The design and implementation of geo-spatial database updating system based on digital map generalization

Abstract

Map generalization is very old topic, but the combination of map generalization technology with geo-spatial database updating is relatively new. After the larger scale database is updated, the smaller scale database should be updated correspondingly in order to keep the consistency of multi-scale geo-spatial database. In this situation, it is very reasonable to apply map generalization technology into the process of geo-spatial database updating. This paper is focused on the application map generalization into the updating of geo-spatial database. A method is described to update geo-spatial database based map generalization technology in this study. The requirements of the application of map generalization into spatial database updating are analyzed firstly. And then, the design is outlined and some key technology details are emphasized on. Finally, the practical implementation in the national 1:50000 geo-spatial database updating project is introduced. This study shows the method is less cost and more efficient in the multi-scale geo-spatial database updating.

Land Use Patch Generalization Based on Semantic Priority

“…If the dataset meeting condition (1) was empty, Step 2 was conducted: retrieve feature dataset FeaSet{} based on condition (2) SemRel ( , ) = 0 and TopoRel ( , ) = 1; the feature with the largest area in the buffer of the DFeature radius of Feature Fj was the desired one. Taking 7 in Figure 2(b) as an example, when the dataset meeting condition (1) was empty, the dataset meeting condition (2) was FeaSet{ 4, 3} and consisted of two features, and the buffer of the 7 Buffer ( 7) was made by taking DFeature as the radius; attention should be paid to 3 and 4 in Buffer ( 7), with 3 as being the desired feature because its area was larger than that of 4 in Buffer ( 7). If the nearest feature was not retrieved after the aforementioned two steps, 1 was added to the value of SemRel ( , ) for recycling, until the most neighboring feature was retrieved.…”

“…Land use generalization is a complicated process involving complex spatial and semantic relationships between land use features, and thus it is very difficult to satisfy such conditions concurrently. A significant amount of research has been conducted in this area: for example, Zongbo [1] discussed proportion image generalization, purpose image generalization, and visual image generalization in image map compilation and elaborated the compilation process on the basis of practice; Chithambaram et al [2] integrated the data based on extracting feature skeletons; that is, secondary patches were compressed into lines or points, secondary lines were compressed into points, and evaluations were given; Ai and Wu [3] conducted neighborhood analysis using the Delaunay triangulation network and carried out a consistency correction for the shared boundary of vector patches after simplification; Ai et al [4] applied the Delaunay triangulation network executing neighborhood analysis to retrieve neighbor patches in patch aggregation and subdivided, merged, and simplified secondary patches by generating skeleton lines using the Delaunay triangulation network; Harrie [5] established appropriate weights for various generalization constraints to solve the balance between constraining conditions and map qualification; Kulik et al [6] proposed an ontology-oriented cartographic generalization and matched the appropriate needs for different users; Zhao et al [7] studied the consistent update system of geospatial databases based on digital map generalization; Li et al [8] and Huang et al [9] discussed the area proportion of each patch after generalization and investigated patch boundary simplification, achieving constraints in the balanced area of various features in boundary simplification and attaining good adaptability; Stoter et al [10] discussed the noncustomized automated cartographic generalization of commercial software, comprehensively considered the elevation results of man and machine, and revealed the possible differences; Qiao and Zhang [11] studied cartographic generalization in a distributed environment, which could be adapted to large quantity spatial data; Dilo et al [12] proposed tGAP to achieve map generalization between two scales in a certain area, with large-scale maps used for generalization and small-scale maps used for constraint; Stanislawski [13] achieved automated generalization in U.S. national hydrological datasets by deleting the corresponding features based on upstream drainage areas; Foerster et al [14] studied the feasibility of geospatial data integration in a network service environment; Ai et al [15] and Liu et al [16], respectively, provided a detailed analysis and calculation models for the semantic similarity of land use data; Zhu et al [17] applied a curve fit algorithm to line generalization and...…”