Algorithms for Labeling Focus Regions
Algorithms for Labeling Focus Regions
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DOI：
10.1109/tvcg.2012.193
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发表时间：
2012-12
影响因子：
5.2
通讯作者：
Martin Fink;J. Haunert;A. Schulz;J. Spoerhase;A. Wolff
中科院分区：
文献类型：
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作者：
Martin Fink;J. Haunert;A. Schulz;J. Spoerhase;A. Wolff
在本文中，我们研究的问题，标记点的网站在焦点区域的地图或图表。例如，当地图服务的用户想要看到拥挤的市中心区域中的餐馆或其他POI的名称，但保持在较大区域上的概览时，会发生该问题。我们的方法是将标签放置在焦点区域的边界处，并通过线性连接将每个站点与其标签连接起来，该连接称为leader。通过这种方式，我们将标签从焦点区域移动到其周围的不太有价值的上下文区域。为了使领导者布局具有良好的可读性，我们提出了排除领导者之间的交叉并优化其他特征（如总领导者长度和标签之间的距离）的算法。这产生了一个新的变种的边界标记问题，这已在文献中研究。在传统的边界标记中，引线通常是示意性的折线，而在其他情况下，我们关注的引线是直线段或Bezier曲线。此外，我们提出的算法，给定的网站，找到一个位置的焦点区域，优化上述特性。我们还考虑了一个变种的问题，我们有更多的网站比空间的标签。在这种情况下，我们假设网站由用户优先考虑。或者，我们采取一个新的设施位置的角度，产生一个集群的网站。我们标记每个聚类的一个代表。如果用户愿意，我们可以将我们的方法应用于集群中的站点，并根据需要提供详细信息。
In this paper, we investigate the problem of labeling point sites in focus regions of maps or diagrams. This problem occurs, for example, when the user of a mapping service wants to see the names of restaurants or other POIs in a crowded downtown area but keep the overview over a larger area. Our approach is to place the labels at the boundary of the focus region and connect each site with its label by a linear connection, which is called a leader. In this way, we move labels from the focus region to the less valuable context region surrounding it. In order to make the leader layout well readable, we present algorithms that rule out crossings between leaders and optimize other characteristics such as total leader length and distance between labels. This yields a new variant of the boundary labeling problem, which has been studied in the literature. Other than in traditional boundary labeling, where leaders are usually schematized polylines, we focus on leaders that are either straight-line segments or Bezier curves. Further, we present algorithms that, given the sites, find a position of the focus region that optimizes the above characteristics. We also consider a variant of the problem where we have more sites than space for labels. In this situation, we assume that the sites are prioritized by the user. Alternatively, we take a new facility-location perspective which yields a clustering of the sites. We label one representative of each cluster. If the user wishes, we apply our approach to the sites within a cluster, giving details on demand.