Demographic analysis device and demographic analysis system

Abstract

To propose a mechanism for analyzing demographic information in a specific time zone in a predetermined region, based on demographic information of the predetermined region.SOLUTION: An information processing system 1000 analyzes demographic information, based on predetermined demographic information including information on the number of people located in each of a plurality of predetermined region meshes included in a region to be analyzed, in a chronological order. An attractive power information estimation unit 2105 estimates aggregate data for each estimation region mesh which is included in the region to be analyzed at a predetermined time and smaller than the predetermined region mesh, and estimates attractive power information indicating relative attractive power for each of the estimation region meshes.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present invention relates to a demographic analysis device and a demographic analysis system for analyzing demographic trends in a specified analysis target area. [Background technology]

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

[0003] 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 population statistical information created using the mechanisms of mobile phone networks. This spatial statistics is demographic information that only shows the number of people in a group, and therefore has the characteristic that it cannot identify individual mobile phone or smartphone users. In this respect, it differs from the treatment of so-called "personal information," and it is possible to provide third parties with population statistical information for a certain area at a certain time without obtaining the consent of the parties to the information.

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

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

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

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

[0011] As shown in Figure 22(a), the statistical target area is the nationwide Docomo service area, and the population distribution across the country can be grasped every hour. Therefore, it is possible to grasp the population distribution every hour, 24 hours a day, 365 days a year.

[0012] As shown in Figure 22(b), this demographic data allows us to understand the population structure by gender and age group, covering people aged 15 to 79.

[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 divided into "trajectory data" and "aggregated data." "Trajectory data" is data related to moving objects such as people, and is often collected by portable devices such as smartphones and car navigation systems.

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

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

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

[0019] FIG. 24 is a diagram showing the procedure for aggregating data in "Mobile Spatial Statistics (registered trademark)" disclosed in Non-Patent Document 2.

[0020] Referring to FIG. 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 statistics is demographic information that only represents the number of people in a group, and therefore cannot identify individual users.

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

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

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

[0025] Alternatively, Patent Document 2 discloses an information processing device that analyzes the actual situation of people in a region specified by a user. More specifically, the information processing device described in Patent Document 2 is a device for analyzing a commercial area. A heat map generation unit generates a heat map associated with map information, dividing each mesh into a predetermined size and representing the number of people or stores within the mesh at a predetermined time in multiple levels. An analysis unit overlays a heat map for a period within the region on a map including the region in accordance with the region and period designation, accepts selection of at least one of the heat map's multiple levels, and defines the multiple meshes in which the heat map having the selected level is located as the area to be analyzed. In this analysis, the resident population trends for each mesh are based on statistical information stored in a statistical information database, and the visitor population trends are generated by the heat map generation unit for each predetermined period based on behavioral information stored in a behavioral information database. Here, the "behavioral information database" refers to user information for each active user periodically transmitted from a user terminal and stored in association with the user ID. Then, the behavioral information generation unit generates behavioral information regarding the behavior of the anonymous user based on the user information stored in the behavioral user information DB, and stores the information in the behavioral information DB.

[0026] FIG. 25 shows an overview of the "Basic Survey on Social Life" (Non-Patent Document 4) published by the Statistics Bureau of the Ministry of Internal Affairs and Communications.

[0027] As shown in Figure 25, the Basic Survey on Social Life and Leisure Activities surveys lifestyle information (behavioral classification) in 15-minute increments by region, age, and sex. [Prior art documents] [Patent documents]

[0028] [Patent Document 1] Patent No. 6226493 [Patent Document 2] Patent No. 6990731 [Non-patent literature]

[0029] [Non-Patent Document 1] https: / / mobaku.jp / [Non-patent document 2] https: / / www.bcm.co.jp / bcm / wp-content / uploads / 2018 / 03 / ntt-docomo-2017-10-01.pdf [Non-patent document 3] Hiroyuki Toda, author: "Understanding and Predicting Urban Pedestrian Flow" https: / / www.jstage.jst.go.jp / article / oubutsu / 90 / 8 / 90_481 / _pdf / -char / ja [Non-patent document 4] https: / / www.stat.go.jp / data / shakai / 2021 / gaiyou.html Summary of the Invention [Problem to be solved by the invention]

[0030] However, the techniques described in Non-Patent Document 1 and Non-Patent Document 2 merely provide hourly statistics such as the age (generation), sex, etc. of people present in a certain area.

[0031] Furthermore, the technology described in Non-Patent Document 3 estimates "people flow" (the number of people moving) from "demographic data," but does not disclose how to display demographic information for a certain area at a certain time in a way that is easy for users to understand.

[0032] The information in Non-Patent Document 4 merely indicates what activities people in each occupation are engaged in at a certain time period.

[0033] Furthermore, the technology described in Patent Document 1 merely discloses a technology for estimating the number of people in a building based on demographic data, and the technology described in Patent Document 2 merely discloses a configuration for collecting data on population trends of visitors, etc., using data collected from user terminals for a pre-set mesh, but does not disclose a configuration for using aggregated data in units other than the pre-set mesh.

[0034] For this reason, even if demographic data is accumulated, it is difficult to estimate the movements of people in a specified area based on demographic information at intervals shorter than the time intervals at which this demographic data is accumulated. Furthermore, Patent Document 2 merely discloses an example in which, when anonymizing a user, attributes of the user, such as age group, gender, hobbies, preferences, residential area, work area, area of ​​activity, educational background, occupation, family structure, and annual income, are used as anonymization factors. Therefore, when estimating such people's movements, there is a problem in that it is difficult to analyze and use information (hereinafter referred to as "lifestyle information") that classifies the movements of people in a specified area during a specified time period.

[0035] The present invention has been made to solve the above-mentioned problems, and its purpose is to provide a demographic analysis device and demographic analysis system that can estimate the tendency of people to gather in a specified area (the force that attracts people: hereinafter referred to as "people attraction") by estimating the population trend in the specified area during a specified time period based on the demographic information of the specified area.

[0036] More specifically, the present invention provides a demographic analysis device and a demographic analysis system that are capable of estimating human movements in a specified area at a specified time based on demographic information for the specified area. [Means for solving the problem]

[0037] In order to achieve the above object, according to one aspect of the present invention, there is provided a demographic analysis device for an analysis target area, the device comprising: a storage device for storing, in a time series, predetermined demographic information including information on the number of people present in each of a plurality of predetermined area meshes included in the analysis target area; lifestyle information; and area location information, the demographic information being aggregated data of demographic data for each first time interval; Within the analysis area , information on the number of people by age and sex present in each predetermined area mesh at a first time interval, the lifestyle information is information for each predetermined time unit, the predetermined time unit being a second time interval shorter than the first time interval, the lifestyle information is information that can associate a person's age, sex, and a predetermined classification of a person's lifestyle and behavior with a person's occupation, and the area location information is location information for each predetermined section, Area of ​​analysis and a computing device for analyzing demographic information, the computing device including information on the total floor area of ​​facilities within the facility and information on occupations within the facility, the computing device including an analysis target area information acquisition means for acquiring analysis target area information indicating an analysis target area designated by a user and information on a predetermined time to be analyzed, and an area life information estimation means for estimating life information for each predetermined section, the area life information estimation means i) for each second time interval based on the demographic information and the life information, Area of ​​analysis and ii) estimating the number of people employed by occupation type in the area, based on the total floor area and the area allocated to the occupation type in each facility, and estimating aggregated data of the number of people in estimated area meshes smaller than the specified area mesh for demographic information, and estimating aggregated data for each estimated area mesh included in the analysis area at a specified time, and estimating people attraction information that shows the relative people attraction for each of the multiple estimated area meshes.

[0038] Preferably, the human attraction information is information that compares the number of people present in each estimated area mesh included in the analysis area at a given time with the average number of people present in all estimated area meshes included in the analysis area.

[0039] Preferably, the storage device stores demographic information and ,raw Live information and , territory The area location information is stored.

[0040] Preferably, the human attraction force information estimation means outputs the human attraction force information superimposed on map information corresponding to the analysis target area, or together with range information indicating a range from a minimum value to a maximum value of the human attraction force information.

[0041] Preferably, the computing device performs a function as human attribute information acquisition means for acquiring human attribute information indicating the attributes of a person designated by a user, and the human attraction force information estimation means further estimates human attraction force information according to the attributes of the designated person based on the human attribute information.

[0042] Preferably, the human attraction information estimation means outputs, for each estimated area mesh, information that expresses both the relationship with the average number of people according to the attributes of people present in each estimated area mesh included in the analysis target area at a given time and the absolute number of people as human attraction information.

[0043] Preferably, the human attraction information estimation means outputs, for each estimated area mesh, the number of people according to the attributes of people present in each estimated area mesh included in the area to be analyzed at a given time divided by the average number of people in all estimated area meshes included in the area to be analyzed as human attraction information.

[0044] Preferably, the human attraction force information estimation means outputs human attraction force information indicating a difference in attraction force at a plurality of predetermined times in a time series.

[0045] Preferably, the human attraction information estimation means outputs human attraction information indicating a comparison between people within a region that covers the analysis target region and people outside a predetermined range, based on the human attribute information.

[0046] Preferably, the human attraction information estimation means outputs human attraction information indicating a comparison of age or sex of a predetermined person based on the human attribute information.

[0047] Preferably, the human attraction information estimation means outputs the human attraction information superimposed on specific location information on the map information.

[0048] Preferably, the human attraction information estimation means outputs the human attraction information together with the event information or the marketing measure information.

[0049] Preferably, the people attraction information estimation means outputs the people attraction information together with route information relating to the predetermined route.

[0050] Preferably, the system further includes an area life information estimation means for estimating area life information, which is life information for each estimated area mesh of the analysis area at a predetermined time, together with the human attraction force information, using the relationship between the life information and specified area location information including information on specified places located within the analysis area.

[0051] Preferably, the people attraction information estimation means outputs the people attraction information as information indicating the effect of a predetermined measure related to a predetermined place according to the area location information included in the analysis target area.

[0052] Preferably, the apparatus further comprises a similar area specifying means for specifying and outputting areas to be analyzed that have similar human attraction forces based on the human attraction force information relating to a plurality of areas to be analyzed.

[0053] According to another aspect of the present invention, there is provided a demographic analysis system, comprising: Within a given area a first data providing server that provides demographic information including information on the number of people located in a group of areas at a predetermined first time interval; wherein the demographic information is aggregated data of demographic data for each first time interval, and is information on the number of people by age and sex who are present in each predetermined area mesh in the first time interval,The system includes a second data providing server that provides life information at a predetermined second time interval including information on people's lives and behaviors, the second time interval being shorter than the first time interval, and a third data providing server that provides area location information including location information for each predetermined section located in a predetermined area, and a demographic analysis device, the demographic analysis device having a storage device that receives and stores the demographic information, life information, and area location information from the first, second, and third data providing servers, and a storage device that stores the demographic information, life information, and area location information. Informing and a computing device that executes analysis processing of demographic information based on the demographic information, and the computing device performs the function of area life information estimation means that estimates life information for each predetermined section, and the area life information estimation means further performs the functions of: i) estimating the number of employed people by occupation type located in the predetermined area for each second time interval based on the demographic information and the life information; ii) apportioning the number of employed people by occupation type to each facility based on the total floor area and the area allocated to the occupation type of each facility, estimating aggregate data of the number of people in an estimated area mesh that is smaller than the predetermined area mesh of the demographic information; and image generation means that generates the estimated result of the people attraction information as image information. [Effects of the Invention]

[0054] According to the present invention, it is possible to estimate the trends in the movement of people in a given area at a given time based on demographic information, and the estimated demographic information can be used in a variety of ways by businesses, researchers, government officials, and others.

[0055] Furthermore, according to the present invention, the relative human attraction power of each of multiple estimated area meshes included in the analysis area specified by the user at a specified time can be presented as a display that the user can easily recognize. [Brief explanation of the drawings]

[0056] [Figure 1]1 is a diagram showing an overview of a lifestyle information estimation system according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a functional block diagram showing a system overview of a lifestyle information service providing server 2000. [Figure 3] FIG. 2 is a diagram illustrating the hardware configuration of a lifestyle information service providing server 2000. [Figure 4] FIG. 10 is a diagram illustrating the contents of data provided by data providing servers 5000.1 to 5000.M. [Figure 5] FIG. 10 is a first diagram illustrating the processing flow of the life information service providing server 2000. [Figure 6] FIG. 10 is a second diagram illustrating the processing flow of the life information service providing server 2000. [Figure 7] FIG. 2 is a diagram showing an outline of data output from a calculation device 2100. [Figure 8] FIG. 10 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a first time point. [Figure 9] FIG. 10 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a second time point. [Figure 10] FIG. 10 is a diagram illustrating the concept of processing for improving the accuracy of spatial resolution in the behavior-specific number of people estimation processing (S120, S210). [Figure 11] FIG. 10 is a diagram showing another example of a map image and a superimposed image of the number of people distribution output by the map output unit 2104. [Figure 12] FIG. 2 is a diagram showing functional blocks when the lifestyle information service providing server 2000 functions as an analysis device for demographic information. [Figure 13] FIG. 10 is a diagram illustrating a process flow for analyzing demographic information. [Figure 14] FIG. 10 is a diagram showing a state in which an analysis target area AR is specified on an estimated area mesh. [Figure 15] FIG. 20 is a diagram showing an example of displaying the results of the human attraction information estimation unit 2105 estimating the human attraction at different times for a specified analysis target region. [Figure 16] 10 is a diagram for explaining another mode of displaying the estimation result by the human attraction force information estimation unit 2105. FIG. [Figure 17] FIG. 1 is a conceptual diagram showing the hierarchy of data and the hierarchy of data processing used in a lifestyle information estimation system. [Figure 18] FIG. 1 is a conceptual diagram showing an overview of the flow of data processing. [Figure 19] FIG. 1 is a diagram showing an overview of services realized using a lifestyle information estimating system 1000. [Figure 20] FIG. 20 is a conceptual diagram showing an example of the service of FIG. 19 provided via a smartphone. [Figure 21] FIG. 10 is a diagram illustrating another example of data visualization by integrated analysis. [Figure 22] FIG. 1 is a diagram showing an overview (1) of conventional mobile spatial statistics. [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 of the prior art. [Figure 25] FIG. 1 is a diagram showing the contents of the Basic Survey on Social Life in the Background Art. DETAILED DESCRIPTION OF THE INVENTION

[0057] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0058] The following describes an information processing system for analyzing demographic trends (hereinafter referred to as a "demographic analysis system") and the configuration of a demographic analysis device for analyzing demographic information in the demographic analysis system.

[0059] As will be explained below, the demographic analysis device in this embodiment is a device that estimates the movement of people in a specified area during a specified time period based on demographic information collected as a series of time series for the area, more specifically, the tendency for people to gather in a specific place (people attraction).

[0060] Therefore, in other words, the demographic analysis device is an analysis device that shows the relative human attraction for each of the multiple estimated area meshes, as described below, included in the analysis area specified by the user for a given time.

[0061] FIG. 1 is a diagram showing an overview of a demographic analysis system according to a first embodiment of the present invention.

[0062] Referring to FIG. 1, a demographic analysis system 1000 according to the first embodiment of the present invention includes a lifestyle information service providing server 2000 that operates as a demographic analysis device, a lifestyle information user terminal 3000 (for example, there may be a plurality of terminals 3000.1 to 3000.N, where N is a natural number of 2 or more; hereinafter, collectively referred to as "life information user terminal 3000") of a user that receives services from the lifestyle information service providing server 2000, data providing servers 5000.1 to 5000.M (M is a natural number) that provide the lifestyle information service providing server 2000 with data necessary for estimating lifestyle information as described below, and a mobile phone or smartphone 200 (for example, there may be a plurality of smartphones 200.1 to 200.M, where M is a natural number of 2 or more; hereinafter, collectively referred to as "smartphone 200") that is used by a user 100 and that receives the provision of services as described below.

[0063] FIG. 2 is a functional block diagram showing an outline of a system when the lifestyle information service providing server 2000 functions as a lifestyle information estimating device.

[0064] 4 is a diagram for explaining the contents of data provided by the data providing servers 5000.1 to 5000.M. Note that hereinafter, when the data providing servers 5000.1 to 5000.M are collectively referred to, they will simply be referred to as "data providing servers 5000."

[0065] Referring to FIG. 2, the life information service providing server 2000 includes a computing device 2100 and a storage device 2300. The computing device 2100 and the storage device 2300 include: a processor 2100;

[0066] 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.

[0067] Here, "predetermined time" refers to, for example, a first predetermined time interval, for example, every hour, and "predetermined area" refers to, for example, an area in a specific region, for example, a first area mesh unit, for example, a 500m mesh (500m square). "Predetermined demographic information" refers to "demographic data" and "aggregated data," and corresponds to, for example, the aforementioned "Mobile Spatial Statistics Information (registered trademark)," as shown in Figure 4.

[0068] Furthermore, the storage device 2300 stores predetermined "lifestyle information 2306." The "lifestyle information 2306" includes at least information related to a person's life. Here, the predetermined "lifestyle information 2306" is, more specifically, information on a classification of predetermined activities by region, age, and gender, at a second time interval that is shorter than a first predetermined time interval and is preferably a divisor of the first time interval. Here, the "lifestyle information" corresponds to, for example, information from a database based on the Ministry of Internal Affairs and Communications' "Basic Survey on Social Life," which classifies activities into 20 predetermined types by region, age, and gender, and in 15-minute increments, as shown in FIG. 4. Here, the "categorization of activities" corresponds to, for example, categories such as "sleeping," "commuting," "school commuting," "working," and "scholarship."

[0069] The storage device 2300 also stores "employment information by occupation 2308." Here, the "employment information by occupation 2308" is data indicating the employment ratio of each occupation by age and sex, and corresponds to, for example, the information in the Ministry of Internal Affairs and Communications' Labor Force Survey Database, as shown in FIG. 4.

[0070] The "predetermined lifestyle information" may include such "occupation-specific employment information."

[0071] Therefore, using the "demographic information 2302" and the "predetermined lifestyle information 2306," the lifestyle information estimation unit 2110 can estimate the number of people by activity (hereinafter referred to as "number of people by activity") present in the "predetermined area" set by the area setting unit 2106 in response to a user's instruction during the "second time interval." Furthermore, by applying the "employment information by occupation" to people with activity categories such as work and study using the "number of people by activity" estimated in this way, the lifestyle information estimation unit 2110 can calculate data on the "occupation" classification of people present in the "predetermined area" during the "second time interval." Note that "occupation" does not only refer to so-called "type of occupation," but also to "work" such as "study" that is not business but in which a person is explicitly engaged in the same activity for a certain period of time or more in a certain location (facility or building).

[0072] The storage device 2300 further stores predetermined "area location information 2310." Here, "predetermined area location information" refers to "information on predetermined locations (including information on the occupations at those locations) that exist in the above-described predetermined area." For example, the "area location information" preferably includes information on buildings and facilities within the second area mesh unit, and information on the occupations performed within those buildings and facilities, for an area of ​​a second area mesh unit that is smaller than the above-described first area mesh unit, preferably a rectangle whose sides are divisors of the first area mesh unit. For example, as shown in FIG. 4, the "area location information 2310" corresponds to map data in 62.5-meter mesh units, including information on city buildings and facilities, information on the occupations performed within those buildings and facilities, and information on roads and water areas. Some of this "map data" is provided by private companies.

[0073] 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 located in the "specified location" of the "specified area" set by the area setting unit 2106.

[0074] Therefore, the lifestyle information estimation unit 2110 of the calculation device 2100 of the lifestyle information service providing server 2000 calculates and outputs information on the work of people who are located at a specified location in a specified area at the second time interval (hereinafter referred to as "people's area lifestyle information") by expanding (for example, by proportional allocation) the "number of people per occupation" into the "area corresponding to each occupation" of the "specified location."

[0075] 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.

[0076] As a relationship between predetermined lifestyle information and predetermined area location information, for example, during a certain time period, it is possible to assume that doctors and nurses work in hospital facilities, farmers work in farmland, fishermen work in fishing grounds, housewives raise children, or pensioners live in homes. Also, during commuting hours, a certain percentage of people will have the attribute "movement." Specific processing for this will be described later.

[0077] Furthermore, the map output unit 2104 generates and outputs a screen that displays the estimated "people's area life information" in association with each "predetermined place" in the "predetermined area" (for example, superimposed on the map information). By generating such an output, the user can visually grasp the life information of people in the "predetermined area" that they have specified.

[0078] As explained above, since the "number of people by activity" or the "number of people corresponding to people's area life information" is calculated for each area in the second area mesh unit and at the second time interval, the people flow path information estimation unit 2102 can be configured to track changes in the number of people distribution for the second area mesh unit area and its surrounding areas along the time axis and integrate this with "information on routes that people can move along" on the map data (for example, roads, railways, etc.) to estimate and output people flow path information (the routes along which people moved). The output result of the people flow path information output unit 2102 is stored in people flow path information 2312 in the storage device 2300.

[0079] The storage device 2300 also stores "traffic route information 2304." Here, the "traffic route information 2304" includes the following data located in a predetermined area: Route information on road maps, information on traffic route nodes such as the location of highway interchanges (ICs) - Average travel speed on a road at a given time on a given day based on past road congestion information Location of public transport (train, bus, etc.) stations and stops Public transport (train, bus, etc.) routes on maps Public transport timetables (average travel speed can also be calculated based on timetable and route information)

[0080] However, the data included in the transportation route information 2304 is not limited to these, as long as it is information related to transportation facilities.

[0081] The traffic route information 2304 stores information as described below, and by relating people flow information to traffic route information, the people flow path information estimation unit 2102 can also estimate information on the amount of people moving along each traffic route, and stores the estimation results in the storage device 2300 as people flow path information 2312.

[0082] FIG. 3 is a diagram illustrating the hardware configuration of the life information service providing server 2000. As shown in FIG.

[0083] 3, the life information service providing server 2000 may be configured such that an internal arithmetic unit (CPU: Central Processing Unit) executes arithmetic processing, or such that part of the program processing is executed on another server. In the following description, it is assumed that an internal arithmetic unit executes arithmetic processing.

[0084] Referring to FIG. 3, the server 2000 includes a computer device 2010, a network communication unit 2012 for communicating with a network, a recording medium (e.g., a memory card) 2210 for recording data from the outside and providing the data to the computer device 2010, a keyboard 2400 as an input device, and a display 2420 as a display device.

[0085] For example, a USB memory, a memory card, or an external storage device can be used as recording medium 2210. Also, for example, a wired LAN or wireless LAN communication function can be used as network communication unit 2012. Network communication unit 2012 and input / output interface 2090 constitute a communication interface.

[0086] As shown in FIG. 3, the computer main body constituting this computer device 2010 includes, in addition to a disk drive 2030 and a memory drive 2020, a CPU (Central Processing Unit) 2100, memory 2200 including a ROM (Read Only Memory) 2200.1 and a RAM (Random Access Memory) 2200.2, each connected to a bus 2050, a nonvolatile rewritable storage device 2300, and an input / output interface 2090 for communicating via a network and exchanging data with external devices. The nonvolatile storage device 2300 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD). The following description will be given assuming that it is an SSD. An optical disk can be inserted into the disk drive 2030. A memory card 2210 can be inserted into the memory drive 2020.

[0087] When the computer device 2010 programs run, the data and programs that store the information that is the basis for the computer's operation will be described as being stored in the SSD 2300.

[0088] 3, the medium capable of recording information such as a program to be installed in the computer main body may be, for example, a DVD-ROM (Digital Versatile Disc), a memory card, a USB memory, etc. To accommodate such cases, the computer main body is provided with a drive device (memory drive 2020, disk drive 2030) capable of reading these media.

[0089] The main components of the computer device 2010 are computer hardware and software executed by the CPU 2100. Generally, such software is stored in a storage medium and distributed or distributed via a network, and is obtained via the disk drive 2030 or the network communication unit 2012 and temporarily stored in the SSD 2300. The software is then read from the SSD 2300 into the RAM 2200.2 in the memory and executed by the CPU 2100. Note that when connected to a network, the software may be directly loaded into the RAM and executed without being stored in the SSD 2300.

[0090] When distributing a program for functioning as computer system 2010, the program does not necessarily include an operating system (OS) that causes computer system 2010 to execute functions such as an information processing device. The program only needs to include instructions that call appropriate functions (modules) in a controlled manner and achieve the desired results. How computer system 2010 operates is well known, and a detailed description thereof will be omitted.

[0091] Furthermore, the CPU 2100 may be a single-core processor or a multi-core processor. That is, it may be a single-core processor or a multi-core processor. The server 2000 may also be configured with multiple servers to perform distributed processing.

[0092] 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. (Estimation process of life information within a specified area)

[0093] Below, as a premise for the process of estimating demographic information by the life information service providing server 2000, the process of estimating the number of people by activity and the number of people in a facility based on the life information of people present in a predetermined area during a predetermined time period will be described.

[0094] Fig. 5 is a first diagram illustrating the processing flow of the activity-specific number of people estimation and facility number of people estimation performed by the life information service providing server 2000. Fig. 6 is a second diagram illustrating the processing flow of the activity-specific number of people estimation and facility number of people estimation performed by the life information service providing server 2000.

[0095] FIG. 7 is a diagram showing an outline of data output from the arithmetic device 2100. As shown in FIG.

[0096] FIG. 8 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number-of-people estimation at a first time point.

[0097] FIG. 9 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a second time point.

[0098] 5 and 6, when the processing for estimating the number of people by behavior and estimating the number of people in a facility starts, the arithmetic device 2100 acquires spatial statistical data via the network (S110, S200).

[0099] As mentioned above, spatial statistical data can be obtained using Mobile Spatial Statistics (registered trademark) provided by NTT Docomo. Spatial statistical data is, for example, data on the number of people by age and sex within a mesh of a predetermined width (for example, 500m square) for a predetermined time period (for example, 1 hour).

[0100] Next, for a "predetermined area" designated by the area setting unit 2106 based on the user's specification, the lifestyle information estimation unit 2110 classifies people (whose age and gender are obtained from the spatial statistical data) who are staying in a specific area (for example, the Umeda area of ​​Osaka) during a specific time period (for example, a specific hour) into states (behavior classification information) such as work and movement (for example, commuting) in more detailed time units (for example, 15 minutes) within this specific time period, referring to the Basic Survey on Social Use and Leisure Activities database, and estimates the number of people (attributes of age and gender) in each category (S120, S210).

[0101] The bottom part of Figure 7(a) shows an overview of this type of behavior-specific number estimation.

[0102] The lower graph in Figure 8 shows the estimated number of people by activity in a specific area at 2 a.m. Judging from the time of day, most people's activity is classified as "sleeping."

[0103] The bottom graph in Figure 9 shows the estimated number of people by activity in a specific area at 3:00 PM (3:00 PM). Judging from the time of day, most people are classified as "working" or "studying."

[0104] Returning to Figures 5 and 6, the lifestyle information estimation unit 2110 further estimates the number of employees (age and gender attributes) by occupation type for every 15 minutes for the specific area based on the estimated number of people (age and gender attributes) for each behavior category, for example, based on the Ministry of Internal Affairs and Communications Labor Force Survey database (S130, S220).

[0105] Next, the area location information estimation unit 2108 distributes (for example, proportionally apportions) the estimated number of employees (attributes of age and gender) by occupation every 15 minutes to each building (facility) based on the total floor area of ​​the facilities and buildings on the map and the area allocated to the occupation of each facility and building, and estimates aggregated data on the number of people in a mesh smaller than the unit mesh of the spatial statistical data (for example, a 62.5 m mesh) (S140, S230).

[0106] The upper part of Figure 7(a) shows an overview of such facility occupancy estimation.

[0107] The upper diagram in Figure 8 shows the results of facility population estimation in a specific area at 2 a.m. Judging from the time of day, most people are located in "residences" or "hotels and inns."

[0108] The upper graph in Figure 9 shows the results of facility population estimation in a specific area at 3:00 PM (15:00). Based on the time of day, it can be seen that most people in each facility are assigned to the "occupation" or "task" that corresponds to the facility or building.

[0109] 5 and 6, the map output unit 2104 further superimposes the number of people displayed for each facility or building on a map and outputs the number of people by color-coding, for example, a heat map (S140, S240). Here, when displaying the heat map, a chart showing the proportion of people performing tasks at that location may be displayed by color-coding.

[0110] FIG. 7(b) is a diagram showing an example of an output that visualizes such a population distribution.

[0111] FIG. 10 is a diagram showing the concept of the process for improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210).

[0112] 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).

[0113] Because the units of the data do not match, analysis usually ends up being performed based on data with larger units.

[0114] However, as mentioned above, in the life information service providing server 2000, people flow data (including population distribution data at a specific time point), building data, facility data, land data (including information on roads, railways, roads and water areas), etc. are all decomposed and converted into the same unit (area mesh) and stored.

[0115] Such a database stored in a uniform unit will be referred to as a "data lake." Therefore, for example, it is possible to perform real-time analysis in the same unit (area mesh) at any point in Japan.

[0116] As described above, the same area mesh is a mesh with one side measuring 62.5 m, as shown in FIG.

[0117] If we set the resolution to 62.5 m, since Mobile Spatial Statistics (registered trademark) uses a mesh with sides of 500 m, we can create an area mesh with sides that are 1 / 8 (1 / 8) of this power of 2, which is approximately the distance a person walks in one minute. When using other spatial statistics, it is also possible to unify the area mesh with sides that correspond to the distance a person walks in one minute in a similar manner. By using the distance a person walks in one minute, we can say that this resolution is suitable for analyzing people flow, including people who are moving by foot, for example.

[0118] However, it is possible to divide it into units smaller than 62.5 m (for example, the smallest unit of 1 m). However, this would increase the amount of data to be analyzed and the amount of calculation, so from the perspective of cost-effectiveness, it is desirable to use, for example, 62.5 m.

[0119] FIG. 11 is a diagram showing another example of a map image and a superimposed image of the number of people distribution output by the map output unit 2104. In FIG.

[0120] As shown in Figure 11, it is possible to designate an area of ​​a certain size as the predetermined area, and then allow the user to further designate a portion of that "predetermined area" in which the estimation process will be performed. (Estimation of demographic information)

[0121] The following describes the process of analyzing and displaying demographic information of people present in a predetermined area during a predetermined time period, performed by the life information service providing server 2000.

[0122] FIG. 12 is a diagram showing functional blocks when the life information service providing server 2000 functions as an analysis device for demographic information.

[0123] The configuration of Figure 12 explains the software functions for executing the "demographic analysis processing" as described below in the configuration of the life information service providing server 2000 shown in Figure 2, and the configuration of the data required to execute such functions.

[0124] There are many common parts between the processing described in the configuration of Fig. 2 and the processing executed in the configuration of Fig. 12. Therefore, although the functional blocks described in Fig. 2 may be omitted from the illustration as appropriate, the same functions are executed by the same functional blocks in Fig. 2 and Fig. 12. Furthermore, the same parts in Fig. 2 and Fig. 12 are given the same reference numerals, and the description thereof will not be repeated.

[0125] Referring to Figure 12, when performing the process of estimating demographic information, the arithmetic device 2100 of the lifestyle information service providing server 2000 executes, in addition to the functions of Figure 2, the following functions: an analysis target area information acquisition unit 2101 that acquires information on the area to be analyzed specified by the user via the user terminal 3000 (hereinafter, "analysis target area information") and the time or time period to be analyzed; a person attribute information acquisition unit 2103 that acquires information on the attributes of people to be analyzed within the analysis target area (hereinafter, "person attribute information") based on the user's specification on the user terminal 3000; and a person attraction information estimation unit 2105 that indicates the relative person attraction for each area unit (each estimated area mesh) within the analysis target area.

[0126] Here, "people attraction" refers to, for example, the number of people present in each estimated area mesh included in a specified analysis area at a given time compared to the average number of people present in all estimated area meshes included in the specified analysis area, and more specifically, the number of people present in each estimated area mesh divided by the average. Therefore, within the analysis area, an estimated area mesh that has more people gathered there than average compared to other estimated area meshes is defined to have a greater ability to attract people (people attraction).

[0127] Furthermore, the arithmetic device 2100 performs the functions of a region life information estimation unit 2107 that estimates region life information, which is life information for each estimated region mesh about people who are located in the region to be analyzed at a specified time, in addition to estimated information about human attraction force information based on the above-mentioned "relationship between specified life information and specified region location information," and a similar region identification unit 2109 that identifies and outputs regions to be analyzed that have similar human attraction force based on human attraction force information for multiple regions to be analyzed.

[0128] Here, "similar human attraction" may mean that there is a high degree of similarity between the magnitude of human attraction (relative to the average value) and the spatial distribution of estimated area meshes of a predetermined size or larger, or that there is a high degree of similarity between the area life information in estimated area meshes of a predetermined size or larger.

[0129] The arithmetic device 2100 also performs the functions of a user matching unit 2111 that notifies the user who specified a plurality of analysis target areas with similar human attraction identified by the similar area identification unit 2109, for example, to other user terminals 3000.i, that there are other users who are interested in areas with similar human attraction; an area evaluation unit 2115 that generates evaluation results for the analysis target area based on the strength of human attraction, the spatial distribution of areas where human attraction is at or above a predetermined level, and area lifestyle information for areas where human attraction is at or above a predetermined level; and an analysis report distribution unit 2113 that distributes the results of the demographic analysis analyzed by a single user or multiple users and generated by the area evaluation unit 2115 as a report.

[0130] The function of the user matching unit 2111 enables a user of the lifestyle information service providing server 2000 to obtain information such as, for example, how many other users exist within a certain period of time who are interested in an analysis area that has characteristics similar to the analysis area that the user has or will be analyzing.

[0131] In addition to the information stored in FIG. 2, the storage device 2300 stores analysis target area information 2301 specified by the user, human attribute information 2303, human attraction force information 2305 estimated by the human attraction force information estimation unit 2105, and area life information 2311 estimated by the area life information estimation unit 2107.

[0132] FIG. 13 is a diagram illustrating the flow of analysis processing of demographic information.

[0133] When the process starts, the analysis target area information acquisition unit 2101 of the arithmetic device 2100 acquires the analysis target area information and the information on the time or time period to be analyzed in response to an instruction from the user terminal 3000 (S102).

[0134] FIG. 14 is a diagram showing the state in which the analysis target area AR has been identified on the estimated area mesh. For ease of explanation, FIG. 14 illustrates the analysis target area AR as having a rectangular shape. However, the shape of the analysis target area AR is not limited to such a rectangular shape and may have any shape in response to an instruction from the user terminal 3000. While not particularly limited, the any shape may or may not follow the shape of the estimated area mesh MC. If the shape does not follow the shape, the number of people within the estimated area mesh can be proportionally apportioned according to the size of the area divided by the boundary between the estimated area meshes.

[0135] In FIG. 13, the process from steps S110 to S140 is the same as the process of estimating life information described in FIG. 5, and therefore description thereof will not be repeated.

[0136] In practice, for example, the data calculated in the processing from steps S110 to S140 in the data lake can be temporarily stored as common data, and can be configured to be shared in the processing for estimating the number of people by behavior and the number of people in facilities described in Figure 5 and the processing for analyzing demographic information described in Figure 13.

[0137] 13, following the processing of steps S110 to S140, the human attraction information estimation unit 2105 of the arithmetic device 2100 calculates human attraction information for each estimation area mesh (S170). As will be described later, at this time, if the human attribute to be analyzed is designated by the user and the human attribute information acquisition unit 2103 has received information on the human attribute, the human attraction is estimated for a person having the designated human attribute.

[0138] Then, the human attraction force information estimation unit 2105 of the arithmetic device 2100 generates data for displaying the human attraction force information as described below, and returns the data to the user terminal (S180).

[0139] Therefore, when the lifestyle information service providing server 2000 executes an analysis process of demographic information of people located within a specified area during a specified time period, the analysis target area information acquisition unit 2101 acquires analysis target area information indicating the analysis target area specified by the user, and the people attraction information estimation unit 2105 estimates people attraction information indicating the relative people attraction for each second area mesh unit (estimated area mesh) smaller than the first area mesh unit, based on the specified demographic information including information on the number of people located in multiple first area mesh units included in the analysis target area at a "specified time (hour or time period)."

[0140] Here, as in the case of Figure 2, "predetermined time" refers to, for example, "first predetermined time intervals," e.g., every hour, and "predetermined area" refers to, for example, an area in a specific region in a first area mesh unit, e.g., a 500m mesh (500m square). "Predetermined demographic information" refers to "demographic data" and "aggregated data," such as the aforementioned "Mobile Spatial Statistics Information (registered trademark)."

[0141] Furthermore, the predetermined "lifestyle information 2306" here, more specifically, refers to information on the classification of predetermined activities by region, age, and gender, over a "second time interval" shorter than the first predetermined time interval, preferably a divisor of the first time interval. Here, "lifestyle information" corresponds to, for example, information from a database based on the Ministry of Internal Affairs and Communications' "Basic Survey on Social Life," which classifies activities into 20 predetermined types by region, age, and gender, and in 15-minute increments. Furthermore, "predetermined area location information" refers to "information on predetermined locations within the above-mentioned predetermined area (including information on the occupations at those locations)." Preferably, the "area location information" includes information on buildings and facilities within the area of ​​a second area mesh unit (estimated area mesh), which is smaller than the above-mentioned first area mesh unit and preferably has sides that are divisors of the first area mesh unit when the first area mesh unit is considered as a rectangle, and information on the occupations performed within those buildings and facilities.

[0142] FIG. 15 is a diagram showing an example of displaying the results of the human attraction information estimation unit 2105 estimating the human attraction at different times for the analysis target area specified as in FIG.

[0143] Here, the different times are assumed to be the time t=T1 and a later time t=T2.

[0144] For example, as shown in Figure 15(a), at time t = T1, areas B01 and B02 within the analysis area AR contain areas where more people than the average number of people within the analysis area AR are gathered (areas with relatively high human attraction).

[0145] In this case, the display is assumed to be a heat map color-coded into multiple stages (for example, seven stages in FIG. 15) within the range of the maximum and minimum values ​​of the number of people (or people attraction force) in the estimated area mesh within the area to be analyzed during the time period from time T1 to T2. Note that if only a specific time is the subject of analysis, the heat map display may be within the range of the maximum and minimum values ​​of the number of people (or people attraction force) in the estimated area mesh within the area to be analyzed at that time.

[0146] Furthermore, as shown in Figure 15(b), at time t = T2, areas with relatively high human attraction are concentrated in area B03 within the analysis area AR, and area B03 has a greater human attraction than in Figure 15(a).

[0147] 15 is configured to display the results of estimating human attraction force at different times, but the display form of the estimation results by the human attraction force information estimation unit 2105 may also be a difference in attraction force between two times in a time series. This difference can also be displayed as a heat map.

[0148] FIG. 16 is a diagram for explaining another mode of displaying the estimation result by the human attraction force information estimation unit 2105.

[0149] In FIG. 16, map information is displayed superimposed on the estimated results of people attraction for the analysis target area AR.

[0150] 16 shows an example of map information that displays information such as roads Rd, railways RT, and stations ST. However, the map information is not limited to this, and may also display information on man-made objects such as buildings, rivers, and elevations.

[0151] In the example of FIG. 16, the area of ​​station ST has a high attraction for people, and the heat map using range information shows that people are densely located there.

[0152] 16, the heat map is displayed so that it is possible to compare the range (range) of the maximum and minimum values ​​of the number of people (or human attraction) in the estimated area mesh at the time of analysis with the average value. However, the representative value of the human density in the area to be analyzed is not limited to the average value, and other statistical representative values ​​such as the median number of people may also be used.

[0153] Furthermore, rather than simply comparing with the "average value," it is also possible to adopt expressions using "standardization" or "normalization" for the numerical values ​​of the human attraction force within the estimated area mesh.

[0154] Furthermore, the heat map display can be not only a relative comparison, such as a comparison with the average value within the range of the maximum and minimum number of people, but also a step-by-step display of the absolute values ​​of the number of people or human density within the estimated area mesh. In Figure 16, the user's selection of relative value display is indicated by a rectangular frame around the display area of ​​the relative value level. The user can also select to switch to absolute value display.

[0155] It should be noted that the user may be allowed to select whether or not to display the information superimposed on the map information.

[0156] Therefore, the people attraction information estimation unit 2105 can generate display data of the people attraction information by overlaying it on map information corresponding to the analysis target area or together with the range information of the people attraction information. The map information and range information may be displayed simultaneously. This allows the user to recognize the people attraction information for each estimated area mesh while recognizing the relationship with the map information in an intuitive and easy-to-understand manner and the range information that indicates the characteristics of the analysis target area and the time-series analysis target period.

[0157] FIG. 16 also illustrates a configuration in which the user can select age (generation) as a human attribute for which human attraction is to be estimated.

[0158] For example, the ages of people to be estimated are divided into stages such as "teens," "twenties," "60s," and "over 70." Figure 16 shows the state in which the user has selected "teens," "twenties," and "50s" as ages to be estimated (selected age groups are displayed in gray).

[0159] Of course, the attributes of people to be estimated are not limited to age (generation), but may also be other attributes included in demographic data, such as place of residence, base of life, and gender.

[0160] Therefore, the human attraction information estimation unit 2105 estimates human attraction information according to the specified human attributes, based on the human attribute information indicating the attributes of people specified by the user, acquired by the human attribute information acquisition unit 2103. This makes it possible to analyze the relative human attraction for each estimation area mesh according to the human attributes specified by the user.

[0161] 16, the people attraction information estimation unit 2105 is configured to display people attraction together with the means of transportation, making it possible to display people attraction information overlaid on route information (for example, route location information, information on timetables for transportation (railways, buses, etc.), road width information, distance and elevation difference between bus stops, etc.) related to a specific route (for example, roads, railways, buses, airports, etc.). This makes it possible to grasp, for example, the usage status of each bus stop on a certain bus route by time period and, if necessary, by person attributes.

[0162] Furthermore, the people attraction information estimation unit 2105, although not limited to, estimates information expressing either a relative relationship with the average number of people according to the attributes of the people present, or an absolute number of people, or both relative and absolute values, as people attraction information for the analysis target area at a given time for each estimated area mesh included in the analysis target area, and outputs a corresponding image.Then, it is possible to configure the unit to generate and output such images as image information that can be compared simultaneously for at least two analysis target areas.

[0163] This allows users to simply view and evaluate the output information, clearly distinguishing between above-average and below-average levels of human attraction in relation to the average value, while simultaneously recognizing the absolute level. This allows accurate evaluation within each analysis target area, while also enabling evaluation across multiple analysis target areas.

[0164] Furthermore, by changing the method for calculating the relative values ​​of the heat map when the human attraction information estimation unit 2105 displays the human attraction information in a heat map according to the purpose, the human attraction information for each area can be easily recognized as a deviation from the average value.

[0165] For example, suppose you specify "Osaka Prefecture" as the area to be analyzed and "outbound visitors" as a person attribute. In this case, if you want to know the "attraction power of outbound visitors to a specific theme park," the denominator for calculating the relative value is "number of outbound visitors located within Osaka Prefecture / number of estimated area meshes in Osaka Prefecture" = (outbound visitors per estimated area mesh). By setting the denominator in this way, you can calculate the attraction power by dividing the number of outbound visitors within each estimated area mesh by this denominator. If you display a heat map of people attraction for estimated area meshes that include a specific theme park within the analysis area, this will show the attraction power of the specific theme park for visitors from outside the prefecture.

[0166] Similarly, if you specify "15 years old" as a person attribute and want to know the "attraction of a specific theme park to 15-year-olds," the denominator would be "number of 15-year-olds located within Osaka Prefecture / number of estimated area meshes in Osaka Prefecture" = (number of 15-year-olds per estimated area mesh), and using this as the denominator, you can display a heat map of the human attraction for the estimated area meshes included in the specific theme park, which will show the attraction of the specific theme park to the population of 15-year-olds in the prefecture.

[0167] Alternatively, if you specify "Izumo City" as the area to be analyzed and "30-year-old women" as the person attribute and want to know the "attraction power of Izumo Taisha Shrine for 30-year-old women," the denominator would be "Number of 30-year-old women located in Izumo City / Estimated number of area meshes for Izumo City" = (Number of 30-year-old women per estimated area mesh).

[0168] Furthermore, if the place of residence is selected as the attribute information of a person, the people attraction information estimation unit 2105 can estimate people attraction information that indicates the comparison between people inside and outside a specified area to be analyzed at a specified time.

[0169] Furthermore, the people attraction information estimation unit 2105 can generate data that displays people attraction information overlaid with location information within the area to be analyzed, rather than simply map information. Here, "location information" refers to, for example, information about stores located in the building (e.g., information about medical departments located in a hospital building, information about convenience stores, etc.), information about fishing grounds included in the water area, etc.

[0170] Furthermore, the people attraction information estimation unit 2105 can generate data that displays people attraction information overlaid with event information for a specific location within the area to be analyzed, rather than simply map information. Here, "event information" includes not only events such as concerts that are actually held at the location, but also marketing measure information (measures and events that attract people to the location, such as television commercials and online advertisements)).

[0171] This makes it possible to evaluate the performance of actual events such as concerts, evaluate methods of attracting customers (marketing measures) such as commercials, and analyze the difference with the target number of customers for local government facilities. For example, if people with attributes that should be expected to attend an event are not attending, it becomes possible to take measures to target those attributes.

[0172] The area lifestyle information estimation unit 2107 can also be configured to estimate area lifestyle information of people located in each estimated area mesh of the analysis target area at a given time, along with estimating people attraction information, based on the relationship between predetermined lifestyle information (e.g., 20 types of behaviors classified according to the Ministry of Internal Affairs and Communications' "Basic Survey on Social Life") and predetermined area location information for each estimated area mesh. The people attraction information estimation unit 2105 can also generate people attraction information as information indicating the effects of predetermined measures or events (e.g., store openings, bus route developments, station redevelopment, etc.) related to the area location information included in the analysis target area by analyzing the area lifestyle information together with the people attraction information. For example, if the effect of a store opening is to estimate the people attraction of the estimated area mesh including the store, it is possible to identify people whose behavior is "shopping" and estimate the number of people in the estimated area mesh.

[0173] The human attraction information estimation unit 2105 can also be configured to identify and output analysis target areas with similar human attraction based on human attraction information for multiple analysis target areas. In this case, by using a generation AI as an interface with the user to lower the hurdle for information input, it becomes possible to collect information from many users and provide users with information on analysis target areas with similar human attraction.

[0174] For example, the user matching unit 2111 can be configured to notify a user who has specified multiple areas to be analyzed that have similar human attraction forces as identified by the human attraction information estimation unit 2105 in the above manner (including in an interactive format via chat).

[0175] In this way, it is possible to identify similar areas from the perspective of, for example, places where "teenage girls" gather to "shop" and show them to the user.

[0176] FIG. 17 is a conceptual diagram showing the hierarchy of data and the hierarchy of data processing used in the demographic analysis system.

[0177] FIG. 18 is a conceptual diagram showing an outline of the flow of data processing.

[0178] As shown in Figure 17, the "public domain," which is a public and open domain, includes, for example, the "urban OS" and data integration platform, which are "software that supports urban infrastructure."

[0179] In Japan, for example, the following Cabinet Office documents are publicly available. Publicly known documents: https: / / www8.cao.go.jp / cstp / stmain / a-whitepaper3_200331.pdf

[0180] Here, there are three challenges to realizing smart cities in Japan: i) reuse and horizontal deployment of services, ii) utilization of data across fields, and iii) low scalability.

[0181] i) In terms of service reuse and horizontal expansion, traditionally systems have been individually specialized for each field or organization, which makes it difficult to reuse or expand services to other regions.

[0182] ii) In the case of cross-sector data utilization, traditional services have data that is independent for each sector and organization, making it difficult to build new cross-sector services.

[0183] iii) Regarding low scalability, conventional specialized systems have the problem that the cost and effort required for functional expansion is high, making it difficult to continuously and easily evolve services.

[0184] To address these challenges in realizing smart cities in Japan, the City OS will be designed to have the following characteristics: i) interoperability (connectivity), ii) data distribution (flow), and iii) ease of expansion (continuity).

[0185] The "demographic analysis system area" of this embodiment is conceptually structured on such a foundation.

[0186] As shown in Figure 17, in addition to "Docomo Spatial Statistics (registered trademark)" as shown in Figure 4, the demographic analysis system 100 is also expected to use weather data for a specific time period in a specific area obtained from the Japan Meteorological Agency server, and "disaster risk information" obtained from the National Research Institute for Earth Science and Disaster Prevention.

[0187] When such data is stored in the aforementioned "data lake," it is first stored as raw, general-purpose data, and then finalized for a certain time period, and stored as a "uniform format for demographic analysis systems (for example, unifying the unit area mesh)," as described above.

[0188] 17 and 18, it is anticipated that data in a general-purpose data format provided to the "Data Lake" will be submitted from a smartphone via a smartphone application, for example. The submitted data may be subjective evaluation data on the congestion situation at a specific facility in a specific location, or may be photographic or video data of the congestion situation.

[0189] Furthermore, as shown in Figures 17 and 18, above the "data lake" there is a data analysis layer, which corresponds to the data integration and analysis processing performed by the lifestyle information service providing server 2000 described above.

[0190] The system is structured so that the part that adds some kind of evaluation to the analysis results, or collaborates with other companies on the analysis results, and finally visualizes and presents them exists at a higher data display level.

[0191] In particular, while spatial statistics are based on an hourly basis, the processing described above makes it possible to analyze and visualize data in 15-minute increments, which is almost real time.

[0192] As shown in Figure 18, after the above-mentioned integrated analysis process is performed, facilities in the city are evaluated based on the information, and people are allocated to each facility. Therefore, since the people flow distribution is created from independent information (people flow → behavior → city information), the people flow distribution also changes when the "city information (general map information)" is updated. Therefore, it is possible to visualize not only current real-time information, but also people flow distribution in the city in the past or future, which differ from the present.

[0193] For example, using city information from the Edo period allows us to estimate the distribution of people at that time and visualize life in the city at that time, while using city information from virtual worlds such as movies, anime, and games makes it possible to estimate the distribution of people in virtual worlds. Of course, it also makes it possible to visualize life in a specific city in the future (e.g., Osaka City).

[0194] These analysis results can also be used for urban planning, environmental optimization, energy optimization, real estate value assessment, logistics optimization, etc. For example, when assessing real estate value, it is possible to analyze and evaluate what kind of changes in population distribution may occur if a certain facility is hypothetically constructed at a certain location in the future, and this information can be taken into account in the real estate value assessment.

[0195] Another possible use of data integration analysis is to distribute admission tickets to product promotional events (whether at a physical or virtual venue) via smartphone based on users' behavioral history, etc.

[0196] FIG. 19 is a diagram showing an overview of services realized using the demographic analysis system 1000.

[0197] FIG. 20 is a conceptual diagram showing an example of the service shown in FIG. 19, which is provided via a smartphone.

[0198] As shown in Figure 19, maps, people flow, various sensor data, weather data, satellite data, real estate data, and data posted via smartphones stored in the data lake can be used to analyze the condition of each region (artificial intelligence prediction), calculate a score for each region for specified evaluation items, and map and visualize each region.

[0199] In this case, the output of the integrated analysis platform will have interfaces suitable for residents, local businesses, and local governments.

[0200] For example, residents could be encouraged to make behavioral changes through a smartphone application to improve the score for their neighborhood or area of ​​residence.

[0201] For example, as shown in Figure 20, in the real world, it is possible to imagine people moving around (or the smartphones they carry and move around with) as a kind of sensor that accumulates data in a data lake via a smartphone application. By using city information posted by smartphone users (which may include not only crowding data but also data on weather, accidents, crime, etc.), it is possible to provide a point analysis platform as a service in the digital space that provides the distribution of people within walking distance, or the distribution of people in the surrounding area, and even statistical data on the state of the city in a larger living area.

[0202] This configuration makes it possible to collect information about the town's attractions directly from local residents, and also promote behavioral changes among people who can share information via smartphone (such as visiting the town).

[0203] Furthermore, by using detailed analysis reports and town simulation tools to base policy decisions and implementation effectively and efficiently on objective evidence, the government will be able to keep the cycle of town development for everyone going.

[0204] Based on the scores obtained from the integrated analysis platform, local businesses will be able to carry out area marketing such as store development and sales promotion.

[0205] FIG. 21 is a diagram showing another example of data visualization by integrated analysis.

[0206] Above, we have explained how the current state of a city can be displayed in real time by using an integrated analysis platform.

[0207] In this case, the types of data that can be superimposed on the map information include "distribution of the number of people (including people flow. People flow can also be shown using vectors, for example)" and "elevation," as well as the type of use of each location within the area, such as "commercial use," "residential use," and "office use," the spatial utilization rate, and even the distribution of road maintenance status.

[0208] As described above, by utilizing the integrated analysis by the life information service providing server 2000, more precise estimations can be made regarding employment, which is an important part of a person's life, based on employment information by occupation.

[0209] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, the analyzed demographic information can be presented to the user in a form that is easily understandable to the user, such as visualized information in charts.

[0210] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, users can easily understand the analyzed demographic information by relating it to locations on a map.

[0211] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, accurate estimation can be made for each area (62.5 m mesh) in which a person can move in one minute, for example.

[0212] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, it is possible to perform time series analysis over a predetermined period of time.

[0213] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, it is possible to simulate life information in the past or future.

[0214] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, when predetermined area location information cannot be obtained as statistical information or the like, it can be supplemented by estimation.

[0215] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, when predetermined demographic information cannot be obtained as statistical information or the like, it can be supplemented by estimation.

[0216] Furthermore, by utilizing the integrated analysis by the lifestyle information service providing server 2000, it is possible to estimate demographic information for a specific location at a specific time based on demographic information that includes at least information about the source of movement (for example, people commuting from home, people returning home from a restaurant, etc.).

[0217] Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, etc. within the scope of achieving the object of the present invention are included in the present invention.

[0218] Furthermore, for example, the above-described series of processes can be executed by hardware or software.

[0219] In other words, the functional configurations in FIGS. 1 to 21 are merely examples and are not particularly limited.

[0220] That is, it is sufficient for the information processing system to have the function of executing the above-described series of processes as a whole, and the type of functional block used to realize this function is not limited to the examples in Figures 1 to 21. Furthermore, the locations of the functional blocks and databases are not particularly limited to those in Figures 1 to 21 and may be arbitrary. For example, at least some of the functional blocks and databases required to execute various processes may be transferred to a user terminal or the like. Conversely, the functional blocks and databases of the user terminal may be transferred to a server or the like.

[0221] Furthermore, one functional block may be configured as a single piece of hardware, a single piece of software, or a combination thereof.

[0222] When a series of processes is executed by software, the programs that make up the software are installed into a computer or the like from a network or a recording medium.

[0223] The computer may be a computer built on dedicated hardware.

[0224] The computer may also be a computer capable of executing various functions by installing various programs, such as a server, a general-purpose smartphone, or a personal computer.

[0225] The recording medium containing such a program may be composed of not only a removable medium (not shown) that is distributed separately from the device main body in order to provide the program to users, etc., but also a recording medium that is provided to users, etc. in a state where it is pre-installed in the device main body.

[0226] In this specification, the steps describing the program to be recorded on the recording medium include not only processes that are performed in chronological order, but also processes that are not necessarily performed in chronological order but are performed in parallel or individually.

[0227] In addition, in this specification, the term "system" refers to an overall device that is made up of a plurality of devices, a plurality of means, etc.

[0228] The embodiments disclosed herein are merely examples of configurations for specifically implementing the present invention, and do not limit the technical scope of the present invention. The technical scope of the present invention is defined by the claims, not by the description of the embodiments, and is intended to include modifications within the literal scope of the claims and within the scope of equivalent meanings. [Explanation of symbols]

[0229] 2 network, 100 user, 200 smartphone, 1000 lifestyle information estimation system, 2000 lifestyle information service providing server, 2100 computing device, 2101 analysis target area information acquisition unit, 2102 people flow path information estimation unit, 2103 people attribute information acquisition unit, 2104 map output unit, 2105 people attraction information estimation unit, 2106 area setting unit, 2107 area lifestyle information estimation unit, 2108 area location information estimation unit, 2109 similar area identification unit, 2111 user matching unit, 2113 analysis report distribution unit, 2115 area evaluation unit, 2110 lifestyle information estimation unit, 2300 storage device, 2301 analysis target area information, 2302 demographic information, 2303 people attribute information, 2304 traffic route information, 2305 people attraction information, 2306 lifestyle information, 2308 Employment information by occupation, 2310 Area location information, 2312 People flow information, 3000 Life information user terminals, 5000.1~5000.M Data provision server.

Claims

1. A demographic analysis device for an analysis target area, comprising: a storage device that stores, in a time series, predetermined demographic information including information on the number of people present in each of a plurality of predetermined area meshes included in the analysis target area, lifestyle information, and area location information; The demographic information is aggregated data of demographic data for each first time interval, and is information on the number of people by age and sex present in each of the predetermined area meshes within the analysis area during the first time interval; the lifestyle information is information for each predetermined time unit, the predetermined time unit being a second time interval shorter than the first time interval, and the lifestyle information is information that can associate the age, sex, and predetermined classification of the person's lifestyle and behavior with the person's occupation; the area location information is location information for each predetermined section, and includes information on the total floor area of ​​facilities within the analysis target area and job types within the facilities; a computing device that analyzes the demographic information, an analysis target area information acquisition means for acquiring analysis target area information indicating the analysis target area designated by a user and information on a predetermined time to be analyzed; a region life information estimation means for estimating the life information for each of the predetermined sections; The area life information estimation means i) estimating the number of employed people by occupation type in the analysis target area for each second time interval based on the demographic information and the lifestyle information; ii) Proportionally allocating the number of employees by occupation type to each facility based on the total floor area and the area allocated to the occupation type of each facility, and estimating aggregated data of the number of people in an estimated area mesh smaller than the specified area mesh for the demographic information; and a human attraction information estimation means that is included in the analysis target area at the specified time, estimates aggregated data for each of the estimated area meshes, and estimates human attraction information that indicates the relative human attraction for each of the plurality of estimated area meshes.

2. 2. The demographic analysis device of claim 1, wherein the human attraction information is information obtained by comparing the number of people present in each of the estimated area meshes included in the analysis area at the specified time with the average number of people present in all of the estimated area meshes included in the analysis area.

3. 2. The demographic analysis device according to claim 1, wherein the storage device stores the demographic information, the lifestyle information, and the area location information as data acquired from an external server via a network.

4. 2. The demographic analysis device according to claim 1, wherein the human attraction information estimation means outputs the human attraction information superimposed on map information corresponding to the analysis target area, or together with range information indicating a range from a minimum value to a maximum value of the human attraction information.

5. the computing device executes a function as person attribute information acquisition means for acquiring person attribute information indicating attributes of the person designated by the user, The demographic analysis device according to claim 4 , wherein the person attraction information estimation means further estimates the person attraction information according to attributes of the person specified based on the person attribute information.

6. 5. The demographic analysis device according to claim 4, wherein the human attraction information estimation means outputs, for each estimated area mesh, information that expresses both the absolute number and a relationship with an average number of people according to the attributes of the people present in each estimated area mesh included in the analysis target area at the predetermined time.

7. 5. The demographic analysis device according to claim 4, wherein the human attraction information estimation means outputs, for each estimated area mesh, the number of people according to the attributes of the people present in each estimated area mesh included in the analysis target area at the predetermined time divided by the average number of people in all estimated area meshes included in the analysis target area as the human attraction information.

8. The demographic analysis device according to claim 4 , wherein the human attraction information estimation means outputs the human attraction information indicating a difference in the attraction at a plurality of the predetermined times in a time series.

9. 6. The demographic analysis device according to claim 5, wherein the human attraction information estimation means outputs the human attraction information indicating a comparison between people within the area to be analyzed and people outside a predetermined range, based on the human attribute information.

10. The demographic analysis device according to claim 5 , wherein the human attraction information estimation means outputs the human attraction information indicating a comparison of ages or genders of predetermined people based on the human attribute information.

11. The demographic analysis device according to claim 4 , wherein the person attraction information estimation means outputs the person attraction information in a manner that is superimposed on specific place information on the map information.

12. The demographic analysis device according to claim 4 , wherein the person attraction information estimation means outputs the person attraction information together with event information or marketing measure information.

13. The demographic analysis device according to claim 4 , wherein the person attraction information estimation means outputs the person attraction information together with route information relating to a predetermined route.

14. The demographic analysis device according to claim 1, further comprising a regional life information estimation means for estimating regional life information, which is life information for each estimated region mesh of the analysis region at the specified time, together with the human attraction force information, using the relationship between the life information and specified region location information including information on specified places located within the analysis region.

15. 15. The demographic analysis device according to claim 14, wherein the human attraction information estimation means outputs the human attraction information as information indicating an effect of a predetermined measure related to a predetermined place according to the area location information included in the analysis target area.

16. 16. The demographic analysis device according to claim 15, further comprising a similar area specifying means for specifying and outputting analysis target areas having similar information on human attraction based on the information on human attraction relating to a plurality of the analysis target areas.

17. a first data providing server that provides demographic information including information on the number of people located in a group of areas within a predetermined area in a time series at a predetermined first time interval; The demographic information is aggregated data of demographic data for each of the first time intervals, and is information on the number of people by age and sex present in each of the predetermined area meshes in the predetermined area during the first time interval; a second data providing server that provides lifestyle information at a predetermined second time interval including information on a person's lifestyle and behavior, the lifestyle information being information for each predetermined time unit, the predetermined time unit being a second time interval that is shorter than the first time interval, and being information that can associate the age, sex, and predetermined classification of the person's lifestyle and behavior with the occupation of the person; a third data providing server that provides area location information including location information for each predetermined section located in the predetermined area; the area location information is location information for each of the predetermined sections, and includes information on the total floor area of ​​facilities within the predetermined area and job types within the facilities; a demographic analysis device, a storage device that receives and stores the demographic information, the lifestyle information, and the area location information from the first, second, and third data providing servers; a computing device that executes an analysis process of the demographic information based on the demographic information, the lifestyle information, and the area location information; The computing device The function of the area life information estimation means is to estimate the life information for each of the predetermined sections, and the area life information estimation means i) estimating the number of employed people by occupation type in the predetermined area for each second time interval based on the demographic information and the lifestyle information; ii) Proportionally allocating the number of employees by occupation type to each facility based on the total floor area and the area allocated to the occupation type of each facility, and estimating aggregated data of the number of people in an estimated area mesh that is smaller than the predetermined area mesh of the demographic information; demographic information estimation means for estimating people attraction information indicating the relative people attraction of each estimated area mesh at a predetermined time according to the aggregated data of the number of people in the estimated area mesh; and an image generating means for generating the estimated result of the human attraction information as image information.

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