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# Scattered Data Visualization - PowerPoint PPT Presentation

Scattered Data Visualization. Shanthanand Kutuva Rabindranath Kiran V Bhaskar. Contents. Scattered Data….a brief introduction. Data Description. Several Possible Approaches. Delaunay Triangulation. Limitations to visualize scattered data. Our Approach-system design

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### Scattered Data Visualization

Shanthanand Kutuva Rabindranath

• Scattered Data….a brief introduction.

• Data Description.

• Several Possible Approaches.

• Delaunay Triangulation.

• Limitations to visualize scattered data.

• Our Approach-system design

• Implementation Considerations

• Demo.

• Future work.

• Topology & Geometry

• Examples- Geophysical and Bio-physical data

Fig: Geophysical Data in 3D space

• Three Dimensional Data.

• Three columns representing each axes.

• Fourth column representing the scalar value.

• Data Normalization.

• Splatting

• Interpolation

• “An optimal triangulation, which satisfies, the circum-sphere condition “.

• Optimal Triangulation: “A triangulation which generates maximized minimum angles”.

• Circum-sphere Condition.

• 2D-“The minimum interior angle of a triangle in Delaunay’s triangulation is greater than or equal to the minimum interior angle of any other possible triangulation.”

• Edge Swapping

•  Scattered density data can be difficult to visualize, particularly when the data do not lie on a regular grid.

•  It is difficult to visualize scattered data if it contains regions of sparse measurements. This often occurs in geophysical or biophysical data.

•  Even using Triangulation, if the points are arranged on a regular lattice(degenerate points), there are several possible triangulations possible. The choice of triangulation depends on the order of data input.

•  Points that are coincident (or nearly so) may be discarded by the algorithm. This is because the Delaunay triangulation requires unique input points. This can be overcome by controlling definition of coincidence using the “tolerance” instance variable.

• Data set we have is a 3D dataset

• Store it in a double array

• Store the concentration value as a scalar value.

• A Count Id to keep track of points used.

We are expected to consider the following conditions before implementing.

• ·preserve input data values.

• ·produce meaningful output values.

• ·provide error estimations.

• ·accept additional constraints.

• · reduce the requirement on the sampling intensity.