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Shapes on a plane: evaluating the impact of projection distortion on spatial binning

机译:平面上的形状:评估投影变形对空间合并的影响

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摘要

One method for working with large, dense sets of spatial point data is to aggregate the measure of the data into polygonal containers, such as political boundaries, or into regular spatial bins such as triangles, squares, or hexagons. When mapping these aggregations, the map projection must inevitably distort relationships. This distortion can impact the reader's ability to compare count and density measures across the map. Spatial binning, particularly via hexagons, is becoming a popular technique for displaying aggregate measures of point data sets. Increasingly, we see questionable use of the technique without attendant discussion of its hazards. In this work, we discuss when and why spatial binning works and how mapmakers can better understand the limitations caused by distortion from projecting to the plane. We introduce equations for evaluating distortion's impact on one common projection (Web Mercator) and discuss how the methods used generalize to other projections. While we focus on hexagonal binning, these same considerations affect spatial bins of any shape, and more generally, any analysis of geographic data performed in planar space.
机译:处理大量密集的空间点数据集的一种方法是将数据的度量汇总到多边形容器(例如,政治边界)或规则的空间箱(例如,三角形,正方形或六边形)中。映射这些聚合时,地图投影不可避免地会扭曲关系。这种失真会影响读者在整个地图上比较计数和密度度量的能力。空间合并,特别是通过六边形,正在成为一种流行的技术,用于显示点数据集的聚合度量。我们越来越多地看到该技术的可疑使用,而没有随之而来的关于其危害的讨论。在这项工作中,我们将讨论何时以及为什么进行空间合并,以及地图制作者如何更好地理解由投影到平面导致的变形所造成的限制。我们介绍了用于评估变形对一个共同投影的影响的方程式(Web Mercator),并讨论了所使用的方法如何推广到其他投影。尽管我们专注于六边形合并,但这些相同的考虑因素会影响任何形状的空间分类,并且更普遍地影响平面空间中执行的地理数据的任何分析。

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