Research On Multi-scale Settlement Incremental Propagating Updating Techniques

With the rapid development of science and technology, information technique is widely used in many aspects of socials. As one of the the most important parts of information industry, spatial information gradually becomes a basic support in industry, agriculture and our daily lives. However, the bottlenecks in updating of spatial data keep spatial data services distinctly lagging behind surging demands. With the shortage of longer updating period and lower updating efficiency, now updating methods can’t perfectly fulfill the various updating requirements. In this situation, multi-scale spatial data incremental propagating updating mode receives high attention for its merits such as less updating workload, flexibility and higher data consistency. Here, the basic theory and core techniques of this updating method are fully studied and settlement feature is taken as an example for its high change frequency and huge quantity. Main works and creations are as the following:1. A new updating method named as multi-scale settlement incremental propagating updating mode is brought forward and becomes the guideline of the whole thesis.2. A multi-scale spatial data link and index structure is built which perfectly support incremental propagating updating process. After achieving the corresponding relation between neighborhood scale data, a multi-scale spatial data link tree is proposed. And cartographic generalization indexes of settlement based on nature grid are built with the considerations of city frame integrality, main roads stroke, balance of road grid density and self-adaptation cluster of buildings in grid. At the end, by merging these two structures, the multi-scale spatial data link and index structure is realized with the focus of longitudinal relation and transverse cartographic generalization index.3. Changed information is extracted with multiple settlement matching algorithms. First, through multi-layer questionnaire inquiry towards cartography workers with different knowledge background, the cognition habits and action characteres in variety matching scenes are captured, and the principles of settlement matching are discussed associating with currently matching researches. Second, for the difficulty in accurately quantitating the weights and thresholds of every feature which involves in the matching algorithm, the ANN(Artificial Neural Network) technique is introduced for its advantages in solving problems with complexity, illegibility and multi-features characters. Third, enlightened by the cognition habits of mankind in finding unfamiliar buildings, spatial relationship similarity is used to assist the settlement matching process. And tests show that this method can effectively improve the matching precision in the case of hardly data displacement and high settlement shape homogeneity. Finally, changed information is extracted based on the settlement matching results.4. Links which connect neighborhood scale settlements are constructed by the support of neighborhood scale settlement matching method which based on multi-level similarity cognition. First, the past researches and the problems of neighborhood scale settlement matching are studied, and the characteristics of this type matching are extracted. Second, through deeply analyzing the instances of multi-to-multi relationship, the forming reason of multi-to-multi relationship is found and the measure is gain through comparing the similarity of shape, structure, area and location of settlement cluster. Third, the position correction and bulk selection of matching targets are realized by the assistance of spatial relationship similarity among matched settlements. Forth, after merging the settlements which are involved in one-to-multi relation, the accurately matching process which includes shape identifying, part character matching, and tracing of outlines is brought into effect successfully. Finally, the links are constructed on the bases of matching results.5. Scale-transformation of changed information is intelligently fulfilled with the assistance of artificial intelligence techniques and incremental generalization algorithms. After reviewing and analyzing existing settlement scale-transformation methods, an improving approach for map generalization algorithms is put forward through comparing the process of incremental generalization and batch generalization. Because of the multi-scale data and various data types, it is difficult to choose appropriate generalization algorithms. So the algorithm-lab, knowledge-lab and case-base are constructed to support the self adapting selecting and using of scale-transforming algorithm. Then, an incremental generalization mode based on influencing domain gradually expanding is proposed, and examples illustrate that this mode is more effective to realize the scale-transforming and updating for large scale maps.6. The propagating of updated information and incremental information quality evaluation are achieved. First, the geometry similarity of updated object between its corresponding source object is expressed and computed. Second, the spatial relation similarity of neighborhood of updated object between its corresponding regions is calculated at the same time. Third, the quality evaluation sequence is ascertained based on the principle of maximizing the reliability of quality evaluation. Finally, a two-stage evaluation method is designed based on the characteristics of adjacent objects. After quality evaluation, the updating results are transferred to next scale data through links between these two layer data.Finally, multi-scale settlement incremental propagating updating prototype system is realized according to the software engineering rules.