Improvement of task-oriented visual interpretation of VGI point data (TOVIP)

改进 VGI 点数据的面向任务的视觉解释 (TOVIP)

• 批准号: 424977732
• 负责人: Professor Dr.-Ing. Jochen Schiewe
• 金额: --
• 依托单位: Lab for Geoinformatics and Geovisualization (g2lab)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/424977732?language=en
• 关键词: Improvement; task; oriented; visual; interpretation

Volunteered Geographic Information (VGI) has already shown great potential for a huge variety of social and commercial applications. With a variety of services and software solutions, both experts and non-expert are able to collect and to display data via the internet.VGI is very often generated as point data, representing points of interest, other qualities or quantities. Typical examples are environmental data (such as measurements of traffic noise, PM10 values, traffic volume), or data about accident or crime spots.VGI data typically shows a very large volume of data as well as semantic and temporal heterogeneity. Both aspects can drastically reduce the usability in visual presentation and exploration, in particular, if the interpretation of high-level (synoptic) patterns is of interest. If the focus is on point data, a decline in rendering performance or the effects of geometric or thematic point clutter are possible.Typically, generalization methods are applied in order to overcome these clutter problems. Instead of looking at a holistic approach or isolated generalization operations only, the focus in this project will be on optimizing generalization workflows designed for specific visual interpretation tasks (such as detecting hot spots or extreme values). When using constraint-based approaches, there are two potentially contradictory aspects to consider: preservation and readability of the spatial patterns. However, constraint-based approaches still have limitations in defining constraints. In addition, research to trigger the generalization process through constraints has been quite limited so far.Especially VGI point data is produced either in multiple scale levels, or over longer periods of time or even in real-time, both requiring non-static displays. However, the improvement in the generalization process of such representations has not been thoroughly investigated. As an example, interactive multi-scale views require consideration of scale transitions, i.e., changes to constraints must be formalized as a function of the task being considered. When multi-temporal representations of static or even moving points are used, the added complexity and limitations of the cognitive workload related to these animations must also be considered.Consequently, the overall goal of this project is to improve the visual interpretability of VGI point data displays – taking into account specific high-level (synoptic) tasks based on static, multi-scale or multi-temporal displays.From a methodological point of view, the project begins with the definition of relevant synoptic tasks. Analytical and empirical investigations define a minimum of constraints. Based on this, agent-based models are developed with the aim of optimizing the entire generalization process. Finally, empirical studies evaluate the assumed progress of the extended set of constraints together with the proposed agent-based optimization method.

志愿地理信息(VGI)已经在各种社会和商业应用中显示出巨大的潜力。通过各种服务和软件解决方案，专家和非专家都能够通过互联网收集和显示数据。VGI通常以点数据的形式生成，表示兴趣点、其他质量或数量。典型的例子是环境数据(如交通噪声、PM10值、交通量的测量)，或者关于事故或犯罪点的数据。VGI数据通常显示出非常大量的数据以及语义和时间的异构性。这两个方面都会极大地降低可视化表示和探索的可用性，特别是如果对高级(概要)模式的解释感兴趣的话。如果焦点集中在点数据上，绘制性能可能会下降，或者几何或主题点杂波的影响。通常情况下，会应用泛化方法来克服这些杂波问题。本项目的重点将不是着眼于整体方法或孤立的概括操作，而是优化为特定视觉解释任务(如检测热点或极值)而设计的概括工作流程。当使用基于约束的方法时，有两个潜在的矛盾方面需要考虑：空间模式的保存性和可读性。然而，基于约束的方法在定义约束方面仍然有局限性。此外，到目前为止，通过约束来触发概化过程的研究还相当有限，特别是VGI点数据的产生要么是在多个比例尺级别上，要么是在更长的时间段甚至是实时的，这两者都需要非静态显示。然而，这种表述在泛化过程中的改进还没有得到彻底的调查。例如，交互式多比例视图需要考虑比例转换，即，对约束的更改必须作为正在考虑的任务的函数来形式化。当使用静态甚至移动点的多时相表示时，还必须考虑到与这些动画相关的认知工作量的增加的复杂性和局限性。因此，本项目的总体目标是提高VGI点数据显示的视觉可解释性-考虑基于静态、多尺度或多时相显示的特定高级(天气)任务。从方法论的角度来看，项目从相关天气任务的定义开始。分析性和经验性调查定义了最低限度的限制。在此基础上，建立了以优化整个综合过程为目标的基于智能体的模型。最后，实证研究对扩展约束集的假设进度和所提出的基于代理的优化方法进行了评估。