Structural equations of modeling in creating defensible … France/WS18/paper...Structural equations of modeling in creating defensible spaces of residential complex,"Case study: LALE

WS-18 Residential Environments and People

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Structural equations of modeling in creating defensible spaces of residential complex, Case study: LALE & MILAD complexes in Ardabil province, Iran

Hassan Feridonzadeh

M.A Architecture, SAMA technical and vocational training college, Islamic Azad University, Ardabil Branch, Ardabil, IRAN

Email:[email protected]

Abstract: The main purpose of the research was to study the role of defensible of residential complex spaces in Structural Equations of Modeling (SEM). Nowadays, increase in number of population and the genesis residential complexes in cities, the peoples from different races, income levels and education are forced together to living. In the meantime, access the largest source for crime prevention, done is defensible space design to strengthen the control of sense the environment in the people. The idea that was presented in 1972 by Oscar Newman, In addition was to being caused higher self esteem of low-income families, gave the opportunity that are important part of mainstream society.28 family (14 family from the LALE residential complex and 14 family from the MILAD residential complex)in Ardabil city were selected by random cluster Sampling. They were asked to their own beliefs in complexes included in the 26 questions. Validity of the instrument calculated by content validity and the understudying construct showed that the instrument had proper validity. Direct and indirect effects of variables on defensible of space complexes were calculated through path analysis and regression tests. The results of the present study with use the Lisrel 8.5 revealed that; Variables such as neighborhood relations, Soci-Economic Status (SES), and apartment rules have direct effect on defensible of residential complex spaces. In the above components, the apartment rules have significant effects on defensible of space complexes with regression coefficient=0.84( ! ) and neighborhood relations with Beta coefficient=0.06 have the lowest effect in among observed variables. Moreover, other variables of research consist of building height, total units with shared hallway and units density have inverse effects on defensible of space complexes that with increase in amount of them, is reduced degree of defend the space. Too, findings showed that factors such as education, income levels, age the resident people have direct effect on the defensible spaces of the residential complex. Key word: Defensible spaces- Architecture- SES- Apartments rules- Units density- Building height 1. Introduction: All Defensible Space programs have a common purpose: They restructure the physical layout of communities to allow residents to control the areas around their homes. This includes the streets and grounds outside their buildings and the lobbies and corridors within them. The programs help people preserve those areas in which they can realize their commonly held values and lifestyles. Defensible Space relies on self-help rather than on government intervention, and so it is not vulnerable to government’s withdrawal of support. It depends on resident involvement to reduce crime and remove the presence of criminals. It has the ability to bring people of different incomes and race together in a mutually beneficial union. For low-income people, Defensible Space can provide an introduction to the benefits of mainstream life and an opportunity to see how their own actions can better the world around them and lead to upward mobility (Newman,1996). Other studies have found that people judge defensible space measures to be quite effective (Martin et al. 2007; Bright and Burtz 2003; Hodgson 1995; Nelson et al.2004). The degree to which this important security function is provided by homes can vary with the type of housing and with the physical design of the area around the homes (Dingemans,1978; Robert Gold,1970).

Defensible space builds on ideas of territoriality and ownership of space, arising from specific design features, building form, and location, combined with a symbolic understanding of the image of a


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place. It evolved from a theoretical explanation of the relationship of physical space and crime, into a practical design-led solution to social problems. Since the 1970s, the concept has been used to justify the radical remodeling of failing urban mass housing, with often extravagant claims to its potential benefits (Warwick, 2009). Surveillance refers to the ability of residents to survey the open space around their homes from within the house. The proper juxtaposition of windows with home entrances, garages, greenbelts, and recreation facilities permits the residents to observe and become familiar with their neighbors. Suspicious intruders and improper behavior should be more easily noticeable by residents who are kept in close contact with their surroundings while going about their normal household activities (Dingemans,1978). During this time, the idea has proved sufficiently ambiguous to support diverse interdisciplinary interpretations, despite there being equally vocal critics and supporters of the concept. The debate around its legitimacy hinges on the relative significance of physical environment to social factors on an individual’s behavior. Yet, the design of our surroundings cannot be dismissed as irrelevant, or the impact of environments as behaviorally neutral. In trying to define the role of spaces in society, particularly residential spaces, defensible space can be seen as a useful concept in several ways: as a model of social and physical interactions, as the mechanisms that shape space in the minds of its occupants, and as a description of how the quality of designed space can contribute to the quality of urban life (Warwick,2009). 1.1. Summary of the effect of building type on behavior Providing its inhabitants with a sense of security from crime is one of the most important functions of any housing environment. Cooper and Rainwater, for example, rank the need for security second only to the need for shelter. Keith Harries, a geographer at the University of Oklahoma, has asserted that "burglary and robbery may be relatively susceptible to control via urban and structural design(Clare Cooper,1975). A family’s claim to a territory diminishes proportionally as the number of families who share that claim increases. The larger the number of people who share a territory, the less each individual feels rights to it. Therefore, with only a few families sharing an area, whether it be the interior circulation areas of a building or the grounds outside, it is relatively easy for an informal understanding to be reached among the families as to what constitutes acceptable usage. When the numbers increase, the opportunity for reaching such an implicit understanding diminishes to the point that no usage other than walking through the area is really possible, but any use is permissible. The larger the number of people who share a communal space, the more difficult it is for people to identify it as theirs or to feel they have a right to control or determine the activity taking place within it. It is easier for outsiders to gain access to and linger in the interior areas of a building shared by 24 to 100 families than it is in a building shared by 6 to 12 families (Warwick group,2009). 1.2. The effect of building type on residents’ control of streets Oscar Newman and other architects have explored some specific interrelationships between the physical designs of housing environments and the security from crime afforded to their inhabitants (Oscar newman,1972; Robert Gold, 1970 & Ray Jeffery1971). Figures I–12, I–13, and I–14 graphically summarize the major differences between residents’ ability to control the areas around their homes and public streets. The three illustrations show the same four-block area of a city, each developed using a different building type.


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Fig.1. A four-city-block row-house development. Only the central portion of the roadbed can be considered fully public.

Fig.2. A four-city-block garden apartment development. The streets and grounds are encompassed within the domain of the multifamily dwellings.

Fig.3. A four-city-block high-rise development. All the streets and grounds are public.

Newman's principles of defensible space design apply mainly under conditions where public open space is an important part of housing environments.Fig.1 is an illustration of a rowhouse development built at a density of 18 units to the acre. Each city block has been subdivided so that all the grounds, except for the streets and sidewalks, are assigned to individual families. The front lawns, because each belongs to an individual family, are designated semiprivate. The rear yards, which are fully enclosed, are private. In fact they are only accessible from the interior of the dwelling units. The close juxtaposition of each dwelling unit and its entry to the street contributes to the incorporation of the sidewalk into the sphere of influence of the inhabitants of the dwelling. This is further reinforced by the fact that their semiprivate lawn abuts the sidewalk, and the family car is parked at the curb. Residents’ attitudes suggest that they consider this sidewalk and parking area as semipublic, rather than public (Newman, 1972). Cluster housing has been praised for bringing to the suburbs a more efficient use of valuable land, lower cost housing, and aesthetic improvements over low density home subdivisions (William Whyte,1964). Fig.2 shows the same four-block area, this time accommodating 3-story garden apartments built at a density of 36 units to the acre. The rear courts within the interior of each cluster have been assigned both to individual families and to all the families sharing the cluster. The families living on the ground floor have been given their own patios within the interior courts, with access to them from the interior of their unit. These patios are therefore private. The remainder of the interior court belongs to all the families sharing a cluster and is only accessible from the semiprivate interior circulation space of each building, making the remainder of the interior cluster semiprivate. The small front lawn adjacent to each building entry is the collective area for that entry’s inhabitants and is therefore semiprivate. As in the row-house scheme in figure 1, all the entries face the street, but each entry now serves six families rather than one and is thus semiprivate rather than private. Parking again is on the street immediately in front of each dwelling. Because of the semiprivate nature of the grounds, the sidewalk and street are not clear extensions of the realms of individual dwelling units. But even with all these limitations, the neighboring sidewalk and parking zone on the street are considered by many residents as areas over which they exert some control. Fig.3 is the same four-block area shown in figures 1 and 2, but now developed as a high-rise superblock at a density of 50 dwelling units to the acre. Each building entry serves 50 families by means of an interior circulation system consisting of a public lobby, elevators, fire stairs, and corridors. The grounds around the buildings are accessible to everyone and are not assigned to


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particular buildings. The residents, as a result, feel little association with or responsibility for the grounds and even less association with the surrounding public streets.

Based on the above images was analyzed Oscar Newman, in this research two samples residential complex from Ardebil province selected and studied. These two residential complexes are located in the southern city of Ardebil and Near Shourabil Lake.

Fig.4. Location the residential complex in Ardebil city Reference: Google earth, 2011

Because in defensible spaces several factors were involved. So tried, both complexes to investigate be chosen from a point the Ardebil city. Too, another important factors that will spill over into this research, There was the police office near the complexes. But this problem was involved in both complexes. Therefore, has been ignored in this study. In this research one sample high-rise building and other sample Short-story building. The high–rise building is MILAD complex and short-story is LALE complex that show in below figures.

Fig.5. MILAD residential complex. High-rise case

study, Source: Author, 2011

Fig.6. LALE residential complex. Short-story case study,

Source: Author, 2011

In both these residential complexes all the open spaces are public spaces for people. Even because there are more tow units in every floor, the circulation spaces in interior blocks are public spaces for the units. The physical factors that correlate most strongly with crime rates are, in order of importance: the height of the buildings, which in turn correlates highly with the number of apartments sharing the entry to a building; the size of the housing project or “the total number of dwelling units in the project”; and the number of other publicly assisted housing projects in the area. In table.1 showed


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the distribution status factors and architecture variables to separate LALE and MILAD residential complexes

Table.1. Physical factors in selecting the two case studies

Factors MILAD residential LALE residential

Density units

0.173 (52units/3000m2) 0.142 (192 units /13500 m2)

Units with share entrances 52 units 8-12-14-16 units

Building height

10 Floor 4 floor

Described above were important physical factors influences in the defending space. The physical factors that correlate most strongly with crime rates are, in order of importance: the height of the buildings, which in turn correlates highly with the number of apartments sharing the entry to a building; the size of the housing project or “the total number of dwelling units in the project”; and the number of other publicly assisted housing projects in the area (Newman,1972). Table.1 showed that Compression ratio of the units in the complex MILAD is more than LALE complex. So that, MILAD complex built in one block and LALE complex built in 12 blocks. But MILAD design for 52 apartments with share entrances and LALE design in several blocks from 8 units with share entrances until 16 units share entrances. Density units in MILAD complex is 0.173 Namely 52 units in 3000 m2 , But this density units for LALE is 0.142 namely 192 units in 13500m2.

1.3. Social factors and their interaction with the physical If need for residential security were the crucial design criterion, then the lower cost townhouses should have better defensible space characteristics (Dingemans,1978). An understanding of the interaction of the social and physical factors that create high crime rates in low- and moderate-income housing developments is useful not only for devising remedies to solve their problems but also for developing strategies for stabilizing neighboring communities composed of single-family housing. Those social variables that correlated highly with different types of crime also correlated highly with each other (Newman,1972). Residents in these homes often cannot afford to pay for supplementary private security personnel or security devices, and they are often located in neighborhoods with higher crime rates. For higher income households living in more expensive townhouse developments, good defensible space design is less critical and merely one alternative against the threat of crime (Dingemans,1978). In this research for social factors answered the residents for some questions, For example, some the question was for about: Family income rate or the cost of living, Education, Amount of social relations with neighbors, Self-sacrifice, Job type, History of residence in apartment and Number of family members. Examining the income of two complexes showed that ratio the family income in MILAD complex with LALE complex impossible to compare. Lowest income for a family with 3 members in LALE was 250 USD, but the same conditions for a family in MILAD complex 8333 USD. So, residents in these homes often cannot afford to pay for supplementary private security personnel or security devices in LALE complex. But this situation in the MILAD complex is contrary. High income in the MILAD also has problems for complex security. For example, because high income in this resident very units from MILAD buying by those use units as a temporary residence and this problem is caused that from 52 units, 18 units used and other units use in Spring and summer Seasons that best the weather in Ardebil city.


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2. A brief introduction to LISREL The main methodology (or mathematical model) used in this study is LISREL. LISREL is an acronym for the linear structural relations model. Properly speaking, LISREL is a computer program that analyzes covariance structures, but the widespread use of the LISREL software has identified the name of the program with the statistical procedures it performs. It is considered the most general method for the analysis of causal hypotheses on the basis of non-experimental data. LISREL for Windows, version 8.5, by Scientific Software International was used in this study. There are two basic types of variables in LISREL, the latent variables represented by lower case letters inside the round circles in Fig.7 and the observed variables represented by upper case letters inside rectangles in Fig.7. Latent variables are those that are formulated in terms of theoretical or hypothetical concepts, i. e. constructs which are not directly measurable or observable. Observed variables are those that are directly measurable or observable and that can be used as indicators of latent variables. In other words, latent variables are represented or measured by one or more observed variables. The relationships between the latent variables and the observed variables of the LISREL model are displayed in a path diagram such as the one shown in Fig.7. In the following discussion, variable names are indicated by italics. Variables on the right/left side of Fig.1 are dependent/independent (input or output) variable Physical Factors/Social factors are the dependent/independent latent variable (by precedence) Density units, Units with shared entrances and Chunk height/Neighborhood Relations, Soci-Economic Status (SES) and Apartments Rules are the dependent/independent observed variables measuring this concept. The relationships among the latent variables determine how the independent variables influence or affect the dependent variables.

Fig.7. Empirical model and the hypotheses

Fig.7 presents the hypothesized Social Factors impressed/impressing of the Physical Factors model that is assessed. The model proposes that overall Physical Factors is impressed/ impressing by the dimensions of Social Factors impact. The details of each construct were discussed, and the validity and reliability of measurement scales were confirmed earlier. In this section, the proposed structural model for (Fig.7 Empirical model and the hypotheses) is assessed. 3. Methodology of Defensible of Residential Complex Spaces 3.1. The model design The first step in building a LISREL model, identifying variables to be used and specifying a initial model, is crucial to the success of it. This important step was a very difficult one for this study because of the complexity of factors involved in Defensible of Residential Complex Spaces. This model hypothesizes that Neighborhood Relations, Soci-Economic Status (SES) and Apartments Rules influence on Defensible Spaces or Density units, Units with shared entrances and building

Social factors

Physical Factors

Neighborhood Relations

Soci-Economic Status

Apartments rules

Density units

Units with share entrances

Building height


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height influence on Defensible Spaces (in Fig.7). It should be emphasized that in the LISREL model the contribution of information to Defensible Spaces is not being measured in isolation, but rather is being measured together with the contributions of other variables. This reflects the reality of Defensible of spaces, where multiple factors of societal& Economic variables together to produce a Defensible Spaces. The relative magnitude of the contributions of different factors (variables) will be determined by the path coeficients which will be available once data is collected and inputted into the model. In other words, the path coefficient of the Social Factors/Physical Factors variables, which will be discussed later, will allow us to determine the contribution of information to Defensible Spaces relative to the contributions of other variables.

3.2. Research Method

3.2.1. Participants

A sample of residents were selected from of two residential complex MILDA and LALE of Ardabil City In Iran with the sample-size ratio of 14 to 14, to participate in this study.

3.3. Data collection 3.3.1. Comparison of body the MILAD and LALE complexes The physical factors that correlate most strongly with crime rates are in order of importance: the height of the buildings, which in turn correlates highly with the number of apartments sharing the entry to a building; the size of the housing project or “the total number of dwelling units in the project”; and the number of other publicly assisted housing projects in the area (Newman, 1972).

Fig.8. Plan the MILAD residential complex Fig.9. Plan the LALE residential complex

a) Density units The site for the MILAD complex is 3000m2. All number of the design units for this site is 52 units, that namely (52units/3000m2). These called showed that ratio density for this residential is 0.173. The 10-story building at the right in Fig.8 (MILAD) has 52 families sharing common interior areas. Because of the large number of people sharing them, these interior areas can only be designated as semipublic or even public. Even the corridors on each floor are shared by 4 families and are accessible from 1 set of stairs and 1 elevator that are very public. But the site for the LALE complex is 13500 m2.


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All number of the design units for this site is 192 units, namely (192 units /13500 m2). These called showed that ratio density for this residential is 0.142. The 4-story building at the above in Fig.9 (LALE) has 16 families sharing common interior areas. Because of the medium number of people sharing them, these interior areas can only be designated as semipublic. Even the corridors on each floor are shared by 4 families and are accessible from 1 set of stairs and 1 elevator that are very public. With a comparative study between MILAD and LALE residential complexes showed that ratio defensible in LALE complex is more from MILAD complexes. Even this subject, cause for Strengthen the social foundation in LALE residential. b) Number units with share entrances Review the plans in the two complexes see that: As mentioned above, MILAD residential with 52 units and with one circulation for all the units. This called opposed with the theory defensible space. All the 52 units commute from a staircase circulation and in every floor with 4 units. So, public space between 4 units and this subject reason for reduction the defensible space in residential complexes. But is LALE residential with 192 units and 12 blocks. All the blocks for this residential is from 8 units until 16 units in every blocks. This 8 unit block is 4 floors. So, In every floor 2 unit. This is example for pattern for Defensible space. The plan showed for blocks the 16 units that there are 4 units in every floor. In these blocks the defensible space ratio the blocks 8 units (2 units in every floor) is very low. Too, the walkup building is subdivided so that eight families share a common entry and interior circulation stair. Two families per floor share a common landing. Entrances from the common staircase usually exit to the outside (Newman,1972). B) Building height In other research have shown that the relationship between the increase in crime and increased building height and that crime is mostly located within public areas (Oscar newman,1972; Robert Gold, 1970 & Ray Jeffery1971).

Fig.10. Graph showing the relationship between the increase in crime and increased building height and that crime is mostly located within public areas.

3.3.2. Distribution of the questionnaire

As explained earlier in the paper, data collection for the observed variables of the LISREL model was carried out through a self administered questionnaire. The unit of data collection is residents of complexes (14 families from the LALE residential complex and 14 families from the MILAD residential complex) then in each complex of residents recruited to the study filled out one questionnaire. In the first three rounds of data collection in the Defensible of spaces study in LALE, the questionnaire was sent to a sample of complex randomly selected from houses in these complexes. Unfortunately, the answering rate in MILAD was very low or non-cooperate, despite efforts, to encourage participation in the survey. Therefore the procedure of the research is survey method and its statistical samples is the 28 family (14 family from the LALE residential complex and 14 family from the MILAD residential complex)


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in Ardabil city by random cluster Sampling. The data are gathered from questionnare as a tool in survey method related to Defensible of Residential Complex Spaces in area of Ardabil city. 3.4. Data analysis Defensible Space relies on self-help rather than on government intervention, and so it is not vulnerable to government’s withdrawal of support. It depends on resident involvement to reduce crime and remove the presence of criminals. It has the ability to bring people of different incomes and race together in a mutually beneficial union. For low-income people, Defensible Space can provide an introduction to the benefits of main-stream life and an opportunity to see how their own actions can better the world around them and lead to upward mobility(Newman,1996)(Fig.1). That is, the structural or casual relationships between Social Factors and Physical Factors, through a LISREL analysis, were explored. LISREL is a computer program for covariance structure analysis, and its prevalent use has always identified the name of the program with the statistical procedures it conducts. LISREL is perceived as the most general method for carrying out confirmatory factor analysis and the causal relationships among latent variables (Anderson &Gerbing, 1988; Jeoreskog & Seorbom, 1989). LISREL version 8.5 was used throughout the analyses. 4. Results 4.1. Regression model Fig.3. indicates that Soci-Economic Status (SE), Neighborhood Relations(NR), and Apartments Rules(AR) as independent variables influence on Physical Factors(PHF) by Standardized coefficients in regression model. In the components of social Factors, Apartments Rules have more effect with the regression coefficient (-0.84), and the Neighborhood Relations have the lowest effect with the regression coefficient (-0.06).

Fig.11. Regression model for Physical Factors

The regression model reflecting Equation for explanation of variances with Physical Factors that: Physical Factors = - 0.15* Soci-Economic Status - 0.06* Neighborhood Relations - 0.84* Apartments Rules R² = 0.87 The R-squared for the regression model with Physical Factors is 0.87 (i.e. 87% of the variation in Defensible Space can be explained by the variations in Soci-Economic Status (SE), Neighborhood Relations (NR), and Apartments Rules (AR).

Fig.4. indicates that Density unit (DU), Units with shared entrances (UE) and Building height (BH) as independent variables influence on Social Factors by Standardized coefficients in regression model. In the components of Physical Factors, Apartments Rules have more effect with the regression


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coefficient (- 0.84), and the Neighborhood Relations have the lowest effect with the regression coefficient (-0.07).

Fig.12. Regression model for Social Factors

The regression model reflecting Equation for explanation of variances with Social Factors that: Social Factors = - 0.38 Building height =+ 0.34 Units with shared entrances= + 0.00 Density units R² = 0.38 The R-squared for the regression model with Social Factors is 0.38 (i.e. 38% of the variation in Defensible Space can be explained by the variations in Density units (DU), Units with shared entrances (UE) and building height (BH). 4.2. Structural model The proposed structural relationships between variables can be conducted through the LISREL analysis (Kelloway,1998).The three scales related to residents perceptions of the Social Factors (described in Fig.1) were used as predictor variables, and the other three scales related to Defensible Factors (also Fig.1) were used as the outcome variables for the analysis. The structural model of this study is presented in Fig. 2.

Fig.13. Accepted structural model. Note:SO= Social Factors PHF=Physical Factor DU= Density units UE= Units with shared entrances BH= Building height SES=Soci-Economic Status NR=Neighborhood Relations AR=Apartments Rules

The fit of the model was evaluated with various measures (Bentler,1995; Seorbom & Jeoreskog,1982). Kelloway (1998) has suggested that the use of chi-square test is reasonable when the study involves a large sample. However, as the chi-square is very sensitive to sample size, the degree of freedom can be used as an adjusting standard by which to judge whether chi-square is large or small (Jeoreskog & Seorbom, 1989). Therefore, in this study, the chi-square per degree of freedom

DU

UE

BH

0.82

SES

NR

AR

0.44

0.54

SO PHF


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can be used, and a ratio below five shows reasonable fit while a ratio between one and two is excellent fit. The ratio of the model in Fig.4 was 2.95, indicating a fairly good fit. Other types of goodness-of-fit measures include Root Mean Squared Error of Approximation (RMSEA), normed fit index (NFI), non-normed fit index (NNFI), and the comparative fit index (CFI). A RMSEA value close to zero shows a near perfect fit. The NFI, NNFI, CFI are always between zero and one, with any value above 0.9 indicating a good fit and the value one suggesting a perfect fit. The model in Fig. 1 had a RMSEA of 0.074, and the NFI, NNFI, and CFI values over 0.9, showing that the model had a highly satisfactory fit. Fig.4 shows the summary of the maximum likelihood parameter estimates, lambda, and the significance of the t-values as indicated by asterisks for the model. The statistically significant relationships are shown. In a LISREL diagram (Fig.4) the number on each arrow pointing from a latent variable to an observed variable is the loading, which can be interpreted as the validity coefficient of the observed variable for the latent variable. For the Defensible Spaces (Fig.4) a comparison of the two loading values for the social factors variable shows that the use of AR (Apartments Rules) is a more valid indicator than the use of SES (Soci-Economic Status), NR (Neighborhood Relations). In other words, AR (Apartments Rules) is more important than SES (Soci-Economic Status), NR (Neighborhood Relations) for Social Factors. Moreover, the coefficient difference between AR and SES&NR there is greater (0.82 versus 0.74 and 0.62) which suggests a heavier reliance on AR by Social Factors. Diagram shows path estimates for the accepted structural model. Social Factors results indicate that social factors are more likely to report social subjects than non-social (B=0.44).One another hand, this research showed that other variables such as education, income levels, age the resident people have direct effect in the defensible spaces of the residential complex. So, For the Defensible Spaces (Fig.13) a comparison of the two loading values for the physical factors variable shows that the use of Building height (BH) is a more valid indicator than the use of Density units(DU) and UE (Units with shared entrances). In other words, Building height (BH) is more important than Density units (DU) and UE (Units with shared entrances) for Physical Factors. Moreover, the coefficient difference between BH and DU&UE there is greater (0.77 versus 0.55 and 0.69) which suggests a heavier reliance on BH by Physical Factors. Physical Factors results indicate that Physical factors are more likely to report social subjects (B=0.54).One another hand, this research showed that Physical Factors explain the most variances of social variables. Then, those social variables that correlated highly with different types of physical factors also correlated highly with each other. 5. Conclusion SEM modeling is a powerful tool enabling researchers to go beyond factor analysis into the arena of determining whether one set of unobserved constructs (dimensions) can cause (be seen to be likely to determine) another set of dimensions. In defensible studies, it is often the case that the variables under study cannot be directly observed or measured (for example, social factors) yet these unobserved variables might be hypothesized to cause one another. SEM analysis is a methodology capable of handling this type of analysis, along with more conventional regression, models, and simultaneous regression models, whilst accounting for multi collinearity and, with appropriate care, other assumptions of regression modeling. But in this search showed that: All number of the design units for this site is 52 units, that namely (52units/3000m2). These called showed that ratio density for this residential is 0.173 and in LALE complex showed that ratio density for this residential is 0.142. With a comparative study between MILAD and LALE residential complexes showed that ratio defensible in LALE complex is more from MILAD complexes. Even this subject, cause for Strengthen the social foundation in LALE residential. Review the plans in the two complexes see that: As mentioned above, MILAD residential with 52 units and with one circulation for all the units. But is LALE residential with 192 units and 12 blocks.


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The plan showed for blocks the 16 units that there are 4 units in every floor. In these blocks the defensible space ratio the blocks 8 units (2 units in every floor) is very low. In these blocks, the walkup building is subdivided so that eight families share a common entry and interior circulation stair. Two families per floor share a common landing. Entrances from the common staircase usually exit to the outside. In this research have shown that the relationship between the increase in crime and increased building height and that crime is mostly located within public areas. 6. References Anderson, J.C & Gerbing, D.W.(1988). Structural equation modeling in practice: A review and recommended two step approach. Psychological Bulletin, 103, 411–423.

Bentler, P. M. (1995). EQS: Structural equations program manual. Encino, CA: Multivariate Software, Inc.

Clare Cooper, Easter Hill Village (New York: The Free Press, 1975); Lee Rainwater, "Fear and the House-as-Haven in the Lower Class," Journal of the American Institute of Planners 32 (January 1966), pp. 23-31. Dennis Dingemans, The Townhouse in the Suburbs: Changing Urban Morphology and Social Space in American Suburbs, 1960-1974, unpublished dissertation in geography. University of California, Berkeley, 1975.

E. Warwick. Defensible Space. King’s College London, London, UK, July 2009.

Jeoreskog, K. G.&Seorbom, D.(1989). LISREL 8:User_s reference guide (2nd ed.).Lincolnwood, IL: SSI.

Kelloway, E. K. (1998). Using LISREL for structural equation modeling: A researcher_s guide. Newbury Park, CA: Sage.

Kieth Harries, The Geography of Crime and Justice (New York: McGraw-Hill, 1974), p. 78.

Newman, Oscar. Creating Defensible Space, 1996.

Newman, Oscar. Analysis of 50 sites in nine competing CCP cities, Report to the U.S. Department of Justice on the suitability of applying Defensible Space technology. Institute for Community Design Analysis: Great Neck, NY. 1994

Newman, Oscar cites four aspects of defensible space, two of which ("image" and "milieu") will not be discussed in this paper because they pertain mostly to public housing. See also: Oscar Newman, Design Guidelines for Creating Defensible Space (Washington, D.C: HUD, 1976).

Newman, Oscar Creating Defensible Space: Crime Prevention Through Urban Design (New York: Macmillan, 1972); Robert Gold, "Urban Violence and Contemporary Defensive Cities, " Journal of the American Institute of Planners 36 (May 1970), pp. 146-160; C. Ray Jeffery, Crime Prevention. Through Environmental Design (Beverly Hills: Sage, 1971),

Newman, Oscar and K. Frank. Factors Influencing Crime and Instability in Urban Housing Developments. U.S. Department of Justice: Washing-ton, D.C. 1976

William Whyte, Cluster development(New York: The Conservation Foundation, 1964); Dennis Dingemans, op. cit., footnote 3, Chapter Two.

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Structural equations of modeling in creating defensible … France/WS18/paper...Structural equations of modeling in creating defensible spaces of residential complex,"Case study: LALE