Adaptive Geographic Scene Expression Driven by User Needs: A Case Study of the Twin Highway Tunnel Scene
Year: 2025
Type: article
Abstract: ABSTRACT Virtual geographic scenes, as a key component of virtual geographic environments (VGEs), play an essential role in conveying complex geographic information. However, the existing geographic visualization methods have problems such as separation from user needs and poor multi‐terminal adaptation, resulting in low user cognitive efficiency. To address these limitations, an adaptive geographic scene representation framework grounded in user requirements is proposed. A systematic mapping relationship between user requirements and scene representations was first established through rigorous analysis of multi‐platform user interaction patterns. Subsequently, data‐driven strategies for dynamic scene organization and scheduling were developed based on comprehensive terminal capability profiling. Furthermore, an integrated closed‐loop framework was developed, incorporating requirement analysis, task definition, scene organization, and interaction refinement to support adaptive scene description, analysis, and exploration. The efficacy of the proposed method was systematically assessed through eye‐tracking cognitive experiments and participant‐based questionnaires. Experimental results demonstrated that the proposed methodology facilitated smooth scene interaction rendering on contemporary terminal devices. In comparison with the original scenes, user cognitive efficiency in the designated areas of interest exhibited a 90.19% improvement, thereby establishing a novel approach for enhanced geographic information transmission.
Source: Transactions in GIS
DOI: 10.1111/tgis.70044
Authors: Jun Zhu, Na Li, Jinbin Zhang, Yuting Rao, Chao Pang, Ranran Tang, Hongyue Zhao
Institution: Southwest Jiaotong University
Language: English
Subfield: Geography, Planning and Development
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
Awards: G4171306671, G4704368447, G7884907075
OpenAlex ID: W4409295904
