Contextual mapping: Visualization of high-dimensional spatial patterns in a single geo-map
Contextual mapping: Visualization of high-dimensional spatial patterns in a single geo-map
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上下文映射：单个地理地图中高维空间模式的可视化
DOI：
10.1016/j.compenvurbsys.2016.08.005
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
V. Moosavi
中科院分区：
文献类型：
--
作者：
V. Moosavi
在这项研究中，我们提出了一个通用的方法来结合高维空间数据，以识别和可视化隐藏的空间模式在一个单层的地理地图。通过使用较少探索的一维自组织映射，我们展示了如何将高维数据转换为一维有序数的谱。这些数字（代码）可以索引一个高维空间，其重要属性是相似的索引表示相似的高维上下文。因此，高维向量将被归因于单个数字，并且这个一维输出可以很容易地呈现为原始地理地图中的新的单个数据层。因此，它同时识别主要的空间集群，并可视化的高维相关性（如果有的话）在一个单一的地理地图。此外，因为所提出的方法的输出是有序索引的集合，所以不需要预先定义固定数量的聚类。最后，我们展示了将所提出的方法应用于几个合成和真实世界数据集的结果。
In this study, we proposed a generic methodology for combining high-dimensional spatial data to identify and visualize the hidden spatial patterns in a single-layer geo-map. By using the less explored one-dimensional self-organizing maps, we showed how the high-dimensional data can be transformed into a spectrum of one-dimensional ordered numbers. These numbers (codes) can index a high-dimensional space with the important property that similar indices refer to similar high-dimensional contexts. Thus, the high-dimensional vectors will be attributed to single numbers, and this one-dimensional output can be easily rendered as a new single data layer in the original geographic map. As a result, it simultaneously identifies the main spatial clusters and visualizes the high-dimensional correlations (if any) in a single geographic map. Further, because the output of the proposed method is a set of ordered indices, there is no need to define a fixed number of clusters in advance.Because these composite spatial layers are identified on the basis of the selected context (i.e., the selected features or aspects of the spatial phenomena), they are calledcontextual maps.Finally, we showed the results of applying the proposed methodology to several synthetic and real-world data sets.