Data processing device, data processing method, and data processing program

Abstract

To improve accuracy in converting administrative boundary data into mesh data.SOLUTION: A data processing device 4 includes an area ratio determination part 421 for determining an area ratio occupied by a region in an individual mesh if the region partitioned to an administrative boundary in map data is partitioned to a plurality of meshes, a proration data generation part 422 for generating proration data obtained by prorating human flow data of the region according to the area ratio in a unit of a mesh, and a data distribution part 423, 424 for distributing numerical data totalized about the region to a plurality of meshes on the basis of ratio of the proration data to the entire multiple meshes.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a data processing device, a data processing method, and a data processing program. [Background technology]

[0002] There are two main ways of displaying (or tabulating) various regional statistical data (e.g., population, number of households, etc.) published by the government. One is based on data in administrative districts (also called "administrative boundaries"), and the other is based on data in units of meshes (which may be called "regional meshes") that divide regions into grid-like areas (hereafter also referred to as "mesh data").

[0003] Recently, there has been an increasing demand for companies to create administrative boundary data based on their own member (or customer) data, etc., and compare and analyze it with statistical data. In addition, there is also an increasing demand for analyzing mesh data that is easy to understand visually. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Ministry of Land, Infrastructure, Transport and Tourism, National Institute for Land and Infrastructure Management, Urban Research Department, "Population Information Mesh Allocation Program Operation Manual Version: 2.2," October 18, 2019 Summary of the Invention [Problem to be solved by the invention]

[0005] As an example of a method for converting administrative boundary data into mesh data, Non-Patent Document 1 shows a method (program) for distributing population data of towns and blocks, which is an example of administrative boundary data, into 100m mesh data. In this method, population data is distributed equally only to meshes classified as "building sites" in the "land use subdivision mesh data" of the Ministry of Land, Infrastructure, Transport and Tourism's National Land Numerical Information, among the meshes contained in the town and block divisions.

[0006] However, in the method of Non-Patent Document 1, since the data is allocated equally to meshes classified as "building sites", there is room for improvement in terms of the accuracy of the obtained mesh data.

[0007] One of the objects of the present invention is to improve the accuracy when converting administrative boundary data into mesh data. [Means for solving the problem]

[0008] A data processing device according to one embodiment of the present invention includes an area ratio determination unit that determines the area ratio that an area occupies in each of a plurality of meshes when an area divided into administrative boundaries in map data is divided into the plurality of meshes, an allocation data generation unit that generates allocation data by apportioning people flow data for the area in accordance with the area ratio in units of the meshes, and a data distribution unit that distributes numerical data compiled for the area to the plurality of meshes based on the ratio of the allocation data to the entirety of the plurality of meshes. Effect of the Invention

[0009] According to the present invention, it is possible to improve the accuracy when converting administrative boundary data into mesh data. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 illustrates an example of a data processing system according to an embodiment. [Diagram 2] FIG. 13 is a diagram showing an example of purchase data for a store located in a specific administrative boundary area. [Diagram 3] 3A to 3C are diagrams showing modified examples of the purchase data exemplified in FIG. [Figure 4] 1 is a block diagram illustrating an example of a functional configuration of a computer as an example of a data processing device according to an embodiment. [Diagram 5] 5 is a flowchart showing an example of the operation of the computer illustrated in FIGS. 1 and 4. [Figure 6]FIG. 6 is a diagram showing an example of polygon data (section data) used in the operation example of FIG. 5. [Figure 7] 6 is a diagram for explaining an example of an area ratio for each mesh calculated in the operation example of FIG. 5. FIG. [Figure 8] 6 is a diagram showing an example of people flow mesh data used in the operation example of FIG. 5. FIG. [Figure 9] 6 is a diagram showing an example of people flow mesh data apportioned according to area ratio in the operation example of FIG. 5. FIG. [Figure 10] 10 is a diagram showing an example of mesh composition ratio data determined based on the people flow mesh data of FIG. 9 in the operation example of FIG. 5. FIG. [Figure 11] 11 is a diagram showing an example of town-district sales mesh data in which the sales data shown in FIG. 5 in the operation example of FIG. 4 is distributed according to the component ratio for each mesh shown in FIG. 10. FIG. [Figure 12] 12 is a diagram showing an example of the town-and-district sales mesh data shown in FIG. 11 displayed in a heat map format. FIG. [Figure 13] FIG. 2 is a diagram illustrating an example of a hardware configuration of a computer according to an embodiment. [Figure 14] FIG. 13 is a diagram showing an example of customer number data displayed on a map in a heat map format by block. [Figure 15] FIG. 15 is a diagram showing another example of FIG. 14. [Figure 16] FIG. 13 is a diagram illustrating an example of the relationship between the area, number of customers, and heat map color for each block. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, the embodiments and examples will be described in detail with reference to the drawings. The same or similar reference numerals indicate the same or similar elements, and repeated explanations may be omitted. In the following description, when a numerical value is described, the numerical value is merely an example, and other numerical values ​​may be used in addition or instead.

[0012] [overview] Administrative boundary data includes data divided by prefecture, city, ward, town, village, district, or block.Mesh data, on the other hand, is data in which a specific area is divided into a number of grid-like sections (meshes) without depending on administrative boundary divisions.Mesh data includes, for example, data in which the length of one side of a square section varies, such as 1km mesh, 500m mesh, 250m mesh, 125m mesh, 100m mesh, or 50m mesh.

[0013] Displays based on mesh data are often used in weather forecasts, for example, because the area of ​​each mesh is constant, making them easy to understand visually. However, it can also be difficult to visualize specific locations (e.g. addresses).

[0014] On the other hand, when it comes to data related to regions held by companies such as retailers (hereinafter referred to as "region-related data"), most of it is personal data such as membership data, and there is little data aggregated by administrative boundaries. If you want to compare and analyze data aggregated by administrative boundaries with your own company data, such as membership data, on the same regional basis, you will have to perform the aggregation work yourself to create administrative boundary data from your own company data.

[0015] For example, you can create data by administrative boundary by tabulating the address information of each individual obtained from your company's member data. Taking a store such as a supermarket as an example, you can obtain customer count data by block-level area (for example, 2-chome, ZZ town, YY city) by tabulating the customers in that area based on the address information in the customer data.

[0016] This data is administrative boundary data by block. Administrative boundary data can be used, for example, for comparison and analysis with census data. Through this comparison and analysis, it is possible to obtain information such as "the membership ratio to the population of ZZ block 2 is XX%."

[0017] Another method for aggregating administrative boundary data from a company's member data is to plot the addresses of members at the parcel number level on a map, assign coordinates (e.g., latitude and longitude), and compare them with administrative boundary data to create statistical data in mesh units. However, processing using addresses at the parcel number level is often restricted in terms of personal information protection, making it difficult to use.

[0018] Fig. 14 is a diagram showing an example of customer number data displayed on a map in a heat map format by block. Fig. 14 shows an example in which store A, such as a supermarket, exists and each area including store A is divided at the block level. Each area (block) is displayed in a different color according to the number of customers of store A. For example, an area with a larger number of customers is displayed in warmer colors, and an area with a smaller number of customers is displayed in cooler colors.

[0019] 14, for example, several areas in the northeast area including the area where store A is located are displayed in "red" (R), and several areas away from store A to the south are displayed in "blue" (B). In this case, it can be visually and intuitively seen that there are many customers to the northeast of store A and few customers to the south of store A.

[0020] If a competing store is located to the south of store A, it is conceivable that the competing store will take away customers. Therefore, store A can plan sales promotions to attract customers in the area to the south of store A, for example, based on such a heat map.

[0021] In the heat map shown in Fig. 14, each block has a roughly uniform area, making it easy to visually grasp the distribution of customer numbers. However, for example, as shown in Fig. 15, when the area of ​​one block K is significantly larger than the areas of other blocks, the relationship between the simple number of customers and the number of customers per unit area is reversed, which can make it difficult to visually grasp (or easy to misunderstand).

[0022] For example, as shown in Figure 16, the area of ​​"ZZ Town 1-chome" is 2 km2 And the number of customers (or population) is 100, while the area of ​​"ZZ Town 2-chome" is 10 km 2 Assume that the number of customers is 110.

[0023] In this case, the customer density is "ZZ Town 2-chome" (11 people / km 2 ) than "ZZ Town 1-chome" (50 people / km 2 ) is larger. However, the absolute number of customers in "ZZ Town 2-chome" (110 people) is greater than that in "ZZ Town 1-chome" (100 people), so in the heat map, for example, "ZZ Town 2-chome" will be displayed in "red" and "ZZ Town 1-chome" will be displayed in "blue."

[0024] Therefore, even though in reality Store A's customers are more concentrated in "ZZ Town 1-chome" than in "ZZ Town 2-chome," a reversal phenomenon can occur, making it appear as if Store A's customers are more concentrated in "ZZ Town 2-chome," which is displayed in red, which can lead to misunderstandings.

[0025] Administrative boundary data (for example, town and street data) tends to have smaller area boundaries in densely populated areas such as urban areas, and larger area boundaries in rural areas. Therefore, in areas where towns and streets of different sizes coexist, the above-mentioned misunderstandings are likely to occur. If the area of ​​each town and street is constant, then the customer density will also be the same for towns and streets with the same number of customers, and so such misunderstandings are unlikely to occur.

[0026] Therefore, for example, by using a heat map based on mesh data as a representation method in which the area of ​​each block is constant (the same), it is possible to prevent or reduce the occurrence of misunderstandings such as those mentioned above. In other words, by converting administrative boundary data created from your company's customer data into mesh data, you can provide a heat map that is easy for anyone to visually understand and less likely to cause misunderstandings.

[0027] For example, while it is relatively easy to compile administrative boundary data based on member address data, it is not easy to compile mesh data that does not necessarily match the regional divisions in the administrative boundary data. Therefore, there is a demand for converting administrative boundary data into mesh data. The process or work involved in such data conversion is sometimes called "distribution." The term "distribution" may be interchangeably interpreted as other terms such as "allocation," "assignment," "association," and "linking."

[0028] Here, in the method described in Non-Patent Document 1, it may be possible to consider distributing "membership data" to mesh data by applying "membership (or customer) data" for each block instead of "population data" for each block. However, as described above, the method described in Non-Patent Document 1 distributes data equally to meshes classified as "building sites," so there is room for improvement in the accuracy of the resulting mesh data.

[0029] For example, in a mesh that includes boundaries between towns and blocks, such as prefectural borders, part of the town or block is missing due to the boundary, so the area proportion that the town or block occupies within the mesh is smaller than in a mesh that does not include boundaries.

[0030] Therefore, if the "membership data" is distributed equally to each mesh without considering the ratio of area that the town-district area occupies within the mesh (hereinafter sometimes abbreviated as "area ratio"), a large error will occur in the number of members distributed to the mesh. Therefore, if the number of people per mesh is displayed as a heat map, the misunderstanding described above in Figure 16 may occur.

[0031] Therefore, below we will describe a non-limiting embodiment that contributes to providing mesh data with improved accuracy, for example by determining (e.g., apportioning) the data to be distributed to a mesh based on the area ratio that town-district areas occupy within the mesh.

[0032] [System configuration example] Fig. 1 is a diagram showing an example of a data processing system 1 according to an embodiment. The data processing system 1 shown in Fig. 1 illustratively includes an ID-POS system 10, an administrative boundary data server 20, a people flow data server 30, and a computer 40, which are communicatively connected to each other via a network (NW) 50. A display 41 that displays the output of the computer 40 is connected to the computer 40, for example.

[0033] The ID-POS system 10 is installed in retail stores such as supermarkets, convenience stores, and drug stores, and is used to investigate and analyze customer purchasing behavior based on the purchasing data of customers who visit the store. Here, the purchasing data obtained by the ID-POS system 10 is associated (or "linked") with customer information (e.g., customer attribute information such as age, sex, and address). The purchasing data linked with customer attribute information may be called "ID-POS data."

[0034] Fig. 2 is a diagram showing an example of ID-POS data for store X located in a specific administrative boundary area. As shown in Fig. 2, the ID-POS data for store X is, for example, data in which customer attribute information such as age, gender, postal code, and address are linked to purchase data such as purchase date and time, purchased product, quantity, and amount by customer (or member) ID. Note that when ID-POS data for multiple stores is managed in a centralized manner, the ID-POS data may be provided with information indicating the ID of each store.

[0035] In ID-POS data, one or both of the postal code and address may be omitted, and the address may be limited to the city, ward, town, or village, or to the district, for example. Figures 3(A) and 3(B) show examples of ID-POS data in which the postal code is omitted, with the address limited to the city, ward, town, or village in Figure 3(A) and the address limited to the city, ward, town, or village in Figure 3(B). Figure 3(C) shows an example of ID-POS data in which the address is omitted, with the postal code and address limited to the city, ward, town, or village in Figure 3(A).

[0036] In this way, ID-POS data is data that can identify, for example, "when," "who," "where," "what," "with what," "how many," and "how much" a purchase was made. Store X can use this ID-POS data, for example, for analysis to increase sales.

[0037] Data that a company or store collects and holds about its customers or visitors, such as ID-POS data, is sometimes called "customer data" or "1st party data." In contrast to "1st party data," there is also data called "2nd party data" and "3rd party data."

[0038] Both "second party data" and "third party data" are different from one's own data and are data provided to a company or the like from outside. For example, "second party data" is data provided from other companies or institutions that have a partnership with one's own company, and "third party data" is data provided from one's own company or a third party such as a company or institution that has no partnership with one's own company. The administrative boundary data provided by the administrative boundary data server 20 and the people flow data provided by the people flow data server 30 may be, for example, "second party data" or "third party data".

[0039] In customer data, information that can identify "who" such as age, sex, address, and family structure may be obtained from information that a customer submits as a response to a questionnaire or the like when applying for membership or registration at Store X, which issues membership cards and point cards. However, with regard to addresses, from the perspective of protecting personal information, Store X does not require information at the level of a lot number from customers, and may limit it to information at the level of a street, block, or postal code.

[0040] For example, store X can analyze "where (from)" customers (purchasers) visit store X based on address information in ID-POS data, and can analyze differences in purchasing behavior due to differences in customers' residential areas. This analysis can be referred to as "visitor residential area analysis and from analysis."

[0041] This analysis makes it possible to understand regional differences, such as the tendency for "aa products" to be purchased at competing stores depending on the area of ​​residence. In addition, it is also possible to understand regional differences, such as the tendency for "bb products" to sell better in areas with many high-rise apartment buildings compared to other areas, where the wealthy and certain generations tend to be concentrated.

[0042] Here, conventionally, analysis (e.g., sales data analysis) based on ID-POS data by administrative boundary (or by postal code) has been mainstream. In contrast, this embodiment makes it possible to analyze ID-POS data by mesh, and to display data by mesh in a way that is visually easy to understand and unlikely to cause misunderstanding. Note that instead of or in addition to ID-POS data, customer membership card or point card registration information, questionnaire response information, etc. may be used in the analysis.

[0043] Returning to Figure 1, the administrative boundary data server 20 is a server computer that stores and manages data (hereinafter referred to as "administrative boundary data") on areas divided into administrative boundaries in map data, such as by prefecture, city, ward, town, village, oaza, town, chome, or town, block, or village.

[0044] The administrative boundary data is an example of information (geospatial information) in which numerical data and the like are associated with geographical or spatial positions. The administrative boundary data provided by the administrative boundary data server 20 may be data under the management of a public institution, or may be data under the management of a private company or institution.

[0045] The people flow data server 30 is, for example, a server computer that stores and manages people flow data in any area of ​​an administrative boundary. The people flow data server 30 and the administrative boundary data server 20 may be functionally integrated into one server.

[0046] "People flow data" is, for example, data on the movement and stay of "people" or "things" carried by people, and can be acquired using devices such as sensors, cameras, smartphones, and base stations (or access points). To acquire "people flow data", for example, multiple devices of the same type may be used, or multiple devices of different types may be used in combination (or mutually complementary) depending on the geographical or spatial range.

[0047] "People flow data" can be obtained by, for example, regarding each unique identifier (terminal ID) of a mobile terminal such as a smartphone as one person, and storing movement histories based on location information for each terminal ID in a database (DB). Also, "people flow mesh data" for store X, which will be described later with reference to FIG. 8, can be obtained, for example, as follows.

[0048] In the movement history DB of the terminal ID, a place (for example, a mesh) where the terminal ID (person) stayed for a long period of time (first reference time) such as several hours at night is estimated to be the mesh of the person's residential area. A device ID (person) that stays on the premises of Store X for a certain period of time (second reference period), such as 10 minutes or more, is counted as one visitor to Store X, and the device ID is linked to the residential area mesh estimated for that device ID.

[0049] One or both of the first and second reference times can be specified by the user in the people flow data server 30 or the computer 40. Examples of the method of counting terminal IDs include the following examples (1) and (2). The counting method shown in (1) or (2) can be specified (selected) by the user in the people flow data server 30 or the computer 40.

[0050] (1) Even if the same terminal ID visits store X multiple times during a specific period (for example, one day), it is counted as one visit. (2) If the same terminal ID visits store X multiple times during a specific period (for example, one day), the total number of visits is counted.

[0051] "People flow data" may include, for example, any one or any combination of two or more of multiple types of data referred to as "count data," "stay data," "OD data," and "movement trajectory data."

[0052] "Count data" is, for example, data indicating the number of people passing through a specific point (e.g., traffic volume data), and may be acquired by time or direction of movement. "Stay data" is, for example, data indicating the number of people staying at a specific point or in a space for a certain period of time, and may be acquired by time.

[0053] "OD data" is, for example, data showing the number of people who moved from a starting point (origination) to a destination (destination) and can be used to grasp and analyze the flow of people (regardless of the route) between two points. "Movement trajectory data" is, for example, data showing the movement trajectory of an individual within a specific geographical or spatial range.

[0054] The people flow data used in this embodiment may include, for example, OD data and staying data in a specific area of ​​an administrative boundary (for example, the block area where store X is located). By using the "people flow data" including OD data and staying data, it is possible to grasp, for example, the number of people who visit a second point (for example, store X, which is the destination) from one or more first points (starting points) in the block area where store X is located.

[0055] Such people flow data may be, as a non-limiting example, data using location information of mobile terminals obtained through base stations (or access points) at mobile communication carriers, or processed data thereof. The processed data is, for example, data from the viewpoint of protecting personal information or privacy, from which information that can identify individuals, such as personal and parcel-level addresses, has been removed or concealed.

[0056] Based on the people flow data, it is possible to estimate, for example, the number of visitors who stayed at store X for a certain period of time during store X's business hours, and also to estimate the residence of people who visited store X (where they came from) based on the duration of their stay outside store X's business hours (for example, at night).

[0057] Note that the number of visitors to Store X estimated from people flow data at a specific date, time or time period may include the number of visitors who are not members of Store X, and may also include the number of visitors who did not purchase any products, etc. Therefore, the number of visitors estimated from people flow data at a specific date, time or time period does not necessarily match the number of customers obtained from ID-POS data.

[0058] However, it is possible to adequately grasp the geographical distribution trends, i.e., the average number of people who actually visit store X in the block-and-district area and "where" (the departure point) those visitors to store X came from within the block-and-district area.

[0059] Therefore, the people flow data server 30 can generate data that, for example, divides a town / district area into a plurality of meshes, and assigns the number of people who visited store X with a point belonging to the mesh as the starting point (or estimated place of residence). This data is an example of "people flow mesh data." For convenience, such people flow mesh data may be referred to as "visitor-resident data (mesh from data)."

[0060] The people flow data server 30 may provide (for example, to the computer 40) people flow mesh data of different sizes (fineness or granularity), such as lengths of one side of 1 km, 500 m, 250 m, 125 m, 100 m, and 50 m. Note that the people flow mesh data may be generated, for example, in the computer 40 that has received the people flow data.

[0061] The mesh size of people flow mesh data may be determined according to, for example, the data provision standards of mobile communication carriers that handle the location information of mobile terminals, and the data processing capabilities of the data recipient (e.g., computer 40). For example, since a 50m mesh may have too much data capacity and the processing load or processing time may exceed the allowable values, a mesh size with a smaller data capacity, such as a 125m mesh, a 250m mesh, or a 500m mesh, may be applied.

[0062] It is said that the minimum mesh size that mobile communication carriers consider to be "summarized so that personal information cannot be identified" is 50m mesh. Therefore, if the mesh size is 50m or more, there is no need to individually process people flow mesh data by removing or concealing personal information.

[0063] The computer 40 is an example of a data processing device, and for example, communicates with the ID-POS system 10, the administrative boundary data server 20, and the people flow data server 30 via the NW 50 to acquire and process ID-POS data, administrative boundary data, and people flow data. The NW 50 may be either a wireless or wired network, or a mixed wireless and wired network.

[0064] Then, the computer 40 generates block-district sales data by aggregating store X's sales data by block-district based on, for example, the ID-POS data and administrative boundary data, and converts (or distributes) the generated block-district sales data into mesh data.

[0065] The town-district sales data may be generated in a computer other than computer 40 and received or input to computer 40. Computer 40 may be a server computer or a client computer. The processing executed by computer 40 may be distributed and executed in a plurality of computers including computer 40, for example. The plurality of computers may be, for example, a group of cloud servers virtualized by virtualization technology.

[0066] [Example of computer 40 configuration] An example of the configuration of a computer 40 that focuses on the data conversion (or distribution) function as described above is shown in Fig. 4. Fig. 4 is a block diagram showing an example of the functional configuration of a computer 40 as an example of a data processing device according to an embodiment.

[0067] 4, the computer 40 includes, for example, a data acquisition unit 401, a processing unit 402, a data output unit 403, and a storage unit 404. In the following, the term "unit" may be interchangeably read as other terms such as "circuit", "block", "module", or "means".

[0068] The data acquisition unit 401 acquires, for example, ID-POS data, administrative boundary data, and people flow data from the ID-POS system 10, the administrative boundary data server 20, and the people flow data server 30, respectively, via the NW 50. These data may be temporarily stored in the storage unit 404, for example.

[0069] The processing unit 402 processes the data acquired by the data acquisition unit 401, thereby converting, for example, town-and-chome sales data based on administrative boundary data into mesh data. Here, the town-and-chome sales mesh data obtained by this conversion is generated based on people flow mesh data of the town-and-chome area and the area ratio that the town-and-chome area occupies in each mesh, as described below. Therefore, it is possible to suppress errors that may occur due to inconsistencies in the area ratio between the town-and-chome area and the mesh, and to improve the accuracy of the town-and-chome sales mesh data.

[0070] As a non-limiting example, the processing unit 402 includes an area ratio determination unit 421, an apportionment data generation unit 422, a mesh configuration ratio determination unit 423, and a data distribution unit 424, as shown in FIG.

[0071] The area ratio determination unit 421, for example, divides the block-chome area where the store X is located into a plurality of meshes and determines the area ratio that the block-chome area occupies in each mesh. For convenience, the block-chome area where the store X is located may be referred to as "block-chome X."

[0072] The area ratio is determined based on image processing using, for example, polygon data corresponding to the planar shape of block X (hereinafter abbreviated as "block polygon") and mesh data dividing block X into a plurality of meshes, as will be described later with reference to Figures 5 and 6. The polygon data is an example of polygonal surface data representing a block, and may also be called "block data".

[0073] The apportionment data generation unit 422 generates mesh data by apportioning the people flow data for each mesh in the people flow mesh data according to the area ratio, based on the people flow mesh data and the area ratio determined by the area ratio determination unit 421.

[0074] As will be described later with reference to Figure 7, "people flow mesh data" is data in which the number of people who visited store X with a certain mesh as the starting point (from) is assigned to each starting point, i.e., to each mesh. For convenience, this type of "people flow mesh data" may be referred to as "visitor resident data (mesh from data)."

[0075] The apportionment data generation unit 422, for example, multiplies the number of people in each mesh in the "visitor resident data (mesh from data)" by the area ratio for the corresponding mesh, thereby apportioning the number of people in each mesh according to the area ratio. An example of the apportionment process will be described later with reference to FIG. 8.

[0076] The mesh composition ratio determination unit 423, for example, determines the composition ratio of people flow data (number of visitors) in each mesh after the apportionment based on the people flow mesh data after the apportionment, with the entire block (polygon) being "1 (100%)." An example of the composition ratio determination process will be described later with reference to Figs. 8 and 9.

[0077] The data distribution unit 424 generates town-district sales mesh data by distributing, for example, the sales data S of block X to each mesh according to the composition ratio determined by the mesh composition ratio determination unit 423. An example of this distribution process will be described later with reference to Figs. 9 and 10.

[0078] It should be noted that the functions of the units 421 to 424 in the processing unit 402 described above may be partially or entirely integrated into one block.

[0079] The data output unit 403 outputs, for example, the processing result by the processing unit 402 to the display 41. The data output unit 403 outputs, for example, the town-district sales mesh data, which is the processing result by the data distribution unit 424, to the display 41 for display.

[0080] The data output unit 403 may output the results of one or more of the processing of the area ratio determination unit 421, the proportional division data generation unit 422, and the mesh composition ratio determination unit 423 to the display 41. By displaying the results of one or more of the processing of these units 421 to 423 on the display 41, for example, a part or all of the processing steps in the process of obtaining the town-block sales mesh data can be visually confirmed.

[0081] The data output unit 403 may also function as a display control unit that controls the display mode of data displayed on the display 41. For example, the data output unit 403 as a display control unit may cause the display 41 to display the town-district sales mesh data M5 in a heat map format.

[0082] As a non-limiting example, the display control unit 403 may set a warmer color as the display color for a block-block area (in other words, a block-block polygon area) that belongs to a mesh with higher sales data. Also, the display control unit 403 may set a cooler color as the display color for a block-block polygon area that belongs to a mesh with lower sales data.

[0083] The storage unit 404 stores, for example, data acquired by the data acquisition unit 401, and programs and data for causing the computer 40 to function as the above-mentioned data acquisition unit 401, processing unit 402, and data output unit 403. For example, when the processing unit 402 of the computer 40 generates "people flow mesh data", information specifying one or both of the above-mentioned first reference time and second reference time may be stored in the storage unit 404 via the data acquisition unit 401, and information specifying the above-mentioned method of counting terminal IDs may also be stored.

[0084] [Example] An example of the operation of the computer 40 configured as described above (for example, an example of a process for distributing block data, which is an example of administrative boundary data, into mesh data) will be described below with reference to Figs. 5 to 12.

[0085] Fig. 5 is a flow chart showing an example of the operation of the computer 40, and Figs. 6 to 12 are diagrams showing an example of data used in the process of distributing block data into mesh data. Note that Figs. 6 to 12 may be understood to correspond to examples of data display screens on the display 41.

[0086] As illustrated in Fig. 5, the computer 40 acquires data by the data acquisition unit 401 (S10). The acquired data is, for example, polygon data (cho-cho-me polygon) Px of block X as illustrated in Fig. 6, mesh division data M1 that divides the block polygon into a mesh as illustrated in Fig. 7, and people flow mesh data M2 as illustrated in Fig. 8. Note that in Fig. 6, the block polygon Px is accompanied by block sales data S (e.g., S = 28,425,000 yen). The block polygon P shown in Fig. 6 may be understood to be data linked to the sales data S for each block.

[0087] The data acquisition unit 401 inputs the various acquired data to the area ratio determination unit 421. The area ratio determination unit 421, for example, superimposes the block-block polygon Px with the mesh partition data M1 as shown in Fig. 7. In the superimposed image data, the area ratio determination unit 421 determines the area ratio of the block-block polygons included in each partition (mesh) by, for example, detecting the ratio of the number of pixels of the block-block polygon P to the number of pixels per mesh (S20 in Fig. 5).

[0088] For example, in Fig. 7, when the block-district polygon P is divided into 4 x 4 = 16 meshes #1 to #16, the area ratio determination unit 421 detects, for example, mesh #i that includes a line segment (also called a "polyline") that corresponds to the outer edge (in other words, the boundary of the block) of the block-district polygon P. Note that i is an integer that satisfies 1 ≤ i ≤ 16. In the example of Fig. 7, 12 meshes #1 to #5, #8, #9, and #12 to #16 are detected as meshes that include polylines.

[0089] In each of these 12 meshes, the number of pixels in the mesh is greater than the number of pixels in the block polygon, so the area ratio for each of meshes #1 to #5, #8, #9, and #12 to #16 is less than 100%. As a non-limiting example, the area ratio determination unit 421 determines the area ratio for each of the 12 meshes #1 to #5, #8, #9, and #12 to #16 in the example of FIG. 7 as 5%, 75%, 93%, 35%, 81%, 48%, 65%, 19%, 2%, and 27%, respectively.

[0090] The remaining four meshes #6, #7, #10, and #11 corresponding to the central portion of the block polygon P do not contain polylines, so "the number of pixels of the mesh = the number of pixels of the block polygon." Therefore, the area ratio determination unit 421 may determine the area ratio to be 100% for each of the meshes #6, #7, #10, and #11 that were not detected as meshes containing polylines.

[0091] In this way, by determining the area ratio of meshes that are not detected as meshes containing polylines as 100% in the area ratio determination process, the processing load can be reduced compared to the case where the area ratio is determined individually for all meshes. Note that the area ratio may alternatively be determined by, for example, spatial calculation processing.

[0092] After determining the area ratio, the processing unit 402 performs processing, for example, by the proportionate division data generating unit 422 (S30 in FIG. 5). The proportionate division data generating unit 422 generates mesh data M3 in which the people flow data for each mesh #i is proportionately divided according to the area ratio, based on the people flow mesh data M2 as shown in FIG. 8 and the area ratio determined as described above.

[0093] The people flow mesh data M2 shown in Fig. 8 is data on the number of people who visited store X starting from mesh #i, assigned to each mesh #i by starting point, and may be referred to as "visitor resident data (mesh from data)" for convenience. For example, in Fig. 8, the number of people who visited store X starting from mesh #1 is "329", and the number of people who visited store A starting from mesh #2 is "108". The same is true for the other meshes #3 to #16.

[0094] The apportionment data generation unit 422 apportions the number of people in each mesh #i in the people flow mesh data M2 by the area ratio (%) of the corresponding mesh #i determined as shown in Fig. 7. Fig. 9 shows an example of people flow mesh data M3 after apportionment.

[0095] For example, in Fig. 9, the number of people (329 people) in mesh #1 shown in Fig. 8 is multiplied by 5% of the area ratio of mesh #1 shown in Fig. 7, resulting in approximately 16.5 people. Similarly, the number of people (108 people) in mesh #2 is multiplied by 75% of the area ratio of mesh #1 shown in Fig. 7, resulting in approximately 81 people. The same applies to the other meshes #3 to #16.

[0096] In addition, for meshes #6, #7, #10, and #11, the area ratio is 100%, so the numbers in Fig. 9 are the same as those shown in Fig. 8 (138 people, 203 people, 48 people, and 38 people). The total number of people in each of meshes #1 to #16 after the allocation is 1468 people.

[0097] After the apportionment process, the processing unit 402, for example, performs processing by the mesh composition ratio determination unit 423 (S40 in FIG. 5). For example, the mesh composition ratio determination unit 423 determines the composition ratio of people flow data (number of visitors) in each mesh #i after apportionment, assuming the entire block (polygon) to be "1 (100%)" based on the people flow mesh data M3 after apportionment. FIG. 10 shows an example of mesh composition ratio data M4 determined based on the people flow mesh data M3 in FIG. 9.

[0098] For example, in Fig. 10, the composition ratio for mesh #1 is [16.5 (people) ÷ 1468 (people)] × 100 (%) ≒ 1.12%, and the composition ratio for mesh #2 is [81.0 (people) ÷ 1468 (people)] × 100 (%) ≒ 5.52%. The same applies to the other meshes #3 to #16.

[0099] After determining the composition ratio, the processing unit 402, for example, performs processing by the data distribution unit 424 (S50 in FIG. 5). For example, the data distribution unit 424 distributes the sales data S of block X to each mesh #i according to the composition ratio determined by the mesh composition ratio determination unit 423. FIG. 11 shows an example of block-block sales mesh data M5 in which the sales data S (e.g., 28,425,000 yen) in FIG. 5 is distributed according to the composition ratio for each mesh #i illustrated in FIG. 10. Note that the numerical unit of the sales data added to each mesh #i in FIG. 11 is [thousand yen].

[0100] After the distribution process, the processing unit 402, for example, outputs the processing result to the display 41 by the data output unit 403 (S60 in FIG. 5). For example, the data output unit 403 outputs the town-district sales mesh data M5, which is the distribution result by the data distribution unit 424, to the display 41 for display.

[0101] The data output unit 403 may output the results of one or more of the processing of the area ratio determination unit 421, the proportional division data generation unit 422, and the mesh composition ratio determination unit 423 to the display 41. By displaying the results of one or more of the processing of these units 421 to 423 on the display 41, for example, a part or all of the processing steps until the town-district sales mesh data M5 is obtained can be visually confirmed.

[0102] The data output unit 403 may also function as a display control unit that controls the display mode of data to be displayed on the display 41. For example, the data output unit 403 functioning as a display control unit causes the display 41 to display the town-district sales mesh data M5 in a heat map format.

[0103] For example, the display control unit 403 sets warmer colors as the display color for block-block areas (in other words, block-block polygon areas) belonging to mesh #i with higher sales data, and sets cooler colors as the display color for block-block polygon areas belonging to mesh #j with lower sales data. Note that "j" is an integer satisfying j ≠ i and 1≦j≦16. The threshold value or range of sales data that serves as the basis for color coding may be set, for example, by setting data acquired through the data acquisition unit 401, or setting data pre-stored in the storage unit 404.

[0104] Fig. 12 shows an example of the town-chome sales mesh data M5 shown in Fig. 11 displayed in heat map format. In Fig. 12, for example, the town-chome areas belonging to meshes #5, #7, and #14 that satisfy sales data S>3 million yen are displayed in "red," and the town-chome areas belonging to meshes #6, #8, and #9 that satisfy 2.5 million yen≦S<3 million yen are displayed in "orange." In addition, the town-chome areas belonging to meshes #10 to #13, #15, and #16 that satisfy S<1.5 million yen are displayed in cool colors such as "light blue" and "blue."

[0105] Such a display in the form of a heat map allows users to visually and intuitively grasp which areas in a block have a high and low contribution to the sales of store X. Therefore, for example, store X can prioritize sales promotion to attract customers to areas with a relatively low contribution to sales.

[0106] Here, as explained in Figures 7 to 9, the sales data for each mesh #i is obtained based on data obtained by apportioning the people flow data for each mesh #i in proportion to the area occupied by the block area in each mesh #i.

[0107] As a result, it is possible to prevent or suppress the inversion phenomenon of numerical data as described using Figures 14 to 16, and therefore, for example, in a heat map format display mode, it is possible to prevent or suppress the inversion phenomenon of color coding between warm and cool colors, which may lead to misunderstandings in understanding the numerical data.

[0108] [Additional information] In the above-described embodiment, an example is shown in which a heat map format is used as a highlighting format for numerical data in mesh units, but this is not limited to this, and other highlighting formats may be used to highlight numerical data in mesh units.

[0109] In addition, in the above-described embodiment, data divided by block / district is shown as an example of administrative boundary data, but the above-described embodiment may also be applied to data divided by prefecture, city / ward / town / village, oaza, or by area divided by block / district or postal code (postal code area).

[0110] Furthermore, in the above-described embodiment, an example was shown of distributing sales data S in block X to mesh #i as an example of numerical data, but other numerical data, for example, the number of customers (or number of members) in block X may also be distributed to mesh #i.

[0111] The sales data S for block X may be data collected and acquired by a system different from the ID-POS system 10, for example, a POS system that does not collect customer attribute information, or other sales management system.

[0112] The sales data S may be, for example, sales data collected by product or service. For example, when the present embodiment is applied to the sales data S by product or service, the visibility of information such as which product or service is selling well or poorly in which area (in other words, mesh) of block X can be improved.

[0113] In the above-described embodiment, the people flow data in relation to store X may include, for example, data indicating the flow (movement) of people by means of transportation such as vehicles. For example, vehicle flow data based on position information acquired by in-vehicle communication devices or sensors may be treated as people flow data indicating the flow of people.

[0114] In addition, the data on the flow of people in relation to store X may be obtained by combining the population and number of households data for block X published by the government with the Huff model for store X.

[0115] The "Huff Model" is a trade area analysis method that estimates the customer attraction rate (or customer attraction rate) of Store X based on parameters such as the distance between Store X and the customer's residence, the presence of competing stores in the area where Store X is located, and the size (e.g., area) of Store X. For example, in cases where it is difficult to obtain or use actual people flow data obtained using base stations, data estimated using the "Huff Model" can be used in place of people flow data.

[0116] The alternative people flow data estimated using the "Huff Model" may result in lower accuracy of the mesh data obtained compared to the actual people flow data used in the above-described embodiment. However, it is useful, for example, in cases where the possible decrease in accuracy is assumed to be within an acceptable range, or when it is desired to easily obtain distribution results for mesh data.

[0117] In addition, for example, the accuracy of alternative people flow data can be improved by using a "modified Hough model" or an "advanced Hough model" in which the parameters for calculating the suction rate are modified or improved.

[0118] In the above embodiment, data processing for one block was described, but for example, similar data processing can be applied to adjacent blocks by block using the block polygons corresponding to each block. For example, in determining the area ratio shown in S20 of Fig. 5, a mesh including one or more polylines is detected. Then, the area ratio is determined for each block by assuming that each polygon area divided by the polylines in the mesh belongs to a different block.

[0119] In addition, in the above-described embodiment, an example of administrative boundary data was described in which data divided by block was processed; however, for example, the data processing of the above-described embodiment may be applied by replacing "block" with "prefecture," "city / ward / town / village," "oaza," "cho-cho-aza," or "zip code area."

[0120] [Example of hardware configuration for computer 40] 13 is a diagram showing an example of the hardware configuration of a computer 40 according to an embodiment. The computer 40 may be physically configured to include, for example, a central processing unit (CPU) 4001, a memory 4002, a storage device 4003, an input device 4004, an output device 4005, and a communication device 4006. These devices are connected to each other via, for example, a bus 4007 so as to be able to communicate with each other.

[0121] The term "apparatus" may be interchangeably read as other terms such as a circuit, a device, a unit, etc. The hardware configuration of computer 40 may be configured to include one or more of the devices shown in FIG. 13, or may be configured to exclude some of the devices.

[0122] Each function in computer 40 is realized, for example, by CPU 4001 reading and executing a program or data used in the execution of the program from memory 4002 or storage device 4003. CPU 4001 may be a single-core CPU or a multi-core CPU.

[0123] The program is a program that causes computer 40 to execute at least a part of the functions or operations of the data processing device described in the above-mentioned embodiment by being read and executed by CPU 4001. For example, it may be understood that data acquisition unit 401, processing unit 402, and data output unit 403 shown in Fig. 4 are realized by CPU 4001 executing a data processing program that embodies the operational examples described with reference to Figs. 5 to 12.

[0124] The program may be stored in advance in storage device 4003, or may be received by computer 40 through a computer-readable storage medium or a network (telecommunication line). The program may also be incorporated as a part of the operating system (OS) of computer 40.

[0125] The memory 4002 and the storage device 4003 are both examples of a computer-readable storage medium. The memory 4002 may be configured with at least one of storage media such as a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), and a random access memory (RAM).

[0126] The storage device 4003 may be composed of at least one of the following storage media: an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a Blu-ray (registered trademark) disk), and a flash memory.

[0127] The input device 4004 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from an external device. The output device 4005 is an output device (e.g., a display 41, a speaker, etc.) that performs output to an external device. The input device 4004 and the output device 4005 may be integrated into one configuration, such as a touch panel.

[0128] The communication device 4006 is a hardware device for communicating with an external device (e.g., the ID-POS system 10, the server 20, or the server 30) via one or both of a wired and wireless network. The communication device 4006 may be called, for example, a network device, a network controller, a network card, or a communication module.

[0129] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the spirit and scope of the present disclosure are not limited to the contents described throughout the present disclosure. The present disclosure can be implemented as modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended for illustrative purposes and does not have any limiting meaning on the spirit and scope of the present disclosure. [Explanation of symbols]

[0130] 1 Data Processing System 10. ID-POS System 20 Administrative Boundary Data Server 30 People Flow Data Server 40 Computers (data processing devices) 41 Display 50 Network (NW) 401 Data Acquisition Department 402 Processing section 403 Data Output Unit 404 Storage section 421 Area Ratio Determination Section 422 Apportionment Data Generation Unit 423 Mesh composition ratio determination part 424 Data Distribution Department 4001 Central Processing Unit (CPU) 4002 Memory 4003 Storage device 4004 Input Device 4005 Output Device 4006 Communication equipment 4007 Bus Px polygon data (chome polygon) M1 mesh division data M2, M3, M4, M5 mesh data S Sales Data

Claims

1. an area ratio determination unit that determines an area ratio that the region occupies in each of a plurality of meshes when an area divided into administrative boundaries in map data is divided into a plurality of meshes; an apportionment data generation unit that generates apportionment data by apportioning the people flow data in the area in units of the meshes according to the area ratio; a data distribution unit that distributes the numerical data collected for the region to the plurality of meshes based on a ratio of the apportionment data to the entirety of the plurality of meshes; A data processing device comprising:

2. The data processing device according to claim 1 , wherein the people flow data is people flow mesh data in which the number of visitors who visit a specific point in the region is assigned to a mesh to which the departure point of the visitor belongs.

3. 3. The data processing device according to claim 1, wherein the area ratio determination unit detects, among the plurality of meshes, a mesh that includes a polyline of polygon data corresponding to the shape of the region, and determines the area ratio occupied by the polygon data in the detected mesh.

4. 4 . The data processing apparatus according to claim 3 , wherein the area ratio determining unit determines the area ratio of a mesh that is not detected as a mesh including the polyline, among the plurality of meshes, to be 100%.

5. 3. The data processing device according to claim 1, further comprising a display control unit that displays mesh data in which the numerical data is allocated to the plurality of meshes on a display in a display format in which the meshes are individually highlighted according to the numerical data.

6. The data processing device according to claim 5 , wherein the display format is a heat map format in which the mesh is color-coded according to the numerical data.

7. 2. The data processing device according to claim 1, wherein the area is an area in which a company or store selling a product or service is located, and the numerical data is customer number data or sales data of the company or store in the area.

8. A process for determining an area ratio of the region in each of the meshes when an area divided into administrative boundaries in map data is divided into a plurality of meshes; A process of generating proportionally divided data by proportionally dividing the people flow data in the area in units of the meshes according to the area ratio; A process of allocating the numerical data collected for the area to the plurality of meshes based on a ratio of the apportionment data to the entirety of the plurality of meshes; (c) a method for processing data,

9. On the computer, A process for determining an area ratio of the region in each of the meshes when an area divided into administrative boundaries in map data is divided into a plurality of meshes; A process of generating proportionally divided data by proportionally dividing the people flow data in the area in units of the meshes according to the area ratio; A process of allocating the numerical data collected for the area to the plurality of meshes based on a ratio of the apportionment data to the entirety of the plurality of meshes; A data processing program that executes the above.

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