Analysis apparatus

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 existing within a specified area. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there are services that provide demographic 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 demographic information created using the mechanisms of mobile phone networks. This spatial statistics is demographic information that shows only the number of people in a group, and so 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 demographic information for a certain area at a certain time without obtaining the consent of the person to whom the information belongs.

[0007] Here, this spatial statistics makes it possible to grasp the population distribution for every hour of Japan, 24 hours a day, 365 days a year. Moreover, this spatial statistics makes it possible to grasp the population structure by gender, age group, and residential area, making it possible to conduct analyses tailored to the purpose. Since the service started in October 2013, it has been widely used by the Japan Tourism Agency, local governments, private companies, etc.

[0008] More specifically, mobile phone networks periodically track the mobile phones located in each base station area so that users can make calls and send emails anytime, anywhere. Using this system, the number of mobile phones can be tallied and the population can be estimated by taking into account the penetration rate of mobile phone carriers. This is Mobile Spatial Statistics (registered trademark).

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

[0010] This spatial statistics makes it possible to analyze data by gender, age group, and residential area (domestic residents), and by country and region (foreign visitors to Japan), and by time period according to purpose.

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

[0012] As shown in Figure 27(b), this demographics makes it possible to grasp the population structure by gender and age group, covering the age range of 15 to 79 years old.

[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 classified into "trajectory data" and "aggregated data." "Trajectory data" is data related to moving objects such as people, and is often acquired by portable terminals 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 by equipment installed in specific locations, once the equipment is installed, data can be collected fairly comprehensively. In addition, because the data is not tied to individual subjects, there are fewer privacy concerns, and it is highly likely that data measured once can be used for a variety of purposes. On the other hand, because information across locations cannot generally be obtained, there are limitations to its use when understanding the flow of people in a city.

[0017] One type of aggregate data is "demographic information," which is data that records the population by location and time in a city. Most demographic information records population information by area and time. When targeting information over a wide area, mobile phone information is often used in consideration of comprehensiveness, and the number of terminals in each area is counted based on terminal connection information at mobile phone base stations and information such as CDR (Call Data Record), and the actual population is estimated while taking into account the utilization rate 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 a data aggregation procedure 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 statistic is demographic information that represents only the number of people in a group, and therefore cannot identify individual users.

[0022] The spatial statistics are created through the following steps: i) De-identification: The process of removing identifying information, such as name, telephone number, date of birth, etc. from operational data. ii) Aggregation processing: Processing to derive statistical "group-related information" by making statistical inferences from de-identified information. iii) Confidentiality processing: Processing to prevent figures from small population areas from being included in the tabulation results

[0023] In addition, 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 that, it executes a task of outputting the amount of movement between cells between times.

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

[0025] FIG. 30 shows an overview of the "Basic Survey on Social Life" (Non-Patent Document 4) issued 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 collects lifestyle information (behavioral classification) in 15-minute increments by region, age, and sex.

[0027] In addition, 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 of the points, as well as search results by the facility search means in association with each of the points.

[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 subject of a search by the query, a determination unit that determines search result candidates corresponding to the acquired query based on the acquired regional information and characteristics of the region corresponding to the acquired query, and a provision unit that provides information indicating the determined search result candidates to the user's terminal device. [Prior art documents] [Patent documents]

[0029] [Patent Document 1] Patent No. 6226493 [Patent Document 2] JP 2024-22109 A [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 of "Understanding and Predicting People Flow in Cities" 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 statistics such as the age (generation), sex, etc. of people present in a certain area on an hourly basis.

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

[0033] The information in Non-Patent Document 4 merely indicates what activities people in different occupations are engaged in at certain times.

[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 led to the problem 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] In addition, when collecting "demographic information," the area is divided into, for example, 500m x 500m "meshes" to tally up people, and no consideration has been given to estimating the distribution and flow of people in relation to an area desired by the user, or 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 target area" corresponding to an analysis target point of a type arbitrarily set by the user, in a specific area (for example, Osaka Prefecture) designated by the user, based on demographic information within the specific area. This is not disclosed in Patent Document 2 or Patent Document 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 user's desired analysis subject (category), and therefore it was not possible to analyze people flow information in relation to such an analysis area.

[0038] The present invention has been made to solve the above-mentioned problems, and its purpose is to provide an analysis device that is capable of estimating people flow information in relation to an area to be analyzed, during a specified time period in a specified area, based on demographic information of 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 on 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 in accordance with 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 the plurality of analysis target points corresponding to the type, and a people 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 calculation device further include an attribute designation means for receiving designation of target attributes to be analyzed from among the attributes of people by a user, 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 an orbit 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 person's attributes and the distance that can be traveled per unit time depending on 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 a person can use each facility by means of transportation depending on the person's attributes.

[0042] Preferably, the apparatus further comprises a superimposed display generating means for generating information for displaying information relating to the distribution of the analysis target area and the number of people so as to be superimposed on each other.

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

[0044] Preferably, 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, for each time period, the distribution of people of a target attribute related to the distribution of the analysis target points based on demographic information, and the overlay display generation means generates information for overlaying and displaying the results of the analysis by the people distribution information analysis means for each 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 by which people stay in a specified area, and the people 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 category of residents or workers in the specified area. Effect of the Invention

[0047] According to the present invention, it is possible to estimate 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 utilized in various ways by business operators, researchers, government officials, and others.

[0048] Furthermore, according to the present invention, it is possible to estimate the distribution and movement of people associated with an analysis area around a particular point designated by a user based on demographic information. [Brief description of the drawings]

[0049] [Figure 1] FIG. 1 is a diagram showing an overview of a lifestyle information estimating system according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a functional block diagram showing an outline of the system of a life information service providing server 2000. [Diagram 3] 2 is a diagram for explaining the hardware configuration of a life information service providing server 2000. FIG. [Figure 4] FIG. 1 is a diagram for explaining the contents of data provided from data providing servers 5000.1 to 5000.M. [Diagram 5] FIG. 11 is a first diagram illustrating a processing flow of the life information service providing server 2000. [Figure 6] FIG. 2 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. 13 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. 13 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. 13 is a diagram showing the concept of the process of improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210). [Figure 11] 13 is a diagram showing another example of the map image and the superimposed image of the number of people distribution output by the map output unit 2104. FIG. [Figure 12] FIG. 11 is a functional block diagram for explaining a configuration of a people flow information service providing server 2000′ according to a second embodiment. [Figure 13] 13 is a flowchart for explaining the process 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. 11 is a first conceptual diagram showing an aspect in which an analysis target area is specified on a digital map. [Figure 16] FIG. 2 is a second conceptual diagram showing an aspect in which an analysis target area is specified 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. [Figure 19] FIG. 13 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. 13 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. 11 is a diagram illustrating a comparison between an analysis target area and a person distribution. [Figure 22] FIG. 1 is a diagram showing the distribution of analysis target areas for specified facilities distributed in a specified region. [Diagram 23] FIG. 1 is a diagram showing the distribution of analysis target areas for specified facilities distributed in a specified region. [Figure 24] 13 is a flowchart illustrating the process of a people flow information service providing server 2000′ according to the third embodiment. [Diagram 25] This is a heat map showing the distribution of the number of people per area mesh (500m square) within a given area, taking into account visit attributes. [Figure 26] This is a heat map showing the distribution of the number of people per area mesh (500m square) within a given area, taking into account visit attributes. [Figure 27] FIG. 1 is a diagram showing an overview (1) of mobile spatial statistics of the prior art. [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 according to the prior art. [Diagram 30] FIG. 1 is a diagram showing the contents of the Basic Survey on Social Life as a background art. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[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, information on the distribution of people, during a specified time period related to an analysis area surrounding a specific type of point specified by a user, based on demographic information of the specified area.

[0053] Here, "area related to the area to be analyzed" means 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 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 life information estimation system 1000 according to a first embodiment of the present invention includes a life information service providing server 2000 that operates as a life information estimation device, a life information user terminal 3000 of a user who receives services from the life information service providing server 2000, data providing servers 5000.1 to 5000.M (M: a natural number) that provide the life information service providing server 2000 with data necessary for estimating life 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 to receive 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 (floor plan, 2D or 3D polygon data, etc.). In addition, visitors to the building may register information such as photos of the interior of the building. In this case, the building information registration terminal 4000 may be, for example, a mobile terminal such as a smartphone having a photo-taking function. For example, in the case of a smartphone, the life information service providing server 2000 may be configured to receive notifications from users who use terminals that have installed application software having an input format that notifies the user of the building name, number of floors, and area information (for example, store name and type of store) 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 from 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 server 5000."

[0060] With reference to FIG. 2, the life information service providing server 2000 includes a computing device 2100 and a storage device 2300 .

[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, the "predetermined time" refers to, for example, a first predetermined time interval, for example, every hour, and the "predetermined area" refers to, for example, an area in a specific region, for example, a first area mesh unit, for example, a 500m mesh (a square of 500m on each side). The "predetermined demographic information" is "demographic data" and means "aggregated data", and corresponds to, for example, the above-mentioned "Mobile Spatial Statistics Information (registered trademark)" as shown in FIG. 4.

[0063] Furthermore, the storage device 2300 stores a 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 a predetermined behavior by region, age, and sex, in 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 information in a database based on the "Basic Survey on Social Life" by the Ministry of Internal Affairs and Communications, which classifies behaviors into 20 predetermined types by region, age, and sex, and in 15-minute increments, as shown in FIG. 4. Here, the "classification of behavior" corresponds to classifications such as "sleep", "commuting", "school", "work", and "academic work".

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

[0065] In addition, the "predetermined living information" may include such "occupation-specific employment information."

[0066] Therefore, the life information estimation unit 2110 can estimate the number of people by behavior (hereinafter, referred to as "number of people by behavior") who are present in the "predetermined area" set by the area setting unit 2106 in response to an instruction from a user, during the "second time interval", based on the "demographic information 2302" and the "predetermined life information 2306". Furthermore, by applying the "employment information by occupation" to the "number of people by behavior" estimated in this way for people with the behavior classification of working or studying, the life information estimation unit 2110 can calculate the classification data of the "occupation" of people who are present in the "predetermined area" during the "second time interval". Note that the "occupation" may not only be the so-called "type of occupation", but may also be any "task" such as "study" that is not a business, but in which a person is obviously engaged in the same behavior for a certain period of time or more in that place (facility / building).

[0067] Although the life information 2306 is described as a "classification of a predetermined behavior" at a second time interval that is shorter than the first time interval and is 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, after a "classification of a predetermined behavior" is roughly estimated at the first time interval and life information, people flow, and people distribution are presented to the user, a "classification of a predetermined behavior" at a finer "second time interval" may be estimated in response to an instruction from the user, and life information, people flow, and people distribution may be presented to the user.

[0068] The storage device 2300 further stores a predetermined "area location information 2310". Here, the "predetermined area location information" refers to "information on a predetermined place existing in the above-mentioned predetermined area (including information on the type of job at that place)". For example, the "area location information" preferably includes information on buildings and facilities in the area of ​​the second area mesh unit, which is smaller than the above-mentioned first area mesh unit and preferably has a divisor of each side when the first area mesh unit is a rectangle, and information on the types of jobs performed in the buildings and facilities. For example, as shown in FIG. 4, the "area location information 2310" corresponds to map data including information on buildings and facilities in a town, information on the types of jobs performed in the buildings and facilities, and information on roads and water areas in 62.5m mesh units. Some of this "map data" is provided by private companies.

[0069] Based on such 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 existing in the "predetermined place" of the "predetermined area" set by the area setting unit 2106. Note that such estimation is preferably performed in areas in units of second area meshes. However, depending on the user's settings, such 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 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, proportionally allocating) 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] As the relationship between the predetermined life information and the predetermined area location information, for example, it is possible to assume 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, pensioners live in houses, etc. The specific processing will be described later.

[0073] Furthermore, the map output unit 2104 generates and outputs a screen that displays the thus estimated "personal 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 he or she has specified.

[0074] As described above, since the "number of people by behavior" 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 estimate and output people flow path information (the route along which people moved) by tracking changes in the number of people distribution for the area in the second area mesh unit and its surrounding areas along the time axis and integrating this with "information on routes along which people can move" (for example, roads, railways, etc.) on the map data. The output result of the people flow information output unit 2102 is stored in the people flow path information 2312 of the storage device 2300.

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

[0076] FIG. 3 is a diagram for explaining 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 a calculation device (CPU: Central Processing Unit) in its own housing executes calculation processing, or a part of the program processing may be further executed on another server. In the following, the description will be given assuming that the calculation device in its own housing executes the calculation processing.

[0078] Referring to FIG. 3, 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 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 the recording medium 2210. Also, for example, a communication function of a wired LAN or a wireless LAN can be used as the network communication unit 2012. The network communication unit 2012 and the input / output interface 2090 configure a communication interface.

[0080] As shown in FIG. 3, the computer main body constituting the computer device 2010 includes, in addition to the disk drive 2030 and the memory drive 2020, a CPU (Central Processing Unit) 2100 connected to a bus 2050, a memory 2200 including a ROM (Read Only Memory) 2200.1 and a RAM (Random Access Memory) 2200.2, a non-volatile rewritable non-volatile storage device 2300, and an input / output interface 2090 for communicating via a network and sending and receiving data with the outside. For example, a HDD (Hard Disc Drive) or an SSD (Solid State Drive) is used as the non-volatile storage device 2300. In the following description, it is assumed to be an SSD. An optical disk can be attached to the disk drive 2030. A memory card 2210 can be attached to the memory drive 2020.

[0081] When the programs of the computer device 2010 operate, the data and programs that store the information that is the basis for the operation of the computer are 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. In order to accommodate such cases, the computer main body is provided with a drive device (memory drive 2020, disk drive 2030) capable of reading such 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 acquired 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 to 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 a program for functioning as computer device 2010 is distributed, the program does not necessarily need to include an operating system (OS) that causes computer main body 2010 to execute functions of an information processing device, etc. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner and produces desired results. How computer system 2010 operates is well known, and a detailed description will be omitted.

[0085] Furthermore, the CPU 2100 may be a one-core processor or a multiple-core processor, i.e., a single-core processor or a multi-core processor. The server 2000 may also be configured with multiple servers to execute 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 population estimation is performed from behavior-specific population estimation at a first time point.

[0090] FIG. 9 is a conceptual diagram showing data generated when facility population 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, to obtain spatial statistical data, it is possible to use "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 in a given time unit (for example, 1 hour) within a mesh of a given width (for example, 500m square).

[0093] Next, for a "predetermined area" specified by the area setting unit 2106 based on the user's designation, 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 certain 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 of Social Life database, and estimates the number of people in each category (attributes of age and gender) (S120, S210).

[0094] The lower part of Figure 7(a) shows an overview of such estimation of the number of people by behavior.

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

[0096] The lower graph in Figure 9 shows the estimated number of people by activity in a specific area at 3:00 p.m. (15:00). Judging from the time of day, most people are classified as "working" or "studying."

[0097] Returning to FIG. 5 and FIG. 6 , further, 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) 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 assigns (for example, proportionally apportions) the estimated number of employees (attributes of age and sex) by occupation for 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 Fig. 7(a) shows an overview of such facility occupancy estimation.

[0100] The upper diagram in Figure 8 shows the results of estimating the number of people in facilities 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 diagram in Figure 9 shows the results of facility population estimation in a specific area at 3:00 p.m. (15:00). Based on the time of day, it can be seen that most people are assigned to the "occupation" or "task" corresponding to the facility or building in each facility.

[0102] 5 and 6, the map output unit 2104 further superimposes the number of people deployed for each facility / building on the map and outputs it by color-coding, for example, using a heat map method. Here, when displaying the heat map, a method of displaying a chart showing the ratio of the number of people doing the work at that location by color coding may be used.

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

[0104] FIG. 10 is a diagram showing the concept of the process of 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 unit).

[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 described above, in the life information service providing server 2000, people flow data (including number of people 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] Hereinafter, such a database stored in the same unit will be referred to as a "data lake." Therefore, for example, it will be possible to perform real-time analysis in the same unit (estimated area mesh) at any point in Japan.

[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 resolution is 62.5 m, since Mobile Spatial Statistics (registered trademark) is a mesh with sides of 500 m, it is possible to create an area mesh with sides of 1 / 8 (1 / 8) of this 500 m, which is approximately equal to the distance a person walks in one minute. When using other spatial statistics, it is possible to unify the sides to an estimated area mesh equivalent to the distance a person walks in one minute in the same way. By using the distance a person walks in one minute, it can be said that the resolution is suitable for analyzing people flow, including people who are moving by foot, 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 viewpoint of cost-effectiveness, it is preferable to use, for example, 62.5 m.

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

[0113] As shown in FIG. 11, it is also possible to designate an area of ​​a certain size as the specified region, and furthermore, to allow the user to specify a portion of that area as the "specified region" 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 the 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 similar to that described in FIG. 3, and therefore description thereof will not be repeated.

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

[0117] Concerning the people flow information service providing server 2000', components that are the same as those of the life information service providing server 2000 in the first embodiment are given 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′ of the second embodiment includes, as a function executed according to a program, a people distribution information analysis unit 2103 instead of the people flow path information estimation unit 2102. The functions of the arithmetic device 2100 further include 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 for area meshes within a predetermined range around 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 and 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 information such as the date and time and the time period to be analyzed by the people distribution information analysis unit 2103 in addition to the attributes of the person itself such as “age”, “gender”, “residential area”, and “occupation (job)”.

[0120] The population 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, an administrative district, etc. In contrast, the area setting unit 2107 has a function of setting an analysis target area for each analysis target point for the area mesh within a predetermined range around the position of the analysis target point of the type specified by the user.

[0122] Here, the attribute designation unit 2114, which is a function executed by the calculation device 2100, accepts designation of target attributes to be analyzed by the user 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" is an area 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 amount according to the target attribute of the person specified by the user. For example, if age is the target attribute, the walking distance is different between people in their 20s and 30s and people in their 70s and above, so it is assumed that the size of the radius is changed. Furthermore, the "predetermined distance" may be a distance calculated based on the attributes of the person and the travelable distance per unit time according to the means of transportation corresponding to the predetermined area. For example, if there is a train station within the predetermined area, it may be a distance that can be traveled by train within 30 minutes. Alternatively, if the attributes of the person include the attribute of being a private car owner, it may be a distance that can be traveled by car within 20 minutes from the analysis target point. Not limited to these examples, the "predetermined distance" is the "travelable distance" per predetermined unit time (for example, a time specified by the user, such as 10 minutes, 20 minutes, or 1 hour) that takes into consideration the "road standard (general road, expressway)", "road width (number of lanes on one side)", and "transportation means (walking, elevation difference, bicycle, senior car, bus, community bus, flat-rate taxi, taxi, etc.)". In this sense, the "predetermined distance" includes a virtual distance that should be called the "time distance" that can be traveled within a unit time, rather than a physical distance. In addition, if the means of transportation has a congestion degree for each time period to be analyzed, a coefficient according to the congestion degree may be multiplied by the time related to the travel. In that case, for example, the congestion degree of the road can be configured to take into account not only 2D information but also 3D information such as an intersection.

[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 in which people can use these facilities by transportation, depending on the person's 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 time-space 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, "administrative district" designated by a user) in a time series of a series of time units. The calculation device 2100 then performs analysis on people distributed in the predetermined area. The functions executed by the calculation device 2100 include a point distribution information acquisition unit 2112 that acquires the distribution of analysis target points existing in the predetermined area according to the type of analysis target point designated by a user in the predetermined area, an area setting unit 2107 that sets an analysis target area for each analysis target point for area meshes within a predetermined range around the plurality of analysis target points corresponding to this 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 actions estimated in the estimated area mesh that exists within the analysis target area.

[0128] Furthermore, although not limited thereto, similarly to the first embodiment, it is desirable that the lifestyle information estimation unit 2110 not only performs estimation for 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 an area mesh included in the analysis target area for each second time unit 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] 13, in the people flow information service providing server 2000', first, the point distribution information acquiring unit 2112 accepts the setting of the type of analysis target point after the user sets a predetermined area through the user's operation (S10).

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

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

[0133] Next, the area setting unit 2107 sets an analysis target area for each analysis target 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 FIG. 14, the people distribution information analysis unit 2103 of the people flow information service providing server 2000' sets the settings of a predetermined area (specific area) and an analysis target area set by the area setting unit 2107 in accordance with a specification from a user as a premise for analyzing the people distribution information (S102).

[0138] The subsequent processes from S110 to S140 are similar to those 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 target area based on the classification and aggregated data of people's behavior estimated in the estimated area mesh existing within the analysis target area, and calculates the number of people distribution for each specified attribute within the analysis target 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, an analysis target area is set at each of analysis target points DR01 to DRN.

[0142] 15, the analysis target point is assumed to be, for example, a "bus stop" although 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 a person aged 60 or older can reach on average within 10 minutes on foot.

[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, an analysis target area DR is also set at each of the analysis target points DR01 to DRN, as indicated by a circle.

[0145] Here again, although there is no particular limitation, for example, the analysis target point is assumed to be a "bus stop." And the radius r2 of the circle indicating the analysis target area is assumed to be the distance that a person in his / her twenties can reach on average within 10 minutes on foot.

[0146] In this way, the analysis area indicates, for each analysis point, the range within which it is possible to travel to the 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 sides, 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] Also, for example, the road information may be divided into "basic road data" and "minor road data."

[0152] Here, the "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, the "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 type, 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 position 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 time), speed limit and other traffic regulations), position coordinates and elevation of interpolation points, emergency transportation road classification, expressway numbering, etc. "Narrow road link data" further includes the corresponding basic road link number.

[0155] "Link attribute data" (a collective term for "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, pedestrian bridges, 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 types of links.

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

[0158] A digital map may also have other layers that contain information on the map.

[0159] The road information may conform to the contents of the following databases. 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 on each building, facility, or location (such as a park) having 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 information such as 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 a 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 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 attributes (purpose) of the same building (facility, location) differ depending on the date, time, and time of day, 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 exterior shape of the building or house, in the height direction.

[0171] Furthermore, the specifications of the indoor information are not particularly limited, but may conform to, for example, the following "3D Indoor Geospatial Information Data Specification (draft)." 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 having 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 terminals that have installed 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 of store) along with the captured photos.

[0173] The people flow information service providing server 2000 ′ may be configured to store information about buildings 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 human distribution, and the building information registration terminal 4000 accepts registration of building information (including 2D or 3D polygon information) to the database. Since buildings, etc. are constantly 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 analysis point, when considering the distance of x minutes on foot, a predetermined coefficient according to the difference in elevation may be applied so that the distance becomes shorter if the difference in elevation is large. 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 the bus stops of all bus routes that pass through Osaka City, based on the travel speed for each age and gender, and then draws a circle centered on the bus stop using the calculated distance.

[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 person distribution.

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

[0183] Alternatively, by displaying the locations of public facilities such as hospitals superimposed with a specified mark of a specified color, it is possible to evaluate information such as the extent to which facilities such as hospitals are covered within a public transportation area.

[0184] FIG. 22 is a diagram showing the distribution of analysis target areas for a given facility distributed in a given region.

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

[0186] With this type of display, it is possible to analyze the distribution and flow of people within the analysis region within the visualized area, 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 the areas analyzed in this way 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 a given facility distributed in a given region.

[0190] FIG. 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 given business hour, which makes it possible to consider plans for new store openings or 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] As mentioned above, digital maps also contain information about road size (width), so it is possible to calculate, for example, the "road area ratio" in an area mesh (estimated area mesh). Since the size of an area mesh (estimated area mesh) is constant, it is possible to understand that an area with a large road area ratio has relatively wide roads, whereas an area with a small road area ratio has only narrow roads. For example, it is also possible to obtain reference information such as a plan for 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 for each time period based on demographic information, and the overlay display generation unit 2116 can be configured to display the results of the analysis by the people distribution information analysis unit 2103 in a superimposed manner for each 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 points by time based on demographic information, and the overlay display generation unit 2116 may generate information for overlaying and displaying the results of the analysis by the people distribution information analysis unit 2103 by time period.

[0198] Furthermore, if the facility that is the analysis target point is a commercial facility, the information generated by the superimposed display generating unit 2103 indicates a location where a new commercial facility can be opened. In addition to commercial facilities, if the facility is an entertainment facility, it is also possible to estimate a location where the entertainment facility can be installed. [Embodiment 3]

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

[0200] In other words, the flow is roughly as follows:

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

[0202] ii) For people who are 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 to classify the people into behavioral classification information for each second time interval that is shorter than the first time interval, and the number of people in each classification is estimated.

[0203] iii) For the area to be analyzed, the number of people by occupation 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 to be analyzed, 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 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 apportioning the number of employees does not necessarily take into account the following points:

[0207] i) For example, during the night, when the behavior is classified as sleeping, residents in the analysis area should be preferentially allocated to residential buildings, 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 having attributes such as commercial facilities and business offices.

[0209] Therefore, in the third embodiment, it is assumed that the people flow information service providing server 2000' is configured in advance with priorities set so that an attribute of "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 numbers of people is changed according to the attributes of the time period and location.

[0210] It should be noted that, although not limited thereto, 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 the residential area.

[0212] Here, during the de-identification process in Fig. 29, it is possible to extract and count the number of "residents" of the analysis target area and residents outside that area who regularly move to the analysis target area on weekdays (workers). It is also possible to classify "persons who are neither residents nor workers" who are in the analysis target area during a certain period of time as "temporary visitors" and count them.

[0213] In this case, a "temporary visitor" would be, for example, a tourist or shopper, so if there are entertainment or tourist facilities such as theme parks within the area being analyzed, it is possible to set it up so that priority is given to these.

[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 preferentially allocate "transient visitors" to such entertainment and tourist facilities.

[0215] In addition, because a "temporary resident" is defined as "someone who is neither a resident nor an employee in a specified area," attributes related to the "place of residence" before moving to a specified area are not necessarily used for "temporary resident."

[0216] For example, as a result of the concealment process in FIG. 29, in Mobile Spatial Statistics (registered trademark), a process is executed in which "area meshes" in which 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" at a certain date and time are simply counted as "temporary visitors," visitors who come from areas where such a small number of people live may be missed from the count. In this respect, the method of specifying "persons who are neither residents nor workers of 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 life information service providing server 2000', and the area location information 2310 is set in advance with a priority of "stay type" for distribution of the attributes of buildings, facilities or locations (residential, 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 embodiment 3. Fig. 24 shows a processing flow of the embodiment 3, which is compared with the processing of Fig. 14, on the basis of the processing of Fig. 13 of the embodiment 2.

[0219] Referring to FIG. 24, the people distribution information analysis unit 2103 of the people flow information service providing server 2000' sets the settings of a predetermined area (specific area) and an analysis target area set by the area setting unit 2107 in accordance with a specification from a user as a 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 period for a predetermined area (S110), and acquires information on stay attributes from the spatial statistical information (S112).

[0221] As mentioned above, to obtain spatial statistical data, it is possible to use "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 in a given time unit (for example, 1 hour) within a mesh of a given width (for example, 500m square).

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

[0223] Next, for a "predetermined area" specified by the area setting unit 2106 based on the user's designation, 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 certain 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 based on the spatial statistical data, with reference to the Basic Survey on Social Life 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 in 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 expands (for example, proportionally allocates) the estimated number of employees by occupation for 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 of 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 target 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 target area (S150).

[0227] In addition, when distributing the number of people in a certain mesh to each building (facility / place) according to the staying attribute, if an upper limit of the number of people that the building (facility / place) can accommodate is set, the people flow information estimation unit 2103 can also calculate the excess number of people for the building (facility / place). For example, such excess number of people can be distributed with the second highest priority to roads around the building (facility / place) and stations of major transportation facilities (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 area location information estimation unit 2108 has completed the process of distributing the number of people for all types of specified stay attributes (Y in S160), it ends the process.

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

[0230] FIG. 25(a) shows the distribution of all people visiting a 500m mesh, while FIG. 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, while Figure 26(b) shows the distribution of temporary residents in a 500m mesh (people in a mesh who do not visit the mesh regularly, such as tourists or visitors from far away).

[0232] In this manner, 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 that can achieve 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 in FIGS. 2 to 12 are merely examples and are not particularly limited.

[0236] That is, it is sufficient that the information processing system is provided with a function capable of executing the above-mentioned 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 a part of the functional blocks and databases required for executing 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 of both.

[0238] When the series of processes is executed by software, the program constituting the software is installed into a computer or the like from a network or a recording medium.

[0239] The computer may be a computer implemented with dedicated hardware.

[0240] Furthermore, the computer may be a computer capable of executing various functions by installing various programs thereon, such as a server, a general-purpose smartphone, or a personal computer.

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

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

[0243] In addition, in this specification, the term "system" refers to an overall device that is composed 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 indicated 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 calculation 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 designation unit, 2116 superimposed display generation unit, 2300 storage device, 2302 demographic information, 2302' demographic information, 2304 traffic 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; and a computing device that performs an analysis of people distributed within the predetermined area, the functions of which are: a point distribution information acquisition means for acquiring a distribution of the analysis target points existing within the predetermined area according to a type of the analysis target point designated by a user within the predetermined area; an area setting means for setting an analysis target area for each of the plurality of analysis target points corresponding to the type of the analysis target points with respect to the area mesh within a predetermined range around the plurality of analysis target points; and a population distribution information analysis means for extracting information on the number of people distributed in the specified area based on the demographic information.

2. The functions executed by the arithmetic device further include an attribute designation unit that receives designation of a target attribute to be analyzed by the user from among 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 described in 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 movable 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 in 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 regarding the number of people so as to be superimposed on each other.

5. The person distribution information analysis means analyzes a distribution of people having the target attribute with respect to a 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. The specified facility is any one of a transportation facility, a public institution, a commercial facility, and an entertainment facility; The person distribution information analysis means analyzes a distribution of people having the target attribute related to a 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 specified 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 has 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 a resident or a worker of the predetermined area.

Images