Method to optimize the visualization of a map's projection based on data and tasks

• US 9,626,796 B2
• Filed: 10/28/2015
• Issued: 04/18/2017
• Est. Priority Date: 01/06/2015
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bak, Peter, Saadoun, Gilad M., Ship, Harold J., Statchuk, Craig A., Yaeli, Avi
• Primary Examiner(s): Thomas, Ashish K
• Application Number:
  US14/924,751
• Publication Number:
  US 20160196670A1
• Time in Patent Office: 538 Days
• Field of Search: None
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06T 11/20 Drawing from basic elements, e.g. lines or circles
• CPC Class Codes: G06T 17/05 Geographic models
• CPC Class Codes: G06T 19/00 Manipulating 3D models or images for computer graphics
• CPC Class Codes: G06T 5/80 Geometric correction
• CPC Class Codes: G06V 30/422 Technical drawings; Geographical maps
• CPC Class Codes: G09B 29/005 Map projections or methods associated specifically therewith

Abstract

A method and system for generating a map using a computer is based on data and weighted factors to minimize corresponding projection distortions. The method and system includes determining visualization goals from analyzing a set of datasets for a map using the computer. A set of visualization characteristics are calculated for each dataset based on the visualization goals using the computer. The visualization characteristics are analyzed to weight factors for each of the datasets. Each of the weighted factors is adjusted based on the relevance of each of the datasets for visualization of the map. An aggregate vector of weighted factors is calculated based on all of the datasets, and the map for visualization is generated based on the aggregate vector of weighted factors.

Claims

1. A computer-implemented method to generate a map based on data and weighted factors to minimize corresponding projection distortions, comprising:

determining visualization goals from analyzing a set of datasets for a map using a computer;

calculating a set of visualization characteristics for each dataset based on the visualization goals using the computer, wherein the calculating of a set of visualization characteristics includes determining elements contained within a geographical extent;

analyzing the visualization characteristics to weight factors for each of the datasets;

adjusting each of the weighted factors based on relevance of each of the datasets for visualization of the map;

calculating an aggregate vector of weighted factors based on all of the datasets;

generating the map for visualization based on the aggregate vector of weighted factors;

building a list of shape files for each of the elements, each shape file being constructed based on a map projection, wherein the weighting of factors considers the shape files of each of the elements;

ranking the shape files by;

comparing an area of the shape file to a geographical area of the element associated with the shape file;

comparing a great circle distance between the center of the shape area and the geographical center of the shape file'"'"'s associated element; and

comparing azimuth errors for a set of contained points in a shape associated with each of the shape files; and

selecting a map projection that minimizes the differences in area and in distance between a plurality of projections and geographical measures, and minimizes aggregated azimuth errors.

2. The method of claim 1, wherein the datasets include metadata describing the datasets.

3. The method of claim 1, wherein the visualization characteristics include data which includes bounding box, center point, center of mass, and data orientation.

4. The method of claim 1, wherein each of the visualization goals is associated with one of the datasets.

5. The method of claim 1, further comprising:

weighting the datasets which are always visible in the map more than the datasets that are only shown upon interactions.

6. The method of claim 1, further comprising:

weighting the datasets which relate to visualization more than other datasets.

7. The method of claim 1, further comprising:

weighting datasets which have datapoints in a visualization bounding box.

8. The method of claim 1, wherein the datasets include visualization preferences from a user.

9. A computer-implemented method to determine an optimal map projection for a geographical extent, comprising:

accepting a user request for a map for a geographical extent for display using a computer;

determining a set of elements that are contained within the geographical extent, using the computer;

building a list of shape files for the elements, each shape file being constructed based on a map projection;

ranking the shape files by comparing an area of the shape file to a geographical area of the shape file'"'"'s associated element;

re-rank the shape files by comparing a great circle distance between a center of the area of the shape file and a geographical center of the shape file'"'"'s associated element;

re-rank the shape files by comparing azimuth errors for a set of contained points in the shape associated with each of the shape files; and

selecting a map projection that minimizes differences in area and distance as compared to the geographical extent.

10. The method of claim 9, further comprising:

selecting the map projection that minimizes differences in area and distance between a plurality of projections and geographical measures, and minimizes aggregated azimuth errors.