  The exponential growth of available data has increased the need for
interactive exploratory analysis. Dataset can no longer be understood through
manual crawling and simple statistics. In Geographical Information Systems
(GIS), the dataset is often composed of events localized in space and time; and
visualizing such a dataset involves building a map of where the events
occurred.
  We focus in this paper on events that are localized among three dimensions
(latitude, longitude, and time), and on computing the first step of the
visualization pipeline, space-time kernel density estimation (STKDE), which is
most computationally expensive. Starting from a gold standard implementation,
we show how algorithm design and engineering, parallel decomposition, and
scheduling can be applied to bring near real-time computing to space-time
kernel density estimation. We validate our techniques on real world datasets
extracted from infectious disease, social media, and ornithology.
