POSITION INFORMATION ANALYSIS DEVICE AND POSITION INFORMATION ANALYSIS METHOD

• US 20120135749A1
• Filed: 03/25/2010
• Published: 05/31/2012
• Est. Priority Date: 04/01/2009
• Status: Abandoned Application

• Current Assignee: NTT Docomo Incorporated (Nippon Telegraph and Telephone Corporation)
• Original Assignee: NTT Docomo Incorporated (Nippon Telegraph and Telephone Corporation)
• Inventors: Kawakami, Hiroshi, Jo, Manhee, Suzuki, Toshihiro, Kobayashi, Motonari, Nagata, Tomohiro, Okajima, Ichiro, Ochi, Daisuke, Oyabu, Yuki
• Application Number:
  US13/258,157
• Publication Number:
  US 20120135749A1
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

To effectively analyze location information of a large number of users that is obtained easily and to quickly collect data with regard to macroscopic user tendencies. A location information analysis device includes: an input module that is adapted to input point data across a plurality of time points with regard to a plurality of users, the point data including location information indicating a position of a user, time information indicating time at which the location information is obtained, and user identifier information with regard to the user; a haunt area extraction module that extracts an area, as a haunt area where the plurality of users frequently haunt, the area in which the point data is concentrated at or more than a predetermined level, based on a distribution status of the input point data plotted on two dimensional map data; and a storage module that stores the extracted haunt area information.

Claims

1. A location information analysis device comprising:

an input module that is adapted to input point data across a plurality of time points with regard to a plurality of users, the point data including location information indicating a position of a user, time information indicating time at which the location information is obtained, and user identifier information with regard to the user;

a haunt area extraction module that extracts an area, as a haunt area where the plurality of users frequently haunt, the area in which the point data is concentrated at or more than a predetermined level, based on a distribution status of the input point data plotted on two dimensional map data; and

a storage module that stores extracted haunt area information,

wherein

the haunt area extraction calculates density of the input point data of all the users, a distance between the input point data of all the users or a distance between the input point data of each of all the users as the distribution status of the input point data, and extracts a area, as the haunt area where the point data is concentrated at, based on predetermined level and the density of the input point data of all the users, the distance between the input point data of all the users or the distance between the input point data of each of all the users.

2. The location information analysis device according to claim 1, wherein

the haunt area extraction module comprises:

an all-user density estimation module that estimates density of the input point data of all the users in each of a plurality of zones partitioned on the two dimensional map data in advance; and

a first extraction module that extracts an area, as the haunt area, the area in which the estimated density of the point data of all the users is equal to or more than a predetermined level.

3. The location information analysis device according to claim 1, wherein

the haunt area extraction module comprises:

a grouping module that calculates a distance between the input point data of all the users plotted on the two dimensional map data, and makes a group of point data of which calculated distance is equal to or less than a predetermined reference distance; and

a second extraction module that extracts an area, as the haunt area, including a plurality of pieces of grouped point data on the two dimensional map data.

4. The location information analysis device according to claim 1, wherein

the haunt area extraction module comprises:

a classification module that classifies the input point data of all the users for each user;

a per-user density estimation module that estimates density of the point data for each user based on the classified point data for each user in each of a plurality of zones partitioned on the two dimensional map data in advance;

a summation module that totals the estimated density of the point data for each user in each zone and obtains density of the point data of all the users in each zone; and

a third extraction module that extracts an area, as the haunt area, the area in which the obtained density of the point data of all the users is equal to or more than a predetermined level.

5. The location information analysis device according to claim 1, wherein

the haunt area extraction module comprises:

a classification module that classifies the input point data of all the users for each user;

a per-user grouping module that calculates a distance between the classified point data of each user plotted on the two dimensional map data, and makes a group of point data of which calculated distance is equal to or less than a predetermined reference distance;

an overlaying module that overlays an area including a plurality of pieces of grouped point data for each user on the two dimensional map data on the two dimensional map data for all the users; and

a fourth extraction module that extracts an area, as the haunt area, that is obtained through the overlaying.

6. The location information analysis device according to claim 1, further comprising:

a concatenation module that classifies the input point data of all the users for each user and concatenates the point data for each user with the extracted haunt area on the two dimensional map data;

a translocation history derivation module that obtains translocation history information between haunt areas for each user with the data concatenating the point data for each user with the haunt area on the two dimensional map data obtained through the concatenation based on time sequential transition with regard to relative positions of the point data for each user for the haunt area; and

a travel derivation module that integrates the translocation history information between the haunt areas for each of all users and obtains travel information between the haunt areas with regard to all the users, based on the obtained translocation history information between the haunt areas of all the users, wherein

the storage module further stores travel information between the haunt areas with regard to all the users.

7. The location information analysis device according to claim 1, further comprising:

a concatenation module that classifies the input point data of all the users for each user and concatenates the point data for each user with the extracted haunt area on the two dimensional map data;

a staying time derivation module that calculates staying time for each user with regard to each haunt area with the data concatenating the point data for each user with the haunt area on the two dimensional map data obtained through the concatenation, based on the time information of the point data of the user located in each haunt area; and

a staying time statistic derivation module that integrates staying time information obtained for each user with regard to each haunt area for all the users and calculates predetermined statistics for all users with regard to the staying time for each haunt area based on the obtained staying time information for all the users, wherein

the storage module further stores the predetermined statistics for all the users with regard to the staying time for each haunt area.

8. The location information analysis device according to claim 7, further comprising

an active time period derivation module that gains information with regard to active time periods of all users for each haunt area based on attribute data including address information of each user input from outside or stored in advance and the staying time for each user in each haunt area derived by the staying time derivation module, for a certain user, by defining a staying time in a haunt area corresponding to the address information of the user as a staying time at home and determining a time period excluding the staying time at home as an active time period of the user, thereby obtaining the active time period of each user, and by integrating information of the active time period of each user for each haunt area, wherein

the storage module stores the information with regard to the active time periods of all the users for each haunt area.

9. The location information analysis device according to claim 1, further comprising:

a read-out module that reads out the information stored in the storage module; and

an output module that outputs the read-out information.

10. A location information analysis method performed by a location information analysis device, the location information analysis method comprising:

an input step of inputting point data across a plurality of time points with regard to a plurality of users to the location information analysis device by the location information analysis device, the point data including location information indicating a position of a user, time information indicating time at which the location information is obtained, and user identifier information with regard to the user;

a haunt area extraction step of extracting an area, as a haunt area where the plurality of users frequently haunt by the location information analysis device, the area in which the point data is concentrated at or more than a predetermined level, based on a distribution status of the input point data plotted on two dimensional map data; and

a storing step of storing the extracted haunt area information by the location information analysis device,

wherein

the haunt area extraction step calculates density of the input point data of all the users, a distance between the input point data of all the users or a distance between the input point data of each of all the users as the distribution status of the input point data, and extracts a area, as the haunt area where the point data is concentrated at, based on predetermined level and the density of the input point data of all the users, the distance between the input point data of all the users or the distance between the input point data of each of all the users.