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Open AccessArticle

Evaluation of the Space Syntax Measures Affecting Pedestrian Density through Ordinal Logistic Regression Analysis

1
Department of Geomatic Engineering, Yildiz Technical University, 34220 Istanbul, Turkey
2
Department of Geomatics Engineering, Istanbul Technical University, 34469 Istanbul, Turkey
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(10), 589; https://doi.org/10.3390/ijgi9100589
Received: 15 September 2020 / Revised: 29 September 2020 / Accepted: 4 October 2020 / Published: 7 October 2020
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
This paper examines the relationship between pedestrian density and space syntax measures in a university campus using ordinal logistic regression analysis. The pedestrian density assumed as the dependent variable of regression analysis was categorised in low, medium, and high classes by using Jenks natural break classification. The data elements of groups were derived from pedestrian counts performed in 22 gates 132 times. The counting period grouped in nominal categories was assumed as an independent variable. Another independent was one of the 15 derived measures of axial analysis and visual graphic analysis. The statistically significant model results indicated that the integration of axial analysis was the most reasonable measure that explained the pedestrian density. Then, the changes in integration values of current and master plan datasets were analysed using paired sample t-test. The calculated p-value of t-test proved that the master plan would change the campus morphology for pedestrians. View Full-Text
Keywords: space syntax; pedestrian density; axial analysis; visual graph analysis; integration space syntax; pedestrian density; axial analysis; visual graph analysis; integration
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MDPI and ACS Style

Hacar, Ö.Ö.; Gülgen, F.; Bilgi, S. Evaluation of the Space Syntax Measures Affecting Pedestrian Density through Ordinal Logistic Regression Analysis. ISPRS Int. J. Geo-Inf. 2020, 9, 589.

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