People flow analysis device and people flow analysis system

Abstract

To propose a mechanism for estimating people flow information in an event 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 staying in an event region surrounded by a boundary set by a user when an event occurs, on the basis of a relationship between predetermined living information and predetermined region location information, and baseline information before the occurrence of a specific event, 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 an area where a specified event occurs. [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] 22 to 24 are diagrams showing an overview of a service that provides such demographic information.

[0004] 22 to 24, "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 22(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 22(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 23, 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. 24 is a diagram showing a data aggregation procedure in "Mobile Spatial Statistics (registered trademark)" disclosed in Non-Patent Document 2.

[0020] Referring to FIG. 24, 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. 25 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 25, the Basic Survey on Social Life surveys collects lifestyle information (behavioral 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.

[0028] Furthermore, the information processing device disclosed in Patent Document 2 generates an appropriate model corresponding to regional characteristics and time characteristics, and executes prediction for each area (section) using the generated model. A configuration for improving the accuracy of prediction of data (e.g., people flow) using the model is disclosed. [Prior art documents] [Patent documents]

[0029] [Patent Document 1] Patent No. 6226493 [Patent Document 2] Patent Publication No. 2022-16920 [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, while Patent Document 2 does not disclose a configuration for estimating the flow or distribution of people in a given section 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 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 for an "event area" whose boundaries are arbitrarily set by the user with the intention of analyzing people distribution and people flow in the area when an event, etc., occurs, based on demographic information within the specific area (for example, Osaka Prefecture) specified by the user.

[0037] Therefore, no mechanism has been proposed to estimate lifestyle information, people flow, and people distribution in a specified time period in an event area based on demographic information of the specified area.

[0038] 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]

[0039] In order to achieve the above 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, the calculation device including: an area boundary designation means that receives designation of a boundary of an analysis target area, which is an area where a predetermined event occurs, by a user; a human behavior information estimation means that estimates the number of people for each classification of human behavior located in an area mesh included in the analysis target area for each predetermined time based on the demographic information and the predetermined life information; an area location information estimation means that estimates 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 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 people's attributes at the time of the event in the analysis target area, and the people flow information estimation means distributes the maximum capacity set in the analysis target area where the predetermined event occurs to the analysis target area, and then distributes the remaining number of people outside the analysis target area.

[0040] 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.

[0041] Preferably, the people flow information estimation means includes a maximum capacity setting means for setting a maximum capacity of the analysis target area in which a predetermined event occurs, and a baseline setting means for aggregating a baseline of the number of people in the analysis target area during a predetermined period before the event is held, and the people flow information estimation means calculates the number of people gathered in the analysis target area at the time of the event based on the estimated number of people. vinegar The distribution of people in the analysis area is estimated based on the difference from the line and the maximum capacity, and if the number of people exceeds the maximum capacity, they are distributed to area meshes outside the analysis area.

[0042] Preferably, when estimating the number of people gathered in the analysis target area, the people flow information estimation means estimates that people residing outside the analysis target area are people staying for the event, based on the stay attributes in the demographic information.

[0043] Preferably, the people flow information estimation means, in distributing the remaining number of people to area meshes outside the analysis target area, preferentially distributes the number of people to area meshes outside the analysis target area that have a predetermined location attribute.

[0044] Preferably, the analysis target area is included within a specified region, and the people flow information estimation means, in distributing the remaining number of people to area meshes outside the analysis target area, preferentially distributes the number to area meshes outside the analysis target area that have location attributes that are set in advance for each specified region.

[0045] Preferably, the analysis target area is included in a specified region, and the people flow information estimation means has a learning means which, when distributing the remaining number of people to area meshes outside the analysis target area, learns, for each specified area, a weighting coefficient corresponding to a location attribute in the estimated area mesh outside the analysis target area to which the distribution is to be made, in accordance with actual values ​​after a specified event.

[0046] Preferably, the area boundary designation means designates a predetermined administrative district, building, road, etc., in response to a user's selection and designation. Road Specify the boundaries to match the region.

[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 analysis results of the people flow analysis device via the network, the people flow analysis device includes a storage device for storing 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 for analyzing the people flow information within an analysis target area, the calculation device including area boundary designation means for receiving designation of a boundary of an analysis target area in which a predetermined event occurs by a user, human behavior information estimation means for estimating the number of people for each behavior category 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, and a user terminal for receiving an estimation result of the human behavior information estimation means. and territory The system includes an area location information estimation means that estimates aggregated data for each area mesh within the analysis area by distributing the number of people estimated by the human behavior information estimation means to the area mesh based on 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 people's attributes at the time of an event within the analysis area, and the people flow information estimation means distributes the maximum capacity set in the analysis area where a specified event occurs to the analysis area, and then distributes the remaining number of people outside the analysis 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 present in an event 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 event area designated by a user by drawing a boundary on a map based on demographic information. [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] 13 is a flowchart showing the “62.5 m aggregated data analysis routine” according to the second embodiment. [Figure 15]FIG. 13 is a conceptual diagram showing an aspect in which an event area is specified on a digital map. [Figure 16] FIG. 23 is a diagram showing an example of a screen when the area boundary setting unit 2107 sets an event area and estimates the distribution of people in an estimated area including the event area. [Figure 17] FIG. 2 is a diagram showing an example of people flow information estimation performed by the people flow information estimation unit 2103 for an uncompleted facility. [Figure 18] 13 is a flowchart illustrating the process of a people flow information service providing server 2000′ according to the third embodiment. [Figure 19] 13 is a flowchart showing the “62.5 m aggregated data analysis routine” according to the third embodiment. [Figure 20] 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 21] 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 22] FIG. 1 is a diagram showing an overview (1) of mobile spatial statistics of the prior art. [Figure 23] FIG. 1 is a diagram showing an overview (2) of mobile spatial statistics of the prior art. [Figure 24] FIG. 1 is a diagram showing an overview (3) of mobile spatial statistics according to the prior art. [Diagram 25] 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] The following describes 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, during a specified time period in an area surrounded by a boundary specified by a user and in which an event occurs, based on demographic information of the specified area.

[0054] In this specification, the term "event" literally means "an event held temporarily in the area being analyzed" such as a concert, a specified event, a festival, or fireworks, as well as "an event in which a non-regular occurrence occurs in the area being analyzed" such as "a disaster that temporarily occurs in the area being analyzed." An "event area" refers to an area whose boundaries are specified by the user as an area for analyzing changes in people flow or distribution in response to an "occurrence of an event." [Embodiment 1]

[0055] FIG. 1 is a diagram showing an overview of a lifestyle information estimating system according to a first embodiment of the present invention.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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."

[0061] With reference to FIG. 2, the life information service providing server 2000 includes a computing device 2100 and a storage device 2300 .

[0062] 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.

[0063] 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.

[0064] 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".

[0065] 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.

[0066] In addition, the "predetermined living information" may include such "occupation-specific employment information."

[0067] 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).

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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."

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] FIG. 3 is a diagram for explaining the hardware configuration of the life information service providing server 2000. As shown in FIG.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] FIG. 7 is a diagram showing an outline of data output from the arithmetic device 2100. As shown in FIG.

[0090] 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.

[0091] 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.

[0092] 5 and 6, when the process starts, the calculation device 2100 acquires spatial statistical data via the network (S110, S200).

[0093] 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).

[0094] 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).

[0095] The lower part of Figure 7(a) shows an overview of such estimation of the number of people by behavior.

[0096] 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."

[0097] 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."

[0098] 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).

[0099] 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).

[0100] The upper part of Fig. 7(a) shows an overview of such facility occupancy estimation.

[0101] 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."

[0102] 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.

[0103] 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.

[0104] FIG. 7(b) is a diagram showing an example of an output that visualizes such a distribution of the number of people.

[0105] 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).

[0106] 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).

[0107] Because the units of the data do not match, analysis usually ends up being performed based on data with larger units.

[0108] 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.

[0109] 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.

[0110] As described above, the same estimated area mesh is a mesh with one side measuring 62.5 m, as shown in FIG.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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]

[0115] 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.

[0116] 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.

[0117] In this specification, "people flow information" refers to information expressing the "pattern of people movement" during a specified period of time, and includes, for example, information on the estimated people distribution regarding the number of people staying in an area surrounded by a boundary specified by a user (hereinafter, "event area") at a certain time or time period for set people attributes. By tracking the change over time in such people distribution information, it is possible to estimate information expressing the flow of people. Note that, in this case, "people attributes" include, for example, age, generation, gender, occupation classification, etc., but are not limited to these.

[0118] 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.

[0119] 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.

[0120] The people flow information estimation unit 2103 will be described later.

[0121] 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 an instruction from a user. In contrast, the area boundary setting unit 2107 has a function of specifying such a "predetermined area (hereinafter, referred to as an "estimated area")" and, in addition, when a user operates a mouse, touch panel, or the like to enclose an area on the displayed map with a continuous line, specifying this area (an "event area") as an area to be analyzed.

[0122] The process of specifying the event area will be described in more detail later.

[0123] 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.

[0124] Therefore, the people flow information service providing server 2000' comprises a memory 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 in time-space statistics, for example, 500 m square) in a time series of a series of first time units, an area boundary setting unit 2107 that accepts the user's specification of the boundary of the area to be analyzed, and a calculation device 2100 that analyzes the people flow information within the area to be analyzed. The arithmetic device 2100 includes, as functions realized by operating based on a computer program, an area boundary designation unit 2107 that accepts designation of the boundary of an analysis target area, which is an area where a predetermined event occurs, by a user, a life information estimation unit 2110 that estimates the number of people for each classification of human behavior located in an area mesh included in the analysis target area for each predetermined time based on demographic information and predetermined life information, an area location information estimation unit 2108 that estimates aggregate data for each area mesh in the analysis target area by distributing the number of people estimated by the life information estimation unit 2110 to the area mesh based on the estimation result of the life information estimation unit 2110 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 unit 2103 that estimates the distribution of people's attributes at the time of the event in the analysis target area. The people flow information estimation unit 2103 distributes the maximum capacity set in the analysis target area where the predetermined event occurs to the analysis target area, and then distributes the remaining number of people outside the analysis target area.

[0125] In addition, 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 area meshes existing within the analysis target area and the aggregated data.

[0126] 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.

[0127] The people flow information estimation unit 2103 may be configured to estimate people flow information for each area mesh. However, if the size and type of place are specified for an estimated area mesh that is finer than the area mesh (divided from the area mesh), the people flow information estimation unit 2103 estimates the distribution of the number of people and the distribution of people's attributes for each estimated area mesh (for example, 62.5 m mesh) in the analysis target area based on the classification and aggregated data of the human behavior estimated in the area mesh that exists in the analysis target area.

[0128] In the following description, it is assumed that the people flow information estimation unit 2103 estimates people flow information in a "62.5 m mesh."

[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, the people flow information service providing server 2000' first receives setting of an area for estimating people flow information (hereinafter, "estimated area") through an operation by a user (S200).

[0131] Here, although not particularly limited, the "estimated area" can be specified in units of administrative districts (prefectures, cities, towns, villages, chome, etc.).

[0132] Next, the boundary area setting unit 2107 of the people flow information service providing server 2000' executes a process of setting the boundary of the analysis target area (a process of setting the event area) (S202). At this time, the maximum capacity of the event area is also set according to the user's specification.

[0133] Then, the people flow information estimation unit 2103 sets the "baseline analysis period" and the analysis time step to estimate the "baseline information" when performing analysis of the area to be analyzed, according to the pre-set settings (default values) or settings from the user (S204).

[0134] Here, the term "baseline information" refers to information estimating the distribution of the number of people (may be by human attributes such as gender, age, etc.) in each area mesh (for example, 500m mesh) in a steady state in the absence of an event during a certain period before the occurrence of a specific event (this is called the "baseline analysis period") for an estimated area. For example, it means the distribution of the average number of people during the "baseline analysis period" for an area mesh. The "baseline analysis period" can be a period set by the user, for example, for a period of about 1 to 3 months. Furthermore, the "baseline information" may be, for example, an average of only the holidays (specific time periods) of the "baseline analysis period" if the event is on a holiday, or an average of the weekdays (specific time periods) of the "baseline analysis period" if the event is on a weekday. The "analysis step" is information for specifying how to sequentially select which days or dates and times of the "baseline analysis period" (for example, by weekday, by holiday, by certain day of the week, etc.).

[0135] Next, the pedestrian flow information estimation unit 2103 executes the "analysis routine for 62.5 m aggregated data" as described below (S206). If the estimation of baseline information for the baseline analysis period according to the analysis step is not completed (N in S208), the pedestrian flow information estimation unit 2103 sets the analysis step to the next step (S210) and returns the process to step S206.

[0136] On the other hand, when the estimation of the baseline information for the baseline analysis period is completed according to the analysis step (Y in S208), the people flow information estimation unit 2103 calculates a baseline value for each area mesh for the estimation area using, for example, the average value of the baseline analysis period of the baseline information (S212).

[0137] Next, the people flow information estimation unit 2103 sets the event date and time to be analyzed as specified by the user, and sets the priority for distributing the number of people estimated as aggregated data to the area to be analyzed and outside the area to be analyzed (S220).

[0138] Here, the "priority" is the priority for distributing the number of people in the aggregated data to each estimated area mesh when the number of people staying in the area mesh exceeds the baseline value on the date and time of the event. For example, the "event area" is distributed with the highest priority (first priority in this case), and when the "maximum capacity of the event area" is exceeded, the distribution to a location "outside the event area" is specified according to this priority. For example, when the "maximum capacity of the event area" is exceeded, the next priority (second priority in this case) is given to transportation stations and stops that exist within XX meters outside the event area, and roads used for walking from these transportation stations and stops to the event area. Maximum capacity may also be set for these stations and stops and roads. Furthermore, after distributing the number of people in the aggregated data of the estimated area up to the maximum capacity of the stations and stops and roads, the next distribution location may be specified (third priority in this case). For example, in addition to stations and stops and roads, the third priority may be set for parking lots that exist within XX meters outside the event area. The number of priorities may be more than this.

[0139] Next, the people flow information estimation unit 2103 executes the "analysis routine of 62.5m aggregated data" for the event date and time (S222).

[0140] Based on this, the people flow information estimation unit 2103 estimates the number of people who gathered in the event area during the event and should be distributed to the event area and elsewhere, using the difference between the result of the "62.5 m aggregated data analysis routine" for the event date and time and the baseline value, and performs an estimation of the people distribution (which may be by person attributes) in the analysis target area and surrounding areas (within the estimated area) (S224).

[0141] The map output unit 2104 outputs display data of the human distribution in the analysis target area and its surrounding area (within the estimated area) generated by the people flow information estimation unit 2103 (S226).

[0142] That is, the people flow information estimation unit 2103 estimates the distribution of people's attributes inside and outside the analysis target area based on the classification of people's behavior estimated in the estimated area mesh existing within the analysis target area and the aggregated data, and the map output unit 2104 superimposes the number of people expanded for each facility, building, and location on a map and outputs it by color-coding it, for example, using a heat map method.

[0143] In the above description, it has been explained that the first to third priorities are set for buildings, facilities, and places depending on the user's designation. However, for example, it is assumed that the analysis target area (event area) is included in the estimated area (administrative district), and the map information 2314 also stores the attributes of the buildings, facilities, and places in this estimated area. For example, the "attributes of buildings, facilities, and places" are information indicating the use of the buildings, facilities, and places, such as "commercial facilities," "business facilities," and "entertainment facilities (including cases where they are used as entertainment facilities on a specific date and time)." At this time, it is assumed that the area place information estimation unit 2108, in distributing the remaining number of people to area meshes outside the analysis target area, sets the weight coefficients of the estimated area meshes outside the analysis target area to be distributed in advance according to the attributes of the buildings, facilities, and places and the distance from the event area. The people flow information estimation unit 2103 may be configured to learn the weight coefficients for each estimated area according to the performance value after a predetermined event. Furthermore, such weighting coefficients may be set so that, for example, even for the same location attribute, the weighting coefficients differ depending on the estimated region (for example, depending on the prefecture or city).

[0144] For example, in the case of a fireworks display, if there is a condition that a certain plaza in a certain region of a certain prefecture is opened as a "parking lot," the weighting coefficient for this "certain plaza" may be initially set low, but based on past performance, the weighting coefficient for the "certain plaza" may be learned to become a larger value.

[0145] In addition, for example, the initial value of the weighting coefficient may be set according to a function in which the weighting coefficient is initially lower as the distance from the event area increases, or the initial value of the weighting coefficient may be set by multiplying a predetermined value for each attribute by a coefficient in which the weighting coefficient is initially lower in stages according to the rank of the distance.

[0146] FIG. 14 is a flowchart showing the “62.5 m aggregated data analysis routine” according to the second embodiment.

[0147] Referring to FIG. 14, when the analysis routine for the 62.5 m aggregated data is started, the people flow information service providing server 2000' acquires spatial statistical data (demographic data) for the set date and time (S310).

[0148] 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).

[0149] Next, for the "estimated area", the lifestyle information estimation unit 2110 classifies people (whose age and gender are obtained from the spatial statistical data) who are staying in the estimated area during a certain 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, and estimates the number of people in each category (for each attribute of age and gender) (S320).

[0150] Furthermore, the lifestyle information estimation unit 2110 estimates the number of people (age and gender attributes) working by occupation type for every 15 minutes in 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 (S330).

[0151] Next, the area location information estimation unit 2108 expands (e.g., proportionally apportions) the estimated number of employees by job type every 15 minutes (by attributes such as age and gender) to each building (facility / location) based on the total floor area of ​​the facilities / buildings / locations on the map and the area allocated to the job type of each facility / building / location, and further based on the priority of that building / facility or location, estimates aggregated data on the number of people in a mesh (e.g., a 62.5 m mesh) that is smaller than the unit mesh of the spatial statistical data (S340), and then returns to the main routine (main function).

[0152] Finally, the people flow information estimation unit 2103 estimates the distribution of people's attributes and numbers within the analysis target area based on the aggregated data of the classification of human behavior estimated in the estimated area mesh existing within the analysis target area, and the map output unit 2104 displays the number distribution of people for each specified attribute within the analysis target area (S152).

[0153] FIG. 15 is a conceptual diagram showing an aspect in which an event area is specified on a digital map.

[0154] A predetermined area RM is displayed as a digital map, as shown in Fig. 15. At this time, a road Rd and a river RV are displayed on the digital map, and an event area DR is specified as a corner surrounded by the road Rd and the river, as indicated by the diagonal lines.

[0155] The digital map data as shown in FIG. 15 is made up of multiple layers.

[0156] 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.

[0157] For example, layer 1 is road information, and is stored in the map information 2314 as a road information DB.

[0158] Also, for example, the road information may be divided into "basic road data" and "minor road data."

[0159] 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.

[0160] 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.

[0161] "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, and expressway numbering. "Northern road link data" further includes the corresponding basic road link number.

[0162] "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.).

[0163] 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.

[0164] 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.).

[0165] A digital map may also have other layers that contain information on the map.

[0166] The road information may conform to the contents of the following database. https: / / www.drm.jp / database /

[0167] Furthermore, the information on administrative boundaries may be structured in accordance with the Geospatial Information Authority of Japan database.

[0168] The map information 2314 also includes building information. The building information is a collection of information on each building or facility having the following configuration, for example.

[0169] 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.

[0170] The "representative point of a building" is, for example, the position of the entrance to the building.

[0171] The "reference point of height" is, for example, the ground surface.

[0172] 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.).

[0173] The building information also includes two-dimensional or three-dimensional polygon information (not shown) of the building in association with the building ID.

[0174] 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.

[0175] 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

[0176] Based on the above assumptions, as shown in FIG. 15, the user specifies the boundaries of an event area on the displayed map using a mouse, touchpad, or the like, and the area boundary setting unit 2107 sets the event area.

[0177] First, it is assumed that when a user specifies an event area, an attribute that is prioritized as a boundary of the event area is set in advance. Such a boundary of an attribute is hereinafter referred to as a "priority boundary." Here, the priority attribute may be, but is not limited to, a road or building boundary on a digital map, a river boundary, or an administrative district boundary.

[0178] When the event area setting process starts and the user specifies the boundary of the area to be analyzed by operating the mouse or touch panel (hereinafter such a boundary will be referred to as the "specified boundary"), 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.

[0179] If there is a designated boundary that is equal to or smaller than the predetermined distance, the area boundary setting unit 2107 executes a process of matching the designated boundary with the priority boundary having the set attribute. The area boundary setting unit 2107 sets the matched boundary as the analysis target area and stores it in the storage device 2300 as set area information 2304.

[0180] On the other hand, if there is no designated boundary that is within the predetermined distance, 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 .

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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 .

[0185] 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.

[0186] FIG. 16 is a diagram showing an example of a screen in which the area boundary setting unit 2107 sets an event area and estimates the distribution of people in an estimated area including the event area.

[0187] As shown in Fig. 16(a), an event area DR is set on a map, and the distribution of the number of people is estimated for a specific area (estimation area) including the event area DR, as shown in Fig. 16(b). Note that in Fig. 16(b), the number of people is estimated for the estimation area mesh (every 62.5 m).

[0188] FIG. 17 is a diagram showing an example of people flow information estimation performed by the people flow information estimation unit 2103 for an uncompleted facility.

[0189] For example, it is possible to accept design data for buildings and facilities that have not yet been started or are not completed within the area to be analyzed from the building information registration terminal 4000, and to predict and estimate the future number of visitors to the buildings and facilities based on this design data.

[0190] In this case, for example, in the case where a building or facility that has not yet been started or is not completed is set as the event area in the design data, if the attributes of each floor area are specified, the number of people staying in the analysis target area (the building or facility that is the event area) can be estimated by multiplying the number estimated from the current demographic information by a predetermined coefficient (for example, 1.3) to obtain the expected number of people staying in the analysis target area, and distributing the number of people within the analysis target area, thereby making such prediction and estimation possible. Here, the predetermined coefficient can also be configured to be determined, for example, according to the ratio between the total floor area of ​​the building or facility that has not yet been started or is not completed and the total floor area of ​​the building or facility in the analysis target area that is currently completed. [Embodiment 3]

[0191] In the first and second embodiments, the distribution of people within a predetermined analysis target area is estimated in the following manner.

[0192] In other words, the flow is roughly as follows:

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] As described in the first embodiment, the first time interval and the second time interval may be the same.

[0198] However, this method of apportioning the number of employees does not necessarily take into account the following points:

[0199] 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.

[0200] 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.

[0201] 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.

[0202] It should be noted that, although not limited thereto, for example, the attribute "stay type" can be acquired from demographic data.

[0203] That is, as explained in FIG. 26 and FIG. 27, first, "Mobile Spatial Statistics (registered trademark)" includes information on the residential area.

[0204] 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.

[0205] 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.

[0206] That is, as in the second embodiment, when estimating "the distribution of the number of people within an event area" when "a specific event occurs", a configuration is adopted in which "transient visitors" are given priority in allocation.

[0207] In addition, because a "temporary resident" is defined as "someone who is neither a resident nor a worker in the event area," attributes related to the "place of residence" before moving to the event area are not necessarily used for "temporary resident."

[0208] For example, as a result of the concealment process in FIG. 24, 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 event area" who are staying in the "event 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 in the event 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 event area."

[0209] 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 for the attributes of buildings, facilities or locations (residential, commercial facilities, offices, accommodation facilities, entertainment and tourist facilities, etc.).

[0210] Fig. 18 is a flowchart for explaining the processing of the people flow information service providing server 2000' according to the third embodiment. Fig. 18 corresponds to Fig. 13 of the second embodiment.

[0211] 18, the people flow information service providing server 2000' first receives setting of an area for estimating people flow information (hereinafter, "estimated area") through an operation by a user (S200).

[0212] Here, although not particularly limited, the "estimated area" can be specified in units of administrative districts (prefectures, cities, towns, villages, chome, etc.).

[0213] Next, the boundary area setting unit 2107 of the people flow information service providing server 2000' sets the boundary of the analysis target area (event area setting process) and also sets the stay attributes to be distributed to such event areas (S202'). At this time, the maximum capacity of the event area is also set according to the user's specification.

[0214] The subsequent processing is similar to that in FIG. 13, and therefore will not be described repeatedly.

[0215] FIG. 19 is a flowchart showing the “62.5 m aggregated data analysis routine” according to the third embodiment.

[0216] Referring to FIG. 19, when the analysis routine for the 62.5 m aggregated data is started, the people flow information service providing server 2000' acquires spatial statistical data (demographic data) for the set date and time (S310). Here, the spatial statistical data (demographic data) is assumed to be data classified by "stay attribute" as described above. At this time, the stay attribute of people to be distributed to the "event area (analysis target area)" is also set according to the type of event. For example, if the event is a "tourist event" such as a "fireworks display," the stay attribute is set to "temporary visitor."

[0217] Next, for the "estimated area", the lifestyle information estimation unit 2110 classifies people (whose age and gender are obtained from the spatial statistical data) who are staying in the estimated area during a certain 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, and estimates the number of people in each classification (for example, age and gender attributes and stay attributes) (S320).

[0218] Furthermore, the lifestyle information estimation unit 2110 estimates the number of people working (by age and sex attributes and stay attributes) by occupation type for every 15 minutes in the above specific area based on the estimated number of people for each behavior category (by age and sex attributes and stay attributes), for example, based on the Ministry of Internal Affairs and Communications Labor Force Survey database (S330).

[0219] Next, the area location information estimation unit 2108 expands (e.g., proportionally apportions) the estimated number of employees by occupation for every 15 minutes (by age and gender attributes, and stay attributes) to each building (facility / location) based on the total floor area of ​​the facilities / buildings / locations on the map and the area allocated to the occupation of each facility / building / location, and further based on the priority of that building / facility or location, estimates aggregated data of the number of people in a mesh (e.g., a 62.5 m mesh) that is smaller than the unit mesh of the spatial statistical data for the set stay attributes (S340), and then returns to the main routine (main function).

[0220] 20 and 21 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.

[0221] Fig. 20(a) shows the distribution of all people visiting a 500m mesh, while Fig. 20(b) shows the distribution of only residents in the 500m mesh (e.g., people who regularly stay within that mesh overnight).

[0222] Figure 21(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 21(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).

[0223] 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.

[0224] 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.

[0225] Furthermore, for example, the above-described series of processes can be executed by hardware or software.

[0226] In other words, the functional configurations in FIGS. 2 to 12 are merely examples and are not particularly limited.

[0227] 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.

[0228] Furthermore, one functional block may be configured as a single piece of hardware, a single piece of software, or a combination of both.

[0229] 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.

[0230] The computer may be a computer implemented with dedicated hardware.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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]

[0236] 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, an area boundary designation means for accepting designation of the boundary of the analysis target area, which is an area in which a predetermined event occurs, by a user; 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 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 the human behavior for each area mesh; a people flow information estimation means for estimating a distribution of attributes of people at the time of the event within the analysis target area, The people flow information estimation means distributes a maximum capacity set in the analysis target area in which the specified event occurs to the analysis target area, and then distributes the remaining number of people to outside the analysis target area.

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. The people flow information estimation means includes: a maximum capacity setting means for setting a maximum capacity of the analysis target area in which the predetermined event occurs; a baseline setting means for counting a baseline of the number of people in the analysis area during a predetermined period before the event is held; 3. The people flow analysis device according to claim 1, wherein the people flow information estimation means estimates a population distribution in the analysis target area based on a difference between an estimated number of people gathered in the analysis target area during the event and the baseline, and based on the maximum capacity, and if the number of people exceeds the maximum capacity, distributes the number of people to area meshes outside the analysis target area.

4. The people flow analysis device according to claim 3, wherein the people flow information estimation means, in estimating the number of people gathered in the analysis target area, estimates people who reside outside the analysis target area to be people staying during the event, based on the stay attributes in the demographic information.

5. The people flow analysis device according to claim 3 , wherein the people flow information estimation means, in distributing the remaining number of people to area meshes outside the analysis target area, preferentially distributes the remaining number of people to area meshes outside the analysis target area that have a predetermined location attribute.

6. 4. The people flow analysis device according to claim 3, wherein the analysis target area is included in a predetermined region, and the people flow information estimation means, in distributing the remaining number of people to area meshes outside the analysis target area, preferentially distributes the remaining number of people to area meshes outside the analysis target area having location attributes that are set in advance for each of the predetermined regions.

7. 4. The people flow analysis device according to claim 3, wherein the analysis target area is included in a predetermined region, and the people flow information estimation means has a learning means that learns, for each of the predetermined regions, a weighting coefficient corresponding to a location attribute in an estimated area mesh outside the analysis target area to which the remaining number of people is to be distributed, in accordance with a performance value after the predetermined event.

8. 4. The people flow analysis device according to claim 3, wherein said area boundary designation means designates said boundaries so as to coincide with areas of predetermined administrative districts, buildings, and roads in response to selection and designation by a user.

9. 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 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, an area boundary designation means for accepting designation of the boundary of the analysis target area, which is an area in which a predetermined event occurs, by a user; 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 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 the human behavior for each area mesh; a people flow information estimation means for estimating a distribution of attributes of people at the time of the event within the analysis target area, The people flow information estimation means distributes a maximum capacity set in the analysis target area in which the specified event occurs to the analysis target area, and then distributes the remaining number of people outside the analysis target area.

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