Abstract
We used buffer superposition, Delaunay triangulation skeleton line, and other methods to achieve the aggregation and amalgamation of the vector data, adopted the method of combining mathematical morphology and cellular automata to achieve the patch generalization of the raster data, and selected the two evaluation elements (namely, semantic consistency and semantic completeness) from the semantic perspective to conduct the contrast evaluation study on the generalization results from the two levels, respectively, namely, land type and map. The study results show that: (1) before and after the generalization, it is easier for the vector data to guarantee the area balance of the patch; the raster data’s aggregation of the small patch is more obvious. (2) Analyzing from the scale of the land type, most of the land use types of the two kinds of generalization result’s semantic consistency is above 0.6; the semantic completeness of all types of land use in raster data is relatively low. (3) Analyzing from the scale of map, the semantic consistency of the generalization results for the two kinds of data is close to 1, while, in the aspect of semantic completeness, the land type deletion situation of the raster data generalization result is more serious.
Highlights
With the development of computer technology and the constant deepening of the GIS application technology, the traditional paper maps are gradually being replaced by digital maps [1, 2], while as a frontier and hot issue in cartography, cartographic generalization has faced unprecedented changes [3]
In the early 1980s, Monmonier had applied mathematical morphology to the research on the cartographic generalization of the planar elements and proposed that the raster data structure was more suitable for research on the cartographic generalization of land use [10]; subsequently, Su et al discussed the processing methods of the raster data such as feature simplification, integration, and displacement using mathematical morphology [11, 12]; based on the mixed data of the raster vector, Huilian et al proposed the GABP neural network model and achieved the simplification of the buildings [13]; at the beginning of this century, Li et al of British Kingston University first applied cellular automata to the cartographic generalization, bringing progress and breakthrough to the cartographic generalization based on the raster mode [14]
For the vector data, we comprehensively considered the auxiliary spatial topological relations of the land type in order to achieve the amalgamation of the adjacent patches and established the polygon through the buffer intersection nodes to achieve the aggregation of the adjacent patches; for the raster data, we used the closing operation in the mathematical morphology to achieve the aggregation of the patches and added the semantic conception of the cellular automata operation of the mode filtering operation rules to achieve the generalization
Summary
With the development of computer technology and the constant deepening of the GIS application technology, the traditional paper maps are gradually being replaced by digital maps [1, 2], while as a frontier and hot issue in cartography, cartographic generalization has faced unprecedented changes [3]. The cartographic generalization refers to the process where, under the premise of maintaining the structures and characteristics of the spatial entities, the extraction and processing are conducted for the map data of the cartographic regions through appropriate selection, generalization, and other operations according to the factors such as scale and use of the map as well as geographical characteristics of cartographic regions, so as to achieve the purpose of passing more and more important spatial information on to the limited representation media [4,5,6,7] Due to factors such as the various changes of geographical space itself and the relative uncertainty of generalization results, the cartographic generalization has always been a difficult international problem in the field of geographical science. For the vector data, we comprehensively considered the auxiliary spatial topological relations of the land type in order to achieve the amalgamation of the adjacent patches and established the polygon through the buffer intersection nodes to achieve the aggregation of the adjacent patches; for the raster data, we used the closing operation in the mathematical morphology to achieve the aggregation of the patches and added the semantic conception of the cellular automata operation of the mode filtering operation rules to achieve the generalization
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