JP7453934B2 - Behavior analysis device and method - Google Patents

Description

The present invention relates to a technique for analyzing participant behavior. In particular, it relates to techniques for analyzing social participation behavior in unit societies such as participants and regions. Note that participants include various concepts related to people such as residents, customers, users, and related parties.

Currently, data analysis is being performed in various fields. Among these, analysis of people's movement behavior for purposes such as purchasing trends, crime prevention, and logistics efficiency is attracting attention. Additionally, as we enter an aging society, health problems for the elderly, such as dementia and nursing care issues, are becoming more apparent. It is becoming clear that by activating the social participation behavior (including social participation activities) of the elderly, it is possible to suppress the onset of dementia and the transition to nursing care in the elderly. In other words, it is important to visualize social participation behavior as a health indicator, in addition to traditional diet and exercise.

For example, in Patent Document 1, information on factors that influence health conditions is acquired based on historical information regarding social participation behavior of users (participants) related to date, time, and location, and risks that require attention for users are acquired. A technique for providing benefit information related to is disclosed.

Japanese Patent Application Publication No. 2019-057069

Here, in Patent Document 1, risks that affect the health condition are only presented based on the user's history information such as location. Here, social participation behavior is a benefit that can suppress the transition to needing nursing care. In addition, the degree of suppression of support required for nursing care differs not only depending on the location but also the type (category) of social participation activities such as work, hobby meetings, sports meetings, volunteer meetings, shopping, and work. In addition, regarding social participation behavior, the diversity (complexity) of behavior, such as the length of stay at the destination depending on the type of activity, the route to the destination, and the type and number of facilities visited at the same time, There are various influences that affect the degree to which nursing care support is suppressed, such as the frequency of contact with other people.

Furthermore, it is difficult for participants, mainly elderly people, to understand all the complex factors, and a method of presenting information that allows them to understand both overall indicators and areas for improvement in terms of social participation behavior is needed. The above-mentioned conventional technology does not sufficiently take these issues into consideration.

Furthermore, for governments and private businesses that serve elderly people as customers, it is important to quantify the effects of such measures from the perspective of social participation when implementing measures (opening facilities and awareness activities) to encourage social participation among the elderly. It is desirable to conduct evaluations that are both objective and visually easy to understand. Furthermore, it is also necessary to quantitatively and visually evaluate the current status of how local residents are conducting their activities from the perspective of social participation. In addition, social participation activities are easily influenced by the surrounding environment of the residential area (ease of access and convenience to shops for shopping, gathering places, libraries, etc.), and residents from different living environments may The problem is that comparisons are difficult.

Furthermore, there is no consideration given to quantitative comparisons to understand the differences between regions from the perspective of social participation, and there is a problem in that it is not possible to adequately understand issues and formulate measures for social participation behavior.

Moreover, in Patent Document 1, only individual participants can be evaluated, and social participation behavior in terms of "environment" such as region or organization is not considered. For example, the ease of participating in social participation activities based on facilities in the area was not analyzed.

An object of the present invention is to solve the above-mentioned problems and to enable the evaluation of social participation behaviors that lead to health conditions such as disease prevention and nursing care prevention from various viewpoints.

In order to solve the above problems, the present invention provides a behavior analysis device for analyzing social participation behavior of participants, which includes an input section that receives location information indicating the movement history of each of a plurality of participants; , a discrimination unit that discriminates a social participation category that indicates the classification of the social participation behavior of the participant; and a social participation indicator that is determined according to the social participation category and indicates the social involvement status of each of the participants. The behavior analysis device includes an index calculation unit that calculates an index.

The present invention also provides a behavior analysis device for analyzing social participation behavior of participants, which includes a database that stores behavior history information indicating the behavior history of the participants, and a database that stores behavior history information that indicates the behavior history of the participants. Also included is a behavior analysis device having a data analysis unit that calculates an environmental index indicating the ease of social participation for each unit society using the social participation category of the facility related to the participant.

Furthermore, it also includes a behavior analysis method using each of the behavior analysis devices described above, a computer program for causing a computer to execute this method, and a storage medium storing the same.

According to the present invention, it is possible to perform detailed behavior analysis in accordance with the actual situation.

FIG. 1 is a diagram showing the concept of an embodiment of the present invention. FIG. 1 is a system configuration diagram of a behavior analysis support system according to an embodiment of the present invention. FIG. 1 is a configuration diagram of a behavior analysis device in an embodiment of the present invention. FIG. 2 is a sequence diagram showing the entire process in an embodiment of the present invention. FIG. 3 is a diagram showing an example of participant information used in an embodiment of the present invention. FIG. 3 is a diagram showing an example of position information used in an embodiment of the present invention. 5 is a flowchart showing social participation category discrimination processing in an embodiment of the present invention. FIG. 3 is a diagram showing the concept of action history information used in an embodiment of the present invention. FIG. 3 is a diagram showing an example of a POI table used in an embodiment of the present invention. FIG. 3 is a diagram showing action history information used in an embodiment of the present invention. FIG. 3 is a diagram showing an example of a social participation classification table used in an embodiment of the present invention. The figure which shows an example of the flowchart which shows the detail of social participation category discrimination processing in one Example of this invention. The figure which shows an example of the flowchart which shows the detail of social participation index calculation processing in one Example of this invention. FIG. 3 is a diagram showing an example of a social participation weight table used in an embodiment of the present invention. FIG. 3 is a diagram showing an example of residence time related information in a graph format according to an embodiment of the present invention. FIG. 3 is a diagram showing, in a graph format, an example of relational information on the number of combinations of transportation means according to an embodiment of the present invention. FIG. 3 is a diagram showing, in a graph format, an example of related information on the number of visited facilities according to an embodiment of the present invention. FIG. 3 is a diagram showing, in a graph format, an example of related information on the number of visitors according to an embodiment of the present invention. FIG. 3 is a diagram showing an example (part 1) of displaying a social participation index or the number of times of social participation in an embodiment of the present invention. The figure which shows the example (2) of a display of a social participation index or the number of social participation in one Example of this invention. 1 is a flowchart showing processing of data analysis (1) in an embodiment of the present invention. The figure which shows the display content of data analysis (1) in one Example of this invention. 3 is a flowchart showing data analysis (2) processing in an embodiment of the present invention. FIG. 2 is a diagram illustrating person-specific total information created in an embodiment of the present invention in the form of a person-specific total information table (UT). 3 is a flowchart showing the processing of data analysis (3) in an embodiment of the present invention. FIG. 2 is a diagram showing facility-specific aggregate information created in an embodiment of the present invention in the form of a facility-specific aggregate information table (PT). The figure which shows the display content of the social participation index of local residents in one Example of this invention. FIG. 3 is a diagram showing display contents representing facility usage status in an embodiment of the present invention. FIG. 3 is a diagram showing display contents representing regional facility usage status in an embodiment of the present invention. 7 is a flowchart showing data analysis (4) processing in an embodiment of the present invention. FIG. 3 is a diagram showing display contents representing comparison results of social participation indicators between people and regions in an embodiment of the present invention. FIG. 3 is a diagram showing display contents representing comparison results of social participation indicators between people and regions in an embodiment of the present invention. FIG. 3 is a diagram showing display contents representing comparison results of social participation indicators between people and regions in an embodiment of the present invention. 12 is a flowchart showing the processing of data analysis (5) in an embodiment of the present invention. FIG. 13 is a diagram showing a display content showing a comparison result of environmental indicators of inter-regional social participation in one embodiment of the present invention. The figure which shows the display content which shows the comparison result of the environmental index of social participation between regions by relative ratio in one Example of this invention.

An embodiment of the present invention will be described below with reference to the drawings. First, the concept of this embodiment will be explained using FIG. In Region A and Region B, participants such as residents carry respective mobile terminals 20-1, 20-2, and 20-3 and carry out social participation activities. Therefore, each of the mobile terminals 20-1, 20-2, and 20-3 uses a GPS function or the like to obtain location information associated with the social participation behavior of the participants. Note that a region is a concept that includes so-called districts, and the unit can be arbitrarily set such as a prefecture. Further, a region is an example of a unit society in this embodiment. The unit society includes companies, organizations such as health insurance associations, and local communities.

Here, this social participation behavior includes movement to and activities at facilities such as home, factory (workplace), store, fitness club (gymnasium), and office (workplace). In addition, participants are not limited to actions within their local area, but engage in activities that extend across regions.

Then, the behavior analysis device 10 installed in the analysis service center 1 collects location information from each mobile terminal 20-1, 20-2, and 20-3, and uses this and the data in the database 17 to Analyze social participation behavior. Further, the location information of the participants can also be acquired from a facility such as a fitness club.

The analysis results are output from the behavior analysis device 10 to each operation management terminal 18-1, 18-2, each mobile terminal 20-1, 20-2, 20-3, and the data utilization system. Here, a social participation index mainly indicating the state of involvement with society is output to each of the mobile terminals 20-1, 20-2, and 20-3.

Furthermore, analysis results according to the analysis instructions are output to the data utilization system. The data usage system is assumed to be a system used by administrative agencies in regions A and B, insurance companies, nursing care facilities, facility management companies such as fitness clubs, and point management companies. Furthermore, the analysis results include environmental indicators that indicate the ease of participation for each unit society such as region, various aggregated information, policy plans using these, and information to support this.

Next, FIG. 2 shows a system configuration diagram of a behavior analysis system for realizing the idea shown in FIG. 1. This behavior analysis support system includes a behavior analysis device 10 and various devices operating in each facility 2-1, 2-2, and 2-3. The behavior analysis support system also includes a data usage system connected via a network 80 such as the Internet. The data usage systems include computer systems of various organizations such as a fitness club system 30, an insurance company system 40, a nursing care facility management company system 50, a point management company system 60, and regional administrative agency systems 70-1 and 70-2. It will be done.

Each device constituting the behavior analysis system will be described below. The behavior analysis device 10 is realized by a computer device such as a server provided in the analysis service center 1. The behavior analysis device 10 includes an input section 11 , an output section 12 , a discrimination section 13 , an index calculation section 14 , a point calculation section 15 , a data analysis section 16 , and a database 17 .

The input section 11 and the output section 12 have an interface function for connecting to the network 80 and each operation management terminal 18-1, 18-2.

The determining unit 13 determines the social participation category of the participant based on the location information. The index calculation unit 14 calculates the social participation index of the participant based on the social participation category. Further, the point calculation unit 15 calculates points that serve as incentives for participants. Further, the data analysis unit 16 executes various data analyzes using social participation indicators and the like. Details of these will be described later, but these can be realized by a computer program (software), dedicated hardware, or FPGA (Field-Programmable Gate Array). Further, the database 17 stores various data used by the behavior analysis device 10.

Further, the analysis service center 1 is installed with operation management terminals 18-1 and 18-2 connected to the behavior analysis device 10. Each operation management terminal 18-1, 18-2 outputs instructions regarding information processing to the behavior analysis device 10, and displays processing results of the behavior analysis device 10.

Next, various devices operating in each of the facilities 2-1, 2-2, and 2-3 will be explained. First, in the fitness club 2-1, a mobile terminal 20-1 carried by a participant, a detection device 21 for detecting a participant, and a counter terminal 22 are operated. Each of these devices acquires position information of the participant, and outputs the acquired position information to the behavior analysis device 10 via the network 80. Here, the mobile terminal 20-1 is realized by a smartphone, a wearable GPS sensor, or the like, and can acquire position information using a GPS function. The detection device 21 can be implemented with a camera or the like, and identifies participants using image recognition technology.The counter terminal 22 also identifies participants using member information and acquires position information of the fitness club 2-1. .

Note that the detection device 21 and the counter terminal 22 are connected to the fitness club system 30 via an intranet 90. Furthermore, the mobile terminal 20-1 holds participant identification information and is connected to the behavior analysis device 10 via a network 80 such as an intranet 90 or the Internet.

Next, the data usage system will be explained. These are realized by computer systems used by various institutions that use the data. First, the fitness club system 30 is available at the headquarters of the fitness club, and can be connected via the intranet 90 to the detection device 21 and counter terminal 22 installed in the fitness club 2-1. The fitness club system 30 then uses the analysis results of the behavior analysis device 10 to support the formulation of various measures. Measures include creating menus for fitness club members, using points granted by point management companies, and planning store openings.

Further, the insurance company system 40 is used by an insurance company that sells life insurance and the like. The insurance company system 40 then uses the analysis results of the behavior analysis device 10 to support the formulation and management of various measures. Measures include the development of insurance products. The nursing care facility management company system 50 is used by a nursing care facility management company that operates nursing care facilities. The nursing facility management company system 50 uses the analysis results of the behavior analysis device 10 to support the formulation and management of various measures. Measures include construction of facilities, formulation of expansion plans, and formulation and development of operational policies. Further, the point management company system 60 is used by a point management company that operates and manages a point system that can be acquired and used in multiple services. Then, the point management company system 60 uses the information processing results of the behavior analysis device 10 to award points to the participants.

Furthermore, the administrative agency systems 70-1 and 70-2 are used at city halls, prefectural offices, and the like. Then, the administrative agency systems 70-1 and 70-2 use the analysis results of the behavior analysis device 10 to support the formulation and management of various measures. Measures in the administrative agency systems 70-1 and 70-2 include various policies regarding nursing care, public insurance, etc. Policies regarding nursing care include understanding the supply and demand balance of services, such as understanding the relationship between the elderly population and the supply of nursing care services in a region (municipal unit or school district unit such as elementary and junior high schools), and identifying high demand. We will implement measures such as supporting the entry of nursing care service providers into areas where the government is expected to enter. Policies regarding public insurance include, for example, determining insurance premium rates that take into account future regional population and insurance premium payments, health projects and preventive care projects, and formulating measures to popularize health projects and preventive care projects. .

Here, the configuration of the data utilization system will be briefly explained using administrative agency systems 70-1 and 70-2. Administrative agency systems 70-1 and 70-2 execute main processing using administrative agency servers 71-1 and 71-2, respectively. Administrative agency servers 71-1 and 71-2 are connected to administrative agency terminals 72-1 and 72-2, and receive instructions regarding processing and output processing results. Note that multiple administrative agency terminals 72-1 and 72-2 may be provided. Note that the various institutions that use the data may include health-related institutions such as hospitals and health insurance associations.

This completes the explanation of the entire behavior analysis support system, and details of the behavior analysis device 10 that executes the main processing of this embodiment will be explained. FIG. 3 is a configuration diagram of the behavior analysis device 10. In this embodiment, the behavior analysis device 10 is realized by a server that executes processing according to a program. For this reason, the behavior analysis device 10 includes a processing device 101, an I/F device 102, a main storage device 103, and a storage device 104, which are connected to each other via a communication path.

Here, the processing device 101 can be realized by a processor such as a CPU, and executes calculations according to various programs described below. The I/F device 102 has an interface function to connect to the network 80 and the operation management terminals 18-1 and 18-2, and corresponds to the input section 11 and output section 12 in FIG.

The main storage device 103 can be implemented as a so-called memory, and programs are developed for calculations in the processing device 101. The storage device 104 can be realized by a storage such as a hard disk drive, and stores programs and various information. Here, the programs include a discrimination program 130, an index calculation program 140, a point calculation program 150, and a data analysis program 160.

Note that each of these programs has the following correspondence with each part in FIG.
Discrimination program 130: Discrimination unit 13
Indicator calculation program 140: Indicator calculation unit 14
Point calculation program 150: Point calculation section 15
Data analysis program 160: Data analysis section 16
Further, each program is distributed via the network 80 or stored in a storage medium, and installed in the behavior analysis device 10. Furthermore, each program is not an independent program, but each program can be implemented as a constituent module.

The storage device 104 also stores participant information 171, location information 172, POI table 173, facility category table 174, action history information 175, social participation classification table 176, social participation weight table 177, and social participation analysis data 178 as information. These can be treated as the database 17 shown in FIG. Note that these various types of information will be explained together with the processing flow explained below.

Next, the processing flow of this embodiment will be explained. Note that in the explanation of the processing flow, each part shown in FIG. 2 will be mainly explained. FIG. 4 is a sequence diagram showing the entire process in this embodiment. This process specifies the social participation behavior of participants such as residents, and the behavior analysis device 10 stores participant information 171 regarding these participants. An example of participant information 171 is shown in FIG. Participant information 171 includes UID, registration date, gender, date of birth, home longitude, home latitude, home altitude, home facility name, work longitude, work latitude, work altitude, work altitude, and work facility name. including. The UID is information that identifies the participant, and may also include a name. The registration date indicates the date and time when the corresponding participant information was registered. However, this information may be up to date. Regarding gender, 1 is male and 2 is female.

The date of birth is the date of birth of the corresponding participant. The home latitude, work latitude, and home altitude are the location information of the participant's home, and may be substituted with the address. As the home facility name, the home apartment name or building name can be used.

The workplace longitude, workplace latitude, and workplace altitude are the location information of the workplace of the relevant participant, and may be substituted with the address. The workplace facility name can be the workplace building name, factory name, or company name. As described above, the participant information 171 indicates the attributes of the participants, particularly information regarding their locations. Each step will be explained below.

First, in step S1, each of the mobile terminals 20-1, 20-2, and 20-3 (hereinafter simply referred to as mobile terminal 20) acquires position information associated with the movement of the participant. Here, an example of the position information 172 is shown in FIG. As shown in FIG. 6, the position information 172 is information indicating the movement history of the participant. FIG. 6 shows a plurality of pieces of position information 172 stored in the behavior analysis device 10. Therefore, each record in FIG. 6 represents one piece of position information 172 detected by the mobile terminal 20.

Here, the location information 172 includes UID, number, positioning time, latitude, longitude, positioning accuracy, altitude, moving speed, moving direction, and number of steps. No. is information that identifies the position information for each participant, that is, each UID. The positioning time indicates the time when the mobile terminal 20 acquires the position information, for example, positions the mobile terminal 20 . As the positioning time, a time indicated by the OS of the mobile terminal 20 or a time obtained as a positioning result such as GPS time can be used. The latitude and longitude indicate location information acquired by the mobile terminal 20. Positioning accuracy is accuracy in the horizontal direction.

The altitude, moving direction, moving speed, and moving acceleration can be obtained as positioning results using GPS. It is a high altitude. Note that the moving direction can be described, for example, with north as zero degrees. The number of steps can be obtained using an acceleration sensor or GPS positioning results, and indicates the number of steps taken by the participant.

Acquisition of such position information 172 can be realized by using a position detection function such as a GPS function or a gyro of the mobile terminal 20. Here, the mobile terminal 20 can be realized by a smartphone or the like as described above. Further, this step may be executed by the detection device 21 or counter terminal 22 provided at the facility that is the participant's destination. Each of these pieces of positional information is an installation position, and participant tail positional information can be specified by detecting and identifying participants who have visited the facility.

Next, in step S2, the mobile terminal 20 outputs the acquired position information to the behavior analysis device 10. For this purpose, the mobile terminal 20 transmits position information at predetermined intervals via the network 80. Further, when the position information 172 is acquired by the detection device 21 or the counter terminal 22, the position information is further transmitted via the intranet 90 using communication equipment installed in the fitness club system 30 or the fitness club 2-1. Submit information.

Next, in step S3, the input unit 11 of the behavior analysis device 10 receives position information 172 from the mobile terminal 20. Then, in step S4, the determining unit 13 determines the social participation category of the participant's behavior based on the position information 172. As the name suggests, this social participation category is information indicating the classification of social participation behavior of participants. Here, details of the social participation category discrimination process will be explained using the flowchart shown in FIG.

First, in step S41, the determination unit 13 creates action history information 175 from the position information 172 and the POI table 173. The concept of this behavior history information 175 will be explained using FIG. 8. FIG. 8 shows how an arbitrary participant leaves home P0, stops at various locations (P1 to P6), and returns to home P0. First, the participant moved from home P0 to location P1 as action B(0). Next, the participant performed action B(1) at location P1. After action B(1), the participant moved to the next destination, location P2, by executing action B(2). In this way, the behavior history information 175 indicating the behavior history of the participant is specified by a set of movement at the target value and behavior at the target (for example, repetition).

Based on this idea, the determining unit 13 creates behavior history information 175. Therefore, in creating the action history information 175, the determination unit 13 first extracts the location information of the destination from the received location information 172. For this purpose, the determination unit 13 determines, among the location information 172, location information whose latitude and longitude are within a predetermined range and for a predetermined number of times (predetermined time) as the destination location information. Note that, at this time, it is preferable that the determination unit 13 extracts the first to last position information included in the predetermined number of position information as a representative. Then, the determination unit 13 creates action history information 175 from the extracted position information and the POI table 173.

Here, first, an example of the POI table 173 is shown in FIG. 9A. The POI table 173 is a table indicating Points of Interest, and information regarding locations (latitude and longitude, etc.) and facilities are associated with each other. For example, the POI table 173 may be implemented as a map database. The POI table 173 includes PID, registration time, latitude, longitude, altitude, facility size, facility name, address, parent facility PID, and facility category. Here, PID is information that identifies a destination. The registration time records the timing at which the facility was registered. Latitude, longitude, and altitude indicate the location of the facility in question. The facility name and address indicate the name of the facility and the address where it exists, respectively. Further, the parent facility PID is the PID of the facility where the corresponding facility exists. For example, in the example of FIG. 9A, if "XX Pharmacy" with PID = 2 and "XX Cafeteria" with PID = 3 are operated by "XX Shopping Center" with PID = 7, which is a parent facility, these parent PIDs becomes "7". Note that for facilities that do not have a parent facility, "-" indicating null is recorded in the parent facility PID. The facility category indicates the category of the facility.

Also. The facility category table 174 is a table in which facility names and facility PIDs are associated with facility categories indicating their classifications. Note that the POI table 173 and the facility category table 174 may be configured as one table.

Therefore, the determining unit 13 searches the POI table 173 using the latitude and longitude of the extracted location information as keys, and identifies the facility size, facility name, address, parent facility PID, and facility category. Furthermore, the facility category can be specified from the facility name and facility PID. As a result, action history information 175 shown in FIG. 9B is created. Further, the determining unit 13 can further identify the facility name "home" and "work place" using the participant information 171. However, "home" and "work place" in the facility name may be specified by inputting them on the operation management terminals 18-1 and 18-2, or by using the resident record in the administrative agency systems 70-1 and 70-2. Information may also be used.

Here, the action history information 175 is shown in FIG. 9B. The action history information 175 is created by the determination unit 13 from the received position information 172 and the POI table 173, and includes UID, BID, movement start time, movement start facility PID, movement end time, movement end facility PID, action type, and movement. Includes distance, steps, and calories burned. Among these, BID is information that identifies the behavior of the behavior history information 175, and is given by the determination unit 13. The movement start time and movement end time are the time of departure from the destination and the time of arrival in the corresponding action. The movement start facility PID and the movement end facility PID are information that identifies the facility serving as the starting point and the facility serving as the destination in the corresponding action. As for the action type, 0 indicates stay and 1 indicates movement, and can be determined by the movement speed of the position information 172. Similarly, the means of transportation can be determined based on the speed of movement. For example, this can be determined by associating the means of transportation for each speed of travel. Distance traveled, number of steps, and calories burned indicate the distance traveled, number of steps, and calories burned when the corresponding action is moving. These may be identified based on the body shape of the participant recorded in the participant information 171. This concludes the explanation of the action history information 175 created in step S41, and proceeds to the explanation of step S42.

In step S42, the determining unit 13 performs behavior determination using the behavior history information 175. In other words, each destination is determined. Specifically, from the action history information 175, the action type=0, that is, the movement end facility PID in the stay record is extracted as the destination. The determining unit 13 then compares the movement end facility PID with the POI table 173 to identify the facility category.

Next, in step S43, the determining unit 13 determines whether the identified facility category can be collated with the social participation classification table 176. As a result, if the comparison is possible (possible), the process moves to step S45. Then, in step S45, the determining unit 13 collates the social participation classification table 176 to identify the social participation category that corresponds to the specified facility category. The social participation classification table 176 is shown in FIG. The social participation classification table 176 shows the correspondence between social participation categories and facility categories. Here, the facility category can be set by referring to the classification item names of the Japan Standard Industrial Classification (revised October 2013) (enacted April 1, 2014). Furthermore, if a single facility implements multiple social participation categories, these may be additionally input. Further, a dialog may be displayed on the user screen of the operation management terminals 18-1, 18-2 or the mobile terminal 20, and input of the social participation category may be accepted in response. Alternatively, the social participation category may be estimated from calendar information of the user such as the mobile terminal 20 and automatically input.

Further, in step S43, if the comparison cannot be made with the social participation classification table 176 (impossible), the process moves to step S44.

Then, in step S44, the determining unit 13 executes social participation category determining processing. The details of this social participation category discrimination process will be explained using FIG. 11.

First, in step S450, the determination unit 13 initializes the BID of the target behavior history information, that is, sets it to 0. Then, in step S451, the determining unit 13 identifies the facility category of the action history information. For this, the one specified in step S42 may be used.

Next, in step S452, the determining unit 13 acquires the participant's schedule data. This schedule data can be acquired from the mobile terminal 20 or the like. Next, in step S453, the determining unit 13 determines whether the schedule of the acquired schedule data is for a sports hobby. For this purpose, the determining unit 13 uses the schedule information to identify the schedule that corresponds to the target action history information. Then, using language processing or the like, it is determined whether the contents described as the schedule are sports-related hobbies.

As a result, if the hobby is physical education (Y), the process moves to step S457. If the hobby is not related to sports (N), the process moves to step S454.

Next, in step S454, the determination unit 13 acquires acceleration information of the participant from the action history information or position information 172. Then, in step S455, the determining unit 13 determines whether the amount of physical activity is high based on whether the acquired acceleration information is greater than or equal to a predetermined value. In other words, if it is a predetermined value, it is determined that the amount of physical activity is high. As a result, if it is high (Y), the process moves to step S457, and if it is not high (N), the process moves to step S456.

Next, in step S456, the determining unit 13 identifies the relevant behavior as a cultural hobby (hobby (cultural)).In addition, in step S457, the determining unit 13 identifies the relevant behavior as a sports-related hobby (hobby (cultural)). Physical education).

Next, in step S458, the determining unit 13 determines whether there is any other data to be processed, that is, there is no record. As a result, if it does not exist (N), the process ends. Further, if there is another one (Y), the process moves to step S459. Then, in step S459, the determination unit 13 updates the BID, that is, adds +1 to it and returns to step S451 to process the next record. Note that the determination unit 13 may set the results of the social participation category determination process (steps S456 and S457) in the social participation classification table 176. Furthermore, the determining unit 13 may specify the social participation category according to the time period. For example, if the working hours are from 9:00 to 17:00 or around this time, the social participation category can be specified as working. Further, the determining unit 13 may directly identify the social participation category from the schedule data. This concludes the explanation of FIG. 11 and returns to the explanation of FIG. 7.

After receiving the above steps S44 and S45, the determining unit 13 determines the social participation category in step S46.

This concludes the explanation of FIG. 7, that is, the detailed explanation of step S4, and returns to FIG. 4 to explain the processing from step S5 onwards.

In step S5, the index calculation unit 14 calculates a social participation index based on the determined social participation category. The details of this social participation index calculation process will be explained using FIG. 12.

In step S51, the index calculation unit 14 specifies the specified social participation category and the weight corresponding to this category. For this purpose, the index calculation unit 14 uses a social participation weight table 177 shown in FIG. 13. That is, the weight table 177 is searched for the weight corresponding to the identified social participation category. As a result, social participation categories and their weights are obtained from the social participation classification table 176 and the social participation weight table 177. It should be noted that the social participation weight table 177 may be set based on input at the operation management terminals 18-1 and 18-2, or may be set using data analysis results for historical data. Further, the weight is information indicating the weight for the diversity variable.

Furthermore, in steps S52 to S55, the index calculating unit 14 calculates a diversity variable that is a variable of the social participation index. Diversity variables are indicators that indicate the degree of diversity and complexity of social activities in which participants participate. It can be assumed that the greater the degree of diversity and complexity, the more active the participant's behavior is, and the more likely it is possible to reduce risks related to health conditions. Note that it is more desirable that the diversity variable be a value that corresponds to the state of involvement in social activities.

In step S52, the index calculation unit 14 first calculates the length of stay at the facility in the target action history information. For this purpose, the index calculation unit 14 calculates the difference between the movement start time and the movement end time as the stay time. Then, the index calculating unit 14 identifies a weight, which is one of the diversity variables related to the stay time, from pre-stored relationship information between the stay time and a weight, which is one of the diversity variables. Here, an example of the relational information is shown in a graph format in FIG. 14. As shown in FIG. 14, the weight increases according to the stay time until the stay time reaches a predetermined value (around 30), and thereafter tends to decrease.

Further, in step S53, the index calculating unit 14 calculates the diversity of means of transportation (for example, means of transportation) to the facility. The diversity of transportation means is information indicating the degree of diversity, such as the number and types of transportation means used by the participants. For this purpose, the index calculation unit 14 may use the number of transportation means included in the action history information. Then, the index calculation unit 14 identifies a weight, which is one of the diversity variables, regarding the combination of transportation means, from the pre-stored relationship information between the number of combinations of transportation means and the weight, which is one of the diversity variables. . Here, an example of the relational information is shown in a graph format in FIG. 15. As shown in FIG. 15, the weight increases as the number of combinations of transportation methods used by participants increases, but the degree of increase tends to decrease as the weight value increases.

Furthermore, in step S54, the index calculation unit 14 calculates the number of facilities that are destinations in the series of actions. That is, the number of locations in the action history, which is a series of actions in FIG. 8, is counted. At this time, locations are counted based on the total number of locations visited. Then, the index calculation unit 14 calculates the weight, which is one of the diversity variables, regarding the number of visited facilities visited by the participant, from the pre-stored relationship information between the number of visited facilities and the weight, which is one of the diversity variables. Identify. Here, an example of the relational information is shown in a graph format in FIG. 16. As shown in FIG. 16, the weight increases as the number of facilities visited by the participant increases.

Further, in step S55, the index calculation unit 14 calculates the number of people staying at the visited facility, that is, the number of people. For this purpose, it is possible to use a people flow analysis service using location information provided by a mobile phone company or location information 172 to action history information 175. Furthermore, it is also possible to implement this by using a contact application between the mobile terminals 20. In addition, it is also possible to use traffic congestion information and manpower information from map information services. Then, the index calculation unit 14 specifies a weight, which is one of the diversity variables regarding the number of guests staying, from pre-stored relationship information between the number of guests staying and the weight, which is one of the diversity variables. Here, an example of the relational information is shown in a graph format in FIG. 17. As shown in FIG. 17, the weight increases as the number of people staying at the destination visited by the participant increases, but the degree of increase tends to decrease as the weight value increases.

Note that the processing order of steps S51 to S55 does not matter, and parallel processing may be performed. Furthermore, at least one of these diversity variables may be calculated. Furthermore, in addition to these, the value of the diversity variable may be increased as the number of visited facilities increases even within the same facility category.

Next, in step S56, the index calculating unit 14 calculates the number of social participations and the social participation index. Note that in this step, the calculation may be limited to the social participation index. In this case, it is desirable to use a social participation index that reflects the number of social participation. For this purpose, the index calculating unit 14 calculates the number of social participations and the social participation index using the behavior history information, the diversity variable that is the calculation result, the social participation category, and the weight as parameters. As a result, the social participation index and number of times can be determined for each behavioral history and social participation category.

Then, in step S57, the index calculation unit 14 stores the result of step S56 in the storage device 104 as one piece of social participation analysis data 178. Further, the index calculation unit 14 outputs the results to the operation management terminals 18-1 and 18-2 in accordance with the operations on the operation management terminals 18-1 and 18-2. This concludes the explanation of FIG. 11 and returns to the explanation of FIG. 4.

Next, in step S6, the point calculation unit 15 calculates points as an incentive for each participant (PID) using at least one of the social participation index and the number of social participations calculated in step S5. do. Note that the point calculation process in step S6 may be performed by the point management company system 60. For this purpose, the point calculation unit 15 transmits the social participation index and the number of social participation for each participant (PID) via the output unit 12. Furthermore, in order to protect the privacy of participants, a social participation index or another index obtained by converting the number of social participations may be transmitted, or may be encrypted or anonymized before being transmitted.

Then, the point calculation unit 15 accumulates the social participation index, the number of times of social participation, and points in the participant information 171.

Next, in step S8, the mobile terminal 20 reads and displays the social participation index, the number of social participations, and points from the behavior analysis device 10 in accordance with the participant's operation. However, this display may be push-distributed from the behavior analysis device 10 using the accumulation in step S7 as a trigger. Furthermore, a terminal device such as a PC used by a participant other than the mobile terminal 20 may execute step S8. Furthermore, it is desirable that the social participation index and the number of social participations can be displayed on the operation management terminals 18-1 and 18-2 and the administrative agency terminals 72-1 and 72-2. In this case, from the perspective of privacy protection, it is desirable to display information without identifying individuals, such as names. Note that the social participation index and points displayed on the mobile terminal 20 are limited to those of the corresponding participant.

Here, the display contents in step S8 are shown in FIGS. 18 and 19. FIG. 18 is a display example (Part 1) of the social participation index or the number of social participations. As shown in FIG. 18, the display content is shown in a graph format showing the values of each social participation category. The average for the area, which is an example of that environment, is also listed. In addition, the numerical values for each social participation category indicate values or average values over a certain period of time, such as one week or 11 months. Furthermore, it is desirable that the overall evaluation be an average value obtained by performing weighting correction on the values of each social participation category.

Furthermore, changes over a certain period of time immediately before may be displayed, cumulative values from the start of participation in this service may be displayed, and comparisons can be made with participants of the same attributes such as the same sex/age in the area. You may also do so. Furthermore, a combination of these may be displayed. Furthermore, both the social participation index or the number of social participation times may be written together.

Next, FIG. 19 is a display example (Part 2) of the social participation index or the number of social participations. The difference from Figure 18 is that "employment" is excluded from the social participation category. This is because the presence or absence of employment has a large influence on the social participation index and the number of social participations, and it is not easy to get a new job compared to other social participation categories, so we have made it possible to select the display of this category. There is. Here, "selectable" may mean that it can be switched by the operator of the mobile terminal 20 or the operation management terminals 18-1, 18-2, or as a function of the behavior analysis device 10. Particularly in the latter case, if the participant information 171 includes workplace information (work facility name, etc.), it is desirable that the contents of Figure 18 be displayed, and if not, the contents of Figure 19 should be displayed. .

Then, in step S9, the mobile terminal 20 becomes able to use points using the point application.

Furthermore, in step S10, the data analysis unit 16 determines whether the analysis conditions are satisfied. That is, it is determined whether the analysis time has come or whether a data analysis instruction has been received from the operation management terminals 18-1, 18-2 or the administrative agency terminals 72-1, 72-2. As a result, if the condition is not satisfied (N), the process returns to step S3. If the conditions are met (Y), the process moves to step S11.

Next, in step S11, the data analysis unit 16 executes data analysis regarding the health condition using various information. Then, in step S12, the data analysis unit 16 outputs data from the operation management terminals 18-1, 18-2 or administrative agency terminals 72-1, 72-2 (hereinafter simply referred to as administrative agency terminals 72) via the output unit 12. Output the results to . In this embodiment, the administrative agency terminal 72 is used, but the results may be output to other data usage systems.

Next, in step S13, the administrative agency terminal 72 displays the analysis results. Then, in step S14, the administrative agency system 70 and other data utilization systems support the planning of various measures in accordance with the analysis results and operator input.

As described above, in this embodiment, the social participation index weighted as shown in FIG. 13 can be calculated and displayed according to the procedure shown in FIG. 12 according to the social participation category shown in FIG. 10. Furthermore, as shown in FIGS. 14 to 16, various weightings can be applied depending on length of stay, variety of means of transportation, going out to multiple facilities, number of people staying at visited facilities, etc. As a result, the influence of social participation on nursing care prevention depends not only on the place of social participation, but also on the type (category) of social participation behavior, such as work, hobby groups, sports groups, volunteer meetings, shopping, and work. There are differences in the degree of suppression of support required for nursing care, and diversity in social participation behavior, such as the length of stay at the destination depending on the type of activity, the route to the destination, and the type and number of facilities visited at the same time. This will have the remarkable effect of solving the problem of various influences related to the degree of suppression of nursing care support, such as (complexity) and the frequency of contact with people at the place visited.

Furthermore, as shown in FIGS. 18 and 19, it is now possible to simultaneously compare and display the social participation index as a comprehensive evaluation and the number of social participation by social participation category. Therefore, it is difficult for participants, mainly elderly people, to understand all the complex factors, and there is a need for a method of presenting information that allows them to understand both overall indicators and improvements in social participation behavior. A wonderful effect can be obtained that can solve the problem.

Further, with the above processing, in this embodiment, it becomes possible for an institution to perform various data analyzes as shown below and to understand the social participation behavior of participants.

Here, steps S11 to S13 will be explained again using five analysis methods as examples.
《Data analysis (1)》
As data analysis (1), we will explain data analysis to understand the usage status of each facility. FIG. 20 is a flowchart showing the processing of data analysis (1). This flowchart is executed for each facility, that is, for each PID. In step S101, the data analysis unit 16 sets the PID, period, and participants of the target facility. This may be specified by accepting a designation from the administrative agency terminal 72, or may be set by applying a predetermined rule. Note that here, the target participants can be assumed to be a plurality of participants, such as residents of any region.

Next, in step S102, the data analysis unit 16 sets initial settings, for example, 0, to the UID, BID, Nv, and Nu to be processed below. Here, Nv and Nu will be explained. Nv indicates the total number of visits indicating the total number of users who used the facility. Therefore, the total number of visits is the sum of the total number of visits for each participant. Then, Nv can be used as the number of times of social participation, and a social participation index can be calculated based on this. Further, Nu is the actual number of users of the facility. In other words, it indicates the number of people who do not count the same person twice.

Next, in steps S103 to S111, the data analysis unit 16 counts the number of behavior history information 175 (records) that match the target PID and UID. This result is defined as the total number of times the facility has been used and the number of actual users. This is included in one of the social participation analysis data 178. Each step of the process will be explained below.

First, in step S103, the data analysis unit 16 reads a predetermined record from the action history information 175 from the database 17. Next, in step S104, the data analysis unit 16 determines whether the read action history information 175 matches the conditions set in step S101. Specifically, it is determined whether the movement start time and the movement end time of the read action history information are included. At this time, the determination may be made based on whether either one is included in the investigation period. Also, it is determined whether both the PID and UID match. If these match (Y), the process moves to step S105. If they do not match (N), the process moves to step S106.

Next, in step S105, the data analysis unit 16 adds 1 to Nv set in step S102, that is, the current target. Then, in step S106, the data analysis unit 16 determines whether the UID set in step S102, that is, the current target UID, is the first value in this flow. As a result, if it is the first UID (Y), the process moves to step S107. If it is not the first UID (N), the process moves to step S108.

Next, in step S107, the data analysis unit 16 adds 1 to Nu, which was set in step S102, that is, the current target. Then, in step S108, the data analysis unit 16 determines whether there is any action history information 175 (record) that has not been read out. If the result is yes (Y), the process moves to step S109. If not (N), the process moves to step S110.

Next, in step S109, the data analysis unit 16 updates the BID set in step S102, that is, the current target BID. For this purpose, the data analysis unit 16 specifies, for example, a BID that identifies the next action from among the read action history information. Then, the process returns to step S103.

Furthermore, in step S110, the data analysis unit 16 determines whether there is any other UID to be targeted. As a result, if there is another UID (Y), the process moves to step S111. Further, if there is no other UID (N), the process moves to step S112.

Next, in step S111, the data analysis unit 16 updates the target UID, and proceeds to step S103.

Further, in step S112, the data analysis unit 16 displays the analysis results Nv and Nu on the administrative agency terminal 72 via the output unit 12. The contents of this display are shown in FIG. FIG. 21(a) displays the analysis results in a pie chart for each facility. Here, the size of the pie chart indicates the social participation index and the number of social participation of the corresponding facility. Furthermore, the density of the pie chart indicates the number of social participants at the corresponding facility. Here, it is desirable that the data analysis unit 16 reflect the calculation results in steps S52 and S55 regarding the social participation index. In other words, adjustments may be made based on the length of stay at the facility and the number of people staying at the facility. Furthermore, it is desirable to reflect the calculation results in steps S53 and S54 regarding the social participation index. In other words, based on the social participation index of the facility in question, the social participation index of the means of transit to the facility (transportation, other facilities) is adjusted. Conversely, the social participation index of the relevant facility may be adjusted based on the social participation index of the transit institution. In this way, the social participation indicators of transit institutions and destination facilities may be distributed.

Further, when a designation is received from the operator for the pie chart in FIG. 21(a), the display changes to that shown in FIG. 21(b). FIG. 21(b) displays a specified pie chart, that is, the social participation index and the number of social participants for each date and time at the corresponding facility. Note that the graph is not limited to a circular display, but may be displayed in a form of size, length, etc. according to the social participation index or the number of social participation. According to this data analysis (1), it is possible to grasp the usage status of each facility, that is, the status of social participation activities for each facility. Therefore, it is possible to grasp the extent to which each facility contributes to revitalizing the social participation of local residents. Alternatively, it will be possible to understand changes in social participation indicators and the number of social participants over time, so we can evaluate the effects of measures such as installing or improving facilities or holding new events for social participation on social participation. Be able to understand quantitatively. In addition, since the number of social participants and the social participation index are displayed at the same time, it is possible to check whether the same residents are using the facility repeatedly (the increase in the social participation index is larger than the increase in the number of social participants). ) can also be understood, which is a remarkable effect. This concludes the explanation of data analysis (1).
《Data analysis (2)》
Next, as data analysis (2), creation of person-specific aggregate information, which is one type of social participation analysis data, that is, data analysis for each participant will be explained. FIG. 22 is a flowchart showing the process of data analysis (2). This flowchart is executed for each participant, that is, for each PID.

First, in step S201, the data analysis unit 16 sets the PID, period, and UID of the target facility. First, the number of UID measurements (N) is initially set to 0. These may be specified by accepting specifications from the administrative agency terminal 72, or may be set by applying predetermined rules.

Next, in step S202, the data analysis unit 16 secures a storage area in the database 17 for storing a per-person total information table (UT) indicating per-person total information. Then, in step S203, a predetermined record is read from the action history information 175 from the database 17.

Next, in step S204, the data analysis unit 16 determines whether the behavior type in the read behavior history information (record) is stay. In the case of staying (Y), the process moves to step S205. If not, the process moves to step S210.

Next, in step S205, the data analysis unit 16 determines whether the read behavior history information 175 matches the conditions set in step S201. Specifically, it is determined whether the movement start time and the movement end time of the read action history information are included. At this time, the determination may be made based on whether either one is included in the investigation period. Also, it is determined whether both the PID and UID match. If these match (Y), the process moves to step S206. If they do not match (N), the process moves to step S210.

Next, in step S206, the data analysis unit 16 determines whether the target PID is the first one. As a result, if it is the first PID (Y), the process moves to step S208. If it is not the first time (N), the process moves to step S207.

Next, in step S207, the data analysis unit 16 updates the total number of visits and total visit time of each PID for the UID corresponding to the read action history information.

Furthermore, in step S208, the total number of visits and total visit time for the corresponding UID and PID are newly created. Then, in step S209, the value of N for the corresponding UID is updated.

Next, in step S210, the data analysis unit 16 determines whether there is any action history information 175 (record) that has not been read out. If the result is yes (Y), the process moves to step S211. If not (N), the process moves to step S212.

Next, in step S211, the data analysis unit 16 updates the current target BID. For this purpose, the data analysis unit 16 specifies, for example, a BID that identifies the next action from among the read action history information. Then, the process returns to step S203.

Furthermore, in step S212, the data analysis unit 16 creates the total number of visits and total length of stay for the corresponding UID by accumulating the number of visits and length of stay.

Next, in step S213, it is determined whether there are any other UIDs to be targeted. As a result, if there is another UID (Y), the process moves to step S214. Furthermore, if there is no other UID (N), the process ends. Note that when terminating the process, the data analysis unit 16 can display the created person-specific aggregate information on the administrative agency terminal 72 via the output unit 12. Furthermore, in step S214, the data analysis unit 16 updates the target UID, and the process proceeds to step S202.

Here, the per-person total information created in the process of FIG. 22 is shown in FIG. 23 in the form of a per-person total information table (UT). The aggregate information for each person includes the applicable UID, the aggregate start time, the aggregate end time, the total number of visits to the applicable PID, the total length of stay, the N of the UID, the PID of the facility visited, the total number of visits to the PID, and the total length of stay. . According to this data analysis (2), it is possible to grasp the usage status of each participant's facility, that is, the status of each participant's social participation activities. This concludes the explanation of data analysis (2).
《Data analysis (3)》
Next, as data analysis (3), the creation of aggregate information by facility, which is one type of social participation analysis data, that is, the data analysis for each facility will be explained. FIG. 24 is a flowchart showing the process of data analysis (3).

First, in step S301, the data analysis unit 16 sets the PID, period, and UID of the target facility. First, the number of UID measurements (N) is initially set to 0. These may be specified by accepting specifications from the administrative agency terminal 72, or may be set by applying predetermined rules.

Next, in step S302, the data analysis unit 16 secures a storage area in the database 17 for storing a facility-specific aggregate information table (PT) indicating facility-specific aggregate information. Then, in step S303, the data analysis unit 16 sets the UID of the target facility.

Next, in step S304, a predetermined record is read from the action history information 175 from the database 17. Then, in step S305, the data analysis unit 16 determines whether the behavior type in the read behavior history information (record) is stay. In the case of staying (Y), the process moves to step S306. If not, the process moves to step S311.

Next, in step S306, the data analysis unit 16 determines whether the read behavior history information 175 matches the conditions set in step S301. Specifically, it is determined whether the movement start time and the movement end time of the read action history information are included. At this time, the determination may be made based on whether either one is included in the investigation period. Also, it is determined whether both the PID and UID match. If these match (Y), the process moves to step S307. If they do not match (N), the process moves to step S311.

Next, in step S307, the data analysis unit 16 determines whether the target UID set in step S303 is the first one. As a result, if it is the first PID (Y), the process moves to step S309. If it is not the first time (N), the process moves to step S308.

Next, in step S308, the data analysis unit 16 updates the total number of visits and total visit time of each PID in the corresponding UID using the read action history information.

Furthermore, in step S309, the total number of visits and total visit time for the corresponding UID and PID are newly created. Then, in step S310, the value of N of the corresponding PID is updated.

Next, in step S311, the data analysis unit 16 determines whether there is any action history information 175 (record) that has not been read out. If the result is yes (Y), the process moves to step S312. If not (N), the process moves to step S313.

Next, in step S312, the data analysis unit 16 updates the current target BID. For this purpose, the data analysis unit 16 specifies, for example, a BID that identifies the next action from among the read action history information. Then, the process returns to step S304.

Further, in step S313, the data analysis unit 16 creates the total number of visits and total length of stay for the corresponding PID by accumulating the number of visits and length of stay.

Next, in step S314, it is determined whether there are any other UIDs to be targeted. As a result, if there is another UID (Y), the process moves to step S315. Furthermore, if there is no other UID (N), the process ends. Note that when terminating the process, the data analysis unit 16 can display the created person-specific aggregate information on the administrative agency terminal 72 via the output unit 12. Furthermore, in step S315, the data analysis unit 16 updates the target UID, and the process proceeds to step S303.

Here, the facility-by-facility tally information created in the process of FIG. 24 is shown in FIG. 25 in the form of a facility-by-facility tally information table (PT). Facility-specific aggregate information includes the target PID, aggregation start time, aggregation end time, total number of visits at the target facility, total length of stay, N of PID, UID visited by the visitor, total number of visits at the UID, and total length of stay. including.
《Display contents using data analysis (2) and (3)》
The above is an explanation of the processing content of data analysis (3), but it is possible to perform various displays using the aggregated information by person and aggregated information by facility, which are the results of data analysis (2) and (3). Can be done. At this time, by applying the procedure shown in Figures 7 and 12 or data analysis (1) before or after aggregation, social participation categories, number of visits, and A social participation index may be calculated and displayed based on information such as , stay time, etc. Alternatively, in order to simplify the process, the total number of visits compiled in data analysis (2) and (3) may be displayed as a social participation index. This content will be explained below. The display contents described here can be displayed on various terminal devices such as the administrative agency terminal 72. However, it is desirable that these display contents be limited depending on the terminal device and its user. For example, in the case of the administrative agency terminal 72, it is desirable to display only information about the relevant area.

First, FIG. 26A shows the display contents of the social participation index of local residents. This display has display areas divided by region. Based on the aggregated information for each person, each region is displayed at a display density that corresponds to the average value per person of the social participation index of the residents. For example, the higher the social participation index, the higher the display density. Additionally, based on aggregate information by facility, facilities with social participation indicators above the threshold are extracted and displayed. In the example of FIG. 26A, it is displayed as a black circle. Note that the average value may be of any type, such as an arithmetic average, a geometric average, or a logarithmic average. Further, a representative value per person other than the average value may be used.

Here, the display can be changed according to specifications from the terminal device. First, when a facility (black circle in the figure) is specified, the display is changed to FIG. 26B. Further, when a display area, that is, a region is specified, the display is changed to that shown in FIG. 26C. Note that FIGS. 26A to 26C can be displayed on map information or as map information.

FIG. 26B shows display content representing the facility usage status. In other words, the display area is displayed at a display density corresponding to the number of residents (participants) using the facility (PID=1) specified in FIG. 26A. For example, areas with more residents using the facility are displayed darker than others. This makes it possible to understand how much a designated facility contributes to the social participation index of each local resident.

Moreover, FIG. 26C is display content showing the usage status of local facilities. Here, the facilities used by residents of the relevant area are displayed. In other words, facilities that are used by residents (participants) of the area designated in FIG. 26A (501 in the figure) more than a predetermined condition are extracted and displayed. In this figure, by specifying a region (501 in this example), it is possible to understand the facilities that contribute to the social participation index for local residents. You can understand which facilities are contributing to the participation index.

As described above, in this example, as shown in FIGS. 26A to 26C, the social participation index of local residents and the facilities that contribute to the social participation index are simultaneously displayed on the map, and furthermore, the designated facility is used. It is now possible to display facilities that are contributing to improving the social participation index of local residents, or the social participation index of designated local residents. Therefore, when implementing measures (opening of facilities or educational activities) to encourage social participation among the elderly, governments and private businesses that serve elderly people as customers must quantitatively and quantitatively evaluate the effects of the measures from the perspective of social participation. This has the wonderful effect of making evaluations easier to understand visually. Furthermore, a remarkable effect can be obtained in that it becomes possible to quantitatively and visually evaluate the current status of local residents' activities from the perspective of social participation in an easy-to-understand manner.
《Data analysis (4)》
Next, as data analysis (4), a comparison of social participation indicators between people and regions, which is one type of social participation analysis data, will be explained. In other words, it calculates social participation indicators for each participant and region and makes it possible to compare them. FIG. 27 is a flowchart showing the data analysis (4) process.

First, in step S401, the data analysis unit 16 sets the UID of each participant targeted for this process. That is, the data analysis unit 16 sets the UID of the participant for which the social participation index is to be created, and the UIDs of N people to be compared with this UID. Further, the data analysis unit 16 sets a search range (x) to be compared. These may be specified by accepting specifications from the administrative agency terminal 72, or may be set by applying predetermined rules. Here, let the UID of the participant who creates the social participation index be uid_t, and let the UIDs of N comparison subjects be uid_c(n).

Next, in step S402, the data analysis unit 16 reads the residential facility of the participant indicated by uid_t, that is, the home from the participant information 171.

Next, in step S403, the data analysis unit 16 calculates, from the POI table 173, the number of facilities Np(c) for each social participation category included in the search range (x) of the home that is the residential facility of the participant uid_t. do. Here, it is desirable that the search range (x) of the home, which is a residential facility, be a social unit such as a region.

Then, in step S404, the data analysis unit 16 multiplies the calculated Np(c) by a weight according to the social participation category and substitutes the product-sum calculation result into Kt (social participation index). Here, the weights are specified using a social participation weight table 177. Therefore, in steps S405 and S406, i=0 and Kc(i)=0 are set for Kc(i).

Next, in step S407, the data analysis unit 16 calculates the number of facilities Np(c) for each social participation category within the search range (x) of residential facilities of uid_c(i). Here, as in step S403, the POI table 173 is used. Then, in step S408, the data analysis unit 16 multiplies Np(c) calculated in step S407 by a weight according to the social participation category and substitutes the product-sum calculation result into Kc(i).

Next, in step S411, the data analysis unit 16 determines whether the process has been executed for all comparison subjects (N people). As a result, if there are any remaining (N), the process moves to step S410, in which i is incremented by 1 and the process returns to step S406. In other words, the process is repeated for all comparison subjects (N people). Furthermore, if the processing for all comparison subjects is completed (Y), the process moves to step S412.

Next, in step S412, the data analysis unit 16 calculates the average value of Kc(i) of N comparison subjects and substitutes this for Kc. Then, in step S413, the correction coefficient is set to Kc/Kt, and this is specified as the comparison result.

FIG. 28 shows the display contents of the comparison results based on this process. The display contents described here can be displayed on various terminal devices such as the administrative agency terminal 72. However, it is desirable that these display contents be limited depending on the terminal device and its user. For example, in the case of the administrative agency terminal 72, it is desirable to display only information about the relevant area.

FIGS. 28A to 28C are examples of displays comparing social participation indicators between people and regions. FIG. 28A is a display example when the correction according to the steps shown in FIG. 27 is not performed. The correction selection switch at the top right of the screen is in the OFF state, and the social participation index of Mr. T, the participant indicated by uid_t, is calculated and displayed as 30.1, and the social participation index of Mr. C, one of the comparison subjects. In this example, the area where Mr. C's home is located is distinguished from other areas, and the average value of the social participation index for that area is calculated and displayed as 37.1. FIG. 28B is a display example when the correction shown in FIG. 27 is performed. The correction selection switch at the top right of the screen is in the ON state. In this example, compared to Mr. T's residential environment, the comparison target's residential area has more facilities that lead to social participation, and the correction coefficient in step S413 is 1.5. This is an example. In Figure 28B, this difference in living environment is corrected, and Mr. T's social participation index is displayed higher than that of Mr. C, as shown in the figure, and it is recognized that Mr. T is actively participating in society. can do. It will be possible to display a display that reflects the hardships and efforts of the person in accordance with their surrounding environment, which will lead to increased motivation of participants to participate in society. It is not easy to change the surrounding environment of a place of residence through individual efforts alone, and such display is very important in objectively expressing the hardships and efforts of individuals. Figure 28C is another example of the display in Figure 28B, where the index for Mr. C, who wants to understand social participation activities, is set at 100%, and the average values for Mr. T and Mr. T's residential area are displayed as percentages. This is an example. The index based on Mr. T can be intuitively understood as a comparison result.

As described above, in this embodiment, as shown in FIGS. 28A to 28C, it is possible to compare and display social participation indicators between people and regions. Furthermore, by using the correction coefficient calculated by the procedure shown in FIG. 27, it is possible to correct the influence of differences in living environment on the social participation index. Therefore, social participation activities are easily influenced by the surrounding environment of the residential area (ease of access and convenience to shops for shopping, gathering places, libraries, etc.), and residents from different living environments This will have the remarkable effect of solving the problem of difficulty in making comparisons. This concludes the explanation of data analysis (4).
《Data analysis (5)》
Next, as data analysis (5), a comparison of environmental indicators of social participation between regions, which is one of the social participation analysis data, will be explained. In other words, data analysis (5) makes it possible to compare the ease, ease, or convenience (hereinafter simply referred to as ease) of social participation in each region. FIG. 29 is a flowchart showing the process of data analysis (5).

First, in step S501, the data analysis unit 16 sets the UID (hereinafter, uid_(c)) of the target persons (N people) for calculating the target person index of the environmental index. The data analysis unit 16 also sets the search range (x). These may be specified by accepting specifications from the administrative agency terminal 72, or may be set by applying predetermined rules. Note that the following processing flow is executed for each search range (x), that is, each region.

Next, in steps S502 and S503, the data analysis unit 16 sets i=0 and Kc(i)=0 for Kc(i) in order to calculate K.

Next, in step S504, the data analysis unit 16 calculates the number of residential facilities of uid_c(i), that is, the number of facilities Np(c) for each social participation category located at (x) in the search range. This allows you to identify the number of facilities in each category in your home area.

Next, in step S505, the data analysis unit 16 substitutes the product-sum calculation result obtained by multiplying Np(c) by a weight according to the social participation category into K(i).

Next, in step S506, the data analysis unit 16 determines whether the process has been executed for all comparison subjects (N people). As a result, if there are any remaining (N), the process moves to step S507, in which i is incremented by 1 and the process returns to step S503. In other words, the process is repeated for all comparison subjects (N people). Further, if the processing for all comparison subjects is completed (Y), the process moves to step S508.

Then, in step S508, the data analysis unit 16 assigns the average value of N K(i) to K. With this, the average value of K(i) can be specified as an environmental index in the relevant area.

Display contents of comparison results based on the above processing are shown in FIGS. 30A and 30B. Note that the display contents described here can be displayed on various terminal devices such as the administrative agency terminal 72. However, it is desirable that these display contents be limited depending on the terminal device and its user. For example, in the case of the administrative agency terminal 72, it is desirable to display only information about the relevant area.

FIG. 30A shows display content in which absolute values of environmental indicators are displayed in display areas associated with each region. Then, when a region is designated using various terminal devices, the environmental index of each region is shown as a relative value, as shown in FIG. 30B. When the area "30.7" in FIG. 30A is specified, the environmental index for other areas is expressed as a relative value (%) with that area set as 100.0%. Note that FIGS. 30A and 30B can be displayed on map information or as map information.

As described above, in this embodiment, according to this data analysis (5), it becomes possible to compare the social participation index for each region weighted according to the social participation category. Therefore, by comparing the number of residents and elderly people in the area, as well as the predicted future numbers of these, we can determine the establishment and reinforcement of facilities such as community centers, the expansion of gathering places, and the expansion of commercial facilities, which will lead to more active participation in society. You will be able to plan and implement specific measures such as attracting foreign students. Alternatively, by predicting the number of people in need of care based on these indicators of social participation, a remarkable effect can be achieved in that it becomes possible to systematically set nursing care insurance premiums and expand the number of nursing care service facilities. In particular, by displaying as shown, the comparison results can be intuitively grasped. Therefore, quantitative comparisons have not been taken into consideration to understand the differences between regions from the perspective of social participation, and it is not possible to fully understand the issues and formulate measures for social participation. A remarkable effect can be obtained. Or, it is wonderful that it can solve the problem that conventionally only the evaluation of individual participants has been possible, and the analysis, evaluation, and utilization of results that take into account social participation behavior in the "environment" unit such as region and organization are insufficient. Effects can be obtained.

This concludes the explanation of this embodiment. Each display is performed by the determination section 13, index calculation section 14, point calculation section 15, and data analysis section 16 using the output section 12 to output to a terminal device, etc. It will be displayed.

Note that the present invention is not limited to this embodiment, and can be realized in various ways. For example, the behavior analysis device 10 may be provided with a display section and realized as a so-called stand-alone device. Furthermore, advice regarding specific social participation behavior may be output based on data analysis.

DESCRIPTION OF SYMBOLS 1... Analysis service center, 10... Behavior analysis device, 11... Input section, 12... Output section, 13... Discrimination section, 14... Index calculation section, 15... Point calculation section, 16... Data analysis section, 17... Database, 18 -1, 18-2...Operation management terminal, 2-1...Fitness club, 2-2...Store, 2-3...Home, 20-1, 20-2, 20-3...Mobile terminal, 21...Detection device 21 , 22...Counter terminal, 30...Fitness club system, 40...Insurance company system, 50...Nursing facility management company system, 60...Point management company system, 70-1, 70-2...Administrative organization system, 71-1, 71 -2... Administrative agency server, 72-1, 72-2... Administrative agency terminal, 80... Network, 90... Intranet

Claims (15)

In a behavior analysis device that analyzes the social participation behavior of participants,
an input unit that accepts location information indicating the movement history of each of the plurality of participants;
a determination unit that determines a social participation category indicating a classification of social participation behavior of the participant based on the location information;
A behavior analysis device comprising: an index calculation unit that calculates a social participation index that is an index according to the determined social participation category and that indicates a social involvement status of each of the participants. The behavior analysis device according to claim 1,
The index calculation unit is a behavior analysis device that calculates the social participation index according to a diversity variable indicating diversity in the behavior of the participants. The behavior analysis device according to claim 2,
The index calculation unit is a behavior analysis device that uses weight information based on at least one of time, means of transportation, number of visited facilities, and number of people staying as the diversity variable. The behavior analysis device according to claim 1,
The discrimination section is
Using the location information, create action history information including the end facility of the participant,
Using the action history information, specifying a facility category for classifying the moved facility;
A behavior analysis device that determines the social participation category determined according to the facility category. The behavior analysis device according to any one of claims 1 to 4,
A behavior analysis device further comprising an output unit that outputs the social participation index for each applicable facility in a format according to the social participation index. In a behavior analysis method that analyzes participants' social participation behavior using a behavior analysis device,
The input section accepts location information indicating the movement history of each of the multiple participants,
a determination unit determines a social participation category indicating a classification of the social participation behavior of the participant based on the location information;
A behavior analysis method in which an index calculation unit calculates a social participation index that corresponds to the determined social participation category and indicates a state of social involvement of each of the participants. The behavior analysis method according to claim 6,
A behavior analysis method, wherein the index calculation unit calculates the social participation index according to a diversity variable indicating diversity in the behavior of the participant. The behavior analysis method according to claim 7,
A behavior analysis method in which the index calculation unit uses weight information based on at least one of time, means of transportation, number of visited facilities, and number of residents as the diversity variable. In the behavior analysis method according to claim 6,
By the discrimination section,
Using the location information, create action history information including the end facility of the participant,
Using the action history information, specifying a facility category for classifying the moved facility;
A behavior analysis method for determining the social participation category determined according to the facility category. The behavior analysis method according to any one of claims 6 to 9,
A behavior analysis method in which an output unit outputs the social participation index for each applicable facility in a form corresponding to the social participation index. In a behavior analysis device that analyzes the social participation behavior of participants,
a database that stores behavior history information indicating the behavior history of the participant;
and a data analysis unit that calculates an environmental index indicating ease of social participation for each unit society using the action history information and the social participation category of a facility related to a participant belonging to the unit society. Analysis equipment. The behavior analysis device according to claim 11,
In the behavior analysis device, the unit society is a region. The behavior analysis device according to claim 11,
Furthermore, the behavior analysis device includes an output unit that compares and outputs environmental indicators for each unit society. The behavior analysis device according to claim 13,
The output unit is a behavior analysis device that outputs the environmental index for each unit society as a relative value. The behavior analysis device according to any one of claims 11 to 14,
The data analysis department is
Calculate the number of facilities for each social participation category related to each participant in the unit society,
A behavior analysis device that calculates the environmental index based on the number of facilities for each social participation category.

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