Length measurements calculated from the geometry of vector geographic objects, called geometric measurements, are inherently imprecise. The imprecision of the measurements is due to the accumulation of causes of various origins, related to the production processes, and the rules of data representation. In order to reduce the overall imprecision of geometric length measurements, this article proposes to identify the causes of measurement error in the data, to model their respective impact, and finally to combine these different impacts. To do so, five causes of geometric measurement error have been modeled: map projection, terrain disregard, polygonal approximation of curves, digitizing error, and cartographic generalization. To estimate the overall measurement imprecision, three combination methods are proposed: selection of the maximum error, sum of the errors, and quadratic aggregation of the errors. An experiment conducted on a sample of roads represented at a medium scale demonstrates that quadratic error aggregation is the most effective combination method for reducing the imprecision of geometric length measurements.

中文翻译：

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结合误差模型降低矢量数据库几何长度测量的不精确性

根据矢量地理对象的几何形状计算的长度测量（称为几何测量）本质上是不精确的。测量的不精确性是由于各种原因的积累造成的，这些原因与生产过程和数据表示规则有关。为了减少几何长度测量的整体不精确性，本文提出识别数据中测量误差的原因，对它们各自的影响进行建模，最后将这些不同的影响结合起来。为此，对几何测量误差的五个原因进行了建模：地图投影、地形忽视、曲线的多边形近似、数字化误差和制图概括。为了估计总体测量不精确度，提出了三种组合方法：选择最大误差、误差之和以及误差的二次聚合。对中等比例的道路样本进行的实验表明，二次误差聚合是降低几何长度测量不精确性的最有效组合方法。