Researches On Spatial Topological Consistent Maintenance

With the increasing demand of spatial data in the information-technology applications, Geographic Information Systems (GIS) play an increasingly large role in the decision-making processes in numerous disciplines such as resource management, intelligent transportation systems, municipal management, land development, tax assessment, real-estate, emergency response, agricultural management, economic development, insurance, marketing, utility plant design and others. The development of the GIS is paid more and more attention; it becomes the essential constituent of the information-technology application.In the application domain, the accuracy and reliability of the analysis result are in higher request to the spatial data. Therefore, the understanding of the uncertainty in the spatial data, discovering the corresponding detection and maintenance techniques to enhance the spatial data analysis result is essential. The important aspects of uncertainty are error, accuracy, precision and consistency. Consistency of spatial data refers to the absence of apparent contradictions among objects in spatial data. This topic is less researched than error, accuracy and precision, but is of the utmost importance in the understanding and management of uncertainties in spatial data.This thesis presents a novel view of spatial data consistency and introduces innovative techniques for the measurement and enhancement of the quality of spatial data. A data model called VDM is proposed that captures the implicit topological relations between neighboring objects. New geostatistical techniques are applied to discover patterns of object interactions. These patterns are formulated as rules that govern topological relations between pairs of objects in a GIS, and take into account the semantic information of the objects. The rules are then used in a process of detecting inconsistencies. When the inconsistencies are detected, the corresponding approaches are implemented in adjustment of inconsistencies among the spatial objects. Experiments indicate that the proposed techniques improve the quality of spatial data, and increase the levels of confidence in the use of spatial data.