Interactive visualization of streaming data with Kernel Density Estimation
In this paper, we discuss the extension and integration of the statistical concept of Kernel Density Estimation (KDE) in a scatterplot like visualization for dynamic data at interactive rates. We present a line kernel for representing streaming data, we discuss how the concept of KDE can be adapted to enable a continuous representation of the distribution of a dependent variable of a 2D domain. We propose to automatically adapt the kernel bandwith of KDE to the viewport settings, in an interactive visualization environment that allows zooming and panning. We also present a GPU based realization of KDE that leads to interactive frame rates, even for comparably large datasets. Finally, we demonstrate the usefulness of our approach in the context of three application scenarios one studying streaming ship traffic data, another one from the oil gas domain, where process data from the operation of an oil rig is streaming in to an on shore operational center, and a third one studying commercial air traffic in the US spanning 1987 to 2008.
2011 IEEE Pacific Visualization Symposium