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Hierarchical visualization of geographical areal data with spatial attribute association

机译：具有空间属性关联的地理区域数据的分层可视化

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摘要

Geographical areal data usually presents hierarchical structures, and its characteristics vary at different scales. At the higher scales, the visualization of geographical areal data is abstract and the detailed features are easily missed. As a difference, more detailed information is presented at the lower scales while the visual perception of global features is easily disturbed due to the overdrawing of visual elements. As the geographical areal data is visualized at a single scale at the same time, it seems impossible to balance the visual perception of both the global features and detailed characteristics. In this paper, we propose a multi-scale geographical areal data visualization method based on spatial attribute association to enhance the visual perception of both the global features and detailed characteristics. Firstly, the geographical areal data is aggregated into hierarchical clusters based on the spatial similarity. Then, the coefficient of variation is applied to estimate the attribute distribution of each cluster in the hierarchy, and a novel geographical areal data visualization scheme is proposed to adaptively present the multi-scale clusters with lower variation coefficients at the same time. In addition, a rich set of visual interfaces and user-friendly interactions are provided enabling users to specify those clusters of interest at different scales and compare multi-scale visualizations with different hierarchies. Finally, we implement a geographical areal data visualization framework, allowing users to visually explore the global features and detailed characteristics at the same time and get deeper insights into the potential features in the geographical areal data. Case studies and quantitative comparisons based on real-world datasets have been conducted to demonstrate the effectiveness of the proposed multi-scale visualization method for in-depth visual exploration of geographical areal data.
机译：地理区域数据通常呈现分层结构，其特性在不同的尺度上变化。在较高的尺度上，地理区域数据的可视化是抽象的，并且很容易错过详细的功能。作为差异，在较低的尺度上呈现更详细的信息，而由于视觉元素的透支由于视觉元素的透支而容易受到干扰的视觉感知。随着地理区域数据的同时以单一规模可视化，似乎无法平衡全局特征和详细特征的视觉感知。在本文中，我们提出了一种基于空间属性关联的多尺度地理区域的数据可视化方法，增强了全局特征和详细特征的视觉感知。首先，地理区域数据基于空间相似性聚集到分层集群中。然后，应用变化系数来估计层次结构中每个群集的属性分布，并且提出了一种新的地理区域可视化方案，以便同时自适应地呈现具有较低变化系数的多尺度簇。此外，还提供了丰富的视觉接口和用户友好的交互，使用户能够在不同的尺度下指定这些感兴趣的群集，并比较具有不同层次结构的多尺度可视化。最后，我们实施了一个地理区域数据可视化框架，允许用户在视觉上同时探索全球特征和详细特性，并在地理区域数据中的潜在功能深入了解。已经进行了基于现实世界数据集的案例研究和定量比较，以证明所提出的多尺度可视化方法对地理区域数据的深入视觉探索的有效性。

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