Estimation of Effective Spatial Variables When Visiting Public Squares through Factor Analysis Model

DİNÇ G., GÜL A.

JOURNAL OF URBAN PLANNING AND DEVELOPMENT, vol.148, no.3, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 148 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.1061/(asce)up.1943-5444.0000844
  • Journal Name: JOURNAL OF URBAN PLANNING AND DEVELOPMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, ICONDA Bibliographic, INSPEC, Metadex, Political Science Complete, Pollution Abstracts, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Public square, Spatial variables, Estimation of effective features, Factor analysis, BUILT ENVIRONMENT, QUALITY, SPACES, DESIGN
  • Süleyman Demirel University Affiliated: Yes

Abstract

In this paper, a new strategy based on the use of factor analysis (FA) with the varimax rotation technique was proposed to assess the effects of spatial variables on the visitation of nine typical public squares. These public squares are Times, Duomo, Oldtown, Red, Taksim, St. Peter's, Grand Place, Trafalgar, and Rynek Glowny. This study aimed to measure the effect of the built environment on human choices and offer recommendations for more successful planning and design studies in cities. Data of the spatial variables were collected and processed by using a factor analysis model to extract the ranking of the effective spatial variables on the people's choices who visit public squares. By applying the FA model, when considering the rotated loading factors, it was observed that the first factor accounting for 81.00% of the total variance would be sufficient to reveal the effectiveness order of spatial variables on the relevant public square's visit. This study showed that the FA model could provide an opportunity to identify the most important variable affecting the human preference (or people choice) of a square and to create better planning and design ideas for squares. The results obtained from the FA application to spatial variables proved that the amount of green area within the square and the amount of green area surrounding the square are the most important variables affecting the people's choice of a square.