Data visualization methods

• US 8,717,364 B2
• Filed: 11/13/2009
• Issued: 05/06/2014
• Est. Priority Date: 11/15/2008
• Status: Active Grant

• Current Assignee: New BIS Safe Luxco S.A R.L
• Original Assignee: New BIS Safe Luxco SARL (New BI US LLC)
• Inventors: Cardno, Andrew John, Ingham, Peter Stewart, Lewin, Bart Andrew, Singh, Ashok Kumar
• Primary Examiner(s): Richer, Joni
• Assistant Examiner(s): Wang, Yi
• Application Number:
  US13/129,244
• Publication Number:
  US 20120075324A1
• Time in Patent Office: 1,635 Days
• Field of Search: None
• US Class Current: 345/440
• CPC Class Codes: G06F 16/34 Browsing; Visualisation therefor
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs

Abstract

A method of creating a graphical representation of data points in the form of a heatmap wherein the heatmap represents a plurality of multi-directional flow values between the data points, the method including the steps of positioning data points on a heatmap for graphical representation, calculating bi-directional flow values between at least a first and second data point, determining the intensity and direction of a predominant flow from the bi-directional flow values, and graphically representing the predominant flow and bi-directional flows between the data points in the heatmap.

Claims

1. A method implemented on an electronic computing device of creating a graphical representation of data points in the form of a heatmap wherein the heatmap represents a plurality of multi-directional flow values between the data points, the method including the steps of:

on the electronic computing device, positioning data points on a heatmap for graphical representation;

calculating bi-directional flow values between at least a first and a second data point;

determining the intensity and direction of a predominant flow from the bi-directional flow values;

determining heatmap colors for flow values between the at least first and second data points, the heatmap colors corresponding to the intensity of the bi-directional flow values, the heatmap colors being selected from colors in a range from a first color representing a highest intensity to a second color representing a lowest intensity; and

graphically representing the predominant flow and bi-directional flows between the data points in the heatmap using the determined heatmap colors, wherein a width of a heatmap color corresponding to a data point on the graphical representation represents the intensity of a bi-directional flow value at the data point, the width increasing as the intensity increases.

2. The method of claim 1 further including the steps of:

calculating weighted heat values for an area on the heatmap based on the bi-directional flow values near the area and the distance the bi-directional flow values are from the area.

3. The method of claim 2 further including the step of calculating weighted heat values based on both bi-directional flow values and flow start values for the data points.

4. The method of claim 2 further including the step of calculating the weighted heat values by calculating a weighted distance function.

5. The method of claim 4, wherein the weighted distance function is an inverse distance weighted (IDW) function.

6. The method of claim 1 further including the step of:

calculating a trapezoid distribution between the first and second data point, where the length of the parallel surfaces of the trapezoid distribution at the data points is based on the value of the calculated flow emanating from each represented data point.

7. The method of claim 1 further including the step of:

calculating a triangular prism distribution between the first and second data point, where the height of the triangular prism at each data point is based on the value of the calculated flow emanating from each represented data point.

8. The method of claim 7 wherein the prism height corresponds to one or more of a range of colors or shadings.

9. The method of claim 1 further including the steps of representing flow start values at the data points by calculating conical data distribution values around the data points based on the flow start value associated with that data point.

10. The method of claim 9, wherein the conical data distribution values are calculated by applying one of a frustoconical distribution, an exact conical distribution or a skewed conical distribution to the data value.

11. The method of claim 9 further including the steps of:

determining whether the data distribution values for the data point are greater than the data value associated with that data point, and,

upon a positive determination, calculating inverse conic data distribution values around the data point based on the data value.

12. The method of claim 11 wherein the inverse conical data distribution values are calculated by applying one of an inverse frustoconical distribution, an exact inverse conical distribution or a skewed inverse conical distribution to the data value.

13. A graphical computing system for creating a graphical representation of data points in the form of a heatmap wherein the heatmap represents a plurality of multi-directional flow values between the data points, the system including:

a processor; and

a memory device, the memory device including instructions that when executed by the processor cause the graphical computing system to implement;

a node determination module arranged to position data points on a heatmap for graphical representation;

a flow determination module arranged to calculate bi-directional flow values between at least a first and a second data point;

a heat determination module arranged to determine the intensity and direction of a predominant flow from the bi-directional flow values; and

a rendering module arranged to graphically representing the predominant flow and bi-directional flows between the data points in the heatmap, wherein a width of a heatmap color corresponding to a data point on the graphical representation represents the intensity of a bidirectional flow value at the data point, the width increasing as the intensity increases.

14. The system of claim 13 wherein the heat determination module is further arranged to calculate weighted heat values for an area on the heatmap based on the bi-directional flow values near the area and the distance the bi-directional flow values are from the area.

15. The system of claim 14 wherein the heat determination module is further arranged to calculate weighted heat values based on both bi-directional flow values and flow start values for the data points.

16. The system of claim 14 wherein the heat determination module is further arranged to calculate the weighted heat values by calculating a weighted distance function.

17. The system of claim 16, wherein the weighted distance function is an inverse distance weighted (IDW) function.

18. The system of claim 13 wherein the heat determination module is further arranged to calculate a trapezoid distribution between the first and second data point, where the length of the parallel surfaces of the trapezoid distribution at the data points is based on the value of the calculated flow emanating from each represented data point.

19. The system of claim 13 wherein the heat determination module is further arranged to calculate a triangular prism distribution between the first and second data point, where the height of the triangular prism at each data point is based on the value of the calculated flow emanating from each represented data point.

20. The system of claim 19 wherein the prism height corresponds to one or more of a range of colors or shadings.

21. The system of claim 13 further including a cone generation module arranged to represent flow start values at the data points by calculating conical data distribution values around the data points based on the flow start value associated with that data point.

22. The system of claim 21, wherein the conical data distribution values are calculated by applying one of a frustoconical distribution, an exact conical distribution or a skewed conical distribution to the data value.

23. The system of claim 21 wherein the cone generation module is further arranged to determine whether the data distribution values for the data point are greater than the data value associated with that data point, and, upon a positive determination, calculate inverse conic data distribution values around the data point based on the data value.

24. The system of claim 23 wherein the inverse conical data distribution values are calculated by applying one of an inverse frustoconical distribution, an exact inverse conical distribution or a skewed inverse conical distribution to the data value.