SOCR Motion Charts: An Efficient, Open-Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data

Abstract

The amount, complexity and provenance of data have dramatically increased in the past five years. Visualization of observed and simulated data is a critical component of any social, environmental, biomedical or scientific quest. Dynamic, exploratory and interactive visualization of multivariate data, without preprocessing by dimensionality reduction, remains a nearly insurmountable challenge. The Statistics Online Computational Resource (www.SOCR.ucla.edu) provides portable online aids for probability and statistics education, technology-based instruction and statistical computing. We have developed a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We validated this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object-oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at www.socr.ucla.edu/SOCR_MotionCharts. It can be used as an instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis.

Figure 1

Geographic Map of the California Ozone Layer Data.

Figure 2

SOCR Motion Charts chart for Ice Thickness data at Key=“Oct. 23, 1984”

Figure 3

SOCR Motion Charts chart for Consumer Price Index data (Year=1999)

Figure 4

SOCR Motion Charts chart for California Ozone data (Year=2005).

Figure 5

The relationship of core classes with each other and with the GUI

Figure 6

The relationship between all the classes in Motion Charts.

Figure 7

A future SOCR Motion Charts improvement will allow input of significantly skewed data (left, A). Instead of using the default uniform color distribution over the support of the data distribution (middle, B), the new approach will employ a quantile-based color indexing to assign colors according to the true data density (right, C).

Figure 8

Another future SOCR Motion Charts improvement will provide blob-appearance cues. For example, the blob fill pattern may indicate data frequency (left, A), and the shape of the blob boundary may represent another variable (right, B).