Efficient compression of vector data map based on a clustering model

Abstract

This paper proposes a new method for the compression of vector data map. Three key steps are encompassed in the proposed method, namely, the simplification of vector data map via the elimination of vertices, the compression of removed vertices based on a clustering model, and the decoding of the compressed vector data map. The proposed compression method was implemented and applied to compress vector data map to investigate its performance in terms of the compression ratio and distortions of geometric shapes. The results show that the proposed method provides a feasible and efficient solution for the compression of vector data map and is able to achieve a promising ratio of compression and maintain the main shape characteristics of the spatial objects within the compressed vector data map.