**Particles Separation in Microfluidic Devices**

Printed Edition of the Special Issue Published in *Micromachines* Takasi Nisisako and Naotomo Tottori Edited by

www.mdpi.com/journal/micromachines

## **Particles Separation in Microfluidic Devices**


**4. The measurement process of physical environmental attributes**

The measurement of physical environmental attributes, which were addressed in the previous section, are applied to the case studies and discussed in this section based on the cadastral maps of the three case studies. The raw information concerning the essential structure of the case studies is elicited from cadastral maps which were in AutoCAD and QGIS formats (**Figures 3**–**5**), the numerical attributes were entered into an excel sheet and then SPSS software. Then, each individual indicator is computed based on its defined equation and coded in the SPSS based on their initial letters, either on 400-m radius or 600-m radius (**Table 1**). For example, the density indicator has three variables for each case study; thus, it was measured two times to produce six numerical values for three neighborhoods. The two scales are (400-m radius), and (600-m radius). Thus, the codes of the three density variables are DnS1 and DnS2, respectively; this coding is continued for the rest of the independent variables (**Table 1**). From this, independent variables for measures of the physical environment attributes are produced (**Table 2**). Twenty-two independent variables were developed in this study, which resulted from the application of the measured physical environment indicators to the case studies.

#### **4.1 Block and housing units density**

The area of the blocks was computed on the two scales 400-m radius and 600-m radius, and the density equations Eqs. (1) and (2) were applied with the assistance of Excel software. Although the block density indicator is computed on two scales, the housing unit density is computed on one scale. Thus, three independent variables were calculated for the densities, which are labeled as BDnSi i = 1, and 2, and HDnS1 (**Table 1**). Moreover, the block density has been measured on the two scales, for the block density on the 400-m radius scale, the highest density was found in Al-Saymmar (0.71), while in Al-Mugawlen and Al-Abassya differed slightly from (0.65) to (0.67), respectively. Furthermore, the block density on the 600-m radius scale was slightly degraded from Al-Saymmar (0.78) to Al-Mugawlen (0.77) to Al-Abassya (0.72) (**Table 2**). The intensity of housing units (HDnS1) was measured only on scale (400-m radius); however, a divergence was noted from Al-Saymmar (41.9) to Al-Mugawlen (25) to Al-Abassya (17.25), in light of the single-family housing per hectare (**Table 2**).

**375**

**Table 2.**

**Table 1.**

**4.2 Mixed land use**

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

The diversity of land use was computed by the entropy equation Eq. (3) and the variables used for that purpose were the different land uses measured by the area. From this, the equation was applied with the assistance of MATLAB software and the categories of land use, for instance, the retail shops and workshops, were

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

*The measurement indicators of the physical environment attributes.*

*The computed indicators of the physical environment attributes.*

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

#### **Table 1.**

*Sustainability in Urban Planning and Design*

space, or street [53].

represents the best possible street quality [50].

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

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

**4. The measurement process of physical environmental attributes**

measured physical environment indicators to the case studies.

**4.1 Block and housing units density**

The measurement of physical environmental attributes, which were addressed in the previous section, are applied to the case studies and discussed in this section based on the cadastral maps of the three case studies. The raw information concerning the essential structure of the case studies is elicited from cadastral maps which were in AutoCAD and QGIS formats (**Figures 3**–**5**), the numerical attributes were entered into an excel sheet and then SPSS software. Then, each individual indicator is computed based on its defined equation and coded in the SPSS based on their initial letters, either on 400-m radius or 600-m radius (**Table 1**). For example, the density indicator has three variables for each case study; thus, it was measured two times to produce six numerical values for three neighborhoods. The two scales are (400-m radius), and (600-m radius). Thus, the codes of the three density variables are DnS1 and DnS2, respectively; this coding is continued for the rest of the independent variables (**Table 1**). From this, independent variables for measures of the physical environment attributes are produced (**Table 2**). Twenty-two independent variables were developed in this study, which resulted from the application of the

The area of the blocks was computed on the two scales 400-m radius and 600-m radius, and the density equations Eqs. (1) and (2) were applied with the assistance of Excel software. Although the block density indicator is computed on two scales, the housing unit density is computed on one scale. Thus, three independent variables were calculated for the densities, which are labeled as BDnSi i = 1, and 2, and HDnS1 (**Table 1**). Moreover, the block density has been measured on the two scales, for the block density on the 400-m radius scale, the highest density was found in Al-Saymmar (0.71), while in Al-Mugawlen and Al-Abassya differed slightly from (0.65) to (0.67), respectively. Furthermore, the block density on the 600-m radius scale was slightly degraded from Al-Saymmar (0.78) to Al-Mugawlen (0.77) to Al-Abassya (0.72) (**Table 2**). The intensity of housing units (HDnS1) was measured only on scale (400-m radius); however, a divergence was noted from Al-Saymmar (41.9) to Al-Mugawlen (25) to Al-Abassya (17.25), in light of the single-family hous-

**374**

ing per hectare (**Table 2**).

*The measurement indicators of the physical environment attributes.*

#### **Table 2.**

*The computed indicators of the physical environment attributes.*

#### **4.2 Mixed land use**

The diversity of land use was computed by the entropy equation Eq. (3) and the variables used for that purpose were the different land uses measured by the area. From this, the equation was applied with the assistance of MATLAB software and the categories of land use, for instance, the retail shops and workshops, were

entered as a variable of the equation (X1, X2, …, Xi) in the MATLAB format. Additionally, because the land use categories are not unified across the three case studies, they could have different nature of influence on residents' lives. This study considers different combinations of land uses, or different type-based bundles. The first bundle involved all the commercial land uses, the second bundle involved the retail shops, which are the commercial land use without parking, workshops, and wholesale, and the third bundle included all the non-residential land uses, which are the commercial plus the civic buildings, such as mosques. Moreover, this indicator was applied to the two scales; 400-m radius, and 600-m radius. Thus, six independent variables were calculated for the land use diversity, which were labeled as LUDiv1S1, LUDiv1S2, LUDiv2S1, LUDiv2S2, LUDiv3S1, and LUDiv3S2, (**Table 1**). In terms of the commercial land use variable (LUDiv1S1), on a 400-m radius scale, the degree of diversity demonstrated a significant difference between the Al-Saymmar neighborhood (0.94) and the Al-Mugawlen and Al-Abassya neighborhoods, (0.7, and 0.79), respectively.

Moreover, the same variables, on a 600-m radius scale, had approximately a similar pattern of variance among the three case studies (0.94, 0.75, and 0.73), respectively. In terms of the variable for commercial land use without parking, workshops and wholesale (LUDiv2S1), on a 400-m radius scale the degree of diversity adequately differed between Al-Saymmar (0.98) and the other two neighborhoods, Al-Mugawlen and Al-Abassya, (0.67, and 0.68), respectively. Moreover, the same variable, on a 600-m radius scale, showed an approximately similar pattern of variance among the three case studies; Al-Saymmar brought about a 0.97 degree of variance, whereas, Al-Mugawlen and Al-Abassya each brought about 0.76 degree. In terms of the non-residential land use variable (LUDiv3S1), on scale 400-m radius, the degree of diversity adequately differed between Al-Saymmar (0.39) and the other two neighborhoods (0.56, and 0.52) for Al-Mugawlen and Al-Abassya, respectively. Moreover, the same variable, on a 600-m radius scale, had approximately shown a similar pattern of variance among the three case studies: Al-Saymmar brought about (0.45) degree of variance, whereas Al-Mugawlen and Al-Abassya each brought about (0.63) degree (**Table 2**). Thus, the land use diversity of Al-Saymmar, as measured by the six variables (**Table 2**), is significantly different from the other two case studies, namely the Al-Mugawlen and Al-Abassya neighborhoods.

### **4.3 Streets connectivity**

The QGIS software was used to compute the number of segments, length of each segment, and number of nodes, and these were transferred to an Excel sheet. Moreover, the streets' segments are represented as polyline between two adjacent nodes, or from a node to a dead-end street. The nodes are either X-intersection or T-intersection types. This procedure is conducted twice, on a 400-m radius scale and on scale 600-m radius. In this study, four indicators defined the connectivity, namely: intersections density, street intensity, link-node ratio, and external connectivity. Moreover, each indicator was applied to two scales, (400- and 600-m); however, the external connectivity was only applied to the 400-m radius scale because the 600-m radius scale did not define neighborhood boundaries, but instead the walking ranges. Thus, the total number of variables for this indicator is five namely, NodDnSi i = 1, 2, StDnSi, ExtConS1 (**Table 1**). Moreover, three equations were used to compute these indicators Eqs. (4)-(6).

The intensity of nodes (NodDnS1) on the 400-m radius scale in the Al-Saymmar neighborhood was (4.16), which is approximately double the number in for both Al-Mugawlen and Al-Abassya (2.89, and 2.03), respectively. Moreover, the node density (NodDnS2) on a 600-m radius scale showed a decline in the node intensity

**377**

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

per hectare, from Al-Saymmar (3.36) to Al-Mugawlen (2.33) to Al-Abassya (1.8), (**Table 2**). The intensity of street lengths (StDnS1) on a 400-m radius scale showed a significant reduction in total street lengths, from 387 m/ha for Al-Saymmar to 324 m/ha for Al-Mugawlen and 267 m/ha for Al-Abassya. However, the intensity of street lengths (StDnS2) on a 600-m radius scale was the highest in Al-Mugawlen 368 at m/ha, while Al-Saymmar was slightly lower at 346 m/ha, and Al-Abassya illustrated the lowest street density in terms of length at 250 m/ha (**Table 2**). The external connectivity (ExtConS1) on a 400-m radius scale demonstrated an adequate reduction in the number of entrances per mile length, while the Al-Saymmar neighborhood showed the highest score with 28.1 entrance/mile, and the Al-Mugawlen neighborhood showed a moderate score at 20 entrance/mile; meanwhile, the lowest score was in the Al-Abassya neighborhood at 16 entrance/

To apply the PCA indicator, the center of each case study is the center of the 400-m radius, as sampled in the cadastral maps. Every single block was considered a destination that needs to be accessed from the center of the neighborhood within 10 min of walk along the street network. The QGIS Road-Graph tool was utilized to measure the shortest network distance between two spatial points, which are the center of the case study and each individual block falls within the 400-m radius. After the adjustment of the human speed to 5 km/h, only the blocks within a 10-min walk were considered in determining the total accessible area in each neighborhood. Thus, the accessible blocks in ≤10 min were added up, and the resulting total accessible block area was represented as percentage area out of the total block area within a 400-m radius. Thus, only one independent variable was noted in applying this indicator, PCAS1 (**Table 1**). Therefore, the PCA variable illustrates that, in the

of accessible area in 10-min of network walking within the 400-m radius area; this is 61.67% of the total built-up area. In the Al-Mugawlen neighborhood, out of

network walking within a 400-m radius area; this is 73.18% of the total built-up

To apply the PRDR indicator, the center of each case study is the center of the 400-m radius, as sampled in the cadastral maps. The retailers are those that inhabitants want to access from the center of the neighborhood in 10 min of walk along street networks. In this regard, the shapefile maps were generated to create the blocks, blocks centroids, and streets networks on the 400-m radius and the 600-m scale. The QGIS Road-Graph tool was utilized to measure the shortest network distance between two spatial points, which are the center of the case study and each individual retailer within the 400-m radius and the 600-m radius. Moreover, because the indicator concerns how well the street network is connected between the destinations and residents' houses, this study designed an approach to test the PRDR for 16 destinations within each case study on each scale. The approach divided the circles of the two scales into 16 sectors then the intersection point of the radiuses with the circles (for the 400-m and the 600-m radiuses) are defined; from this, the nearest destination to those points are considered to compute the

of accessible area in a 10-min network walk within the 400-m radius

of built-up area, there was 219,635 m2

of accessible area in 10-min of

of built-up area, there was

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

**4.4 Pedestrian catchment area (PCA)**

Al-Saymmar neighborhood, out of 356,135 m2

of built-up area, there was 219,635 m2

area. In Al-Abassya neighborhood, out of 333,600 m2

area; this is 70.4% of the total built-up area (**Table 2**).

**4.5 Pedestrian route directness ratio (PRDR)**

mile (**Table 2**).

326,500 m2

2235,600 m2

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

per hectare, from Al-Saymmar (3.36) to Al-Mugawlen (2.33) to Al-Abassya (1.8), (**Table 2**). The intensity of street lengths (StDnS1) on a 400-m radius scale showed a significant reduction in total street lengths, from 387 m/ha for Al-Saymmar to 324 m/ha for Al-Mugawlen and 267 m/ha for Al-Abassya. However, the intensity of street lengths (StDnS2) on a 600-m radius scale was the highest in Al-Mugawlen 368 at m/ha, while Al-Saymmar was slightly lower at 346 m/ha, and Al-Abassya illustrated the lowest street density in terms of length at 250 m/ha (**Table 2**). The external connectivity (ExtConS1) on a 400-m radius scale demonstrated an adequate reduction in the number of entrances per mile length, while the Al-Saymmar neighborhood showed the highest score with 28.1 entrance/mile, and the Al-Mugawlen neighborhood showed a moderate score at 20 entrance/mile; meanwhile, the lowest score was in the Al-Abassya neighborhood at 16 entrance/ mile (**Table 2**).

#### **4.4 Pedestrian catchment area (PCA)**

*Sustainability in Urban Planning and Design*

neighborhoods, (0.7, and 0.79), respectively.

**4.3 Streets connectivity**

to compute these indicators Eqs. (4)-(6).

entered as a variable of the equation (X1, X2, …, Xi) in the MATLAB format. Additionally, because the land use categories are not unified across the three case studies, they could have different nature of influence on residents' lives. This study considers different combinations of land uses, or different type-based bundles. The first bundle involved all the commercial land uses, the second bundle involved the retail shops, which are the commercial land use without parking, workshops, and wholesale, and the third bundle included all the non-residential land uses, which are the commercial plus the civic buildings, such as mosques. Moreover, this indicator was applied to the two scales; 400-m radius, and 600-m radius. Thus, six independent variables were calculated for the land use diversity, which were labeled as LUDiv1S1, LUDiv1S2, LUDiv2S1, LUDiv2S2, LUDiv3S1, and LUDiv3S2, (**Table 1**). In terms of the commercial land use variable (LUDiv1S1), on a 400-m radius scale, the degree of diversity demonstrated a significant difference between the Al-Saymmar neighborhood (0.94) and the Al-Mugawlen and Al-Abassya

Moreover, the same variables, on a 600-m radius scale, had approximately a similar pattern of variance among the three case studies (0.94, 0.75, and 0.73), respectively. In terms of the variable for commercial land use without parking, workshops and wholesale (LUDiv2S1), on a 400-m radius scale the degree of diversity adequately differed between Al-Saymmar (0.98) and the other two neighborhoods, Al-Mugawlen and Al-Abassya, (0.67, and 0.68), respectively. Moreover, the same variable, on a 600-m radius scale, showed an approximately similar pattern of variance among the three case studies; Al-Saymmar brought about a 0.97 degree of variance, whereas, Al-Mugawlen and Al-Abassya each brought about 0.76 degree. In terms of the non-residential land use variable (LUDiv3S1), on scale 400-m radius, the degree of diversity adequately differed between Al-Saymmar (0.39) and the other two neighborhoods (0.56, and 0.52) for Al-Mugawlen and Al-Abassya, respectively. Moreover, the same variable, on a 600-m radius scale, had approximately shown a similar pattern of variance among the three case studies: Al-Saymmar brought about (0.45) degree of variance, whereas Al-Mugawlen and Al-Abassya each brought about (0.63) degree (**Table 2**). Thus, the land use diversity of Al-Saymmar, as measured by the six variables (**Table 2**), is significantly different from the other

two case studies, namely the Al-Mugawlen and Al-Abassya neighborhoods.

The QGIS software was used to compute the number of segments, length of each segment, and number of nodes, and these were transferred to an Excel sheet. Moreover, the streets' segments are represented as polyline between two adjacent nodes, or from a node to a dead-end street. The nodes are either X-intersection or T-intersection types. This procedure is conducted twice, on a 400-m radius scale and on scale 600-m radius. In this study, four indicators defined the connectivity, namely: intersections density, street intensity, link-node ratio, and external connectivity. Moreover, each indicator was applied to two scales, (400- and 600-m); however, the external connectivity was only applied to the 400-m radius scale because the 600-m radius scale did not define neighborhood boundaries, but instead the walking ranges. Thus, the total number of variables for this indicator is five namely, NodDnSi i = 1, 2, StDnSi, ExtConS1 (**Table 1**). Moreover, three equations were used

The intensity of nodes (NodDnS1) on the 400-m radius scale in the Al-Saymmar neighborhood was (4.16), which is approximately double the number in for both Al-Mugawlen and Al-Abassya (2.89, and 2.03), respectively. Moreover, the node density (NodDnS2) on a 600-m radius scale showed a decline in the node intensity

**376**

To apply the PCA indicator, the center of each case study is the center of the 400-m radius, as sampled in the cadastral maps. Every single block was considered a destination that needs to be accessed from the center of the neighborhood within 10 min of walk along the street network. The QGIS Road-Graph tool was utilized to measure the shortest network distance between two spatial points, which are the center of the case study and each individual block falls within the 400-m radius. After the adjustment of the human speed to 5 km/h, only the blocks within a 10-min walk were considered in determining the total accessible area in each neighborhood. Thus, the accessible blocks in ≤10 min were added up, and the resulting total accessible block area was represented as percentage area out of the total block area within a 400-m radius. Thus, only one independent variable was noted in applying this indicator, PCAS1 (**Table 1**). Therefore, the PCA variable illustrates that, in the Al-Saymmar neighborhood, out of 356,135 m2 of built-up area, there was 219,635 m2 of accessible area in 10-min of network walking within the 400-m radius area; this is 61.67% of the total built-up area. In the Al-Mugawlen neighborhood, out of 326,500 m2 of built-up area, there was 219,635 m2 of accessible area in 10-min of network walking within a 400-m radius area; this is 73.18% of the total built-up area. In Al-Abassya neighborhood, out of 333,600 m2 of built-up area, there was 2235,600 m2 of accessible area in a 10-min network walk within the 400-m radius area; this is 70.4% of the total built-up area (**Table 2**).

#### **4.5 Pedestrian route directness ratio (PRDR)**

To apply the PRDR indicator, the center of each case study is the center of the 400-m radius, as sampled in the cadastral maps. The retailers are those that inhabitants want to access from the center of the neighborhood in 10 min of walk along street networks. In this regard, the shapefile maps were generated to create the blocks, blocks centroids, and streets networks on the 400-m radius and the 600-m scale. The QGIS Road-Graph tool was utilized to measure the shortest network distance between two spatial points, which are the center of the case study and each individual retailer within the 400-m radius and the 600-m radius. Moreover, because the indicator concerns how well the street network is connected between the destinations and residents' houses, this study designed an approach to test the PRDR for 16 destinations within each case study on each scale. The approach divided the circles of the two scales into 16 sectors then the intersection point of the radiuses with the circles (for the 400-m and the 600-m radiuses) are defined; from this, the nearest destination to those points are considered to compute the

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**).