1. Scattered Data Visualization Shanthanand Kutuva Rabindranath Kiran V Bhaskar

2. Contents  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.

3. Scattered Data-A brief introduction Topology & Geometry Examples- Geophysical and Bio-physical data Fig: Geophysical Data in 3D space

4. Data Description Three Dimensional Data. Three columns representing each axes. Fourth column representing the scalar value. Data Normalization.

5. Several Approaches  Splatting  Interpolation

6. Delaunay Triangulation “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

7. Limitations to visualize scattered data   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.

8. System Design 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.

9. Implementation Considerations 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.

10. Demo  Click here

11. Future Work  We wish to extend the same to a larger dataset.  A high resolution color table  Visualize the same data using other methods and compare.