JP7635449B2 - Lifestyle information estimation device and lifestyle information estimation system - Google Patents

Description

The present invention relates to a lifestyle information estimation device and lifestyle information estimation system that estimate lifestyle information for a specified area.

Conventionally, there are services that provide population statistical information created using the mechanisms of mobile phone networks (for example, see non-patent document 1).

Figures 24 to 26 show an overview of a service that provides such demographic information.

Figures 24 to 26 explain "Mobile Spatial Statistics (registered trademark)" provided by NTT DoCoMo, Inc. as an example.

This type of "Mobile Spatial Statistics (registered trademark)" is explained, for example, in Non-Patent Document 2.

"Mobile Spatial Statistics (registered trademark)" is demographic information created using the mechanisms of mobile phone networks. This spatial statistics is demographic information that only indicates the number of people in a group, and so has the unique feature 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 pertains.

Here, using this spatial statistics, it is possible to grasp the hourly population distribution across Japan, 24 hours a day, 365 days a year. Moreover, using this spatial statistics, it is 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 was launched in October 2013, it has been widely used by the Japan Tourism Agency, local governments, private companies, and others.

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

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 used widely throughout society. As mentioned above, the service was made available in October 2013 so that it could be used not only in the public sector, but also in academic and industrial fields.

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

As shown in FIG. 24(a), the statistical target area is the nationwide DoCoMo service area, and it is possible to grasp the population distribution throughout the country on an hourly basis. Therefore, it is possible to grasp the population distribution on an hourly basis 24 hours a day, 365 days a year.

As shown in Figure 24(b), this demographics makes it possible to grasp the population structure by gender and age group. In this case, the target age group is 15 to 79 years old.

Furthermore, as shown in Figure 25, 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 the registered mobile phone information.

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).

According to Non-Patent Document 3, "demographic data" can be divided into "trajectory data" and "aggregated data." "Trajectory data" is data associated with moving objects such as people, and is often collected by portable devices such as smartphones and car navigation systems.

On the other hand, "aggregated data" is data associated with a location, and is collected by traffic volume sensors installed on roads, wireless base stations, and so on. Because data is acquired using equipment installed in specific locations, once the equipment is installed, data can be collected fairly comprehensively. In addition, because the data is not linked 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 that crosses locations cannot generally be obtained, there are limitations to its use when understanding the flow of people in cities.

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. 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 utilization rates by attribute, and this is used as demographic information.

The "spatial statistics" described in the above-mentioned non-patent documents 1 and 2 correspond to "demographic information" in this sense.

Figure 26 shows the data aggregation procedure in "Mobile Spatial Statistics (registered trademark)" disclosed in Non-Patent Document 2.

Referring to Figure 26, this "spatial statistics" performs the following processing to eliminate any personally identifiable information and ensure safety.

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

The spatial statistics are created through the following steps:
i) De-identification processing: Processing to remove identifying information such as names, telephone numbers, and dates of birth from operational data. ii) Aggregation processing: Processing to derive statistical "group-related information" by performing statistical estimation from de-identified information. iii) Concealment processing: Processing to prevent figures from small population areas from being included in the aggregation results.

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

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

Figure 27 shows an overview of the "Basic Survey of Social Life" (Non-Patent Document 4) published by the Statistics Bureau of the Ministry of Internal Affairs and Communications.

As shown in Figure 27, the Basic Survey on Social Life surveys collects lifestyle information (behavioral classification) by region, age, and sex every 15 minutes.

Patent No. 6226493

https://mobaku.jp/ https://www.bcm.co.jp/bcm/wp-content/uploads/2018/03/ntt-docomo-2017-10-01.pdf Hiroyuki Toda, author: "Understanding and predicting pedestrian flow in cities" https://www.jstage.jst.go.jp/article/oubutsu/90/8/90_481/_pdf/-char/ja https://www.stat.go.jp/data/shakai/2021/gaiyou.html

However, the technology described in Non-Patent Document 1 and Non-Patent Document 2 only provides statistics on the age (generation), gender, and other information of people in a certain area on an hourly basis.

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

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

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.

This posed a problem in that it was difficult to analyze information that classifies the behavior of people in a given area (hereafter referred to as "lifestyle information") at intervals shorter than the time units at which demographic data is collected.

Therefore, no mechanism has been proposed to estimate lifestyle information for a specific area during a specific time period based on demographic information for that area.

The present invention has been made to solve the problems described above, and its purpose is to provide a lifestyle information estimation device and lifestyle information estimation system that can estimate lifestyle information and people flow information for a specified area during a specified time period based on demographic information for the specified area.

In order to achieve the above object, according to one aspect of the present invention , there is provided a lifestyle information estimation device, comprising: a storage device for storing predetermined demographic information including information on the number of people present in a predetermined area at a predetermined time ; lifestyle information for a predetermined unit of time including information on the lives and behavior of people ; and area location information including location information for each predetermined section present in the predetermined area, wherein the demographic information is aggregated data of demographic data for a first time interval, and is information on the number of people by age and sex present in the predetermined area for each area mesh in the first time interval; the lifestyle information includes employment information by predetermined occupation type; the predetermined time unit of the lifestyle information is a second time interval shorter than the first time interval; and the lifestyle information includes information on the age, sex and location of people. This information can associate specified classifications of life and behavior with people's occupations, and the location information for each specified section of the area location information includes information on the total floor area of facilities within the specified area and the occupations within the facilities.The area life information output means further includes an area life information output means for outputting area life information of people located in the specified area at a specified time based on the demographic information, life information, and area location information, and the area life information output 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 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 a mesh smaller than the area mesh of the demographic information .

Preferably, the device further includes a chart output means for outputting lifestyle information of people in a specified area at an estimated specified time as a chart showing the proportion by number of people.

Preferably, the device further includes a map output means for outputting on a map lifestyle information of people located in a specified area at an estimated specified time, with the proportion of people associated with the location.

Preferably, the area life information output means outputs estimated life information of people located in a specific area based on predetermined area location information for each specific area.

Preferably, the storage device stores demographic information, lifestyle information, and area location information as data acquired from an external server via the network .

Preferably, the area life information output means outputs area life information at multiple times based on specified demographic information at multiple specified times included in a specified period of time.

Preferably, the area life information output means outputs area life information based on predetermined area location information set in the past or future.

Preferably, the device further includes area location information estimation means for estimating predetermined area location information.

Preferably, the device further includes a lifestyle information estimation means for estimating predetermined lifestyle information based on the predetermined area location information.

Preferably, the area life information output means outputs area life information including information relating to people flow paths based on demographic information including information relating to the origin of movement .

According to another aspect of the present invention , a life information estimation system includes a first data providing server that provides predetermined demographic information at predetermined time intervals, the first data providing server providing life information for a predetermined time unit , the first data providing server providing life information for a predetermined time unit, the third data providing server providing area location information, the third data providing server providing area location information, the third data providing server receiving and storing demographic information, life information, and area location information from the first, second, and third data providing servers, the demographic information being aggregated data of demographic data for the first time interval, the demographic information being information on the number of people by age and sex present in the predetermined area for each area mesh at the first time interval, and the life information being information on employment by occupation. The specified time unit of the life information is a second time interval shorter than the first time interval, the life information is information capable of correlating a person's age, gender, and a specified classification of a person's life and behavior with a person's occupation, and the location information for each specified section of the area location information includes information on the total floor area of facilities within the specified area and the occupation within the facility, and the area life information output means further includes an area life information output means for outputting area life information of people located in the specified area at a specified time based on the demographic information, life information, and area location information, and the area life information output means i) estimates the number of employed people by occupation type located in the specified area for each second time interval based on the demographic information and life information, and ii) apportions 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, and estimates aggregate data of the number of people in a mesh smaller than the area mesh of the demographic information .

According to yet another aspect of the present invention, there is provided a lifestyle information estimation device, comprising: a storage device for storing predetermined demographic information including information on the number of people present in a predetermined area at a predetermined time; lifestyle information for a predetermined time unit including information on the lives and behavior of people; and area location information including location information for each predetermined section present in the predetermined area, wherein the demographic information is aggregated data of demographic data for a first time interval, and is information on the number of people for each attribute of people present in the predetermined area for each area mesh in the first time interval, the person attributes include stay type information for people staying in the predetermined area, the lifestyle information includes employment information by predetermined occupation type, the predetermined time unit of the lifestyle information is a second time interval shorter than the first time interval, and the lifestyle information includes age of people, This information can associate a predetermined classification of gender and a person's life and behavior with a person's occupation, and the location information for each predetermined section of the area location information includes information on the total floor area of facilities within the predetermined area and the occupation within the facility.The area life information output means further includes an area life information output means for outputting area life information of people located in the specified area at a predetermined time based on the demographic information, life information, and area location information, and the area life information output 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 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 a mesh smaller than the area mesh of the demographic information .

Preferably, the stay type information includes attributes of residents in a given area, attributes of workers in a given area, and attributes other than residents and workers.

Preferably, the area life information output means includes a people flow estimation means for allocating the number of people based on the demographic information to each location within a specified area according to the attributes of the area location information corresponding to the stay type information, and the people flow estimation means prioritizes the attribute of residence if the stay type is a resident, prioritizes business facilities if the stay type is an employee, and prioritizes short-term stay locations and entertainment facilities if the stay type is other than a resident or employee.

Preferably, the specified time unit of the lifestyle information is a second time interval that is shorter than the first time interval, and the lifestyle information is information that can associate a person's age, sex, and a specified classification of the person's lifestyle and behavior with the person's occupation.

Preferably, the area life information output means allocates people to locations taking into account the attributes of the locations for each time period.

The present invention makes it possible to estimate lifestyle information about people who are in a specific area at a specific time, allowing businesses, researchers, government officials, and others to use the estimated lifestyle information in a variety of ways.

Furthermore, according to the present invention, it is possible to estimate the movement of people in a given area based on demographic information. More specifically, it is possible to estimate the crowding situation based on the crowding of people in a mesh that includes Hankyu Umeda Station, a mesh that includes JR Osaka Station, and a mesh that includes an anime event venue, for example, by age group 15-20/20-25 and by gender.

1 is a diagram showing an overview of a lifestyle information estimation system according to a first embodiment of the present invention. FIG. 2 is a functional block diagram showing a system outline of a life information service providing server 2000. 2 is a diagram for explaining the hardware configuration of a life information service providing server 2000. [0023] FIG. FIG. 10 is a diagram for explaining the contents of data provided from data providing servers 5000.1 to 5000.M. FIG. 11 is a first diagram illustrating a processing flow of the life information service providing server 2000. FIG. 2 is a second diagram illustrating the processing flow of the life information service providing server 2000. FIG. 2 is a diagram showing an overview of data output from the arithmetic device 2100. 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 instant; 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. FIG. 11 is a diagram showing the concept of a process for improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210). FIG. FIG. 13 is a diagram showing another example of a map image and a superimposed image of a number of people distribution output by the map output unit 2104. FIG. 11 is a functional block diagram for explaining the configuration of a people flow information service providing server 2000′ according to a second embodiment. FIG. 2 is a conceptual diagram showing a state in which a predetermined area RM is designated on a digital map. FIG. 2 is a conceptual diagram for explaining the data structure of a digital map. FIG. 13 is a conceptual diagram showing an example of building information. 13 is a flowchart showing a process of estimating a number of people distribution in an analysis target area, taking into account stay attributes. This is a heat map showing the distribution of the number of people per area mesh (500 m square) within a specified area, taking into account the attributes of visitors. This is a heat map showing the distribution of the number of people per area mesh (500 m square) within a specified area, taking into account the attributes of visitors. FIG. 1 is a conceptual diagram showing a hierarchy of data and a hierarchy of data processing used in the lifestyle information estimation system. FIG. 2 is a conceptual diagram showing an overview of the flow of data processing. FIG. 1 is a diagram showing an overview of services realized using the lifestyle information estimating system 1000. FIG. 22 is a conceptual diagram showing an example of a service provided via a smartphone for the service of FIG. 21 . FIG. 13 is a diagram showing another example of data visualization by integrated analysis. FIG. 1 is a diagram showing an overview (1) of mobile spatial statistics of the prior art. FIG. 1 is a diagram showing an overview (2) of mobile spatial statistics of the prior art. FIG. 1 is a diagram showing an overview (3) of mobile spatial statistics of the prior art. FIG. 1 is a diagram showing the contents of the Basic Survey on Social Life as a background art.

The following describes an embodiment of the present invention with reference to the drawings.

Below, we will explain the configuration of an information processing system for estimating lifestyle information (hereinafter referred to as the "lifestyle information estimation system") and a lifestyle information estimation device for estimating lifestyle information in the lifestyle information estimation system.

As described below, the lifestyle information estimation device in each of the following embodiments is a device that estimates lifestyle information for a specific area during a specific time period based on demographic information for the specific area.

The lifestyle information estimation device in each of the following embodiments is a device that estimates and analyzes people flow information, for example, people distribution information, during a specific time period related to an analysis target area specified by a user, based on demographic information for the specific area.

Figure 1 is a diagram showing an overview of a lifestyle information estimation system according to embodiment 1 of the present invention.

Referring to FIG. 1, the lifestyle information estimation system 1000 according to the first embodiment of the present invention includes a lifestyle information service providing server 2000 that operates as a lifestyle information estimation device, a lifestyle information user terminal 3000 of a user that receives services from the lifestyle information service providing server 2000, data providing servers 5000.1 to 5000.M (M: natural number) that provide the lifestyle information service providing server 2000 with data necessary for estimating lifestyle information as described below, a building information registration terminal 4000 that registers building information as described below, and a mobile phone or smartphone 200 that is used by a user 100 and receives services as described below.

Here, the building information registration terminal 4000 is a terminal for a building owner or manager to register building information (such as floor plans and 2D or 3D polygon data). Visitors to the building may also register information such as photos of the interior of the building. In this case, the building information registration terminal 4000 may be, for example, a mobile terminal such as a smartphone with a photo-taking function. For example, in the case of a smartphone, the life information service providing server 2000 may be configured to receive notifications from users who use terminals that have installed application software having an input format that notifies them of the building name, floor number, and area information (such as store name and type of store) along with the photos they have taken.

Figure 2 is a functional block diagram showing an overview of the system of the lifestyle information service providing server 2000.

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

Referring to FIG. 2, the lifestyle information service providing server 2000 includes a computing device 2100 and a storage device 2300.

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.

Here, "a specified time" refers to, for example, a first specified time interval, for example, every hour, and "a specified area" refers to, for example, an area in a specific region, in a first area mesh unit, for example, a 500m mesh (a square of 500m on each side). "Specified demographic information" means "demographic data" and "aggregated data", and corresponds to, for example, the aforementioned "Mobile Spatial Statistics Information (registered trademark)" as shown in Figure 4.

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

The storage device 2300 also stores "occupation-specific employment information 2308." Here, "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. 4.

In addition, the "prescribed lifestyle information" may include such "occupation-specific employment information."

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

Note that while the lifestyle information 2306 is described as a "classification of a predetermined behavior" at a second time interval that is shorter than the first time interval and is preferably a divisor of the first time interval, the second time interval may be, for example, the same as the first time interval. For example, a "classification of a predetermined behavior" may be roughly estimated at the first time interval, and lifestyle information, people flow, and people distribution may be presented to the user, and then a "classification of a predetermined behavior" may be estimated at a finer "second time interval" in response to the user's instructions, and lifestyle information, people flow, and people distribution may be presented to the user.

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 that exists 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 within the second area mesh unit area, which is smaller than the above-mentioned first area mesh unit and preferably has a side that is a divisor of the first area mesh unit when the first area mesh unit is a rectangle, and information on the types of jobs performed within the buildings and facilities. For example, as shown in FIG. 4, the "area location information 2310" corresponds to map data in 62.5 m mesh units that includes information on buildings and facilities in the city, information on the types of jobs performed within the buildings and facilities, and information on roads and water areas. Some of this "map data" is provided by private companies.

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 that exist in the "predetermined location" of the "predetermined area" set by the area setting unit 2106. Note that it is preferable to perform such estimation in areas in second area mesh units. However, depending on the user's settings, it is also possible to perform such estimation in first area mesh units, each side of which is a multiple of the second area mesh.

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 during 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."

For example, when calculating such "human area life information," the method described above is used to calculate the classification of "tasks" that people are engaged in at a certain location (facility/building) within the target area.

As a relationship between specified lifestyle information and specified area location information, for example, it is possible to assume that during a certain time period, doctors and nurses are working in hospital facilities, farmers are working on farmland, fishermen are working in fishing grounds, housewives are raising children, and pensioners are living in houses. The specific processing of this will be described later.

Furthermore, the map output unit 2104 generates and outputs a screen that displays the "person's area life information" thus estimated in association with each "predetermined location" in the "predetermined area" (for example, superimposed on top of 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.

As described above, 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, so that 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" on the map data (for example, roads, railways, etc.). 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.

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

Figure 3 is a diagram explaining the hardware configuration of the lifestyle information service providing server 2000.

Referring to FIG. 3, the life information service providing server 2000 may be configured so that a computing device (CPU: Central Processing Unit) within its own housing executes the arithmetic processing, or a part of the program processing may be executed on another server. In the following description, it is assumed that a computing device within its own housing executes the arithmetic processing.

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

For example, the recording medium 2210 may be a USB memory, a memory card, or an external storage device. The network communication unit 2012 may be a wired LAN or wireless LAN communication function. The network communication unit 2012 and the input/output interface 2090 together constitute a communication interface.

3, the computer 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 the 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 to and from 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 2030. A memory card 2210 can be inserted into the memory drive 2020.

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

In FIG. 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, or a USB memory. In such cases, the computer main body is provided with a drive device (memory drive 2020, disk drive 2030) capable of reading these media.

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. It 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.

When a program that functions as computer device 2010 is distributed, the program does not necessarily have 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 the desired results. How computer system 2010 operates is well known, and a detailed explanation will be omitted.

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

The hardware configurations of the lifestyle information user terminal 3000 and the data providing server 5000.i (1≦i≦M) are basically the same, so the description will not be repeated.

Figure 5 is a first diagram explaining the processing flow of the life information service providing server 2000. Figure 6 is a second diagram explaining the processing flow of the life information service providing server 2000.

Figure 7 shows an overview of the data output from the computing device 2100.

Figure 8 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific occupancy estimation at a first time point.

Figure 9 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific occupancy estimation at a second time.

Referring to Figures 5 and 6, when processing starts, the calculation device 2100 acquires spatial statistical data via the network (S110, S200).

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 gender within a mesh of a certain width (for example, 500m square) for a certain time period (for example, 1 hour).

Next, for the "predetermined area" specified by the area setting unit 2106 at the user's request, the lifestyle information estimation unit 2110 classifies people (whose age and gender are obtained from the spatial statistical data) who are in a specific area (e.g., the Umeda area of Osaka) during a specific time period (e.g., a certain hour) based on the spatial statistical data, into work, travel (e.g., commuting) and other status (behavioral classification information) for more detailed time periods (e.g., 15 minutes) within this specific time period, with reference to the Basic Survey of Social Life database, and estimates the number of people in each category (age and gender attributes) (S120, S210).

The lower part of Figure 7(a) shows an overview of this estimation of the number of people by behavior.

The lower graph in Figure 8 shows the estimated number of people by activity in a specific area at 2:00 a.m. Based on the time of day, most people's activities are classified as "sleeping."

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's activities are classified as "work" or "study."

Returning to Figures 5 and 6, the lifestyle information estimation unit 2110 further estimates the number of people (age and gender attributes) employed 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).

Then, the area location information estimation unit 2108 spreads (e.g., proportionally apportions) the estimated number of employees (attributes of age and gender) by occupation for every 15 minutes to each building (facility) on the map based on the total floor area of the facility/building and the area allocated to each facility/occupation in the building, and estimates aggregated data on the number of people in a mesh (e.g., 62.5 m mesh) that is smaller than the unit mesh of the spatial statistical data (S140, S230).

In the following, when the estimation processing functions of the "area location information estimation unit 2108" and the "lifestyle information estimation unit 2110" are collectively referred to as the "area lifestyle information output unit."

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

The upper graph in Figure 8 shows the results of estimating the number of people in facilities in a specific area at 2:00 a.m. Judging from the time of day, most people are located in "residences" or "hotels and inns."

The upper graph in Figure 9 shows the results of estimating the number of people in a facility in a specific area at 3:00 p.m. (15:00). Based on the time of day, we can see that most people in each facility are assigned to the "occupation" or "task" that corresponds to the facility or building.

Returning to Figures 5 and 6, the map output unit 2104 further superimposes the number of people displayed for each facility/building on the map and outputs it in a color-coded manner, for example, using a heat map method. Here, when displaying the heat map, a color-coded chart showing the proportion of people performing tasks at that location may be used.

Figure 7 (b) shows an example of an output that visualizes such a population distribution.

Figure 10 shows the concept of the process for improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210).

As shown in Figure 10, when performing analysis using various information such as spatial statistical data (or people flow data) and map information, some data may have a spatial resolution of 500 m, other data may have a spatial resolution of 100 m, and still other data may have a spatial resolution of land lot number (address).

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

However, as mentioned above, in the lifestyle 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 areas), etc. are all broken down and converted into the same unit (area mesh) and stored.

A database in which data is stored in the same units like this will be referred to as a "data lake" below. Therefore, for example, it is possible to perform real-time analysis in the same units (area mesh) at any point in Japan.

As mentioned above, the same area mesh has a side length of 62.5 m, as shown in Figure 10.

If we use 62.5 m, since Mobile Spatial Statistics (registered trademark) is a mesh with sides of 500 m, we can create an area mesh with sides of 1/8 (1/2) of 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 a similar manner. By using the distance a person walks in one minute, it can be said that this resolution is suitable for analyzing people flow, including people who are moving by foot, for example.

However, it is also possible to divide it into units smaller than 62.5 m (for example, a minimum of 1 m). However, this would increase the amount of data to be analyzed and the amount of calculations, so from a cost-effectiveness perspective, it is desirable to use, for example, 62.5 m.

Figure 11 shows another example of a map image and a superimposed image of the number of people distribution output by the map output unit 2104.

As shown in FIG. 11, a certain area is designated as the specified region, and further, the user can specify a portion of the "specified region" in which the estimation process will be performed.
[Embodiment 2]

Figure 12 is a functional block diagram for explaining the configuration of the people flow information service providing server 2000' in embodiment 2.

The hardware configuration of the people flow information service providing server 2000' is the same as that described in FIG. 3, so the description will not be repeated. In this specification, the "lifestyle information service providing server 2000" and the "people flow information service providing server 2000'" are collectively referred to as the "lifestyle information estimation device."

In this specification, "people flow information" refers to information that expresses the "pattern of people movement" during a specified period of time, and includes, for example, information on the estimated distribution of people regarding the number of people staying in a user-specified area (hereinafter, "specific area") at a specific time or time period for set human attributes. By tracking changes in such information on human distribution over time, it is possible to estimate information that expresses the flow of people. Note that "people attributes" here include, for example, but are not limited to, age, generation, gender, and occupation classification.

Components that are the same as those in the life information service providing server 2000 of embodiment 1 are given the same reference numerals, and as a general rule, descriptions will not be repeated.

Referring to FIG. 12, the computing device 2100 of the people flow information service providing server 2000' of the second embodiment includes, as functions executed according to a program, a people flow information estimation unit 2103 instead of the people flow path information estimation unit 2102, and an area setting unit 2107 instead of the area setting unit 2106.

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

The area setting unit 2106 has a function of specifying a "predetermined area (a "specific area" specified by the user as the area to be analyzed)" by specifying, for example, a building or administrative district in response to instructions from the user. In contrast, the area setting unit 2107 has a function of specifying a "specific area" as the area to be analyzed by the user specifying a building or administrative district on the displayed map, as well as specifying the boundaries of the area by operating a mouse, touch panel, or the like.

In addition, the storage device 2300 of the people flow information service providing server 2000' stores map information 2314 as a digital map, as described below.

Therefore, the storage device 2300 of the people flow information service providing server 2000' stores demographic information (e.g., "Mobile Spatial Information (registered trademark)") equivalent to demographic information, lifestyle information, and area location information as data acquired from an external server via the network. The demographic information is aggregated data of demographic data for each first time interval (e.g., 1 hour), and is information on the age, sex, and number of people by place of residence of people located in each area mesh (e.g., 500 m square) in a specified area at the first time interval. On the other hand, the specified time unit of the lifestyle information is a second time interval (e.g., 15 minutes) that is shorter than the first time interval, and the lifestyle information is information that can associate a person's age, sex, and a specified classification of a person's life and behavior with a person's occupation. The location information for each specified section of the area location information includes information on the total floor area of facilities in the specified area and the occupations within the facilities. The computing device 2100 of the people flow information service providing server 2000' executes a function as an area life information output unit that estimates and outputs area life information of people located in a specified area at least at a specified time based on demographic information, life information, and area location information based on the relationship between the specified life information and the specified area location information based on the program. More specifically, the life information estimation unit 2110 i) estimates the number of employed people by occupation type located in the specified area for each second time interval based on the demographic information and the life information, and ii) allocates 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, and estimates aggregate data of the number of people in a mesh smaller than the area mesh of the demographic information.

Furthermore, the storage device 2300 stores stay type information in association with the demographic data. As described above, the demographic information is aggregated data of the demographic data for each first time interval, and is information on the number of people for each person attribute (e.g., age and gender) present in each area mesh in a specified area during the first time interval. Here, the person attributes include stay type information on the stay of a person in the specified area.

As described below, "stay type information" includes attributes of residents of a specified area, attributes of workers of a specified area, and attributes other than residents and workers.

The computing device 2100 further executes the function of the people flow information estimation unit 2103 that analyzes people flow information within the analysis target area. The life information estimation unit 2110 of the computing device 2100 estimates the number of people for each classification of human behavior located in the area mesh included in the analysis target area for each first time interval based on demographic information and predetermined life information (behavior information and occupation information), and performs human behavior information estimation. The area location information estimation unit 2108 estimates aggregate data for each estimated area mesh in the analysis target area by distributing the number of people estimated by the life information estimation unit 2110 to the estimated area mesh based on the estimation result of the life information estimation unit 2110 and the type and size of the place (building, facility, road, transportation, etc.) corresponding to the classification of human behavior for each "estimated area mesh (for example, 62.5 m square)". The people flow information estimation unit 2103 estimates the distribution of people's attributes (including people's gender, age or generation, occupation, type of stay, etc.) specified by the user within the analysis target area based on the classification and aggregated data of people's behavior estimated in the estimated area mesh existing within the analysis target area.

Furthermore, although not particularly limited, similarly to embodiment 1, it is desirable that the lifestyle information estimation unit 2110 not only performs estimation in a first time unit based on demographic information and predetermined lifestyle information, but also estimates the number of people for each category of human behavior located in the area mesh included in the analysis target area for each second time unit that is shorter than the first time unit.

Figure 13 is a conceptual diagram showing how a specific area RM is specified on a digital map.

As shown in FIG. 13, a specified area RM is displayed as a digital map. At this time, roads Rd and rivers RV are displayed on the digital map.

Figure 14 is a conceptual diagram to explain the data structure of a digital map.

As shown in Figure 14, digital map data consists of multiple layers.

For example, the map information stored as "management data" (not shown) includes the secondary mesh code, base map used, geomagnetic declination, actual distance of the corner sides, update date of each data, number of records for each data, number of items for each data, etc.

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

For example, road information may be divided into "basic road data" and "minor road data."

Here, "basic road data" is not limited to, but includes, for example, "basic road node data," "basic road link data," and "attribute data within basic road links." Also, "narrow road data" is not limited to, but includes, for example, "narrow road node data," "narrow road link data," and "attribute data within narrow road links."

Here, "node data" (a collective term for "basic road node data" and "minor road node data") includes node numbers, position coordinates, elevation, node type, number of connecting links, connecting node numbers, etc. In addition, "basic road node data" also includes intersection names.

"Link data" (a collective term for "basic road link data" and "minor road link data") includes the link number (start and end node number), road administrator, road type, administrative district code, link length, road width classification, number of lanes, and position coordinates and elevation of interpolation points. "Basic road link data" further includes route number, roadway width, road census data (median lane width, 12-hour traffic volume, travel speed (peak hours), speed limits and other traffic regulations), position coordinates and elevation of interpolation points, emergency transport road classification, and expressway numbering. "Narrow road link data" further includes the corresponding basic road link number.

"Link attribute data" (a collective term for "basic road link attribute data" and "minor road link attribute data") includes the location and name of link attributes (bridges, overpasses, tunnels, caverns, railroad crossings, footbridges, toll booths, underpasses, areas expected to be flooded, etc.).

Therefore, road information generally consists of node data for identifying the positions of road intersections, link data indicating the nature of the roads connecting the nodes, and link attribute data indicating the type of link.

Layer 2 is, for example, building information as described below, layer 3 is, for example, contour lines, layer 4 indicates, for example, water bodies (rivers, lakes, ponds, etc.), and layer 5 indicates, for example, administrative divisions (prefectural borders, city/ward/town/village boundaries, block boundaries, etc.).

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

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

In addition, administrative boundary information may be structured in accordance with the Geospatial Information Authority of Japan database.

Figure 15 is a conceptual diagram showing an example of building information.

Building information is, for example, a collection of information about each building or facility, with the following structure:

Information about individual buildings consists of information such as the building ID, building name, latitude and longitude of the building's representative point, building use, building height (height from the reference point), number of floors (underground, above ground), total floor area, and floor plan information for each floor.

A "representative point of a building" is, for example, the location of the building's entrance.

The "height reference point" is, for example, the ground surface.

The "floor plan information for each floor" includes, but is not limited to, the identification number FPID of each floor, the use of the areas on that floor (parking lot, store (type of store), office, etc.), the area of each area, and the attributes of each area by time period (residential facility, commercial facility, tourist facility, accommodation facility, etc.).

In other words, in the case of building information (including facility information and location information), if the attributes (uses) of the same building (facility, location) differ depending on the date, time, and time of day, the building information may be configured to specify the respective attributes for each date, time, and time.

The building information also includes two-dimensional or three-dimensional polygon information (not shown) for the building, associated with the building ID.

Two-dimensional polygon information is the representation of the shape of a building or house as a polygon in a GIS (geographic information system). Three-dimensional polygon information is the representation of the shape of each floor of a building or house as well as the outer shape of the building in the height direction.

In addition, the specifications of the indoor information are not particularly limited, but may conform to, for example, the following "3D Indoor Geospatial Information Data Specification (Draft)."
https://www.gsi.go.jp/common/000212582.pdf

As described in the first embodiment, the building owner or manager may register building information (such as floor plans and 2D or 3D polygon data) via the building information registration terminal 4000. The building information registration terminal 4000 may also be configured so that visitors to the building register information such as photos of the interior of the building as building information, using a mobile terminal such as a smartphone with a photography function. For example, in the case of a smartphone, the people flow information service providing server 2000' may receive notifications from users who use a terminal that has installed application software having an input format that notifies the user of the building name, number of floors, and area information (such as store name and type of store) along with the photos they have taken.

The people flow information service providing server 2000' may be configured to store building information in the storage device 2300 as map information 2314.

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

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

Note that the above explanation was based on the assumption that the distribution of people within a specified analysis area is estimated in the following manner. In other words, the process is roughly as follows:

i) For a specified area, obtain "spatial statistical data" that is aggregated based on mobile phone usage information, etc., for each specified area mesh and for each first time interval.

ii) For people who are in a specified area (analysis target area) during a first time interval, the Basic Survey on Social Life DB is referenced to classify the people into behavioral classification information for each second time interval that is shorter than the first time interval, and the number of people in each category is estimated.

iii) For the area under analysis, estimate the number of people by occupation for each second time interval by referring to the Ministry of Internal Affairs and Communications Labor Force Survey Database, etc.

iv) For the area to be analyzed, the number of people by occupation type for each second time interval is apportioned based on the total floor area of each area of buildings on the map and the information on the occupation types of each building, thereby estimating the distribution of people (aggregated data) for a mesh (estimated area mesh) finer than the specified area mesh of the spatial statistical data.

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

However, this method of apportioning the number of people does not necessarily take into account the following points:

i) For example, at night, if the behavior is classified as sleeping, residents in the analysis area should be preferentially allocated to residential buildings. On the other hand, people who are neither residents nor workers in the analysis area (transient visitors) should be preferentially allocated to accommodation facilities (e.g. hotels) in the analysis area.

ii) Similarly, during daytime hours on weekdays, workers in the analysis area should be preferentially allocated to buildings that have attributes such as commercial facilities and business offices.

Therefore, in the second embodiment, the attribute "stay type" is assigned to people who stay in the analysis area during each time period, and priorities are set in advance in the people flow information service providing server 2000' so that the priority of the distribution of numbers of people is changed according to the attributes of the time period and location.

Note that, although not limited thereto, for example, the attribute "stay type" can be obtained from demographic data.

The area location information estimation unit 2108 then assigns priority to residential attributes if the person is a resident, priority to business facilities (business buildings, stores in commercial facilities, etc.) if the person is an employee, and priority to short-term stay locations and entertainment facilities if the person is not an employee (short-term stay locations are assumed to be accommodation facilities such as hotels and inns, and entertainment facilities are assumed to be theme parks, parks, museums, various experience facilities, aquariums, etc.).

The area location information estimation unit 2108 also considers the location attributes for each time period when allocating people to locations. For example, if the attribute is "resident" + behavior information is "sleeping", it will preferentially allocate to a residence, and if the attribute is "non-resident/non-worker" + behavior information is "sleeping", it will preferentially allocate to a hotel.

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

Here, during the de-identification process in FIG. 27, it is possible to extract and count the number of "residents" of the analysis target area and residents outside that area who regularly travel to the analysis target area on weekdays (workers). It is then possible to classify "persons who are neither residents nor workers" who are in the analysis target area during a certain period of time as "temporary visitors" and count them.

In this case, "temporary visitors" are, for example, tourists or shoppers, so if there are entertainment or tourist facilities such as theme parks within the area being analyzed, it is possible to set it up so that priority is given to these facilities during their operating hours.

As described above, the attributes of buildings or locations are registered in the map information 2314 of the lifestyle information service providing server 2000, and the area location information 2310 is set in advance with a priority for the "stay type" when allocating the attributes of the buildings or locations (such as housing, commercial facilities, offices, accommodation facilities, entertainment and tourist facilities, etc.).

Figure 16 is a flowchart showing the process of estimating the number of people distribution within the area to be analyzed, taking into account the stay attributes.

Referring to FIG. 16, in the people flow information service providing server 2000', first, the area setting unit 2107 executes a process of setting an area to be analyzed in response to an operation by a user (S102). At this time, the area setting unit 2107 also receives settings of the date, time, and time period to be analyzed from the user.

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

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 gender within a mesh of a certain width (for example, 500m square) for a certain time period (for example, 1 hour).

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

Next, for the "predetermined area" specified by the area setting unit 2106 at the user's request, the lifestyle information estimation unit 2110 classifies people (whose age and gender are obtained from the spatial statistical data) who are in a specific area during a specific time period (for example, a certain hour) based on the spatial statistical data, into work, movement (for example, commuting), etc. (behavior classification information) in more detailed time units (for example, 15 minutes) within this specific time period, with reference to the Basic Survey of Social Life database, and estimates the number of people in each category (for example, age and gender attributes, and stay attributes) (S120).

Furthermore, the lifestyle information estimation unit 2110 estimates the number of people (age and gender attributes) employed by occupation type for every 15 minutes for the above-mentioned specific area based on the estimated number of people for each behavior category (age and gender attributes, stay attributes), for example, based on the Ministry of Internal Affairs and Communications Labor Force Survey database (S130).

Next, the area location information estimation unit 2108 expands (e.g., proportionally apportions) the estimated number of employees by job type every 15 minutes (by age/gender attributes and dwelling attributes) to each building (facility) based on the total floor area of the facilities/buildings on the map, the area allocated to the job type of each facility/building, and the attributes of each building (facility), and estimates aggregate data on the number of people in a mesh smaller than the unit mesh of the spatial statistical data (e.g., 62.5 m mesh) (S141).

Finally, the people flow information estimation unit 2103 estimates the distribution of people's attributes within the analysis target area based on the classification and aggregated data of people's behavior estimated in the estimation area mesh existing within the analysis target area.

When distributing the number of people in a mesh to each building (facility/location) according to the stay attributes, the area location information estimation unit 2108 can also calculate the excess number of people for the building (facility/location) if an upper limit is set for the number of people that the building (facility/location) can accommodate. For example, such excess number of people can be configured to be distributed with the next highest priority to roads surrounding the building (facility/location) or major transportation stations (S150).

Next, if the area location information estimation unit 2108 has not completed the process of distributing the number of people for all types of specified stay attributes (N in S160), it returns the process to step S120, and if the process of distributing the number of people for all types of specified stay attributes has been completed (Y in S160), it ends the process.

Figures 17 and 18 are heat maps showing the distribution of people per area mesh (500m square) within a given area, taking into account visit attributes.

Figure 17(a) shows the distribution of all people visiting a 500m mesh, while Figure 17(b) shows the distribution of only residents in the 500m mesh (e.g., people who regularly stay in that mesh overnight).

Figure 18(a) shows the distribution of workers (including students, for example, people who are in the same mesh regularly during the day) in a 500 m mesh, and Figure 18(b) shows the distribution of temporary visitors in a 500 m mesh (people in a mesh who do not visit the mesh regularly, such as tourists or visitors from far away).

In this manner, by estimating the distribution of people within the estimated area mesh while taking into account the “stay type,” it is possible to estimate the distribution of people within the analysis target area with greater accuracy than in embodiment 1.
[System Using Lifestyle Information Providing Server 2000 and People Flow Information Providing Server 2000′]

Figure 19 is a conceptual diagram showing the hierarchy of data and data processing used in the lifestyle information estimation system.

Figure 20 is a conceptual diagram showing an overview of the data processing flow.

As shown in Figure 19, the "public domain," which is a public and open domain, includes, for example, "urban OS" as "software that supports urban infrastructure" and data sharing platforms.

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

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.

i) In terms of reusing and expanding services, traditionally systems have been specialized for each field and organization, which makes it difficult to reuse or expand services to other regions.

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.

iii) Low scalability: Conventional specialized systems have the problem that expanding functionality requires significant cost and effort, making it difficult to continuously and easily evolve services.

To address these issues in realizing Japan's smart cities, the City OS will be designed to have the following features: i) interoperability (connection), ii) data distribution (flow), and iii) ease of expansion (sustainability).

The "lifestyle information estimation system domain" of this embodiment is conceptually structured on such a foundation.

As shown in FIG. 19, in addition to "DoCoMo Spatial Statistics (registered trademark)" as shown in FIG. 4, the lifestyle information estimation system 1000 is also expected to use weather data for a specific time period in a specific area obtained from the Japan Meteorological Agency server, "disaster risk information" obtained from the National Research Institute for Earth Science and Disaster Prevention, and the like.

When this type of data is stored in the "data lake" mentioned above, it is first stored as general-purpose raw data, and then finalized for a certain period of time, and stored as a "standardized format for the lifestyle information estimation system (for example, standardization of the unit area mesh)" as mentioned above.

As shown in Figures 19 and 20, 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.

Furthermore, as shown in Figures 19 and 20, above the "data lake" there is a data analysis layer, which corresponds to the data integration and analysis processing performed by the lifestyle information service providing server 2000 and people flow information service providing server 2000' described above.

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

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.

As shown in Figure 20, after carrying out the above-mentioned integrated analysis process, 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), updating the "town information (general map information)" also changes the people flow distribution. Therefore, it is possible to visualize not only current real-time information, but also people flow distribution in a town that differs from the present in the past and future.

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

These analytical results can be used for urban planning, environmental optimization, energy optimization, real estate value assessment, logistics optimization, and so on. 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.

One possible use case for data integration and analysis is distributing admission tickets to product promotional events (whether at a physical or virtual venue) via smartphone based on users' behavioral history, etc.

Figure 21 shows an overview of the services realized using the lifestyle information estimation system 1000.

Figure 22 is a conceptual diagram showing an example of a service provided via a smartphone for the service in Figure 21.

As shown in Figure 21, 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 used to analyze the condition of each region (artificial intelligence prediction), calculate the score of each region for specified evaluation items, and map and visualize each region.

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

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.

For example, as shown in Figure 22, in the real world, it is possible to think of a moving person (or the smartphone they carry with them) as a kind of sensor that accumulates data in a data lake via a smartphone application. Then, by using city information posted by smartphone users (which can include not only crowding but also data on the weather, accidents, crime, etc.), it is possible to provide a location analysis platform as a service in the digital space, such as 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.

By configuring it in this way, it is possible to collect information about the town's attractions directly from local residents, and to encourage behavioral changes among people who can share information via smartphone (such as visiting the town).

In addition, 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.

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.

Figure 23 shows another example of data visualization using integrated analysis.

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

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, for example, using vectors)" and "elevation," as well as 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.

As explained above, by utilizing the integrated analysis by the lifestyle information service providing server 2000 and the people flow information service providing server 2000', more precise estimations can be made regarding employment, which is an important part of people's lives, based on employment information by occupation.

In addition, by utilizing integrated analysis by the lifestyle information service providing server 2000 and the people flow information service providing server 2000', the estimated lifestyle information can be provided to the user in a form that is easily understandable to the user through visualized information such as charts.

In addition, by utilizing the integrated analysis by the lifestyle information service providing server 2000 and the people flow information service providing server 2000', users can easily understand the estimated lifestyle information by relating it to locations on a map.

In addition, by utilizing integrated analysis by the lifestyle information service providing server 2000 and the people flow information service providing server 2000', accurate estimation can be made for each area (62.5 m mesh) in which a person can move in one minute, for example.

In addition, by utilizing integrated analysis by the lifestyle information service providing server 2000 and the people flow information service providing server 2000', it is possible to perform time series analysis over a specified period of time.

In addition, by utilizing integrated analysis by the lifestyle information service providing server 2000 and the people flow information service providing server 2000', it is possible to simulate lifestyle information in the past or future.

In addition, by utilizing integrated analysis by the lifestyle information service providing server 2000 and the people flow information service providing server 2000', it is possible to supplement specific area location information by estimation when it is not available as statistical information, etc.

In addition, by utilizing the integrated analysis by the lifestyle information service providing server 2000 and the people flow information service providing server 2000', it is possible to supplement certain lifestyle information by estimation when it is not available as statistical information, etc.

In addition, by utilizing integrated analysis by the lifestyle information service providing server 2000 and the people flow information service providing server 2000', it is possible to estimate area lifestyle information including at least information related to people flow routes based on demographic information including at least information related to the origin of movement (e.g., people commuting from home, people returning home from a restaurant, etc.).

Although one embodiment of the present invention has been described above, the present invention is not limited to the above-mentioned embodiment, and modifications and improvements that can achieve the object of the present invention are included in the present invention.

For example, the above-mentioned series of processes can be executed by hardware or software.

In other words, the functional configurations in Figures 1 to 23 are merely examples and are not particularly limited.

In other words, it is sufficient that the information processing system is provided with the functionality to execute the above-mentioned series of processes as a whole, and the functional blocks used to realize this functionality are not limited to the examples of Figures 1 to 23. Furthermore, the locations of the functional blocks and databases are not limited to those of Figures 1 to 23 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.

Furthermore, a single functional block may be configured as hardware alone, software alone, or a combination of both.

When a series of processes is executed by software, the programs that make up the software are installed onto a computer or other device from a network or recording medium.

The computer may be a computer built on dedicated hardware.

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

The recording medium containing such a program may be configured not only as a removable medium (not shown) that is distributed separately from the device body in order to provide the program to the user, but also as a recording medium that is provided to the user in a state where it is already installed in the device body.

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.

In addition, in this specification, the term "system" refers to an overall device that is composed of multiple devices, multiple means, etc.

The embodiments disclosed herein are illustrative 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.

2 Network, 100 User, 200 Smartphone, 1000 Lifestyle information estimation system, 2000 Lifestyle information service providing server, 2100 Calculation device, 2102 People flow path information estimation unit, 2103 People flow information estimation unit, 2104 Map output unit, 2106, 2107 Area setting unit, 2108 Area location information estimation unit, 2110 Lifestyle information estimation unit, 2300 Storage device, 2302 Demographic information, 2304 Traffic route information, 2306 Lifestyle information, 2308 Occupation-specific employment information, 2310 Area location information, 2312 People flow path information, 2314 Map information, 3000 Lifestyle information user terminal, 5000.1 to 5000. M Data providing server.

Claims (17)

a storage device for storing predetermined demographic information including information on the number of people present in a predetermined area at a predetermined time , life information for a predetermined time unit including information on people 's lives and behavior , and area location information including information on the location of each of predetermined sections present in the predetermined area;
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 area mesh in the specified area during the first time interval;
the life information includes employment information by predetermined occupation type, the predetermined time unit of the life information is a second time interval shorter than the first time interval, and the life information is information capable of associating the age, sex, and predetermined classification of the person's life and behavior with the occupation of the person;
The location information for each of the predetermined sections of the area location information includes information on the total floor area of a facility within the predetermined area and information on the type of job within the facility,
Further comprising an area life information output means for outputting area life information of a person located in a predetermined area at a predetermined time based on the demographic information, the life information, and the area location information;
The area life information output means includes:
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) A lifestyle information estimation device that allocates 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 estimates aggregated data of the number of employees in a mesh smaller than the area mesh of the demographic information . The lifestyle information estimation device according to claim 1 , further comprising a chart output means for outputting the lifestyle information of the people present in the predetermined area at the estimated predetermined time as a chart showing a ratio by number of people. The lifestyle information estimation device according to claims 1 to 2, further comprising a map output means for outputting lifestyle information of people located in the specified area at the estimated specified time on a map by correlating the proportion of people with locations. The life information estimation device according to claim 1 , wherein the area life information output means outputs an estimate of the life information of a person located in the specific area based on predetermined area location information for each specific area. The lifestyle information estimation device according to claim 1 , wherein the storage device stores the demographic information, the lifestyle information, and the area location information as data acquired from an external server via a network. The life information estimation device according to claim 1 , wherein the area life information output means outputs the area life information at the plurality of times based on the predetermined demographic information at the plurality of predetermined times included in a predetermined period. The life information estimation device according to claim 1 , wherein the area life information output means outputs the area life information based on predetermined area location information set in the past or future. The lifestyle information estimation device according to claim 1 , further comprising an area location information estimation means for estimating the predetermined area location information. The lifestyle information estimating device according to claim 8 , further comprising lifestyle information estimating means for estimating the predetermined lifestyle information based on the predetermined area location information. The lifestyle information estimation device according to claim 1 , wherein the area lifestyle information output means outputs the area lifestyle information including information on people flow paths based on the demographic information including information on a movement origin. a first data providing server that provides predetermined demographic information at predetermined time intervals, the demographic information including information on the number of people present in a predetermined area at a predetermined time;
a second data providing server that provides life information for a predetermined time unit, the life information including information on a person 's life and behavior;
A third data providing server that provides area location information including location information for each predetermined section located in the predetermined area;
A lifestyle information estimation 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;
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 area mesh in the specified area during the first time interval;
the life information includes employment information by predetermined occupation type, the predetermined time unit of the life information is a second time interval shorter than the first time interval, and the life information is information capable of associating the age, sex, and predetermined classification of the person's life and behavior with the occupation of the person;
The location information for each of the predetermined sections of the area location information includes information on the total floor area of a facility within the predetermined area and information on the type of job within the facility,
Further comprising an area life information output means for outputting area life information of a person located in a predetermined area at a predetermined time based on the demographic information, the life information, and the area location information;
The area life information output means includes:
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) A lifestyle information estimation system that allocates 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 estimates aggregated data of the number of employees in a mesh smaller than the area mesh of the demographic information . a storage device for storing predetermined demographic information including information on the number of people present in a predetermined area at a predetermined time, life information for a predetermined time unit including information on people's lives and behavior, and area location information including information on the location of each of predetermined sections present in the predetermined area;
The demographic information is aggregated data of demographic data for each first time interval, and is information on the number of people for each attribute of people present in each area mesh in the specified area during the first time interval;
The attributes of the person include stay type information of the person staying in the specified area,
the life information includes employment information by predetermined occupation type, the predetermined time unit of the life information is a second time interval shorter than the first time interval, and the life information is information capable of associating the age, sex, and predetermined classification of the person's life and behavior with the occupation of the person;
The location information for each of the predetermined sections of the area location information includes information on the total floor area of a facility within the predetermined area and information on the type of job within the facility,
Further comprising an area life information output means for outputting area life information of a person located in a predetermined area at a predetermined time based on the demographic information, the life information, and the area location information;
The area life information output means includes:
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) A lifestyle information estimation device that allocates 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 estimates aggregated data of the number of employees in a mesh smaller than the area mesh of the demographic information . The lifestyle information estimation device according to claim 12 , wherein the stay type information includes attributes of residents of the predetermined area, attributes of workers of the predetermined area, and attributes of persons other than the residents and workers. The area life information output means includes a people flow estimation means for allocating the number of people based on the demographic information to each location within the predetermined area according to an attribute of the area location information corresponding to the stay type information,
The lifestyle information estimation device according to claim 13, wherein the people flow estimation means assigns the stay type preferentially to an attribute of a residence if the person is a resident, assigns the stay type preferentially to a business facility if the person is an employee, and assigns the stay type preferentially to a short-term stay place or an entertainment facility if the person is neither a resident nor an employee. 13. The lifestyle information estimation device according to claim 12, wherein the predetermined time unit of the lifestyle information is a second time interval shorter than the first time interval, and the lifestyle information is information capable of associating an age, a sex, and a predetermined classification of the person's lifestyle and behavior with an occupation of the person . The lifestyle information estimation device according to claim 12 , wherein the area lifestyle information output means performs allocation of people to places taking into consideration attributes of places for each time period. a first data providing server that provides predetermined demographic information at predetermined time intervals, the demographic information including information on the number of people present in a predetermined area at a predetermined time;
a second data providing server that provides life information for a predetermined time unit, the life information including information on a person 's life and behavior;
A third data providing server that provides area location information including location information for each predetermined section located in the predetermined area;
A lifestyle information estimation 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;
The demographic information is aggregated data of demographic data for each first time interval, and is information on the number of people for each attribute of people present in each area mesh in the specified area during the first time interval;
The attributes of the person include stay type information of the person staying in the specified area,
the life information includes employment information by predetermined occupation type, the predetermined time unit of the life information is a second time interval shorter than the first time interval, and the life information is information capable of associating the age, sex, and predetermined classification of the person's life and behavior with the occupation of the person;
The location information for each of the predetermined sections of the area location information includes information on the total floor area of a facility within the predetermined area and information on the type of job within the facility,
Further comprising an area life information output means for outputting area life information of a person located in a predetermined area at a predetermined time based on the demographic information, the life information, and the area location information;
The area life information output means includes:
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) A lifestyle information estimation system that allocates 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 estimates aggregated data of the number of employees in a mesh smaller than the area mesh of the demographic information .

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