**4.6 Clustering coefficient**

The clustering coefficient indicator of the physical environment is measured by applying the equation of the clustering Eq. (9), and the major two components to run the equation are the observed number of links among the destinations and the possible number of links. However, there was no clear explanation about how to measure these two components in the reviewed literature; therefore, the criteria to measure these two components are designed by this study. In this regard, the first component of this equation is the observed links between destinations. It considered 5 min as the maximum walking distance between two destinations, which is a 200-m length. Thus, each destination has a potential relationship with all other destination in the 200-m radius. The reason for such an assumption is because, if the distance between every two destinations is not a complete journey for the walker but rather a sub-journey, then the minimum distances mean a better relationship. Based on this criterion, each destination was defined as a center and a straight line was drawn to all other adjacent destinations in the 200-m radius. The required information was elicited from the cadastral maps with the assistance of AutoCAD 3D map software; in this regard, the shapefile maps were generated to both the links and the destinations on the 400-m radius and 600-m radius scales. From this, the layers were added into QGIS software. The resulting total number of links was considered the observed links (numerator). The second component is the possible number of links, even if they did not exist, between the destinations; for this purpose, the equation used was: the number of possible links = (n<sup>2</sup> − n)/2 (denominator), Eq. (9), where n is the total number of destinations. Thus, two independent variables resulted from this indicator, on the 400-m radius and 600-m radius scales. The two variables were labeled as ClsCofS1, ClsCofS2 (**Table 1**). Thereafter, on the 400-m radius scale, the clustering coefficient variable CICS1, indicated that the Al-Saymmar and Al-Abassya neighborhoods were identical, at 0.05 for each. Meanwhile, Al-Mugawlen was slightly different at 0.04. Moreover, on the 600-m radius scale, the clustering coefficient variable CICS2, indicated that the Al-Saymmar and Al-Abassya neighborhoods are identical at 0.04 for each. Finally, Al-Mugawlen was slightly different at 0.03 (**Table 2**).

#### **4.7 Edges assessment**

The method to assess the quality of street edges was adopted the frontage quality index (SFOMD) [50]: p.108. It depends on a Likert scale of seven points, starting with (1), which is the lowest score of the assessment, and ending with (7), which

**379**

**Table 3.**

*SFOMD index, analysis of the edges.*

**4.8 Enclosure ratio**

*Approaching Urban Design through the Analysis of Structural Differences within Three…*

is the highest score. The application of the method depends on observations is conducted by a specialist team and criteria based sampling of the urban tissue. The computation of the overall index concerning the quality of the area was adopted from Gehl [51] and Hershberger and Clements [52], which combines the five indicators by totaling their raw scores. Therefore, the minimum score of the process is five points which represents the poorest quality streets, whereas, the highest score is 35 points, which represents the best possible quality. Thus, one independent variable was developed in terms of the edges assessment, namely: The Frontage quality index (SFOMDS1) on the 400-m radius scale (**Table 1**). Moreover, the principle of sampling the streets is an important issue to avoid bias and to validate the generalization of the results, therefore, this study depends on the selection of three streets segments based on the hierarchal level of the street. From the hierarchical street levels, they define the main street, a connector street, and a cul-de-sac from each case study. The information of the survey was transferred into Excel-sheets for the purpose of analysis. The individual survey sheets were summarized (**Table 3**). Accordingly, three variables were developed from this analysis, which were: the Frontage quality index of the main street (SFMODS1M), the Frontage quality index of the Connecting street (SFMODS1C), and the Frontage quality index of the Col-

The SFOMD index in Al-Saymmar neighborhood demonstrated the lowest levels of frontage quality, at 21, 20, and 15 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively. The SFOMD index in Al-Mugawlen neighborhood demonstrated moderate levels of frontage quality, at 27, 23, and 22 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively. Finally, the SFOMD index in Al-Abassya neighborhood demonstrated the highest levels of frontage quality, at 30, 27, and 27 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively, (**Tables 2** and **3**). Therefore, the value of the SFOMD index increased in parallel with the increased grid structure among the street typologies. For example, the red level of the SFOMD index, brought about different scores across the three neighborhoods, 21, 27, and 30, for Al-Saymmar,

The enclosure ratio was measured for the sampled streets in **Figures 6–8**, Thus,

three independent variables resulted from applying the enclosure indicator on the 400-m radius scale; these were coded as EnRBS1M, EnRBS1C and EnRBS1CS

*DOI: http://dx.doi.org/10.5772/intechopen.87221*

de-sac (SFMODS1CS) (**Table 1**).

Al-Mugawlen, and Al-Abassya respectively (**Table 2**).

*Approaching Urban Design through the Analysis of Structural Differences within Three… DOI: http://dx.doi.org/10.5772/intechopen.87221*


#### **Table 3.**

*Sustainability in Urban Planning and Design*

**4.6 Clustering coefficient**

indicator. Thus, two independent variables were addressed by the PRDR, which were labeled: PRDRS1, PRDRS2 (**Table 1**). Then, the specified PRDR equation Eq. (8) was utilized to compute the indicators, which must be ≤1. A value of 1 represents an optimum relationship that has identical aerial and real distances; whereas, the smaller ratio illustrates that the real route is longer than the aerial distance. In other words, the street network route distance between the two points relates the user's departure station to the location of a contextual destination; thus, the shorter distance indicates the more accessible destination. The PRDR for the 16 destinations of each case study were averaged to determine how well the destinations of each case study are served by the street network. The PRDRS1 on the 400-m radius scale slightly differed among the three neighborhoods, at 0.73, 0.77, and 0.72 for Al-Saymmar, Al-Mugawlen, and Al-Abassya neighborhoods, respectively. Also, it illustrated similar differences for the 600-m radius scale, at 0.76, 0.79, and 0.76 for Al-Saymmar, Al-Mugawlen, and Al-Abassya neighborhoods, respectively (**Table 2**).

The clustering coefficient indicator of the physical environment is measured by applying the equation of the clustering Eq. (9), and the major two components to run the equation are the observed number of links among the destinations and the possible number of links. However, there was no clear explanation about how to measure these two components in the reviewed literature; therefore, the criteria to measure these two components are designed by this study. In this regard, the first component of this equation is the observed links between destinations. It considered 5 min as the maximum walking distance between two destinations, which is a 200-m length. Thus, each destination has a potential relationship with all other destination in the 200-m radius. The reason for such an assumption is because, if the distance between every two destinations is not a complete journey for the walker but rather a sub-journey, then the minimum distances mean a better relationship. Based on this criterion, each destination was defined as a center and a straight line was drawn to all other adjacent destinations in the 200-m radius. The required information was elicited from the cadastral maps with the assistance of AutoCAD 3D map software; in this regard, the shapefile maps were generated to both the links and the destinations on the 400-m radius and 600-m radius scales. From this, the layers were added into QGIS software. The resulting total number of links was considered the observed links (numerator). The second component is the possible number of links, even if they did not exist, between the destinations; for this purpose, the equation used was: the number of possible links = (n<sup>2</sup> − n)/2 (denominator), Eq. (9), where n is the total number of destinations. Thus, two independent variables resulted from this indicator, on the 400-m radius and 600-m radius scales. The two variables were labeled as ClsCofS1, ClsCofS2 (**Table 1**). Thereafter, on the 400-m radius scale, the clustering coefficient variable CICS1, indicated that the Al-Saymmar and Al-Abassya neighborhoods were identical, at 0.05 for each. Meanwhile, Al-Mugawlen was slightly different at 0.04. Moreover, on the 600-m radius scale, the clustering coefficient variable CICS2, indicated that the Al-Saymmar and Al-Abassya neighborhoods are identical at 0.04 for each. Finally,

**378**

**4.7 Edges assessment**

Al-Mugawlen was slightly different at 0.03 (**Table 2**).

The method to assess the quality of street edges was adopted the frontage quality index (SFOMD) [50]: p.108. It depends on a Likert scale of seven points, starting with (1), which is the lowest score of the assessment, and ending with (7), which

*SFOMD index, analysis of the edges.*

is the highest score. The application of the method depends on observations is conducted by a specialist team and criteria based sampling of the urban tissue. The computation of the overall index concerning the quality of the area was adopted from Gehl [51] and Hershberger and Clements [52], which combines the five indicators by totaling their raw scores. Therefore, the minimum score of the process is five points which represents the poorest quality streets, whereas, the highest score is 35 points, which represents the best possible quality. Thus, one independent variable was developed in terms of the edges assessment, namely: The Frontage quality index (SFOMDS1) on the 400-m radius scale (**Table 1**). Moreover, the principle of sampling the streets is an important issue to avoid bias and to validate the generalization of the results, therefore, this study depends on the selection of three streets segments based on the hierarchal level of the street. From the hierarchical street levels, they define the main street, a connector street, and a cul-de-sac from each case study. The information of the survey was transferred into Excel-sheets for the purpose of analysis. The individual survey sheets were summarized (**Table 3**). Accordingly, three variables were developed from this analysis, which were: the Frontage quality index of the main street (SFMODS1M), the Frontage quality index of the Connecting street (SFMODS1C), and the Frontage quality index of the Colde-sac (SFMODS1CS) (**Table 1**).

The SFOMD index in Al-Saymmar neighborhood demonstrated the lowest levels of frontage quality, at 21, 20, and 15 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively. The SFOMD index in Al-Mugawlen neighborhood demonstrated moderate levels of frontage quality, at 27, 23, and 22 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively. Finally, the SFOMD index in Al-Abassya neighborhood demonstrated the highest levels of frontage quality, at 30, 27, and 27 points for the variables SFOMDS1M, SFOMDS1C, SFOMDS1CS, respectively, (**Tables 2** and **3**). Therefore, the value of the SFOMD index increased in parallel with the increased grid structure among the street typologies. For example, the red level of the SFOMD index, brought about different scores across the three neighborhoods, 21, 27, and 30, for Al-Saymmar, Al-Mugawlen, and Al-Abassya respectively (**Table 2**).

#### **4.8 Enclosure ratio**

The enclosure ratio was measured for the sampled streets in **Figures 6–8**, Thus, three independent variables resulted from applying the enclosure indicator on the 400-m radius scale; these were coded as EnRBS1M, EnRBS1C and EnRBS1CS

**Figure 6.** *Al-Saymmar edges assessment.*

**Figure 7.** *Al-Mugawlen edges assessment.*

**381**

*p* < 0.001, *R*<sup>2</sup>

*Approaching Urban Design through the Analysis of Structural Differences within Three…*

the individuals. The measured attributes of the physical environment were tested in terms of their predictability for the walking minutes. Thus, their variables are considered predictors (X), which need to be tested in terms of their predictability for the walking minutes (Y). In other words, this analysis tests whether the walking outcome variables can be individually predicted by the measured attributes of the physical environment. For such a purpose, the hierarchal regression analysis was chosen because of its flexibility to enter predictors in a split block with extra predictors. Moreover, the *p*-value (<0.05) indicates the significance of the models,

explain the variance of the outcomes. The hierarchical regression analyses were run to test the predictability of the physical environment indicators, which were individually tested with the walking minutes in one model for each indicator to determine the effect significance of the predictor on the outcome variable (*p*-value)

The Block density, the predictability of the model was significant (*F*(173, 1) =

the variance of the total walking minutes. Also, the test shows that the higher walking behavior outcome score was associated with higher block densities (*b* = 11.817, *p* < 0.001). In term of Housing Units density, the predictability of the model was

explain 13.2% of the variances of the total walking minutes. It was showed that the higher scores for walking behavior outcomes were associated with higher housing

the singular model was able to explain 6.1% of the variances of the total walking minutes. Furthermore, the higher walking behavior outcome scores were associated with a higher diversity of all commercial land uses (*b* = 2.478, *p* < 0.001). Regarding the commercial land use without parking, wholesale, and workshops variable on a 400-m radius scale, the predictability of the model was significant

10.4% of the variances of the total walking minutes. Furthermore, the higher scores of walking behavior outcomes were associated with the higher diversity of commercial land use without parking, wholesale, and workshops (*b* = 2.390, *p* < 0.001). In term of the diversity of the non-residential land use on a 400-m radius scale, the predictability of the model was significant with the total walk-

to explain 7.5% of the variances of the total walking minutes. Also, the higher of walking behavior outcomes scores were associated with a lower diversity of non-

Regarding the connectivity indicators; the node (streets intersections) intensity on both scales 400-m and 600-m radius, the predictability of the model was

9.7% of the variances of the total walking minutes on the two scales, respectively. However, the higher walking behavior outcome scores were associated with the higher node intensities only on the 400-m radius scale in terms of the total walking minutes (*b* = 0.573, *p* < 0.001). In term of the street intensity on 400-m and 600-m radius scales, the predictability of the two models was significant for the total

= 0.097), respectively. Thus, the models were able to explain 13.5 and

Diversity of all commercial land use on a 400-m radius scale; the predictability

). Moreover, the SPSS software was utilized for the conducting all the

= 0.089). Thus, the singular model was able to explain 8.9% of

= 0.104). Thus, the model was able to explain

) explain the potential of the predictors to

= 0.132). Thus, the model was able to

= 0.075). Thus, the model was able

= 0.135) and (*F*(173, 1) = 18.678,

= 0.061). Thus,

*DOI: http://dx.doi.org/10.5772/intechopen.87221*

while the determination coefficient (*R*<sup>2</sup>

required statistical analysis in this study.

significant (*F*(173, 1) = 26.231, *p* < 0.001, *R*<sup>2</sup>

ing minutes (*F*(173, 1) = 13.947, *p* < 0.001, *R*<sup>2</sup>

residential land use (*b* = −3.390, *p* < 0.001).

significant (*F*(173, 1) = 26.940, *p* < 0.001, *R*<sup>2</sup>

of the model was significant (*F*(173, 1) = 11.145, *p* < 0.001, *R*<sup>2</sup>

units densities (*b* = 0.040, *p* < 0.001).

(*F*(173, 1) = 20.176, *p* < .001, *R*<sup>2</sup>

and the (*R*<sup>2</sup>

**5.1 Findings**

16.989, *p* < 0.001, *R*<sup>2</sup>

**Figure 8.** *Al-Abassya edges assessment.*

(**Table 1**). The variables required to apply the indicator are the width of the street and the heights of the adjacent buildings, which were measured directly in this study. Then, the function of the indicator was applied to the sections of all the sampled streets segments. In terms of the main streets (EnRBS1M), the three case studies have a broadly similar value of enclosure ratios, at 2.9, 2.7, and 2.7, for Al-Saymmar, Al-Mugawlen, and Al-Abassya, respectively. In terms of the Connecting streets with the green level of betweenness (EnRBS1C), the highest score was noted with the Al-Mugawlen neighborhood (1.7); Al-Abassya showed a moderate enclosure ratio level (1.3), and the lowest level was noticed within Al-Saymmar (1). In terms of the local streets and the blue level of betweenness (EnRBS1CS), the highest score was noted within the Al-Mugawlen neighborhood (1.7); Al-Saymmar showed a moderate enclosure ratio level (1.1), and the lowest level was noticed with Al-Saymmar (0.93) (**Table 2**).
