**4.1 TOPSIS analysis**

**Table 4** presents the positive ideal, negative ideal, closeness coefficient, and TOPSIS rank for each of the cities being analyzed. According to the closeness coefficients, the ranking order for the cities is as follows:

1. Benghazi


6.Al Qubbah

The ranking results were then used to generate a map for final decision-making (**Figure 6**). Cities with green and light green colors are suggested to be prioritized first for development. More details about the TOPSIS analysis can be found in the Appendices.

Based on the results, the importance of each group of factors was evaluated (**Figure 7**). The bar chart shows the importance of the standardized factor weights

**Figure 6.**

**Figure 7.**

**253**

*Importance degree of factor groups.*

**Table 4.**

*The map showing the cities' ranks based on TOPSIS.*

*Ranking of cities on the basis of importance for urban development.*

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

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

**City A+ A Closeness coefficient TOPSIS rank** Darnah 2.58 1.41 0.35 4 Al Jabal Al Akhdar 2.05 1.74 0.46 2 Benghazi 0.98 3.40 0.77 1 Al Marj 2.23 1.57 0.41 3 Al Qubbah 3.13 1.08 0.25 6 Al Hizam Al Akhdar 2.69 1.29 0.32 5

**Figure 5.** *FO after refinement with SVM.*


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

#### **Table 4.**

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

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

**Table 4** presents the positive ideal, negative ideal, closeness coefficient, and TOPSIS rank for each of the cities being analyzed. According to the closeness

The ranking results were then used to generate a map for final decision-making (**Figure 6**). Cities with green and light green colors are suggested to be prioritized first for development. More details about the TOPSIS analysis can be found in the

Based on the results, the importance of each group of factors was evaluated (**Figure 7**). The bar chart shows the importance of the standardized factor weights

coefficients, the ranking order for the cities is as follows:

the selection process.

*Sustainability in Urban Planning and Design*

**4.1 TOPSIS analysis**

1. Benghazi

3. Al Marj

4.Darnah

6.Al Qubbah

Appendices.

**Figure 5.**

**252**

*FO after refinement with SVM.*

2. Al Jabal Al Akhdar

5. Al Hizam Al Akhdar

method.

*Ranking of cities on the basis of importance for urban development.*

**Figure 6.** *The map showing the cities' ranks based on TOPSIS.*

**Figure 7.** *Importance degree of factor groups.*

in each group. Demography and vegetation are the two most influential factors with positive contribution, followed by vegetation and topography. The other factors have negative contribution.

Consequently, the least feasible areas were classified out of 0.001 to 0.40. The categorized suitability map was then validated based on the same randomly selected samples (**Table 5**). The SVM model accurately classified 1178 samples, which is about 78.5% of the total samples tested, which produced kappa index of o.67. The

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

*<sup>K</sup>* <sup>¼</sup> *PC* � *Pexp* 1 � *Pexp*

where *Pc* is the proportion of number of pixels that are correctly classified and

An automated geospatial solution for selecting and ranking cities in Libya for urban development is proposed in this chapter. The suitability map showed that most areas indicated to be suitable are in the northern part of Libya. The results indicate that land use, distance to primary route, distance to large city, rainfall, NPP, and NO2 have negative effects on the level of suitability for the selection process, whereas the other factors have positive effects with population density taking the lead. It is revealed that SVM model accurately classifies 1178 samples, about 78.5% of the total samples tested which produced kappa statistic of 0.67. The high-priority city was selected as Benghazi that is followed by Al Jabal Al Akhdar. The results suggest that demography and vegetation are the two most influential factors contributing to the selection of city for development in Libya. This study is limited to analysis of six cities; the procedure developed through this study can be extended to other cities. It is of the opinion that evaluated criteria can be adjusted according to the environment and the current development

classes are important for the final mapping and ranking of the cities, we also evaluated the results using the area under the ROC curve (**Table 6**), which gives an indication of the global statistical accuracy of the models. If the AUC, which

varies from 0.5 to 1, increases toward 1, it indicates better performance prediction [45]. The highest and lowest success rates are 0.939 and 0.793, while the highest and lowest prediction rates are 0.884 and 0.673, respectively

ð Þ *totalnumberoftrainingpixels* <sup>p</sup> *:* Since both the "not suitable" and "highly suitable"

*totalnumberofpixels*. *Pexp* is the expected agreements and is calculated

(11)

kappa index is calculated using Eq. (11) [44]:

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

is calculated as ð Þ *TP*þ*TN*

(**Figure 8**).

**5. Conclusion**

of the cities.

**255**

as ð Þ *TP*þ*FN* ð Þþ *TP*þ*FP* ð Þ *FP*þ*TN* ð Þ *FN*þ*TN* ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

## **4.2 Accuracy assessment**

Recall that the refined suitability map was recategorized into the three classes of "not suitable," "less suitable," and "highly suitable," These classes reflect the degree of urban development suitability in the study area. The categorized suitability map can be validated accurately through each class. The continuous refined is suitability map ranging from 0 to 1. The most suitable areas that range from 0.751 to 1 fall into high suitable class, while the moderate suitable areas for urban development were extracted from 0.401 to 0.751 from the continuous refined suitability map.


**Table 5.**

*Overall accuracy assessment of SVM modeling.*


#### **Table 6.**

*Accuracy assessment of SVM modeling based on ROC.*

**Figure 8.** *AUC for the SVM.*

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

Consequently, the least feasible areas were classified out of 0.001 to 0.40. The categorized suitability map was then validated based on the same randomly selected samples (**Table 5**). The SVM model accurately classified 1178 samples, which is about 78.5% of the total samples tested, which produced kappa index of o.67. The kappa index is calculated using Eq. (11) [44]:

$$K = \frac{P\_C - P\_{exp}}{1 - P\_{exp}} \tag{11}$$

where *Pc* is the proportion of number of pixels that are correctly classified and is calculated as ð Þ *TP*þ*TN totalnumberofpixels*. *Pexp* is the expected agreements and is calculated as ð Þ *TP*þ*FN* ð Þþ *TP*þ*FP* ð Þ *FP*þ*TN* ð Þ *FN*þ*TN* ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð Þ *totalnumberoftrainingpixels* <sup>p</sup> *:* Since both the "not suitable" and "highly suitable" classes are important for the final mapping and ranking of the cities, we also evaluated the results using the area under the ROC curve (**Table 6**), which gives an indication of the global statistical accuracy of the models. If the AUC, which varies from 0.5 to 1, increases toward 1, it indicates better performance prediction [45]. The highest and lowest success rates are 0.939 and 0.793, while the highest and lowest prediction rates are 0.884 and 0.673, respectively (**Figure 8**).
