**3. Modeling process**

This section describes the modeling process, specifically the application of TOPSIS for scheduling and prioritizing the cities for urban development (**Figure 3**). First, a medium-resolution Landsat-8 satellite image from the study area was acquired and preprocessed. Then, the image was segmented using a multiresolution segmentation algorithm and classified into several classes using object-based image classification. The multiresolution algorithm has three main parameters, namely, scale, shape, and compactness. Since these parameters are data and application dependent, in this study, we had to select them empirically via trial and error. This meant that the best values were determined via visual examination of the segmentation results. After the segmentation process, several attributes were selected and used as class predictors in the classification algorithm. From the spectral attributes, the five bands of the Landsat-8 image were selected. For spatial attributes, shape index, roundness, compactness, and density were used [29, 30]. In the classification step, the support vector

**Figure 3.** *Flowchart of the methodology implemented in this study.*

*Urban Planning Using a Geospatial Approach: A Case Study of Libya DOI: http://dx.doi.org/10.5772/intechopen.86355*

machine (SVM) algorithm was used. Although the SVM is a relatively simple binary classifier, it has very good generalization capabilities if properly trained [31, 32].

Several other digital data such as DEM and population density were also obtained from various online sources. The factors used as described in the previous section are widely reported in the literature for selecting urban projects or relevant projects. Fuzzy overlay (FO) analysis was carried out to determine the importance of each parameter to achieving the goal of the study. The SVM classifier was further applied to refine the results obtained from the FO model. Finally, the cities were sorted according to their importance by applying the TOPSIS model on the results of the SVM.

#### **3.1 Fuzzy overlay and TOPSIS models**

*2.3.5 Air quality*

MODIS Conversion Tool Kit.

*Sustainability in Urban Planning and Design*

**3. Modeling process**

**Figure 3.**

**248**

*Flowchart of the methodology implemented in this study.*

Air quality directly affects the environment and consequently people's health. In

This section describes the modeling process, specifically the application of TOPSIS for scheduling and prioritizing the cities for urban development (**Figure 3**). First, a medium-resolution Landsat-8 satellite image from the study area was acquired and preprocessed. Then, the image was segmented using a multiresolution segmentation algorithm and classified into several classes using object-based image classification. The multiresolution algorithm has three main parameters, namely, scale, shape, and compactness. Since these parameters are data and application dependent, in this study, we had to select them empirically via trial and error. This meant that the best values were determined via visual examination of the segmentation results. After the segmentation process, several attributes were selected and used as class predictors in the classification algorithm. From the spectral attributes, the five bands of the Landsat-8 image were selected. For spatial attributes, shape index, roundness, compactness, and density were used [29, 30]. In the classification step, the support vector

this work, we have considered the CO and NO2 (**Figure 2n** and **o**) air quality indicators. In 2016, higher CO and NO2 levels were measured in the southern part of Benghazi. Benghazi city also recorded high levels of these gasses for the year under investigation. The air quality data was extracted from the MODIS source with a resolution of 0.25°. We utilized the ENVI 5.3 software to process the MODIS imagery. However, in order to prepare unprocessed MODIS satellite images for analysis, they must firstly be converted into ENVI format. This was done using the

> Fuzzy overlay analysis is based on the fuzzy set theory that relies on membership relationship of events to define specific sets or classes [33]. Operationally, FO is similar to overlay analysis but differs in the reclassified values and results from the combination of multiple criteria. It involves problem definition, partitioning into sub-models and determining the significant layers. FO transforms the data to a common scale and defines the likelihood of the data belonging to a specific class, for example, slope values being transformed into the probability of fitting into the favorable suitability set based on a scale of 0 to 1, expressed in terms of membership [34]. Input raster are not weighted in FO since the transformed values indicate the possibility of membership rather than using ratio scale as with weighted overlay and weighted sum. The equation using fuzzy Gaussian function can be given as [35]

$$\mu(\mathbf{x}) = \mathbf{e}^{-f\_i^{\*\left(\mathbf{x}-f\_2\right)^2}} \tag{2}$$

The inputs *fi* and *f* <sup>2</sup> are the spread and the midpoint, respectively. Midpoint can be a user-defined value with a fuzzy membership of 1. The default is the midpoint of the range of values of the input raster. Spread defines the membership of the Gaussian function. It generally ranges from 0.01 to 1. Increasing the spread causes the fuzzy membership curve to become steeper. Fuzzy overlay analysis quantifies the possibilities of each cell or location to a specified set based on membership value.

As previously mentioned, the results of FO are refined using the SVM, which develops a linear regression between suitability status and criteria factors. SVM aims to determine an optimal separating hyperplane (maximizing the margin width) between two classes in feature space [36]. The training points near the hyperplane are called support vectors and are utilized for classification once the decision line/surface is obtained. The separating hyperplane is found as follows:

$$y\_i(w \times x\_i + b) \ge \mathbf{1} - \varepsilon\_i \tag{3}$$

where *w* is the cofficienct vector that defines the hyperplane orientation in the feature space, *b* is the offset of the hyperplane from the origin, and *ε<sup>i</sup>* is the positive slack variables. The optimal hyperplane is found by solving the following optimization problem [36, 37]:

$$\begin{aligned} \text{Minimize} & \sum\_{i=1}^{n} \alpha\_i - \frac{1}{2} \sum\_{i=1}^{n} \sum\_{j=1}^{n} \alpha\_i \alpha\_j y\_j y\_j (\mathbf{x}\_i \mathbf{x}\_j) \\ \text{subject} & \sum\_{i=1}^{n} \alpha\_i y\_i = \mathbf{0}, \ \mathbf{0} \le \alpha\_i \le \mathbf{C} \end{aligned} \tag{4}$$

where *α<sup>i</sup>* is the Lagrange multiplier and *C* is the penalty for data classification. The following decision function is applied as follows:

$$\mathbf{g}(\mathbf{x}) = \text{sign}\tag{5}$$

quality. Therefore, these factors should be thoroughly analyzed to discover the most (and the least) suitable area for urbanization. Hence, in this work, 17 detailed factors were analyzed in order to rank each's importance (via weight assignment) for the selection of a city (or cities) for sustainable urban development. Subsequently, a suitability map was generated based on the FO (**Figure 4a**) method. A continuous scale was used for suitability weightage, which ranges from 0 (less suitable) to 1 (highly suitable). From the generated map, areas indicated as most suitable are located in the northern parts, especially the areas surrounding

*Urban Planning Using a Geospatial Approach: A Case Study of Libya*

*DOI: http://dx.doi.org/10.5772/intechopen.86355*

Benghazi and the northern parts of Al Marj and Al Jabal Al Akhdar. Sole reliance on the generated map, however, does not help much in deciding city development prioritization. As a result, the map was further refined to make it much more distinct for decision-makers. To do this, the map was firstly reclassified into three categorical classes: (i) not suitable, (ii) less suitable, and (iii) highly suitable. This was done using the natural break classification method (**Figure 4b**) where several samples were selected from the not suitable and highly suitable areas (results of FO) to generate training and testing data. These datasets were then used to train a SVM to classify between the two classes. **Table 3** presents the estimated factors and their coefficients. The result indicates that land use, distance to primary route,

**Evaluation criteria SVM weight Criteria code** Land use 0.36 C12 Percent built-up area 0.97 C1 NDVI 2.15 C10 Altitude 0.60 C15 Slope 0.96 C7 Distance to primary route 2.35 C4 Distance to secondary route 0.74 C6 Distance to trail lines 0.27 C8 Distance to capital city 0.62 C13 Population density 4.88 C3 Rainfall 1.17 C5 LST 0.23 C11 NPP 0.10 C2 Carbon monoxide 1.23 C14 Nitrogen dioxide 1.18 C9

**Figure 4.**

**Table 3.**

**251**

*List of criteria, estimated coefficient, and their code.*

*Results of fuzzy overlay in (a) continuous scale and (b) categorical format.*

Developed in [38–40], TOPSIS is a multi-criteria decision tool based on the intuition that a selected alternative has the shortest possible geometric distance from the PIS. In other words, the alternative has the longest geometric distance from the NIS [41]. The analysis compares a set of alternatives by assigning weightage to each criterion to compute the geometric distance between possible alternatives to determine the ideal alternative based on the assumption that the criteria uniformly increases or decreases. TOPSIS allows trade-offs between criteria; a poor result in one criterion can be compensated by a good result in another criterion. TOPSIS provides a more realistic model than non-compensatory methods by including or excluding alternative solutions using hard cutoffs. Consider *Xij* as the inputs for matrix of priorities where there are *i* ¼ 1*,* …*, m* alternatives and *j* ¼ 1*,* …*, n* criteria. There are six steps associated with the implementation of TOPSIS as follows [42]:

Step 1: Construct the normalized decision matrix calculated using Eq. (6):

$$r\_{ij} = \frac{\varkappa\_{ij}}{\sqrt{\sum\_{i=1}^{m} \sum\_{j=1}^{n} \varkappa\_{ij}^2}} \tag{6}$$

Step 2: Construct the weighted normalized decision matrix using Eq. (7):

$$w\_{i\bar{j}} = w\_i r\_{i\bar{j}}, i = 1, \dots, m\bar{j} = 1, \dots, n \tag{7}$$

Step 3: The positive and negative ideal solutions are determined by

$$\begin{aligned} A^{+} &= \{v\_1^{+}, \dots, v\_n^{+}\}, \text{where } v\_j^{+} = \{\max(v\_{\vec{\eta}}) \nexists j \in J; \min(v\_{\vec{\eta}}) \textit{if } j \in J'\} \\ A^{-} &= \{v\_1^{-}, \dots, v\_n^{-}\}, \text{where } v\_j^{\*} = \{\min(v\_{\vec{\eta}}) \textit{if } j \in J; \min(v\_{\vec{\eta}}) \textit{if } j \in J'\} \end{aligned} \tag{8}$$

Step 4: Calculation of separation (positive and negative) measurement using Euclidean distance. Eq. (9) is used to calculate the distance.

$$\mathbf{S}\_{i}^{+} = \sqrt{\sum\_{j=1}^{n} \left(\boldsymbol{v}\_{j}^{+} - \boldsymbol{v}\_{\vec{\eta}}\right)^{2}}, \mathbf{S}\_{i}^{-} = \sqrt{\sum\_{j=1}^{n} \left(\boldsymbol{v}\_{j}^{-} - \boldsymbol{v}\_{\vec{\eta}}\right)^{2}}, \ i = \mathbf{1}, \ldots, m \tag{9}$$

Step 5: Closeness to the ideal solution is calculated using Eq. (10):

$$\mathbf{C}\_{i}^{+} = \frac{\mathbf{S}\_{i}^{-}}{\mathbf{S}\_{i}^{-} + \mathbf{S}\_{i}^{+}}, \mathbf{0} < \mathbf{C}\_{I}^{+} < \mathbf{1}, i = \mathbf{1}, \dots, m \tag{10}$$

Step 6: Ranking alternatives based on closeness to the ideal solution. TOPSIS has been used in different circumstances (e.g., individual and grouping). By applying the TOPSIS model using the results of the FO as input, the cities were sorted according to their importance for proposed urban development projects.

#### **4. Results and discussion**

According to [43], the most contributing factors to urban suitability are topography, land use and infrastructure, vegetation, demography and climate, and air

## *Urban Planning Using a Geospatial Approach: A Case Study of Libya DOI: http://dx.doi.org/10.5772/intechopen.86355*

quality. Therefore, these factors should be thoroughly analyzed to discover the most (and the least) suitable area for urbanization. Hence, in this work, 17 detailed factors were analyzed in order to rank each's importance (via weight assignment) for the selection of a city (or cities) for sustainable urban development. Subsequently, a suitability map was generated based on the FO (**Figure 4a**) method. A continuous scale was used for suitability weightage, which ranges from 0 (less suitable) to 1 (highly suitable). From the generated map, areas indicated as most suitable are located in the northern parts, especially the areas surrounding Benghazi and the northern parts of Al Marj and Al Jabal Al Akhdar. Sole reliance on the generated map, however, does not help much in deciding city development prioritization. As a result, the map was further refined to make it much more distinct for decision-makers. To do this, the map was firstly reclassified into three categorical classes: (i) not suitable, (ii) less suitable, and (iii) highly suitable. This was done using the natural break classification method (**Figure 4b**) where several samples were selected from the not suitable and highly suitable areas (results of FO) to generate training and testing data. These datasets were then used to train a SVM to classify between the two classes. **Table 3** presents the estimated factors and their coefficients. The result indicates that land use, distance to primary route,

#### **Figure 4.**

where *α<sup>i</sup>* is the Lagrange multiplier and *C* is the penalty for data classification.

Developed in [38–40], TOPSIS is a multi-criteria decision tool based on the intuition that a selected alternative has the shortest possible geometric distance from the PIS. In other words, the alternative has the longest geometric distance from the NIS [41]. The analysis compares a set of alternatives by assigning weightage to each criterion to compute the geometric distance between possible alternatives to determine the ideal alternative based on the assumption that the criteria uniformly increases or decreases. TOPSIS allows trade-offs between criteria; a poor result in one criterion can be compensated by a good result in another criterion. TOPSIS provides a more realistic model than non-compensatory methods by including or excluding alternative solutions using hard cutoffs. Consider *Xij* as the inputs for matrix of priorities where there are *i* ¼ 1*,* …*, m* alternatives and *j* ¼ 1*,* …*, n* criteria. There are six steps associated with the implementation of

Step 1: Construct the normalized decision matrix calculated using Eq. (6):

Step 2: Construct the weighted normalized decision matrix using Eq. (7):

*<sup>j</sup>* ¼ max *vij*

*<sup>j</sup>* ¼ min *vij*

Step 4: Calculation of separation (positive and negative) measurement using

r

*,* 0 <*C*<sup>þ</sup>

Step 6: Ranking alternatives based on closeness to the ideal solution. TOPSIS has been used in different circumstances (e.g., individual and grouping). By applying the TOPSIS model using the results of the FO as input, the cities were sorted according to their importance for proposed urban development projects.

According to [43], the most contributing factors to urban suitability are topography, land use and infrastructure, vegetation, demography and climate, and air

∑*<sup>n</sup> <sup>j</sup>*¼<sup>1</sup> *<sup>v</sup>*�

Step 3: The positive and negative ideal solutions are determined by

*, S*� *<sup>i</sup>* ¼

Step 5: Closeness to the ideal solution is calculated using Eq. (10):

Euclidean distance. Eq. (9) is used to calculate the distance.

*<sup>j</sup>* � *vij* � �<sup>2</sup>

> *<sup>i</sup>* <sup>¼</sup> *<sup>S</sup>*� *i S*� *<sup>i</sup>* þ *S*<sup>þ</sup> *i*

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

*C*<sup>þ</sup>

*rij* <sup>¼</sup> *xij* ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ∑*<sup>m</sup> <sup>i</sup>*¼<sup>1</sup>∑*<sup>n</sup> <sup>j</sup>*¼<sup>1</sup>*x*<sup>2</sup> *ij*

*g x*ð Þ¼ *sign* (5)

<sup>q</sup> (6)

� �*if j*∈*J*

� �*if j*∈ *J* <sup>0</sup> � � (8)

*i* ¼ 1*,* …*, m* (9)

*vij* ¼ *wirij, i* ¼ 1*,* …*, mj* ¼ 1*,* …*, n* (7)

� �*if j*∈ *J*; min *vij*

� �*if j*∈*J*; min *vij*

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

*<sup>j</sup>* � *vij* � �<sup>2</sup>

*,*

*<sup>I</sup>* < 1*, i* ¼ 1*,* …*, m* (10)

<sup>0</sup> � �

The following decision function is applied as follows:

*Sustainability in Urban Planning and Design*

TOPSIS as follows [42]:

*A*<sup>þ</sup> ¼ *v*<sup>þ</sup>

*A*� ¼ *v*�

*S*<sup>þ</sup> *<sup>i</sup>* ¼

**4. Results and discussion**

**250**

<sup>1</sup> *;* …*; v*<sup>þ</sup> *n* � �*,* where *v*<sup>þ</sup>

<sup>1</sup> *;* …*; v*� *n* � �*,* where *v* <sup>∗</sup>

> ∑*<sup>n</sup> <sup>j</sup>*¼<sup>1</sup> *<sup>v</sup>*<sup>þ</sup>

r

*Results of fuzzy overlay in (a) continuous scale and (b) categorical format.*


#### **Table 3.**

*List of criteria, estimated coefficient, and their code.*

distance to capital city, rainfall, NPP, and NO2 have negative effects on the suitability level of the selection process. The remaining factors have positive effects. Among the positive factors, population density has the highest effects on the selection process.

Based on the estimated coefficients, the suitability map in **Figure 5** was produced. It can be seen that the map reflects the same thing as in the previous suitability map. However, it is clearly more informative for decision-makers. Based on this, the cities were ranked according to their importance using TOPSIS method.
