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Dynamic Mixed Data Analysis and Visualization.

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Area of Science:

  • Data Science
  • Statistics
  • Computational Biology

Background:

  • Big data presents challenges with heterogeneous, time-evolving datasets.
  • Comparing individuals within dynamic, mixed-type data requires advanced analytical methods.

Purpose of the Study:

  • To propose a protocol for analyzing dynamic mixed data, focusing on individual comparisons and outlier detection.
  • To integrate robust distance metrics with advanced visualization techniques for temporal data analysis.

Main Methods:

  • Utilized a robustified Gower's metric to calculate proximity matrices for individuals across time points.
  • Developed graphical tools including dynamic line graphs, box plots, proximity plots, and multidimensional scaling maps.
  • Implemented the methodology in an R Shiny application for practical data analysis.

Main Results:

  • The protocol effectively tracks pairwise distance evolution and identifies individuals with extreme disparities.
  • Proximity plots successfully visualize outliers and individuals distant from the main group.
  • Dynamic multidimensional scaling maps illustrate the temporal evolution of inter-individual distances.

Conclusions:

  • The proposed protocol offers a robust framework for analyzing dynamic, mixed-type data.
  • The integrated visualization tools provide intuitive insights into individual behavior and outlier patterns over time.
  • This methodology is applicable to various fields, including public health surveillance, as demonstrated with COVID-19 data.