Data Classification for Highlighting Polygons with Local Extreme Values in Choropleth Maps
Data Classification for Highlighting Polygons with Local Extreme Values in Choropleth Maps
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DOI：
10.1007/978-3-319-57336-6_31
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发表时间：
2017-07
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影响因子：
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通讯作者：
J. Schiewe
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文献类型：
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作者：
J. Schiewe
根据地图设计中面向任务的一般要求，这里将研究一项具体的任务：保留和突出高亮地图中的局部极值。极值多边形是指与所有直接相邻的多边形相比，显示更大(局部最大)或更小(局部最小)属性值的多边形。对于分类地图中的视觉识别，这样的多边形必须属于不同于周围多边形的类别。然而，在生成全息图的过程中常用的数据分类方法是数据驱动的，即，仅基于原始值的当前频率分布来确定间隔。在沿着数字线进行这种划分的情况下，基本数据的空间上下文被完全忽略，并且不能保证对局部极值进行所需的分类。为此，人们提出了一种新的方法(称为PLEX)。该方法的应用和有效性将通过真实世界的例子进行演示。
Following the general demand for task-orientation in map design, one specific task will be examined here: the preservation and highlighting of local extreme values in choropleth maps. Extreme value polygons are ones that show a larger (local maximum) or smaller (local minimum) attribute value compared to all directly neighboring polygons. For a visual identification in a classified choropleth map, such a polygon must belong to a class other than the surrounding polygons. However, data classification methods that are commonly used in the process of generating choropleth maps are data-driven, i.e., the intervals are determined solely on the basis of the present frequency distribution of the original values. With such a division along the number line, the spatial context of the underlying data is completely neglected and with that the desired categorization for local extreme values is not guaranteed. As a consequence, a new method (called PLEX) is presented for this purpose. The application and the effectiveness of this method will be demonstrated using real-world examples.