People flow analysis apparatus and people flow analysis system

Abstract

To propose a mechanism for estimating people flow information in a region with a boundary 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 flow information estimation unit 2103 which estimates a distribution of people located in a region surrounded by a boundary set by a 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 at least, 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 a people flow analysis device and a people flow analysis system that estimate information on people flow and distribution in a specified area. [Background technology]

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

[0003] 25 to 27 are diagrams showing an overview of a service that provides such demographic information.

[0004] 25 to 27, "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 25(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 25(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 26, 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. 27 is a diagram showing a data aggregation procedure in "Mobile Spatial Statistics (registered trademark)" disclosed in Non-Patent Document 2.

[0020] Referring to FIG. 27, 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. 28 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 28, the Basic Survey on Social Life surveys collects information on daily life (behavior classification) in 15-minute increments by region, age, and sex.

[0027] There have also been proposals for a system that uses big data from smartphone location information, utilizes only user data from users who have given their individual, explicit consent, and links the location information data to subscriber attributes to enable analysis of gender, age, etc. [Prior art documents] [Patent documents]

[0028] [Patent Document 1] Patent No. 6226493 [Non-patent literature]

[0029] [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]

[0030] 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.

[0031] 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.

[0032] The information in Non-Patent Document 4 merely indicates what activities people in different occupations are engaged in at certain times.

[0033] 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.

[0034] 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.

[0035] 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 people distribution and people flow in an area desired by the user, nor to a user interface for this purpose. In other words, no mechanism has been proposed for estimating people flow information in a specific area designated by the user (for example, Osaka Prefecture) based on demographic information within the specific area, and further for an "enclosed area" whose boundaries the user has arbitrarily set.

[0036] Therefore, no mechanism has been proposed to estimate lifestyle information, people flow, and people distribution in a specified period of time in a enclosed area based on demographic information of the area.

[0037] The present invention has been made to solve the problems described above, and its object is to provide a people flow analysis device and a people flow analysis system that are capable of estimating people flow information in a specified area during a specified time period based on demographic information for the specified area. [Means for solving the problem]

[0038] In order to achieve the above-mentioned object, according to one aspect of the present invention, a people flow analysis device includes a storage device that stores predetermined demographic information including information on the number of people located in each of a plurality of predetermined area meshes in a time series of a first time unit, and a calculation device that analyzes the people flow information within an analysis target area, and the functions executed by the calculation device include an area boundary setting means that accepts a user's designation of the boundary of the analysis target area, a human behavior information estimation means that estimates the number of people for each classification of human behavior located in the area meshes included in the analysis target area for each predetermined time based on the demographic information and the predetermined lifestyle information, an area location information estimation means that estimates aggregated data for each area mesh within the analysis target area by distributing the number of people estimated by the human behavior information estimation means to the area meshes based on the estimation result of the human behavior information estimation means and the type of place and the size of the place corresponding to the classification of human behavior for each area mesh, and a people flow information estimation means that estimates the distribution of human attributes within the analysis target area based on the classification of human behavior and the aggregated data estimated in the area meshes existing in the analysis target area.

[0039] Preferably, the human behavior information estimation means estimates the number of people for each category of human behavior located in an area mesh included in the analysis target area for each second time unit shorter than the first time unit based on demographic information and specified lifestyle information, and the area location information estimation means estimates aggregated data for each estimated area mesh divided into the area mesh.

[0040] Preferably, the people flow information estimation means estimates changes over time in the distribution of attributes of people in the analysis target area for weekdays and holidays.

[0041] Preferably, the people flow information estimation means displays a comparison of changes over time in distribution of people's attributes on weekdays and holidays.

[0042] Preferably, the people flow information estimation means estimates the distribution of people's attributes within the analysis target area based on the classification and aggregated data of human behavior estimated in area meshes existing within the analysis target area, allocating the data in the area ratio of estimated area meshes within the analysis target area to those outside the analysis target area.

[0043] Preferably, the area boundary setting means specifies the boundaries to coincide with areas of predetermined administrative districts, buildings, roads, etc. in response to user selection and specification.

[0044] Preferably, the area boundary setting means further comprises a database for storing polygon information of buildings and facilities used when specifying the boundary, and a registration receiving means for receiving registration of the polygon information in the database.

[0045] Preferably, the number of future visitors to the building is estimated based on design data of the building in the area to be analyzed.

[0046] 2. The people flow analysis device according to claim 1, wherein the demographic information is preferably aggregated data on mobile phone users collected through a mobile phone network, and the predetermined lifestyle information includes employment information by predetermined occupation type.

[0047] According to another aspect of the present invention, a people flow analysis system includes a first database for storing demographic information, a second database for storing lifestyle information, a people flow analysis device capable of acquiring data from the first and second databases via a network, and a user terminal for receiving an analysis result of the people flow analysis device via the network, the people flow analysis device includes a storage device for receiving and storing predetermined 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 first time units, and a calculation device for analyzing the people flow information within an analysis target area, and a function executed by the calculation device includes a region boundary calculation device for accepting a user's designation of a boundary of the analysis target area, The system comprises a boundary setting means, a human behavior information estimation means for estimating the number of people for each human behavior classification located in an area mesh included in the analysis target area for each specified time period based on demographic information and specified lifestyle information, an area location information estimation means for estimating aggregated data for each area mesh within the analysis target area by distributing the number of people estimated by the human behavior information estimation means to the area mesh based on the estimation result of the human behavior information estimation means and the type of place and the size of place corresponding to the human behavior classification for each estimated area mesh, and a people flow information estimation means for estimating the distribution of human attributes within the analysis target area based on the human behavior classification and aggregated data estimated in the area meshes existing in the analysis target area. Effect of the Invention

[0048] According to the present invention, it is possible to estimate lifestyle information, people flow, and people distribution of people located in an area designated by a user by drawing a boundary on a map 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, etc.

[0049] Furthermore, according to the present invention, it is possible to estimate the distribution and movement of people according to the attributes of an area designated by a user by drawing a boundary on a map based on demographic information. More specifically, it is possible to estimate the crowding situation based on the crowding situation in an arbitrary area in front of a specific station, the crowding situation in an area of ​​an indefinite shape such as an anime event venue, etc., for example, by age group 15-20 / 20-25, male / female. [Brief description of the drawings]

[0050] [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] FIG. 13 is a conceptual diagram showing a manner in which an enclosed area is specified on a digital map. [Figure 15] FIG. 2 is a conceptual diagram for explaining the data structure of a digital map. [Figure 16] FIG. 13 is a conceptual diagram showing an example of building information. [Figure 17] 13 is a conceptual diagram showing an example of a screen display when a user specifies an enclosed area. FIG. [Figure 18] 13 is a flowchart for explaining an example of processing by an area boundary setting unit 2107 when a user specifies an enclosed area. [Figure 19] FIG. 13 is a diagram showing the results of estimating the change over time in the distribution of people within a "enclosed area" after the "enclosed area" is specified. [Figure 20] FIG. 20 is a diagram showing a specific example in which estimation and analysis equivalent to those in FIG. 19 are carried out by enclosing a specific area in the Umeda district of Osaka. [Figure 21] This graph shows the distribution of people in Miyakojima Ward, Osaka, on a certain day and at a certain time period, divided into multiple hierarchical levels and color-coded by hierarchical level. [Figure 22] 13 is a flowchart showing a process of estimating a number of people distribution in an analysis target area, taking into account stay attributes. [Figure 23] 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 24] 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. [Diagram 25] FIG. 1 is a diagram showing an overview (1) of mobile spatial statistics of the prior art. [Figure 26] FIG. 1 is a diagram showing an overview (2) of mobile spatial statistics of the prior art. [Figure 27]FIG. 1 is a diagram showing an overview (3) of mobile spatial statistics according to the prior art. [Figure 28] 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

[0051] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0052] In the following, a description will be given of a people flow analysis system for estimating lifestyle information, people flow, and people distribution, and the configuration of a people flow analysis device for estimating people flow information in the people flow analysis system.

[0053] As described below, the people flow analysis device in this embodiment is a device that estimates people flow information, for example, information on the distribution of people, in a specified time period in an area surrounded by a boundary specified by a user, based on demographic information of the specified area. [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 people flow analysis device and the people flow 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 then allow the user to further designate a portion of that area as the "specified region" in which the estimation process will be performed. [Embodiment 2]

[0114] FIG. 12 is a functional block diagram for explaining the configuration of a people flow information service providing server 2000' according to the second embodiment.

[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, information on people distribution estimated regarding the number of people staying in an area surrounded by a boundary specified by a user (hereinafter, "enclosed area") at a certain time or time period for set human 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, gender, occupation classification, etc., but are not limited to these.

[0117] The same components 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] Referring to FIG. 12, the calculation device 2100 of the people flow information service providing server 2000′ of embodiment 2 includes, as functions executed according to a program, a people flow information estimation unit 2103 instead of the people flow path information estimation unit 2102, and an area boundary setting unit 2107 instead of the area setting unit 2106.

[0119] The people flow information estimation unit 2103 will be described later.

[0120] The area setting unit 2106 has a function of specifying a "predetermined area" by specifying, for example, a building, an administrative district, etc., according to instructions from the user. In contrast, the area boundary setting unit 2107 has a function of specifying an area ("enclosed area") as an area to be analyzed when the user encloses an area with a continuous line by operating a mouse, touch panel, or the like on the displayed map.

[0121] The process of specifying the enclosed area will be described in more detail later.

[0122] 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.

[0123] Therefore, the people flow information service providing server 2000' comprises an area boundary setting unit 2107 that accepts the user's specification of the boundary of the area to be analyzed, a storage device 2300 that stores specified demographic information including information on the number of people located in each of a plurality of specified area meshes (area meshes of time-space statistics, for example, 500 m square) in a time series of a series of first time units, and a calculation device 2100 that analyzes the people flow information within the area to be analyzed. The calculation device 2100 includes a lifestyle information estimation unit 2110 that estimates the number of people for each classification of human behavior located in an area mesh included in an analysis target area for each specified time based on demographic information and specified lifestyle information (behavior information and occupation information) and performs human behavior information estimation, and an area place information estimation unit 2108 that estimates aggregated data for each estimated area mesh in the analysis target area by distributing the number of people estimated by the lifestyle information estimation unit 2110 to the estimated area mesh based on the estimation result of the lifestyle information estimation unit 2110 and the type and size of places (buildings, facilities, roads, transportation facilities, etc.) corresponding to the classification of human behavior for each "estimated area mesh (e.g., 62.5 m square)".

[0124] The people flow information estimation unit 2103 estimates the distribution of people's attributes within the analysis target area based on the classification of people's behavior estimated in the estimated area mesh that exists within the analysis target area and the aggregated data.

[0125] 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.

[0126] FIG. 13 is a flowchart for explaining the processing of the people flow information service providing server 2000′ according to the second embodiment.

[0127] 13, in the people flow information service providing server 2000', first, the area boundary setting unit 2107 executes a process of setting the boundary of the area to be analyzed (a process of setting an enclosed area) in response to an operation by a user (S102).

[0128] 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.

[0129] Finally, the people flow information estimation unit 2103 estimates the distribution of people's attributes within the analysis area based on the classification of people's behavior estimated in the estimated area mesh existing within the analysis area and the aggregated data, and the map output unit 2104 displays the number of people distribution for each specified attribute within the analysis area (S152).

[0130] FIG. 14 is a conceptual diagram showing a manner in which an enclosed area is specified on a digital map.

[0131] A predetermined area RM is displayed as a digital map as shown in Fig. 14. At this time, a road Rd and a river RV are displayed on the digital map, and here, as shown by the diagonal lines, an enclosed area DR is specified as a corner enclosed by the road Rd.

[0132] FIG. 15 is a conceptual diagram for explaining the data structure of a digital map.

[0133] As shown in FIG. 15, the digital map data consists of multiple layers.

[0134] 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.

[0135] For example, layer 1 is road information, and is stored in the map information 2314 as a road information DB.

[0136] Also, for example, the road information may be divided into "basic road data" and "minor road data."

[0137] 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.

[0138] 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.

[0139] "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.

[0140] "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.).

[0141] 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.

[0142] 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.).

[0143] A digital map may also have other layers that contain information on the map.

[0144] The road information may conform to the contents of the following databases. https: / / www.drm.jp / database /

[0145] Furthermore, the information on administrative boundaries may be structured in accordance with the Geospatial Information Authority of Japan database.

[0146] FIG. 16 is a conceptual diagram showing an example of building information.

[0147] The building information is a collection of information on each building or facility having the following configuration, for example.

[0148] 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.

[0149] The "representative point of a building" is, for example, the position of the entrance to the building.

[0150] The "reference point of height" is, for example, the ground surface.

[0151] 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.).

[0152] The building information also includes two-dimensional or three-dimensional polygon information (not shown) of the building in association with the building ID.

[0153] 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.

[0154] 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

[0155] FIG. 17 is a conceptual diagram showing an example of a screen display when a user specifies an enclosing area.

[0156] FIG. 18 is a flowchart for explaining an example of the processing of the area boundary setting unit 2107 when a user specifies an enclosed area.

[0157] First, it is assumed that when a user specifies an enclosing area, an attribute that is prioritized as the boundary of the enclosing area is set in advance. Such a boundary attribute is hereinafter referred to as a "priority boundary." Here, the priority attribute may be, but is not limited to, the boundary of a road or building on a digital map, the boundary of a river, or the boundary of an administrative district.

[0158] When the process of setting the enclosed area starts and the user (user) specifies the boundary of the area to be analyzed by operating a mouse or touch panel as shown in FIG. 17(a) (hereinafter such a boundary will be referred to as the "specified boundary") (S202), the area boundary setting unit 2107 determines whether or not there is a specified boundary that is within a predetermined distance from the priority boundary of the attribute previously set by the user (S204).

[0159] If there is a designated boundary that is less than the predetermined distance (Y in S204), the area boundary setting unit 2107 executes a process of matching the designated boundary with the priority boundary having the set attribute (S206). The area boundary setting unit 2107 sets the matched boundary as the analysis target area (see FIG. 7(b)), and stores it in the storage device 2300 as the set area information 2304 (S208).

[0160] On the other hand, if there is no designated boundary that is within the predetermined distance (N in S204), the area boundary setting unit 2107 sets the designated boundary as the area to be analyzed as is, and stores it in the storage device 2300 as set area information 2304 (S208).

[0161] That is, the area boundary setting unit 2107 specifies the boundary so as to coincide with the area of ​​a predetermined administrative district, building, road, etc., in response to the user's selection and designation.

[0162] In addition, when determining the boundaries of buildings and other features, building data in vector tiles provided by the Geospatial Information Authority of Japan's Map Vector (tentative name), as shown below, may be used. https: / / maps.gsi.go.jp / development / vt_fn.html

[0163] As described above, when a rough area is manually specified on a map, the area boundary setting unit 2107 automatically performs a process to match the appropriate boundary, thereby realizing a user interface that allows the user to easily specify an area.

[0164] 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.

[0165] 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 .

[0166] As described above, the map information 2314 is a database that stores polygon information of buildings and facilities used by the area boundary setting unit 2107 when specifying boundaries, and the building information registration terminal 4000 accepts registration of building information (including 2D or 3D polygon information) in the database. Since buildings, etc. are constantly updated, it is desirable to have a configuration that allows polygon information to be provided from outside.

[0167] It is also possible to receive design data of buildings and facilities that have not yet been started or are not completed in the analysis target area from the building information registration terminal 4000, and to predict and estimate the number of visitors to the buildings and facilities in the future based on this design data. In this case, for example, if the design data specifies the attributes of each floor area of ​​the buildings and facilities that have not yet been started or are not completed, the number of people staying in the analysis target area can be predicted and estimated by multiplying the number estimated from the current demographic information by a predetermined coefficient (for example, 1.3) and distributing it within the analysis target area as the expected number of people. Here, the predetermined coefficient can be configured to be determined according to, for example, the ratio of the total floor area of ​​the buildings and facilities that have not yet been started or are not completed to the total floor area of ​​the buildings and facilities in the analysis target area that have been completed.

[0168] FIG. 19 is a diagram showing the results of estimating the change over time in the distribution of people within a "enclosed area" after the "enclosed area" is specified.

[0169] The method for apportioning the number of people within the "enclosed area" is as shown in Figure 13. However, if the boundary of the enclosed area crosses the estimated area mesh, it is desirable to estimate the number of people within the enclosed area by apportioning the number of people within the estimated area mesh based on the area ratio between the area inside the enclosed area of ​​the estimated area mesh and the area outside the enclosed area.

[0170] In other words, the people flow information estimation unit 2103 estimates the distribution of people's attributes within the analysis target area based on the estimated people's lifestyle information (behavior and occupation classification) in the area meshes existing within the analysis target area and the aggregated data, and allocates the information in the area ratio between the estimated area meshes inside and outside the analysis target area.

[0171] In other words, when the people flow information estimation unit 2103 extracts and aggregates elements such as building data, road data, and water area data for each estimated area mesh to determine what is inside the enclosed area, it calculates the element ratio of the area inside and outside the enclosed area and aggregates the number of people in the enclosed area by age and gender by time.

[0172] FIG. 19(a) is a diagram showing the number of people in each age group in the "enclosed area" over time (0:00 to 24:00).

[0173] Here, for example, the area between the line for those in their 20s and the line for those in their 30s corresponds to the number of people in their 30s. This day is a weekday, and we can see that people start gathering around 8:00 a.m., and by 10:00 p.m., they have moved out of the area. In other words, this enclosed area is an area where workers gather, such as an office district.

[0174] FIG. 19(b) is a diagram showing the distribution of the number of people by age and gender within a boxed area for a specific time period in the distribution of the number of people shown in FIG. 19(a).

[0175] In this way, by estimating and displaying the distribution of people within an enclosed area based on attributes such as time, age, and gender, it is possible to grasp the characteristics of people flow and distribution within the enclosed area.

[0176] For example, a user who is planning to open a new store within the enclosed area can estimate in advance the number of people who will gather after the store opens, and then decide whether or not to open the store and consider the product lineup.

[0177] From the standpoint of disaster prevention, it is possible to perform simulations in advance to determine whether the evacuation routes in a surrounded area are sufficient, or to consider methods of evacuation guidance.

[0178] FIG. 20 is a diagram showing a specific example in which estimation and analysis corresponding to FIG. 19 are carried out by enclosing a specific area in the Umeda district of Osaka.

[0179] Figure 20(a) shows the boxed area after it has been specified, and Figure 20(b) shows the change in the number of people in each age group within the “boxed area” over time, as well as the distribution of people by age and gender within the boxed area at a specific time period.

[0180] It is also possible to estimate the change in the number of people in each age group within such an "enclosed area" over time, and the distribution of people in the enclosed area at a specific time period by attributes such as age and gender, for example, on weekdays and holidays, and display a comparison of the distribution on weekdays and holidays.

[0181] That is, the people flow information estimation unit 2103 estimates the time change in distribution of people's attributes in the analysis target area for weekdays and holidays. The people flow information estimation unit 2103 can also be configured to display the time change in distribution of people's attributes on weekdays and holidays in comparison.

[0182] Such comparative information can also be used for store opening plans, evacuation plans, and the like.

[0183] Figure 21 shows the distribution of people in Miyakojima-ku, Osaka during a certain time period on a certain day, divided into multiple hierarchical levels and color-coded by hierarchical level.

[0184] Figure 21(a) shows the distribution of people by setting an enclosed area for each chome in Miyakojima-ku. In this case, the priority boundary is set to the administrative division of chome within the ward.

[0185] FIG. 21(b) is a diagram showing the distribution of the number of people under the same conditions as FIG. 21(a) as a heat map in mesh units.

[0186] As can be seen by comparing Figures 21(a) and 21(b), when considering administrative policies, it may be more desirable to have population distribution by block rather than a heat map by mesh unit. [Embodiment 3]

[0187] In the first and second embodiments, the distribution of people within a predetermined analysis target area is estimated in the following manner.

[0188] In other words, the flow is roughly as follows:

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] As described in the first embodiment, the first time interval and the second time interval may be the same.

[0194] However, this method of apportioning the number of employees does not necessarily take into account the following points:

[0195] 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.

[0196] 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.

[0197] Therefore, in the third embodiment, it is assumed that priorities are set in advance in the people flow information service providing server 2000' so that an attribute 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.

[0198] It should be noted that, although not limited thereto, for example, the attribute "stay type" can be acquired from demographic data.

[0199] That is, as explained in FIG. 26 and FIG. 27, first, "Mobile Spatial Statistics (registered trademark)" includes information on the residential area.

[0200] Here, during the de-identification process in Fig. 27, 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.

[0201] 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.

[0202] As described above, the attributes of buildings or places 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 or places (such as residences, commercial facilities, offices, accommodation facilities, entertainment and tourist facilities, etc.).

[0203] FIG. 22 is a flowchart showing the process of estimating the number of people distribution in an analysis target area, taking into account the stay attributes.

[0204] 22, in the people flow information service providing server 2000', first, the area boundary setting unit 2107 executes a process of setting the boundary of the area to be analyzed (a process of setting an enclosed area) in response to an operation by a user (S102).

[0205] Next, the calculation device 2100 acquires spatial statistical data via the network (S110).

[0206] 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).

[0207] The lifestyle information estimation unit 2110 further acquires information on stay attributes from the spatial statistical data (S112).

[0208] 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).

[0209] 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).

[0210] 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).

[0211] Finally, the people flow information estimation 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 the map output unit 2104 superimposes the number of people spread out for each facility / building on a 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 working at that location by color coding may be used.

[0212] 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).

[0213] 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.

[0214] 23 and 24 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 the visitor.

[0215] FIG. 23(a) shows the distribution of all people visiting a 500m mesh, while FIG. 23(b) shows the distribution of only residents in the 500m mesh (e.g., people who regularly stay within that mesh overnight).

[0216] Figure 24(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 24(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).

[0217] 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.

[0218] 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.

[0219] Furthermore, for example, the above-described series of processes can be executed by hardware or software.

[0220] In other words, the functional configurations in FIGS. 2 to 12 are merely examples and are not particularly limited.

[0221] 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.

[0222] Furthermore, one functional block may be configured as a single piece of hardware, a single piece of software, or a combination of both.

[0223] 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.

[0224] The computer may be a computer implemented with dedicated hardware.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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]

[0230] 2 network, 100 user, 200 smartphone, 1000 people flow information estimation system, 2000 people flow information service providing server, 2100 computing device, 2102 people flow path information estimation unit, 2103 people flow information estimation unit, 2104 map output unit, 2106 area setting unit, 2107 area boundary setting unit, 2108 area location information estimation unit, 2110 lifestyle information estimation unit, 2300 storage device, 2302 demographic information, 2302´ demographic information, 2304 traffic route information, 2306 lifestyle information, 2308 employment information by occupation, 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 that stores predetermined 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 first time units; A calculation device that analyzes people flow information within an analysis target area, and the functions executed by the calculation device include: an area boundary setting means for receiving a user's designation of the boundary of the area to be analyzed; a human behavior information estimation means for estimating the number of people for each classification of human behavior present in the area mesh included in the analysis target area for each predetermined time period based on the demographic information and predetermined life information; an area location information estimation means for estimating aggregate data for each area mesh within the analysis target area by distributing the number of people estimated by the human behavior information estimation means to the area mesh based on the estimation result of the human behavior information estimation means and the type of place corresponding to the classification of the human behavior for each area mesh and the size of the place; and a people flow information estimation means for estimating a distribution of people's attributes within the analysis target area based on a classification of people's behavior estimated in the area mesh existing within the analysis target area and the aggregated data.

2. The human behavior information estimation means estimates the number of people for each classification 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 based on the demographic information and predetermined life information; The people flow analysis device according to claim 1 , wherein the area location information estimation means estimates the aggregated data for each estimated area mesh obtained by dividing the area mesh.

3. 3. The people flow analysis device according to claim 1, wherein the people flow information estimation means estimates a time change in distribution of attributes of people in the analysis target area for weekdays and holidays.

4. 4. The people flow analysis device according to claim 3, wherein said people flow information estimation means displays a comparison of time-dependent changes in distribution of people's attributes on weekdays and holidays.

5. 3. The people flow analysis device according to claim 2, wherein the people flow information estimation means estimates a distribution of people's attributes within the analysis target area by allocating the aggregated data and the classification of human behavior estimated in the area meshes present within the analysis target area in an area ratio between the estimated area meshes within the analysis target area and the estimated area meshes outside the analysis target area.

6. 2. The people flow analysis device according to claim 1, wherein said area boundary setting means specifies said boundaries so as to coincide with areas of predetermined administrative districts, buildings, roads, etc., in response to selection and specification by a user.

7. 7. The people flow analysis device according to claim 6, further comprising: a database for storing polygon information of buildings and facilities used when specifying the boundaries, said area boundary setting means; and a registration receiving means for receiving registration of the polygon information in said database.

8. The people flow analysis device according to claim 7 , further comprising: a device for estimating a future number of visitors to the building based on design data of the building within the area to be analyzed.

9. 2. The people flow analysis device according to claim 1, wherein the demographic information is aggregated data on mobile phone users collected through a mobile phone network, and the predetermined lifestyle information includes employment information by predetermined occupation type.

10. a first database storing demographic information; A second database for storing lifestyle information; A people flow analysis device capable of acquiring data from the first and second databases via a network, and a user terminal that receives an analysis result of the people flow analysis device via the network, A storage device that receives and stores the predetermined 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 first time units; A calculation device that analyzes people flow information within an analysis target area, and the functions executed by the calculation device include: an area boundary setting means for receiving a user's designation of the boundary of the area to be analyzed; a human behavior information estimation means for estimating the number of people for each classification of human behavior present in an area mesh included in the analysis target area for each predetermined time period based on the demographic information and a predetermined lifestyle information; an area location information estimation means for estimating aggregate data for each area mesh within the analysis target area by distributing the number of people estimated by the human behavior information estimation means to the area mesh based on the estimation result of the human behavior information estimation means and the type of place corresponding to the classification of the human behavior for each estimation area mesh and the size of the place; A people flow analysis system comprising: a people flow information estimation means for estimating a distribution of people's attributes within the analysis target area based on a classification of human behavior estimated in the area mesh existing within the analysis target area and the aggregated data.

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