JP7312689B2 - Area analysis system and method - Google Patents

Description

TECHNICAL FIELD The present invention relates to an area analysis system and method, and more particularly to a technique for analyzing features of an area ascertained based on people's movement histories.

In order to create a city where people gather, it is important that the location of local stores and facilities that provide services be convenient for shopping, and that the product lineup of surrounding stores match the needs of users. For this reason, businesses engaged in regional development and companies that provide services are required to understand trends in consumption and movement in their target areas.

Conventionally, various techniques for analyzing the market of an area and techniques for evaluating features have been proposed. For example, Patent Literature 1 discloses a user behavior analysis device that analyzes user behavior trends from the usage history of transportation IC cards or payment means terminals with equivalent functions, and numerically evaluates characteristics such as station convenience and growth rate in consideration of the results. Further, Patent Literature 2 discloses a search information providing system capable of performing analyzes such as trade area analysis using route search results accumulated in a database.

Japanese Patent Laid-Open No. 2013-251011 (Patent No. 5638115) Japanese Patent Application Laid-Open No. 2011-159039 (Patent No. 5296720)

The technique of Patent Literature 1 is an analysis of user behavior with respect to a station, and does not refer to evaluation of the area around the station. It is known that there are facilities that people stop by and facilities that people pass by near the station, and it is not possible to grasp and analyze the usage trends of surrounding facilities based only on the usage information of the station. In addition, each station has a different structure, bus stop, and shops in front of the station, so it is difficult to judge how far the area will benefit from the station. The technique of Patent Document 2 uses route search results to perform analyzes such as trade area analysis, and by grasping the route traveled by the user, it is possible to predict the appropriate place for placing an advertisement or opening a store. However, it is not possible to obtain the purchasing tendency of the user. In addition, people who use the area on a daily basis often do not perform searches, and there is a possibility that the accuracy of the analysis is not good.

In recent years, in view of the declining population along railway lines and the decrease in regular income, major transportation operators are trying to increase profits by expanding lifestyle-related services linked to transportation services. As a means of doing so, it is expected that users will be urged to join transportation IC cards and analysis terminal applications, and consumption at commercial facilities within the group will increase.

SUMMARY OF THE INVENTION Accordingly, an object of the present invention is to provide a technique for defining an area using information on the movement of people involved in facilities such as stations and purchases around the facility, and analyzing the behavior of people in that area.

An area analysis system according to a preferred embodiment of the present invention is an area analysis system that processes information about a target area using an information processing device having a processing unit and a storage unit,
a degree-of-relevance calculation unit that calculates a degree of relevance indicating continuous use of any two facilities out of a plurality of facilities included in the area, based on movement information of people in the area and purchase information or service information;
and an area definition unit that selects a route between two facilities with a high degree of association obtained by the degree of association calculation unit and defines an area that includes the two facilities and a route connecting the two facilities as an area analysis system.
The present invention is also grasped as an area analysis method in the above area analysis system.

According to the present invention, it is possible to define an area using information related to the movement of people involved in a facility and purchases in the surrounding area, and to analyze the behavior of people in that area.

1 illustrates an area analysis system and its functional blocks in one embodiment; FIG. It is a figure which shows the movement history in one Example. It is a figure which shows the purchase history and service utilization history in one Example. It is a figure which shows the facility information and shop information in one Example. It is a figure which shows the user information in one Example. It is a figure which shows the road network information in one Example, and route network information. It is a figure which shows the action history in one Example, and route information. FIG. 4 is a diagram showing area definitions and route lists in one embodiment; FIG. 10 is a diagram showing an example of a display screen of area information in one embodiment; It is a figure which shows the processing flowchart of the action-history preparation part in one Example, a movement route estimation part, and a transportation means estimation part. It is a figure which shows the processing flowchart of the facility relevance degree calculation part in one Example. FIG. 10 is a diagram showing a processing flowchart of area analysis in one embodiment.

An embodiment will be described below with reference to the drawings.

FIG. 1 is a diagram illustrating an area analysis system and its functional blocks according to one embodiment.
The area analysis system is configured by connecting an area analysis server 1 (hereinafter simply referred to as a server) and a terminal 9 via a network. An external server 8 is also connected to the network.

The server 1 is an information processing apparatus having a CPU (processing unit) 12, a communication I/F 13, and a storage unit 11 as hardware resources. The storage unit 11 stores an area analysis program 100 and a DB (database) 110 . The DB 110 stores movement history 200, purchase/facility usage information 300, user information 430, station/facility information 500, surrounding traffic information 600, and map information 700. FIG. The area analysis program 100 has the functions of an action history creation unit 101, a movement route estimation unit 102, a transportation means estimation unit 103, a facility relevance calculation unit 104, and an area definition unit 105. These functions are realized by executing the area analysis program 100 with the CPU 12.

Note that the names of the parts described above are only examples, and other names may be given. For example, the area definition unit 105 may be called an area determination unit. In addition, although the names of the respective functions are given so that the functions of the respective parts can be easily understood, they are not necessarily limited to those shown in the drawings. Further, each of the above information managed by the DB 110 may be referred to as "... information table" or "... information DB".

The terminal 9 has a CPU, a communication I/F, a storage unit, an input device, and a display as hardware resources, and an analysis application 90 is stored in the storage unit. The analysis application 90 has functions of an analysis processing unit 91 and a display unit 92, and these functions are realized by the analysis application 90 being executed by the CPU. The analyst can operate the input device to instruct creation and display of a screen as shown in FIG. The analysis processing unit 91 issues an analysis request including the designated area to the server 1 .

Next, each component of the area analysis program 100 will be described.
The action history creation unit 101 acquires an analysis request from the analysis processing unit 91 of the analysis application 90 of the terminal 9 via the communication I/F 13 . This analysis request contains information for specifying the target area, such as the name of the municipality, the code of the municipality, and the name of the station. In addition, the attribute of the target person, the date and time of the target, and the time period may be included. The action history creation unit 101 acquires entry/exit records at station ticket gates and bus boarding/alighting records in facilities existing in a designated area from the movement history management server 810 of the external server 8 and records them in the movement history 200. Also, purchase information and service information such as purchased items and prices are acquired from the purchase/service usage history management server 830 and recorded in the purchase/service usage history 300 . The action history creation unit 101 further arranges the movement history 200 and the purchase/service use history 300 in chronological order for each user to create an action history 600 .

The movement route estimation unit 102 maps the position information included in the action history 600 of each user to the map information 500 to identify the route traveled by each user. A method generally called map matching can be used to identify the route. The moving means estimating unit 103 obtains the moving distance of the subject from the specified route, and calculates the moving speed using the measurement time included in the moving trajectory. Here, means of transportation refers to means of transportation by which users move, such as trains, cars, bicycles, and walking. The moving speed of the user indicates what means of transportation the user has used. The means of transportation estimation unit 103 determines the means of transportation based on a rule for determining the moving speed of the user. Judgment rules are judgment criteria such as, for example, walking if moving speed is 5 km/h, bicycle if moving speed is 5-20 km/h, car if moving speed is 20-50 km/h, and train if moving speed is 50 km/h or more.

The facility relevance calculator 104 obtains the probability of continuous use of any two facilities from the movement history 200 and the purchase/service use history 300, and calculates the relevance of the user's visits to the two facilities. The area definition unit 105 extracts two facilities whose degree of association calculated here is equal to or higher than a certain value, extracts all routes used for movement between the two facilities from the action history 600, and defines a list of the two facilities and routes as one area. The area definition unit 105 can transmit the defined area information to the analysis application 90 of the terminal 9 .

The external server 8 includes, for example, a movement history management server 810, a facility/store server 820, a purchase/service usage history server 830, a map server 840, and the like. The movement history management server 810 manages movement histories, which are entry/exit records of users passing through station ticket gates, for example. Users can enter/exit ticket gates using transportation media or mobile terminal applications. The facility/store server 820 manages facility information and store information. The purchase/service usage history server 830 manages store purchase information and service usage history for each user. The map server 840 manages map information for each area.

Next, each piece of information managed by the DB 110 will be described with reference to FIGS. 2 to 6. FIG.
FIG. 2(a) shows a movement history 210, which is a record of entering/exiting a ticket gate at a station. The movement history 210 is composed of a user ID 211 that is the identifier of the subject, a ticket gate ID 212 that is the identifier of the ticket gate, an entry/exit classification 213, a station ID 214 that is the station identifier, and a date and time 215 when the entry/exit was recorded.

Users often have mobile terminals such as smartphones. Therefore, it is possible to manage the user's movement history based on the user's location information acquired by the Wi-Fi connection. FIG. 2(b) shows a movement history 220 for each user obtained through Wi-Fi connection. The movement history 220 is composed of a terminal number ID 221 which is a terminal identifier, a Wi-Fi station ID 222 which identifies a Wi-Fi station, a latitude 223 and a longitude 224 which indicate the position of the Wi-Fi station, and a date and time 225 when connection to the Wi-Fi station was detected.

FIG. 3 shows a purchase history 310 and a service usage history 320 included in the purchase/service usage history information 300 acquired from the purchase/service usage history server 830 by the action history creation unit 101 of the area analysis program 100 . A purchase history 310 shown in FIG. 3A is composed of a user ID 311, a purchased item 312, a purchase price 313, a store ID 314 that is the identifier of the store of purchase, and a date and time of use 315. FIG. The service usage history 320 shown in FIG. 3B is composed of a user ID 321, a shop ID 322, a service type used 323, a usage start time 324, and a usage end time 325. FIG.

FIG. 4A shows facility information 410 and store information 420 acquired from the facility/store server 820 by the action history creation unit 101 of the area analysis program 100 .
The facility information 410 shown in FIG. 4A is composed of a facility ID 411 that identifies the facility, a facility name 412, latitude 413 and longitude 414 that indicate the location of the facility, and the name 415 of the operator that manages the facility. The store information 420 shown in FIG. 4B is composed of a store ID 421 that is a store identifier, a store name 422, a latitude 423 and a longitude 424 that indicate the location of the store, a facility ID 425 that is the identifier of the facility to which the store belongs, and a category 426 that occupies the service contents of the store.

FIG. 4B shows user basic information 431 and user ID management information 432 included in user information 430 . User basic information 431 shown in FIG. It should be noted that this user basic information is an example, and the sex 4313 and the date of birth 4314 may be omitted when the analysis according to sex and age is not performed. The user ID management information 432 shown in FIG. 4B (d) is information for linking the common ID 4311 with the user IDs of other services, and in this embodiment, it consists of a common ID 4321 that is the same as the common ID 4311, a service A user ID 4322, a service B user ID 4323, a service C user ID 4324, and a mobile terminal number 445.

FIG. 5 shows road network information 510 and route network information 520 included in map information 500 . The road network information 510 shown in FIG. 5A includes a road ID 511, a name 512, and road position information 513. FIG. Road location information 513 is a set of location information including latitude and longitude. For example, the road with the road ID "111" has the name "OPQ" and indicates that it extends from position 1 to position N, which is the end point.

The route network information 520 shown in FIG. 5( b ) indicates the positions of railroad and bus routes on the map, and includes route IDs 521 , route names 522 , and route position information 523 . Note that the route position information 523 is a set of position information including latitude and longitude. For example, the route name of route ID “011” is “ABC”, which indicates that the route runs from position 1 to position N, which is the end point.

FIG. 6 shows action history 600 . The action history 600 is a history of travel and purchases arranged in chronological order for each user, and includes date and time 601 when the action such as travel and purchase was detected, facility ID 602 where the action was detected, store ID or ticket gate ID 603, action type 604 including entry, exit, purchase, and service use, amount 605, item/service type 606, latitude 607 and longitude 608 indicating the location of the facility or store, and movement between facilities. It is composed of a road ID 609 which is an identifier of a road to be used and transportation means 610 .

FIG. 7 shows area information 700 . Area information 700 consists of area information 710 and route information 720 . The area information 710 shown in FIG. 7(a) is information defining an area, and is composed of an area ID 711, facility IDs 712 and 713 of two facilities, which are identifiers of the facilities or shops that make up the area, a degree of association 714 between the two facilities, a route ID 715, which is the identifier of the route taken by the user when moving between the two facilities, and the number of continuous users between the two facilities 716. The degree of relevance and the standard error of the degree of relevance 714 will be described later.

The route information 720 shown in FIG. 7B is route information indicated by the route ID 715 in the area information 710, and is composed of a route ID 721 that is the same as the route ID 715, a road ID 722, and a selection rate 723 that indicates the rate at which the route is selected.

FIG. 8 is a display example of a screen of area information 710 displayed on the display unit 92 of the analysis application 90. As shown in FIG. The screen shown in FIG. 8A displays the facility with the largest number of users in the area 920 specified by the analyst as a central node 9201, and other facilities (for example, facility 9203) highly related to that facility as nodes connected by links representing the degree of association 9202 between facilities. This screen is generated by screen creation by the analysis processing unit 91 of the analysis application 90 according to the processing result of the area definition unit 105 and the operation of the terminal 9 .

It should be noted that other facilities with a high degree of association can also be displayed as nodes connected by links. If there are a large number of facilities with high relevance, it is possible to limit the display to facilities with a certain degree of relevance or more. In addition, the node representing the facility may change the size and color of the node according to the average number of users per day, the average usage amount per person, and the average usage time, so that the scale of the number of users can be intuitively grasped. Similarly, the link representing the degree of association may change the line thickness and color according to the magnitude of the degree of association between the two facilities.

FIG. 8(b) is a screen displaying the degree of association between two facilities in the area information 710 and the route 9204. FIG. When the analyst operates the terminal 9 and selects any two facilities (for example, facilities 9201 and 9203) on the screen showing the area information 710, the degree of association 714 and the route selection rate 723 of the two facilities are displayed in a pie chart. Using these values, when the number of users of a certain facility such as a station can be estimated, the number of users can be estimated by multiplying the estimated number of users of the facility by the degree of relevance of the other facility. Furthermore, the number of travelers for each travel route can be estimated from the route selection rate 723 . In this way, by visualizing the actual movement of people, the range of excursions defined based on purchase data, the estimated number of excursions, and routes, it is possible to support the following decision-making. For example, when deciding on the location of a new commercial facility in a certain area, by narrowing down the target age group and gender and analyzing the area, it is possible to locate the facility on the route where more target people move. In addition, when developing share bike stations, it is possible to search for two facilities with a large number of people moving, arrange stations based on the assumption of movement between those facilities, and determine the number of share bikes to prepare based on the number of people moving in each time period.

Next, the processing operation of each functional unit will be described.

FIG. 9 is a flow chart showing the processing of the action history creating unit 101, the moving route estimating unit 102, and the transportation means estimating unit 103. As shown in FIG.
First, the action history creation unit 101 acquires the facility information 410 and the store information 420 in the target area from the facility/store server 820 in response to an analysis request (including information specifying the area) transmitted from the analysis application 90 (S101). Further, the action history creation unit 101 acquires the facility travel history 200, purchase history 310, and service usage history 320 from the travel history management server 810 and the purchase/service usage history server 830, respectively (S101). Then, the action history creation unit 101 sorts the acquired facility information, store information, movement history, purchase history, and service usage history in chronological order for each user to create an action history 600 (S102). If the analysis request specifies the user's age and gender, target date and time, and weekday/holiday classification as target person attributes, the movement history 200 and purchase/service usage history 300 that match those conditions will be acquired.

Next, referring to facility information 410 and store information 420 from facility ID 602 or store ID 603 of created action history 600, latitude 413 and longitude 414 (also latitude 423 and longitude 424) indicating these positions are acquired and stored in latitude 607 and longitude 608 of action history 600 (S103).

Next, the movement route estimation unit 102 searches the map information 500 for the road network information 510 and the route network information 520 that exist within a certain range from the latitude 607 and the longitude 608 in the action history 600, and superimposes the acquired road network information 510, the route network information 520, and the movement trajectory using a map matching method to identify the road or route along which the user traveled (S104).

Next, the transportation means estimating unit 103 obtains the length of the road network 510 and the route network 520 that the user is thought to have traveled from the road position information 513 and the route position information 523, determines the travel distance of the user, and obtains the travel speed from the travel distance, the travel start date and time, and the travel end date and time (S105). Then, based on the moving speed, the moving means estimation unit 103 determines the moving means for the action history 600, such as automobile, railroad, and walking (S106). The determination result can be recorded in the item of transportation means 610 included in the action history 600 .
First, the facility relevance calculation unit 104 acquires the facility information 410 and the store information 420 in the target area from the facility/store server 320 based on the information specifying the area included in the analysis request transmitted from the analysis application 90 . Further, the travel history 200, purchase history 310, and service usage history 320 in each facility are acquired from the travel history management server 810 and the purchase/service usage history server 830 (S200).

Then, the facility relevance calculation unit 104 uses the acquired facility information 410, store information 420, movement history 310, purchase history 310, and service usage history 320 to obtain the number of users for each facility, sort them in descending order of the number of users, and create a ranking of the number of facility users (S201). Next, the facility with the largest number of users is determined as the central facility (S202), and the degree of relevance to all other facilities is calculated and recorded in the degree of relevance 714 of the area information 710. FIG.

Here, a method of calculating the degree of association will be described. The facility relevance calculation unit 104 refers to the action history 600 to identify users of the central facility who used other facilities for which relevance is calculated on the same day, and counts the number of such users (S203). Then, the number of users of other facilities with respect to the number of users of the central facility is stored as the degree of association 714 (S204). However, since the accuracy of this destination selection rate varies greatly depending on the number of target users, the standard error is obtained to calculate the fluctuation range of the degree of association. In the calculation of this standard error, the total number is the number of all users of the central facility, but there are users who came to the facility for shopping but did not purchase, and users who did not bring their membership card to the facility and did not keep a shopping record. In order to increase the accuracy including those users, the spatial population obtained from the mobile phone base station connection information may be used as the total number. The number of samples is the number of users for which the degree of association is calculated, and is the number of users of the central facility.

Relevance = Number of consecutive users of the central facility and other facilities / Number of users of the central facility Standard error = √(total - number of samples) / (total - 1) x (relevance x (1 - relevance) / number of samples))
As described above, after calculating the degree of relevance between the central facility and other facilities, the facility relevance calculation unit 104 selects the facility with the next largest number of users from the ranking of the number of users of the facilities, and uses it as the central facility to calculate the degree of relevance with other facilities by the same process. Here, the processing is skipped for the two facilities for which the degrees of association have already been calculated (S205).

If the store is a tenant in the facility, the degree of association is calculated for the facility, but the degree of association may be calculated for each store data 420 as a more detailed analysis. If the store is an independent facility, the facility ID 425 is blank, and the store data 420 is used as the facility data 410 to calculate the degree of association with the store.

The degree of relevance obtained in this way is the ratio of people who have used a certain facility to use other facilities in succession.

FIG. 11 is a flowchart showing area definition processing of the area definition unit 105 .
The area definition unit 105 refers to the area information 710 and selects all facility IDs (facility IDs for each two facilities) that satisfy a condition such as a certain number of users or more from the ranking of the number of facility users (S301). Then, from all the selected facilities, facilities having a degree of relevance above a certain level are selected, defined as one area, and given a unique area ID (S302). Furthermore, by referring to the action history 600, the road ID 609, which is the identifier of the road registered as the route between the two facilities that has been used, is acquired, and the route ID 715 is assigned (S303). The area defined in this way is recorded in the area information 710 together with the area ID 711, facilities 712-713, degree of association 714, route ID 715, etc. (S304).

By knowing the route used to move between the two facilities in this way, it is possible to estimate how much the user with the attribute specified by the analyst travels in what time zone and how much, using the analysis application 90 of the terminal 9 .

As described above, according to this embodiment, it is possible to define people with specific attributes and areas that are frequently used during specific time periods by using movement histories and purchase histories, and to obtain movement and shopping behavior of people between facilities in the defined areas. Furthermore, since the area through which users of a certain facility flow can be determined for each road, it is possible to arrange facilities appropriately. For example, by calculating the percentage of users of a facility based on the highly accurate number of users of the station, it is possible to accurately determine what percentage of people staying in the area the facility appeals to. Furthermore, since it is possible to determine the area through which users of the station pass by road unit, it is possible to arrange appropriate facilities.

It should be noted that the present invention is not limited to the above-described embodiments, and can be implemented in various modifications and alternatives.
For example, in the above embodiment, the degree of association was defined as the probability of using any two facilities in succession, but it may be defined as the probability of using any three facilities in succession. In any case, the utilization probability between two facilities is included.

As another example, for example, a plurality of functional units shown in FIG. 1 may be collectively defined as one functional unit. Actually, the processing flow shown in FIG. 9 is processing by the action history creating unit 101, the moving route estimating unit 102, and the moving means estimating unit 103, but these three functional units may be defined as one functional unit, for example, a “human movement processing unit” or a “human movement information acquisition processing unit”.
In addition, although the present invention is grasped as an area analysis system, it can also be grasped as a people movement analysis system or a people movement information processing system because the analysis of people's movement is the main focus.

1: Area analysis server 2: Network 100: Area analysis program 9: Terminal 12: CPU 13: Communication I/F 11: Storage unit 101: Action history creation unit 102: Movement route estimation unit 103: Transportation means estimation unit 104: Facility relevance calculation unit 105: Area definition unit 110: Database 200: Movement history 300: Purchase/service usage history 410: Facility information 420: Store information 430 : User information 500: Map information 600: Action history 700: Area information 8: External server 810: Movement history management server 820: Facility/store server 830: Purchase/service usage history server 840: Map server 90: Analysis application 91: Analysis processing unit 92: Display unit

Claims (8)

An area analysis system that processes information about a target area using an information processing device having a processing unit and a storage unit,
a degree-of-relevance calculation unit that calculates a degree of relevance indicating continuous use of any two facilities out of a plurality of facilities included in the area, based on movement information of people in the area and purchase information or service information;
an area definition unit that identifies a route traveled by each user from an action history created based on movement information of people in the area and purchase information or service information, selects a route between two facilities with a high degree of association obtained by the degree of association calculation unit from the route, and defines an area including the two facilities and a route connecting the two facilities as an area. The area definition unit assigns a unique area ID and a unique route ID to each of the defined areas,
2. The area analysis system according to claim 1, wherein the storage unit stores area information including facility IDs specifying the two facilities, the degrees of association, and the route IDs, corresponding to the area IDs defined by the area definition unit. an acquisition unit that acquires a designated area, entrance/exit records of the plurality of facilities in the area, and the purchase information or service information in the facility;
The storage unit is acquired by the acquisition unit,
a movement history registered in association with the facility where the person moves and the date and time as the movement information of the person;
a purchase/service usage history that is registered for each person as the purchase information or service information in association with the purchase or service item, the facility, and the date and time ;
Facility information for registering information about the facility;
2. The area analysis system according to claim 1, wherein for each person, said facility to which said person has moved, said purchase information or service information, and an action history for registering location information are stored. The degree-of-association calculation unit
obtaining the number of users of each facility using the movement history, the purchase/service usage history, and the facility information, sorting the number in descending order of the number of users, and creating a ranking of the number of users of the facility;
Determine the facility with a large number of users as the central facility, and calculate the degree of relevance with all other facilities.
The area analysis system according to claim 3. 5. The area analysis system according to claim 4, wherein said degree-of-association calculator calculates the degree of association between two facilities using the following formula.
Degree of relevance = Number of consecutive users of the central facility and other facilities / Number of users of the central facility Standard error = √ (total - number of samples) / (total - 1) x (relevance x (1 - relevance) / number of samples)) A terminal connected to the information processing device has a processing unit that performs information processing and a display unit,
The processing unit issues a request to the information processing device, and in response to an instruction from the information processing device, based on the processing result of the area definition unit, generates a screen that displays facilities with a large number of users in the area, other facilities that are highly related to the facilities, and a route connecting the two facilities,
2. The area analysis system according to claim 1, wherein said display unit displays said screen generated by said processing unit. An area analysis method for processing information about a target area using an information processing device having a processing unit and a storage unit,
a degree-of-relevance calculation step in which the processing unit calculates a degree of relevance indicating continuous use of any two facilities out of a plurality of facilities included in the area, based on movement information of people in the area and purchase information or service information;
an area definition step in which the processing unit identifies a route traveled by each user from the action history created based on movement information of people in the area and purchase information or service information, selects a route between two facilities with a high degree of association obtained in the degree of association calculation step from the route, and defines an area including the two facilities and a route connecting the two facilities;
An area analysis method characterized by comprising: In the area definition step , the processing unit assigns a unique area ID and a unique route ID to each of the defined areas,
8. The area analysis method according to claim 7, wherein the storage unit stores area information including facility IDs specified by the two facilities, the degree of association, and the route ID, corresponding to the area IDs defined by the area defining step .

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