多源地理空间矢量数据融合理论与方法研究
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摘要
随着GIS的应用日益广泛,各行业对空间数据的需求越来越大。由于不同的业务内容存在巨大差异,对地理空间数据的需求也不尽相同,各部门通常针对本行业的需要,对同一地区同一比例尺的空间数据采用不同的数据源、不同的空间数据标准、特定的数据模型和特定的空间物体分类分级体系进行重复采集。这不仅造成了人力、财力的巨大浪费,还引发了空间数据的多种差异。因此,地理空间数据共享成为目前地理信息科学研究热点之一。但是,地理空间数据的需求不断变化,生产新的地理空间数据是地理信息产业发展的主题。本文深入研究了从不同数据源,不同数据精度和不同数据模型的地理空间数据中抽取所需要的信息,按照用户新的应用需求构建新的空间数据的地理空间矢量数据融合理论和方法,这不仅能降低地理数据的生产成本,加快现有地理信息更新速度,对提高现有地理空间数据质量也具有重要的意义。论文主要内容如下:
(1)论文给出了地理空间矢量数据融合的概念及研究范畴、分析了研究现状,指出了存在的主要问题,建立了地理空间矢量数据融合的数据集成、同名实体匹配与识别、几何数据与属性数据融合的框架和流程。
(2)深入分析了多源地理空间数据产生的原因。从空间实体或现象本身不确定性、人类认知表达能力的局限性、空间实体或现象观测的误差、计算机表达地理对象的局限性和空间数据操作产生的误差等方面详细分析了多源地理空间数据产生的客观和主观原因,这对空间数据共享、集成和融合来说,具有重要理论价值。
(3)从空间基准、数据模型和语义编码三个方面分析了多源地理空间数据集成的内容。研究了数据交换、直接数据访问、空间数据互操作、基于本体的地理空间数据集成四种模式的空间数据集成的方法。
(4)从空间关系理论出发,提出了地理空间矢量数据的顺序、双向和并行的多种数据匹配策略,解决了一对一、一对多、多对一、特别是多对多空间实体匹配的难题;总结了拓扑空间关系数学模型和点、线、面实体匹配的几何度量指标;改进了点结构“蜘蛛编码”匹配的算法;提出了基于面积迭置率的数据匹配算法和匹配思路,解决了多对多的面实体匹配的问题;根据地理空间方向关系描述方法,提出了栅格环境下基于方向关系矩阵模型的空间目标方向相似性计算方法和尺寸与形状相似和不相似情况下的计算模型,给出面状实体匹配结果;根据本体匹配基本理论,提出了语义相似度计算方法。
(5)以同一地区数字地形图和数字海图融合为例,验证了论文提出的空间矢量数据的融合模型和算法。
Along with wide and deep application of GIS, the demand for spatial data becomes greater. Because of different operation contents in various fields, the demands on geospatial data are different from each other. According to their particular demand, to get the geospatial data of the same region on the same scale, each department collects them separately, using different data resources, different geospatial data standards, particular data models and particular grading and classing system for spatial objects. This leads not only to great waste of manpower and resource, but also to big differents. So, geospatial data sharing has been a research hotspot of current geographical information sciences. However, the change of demand to geospatial data will definitely bring the change to geospatial data's contents and formats. To meet new demand is the theme of geographical information science development. This paper researched on geospatial vector data fusion theory and method, taking out needful information from different datum, which had different data resource, different data precision and different data model. As can not only save production cost, but also speed the geospatial information updating. It has quite significant meaning to improve current geospatial data quality. Main contents of the paper are as followings:
(1) This paper gave out the conception and research category, research actuality and existing problems of geospatial vector data fusion, and built up its framework and flow of data fusion preprocessing. It conclude: data integration, homonymy entity recognizing, geometry data and attribute data fusion.
(2) This paper deeply analyzed the fountain of multi-source geospatial data. The objective and subjective reasons were labored, including the self-uncertainties of spatial entity and phenomenon, the limitation of human's cognition, the observing error on spatial entity and phenomenon, the computer's expression limitation on geographical phenomenon. It is quite valuable to geospatial data sharing, integration and fusion.
(3) This paper analyzed the main contents of multi-source geographical spatial data integration from three aspects: spatial datum, data model and semantic code. We researched integration modes of four kinds: data exchanging, directly data accessing, spatial data inter-operation and ontology-based spatial data integration.
(4) Based on spatial relation theories, this paper put forward geographical spatial vector data matching methods, including sequence matching, bidirectional matching, and parallel matching, solving the spatial entities' matching problems of one-to-one, one-to-many, many-to-one, especially many-to-many. The paper summarized the topological spatial relations math models, also, the geometrical measurement to point, line and area entities; ameliorates the "spider coding" algorithm for point matching; put forward overlap-area-based area entities matching method and algorithm; solved the problems to many-to-many area matching. According to geospatial direction relationships description method, we brought forward calculating method of spatial objects directions similarity with direction-relationships-based matrix models in grid environment, and the calculating models when size and shape similar or not, and the matching results of line and area entities. According to the base theories of ontology matching, the semantic similarity calculating method was raised.
(5) This paper take the fusion of digital topographic map and digital chart as an example to validate the spatial vector data fusion model and algorithm raised in this paper.
(1)论文给出了地理空间矢量数据融合的概念及研究范畴、分析了研究现状,指出了存在的主要问题,建立了地理空间矢量数据融合的数据集成、同名实体匹配与识别、几何数据与属性数据融合的框架和流程。
(2)深入分析了多源地理空间数据产生的原因。从空间实体或现象本身不确定性、人类认知表达能力的局限性、空间实体或现象观测的误差、计算机表达地理对象的局限性和空间数据操作产生的误差等方面详细分析了多源地理空间数据产生的客观和主观原因,这对空间数据共享、集成和融合来说,具有重要理论价值。
(3)从空间基准、数据模型和语义编码三个方面分析了多源地理空间数据集成的内容。研究了数据交换、直接数据访问、空间数据互操作、基于本体的地理空间数据集成四种模式的空间数据集成的方法。
(4)从空间关系理论出发,提出了地理空间矢量数据的顺序、双向和并行的多种数据匹配策略,解决了一对一、一对多、多对一、特别是多对多空间实体匹配的难题;总结了拓扑空间关系数学模型和点、线、面实体匹配的几何度量指标;改进了点结构“蜘蛛编码”匹配的算法;提出了基于面积迭置率的数据匹配算法和匹配思路,解决了多对多的面实体匹配的问题;根据地理空间方向关系描述方法,提出了栅格环境下基于方向关系矩阵模型的空间目标方向相似性计算方法和尺寸与形状相似和不相似情况下的计算模型,给出面状实体匹配结果;根据本体匹配基本理论,提出了语义相似度计算方法。
(5)以同一地区数字地形图和数字海图融合为例,验证了论文提出的空间矢量数据的融合模型和算法。
Along with wide and deep application of GIS, the demand for spatial data becomes greater. Because of different operation contents in various fields, the demands on geospatial data are different from each other. According to their particular demand, to get the geospatial data of the same region on the same scale, each department collects them separately, using different data resources, different geospatial data standards, particular data models and particular grading and classing system for spatial objects. This leads not only to great waste of manpower and resource, but also to big differents. So, geospatial data sharing has been a research hotspot of current geographical information sciences. However, the change of demand to geospatial data will definitely bring the change to geospatial data's contents and formats. To meet new demand is the theme of geographical information science development. This paper researched on geospatial vector data fusion theory and method, taking out needful information from different datum, which had different data resource, different data precision and different data model. As can not only save production cost, but also speed the geospatial information updating. It has quite significant meaning to improve current geospatial data quality. Main contents of the paper are as followings:
(1) This paper gave out the conception and research category, research actuality and existing problems of geospatial vector data fusion, and built up its framework and flow of data fusion preprocessing. It conclude: data integration, homonymy entity recognizing, geometry data and attribute data fusion.
(2) This paper deeply analyzed the fountain of multi-source geospatial data. The objective and subjective reasons were labored, including the self-uncertainties of spatial entity and phenomenon, the limitation of human's cognition, the observing error on spatial entity and phenomenon, the computer's expression limitation on geographical phenomenon. It is quite valuable to geospatial data sharing, integration and fusion.
(3) This paper analyzed the main contents of multi-source geographical spatial data integration from three aspects: spatial datum, data model and semantic code. We researched integration modes of four kinds: data exchanging, directly data accessing, spatial data inter-operation and ontology-based spatial data integration.
(4) Based on spatial relation theories, this paper put forward geographical spatial vector data matching methods, including sequence matching, bidirectional matching, and parallel matching, solving the spatial entities' matching problems of one-to-one, one-to-many, many-to-one, especially many-to-many. The paper summarized the topological spatial relations math models, also, the geometrical measurement to point, line and area entities; ameliorates the "spider coding" algorithm for point matching; put forward overlap-area-based area entities matching method and algorithm; solved the problems to many-to-many area matching. According to geospatial direction relationships description method, we brought forward calculating method of spatial objects directions similarity with direction-relationships-based matrix models in grid environment, and the calculating models when size and shape similar or not, and the matching results of line and area entities. According to the base theories of ontology matching, the semantic similarity calculating method was raised.
(5) This paper take the fusion of digital topographic map and digital chart as an example to validate the spatial vector data fusion model and algorithm raised in this paper.
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