土地利用数据综合结果的质量评价
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摘要
以质取胜,以精立业。质量是空间数据的生命,质量问题产生于数据的采集、加工与分析应用等多个环节,旨在对空间数据进行简化处理的地图综合操作是空间数据质量问题产生的重要来源之一,与数据采集、结构化组织、参考系变换等一般性数据处理相比,地图综合对数据质量产生的影响要大得多。合理的地图综合要在空间数据质量损失和质量维护中求得平衡。针对地图综合结果的数据质量评价研究具有重要意义,该问题的研究与通用的空间数据质量评价相比有其独特性,表现在质量评价的对象、参考基准、质量问题的来源与评价模型等方面。
地图综合数据对象包括以地形图为目标的通用空间数据,也包括顾及语义特征表达的专题数据,土地利用数据便是专题性空间数据的一种。与通用地图综合关注空间特征不同,土地利用数据综合的质量评价更多的关注专题属性特征,涉及土地利用类型、行政级别、土地实体权属等。目前针对地图综合空间特征质量评价的研究已有不少成果,然而对专题属性特征的质量评价,尚缺乏系统、深入的技术策略和方法研究。随着全国第二次土地调查工作的完成,国土资源和城镇化管理各业务对多尺度土地利用数据的需求日益增长,需要通过地图综合建立不同层次的数据库,如何评价不同尺度的土地利用数据库的可靠性,成为该领域亟需解决的问题。
本文以土地利用数据综合的质量评价为选题,针对尺度变换的特点,在通用空间数据质量评价的基础上拓展研究土地利用数据综合产生的质量问题。从地图综合的多因子约束出发,对土地利用数据综合结果的数据质量进行深入分析,设计了质量评价的模型、算法,从量化的角度在微观(单个对象)、中观(要素群组)和宏观(整个地图)三个空间层次给出评价方法。主要研究内容包括：
(1)在深入分析土地利用综合结果质量问题的来源、影响因素的基础上提出了空间层次与约束相结合的质量评价指标二维体系。土地利用数据综合结果的质量问题主要体现为与综合前数据在空间位置、拓扑、语义、土地分类结构等方面的不一致及面积失衡问题,本文由质量问题出发,基于综合约束,对土地利用数据综合结果的三种几何形态给出了质量评价指标以及其在约束和空间层次下的二维体系。
(2)针对土地利用数据综合结果中的面要素,将三种空间层次下的综合评价指标从图形、统计、语义方面进行了归纳,并针对每种指标给出了对应空间层次下的量化评价方法。
(3)研究了综合前后面积平衡性保持和土地利用类型空间分布特征保持的评价方法。考虑到土地利用数据与区域特征的极大相关性,本文改进了景观指数分析方法,设计了基于空间关联关系的土地分类权重体系支撑下的面积平衡性评价方法；考虑到土地利用分类所具有的空间自相关特性,本文引入探索性空间数据分析的方法,基于Moran's I指数进行综合前后数据各种地类的空间自相关分析,实现了土地分类空间分布特征保持的量化评价。
(4)归纳了土地利用数据综合中语义质量的四个方面：精度、一致性、完整性和相似性,设计了基于语义隶属度变化的语义精度评价模型、基于面积变化的语义一致性评价模型、基于要素数量变化的语义完整性评价模型和基于信息熵的语义相似性评价模型。考虑到土地利用数据在语义上的层次化,设计了基于语义层次和有序量的混合隶属度计算模型,给出了土地利用现状分类体系的语义隶属度矩阵,实现了三个空间层次下语义质量的量化评价。
(5)设计了土地利用数据综合结果质量评价概念框架,将评价过程分为三步：要素特征的描述、评价指标的描述、评价结果的整合。构建了三个空间层次下的质量评价模型,并给出了顾及尺度与区域特征的评价指标权重矩阵。使用两个区域的第二次土地调查的多尺度综合成果(基于DOMAP综合软件生成)进行了验证,试验证明本文提出的顾及空间分布模式与空间格局的统计评价方法能够真实的反映数据在综合后对土地分类空间分布特征的保持度,本文提出的基于混合隶属度计算模型的语义质量评价方法能够揭示“综合尺度跨度越大,综合结果语义精度损失越多”的特征。
本文的研究将通用空间数据质量问题拓展到专业性的数据加工处理中,针对土地利用数据的地图综合与尺度变换的特殊性从多个层次、多个指标进行了研究,提出了三个空间层次下的质量评价指标、评价模型和算法。本研究结合实际应用需求,基于第二次土地资源调查多尺度数据库成果进行了评价指标选择和权重设置,给出了量化的评价方法并进行了试验验证,具有很强的实际意义。对语义质量评价的研究和顾及空间分布模式与空间格局的统计评价方法在理论上是对已有空间数据质量评价研究的补充。
We adhere to the "quality win" and "delicate work" principle in almost every field. As is known to all of us, quality plays an important role in spatial data representation and applications. The quality problem results from the process of data acquisition, data processing, data analysis and data application. Map generalization aims at simplifying data and has been claimed to be one of the most important sources of quality problem. The reason is that map generalization has a greater influence on data quality than the general data processing way, such as data acquisition, transformation of the reference system, etc. On one hand, map generalization needs to delete the details of data. On the other hand, the generalization result must maintain the original spatial distribution and the global information of study area. Therefore, we need to find a balance between quality loss and quality accuracy for a reasonable map generalization. For this reason, the research on quality assessment of the map generalization result has important implications. Compared with the general quality assessment of spatial data, map generalization has its unique features which highlight the objects to be assessed, reference datum, the source of quality problem and the evaluation model.
The objects to be assessed for map generalization include the general spatial data which mainly represented by the topographic map and the thematic data considering the semantic feature. And land use data is a kind of thematic data and it can provide rich information and mainly describe the polygon feature. And there is a great difference in the rule, operator and result from the general map generalization, which contributes to its own uniqueness in the quality problem. The general map generalization mainly focuses on spatial characteristic. However, the quality assessment of land use generalization pays more attention to thematic attribution, such as land use type, administrative level and land ownership, etc. For the moment, there are many research findings on quality assessment of spatial characteristic for map generalization, but fewer researches with the characteristic of system thorough technical strategy and method on the quality assessment of thematic attribution. Following completion of the second state land investigation, the requirement of land use data for state department and other government agencies is increasing. What is needed now is to establish database with multi-level for map generalization. Consequently, how to assess the quality of land use generalization has become a crucial issue.
The subject of this paper is researching the quality assessment of land use generalization. For the characteristic of scale transformation, we expand the research of quality problem generated by land use generalization on the basis of quality assessment for the general spatial data. Starting with the generalization constraint, this paper deeply analyzed the quality of land use data generalization and developed some evaluation model, algorithms and operators. They work together and provide quantitative evaluation results from micro, meso and macro levels. The main contents of the paper are depicted as follows:
(1) On the basis of analyzing the quality problem of land use data generalization, we put forward a two-dimensional quality evaluation index that combined with level and constraint. Land use data is usually composed of polygonal features. And it is characterized with complete coverage, seamlessness, little overlap and hierarchy in semantic. In addition, the constraint, operator and algorithm of land use data generalization are usually different from those of the general map generalization. Thus, there exists particularity in the quality problem of land use data generalization. Specifically, the quality problems appear in the inconformity of topology, area, semantic, land classification structure and spatial location between the original data and the generalization result. Based on the constraints, this paper provided the quality evaluation indexes of the generalization result for land use data by three kinds of geometrical shapes. And it also put forward a two-dimensional quality evaluation index that combined with level and constraint.
(2) Take polygonal feature, for instance. The evaluation indexes of the three levels are generalized into graphical evaluation, statistical evaluation and semantic evaluation. And there is a specific quantitative evaluation method for each index at the corresponding level.
(3) This paper has studied two evaluation methods which can help us understand the maintenance of area balance and spatial distribution of the land classification structure. Land use data is closely related to regional characteristic. Based on this, the landscape index analysis method is improved to evaluate the maintenance of area balance, which took into account the structure similarity and pattern stability of land use classification structure with the spatial correlation analysis. Considering land use type has the property of spatial autocorrelation, this paper brought in the exploratory spatial data analysis and realized the evaluation of spatial distribution for the land use type quantitatively with the overall and local Moran's I index.
(4) We proposed four elements of semantic quality, which were accuracy, consistence, completeness and similarity. And each element has its own evaluation model. Specifically, model for accuracy is based on the semantic membership. Model for consistence is based on area. Model for completeness is based on the quantity of features and model for similarity is based on information entropy. It is known to all that the semantic information of land use data possesses the characteristic of hierarchy. Thus, we built a hybrid membership model which consists of hierarchy structure and order statistic. And further, we constructed the semantic membership matrix for the land use map derived from the second national land survey, which quantitatively evaluated the semantic quality from the three levels mentioned above.
(5) This paper designed the concept frame for quality evaluation of land use data generalization based on the summary of characteristics of land use data generalization, the evaluation index and the evaluation method. And we broke the evaluation process into three steps which were description of feature characteristics, description of evaluation indexes and integration of evaluation results. What is more, this paper constructed the quality evaluation model at three levels. Simultaneously, the weight matrix which took into account the scale and regional characteristic was also designed. The example analysis on real data which came from the results of the DOMAP software verifies the validity of each evaluation index. The experiments proved that all indexes proposed in this paper can truly reflect the quality of the generalization results for land use data. And it can also reveal the changing features of the quality of generalization result with scales.
The research of this paper has expanded quality problem of the general spatial data into the professional data processing. For the particularity of land use generalization and scale transformation, we have put forward the evaluation index, model and algorithm in three spatial levels. In addition, this study combining the application of actual data from the second national land investigation project, selected some evaluation index and the weight. At the same time, some quantitative evaluation methods were given and proved by experiment. Hence, this research turned out to be of great practical significance. Moreover, the research on evaluation of semantic quality and the statistic evaluation method based on spatial distribution pattern and spatial structure is an important supplement to the existing research on quality evaluation for spatial data.
地图综合数据对象包括以地形图为目标的通用空间数据,也包括顾及语义特征表达的专题数据,土地利用数据便是专题性空间数据的一种。与通用地图综合关注空间特征不同,土地利用数据综合的质量评价更多的关注专题属性特征,涉及土地利用类型、行政级别、土地实体权属等。目前针对地图综合空间特征质量评价的研究已有不少成果,然而对专题属性特征的质量评价,尚缺乏系统、深入的技术策略和方法研究。随着全国第二次土地调查工作的完成,国土资源和城镇化管理各业务对多尺度土地利用数据的需求日益增长,需要通过地图综合建立不同层次的数据库,如何评价不同尺度的土地利用数据库的可靠性,成为该领域亟需解决的问题。
本文以土地利用数据综合的质量评价为选题,针对尺度变换的特点,在通用空间数据质量评价的基础上拓展研究土地利用数据综合产生的质量问题。从地图综合的多因子约束出发,对土地利用数据综合结果的数据质量进行深入分析,设计了质量评价的模型、算法,从量化的角度在微观(单个对象)、中观(要素群组)和宏观(整个地图)三个空间层次给出评价方法。主要研究内容包括：
(1)在深入分析土地利用综合结果质量问题的来源、影响因素的基础上提出了空间层次与约束相结合的质量评价指标二维体系。土地利用数据综合结果的质量问题主要体现为与综合前数据在空间位置、拓扑、语义、土地分类结构等方面的不一致及面积失衡问题,本文由质量问题出发,基于综合约束,对土地利用数据综合结果的三种几何形态给出了质量评价指标以及其在约束和空间层次下的二维体系。
(2)针对土地利用数据综合结果中的面要素,将三种空间层次下的综合评价指标从图形、统计、语义方面进行了归纳,并针对每种指标给出了对应空间层次下的量化评价方法。
(3)研究了综合前后面积平衡性保持和土地利用类型空间分布特征保持的评价方法。考虑到土地利用数据与区域特征的极大相关性,本文改进了景观指数分析方法,设计了基于空间关联关系的土地分类权重体系支撑下的面积平衡性评价方法；考虑到土地利用分类所具有的空间自相关特性,本文引入探索性空间数据分析的方法,基于Moran's I指数进行综合前后数据各种地类的空间自相关分析,实现了土地分类空间分布特征保持的量化评价。
(4)归纳了土地利用数据综合中语义质量的四个方面：精度、一致性、完整性和相似性,设计了基于语义隶属度变化的语义精度评价模型、基于面积变化的语义一致性评价模型、基于要素数量变化的语义完整性评价模型和基于信息熵的语义相似性评价模型。考虑到土地利用数据在语义上的层次化,设计了基于语义层次和有序量的混合隶属度计算模型,给出了土地利用现状分类体系的语义隶属度矩阵,实现了三个空间层次下语义质量的量化评价。
(5)设计了土地利用数据综合结果质量评价概念框架,将评价过程分为三步：要素特征的描述、评价指标的描述、评价结果的整合。构建了三个空间层次下的质量评价模型,并给出了顾及尺度与区域特征的评价指标权重矩阵。使用两个区域的第二次土地调查的多尺度综合成果(基于DOMAP综合软件生成)进行了验证,试验证明本文提出的顾及空间分布模式与空间格局的统计评价方法能够真实的反映数据在综合后对土地分类空间分布特征的保持度,本文提出的基于混合隶属度计算模型的语义质量评价方法能够揭示“综合尺度跨度越大,综合结果语义精度损失越多”的特征。
本文的研究将通用空间数据质量问题拓展到专业性的数据加工处理中,针对土地利用数据的地图综合与尺度变换的特殊性从多个层次、多个指标进行了研究,提出了三个空间层次下的质量评价指标、评价模型和算法。本研究结合实际应用需求,基于第二次土地资源调查多尺度数据库成果进行了评价指标选择和权重设置,给出了量化的评价方法并进行了试验验证,具有很强的实际意义。对语义质量评价的研究和顾及空间分布模式与空间格局的统计评价方法在理论上是对已有空间数据质量评价研究的补充。
We adhere to the "quality win" and "delicate work" principle in almost every field. As is known to all of us, quality plays an important role in spatial data representation and applications. The quality problem results from the process of data acquisition, data processing, data analysis and data application. Map generalization aims at simplifying data and has been claimed to be one of the most important sources of quality problem. The reason is that map generalization has a greater influence on data quality than the general data processing way, such as data acquisition, transformation of the reference system, etc. On one hand, map generalization needs to delete the details of data. On the other hand, the generalization result must maintain the original spatial distribution and the global information of study area. Therefore, we need to find a balance between quality loss and quality accuracy for a reasonable map generalization. For this reason, the research on quality assessment of the map generalization result has important implications. Compared with the general quality assessment of spatial data, map generalization has its unique features which highlight the objects to be assessed, reference datum, the source of quality problem and the evaluation model.
The objects to be assessed for map generalization include the general spatial data which mainly represented by the topographic map and the thematic data considering the semantic feature. And land use data is a kind of thematic data and it can provide rich information and mainly describe the polygon feature. And there is a great difference in the rule, operator and result from the general map generalization, which contributes to its own uniqueness in the quality problem. The general map generalization mainly focuses on spatial characteristic. However, the quality assessment of land use generalization pays more attention to thematic attribution, such as land use type, administrative level and land ownership, etc. For the moment, there are many research findings on quality assessment of spatial characteristic for map generalization, but fewer researches with the characteristic of system thorough technical strategy and method on the quality assessment of thematic attribution. Following completion of the second state land investigation, the requirement of land use data for state department and other government agencies is increasing. What is needed now is to establish database with multi-level for map generalization. Consequently, how to assess the quality of land use generalization has become a crucial issue.
The subject of this paper is researching the quality assessment of land use generalization. For the characteristic of scale transformation, we expand the research of quality problem generated by land use generalization on the basis of quality assessment for the general spatial data. Starting with the generalization constraint, this paper deeply analyzed the quality of land use data generalization and developed some evaluation model, algorithms and operators. They work together and provide quantitative evaluation results from micro, meso and macro levels. The main contents of the paper are depicted as follows:
(1) On the basis of analyzing the quality problem of land use data generalization, we put forward a two-dimensional quality evaluation index that combined with level and constraint. Land use data is usually composed of polygonal features. And it is characterized with complete coverage, seamlessness, little overlap and hierarchy in semantic. In addition, the constraint, operator and algorithm of land use data generalization are usually different from those of the general map generalization. Thus, there exists particularity in the quality problem of land use data generalization. Specifically, the quality problems appear in the inconformity of topology, area, semantic, land classification structure and spatial location between the original data and the generalization result. Based on the constraints, this paper provided the quality evaluation indexes of the generalization result for land use data by three kinds of geometrical shapes. And it also put forward a two-dimensional quality evaluation index that combined with level and constraint.
(2) Take polygonal feature, for instance. The evaluation indexes of the three levels are generalized into graphical evaluation, statistical evaluation and semantic evaluation. And there is a specific quantitative evaluation method for each index at the corresponding level.
(3) This paper has studied two evaluation methods which can help us understand the maintenance of area balance and spatial distribution of the land classification structure. Land use data is closely related to regional characteristic. Based on this, the landscape index analysis method is improved to evaluate the maintenance of area balance, which took into account the structure similarity and pattern stability of land use classification structure with the spatial correlation analysis. Considering land use type has the property of spatial autocorrelation, this paper brought in the exploratory spatial data analysis and realized the evaluation of spatial distribution for the land use type quantitatively with the overall and local Moran's I index.
(4) We proposed four elements of semantic quality, which were accuracy, consistence, completeness and similarity. And each element has its own evaluation model. Specifically, model for accuracy is based on the semantic membership. Model for consistence is based on area. Model for completeness is based on the quantity of features and model for similarity is based on information entropy. It is known to all that the semantic information of land use data possesses the characteristic of hierarchy. Thus, we built a hybrid membership model which consists of hierarchy structure and order statistic. And further, we constructed the semantic membership matrix for the land use map derived from the second national land survey, which quantitatively evaluated the semantic quality from the three levels mentioned above.
(5) This paper designed the concept frame for quality evaluation of land use data generalization based on the summary of characteristics of land use data generalization, the evaluation index and the evaluation method. And we broke the evaluation process into three steps which were description of feature characteristics, description of evaluation indexes and integration of evaluation results. What is more, this paper constructed the quality evaluation model at three levels. Simultaneously, the weight matrix which took into account the scale and regional characteristic was also designed. The example analysis on real data which came from the results of the DOMAP software verifies the validity of each evaluation index. The experiments proved that all indexes proposed in this paper can truly reflect the quality of the generalization results for land use data. And it can also reveal the changing features of the quality of generalization result with scales.
The research of this paper has expanded quality problem of the general spatial data into the professional data processing. For the particularity of land use generalization and scale transformation, we have put forward the evaluation index, model and algorithm in three spatial levels. In addition, this study combining the application of actual data from the second national land investigation project, selected some evaluation index and the weight. At the same time, some quantitative evaluation methods were given and proved by experiment. Hence, this research turned out to be of great practical significance. Moreover, the research on evaluation of semantic quality and the statistic evaluation method based on spatial distribution pattern and spatial structure is an important supplement to the existing research on quality evaluation for spatial data.
引文
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