In general,
charts,
graphs and
plots provide the means for summarizing quantitative and qualitative
data using diverse graphical representations. The main limitations of such static types of data exploratory and visualization are the low number of variables that can be shown simultaneously on the chart. Many classical data visualization techniques have limitations in terms of the volume, properties or complexity of the dataset. For instance,
Scatter plots require bivariate data. Many datasets include multiple measurements like time, space, demographic, phenotypic and functional recording. For instance, the annual US Housing Price Index dataset includes dozens of variable including location (State and US region), year, unemployment rate, state population, percent subprime loans, etc. Motion charts provide a dynamic data visualization paradigm that facilitates the representation and understanding of large and multivariate data. Using the familiar 2D
Bubble charts, motion Charts enable the display of large multivariate data with thousands of data points and allow for interactive visualization of the data using additional dimensions like time, the size of the blobs, and color) to show different characteristics of the data. The central object of a motion chart is a blob (or bubble), which is a solid object
homeomorphic to a disc. Blobs have 3 important characteristics – size, position and appearance. Using variable mapping, motion charts allow control over the appearance of the blobs at different time points. This mechanism enhances the dynamic appearance of the data in the motion chart and facilitates the visual inspection of associations, patterns and trends in multivariate datasets. == Examples of motion charts==