Real-Time Displacement of Point Symbols Based on Spatial Distribution Characteristics

Abstract

Maps at different scales have different emphases on the information representation of point data. With a focus on large scales, this paper proposes an improved sequential displacement method. While existing approaches mostly use a fixed order to place points during displacement, the proposed method takes into consideration the spatial distribution characteristics, including the spatial structure and the holistic distance relations of a point group. This method first rapidly extracts feature points through a quadtree index to capture the spatial structure of a point group. Then, it uses map information content to determine the points to be processed. Finally, a global distance matrix for the above two sets of points is established. Overlapping of symbols is resolved by processing the global distance matrix. The algorithm is estimated by comparing with the latest strategy, which has overcome the position drift drawback of traditional sequential displacement methods and the results show that the proposed method can improve the effects of map expression and meet the requirements of real-time processing.

LBS Tag Cloud: A Centralized Tag Cloud for Visualization of Points of Interest in Location-Based Services

“…However, for a large number of points, map legibility is often plagued by problems such as con-gestion and occlusion. Some studies introduced methods in the field of map generalization such as selection [27], label optimization [28], aggregation [12], and displacement [13] to increase legibility, but such methods focused on the spatial information of the POI and gave less consideration to the attribute information. There are also studies converting POIs into continuous density surfaces or statistical diagrams [8] to obtain their spatial distribution, but these methods also ignore the individual attribute information of the POI.…”

“…While map zooming alleviates these issues, it results in a loss of map context and can also cause users to get lost in zooming [11]. Researchers have designed a variety of methods, such as selection, aggregation [12], displacement [13], density surface fitting [14], pagination design [15], and multi-level structure [16], to achieve legible visualization of POIs, but such methods lead to a very limited number of POIs that are visualized simultaneously on the map, which is not conducive to the user's complete understanding of the surrounding environment. Therefore, it is of great research significance and application value to design a new visualization method that can integrate global distribution and local details and help users quickly and efficiently understand the surrounding POIs from a global perspective.…”

ReBankment: displacing embankment lines from roads and rivers with a least squares adjustment

“…The first methods were sequential: finding the best building to displace, or the worst conflict to solve, then displacing this building, and then iteratively displacing other buildings until all conflicts are solved [9]. Several algorithms were proposed in recent years following this iterative or sequential approach [10,11,12]. There were more global approaches proposed such as the computation of a vector field taking all conflicts and relations to preserve in a block into account [13].…”

A Method for Generating Variable-Scale Maps for Small Displays

“…During map generalization, the main distribution patterns of point clusters should be retained. Many methods have been proposed to simplify point data [20][21][22]. Delaunay triangulation (DT), which is the dual graph of the Voronoi diagram, is a powerful tool in the computational geometry domain for proximity analysis between spatial objects [23].…”