JP2023022623A - Human flow visualization system, human flow visualization apparatus, human flow visualization method, and program - Google Patents

Abstract

To provide a human flow visualization system, human flow visualization apparatus, human flow visualization method, and program, capable of appropriately providing information on a human flow according to a user's intention.SOLUTION: A human flow visualization apparatus 30 comprises: a data acquisition unit 301 that acquires data including positions of portable terminal devices 10 held by persons, unique identifier, and time; and an analysis unit 305 that, on the basis of the data, creates human flow data showing movement of the persons and displays it on a map as loci. According to a reduced scale of the map designated by a user, the analysis unit 305 displays the human flow data when its movement speed, movement distance, or movement time satisfies a predetermined condition.SELECTED DRAWING: Figure 1

Description

The present invention relates to a people flow visualization system, a people flow visualization device, a people flow visualization method, and a program, and more particularly, to a technique for accurately providing information on the flow of people according to user's intention.

Conventionally, techniques for analyzing the trajectory of people's movement (hereinafter referred to as "people flow") have been proposed. Patent Document 1 discloses a system that analyzes sensor data from sensors installed at a plurality of points and visualizes the size of the flow of people between points, that is, the scale of the number of people who moved between the points.

JP 2019-175240 A

In the system described in Patent Document 1, sensor data acquisition conditions (date and time, area, transmission frequency, etc.) and people flow visualization conditions (trajectory generation method, display method, etc.) are fixed. Even when it is desired to change these conditions according to the purpose of visualizing the flow of people or the user's intentions from time to time, the conventional system cannot accurately meet such demands.

The present invention has been made to solve such problems, and provides a people flow visualization system, a people flow visualization device, a people flow visualization method, and a program that can accurately provide information on the flow of people according to the user's intention. With the goal.

Other problems and novel features will become apparent from the description of the specification and the accompanying drawings.

According to one embodiment, a people flow visualization device includes a data acquisition unit that acquires data including the position of a mobile terminal device held by a person, a unique identifier, and time, and shows the movement of the person based on the data. an analysis unit that generates people flow data and displays it as a trajectory on a map; to display the people flow data that satisfies
According to one embodiment, the data acquisition unit of the people flow visualization device determines the acquisition frequency of the data according to the scale of the map designated by the user.
According to one embodiment, the analysis unit of the people flow visualization device hides a section in which the movement distance, movement time, or movement speed exceeds a predetermined threshold among the sections constituting the people flow data. According to the aspect, the people flow visualization system includes a mobile terminal device held by a person, and a data analysis device for acquiring and analyzing the position of the mobile terminal device, the data analysis device a data acquisition unit that acquires data including a position, a unique identifier, and a time; and an analysis unit that generates people flow data indicating the movement of the person based on the data and displays it as a trajectory on a map, The analysis unit displays the people flow data that satisfies a predetermined condition of moving speed, moving distance, or moving time according to the reduced scale of the map specified by the user.
According to one embodiment, a people flow visualization method includes a data acquisition step of acquiring data including the position of a mobile terminal device held by a person, a unique identifier and time, and showing the movement of the person based on the data. an analysis step of generating people flow data and displaying it as a trajectory on a map; to display the people flow data that satisfies
According to one embodiment, a program causes a computer to perform the above method.

INDUSTRIAL APPLICABILITY According to the present invention, it is possible to provide a people flow visualization system, a people flow visualization device, a people flow visualization method, and a program capable of accurately providing information on the flow of people according to user's intention.

BRIEF DESCRIPTION OF THE DRAWINGS It is a figure which shows the whole image of the people flow visualization system 1 concerning Embodiment 1. FIG. BRIEF DESCRIPTION OF THE DRAWINGS It is a figure which shows the whole image of the people flow visualization system 1 concerning Embodiment 1. FIG. BRIEF DESCRIPTION OF THE DRAWINGS It is a figure which shows the whole image of the people flow visualization system 1 concerning Embodiment 1. FIG. BRIEF DESCRIPTION OF THE DRAWINGS It is a figure which shows the whole image of the people flow visualization system 1 concerning Embodiment 1. FIG. BRIEF DESCRIPTION OF THE DRAWINGS It is a figure which shows the whole image of the people flow visualization system 1 concerning Embodiment 1. FIG. 1 is a diagram showing a hardware configuration of the people flow visualization system 1 according to the first embodiment; FIG. 3 is a diagram showing a functional configuration of a data analysis device 30; FIG. It is a display example of people flow data. It is a display example of the people flow data when the trajectory cutting condition is reflected. It is a display example of people flow data. It is a display example of the people flow data when the display form of the trajectory is adjusted. It is a display example of people flow data when a visualization target is selected according to user needs. It is a display example of people flow data when a visualization target is selected according to user needs. It is a display example of people flow data when a visualization target is selected according to user needs. FIG. 10 is a diagram showing an overview of a people flow visualization system 1 according to a second embodiment; FIG. FIG. 10 is a diagram showing an overview of a people flow visualization system 1 according to a second embodiment; FIG. FIG. 10 is a diagram showing an overview of a people flow visualization system 1 according to a second embodiment; FIG. FIG. 10 is a diagram showing an overview of a people flow visualization system 1 according to a second embodiment; FIG. FIG. 10 is a diagram showing an overview of a people flow visualization system 1 according to a second embodiment; FIG. FIG. 10 is a diagram showing a hardware configuration of a people flow visualization system 1 according to a second embodiment; FIG.

Hereinafter, specific embodiments to which the present invention is applied will be described in detail with reference to the drawings.

<Embodiment 1>
An overview of a people flow visualization system 1 according to a first embodiment of the present invention will be described with reference to FIGS. 1 to 5. FIG.

A people flow visualization system 1 includes a plurality of mobile terminal devices 10 and a data analysis device 30 (FIG. 1). The data analysis device 30 is also called a people flow visualization device 30 . The mobile terminal device 10 and the data analysis device 30 can communicate via a communication network.

The user of the people flow visualization system 1 directly operates the data analysis device 30 . The main function of the data analysis device 30 is to display the trajectories of people (people flow) as visualized information. Typically, line segments representing the flow of people are drawn on the map.

Basic information (people flow data) for displaying the flow of people is generated by the mobile terminal devices 10 held by people moving in the city (FIG. 2). Each mobile terminal device 10 acquires its own location information by GPS (Global Positioning System) or the like, and transmits the location information to the data analysis device 30 at a predetermined frequency. A communication network such as a wireless LAN or the Internet is used for transmission. These processes are typically automatically executed by the mobile terminal device 10 in the background.

The transmitted position information is accumulated in a storage area (storage) that is part of the data analysis device 30 . The storage may be a so-called cloud storage or provided in a system operated by a communication carrier or the like. The data analysis device 30 accesses the storage when a user requests to display people flow, extracts necessary data from the accumulated people flow data, and processes it (FIG. 3). This realizes the display of the flow of people on the map.

The user can change the display range of the map using a command button, a drag operation, or the like, or change the scale using a command button, a pinch operation, or the like. At this time, the data analysis device 30 estimates the intention of the user who operated the map and dynamically changes the information displayed on the map (FIG. 4). For example, it is possible to change how the flow of people is shown by displaying only the flow of people moving on foot or only the flow of people moving by means of transportation. It is also possible to guess the intention of the user and change the method of acquiring people flow data in the first place (Fig. 5). For example, the data analysis device 30 can issue an instruction to the mobile terminal device 10 to change the acquisition frequency of the people flow data.

FIG. 6 is a block diagram schematically depicting the hardware configuration of the people flow visualization system 1 according to the first embodiment of the present invention. The people flow visualization system 1 includes a mobile terminal device 10 and a data analysis device 30 .

The mobile terminal device 10 is an information processing device held by a person, and is typically a smart phone, a wearable terminal, or the like. The mobile terminal device 10 has a function of measuring location information (typically represented by latitude and longitude, but not limited to this) of the mobile terminal device 10 at predetermined intervals. Typically, by using a satellite positioning system (GNSS: Global Navigation Satellite System) such as GPS (Global Positioning System), GLONASS (Global Navigation Satellite System), Galileo, QZSS (Quasi-Zenith Satellite System) Positional information can be measured. Location information can also be measured based on distance from multiple wireless base stations (eg, femtocell base stations, indoor small base stations, etc.) or Wi-Fi base stations. Any other known technique may be used to calculate position information.

The mobile terminal device 10 transmits the measured position information to the data acquisition unit 301 (described later) of the data analysis device 30 together with a unique identifier that can uniquely identify the mobile terminal device 10 . At this time, the date and time of measurement, attribute information (described later), etc. may be included in the transmission. In addition, the mobile terminal device 10 can start or stop transmission of location information and change the measurement interval of location information in response to a request from a data acquisition condition setting unit 303 (described later) of the data analysis device 30. .

The mobile terminal device 10 also includes information indicating personal attributes such as age, sex, place of residence (prefecture, etc.), nationality, etc. of the person using the mobile terminal device 10, operating system version, etc. of the mobile terminal device 10. Information indicating attributes, information indicating actions such as spots where the mobile terminal device 10 has recently stayed, and the like can be held as attribute information.

The data analysis device 30 is an information processing device that can communicate with a plurality of mobile terminal devices 10, and is typically a PC (Personal Computer), a server computer, a cloud server, or the like. The data analysis device 30 typically communicates with the mobile terminal device 10 via a mobile phone wireless base station (for example, a femtocell base station, an indoor small base station, etc.), a Wi-Fi base station, a gateway, a relay server, or the like. I do. The data analysis device 30 collects unique identifiers and location information of the mobile terminal devices 10 from these mobile terminal devices 10 . It also analyzes the collected unique identifiers and location information to generate people flow data.

FIG. 7 is a diagram showing the functional configuration of the data analysis device 30. As shown in FIG. The data analysis device 30 has a data acquisition unit 301 , a data acquisition condition setting unit 303 , an analysis unit 305 , a visualization condition setting unit 307 , a display unit 309 and a storage unit 311 . These processing units do not necessarily have to be mounted on the same information processing device, and may be distributed and mounted on a plurality of information processing devices as necessary. Moreover, some or all of the processing units may be constructed using a service provided by a third party (such as a cloud computing environment).

The storage unit 311 stores data acquired by the data acquisition unit 301, people flow data generated by the analysis unit 305, data acquisition conditions set by the data acquisition condition setting unit 303, visualization conditions set by the visualization condition setting unit 307, map data, and the like. This is a storage area (storage) for storing. The storage unit 311 is installed by the operator of the data analysis device 30, is provided by a third party as a cloud service, or is provided in a system operated by a communication carrier or other infrastructure provider. I don't mind. That is, any storage area that can be accessed by other processing units of the data analysis device 30 may be used.

The data acquisition unit 301 communicates with a plurality of mobile terminal devices 10 via mobile phone radio base stations (for example, femtocell base stations, indoor small base stations, etc.), Wi-Fi base stations, gateways, relay servers, and the like. , the location information and the unique identifier of each mobile terminal device 10 are obtained. The data acquisition unit 301 acquires data according to data acquisition conditions set by the data acquisition condition setting unit 303 .

The data acquisition condition setting unit 303 sets data acquisition conditions. The data acquisition conditions include, for example, data acquisition area, date and time of acquisition, frequency of acquisition, and the like. The data acquisition condition setting unit 303 allows the user to specify a data acquisition area by, for example, inputting an address or specifying a range on a map. It also accepts the input of the acquisition start date and time, the acquisition end date and time, and the acquisition interval (for example, how many minutes to measure the position information) from the user. When the acquisition start time arrives, the data acquisition condition setting unit 303 measures the location information at the designated acquisition frequency to the portable terminal devices 10 existing in the data acquisition area, and continues to transmit the location information until the acquisition end time. demand that

Also, the data acquisition condition setting unit 303 may allow the user to specify attribute information. For example, the data acquisition condition setting unit 303 accepts designation of age, sex, place of residence (prefecture, etc.), nationality, version of the operating system, spots recently visited, and the like. In this case, the data acquisition condition setting unit 303 requests transmission of location information only to the mobile terminal device 10 holding the designated attribute information.

The data acquisition condition setting unit 303 may hold preset values of attribute information such as age, gender, place of residence (prefecture, etc.), nationality, version of the operating system, and recently visited spots as personas. For example, for each segment such as A layer, B layer, C layer, etc., a combination of setting values such as age, gender, place of residence (prefecture, etc.), nationality, operating system version, and recently visited spots is defined in advance. Keep When the user designates a category, the data acquisition condition setting unit 303 assigns set values such as age, gender, place of residence (prefecture, etc.), nationality, operating system version, recently visited spot, etc. corresponding to the category. Informational.

Further, the data acquisition condition setting unit 303 may allow the user to specify a threshold value S (described later) for determining whether the person is moving or staying.

The data acquisition condition setting unit 303 may hold preset values such as the data acquisition area, date and time of acquisition, frequency of acquisition, threshold value S, and attribute information. For example, for each project such as traffic volume survey, event crowd survey, and sales forecast for opening a commercial store, a combination of setting values such as data acquisition area, acquisition date and time, acquisition frequency, threshold value S, and attribute information is defined in advance. Keep When the user designates a project, the data acquisition condition setting unit 303 designates set values such as a data acquisition area, acquisition date and time, acquisition frequency, threshold value S, and attribute information corresponding to the project as data acquisition conditions.

Also, the data acquisition condition setting unit 303 may be configured to set a regular data acquisition schedule. For example, when the user inputs a day of the week and a time slot as a regular schedule, the data acquisition condition setting unit 303 sets the data acquisition area, acquisition frequency, threshold value S, etc. specified in advance by the user each time the day of the week and time slot arrive. are set as data acquisition conditions.

Also, the data acquisition condition setting unit 303 may have a function of dynamically changing the data acquisition condition in response to a request from the visualization condition setting unit 307 (described later).

The analysis unit 305 analyzes the data acquired by the data acquisition unit 301 and generates people flow data. People flow data is data that indicates where people have moved from which point to which point. For example, when the unique identifier X acquired by the base station 20a (located at A) at time T1 is acquired by the base station 20b (located at B) at time T2, the analysis unit 305 obtains X is determined to have moved from point A to point B between times T1 and T2. Alternatively, when the unique identifier X acquired by the base station 20a (point A) at time T1 is also acquired by the base station 20a (point A) at time T2, the analysis unit 305 determines that X stays at point A. determined to be Staying for a time exceeding a predetermined threshold S can also be determined as a stay. For example, the analysis unit 305 determines that X stays at point A when X stays at point A for 5 minutes or longer. People flow data with little noise can be generated by not using the data of the time zone determined to be staying for generation of people flow data. Note that the threshold value S for stay determination can be arbitrarily set using the data acquisition condition setting unit 303 .

The analysis unit 305 visualizes the people flow data and displays it on the display unit 309 . FIG. 8 is an example of mapping people flow data in a certain time zone on a map. A straight line connecting a plurality of points indicates the locus of movement of people, that is, the flow of people.

An example of analysis and visualization processing of people flow data by the analysis unit 305 is shown. The analysis unit 305 acquires the following data from the multiple base stations 20 .
{uid, lat, lng, t1}, {uid, lat, lng, t2}, . . . , {uid, lat, lng, tn}
Here, uid is the unique identifier of the mobile terminal device 10, lat and lng are the location (latitude and longitude) of the base station 20, and t is the data acquisition time. That is, each piece of data represents approximate coordinates of the mobile terminal device 10 at a certain time. The analysis unit 305 collects this by uid, arranges the coordinates in chronological order, and generates a trajectory by forming line segments between the coordinates.
{uid, [{lat, lng, t1}, {lat, lng, t2}, . . . , {lat, lng, tn}]}

Moreover, when the visualization condition is set by the visualization condition setting unit 307, the analysis unit 305 visualizes the people flow data according to the visualization condition.

A visualization condition setting unit 307 sets a visualization condition. The visualization conditions include, for example, trajectory cutting conditions. The trajectory cutting condition is a condition for hiding any section included in the trajectory. First, the need to hide an arbitrary section will be explained. In the example of FIG. 8, a part of the trajectory is displayed on the sea or the like where there is no moving route. Such an event occurs, for example, when the unique identifier obtained at point A at time t1 is then obtained at point B (opposite bank of point A, etc.) at time t2. During this time, the mobile terminal device 10 is actually moving on a land route along the bay, but if the moving speed is high or the frequency of data acquisition by the base station 20 is low, the actual moving route is Differently, there will be a section that straddles the sea. Trajectory cutting conditions can be used to avoid displaying such unrealistic sections.

Therefore, the visualization condition setting unit 307 receives inputs such as the length of a section (distance between coordinates [lat, lng]), time (interval between times t), and speed as trajectory cutting conditions. If the trajectory includes a section that satisfies these conditions, the visualization condition setting unit 307 hides the section and displays only other sections. As a result, the trajectory is displayed as if it were partially cut off. FIG. 9 is a display example of people flow data when the locus cutting condition is reflected. The trajectory cutting conditions were "when the data acquisition interval is 1 hour or more and the object has moved 500 m or more during that time" and "when the distance between two continuous points is 5000 m or more". As a result, it can be seen that inappropriate sections, such as the sea, where there are no moving routes, have been deleted.

In this embodiment, the visualization condition setting unit 307 designates a condition (trajectory cutting condition) for hiding an arbitrary section included in the trajectory. However, the present invention is not limited to this, and for example, a condition (trajectory display condition) for displaying an arbitrary section included in the trajectory may be specified. In this case, the visualization condition setting unit 307 receives inputs such as the length of a section (distance between coordinates [lat, lng]), time (interval between times t), speed, etc., as locus display conditions. If the trajectory includes a section that satisfies these conditions, the visualization condition setting unit 307 displays the section and hides the other sections.

In addition, the visualization condition setting unit 307 may have a function of adjusting the display form of the trajectory. For example, as shown in FIG. 10, in sections where many people flow overlap, the trajectory may be crowded and difficult to read. Therefore, the visualization condition setting unit 307, for example, as shown in FIG. 11, sets the display format between a section with a large flow of people (when a predetermined number or more of mobile terminal devices 10 are moving in the same section) and a section with a low flow of people. can be separated. In this example, a section with a large flow of people is highlighted by increasing the density of the display color of the line, and a section with a low flow of people is displayed with a lower density of the display color of the line. This makes it possible to simply grasp the overall trend of people flow.

In addition, the visualization condition setting unit 307 may have a function of appropriately changing the visualization condition of the trajectory according to the user's intention estimated from the screen operation or the like. Here are some examples.

<Selection of visualization targets according to user needs>
The analysis unit 305 provides a function of designating a map scale (zoom level or display range) when the people flow data is visualized and displayed on the display unit 309 . Since various means for designating the scale of an electronic map are known, the description thereof is omitted here. The analysis unit 305 notifies the visualization condition setting unit 307 of the scale specified by the user.

The visualization condition setting unit 307 provides a function of extracting people flow data that satisfies a specific condition among the people flow data that has already been acquired according to the scale specified by the user in the analysis unit 305 as a visualization target. One method for realizing this function is that the visualization condition setting unit 307 holds a correspondence table (Table 1) between the scale SC and the moving speed SP.

Table 1 shows the range of movement speed SP of people flow data extracted as a visualization target by the visualization condition setting unit 307 when the user designates a certain scale SC. That is, if the scale SC≦1/5000, only the people flow data with 18 km/h≦moving speed SP is extracted and the trajectory is displayed on the map. If 1/5000<scale scale SC≦1/2000, only the people flow data with 4 km/h<moving speed SP≦18 km/h is extracted and the trajectory is displayed on the map. If 1/2000<scale scale SC, only the people flow data whose moving speed SP≦4 km/h is extracted and the trajectory is displayed on the map.

When the user displays a wide area (zooms out), it is presumed that the user intends to see people flow data in cases where people move between cities or between bases by means of transportation. be. Therefore, when a scale smaller than a predetermined threshold (1/5000 in the example of Table 1) is specified, the visualization condition setting unit 307 sets the general moving speed of transportation means such as bicycles, automobiles, trains, and aircraft Extract people flow data for a speed range including (over 18 km/h in the example in Table 1).

On the other hand, when the user magnifies (zooms in) an area narrower to some extent, the user intends to see people flow data in cases where people are walking on streets or open spaces. presumed to be In this case, the visualization condition setting unit 307 determines that when a predetermined scale range (more than 1/5000 and less than 1/2000 in the example of Table 1) is specified, the person moves without using transportation means. We extract the people flow data covering the typical speed range (above 4 km/h and 18 km/h in the example of Table 1).

Then, when the user further enlarges (zooms in) a narrower area, the user can see the people flow data in the case where people move around very slowly in commercial facilities, office buildings, leisure facilities, event venues, etc. It is presumed that he has the intention of wanting to see In this case, when a scale exceeding a predetermined threshold (1/2000 in the example of Table 1) is specified, the visualization condition setting unit 307 is lower than the general walking speed (4 km in the example of Table 1). /h and below) Extract people flow data.

In the above-described embodiment, an example using a correspondence table such as Table 1 was shown, but the present invention is not limited to this. Other methods can be adopted as long as they can extract people flow data within a range. For example, the visualization condition setting unit 307 holds in advance a formula (Equation 1) that defines the relationship between the scale SC and the upper and lower limits of the speed SP, and inputs the designated scale SC into this formula. , it is possible to specify the people flow data of what speed SP should be extracted.
[Number 1]
SPmin = f1(SC)
SPmax = f2(SC)
SPmin: Lower limit of speed SP, SPMax: Upper limit of speed SP

In the above-described embodiment, the contents of the correspondence table (Table 1) between the scale SC and the speed SP and the relational expression (Equation 1) are fixed, but these may be variable. For example, assume that a map is divided into a plurality of areas (for example, 1 km square mesh) and each area is given an attribute (for example, a label indicating whether it is a large city or a suburb). In this case, the visualization condition setting unit 307 can extract people flow data for each area using a correspondence table (Table 1) and a relational expression (Equation 1) prepared in advance for each attribute of the area. Alternatively, even if there is only one correspondence table (Table 1) or one relational expression (Equation 1), the contents and coefficients thereof are rewritten each time according to the attributes of the area of interest, and the people flow data of each area is obtained. It is good also as extracting. For example, since the average speed of cars and trains is considered to be different between the center of a large city and the suburbs, the contents of the correspondence table (Table 1) and the relational expression (Equation 1) are different between the large city and the suburbs. This makes it possible to extract people flow data that more accurately reflects the user's intention. Similarly, a correspondence table with different contents according to various conditions related to the acquired people flow data, such as the time period when the people flow data was acquired, the attributes (for example, age) of the person who held the mobile terminal device 10, etc. (Table 1) or a relational expression (Equation 1) can be used.

Alternatively, the visualization condition setting unit 307 may be configured so that the user can arbitrarily switch between a plurality of correspondence tables (Table 1) and relational expressions (Equation 1) according to the purpose of analyzing people flow data. For example, it is conceivable that the user's intention when manipulating the scale is different for the purpose of acquiring people flow data for city planning and for the purpose of acquiring people flow data for increasing sales in commercial facilities. Therefore, by making it possible to select the correspondence table (Table 1) and the relational expression (Equation 1) adjusted according to each purpose, it is possible to perform visualization that more accurately reflects the user's intention.

12 to 14 show experimental examples of this technique. FIG. 12 shows the result of visualization of people flow data when the scale SC≦1/5000 is specified. As a result of extracting only the people flow data with 18 km/h≦moving speed SP, major transportation networks such as railroads and arterial roads with large numbers of users are depicted as dark trajectories. FIG. 13 is a visualization result of people flow data when 1/5000<scale scale SC≦1/2000 is specified. As a result of extracting only the people flow data where 4 km/h<moving speed SP≦18 km/h, streets with many people are drawn as dark trajectories. FIG. 14 shows the result of visualization of people flow data when 1/2000<scale scale SC is specified. As a result of extracting only the people flow data with the moving speed SP≦4 km/h, the densely populated facilities and the like are depicted as dark trajectories.

As described above, according to the present embodiment, it is possible to extract and visualize the people flow data that meets the user's intention from the acquired people flow data in a form in which unnecessary information is eliminated.

In this embodiment, the scale is used as a parameter that reflects the user's intention, and the moving speed is used as a parameter that indicates the conditions for extracting people flow data, but the present invention is not limited to this. For example, it is possible to change extraction conditions for people flow data in accordance with any operation that reflects the user's intention, instead of the scale operation. In addition, instead of moving speed, people flow data can be extracted under arbitrary conditions. For example, only the people flow data of the mobile terminal device 10 having predetermined attribute information may be extracted.

<Selection of data acquisition conditions according to user needs>
The analysis unit 305 provides a function of designating a map scale (zoom level or display range) when the people flow data is visualized and displayed on the display unit 309 . The analysis unit 305 notifies the visualization condition setting unit 307 of the scale specified by the user.

The visualization condition setting unit 307 provides a function of generating conditions (data acquisition conditions) for newly acquiring people flow data according to the scale specified by the user in the analysis unit 305 . When the data acquisition condition is newly generated, the visualization condition setting unit 307 notifies the data acquisition condition setting unit 303 of this. After receiving a new data acquisition condition, the data acquisition condition setting unit 303 causes the data acquisition unit 301 to acquire people flow data according to the new data acquisition condition. Accordingly, when the user changes the scale on the map, the data acquisition frequency is changed in real time.

A typical data acquisition condition is data acquisition frequency. In this embodiment, an example in which the data acquisition frequency is changed in real time when the user changes the scale on the map will be mainly described. One method for realizing this function is that the visualization condition setting unit 307 holds a correspondence table (Table 2) between the scale SC and the data acquisition frequency FR.

Table 2 shows the data acquisition frequency FR generated by the visualization condition setting unit 307 when the user designates a certain scale SC. That is, if the scale SC≦1/5000, the new data acquisition frequency is set to once/min. If 1/5000<scale scale SC≦1/2000, the new data acquisition frequency is set to 2 times/min. If 1/2000<scale scale SC, the new data acquisition frequency is set to 3 times/min.

When the user displays a wide area (zooms out), it is presumed that the user intends to see people flow data in cases where people move between cities or between bases by means of transportation. be. In such a case, even if the data acquisition frequency FR is small (acquisition interval is long), there is no problem in grasping the general trend. Disadvantages such as an increase in number and an increase in processing load are easily noticeable. Therefore, when a scale SC below a predetermined threshold value (1/5000 in the example of Table 2) is designated, the visualization condition setting unit 307 sets the data acquisition frequency FR to a relatively small value of 1 time/min. set.

On the other hand, when the user magnifies (zooms in) an area narrower to some extent, the user intends to see people flow data in cases where people are walking on streets or open spaces. presumed to be In such a case, the data acquisition frequency FR is set somewhat higher (shorter acquisition interval) than in the case of wide-area movement, so that it is easier to capture the movement of the person. Therefore, the visualization condition setting unit 307 sets the data acquisition frequency FR to a larger value when a predetermined scale SC range (more than 1/5000 and 1/2000 or less in the example of Table 2) is specified. Set to 2 times/min.

Then, when the user further enlarges (zooms in) a narrower area, the user can see the image of a person moving around very slowly inside a commercial facility, an office building, a leisure facility, an event venue, or the like. It is presumed that he has an intention to see people flow data. In such a case, the data acquisition frequency FR is preferably set as high as possible (the acquisition interval is shortened) so that even a slight movement of a person can be captured. Therefore, the visualization condition setting unit 307 sets the data acquisition frequency to the maximum of 3 times/min when a scale SC exceeding a predetermined threshold value (1/2000 in the example of Table 2) is specified.

In the above-described embodiment, an example using a correspondence table such as Table 2 was shown, but the present invention is not limited to this. Other methods can be adopted as long as the acquisition frequency can be set. For example, the visualization condition setting unit 307 holds in advance a formula (Equation 2) that defines the relationship between the scale SC and the data acquisition frequency FR. A set value for the frequency FR can be specified.
[Number 2]
FR = f3(SC)

In the above-described embodiment, the contents of the correspondence table (Table 2) between the scale SC and the data acquisition frequency FR and the relational expression (Equation 2) are fixed, but these may be variable. For example, assume that a map is divided into a plurality of areas (for example, 1 km square mesh) and each area is given an attribute (for example, a label indicating whether it is a large city or a suburb). In this case, the visualization condition setting unit 307 can extract people flow data for each area using a correspondence table (Table 2) and a relational expression (Equation 2) prepared in advance for each attribute of the area. Alternatively, even if there is only one correspondence table (Table 2) or one relational expression (Equation 2), the contents, coefficients, etc., are rewritten each time according to the attributes of the area of interest, and people flow data for each area is obtained. It is good also as extracting. For example, the average distance traveled by people per unit time is considered to be different between the center of a large city and the suburbs. , it is possible to extract people flow data that more accurately reflects the user's intention. Similarly, a correspondence table with different contents according to various conditions related to the acquired people flow data, such as the time period when the people flow data was acquired, the attributes (for example, age) of the person who held the mobile terminal device 10, etc. (Table 2) or the relational expression (Equation 2) can be used.

Alternatively, the visualization condition setting unit 307 may be configured so that the user can arbitrarily switch between a plurality of correspondence tables (Table 2) and relational expressions (Equation 2) according to the purpose of analyzing people flow data. For example, it is conceivable that the user's intention when manipulating the scale is different for the purpose of acquiring people flow data for city planning and for the purpose of acquiring people flow data for increasing sales in commercial facilities. Therefore, by making it possible to select the correspondence table (Table 2) and the relational expression (Equation 2) adjusted according to each purpose, it is possible to perform visualization that more accurately reflects the user's intention.

As described above, according to the present embodiment, it is possible to change the people flow data acquisition condition in real time according to the user's intention.

In this embodiment, the scale is used as a parameter that reflects the user's intention, and the acquisition frequency is used as a parameter that indicates the data acquisition conditions, but the present invention is not limited to this. For example, the data acquisition conditions can be changed according to any operation that reflects the user's intention instead of the scale operation. Also, any data acquisition condition can be specified instead of the acquisition frequency. For example, predetermined attribute information may be designated as the data acquisition condition.

<Embodiment 2>
An overview of the people flow visualization system 1 according to the second embodiment of the present invention will be described with reference to FIGS. 15 to 19. FIG.

A people flow visualization system 1 includes a mobile terminal device 10, a base station 20 capable of communicating with a plurality of mobile terminal devices 10, and a data analysis device 30 capable of communicating with a plurality of base stations 20 (FIG. 15). It is assumed that the base station 20 and the data analysis device 30 are connected by a communication network or the like.

The user of the people flow visualization system 1 directly operates the data analysis device 30 . The main function of the data analysis device 30 is to display the trajectories of people (people flow) as visualized information. Typically, line segments representing the flow of people are drawn on the map.

Basic information (people flow data) for displaying the flow of people is generated by the base station 20 and the data analysis device 30 (FIG. 16). The base station 20 constantly updates the list of mobile terminal devices 10 existing within its own communicable range, and transmits this to the data analysis device 30 at a predetermined frequency. A communication network such as a leased line or the Internet is used for transmission.

The list of mobile terminal devices 10 transmitted from the base station 20 is accumulated in a storage area (storage) that is part of the data analysis device 30 . The storage may be a so-called cloud storage or provided in a system operated by a communication carrier or the like. The data analysis device 30 accesses the storage and acquires the accumulated information when the user requests to display the flow of people.

The data analysis device 30 grasps the coordinates of the installation point of the base station 20 in advance. Therefore, the data analysis device 30 can simply regard the installation position of the base station 20 as the position of the mobile terminal device 10 included in the list transmitted from the base station 20 . If the same mobile terminal device 10 is included in the list of mobile terminal devices 10 transmitted from a plurality of base stations 20, the position of the mobile terminal device 10 may be estimated as the midpoint between those base stations 20. .

If the base station 20 can grasp the distance to the mobile terminal device 10, it is preferable to transmit distance information (an index such as radio wave intensity correlated with the distance) together with the list. If the same mobile terminal device 10 is included in the list of mobile terminal devices 10 transmitted from a plurality of base stations 20, the data analysis device 30 uses the distance information to determine the position of the mobile terminal device 10. It can be estimated precisely.

In this way, position information of the mobile terminal device 10 at a certain point in time is generated. This people flow data is stored in the storage. The data analysis device 30 accesses the storage when the user requests to display the flow of people, extracts necessary data from the accumulated data of people flow, and processes the extracted data (FIG. 17). This realizes the display of the flow of people on the map.

The user can change the display range of the map using a command button, a drag operation, or the like, or change the scale using a command button, a pinch operation, or the like. At this time, the data analysis device 30 guesses the intention of the user who operated the map, and dynamically changes the information displayed on the map (FIG. 18). For example, it is possible to change how the flow of people is shown by displaying only the flow of people moving on foot or only the flow of people moving by means of transportation. It is also possible to guess the intention of the user and change the method of acquiring the people flow data in the first place (FIG. 19). For example, the data analysis device 30 can instruct the base station 20 to change the list transmission frequency.

FIG. 20 is a diagram illustrating the hardware configuration of the people flow visualization system 1 according to the second embodiment of the present invention. The people flow visualization system 1 includes a mobile terminal device 10 , a base station 20 and a data analysis device 30 . The functional configuration of the data analysis device 30 is as shown in FIG.

The main difference between Embodiment 1 and this embodiment is that base station 20 is included. Along with this, some operations of the mobile terminal device 10 and the data analysis device 30 are changed. In the following, differences from the first embodiment will be mainly described, and descriptions of items that overlap with the first embodiment will be omitted.

The mobile terminal device 10 is an information processing device held by a person, and is typically a smart phone, a wearable terminal, or the like.

The base station 20 is a wireless communication facility capable of communicating with a plurality of mobile terminal devices 10 existing within a predetermined geographical range, and is typically a mobile phone wireless base station (for example, a femtocell base station). base stations, small indoor base stations, etc.) and Wi-Fi base stations. The mobile terminal device 10 periodically transmits a unique identifier that can uniquely identify the mobile terminal device 10 to the base station 20 . As a result, the base station 20 can always grasp the unique identifiers of the mobile terminal devices 10 existing within its own communicable range.

The base station 20 transmits the acquired list of unique identifiers of the mobile terminal device 10 to the data acquisition unit 301 of the data analysis device 30 . At this time, the date and time of measurement may be included in the transmission. In addition, the base station 20 can start or stop transmission of the unique identifier and change the unique identifier acquisition interval in response to a request from the data acquisition condition setting unit 303 of the data analysis device 30 .

The data analysis device 30 is an information processing device capable of communicating with a plurality of base stations 20 directly or via a gateway, a relay server, etc., and is typically a PC (Personal Computer), a server computer, a cloud server, or the like. . The data analysis device 30 collects from these base stations 20 the unique identifiers of the mobile terminal devices 10 that have communicated with the base stations 20 . It also analyzes the collected unique identifiers to generate people flow data.

Specifically, the data acquisition unit 301 of the data analysis device 30 communicates with a plurality of base stations 20 directly or via a gateway, a relay server, or the like, and performs unique Get and store an identifier. Data acquisition section 301 collects unique identifiers from base station 20 according to data acquisition conditions set by data acquisition condition setting section 303 . Then, people flow data is generated based on the collected unique identifiers. An example of how to generate people flow data is shown below.

It is assumed that the data acquisition unit 301 has previously grasped the coordinates of the installation point of the base station 20 . The data acquisition unit 301 can simply assume that the mobile terminal device 10 having the unique identifier transmitted from a certain base station 20 exists at the installation position of the base station 20 . Alternatively, if the information received from a plurality of base stations 20 contains the same unique identifier, it may be assumed that the mobile terminal device 10 exists at an intermediate point between those base stations 20 .

Note that if the base station 20 can grasp the distance to the mobile terminal device 10 (an index such as radio wave intensity that is correlated with the distance may be used), the base station 20 can determine the distance to the mobile terminal device 10 together with the unique identifier. is preferred to be sent. If the information received from a plurality of base stations 20 contains the same unique identifier, the data analysis device 30 uses the installation position of the base station 20 and distance information from the base station 20 to The position of the terminal device 10 can be precisely estimated.

The data acquisition unit 301 stores the location information of the mobile terminal device 10 at a certain time estimated in this way in the storage unit 311 as people flow data.

The data acquisition condition setting unit 303 sets data acquisition conditions. The data acquisition conditions include, for example, data acquisition area, date and time of acquisition, frequency of acquisition, and the like. The data acquisition condition setting unit 303 allows the user to specify a data acquisition area by, for example, inputting an address or specifying a range on a map. It also accepts the input of the acquisition start date and time, the acquisition end date and time, and the acquisition interval (for example, how many minutes to measure the position information) from the user. When the acquisition start time arrives, the data acquisition condition setting unit 303 assigns the unique identifier of the mobile terminal device 10 existing within the communicable range to the base station 20 covering the data acquisition area. , and request to continue sending until the acquisition end time.

Also, the data acquisition condition setting unit 303 may allow the user to specify attribute information. In this case, the data acquisition condition setting unit 303 associates the unique identifier of the mobile terminal device 10 with attribute information (age, sex, place of residence (prefecture, etc.), nationality of the person using the mobile terminal device 10). (including information indicating personal attributes such as personal attributes, information indicating attributes of the mobile terminal device 10 such as the version of the operating system, and information indicating actions such as the spot where the mobile terminal device 10 has recently stayed)). . When the data acquisition condition setting unit 303 accepts the attribute information specified by the user, the base station 20 holding the unique identifier corresponding to the specified attribute information broadcasts a request to transmit the unique identifier.

Other functions of the data acquisition condition setting unit 303, functions of the analysis unit 305, visualization condition setting unit 307, display unit 309, and storage unit 311 are the same as those in the first embodiment.

According to Embodiment 2, the base station 20 collects the unique identifier of the mobile terminal device 10 and the data analysis device 30 tracks the location of the mobile terminal device 10 by obtaining the unique identifier from the base station 20 . As a result, it is possible to collect and analyze people flow data without adding a special function to the mobile terminal device 10 .

<Other embodiments>
It should be noted that the present invention is not limited to the above embodiments, and can be modified as appropriate without departing from the scope of the invention. For example, in Embodiment 1, only the example in which the mobile terminal device 10 holds the attribute information was shown, but as in the case of the second embodiment, the data analysis device 30 holds the unique identifier and the attribute information in association with each other. You can configure it like this.

In addition, in the above-described embodiment, it is assumed that the mobile terminal device 10 is held by a person, but it is not limited to this, and the mobile terminal device 10 is attached to an animal or an article (vehicle, aircraft, robot, etc.). It's okay to be there.

Further, the various processes disclosed in the above embodiments may be executed by hardware, or may be realized by causing a CPU (Central Processing Unit) to execute a computer program. In this case, the computer program can be stored and provided to the computer using various types of non-transitory computer readable medium. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (eg, flexible discs, magnetic tapes, hard disk drives), magneto-optical recording media (eg, magneto-optical discs), CD-ROMs (Read Only Memory), CD-Rs, CD-R/W, semiconductor memory (eg, mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flash ROM, RAM (random access memory)). The program may also be supplied to the computer on various types of transitory computer readable medium. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. Transitory computer-readable media can deliver the program to the computer via wired channels, such as wires and optical fibers, or wireless channels.

1 people flow visualization system 10 mobile terminal device 20 base station 30 data analysis device (people flow visualization device)
301 data acquisition unit 303 data acquisition condition setting unit 305 analysis unit 307 visualization condition setting unit 309 display unit 311 storage unit

According to one embodiment, a people flow visualization device includes a data acquisition unit that acquires data including the position of a mobile terminal device held by a person, a unique identifier, and time; an analysis unit that displays the people flow data that satisfies a predetermined visualization condition as a trajectory on a map as a visualization target; and a visualization condition that changes the visualization condition when the scale of the map is changed. a setting unit, wherein when the visualization condition is changed by the visualization condition setting unit, the analysis unit displays the people flow data that satisfies the changed visualization condition.
According to one embodiment, the data acquisition unit acquires the data according to a predetermined data acquisition condition, and changes the data acquisition condition when the visualization condition is changed by the visualization condition setting unit.
According to one embodiment, the visualization condition includes the moving speed of the mobile terminal device.
According to one embodiment, the visualization conditions include attributes of the mobile terminal device, and the attributes include age, sex, place of residence, nationality, spot recently visited, or Contains any of the operating systems of the terminal device.
According to one embodiment, the data acquisition condition includes the acquisition frequency of the data.
According to one embodiment, the data acquisition condition includes attributes of the mobile terminal device, and the attributes include the age, gender, place of residence, nationality, spot recently visited, or the Includes any of the operating systems of the mobile terminal device.
According to one embodiment, the analysis unit hides a section in which the movement distance, the movement speed, or the data acquisition interval exceeds a predetermined threshold, among the sections forming the people flow data.
According to one embodiment, a people flow visualization system includes a mobile terminal device held by a person, and a data analysis device for acquiring and analyzing the position of the mobile terminal device, wherein the data analysis device A data acquisition unit that acquires data including the position, unique identifier, and time of the held mobile terminal device, and people flow data that indicates the movement of the people based on the data, and the people flow that satisfies a predetermined visualization condition. and a visualization condition setting unit that changes the visualization condition when the scale of the map is changed, wherein the analysis unit performs the visualization When the visualization condition is changed by the condition setting unit, the people flow data satisfying the changed visualization condition is displayed.
According to one embodiment, a people flow visualization method includes a data acquisition step of acquiring data including the position of a mobile terminal device held by a person, a unique identifier, and time; an analysis step of displaying the people flow data that satisfies a predetermined visualization condition as a trajectory on a map as a visualization target; and a visualization condition of changing the visualization condition when the scale of the map is changed. and displaying the people flow data that satisfies the changed visualization condition.
According to one embodiment, a program causes a computer to perform the above method.

Claims (6)

a data acquisition unit that acquires data including the location, unique identifier, and time of a mobile terminal device held by a person;
an analysis unit that generates people flow data indicating the movement of the people based on the data and displays it as a trajectory on a map;
The analysis unit displays the people flow data that satisfies a predetermined condition of a moving speed, a moving distance, or a moving time according to a reduced scale of the map designated by a user. People flow visualization device. The people flow visualization device according to claim 1, wherein the data acquisition unit determines the acquisition frequency of the data according to the scale of the map specified by a user. The people flow visualization device according to claim 1, wherein the analysis unit hides a section in which the movement distance, the movement time, or the movement speed exceeds a predetermined threshold among the sections constituting the people flow data. a mobile terminal device held by a person;
a data analysis device that acquires and analyzes the position of the mobile terminal device,
The data analysis device is
a data acquisition unit that acquires data including the location, unique identifier, and time of the mobile terminal device;
an analysis unit that generates people flow data indicating the movement of the people based on the data and displays it as a trajectory on a map;
The people flow visualization system, wherein the analysis unit displays the people flow data that satisfies a predetermined condition of moving speed, moving distance, or moving time according to the reduced scale of the map designated by the user. a data acquisition step of acquiring data including the location, unique identifier and time of a mobile terminal device held by a person;
an analysis step of generating people flow data indicating the movement of the people based on the data and displaying it as a trajectory on a map;
In the analyzing step, the people flow data that satisfies a predetermined condition of moving speed, moving distance, or moving time is displayed according to a reduced scale of the map specified by a user. A program for causing a computer to execute the method according to claim 5.

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