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

[0003] 22 to 24 are diagrams showing an overview of a service that provides such demographic information.

[0004] 22 to 24, "Mobile Spatial Statistics (registered trademark)" provided by NTT DoCoMo, Inc. is described as an example.

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

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

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

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

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

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

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

[0012] As shown in Figure 22(b), this demographics makes it possible to grasp the population structure by gender and age group, covering the age range of 15 to 79 years old.

[0013] Furthermore, as shown in Figure 23, this spatial statistics makes it possible to grasp the population by residential area in a specific time period (every hour) in a specific area, which, as mentioned above, makes it possible to grasp the population distribution by residential area based on registered mobile phone information.

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

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

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

[0017] One type of aggregate data is "demographic information," which is data that records the population by location and time in a city. Most demographic information records population information by area and time. When targeting information over a wide area, mobile phone information is often used in consideration of comprehensiveness, and the number of terminals in each area is counted based on terminal connection information at mobile phone base stations and information such as CDR (Call Data Record), and the actual population is estimated while taking into account the utilization rate by attribute, and this is used as demographic information.

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

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

[0020] Referring to FIG. 24, in this "spatial statistics", the following processing is carried out to ensure safety by eliminating information that can identify individuals.

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

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

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

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

[0025] Alternatively, Patent Document 2 discloses an information processing device that analyzes the actual situation of people in an area specified by a user. More specifically, the information processing device described in Patent Document 2 is a device for analyzing a commercial area, in which a heat map generating unit generates a heat map that is associated with map information and that divides the map into a predetermined size, and that shows the number of people or stores in the mesh at a predetermined time in the mesh by dividing the mesh into multiple levels. The analysis unit overlays a heat map for a period in the area on a map including the area in accordance with the designation of the area and period, accepts the selection of at least one level out of multiple levels of the heat map, and sets the multiple meshes in which the heat map having the selected level is arranged as the area to be analyzed. Here, in the analysis of the number of people in each mesh, the population transition of residents is based on statistical information stored in the statistical information DB, and the population transition of visitors is created by the heat map generating unit for each predetermined period based on the behavior information stored in the behavior information DB. Here, the "behavior information DB" refers to user information of each behavior user periodically transmitted from a user terminal, and stored in association with a user ID. Then, the behavioral information generating 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 behavioral information in the behavioral information DB.

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

[0027] As shown in Figure 25, the Basic Survey on Social Life surveys collects lifestyle information (behavioral classification) in 15-minute increments by region, age, and sex. [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 of "Understanding and Predicting People Flow in Cities" https: / / www.jstage.jst.go.jp / article / oubutsu / 90 / 8 / 90_481 / _pdf / -char / ja [Non-Patent Document 4] https: / / www.stat.go.jp / data / shakai / 2021 / gaiyou.html Summary of the Invention [Problem to be solved by the invention]

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

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

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

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

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

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

[0036] More specifically, the present invention provides a demographic analysis device and a demographic analysis system 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, a demographic analysis device for an analysis target area includes a storage device that stores predetermined demographic information including information on the number of people located in each of a plurality of predetermined area meshes included in the analysis target area in a series of time series, and a calculation device that analyzes the demographic information, wherein the calculation device performs the functions of an analysis target area information acquisition means that acquires analysis target area information indicating the analysis target area specified by a user and information at a predetermined time to be analyzed, and a human attraction information estimation means that estimates aggregate data for each estimated area mesh that is included in the analysis target area at the predetermined time and is smaller than the predetermined area mesh, and estimates human attraction information that indicates the relative human attraction for each of the plurality of estimated area meshes.

[0038] Preferably, the human attraction information is information comparing 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, lifestyle information, and area location information as data acquired from an external server via a network, the demographic information being aggregated data of demographic data for each first time interval, and being information on the number of people by age and sex present in each predetermined area mesh in the first time interval, the lifestyle information being 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 being information corresponding to a predetermined classification of a person's age, sex, and life and behavior, and a person's occupation. The area location information is location information for each specified section, and includes information on the total floor area of ​​facilities within the specified area and the occupations within the facilities, and the area life information estimation means further comprises an area life information estimation means for estimating the life information for each specified section, and the area life information estimation means i) estimates the number of employed people by occupation located in the specified area for each second time interval based on the demographic information and the life information, and ii) apportions the number of employed people by occupation to each facility based on the total floor area and the area allocated to the occupation of each facility, and estimates aggregate data of the number of people in the estimated area mesh of the demographic information.

[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 specified by a user, and the human attraction force information estimation means further estimates human attraction force information according to the attributes of the specified person based on the human attribute information.

[0042] Preferably, the human attraction information estimation means outputs, for each estimated area mesh, information expressing 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 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 analysis target area at a given time divided by the average number of people in all estimated area meshes included in the analysis target area as human attraction information.

[0044] Preferably, the human attraction force information estimating 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 the area to be analyzed 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 estimating means outputs the human attraction information so as to overlap with specific place information on the map information.

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

[0049] Preferably, the people attraction information estimating 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 target area at a specified time, together with the human attraction force information, by using the relationship between the life information and specified area location information including information of specified places located within the analysis target area.

[0051] Preferably, the people attraction information estimation means outputs the people 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.

[0052] Preferably, the apparatus further comprises a similar area specifying means for specifying and outputting analysis target areas having similar human attraction force based on human attraction force information related to a plurality of analysis target areas.

[0053] According to another aspect of the present invention, there is provided a demographic analysis system comprising a first data providing server providing demographic information including information on the number of people located in a group of areas in a time series at a first predetermined time interval, a second data providing server providing life information at a second predetermined time interval including information on people's lives and behavior, the second time interval being shorter than the first time interval, a third data providing server providing area location information including information on the location of each predetermined block located in the predetermined area, and a demographic analysis device, the demographic analysis device comprising a storage device receiving and storing the demographic information, life information and area location information from the first, second and third data providing servers, and a demographic analysis device performing analysis processing of the demographic information based on the demographic information, life information and area location information. and a calculation device that executes a process to estimate the living information for each predetermined section, and the area living information estimation means further executes 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 living 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 estimating people attraction information indicating the relative people attraction of each estimated area mesh at a predetermined time according to the aggregate data of the number of people in the estimated area mesh; and an image generation means that generates the estimated result of the people attraction information as image information. Effect of the Invention

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

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

[0056] [Figure 1] FIG. 1 is a diagram showing an overview of a lifestyle information estimating system according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a functional block diagram showing an outline of the system of a life information service providing server 2000. [Diagram 3] 2 is a diagram for explaining the hardware configuration of a life information service providing server 2000. FIG. [Figure 4] FIG. 1 is a diagram for explaining the contents of data provided from data providing servers 5000.1 to 5000.M. [Diagram 5] FIG. 11 is a first diagram illustrating a processing flow of the life information service providing server 2000. [Figure 6] FIG. 2 is a second diagram illustrating the processing flow of the life information service providing server 2000. [Figure 7] FIG. 2 is a diagram showing an outline of data output from a calculation device 2100. [Figure 8] FIG. 13 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a first time point. [Figure 9] FIG. 13 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a second time point. [Figure 10] FIG. 13 is a diagram showing the concept of the process of improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210). [Figure 11] 13 is a diagram showing another example of the map image and the superimposed image of the number of people distribution output by the map output unit 2104. FIG. [Figure 12] FIG. 2 is a diagram showing functional blocks when the life information service providing server 2000 functions as an analysis device for demographic information. [Figure 13] FIG. 13 is a diagram illustrating a flow of a process for analyzing demographic information. [Figure 14] FIG. 13 is a diagram showing a state in which an analysis target area AR is specified on an estimated area mesh. [Figure 15]FIG. 23 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] 13 is a diagram for explaining another aspect of displaying the estimation result by the human attraction information estimation unit 2105. FIG. [Figure 17] FIG. 1 is a conceptual diagram showing a hierarchy of data and a hierarchy of data processing used in the lifestyle information estimation system. [Figure 18] FIG. 2 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 by 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. 13 is a diagram showing another example of data visualization by integrated analysis. [Figure 22] FIG. 1 is a diagram showing an overview (1) of mobile spatial statistics of the prior art. [Figure 23] FIG. 1 is a diagram showing an overview (2) of mobile spatial statistics of the prior art. [Figure 24] FIG. 1 is a diagram showing an overview (3) of mobile spatial statistics according to the prior art. [Diagram 25] FIG. 1 is a diagram showing the contents of the Basic Survey on Social Life as a background art. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[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, "demographic analysis system") and the configuration of a demographic analysis device for analyzing demographic information in the demographic analysis system.

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

[0060] Therefore, in other words, the demographic analysis device is an analysis device that indicates the relative human attraction for "each of multiple estimated area meshes" as described below, which are 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, demographic analysis system 1000 of embodiment 1 of the present invention includes a life information service providing server 2000 operating as a demographic analysis device, a life 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 receiving services from life information service providing server 2000, data providing servers 5000.1 to 5000.M (M: a natural number) that provide life information service providing server 2000 with data necessary for estimating life 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") used by user 100 to receive services as described below.

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

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

[0065] With reference to FIG. 2, the life information service providing server 2000 includes a computing device 2100 and a storage device 2300 .

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

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

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

[0070] In addition, the "predetermined living information" may include such "occupation-specific employment information."

[0071] Therefore, the life information estimation unit 2110 can estimate the number of people by behavior (hereinafter, referred to as "number of people by behavior") who are present in the "predetermined area" set by the area setting unit 2106 in response to an instruction from the user, during the "second time interval", based on the "demographic information 2302" and the "predetermined life information 2306". Furthermore, the life information estimation unit 2110 can calculate the classification data of the "occupation" of people who are present in the "predetermined area" during the "second time interval", by applying the "work information by occupation" to people with the behavior classification of working or studying, based on the "number of people by behavior" estimated in this way. Note that the "occupation" does not only refer to the so-called "type of occupation", but may also refer to "tasks" such as "study" that are not business, but in which a person is obviously engaged in the same behavior for a certain period of time or more in that place (facility / building).

[0072] The storage device 2300 further stores a predetermined "area location information 2310". Here, the "predetermined area location information" refers to "information on a predetermined place existing in the above-mentioned predetermined area (including information on the type of job at that place)". For example, the "area location information" preferably includes information on buildings and facilities in the area of ​​the second area mesh unit, which is smaller than the above-mentioned first area mesh unit and preferably has a divisor of each side when the first area mesh unit is a rectangle, and information on the types of jobs performed in the buildings and facilities. For example, as shown in FIG. 4, the "area location information 2310" corresponds to map data including information on buildings and facilities in a town, information on the types of jobs performed in the buildings and facilities, and information on roads and water areas in 62.5m mesh units. Some of this "map data" is provided by private companies.

[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 located at a specified location in a specified area at the second time interval (hereinafter referred to as "people's area lifestyle information") by expanding (for example, proportionally allocating) the "number of people per occupation" into the "area corresponding to each occupation" of the "specified location."

[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 a certain life information and a certain 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 on farmland, fishermen work in fishing grounds, housewives raise children, and pensioners live in houses. Also, during commuting hours, a certain percentage of people will have the attribute "movement." The specific processing will be described later.

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

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

[0079] The storage device 2300 further 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 transportation 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 historical road congestion information Location of public transport stations and stops (railroads, buses, etc.) 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, as long as the information is related to transportation, the data included in the transportation route information 2304 is not limited to these.

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

[0082] FIG. 3 is a diagram for explaining 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 a calculation device (CPU: Central Processing Unit) in its own housing executes calculation processing, or a part of the program processing may be further executed on another server. In the following, the description will be given assuming that the calculation device in its own housing executes the calculation processing.

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

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

[0086] As shown in FIG. 3, the computer main body constituting the computer device 2010 includes, in addition to the disk drive 2030 and the memory drive 2020, a CPU (Central Processing Unit) 2100 connected to a bus 2050, a memory 2200 including a ROM (Read Only Memory) 2200.1 and a RAM (Random Access Memory) 2200.2, a non-volatile rewritable non-volatile storage device 2300, and an input / output interface 2090 for communicating via a network and sending and receiving data with the outside. For example, a HDD (Hard Disc Drive) or an SSD (Solid State Drive) is used as the non-volatile storage device 2300. In the following description, it is assumed to be an SSD. An optical disk can be attached to the disk drive 2030. A memory card 2210 can be attached to the memory drive 2020.

[0087] When the programs of the computer device 2010 operate, the data and programs that store the information that is the basis for the operation of the computer are stored in the SSD 2300.

[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. In order to accommodate such cases, the computer main body is provided with a drive device (memory drive 2020, disk drive 2030) capable of reading such media.

[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 acquired via the disk drive 2030 or the network communication unit 2012 and temporarily stored in the SSD 2300. The software is then read from the SSD 2300 to the RAM 2200.2 in the memory and executed by the CPU 2100. Note that, when connected to a network, the software may be directly loaded into the RAM and executed without being stored in the SSD 2300.

[0090] When a program for functioning as computer device 2010 is distributed, the program does not necessarily need to include an operating system (OS) that causes computer main body 2010 to execute functions of an information processing device, etc. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner and produces desired results. How computer system 2010 operates is well known, and a detailed description will be omitted.

[0091] Furthermore, the CPU 2100 may be a one-core processor or a multiple-core processor, i.e., a single-core processor or a multi-core processor. The server 2000 may also be configured with multiple servers to execute distributed processing.

[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] In the following, 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 behavior and the number of people in a facility based on the life information of people present in a specific area during a specific time period will be described.

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

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

[0098] 5 and 6, when the processing of the behavior-specific number of people estimation and facility number of people estimation starts, the calculation device 2100 acquires spatial statistical data via the network (S110, S200).

[0099] As mentioned above, to obtain spatial statistical data, it is possible to use "Mobile Spatial Statistics (registered trademark)" provided by NTT DoCoMo. Spatial statistical data is, for example, data on the number of people by age and sex in a given time unit (for example, 1 hour) within a mesh of a given width (for example, 500m square).

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

[0101] The lower part of Figure 7(a) shows an overview of such estimation of the number of people by behavior.

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

[0103] The lower graph in Figure 9 shows the estimated number of people by activity in a specific area at 3:00 p.m. (15:00). Judging from the time of day, most people are classified as "working" or "studying."

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

[0105] Next, the area location information estimation unit 2108 expands (e.g., proportionally apportions) the estimated number of employees (attributes of age and gender) by occupation for 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 (e.g., a 62.5 m mesh) (S140, S230).

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

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

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

[0109] 5 and 6, the map output unit 2104 further superimposes the number of people deployed for each facility / building on the map and outputs it by color-coding, for example, using a heat map method (S140, S240). Here, when displaying the heat map, a method of displaying a chart showing the ratio of the number of people doing the work at that location by color coding may be used.

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

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

[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 unit).

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

[0115] Hereafter, such a database stored in the same unit will be referred to as a "data lake." Therefore, for example, it is possible to perform real-time analysis 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 the resolution is 62.5 m, since Mobile Spatial Statistics (registered trademark) is a mesh with sides of 500 m, it is possible to create an area mesh with sides of 1 / 8 (1 / 8) of this 500 m, which is approximately the distance a person walks in one minute. When using other spatial statistics, it is possible to unify the area mesh with sides equivalent to the distance a person walks in one minute in the same way. By using the distance a person walks in one minute, it can be said that the resolution is suitable for analyzing people flow, including people who are moving by foot, for example.

[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 viewpoint of cost-effectiveness, it is preferable to use, for example, 62.5 m.

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

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

[0121] The following describes the process of analyzing and displaying demographic information of people present in a specific area during a specific time period 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 in FIG. 12 illustrates 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 FIG. 2, and the data configuration required for executing such functions.

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

[0125] Referring to FIG. 12, when performing a demographic information estimation process, the calculation device 2100 of the life information service providing server 2000 executes, in addition to the functions of FIG. 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, "human attraction" refers to, for example, the number of people present in each estimated area mesh included in a specified analysis area at a specified 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 area where more people than average gather compared to other estimated area meshes is defined to have a greater ability to attract people (human attraction).

[0127] In addition, the calculation device 2100 executes the functions of an area life information estimation unit 2107 that estimates area life information, which is life information for each estimated area mesh about people located in the analysis target area at a specified time, in addition to the estimated information of human attraction force information based on the above-mentioned "relationship between specified life information and specified area location information", and a similar area identification unit 2109 that identifies and outputs analysis target areas with similar human attraction force based on the human attraction force information related to multiple analysis target areas.

[0128] Here, "similar human attraction" may mean that there is a high 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 similarity in area life information in estimated area meshes of a predetermined size or larger.

[0129] In addition, the calculation device 2100 performs the functions of a user matching unit 2111 that notifies, for example, other user terminals 3000.i, the user who specified a plurality of analysis target areas with similar human attraction identified by the similar area identification unit 2109 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 with human attraction at or above a predetermined level, and area life information of the areas with human attraction 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 life 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 similar characteristics to the analysis area that the user has analyzed or intends to analyze.

[0131] The storage device 2300 stores, in addition to the information that is assumed to be stored in FIG. 2, 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 calculation device 2100 acquires analysis target area information and 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 a state in which the analysis target area AR is specified on the estimated area mesh. In FIG. 14, for the sake of simplicity, the analysis target area AR is described as having a rectangular shape, but the shape of the analysis target area AR is not limited to such a rectangular shape and may have any shape according to an instruction from the user terminal 3000. Although not particularly limited, the arbitrary shape may or may not follow the shape of the estimated area mesh MC. If it does not follow the shape, the number of people in the estimated area mesh may be proportionally apportioned according to the size of the area that the boundary divides the estimated area mesh.

[0135] In FIG. 13, the process from steps S110 to S140 is similar to 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 may be temporarily stored as common data, and may be configured to be shared in the processing for estimating the number of people by behavior and estimating the number of people in facilities described in FIG. 5 and the processing for analyzing demographic information described in FIG. 13.

[0137] 13, following the processing of steps S110 to S140, the human attraction information estimation unit 2105 of the calculation device 2100 calculates human attraction information for each estimated area mesh (S170). As 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 receives information on the human attribute, the human attraction is estimated for a person having the designated human attribute.

[0138] Then, the human attraction information estimation unit 2105 of the arithmetic device 2100 generates data for displaying the human attraction 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 Fig. 2, "a specified time" refers to, for example, a "first specified time interval", for example, every hour, and "a specified area" refers to, for example, a first area mesh unit, for example, a 500m mesh (a square of 500m on each side) unit, of a specific region. "Specified demographic information" refers to "demographic data" and means "aggregated data", such as the above-mentioned "Mobile Spatial Statistics Information (registered trademark)".

[0141] Furthermore, the predetermined "lifestyle information 2306" is more specifically information on the classification of predetermined behaviors in a "second time interval" that is shorter than the first predetermined time interval and is preferably a divisor of the first time interval, by region, age, and sex. Here, the "lifestyle information" corresponds to information in a database based on the "Basic Survey on Social Life" by the Ministry of Internal Affairs and Communications, which classifies behaviors into 20 predetermined types by region, age, and sex, and by 15-minute intervals. Furthermore, the "predetermined area location information" refers to "information on a predetermined place that exists in the above-mentioned predetermined area (including information on the type of job at that place)," and preferably, the "area location information" includes information on buildings and facilities within the area of ​​the second area mesh unit, and information on the type of job performed within the building or facility, for an area of ​​a second area mesh unit (estimated area mesh) that 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 a rectangle.

[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] The different times here refer to time t=T1 and a later time t=T2.

[0144] For example, as shown in FIG. 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 people attraction).

[0145] At this time, the display is assumed to be displayed as a heat map colored in multiple stages (for example, 7 stages in FIG. 15) within the range of the maximum and minimum values ​​of the number of people (or human attraction) in the estimated area mesh in the analysis target area during the time period from time T1 to T2. Note that if only a specific time is the analysis target, the heat map display may be in the range of the maximum and minimum values ​​of the number of people (or human attraction) in the estimated area mesh in the analysis target area at that time.

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

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

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

[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 such as roads Rd, railways RT, and stations ST. However, the map information is not limited to these, and may include information on man-made objects such as buildings, rivers, and altitudes.

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

[0152] 16, the heat map is displayed so that the range between the maximum and minimum values ​​of the number of people (or human attraction) in the estimated area mesh at the time of analysis can be compared with the average value. However, the representative value of the density of people 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 be used.

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

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

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

[0157] FIG. 16 also illustrates a configuration in which a 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 "70 or older." Figure 16 shows that the user has selected "teens," "twenties," ... "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 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 the person 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 estimated area mesh according to the attributes of the person 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, timetable information for transportation (railroad, bus, etc.), road width information, distance and elevation difference between bus stops, etc.) related to a specific route (for example, road, railway, bus, airport, 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 human attraction information estimation unit 2105 estimates, for each estimated area mesh included in the analysis target area at a given time, 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 a relative value and an absolute value, as human attraction information for the analysis target area, and outputs a corresponding image.Then, it is possible to configure the system to generate and output such images as image information that can be compared simultaneously for at least two analysis target areas.

[0163] This allows the user to simply view and evaluate the output information, and perform an analysis and evaluation that clearly distinguishes between above-average and below-average human attraction in relation to the average value, while simultaneously recognizing the absolute magnitude. This allows for accurate evaluation within each analysis target area, while also enabling evaluation across multiple analysis target areas.

[0164] In addition, by changing the method of 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 "out-of-prefecture visitors" as a person attribute. In this case, if you want to know the "attraction of out-of-prefecture visitors to a specific theme park," the denominator for calculating the relative value is "number of out-of-prefecture visitors located within Osaka Prefecture / number of estimated area meshes in Osaka Prefecture" = (out-of-prefecture visitors per estimated area mesh). By setting the denominator in this way, the attraction is calculated by dividing the number of out-of-prefecture visitors in each estimated area mesh by this denominator. If you display the human attraction in a heat map for the estimated area meshes that are included in a specific theme park in the analysis target area, this will show the attraction of the specific theme park to 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 in 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 human attraction for the estimated area meshes contained 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 a person attribute and want to know the "attraction power of 30-year-old women at Izumo Taisha Shrine," 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] In addition, 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 indicating a comparison between people inside and outside a specified analysis target area at a specified time.

[0169] Furthermore, the people attraction information estimation unit 2105 can generate data that displays people attraction information by overlaying it with location information within the area to be analyzed, rather than simply map information. Here, "location information" refers to, for example, information on stores in a building (e.g., information on medical departments in a hospital building, information on convenience stores, etc.), information on 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 by overlaying it with event information of a specific location in the analysis target area, 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 (customer attraction measures and events that attract people to the location (such as television commercials and advertisements on the Internet)).

[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 of local government facilities. For example, if people with attributes that are expected to attend an event are not attending, it becomes possible to take measures to target those attributes.

[0172] The area life information estimation unit 2107 can also be configured to estimate area life information of people located in each estimated area mesh of the analysis target area at a predetermined time, along with estimating people attraction information, based on the relationship between predetermined life information (for example, classified into 20 types of behavior according to the Ministry of Internal Affairs and Communications' "Basic Survey on Social Life") and predetermined area place information for each estimated area mesh. The people attraction information estimation unit 2105 can also generate people attraction information as information indicating the effect of a predetermined policy or event (for example, opening of a store, development of a bus route, redevelopment of a station area, etc.) related to the area place information included in the analysis target area by analyzing together with the area life information. For example, in the case of the effect of opening a store, it is assumed that in estimating the people attraction of the estimated area mesh including the store, people with "shopping" as the behavior classification are identified, and the number of people in the estimated area mesh is estimated.

[0173] The human attraction information estimation unit 2105 can also be configured to specify and output analysis target areas with similar human attraction based on human attraction information for a plurality of 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 information on analysis target areas with similar human attraction to the user.

[0174] For example, the user matching unit 2111 can be configured to notify a user who has specified multiple analysis target areas having similar human attraction 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, for example, it is possible to identify similar areas from the perspective of 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" as "software that supports urban infrastructure" and the data integration platform.

[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 reusing and expanding services, traditionally systems were specialized for each field or organization, which made it difficult to reuse or expand services to other regions.

[0182] ii) When it comes to utilizing data across sectors, traditional services have separate data for each sector and organization, making it difficult to build new services that span sectors.

[0183] iii) Low scalability: Conventional specialized systems have the problem that expanding functions requires large costs and effort, making it difficult to continuously and easily evolve services.

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

[0185] The "demographic analysis system area" of this embodiment is conceptually configured to exist 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 utilize 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 above-mentioned "data lake," it is first stored as general-purpose raw data, and then finalized for a certain time period, and stored as a "unified format for the demographic analysis system (for example, unifying the unit area mesh)" as mentioned above.

[0188] As shown in Figures 17 and 18, data in a general-purpose data format provided to the "Data Lake" may be posted from a smartphone via a smartphone application, for example. The posted data may be subjective evaluation data on the congestion situation at a specific facility in a specific location, or may be photographs or videos 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 above-mentioned life information service providing server 2000.

[0190] The system is structured so that a section that adds some kind of evaluation to the analysis results, or collaborates with other companies on the 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 above-mentioned processing makes it possible to perform data analysis and visualization 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 town 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 → town information), the people flow distribution changes when the "town information (general map information)" is updated. Therefore, it is possible to visualize not only current real-time information, but also people flow distribution in towns that differ from the present in the past and future.

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

[0194] These analytical results can 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 in a certain location in the future, and this information can be added to the real estate value assessment.

[0195] One possible use case for data integration and analysis would be 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 flows, various sensor data, weather data, satellite data, real estate data, and data posted via smartphones stored in the data lake can be analyzed on an integrated analysis platform (artificial intelligence prediction) to analyze the condition of each region, 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 be provided with 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 think of a moving person (or the smartphone 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 can include not only crowding but also data on weather, accidents, crime, etc.), it is possible to provide a location analysis platform as a service in the digital space, which can provide the distribution of people within walking distance, or even the distribution of people in the surrounding area, and even larger statistical data on the state of the city in the living area.

[0202] With this configuration, it is possible to collect information about the town's attractions directly from local residents, and it is also possible to encourage 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] Local businesses will be able to carry out area marketing such as store development and sales promotion based on the scores obtained from the integrated analysis platform.

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

[0206] We have explained above 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 visualizing the type of use of each location within the area, such as "commercial use," "residential use," or "office use," the spatial utilization rate, and even the distribution of road maintenance conditions.

[0208] As described above, by utilizing the integrated analysis by the life information service providing server 2000, more precise estimation 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 provided to the user in a form that is easily understandable to the user by visualizing the information in charts or the like.

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

[0211] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, accurate estimation can be performed 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 a time series analysis for 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 a specific 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 certain demographic information is not available as statistical information or the like, it is possible to make up for it by estimation.

[0216] In addition, by utilizing the integrated analysis by the lifestyle information service providing server 2000, demographic information for a specified location at a specified time can be estimated based on demographic information that includes at least information related to the source of movement (e.g., 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 that can achieve 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 that the information processing system is provided with a function capable of executing the above-mentioned series of processes as a whole, and the type of functional block used to realize this function is not limited to the examples in Figures 1 to 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 a portion 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 of both.

[0222] When the series of processes is executed by software, the program constituting the software is installed into a computer or the like from a network or a recording medium.

[0223] The computer may be a computer implemented with dedicated hardware.

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

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

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

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

[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 indicated by the claims, not by the description of the embodiments, and is intended to include modifications within the literal scope of the claims and within the scope of equivalent meanings. [Explanation of symbols]

[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. An apparatus for analyzing demographic trends in 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; and a computing device for analyzing the demographic information, the computing device comprising: 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; and a human attraction information estimation means for estimating aggregate data for each estimated area mesh that is included in the analysis target area at the specified time and is smaller than the specified area mesh, and estimating human attraction information that indicates the relative human attraction for each of the multiple 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 target area at the specified time with the average number of people present in all of the estimated area meshes included in the analysis target area.

3. 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; 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 predetermined area mesh in the predetermined area during the first time interval; the life 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 life information is information capable of associating the age, sex, and a predetermined classification of the person's life and behavior with the occupation of the person; The area location information is location information for each predetermined section, and includes information on the total floor area of ​​a facility within the predetermined area and the type of job within the facility, The area life information estimation means further includes a region life information estimation means for estimating the life information for each of the predetermined sections, the region life information estimation means including: i) estimating the number of people employed by occupation type in the specified area for each second time interval based on the demographic information and the lifestyle information; ii) apportioning the number of employees by occupation type to each of the facilities based on the total floor area and the area allocated to the occupation type of each of the facilities, and estimating aggregate data of the number of employees in the estimated area mesh of the demographic information.

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 arithmetic device executes a function as a person attribute information acquisition means for acquiring person attribute information indicating attributes of a person designated by the user, The demographic analysis device according to claim 4 , wherein the human attraction information estimation means estimates the human attraction information according to attributes of the person specified based on the human 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 expressing both the relationship with the 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 specified time and the absolute number as the human attraction information.

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 specified 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. 5. The demographic analysis device according to claim 4, 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 4 , wherein the human attraction information estimation means outputs the human attraction information indicating a comparison of age or sex of a predetermined person 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 by superimposing it on specific place information on the map information.

12. The demographic analysis device according to claim 4 , wherein the human attraction information estimation means outputs the human 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 in a manner overlapping with route information relating to a predetermined route.

14. The demographic analysis device of claim 4, further comprising an area life information estimation means for estimating area life information, which is life information for each estimated area mesh of the analysis target area at the specified time, together with the human attraction information, by using a relationship between the life information and specified area location information including information of a specified place located within the analysis target area.

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 human attraction based on the human attraction information 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 in a time series at a first predetermined time interval; a second data providing server that provides life information at a predetermined second time interval including information on the life and behavior of a person, the second time interval being shorter than the first time interval; A third data providing server that provides area location information including location information for each predetermined section located in the predetermined area; 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 demographic information based on the demographic information, the life information, and the area location information; The computing device includes: The function of the area life information estimation means for estimating the life information for each of the predetermined sections is executed, and the area life information estimation means i) estimating the number of people employed by occupation type in the specified area for each second time interval based on the demographic information and the lifestyle information; ii) apportioning the number of employees by occupation type to each of the facilities based on the total floor area and the area allocated to the occupation type of each of the facilities, and estimating aggregate data of the number of people in an estimated area mesh smaller than the specified area mesh of the demographic information; A 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 aggregate data of the number of people in the estimated area mesh; and an image generating means for generating an estimated result of the human attraction information as image information.

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