JP2015207323A - user information output system and user information output method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To output highly accurate user information in relation to an object area.SOLUTION: A user information output system includes: storage means for storing users' positioning information to a first database with respect to a plurality of users; area setting means for setting an object area; first condition setting means for setting a first extraction condition with respect to the positioning information; second condition setting means for setting a second extraction condition with respect to a user attribute; user extraction means which extracts a user who corresponds to positioning information satisfying the set first extraction condition and has user attribute satisfying the set second extraction condition with respect to the set object area with reference to the first database and a second database storing user attributes; and output means for outputting information about the extracted user in association with the set object area.

Description

  The present invention relates to information processing technology using GPS (Global Positioning System) and the like, and more particularly, to technology for processing positioning information of a mobile terminal positioned using GPS.

  Various services using GPS are provided. As an example of such a service, the following Patent Document 1 includes means for setting information related to electronic mail and a transmission point on a route, means for calculating the current vehicle position of the vehicle, and the calculated vehicle position. A navigation device having means for monitoring whether or not the set transmission point has been reached and transmission means for transmitting the set e-mail when the vehicle position has reached the transmission point is disclosed. Yes.

JP 2000-285382 A

  By the way, with the rapid spread of mobile terminals, the Internet, navigation systems, etc., the use of GPS is expanding in various scenes. As a result, among users such as mobile terminals equipped with a GPS function, there is an increasing need for improving the convenience of services using GPS. For example, as an example of a service that uses the GPS function of a mobile terminal, when the current position of the mobile terminal is periodically measured and the current position that has been measured approaches a destination set in advance by the user, that fact is indicated. There are known services for notifying a user and providing information related to the destination.

  Here, the inventor of the present application considered extracting users related to a certain area based on the positioning information measured by the GPS function and analyzing the user, and set conditions for the positioning information. In the method of extracting users related to an area, for example, all users having positioning information in the area are extracted, and it has been found difficult to perform highly accurate user analysis.

  Therefore, the present invention provides a technology that can accurately extract users to be analyzed and output user analysis results (user information) when extracting users related to a certain area based on positioning information. It is to provide.

  The user information output system sets, for a plurality of users, storage means for storing the user's positioning information in a first database, area setting means for setting a target area, and a first extraction condition for the positioning information. The set target area with reference to a first condition setting means, a second condition setting means for setting a second extraction condition for the user attribute, and a second database for storing the first database and the user attribute The user extraction means for extracting the user corresponding to the positioning information satisfying the set first extraction condition and having the user attribute satisfying the set second extraction condition, and the set target area Output means for outputting the extracted user information in association with the user information.

  Further, the first extraction condition includes a condition for a positioning time included in the positioning information.

  In addition, the user attribute includes information on a user's action base.

  Further, the output means classifies the extracted users into groups based on the positioning information of the users, and outputs the extracted user information for a specific group.

  Further, the output means classifies the extracted users into groups based on information on the action bases outside the set target area of the user, and outputs the extracted user information for a specific group. Features. It is preferable that the group based on the information on the user's action base includes a group by home location.

  Further, the output means may enter the target area from any sub-area within the set target area, and / or within the set target area for the extracted user based on the positioning information of the user. Determining from which sub-area the user has left the target area, classifying into groups based on the determined entry sub-area and / or exit sub-area, and outputting the extracted user information for a specific group It is characterized by.

  In addition, at least one of the sub-areas is set based on any of a station, an airport, a port, an interchange, and an intersection in the set target area.

  Further, the area setting means sets a first target area and a second plurality of target areas, and the output means extracts the user's extracted information for both the first target area and the second target area. Information is output in association with the first target area and the second target area.

  Further, in the user information output method, for a plurality of users, a step of storing the positioning information of the users in a first database, a step of setting a target area, and a step of setting a first extraction condition for the positioning information And setting a second extraction condition for the user attribute, and referring to the first database and the second database storing the user attribute, the set first extraction with respect to the set target area Extracting the user corresponding to the positioning information satisfying the condition, the user having a user attribute satisfying the set second extraction condition, and the extracted user information in association with the set target area And a step of outputting.

  Further, the present invention is also realized as a program for causing a computer to execute the above steps. This program can be installed or loaded on a computer through various recording media such as an optical disk such as a CD-ROM, a magnetic disk, or a semiconductor memory, or via a communication network.

  Further, in this specification and the like, the term “means” does not simply mean a physical means, but includes a case where the functions of the means are realized by software. Further, the function of one means may be realized by two or more physical means, or the functions of two or more means may be realized by one physical means.

  According to the present invention, when a user related to a certain area is extracted based on positioning information, a technique capable of accurately extracting a user to be analyzed and outputting a user analysis result (user information). Can be provided.

It is a block diagram which shows schematic structure of a fluid population specific system. It is a figure explaining an example of basic mesh data. It is a figure explaining an example of daily mesh data. It is a figure explaining the time allocation between positioning points. It is a figure explaining an example of the mesh data according to a period. It is a figure explaining the setting of an everyday zone. It is a figure which shows an example of a mesh group. It is a figure explaining an example of an action base mesh group and an action base mesh. It is a figure which shows an example of the data structure of positioning information DB, basic | foundation mesh data DB, and daily mesh DB. It is a figure which shows an example of the data structure of mesh DB classified by period, mesh group DB, and daily life area DB. It is a figure which shows an example of the data structure of action base DB. It is a block diagram which shows schematic structure of a portable terminal. It is a flowchart which shows an example of the whole flow of an everyday sphere setting process. It is a flowchart which shows an example of the flow of a daily mesh data generation process. It is a flowchart which shows an example of the flow of a mesh data generation process according to a period, and a daily sphere setting process. It is a flowchart which shows an example of the whole flow of an action base and a fluid population specific process. It is a flowchart which shows an example of the whole flow of a home mesh area | region identification process. It is a flowchart which shows an example of the flow of the whole work place mesh area | region specific process. It is a figure for demonstrating home confirmation and work place confirmation. It is a flowchart which shows an example of the whole flow of a home confirmation process. It is a flowchart which shows an example of the whole flow of work location confirmation processing. It is a flowchart which shows an example of the whole flow of a fluid population specific process. It is a flowchart which shows an example of the whole flow of a user information output process. The example which outputs the user information of the group according to the prefecture where a house is located about a guest group is shown. An example in which user information is output as the ratio of the number of people entering by subarea and the ratio of the number of people leaving by subarea is shown. An example in which a plurality of target areas are set and user information is output is shown.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The user information output system of the present embodiment uses the positioning information and user attributes from the terminal to extract users related to the target area and outputs the user information. For example, the user's daily life zone (hereinafter referred to as “daily zone”) is automatically set using positioning information from the terminal, and the user's specific action base is specified from the set daily zone. In addition, the information on the action base can be used as a user attribute. As a method for identifying the user's action base from the daily life area, for example, it may be possible to use the number of times of positioning or the like as the action base parameter. However, if only one type of action base parameter is used, the accuracy of specifying the action base is poor. There is a problem. Therefore, it is conceivable to specify an action base by using at least two or more kinds of action base parameters according to their priorities, thereby improving the accuracy of action base specification. Hereinafter, in the present embodiment, the first action base (for example, “home”) that is assumed to have the strongest connection with the user and the base of the life of the user who stays continuously is the base of the user's life. A case will be described in which second action bases (for example, “work location” and “school”) that are not strongly connected to the location are specified and used as user attributes.

[1. Configuration of user information output system]
FIG. 1 is a block diagram illustrating an example of a schematic configuration of a user information output system according to the present embodiment. A user information output system (user information output device) 10 is connected to a mobile mobile terminal device (hereinafter referred to as “mobile terminal”) 20 via a network N.

  The user information output system 10 includes a main control unit 101, a communication unit 102, a positioning information storage unit 103, a basic mesh data generation unit 104, a mesh data generation unit 105 (daily mesh generation unit 106 (stay seconds / position count counting unit). 107, distribution complementing means 108), period-specific mesh generating means 109), daily sphere setting means 110, group processing means 111, action base mesh group specifying means 112, action base specifying means 113, mesh visitor number acquiring means 120 , Base user number acquisition means 121, fluid user number acquisition means 122, area setting means 130, first condition setting means 131, second condition setting means 132, user extraction means 133, user information output means 134, positioning information DB 114 (first 1 database), basic mesh data DB 115, daily Mesh DB116 and period-specific mesh DB117 (second database), everyday speaking DB118, basing its DB119 (third database), mainly includes various function realizing means such as a mesh group DB123.

  Although not shown in the figure, the user information output system 10 includes a CPU, a memory such as a ROM and a RAM, an external storage device for storing various types of information, an input / output interface, a communication interface, and a bus connecting them. For example, when the CPU executes a predetermined program stored in a ROM or the like, it functions as each of the function realizing means. The user information output system 10 may be configured by a single computer or may be configured by a plurality of computers distributed on a network.

  The main control unit 101 controls the entire operation of the user information output system 10 and the operation of each unit described above. The communication means 102 is an interface for transmitting and receiving various types of information by communicating with the mobile terminal 20 via the network N. The communication unit 102 also functions as a reception unit that receives positioning information transmitted from the mobile terminal 20.

  The positioning information storage means 103 stores, for a plurality of users, positioning information transmitted from the user's portable terminal 20 in the positioning information DB 114. Although the positioning information will be described later, a positioning point (coordinate information) where the current position of the mobile terminal 20 is positioned, a positioning time of the positioning point, and user identification information (ID) that uniquely identifies the user of the mobile terminal 20 And are included.

  Based on the positioning information stored in the positioning information DB 114, the basic mesh data generation means 104 is a basic mesh in which a positioning point and a positioning time (stay start time) are associated with a corresponding mesh area of the map for each user and for each positioning date. Generate data. The mesh area is a plurality of divided areas obtained by dividing the map based on the latitude and longitude, and the shape and size of the divided areas can be appropriately set according to specifications and designs. For example, the shape of the mesh region can be a region surrounded by a polygon such as a quadrangle, a hexagon, a rhombus, or a curve such as a circle. Further, for example, when the user wants to specify an area where he / she frequently stays, the size of the mesh area can be set large. On the other hand, when he / she wants to specify a store where the user frequently visits, the mesh area can be set small. Further, the size of the mesh area may not be constant. For example, the urban mesh may be made smaller (finer). You can also change the size of the mesh for each region based on demographic information (for example, make the mesh in a region with a large population smaller), or set the size based on city classification, residential land type, daily life information, etc. May be. The size may be set based on positioning information for a certain period (for example, August, weekday morning, etc.) or current positioning information. For example, it is conceivable to increase the mesh size when the number of users who have been measured during a certain period is small, or when the positioning accuracy of positioning information during a certain period is low. Even if there are many users who have measured the position during a certain period, it is desirable to increase the mesh size if the positioning accuracy is low. The mesh area corresponding to the positioning point means a mesh area to which the coordinate information of the positioning point belongs.

  FIG. 2 is a diagram for explaining how the basic mesh data is generated based on the positioning information stored in the positioning information DB 114. In the same figure, basic mesh data B1 in which a positioning point and a positioning time are associated with each mesh area is described as a user's action trajectory on the corresponding day for the positioning date (March 2010) of the user (Mr. A). ing. The generated basic mesh data is stored in the basic mesh data DB 115.

  Returning to FIG. 1, the mesh data generation means 105 determines the number of positioning days and positioning within a predetermined period in each mesh area based on the basic mesh data stored in the basic mesh data DB 115 for a plurality of users who have received positioning information. At least two of the number of times and the stay time of the user are calculated as action base parameters, and mesh data in which each mesh region is associated with the calculated at least two action base parameters is generated. The number of positioning days is the total number of days that positioning was performed in the corresponding mesh area within a predetermined period (the same day is not double-added) and indicates the number of days the user stayed in the corresponding mesh area. Is called "stay days". The number of positioning times is the total number of times positioning has been performed in the corresponding mesh region within a predetermined period. The user's stay time is the total time that the user stayed in the corresponding mesh area within a predetermined period. In the present embodiment, since the stay time is expressed in seconds, this is also referred to as “stay seconds” hereinafter. In the action base parameter, any combination of at least two of the stay days, the number of positioning times, and the stay seconds can be set according to the content of the action base to be specified. You may add other information. The mesh data includes daily mesh data and period-specific mesh data, which will be described below.

  The daily mesh generation means 106 generates mesh data for each unit period as data representing the number of times of positioning and the number of stay seconds in each mesh region for each predetermined unit period. The predetermined unit period is a minimum unit period for generating mesh data, and can be appropriately set according to specifications / designs such as “one day” and “half day”. In the present embodiment, “1 day” is set as the predetermined unit period, and hence the mesh data by unit period is hereinafter referred to as “daily mesh data”. FIG. 3 is a diagram for explaining how daily mesh data is generated based on the basic mesh data stored in the basic mesh data DB 115. In the figure, mesh data M1 in which the number of stay seconds and the number of times of positioning are associated with each mesh area is described for the positioning date (2010/03/03) of the user (Mr. A). In this embodiment, the case where the number of positioning times and the number of staying seconds are calculated and associated for each mesh region has been described. However, only the number of positioning times or the number of staying seconds may be calculated and associated. The generated daily mesh data is stored in the daily mesh DB 116.

  Returning to FIG. 1, the daily mesh generation unit 106 specifically includes a staying seconds / positioning count totaling unit 107 and a distribution complementing unit 108.

  Based on the basic mesh data stored in the basic mesh data DB 115, the staying seconds / positioning number counting means 107 calculates the number of positioning points in each mesh area and the number of seconds the user of the mobile terminal stays for a predetermined unit period. Each mesh region is associated with the calculated number of positioning times and staying seconds. The number of positioning times in each mesh area is calculated by summing the number of times positioning has been performed in the corresponding mesh area. The staying seconds in each mesh area is calculated by totaling the allocation time allocated to the corresponding mesh area by the distribution complementing means 108 described later.

  The distribution complementing means 108 selects the first positioning point and the second positioning point arranged next to the first positioning point on the time axis from the positioning points associated with the mesh region. The first positioning point is also called a pre-movement point, and the second positioning point is also called a post-movement point. Next, the time from the first positioning time of the first positioning point to the second positioning time of the second positioning point (hereinafter referred to as “time between positioning points”), the second positioning point, and the first positioning point (Hereinafter, referred to as “distance between positioning points”). Then, an allocation condition is determined based on the time between the positioning points and the distance between the positioning points, and the time between the positioning points is set on the movement path from the first positioning point to the second positioning point according to the determined allocation condition. Allocate to the mesh area (including the first mesh area of the first positioning point and the second mesh area of the second positioning point). The allocation includes allocation of 0 hours. In this case, the allocation is substantially the same as not allocation.

  The distribution conditions can be set as appropriate according to the specifications and design, and there is no particular limitation on the contents thereof, but in this embodiment, according to the transmission conditions and configuration of positioning information from the mobile terminal, as follows: Allocation conditions are set. Specifically, the mobile terminal 20 in the present embodiment transmits positioning information to the user information output system 10 at a predetermined transmission time interval (for example, 5 minutes). The positioning information is not transmitted for the reason. That is, when the user is in the radio wave range, the positioning information from the mobile terminal 20 is transmitted at a predetermined transmission time interval only when the user is moving. Therefore, the user's stay at the first positioning point, which is the point before moving, is the time obtained by excluding the moving time from the time between the positioning points between the first positioning time and the second positioning time (hereinafter referred to as “time after moving time subtraction”). Therefore, the movement time subtracted time is allocated to the mesh area of the first positioning point.

  Next, when it is possible to estimate the user's movement content (moving means and movement route) between the first positioning point and the second positioning point (when there is continuity between the first positioning point and the second positioning point) ) Distributes the movement time substantially evenly to the mesh region located on the estimated movement path (hereinafter referred to as “estimated movement path”) from the first positioning point to the second positioning point (first distribution). conditions). That is, the staying seconds are complemented for the mesh region on the estimated movement route. On the other hand, when the user's movement content between the first positioning point and the second positioning point cannot be estimated (when there is no continuity between the first positioning point and the second positioning point), the mesh area of the movement time No allocation is made (0 hours are allocated) (second allocation condition). That is, the number of seconds staying in the mesh area is not complemented.

  The method for determining whether or not the movement content can be estimated can be appropriately set according to the specification and design, and the content thereof is not particularly limited. For example, the time between positioning points and / or the distance between positioning points is a predetermined reference time. If it is less than the distance and / or less than the predetermined reference distance, it is assumed that the movement path of the user can be estimated by assuming that the movement is by walking or the like. On the other hand, when it is not less than the predetermined reference time and / or less than the predetermined reference distance, it can be assumed that the user's movement route cannot be estimated by considering that the movement is, for example, when using the subway.

  In addition, the calculation method of the travel time can be appropriately set according to the specification and design, and the content thereof is not particularly limited. For example, a method of using a transmission time interval of the mobile terminal 20 or a distance between positioning points And a method of calculating the travel time from a predetermined speed according to the travel means (eg, train, car, walk). In the present embodiment, in the case of the first distribution condition, the transmission time interval (for example, 5 minutes) of the positioning information in the mobile terminal 20 is set as the travel time. In the case of the second distribution condition, the travel time from the first positioning point to the second positioning point is calculated based on the distance between the positioning points and a predetermined speed (for example, 30 km) set in advance. Note that the predetermined hourly speed can be appropriately set according to the estimated moving means and the like. For example, for the first distribution condition, the traveling time may be calculated using the hourly walking speed.

  Also, the method for specifying the movement route can be set as appropriate according to the specification and design, and the content thereof is not particularly limited. In the present embodiment, however, the connection is made by connecting the first positioning point and the second positioning point. Represents the travel route. In FIG. 4A, the connection is shown as a straight line, but the connection is not limited to a straight line, and has, for example, a shape along an arbitrary movement route estimated according to a road, a route, a building, or the like. be able to. Further, among the mesh regions located on the connection, mesh regions other than the mesh regions of the first positioning point and the second positioning point are referred to as complementary mesh regions.

  In addition, since the daily mesh generation means 106 manages the positioning information for each predetermined unit period, when the first positioning point and the second positioning point cross the day (for example, the reference time (e.g., 0: 00/24 When)). That is, the first positioning point (hereinafter referred to as “first positioning point”) and the last positioning point (hereinafter referred to as “last positioning point”) in the unit period before the movement to be combined. A positioning point or a post-movement positioning point belongs to another day. Therefore, in the present embodiment, as the third distribution condition, for the first positioning point, since the corresponding pre-movement positioning point belongs to another day, the time from the reference time (for example, 0:00) to the positioning time of the first positioning point. The time is allocated as the staying seconds to the mesh area of the first positioning point. For the last positioning point, since the corresponding post-movement positioning point belongs to another day, the time from the positioning time of the last positioning point to the reference time (for example, 24:00) is displayed in the mesh area of the last positioning point. To distribute. In the third distribution condition, the maximum time allocated to the mesh area can be set, and the content thereof is not particularly limited, but in the present embodiment, a maximum of 6 hours is set.

  FIG. 4A is a diagram illustrating a state in which the staying seconds are distributed according to the first distribution condition. In the figure, first, the travel time obtained by subtracting the travel time (5 minutes) from the time between the positioning points between the first positioning point and the second positioning point (9: 40-9: 30 = 10 minutes). The subtracted time (5 minutes (300 seconds)) is distributed to the mesh area of the first positioning point. Next, in order to supplement the stay time in the mesh area between the first positioning point and the second positioning point, the movement time (5) is set to the six mesh areas located on the connection between the first positioning point and the second positioning point. Minute) is distributed approximately evenly (50 seconds each).

  FIG. 4B is a diagram for explaining a state in which the staying seconds are distributed according to the second distribution condition. In the figure, first, the travel time (20 minutes) between the first positioning point and the second positioning point is calculated from the distance between positioning points (10 km) and a predetermined speed (eg, 30 km / hour). The movement time subtracted time (100 minutes) obtained by subtracting the movement time (20 minutes) from the interval time (11: 30-9: 30 = 120 minutes) is allocated to the mesh area of the first positioning point. Yes. Here, since the mesh region located between the first positioning point and the second positioning point is not complemented, the travel time is not allocated.

  FIG. 4C is a diagram illustrating a state in which the staying seconds are distributed according to the third distribution condition. In the figure, the first positioning point (positioning time 7:30) is allocated 6 hours as the maximum time, and the last positioning point (positioning time 23:30) is from the positioning time to 0:00. 30 minutes are allocated.

Next, returning to FIG. 1, the period-specific mesh generation means 109 will be described. The mesh generator 109 for each period totals the number of positionings and the number of stays included in the daily mesh data for each predetermined counting period, and associates the number of times of positioning, the number of stays, and the number of stays with each mesh area. Total period-specific mesh data (hereinafter referred to as “period-specific mesh data”) is generated.
The predetermined counting period can be set as appropriate according to the design and specifications, and the contents of the period are not particularly limited. For example, the counting period such as the latest 7 days, the latest 30 days, the latest 180 months, the latest 365 days, etc. Can be set. A plurality of total periods may be set at the same time. In addition, if mesh data by period already exists, the daily mesh data for the date to be newly added (for example, the latest positioning date) and the date to be subtracted (for example, the oldest positioning date in the existing counting period) Read from the daily mesh DB 116 and add the stay seconds, positioning times, stay days for the additional target day for each mesh area, while subtracting stay seconds, positioning times, stay days for the subtraction target day for each mesh area To do. FIG. 5 is a diagram for explaining how the period-specific mesh data is generated from the daily mesh data. This figure shows a state in which the number of stay seconds, the number of positioning times, and the number of stay days are associated with each mesh area in the mesh data M6 by period generated from the daily mesh data M5.

  When the basic mesh data DB 115 includes “positioning level”, the daily mesh generation means 106 and the period mesh generation means 109 can create each mesh data according to the positioning level. Each mesh data generated according to the positioning level is stored in the daily mesh DB 116 and the period mesh DB 117, respectively. When creating daily mesh data according to the positioning level, the staying seconds / positioning count totaling means 107 can calculate the positioning count and staying time using only the basic mesh data of the high positioning level. On the other hand, daily mesh data may be created using all basic mesh data regardless of the positioning level.

  The daily area setting unit 110 extracts a mesh region in which at least two combinations of the number of positioning times, the number of staying seconds, and the number of staying included in the period-specific mesh data are equal to or greater than a predetermined threshold, Set the daily life zone.

  FIG. 6 is a diagram for explaining the setting of the daily sphere based on the period-specific mesh data. In the figure, when a mesh area that matches the condition of “stay time is 300 minutes or more and stay days is 3 days or more” is extracted from the mesh data M7 of the most recent 30 days, A case is shown in which the daily sphere is set by extracting a mesh area that matches the condition “stay time is 60 minutes or more and stay days are 2 days or more” from the mesh data M8 of the day. As shown in the figure, the daily life area may include various places such as a user's main base of action, home, work place, transfer station road on a commute route, and a store at a destination.

  Returning to FIG. 1, the group processing unit 111 will be described. The group processing unit 111 executes mesh grouping processing and group-specific calculation processing for at least one of a plurality of users who created mesh data. In the mesh grouping process, mesh areas satisfying a predetermined connection relationship are grouped for mesh areas included in the daily life. When the GPS positioning accuracy of the mobile terminal 20 is low, there is a possibility that the mesh area adjacent to the mesh area where the user is actually located is measured as the current position. For this reason, in this embodiment, mesh regions that satisfy a predetermined connection relationship are grouped and processed. The “predetermined connection relationship” can be set as appropriate according to the specifications and design. For example, at least a part of the mesh region is in contact (if the vertices of different mesh regions are in contact, the sides of the mesh region are Including, but not limited to, a case where a predetermined number (for example, 1) of extraordinary sphere meshes are sandwiched between them. The grouped mesh region is referred to as “mesh group”. When there is no mesh area that satisfies the predetermined connection relationship, the mesh area is handled as one mesh group. FIG. 7A is a diagram for explaining grouping of mesh regions. In FIG. 9A, mesh data M11 indicates the daily sphere before grouping, and mesh data M12 indicates the daily sphere after grouping.

  The group-specific calculation process targets at least one mesh group composed of one or a plurality of mesh regions, and calculates the group stay days, the group stay seconds, and the group positioning times as the group action base parameters. Mesh group data in which the calculated group stay days, group stay sickness, and group positioning count are associated with the mesh group is generated. The number of stays by group is the sum of the stay days of all mesh areas in the same mesh group in the aggregation target period (however, duplicate days are not double-added), or all meshes in the same mesh group in the aggregation target period It can be the maximum number of stays in the region. The group stay seconds are the sum of the stay seconds of all mesh areas in the same mesh group in the aggregation target period, or the maximum of the stay seconds of all mesh areas in the same mesh group in the aggregation target period. Dwell time. The positioning count by group is the total positioning count of all mesh areas in the same mesh group in the aggregation target period, or the maximum positioning count among the positioning counts of all mesh areas in the same mesh group in the aggregation target period. It can be. FIG. 7B shows an example in which the staying time per group and the staying time per group are calculated.

  Next, the behavior base mesh group specifying unit 112 specifies a behavior base mesh group from the mesh group on condition that the mesh group that generated the mesh group data satisfies a predetermined calculation condition. Specifically, first, it is determined whether or not there is a mesh group satisfying a predetermined calculation condition in the mesh group. If the determination result is negative, in principle, the user's action base is unknown. The process ends. Details of the predetermined calculation condition will be described later. On the other hand, if the determination result is correct, the action base mesh group specifying process is executed. The action base mesh group identification process identifies the mesh group with the maximum number of days stayed by group, number of stays by group or number of positionings by group from the mesh groups as the action base mesh group, and is included in the action base mesh group. The mesh number (mesh number in the mesh group) of the mesh area to be stored is stored in the action base DB 119. For example, when the action base is “home”, the mesh group having the maximum “stay period by group” is selected as the “home mesh group”. On the other hand, when the action base is “work location”, the mesh group having the maximum “stay time per group” is selected as the “work location mesh group”.

  When the action base mesh group is specified, the action base specifying unit 113 selects at least two of the stay days, the stay seconds, and the positioning times (behavior base parameters) for the mesh area included in the action base mesh group. By using according to each priority order, the likelihood of action base is calculated. For example, when the action base is “home”, the home-likeness is calculated using “stay days” (first priority), and when the home cannot be identified by this, “stay seconds” (second priority) The order of home is calculated using the ranking. On the other hand, if the location of action is “work location”, the “likeness of stay” (first priority) is used to calculate the likelihood of work location. The work place likelihood is calculated using the second priority). In the case of “home”, “stay days” has a higher priority than “stay seconds” because the departure point is many at home regardless of the destination, including weekends and holidays. This is because it is assumed to be the maximum. On the other hand, in the case of “work location”, “seconds stayed” has a higher priority than “stay days” because the nearest station or transfer station at home is also used on weekends and holidays. This is because it is assumed that the number of stays may be longer than the ground, while the number of stays is assumed to be larger in the work place. Note that the behavior base-likeness calculation method using the behavior base parameter can be set according to the specification or design, but here, the value of the behavior base parameter is a value indicating the behavior base-likeness. Further, “positioning count” (third priority) may be added for the identification processing of the home and work place.

  The behavior base identifying unit 113 identifies a mesh region that is a user's behavior base based on the behavior base-likeness. For example, the mesh region having the highest home-like property can be specified as the user's “home”, and the mesh region having the highest home-like property can be specified as the user's “work location”. FIG. 8A shows an example in which a home mesh group is identified from mesh groups, homeness is calculated for mesh regions in the home mesh group, and a mesh region having the maximum homeness is identified as a home mesh. Is shown. In FIG. 8B, a work area mesh group is identified from the mesh groups, the work area likelihood is calculated for the mesh area in the work area mesh group, and the mesh area having the maximum work place likelihood is the work area mesh. An example identified as

  The mesh visitor number acquisition means 120 is the basic mesh data stored in the basic mesh data DB 115, the date mesh data stored in the daily mesh DB 116, or the mesh data by period stored in the mesh DB 117 by period. Based on the above, the number of visitors in a preselected period (selection period; for example, 1 hour, 1 day, 1 month, etc.) in a preselected mesh area (selected mesh area) is obtained. Specifically, basic mesh data whose positioning time or positioning date is included within the selection period is extracted, and the number of users associated with the selected mesh region is totaled with reference to the extracted basic mesh data. The number of visitors in the selected mesh area. When using date-specific mesh data, extract the date-specific mesh data whose positioning date is included in the selected period, and refer to the extracted date-specific mesh data to determine the user's associated with the selected mesh region. You may count the number. In addition, when using mesh data by period, the number of users associated with the selected mesh area is extracted by extracting mesh data by period whose positioning period is included in the selected period, and referring to the extracted mesh data by date May be counted.

  The selected mesh area and the selection period for obtaining the number of visitors can be configured so that the system user can specify them. In this case, the user information output system 10 includes a user interface (for example, an input terminal) for the system user to specify a selected mesh region and a selection period, a storage unit that stores the specified information, and the like.

  The base user acquisition unit 121 refers to the behavior base DB 119 to obtain the number of users (number of base users) whose selected mesh region is included in the behavior base mesh group. Specifically, with reference to the behavior base DB 119, users who are associated with the selected mesh region as the mesh region of the behavior base mesh group, and users who have been positioned within the selection period (the number of visitors) The total number of users (calculated at the time of obtaining) is totaled and set as the number of base users in the selected mesh area.

  The fluid population acquisition means 122 calculates the number of fluid users for the selected mesh area from the number of visitors determined by the mesh visitor number acquisition means 120 and the number of base users determined by the base user acquisition means 121. Specifically, the number of base users in the selected mesh area is subtracted from the number of visitors in the selected mesh area to obtain the number of fluid users in the selected mesh area.

  The area setting unit 130 sets a target area to be a user information output target (user analysis target). The target area can be set in units of regions such as Kanto, prefectures, municipalities, and the like.

  The first condition setting means 131 sets a first extraction condition for the positioning information. For example, the first extraction condition can be a condition for a positioning point, positioning time, and positioning level (if included in the positioning information) included in the positioning information, and positioning is performed in a certain period within the target area. The positioning information can be set like positioning information that is higher than a certain positioning level.

  The second condition setting unit 132 sets a second extraction condition for the user attribute. For example, the second extraction condition can be a condition for the user's action base as the user attribute, and can be set such that the home area and the work place are not included in the target area.

  Note that the user attributes are not limited to those related to the user's behavior base, but the route to the target area, the means of transportation to the target area (transportation means), age, gender, family structure, hobby, career, behavior history (on the network) Various user attributes associated with the user can be used alone or in combination. For example, when a moving means is used as a user attribute, it is conceivable that the second extraction condition is set such that a target area is entered using a train. In this case, the user information output system 10 refers to one or more databases (for example, action base DB, moving means DB, etc.) stored in advance in the own system or other systems, and stores the user attributes. Can be acquired. Moreover, you may comprise so that a user can directly register a user attribute in a database including a user's action base.

  The target area, the period and positioning level used for the first extraction condition, and the type of action base used for the second extraction condition may be configured so that the system user can specify the same as in the selected mesh area. it can. In this case, the user information output system 10 includes a user interface for the system user to specify a target area.

  The user extraction means 133 refers to the positioning information DB 114, the behavior base DB 119, etc., and is a user corresponding to the positioning information that satisfies the first extraction condition with respect to the target area, and has a user attribute that satisfies the second extraction condition. Extract users. Specifically, the user extracting unit 133 refers to the positioning information DB 114, extracts positioning information that satisfies the first extraction condition for the target area, and extracts a user corresponding to the extracted positioning information. In addition, the user extraction unit 133 refers to the behavior base DB 119 and extracts users who satisfy the second extraction condition regarding the target area. Then, a common user is extracted from the user extracted based on the first extraction condition and the user extracted based on the second extraction condition.

  A user who does not satisfy the second extraction condition is extracted, and a user who satisfies the first extraction condition and does not satisfy the second extraction condition is excluded from the users who satisfy the first extraction condition, and remains. The user may be extracted.

  In addition, one of the user extraction based on the first extraction condition and the user extraction based on the second extraction condition is performed first, and the other extraction is performed on the premise of the user as a result. You may do it.

  The user information output unit 134 outputs the user information (user analysis information) extracted by the user extraction unit 133 in association with the target area. As the user information, the number of extracted users, the extracted users are classified into groups based on positioning information and user attributes, the number of people counted for a specific group, the extracted user flow line information (from where to enter the target area , Where you left, etc.). When there are a plurality of target areas, the number of users extracted in common in the plurality of target areas may be output as user information.

  In addition, as an output method, various forms, such as displaying on a display etc., printing on a printing medium, memorize | storing in a storage medium, and transmitting to another information processing system, can be considered. As a mode of outputting in association with the target area, various graphs and tables for outputting user information in association with identification information (name, etc.) of the target area, outputting user information in association with the target area on the map Various modes such as output in a format are conceivable.

  Next, the structure of each database will be described. Each database includes not only the secondary storage device but also a case where data is temporarily stored in a memory.

  The positioning information DB 114 stores positioning information transmitted from the mobile terminal 20. FIG. 9A shows an example of the data structure. According to the figure, the positioning information DB 114 stores data such as “positioning time”, “user ID”, “latitude”, and “longitude” in association with each other as a history of positioning information.

  The basic mesh data DB 115 stores the basic mesh data generated by the basic mesh data generating unit 104. FIG. 9B shows an example of the data structure. According to the figure, the basic mesh data DB 115 stores data such as “user ID”, “positioning date”, “mesh number”, “stay start time”, “stay end time” in association with each other. . The “mesh number” stores identification information for uniquely identifying a mesh area.

  The daily mesh DB 116 stores the daily mesh data generated by the daily mesh generation means 106. FIG. 9C illustrates an example of the data structure. According to the figure, the daily mesh DB 116 stores data such as “user ID”, “positioning date”, “mesh number”, “staying seconds”, and “number of times of positioning” in association with each other.

  The period-specific mesh DB 117 stores the period-specific mesh data generated by the period-specific mesh generation unit 109. FIG. 10A shows an example of the data structure. According to the figure, data such as “user ID”, “target period”, “mesh number”, “total number of staying seconds”, “total number of positioning times”, “total number of staying days” are stored in the mesh DB 117 by period. Stored in association.

  The mesh group DB 123 stores mesh group data generated by the group processing unit 111. FIG. 10D illustrates an example of the data structure. According to the figure, the mesh group DB 123 includes “user ID”, “target period”, “mesh number in mesh group”, “total number of stay seconds”, “total number of positioning times”, “total number of stay days”, and the like. Data is stored in association with each other.

  The daily area DB 118 stores data relating to the daily area setting, and specifically includes daily area conditions used in the daily area setting and data of the set daily area.

  FIG. 10B is a diagram illustrating an example of the daily sphere conditions included in the daily sphere DB 118. Although the contents according to the specification design can be set in the daily sphere conditions, and there is no particular limitation, in the figure, the daily sphere types and conditions are stored in association with each other. Specifically, “Aggregation Period”, “Number of Stay Days”, and “Number of Stays” are set for Daily Area A, and “Target Count Period”, “Positioning Days”, and “Number of Stays” are set for Daily Area B. And “number of times of positioning” are set, and “daily counting period” and “stay days” are set in the daily life zone C. Further, for each item, an arbitrary period and the number of days, time, and number of times as a threshold are set for each type of daily area. As shown in the figure, the conditions for the daily life can be set according to the type of the daily life to be extracted. For example, if you place importance on the number of days you stay and do not consider the number of seconds to stay or the number of positioning, you can specify the daily area that mainly uses the commute route, and if you do not consider the number of days to stay with emphasis on the number of seconds It is possible to specify a daily life area that includes a place where the number of visits is small but stays long (eg, parents' home). In addition, if you set a long period for the target aggregation period, you can prevent the daily area from disappearing when you go on a long trip or a business trip, while if you set a short period for the target aggregation period, It can be easily identified.

  FIG. 10C is a diagram illustrating an example of a user's daily area included in the daily area DB 118. In the figure, for each user ID, “type of daily life” and “mesh number” are stored in association with each other. According to the figure, it can be seen that the mesh number of the set daily sphere differs depending on the type of daily sphere.

The behavior base DB 119 stores data related to the behavior base identification process. As illustrated in FIG. 11, the behavior base DB 119 includes a calculation condition table that stores calculation conditions for the behavior base and data regarding the behavior base specified for each user. It includes a coefficient table for storing action base data to be stored and coefficients for each positioning level used when calculating the likelihood of the action base.
In the calculation condition table, conditions according to the characteristics of the action base can be set. In FIG. 11A, for example, in the case of home, “the number of stay days by group is not less than a predetermined number of days (n days)”. In the case of a work place, “the number of stays by group is equal to or greater than the predetermined number of days (x seconds)”, and , The number of stay days by group is equal to or greater than a predetermined number of days (y days). n is the minimum number of days stayed (eg, n is 20 days), m is the minimum length of stay (eg, m is 432000 seconds = 15 days × 8 hours × 60 minutes × 60 seconds), x is the minimum working days (eg, x is 234000 seconds = 13 days × 5 hours × 60 minutes × 60 seconds), and y is the minimum working time (for example, y is 13 days).

  In the action base data, as shown in FIG. 11B, “user ID”, “type of action base”, “mesh number” of the action base, “date” specifying the action base, the action base Data such as “mesh number in mesh group” corresponding to is stored as a history of action bases.

  In the coefficient table, as shown in FIG. 11C, “positioning level” and “coefficient” are stored in association with each other. The number of positioning levels and the coefficient values can be set according to specifications and design. In addition, the calculation process of the likelihood of an action base using a positioning level will be described in other embodiments.

[2. Configuration of mobile terminal]
Returning to FIG. 1, the mobile terminal 20 will be described. As the mobile terminal 20, for example, a conventional terminal device having a function of uploading positioning information obtained by measuring the current position at predetermined time intervals using GPS such as a mobile phone, a PDA, and a personal computer can be applied. Although one mobile terminal 20 is shown in FIG. 1, a plurality of mobile terminals 20 can be connected to the user information output system 10 according to the usage mode.

  As shown in FIG. 12, the mobile terminal 20 includes various function realizing means such as a main control means 201, a communication means 202, a display means 203, an operation means 204, a storage means 205, a current position positioning means 206, and a positioning information transmission means 207. Prepare for the main.

  The main control unit 201 includes a CPU (not shown) and a processor including a memory such as a ROM and a RAM, and controls the operation of each unit of the portable terminal 20 by the CPU executing a predetermined program stored in the ROM. . The communication unit 202 is an interface for transmitting and receiving various information to and from the user information output system 10 via the network N. The display unit 203 is a display that displays information such as characters and images, and the operation unit 204 is a button or a touch panel that receives an operation instruction from the user. The storage unit 205 is a memory as a storage device that stores various programs and data.

  The current position positioning means 206 includes, for example, a GPS receiver, and measures the current position (latitude / longitude) of the mobile terminal 20 by receiving and processing GPS satellite signals at predetermined reception intervals.

  The positioning information transmitting unit 207 transmits positioning information including the positioning point and positioning time determined by the current position positioning unit 206 and the user ID of the user who owns the mobile terminal 20 to the user information output system 10. The positioning information can be appropriately set according to the specification / design. However, as described above, in this embodiment, when the user is moving, the positioning information is transmitted according to a predetermined transmission interval. It is configured as follows. Whether or not the user is moving can be determined, for example, by applying a conventional technique using an acceleration sensor (not shown).

  The network N is a communication line for transmitting and receiving information between the user information output system 10 and the mobile terminal 20. For example, it may be any of the Internet, a LAN, a dedicated line, a packet communication network, a telephone line, a corporate network, other communication lines, combinations thereof, and the like, regardless of whether they are wired or wireless.

[3. Action area identification process]
Next, an outline of the operation of the user information output system 10 configured as described above will be described with reference to the flowcharts of FIGS. In addition, each processing step to be described later can be executed in any order or in parallel as long as there is no contradiction in processing contents, and other steps may be added between the processing steps. . Further, a step described as one step for convenience can be executed by being divided into a plurality of steps, while a step described as being divided into a plurality of steps for convenience can be grasped as one step.

[3-1. Daily zone setting process]
First, the daily zone setting process will be described. FIG. 13 is a flowchart showing an example of the overall flow of the daily area setting process. When receiving the positioning information (positioning point, positioning time, user ID) transmitted from the mobile terminal 20 at a predetermined transmission interval, the positioning information storage unit 103 stores the received positioning information in the positioning information DB 114 (S100). Thereby, the history of the positioning information according to the movement of the user is stored in the positioning information DB 114 (see FIG. 3A).

  The basic mesh data generation means 104 rearranges the positioning information in the positioning information DB 114 in time series according to the date for each user, and generates basic mesh data in which the positioning point and the positioning time are associated with the corresponding mesh area for each positioning date for each user. (S200) (refer FIG. 6). The generated basic mesh data is stored in the basic mesh data DB 115 (see FIG. 3B).

  The mesh data generation means 105 (daily mesh generation means 106), based on the generated basic mesh data, determines the number of times of positioning in each mesh area and the user's stay seconds for each predetermined unit period (eg, 1 day) for each user. Daily mesh data representing the number is generated (S300). The generated daily mesh data is stored in the daily mesh DB 116 (see FIGS. 4A and 7). Details of the daily mesh data generation process will be described later.

  The mesh data generation means 105 (period-specific mesh generation means 109) counts the number of positioning times, stay seconds, and stay days of each mesh area in a predetermined counting period based on the generated daily mesh data. The mesh data according to the period in which the total number of positioning times, the staying seconds, and the staying days are associated with each other is generated (S400). The generated period-specific mesh data is stored in the period-specific mesh DB 117 (see FIG. 10).

  The daily area setting means 110 is a mesh area in which any one of the number of positioning times, the number of staying seconds, and the number of staying included in the period-specific mesh data, or an arbitrary combination of two or more thereof satisfies a predetermined daily area condition. Is extracted from the user's daily sphere (S500). The generated daily life data is stored in the daily life DB 118 (see FIGS. 4B and 4C and FIG. 11).

[3-1-1. Daily mesh data generation process]
Next, the flow of daily mesh data generation processing will be described with reference to FIG. First, the daily mesh generation means 106 determines, based on the basic mesh data, from the positioning points on the mesh area, for example, according to the time series, the first positioning point of the positioning point before movement and the second positioning point of the positioning point after movement. When the group is identified and the group can be identified (S301: YES), the process proceeds to step 302 to apply the first distribution condition or the second distribution condition, and when the group cannot be identified (S301). : NO), go to Step 309 to apply the third distribution condition.

  When the daily mesh generating means 106 proceeds to step 302, the daily mesh generating means 106 calculates the distance between the first positioning point and the second positioning point, and calculates the first positioning time of the first positioning point and the second positioning point. The time between positioning points between two positioning times is calculated (S302).

  As an example of the distribution condition, the daily mesh generation unit 106 determines whether the calculated distance between positioning points is less than a predetermined reference distance and whether the calculated time between positioning points is less than a predetermined reference time ( In step S303, if the determination result is right, the process proceeds to step 304 in order to apply the first distribution condition. If the determination result is negative, the process proceeds to step 307 in order to apply the second distribution condition.

  When the daily mesh generation means 106 proceeds to step 304, it sets the transmission time interval of the positioning information in the portable terminal to the travel time (S304). Then, the travel time subtracted time is calculated by subtracting the travel time from the positioning point time, and the calculated travel time subtracted time is allocated to the first mesh region to which the first positioning point belongs as the allocated staying seconds. (S305). Next, the moving time is distributed approximately evenly as the allocated staying seconds to one or a plurality of mesh regions located on the connection between the first positioning point and the second positioning point (S306). As a result, the allocated stay seconds are appropriately distributed to the first mesh region, the second mesh region, and the complementary mesh region (see FIG. 8A).

  On the other hand, when the daily mesh generation means 106 proceeds to step 307, the daily mesh generation means 106 calculates the travel time based on the distance between the positioning points and the predetermined speed information (S307). Then, the travel time subtracted time is calculated by subtracting the travel time from the positioning point time, and the calculated travel time subtracted time is allocated to the first mesh region to which the first positioning point belongs as the allocated staying seconds. (S308). In addition, the allocation stay seconds are not distributed to the second mesh area to which the second positioning point belongs or other mesh areas. Thereby, the allocation staying seconds are appropriately allocated to the first mesh area (see FIG. 8B).

  In addition, since the judgment result is NO in step S301, the daily mesh generation means 106, when proceeding to step 309, is the last when the first positioning point where the target positioning point was first positioned on the corresponding day was last determined. It is determined whether there is a positioning point, and if it is the first measuring point, the time from the reference time 0:00 to the positioning time of the target positioning point is distributed to the target mesh area within a predetermined maximum time ( S310). On the other hand, when the target positioning point is the last positioning point, the time from the positioning time of the target positioning point to 24 o'clock as the reference time is distributed to the target mesh area within a predetermined maximum time (S311). . Thereby, the allocation staying seconds are appropriately allocated to the mesh areas of the first positioning point and the last positioning point (see FIG. 9).

  The daily mesh generation means 106 calculates the staying seconds in the target mesh area based on the allocated staying seconds allocated to the mesh area, and the positioning point is determined in the mesh area. Calculation is performed based on the number of times (S312). For the complementary mesh region, the staying seconds are added, but the positioning point is not added.

  Next, the daily mesh generation means 106 determines whether there is a positioning point to be processed next. If there is a positioning point to be processed next (S313: YES), the process returns to step S301. The above process is repeated. On the other hand, if there is no positioning point to be processed next, the process is terminated (S313: NO).

[3-1-2. Periodic mesh data generation process]
Next, the flow of the period-specific mesh data generation process will be described with reference to FIG. 15A (see FIG. 10). The period-specific mesh generation means 109 determines whether or not the period-specific mesh data of the target aggregation period exists in the period-specific mesh DB 117. If the period-specific mesh data has already been generated (S401: YES), the process proceeds to step S402 and still If not generated (S401: NO), the process proceeds to step S403.

  When the process proceeds to step S402, the period-specific mesh generation unit 109 reads the existing period-specific mesh data from the period-specific mesh DB 117, and reads the daily target mesh data for the addition target date and the subtraction target date from the daily mesh DB 116. For each mesh region, the number of stay seconds, the number of positioning times, and the number of stay days of the addition target day are added, while the number of stay seconds, the number of positioning times, and the number of stay days of the subtraction target day are subtracted (S402).

  When the process proceeds to step S403, the period-specific mesh generation unit 109 reads the daily mesh data for the target total period from the daily mesh DB 116, and totals the stay seconds, positioning times, and stay days for the target total period. (S403).

  The period-specific mesh generation unit 109 stores the generated period-specific mesh data in the period-specific mesh DB 117 (S404).

[3-1-3. Daily zone setting process]
Next, the flow of the daily area setting process will be described with reference to FIG. 15B (see FIG. 11). The daily sphere setting means 110 refers to the daily sphere conditions of the target daily sphere from the daily sphere DB 118 (S501), and reads the corresponding period-specific mesh data from the period-specific mesh DB 117. A matching mesh region is extracted and set as a daily zone (S502). The user information output system 10 stores the set daily life type and mesh number in the daily life DB 118 in association with each other (S503).

[3-2. Action base and current population identification process]
Next, using the daily sphere set as described above, the user's action base is identified, and the information on the action base is used to identify the fluid population in the selected mesh area. Processing will be described. Here, as an example, a case where “home” is specified as the first action base and “work location” is specified as the second action base will be described as an example. FIG. 16 shows the overall flow of the action base and the fluid population identification process.

  That is, the group processing unit 111 has a predetermined connection relation among at least one of a plurality of users who have generated mesh data from a mesh area (hereinafter referred to as “daily area mesh”) included in the daily area of the user. Mesh regions satisfying (for example, 4-adjacent or 8-adjacent relationship) are grouped to generate a mesh group (S1000: see FIG. 7A). Next, for at least one user, at least one of the mesh groups is targeted, and by referring to the period-specific mesh DB 117, the number of stay days by group, the number of stay seconds by group, and the number of positioning times by group are calculated in the aggregation target period. Then, mesh group data associated with the mesh group is generated and stored in the mesh group DB (S1010: see FIG. 7B).

  Next, a home mesh area specifying process is executed for the user who generated the mesh group data (S1020). Also, work area mesh area identification processing is executed (S1030).

  Next, for example, the flow population specifying process is executed for the selected mesh region designated by the system user (S1040). Details of these processes will be described later.

[3-2-1. Home mesh area identification process]
FIG. 17 shows the overall flow of the home mesh area specifying process. The behavior base mesh group specifying unit 112 refers to the home calculation conditions from the behavior base DB 119 (S701), and among the generated mesh groups, the home calculation conditions (e.g., stay days by group ≧ minimum stay days at home n) and the group It is determined whether or not there is a mesh group satisfying another stay seconds ≧ minimum staying seconds m) (S702). If the determination result is negative (S702: NO), it is determined that the home mesh area is unknown for the user (S703).

  On the other hand, when the determination result is “good” (S702: YES), the action base mesh group specifying unit 112 selects the mesh group having the largest “stay days by group” from the mesh groups that match the home calculation conditions. It identifies as a group (S704). When a plurality of home mesh groups having the maximum stay days are specified (S705: YES), the mesh group having the largest “stay time by group” is selected as the home mesh group (S706). Further, when a plurality of home mesh groups having the maximum staying seconds are specified (S707: YES), the mesh group having the largest “number of positioning by group” is selected as the home mesh group (S708). When the home base mesh group is specified, the behavior base mesh group specifying unit 112 associates the user with the mesh number in the home mesh group and stores it in the behavior base DB 119.

  Next, the behavior base specifying unit 113 calculates a value indicating the homeness for each mesh in the home mesh group using the behavior base parameter. Here, “stay days” (first priority), “stay seconds” (second priority), and “number of times of positioning” (third priority) are set in the action base parameters together with the priority. Therefore, first, a value indicating homeness is calculated using the “stay days” of each mesh region (S709).

  When the value indicating the homeness is calculated for all the mesh regions in the home mesh group, the behavior base identifying unit 113 determines the mesh region having the largest value indicating the homeness as the home mesh region (S710). If a plurality of home mesh areas having the maximum stay days are specified (S711: YES), a value indicating homeness is calculated based on the second priority "stay seconds", and the value is the largest. The mesh area is selected as the home mesh area (S712). Further, when a plurality of home mesh areas having the maximum staying seconds are specified (S713: YES), a value indicating homeness is calculated based on the “number of times of positioning” that is the third priority, and the value is the largest. The mesh area is selected as the home mesh area (S714).

  When the home mesh area of the user is determined, the behavior base identifying unit 113 executes a predetermined home confirmation process (S715), and then associates the user with the mesh number of the home mesh area and stores it in the behavior base DB 119. (S716). Details of the home confirmation process will be described later. Through the above process, the user's home mesh area is identified from the daily life.

[3-2-2. Work area mesh area identification process]
FIG. 18 shows the overall flow of the work location mesh area specifying process. The behavior base mesh group specifying unit 112 excludes the home mesh group from the target mesh group, and refers to the work location calculation condition from the behavior base DB 119 (S801). Then, among the target mesh groups after excluding the home mesh group, the mesh group that satisfies the work location calculation condition (for example, the stay days by group ≧ minimum work days x and the stay seconds by group ≧ minimum work seconds y) It is determined whether or not there is (S802).

  If the determination result is right (S802: YES), the action base mesh group specifying unit 112 selects a mesh group having the largest “seconds stayed by group” from the mesh groups that match the work place calculation conditions. The mesh group is specified (S804). If a plurality of work place mesh groups having the maximum stay seconds are specified (S805: YES), the mesh group having the largest “stay days by group” is selected as the work place mesh group (S806). Furthermore, when a plurality of work location mesh groups having the maximum stay days are specified (S807: YES), the mesh group having the largest “number of positioning by group” is selected as the work location mesh group (S808). When the work base mesh group specifying unit 112 specifies the work place mesh group, the action base mesh group is associated with the mesh number in the work place mesh group and stored in the action base DB 119.

  Next, the behavior base specifying unit 113 calculates a value indicating the likelihood of the workplace using the behavior base parameter for each mesh in the workplace mesh group. Here, “staying seconds” (first priority), “staying days” (second priority), and “number of times of positioning” (third priority) are set together with the priority in the action base parameter. Therefore, first, a value indicating the likelihood of work is calculated using “stay seconds” of each mesh region (S809).

  When the value indicating the work place likelihood is calculated for all mesh areas in the work place mesh group, the action base specifying unit 113 determines the mesh area having the largest value indicating the work place likelihood as the work place mesh area ( S810). When a plurality of work place mesh areas having the maximum stay seconds are specified (S811: YES), a value indicating work place likelihood is calculated using the second priority “stay days” as an action base parameter. The mesh area having the maximum value is selected as the work area mesh area (S812). Further, when a plurality of work place mesh areas having the maximum stay days are specified (S813: YES), a value indicating the work place likelihood is calculated using the third priority "positioning count" as an action base parameter, and the value The mesh area having the largest is selected as the work place mesh area (S814).

  When the user's work place mesh area is determined, the action base specifying unit 113 associates the user with the ID of the work place mesh area, and stores it in the action base DB 119 (S812). If it is determined in step S802 that there is no mesh group that satisfies the work location calculation condition (S802: NO), the behavior base likelihood calculation means 112 executes a predetermined work location confirmation process (S813), and the work location confirmation If it is determined that the work location is unknown even after the processing (S814: NO), it is determined that the work location mesh area is unknown for the user (S815). On the other hand, if it is determined by the work place confirmation process that there is a work place (S814: YES), the process proceeds to step 812, and the user and the mesh number of the work place mesh area are associated and stored in the action base DB 119 ( S812). Through the above processing, the work location mesh area of the user is specified from the daily life.

[3-2-3. Home confirmation process]
Next, the home confirmation process will be described. For example, when the user has just moved, the stay days may be distributed over a mesh area including the home before moving and the home after moving (see FIG. 19A). As a result, the number of stays in the mesh area including the work place may be larger than the stay days in the mesh area including the home after moving, and the work place may be determined as the home and the home after moving may be determined as the work place. . Therefore, in this embodiment, in order to identify an appropriate user's action base in consideration of such a change in the action base, a confirmation process for reconfirming the result of the home determination process described above is provided. FIG. 20 shows the overall flow of the home confirmation process.

  When the home mesh area is specified (FIG. 17: S711), the behavior base specifying unit 113 executes the home confirmation process. That is, referring to the mesh number of the work place mesh area (hereinafter referred to as “reference work place mesh area”), which is a work place mesh area specified in the past and matches a predetermined reference condition, from the action base DB 119, It is determined whether or not the mesh number of the home mesh area specified this time matches the mesh number of the reference work area mesh area (S901). As the predetermined reference condition, for example, contents such as “work location mesh region specified last time” or “work location mesh region specified continuously more than a predetermined number of times” can be set.

  If the determination result is NO (= no match), the behavior base specifying unit 113 ends the process and proceeds to step S713 in FIG. 17 (S902: NO). On the other hand, when the determination result is “good” (= match), the work location mesh area is determined as the current work place mesh area, assuming that the work place is erroneously determined as the home (S903). Then, the process moves to step S701 in FIG. 17 so that the mesh group including the reference work area mesh region is excluded from the target mesh group, and the home mesh region specifying process is re-executed for the target mesh group after the exclusion (S904). ).

  According to the above, even if the work location is mistakenly determined as a home due to moving, etc., the error is detected and the home mesh area specifying process is re-executed. Will be able to decide. Note that the home confirmation process is not limited to the case where the workplace is erroneously determined as home, but can be applied when there is a possibility that one action base may be erroneously determined as another action base.

[3-2-4. Work location confirmation process]
Next, work location confirmation processing will be described. For example, when the user's work location is located near his / her home, the work location mesh area may be included in the home mesh group. Since it is excluded, the work area mesh region may not be specified (see FIG. 19B). Therefore, in the present embodiment, in consideration of the positional relationship between the home and the work place, a confirmation process for reconfirming whether the user's work place exists is provided.

  If the work location mesh area is determined to be unknown because there is no mesh group that matches the work location calculation condition (S802: NO in FIG. 18), the behavior base specifying unit 113 executes the work location confirmation process. FIG. 21 shows the overall flow of work location confirmation processing.

  First, the behavior base specifying unit 113 sets a mesh region other than the home mesh region in the home mesh group as a target mesh region (S1001). Then, it is determined whether or not the target mesh region satisfies the predetermined stay days, stay seconds, and positioning times (first extraction criterion) (S1002).

  If the determination result is positive (S1002: YES), it is determined whether or not the target mesh region that satisfies the first extraction criterion is a predetermined distance away from the home mesh region (second extraction criterion) (S1003). If the determination result is positive (S1003: YES), for the target mesh region that satisfies the second extraction criterion, the difference in the number of stays between the target mesh region and the home mesh region, the difference in the number of staying seconds, the difference in the number of positioning times Is a predetermined threshold (third extraction criterion) or not (S1004).

  If the determination result is correct (S1004: YES), whether or not a specific facility (eg, company, factory, school, etc.) is included for the target mesh region that satisfies the third extraction criterion (fourth extraction criterion). ) Is determined (S1005). Then, when the determination result is “good” (S1005: YES), for the target mesh region satisfying the fourth extraction criterion, the work place likelihood is calculated using the behavior base parameter (here, the number of staying seconds) (S1006). .

  When the behavior base specifying unit 113 calculates a value indicating the work place likelihood for the mesh area that matches the first extraction criterion to the fourth extraction reference, the action base specifying unit 113 determines the mesh area having the highest work place likelihood as the work place mesh region. (S1007).

  On the other hand, when the first extraction criterion to the fourth extraction criterion are not satisfied (S1002: NO, S1003: NO, S1004: NO, S1005: NO), the action base specifying unit 113 determines that the work location mesh region is unknown. .

  According to the above, for example, even when the work place and the home are included in the same mesh group, the work place can be appropriately specified. The extraction criteria for the target mesh can be set according to the specifications and design, and the extraction criteria can be deleted, added, or changed for the first to fourth extraction criteria described above. The work location confirmation process is not limited to the relationship between the work location and the home, but can be applied when one action base may be included in a mesh group of another action base.

[3-2-5. Flowing population identification process]
FIG. 22 shows the overall flow of the fluid population identification process. The mesh visitor number acquisition means 120 is the basic mesh data stored in the basic mesh data DB 115, the date mesh data stored in the daily mesh DB 116, or the mesh data by period stored in the mesh DB 117 by period. Based on the above, the number of visitors in the selected period in the selected mesh area is obtained (SS1041). For example, when the number of visitors is obtained using the basic mesh data, if the selection period is designated as March 2010, the mesh visitor number acquisition means 120 determines that the positioning date is 2010 3 from the basic mesh data DB 115. The basic mesh data, which is the month, is extracted, and the number of users associated with the selected mesh area is totaled with reference to the extracted basic mesh data, and the number of visitors in the selected mesh area is obtained. Further, for example, if the selection period is 3 hours from 6 o'clock to 9 o'clock, the mesh visitor number acquisition means 120 is based on the basic mesh data included in the 3 hours of positioning time from 6 o'clock to 9 o'clock from the basic mesh data DB 115. Are extracted, the number of users associated with the selected mesh region is totaled with reference to the extracted basic mesh data, and is set as the number of visitors in the selected mesh region.

  Next, the base user acquisition unit 121 refers to the behavior base DB 119 and obtains the number of users (number of base users) whose selected mesh area is included in the behavior base mesh group (S1042). For example, when home and work location are registered as the behavior base in the behavior base DB 119, the mesh number of the selected mesh area is a user registered as a mesh number in the home mesh group, and positioning is performed within the selection period. The number of users (users counted when obtaining the number of stayers) is counted to obtain the number of home base users in the selected mesh area. Moreover, the mesh number of the selected mesh area is a user who is registered as a mesh number in the work area mesh group, and is a user who has been positioned within the selection period (a user who has been counted when obtaining the number of visitors) The number is counted and used as the number of work site base users in the selected mesh area.

  Next, the fluid population acquisition means 122 calculates the number of fluid users for the selected mesh region from the number of visitors determined by the mesh visitor number acquisition means 120 and the number of base users determined by the base user acquisition means 121 ( S1043). For example, if the number of home base users and the number of work base users are calculated as the number of base users, the number of home base users and the number of work base users are subtracted from the number of stayers in the selected mesh area. Find the number of fluid users in the mesh area.

  In addition, according to the definition of a fluid user, what kind of base user number of bases can be determined from the number of visitors. For example, when a user other than a home base user is defined as a fluid user, only the number of home base users may be subtracted from the number of visitors to obtain the number of fluid users. For example, when defining users other than all base users as fluid users, the number of base users is calculated for all the action bases registered in the action base DB 119 and subtracted from the number of visitors in the selected mesh area. You may comprise.

  In this way, the number of visitors, the number of base users, and the number of fluid users in the selected mesh area obtained by the fluid population identification process are stored in the storage means in the user information output system, and various such as demographic analysis and city planning It is referred to (output) at the time of use in this mode.

  According to the above, since the user's action base can be automatically set only by carrying the portable terminal with the GPS function, various information registration work for setting the action base becomes unnecessary, and the user Convenience can be improved. In addition, the behavior base is calculated by using at least two parameters of the number of positioning times, the number of stays, and the number of days of stay according to the priority order according to the content of the behavior base. It becomes possible to increase the accuracy of the. Furthermore, it becomes possible to obtain the fluid population of a specific area based on the information of the action base.

[Examples of use of action bases]
An example of using the action base set as described above will be described. The usage method of the action base can be determined according to the purpose, etc., and the content thereof is not particularly limited. For example, when a predetermined service providing apparatus provides information related to a certain facility to the user, The content of the information to be provided can be determined after considering the relationship between the facility and the user's action base. For example, when the target facility is located in the vicinity of the user's home / work location mesh area or in the home / work location mesh group, the sale information, lunch information, real estate information, etc. Can be provided.

  In addition, the mesh area in the mesh group excluding the home mesh group and work place mesh group from the daily life area is identified as the “frequently meshed area” as the third action base, and is located in the identified “frequently meshed area” It is also possible to discriminate the user's hobbies (eg, golf, skiing) from information (eg, golf courses, ski resorts) regarding the facility to be performed, and provide information on the discriminated hobbies.

  Furthermore, it is possible to determine the occupation and attributes of the user by using the specified action base mesh group and action base mesh area. For example, a user for whom a work location mesh group is specified can be determined as a company employee or a student, and a user for whom a work location mesh group has not been specified can be determined as a housewife or a self-employed person. In addition, when the work location mesh area includes a university, the user is determined to be a college student, and when the work location mesh group is located in an office district, the user can be determined to be a company employee.

[3-3. User information output processing]
Next, a user information output process for extracting a user for the target area using the positioning information and the action base information as the user attribute, and outputting the extracted user information will be described. FIG. 23 shows the overall flow of the user information output process. For example, the area setting unit 130 sets a target area to which user information is output based on an input received from the user via the user interface (S1051). For example, when the user information output system 10 has an existing map display function, the user interface can be configured such that the user specifies a target area on the displayed map. For example, the target area is set by acquiring polygon data representing the outline of the target area from a database (not shown) inside or outside the system and storing it in the storage means in the system in association with the identification information of the target area. Done.

  Next, the first condition setting unit 131 sets a first extraction condition for the positioning information based on, for example, an input received from the user via the user interface (S1052). For example, when the extraction condition for the positioning time included in the positioning information is set as the first extraction condition, the user interface can be configured so that the user specifies the period for extracting the positioning information.

  Next, the second condition setting unit 132 sets a second extraction condition for the user attribute based on, for example, an input received from the user via the user interface (S1053). For example, when the extraction condition for the user's action base as the user attribute is set as the second extraction condition, the user interface selects the action base type and includes the action base in the target area. It can be configured to specify that the action base is not included in the target area.

  Next, the user extraction unit 133 refers to the positioning information DB 114, the action base DB 119, and the like, and for the target area set by the area setting unit 130, the positioning information that satisfies the first extraction condition set by the first condition setting unit 131. And a user (analysis target user) having a user attribute that satisfies the second extraction condition set by the second condition setting unit 132 is extracted (S1054).

  For example, “Atami City” is set as the setting area, the condition “positioning information in August in the target area” is set as the first extraction condition, and “target area” is set as the second extraction condition. It is assumed that a condition “user who does not include home or work location” is set.

  In this case, the user extraction means 133 refers to the positioning information DB 114, extracts positioning information whose positioning point is in Atami city and whose positioning time is in August, and a user (positioning corresponding to the extracted positioning information User). Further, referring to the action base DB 119, a user (base user) whose home mesh or work place mesh is included in Atami City (for example, the mesh center is included in the region specified by the polygon data of Atami City). To extract. And the user who is a positioning user and a base user is excluded from a positioning user, and the remaining user is extracted as an analysis object user.

  Next, the user information output unit 134 outputs the user information extracted by the user extraction unit 133 in association with the target area (S1055). For example, the number of users to be analyzed may be aggregated and output as “the number of users visiting Atami in August”.

  Further, the user information output unit 134 may classify the analysis target user into groups based on the positioning information and user attributes, and output the number of persons counted for the specific group as user information associated with the target area. .

  For example, the user information output means 134 can classify the analysis target user into a visitor group and other than that, and output the number of persons counted for the visitor group as user information associated with the target area.

  Specifically, the user information output means 134 calculates the stay time in the target area for each analysis target user based on the positioning time included in the positioning information that satisfies the first extraction condition. The staying time can be obtained, for example, by subtracting the earliest positioning time from the latest positioning time among the positioning times included in the positioning information that satisfies the first extraction condition for each analysis target user.

  Next, the user information output unit 134 classifies the analysis target users into groups (stayer groups) whose stay time is equal to or greater than a predetermined threshold T1 and other groups. Then, the number of analysis target users belonging to the visitor group is totaled and output as “the number of visitors in Atami City in August”.

  The user information output means 134 further classifies the analysis target users belonging to the visitor group into groups based on positioning information and user attributes, and counts the number of persons counted for a specific group as user information associated with the target area. It can also be output.

  For example, the user information output unit 134 classifies the analysis target users belonging to the visitor group into a guest group and a day returner group, and outputs the number of persons counted for each group as user information associated with the target area. be able to.

  Specifically, the user information output unit 134 obtains the stay period for the analysis target users belonging to the visitor group based on the positioning time included in the positioning information that satisfies the first extraction condition. For the stay period, for example, for each analysis target user, among the positioning times included in the positioning information satisfying the first extraction condition, the earliest positioning time is set as the stay start time, and the latest positioning time is set as the stay end time. And you can ask for it.

  Next, the user information output unit 134 selects the analysis target users belonging to the visitor group as groups (accommodation) in which the stay period includes a predetermined time (for example, 3 am) and the length of the stay period is equal to or greater than a predetermined threshold T2. Group) and other groups (day trip group). Then, the number of users to be analyzed belonging to the guest group and the day return group is totaled and output as “the number of overnight guests in Atami City in August” and “the number of day visitors in Atami City in August”.

  In addition, you may comprise so that corresponding user information may be output only about either a guest group or a day return person group.

  The user information output means 134 further classifies the analysis target users belonging to the guest group and the day returner group into groups based on the positioning information and user attributes, and corresponds the number of persons counted for the specific group to the target area. It can also be output as attached user information.

  For example, the user information output means 134 classifies the users to be analyzed belonging to the guest group and the day returner group into groups by home location (for example, the prefecture where the home is located), and the total number of people for each group. The user information associated with the target area can be output.

  Specifically, the user information output unit 134 refers to the behavior base DB 119 for the analysis target users belonging to the guest group and the day returner group, and classifies them into groups for each prefecture including the home mesh. Then, for each guest group and day visitor group, for example, the number of users to be analyzed belonging to each group according to prefecture is tabulated, and “Number of August Atami-city guests by home location”, “August Atami” Output as “Number of people who return to the city by day of home”. FIG. 24 shows an example in which user information is output by classifying the users to be analyzed in the guest group into groups by prefecture where the home is located.

  Further, the user information output means 134 classifies the analysis target user into groups based on the flow line information obtained based on the positioning information, and counts the number of persons for the specific group as user information associated with the target area. It may be output.

  For example, the user information output unit 134 classifies the analysis target users into groups that have entered the target area or groups that have left the target area, and outputs the total number of people for each group as user information associated with the target area. be able to.

  Specifically, the user information output unit 134 determines where the analysis target user has entered the target area from where the analysis target user has exited and based on the positioning information. For this purpose, the user information output unit 134 sets a plurality of sub-areas in advance in the target area based on input from the user or by acquiring / using POI information registered in the target area.

  The sub-area can be set based on the sub-area reference point by selecting the sub-area reference point so as to include any of the station, airport, port, interchange, intersection, etc. in the target area. In this case, for example, a building or area related to the subarea reference point, a circular area with a predetermined radius centered on the subarea reference point, or an administrative division including the subarea reference point may be set as the subarea. . Alternatively, the entire target area may be divided into a plurality of sub areas (for example, meshes).

  Next, the user information output unit 134 refers to the positioning information DB 114, extracts positioning information in which the positioning points are included in the subarea within the period used for the first extraction condition for each analysis target user, and extracts the extracted information. The positioning information including the earliest positioning time in the positioning information is determined as approaching positioning information, the positioning information including the latest positioning time is determined as exiting positioning information, and the subarea including the positioning point of the approaching positioning information is analyzed. The subarea including the user entry subarea and the positioning point of the exit positioning information is determined as the exit subarea of the analysis target user.

  Next, the user information output unit 134 classifies the analysis target users into groups for each entry subarea and each exit subarea. At this time, the analysis target user from whom the positioning information included in the subarea is not extracted may be classified into an unknown group. Then, the number of users to be analyzed belonging to each group is totaled and output as the number of ingress users by subarea and the number of exit users by subarea.

  Or, as shown in FIG. 25, the number of people entering by subarea / the number of people entering all subareas, the number of people leaving by subarea / the number of people leaving all subareas are obtained and output as the ratio of people entering by subarea and the number of people leaving by subarea May be.

  Note that only one of the entry subarea and the exit subarea may be determined and the corresponding user information may be output. Moreover, you may set a subarea so that the one part or all part may be outside a target area. When a part or all of the sub-area is set outside the target area, the approach sub-area and the exit sub-area can be determined using the positioning information measured outside the target area.

  According to the above, a user having a specific user attribute among users having positioning information in the target area by extracting the user related to the target area using the extraction condition for the positioning information and the extraction condition for the user attribute Can be extracted as an analysis target user, so that the analysis target user can be extracted with high accuracy and various user analysis results can be output as user information.

[Other Embodiments]
The present invention is not limited to the above-described embodiment, and can be implemented in various other forms without departing from the gist of the present invention. The above-described embodiment is merely an example in all respects, and is not construed as limiting.

  (1) For example, the behavior base likelihood calculation unit 112 may calculate a value indicating the behavior base likelihood by multiplying the value of the behavior base parameter by a coefficient corresponding to the positioning level. The positioning level indicates an error range of the positioning result. For example, the positioning level 3 is “horizontal error <50 m”, the positioning level 2 is “50 m ≦ horizontal error <300 m”, and the positioning level 1 is “300 m ≦ horizontal error”. Is shown. As described above, since the error becomes larger as the positioning level is lower, there is a possibility that there is actually no user in the mesh area indicated by the positioning information. Therefore, the coefficient is set so that the weight becomes smaller as the positioning level is lower (see FIG. 11C). Then, for each mesh region in the action base mesh group, the formula = {(positioning level 1 positioning times × positioning level 1 coefficient × action base parameter) + (positioning level 2 positioning times × positioning level 2 coefficient × action Base parameter) +... + (Positioning frequency of positioning level n × coefficient of positioning level n × action base parameter)}, and the result of the calculation is a value indicating the likelihood of the action base of the mesh region. According to this, since the value indicating the likelihood of the behavior base can be calculated in consideration of the positioning level, it is possible to further improve the identification accuracy of the behavior base mesh region. The coefficient setting includes a case where 1 is set for the coefficients of all the positioning levels, and in this case, it is substantially the same as not considering the positioning level.

  (2) The behavior base likelihood calculation means 112 may obtain a sum of weights by assigning a larger weight to the value of each behavior base parameter that has a higher priority, and use it as a value indicating the likelihood of the behavior base.

  (3) The behavior base attributes can be determined using the behavior base parameters “stay seconds” and “positioning count”. When the mobile terminal 20 is configured to transmit positioning information only when the user moves, the number of positioning increases if the user moves, while the number of positioning decreases when the user does not move much. Accordingly, when a certain mesh area has a small number of staying seconds but a large number of positioning times, it can be assumed that the mesh area is an active action base (for example, a shopping mall). On the other hand, when the number of staying seconds is large but the number of positioning times is small, the mesh area can be assumed to be an inactive action base (for example, a movie or a concert).

  (4) For example, when it is determined that the positioning accuracy of the current position in the mobile terminal 20 is low, a mesh area (hereinafter referred to as “movement path mesh”) located on the movement path from the first positioning point to the second positioning point. One or a plurality of mesh regions located in the vicinity of the “region” may be set as the peripheral mesh region, and the time between positioning points may be allocated to the peripheral mesh region. The surrounding mesh area can be set as appropriate according to the design and specifications. For example, when the positioning accuracy of the first positioning point and / or the second positioning point is low, the first mesh area and / or Alternatively, the mesh area around the second mesh area can be set as the first peripheral mesh area and / or the second peripheral mesh area. For example, when the positioning accuracy of both the first positioning point and the second positioning point is low, a mesh area around the complementary mesh area on the movement path may be set as the complementary peripheral mesh area. When the surrounding mesh area is set, the distribution complementing means 108 is calculated according to the distributing method described in the above embodiment, for example, and the movement route mesh area corresponding to the surrounding mesh area (eg, the first mesh surrounding area). The allocation stay seconds for (example: first mesh area) can be distributed to both the movement path mesh area and the peripheral mesh area (example: first mesh area and first mesh peripheral area). According to this configuration, when a positioning point with low accuracy is received, the number of staying seconds is also distributed around the positioning point. As a result, it is possible to improve the accuracy of the daily life zone.

  (5) In the fluid population identification process of the above embodiment, the number of users included in the action base mesh group in the selected mesh area is obtained as the number of base users. The number of users registered as behavior base mesh areas having high “home-likeness” and “workplace-likeness” may be obtained as the number of base users.

  (6) In the above embodiment, the process of allocating the stay time to the mesh area on the movement path between the first positioning point and the second positioning point is performed by the distribution complementing means 108. The distribution process may not be performed. That is, the process of steps S303 to S308 in FIG. 12 may not be performed and the process may proceed from step S302 to step S312.

  In addition, when the predetermined condition is satisfied, the stay time distribution by the distribution supplement means 107 may not be performed, and the stay time may be distributed by the distribution supplement means 107 in other cases. Examples of such conditions include the following. One is that complementation is not performed if the size of the mesh region is larger than a predetermined size. This is because when the size of the mesh is sufficiently large (for example, 300 m square), the first positioning point and the second positioning point are often included in the same mesh, and the result does not change much even if complementation is performed. The other is that complementation is not performed when the time between positioning points between the first positioning point and the second positioning point is shorter than the threshold value. This is because when the time between positioning points is short (for example, less than 10 minutes), there is not much difference between the results when complementing is performed and when complementing is not performed. The other is that when the transmission time interval of the positioning information in the mobile terminal 20 is shorter than the threshold value, no complement is performed. When the transmission time interval is short (for example, less than 5 minutes), the distance between the first positioning point and the second positioning point is often short, and there is not much difference between the results with and without complementing. Because it is not. The other is that complementation is not performed when the distance for everyday area determination is greater than the threshold. This is because when the distance for everyday area determination is large (for example, 400 m or more), there is not much difference between the results when complementing is performed and when it is not performed. Note that it may be determined whether or not to perform complementation using one of these conditions, or may be determined by combining two or more conditions.

  (7) For example, a plurality of target areas may be set in the user information output process. In this case, the user information output means 134 may output the number of users extracted in common in a plurality of target areas as user information. For example, assuming that “Atami City” and “Hakone City” are set as the target areas, the user information output means 134 uses, for example, the above-described method, for each of Atami City and Hakone City, a guest group, a day return group. Ask for.

  Next, the user information output means 134 includes the number A belonging to the guest group in Atami City, the number B belonging to the day visitor group in Atami City, the user belonging to the guest group in Atami City and the guest group in Hakone City. The number of users C, the number D of users belonging to the Atami city guest group and belonging to the day trip group of Hakone city, the number of users E belonging to the day trip group of Atami city and belonging to the guest group of Hakone city, Atami city The number of users F belonging to the day trip group and the day trip group in Hakone City is totaled.

  And, for example, as schematically shown in FIG. 26, among the guests A in Atami City in August, the number of people staying in Hakone City in the same month is C, and the number of people who have taken a day is D. The user information indicating that the number of people who have stayed in Hakone city among the day-goers B in Atami city is E, and the number of people who have made a day trip is F is output.

  (8) Further, for example, various user information may be output for the sub-areas in the target area by the same method as described above for the target area. Further, for a plurality of subareas in one or more target areas, the number of users extracted in common in the plurality of subareas is output as user information by the same method as described above in (7). Also good. By performing such user analysis, it is possible to output information on the user's migratory behavior based on the subarea.

  (9) In the case of using a movement route or movement means to the target area as the user attribute, the user information output system 10 uses the same method as the movement route or movement means estimation method described in the above embodiment to move the user. A route and a moving means may be specified and used. Alternatively, the user information output system 10 includes a movement route estimation system that estimates a user's movement route with reference to the positioning information DB 114, a network information database (DB) that stores information on road networks and route networks, and the like. Alternatively, the user's movement route information may be acquired from an external movement route estimation system.

  Such a movement route estimation system specifies, for example, a road and / or route that is within a predetermined range (for example, the nearest) from the positioning point of each positioning information as a reference point (departure point, waypoint, arrival point, etc.). A reference point that stores, in the reference point information storage unit, reference point information in which position information indicating the position of the reference point, type information indicating the type of road and / or route, and positioning time of the positioning point are associated with each other A plurality of paths between the first and second reference points based on the position information and the type information of the first and second reference points with reference to the specifying unit and the reference point information storage unit Walking route, walking / train route, bicycle route, car route, etc.) and the route search unit for searching for the required time of the route, and the required route from the plurality of routes searched by the route search unit Time and the first and second reference points Based on the positioning time interval between the first and second positioning points to estimate the movement path between the first and second positioning points (for example, among the plurality of paths, the required time, And a movement path estimation unit that selects a movement path with the smallest difference between the positioning time intervals between the first and second positioning points. In this case, the estimated moving route and moving means corresponding to the moving route can be used as user attributes.

  (10) In the user information output process, after setting the target area, the action base is specified for the set target area by the method described in the above embodiment, and the specified action base is set as the second extraction condition. You may comprise so that it may be used for the user extraction by.

  (11) When obtaining the stay time in the user information output process, positioning information measured outside the target area for the analysis target user may be used. For example, the “latest positioning time” when obtaining the stay time may be the positioning time included in the positioning information first positioned after leaving the target area for the analysis target user, or the “earliest positioning time” It is good also as positioning time contained in the positioning information measured last before approach of the object area about the analysis object user.

  (12) In the user information output process, each group can be arbitrarily combined to take a product set or a union set, or can independently output corresponding user information. The information on the number of people can be output instead of the information on the number of people according to the design.

  DESCRIPTION OF SYMBOLS 10 ... User information output system, 20 ... Portable terminal, 101 ... Main control means, 102 ... Communication means, 103 ... Positioning information storage means, 104 ... Basic mesh data generation means, 105 ... Mesh data generation means, 106 ... Daily mesh generation means, 107 ... Positioning frequency counting means, 108 ... Distribution complementing means, 109 ... Periodic mesh generation means, 110 ... Daily life Setting means 111... Group processing means 112... Action base mesh group specifying means 113 113 Action base specifying means 114 114 Positioning information DB 115 115 Basic mesh data DB 116. ..Daily mesh DB, 117 ... Period mesh DB, 118 ... Daily service area DB, 119 ... Behavior base DB, 120 ... Mesh visitor number acquisition means, 21 ... Base user number acquisition means, 122 ... Current user number acquisition means, 123 ... Mesh group DB, 130 ... Area setting means, 131 ... First condition setting means, 132 ... Second condition setting means, 133 ... User extraction means, 134 ... User information output means, 201 ... Main control means, 202 ... Communication means, 203 ... Display means, 204 ... Operation means, 205 ... Storage means, 206 ... Current position positioning Means, 207 ... Positioning information transmission means, N ... Network

Claims (11)

For a plurality of users, storage means for storing the user's positioning information in the first database;
Area setting means for setting the target area;
First condition setting means for setting a first extraction condition for positioning information;
Second condition setting means for setting a second extraction condition for the user attribute;
A user corresponding to the positioning information that satisfies the first extraction condition that has been set with respect to the set target area with reference to the first database and the second database that stores user attributes, and the set User extraction means for extracting a user having a user attribute that satisfies the second extraction condition;
An output means for outputting the extracted user information in association with the set target area;
A user information output system comprising:   The user information output system according to claim 1, wherein the first extraction condition includes a condition for a positioning time included in the positioning information.   The user information output system according to claim 1, wherein the user attribute includes information on a user's action base.   The output means classifies the extracted users into groups based on positioning information of the users, and outputs the extracted user information for a specific group. The user information output system according to item 1.   The output means classifies the extracted users into groups based on the information of the action bases outside the set target area of the user, and outputs the extracted user information for a specific group. The user information output system according to any one of claims 1 to 4.   The user information output system according to claim 5, wherein the group based on information on the user's action base includes a group for each home location.   The output means enters the target area from which sub-area in the set target area, and / or which of the set target area for the extracted user based on the positioning information of the user It is determined whether or not the sub-area has been moved out of the target area, classified into groups based on the determined entry sub-area and / or exit sub-area, and the extracted user information for a specific group is output. The user information output system according to any one of claims 1 to 6.   8. The user information output system according to claim 7, wherein at least one of the sub-areas is set based on any of a station, an airport, a port, an interchange, and an intersection in the set target area. The area setting means sets a first target area and a second plurality of target areas;
The output means outputs user information extracted for both the first target area and the second target area in association with the first target area and the second target area. Item 9. The user information output system according to any one of Items 1 to 8. For a plurality of users, storing the user's positioning information in a first database;
Setting the target area;
Setting a first extraction condition for positioning information;
Setting a second extraction condition for the user attribute;
A user corresponding to the positioning information that satisfies the first extraction condition that has been set with respect to the set target area with reference to the first database and the second database that stores user attributes, and the set Extracting a user having a user attribute that satisfies the second extraction condition;
Outputting the extracted user information in association with the set target area;
A user information output method comprising: Storing a plurality of users' positioning information in a computer in a first database;
Setting the target area;
Setting a first extraction condition for positioning information;
Setting a second extraction condition for the user attribute;
A user corresponding to the positioning information that satisfies the first extraction condition that has been set with respect to the set target area with reference to the first database and the second database that stores user attributes, and the set Extracting a user having a user attribute that satisfies the second extraction condition;
Outputting the extracted user information in association with the set target area;
User information output program to execute.