Comparing self-navigation and video mode in a choice experiment to measure public space preferences. (July 2022)

• Record Type: Journal Article
• Authors: Zhao, Yuwen; van den Berg, Pauline E.W.; Ossokina, Ioulia V.; Arentze, Theo A.
• Is Part Of: Computers, environment and urban systems. Volume 95(2022)
• Publication Date: 2022-07
• Subjects: 3D dynamic visualization -- Stated choice experiment -- Neighborhood public spaces -- Virtual environment -- Self-navigation; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2022.101828
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: 3D dynamic visualization technologies are increasingly used in studying residents' preferences for urban planning and design scenarios. The techniques help concentrate respondent attention and improve the measurement quality of environmental preferences. However, little is known about differences in measurement quality between different 3D dynamic visualization modes. This paper applies two modes – a self-navigation mode and a video mode - in a virtual environment-based stated choice experiment with the aim of measuring neighborhood public spaces preferences. Based on data from 276 experiment participants and applying conditional and mixed logit techniques, we find no statistically significant differences in the model fit between the two modes. Out of six public space attributes, only one shows statistically significant differences in valuation between modes, namely 'vertical green'. Our results suggest that the choice between video and self-navigation modes can be based on other (secondary) considerations, i.e., required sample size, respondents' experiences with navigation interfaces, the specific goals of the study or application in practice, and the costs and effort needed to conduct the experiment. Highlights: We compare a self-navigation mode and video's of a virtual environment in a choice experiment on public space valuation. No significantly difference in models' error variance was found between the two 3D dynamic visualization modes. In the self-navigation mode vertical green is valued higher than in the video mode. The video mode shows a higher level of enjoyment and authenticity than the self-navigation mode. The video mode also benefits from a lower level of dizziness and a shorter time to complete.