多尺度地图数据间不一致性同化建模与处理方法研究

Spatial inconsistency is a vital factor which determines the quality of spatial data. In the meanwhile, spatial inconsistency is one of the common concerns in the international geographical information science community. Currently, main progress on inconsistency issues is focused on topological inconsistency between spatial objectives from maps of the same or similar scale, but no achivements are obtained about the generation mechanism, types of inconsistency and synergistic mechanism among all kinds of types of inconsistencies. In particular, no progresses are made on fundamental problems like regularity and processing methods of inconsistency between multi-scale map data under different scales. Data assimilation is recently concerned as an effective means to be used to deal with spatial data inconsistencies. With regards to this, research on congnition and description of characteristics of inconsistencies which changing with map scales are made. Then, inconsistencies among multi-scale map data are detected. At last, theory and methodology of inconsistency assimilation modeling and handling are proposed, which include: (1) inconsistency map scale relied change characterising and detecting; (2) inconsistency assimilation modeling and performance analysis; (3) inconsistency assimilation handling and results' quantitative evaluation. The purpose of this study is twofold. First, to explore a novel method for inconsistency handling, especially for multi-scale map data. Second, to expand the application fields of data assimilation techniques, meanwhile, to assess the ability of data assimilation techniques for complex applications of geospatial data, such as data integration, map updating, etc. Further research achievements gained from this project will shed light on solutions to make geospatial information with multi-source, multi-scale, and multi-temporal coordinated and uniform in semantics, target expression, and spaital reference, etc. Finally, map based value-added services will be implemented.

空间数据不一致性是影响空间数据质量的关键因素之一，亦是国际地理信息科学领域长期关注的热点问题。当前空间数据不一致性研究主要集中在相同或相近比例尺地图中拓扑不一致性的处理等方面，而对多尺度地图数据间不一致性的探测处理等基础问题关注很少。为此，本项目基于数据同化的思想，在发掘多尺度地图数据间不一致性尺度变化特性的基础上，深入研究多尺度地图数据间不一致性的探测、同化建模、处理的理论与方法，具体包括：（1）不一致性的探测及尺度变化特性；（2）不一致性同化建模及性能分析；（3）不一致性同化处理及定量评价。本项目的深入研究将有利于实现多尺度地图数据协调表达与级联更新，进而服务于多源、多尺度、多时态地理空间信息综合应用，实现地图增值服务。

空间数据不一致性是影响空间数据质量的关键因素之一，亦是国际地理信息科学领域长期关注的热点问题。当前空间数据不一致性研究主要集中在相同或相近比例尺地图中拓扑不一致性的处理等方面，而对多尺度地图数据间不一致性的探测处理等基础问题关注很少。为此，本项目基于数据同化的思想，在发掘多尺度地图数据间不一致性尺度变化特性的基础上，深入研究了多尺度地图数据间不一致性的探测、同化建模、处理的理论与方法，具体包括：（1）研究了不一致性的探测及尺度变化特性；（2）研究了基于Morphing连续变形的不一致性同化建模及性能分析；（3）研究了不一致性同化处理及定量评价。本项目的深入研究一方面将有力地完善并丰富空间数据质量研究理论与技术方法，另一方面将有利于实现多尺度地图数据协调表达与级联更新，进而服务于多源、多尺度、多时态地理空间信息综合应用，实现地图增值服务。