Method for analyzing demographic data

• US 20080016051A1
• Filed: 01/11/2006
• Published: 01/17/2008
• Est. Priority Date: 12/21/2001
• Status: Active Grant

• Current Assignee: Location Incorporated Group Corporation (CoreLogic Incorporated)
• Original Assignee: Location Incorporated Group Corporation (CoreLogic Incorporated)
• Inventors: Schiller, Andrew
• Granted Patent: US 7,680,859 B2
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: Y10S 707/918 Location
• CPC Class Codes: Y10S 707/919 Geographic

Abstract

A computer implemented method of generating an ordered list of geographical locations having similarities in preselected categories relative to a first geographical location.

Claims

1-8. -8. (canceled)

9. A computer-implemented method of identifying a plurality of geographic locations having similarities to another geographic location in at least one category, the method comprising the steps of:

providing a data base containing geographic locations and at least one characteristic associated with each of said geographic locations;

calculating a numeric value that is a quantitative representation of said at least one characteristic and associating said numeric value with each of said geographic locations;

allowing a user to select a first geographic location from the data base;

comparing the numeric value associated with said first geographic location in the data base with the numeric values associated with each of the other geographic locations in the data base to determine the overall dissimilarities in said numeric values; and

generating an ordered list of geographic locations that are most similar to the first geographic location based on the smallest dissimilarities in said numeric values

10. A method according to claim 9 wherein the step of comparing the first geographic location in the data base with other geographic locations in the data base and generating an ordered list of other geographic locations most similar to the first geographic location comprises generating a dissimilarity matrix in which an overall dissimilarity value is assigned to each of the other geographic locations relative to the first location, and generating an ordered list of the other geographic locations therefrom.

11. A method according to claim 10 wherein the step of generating a dissimilarity matrix comprises the step of applying an extraction method to the raw data, the extraction method including a factor analysis to extract factors from the data base, and applying a squared Euclidian distance measure to the extracted factors.

12. The method according to claim 9, wherein said step of comparing the numeric value associated with said first geographic location in the data base with the numeric values associated with each of the other geographic locations in the data base further comprises:

calculating a squared difference between a numeric value associated with a first chosen characteristic of each geographic location to a numeric value associated with said first chosen characteristic of said first geographic location;

repeating said step of calculating a squared difference using each respective identifiable characteristic related to each respective geographic location; and

adding each squared difference that is calculated together to create a dissimilarity value between said first geographic location and each of said other geographic locations.

13. A computer-implemented method of identifying a plurality of geographic locations having similarities to at least one user selected keyword descriptor, the method comprising the steps of:

providing a data base of geographic locations and at least one characteristic associated with each of said-geographic locations;

calculating a numeric value that is a quantitative representation of said at least one characteristic and associating said numeric value with each of said geographic locations;

converting said at least one keyword descriptor to a corresponding quantitative numeric value;

allowing a user to select at least one keyword descriptor;

comparing the numeric value associated with said at least one keyword descriptor with the numeric values associated with the other geographic locations in the data base to determine the overall dissimilarities in said numeric values; and

generating an ordered list of geographic locations that are most similar to the at least one keyword descriptor, wherein said order is based on the smallest dissimilarities in said numeric values among the geographic locations.

14. A method according to claim 13 wherein the step of comparing the numeric value associated with said at least one keyword descriptor with the numeric values associated with the geographic locations in the data base and generating an ordered list of all geographic locations most similar to the at least one keyword descriptor further comprises:

generating a dissimilarity matrix in which an overall dissimilarity value is assigned to each of the geographic locations relative to the numeric value of said at least one keyword descriptor, and generating an ordered list of the geographic locations therefrom.

15. A method according to claim 14 wherein the step of generating a dissimilarity matrix comprises the step of applying an extraction method to the raw data, the extraction method including a factor analysis to extract factors from the data base, and applying a squared Euclidian distance measure to the extracted factors.

16. The method according to claim 13 wherein said step of selecting at least one keyword descriptor comprises:

selecting a plurality of keyword descriptors.

17. The method according to claim 13 wherein said step of selecting at least one keyword descriptor further comprises:

selecting a category that includes a preselected group of keyword descriptors.

18. The method according to claim 16, wherein said step of comparing the numeric values associated with said at least one keyword descriptor with the numeric values associated with geographic locations in the data base further comprises:

calculating a squared difference between a numeric value associated with a first chosen keyword descriptor to a corresponding numeric value associated with said same characteristic for each of said geographic locations within said database;

repeating said step of calculating a squared difference using each of said plurality of keyword descriptors for corresponding numeric values associated with said same characteristic related to each geographic location; and

adding each squared difference that is calculated together to create a dissimilarity value between said plurality of keyword descriptors and each of said geographic locations within said database.

19. A computer-implemented method of identifying a plurality of geographic locations having similarities to a another geographic location as modified by at least one user selected keyword descriptor, the method comprising the steps of:

providing a data base of geographic locations and characteristics associated with each of said geographic locations;

calculating a numeric value that is a quantitative representation of each of said characteristics and associating said numeric values with each of said geographic locations;

allowing a user to select a first geographic location from the data base;

generating a set of keyword descriptors corresponding to said numeric values for said characteristics of said user selected first geographic location;

allowing said user to modify said set of keyword descriptors;

converting said modified set of keyword descriptors to a corresponding numeric values;

comparing the numeric values associated with said modified set of keyword descriptors with the numeric values associated with the other geographic locations in the data base to determine the overall dissimilarities in said numeric values; and

generating an ordered list of geographic locations that are most similar to said user selected geographic location as modified by said user based on the smallest dissimilarities in said numeric values.

20. A method according to claim 19 wherein the step of comparing the numeric values associated with said user selected geographic location as modified by said user with the numeric values associated with the other geographic locations in the data base and generating an ordered list of other geographic locations most similar to the modified set of keyword descriptors further comprises:

generating a dissimilarity matrix in which an overall dissimilarity value is assigned to each of the other geographic locations relative to the numeric value of said set of keyword descriptors of said user selected geographic location as modified by said user, and generating an ordered list based on the smallest dissimilarity between all of the geographic locations within the data base.

21. A method according to claim 20 wherein the step of generating a dissimilarity matrix comprises the step of applying an extraction method to the raw data, the extraction method including a factor analysis to extract factors from the data base, and applying a squared Euclidian distance measure to the extracted factors.