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March 28, 2018
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.
