TraVis -一个城市微气候环境下移动样带数据集的可视化框架

Kathrin Häb, A. Middel, B. Ruddell, H. Hagen
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引用次数: 11

摘要

在城市小气候研究中,利用移动样带观测大气变量与城市环境的关系。然而,由移动测量产生的数据集是复杂的:它们是空间依赖的、多变量的,而且往往是多时间的。同时,每次观测的空间背景——它的视野和所代表的区域——在物理上是复杂和动态的。这些特性使分析和可视化具有挑战性。我们提出了一个原型可视化框架,帮助研究人员分析移动样条测量。该系统使用户能够可视化和探索观察结果,就像在地图上描绘横断面路线的墙壁一样。观察到的属性在这些墙内像缎带一样堆叠在一起,以方便空间变异性和多变量相关性的定性分析。通过沿着横断面路线移动滑块,可以交互式地探索观测结果与空间环境之间的关系。对于轨道上的每次观测,将显示空间上下文源区域,并链接到源区域内包含的土地覆盖类别的部分视图。这些定性分析功能由交互式聚类界面补充,该界面允许根据用户定义的观察集之间的多变量关系的连贯模式对样条段进行分类。该框架是由可视化和城市微气候研究人员组成的团队开发的,一个案例研究显示了它在这一专业应用中的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TraVis - A visualization framework for mobile transect data sets in an urban microclimate context
In urban microclimate research, mobile transects are utilized to observe the relationship between atmospheric variables and the urban environment. However, the data sets resulting from mobile measurements are complex: They are spatially dependent, multivariate, and often-times multitemporal. At the same time, the spatial context of each observation - its field of view and area represented - is physically complex and dynamic. These properties make analysis and visualization challenging. We present a prototype visualization framework that assists researchers in the analysis of mobile transect measurements. The system enables users to visualize and explore observations as walls that delineate the transect route on a map. The observed attributes are stacked upon each other within these walls as ribbons to facilitate the qualitative analysis of spatial variability and multivariate correlations. The relationship between observations and spatial context can interactively be explored by moving a slider along the transect route. For each observation on the track, the spatially contextual source area is displayed and linked to a view of the fraction of land cover classes contained within the source area. These qualitative analysis capabilities are complemented by an interactive clustering interface, which allows for the classification of transect segments according to a coherent pattern of multivariate relationships between a user-defined set of observations. The framework was developed by a team comprising both visualization and urban microclimate researchers, and a case study shows its utility for this specialized application.
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