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.
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