A Study On Multi-scale Vector Map Data Inconsistency Detection And Handling Methods

Scale and time are two basic characteristics of map data. Due to different representation details of spatial objects in different scale maps, the same geographic entity may have different geometry, attribute and topology in multi-scale maps, which contain real change, error (or allowable variation) and inconsistency, etc. To improve the quality of spatial data and accuracy of spatial analysis results, inconsistency must be treated rather than real variation and allowable variation.Inconsistency is one of the internationally recognized spatial data quality indices, which can be divided into spatial inconsistency, property inconsistency and semantic inconsistency. Current researches are mostly presented for the same type of spatial objects with same or similar scales, especially for line objects. However, there is little research on inconsistency detection in multi-scale maps. Novel methods must be developed to detect and handle the inconsistency problems among multi-scale maps. For this purpose, existing methods of classification and representation of multi-scale map data variation are summarized systematically. Through the detection of multi-scale map data variation, the compound information including real variation of geographic entity, allowable variation of geographic entity changing with scale and inconsistency of map data are obtained. This thesis mainly focus on the spatial inconsistency of multi-scale map data, which is divided into four classes, including geometry inconsistency, topological inconsistency, metric inconsistency and direction inconsistency. The representation of individual spatial relation and the inconsistency in detection method are discussed from topological relation, direction relation and distance relation. Further, the idea of decomposition and combination is used to hierarchically analyze the complex spatial relation, and the spatial relation based on topology chain is represented integratedly through combination of basic spatial relation types. Based on the method desiged for type analysis, detection and processing of multi-scale map data inconsistency, the experimental results of analysis of line objects and region objects, such as rivers, contour lines, boundaries and administrative regions, have testified the feasibility of the proposed method, which will provide some referenced value for improving spatial data quality and promoting basic geographic information service level.The main contents and contributions of this thesis are presented as follows.1. The significance of inconsistency is elaborated from the aspect of spatial data quality. A detailed review of the progress on spatial data inconsistency in the fields of geographical information science (GIS) is made. Main issues involved in current methods are pointed out, and the urgency of developing inconsistency detection methods for multi-scale map data is analyzed.2. According to the basic spatial object types, the classification and formal representation of variation type of multi-scale map spatial objects are discussed respectively. A method adopting4-intersection-and-difference model to detect the variation of homonym objects is prosposed, which can effectively separate real variation from geometric diffence information caused by map generation and thus lay the foundation for analyzing the sources, classification and respresentation of multi-scale map data inconsistency.3. The main sources of multi-scale map data inconsistency are systematically studied from spatial data quality, thematic properties, storage and representation, and analysis and manipulation, and classified into geometry inconsistency, topological inconsistency, metric inconsistency and direction inconsistency. The four types of representations are given to provide the basis for detecting and processing the multi-scale map data inconsistency.4. Based on the representation of basic relation between spatial objects, the detection method of the inconsistency of the same type of spatial relations is discussed. Further, the complex spatial relation is hierarchically analyzed using the decomposition and combination strategy. An integrated representation method of complex spatial relation based on topology chain is proposed and applied to detect the complex spatial relation inconsistency. The experiments of spatial inconsistency detection among rivers and contour lines have verified the validity and effectiveness of the method.5. The shortage and limitation of existing spatial data inconsistency methods are analyzed. An inconsistency processing model and method are presented for point objects, line objects and poly-line objects based on node capture. An error estimation formula is derived for the processing results of poly-line object inconsistency. Based on integrated representation method of complex spatial relation on topology chain, the integrated spatial relation inconsistency processing procedure is discussed taking the rivers and contour lines as examples. A comparison with existing method verified the accuracy advantage of the method proposed in this thesis.