Dynamic mixed data analysis and visualization Articles uri icon

publication date

  • October 2022

start page

  • 1

end page

  • 12

issue

  • 10, 1399

volume

  • 24

International Standard Serial Number (ISSN)

  • 1099-4300

abstract

  • One of the consequences of the big data revolution is that data are more heterogeneous than ever. A new challenge appears when mixed-type data sets evolve over time and we are interested in the comparison among individuals. In this work, we propose a new protocol that integrates robust distances and visualization techniques for dynamic mixed data. In particular, given a time 𝑡∈𝑇={1,2,…,𝑁}, we start by measuring the proximity of n individuals in heterogeneous data by means of a robustified version of Gower"s metric (proposed by the authors in a previous work) yielding to a collection of distance matrices {𝐃(𝑡),∀𝑡∈𝑇}. To monitor the evolution of distances and outlier detection over time, we propose several graphical tools: First, we track the evolution of pairwise distances via line graphs; second, a dynamic box plot is obtained to identify individuals which showed minimum or maximum disparities; third, to visualize individuals that are systematically far from the others and detect potential outliers, we use the proximity plots, which are line graphs based on a proximity function computed on {𝐃(𝑡),∀𝑡∈𝑇}; fourth, the evolution of the inter-distances between individuals is analyzed via dynamic multiple multidimensional scaling maps. These visualization tools were implemented in the Shinny application in R, and the methodology is illustrated on a real data set related to COVID-19 healthcare, policy and restriction measures about the 2020-2021 COVID-19 pandemic across EU Member States.

subjects

  • Economics

keywords

  • mixed data; robustness; outliers; time series; data visualization