权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Task-oriented data classification and design of choropleth maps (aChor)

面向任务的数据分类和分区统计图设计 (aChor)

基本信息

批准号：
355807088
负责人：
Professor Dr.-Ing. Jochen Schiewe
金额：
--
依托单位：
Lab for Geoinformatics and Geovisualization (g2lab)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/355807088?language=en
关键词：
Task oriented data classification design

项目摘要

Choropleth maps are the type of thematic maps that are probably most frequently used. To obtain a better overview and faster readability, the displayed attribute values are often classified in advance.The classification methods commonly used and implemented in software packages (such as equidistance, Quantile, Jenks, etc.) are data-driven, i.e., the intervals are determined solely on the basis of the present frequency distribution of the original values. The spatial context of the underlying data, which is relevant for many applications, is completely neglected when using such a division along the number line. With that information about spatial relationships or patterns (or even desired "messages" of a map) might be lost; in practice (and in a best case), a manual-subjective selection out of several classification options is performed.Therefore, in this project a task-oriented approach is advocated. Although it is not possible to assign a specific task or "message" to each choropleth in advance, there are enough applications in absolute numbers to justify the following developments.The project will develop and test methods that produce optimal data classifications for synoptic tasks which can be performed by using choropleth maps. These tasks include the detection of values differences between polygons, hot and cold spots, global or local extreme values as well as cluster regions.After the creation of classes those have to be visualized by a suitable color scheme, that adequately preserves (or even stresses) the original attribute value differences between adjacent polygons or groups of polygons. A special focus will be laid on the transformation of value differences into (proportional) color differences. So far, the selection and evaluation of a color scheme taking account of specific use purposes have been treated only in a few publications.Finally, the question remains whether the potential added value of a task-oriented data classification and a corresponding visualization is actually perceived by the user - and can thus lead to good decisions. For this purpose, comprehensive empirical user studies are carried out, that also take into account various parameters of the data classification step.

Choropleth地图可能是最常用的专题地图类型。为了获得更好的概览和更快的可读性，显示的属性值通常是预先分类的。软件包中常用和实现的分类方法（如等距、分位数、詹克斯等）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。当使用这样的沿着沿着数字线的划分时，与许多应用相关的底层数据的空间上下文被完全忽略。由于可能会丢失有关空间关系或模式的信息（甚至是地图的期望“信息”）;在实践中（在最好的情况下），需要从多个分类选项中进行人工主观选择。因此，在本项目中提倡采用任务导向的方法。虽然不可能事先为每一个分区图指定一项具体的任务或“信息”，但有足够的绝对数量的应用证明以下发展是合理的，该项目将开发和测试为可通过使用分区图执行的天气任务产生最佳数据分类的方法。这些任务包括检测多边形、热点和冷点、全局或局部极值以及聚类区域之间的值差异。创建类后，必须通过合适的配色方案将其可视化，以充分保留（甚至强调）相邻多边形或多边形组之间的原始属性值差异。一个特别的重点将放在价值差异转化为（比例）色差。到目前为止，只有少数出版物讨论了考虑特定使用目的的配色方案的选择和评估。最后，问题仍然是面向任务的数据分类和相应的可视化的潜在附加值是否被用户实际感知-从而导致良好的决策。为此，进行了全面的经验用户研究，其中还考虑到数据分类步骤的各种参数。