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Skeleton line extraction is a key step in the dissolution and collapse operation of small patches in digital map generalization. Most parts of skeleton lines can be extracted well by existing methods, but these methods ignore skeleton line extraction at the local transition areas near the boundary, which is complex and diverse, resulting in topological inconsistency and visual noncontinuity. Our research focuses on this problem and puts forward a pattern recognition and correction method for skeleton lines at small patch boundaries. First, according to the semantic, topological, and distance characteristics of the nodes on the boundaries, the topological inconsistencies of skeleton lines at the boundaries are defined and classified into four patterns. Second, a classification and regression tree (CART) is trained to identify various patterns automatically. Finally, cubic Bezier curves are used to correct different patterns by considering the extension directions of the boundary and the skeleton line as constraints. The proposed method is validated using the National Geographic Census data in Guizhou Province, China. The results demonstrate that the recognition rate of topological inconsistency patterns can reach 100%, the topological relationships at boundaries are preserved, and the corrected skeleton lines are natural and smooth. [ABSTRACT FROM AUTHOR]

Collapse is a basic generalization operation in land-use data multi-scale representation. It is a compute-intensive operation and involves complex topological transformations. A feasible way to improve the computation efficiency is to adopt a parallel strategy, which means that the collapse of a large spatial range of long and narrow (LN) patches via a partitioning-based approach. However, many topological inconsistencies occur around the partition-cell boundaries. Hence, we propose a partitioned collapse method for LN patches and focus on the reconstruction problem of topological inconsistencies. First, topological inconsistencies were classified into four patterns based on their morphological characteristics. Second, for each of these patterns, a corresponding algorithm was formulated to reconcile the topological inconsistencies. Finally, our approach was validated using the national geographical condition data of Chishui City, Guizhou Province. The experiment results show that our method generates smooth split lines from land-use patches by effectively eliminating topology inconsistency patterns near the partition-cell boundary lines and is 8.7 times more efficient than the traditional unpartitioned method within a city region. • Partition methodology of collapse operation for long and narrow patches generalization. • Topology consistency maintenance during partitioned collapse operation. • Reliability and efficiency analysis by comparing with an un-partitioned collapse method with a small-area data. • Efficiency and superiority verification by statistical analysis with a large-area data. • No potential conflict of interest was reported by the authors. [ABSTRACT FROM AUTHOR]

Automatic thematic map generalization always needs to deal with large datasets whose processes are computationally intensive, and partitioning strategy has been proved to be one of the effective ways to make the generalization of massive data scalable and useable in practice. Nevertheless, when partitioning strategy is introduced to the dissolution operation of long and narrow patches (LN patches) in land-use data, traditional partitioning methods are not conducive to balancing the computation load on machines for each cell in the grid, and grid boundaries easily affect dissolution accuracy and stability of the process. Hence, this paper proposes an improved partitioning method for dissolving LN patches. First, massive patches are meticulously partitioned into varieties of cells based on the area balance of LN patches. Second, the grid boundaries are corrected considering the shape characteristics of the adjacent LN patches. Finally, the actual data from the national geographic census at 1:10000 scale in Guizhou and Yunnan Province in China are used for validation. The experiment shows that the proposed method can improve the computation efficiency and preserve the accuracy at the boundary for dissolution operation of massive data. • Partitioning method for dissolution operation considering the computation load balancing. • Partitioning grids are meticulously partitioned accounting for the area balance of long and narrow patches. • Avoid forming new fragmented patches at boundaries by grid boundary correction. • Efficiency and rationality of the proposed method are verified through massive data. [ABSTRACT FROM AUTHOR]

Filling dot maps with a regular pattern is a key step in visual representation of land use data. The traditional methods cannot adapt well to shape features of complex areas, leading to an unreasonable symbol arrangement in the inner region and area boundaries during symbol filling. For this reason, a dot symbol auto-filling method of complex areas considering shape features is proposed in this paper. First, based on the constrained Delaunay triangulation, the internal structure of a complex area is divided into three simple filling areas denoted tile type, narrow type, and point type. Next, according to the geometric shape features, these three type areas are filled with plane, line, and point level of symbols respectively. Finally, the dot symbols near to boundaries are adjusted on the basis of the boundary constraint to optimize the symbol-filling effect. Based on the national data of a region in Guizhou Province, the method proposed in this paper is compared to the traditional symbol filling methods for validation. The experimental results show that the proposed method improves the dot symbol sufficiency of complex areas, and the edge of dot symbol group closely adhere to the boundary and conform to the extension characteristics of boundary without creating spatial conflicts such as spatial overlap, the filling result can better reflect the shape features of the areas. [ABSTRACT FROM AUTHOR]