**3. Review of the measurement indicators of the urban physical environment**

A proponent study by Cervero and Kockelman, addressed three dimensions that are considered responsible for moving demands of residents in the built environment; these include density, diversity, and design. The so-called '3Ds' represent a general umbrella for the measurement of the physical dimension of the urbanism phenomenon. Their study showed how these three dimensions contributed to an increase in the number of walkable streets in San-Francisco [19]. Regarding the

issue of density, the intensity of users in urban areas is associated with the concentration of built-up urban developments. Density influences how well human activity and place are related since it influences the availability of urban space. Moudon et al. demonstrated that density and walking are strongly associated and higher density areas are more vibrant and walkable [20]. Although the locations of destinations are defined by the distance factor, the density of the urban area is influenced by locations of activities. This is because the geometrical relationship among components of a higher density area imposes closer distances. Moreover, Frank et al. demonstrated that the residential typologies and their physical layouts could provide a conception of population density. For example, a high-density place could include multi-family housing, apartments, and small residential lots. Moreover, high density is suggested as >6 housing units per acre, and low density is defined as <3 units per acre, while a medium density falls between these values [21]. Therefore, density could be rather a parametric concept that abstractly defines the neighborhood typology. Although density is a reliable measurement in urban planning, there is no particular level of acceptance concerning density. It shifts according to different factors, such as social and cultural contexts. For example, what could be considered as high density in some Western countries could be seen as low density in China or India.

Eq. (1): Block density equation [21]

$$\text{Blocks density} = \mathbf{B}\_{\text{tot}} / \mathbf{A}\_{\text{tot}} \tag{1}$$

B is the total area of blocks, includes the open space inside the block and A is the total area measured by m2 .

Eq. (2): Housing unit density equation [21]

$$\text{Housing density} = \mathbf{H}\_{\text{tot}} / \mathbf{A}\_{\text{tot}} \tag{2}$$

H is the total number of housing units and A is the total area measured by square hectare.

The mix of land use measure is the degree of difference between the land uses that occupy a certain urban area. In other words, this means the degree of proportionating between different types of land use areas within the total area. Similar to density, the mix of land uses is considered to be associated with the increase of walkers in urban areas. In other words, when a place has diverse facilities or destinations, this will encourage users to walk [19, 21, 22]. The impact of land use diversity on users is manifested in the way in which users associate themselves with their neighborhood or with the wider urban area of the city. This is because it provides them with more opportunities. Also, it facilitates their imagination, in accordance with the notion of a cognitive map, as posited by Kevin Lynch. Thus, diverse land use adds to the experience of people providing more motives and resolutions, which encourage walking. Furthermore, studies used a mix of land use as a criterion of the '3Ds' to probe the quality of place in term of its walkability and transportation, and positive correlations were widely noted among several studies [17, 23–25]. The method to compute land use diversity is addressed by Frank et al. [21]. It adopts a mathematical equation to compute the entropy of land use division to a group of land usages, from a baseline of equality between the different portions [21, 26].

Eq. (3): Land use diversity equation [21]

$$\text{Land use diversity} = -\frac{\Sigma\_k p k \ln p k}{\ln N} \tag{3}$$

**371**

units.

by hectare.

the place.

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

*k* is an individual category of land use, *p* is the proportion of total land use, and

The design dimension based on Cervero and Kockelman [19] is the streets connectivity which is considered the third indicator in urban planning studies. It is based on the notion that a greater flow of movement highly depends on how easily people and cars can gain access within/through urban areas. Thus, more accessible attractions require more connectivity of streets and walkways. Their study outlined design dimension as the shape and number of streets and nodes. Streets could be a grid or curve-lined shapes, and the intersections are nodes of four or three legs. Regarding the quality of the streets, studies depend on street design criteria as a parametric measure to assess their contribution in terms of walkability and transportation [17, 20, 21]. Connectivity and smaller and denser blocks and streets decrease travel distances and encourage users to walk or cycle. Consequently, they increase place accessibility [27]. New Urbanism supported this finding, proposing that more connected and compacted urban form is generally more accessible and results in lower land consumption. Thus, the pedestrian-oriented areas designed with highly connected streets and a lower ratio of wasted land are not just more walkable but safer. As such these can also be considered as more sustainable areas [27, 28]. Both streets and intersections are useful tools for engineering urban tissue, which define its key urban characteristics. Moreover, many researchers emphasized the importance of these two components in measuring people's movement [29, 30]. In this regard, several forms of measurement were addressed by different scholars, such as: (1) the intensity of nodes per area [21, 31]; (2) the external connectivity, which depends on the number of entrances (related to length in m) into a certain urban area [32]; and (3) the number of street segments normalized by the number of accommodated intersections [33]. However, the node density was more com-

monly used among urban planning measures of ecological models.

of three and four legs intersection to the total area in question [34].

Eq. (4): Intersection density equation [19]

Eq. (5): Street density equation [35, 36]

The density of intersections per area was considered a measure of the streets' network connectivity, whereas the presence of three legs intersections or more indicate a greater connectivity, and thus more accessible place [19, 21]. However, different formulas were applied to measure the density of intersections. The first formula was by urban planners, and depends on the ratio of the aggregated number

Intersections density = Ntot/Atot (4)

N is the total number of T and X intersections and A is the total area measured

Street density is the total length of streets included in a certain urban area, as normalized by the total area, was considered a measure of connective urban tissue [35–37]. Thus, the denser the streets per area, the more connected, and accessible,

Street density = Ltot/Atot (5)

Other studies considered other indicators and the text below explains several reliable indicators that were considered in empirical studies and showed considerable association with walking, namely, external connectivity, Pedestrian Catchment

L is the total length to street segments and A is the total area measured in hectare

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

*N* is the total number of categories.

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

*k* is an individual category of land use, *p* is the proportion of total land use, and *N* is the total number of categories.

The design dimension based on Cervero and Kockelman [19] is the streets connectivity which is considered the third indicator in urban planning studies. It is based on the notion that a greater flow of movement highly depends on how easily people and cars can gain access within/through urban areas. Thus, more accessible attractions require more connectivity of streets and walkways. Their study outlined design dimension as the shape and number of streets and nodes. Streets could be a grid or curve-lined shapes, and the intersections are nodes of four or three legs. Regarding the quality of the streets, studies depend on street design criteria as a parametric measure to assess their contribution in terms of walkability and transportation [17, 20, 21]. Connectivity and smaller and denser blocks and streets decrease travel distances and encourage users to walk or cycle. Consequently, they increase place accessibility [27]. New Urbanism supported this finding, proposing that more connected and compacted urban form is generally more accessible and results in lower land consumption. Thus, the pedestrian-oriented areas designed with highly connected streets and a lower ratio of wasted land are not just more walkable but safer. As such these can also be considered as more sustainable areas [27, 28]. Both streets and intersections are useful tools for engineering urban tissue, which define its key urban characteristics. Moreover, many researchers emphasized the importance of these two components in measuring people's movement [29, 30]. In this regard, several forms of measurement were addressed by different scholars, such as: (1) the intensity of nodes per area [21, 31]; (2) the external connectivity, which depends on the number of entrances (related to length in m) into a certain urban area [32]; and (3) the number of street segments normalized by the number of accommodated intersections [33]. However, the node density was more commonly used among urban planning measures of ecological models.

The density of intersections per area was considered a measure of the streets' network connectivity, whereas the presence of three legs intersections or more indicate a greater connectivity, and thus more accessible place [19, 21]. However, different formulas were applied to measure the density of intersections. The first formula was by urban planners, and depends on the ratio of the aggregated number of three and four legs intersection to the total area in question [34].

Eq. (4): Intersection density equation [19]

$$\text{Interactions density} = \mathbf{N}\_{\text{tot}} / \mathbf{A}\_{\text{tot}} \tag{4}$$

N is the total number of T and X intersections and A is the total area measured by hectare.

Street density is the total length of streets included in a certain urban area, as normalized by the total area, was considered a measure of connective urban tissue [35–37]. Thus, the denser the streets per area, the more connected, and accessible, the place.

Eq. (5): Street density equation [35, 36]

$$\text{Strecet density} = \mathbf{L}\_{\text{tot}} / \mathbf{A}\_{\text{tot}} \tag{5}$$

L is the total length to street segments and A is the total area measured in hectare units.

Other studies considered other indicators and the text below explains several reliable indicators that were considered in empirical studies and showed considerable association with walking, namely, external connectivity, Pedestrian Catchment

*Sustainability in Urban Planning and Design*

as low density in China or India.

total area measured by m2

hectare.

Eq. (1): Block density equation [21]

. Eq. (2): Housing unit density equation [21]

Eq. (3): Land use diversity equation [21]

Land use diversity <sup>=</sup> <sup>−</sup>∑*kpk* ln*pk* \_\_\_\_\_\_\_\_\_

issue of density, the intensity of users in urban areas is associated with the concentration of built-up urban developments. Density influences how well human activity and place are related since it influences the availability of urban space. Moudon et al. demonstrated that density and walking are strongly associated and higher density areas are more vibrant and walkable [20]. Although the locations of destinations are defined by the distance factor, the density of the urban area is influenced by locations of activities. This is because the geometrical relationship among components of a higher density area imposes closer distances. Moreover, Frank et al. demonstrated that the residential typologies and their physical layouts could provide a conception of population density. For example, a high-density place could include multi-family housing, apartments, and small residential lots. Moreover, high density is suggested as >6 housing units per acre, and low density is defined as <3 units per acre, while a medium density falls between these values [21]. Therefore, density could be rather a parametric concept that abstractly defines the neighborhood typology. Although density is a reliable measurement in urban planning, there is no particular level of acceptance concerning density. It shifts according to different factors, such as social and cultural contexts. For example, what could be considered as high density in some Western countries could be seen

Blocks density = Btot/Atot (1)

Housing density = Htot/Atot (2)

The mix of land use measure is the degree of difference between the land uses that occupy a certain urban area. In other words, this means the degree of proportionating between different types of land use areas within the total area. Similar to density, the mix of land uses is considered to be associated with the increase of walkers in urban areas. In other words, when a place has diverse facilities or destinations, this will encourage users to walk [19, 21, 22]. The impact of land use diversity on users is manifested in the way in which users associate themselves with their neighborhood or with the wider urban area of the city. This is because it provides them with more opportunities. Also, it facilitates their imagination, in accordance with the notion of a cognitive map, as posited by Kevin Lynch. Thus, diverse land use adds to the experience of people providing more motives and resolutions, which encourage walking. Furthermore, studies used a mix of land use as a criterion of the '3Ds' to probe the quality of place in term of its walkability and transportation, and positive correlations were widely noted among several studies [17, 23–25]. The method to compute land use diversity is addressed by Frank et al. [21]. It adopts a mathematical equation to compute the entropy of land use division to a group of land usages, from a baseline of equality between the different portions [21, 26].

H is the total number of housing units and A is the total area measured by square

ln*<sup>N</sup>* (3)

B is the total area of blocks, includes the open space inside the block and A is the

**370**

Area (PCA), pedestrian route directness ratio (PRDR), the clustering coefficient of destinations, the quality of edges, and the enclosure ratio. The external connectivity is the ratio of Ingress/Egress (access) points of the neighborhood to the total length of peripheral streets reveal the extent to which the neighborhood is connected to external urban areas; thus, the greater the distance, the poorer the external connectivity [32].

Eq. (6): External connectivity equation [32]

$$\text{External connectivity} = \text{L}\_{\text{tot}} / \text{E}\_{\text{tot}} \tag{6}$$

L is the total length of peripheral boundaries of the neighborhood, and E is the total number of entrances into the neighborhood.

Pedestrian Catchment Area (PCA): The PCA is the accessible area via the street network, assesses the efficiency of the street network to serve certain destinations or built up urban areas within an acceptable Euclidean walking distance, such as a 200, 400, or 800-m radius, from a given point or important facility, like a transport station [37, 38]. Furthermore, the PCA ascertains that, measuring the extent to which the street network serves blocks, demonstrates a certain level of accessibility into the built-up area, as sampled by a circle (e.g., 200-m radius). The center of the circle is hypothetically considered as the pedestrian departure point and a 200-m radius ring is the proposed walkable shed. Thereby, the total accessible built-up area in a 200-m network distance is normalized by the total built-up area inside the circle, indicating the efficiency of the street network defined by the sampling circle. In some research this is referred to the 'Pedshed ratio' [37].

Eq. (7): Pedestrian Catchment Area equation (PCA) [37]

$$\text{PCA} = \mathbf{AA}\_{\text{tot}} / \mathbf{A}\_{\text{tot}} \tag{7}$$

**373**

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

of possible links, and reported the significant influence of walking.

Clustering coefficient = observed links/possible links (n2 − n)/2 (9)

The quality of edges: the studies of walking urban areas linked the quality of

quality of streets and street life. A street facilitates the interaction between people because it brings them together, even those who do not know each other. In a street, people do their favorite things: walking, watching, sitting, or choosing their favorite viewpoint. A good street has clearly designed edges, geometry and carefully delineated transparency [2]. The block frontage is an important component of block structure, which impacts on human perception, traffic and pedestrian flow [32, 47–49]. In this study, the method to assess the quality of street edges was adopted from Remali et al. [50], and depends on five factors to assess the quality of elevations. These factors are: the "number of visible units accessible from the street (S); visible diversity of function (F); openness to the public street (O); level of maintenance (M); and level of detail and quality of materials (D)". The frontage quality index (SFOMD) method depends on a Likert scale of seven points, starting with (1), which is the lowest score of the assessment, and ending with (7), which the

<sup>1</sup> The formula (n2 − n)/2) to compute the possible number of links amongst n nodes is addressed by this

study because the original reference did not mention this part: (authors).

Jacobs in [2] asserted the link between the

Eq. (9): Clustering coefficient equation [46]

street edges with the number of walkers.1

(PRDR = Euclidian distance/network length of a route) (8)

The clustering coefficient of destinations suggests that people perceive this as more than a single destination because each cluster provides a range of options. Thus, the cluster has a greater chance of meeting users' needs than an individual destination [22, 43]. Thus, a cluster of destinations is a design process that serves the proximity by compromising the distance and geometrics of destinations; therefore, users maintain a cognitive map of the proximity of their houses to non-residential clusters. However, there is no study that validates any standard measures of a cluster; instead it is simply perceived as a group of destinations proximate to each other to which people take themselves. Each cluster accommodates a bundle of different types of uses instead of single type of land use. The depending bundle criterion was devised by Canter and Tagg, who defined how many clusters could serve a particular urban area [44]. The clustering coefficient is a graph-based measure, developed by Watts and Strogatz, to calculate social networks, which were considered 'small world' networks. If a group of destinations is represented as nodes and a graph was made through connecting the nodes by hypothetical links, then the clustering coefficient is the number of links between all the nodes divided by the total number of links that was postulated as a rational relationship among the nodes. Also, the coefficient represents the degree to which a group of nodes are clustered, by normalizing the number of observed Links to the number of possible Links among the same group of nodes. The implication of a small-world phenomenon, as defined by network theorists Watts and Strogatz [45], to measure the degree of proximities between a group of destinations is promising because there is no equivalent topological measure to represent the relationship among a group of proximate destinations. However, there is no available method to produce a standard measurement of accessibility from this coefficient. In this respect, van der Westhuizen [46] used the ratio of a number of realized links between destinations, normalized by the number

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

Eq. (8): PRDR equation [42]

AA is the total accessible area and A is the total built-up area.

Pedestrian route directness ratio (PRDR) concerns the proximity of the distributed destination in the urban area, which was frequently used in accessibility and transportation related research. This involves the distance, either aerial or real, between a resident's house and the destination (calculated in walking distances, e.g., ¼ mile, ½ mile, and 1 mile). This was considered an influential factor in facilitating the activity of users, especially walking to the nearest destinations [17, 22, 23, 39–41]. Moreover, Randal and Baetz [42] developed the pedestrian route directness ratio (PRDR), which is the ratio between the aerial distance and the real distance. It is an expressive formula because it explains the ease or probability to access certain destinations located within a certain distance-range of residences. Thus, the higher ratios (up to 1) represent the best proximate relationships between origin and destination. In this study, the ratio was adopted to express the proximity caused by the street network design, and the number of destinations measure was considered in different measurement levels. The (PRDR) measure was considered on the neighborhood level of measurement, Eq. (8), and the numerical range of this ratio is ≤1. The 1 value represents an optimum relationship that has identical aerial and real distances, whereas a smaller ratio illustrates that the real route is longer than the aerial distance. In streets, network routes relate between two points, the user's departure station and contextual locations or destinations; meanwhile, the Euclidian distance is the aerial distance between the two points. In this study, a certain number of destinations and one origin are defined for each case study, where the origin is the postulated departure point and the destinations are defined by the survey.

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

Eq. (8): PRDR equation [42]

*Sustainability in Urban Planning and Design*

Eq. (6): External connectivity equation [32]

total number of entrances into the neighborhood.

In some research this is referred to the 'Pedshed ratio' [37]. Eq. (7): Pedestrian Catchment Area equation (PCA) [37]

AA is the total accessible area and A is the total built-up area.

nectivity [32].

Area (PCA), pedestrian route directness ratio (PRDR), the clustering coefficient of destinations, the quality of edges, and the enclosure ratio. The external connectivity is the ratio of Ingress/Egress (access) points of the neighborhood to the total length of peripheral streets reveal the extent to which the neighborhood is connected to external urban areas; thus, the greater the distance, the poorer the external con-

External connectivity = Ltot/Etot (6)

L is the total length of peripheral boundaries of the neighborhood, and E is the

Pedestrian Catchment Area (PCA): The PCA is the accessible area via the street network, assesses the efficiency of the street network to serve certain destinations or built up urban areas within an acceptable Euclidean walking distance, such as a 200, 400, or 800-m radius, from a given point or important facility, like a transport station [37, 38]. Furthermore, the PCA ascertains that, measuring the extent to which the street network serves blocks, demonstrates a certain level of accessibility into the built-up area, as sampled by a circle (e.g., 200-m radius). The center of the circle is hypothetically considered as the pedestrian departure point and a 200-m radius ring is the proposed walkable shed. Thereby, the total accessible built-up area in a 200-m network distance is normalized by the total built-up area inside the circle, indicating the efficiency of the street network defined by the sampling circle.

PCA = AAtot/Atot (7)

Pedestrian route directness ratio (PRDR) concerns the proximity of the distributed destination in the urban area, which was frequently used in accessibility and transportation related research. This involves the distance, either aerial or real, between a resident's house and the destination (calculated in walking distances, e.g., ¼ mile, ½ mile, and 1 mile). This was considered an influential factor in facilitating the activity of users, especially walking to the nearest destinations [17, 22, 23, 39–41]. Moreover, Randal and Baetz [42] developed the pedestrian route directness ratio (PRDR), which is the ratio between the aerial distance and the real distance. It is an expressive formula because it explains the ease or probability to access certain destinations located within a certain distance-range of residences. Thus, the higher ratios (up to 1) represent the best proximate relationships between origin and destination. In this study, the ratio was adopted to express the proximity caused by the street network design, and the number of destinations measure was considered in different measurement levels. The (PRDR) measure was considered on the neighborhood level of measurement, Eq. (8), and the numerical range of this ratio is ≤1. The 1 value represents an optimum relationship that has identical aerial and real distances, whereas a smaller ratio illustrates that the real route is longer than the aerial distance. In streets, network routes relate between two points, the user's departure station and contextual locations or destinations; meanwhile, the Euclidian distance is the aerial distance between the two points. In this study, a certain number of destinations and one origin are defined for each case study, where the origin is the postulated departure point and the destinations are

**372**

defined by the survey.

(PRDR = Euclidian distance/network length of a route) (8)

The clustering coefficient of destinations suggests that people perceive this as more than a single destination because each cluster provides a range of options. Thus, the cluster has a greater chance of meeting users' needs than an individual destination [22, 43]. Thus, a cluster of destinations is a design process that serves the proximity by compromising the distance and geometrics of destinations; therefore, users maintain a cognitive map of the proximity of their houses to non-residential clusters. However, there is no study that validates any standard measures of a cluster; instead it is simply perceived as a group of destinations proximate to each other to which people take themselves. Each cluster accommodates a bundle of different types of uses instead of single type of land use. The depending bundle criterion was devised by Canter and Tagg, who defined how many clusters could serve a particular urban area [44]. The clustering coefficient is a graph-based measure, developed by Watts and Strogatz, to calculate social networks, which were considered 'small world' networks. If a group of destinations is represented as nodes and a graph was made through connecting the nodes by hypothetical links, then the clustering coefficient is the number of links between all the nodes divided by the total number of links that was postulated as a rational relationship among the nodes. Also, the coefficient represents the degree to which a group of nodes are clustered, by normalizing the number of observed Links to the number of possible Links among the same group of nodes. The implication of a small-world phenomenon, as defined by network theorists Watts and Strogatz [45], to measure the degree of proximities between a group of destinations is promising because there is no equivalent topological measure to represent the relationship among a group of proximate destinations. However, there is no available method to produce a standard measurement of accessibility from this coefficient. In this respect, van der Westhuizen [46] used the ratio of a number of realized links between destinations, normalized by the number of possible links, and reported the significant influence of walking.

Eq. (9): Clustering coefficient equation [46]

Clustering coefficient = observed links/possible links (n2 − n)/2 (9)

The quality of edges: the studies of walking urban areas linked the quality of street edges with the number of walkers.1 Jacobs in [2] asserted the link between the quality of streets and street life. A street facilitates the interaction between people because it brings them together, even those who do not know each other. In a street, people do their favorite things: walking, watching, sitting, or choosing their favorite viewpoint. A good street has clearly designed edges, geometry and carefully delineated transparency [2]. The block frontage is an important component of block structure, which impacts on human perception, traffic and pedestrian flow [32, 47–49]. In this study, the method to assess the quality of street edges was adopted from Remali et al. [50], and depends on five factors to assess the quality of elevations. These factors are: the "number of visible units accessible from the street (S); visible diversity of function (F); openness to the public street (O); level of maintenance (M); and level of detail and quality of materials (D)". The frontage quality index (SFOMD) method depends on a Likert scale of seven points, starting with (1), which is the lowest score of the assessment, and ending with (7), which the

<sup>1</sup> The formula (n2 − n)/2) to compute the possible number of links amongst n nodes is addressed by this study because the original reference did not mention this part: (authors).

highest score of the assessment. The computing of the overall index was adopted from Gehl [51] and Hershberger [52], and combines the five indicators by totaling their raw scores. Thereby, the minimum score for the process is five points, which represents the poorest street quality, whereas, the highest score is 35 points which represents the best possible street quality [50].

Enclosure ratio: studies in urban design have developed different ideas on the relationship between human perception and street room. The enclosure notion defines the sense of place in connection with the relationship between street widths and adjacent building heights. From an architectural point of view, Cullen illustrated that enclosure is an important tool that influences the human perception of a place or the "hereness". Accordingly, the quality of enclosure is defined as a highly-required dimension of a streetscape, because the street-building proportions represent the "outdoor room" of walkers. For example, Ewing et al. indicate that building height and other vertical elements are milestones to establishing welldefined outdoor spaces when they are proportionate with the width of the counter space, or street [53].
