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Mapping trajectories and flows: facilitating a human-centered approach to movement data analytics
Cartography and Geographic Information Science ( IF 2.354 ) Pub Date : 2021-05-21 , DOI: 10.1080/15230406.2021.1913763
Somayeh Dodge 1 , Evgeny Noi 1
Affiliation  

ABSTRACT

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.



中文翻译:

绘制轨迹和流动:促进以人为中心的运动数据分析方法

摘要

本文主张采用一种“以人为中心”的方法,通过使用可视化和映射从运动数据中发现知识。随着运动数据在维度和分辨率上变得更加可用和多样化,映射在运动轨迹的探索性分析和捕获大型起点-终点流数据集中的模式和结构中变得尤为重要。运动现象(例如,从人和动物的微观运动到宏观水平的运动、迁移流动、病毒的传播)是复杂的动态过程,在多维位置-时间-上下文空间中实现。本文全面概述了通过制图镜头绘制运动的各种可视化技术,并特别关注“人类用户”(例如数据科学家、分析师、领域专家等)。我们根据可用技术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,系统地描述和分类可用技术。这些元素有利于增强用户的地图推理和知识发现能力。讨论了过去 10 年运动可视化文献中的趋势和差距。我们的评论表明,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,同时提供映射不确定性和保护个人隐私的功能。这些工具应以与运动特别相关的制图框架和视觉原则为指导。我们根据可用技术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,系统地描述和分类可用技术。这些元素有利于增强用户的地图推理和知识发现能力。讨论了过去 10 年运动可视化文献中的趋势和差距。我们的评论表明,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,同时提供映射不确定性和保护个人隐私的功能。这些工具应以与运动特别相关的制图框架和视觉原则为指导。我们根据可用技术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,系统地描述和分类可用技术。这些元素有利于增强用户的地图推理和知识发现能力。讨论了过去 10 年运动可视化文献中的趋势和差距。我们的评论表明,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,同时提供映射不确定性和保护个人隐私的功能。这些工具应以与运动特别相关的制图框架和视觉原则为指导。

更新日期:2021-06-04
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