Influence of different colormaps on the perceptual interpretation of numerical values produced by a self-organizing feature map
Sammanfattning: To visualize important information and generating a well suited colormap to represent the features of interest in a dataset is complicated. Some colormaps provide with less accurate insights of the data. Meanwhile other colormaps can provide effectively a stronger understanding and identifcation of features in the data. Therefore, a survey was made to investigate how participants interpet numerical values from colormaps and determining relative distances in the neural network Self-organizing map's grid. We can conclude that using the colormaps in some contexts seemed to work fine. This can be shown from violinplots about the relative distances where some results were quite good. The combination of where the best matching units were located in the grid and choice of colormap can be very effective. However, some cases it might be ineffective due to knowledge of the mechanics in a SOM, familiarity of colormaps or even possible color vision deficiency. Also, when test participants were asked to interpret the underlying distribution in colors as a whole, there were misconceptions. In any case, this thesis can be seen as a start of investigating if newly developed perceptually uniform colormaps differ than older colormaps in how users interpret rendered numerical values. Possibly contribute to the guidelines as visualization designers or engineers. Nonetheless when used in the context of determining the BMUs relative distance, some cases were relatively accurate. There could be implications that maximum and minimum associations were not necessary. Significance testing with the Kruskal-Wallis H-test on the colormaps indicated that there were no difference between the medians of the different colormaps. This meant that one can conclude that the colormaps belong to the same distribution. In this thesis there were 62 test participants. To be able to draw other conclusions with high statistical power regarding the different colormaps, more participants would be required for further investigations .
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