DeepBionics


Univariate and Mutlivariate Data Visualizations

Here several visualization approaches for data anaylisis will be presentend. They are either based on [Thrun, 2018a] or an extension of [Thrun/Ultsch, 2018] or only published as a techical report here. The corresponding R package can be downloaded from CRAN here .

Choropleth Maps, Political Maps and Geographical Maps

Two-dimensional Gaussian Mixture Clustering (Bayes Boundaries) mapped with German postal (zip) codes is shown on an political map and an Choropleth (geographical) map.

To use the function 'plotChoroplethMap' of the CRAN package DataVisualizations you have to dowwnload the dataset GermanPostalCodesShapes.rda and load it in R with load(file='GermanPostalCodesShapes.rda'). Then you can pass the shape file 'GermanPostalCodesShapes' as the following argument plotChoroplethMap(...,PostalCodesShapes=GermanPostalCodesShapes).

Choropleth map Choropleth - Geographic

The figure shows that the payout system of income taxes discriminates between low quota and high quota classes of municipalities. The geographical distribution of the low-quota vs. high-quota municipalities revealed an evident east-west disparity





Databionic swarm clustering of the world-gross-domestic product from 1970-2010 reveals two major classes [Thrun, 2018b].

WorldGDP

For coutries marked in grey no data could be obtained. Red marks the outlier. The function 'plotWorldmap' of the CRAN package DataVisualizations in R was used. The figure shows a discrimination which is clearly spatially distributed.





Databionic swarm clustering of the Tetrangula bees [Thrun, 2018a].

Choropleth - Geographic

The location of the bee swarms war not used in the clustering. It is marked in Google Maps figure below. The function 'GoogleMapsCoordinates' of the CRAN package DataVisualizations in R was used.





Fanplot versus Pie Chart

A normal pie plot is dificult to interpret for a human observer, because humans are not trained well to observe angles [Gohil, 2015, p. 102]. Therefore, the fan plot is used. As proposed in [Gohil 2015] the \code{fan.plot}() of the \code{plotrix} package is used to solve this problem. If Number of Slices is higher than MaxNumberOfSlices then \code{ABCanalysis} is applied (see [Ultsch/Lotsch, 2015]) and group A chosen. This site is currently under construction...

References

[Thrun, 2018a] Thrun, M. C.: Projection-Based Clustering through Self-Organization and Swarm Intelligence , Springer, Heidelberg, ISBN: 978-3658205393, 2018.

[Ultsch/Lotsch, 2015] Ultsch. A ., Lotsch J.: Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data, PloS one, Vol. 10(6), pp. e0129767. doi 10.1371/journal.pone.0129767, 2015.

[Gohil, 2015] Gohil, Atmajitsinh. R data Visualization cookbook. Packt Publishing Ltd, 2015.

[Thrun/Ultsch, 2018] Thrun, M. C. & Ultsch A.: Effects of the payout system of income taxes to municipalities in Germany, 12th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena, Foundation of the Cracow University of Economics, Zakopane, Poland, accepted, 2018.

[Thrun, 2018b] Thrun, M. C. : Cluster Analysis of the World Gross-Domestic Product Based on Emergent Self-Organization of a Swarm, 12th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena, Foundation of the Cracow University of Economics, Zakopane, Poland, accepted, 2018.