Contextual mapping: Visualization of high-dimensional spatial patterns in a single geo-map

Abstract

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 called contextual maps.Finally, we showed the results of applying the proposed methodology to several synthetic and real-world data sets.