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A method to generalize stream flowlines in small-scale maps by a variable flow-based pruning threshold

机译:一种基于可变流量的修剪阈值在小比例尺地图中概括流线的方法

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The aim of this paper is to explore and describe a method of automated generalization designed to produce a map which strikes a balance between cartographic and hydrologic representations. Following a discussion of scholarly literature on generalization, we describe a novel method for automated generalization of hydrographic stream data, using the National Hydrography Data Set (NHDPlus) as an example. Traditional hydrography shows a fairly uniform density of stream flowlines over space. While this is pleasing to the eye, traditional methods tend to under-represent rivers in humid areas and over-represent them in arid areas. We address this problem through a method in automated generalization to produce a high-quality presentation of hydrographic data, suitable for display as a wall map or in an atlas. Streams are pruned based on a variable flow threshold, derived from the local mean annual precipitation by a regression equation. After running the model using different parameters, we produce a more satisfactory portrayal of stream networks in the United States that communicates the flow of water through rivers and reflects the regional climate. Specific advantages in generalizing with variable flow threshold include (1) the method allows for fine gradations in output scale; (2) the output maps tend to minimize density variations in the raw data; (3) the subjective criteria are easily derived; and (4) the method can be performed rapidly on large data sets, as long as the stream data has been enriched with reliable flow rates.
机译:本文的目的是探索和描述一种自动综合的方法,该方法旨在生成可在制图和水文制图表达之间取得平衡的地图。在讨论了有关泛化的学术文献之后,我们以国家水文数据集(NHDPlus)为例,介绍了一种自动泛化水文河流数据的新方法。传统的水文学表明整个空间内的流线密度相当均匀。尽管这令人赏心悦目,但传统方法往往使湿润地区的河流代表不足,而在干旱地区过多地代表河流。我们通过一种自动综合的方法来解决此问题,以产生高质量的水文数据表示形式,适用于以地图或地图集的形式显示。根据可变流量阈值对河流进行修剪,该阈值通过回归方程从当地年平均降水量得出。使用不同的参数运行模型后,我们对美国的河流网络进行了更为令人满意的描述,该网络传达了河流中的水流并反映了区域气候。用可变流量阈值进行概括的具体优势包括:(1)该方法可以实现输出规模的精细渐变; (2)输出图倾向于使原始数据中的密度变化最小化; (3)主观标准容易推导; (4)该方法可以在大数据集上快速执行,只要流数据已经充实了可靠的流量即可。

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