Mapping trajectories and flows: facilitating a human-centered approach to movement data analytics
Mapping trajectories and flows: facilitating a human-centered approach to movement data analytics
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DOI：
10.1080/15230406.2021.1913763
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发表时间：
2021-05-22
影响因子：
2.5
通讯作者：
Noi, Evgeny
中科院分区：
文献类型：
--
作者：
Dodge, Somayeh;Noi, Evgeny
本文提出了一种“以人为中心”的方法，通过使用可视化和绘图从运动数据中发现知识。随着运动数据的可用性和维度和分辨率的多样化，映射在运动轨迹的探索性分析以及在大型始发-目的地流数据集中捕获模式和结构方面变得尤为重要。运动现象（例如，从人类和动物的微观运动到宏观流动，再到移民流动，再到病毒传播）是复杂的动态过程，是在多维的地点-时间-背景空间中实现的。本文通过地图学的视角全面概述了各种用于地图运动的可视化技术，并特别关注“人类用户”（例如数据科学家、分析师、领域专家等）。我们根据它们的视觉规格和人类控制、地图交互和设计灵活性的功能能力，系统地描述和分类了可用的技术。这些元素有利于提高用户的地图推理能力和知识发现能力。讨论了过去10年运动可视化文献的发展趋势和差距。我们的研究表明，未来的研究应该更多地关注“人”在开发以人为中心的视觉分析和探索工具中的作用，同时在运动知识发现中提供映射不确定性和保护个人隐私的功能。这些工具应以制图框架和与运动特别相关的视觉原则为指导。
This paper argues for a "human-centered" approach to knowledge discovery from movement data through the use of visualization and mapping. As movement data becomes more available and diverse in dimension and resolution, mapping becomes particularly important in the exploratory analysis of movement trajectories and for capturing patterns and structures in large origin-destination flow data sets. Movement phenomena (e.g. ranging from micro-movements of humans and animals to macro-level mobility, to migration flows, to spread of viruses) are complex dynamic processes which are realized in a multidimensional location-time-context space. This paper provides a comprehensive overview of various visualization techniques for mapping movement through the lens of cartography and with a special focus on the "human user" (e.g. data scientist, analyst, domain expert, etc.). We systematically characterize and categorize available techniques based on their visual specifications and functional capacities for human control, map-interaction, and design flexibility. These elements are beneficial to enhance the user's capacities for map reasoning and knowledge discovery. Trends and gaps in the literature on movement visualization over the past 10 years are discussed. Our review suggests that future research should focus more on the role of the "human" in the development of human-centered visual analytic and exploratory tools, while providing functionalities for mapping uncertainty and protecting individual privacy in knowledge discovery of movement. These tools should be guided by a cartographic framework and visual principles specifically pertinent to movement.