DG Team Re-Imagines the Helix
Researchers Develop a Simple, Powerful Tool for Analyzing Geospatial Data Over Time
By Karen Heyman
For the DGRC

Managing Time-Series Data
	Researcher profile: Peggy Agouris Project profile: "Knowledge Management Over Time-Varying Geospatial Datasets" Project home page

One of the great challenges of contemporary computing is handling large data sets, especially those associated with image processing. It is a challenge not only of creating powerful enough hardware and software, but of comprehension, for human researchers trying to interpret the reams of data that are produced.

One way of handling the data is to translate it into visual graphs, where complex quantitative ideas can become instantly visually legible.

But with multi-dimensional datasets, even graphs become confusing, inadequate representations of the many variables that must be displayed. To address that problem, Digital Government researcher Peggy Agouris and her collaborators have developed a new way of modeling and representing spatio-temporal data.

They are particularly concerned with time series information - whether in the form of a sequence of still pictures, as in satellite observations, or as taken by a video camera.

Suppose, for example, you are at the EPA trying to understand a recent flooding event, or suppose you are at the DOT trying to understand traffic flow: If you have video footage of the events, there's only so much you can understand with the naked eye. There is too much extraneous data, and of course, you have to keep looking at the thing in real-time.

Agouris's method lets an analyst glance quickly at a visual representation and understand what are the most significant events. Called a "Spatiotemporal Helix," (pictured at right) it appears as a twig-like structure in the central part of a three-dimensional space with spatial (x,y) and temporal (z) coordinates.

"Each notch of the axis corresponds to frames over time as the video evolves," says Agouris, Associate Professor in the Department of Spatial Information Science and Engineering at the University of Maine, "It can describe instances in the video where something important has happened. It's a visual thing, and we see the helix as the mathematical foundation behind it."

The helix has a "spine," the backbone that models the trajectory of the object over time. On it are "nodes," which are circles that note acceleration, deceleration, and rotation. "Nodes represent important moments, they are indicative of a level of generalization, so you can have more or less, as you require," says Agouris.

Pointing at the spine are "prongs," little red arrows that express the deformation of an object over time. In other words, this model can show both the direction in which a lake is overflowing and how the lake's shape is changing.

The great advantage in using this method is that now, rather than fast-forwarding through an hour of video tape, an analyst can look at the helix and see instantly the point where that lake first overflowed or traffic went from creeping to standstill. "The helix is a summary of the behavior of the object, not only the movement, but also the shape of the object as time passes," says Agouris.

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And the helix is not limited to the few hours that a video camera can capture; it can model anything from one individual event on one particular morning (such as a traffic jam) or larger event taking place over decades (such as the city's encroachment on its outlying suburbs).

Nor is the helix limited to modeling: it can be used to summarize, generalize and index data, because the nodes allow you to home in on specific moments in the time sequence. If a researcher so chooses, it can be set up as a visual interface - so that one can click on a node and it will pick up the frame in a video or still time series where the corresponding event has happened.

Because of its versatility, Agouris's work has attracted interest from a wide range of federal agencies who contributed datasets and feedback. They include NASA's Goddard Space Flight Center, the National Geospatial-Intelligence Agency, the US Geological Survey, the Army Corps of Engineers and the National Cancer Institute. NASA researchers are using the technology to analyze satellite images - not of distant planets, but of invasive plant species throughout the Colorado plateau.

The helix allows hundreds of images of thousands of acres to be reduced to a few inches of graph. Because it is a spatial-temporal representation of how an object spreads over time, it can be used by the researchers to predict the likely geographic range of a non-native plant, giving some warning about future infestations. The helix is an advanced on current modeling tools because it allows more finely detailed modeling of the behaviors of groups of objects, so it is easy to see what all members of a given invasive species are likely to do.

The DG work has proved so successful, Agouris says, that she and her team have now entered a new phase: They are engaged in technology transfer, working with a private company to develop a stable, commercial version of the software.