JP7246037B2 - Data analysis device, data analysis system, data analysis method and program - Google Patents

Description

The present disclosure relates to a data analysis device, data analysis system, data analysis method, and program.

2. Description of the Related Art In recent years, commercial area analysis for formulating sales strategies and store opening strategies has been performed using a large amount of sales of products and services obtained via communication networks, information on customer visit rates, and the like. For example, Patent Literature 1 discloses a system that uses a map application provided through the Internet to enable user-friendly trade area analysis.

JP-A-10-307868

However, the system described in Patent Literature 1 selects a target area on the map data and only shows the sales situation at the store for each selected target area, which affects sales. It is difficult to conduct trade area analysis that focuses on characteristic areas.

Therefore, the present disclosure has been made to solve the above problems, and the purpose thereof is to provide a data analysis device capable of identifying an area with a characteristic visit rate to a point set on a map, and a data analysis device. It is to provide a system, data analysis method and program.

In order to achieve the above object, a data analysis device according to a first aspect of the present disclosure is a device for analyzing a commercial area of a point set on a map, and for each predetermined area on the map, in the area Calculated by the visit rate calculation unit that calculates the visit rate to the point where the customer is active, the average calculation unit that calculates the average visit rate to the point where the customer is active at that distance for each distance from the point, and the visit rate calculation unit an evaluation unit that evaluates the visit rate for a point estimated for each predetermined area on the map based on the visit rate for each predetermined area on the map calculated by the average calculation unit and the average visit rate calculated by the average calculation unit; and an output unit for outputting the result.

Further, in order to achieve the above object, a data analysis system according to a second aspect of the present disclosure is a system for analyzing a commercial area of a point set on a map, and for each predetermined area on the map, A visit rate calculation unit that calculates a visit rate to a point of customers who are active in an area, an average calculation unit that calculates an average visit rate to a point of customers who are active at that distance for each distance from the point, and a visit rate calculation unit an evaluation unit that evaluates the visit rate for a point estimated for each predetermined area on the map based on the visit rate for each predetermined area on the map calculated by and the average visit rate calculated by the average calculation unit; and an output unit for outputting the evaluation result.

Further, in order to achieve the above object, a data analysis method according to a third aspect of the present disclosure is a method of analyzing a commercial area of a point set on a map, wherein for each predetermined area on the map, the a step of calculating a visit rate to a point of customers who are active in an area; a step of calculating an average visit rate to a point of customers who are active in that distance for each distance from the point; a step of evaluating the visit rate for a point estimated for each predetermined area on the map based on the visit rate for each predetermined area of and the average visit rate calculated by the average calculation unit; and outputting the evaluation result a step;

Moreover, in order to achieve the above object, a program according to a fourth aspect of the present disclosure causes a computer to execute the above data analysis method.

According to the present disclosure, it is possible to analyze a characteristic area for a visiting place set on a map.

1 is a configuration diagram of a data analysis system 1 according to this embodiment; FIG. 2 is a functional block diagram showing an example of the functional configuration of the customer terminal 100; FIG. 3 is a block diagram showing an example of a functional configuration of a server device 300; FIG. 3 is a diagram showing an example of customer information 323. FIG. 3 is a diagram showing an example of position information 324. FIG. It is a figure which shows an example of estimation of an address. FIG. 10 is a diagram showing a map in which visited places are set; It is a figure which shows an example of the visit rate of area|region A1 and A2. It is a figure which shows an example of the visit rate of area|region A3 and A4. It is a figure which shows an example of an output result. 4 is a flow chart showing the flow of processing of the server apparatus 300. FIG. It is a specific example of a graph showing the distance from the visited place and the visit rate. It is a specific example of a graph showing the distance from the visited place and the visit rate for the feature area. 3 is a block diagram showing the hardware configuration of the server device 300; FIG.

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all the drawings for explaining the embodiments, common constituent elements are given the same reference numerals, and repeated explanations are omitted. It should be noted that the embodiments described below do not unduly limit the content of the present disclosure described in the claims. Also, not all the components shown in the embodiments are essential components of the present disclosure.

<Embodiment>
A data analysis system 1 according to this embodiment is a system for analyzing a trade area of a visited place set on a map. Trade area analysis is the analysis of, for example, which region customers come to a certain store from, and what kind of products and services sell in a certain region. is performed on

In this embodiment, an area with a characteristic visit rate to a certain point (for example, a store) is specified. In general, it is known that there is a correlation between the distance from the store and the visit rate, and the visit rate decreases as residents live in areas farther from the store. However, in reality, due to local traffic conditions (e.g., good access to expressways) and surrounding facilities (e.g., existence of competing stores), areas showing visit rates that are not in such a correlation There is Therefore, in this embodiment, a characteristic area showing such a visit rate is specified based on the relationship with the average visit rate. As a result, the data analyst can be presented with the area in question as an area that particularly requires analysis, and can be used as a strategy map for store opening strategies and sales strategies.

FIG. 1 is a configuration diagram of a data analysis system 1 according to this embodiment. The configuration and outline of the data analysis system 1 will be described with reference to FIG.

In FIG. 1, a data analysis system 1 is a system for analyzing a commercial area of a point set on a map, and includes a plurality of customer terminals 100, an information processing terminal 200, and a server device 300. The server device 300 is communicably connected to a plurality of customer terminals 100 and information processing terminals 200 via a network NW. The network NW is, for example, a WAN (World Area Network). Here, the points set on the map are, for example, shopping centers, supermarkets, shops such as department stores, etc., but are not limited to these, tourist spots, public facilities such as libraries and hospitals, stations, etc. transportation may be used. Note that in the following description, the point that is the target of the trade area analysis may be referred to as a visited place.

The customer terminal 100 is an information processing terminal used by a customer, and the customer terminal 100 is implemented by, for example, a smart phone, a tablet terminal, or the like. In the present embodiment, a customer is a person who has an information processing terminal installed with a program (for example, a smartphone application) that causes the server device 300 to acquire location information used in the data analysis system 1. However, it is not limited to this, as long as the server device 300 can acquire the location information of the terminal itself, it may be in any form. Customers may include customers who simply drop by without performing any service or transaction.

The customer terminal 100 continuously acquires location information via a program (for example, an application dedicated to smartphones) installed in the customer terminal 100 . For example, the GPS (Global Positioning System) is used to detect location information indicating the location of the device itself. Then, the customer terminal 100 continuously transmits the detected location information to the server device 300 according to a request from the server device 300, for example.

The information processing terminal 200 is an information processing terminal used by a user who performs trade area analysis, and is realized by, for example, a smart phone, a tablet terminal, a PC (Personal Computer), or the like. The information processing terminal 200 receives the operation of the user who conducts the trade area analysis, for example, starts a browser application program, and sends information indicating the visited place set by the user (point to be the target of the trade area analysis) to the server. Send to device 300 . Note that the number of information processing terminals 200 is not limited to that shown in FIG. 1, and a plurality of terminals may be provided.

The server device 300 is a server device corresponding to a data analysis device that identifies areas with characteristic visit rates to points set on a map. When the server device 300 acquires the information indicating the visited place from the information processing terminal 200 , the server device 300 calculates the visit rate based on the position information received from the customer terminal 100 . Then, an area (characteristic region) having a characteristic visit rate is specified, the characteristic region is superimposed on the map, output, and transmitted to the information processing terminal 200 . At this time, the degree to which the visit rate deviates from the average visit rate may be evaluated using a score or the like, and the score may be output. A method of identifying the characteristic region will be described later.

As described above, by superimposing and displaying areas with characteristic visit rates on the map, a user who conducts trade area analysis can easily grasp the area as an area that particularly requires analysis.

FIG. 2 is a functional block diagram showing an example of the functional configuration of the customer terminal 100. As shown in FIG. A functional configuration of the customer terminal 100 will be described with reference to FIG. Note that the customer terminal 100 of the present embodiment may have a configuration in which some of the constituent elements (each unit) in FIG. 2 are omitted. Further, the information processing terminal 200 of this embodiment may have the same functional configuration as the customer terminal 100, and the description of the functional configuration of the information processing terminal 200 will be omitted.

The customer terminal 100 is, for example, a portable terminal such as a mobile phone including a smart phone, a tablet terminal, a notebook PC, or a PDA (Personal Digital Assistant). The customer terminal 100 also includes a wearable device, which is an information processing terminal in the form of spectacles or a watch. Furthermore, the customer terminal 100 may include various smart devices having information processing functions for acquiring location information. For example, customer terminal 100 may include a smart vehicle such as an automobile.

2, the customer terminal 100 has a communication section 110, an input section 120, a display section 130, a detection section 140, and a control section 150. As shown in FIG.

The communication unit 110 performs various controls for communicating with the server apparatus 300 via the network NW, and its functions can be realized by hardware such as various processors or communication ASICs, programs, and the like.

The input unit 120 is an input interface that receives various operations from the user. For example, the input unit 120 is implemented by operation keys, buttons, a touch panel, a microphone for voice input, etc. provided on the customer terminal 100 .

The display unit 130 is a display device for displaying various types of information, and is realized by, for example, a liquid crystal display. When a touch panel is adopted for the customer terminal 100, part of the input section 120 and the display section 130 are integrated.

The detection unit 140 detects various types of information regarding the customer terminal 100 . Specifically, the detection unit 140 detects the physical state of the customer terminal 100 . In the example shown in FIG. 2 , the detection section 140 has a position detection section 141 .

The position detector 141 detects the current position of the customer terminal 100 . Specifically, the position detection unit 141 receives radio waves transmitted from the satellite positioning system, and detects position information (for example, latitude and longitude) indicating the current position of the customer terminal 100 based on the received radio waves. Note that the position detection unit 141 may acquire position information using a different technique. For example, if the customer terminal 100 has a function equivalent to that of a contactless IC card used at a station ticket gate or a store (or if the customer terminal 100 has a function of reading the history of the contactless IC card). case), the customer terminal 100 records the information that the fare is settled at the station and the location of use. The position detection unit 141 detects this information and acquires it as position information. Further, the position detection unit 141 may detect position information that can be acquired from an access point when the customer terminal 100 communicates with a specific access point. Further, the position detection unit 141 may detect the estimated position of the customer terminal 100 based on the identification information (IP address, etc.) when the customer terminal 100 is connected to the Internet.

Note that the detection unit 140 is not limited to the position detection unit 141 and may include various devices that detect various states of the customer terminal 100 . For example, the detection unit 140 includes a microphone that collects sounds around the customer terminal 100, an illuminance sensor that detects the illuminance around the customer terminal 100, an acceleration sensor that detects physical movement of the customer terminal 100 (or gyro sensor, etc.), a humidity sensor that detects the humidity around the customer terminal 100, a geomagnetic sensor that detects the magnetic field at the location where the customer terminal 100 is located, and the like. Further, the detection unit 140 may detect various information using the function of a sensor. For example, the detection unit 140 may detect the number of steps of the customer using the customer terminal 100 using the function of an acceleration sensor. Further, the detection unit 140 uses the function of an acceleration sensor to detect motion information indicating whether the customer terminal 100 is operating or stationary at regular intervals or at each timing when the customer terminal 100 operates. You may

The control unit 150 is, for example, a controller, and a CPU (Central Processing Unit), MPU (Micro Processing Unit) or the like transfers various programs stored in a storage device inside the customer terminal 100 to a RAM (Random Access Memory). ) as a work area. Also, the control unit 150 is a controller, and is implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

The control unit 150 controls the processing of providing the location information of the customer terminal 100 to the server device 300 . For example, the control unit 150 executes and controls a program (for example, a smartphone application) for implementing the process of providing position information of the customer terminal 100 . The program may be pre-installed in the customer terminal 100, or may be downloaded from the server device 300 (or an external server that provides various programs) and installed in the customer terminal 100 according to the operation of the customer who owns the customer terminal 100. may

The control unit 150 realizes or executes the functions and effects of information processing described below by executing the above-described programs using the RAM as a work area.

The control unit 150 acquires various information detected by the detection unit 140 as position information by controlling the detection unit 140 . For example, the control unit 150 controls the position detection unit 141 to acquire position information of the customer terminal 100 and time information corresponding to the time when the position information was detected as position information.

Also, the control unit 150 may acquire information about the status of communication performed by the communication unit 110 as the position information. For example, the control unit 150 may acquire mutual communication status between the customer terminal 100 and a predetermined access point. In addition, when the customer terminal 100 has a call function, the control unit 150 may acquire information such as the time of the call, the destination of the call, and the duration of the call. In addition, when the customer terminal 100 has a photographing function, the control unit 150 may acquire information such as the time when the photographing was performed, the position information at which the photographing was performed, and the photographing time. Then, the control unit 150 may acquire the location information of the customer terminal 100 based on these pieces of information.

Also, the control unit 150 may acquire position information periodically (every minute, every three minutes, every five minutes, every hour, etc.), for example. Also, the timing at which the control unit 150 acquires the position information may be set by the server device 300 . Also, the control unit 150 may acquire the position information at the timing when a predetermined event occurs. For example, the control unit 150 acquires position information according to a predetermined event, such as when the above-described non-contact IC card function is used, or when a camera is photographed.

Further, the control unit 150 controls the communication unit 110 to transmit the acquired position information to the server device 300 . For example, under the control of the control unit 150, the communication unit 110 transmits identification information for identifying the customer terminal 100, location information detected by the detection unit 140, and the date and time when the location information was detected to the server device 300. Send to At this time, the control unit 150 may transmit the position information and the like to the server device 300 each time the position information is acquired, or may transmit the position information and the like to the server device 300 at predetermined intervals. For example, the control unit 150 controls the communication unit 110 to periodically (every minute, every three minutes, every five minutes, every hour, etc.) transmit the position information to the server device 100 .

FIG. 3 is a block diagram showing an example of the functional configuration of the server device 300. As shown in FIG. A functional configuration of the server device 300 will be described with reference to FIG.

The server device 300 has a communication section 310 , a storage section 320 and a control section 330 . The server device 300 includes an input unit (for example, a keyboard, a mouse, etc.) that receives various operations from an administrator or the like who uses the server device 300, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. may have.

The communication unit 310 performs various controls for communicating with the customer terminal 100 and the information processing terminal 200 via the network NW. etc.

The storage unit 320 is realized by, for example, a semiconductor memory device such as a RAM or flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 320 has map information 321 , visited place information 322 , customer information 323 and location information 324 .

The map information 321 stores map data. Map data can specify a position, for example, by latitude and longitude. Alternatively, the map data may be divided into a mesh and the position may be specified by identification information for identifying each divided area.

The visited place information 322 stores information of visited places transmitted from the information processing terminal 200 . The information on the visited place is, for example, an address, an ID for identifying the visited place, and position information indicating the position of the visited place such as latitude and longitude. Further, in the map data stored in the map information 321, identification information for identifying each area divided into a mesh may be stored as position information.

The customer information 323 stores information about customers. Location information 324 stores customer location information. Here, an example of the customer information 323 and the location information 324 will be described with reference to FIGS. 4 and 5. FIG.

FIG. 4 is a diagram showing an example of the customer information 323. As shown in FIG. As shown in FIG. 4, the customer information 323 has items such as "customer ID" and "address".

"Customer ID" indicates identification information for identifying a customer. In addition, in this embodiment, identification information shall correspond with the reference code used for description. For example, a customer identified by customer ID "C1" indicates customer C1.

"Address" is the customer's home address, which is estimated by the estimation unit 332, which will be described later. It should be noted that information input by the user, such as member information of the store, may be used. Moreover, the customer information is not limited to the address, and may include other information about the customer such as age, place of work, and occupation. In other words, the information about the customer is information indicating the area in which the customer is active. It may be a place that represents the lifestyle of In addition, in this embodiment, the area where the customer resides will be described as the area where the customer is active.

In FIG. 4, the address of the customer information is indicated by a concept such as "A01", but in practice it is indicated by specific information. For example, in the map data, the address item is indicated by identification information indicating mesh-like divided areas, prefecture names, ward/town/village names, and the like.

That is, FIG. 4 shows that the address of customer C1 indicated by customer ID "C1" is "A01".

FIG. 5 is a diagram showing an example of the position information 324. As shown in FIG. As shown in FIG. 5, the location information 324 has items such as "terminal ID", "customer ID", "date and time of acquisition", and "location information".

"Terminal ID" indicates identification information for identifying a customer terminal. For example, the terminal identified by the terminal ID "100-1" indicates the customer terminal 100-1.

“Acquisition date and time” indicates the date and time when the location information was acquired by the customer terminal 100 or the server device 300 . Note that FIG. 5 shows an example in which position information is stored every two hours or every day, but in reality, position information is acquired at more detailed intervals (for example, every five minutes). good too. “Position information” indicates the position information of the customer terminal 100 . In FIG. 5, "location information" is represented by a concept such as "AA01", but in reality, it is represented by measured values such as longitude and latitude, location information of access points, location of station ticket gates, and the like. may be Further, it may be identification information indicating a divided area in the map data.

That is, FIG. 5 shows that the customer terminal 100-1 identified by the terminal ID "100-1" is used by the customer C1 identified by the customer ID "C1". Further, as an example of the position information, the position information acquired at "8:00 on August 1, 2018" indicates that the customer terminal 100-1 is located at the position indicated by the position information "AA01". Further, as another example of the location information, the location information acquired at “10:00 on August 1, 2018” indicates that the customer terminal 100-1 is located at the location indicated by the location information “AA02”. there is

Returning to FIG. 3, the controller 330 will be described. The control unit 330 is, for example, a controller, and various programs (equivalent to an example of a determination program) stored in a storage device inside the server device 300 are executed by the CPU, MPU, or the like using the RAM as a work area. Realized. Also, the control unit 330 is a controller, and is implemented by an integrated circuit such as an ASIC or FPGA, for example.

The control unit 330 includes an acquisition unit 331, an estimation unit 332, a visit rate calculation unit 333, an average calculation unit 334, a specification unit 335, and an output unit 336, and has information processing functions described below. achieve or perform any action. Note that the internal configuration of the control unit 330 is not limited to the configuration shown in FIG. 3, and may be another configuration as long as it performs information processing described later.

The acquisition unit 331 acquires various types of information. For example, the acquisition unit 331 acquires information on the visited place from the information processing terminal 200 and stores it in the visited place information 322 . Further, for example, the acquisition unit 331 identifies the customer terminal 100 based on information when the customer terminal 100 uses a dedicated application or website, and continuously obtains the location information of the customer terminal 100 from the identified customer terminal 100. to get to. Specifically, the acquisition unit 331 acquires various types of information detected or acquired by the customer terminal 100 as position information. The acquisition unit 331 acquires position information from the customer terminal 100 at predetermined time intervals, and stores the acquired position information in the position information 324 of the storage unit 320 .

Note that the acquisition unit 331 does not need to acquire the position information itself used by each processing unit, which will be described later. For example, the acquisition unit 331 may acquire location information of the customer terminal 100 based on information detected from the customer terminal 100 . Specifically, when the visit rate calculation unit 333, which will be described later, calculates the visit rate, even if the information indicating the area where the customer resides is used, the acquisition unit 331 obtains the location of the customer from the customer terminal 100. It is not necessary to acquire the area itself to be used. That is, the acquiring unit 331 acquires position information indicated by longitude and latitude from the customer terminal 100, and converts the information detected from the acquired longitude and latitude into identification information for identifying divided areas on the map. and the converted information may be stored in the position information 324 .

Further, the acquisition unit 331 may acquire position information together with date and time information including day of the week information. For estimating the customer's home, for example, the relationship with the date and time and the day of the week is useful, such as the position where the customer is located early in the morning on weekdays, the position where the customer is located during the daytime on weekdays, and the position where the customer is located on holidays. Conceivable. For this reason, the acquisition unit 331 may acquire location information together with date and time information including day of the week information to increase the amount of location information to be acquired, so that estimation with higher accuracy may be performed.

Also, the acquisition unit 331 does not necessarily acquire the location information at the same time as the customer terminal 100 acquires the location information. That is, the acquisition unit 331 may acquire the location information stored in the storage unit of the customer terminal 100 when using it for processing instead of acquiring the location information in real time. Thereby, the server apparatus 300 can reduce the processing amount for communication.

Further, the acquisition unit 331 may acquire the status of communication between the customer terminal 100 and another device as the position information. Specifically, the acquisition unit 331 acquires the communication status in which the customer terminal 100 accesses the Internet or the like via an access point. In this case, the acquisition unit 331 acquires information detected from the communication status with the access point as the position information. Specifically, the acquisition unit 331 may determine the installation position of the access point communicating with the customer terminal 100 and acquire the determined installation position of the access point as the position information of the customer terminal 100 . In addition, the acquisition unit 331 acquires the location information of the customer terminal 100 by detecting communication between the device of the station ticket gate and the customer terminal 100, or acquires the location information based on the IP address of the customer terminal 100, as described above. may be obtained. Further, the acquisition unit 331 may acquire the location information through a customer's SNS (Social Networking Service) or the like.

The estimation unit 332 estimates the area where the customer resides. For example, the estimating unit 332 estimates the area where the customer who owns the customer terminal 100 is estimated to be at home, based on the location information of the customer terminal 100 in the early morning or at night on weekdays. Here, an example of estimating the address of customer C1 who owns customer terminal 100-1 will be described with reference to FIG.

FIG. 6 is a diagram showing an example of address estimation. In FIG. 6, a map 60 is divided into a mesh. With the upper left corner of the map 60 as the origin, the range indicated by the coordinates of 1 in the vertical direction and 1 in the horizontal direction is defined as an area A11. Similarly, the range indicated by the coordinates of 2 vertically and 1 horizontally counting from the origin of the map 60 is area A12, and the range indicated by the coordinates of 3 vertically and 1 horizontally counting from the origin of the map 60 is area A13. and

For example, as shown in FIG. 6, the estimation unit 332 maps the predetermined range in which the customer terminal 100-1 exists to the coordinates in light of the position information. In the map 60, in the location information acquired from the customer terminal 100-1, hatching indicates areas where the customer terminal 100-1 is likely to be located (that is, areas with a high probability distribution). Note that the probability distribution may indicate the probability of all the location information acquired regarding the customer, or indicate the probability that the customer is located within a predetermined range when narrowed down to a specific time period or day of the week. can be anything.

In the example shown in FIG. 6, the area A55 is hatched. This indicates that the customer terminal 100-1 has been in the range indicated by the area A55 for a relatively long time. For example, an area A55 indicates that the location information corresponds to a range indicating a distribution value exceeding a predetermined threshold in the probability distribution indicating the probability that the customer terminal 100-1 is located in the range shown on the map 60. FIG.

Further, the estimating unit 332 determines from the location information of the customer terminal 100-1 stored in the location information 324 that relatively many customer terminals 100-1 are located in the range indicated by the area A55 during early mornings, evenings, and holidays on weekdays. Then judge. In this case, the estimation unit 332 estimates that the area indicated by the area A55 is the area where the home address of the customer C1 who owns the customer terminal 100-1 exists, that is, the area where the customer resides.

For example, the estimating unit 332 receives settings for performing such estimation in advance, and executes the above-described estimation processing by comparing the received settings with the position information. Specifically, the estimation unit 332 estimates that the address of the customer C1 exists in the range when the probability distribution of the predetermined range exceeds a predetermined threshold. As an example, the estimating unit 332 uses the acquired location information of the customer terminal 100-1 (for example, the acquired location information of the customer C1 narrowed down to the time period from 6:00 to 8:00 in a period of one week). ), if the probability distribution indicating the probability that the customer C1 is located in the predetermined range exceeds "0.5 (50%)", it is estimated that the address of the customer C1 exists in the range.

The estimating unit 332 stores the estimated area where the customer lives as described above in the customer information 323 of the storage unit 320 as the customer's address. In the above description, the customer's address was given as an example of the area where the customer is active, which is estimated by the estimation unit 332. Similarly, the customer's place of work, etc. can also be estimated. That is, the estimating unit 332 can calculate a customer's frequent appearance pattern based on the probability that the customer is located within a predetermined range, and can estimate the location representing the customer's lifestyle.

As described above, in this embodiment, the area where the customer lives is described as the area where the customer is active.

Returning to FIG. 3, the visit rate calculation unit 333 calculates the visit rate of customers to the points (visited places) transmitted from the information processing terminal 200 for each predetermined area on the map.

Specifically, the visit rate calculation unit 333 extracts customers who live in a predetermined area of the map data based on the customer's address stored in the customer information 323, and among the customers who live in the area, Calculate the percentage of customers who visited the destination. Note that the predetermined area may be a mesh-like divided area or an administrative division, and can be freely set by the user who conducts the trade area analysis.

That is, the visit rate of customers in an area Ai (i=1 to n, where n is a natural number) obtained by dividing the target area for trade area analysis into predetermined areas can be expressed by the following equation (1).

(visit rate of customers in area Ai)=(number of customers residing in area Ai who have visited the visited place)/(number of customers residing in area Ai) Equation (1)

Here, whether or not a certain customer has visited the visited place is determined from the acquired location information of the customer. For example, based on the position information 324, the visit rate calculation unit 333 determines whether the position indicated by the position information of the customer is included in the area where the visited place exists. At this time, based on the date and time when the position information was detected, if the customer's position is included in the area where the visited place exists for a predetermined period of time (for example, 30 minutes or longer), the customer visits the visited place. It can be judged that In addition, if the ratio of the number of location information data included in the area where the visited location exists to the total location information data acquired for a customer is greater than or equal to a predetermined threshold, it is determined that the customer has visited the location. You may

The average calculation unit 334 calculates the average visit rate of customers for each distance from the visited place. Specifically, the average calculation unit 334 extracts customers who live within a predetermined distance from the visited place based on the addresses of the customers stored in the customer information 323, and Percentages are calculated as the average visit rate for that distance.

That is, the average visit rate of the distance Ri (i=1 to m: m is a natural number) from the visited place in the target area of the trade area analysis can be expressed by the following equation (2).

(Average visit rate of customers at a distance Ri)=(Number of customers who visited the visited place and live at a distance Ri from the visited place)/(Number of customers living at a distance Ri) Equation (2)

Here, the visit rate of customers for each area calculated by the visit rate calculation unit 333 and the average visit rate of customers for each distance calculated by the average calculation unit 334 will be specifically described with reference to FIG.

FIG. 7 is a diagram showing a map on which visited places are set. The map 60 shown in FIG. 7 is the same as the map 60 described with reference to FIG. 6, so repeated description will be omitted.

When the visited place P is set on the map 60 as shown in FIG. 7, the visit rate calculation unit 333 calculates the visited place P Calculate the percentage of customers who visited the site as the visit rate. That is, the visit rate calculation unit 333 extracts customers living in each area based on the customer's address stored in the customer information 323, and calculates the percentage of customers who have visited the visited place P from the location information of the customer. calculate.

Further, the average calculation unit 334 calculates the ratio of customers who visited the visited place P among the customers living on the concentric circles R1, R2, R3, . , is calculated as the average visit rate at that distance.

In this embodiment, the identifying unit 335, which will be described later, identifies the characteristic region based on the visit rate calculated as described above. Specifically, the visit rate in a certain region is compared with the visit rate (average visit rate) in the distance from the visited place to that region, and if there is a significant difference, that region is specified as a feature region. In the example of FIG. 7, the visit rate of area A31 (distance r3 from visited place P) is compared with the visit rate (average visit rate) on concentric circle R3 (distance r3 from visited place P). In another example of FIG. 7, the visit rate of area A65 (distance r2 from visited place P) and the visit rate (average visit rate) on concentric circle R2 (distance r2 from visited place P) are compare. That is, the visit rate of an area is compared with the average visit rate of concentric circles included in the area.

Returning to FIG. 3, the identification unit 335 corresponds to one function of an evaluation unit (not shown) that evaluates the visit rate. Specifically, based on the visit rate calculated as described above, the identification unit 335 estimates the visit rate for each predetermined area on the map in a confidence interval with a predetermined reliability (for example, 95%). . Then, a characteristic region is identified based on the confidence interval of the estimated visit rate. Specifically, a feature area is identified in which the confidence interval of the estimated visit rate in each area deviates from the average visit rate for the distance from the visited place to the area.

The reason for specifying the feature region based on the confidence interval is as follows. That is, in commercial area analysis, since it is not realistic to survey the visit rate of all residents in a certain area, sample surveys are generally conducted. However, since the visit rate differs depending on which residents are extracted as samples, the visit rate of all residents in a certain area can be calculated from only the value of the visit rate of customers in a certain area calculated by the visit rate calculation unit 333 as described above. It is imprecise to assert that Therefore, in this embodiment, the visit rate is calculated with a certain width (confidence interval). That is, the visit rate is estimated based on the assumption that "the visit rate exists in that section with a certain degree of reliability (for example, 95%)." (The smaller end point of the interval is called the “lower confidence limit” and the larger end point is called the “upper confidence limit.”) In this way, by giving accuracy to the calculated visit rate, it is possible to accurately evaluate the calculation results. become able to.

In this embodiment, the Wilson score method is used as a method for obtaining a confidence interval. adjustment Wald method), Jeffeys method, etc. can be used. Since these are well-known techniques, detailed description thereof is omitted.

Identification of a characteristic region will be further described with reference to FIGS. 8 and 9. FIG.

FIG. 8 is a diagram showing an example of visit rates of areas A1 and A2. In FIG. 8, areas A1 and A2 are areas at the same distance r from the visited place, and the confidence intervals 81 and 82 (for example, confidence level 95%) of the visit rates of areas A1 and A2 and the average at distance r Visitor rates are indicated.

Here, the average visit rate is included in the confidence interval 81 of the visit rate of area A1. In this case, since it matches the estimation result, in the present embodiment, it is determined that there is no significant difference in the visit rate of the area A1. Similarly, since the average visit rate is included in the confidence interval 82 of the visit rate of area A2, it is determined that there is no significant difference in the visit rate of area A2.

That is, areas A1 and A2 have no significant difference in visit rate, and are determined not to be feature areas.

FIG. 9 is a diagram showing an example of visit rates of areas A3 and A4. In FIG. 9, areas A3 and A4 are areas at the same distance r from the visited place, and the confidence intervals 91 and 92 (for example, confidence level 95%) of the visit rates of areas A3 and A4 and the average at distance r Visitor rates are indicated.

Here, the confidence interval 91 of the visit rate of the area A3 and the confidence interval 92 of the visit rate of the area A4 both deviate from the average visit rate. That is, for the confidence interval 91, the lower confidence limit exceeds the average visit rate, and for the confidence interval 92, the upper confidence limit does not exceed the average visit rate. In this case, since the estimation results do not match, in the present embodiment, it is determined that there is a significant difference between the visit rates of the areas A3 and A4, and the areas A3 and A4 are defined as feature areas. If the lower confidence limit exceeds the average visit rate, it means that the number of people visiting the visited site from that area is higher than the average. Also, if the upper confidence limit does not exceed the average visitor rate, it means that less than average people visit the site from that region.

As described above, the identifying unit 335 identifies a characteristic area based on whether the confidence interval of the visit rate estimated for each predetermined area is outside the average visit rate for the distance from the visited place to the area.

The identification unit 335 may also include a calculation unit (not shown) that calculates a feature score indicating the extent to which the average visit rate deviates from the confidence interval. Then, a feature region is specified based on the feature score.

For example, the visit rate of the area A located at the distance R from the visited place and the feature score for the average visit rate of the distance R can be expressed by the following formula.

(feature score) =
(lower confidence limit of visit rate in area A)/(average visit rate of customers at distance R) (when lower confidence limit > average visit rate)
(Reliable upper limit of visit rate of region A)/(Average visit rate of customers at distance R) (if confidence upper limit < average visit rate)
1 ... (if none of the above)

For example, if the average visit rate is 0.50 and the visit rate estimated with 95% confidence is 0.35 (lower confidence limit) < (visit rate) < 0.42 (upper confidence limit), then the upper confidence limit is the average visit rate is less than, the feature score is 0.84 (=0.42/0.50). The feature score is 1 when the average visit rate is 0.28, 0.24 (lower confidence limit) < (visit rate) < 0.32 (upper confidence limit).

Note that the feature score is not limited to the calculation method described above, and may be calculated as follows.
(feature score) =
Logarithm of (lower confidence limit of visit rate of region A)/(average visit rate of customers at distance R)... (if confidence lower limit > average visit rate)
Logarithm of (confidence upper limit of visit rate of region A)/(average visit rate of customers at distance R) (in the case of upper confidence limit < average visit rate)
0 ... (if none of the above)

The specifying unit 335 may determine the significant difference from the feature score calculated as described above, and specify the feature region based on the value of the feature score.

Returning to FIG. 3, the output unit 336 outputs the feature regions and/or feature scores identified as described above. For example, the output unit 336 superimposes the characteristic area on the map data of the map information 321 stored in the storage unit 320 and transmits the result to the information processing terminal 200 via the communication unit 310 . The user of the information processing terminal 200 can easily grasp where the area particularly requiring analysis is based on the feature area and feature score superimposed on the transmitted map data.

FIG. 10 is a diagram showing an example of output results. The map 60 in FIG. 10 is the same as the diagram described in FIG. 7, and the characteristic regions are hatched. For example, areas A41, 51, 26 and 36 are hatched lightly, and areas A13 and A74 are hatched darkly. Thin hatching means that the lower confidence limit exceeds the average visit rate, that is, more people visit the visited place P than the average. Dark hatching means that the confidence limit does not exceed the average visit rate, that is, fewer people than the average visited the visited site P. From the presentation of such characteristic areas, data analysts may think that areas A26, A36 and areas A41, 51 are close to the station as a factor when considering sales strategies, and areas A13 and A74 have competing stores. It becomes easier to make hypotheses such as

By presenting the characteristic regions in this way, it becomes easier to hypothesize that there is some factor that affects the visit rate. For example, there are competing stores in such a characteristic area, so the visit rate is low, and because access to the expressway is good, the visit rate is high even for stores that are far away. presumed to exist. When analyzing a commercial area, the user can obtain an opportunity to analyze the circumstances peculiar to the area more carefully by being presented with the characteristic area.

FIG. 11 is a flow chart showing the processing flow of the server device 300. As shown in FIG. The flow of processing in the server device 300 will be described with reference to FIG. Note that the processing illustrated in FIG. 11 is an example of the processing of the server device 300 . As the processing of the server device 300, in addition to this, the processing of each functional configuration of the server device 300 described above may be combined.

In step S<b>101 , the acquisition unit 331 determines whether the communication unit 310 has received location information from the customer terminal 100 . If it is determined that position information has not been received (N in step S101), the process returns to step S101.

On the other hand, if it is determined that position information has been received (Y in step S101), the acquisition unit 331 acquires position information from the communication unit 310 and stores it in the position information 324 of the storage unit 320 in step S102.

In step S<b>103 , the acquisition unit 331 determines whether the communication unit 310 has received the information of the visited place, which is the target of trade area analysis, from the information processing terminal 200 . If it is determined that the information on the visited place has not been received (N in step S103), the process returns to step S101.

On the other hand, if it is determined that the information on the visited place has been received (N in step S103), in step S104 the visit rate calculation unit 333 calculates, for each predetermined area on the map, the number of people living in the visited place. Calculate the customer visit rate. Note that the estimating unit 332 may estimate the area where the customer resides when calculating the visit rate, or estimate the area where the customer resides in advance based on the position information acquired during a predetermined period of time. may be stored in In addition, based on the information about the customer (the area in which the customer is active) stored in the customer information 323, such as a customer who works in the area, a customer who often stays on holidays, and not a customer who lives in the area, You can also calculate the visit rate by attribute.

In step S105, the average calculation unit 334 calculates the average visit rate of customers to the visited place for each distance from the visited place.

In step S106, the identifying unit 335 identifies a characteristic region. That is, based on the customer's visit rate calculated for each predetermined area, the visit rate is estimated with a predetermined degree of reliability in the area. Then, if the confidence interval of the estimated visit rate is outside the average visit rate for the distance from the visited place to the area, it is identified as a feature area.

In step S107, the output unit 336 outputs the characteristic regions identified in step S106. For example, the output unit 336 superimposes the specific area on the map data stored in the map information 321 and transmits it to the information processing terminal 200 via the communication unit 310 .

FIG. 12 is a specific example of a graph showing the distance from the visited place and the visit rate. In FIG. 12, the vertical axis indicates the visit rate, the horizontal axis indicates the distance from the visited place, and reference numeral 10 indicates the average visit rate for each distance. Also, the "▴" indicated by reference numeral 11 indicates the upper limit of confidence in the visit rate in a certain region, and the "▼" indicated by reference numeral 12 indicates the lower limit of confidence in the visit rate in the certain region.

That is, in the graph shown in FIG. 12, the upper confidence limit “▲” and the lower confidence limit “▼” are displayed as a pair for each region. It can be seen that some average visit rates are within the confidence intervals, while others have average visit rates outside the confidence intervals.

FIG. 13 is a specific example of a graph showing the distance from the visited place and the visit rate for the feature area. In FIG. 13, the vertical axis indicates the visit rate, the horizontal axis indicates the distance from the visited place, and reference numeral 20 indicates the average visit rate for each distance. "▲" indicated by reference numeral 21 indicates the upper limit of confidence in the visit rate in a certain characteristic area, and "▼" indicated by reference numeral 22 indicates the lower limit of confidence in the visit rate in a certain characteristic area.

Since the graph shown in FIG. 13 shows the feature area, unlike the graph shown in FIG. 12, the upper confidence limit “▲” does not exceed the average visit rate, or are shown.

Note that FIG. 13 shows the average visit rate estimated with a predetermined degree of reliability. That is, reference numeral 30 indicates the upper confidence limit of the average visit rate, and reference numeral 40 indicates the lower confidence limit of the average visit rate. For example, it may be determined that there is a significant difference when the upper confidence limit of the visit rate does not exceed the lower confidence limit of the average visit rate. By giving precision to the average visit rate, more accurate estimation results can be presented.

(Hardware configuration diagram)
FIG. 14 is a block diagram showing the hardware configuration of the server device 300. As shown in FIG. Server device 300 is implemented in computer 401 . The computer 401 includes a CPU 402 , a main memory device 403 , an auxiliary memory device 404 and an interface 405 .

The operation of each component of server device 300 is stored in auxiliary storage device 404 in the form of a program. The CPU 402 reads out the program from the auxiliary storage device 404, develops it in the main storage device 403, and executes the above processing according to the program. Also, the CPU 402 secures a storage area in the main storage device 403 according to the program. Specifically, the program is a data analysis program that causes the computer 401 to analyze the trade area of the point set on the map.

Note that the auxiliary storage device 404 is an example of a non-temporary tangible medium. Other examples of non-transitory tangible media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, semiconductor memories, etc. that are connected via the interface 405 . Also, when this program is distributed to the computer 401 via a network, the computer 401 receiving the distribution may develop the program in the main storage device 403 and execute the processing.

Also, the program may be for realizing part of the functions described above. Further, the program may be a so-called difference file (difference program) that implements the above-described functions in combination with another program already stored in the auxiliary storage device 404 . It should be noted that the hardware configuration shown in FIG. 14 may be configured similarly to the customer terminal 100 and the information processing terminal 200 . The operation of each component in these devices is realized by the CPU according to the program stored in the auxiliary storage device, as in the server device 300 described above.

(Explanation of effect)
As described above, the data analysis system according to the present embodiment evaluates the visit rates of visited locations set on a map, and identifies areas with characteristic visit rates. In general, it is known that there is a correlation between the distance from the store and the visit rate, and the visit rate decreases as residents live in areas farther from the store. However, in reality, there are areas that exhibit visit rates that do not have such a correlation, depending on local traffic conditions, surrounding facilities, and the like. Therefore, in this embodiment, a characteristic area showing such a visit rate is specified based on the relationship with the average visit rate.

As a result, the data analyst can be presented with the area in question as an area that particularly requires analysis, and can be used as a strategy map for store opening strategies and sales strategies. In addition, since the data analyst can easily grasp the areas that particularly require analysis, it is easier to formulate store opening strategies than simply being presented with the status of the visit rate.

In the present embodiment, feature regions are identified based on confidence intervals. That is, in commercial area analysis, since it is not realistic to survey the visit rate of all residents in a certain area, sample surveys are generally conducted. However, since the visit rate differs depending on which residents are sampled, it is inaccurate to conclude that it is the visit rate of all residents in a certain area based only on the value of the visit rate of customers in a certain area. Therefore, in the present embodiment, the visit rate is calculated with a predetermined degree of reliability, and accuracy is given to the calculated visit rate, so that the calculation result can be accurately evaluated. Therefore, the accuracy-guaranteed trade area analysis enables the user to develop more useful store opening strategies and sales strategies.

Further, in the present embodiment, the feature area is specified based on the feature score. It is possible to determine how significant the difference is based on the feature score, and to perform more accurate trade area analysis.

This embodiment can be implemented in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and their modifications are intended to be included in the invention described in the claims and their equivalents as well as included in the scope and gist of the invention.

1 data analysis system, 100 customer terminal, 200 information processing terminal, 110 communication unit, 120 input unit, 130 display unit, 140 detection unit, 141 position detection unit, 150 control unit, 300 server device, 310 communication unit, 320 storage unit , 321 map information, 322 visited place information, 323 customer information, 324 location information, 330 control unit, 331 acquisition unit, 332 estimation unit, 333 visit rate calculation unit, 334 average calculation unit, 335 identification unit, 336 output unit, 401 computer, 402 CPU, 403 main storage device, 404 auxiliary storage device, 405 interface

Claims (13)

A data analysis device for analyzing a commercial area of a point set on a map,
a visit rate calculation unit that calculates, for each predetermined area on the map, a visit rate of customers who are active in the area to the point;
An average calculation unit that calculates an average visit rate of the customers who are active at the distance from the point to the point, for each distance from the point;
estimated for each predetermined area on the map based on the visit rate for each predetermined area on the map calculated by the visit rate calculation unit and the average visit rate calculated by the average calculation unit an evaluation unit that evaluates the degree to which the visit rate for the point deviates from the average visit rate for the distance from the point to the area ;
an output unit that outputs the evaluated result;
A data analysis device comprising The evaluation unit evaluates whether or not the confidence interval of the visit rate estimated in the predetermined area on the map is outside the average visit rate in the distance from the point to the area. 2. The data analysis device according to 1. 2. The data analysis apparatus according to claim 1 , wherein said evaluation unit includes a specification unit that specifies a characteristic area characterized by a visit rate estimated in said predetermined area on said map based on said degree. With respect to the visit rate of the predetermined area on the map, the identification unit determines that a confidence interval of the visit rate estimated with a predetermined degree of reliability is the average visit rate in the distance from the point to the area. 4. The data analysis device according to claim 3, wherein a region outside of is specified as the feature region. The specifying unit determines whether the upper limit of the confidence interval of the estimated visit rate in the predetermined area on the map does not exceed the average visit rate, or the lower limit of the confidence interval of the visit rate does not exceed the average visit rate. 5. The data analysis device according to claim 4, wherein said area is specified as said characteristic area when the area exceeds . The evaluation unit includes a calculation unit that calculates a feature score indicating the degree to which the average visit rate deviates from the confidence interval,
5. The data analysis device according to claim 4, wherein said specifying unit specifies said feature area based on said feature score. 7. The data analysis device according to claim 6, wherein said output unit outputs said feature score and/or said feature area as said evaluation result. further comprising a storage unit for storing map information,
The data analysis apparatus according to any one of claims 3 to 7, wherein the output unit outputs the feature area superimposed on the map information stored in the storage unit. An estimating unit that estimates the active area of the customer who visited the point,
The data analysis device according to any one of claims 1 to 8, wherein the visit rate calculation unit calculates the visit rate based on the area where the customer is active estimated by the estimation unit. Further comprising an acquisition unit that acquires the location information of the customer,
10. The data analysis device according to claim 9, wherein said estimation unit estimates said active area of said customer based on said customer's location information. A data analysis system for analyzing a commercial area of a point set on a map,
a visit rate calculation unit that calculates, for each predetermined area on the map, a visit rate of customers who are active in the area to the point;
An average calculation unit that calculates an average visit rate of the customers who are active at the distance from the point to the point, for each distance from the point;
estimated for each predetermined area on the map based on the visit rate for each predetermined area on the map calculated by the visit rate calculation unit and the average visit rate calculated by the average calculation unit an evaluation unit that evaluates the degree to which the visit rate for the point deviates from the average visit rate for the distance from the point to the area ;
an output unit that outputs the evaluated result;
data analysis system. A data analysis method in which a computer having a control unit analyzes a commercial area of a point set on a map,
The control unit
a visit rate calculation step of calculating, for each predetermined area on the map, a visit rate of customers who are active in the area to the point;
An average calculation step of calculating an average visit rate of the customers who are active at the distance to the point for each distance from the point;
Based on the visit rate for each predetermined area on the map calculated in the visit rate calculating step and the average visit rate calculated in the average calculating step , for each predetermined area on the map an evaluation step of evaluating the degree to which the visit rate for the point estimated to deviate from the average visit rate for the distance from the point to the area ;
an output step of outputting the evaluated result;
data analysis methods that perform A program for causing a computer to execute the data analysis method according to claim 12.

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