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作 者:钟其洋 郭庆胜[1,2] 王勇[3] 罗安[3] ZHONG Qiyang;GUO Qingsheng;WANG Yong;LUO An(School of Resource and Environmental Sciences,Wuhan University,Wuhan 430079,China;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;Chinese Academy of Surveying and Mapping,Beijing 100830,China)
机构地区:[1]武汉大学资源与环境科学学院,湖北武汉430079 [2]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079 [3]中国测绘科学研究院,北京100830
出 处:《测绘通报》2022年第8期117-122,共6页Bulletin of Surveying and Mapping
基 金:国家自然科学基金(41871378)。
摘 要:志愿者地理信息近年已得到广泛应用,鉴于有些来源数据可能缺少比例尺说明,因此对矢量空间数据比例尺的估算十分必要。本文提出了一种通过计算和统计矢量空间数据的空间粒度,估算矢量空间数据比例尺的方法。首先,以道路要素为例选取多种空间粒度,包括单类别道路空间数据的最短直线段长度和弯曲的最小面积等,并通过线性插值方法拟合空间数据比例尺估算函数;其次,以北京市1∶25万、1∶100万、1∶400万比例尺道路矢量空间数据作为试验样本数据,详细阐述了基于空间粒度估算比例尺的过程;然后,统计了不同类型空间粒度的数量分布,基于开方根规律量化了比例尺与空间粒度单元数量之间的对应关系;最后,试验验证了该方法的可行性和有效性,有利于多尺度多源数据的融合和应用。Volunteered geographic information has been widely used in recent years. The data from some sources may lacks scale description. Therefore, it is necessary to estimate the scale of vector spatial data. This paper proposes a method to estimate the scale by calculating and counting the spatial granularity of vector spatial data. The road elements are used as examples, and this paper selects a variety of spatial granularity indicators, including the shortest straight segment length of road spatial object with the single-category and the smallest bend area of the same spatial object. The function of estimating vector spatial data scale is fitted by a linear interpolation. The process of estimating scale based on spatial granularity is described in detail, and the data sets include 1∶250 000 scale data, 1∶1 000 000 scale data and 1∶4 000 000 scale data in Beijing city. In addition, we also count the number of different spatial granularity units, and establish the relationships between the number of spatial granularity unit and the scale based on the radical law. Finally, experiments verify the feasibility and effectiveness of the proposed method in this paper, which is conducive to the fusion and application of multi-scale and multi-source data.
分 类 号:P208[天文地球—地图制图学与地理信息工程]
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