4.2. Ordinal Logistic Regression Analysis
The dependent variable of the ordinal logistic regression process is based on gate-count data in this study. The mean and the standard deviation values of totally 132 data elements were calculated as 189.16 and 228.97, respectively. Depending on these values, z
-scores were generated using Eq.1 for each element, and those that could be outliers were determined. Figure 3
shows the distribution range of the gate-count data. In this graph, three data elements with z
-score values outside the ±2.5 default threshold can be considered as potential outliers (shown with the blue rings). Attention should be paid to these elements in a classification process using the JNB algorithm.
According to the first JNB classification with 132 data elements, 106 data were assigned to the low, 25 data to the medium, and only one data to the high classes (Table 3
). The GVF value for this classification is calculated as 0.823. Assigning only one element to the high class and the GVF value lower than 0.850 led the authors to exclude the outliers in the first JNB classification. According to the second classification with 129 data elements, 75 data were assigned to low, 44 data to medium, and 10 data to high classes (Table 3
). The GVF value for this classification is 0.870. The more successful distribution of the data to the classes and the GVF value higher than 0.850 showed that this procedure was applicable. Besides, the three outliers were added to the high class and the number of data of this class was 13.
Descriptive information about the dependent and independent variables used in this study is presented in Table 4
. A total of 16 different models were created. The dependent variable pedestrian density and the independent variable periods were used in each model. The independent variable measures were included in each model one by one.
The significance of the models created in this study was examined by the model fitting and parallel lines tests (Table 5
). The goodness of fit and pseudo R2
tests were not evaluated because the data contained a large number of empty cells and sorting the models was unnecessary. All of the 16 models provided the assumption of parallel lines because of p
> 0.05. When the fit values explaining the significance of the model were evaluated, the four models highlighted in gray in Table 5
were found statistically significant because of p
The effects of independent variables (periods and measures) on the dependent variable (pedestrian density) were analysed with parameter estimation statistics for the four significant models identified above. Parameter estimation uses a table of statistics to explain the direction, strength, and statistical significance of the relationships between variables.
gives the model results of the integration measure obtained from AA. Since the authors demanded to analyze the variables that increased the pedestrian density, they selected the high pedestrian density group as a reference in this study. This preference causes ß
-value of the independent variables that increase the pedestrian density to be positive. The p
-value lower than 0.05 for
notifies that integration has a significant effect on the established model. According to periods variable, T2 was statistically significant (p
< 0.05); T1 and T3 were not significant (p
> 0.05), when T4 was selected as a reference. The non-significant effects for T1 and T3 explain that the pedestrian density in these periods was not significantly different from T4. ß
is a coefficient that expresses how the effect of variables on the dependent variable proportionally. A variable with a high ß
-value has more effect in the model than others. In this study, since
has greater ß
-value than the others, integration was accepted as the most significant variable in explaining the pedestrian density. On the other hand, the ß
-values of the periods are not statistically as effective as the integration in explaining the pedestrian density. eβ
-values are used to interpret the effects of variables like ß
-value takes values less than one when ß
-value is negative and greater than one when it is positive. A high ß
coefficient for a variable indicates that its effect on the dependent variable is considerable. Briefly, AA integration in this model was more effective than the periods in explaining the pedestrian density.
gives the model results of the mean depth measure obtained from AA. The p
-value lower than 0.05 for
notifies that mean depth has a significant effect. The results for periods variable are similar to the model established with
is an inverse of
, its ß
-value is negative, and therefore its eβ
-value is lower than one. The ß
-value, and w
were lower than the values of
has. These statistical results show that
does not reflect pedestrian density as successful as
gives the model results of the choice measure obtained from AA. The p
-value higher than 0.05 for
notifies that choice does not have a significant effect on pedestrian density. Therefore the authors could not evaluate ß
-value, and w
-value for choice. The results for periods variable are similar to the models established with
gives the model results of the control measure obtained from VGA. The p
-value higher than 0.05 for
notifies that control does not have a significant effect on pedestrian density. Therefore the authors could not evaluate ß
-value, and w
-value for control. The results for periods variable are similar to the models established with
The four tables prepared for parameter estimations of ordinal logistic regression analyses verify that integration was the most effective measure to predict the pedestrian density in this study area. Since the p-values of T1 and T3 groups, which were among the periods used in all models, were higher than 0.05, the counts made in these periods were not significantly different from the reference T4 in determining the pedestrian density. The effect of the T2 group, which was statistically significant in each model, was negative compared to the reference T4 group. The significant effect for T2 implies that the pedestrian volume at T2 was significantly lower than at the reference T4. If it were aimed to determine the low pedestrian density of the study area, it would be appropriate to make the counts in the period corresponding to the T2 group.
4.3. Evaluation of Current and Master Plan Datasets
a,b show the axial maps of Davutpaşa Campus created according to normalized AA integration values, respectively. Classes (low: green, medium: blue, and high: pink) representing integration values with colours were designed using the JNB classification algorithm. Standard class limits were used in both maps to facilitate visual comparison.
The new axial lines that were planned to be connected to the axes categorized in high class in the current axial map were generally grouped in high class. These axial lines often appeared in the central part of the campus in the master plan. However, other new axes planned to be added around the campus boundaries were in the medium and low classes. They increased overall accessibility by helping spread the centrality to the edges as well.
shows the paired sample t
-test results for the current and master plan datasets. There are 96 axial lines in the current dataset, and their mean integration value is 0.388. According to the master plan, when 41 new walkways were added to the campus area, the mean integration value decreased to 0.356. Paired sample t
-test was performed for 96 common walkways in both datasets. The mean integration value of these 96 paths in the master plan was calculated as 0.370. These differences between the mean values showed that new paths to be added would reduce the mean value of the current ones. Besides, the mean values of the new paths were lower than the current ones. The p
-value of 0.002 indicated that the changes in the means mentioned above were statistically significant. The value proved that the master plan would change the campus morphology for pedestrians.