Analytical device

Abstract

To propose a mechanism for estimating people flow information by setting an analysis target region around a specific spot designated by a user, on the basis of demographic information of a predetermined region.SOLUTION: A people flow information estimation apparatus 2000' includes a people distribution information analysis unit 2103 which estimates a distribution of people located in a region related to an analysis target region around a specific spot designated by user, on the basis of a relationship between predetermined living information and predetermined region location information, on the basis of predetermined demographic information 2302'including information on the number of people located in a predetermined region at a predetermined time, living information 2306 in units of a predetermined length of time including information related to living and activity of the people, and region location information 2310 including information on a place for each predetermined section located in the predetermined region at least.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present invention relates to an analysis device that estimates information on people flow and distribution in relation to the distribution of points within a predetermined area. [Background technology]

[0002] BACKGROUND ART Conventionally, there are services that provide demographic statistical information created using the mechanisms of mobile phone networks (see, for example, Non-Patent Document 1).

[0003] 27 to 29 are diagrams showing an overview of a service that provides such demographic information.

[0004] 27 to 29, "Mobile Spatial Statistics (registered trademark)" provided by NTT DoCoMo, Inc. is described as an example.

[0005] Such "Mobile Spatial Statistics (registered trademark)" is explained in, for example, Non-Patent Document 2.

[0006] "Mobile Spatial Statistics (registered trademark)" is population statistical information created using the mechanisms of mobile phone networks. This spatial statistics is demographic information that only shows the number of people in a group, and therefore has the characteristic that it cannot identify individual mobile phone or smartphone users. In this respect, it differs from the treatment of so-called "personal information," and it is possible to provide third parties with population statistical information for a certain area at a certain time without obtaining the consent of the parties to the information.

[0007] Using this spatial statistics, it is possible to grasp the hourly population distribution across Japan, 24 hours a day, 365 days a year. Furthermore, using this spatial statistics, it is possible to grasp the population structure by gender, age group, and residential area, enabling analysis tailored to the purpose. Since the service began in October 2013, it has been widely used by the Japan Tourism Agency, local governments, private companies, and others.

[0008] More specifically, mobile phone networks periodically track the mobile phones located in each base station area so that users can make calls, send emails, and use other services anytime, anywhere. By using this system to tally the number of mobile phones and taking into account the penetration rate of mobile phone carriers, it is possible to estimate the population. This is Mobile Spatial Statistics (registered trademark).

[0009] Demonstration experiments were conducted in public fields such as urban development and disaster prevention planning, and it was confirmed that this spatial statistics could be used widely in society. As mentioned above, the service was launched in October 2013 so that it could be used not only in the public field but also in academic and industrial fields.

[0010] This spatial statistics allows for analysis by gender, age group, residential area (domestic residents), country / region (foreign visitors to Japan), and time of day, depending on the purpose.

[0011] As shown in Figure 27(a), the statistical target area is the nationwide Docomo service area, and the population distribution across the country can be grasped every hour. Therefore, it is possible to grasp the population distribution every hour, 24 hours a day, 365 days a year.

[0012] As shown in Figure 27(b), this demographic data also allows us to understand the population structure by gender and age group, covering people aged 15 to 79.

[0013] Furthermore, as shown in Figure 28, this spatial statistics makes it possible to grasp the population by residential area in a specific time period (every hour) in a specific area, which, as mentioned above, makes it possible to grasp the population distribution by residential area based on registered mobile phone information.

[0014] Furthermore, assuming that spatial statistical information is available, technology has been proposed for understanding and predicting "people flow" using demographic information as input data (see, for example, Non-Patent Document 3).

[0015] According to Non-Patent Document 3, "demographic data" can be divided into "trajectory data" and "aggregated data." "Trajectory data" is data related to moving objects such as people, and is often collected by portable devices such as smartphones and car navigation systems.

[0016] On the other hand, "aggregated data" is data associated with a location, and is collected by traffic volume sensors installed on roads, wireless base stations, and so on. Because data is acquired using equipment installed in specific locations, once the equipment is installed, data can be collected fairly comprehensively. Furthermore, because the data is not tied to individual subjects, there are fewer privacy concerns, and once measured, the data can be used for a variety of purposes. However, because cross-location information is generally not available, there are limitations to its use when understanding the flow of people in a city.

[0017] One type of aggregated data is "demographic information," which records the population of a city by location and by time. Most demographic information records population information by area and by time. When targeting information over a wide area, mobile phone information is often used to ensure comprehensiveness. The number of devices in each area is counted based on information such as device connection information at mobile phone base stations and CDRs (Call Data Records), and the actual population is estimated while taking into account factors such as utilization rates by attribute, and this is used as demographic information.

[0018] The "spatial statistics" described in the above-mentioned Non-Patent Documents 1 and 2 correspond to "demographic information" in this sense.

[0019] FIG. 29 is a diagram showing the procedure for aggregating data in "Mobile Spatial Statistics (registered trademark)" disclosed in Non-Patent Document 2.

[0020] Referring to FIG. 29, in this "Spatial Statistics", the following processing is carried out to ensure safety by eliminating information that can identify individuals.

[0021] As mentioned above, in order to strictly protect customer privacy, this spatial statistics is demographic information that only represents the number of people in a group, and therefore cannot identify individual users.

[0022] This spatial statistics is created by the following procedure. i) De-identification: The process of removing identifying information such as names, phone numbers, and dates of birth from operational data. ii) Aggregation processing: Processing to derive statistical "group information" by making statistical inferences from de-identified information. iii) Confidentiality processing: Processing to prevent the inclusion of numbers from areas with small numbers of people in the aggregated results

[0023] Non-Patent Document 3 discloses a technology for performing people flow analysis in order to more effectively utilize demographic information, in which input is demographic information corresponding to multiple time periods, and based on this information, the task of outputting the amount of movement between cells between times is executed.

[0024] On the other hand, when using two-dimensional information, there is a technology for a system that distributes demographic data from large meshes to small meshes based on estimated information on building areas (see, for example, Patent Document 1).

[0025] FIG. 30 shows an overview of the "Basic Survey on Social Life" (Non-Patent Document 4) published by the Statistics Bureau of the Ministry of Internal Affairs and Communications.

[0026] As shown in Figure 30, the Basic Survey on Social Life surveys surveys lifestyle information (behavioral classification) in 15-minute increments by region, age, and sex.

[0027] Furthermore, the information processing system disclosed in Patent Document 2 includes a section identification means for identifying a section of a transportation route, a type determination means for determining one or more types of facilities to be searched for, a facility search means for searching for facilities in the vicinity of each point included in the section based on the type, and a display control means for displaying on a display unit a list of each point and the search results by the facility search means in association with each point.

[0028] In addition, the information processing device disclosed in Patent Document 3 includes an acquisition unit that acquires a query input by a user and regional information that is the target of a search using the query, a determination unit that determines candidate search results corresponding to the acquired query based on the characteristics of the region corresponding to the acquired regional information, and a provision unit that provides information indicating the determined candidate search results to the user's terminal device. [Prior art documents] [Patent documents]

[0029] [Patent Document 1] Patent No. 6226493 [Patent Document 2] Japanese Patent Application Publication No. 2024-22109 [Patent Document 3] Patent No. 7429741 [Non-patent literature]

[0030] [Non-Patent Document 1] https: / / mobaku.jp / [Non-patent document 2] https: / / www.bcm.co.jp / bcm / wp-content / uploads / 2018 / 03 / ntt-docomo-2017-10-01.pdf [Non-patent document 3] Hiroyuki Toda, author: "Understanding and Predicting Urban Pedestrian Flow" https: / / www.jstage.jst.go.jp / article / oubutsu / 90 / 8 / 90_481 / _pdf / -char / ja [Non-patent document 4] https: / / www.stat.go.jp / data / shakai / 2021 / gaiyou.html Summary of the Invention [Problem to be solved by the invention]

[0031] However, the techniques described in Non-Patent Document 1 and Non-Patent Document 2 merely provide hourly statistics such as the age (generation), sex, etc. of people present in a certain area.

[0032] Furthermore, the technology described in Non-Patent Document 3 estimates "people flow" (the number of people moving) from "demographic data," but it cannot grasp what kind of people are doing what kind of activities in a certain area at a certain time.

[0033] The information in Non-Patent Document 4 merely indicates what activities people in each occupation are engaged in at a certain time period.

[0034] Furthermore, the technology described in Patent Document 1 merely discloses a technology for estimating the number of people in a building based on demographic data.

[0035] This has posed a challenge in that it is difficult to analyze information that classifies the behavior of people in a given area (hereinafter referred to as "lifestyle information") at intervals shorter than the time units at which demographic data is accumulated.

[0036] Furthermore, when collecting "demographic information," the area is divided into, for example, 500m x 500m "meshes" and people are tallied, but no consideration is given to estimating people distribution and people flow in relation to a user-specified area, nor to a user interface for this purpose. In other words, no mechanism has been proposed for estimating people flow information for an area related to an "analysis area" corresponding to a type of analysis point arbitrarily set by the user, based on demographic information within the specific area (e.g., Osaka Prefecture). This is not disclosed in Patent Documents 2 and 3.

[0037] Therefore, in the past, there was no method for setting an analysis area (survey mesh) around a specific point specified by the user according to the analysis target (category) desired by the user, and therefore it was not possible to analyze people flow information in relation to such an analysis target area.

[0038] The present invention has been made to solve the above-mentioned problems, and its purpose is to provide an analysis device that can estimate people flow information related to an area to be analyzed, during a specified time period in a specified area, based on demographic information for the specified area. [Means for solving the problem]

[0039] In order to achieve the above-mentioned object, according to one aspect of the present invention, an analysis device includes a storage device that stores demographic information including information on the number of people located in each of a plurality of specified area meshes in a time series of a series of time units within a specified area, and a calculation device that performs analysis of people distributed within the specified area, and the functions performed by the calculation device include a point distribution information acquisition means that acquires the distribution of analysis target points located within the specified area according to the type of analysis target point specified by a user within the specified area, an area setting means that sets an analysis target area for each analysis target point for area meshes within a specified range surrounding a plurality of analysis target points corresponding to the type, and a person distribution information analysis means that extracts information on the number of people distributed in the specified area based on the demographic information.

[0040] Preferably, the functions executed by the computing device further include an attribute designation means for receiving designation of target attributes to be analyzed from the user among the attributes of people, and the people distribution information analysis means acquires information on the attributes of people within a specified area or analysis area from demographic information, and the specified range around the analysis target point is an area within a trajectory of a specified distance centered on the analysis target point corresponding to the type designated by the user, and the specified distance is variable depending on the target attributes of people designated by the user.

[0041] Preferably, the specified distance is a distance calculated based on the distance that can be traveled per unit time according to the person's attributes and the means of transportation corresponding to the specified area, the points to be analyzed are specified facilities designated by type, and the area within the trajectory corresponds to the range in which the person can use each facility by means of transportation according to the person's attributes.

[0042] Preferably, the apparatus further comprises a superimposed display generating means for generating information to be displayed so that the distribution of the analysis target area and the information relating to the number of people are superimposed.

[0043] Preferably, the people distribution information analysis means analyzes the distribution of people with target attributes relative to the distribution of analysis target points by time based on demographic information, and the superimposed display generation means displays the results of the analysis by the people distribution information analysis means by superimposing them for each time period.

[0044] Preferably, further comprising a superimposed display generating means for generating information to be displayed so that the distribution of the analysis target area and the information regarding the number of people are superimposed; The specified facility is either a transportation facility, a public institution, a commercial facility or an entertainment facility, and the people distribution information analysis means analyzes the distribution of people with target attributes related to the distribution of the analysis target point by time based on demographic information, and the superimposed display generation means generates information for superimposing and displaying the results of the analysis by the people distribution information analysis means by time period.

[0045] Preferably, the predetermined facility is a commercial facility, and the information generated by the superimposed display generating means indicates locations where new stores of the commercial facility are available for opening.

[0046] Preferably, the demographic information includes information on the type of stay that people have in a specified area, and the person distribution information analysis means estimates the distribution of people in the specified area according to the type of stay, and the type of stay is information that identifies residents of the specified area, workers in the specified area, and people who do not belong to either the resident or worker category in the specified area. [Effects of the Invention]

[0047] According to the present invention, it is possible to estimate people's lifestyle information, people flow, and people distribution related to an analysis area around a specific point designated by a user at a given time, so that the estimated lifestyle information and people flow information can be used in a variety of ways by businesses, researchers, government officials, and others.

[0048] Furthermore, according to the present invention, it is possible to estimate the distribution and movement of people related to an area to be analyzed around a specific point designated by a user based on demographic information. [Brief explanation of the drawings]

[0049] [Figure 1] 1 is a diagram showing an overview of a lifestyle information estimation system according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a functional block diagram showing a system overview of a lifestyle information service providing server 2000. [Figure 3] FIG. 2 is a diagram illustrating the hardware configuration of a lifestyle information service providing server 2000. [Figure 4] FIG. 10 is a diagram illustrating the contents of data provided by data providing servers 5000.1 to 5000.M. [Figure 5] FIG. 10 is a first diagram illustrating the processing flow of the life information service providing server 2000. [Figure 6] FIG. 10 is a second diagram illustrating the processing flow of the life information service providing server 2000. [Figure 7] FIG. 2 is a diagram showing an outline of data output from a calculation device 2100. [Figure 8] FIG. 10 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a first time point. [Figure 9] FIG. 10 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a second time point. [Figure 10] FIG. 10 is a diagram illustrating the concept of processing for improving the accuracy of spatial resolution in the behavior-specific number of people estimation processing (S120, S210). [Figure 11] FIG. 10 is a diagram showing another example of a map image and a superimposed image of the number of people distribution output by the map output unit 2104. [Figure 12] FIG. 10 is a functional block diagram for explaining the configuration of a people flow information service providing server 2000′ according to a second embodiment. [Figure 13] 10 is a flowchart for explaining the processing of a people flow information service providing server 2000′ according to the second embodiment. [Figure 14] 14 is a flowchart for explaining the processing of analyzing people distribution information in step S50 of FIG. 13. [Figure 15] FIG. 1 is a first conceptual diagram showing an aspect in which an analysis target area is designated on a digital map. [Figure 16]FIG. 2 is a second conceptual diagram showing an aspect in which an analysis target area is designated on a digital map. [Figure 17] FIG. 2 is a conceptual diagram for explaining the data structure of a digital map. [Figure 18] 18 is a conceptual diagram showing an example of building information stored in the building information DB shown in FIG. 17. FIG. [Figure 19] FIG. 10 is a conceptual diagram showing a state in which an analysis area is set around an analysis point on an actual map. [Figure 20] FIG. 10 is a diagram showing an analysis area that is set when the analysis point is set to a "bus stop" in Osaka city. [Figure 21] FIG. 10 is a diagram illustrating a comparison between an analysis target area and a distribution of people. [Figure 22] FIG. 1 is a diagram showing the distribution of analysis target areas for predetermined facilities distributed in a predetermined region. [Figure 23] FIG. 1 is a diagram showing the distribution of analysis target areas for predetermined facilities distributed in a predetermined region. [Figure 24] 11 is a flowchart for explaining the processing of a people flow information service providing server 2000′ according to the third embodiment. [Figure 25] This is a heat map showing the distribution of people per area mesh (500m square) within a specified area, taking into account visitor attributes. [Figure 26] This is a heat map showing the distribution of people per area mesh (500m square) within a specified area, taking into account visitor attributes. [Figure 27] FIG. 1 is a diagram showing an overview (1) of conventional mobile spatial statistics. [Figure 28] FIG. 1 is a diagram showing an overview (2) of mobile spatial statistics of the prior art. [Figure 29] FIG. 1 is a diagram showing an overview (3) of mobile spatial statistics of the prior art. [Figure 30] FIG. 1 is a diagram showing the contents of the Basic Survey on Social Life in the Background Art. DETAILED DESCRIPTION OF THE INVENTION

[0050] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0051] Below, we will explain the configuration of an analysis system for estimating people's lifestyle information, people flow, and people distribution in an area related to an analysis target area surrounding a specific point specified by a user, and an analysis device for analyzing people flow information in the analysis system.

[0052] As described below, the analysis device in this embodiment is a device that estimates and analyzes people flow information, for example, people distribution information, during a specified time period related to an analysis area surrounding a specific type of location specified by a user, based on demographic information for the specified area.

[0053] Here, "areas related to the area to be analyzed" refers to the area to be analyzed itself or a specified area that serves as the background for setting the area to be analyzed (for example, an administrative district specified by the user). [Embodiment 1]

[0054] FIG. 1 is a diagram showing an overview of a lifestyle information estimating system according to a first embodiment of the present invention.

[0055] As will be described later, the configuration of the lifestyle information estimation system is a prerequisite for the configuration and operation of the analysis device and analysis system.

[0056] Referring to FIG. 1, a lifestyle information estimation system 1000 according to a first embodiment of the present invention includes a lifestyle information service providing server 2000 that operates as a lifestyle information estimation device, a lifestyle information user terminal 3000 of a user who receives services from the lifestyle information service providing server 2000, data providing servers 5000.1 to 5000.M (M: natural number) that provide the lifestyle information service providing server 2000 with data necessary for estimating lifestyle information as described below, a building information registration terminal 4000 that registers building information as described below, and a mobile phone or smartphone 200 that is used by a user 100 and receives services as described below.

[0057] Here, the building information registration terminal 4000 is a terminal for a building owner or manager to register building information (such as floor plans and 2D or 3D polygon data). Visitors to the building may also register information such as photos of the interior of the building. In this case, the building information registration terminal 4000 may be a mobile terminal such as a smartphone with a photography function. For example, in the case of a smartphone, the lifestyle information service providing server 2000 may be configured to receive notifications from users who use a terminal that has installed thereon application software having an input format that notifies the user of the building name, number of floors, and area information (such as store name and type) along with the captured photo.

[0058] FIG. 2 is a functional block diagram showing an outline of the system of the life information service providing server 2000. As shown in FIG.

[0059] 4 is a diagram for explaining the contents of data provided by the data providing servers 5000.1 to 5000.M. Note that hereinafter, when the data providing servers 5000.1 to 5000.M are collectively referred to, they will simply be referred to as "data providing servers 5000."

[0060] Referring to FIG. 2, the life information service providing server 2000 includes a computing device 2100 and a storage device 2300. The computing device 2100 and the storage device 2300 include: a processor 2100;

[0061] The storage device 2300 first stores predetermined "demographic information 2302." Here, the "demographic information 2302" includes at least information on the number of people present in a predetermined area at a predetermined time.

[0062] Here, "predetermined time" refers to, for example, a first predetermined time interval, for example, every hour, and "predetermined area" refers to, for example, an area in a specific region, for example, a first area mesh unit, for example, a 500m mesh (500m square). "Predetermined demographic information" refers to "demographic data" and "aggregated data," and corresponds to, for example, the aforementioned "Mobile Spatial Statistics Information (registered trademark)," as shown in Figure 4.

[0063] Furthermore, the storage device 2300 stores predetermined "lifestyle information 2306." The "lifestyle information 2306" includes at least information related to a person's life. Here, the predetermined "lifestyle information 2306" is, more specifically, information on a classification of predetermined activities by region, age, and gender, at a second time interval that is shorter than a first predetermined time interval and is preferably a divisor of the first time interval. Here, the "lifestyle information" corresponds to, for example, information from a database based on the Ministry of Internal Affairs and Communications' "Basic Survey on Social Life," which classifies activities into 20 predetermined types by region, age, and gender, and in 15-minute increments, as shown in FIG. 4. Here, the "categorization of activities" corresponds to, for example, categories such as "sleeping," "commuting," "school commuting," "working," and "scholarship."

[0064] The storage device 2300 also stores "employment information by occupation 2308." Here, the "employment information by occupation 2308" is data indicating the employment ratio of each occupation by age and sex, and corresponds to, for example, the information in the Ministry of Internal Affairs and Communications' Labor Force Survey Database, as shown in FIG. 4.

[0065] The "predetermined lifestyle information" may include such "occupation-specific employment information."

[0066] Therefore, using the "demographic information 2302" and the "predetermined lifestyle information 2306," the lifestyle information estimation unit 2110 can estimate the number of people by activity (hereinafter referred to as "number of people by activity") present in the "predetermined area" set by the area setting unit 2106 in response to a user's instruction during the "second time interval." Furthermore, by applying the "employment information by occupation" to the "number of people by activity" estimated in this way for people with activity categories such as work and study, the lifestyle information estimation unit 2110 can calculate data on the "occupation" classification of people present in the "predetermined area" during the "second time interval." The "occupation" does not only refer to so-called "type of occupation," but also to "work" such as "study" that is not business but in which a person is explicitly engaged in the same activity for a certain period of time or more in a certain place (facility or building).

[0067] Although the lifestyle information 2306 is described as a "predetermined behavior classification" at a second time interval that is shorter than the first time interval and preferably a divisor of the first time interval, the second time interval may be, for example, the same as the first time interval. For example, a "predetermined behavior classification" may be coarsely estimated at the first time interval, and lifestyle information, people flow, and people distribution may be presented to the user. After that, a "predetermined behavior classification" may be estimated at a finer "second time interval" in response to a user's instruction, and lifestyle information, people flow, and people distribution may be presented to the user.

[0068] The storage device 2300 further stores predetermined "area location information 2310." Here, "predetermined area location information" refers to "information on predetermined locations (including information on the occupations at those locations) that exist in the above-described predetermined area." For example, the "area location information" preferably includes information on buildings and facilities within the second area mesh unit, and information on the occupations performed within those buildings and facilities, for an area of ​​a second area mesh unit that is smaller than the above-described first area mesh unit, preferably a rectangle whose sides are divisors of the first area mesh unit. For example, as shown in FIG. 4, the "area location information 2310" corresponds to map data in 62.5-meter mesh units, including information on city buildings and facilities, information on the occupations performed within those buildings and facilities, and information on roads and water areas. Some of this "map data" is provided by private companies.

[0069] Based on this area location information 2310, the area information estimation unit 2108 estimates the area corresponding to each job type in the total floor area of ​​facilities and buildings located in the "predetermined location" of the "predetermined area" set by the area setting unit 2106. Note that this estimation is preferably performed in units of second area meshes. However, depending on the user's settings, this estimation can also be performed in units of first area meshes, each side of which is a multiple of the second area mesh.

[0070] Therefore, the lifestyle information estimation unit 2110 of the calculation device 2100 of the lifestyle information service providing server 2000 calculates and outputs information on the work of people who are located at a specified location in a specified area at the second time interval (hereinafter referred to as "people's area lifestyle information") by expanding (for example, by proportional allocation) the "number of people per occupation" into the "area corresponding to each occupation" of the "specified location."

[0071] For example, when calculating such "human area life information," the classification of "tasks" that people are engaged in at a certain location (facility / building) within the target area is calculated according to the method described above.

[0072] The relationship between predetermined lifestyle information and predetermined area location information can be assumed to be, for example, that during a certain time period, doctors and nurses work at hospital facilities, farmers work on farmland, fishermen work at fishing grounds, housewives raise children, or pensioners live in homes, etc. Specific processing of this will be described later.

[0073] Furthermore, the map output unit 2104 generates and outputs a screen that displays the estimated "people's area life information" in association with each "predetermined place" in the "predetermined area" (for example, superimposed on the map information). By generating such an output, the user can visually grasp the life information of people in the "predetermined area" that they have specified.

[0074] As explained above, since the "number of people by activity" or the "number of people corresponding to people's area life information" is calculated for each area in the second area mesh unit and at the second time interval, the people flow path information estimation unit 2102 can be configured to track changes in the number of people distribution for the second area mesh unit area and its surrounding areas along the time axis and integrate this with "information on routes that people can move along" (e.g., roads, railways, etc.) on the map data, thereby estimating and outputting people flow path information (the routes along which people moved). The output result of the people flow information output unit 2102 is stored in people flow path information 2312 in the storage device 2300.

[0075] In addition, by associating information on people flow with information on traffic routes, the people flow path information estimation unit 2102 can also estimate information on the amount of people moving along each traffic route, and stores the estimation results in the storage device 2300 as traffic route information 2304.

[0076] FIG. 3 is a diagram illustrating the hardware configuration of the life information service providing server 2000. As shown in FIG.

[0077] 3, the life information service providing server 2000 may be configured such that an internal arithmetic unit (CPU: Central Processing Unit) executes arithmetic processing, or such that part of the program processing is executed on another server. In the following description, it is assumed that an internal arithmetic unit executes arithmetic processing.

[0078] Referring to FIG. 3, the server 2000 includes a computer device 2010, a network communication unit 2012 for communicating with a network, a recording medium (e.g., a memory card) 2210 for recording data from the outside and providing the data to the computer device 2010, a keyboard 2400 as an input device, and a display 2420 as a display device.

[0079] For example, a USB memory, a memory card, or an external storage device can be used as recording medium 2210. Also, for example, a wired LAN or wireless LAN communication function can be used as network communication unit 2012. Network communication unit 2012 and input / output interface 2090 constitute a communication interface.

[0080] As shown in FIG. 3, the computer main body constituting this computer device 2010 includes, in addition to a disk drive 2030 and a memory drive 2020, a CPU (Central Processing Unit) 2100, memory 2200 including a ROM (Read Only Memory) 2200.1 and a RAM (Random Access Memory) 2200.2, each connected to a bus 2050, a nonvolatile rewritable storage device 2300, and an input / output interface 2090 for communicating via a network and exchanging data with external devices. The nonvolatile storage device 2300 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD). The following description will be given assuming that it is an SSD. An optical disk can be inserted into the disk drive 2030. A memory card 2210 can be inserted into the memory drive 2020.

[0081] When the computer device 2010 programs run, the data and programs that store the information that is the basis for the computer's operation will be described as being stored in the SSD 2300.

[0082] 3, the medium capable of recording information such as a program to be installed in the computer main body may be, for example, a DVD-ROM (Digital Versatile Disc), a memory card, a USB memory, etc. To accommodate such cases, the computer main body is provided with a drive device (memory drive 2020, disk drive 2030) capable of reading these media.

[0083] The main components of the computer device 2010 are computer hardware and software executed by the CPU 2100. Generally, such software is stored in a storage medium and distributed or distributed via a network, and is obtained via the disk drive 2030 or the network communication unit 2012 and temporarily stored in the SSD 2300. The software is then read from the SSD 2300 into the RAM 2200.2 in the memory and executed by the CPU 2100. Note that when connected to a network, the software may be directly loaded into the RAM and executed without being stored in the SSD 2300.

[0084] When distributing a program for functioning as computer system 2010, the program does not necessarily include an operating system (OS) that causes computer system 2010 to execute functions such as an information processing device. The program only needs to include instructions that call appropriate functions (modules) in a controlled manner and achieve the desired results. How computer system 2010 operates is well known, and a detailed description thereof will be omitted.

[0085] Furthermore, the CPU 2100 may be a single-core processor or a multi-core processor. That is, it may be a single-core processor or a multi-core processor. The server 2000 may also be configured with multiple servers to perform distributed processing.

[0086] The hardware configurations of the lifestyle information user terminal 3000 and the data providing servers 5000.i (1≦i≦M) are basically the same, and therefore the description thereof will not be repeated.

[0087] Fig. 5 is a first diagram illustrating the processing flow of the life information service providing server 2000. Fig. 6 is a second diagram illustrating the processing flow of the life information service providing server 2000.

[0088] FIG. 7 is a diagram showing an outline of data output from the arithmetic device 2100. As shown in FIG.

[0089] FIG. 8 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number-of-people estimation at a first time point.

[0090] FIG. 9 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a second time point.

[0091] 5 and 6, when the process starts, the calculation device 2100 acquires spatial statistical data via the network (S110, S200).

[0092] As mentioned above, spatial statistical data can be obtained using Mobile Spatial Statistics (registered trademark) provided by NTT Docomo. Spatial statistical data is, for example, data on the number of people by age and sex within a mesh of a predetermined width (for example, 500m square) for a predetermined time period (for example, 1 hour).

[0093] Next, for a "predetermined area" designated by the area setting unit 2106 based on the user's specification, the lifestyle information estimation unit 2110 classifies people (whose age and gender are obtained from the spatial statistical data) who are staying in a specific area (for example, the Umeda area of ​​Osaka) during a specific time period (for example, a specific hour) into states (behavior classification information) such as work and movement (for example, commuting) in more detailed time units (for example, 15 minutes) within this specific time period, referring to the Basic Survey on Social Use and Leisure Activities database, and estimates the number of people (attributes of age and gender) in each category (S120, S210).

[0094] The bottom part of Figure 7(a) shows an overview of this type of behavior-specific number estimation.

[0095] The lower graph in Figure 8 shows the estimated number of people by activity in a specific area at 2 a.m. Judging from the time of day, most people's activity is classified as "sleeping."

[0096] The bottom graph in Figure 9 shows the estimated number of people by activity in a specific area at 3:00 PM (3:00 PM). Judging from the time of day, most people are classified as "working" or "studying."

[0097] Returning to Figures 5 and 6, the lifestyle information estimation unit 2110 further estimates the number of employees (age and gender attributes) by occupation type for every 15 minutes for the specific area based on the estimated number of people (age and gender attributes) for each behavior category, for example, based on the Ministry of Internal Affairs and Communications Labor Force Survey database (S130, S220).

[0098] Next, the area location information estimation unit 2108 distributes (for example, proportionally allocates) the estimated number of employees (attributes of age and gender) by occupation every 15 minutes to each building (facility) on the map based on the total floor area of ​​the facility / building and the area allocated to the occupation of each facility / building, and estimates aggregated data of the number of people in a mesh (for example, a 62.5 m mesh) that is smaller than the unit mesh of the spatial statistical data (S140, S230).

[0099] The upper part of Figure 7(a) shows an overview of such facility occupancy estimation.

[0100] The upper diagram in Figure 8 shows the results of facility population estimation in a specific area at 2 a.m. Judging from the time of day, most people are located in "residences" or "hotels and inns."

[0101] The upper graph in Figure 9 shows the results of facility population estimation in a specific area at 3:00 PM (15:00). Based on the time of day, it can be seen that most people in each facility are assigned to the "occupation" or "task" that corresponds to the facility or building.

[0102] 5 and 6, the map output unit 2104 further superimposes the number of people displayed for each facility or building on a map and outputs the number of people by color-coding it using, for example, a heat map format. Here, when displaying the heat map, a chart showing the proportion of people performing tasks at that location may be displayed by color-coding it.

[0103] FIG. 7(b) is a diagram showing an example of an output that visualizes such a population distribution.

[0104] FIG. 10 is a diagram showing the concept of the process for improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210).

[0105] As shown in Figure 10, when conducting analysis using various information such as spatial statistical data (or people flow data) and map information, some data may have a spatial resolution of 500m, other data may have a spatial resolution of 100m, and still other data may have a spatial resolution of land lot number (address).

[0106] Because the units of the data do not match, analysis usually ends up being performed based on data with larger units.

[0107] However, as mentioned above, in the life information service providing server 2000, people flow data (including population distribution data at a specific time point), building data, facility data, land data (including information on roads, railways, roads and water areas), etc. are all decomposed and converted into the same unit (estimated area mesh) and stored.

[0108] Such a database stored in the same unit will be referred to as a "data lake." Therefore, for example, it is possible to perform real-time analysis at any point in Japan using the same unit (estimated area mesh).

[0109] As described above, the same estimated area mesh is a mesh with one side measuring 62.5 m, as shown in FIG.

[0110] If the distance is set to 62.5 m, since Mobile Spatial Statistics (registered trademark) uses a mesh with sides of 500 m, it is possible to create an area mesh with sides of 1 / 8 (1 / 8) of this power of 2, which is approximately the distance a person walks in one minute. When using other spatial statistics, it is also possible to unify the estimated area mesh with sides equivalent to the distance a person walks in one minute in a similar manner. By using the distance a person walks in one minute, it can be said that this resolution is suitable for analyzing people flow, including people who are walking, for example.

[0111] However, it is possible to divide it into units smaller than 62.5 m (for example, the smallest unit of 1 m). However, this would increase the amount of data to be analyzed and the amount of calculation, so from the perspective of cost-effectiveness, it is desirable to use, for example, 62.5 m.

[0112] FIG. 11 is a diagram showing another example of a map image and a superimposed image of the number of people distribution output by the map output unit 2104. In FIG.

[0113] As shown in Figure 11, it is possible to designate an area of ​​a certain size as the predetermined area, and then allow the user to further designate a portion of that "predetermined area" in which the estimation process will be performed. [Embodiment 2]

[0114] 12 is a functional block diagram for explaining the configuration of a people flow information service providing server 2000' according to Embodiment 2. The people flow information service providing server 2000' corresponds to the above-mentioned "analysis device."

[0115] The hardware configuration of people flow information service providing server 2000' is the same as that described with reference to FIG. 3, and therefore description thereof will not be repeated.

[0116] In this specification, "people flow information" refers to information that expresses the "pattern of people movement" over a specified period of time, and includes, for example, estimated people distribution information regarding the number of people staying in an analysis area surrounding a specific point designated by a user and in a specified area including the analysis area, for set people attributes at a specific time or time period. By tracking changes in such people distribution information over time, it is possible to estimate information representing people flow. Note that "people attributes" here include, but are not limited to, age, generation, gender, and occupation classification.

[0117] Regarding the people flow information service providing server 2000', the same components as those of the life information service providing server 2000 in the first embodiment are designated by the same reference numerals, and in principle, description thereof will not be repeated.

[0118] 12 , the arithmetic device 2100 of the people flow information service providing server 2000′ according to the second embodiment includes, as a function executed in accordance with a program, a people distribution information analysis unit 2103 instead of the people flow path information estimation unit 2102. The arithmetic device 2100 further includes, as functions thereof, a point distribution information acquisition unit 2112 that acquires information on the positions of analysis target points of a type designated by a user from map information 2314 (described later), an area setting unit 2107 that sets an analysis target area for each analysis target point within a mesh area within a predetermined range surrounding the analysis target point, an attribute designation unit 2114 that receives designation of attributes of people to be analyzed within the predetermined area, and a superimposed display generation unit 2116 that generates information for superimposing and displaying the analysis target areas set by the area setting unit 2107 on the distribution of people with the designated attributes estimated and analyzed by the people distribution information analysis unit 2103.

[0119] In addition, the attributes of people accepted by the attribute specification unit 2114 may include not only the attributes of the person themselves, such as "age," "gender," "residential area," and "occupation (job)," but also information such as the date, time, and time period to be analyzed by the people distribution information analysis unit 2103.

[0120] The people distribution information analysis unit 2103 will be described later.

[0121] The area setting unit 2106 has a function of specifying a "predetermined area (a specific area as the largest area for analysis)" by specifying, for example, a building, administrative district, etc. In contrast, the area setting unit 2107 has a function of setting an analysis area for each analysis point within a mesh area within a predetermined range around the position of the analysis point of the type specified by the user.

[0122] Here, the attribute specification unit 2114, which is a function executed by the calculation device 2100, accepts the specification by the user of target attributes to be analyzed from among the attributes of people, and the people distribution information analysis unit 2103 obtains information on the attributes of people within a specified area or analysis area from demographic information.

[0123] The "predetermined range around the analysis target point" refers to a region within a trajectory of a predetermined distance centered on the analysis target point corresponding to the type specified by the user. The "predetermined distance" here is a variable quantity depending on the target attributes of the person specified by the user. For example, if age is the target attribute, the walking distance differs between people in their 20s and 30s and people in their 70s and older, so the radius size may be changed. Furthermore, the "predetermined distance" may be calculated based on the person's attributes and the travelable distance per unit time corresponding to the mode of transportation corresponding to the predetermined area. For example, if there is a train station within the predetermined area, the distance may be within 30 minutes of travel by train. Alternatively, if the person's attributes include car ownership, the distance from the analysis target point may be within 20 minutes of travel by car. Without being limited to these examples, the "predetermined distance" refers to the "travelable distance" per predetermined unit of time (e.g., a user-specified time, such as 10 minutes, 20 minutes, or 1 hour) that takes into account factors such as road specifications (general roads, expressways), road width (number of lanes on each side), and transportation methods (walking, elevation change, bicycles, mobility scooters, buses, community buses, flat-rate taxis, taxis, etc.). In this sense, the "predetermined distance" refers not only to a physical distance but also to a virtual distance that should be called the "time distance" that can be traveled within a unit of time. Furthermore, if a transportation method has a congestion level for each time period being analyzed, a coefficient based on the congestion level may be applied to the travel time. In this case, for example, the road congestion level can be calculated by taking into account not only 2D information but also 3D information such as overpasses.

[0124] For example, the "analysis target point" is a "predetermined facility" designated by type, and may be any of transportation facilities (stations, bus stops, etc., meaning "places where people can get on and off transportation"), public institutions (including not only government offices but also hospitals), commercial facilities, or entertainment facilities. In this case, the "area within the track" corresponds to the range within which people can use these facilities by transportation, depending on their attributes.

[0125] Furthermore, the storage device 2300 of the people flow information service providing server 2000' stores map information 2314 in the form of a digital map, as will be described later.

[0126] From the above, the people flow information service providing server 2000′ includes a storage device 2300 that stores predetermined demographic information, including information on the number of people located in each of a plurality of predetermined area meshes ("area meshes" in spatiotemporal statistics, which may be, for example, 500 m square or "estimated area meshes (for example, 62.5 m square)") in a predetermined area (for example, an "administrative district" designated by a user) in a time series of a series of time units. The calculation device 2100 then performs analysis of people distributed within the predetermined area. The calculation device 2100 executes functions including a point distribution information acquisition unit 2112 that acquires the distribution of analysis target points within the predetermined area according to the type of analysis target point designated by the user within the predetermined area; an area setting unit 2107 that sets an analysis target area for each analysis target point within the area meshes within a predetermined range surrounding the plurality of analysis target points corresponding to the type; and a people distribution information analysis unit 2103 that extracts, estimates, or analyzes information on the number of people distributed in the predetermined area based on the demographic information.

[0127] The person distribution information analysis unit 2103 estimates the distribution of people's attributes within the analysis target area based on the classification and aggregated data of people's behavior estimated in the estimated area mesh that exists within the analysis target area.

[0128] Furthermore, although not limited to this, similarly to the first embodiment, it is desirable that the lifestyle information estimation unit 2110 not only performs estimation in a first time unit based on demographic information and predetermined lifestyle information, but also estimates the number of people for each category of human behavior present in the area mesh included in the analysis target area in a second time unit that is shorter than the first time unit.

[0129] FIG. 13 is a flowchart for explaining the processing of the people flow information service providing server 2000′ according to the second embodiment.

[0130] Referring to FIG. 13, in the people flow information service providing server 2000′, first, the point distribution information acquisition unit 2112 accepts the setting of the type of point to be analyzed after the user sets a predetermined area through operation by the user (S10).

[0131] Next, the point distribution information acquisition unit 2112 acquires information on the positions of the analysis target points within the predetermined area from the map information 2314 (S20).

[0132] Next, the attribute specification unit 2114 accepts and sets the specification of the attributes of the person to be analyzed (gender, age, date and time of analysis, time period, etc.) (S30).

[0133] Next, the area setting unit 2107 sets an analysis area for each analysis point (S40).

[0134] Based on the above settings, the people flow distribution information analysis unit 2103 executes an analysis of the people distribution information (S50). The analysis of the people distribution information will be described later.

[0135] The map output unit 2104 or the superimposed display generation unit 2116 generates information for displaying the analysis target area and the people distribution information, and outputs data for display on the user terminal 3000 (S60).

[0136] FIG. 14 is a flowchart for explaining the process of analyzing the people distribution information in step S50 of FIG.

[0137] Referring to Figure 14, the people distribution information analysis unit 2103 of the people flow information service providing server 2000' sets the predetermined area (specific area) and the area to be analyzed set by the area setting unit 2107 in accordance with the user's instructions as the premise for analyzing the people distribution information (S102).

[0138] The subsequent processing from S110 to S140 is similar to the processing of the life information service providing server 2000 in the first embodiment shown in FIG. 5, so the same parts are given the same reference numerals and description thereof will not be repeated.

[0139] Finally, the people distribution information analysis unit 2103 estimates the distribution of people's attributes within the analysis area based on the classification and aggregated data of people's behavior estimated in the estimated area mesh existing within the analysis area, and calculates the number of people distribution for each specified attribute within the analysis area (S152).

[0140] FIG. 15 is a first conceptual diagram showing an aspect in which an analysis target area is designated on a digital map.

[0141] As shown in Fig. 15, a predetermined area RM is displayed as a digital map. At this time, roads Rd and rivers RV are displayed on the digital map, and here, as shown by circles, analysis target areas are set at analysis target points DR01 to DRN, respectively.

[0142] 15, the analysis target point is assumed to be, for example, a "bus stop," though it is not particularly limited thereto. The radius r1 of the circle indicating the analysis target area is assumed to be, for example, a distance that can be reached on foot within 10 minutes by an average person aged 60 or older.

[0143] FIG. 16 is a second conceptual diagram showing an aspect in which an analysis target area is designated on a digital map.

[0144] As shown in FIG. 16, the analysis target area DR is also set at each of the analysis target points DR01 to DRN, as indicated by the circle.

[0145] Here again, although not particularly limited, the analysis target point may be, for example, a "bus stop." The radius r2 of the circle representing the analysis target area is assumed to be the distance that an average person in their twenties can reach on foot within 10 minutes.

[0146] In this way, the analysis area indicates the range of travel possible to each analysis point within a specified time by a specified means of transportation, depending on the attributes of the person being analyzed.

[0147] FIG. 17 is a conceptual diagram for explaining the data structure of a digital map.

[0148] As shown in FIG. 17, the digital map data consists of multiple layers.

[0149] For example, the map information stored as "management data" (not shown) includes the secondary mesh code, base map used, geomagnetic declination, actual distance of the corner edges, update date of each data, number of records for each data, number of items for each data, etc.

[0150] For example, layer 1 is road information, and is stored in the map information 2314 as a road information DB.

[0151] Furthermore, for example, the road information may be divided into "basic road data" and "minor road data."

[0152] Here, "basic road data" is not particularly limited, but includes, for example, "basic road node data," "basic road link data," "basic road link attribute data," etc. Also, "narrow road data" is not particularly limited, but includes, for example, "narrow road node data," "narrow road link data," "narrow road link attribute data," etc.

[0153] Here, "node data" (a collective term for "basic road node data" and "minor road node data") includes node numbers, position coordinates, altitude, node types, number of connecting links, connecting node numbers, etc. In addition, "basic road node data" also includes intersection names.

[0154] "Link data" (a collective term for "basic road link data" and "minor road link data") includes the link number (start and end node number), road administrator, road type, administrative district code, link length, width classification, number of lanes, and location coordinates and elevation of interpolation points. "Basic road link data" further includes route number, roadway width, road census data (median lane width, 12-hour traffic volume, travel speed (peak hours), traffic regulations such as speed limits), location coordinates and elevation of interpolation points, emergency transportation road classification, and expressway numbering. "Minor road link data" further includes the corresponding basic road link number.

[0155] "Link attribute data" (collectively "basic road link attribute data" and "minor road link attribute data") includes the location and name of link attributes (bridges, overpasses, tunnels, caverns, railroad crossings, footbridges, toll booths, underpasses, areas expected to be flooded, etc.).

[0156] Therefore, road information generally consists of node data for identifying the positions of road intersections, link data indicating the types of roads connecting the nodes, and link attribute data indicating the type of link.

[0157] Layer 2 may be, for example, building information as described below, layer 3 may be, for example, contour lines, layer 4 may indicate water bodies (rivers, lakes, ponds, etc.), and layer 5 may indicate administrative divisions (prefectural borders, city / ward / town / village boundaries, block boundaries, etc.).

[0158] The digital map may also have other layers containing information on the map.

[0159] The road information may be based on the contents of the following database. https: / / www.drm.jp / database /

[0160] Furthermore, the information on administrative boundaries may be structured in accordance with the Geospatial Information Authority of Japan database.

[0161] FIG. 18 is a conceptual diagram showing an example of building information stored in the building information DB shown in FIG.

[0162] The building information is a collection of information about each building, facility, or location (such as a park) that has the following configuration, for example.

[0163] In the following, an example will be described in which the building information is information about a specific building.

[0164] Information on individual buildings consists of the building ID, building name, latitude and longitude of the building's representative point, building use, building height (height from the reference point), number of floors (underground, above ground), total floor area, and floor plan information for each floor.

[0165] The "representative point of the building" is, for example, the position of the entrance to the building.

[0166] The "reference point of height" is, for example, the ground surface.

[0167] The "floor plan information for each floor" includes, but is not limited to, the identification number FPID of each floor, the use of the areas on that floor (parking lot, store (type of store), office, etc.), the area of ​​each area, and the attributes of each area by time period (residential facility, commercial facility, tourist facility, accommodation facility, etc.).

[0168] In other words, in building information (including facility information and location information), if the same building (facility, location) has different attributes (uses) depending on the date, time, and time period, the building information may be configured to specify the respective attributes for each date, time, and time.

[0169] The building information also includes two-dimensional or three-dimensional polygon information (not shown) of the building in association with the building ID.

[0170] 2D polygon information is a representation of the shape of a building or house using polygons in a GIS (geographic information system). 3D polygon information is a representation of the shape of each floor of a building or house, as well as the outer shape of the building or house, in the height direction.

[0171] Furthermore, the specifications for indoor information are not particularly limited, but may conform to the following "3D Indoor Geospatial Information Data Specification (Draft)," for example. https: / / www.gsi.go.jp / common / 000212582.pdf

[0172] As described in the first embodiment, the building owner or manager may register building information (such as floor plans and 2D or 3D polygon data) via the building information registration terminal 4000. Alternatively, the building information registration terminal 4000 may be configured so that visitors to the building register information such as photos of the interior of the building as building information using a mobile terminal such as a smartphone with a photography function. For example, in the case of a smartphone, the people flow information service providing server 2000' may receive notifications from users who use a terminal on which application software having an input format that notifies the user of the building name, number of floors, and area information (such as store name and type) along with the photos they have taken has been installed.

[0173] The people flow information service providing server 2000′ may be configured to store building information as the map information 2314 in the storage device 2300.

[0174] As described above, the map information 2314 is a database that stores polygon information of buildings and facilities used when estimating and analyzing people distribution, and the building information registration terminal 4000 accepts registration of building information (including 2D or 3D polygon information) into the database. Because buildings and the like are constantly being updated, it is desirable to have a configuration that allows polygon information to be provided from outside.

[0175] FIG. 19 is a conceptual diagram showing a state in which an analysis target area is set around an analysis target point on an actual map.

[0176] In FIG. 19, the area to be analyzed is set to be an area that a man in his twenties can walk for x minutes (for example, 10 minutes) around a point to be analyzed (for example, a bus stop).

[0177] For example, if there is a difference in elevation around the point to be analyzed, when considering the distance of x minutes on foot, if the difference in elevation is large, a predetermined coefficient according to the difference in elevation may be applied so that the distance becomes shorter. In other words, the predetermined coefficient is set so that the greater the difference in elevation, the shorter the distance becomes.

[0178] FIG. 20 is a diagram showing an analysis area that is set when the analysis point is set to a "bus stop" in Osaka city.

[0179] In other words, the system calculates the distance that can be traveled within a specified time from bus stops on all bus routes that pass through Osaka City based on travel speeds for each age and gender, and then uses the calculated distance to draw a circle centered on the bus stop.

[0180] For example, FIG. 20(a) is a diagram showing an analysis target area based on distances calculated for a man in his 60s, and FIG. 20(b) is a diagram showing an analysis target area based on distances calculated for a man in his 20s.

[0181] FIG. 21 is a diagram showing a comparison between the analysis target area and the distribution of people.

[0182] Figure 21(a) is the analysis target area shown in Figure 20(a), and Figure 21(b) is a diagram showing the distribution of people of the same age in Osaka City. If the map output unit 2104 outputs display information so that Figure 21(a) and Figure 21(b) can be compared, or if the superimposed display generation unit 2115 outputs display information so that the information in Figure 21(a) and the information in Figure 21(b) are superimposed, it becomes possible to grasp the transportation gap zone VR (for bus transportation) by age and gender. In this case, attention will be paid to the distribution of people outside the analysis target area.

[0183] Alternatively, if the locations of public facilities such as hospitals are displayed superimposed with a specific mark in a specific color, it will be possible to evaluate information such as the extent to which hospitals and other facilities are covered within a public transportation area.

[0184] FIG. 22 is a diagram showing the distribution of analysis target areas for predetermined facilities distributed in a predetermined area.

[0185] Figure 22(a) shows a 10km circle drawn around a famous tourist spot in the Kinki region.

[0186] This type of display makes it possible to analyze the distribution and flow of people within the analyzed area within the visualized area, thereby making it possible to compare the level of tourist flow for each tourist destination.

[0187] Figure 22(b) shows a circle with a radius of 1 km drawn around an elementary school in Shizuoka City.

[0188] By comparing the distribution of this analysis area with the distribution of the number of elementary school students in Shizuoka Prefecture, it is possible to consider the establishment of new schools or the consolidation and closure of schools, and to visualize areas where it is difficult to commute to school.

[0189] FIG. 23 is a diagram showing the distribution of analysis target areas for predetermined facilities distributed in a predetermined region.

[0190] Figure 23(a) shows a 500m circle drawn around a hamburger chain restaurant in Osaka City.

[0191] This type of display makes it possible to visualize the regional coverage of chain stores and the like.

[0192] For example, it is possible to know the distribution of a target customer demographic within the area being analyzed during a specific business hour, which makes it possible to consider new store opening plans and stores that should be closed.

[0193] Figure 23(b) shows a circle with a radius of 1 km drawn around a fire station in one of Tokyo's 23 wards.

[0194] This type of display makes it possible to visualize the area that a fire station can cover and can be used as a reference for formulating disaster prevention plans.

[0195] Furthermore, as mentioned above, digital maps also contain information about the size (width) of roads, so it is possible to calculate, for example, the "road area ratio" in an area mesh (estimated area mesh). Because the size of an area mesh (estimated area mesh) is constant, it is possible to understand that areas with a large road area ratio have relatively wide roads, whereas areas with a small road area ratio have only narrow roads. For example, it is also possible to obtain reference information for planning the placement of fire engines of different sizes.

[0196] In addition, the people distribution information analysis unit 2103 can be configured to analyze the distribution of people with target attributes related to the distribution of the analysis target points by time based on demographic information, and the superimposed display generation unit 2116 can be configured to superimpose and display the results of the analysis by the people distribution information analysis unit 2103 by time period.

[0197] Alternatively, the specified facility that is the analysis target point may be any one of a transportation facility, a public institution, a commercial facility, or an entertainment facility, and the people distribution information analysis unit 2103 may analyze the distribution of people with target attributes related to the distribution of the analysis target point by time based on demographic information, and the superimposed display generation unit 2116 may generate information for superimposing and displaying the results of the analysis by the people distribution information analysis unit 2103 by time period.

[0198] Furthermore, if the predetermined facility that is the point to be analyzed is a commercial facility, the information generated by the superimposed display generating unit 2103 indicates possible locations for new commercial facility openings. Note that, in the case of entertainment facilities as well as commercial facilities, it is also possible to estimate possible locations for installing entertainment facilities. [Embodiment 3]

[0199] In the first and second embodiments, the distribution of people within a predetermined area to be analyzed is estimated in the following manner.

[0200] That is, the flow is roughly as follows:

[0201] i) For a predetermined area, "spatial statistical data" is obtained for each predetermined mesh and for each first time interval, which data is compiled based on information on mobile phone usage, etc.

[0202] ii) For people staying in a specific area (analysis target area) within a specified area during a first time interval, the Basic Survey on Social Life DB is referenced, and the people are classified into behavioral classification information for each second time interval that is shorter than the first time interval, and the number of people in each category is estimated.

[0203] iii) For the area to be analyzed, the number of people by occupation type for each second time interval is estimated by referring to the Ministry of Internal Affairs and Communications Labor Force Survey DB, etc.

[0204] iv) For the area under analysis, the number of people by occupation type for each second time interval is apportioned based on the total floor area of ​​each area of ​​buildings on the map and the information on the occupation types of each building, thereby estimating the distribution of people (aggregated data) for a mesh (area mesh) finer than the specified mesh of the spatial statistical data.

[0205] As described in the first embodiment, the first time interval and the second time interval may be the same.

[0206] However, this method of allocating personnel does not necessarily take into consideration the following points:

[0207] i) For example, if the behavior is classified as sleeping at night, residents in the analysis area should be preferentially allocated to residential properties, while those who are neither residents nor workers in the analysis area (temporary visitors) should be preferentially allocated to accommodation facilities (e.g., hotels) in the analysis area.

[0208] ii) Similarly, during the daytime on weekdays, workers in the analysis area should be preferentially allocated to buildings with attributes such as commercial facilities and business offices.

[0209] Therefore, in the third embodiment, it is assumed that priorities are set in advance in the people flow information service providing server 2000' so that an attribute called "stay type" is assigned to people who stay in the area to be analyzed during each time period, and the priority of the distribution of the number of people is changed according to the attributes of the time period and location.

[0210] Although not particularly limited, for example, the attribute "stay type" can be acquired from demographic data.

[0211] That is, as explained in FIG. 27 and FIG. 28, first, "Mobile Spatial Statistics (registered trademark)" includes information on residential areas.

[0212] 29, it is possible to extract and count the number of people for the area to be analyzed, including "residents" of that area and residents outside that area who regularly travel to the area to be analyzed on weekdays (workers). It is also possible to classify "people who are neither residents nor workers" who are in the area to be analyzed during a certain time period as "temporary visitors" and count them.

[0213] In this case, "temporary visitors" are tourists or shoppers, for example, so if there are entertainment or tourist facilities such as theme parks within the area being analyzed, it is possible to set up the system so that priority is given to distribution to those facilities.

[0214] In other words, when estimating the "distribution of the number of people in a specified area" during a "specified time period," the system is configured to allocate "temporary visitors" preferentially to such entertainment and tourist facilities.

[0215] Furthermore, since a "temporary resident" is defined as "someone who is neither a resident nor a worker in a designated area," the attributes of the "place of residence" before moving to the designated area are not necessarily used for "temporary resident."

[0216] For example, as a result of the concealment process in Figure 29, Mobile Spatial Statistics (registered trademark) executes a process in which "area meshes" where only a certain number of people or less are staying are excluded from the statistics. For this reason, if "residents of areas other than the specified area" who are staying in a "specified area" on a certain date and time are simply counted as "temporary visitors," there is a possibility that visitors who visit from areas where such a small number of people reside will be missed from the count. In this regard, the method of identifying "persons who are neither residents nor workers in the specified area" has the advantage of reducing the possibility of such omissions from the count, rather than classifying the people who make up the number of visitors as "residents of areas other than the specified area."

[0217] As described above, the attributes of buildings, facilities, or locations are registered in the map information 2314 of the lifestyle information service providing server 2000', and the area location information 2310 is set in advance with a priority of "stay type" when allocating the attributes of buildings, facilities, or locations (such as residences, commercial facilities, offices, accommodation facilities, entertainment and tourist facilities, etc.).

[0218] Fig. 24 is a flowchart for explaining the processing of the people flow information service providing server 2000' according to the third embodiment. Fig. 24 shows the processing flow of the third embodiment, which is compared with the processing of Fig. 14, based on the processing of Fig. 13 according to the second embodiment.

[0219] Referring to Figure 24, the people distribution information analysis unit 2103 of the people flow information service providing server 2000' sets the predetermined area (specific area) and the area to be analyzed set by the area setting unit 2107 in accordance with the user's instructions as the premise for analyzing the people distribution information (S102).

[0220] Next, the people distribution information analysis unit 2103 acquires spatial statistical data for a specified time or date for a predetermined area (S110), and also acquires information on stay attributes from the spatial statistical information (S112).

[0221] As mentioned above, spatial statistical data can be obtained using Mobile Spatial Statistics (registered trademark) provided by NTT Docomo. Spatial statistical data is, for example, data on the number of people by age and sex within a mesh of a predetermined width (for example, 500m square) for a predetermined time period (for example, 1 hour).

[0222] Furthermore, spatial statistical data (demographic data) is assumed to be data classified by "stay attribute" as described above. In this case, depending on the type of analysis, the stay attribute of people to be distributed to "specified buildings, facilities, and locations" within the "analysis area" or "specified area (specific area)" is also set. For example, if the specified building is a hotel, the stay attribute is set as "temporary resident."

[0223] Next, for the "predetermined area" designated by the area setting unit 2106 based on the user's specification, the lifestyle information estimation unit 2110 classifies people (whose age and gender are obtained from the spatial statistical data) who are staying in a specific area during a specific time period (for example, a specific hour) into states (behavior classification information) such as work and movement (for example, commuting) in more detailed time units (for example, 15 minutes) within this specific time period, referring to the Basic Survey on Social Life and Leisure Activities database, and estimates the number of people in each category (for example, age and gender attributes and stay attributes) (S120).

[0224] Furthermore, the lifestyle information estimation unit 2110 estimates the number of employees (age and gender attributes) by occupation type for every 15 minutes for the above-mentioned specific area based on the estimated number of people (age and gender attributes, stay attributes) for each behavior category, for example, based on the Ministry of Internal Affairs and Communications Labor Force Survey database (S130).

[0225] Next, the area location information estimation unit 2108 distributes (for example, proportionally apportions) the estimated number of employees by occupation every 15 minutes (by age and gender attributes and stay attributes) to each building (facility) based on the total floor area of ​​the facilities and buildings on the map, the area allocated to the occupation of each facility and building, and the attributes of each building (facility), and estimates aggregated data on the number of people in a mesh smaller than the unit mesh of the spatial statistical data (for example, a 62.5 m mesh) (S141).

[0226] Finally, the people flow information estimation unit 2103 estimates the distribution of people's attributes within the analysis area or a specified area based on the classification and aggregated data of people's behavior estimated in the estimated area mesh existing within the analysis area (S150).

[0227] When distributing the number of people in a mesh to each building (facility / place) according to the stay attributes, the people flow information estimation unit 2103 can also calculate the excess number of people for the building (facility / place) if an upper limit on the number of people that the building (facility / place) can accommodate is set. For example, such excess number of people can be distributed with the next highest priority to roads around the building (facility / place) or major transportation stations (S150).

[0228] Next, if the area location information estimation unit 2108 has not completed the process of distributing the number of people for all types of specified stay attributes (N in S160), it returns the process to step S120, and if the process of distributing the number of people for all types of specified stay attributes has been completed (Y in S160), it ends the process.

[0229] 25 and 26 are heat maps showing the distribution of the number of people in each area mesh (500m square) within a given area, taking into account the stay attributes.

[0230] Figure 25(a) shows the distribution of all people visiting the 500m mesh, while Figure 25(b) shows the distribution of only residents in the 500m mesh (e.g., people who regularly stay within that mesh overnight).

[0231] Figure 26(a) shows the distribution of workers (including students, for example, people who are in the same mesh regularly during the day) staying in a 500m mesh, and Figure 26(b) shows the distribution of temporary residents in a 500m mesh (people in the mesh who do not come to the mesh regularly, such as tourists or visitors from far away).

[0232] In this way, by estimating the distribution of people within the estimated area mesh while taking into account the "stay type," it is possible to estimate the distribution of people within the area to be analyzed with greater accuracy than in the first and second embodiments.

[0233] Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, etc. within the scope of achieving the object of the present invention are included in the present invention.

[0234] Furthermore, for example, the above-described series of processes can be executed by hardware or software.

[0235] In other words, the functional configurations shown in FIGS. 2 to 12 are merely examples and are not particularly limited.

[0236] That is, it is sufficient for the information processing system to have the function of executing the above-described series of processes as a whole, and the type of functional block used to realize this function is not limited to the examples in Figures 1 to 16. Furthermore, the locations of the functional blocks and databases are not particularly limited to those in Figures 1 to 16 and may be arbitrary. For example, at least some of the functional blocks and databases required to execute various processes may be transferred to a user terminal or the like. Conversely, the functional blocks and databases of the user terminal may be transferred to a server or the like.

[0237] Furthermore, one functional block may be configured as a single piece of hardware, a single piece of software, or a combination thereof.

[0238] When a series of processes is executed by software, the programs that make up the software are installed into a computer or the like from a network or a recording medium.

[0239] The computer may be a computer built on dedicated hardware.

[0240] The computer may also be a computer that can execute various functions by installing various programs, such as a server, a general-purpose smartphone, or a personal computer.

[0241] The recording medium containing such a program may be composed of not only a removable medium (not shown) that is distributed separately from the device main body in order to provide the program to users, etc., but also a recording medium that is provided to users, etc. in a state where it is pre-installed in the device main body.

[0242] In this specification, the steps describing the program to be recorded on the recording medium include not only processes that are performed chronologically in accordance with the order, but also processes that are not necessarily performed chronologically but are performed in parallel or individually.

[0243] In addition, in this specification, the term "system" refers to an overall device that is made up of a plurality of devices, a plurality of means, etc.

[0244] The embodiments disclosed herein are merely examples of configurations for specifically implementing the present invention, and do not limit the technical scope of the present invention. The technical scope of the present invention is defined by the claims, not by the description of the embodiments, and is intended to include modifications within the literal scope of the claims and within the scope of equivalent meanings. [Explanation of symbols]

[0245] 2 network, 100 user, 200 smartphone, 1000 people flow information estimation system, 2000 lifestyle information service providing server, 2000' people flow information service providing server, 2100 computing device, 2102 people flow path information estimation unit, 2103 people distribution information estimation unit, 2104 map output unit, 2106 area setting unit, 2107 area setting unit, 2108 area location information estimation unit, 2110 lifestyle information estimation unit, 2112 point distribution information acquisition unit, 2114 attribute specification unit, 2116 superimposed display generation unit, 2300 storage device, 2302 demographic information, 2302' demographic information, 2304 transportation route information, 2306 lifestyle information, 2308 occupational employment information, 2310 area location information, 2312 people flow path information, 3000 Lifestyle information user terminal, 5000.1~5000.M data providing server.

Claims

1. a storage device for storing demographic information including information on the number of people present in each of a plurality of predetermined area meshes in a time series of a series of time units within a predetermined area; a computing device that performs an analysis of people distributed within the predetermined area, and the functions performed by the computing device include: a point distribution information acquisition means for acquiring a distribution of the analysis target points existing within the predetermined area in accordance with the type of the analysis target points designated by a user within the predetermined area; an area setting means for setting an analysis area for each of the plurality of analysis target points corresponding to the type of the analysis target points in the area mesh within a predetermined range around the plurality of analysis target points; and a person distribution information analysis means for extracting information on the number of people distributed in the predetermined area based on the demographic information.

2. The functions executed by the arithmetic device further include an attribute designation means for receiving, from the user, a target attribute to be analyzed from among the attributes of a person; The person distribution information analysis means acquires information on attributes of people within the predetermined area or analysis area from the demographic information, The analysis device of claim 1, wherein the specified range around the analysis target point is an area within a trajectory of a specified distance centered on the analysis target point corresponding to the type specified by the user, and the specified distance is variable depending on the target attributes of the person specified by the user.

3. the predetermined distance is a distance calculated based on the attributes of the person and a travelable distance per unit time according to a means of transportation corresponding to the predetermined area, the analysis target point is a predetermined facility designated by the type, The analysis device according to claim 2 , wherein the area within the trajectory corresponds to a range within which the person can use each of the facilities by the means of transportation according to an attribute of the person.

4. The analysis device according to claim 1 , further comprising a superimposed display generating means for generating information that displays the distribution of the analysis target area and the information about the number of people so that they are superimposed on each other.

5. The present invention further comprises a superimposed display generating means for generating information to be displayed so that the distribution of the analysis target area and the information regarding the number of people are superimposed, The person distribution information analysis means analyzes the distribution of people with the target attribute relative to the distribution of the analysis target points for each time period based on the demographic information, The analysis device according to claim 2 , wherein the superimposed display generating means displays the results of the analysis by the people distribution information analyzing means in a superimposed manner for each time period.

6. a superimposed display generating means for generating information to be displayed so that the distribution of the analysis target area and the information regarding the number of people are superimposed, The predetermined facility is one of a transportation facility, a public institution, a commercial facility, or an entertainment facility; The person distribution information analysis means analyzes the distribution of people with the target attribute related to the distribution of the analysis target points for each time period based on the demographic information, The analysis device according to claim 3 , wherein the superimposed display generating means generates information for superimposing and displaying the results of the analysis by the people distribution information analyzing means for each time period.

7. the predetermined facility is a commercial facility, The analysis device according to claim 6 , wherein the information generated by the superimposed display generating means indicates locations in the commercial facility where new stores can be opened.

8. The demographic information includes information on the type of stay that the person will have in the specified area, The person distribution information analysis means estimates a distribution of people in the predetermined area according to the stay type, The analysis device according to claim 1 , wherein the stay type is information for identifying a resident of the predetermined area, a worker of the predetermined area, or a person who does not belong to either the resident or worker category of the predetermined area.

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