面向交互式和可视化勘探的地图地理空间数据高效选择

Tao Guo, Kaiyu Feng, G. Cong, Z. Bao
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引用次数: 46

摘要

随着移动设备的普及,大量地理空间数据变得可用,比如带有地理标记的照片。地图绘制系统在向最终用户呈现如此大的地理空间数据集方面发挥着重要作用。我们建议这样的系统应该支持以下可取的特性:代表性、可见性约束、缩放一致性和平移一致性。前两个约束是地图探索系统的基本挑战,其目的是从用户感兴趣的当前区域有效地选择一小组具有代表性的对象,并且任何两个被选中的对象都不应该太靠近,以免用户在有限的屏幕空间中区分。我们将其形式化为空间目标选择(SOS)问题,证明了它是一个np困难问题,并开发了一种新的具有性能保证的近似算法。为了进一步支持地图上地理空间数据的交互式探索,我们提出了交互式SOS (ISOS)问题,其中我们用缩放一致性和平移一致性约束丰富了SOS问题。ISOS的目标是为最终用户提供无缝体验,通过导航地图交互式地探索数据。我们扩展了SOS问题的算法来解决SOS问题,并提出了一种基于预取的新策略,显著提高了效率。最后,我们进行了大量的实验来证明我们的方法的效率和可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Selection of Geospatial Data on Maps for Interactive and Visualized Exploration
With the proliferation of mobile devices, large collections of geospatial data are becoming available, such as geo-tagged photos. Map rendering systems play an important role in presenting such large geospatial datasets to end users. We propose that such systems should support the following desirable features: representativeness, visibility constraint, zooming consistency, and panning consistency. The first two constraints are fundamental challenges to a map exploration system, which aims to efficiently select a small set of representative objects from the current region of user's interest, and any two selected objects should not be too close to each other for users to distinguish in the limited space of a screen. We formalize it as the Spatial Object Selection (SOS) problem, prove that it is an NP-hard problem, and develop a novel approximation algorithm with performance guarantees. % To further support interactive exploration of geospatial data on maps, we propose the Interactive SOS (ISOS) problem, in which we enrich the SOS problem with the zooming consistency and panning consistency constraints. The objective of ISOS is to provide seamless experience for end-users to interactively explore the data by navigating the map. We extend our algorithm for the SOS problem to solve the ISOS problem, and propose a new strategy based on pre-fetching to significantly enhance the efficiency. Finally we have conducted extensive experiments to show the efficiency and scalability of our approach.
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