The impact of spatial scale on layout learning and individual evacuation behavior in indoor fires: single-scale learning perspectives

Abstract

The detail and representation of a spatial layout varies with scale. This affects an individual’s learning effectiveness and understanding, in turn directly influencing their behavior in a fire evacuation. However, the impact of layout learning methods with different spatial scales on fire evacuation behavior, and the relationship between spatial cognition and evacuation effects, remains unclear. We conducted spatial layout learning across three scales with 81 participants and simulated a fire evacuation scenario in a mobile virtual reality for groups. We collected evacuation decision-making and user experience questionnaires as supplementary data. The results demonstrate that small-scale learning objects are the easiest for participants to understand in terms of spatial layout and relationships, but their performance in fire evacuation is poor. Large-scale learning objects significantly improve participants’ evacuation efficiency. Spatial layout learning plays a crucial role in fire evacuation outcomes, but traditional spatial knowledge acquisition measurement methods cannot predict fire evacuation performance. This study sheds light on how spatial cognition influences fire evacuation behavior and provides a more reliable fire evacuation simulation method based on mobile virtual reality (MVR).

Keywords:

• Multiscale spatial learning
• mobile virtual reality
• visual attention
• evacuation behavior
• wayfinding

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available in figshare.com with the identifier https://doi.org/10.6084/m9.figshare.21779741.v2 . The experiment was reviewed and approved by the university’s institutional review board (Approval No. SWJTU-2301- NSFC (098)). All participants provided written consent.

Additional information

Funding

This study were funded National Natural Science Foundation of China; National Natural Science Foundation of China; National Natural Science Foundation of China; National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program.

Notes on contributors

Jun Zhu

Jun Zhu earned his M.S. in geodesy and survey engineering from Southwest Jiaotong University in 2003 and a Ph.D. in cartography and GIS from the Chinese Academy of Sciences in 2006. He’s now a Professor at Southwest Jiaotong University’s Faculty of Geosciences and Environmental Engineering, specializing in computer vision, 3-D GIS, and virtual geographic environments. He provided a crucial dataset for this research and offered valuable suggestions on data analysis methods, ensuring the accuracy and completeness of the study.

Pei Dang

Pei Dang obtained his B.S. in GIS from Southwest Jiaotong University in 2016 and an M.S. in survey engineering in 2022 from the same institution. He’s currently a Ph.D. candidate there, researching virtual geographic environments, spatial cognition, and GeoAI. He played a key role in the literature review section, helping to determine the direction and framework of the research, and provided several important references.

Jinbin Zhang

Jinbin Zhang obtained his B.S. in GIS from Southwest Jiaotong University in 2022. He is currently a doctoral student at Southwest Jiaotong University, researching geographic knowledge graphs and 3D GIS. He played a key role in the literature review section, helping to determine the direction and framework of the research, and provided several important references.

Yungang Cao

Yungang Cao obtained his Ph.D. in cartography and geographic information systems from the Chinese Academy of Sciences in 2006. He is currently a professor at Southwest Jiaotong University. His research includes 3D reconstruction and environmental remote sensing. He provided technical support for this research, especially in software and programming, ensuring smooth and accurate data processing.

Jianlin Wu

Jianlin Wu obtained his B.S. from Lanzhou Jiaotong University in 2020. He is currently a doctoral student at Southwest Jiaotong University, researching 3D GIS and holographic visualization. He provided key feedback and revision suggestions during the paper writing process, enhancing the logical flow and readability of the paper.

Weilian Li

Weilian Li obtained his Ph.D. from Southwest Jiaotong University in 2021. He is currently conducting research on 3D GIS and spatial cognition at the University of Bonn in Germany and in Shenzhen. He provided valuable field investigation data and observations during the research process, offering empirical support for the study.

Ya Hu

Ya Hu obtained his M.S. degree from Southwest Jiaotong University in 2005 and his Ph.D. from the same university in 2019. His research focuses on geographic visualization. He played a role in project management throughout the research process, ensuring the progress and quality of the research, and continuously provided encouragement and support to the team.

Jigang You

Jigang You obtained his B.S. degree from Southwest Jiaotong University in 2019. He is currently pursuing his Ph.D. at the same university, with a research focus on geographic augmented visualization. He actively collaborated in the data collection phase, utilizing her expertise in fieldwork to gather high-quality data. Her insights into the research topic also enriched the discussions and contributed to refining the research questions.