HoLens:A visual analytics design for higher-order movement modeling and visualization  

作　　者：Zezheng Feng Fang Zhu Hongjun Wang Jianing Hao Shuang-Hua Yang Wei Zeng Huamin Qu 

机构地区：[1]Department of Computer Science and Engineering,The Hong Kong University of Science and Technology,Hong Kong,China [2]Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China [3]Thrust of Computational Media and Arts(CMA),The Hong Kong University of Science and Technology(Guangzhou),Guangzhou 511458,China [4]Department of Computer Science,University of Reading,Berkshire RG66AH,UK [5]Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet,Southern University of Science and Technology,Shenzhen 518055,China

出　　处：《Computational Visual Media》2024年第6期1079-1100,共22页计算可视媒体(英文版)

基　　金：supported in part by the Shenzhen Science and Technology Program(No.ZDSYS20210623092007023);in part by the National Natural Science Foundation of China(No.62172398);the Guangdong Basic and Applied Basic Research Foundation(No.2021A1515011700).

摘　　要：Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the DAG.However,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban environments.To overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment.HoLens mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral variability.Second,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions.Two real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using HoLens.We also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.

关 键 词：data visualization movement modeling state sequence visualization movement visualization urban visual analytics 

分 类 号：O17[理学—数学]