. Exerc. 2006, 38, 1682–1691. https://doi.org/10.1249/01.mss.0000227639.83607.4d. 42. Berkman, L.F.; Syme, S.L. Social networks, host resistance, and mortality: A nine-year follow-up study of Alameda County residents. Am. J. Epidemiol. 1979, 109, 186–204. 43. Acedo, A.; Oliveira, T.; Naranjo-Zolotov, M.; Painho, M. Place and city: Toward a geography of engagement. Heliyon 2019, 5, e02261. https://doi.org/10.1016/j.heliyon.2019.e02261. 44. Craig, C.L.; Marshall, A.L.; Sjöström, M.; Bauman, A.E.; Booth, M.L.; Ainsworth, B.E.; Oja, P. International physical activity questionnaire: 12-Country reliability and validity. Med. Sci. Sport. Exerc. 2003, 35, 1381–1395. https://doi.org/10.1249/01.MSS.0000078924.61453.FB. 45. Ryu, E. Effects of skewness and kurtosis on normal-theory based maximum likelihood test statistic in multilevel structural equation modeling. Behav. Res. Methods 2011, 43, 1066–1074. https://doi.org/10.3758/s13428-011-0115-7. 46. Halkos, G.; Leonti, A.; Sardianou, E. Activities, motivations and satisfaction of urban parks visitors: A structural equation modeling analysis. Econ. Anal. Policy 2021, 70, 502–513. https://doi.org/10.1016/j.eap.2021.04.005. 47. Hair, J.F., Jr.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis; 7th ed.; Upper Saddle River: Pearson Education Limited: Hoboken, NJ, USA, 2009; p. 761. 48. Hur, M.; Nasar, J.L. Physical upkeep, perceived upkeep, fear of crime and neighborhood satisfaction. J. Environ. Psychol. 2014, 38, 186–194. https://doi.org/10.1016/j.jenvp.2014.02.001. 49. Riger, S.; Lavrakas, P.J. Community ties: Patterns of attachment and social interaction in urban neighborhoods. Am. J. Community Psychol. 1981, 9, 55–66. https://doi.org/10.1007/BF00896360. 50. Foster, S.; Giles-Corti, B.; Knuiman, M. Creating safe walkable streetscapes: Does house design and upkeep discourage incivilities in suburban neighbourhoods? J. Environ. Psychol. 2011, 31, 79–88. https://doi.org/10.1016/j.jenvp.2010.03.005. 51. Zhang, G.; He, B.J. Towards green roof implementation: Drivers, motivations, barriers and recommendations. Urban Forestry and Urban Greening; Elsevier GmbH: Amsterdam, The Netherlands, 2021. https://doi.org/10.1016/j.ufug.2021.126992. 52. Thomas, J.; Thirlaway, K.; Bowes, N.; Meyers, R. Effects of combining physical activity with psychotherapy on mental health and well-being: A systematic review. J. Affect. Disorders. 2020, 265, 475–485. https://doi.org/10.1016/j.jad.2020.01.070. 53. Zeng, E.; Dong, Y.; Li, F.; Che, L. The Impact of Built Environment Characteristics on Perceived Safety of City Dwellers: A Case Study in Mianyang (China). In Proceedings of the 57th ISOCARP World Planning Congress, Doha, Qatar, 8–11 November 2021. pp. 970–981. 54. Lund, H. Pedestrian environments and sense of community. J. Plan. Educ. Res. 2002, 21, 301–312. https://doi.org/10.1177/0739456X0202100307. 55. Kondo, M.C.; Fluehr, J.M.; McKeon, T.; Branas, C.C. Urban green space and its impact on human health. Int. J. Environ. Res. Public Health. 2018, 15, 445. https://doi.org/10.3390/ijerph15030445. 56. Koohsari, M.J.; Mavoa, S.; Villianueva, K.; Sugiyama, T.; Badland, H.; Kaczynski, A.T.; Giles-Corti, B. Public open space, physical activity, urban design and public health: Concepts, methods and research agenda. Health Place 2015, 33, 75–82. https://doi.org/10.1016/j.healthplace.2015.02.009. 57. An, R.; Shen, J.; Yang, Q.; Yang, Y. Impact of built environment on physical activity and obesity among children and adolescents in China: A narrative systematic review. J. Sport Health Sci. 2019, 8, 153–169. https://doi.org/10.1016/j.jshs.2018.11.003. 58. Foster, S.; Giles-Corti, B. The built environment, neighborhood crime and constrained physical activity: An exploration of inconsistent findings. Prev. Med. 2008, 47, 241–251. https://doi.org/10.1016/j.ypmed.2008.03.017. 59. Aliyas, Z. Why some walk and others don't: Neighborhood safety and the sociodemographic variation effect on walking for leisure and transportation. J. Public Health Manag. Pract. 2019, 26, 24–32. http://doi.org/10.1097/PHH.0000000000000992 Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Journal
Buildings – Multidisciplinary Digital Publishing Institute
Published: Dec 20, 2022
Keywords: perceived safety; built environment; social environment; physical activity; mediation effect; structural equation model