Vector tile technology is developing rapidly and has received increasing attention in recent years. Compared to the raster tile, the vector tile has shown incomparable advantages, such as flexible map styles, suitability for high-resolution screens and ease of interaction. Recent studies on vector tiles have mostly focused on improving the efficiency on the server side and have overlooked the efficiency on the client side, which affects user experience. Parallel computing provides solutions to this issue. Parallel visualization of vector tiles is a typical example of embarrassing parallelism; thus, estimating the computing times of each tile accurately and decomposing the workload into multiple computing units evenly are key to the parallel visualization of vector tiles. This article adopts mainstream parallel computing and proposes an efficient tile-based parallel method for accelerating geographical feature visualization by building computational weight functions (CWFs) of geographical feature visualizations. The computing time of each vector tile is estimated by the CWF, and an effective workload decomposition strategy is proposed such that the efficiency of vector tile visualization is improved on the client side. Furthermore, a tile-based reconstruction scheme for geographical features is also proposed. Experiments show that the R-squared value of the estimated computing times of vector tiles is 0.914 and that the computational efficiency of the parallel visualization of vector tiles with the proposed workload decomposition strategy is 18.6% higher than that of common parallel visualization. Finally, users can obtain the entire set of features effectively and accurately based on the proposed reconstruction scheme.
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