JP2007011391A - Subjective map generating system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a subjective map generating system, with which a subjective map can be easily generated in which a utilization value is very high for a user based on an individual viewpoint of the user. <P>SOLUTION: The subjective map generation is performed by; relating and collecting position information and time information by an information collecting means 21; relating and storing action information, the position information and the time information which are collected, by an information storing means 22; storing an existing map data by an existing map storing means 23; dividing the existing map data into a plurality of regions by a region dividing means 24; counting a moving cycle to surrounding regions afterwards, by setting the region in which the position information makes a counterpart of the time information which shows the oldest time is located, to a start region by moving cycle counting means 25; and generating a network for connecting all moved regions in a moving time order, and weighting the moving network with a weighting value which is determined for each between-regions, and embedding the extracted subjective information to an existing objective map data, by a moving network generating means 26. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The invention of this application relates to a subjective mapping system. More specifically, the invention of this application relates to a new subjective map creation system capable of easily creating a map rich in subjectivity as viewed from the user.

  A map that has been used by society in general is a collection of objective data for the user, that is, the latitude, longitude, azimuth, distance, etc. measured regardless of the user's intention, and the predetermined area name / route name. -It is only a collection of landmark names, etc., and the user can simply refer to the objective data shown on the map.

  On the other hand, in recent years, a town information map called “Town Navi” that introduces various shops / famous places in each region on the existing map has also appeared. It is considered that information collected through electronic bulletin boards and e-mail notifications is often used, and it is a collection of subjective data to some extent for each user. This is because the questionnaire or the like provides subjective information that reflects the preferences, emotions, ideas, and the like obtained by the user's own actions.

  However, the creation of a subjective map composed of such subjective data is very cumbersome because it takes time and effort for the user to provide information, to statistic the collected information, and to adapt the statistical data to the map data. Accompanied by work.

  Moreover, even if said town navigation is a subjective map, since it is thought that the questionnaire information from a user etc. and the text information showing it are the main information sources, it can be said that it is still poor in subjectivity and information property.

  For example, various techniques described in Patent Documents 1 to 5 below have been proposed as techniques for supporting the creation of a map. However, none of them are related to a subjective map, and the present situation is that no technology for facilitating the creation of a subjective map has been realized.

In addition, it is not a map creation support, but it creates a database that acquires information related to the location of the location and multimedia information such as video and audio related to the status of the location at any outdoor location and stores them in association with each other As a support technique, what is described in the following Patent Document 6 has been proposed, and this Patent Document 6 further proposes mapping the multimedia information extracted from the database on a map. However, this also has poor subjectivity with respect to the collected multimedia information, and merely displays the multimedia information next to the map display, so this technique is temporarily applied to the creation of a subjective map. Even if it is possible, it is considered that only subjectivity and information properties of the same degree as the town information map can be realized.
JP 2002-318533 A JP 2002-15331 A JP 2001-338305 A Japanese Patent Laid-Open No. 10-332391 JP-A-10-63181 JP 2000-235576 A

  Therefore, in view of the circumstances as described above, the invention of this application is a subjective map creation system that can easily create a subjective map with extremely high utility value from the viewpoint of each user. The issue is to provide.

  The invention of this application solves the above-mentioned problem. First, action information that is information born by an action performed at an arbitrary point as illustrated in the functional block diagram of FIG. The information collection means (21) that collects the position information, which is information related to the position of an arbitrary point, and time information in association with each other, and the action information, the position information, and the time information collected by the information collection means (21) in association with each other Information storage means (22) for storing, existing map storage means (23) for storing existing map data, and area dividing means for dividing the map data stored in the existing map storage means (23) into a plurality of areas (24) and the number of movements between each area of the map data divided by the area dividing means (24) based on the position information and time information stored in the information storage means (22). And counting the number of movements with respect to the surrounding area that has been moved thereafter, and a movement number counting means (25) for counting the number of movements with respect to the surrounding area. A network connecting all the regions in order of movement time is created, and a weighting value corresponding to the number of movements counted by the movement number counting means (25) is determined between the regions, and the mobile network is used with the weighted value. And a mobile network creation means (26) that weights the subjective map creation system (2).

  Second, it further comprises data embedding means (27) for embedding the mobile network created by the mobile network creation means (26) in the map data stored in the existing map storage means (23). Third, the weighting value is a movement frequency value indicating how much the number of movements between the regions is relative to the total number of movements. A subjective mapping system (2) characterized by being provided is provided.

  The invention of this application provides a subjective map creation system that can easily create a subjective map that is extremely useful to the user from the viewpoint of each user.

  Observing the streets of recent times, many people like various situations, such as carrying around various electronic devices such as mobile phones, mobile terminals, and digital cameras, walking, stopping, and sitting in the store. You can see that you frequently perform various actions that touch electronic information, such as making a call, sending an e-mail, taking a digital image, etc. when you like in any place. Naturally, various kinds of electronic information born from such actions represent the subjectivity itself such as the intention, idea, emotion, and preference of the person who performed the action. For example, the act of sending an e-mail is in accordance with its own intention, and the content of the sent e-mail also expresses its own feelings. The image capturing action of the digital image is also in accordance with his / her intention, and the captured image also expresses his / her preference or the like. On the other hand, it is considered that these various types of electronic information often include various information regarding places. For example, emails may include words that describe the sending location, related words around the location, or the location where you actually visited, but not the sending location (the email itself is sent away from the location) In many cases, the image of the shooting location or a characteristic object at the shooting location is reflected in the image. That is, the electronic information generated by the action performed at a certain point on the earth is the subjective information of the person who performed the action, and the information regarding the location included therein is also the subjective information.

  In view of the above, the inventor of the invention of this application regards the heterogeneous electronic information caused by the user's action as action information collectively, collects and accumulates it together with the position information (FIG. 1: step S1), And by extracting the actor's subjective information about the place from that (Fig. 1: Step S2) and mapping it to the existing objective map (Fig. 1: Step S3), we act in the real world. It was found that an entirely new map which is composed of subjective information of people who are rich in subjectivity and information, and which has never existed before, can be used to come up with the invention of this application. The map created in this way is called a “subjective map”.

  Therefore, although the town information map such as Town Navi is called a subjective map in the description of the prior art, it is actually a concept that is far from the “subjective map” considered by the inventor of the present invention. However, there seems to be no “subjective map” of the same concept.

  Hereinafter, embodiments of the invention of this application having the above-described features will be described in detail for each of the various means. Note that what is called processing means and storage means corresponds to various means described here. Further, the embodiment is described including the basis of the embodiment described later.

<< First Embodiment >>
[Information collection means]
The information collecting means (FIG. 2: (11)), for example, as illustrated in FIG. 5, the behavior information collecting unit (411) and the position information collecting unit (412) that collect and collect behavior information and position information at an arbitrary point. ) (FIG. 6: “Information collection process”), for example, a remote control type or program including a behavior information collection unit (411) and a position information collection unit (412) Examples include self-propelled robots, mobile phones, and mobile terminals. In the above description of the “subjective map”, the subjective behavioral information of human beings moving in the real world is used. However, devices such as robots that move and act according to programs based on remote operations or programs based on human subjectivity can also be used. is there.

  Here, “behavior information” means information born by an action performed at an arbitrary point, for example, text data such as e-mail or diary data input at an arbitrary point, recorded voice, etc. Various kinds of information such as auditory data, visual data such as captured still images and moving images can be considered. Text and speech can also be classified as language data. In addition, body information measured at an arbitrary point (for example, body temperature, pulse, blood pressure, myoelectric potential, respiratory state, brain wave, etc. for humans, battery voltage, motor current, internal temperature, motor temperature for robots) , Circuit temperature, torque addition, etc.) can also be included in the “behavior information” here. This is because the physical information can be said to be information born by various actions such as walking, climbing, and descending performed by humans and robots at arbitrary points. Since these are measured by a detection device such as various sensors (see “behavior information collection unit (411)” described later), it can also be called “behavior sensing information”. Furthermore, information related to individual user actions included in diaries, bulletin boards, travel diaries, and the like published on the Internet Web can also be included in the “behavior information” here. In other words, not only the information (including behavioral sensing data) born from the actual movement destination as described above, but also the past destination location entered / transmitted after moving to another location. It is also possible to consider the behavior information. For example, if you went to a park but didn't send emails, you moved to another place, such as a store or home, and sent information about the park or published it on the web. By using it to create a subjective map intended by the invention of the application, the subjectivity of the person can be covered. Of course, all of the above heterogeneous behavior information may be used for creating a map, or may be used in one or a combination thereof. A subjective map can be created by using at least one type of behavior information, but it goes without saying that subjectivity for multiple types of behavior information is more subjective and informative.

  “Position information” means information regarding the position of an arbitrary point, and for example, longitude and latitude, altitude, and address can be considered. This position information may be data that can associate the generation position of the behavior information with the position on the objective map when mapping the behavior information to the existing objective map data. For example, mapping based only on longitude and latitude, mapping only based on address, and a combination thereof are mentioned (this will be described in more detail in the explanation of the information distribution means). In addition, when the behavior information is not from the actual destination as described above but after being moved to another location, the position of the past destination point included in the latter behavior information The information on the location is also included in the “location information” here. For example, in the case of the above example in the park, the place where the input / transmission is performed is a shop or home, but even if the location information is acquired, it has nothing to do with the location of the park described in the e-mail or the web. In that case, a word relating to the position of the park (for example, the name of the park) included in the text information is used as position information used for mapping.

  The behavior information collection unit (411) has a function of collecting the behavior information. For example, a keyboard for text data, a microphone for auditory data, a camera for visual data, and various types of behavior sensing data. A search engine or the like can be considered for Web public data such as sensors. Different devices are applied depending on the type of behavior information. What is necessary is just to provide these apparatuses to the said self-propelled robot, a mobile phone, and a portable terminal. For example, various sensors for behavior sensing data are incorporated into a wristwatch in the case of a human being to automatically measure a pulse or the like, and in the case of a robot, the sensor is incorporated into a drive source, a control circuit, etc. It is also possible to consider a mode of automatically measuring the angle. In addition, as a search engine for Web public data, for example, a behavior information collection center can be constructed and used for various search engines when creating a subjective map in the center without having to equip a moving human or robot. Search the diary or bulletin board.

  The position information collection unit (412) has a function of collecting the position information. For example, GPS can be taken into consideration for the position information of the place where the action is actually performed. What is necessary is just to give a GPS function etc. to the said self-propelled robot, a mobile phone, and a portable terminal. Also, for example, if a point where an e-mail or the like is transmitted in a PHS phone is recognized as the position of the nearest base station, and if it is within the allowable range of position accuracy when creating a map, the recognition device also has a position collecting function Can be considered as. In addition, regarding the position information included in the action information after moving to another place (such as the name of the park in the above example of the park), an extraction device having a function of executing various word extraction methods can be considered.

  As described above, the relationship between the action information collected and the position information is such that one piece of position information always corresponds to one piece of action information. For example, in a mobile phone having a GPS function and an e-mail function, every time an e-mail is transmitted, the GPS data at the time of transmission is collected, or the time when the e-mail is transmitted while GPS data is continuously collected GPS data at the same time is extracted, every time body information such as a person or a robot is measured, position information at the time of measurement is collected, or even an action related to a certain place in the Web public data Extract information and location information in pairs.

  As can be seen from these examples, the time information connecting the behavior information and the position information is always related. Therefore, the information collection device (41) collects time information that can also be called link information for associating behavior information and position information simultaneously with the collection of both, and the time information collection unit (413) (see FIG. 5) for that purpose is collected. It can be said that it has. For example, a mode in which an e-mail transmission time is recorded and collected using a mobile phone / portable terminal built-in clock or a physical information measurement time is recorded using a measuring device built-in clock, etc. A mode of extracting and collecting information to be represented can be considered. Information that expresses time includes the date contained in the text data, information using numerical values such as hour, minute, second, various language information such as morning, afternoon, morning, day, morning, evening, noon, early morning, midnight, and various Any information that can grasp the time, such as information such as the creation date and time and the transmission date and time included in the header, can be used, and this may be extracted when collecting the behavior information and the position information.

  Since the above information collection device (41) needs to provide the collected action information, position information, and time information to the information storage means described below, the collected information is transmitted wirelessly or by wire using radio waves or light. Or an information output unit (414) provided in a form other than communication (see FIG. 5). As for forms other than communication, for example, a magnetic or optical recording medium that can suck out collected information from each collecting unit and transfer it to the information recording unit can be considered.

[Information storage means]
The information storage means (FIG. 2: (12)) includes an information input unit (421) that receives collected information from the information collection device (41) and an information input unit (421), as exemplified in FIG. An information database (42) having an information storage unit (422) that stores the input collected information in association with each other can be considered (FIG. 6: “Information Storage Processing”).

  This information database (42) may be separate from the information collection device (41) or may be incorporated in itself.

  In the former case, for example, an information collection / storage center or the like can be constructed and a database device installed in the information collection / storage center can be considered. Collected information is collected from the information output unit (414) of the information collection device (41) by the information input unit (421). The data is received or sucked and sequentially stored in the information storage unit (422) (FIG. 5 shows this form).

  In the latter case, for example, a configuration in which a database device is incorporated in the information collection device (41) itself or a configuration in which a database is built in the built-in storage medium can be considered, and collection information is received in the information collection device (41). (The information output unit (414) and the information input unit (421) are both input / output functions inside the apparatus). In this latter form, the collection of information collection devices (41) may be realized as a distributed database on the network. According to this, for example, each program described later (FIG. 5: 44, 45, 46, 47). , 48) can be realized as an agent that moves between the information collection devices (41) connected to the network while performing the map creation process.

[Existing map storage means]
The existing map storage means (FIG. 2: (13)) includes, for example, a map input unit (431) for inputting existing map data and map data input by the map input unit (431) as illustrated in FIG. Can be considered a map database (43) having a map storage unit (432) for storing.

  "Existing map data" refers to objective map data consisting of a collection of surveyed latitude, longitude, azimuth, distance, etc. and predetermined area names, route names, landmark names, It is a map used as the base of the subjective map which the invention of this application intends to make. Note that this objective map may or may not be maintained.

[Area dividing means]
The area dividing means (FIG. 2: (14)), for example, as shown in FIG. 5, an area dividing unit (441 for dividing the objective map data stored in advance in the map database (43) into a plurality of areas. ) Can be considered.

  This area dividing device (44) can be realized as a device form having the area dividing unit (441) as a program, or a form in which the area dividing unit (441) is a program.

  For specific processing, for example, objective map data is divided into grids, administrative divisions and addresses included in advance in objective map data, or landmarks such as buildings, roads, intersections, green spaces, rivers, etc. Or the like (FIG. 6: “Area Division Processing”). In the former case, the grid may be uniformly divided into the same size, but a process of reading position information from the information database (42) and dividing the frequently appearing position into smaller sizes may be considered. Alternatively, it is also possible to feed back the data amount of action information distributed by the information distribution means described later and automatically adjust the size of each region according to the data distribution, that is, the amount of data (FIG. 6: “Feedback”).

[Information distribution means]
The information distribution means (FIG. 2: (15)) reads the pair information of the action information and the position information from the information database (42) as exemplified in FIG. 5, for example, and the position information and the region dividing device (44). ) And a position matching unit (451) that performs matching with each region position of the objective map data divided by (2), and behavior information that is matched by the position matching unit (451), that is, a pair that matches the position information. It is possible to consider an information distribution device (45) that has a behavior information allocating unit (452) for allocating all of the behavior and position information.

  This information distribution device (45) can be realized as a device configuration having the position matching unit (451) and the behavior information allocation unit (452) as a program, or a configuration itself as a program.

  For specific processing, for example, when the position information is longitude and latitude, it is detected which region falls within the longitude and latitude range, and behavior information is assigned to the corresponding region. The same applies to the case of an address, a combination of longitude and latitude / address, and other positional information. Thereby, it is matched with which area | region on the objective map the actual action was performed.

  In order to accumulate action information assigned to each area, an area information database (400a, 400b, 400c...) May be constructed for each area (FIG. 6: “Information distribution process”). ).

[Information extraction means]
For example, the information extracting means (FIG. 2: (16)) uses the information distributing device (45) for each region of the objective map data divided by the region dividing device (44) as exemplified in FIG. It is possible to consider an information extraction device (46) having an information extraction unit (461) that extracts geographical information from the action information allocated by.

  This information extraction device (46) can be realized in the form of an apparatus having the information extraction unit (461) as a program, or in the form of a program itself.

  For specific processing, for example, for each area database (400a, 400b, 400c...) Of each area, geographical information is extracted from the action information stored in each area (FIG. 6: “ Information extraction process "). For the extraction, for example, when the behavior information is text data, a word is extracted by morphological analysis or a word is extracted by dictionary matching, and when it is image data, an object is extracted by image recognition processing or colors and textures by various image processing. , Graphic extraction, character information extraction by image character recognition processing, sound data identification and extraction by voice recognition processing, extraction of main audio frequency components by frequency analysis, etc. in the case of voice data, etc. Various extraction techniques and recognition techniques can be applied.

  Here, “geographical information” refers to the mountain rivers / land / land / climate / population / community / industry / traffic on the earth, the state and circumstances of the land, the height, width, narrowness and shape of the land, topography, topography, buildings, and architecture It means information about every kind of geography such as things and landscapes, and also includes information about human life and culture in geography. That is, any information that can constitute a subjective map is included. Actually, the information to be extracted is determined in advance, and the extraction process is performed based on the information. Of course, the type of extracted information can be updated at any time.

  The extracted geographical information of each area may be stored in the corresponding area database (400a, 400b, 400c...).

[Feature selection means]
For example, the feature selection means (FIG. 2: (17)) uses the information extracting device (46) for each region of the objective map data divided by the region dividing device (44) as exemplified in FIG. The feature selection device (47) for selecting a geographic feature from the geographic information extracted by the above can be considered.

  “Geographic feature” refers to representative or typical information (significant feature represented by) in particular in the geographic information extracted from the behavior information in a certain region.

  More specifically, in order for a certain piece of geographic information i to be a “geographic feature” in the region R, either or both of the following conditions (a) and (b) are satisfied, or the condition (c) It is necessary to satisfy.

      (A) Among the behavior information obtained in the region R, the appearance frequency of the geographical information i is high (“high reproducibility in the same region”).

      (B) Of the behavior information obtained in areas other than the area R, the appearance frequency of the geographical information i is low. Alternatively, there are a small number of regions where the appearance frequency of the geographic information i is as high as the region R (“rareness in other regions”).

      (C) The user directly designates the geographical information i as an important feature in the region R (“importance by the user”).

  Some specific examples are given below.

  Example 1 (when the conditions (a) and (b) are satisfied): Among the geographical information in the region R, the geographical information i (= “Tokyo Tower”) appears frequently. There were very few areas where the information i appeared with the same high frequency. At this time, the geographical information i is a geographical feature in the region R.

  Example 2 (when the condition (a) is satisfied but (b) is not satisfied): Among the geographical information in the region R, the geographical information i (= “intersection”) appears frequently, but this geography There are many other areas in which the target information i is filed with the same high frequency. At this time, the geographical information i is not a geographical feature in the region R.

  Example 3 (when condition (b) is satisfied but (a) is not satisfied): Geographic information i (= “fire”) included in the geographical information in region R has a very high frequency of appearance in other regions. Although it was small, it appeared only once in the region R. At this time, the geographical information i is not a geographical feature.

  Example 4 (when the conditions (a) and (b) are not satisfied but (c) is satisfied): In the geographical information in the region R, the appearance frequency of the geographical information i (= “shrine”) is compared with other regions. The user specified the geographical information i as the geographical feature of the region R.

  In Examples 1 to 3, the case where both conditions (a) and (b) are simultaneously provided is used as a criterion. However, only one of them may be used as the criterion. In that case, the geographical information in Examples 2 and 3 is used. i is also a geographical feature. However, geographical information having a higher characteristic (importance) in the former is selected as a geographical feature. Moreover, although the condition (c) as in Example 4 is an optional aspect, it is preferable in the case where the subjectivity of the user is more strongly taken up.

  Therefore, the feature selection device (47), for example, the high reproducibility determination unit (471) that determines whether or not the above conditions (a), (b), and (c) are included in the geographical information extracted for each region. The rarity determining unit (472) and the user designation determining unit (473) can be provided, and each region can be determined by statistically comparing the appearance frequency of geographical information between the regions or according to the designation from the user. A geographic feature to be called a landmark is selected (FIG. 6: “Feature selection process”).

  For the high reproducibility judgment unit (471) and the rarity judgment unit (472), various statistics such as “tf (term frequency) / idf (inverted document frequency) method”, “statistical test”, “method based on total information amount”, etc. Implementation of a traditional method is conceivable.

  As for the user designation determined by the user designation determination unit (473), for example, the information collection device (41) also collects user designation information for the geographical information i in the behavior information when collecting the behavior information. When the information database (42) accumulates the designation information together with the action information, the geographic information i is determined to be designated by the user and is selected as a geographic feature in the corresponding region R. Alternatively, it may be possible to accept input from a user separately.

  The extracted geographical features of each area may be accumulated in the corresponding area database (400a, 400b, 400c...).

  This feature selection device (47) has a device form having these high reproducibility judgment unit (471), rarity judgment unit (472), and user designation judgment unit (473) as a program, or a form of itself as a program. realizable.

  The geographical features selected as described above are not simply user subjective data, but should be called subjective landmark data for each point obtained by statistical processing. By using it, a subjective map with extremely high subjectivity and information can be created.

[Data embedding means]
The data embedding means (FIG. 2: (18)) stores the geographical feature data of each area selected by the feature selection device (47) as exemplified in FIG. 5 in the map database (43). A data embedding device (48) having a data embedding unit (481) that maps and embeds in a corresponding region in existing objective map data stored can be considered.

  The data embedding device (48) can be realized as a device configuration having the data embedding unit (481) as a program or a configuration in which the data embedding device (481) is a program.

  In addition to embedding the selected geographical feature in an objective map to make a subjective map, only the geographical feature may be used as a subjective map. In this case, for example, the geographical features stored in the region database (400a, 400b, 400c...) Are used as map-like data positioned in each region, and this feature map is used as it is as a subjective map. According to this, the feature map as the subjective map and the existing objective map can be referred separately.

  Further, the subjective map data may be created by superimposing the feature map data on the objective map data. FIG. 6 shows this form, in which “feature map creation processing” and “superposition processing” are performed in order.

  Each of the above processes creates a subjective and informative subjective map consisting of subjective landmark data for each point, and the user usually performs e-mail transmission, digital image shooting, etc. It is possible to easily support the creation of a subjective map only by the actions that are present.

  You can also easily create a subjective map that can be called a private map based on your own subjective information. In this case, for example, if behavior information is collected and accumulated as data with user ID information, a subjective map of only the user recognized by the ID information can be created.

<< Second Embodiment >>
In the first embodiment described above, a subjective map created by converting a subjective action taken by a user at an arbitrary point into map data and embedding it in an objective map has been described. A subjective map is created using the movement history of the user who moves.

  In the present embodiment, information collection means (FIG. 3: (21)) that collects behavior information, position information, and time information in association with each other, and behavior information, position information, and time information collected by the information collection means (21) Are stored in information storage means (FIG. 3: (22), existing map storage means (FIG. 3: (23)) for storing existing map data, and existing map storage means (23). Area dividing means for dividing the map data into a plurality of areas (FIG. 3: (24)) and each means described below are required. The former four means are, for example, as illustrated in FIG. The information collecting device (51), the information database (52), the map database (53), and the region dividing device (54) having substantially the same functions as those in the first embodiment can be provided.

Each means other than the above will be described in detail below with reference to FIGS. 7 and 8 as appropriate.

[Movement count means]
The movement number counting means (FIG. 3: (25)), for example, the number of movements between the regions of the objective map data divided by the region dividing device (54) as exemplified in FIG. ) Can be considered (FIG. 8: “Movement Count Counting Process”) having a counting unit (551) that counts based on the position information and time information stored in ().

  This movement number counting device (55) can be realized as a device configuration having the counting section (551) as a program, or a configuration in which the device itself is a program.

For specific processing, first, the area corresponding to the position information paired with the time information indicating the oldest time is determined as the start area (area _{RA in} FIG. 8), and based on that area, the surroundings moved thereafter The number of movements for the areas R _B to _I is counted up.

  The counted number of movements of each area may be accumulated in the corresponding area database (500a, 500b, 500c...).

[Mobile network creation means]
The mobile network creation means (FIG. 3: (26)) includes, for example, a network creation unit (561) for creating a network connecting the moved areas as exemplified in FIG. It has a network weighting unit (562) that weights based on the number of movements counted by (55), and thus a moving network creation device (56) that creates a weighted inter-region movement network can be considered (FIG. 8: "Mobile network creation process").

  The mobile network creation device (56) can be realized as a device configuration having the network creation unit (561) and the network weighting unit (562) as a program, or a configuration in which the mobile network creation device (56) itself is a program.

For specific processing, for example, first, a network that connects all the moved areas, that is, all the areas corresponding to the position information in order of movement time is created, and then a weight value corresponding to the number of movements between the areas is created. To decide. As this weighting value, it is possible to use a movement frequency value indicating how much the number of movements is relative to the total number of movements, and weighting is performed using this movement frequency value. For example, if the region transfer times 10 times from R _A to region R _B when the total number of movements is 100 times 0.1, if 5 times the number of movements to the area R _C 0.05, calculating a movement frequency value and so, the region R _B from the region R _a this value may be weighted network to the area R _C.

  According to this, the relationship between the regions can be visualized by the weighted inter-region movement network, and the creation of a subjective map can be realized by using this.

[Data embedding means]
The data embedding means (FIG. 3: (27)) stores the weighted inter-region mobile network created by the mobile network creation device (56) as exemplified in FIG. 7 in the map database (53). A data embedding device (57) having a data embedding unit (571) for embedding in existing stored objective map data can be considered.

  This data embedding device (57) can be realized as a device configuration having the data embedding unit (571) as a program or a configuration in which the data embedding device (571) itself is a program.

  In addition to embedding the created mobile network in an objective map to make a subjective map, only the mobile network may be used as a subjective map. In this case, for example, the weighted inter-region movement network can be displayed as map-like data, and this weighted inter-region movement map is used as it is as a subjective map. According to this, the movement map as a subjective map and the existing objective map can be referred separately.

  Further, the subjective map data may be created by superimposing the moving map data on the objective map data. FIG. 8 shows this form, in which “movement map creation processing” and “superposition processing” are performed in order.

  Through the above processes, a subjective map consisting of subjective movement data between each point and rich in subjectivity and information will be created, and the user usually performs e-mail transmission, digital image shooting, etc. It is possible to easily support the creation of a subjective map only by the actions that are present, and it is also possible to realize a private map that represents your own movement history.

  In the above-described movement map creation process, longitude / latitude data, altitude data, address data, etc. are used as position information. However, various movements such as distance, direction, and speed moved to create a more precise movement map. Information may also be collected and stored as a kind of location information and used when creating a mobile network. For example, various modes such as weighting in consideration of the moving speed in addition to the number of movements between certain areas can be considered.

<< Third Embodiment >>
According to the invention of this application, it is also possible to create a subjective map composed of subjective landmark data and subjective movement data by combining the first embodiment and the second embodiment described above.

  In this case, information collecting means (FIG. 4:31), information storage means (FIG. 4:32), existing map storage means (FIG. 4:33), area dividing means (FIG. 4:34), information distribution means (FIG. 4:35), information extraction means (FIG. 4:36), feature selection means (FIG. 4:37), movement count counting means (FIG. 4:38), and mobile network creation means (FIG. 4:39). Although not shown in FIG. 5, the information collection device (FIG. 5: 41), the information database (FIG. 5: 42), the map database (FIG. 5: 43), the region dividing device (FIG. 5: 44), the information distribution device ( 5:45), information extraction device (FIG. 5:46), feature selection device (FIG. 5:47), movement frequency counting device (FIG. 7:55), mobile network creation device (FIG. 7:56) can be considered. Further, as the data embedding means (FIG. 4: 310), the above-mentioned The data embedding apparatus (Fig. 5:48) and data embedding device can be considered a functional combination of (Fig. 7:57). As for data embedding, if both the geographical feature or geographical map obtained by the feature selection device (47) and the mobile network or mobile map obtained by the mobile network creation device (56) are embedded in the objective map data. Good.

The subjective map obtained in this way can grasp subjective landmark data and subjective movement data at a time, and can realize extremely useful town navigation and the like.

  Here, the Example by the said embodiment of invention of this application is demonstrated. In addition, about an Example, the thing used as the foundation of the Example described after that, and a related thing shall be described.

<< First Example >>
As a first embodiment, consider a case where an e-mail transmitted from a portable terminal with GPS carried by an individual at a point outdoors is used for creating a subjective map. FIG. 9 is a processing flowchart for explaining the present embodiment.

  First, the existing objective map data is stored, the text data of the sent e-mail (behavior information), the longitude and latitude data of the point where it was sent (location information by GPS), and it was sent Time data (time information by the portable terminal built-in clock) is stored in association with each other (FIG. 5: information collecting device (41), information database (42), map database (43)).

  Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In the present embodiment, a uniform size fixed lattice division is performed.

  Next, the text data is allocated to each area based on the longitude and latitude data (FIG. 5: information distribution device (45)). More specifically, it is detected in which region the longitude / latitude data falls, and text data paired with the longitude / latitude data is assigned to the detected region.

  Next, a geographical word is extracted from the text data for each region (FIG. 5: information extracting device (46)). For example, if the text data "This shrine is on a hill and Mt. Fuji ..." is in the upper left corner of the illustration, the geographical words "Shrine", "Saka" and "Mt. Fuji" Cut out.

  Next, a geographic feature word is selected from the geographic words for each region (FIG. 5: feature selection device (47)). For example, in the upper left corner region, the “shrine” of the geographical word data “Shrine”, “Saka”, and “Mt. Fuji” is selected as a geographical feature word by statistical processing such as tf / idf method. That is, “shrine” is a landmark keyword in this area.

  A feature map is created by performing the above extraction process and selection process for all regions, and a subjective map is created by superimposing the feature map (FIG. 5: data embedding device (48)).

<< Second Embodiment >>
As a second embodiment, a case where a person visits a place outdoors and then moves to another place and uses an e-mail containing information on the visited place sent from the mobile phone for the purpose of creating a subjective map. Think. FIG. 10 is a processing flowchart for explaining the present embodiment.

  First, the existing objective map data is stored, and the text data (behavior information) of the transmitted e-mail and the data indicating the name of the visited place included in the text data (position information by word recognition), In addition, time data (time information based on word recognition) indicating the visit time included in the text data is associated and accumulated (FIG. 5: information collection device (41), information database (42), map database ( 43)).

  Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In this embodiment, division based on geographical data is performed.

  Next, text data is allocated to each area based on the place name data (FIG. 5: information distribution device (45)). More specifically, it is detected in which area the place name data indicates the name of the place, and text data paired with the place name data is assigned to the detected area. For example, if the name of a temple is included, it is extracted and text data is distributed to the right middle area in the illustration, which is the area where the temple exists.

  Next, a geographical word is extracted from the text data for each region (FIG. 5: information extracting device (46)). For example, if there is text data “That was dark and cold. It was very quiet” for the temple, the geographical words “dark”, “cold”, and “quiet” are cut out.

  Next, a geographic feature word is selected from the geographic words for each region (FIG. 5: feature selection device (47)). For example, in the right middle region, “quiet” of the geographical word data “dark”, “cold”, and “quiet” is selected as the geographical feature word. As a result, “quiet” becomes a landmark keyword in this area, and subjective information that the temple is quiet can be obtained.

  After that, a feature map is created by performing the above extraction process and selection process for all regions, and a subjective map is created by superimposing the feature map (FIG. 5: Data Embedding Device (48)). )).

<< Third Example >>
As a third embodiment, a diary published on the Web including information on a visited place written by a person who visits a place outdoors and then returns home is used for creating a subjective map. Think about the case. FIG. 11 is a processing flowchart for explaining the present embodiment.

  First, the existing objective map data is stored, and the text data (behavior information) of the public web diary and the data (location information by word recognition) indicating the name of the place included in the text data, The time data (time information by word recognition) indicating the visit time included in the text data is stored in association with each other (FIG. 5: information collection device (41), information database (42), map database (43 )).

  Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In this embodiment, division based on data distribution (with feedback) is performed.

  Next, text data is allocated to each area based on the place name data (FIG. 5: information distribution device (45)). More specifically, it is detected in which area the place name data indicates the name of the place, and text data paired with the place name data is assigned to the detected area. For example, when the name of a certain park is included, it is extracted and the text data is distributed to the upper left corner area in the illustration which is the area where the park exists.

  Next, a geographical word is extracted from the text data for each region (FIG. 5: information extracting device (46)). For example, when there is text data “Sakura blossoms are in full bloom and very beautiful” with respect to the park, the geographical words “Sakura”, “Flowers” and “Beautiful” are cut out from the text data.

  Next, a geographic feature word is selected from the geographic words for each region (FIG. 5: feature selection device (47)). For example, in the upper left corner area, “cherry blossom” of the geographical word data “cherry blossom”, “flower”, and “beautiful” is selected as a geographical feature word. As a result, “sakura” becomes a landmark keyword in this area, and subjective information that the park is famous for cherry blossoms is obtained.

  After that, a feature map is created by performing the above extraction process and selection process for all regions, and a subjective map is created by superimposing the feature map (FIG. 5: Data Embedding Device (48)). )).

  In addition, although the above 1st-3rd Example is classified according to an e-mail and a web public diary, it cannot be overemphasized that they can be utilized collectively. For example, when both e-mail text data and Web open diary text data are allocated in the upper left corner area, various geographical words are extracted from each, and one or a plurality of geographical words statistically extracted from all the words. Features are selected. Such processing is performed for the entire area.

<< 4th Example >>
As a fourth embodiment, a case is considered where image data taken at a certain point outdoors from a digital camera + GPS mobile terminal is used for creating a subjective map. FIG. 12 is a processing flowchart for explaining the present embodiment.

  First, existing objective map data is stored, image data (behavior information), longitude and latitude data (location information by GPS) of the point where the image data was imaged, and time data (portable data) The time information by the terminal built-in clock is stored in association with each other (FIG. 5: information collection device (41), information database (42), map database (43)).

  Subsequently, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)), and image data is allocated to each region based on the longitude and latitude data (FIG. 5: information distribution device (45)).

  Next, a geographical object is extracted from the image data for each region (FIG. 5: information extracting device (46)). For example, when the image data as shown in FIG. 12 is in the upper left corner area, the geographical objects “house”, “tree”, “red roof”, and “car” are cut out by image recognition processing or the like.

  Next, a geographic feature object is selected from the geographic objects for each region (FIG. 5: feature selection device (47)). For example, in the upper left corner area, the “red roof” and “car” among the geographical objects are selected as geographical feature objects by statistical processing. That is, these become landmark objects in this region.

  After that, a feature map is created by performing the above extraction process and selection process for all regions, and a subjective map is created by superimposing the feature map (FIG. 5: Data Embedding Device (48)). )).

<< Fifth embodiment >>
As a fifth embodiment, let us consider a case where image data taken at a certain point outdoors from a portable terminal with a digital camera is used for subjective map creation. FIG. 13 is a processing flowchart for explaining the present embodiment.

  First, existing objective map data is stored, image data (behavior information), image data indicating a shooting location included in the image data (position information by image object recognition), and a time indicating an imaging time Data (time information from the portable terminal built-in clock) is stored in association with each other (FIG. 5: information collection device (41), information database (42), map database (43)).

  Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In the present embodiment, a uniform size fixed lattice division is performed.

  Next, the image data is allocated to each area based on the shooting location image data (FIG. 5: information distribution device (45)). More specifically, it is detected which region the shooting location image data indicates, and image data paired with the shooting location image data is assigned to the detected region. For example, when a certain Mt. Fuji is projected, it is extracted and text data is distributed to the area where Mt. Fuji exists.

  Next, a geographical object is extracted from the image data for each region (FIG. 5: information extracting device (46)). For example, when the image data as shown in FIG. 12 is in the upper left corner area, the geographical objects “Shrine”, “Saka”, and “Mt. Fuji” are cut out by image recognition processing or the like.

  Next, a geographic feature object is selected from the geographic objects for each region (FIG. 5: feature selection device (47)). For example, in the upper left corner area, “Mt. Fuji” among the geographical objects is selected as a geographical feature object by statistical processing. That is, this is a landmark object in this region.

  After that, a feature map is created by performing the above extraction process and selection process for all regions, and a subjective map is created by superimposing the feature map (FIG. 5: Data Embedding Device (48)). )).

<< Sixth Embodiment >>
As a sixth embodiment, let us consider a case where sound data recorded at a certain point outdoors from an IC recorder + GPS mobile terminal is used for creating a subjective map. FIG. 14 is a processing flowchart for explaining the present embodiment.

  First, existing objective map data is stored, sound data (behavior information), longitude and latitude data (location information by GPS) of the point where the sound data was recorded, and time data (portable data) The time information by the terminal built-in clock is stored in association with each other (FIG. 5: information collection device (41), information database (42), map database (43)).

  Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In the present embodiment, a uniform size fixed lattice division is performed.

  Next, sound data is allocated to each area based on longitude and latitude data (FIG. 5: information distribution device (45)). More specifically, it is detected in which region the longitude / latitude data falls, and image data paired with the longitude / latitude data is assigned to the detected region.

  Next, a geographical sound source is extracted from the sound data for each region (FIG. 5: information extracting device (46)). For example, as shown in FIG. 14, when there is sound data such as “Kankankan” and “Zawazawa” in the upper left corner area, and sound data such as “Gya”, “Gau”, “Hugh”, and “Grogro” in the upper right corner area, By means of voice recognition processing or the like, “crossing”, “busy”, “child's voice”, “dog's cry”, “wind” and “lightning” are extracted as geographical sound sources.

  Next, a geographical feature sound source is selected from the geographical sound sources for each region (FIG. 5: feature selection device (47)). For example, in the upper left corner area, “crossing” of the above-mentioned geographical sound sources is selected as a geographical feature object in the statistical processing, and “child voice” in the upper right corner area. That is, they become landmark objects in the corresponding areas.

  After that, a feature map is created by performing the above extraction process and selection process for all regions, and a subjective map is created by superimposing the feature map (FIG. 5: Data Embedding Device (48)). )).

<< Seventh embodiment >>
As a seventh embodiment, when a person wearing a wristwatch type mobile phone with a body temperature / pulse measurement function + GPS function visits a place outdoors, body temperature data which is the person's physical information measured at the place, and Consider the case where pulse data is used for subjective mapping. FIG. 15 is a processing flowchart for explaining the present embodiment.

  First, the existing objective map data is stored, the measured body temperature data and pulse data (behavioral sensing information), the longitude and latitude data of the place where it was measured (position information by GPS), and it is measured. The time data (time information by the mobile phone built-in clock) is stored in association with each other (FIG. 5: information collecting device (41), information database (42), map database (43)).

  Subsequently, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)), and body temperature data and pulse data are allocated to each region based on longitude and latitude data (FIG. 5: information distributing device (45 )).

  Next, geographical body information is extracted from body temperature data and pulse data for each region (FIG. 5: information extraction device (46)). For example, as shown in FIG. 15, geographical body information such as “increased body temperature” if the body temperature is higher than normal and “increase in pulse” if the pulse is higher than normal is recognized and extracted. .

  Next, geographic feature body information is selected from the geographic body information for each region (FIG. 5: feature selection device (47)). For example, in the upper right corner region, “pulse increase” among the geographical body information is statistically selected as the geographical feature body information. Thereby, “increase in pulse” becomes landmark physical information in this area, and subjective information that the pulse increases in this area is obtained.

  After that, a feature map is created by performing the above extraction process and selection process for all regions, and a subjective map is created by superimposing the feature map (FIG. 5: Data Embedding Device (48)). )).

<< Eighth embodiment >>
As an eighth embodiment, when a remotely operated robot with temperature / torque sensor function + GPS function is moved to a location outdoors, the internal temperature data and torque that are the body information of the robot measured at that location Consider the case where load data is used for subjective map creation. FIG. 16 is a processing flowchart for explaining the present embodiment.

  First, existing objective map data is stored, measured internal temperature data and torque load data (behavior sensing information), longitude and latitude data (location information by GPS) of the place where it is measured, and The measured time data (time information from the robot built-in clock) is stored in association with each other (FIG. 5: information collection device (41), information database (42), map database (43)).

  Subsequently, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)), and internal temperature data and torque load data are allocated to each region based on longitude and latitude data (FIG. 5: information distributing device). (45)).

  Next, geographical body information is extracted from the internal temperature data and torque load data for each region (FIG. 5: information extraction device (46)). For example, as shown in FIG. 16, the geographical body information that “internal temperature rise” when the internal temperature is higher than normal and “torque increase” when the torque load is higher than normal is recognized.・ Extracted.

  Next, geographic feature body information is selected from the geographic body information for each region (FIG. 5: feature selection device (47)). For example, in the upper right corner area, “torque load increase” among the geographical body information is statistically selected as the geographical feature body information. Thereby, “torque load increase” becomes landmark robot body information in this region, and subjective information that the torque load increases in this region is obtained.

  After that, a feature map is created by performing the above extraction process and selection process for all regions, and a subjective map is created by superimposing the feature map (FIG. 5: Data Embedding Device (48)). )).

  Although the first to ninth embodiments described above are classified according to text data, image data, and sound data, it goes without saying that they can be used collectively. For example, when different types of behavior information exist in the same region, various types of geographical information are extracted from each information, and one or more geographical features are selected statistically from all the information. Such processing is performed for the entire area.

<< Ninth embodiment >>
As a ninth embodiment, consider a case where a travel map is created using the longitude and latitude data and time data of the e-mail transmission point collected in the first embodiment.

  As illustrated in FIG. 17, first, the existing objective map data is stored, and the text data of the transmitted e-mail is stored in association with the longitude / latitude data and the time data of the point where the e-mail is transmitted. (FIG. 7: information collecting device (51), information database (52), map database (53)), on the other hand, the objective map data is divided into a plurality of regions (125: region dividing device (54)). The steps so far are the same as those in the first embodiment.

Next, the number of movements between the areas is counted, and the movement frequency is calculated (FIG. 7: movement number counting device (55)). For example, the movement frequency = 0.1 from the region R _A to the area R _B, movement frequency = 0.05 to the area R _C, is calculated as that movement frequency = 0.4 to region R _D.

  Next, the mobile network between the regions is weighted with the calculated travel frequency (FIG. 7: mobile network creation device (56)). The weighted network is, for example, thicker as the movement frequency increases and thinner as the movement frequency decreases.

  The mobile network created as described above can be used as a mobile map that clearly visualizes the relationship between regions, and even if this single map is used as a subjective map, it can be used as a subjective map created by superimposing it on an objective map. A map may be used (FIG. 7: data embedding device (57)).

  Of course, not only the first embodiment, but also the location data of the visited place and the time data of the visited time included in the e-mail and the Web public diary collected in the second and third embodiments, Alternatively, it goes without saying that a mobile network / moving map can be created in the same manner using the position data and time data collected in the fourth to eighth embodiments.

<< Tenth embodiment >>
FIG. 18 shows a case where a subjective map is created by superimposing both a feature map and a movement map created as described above on an objective map.

  As is clear from FIG. 18, this subjective map accurately displays the landmarks that the user has noticed and felt on a conventional objective map, as well as the movement history between them. It is clearly displayed, so that objective information and subjective information can be easily grasped at a time.

<< Eleventh embodiment >>
If the above subjective map is used, various applications such as the following are considered possible.

I. "Soft" navigation (location estimation and route search based on words and perceptual information)
Uses information such as landmark keywords, key objects, and key sound sources embedded in the subjective map to provide route guidance.

II. Creation of “Summary Map” Suitable for Individuals By emphasizing landmark objects on the subjective map more than others, a simplified map specialized for individual interest and physicality is created.

III. Support for acquisition and organization of spatial knowledge of each individual (treatment of direction sounds, etc.)
The landmark keywords, key objects, key sound sources, etc. embedded in the subjective map are presented to the user in a timely manner, thereby helping memorize the geographical information of the land.

IV. Promote sharing and exchange of spatial information (community support, etc.)
Within a group that shares interests and residential areas, each individual supports the exchange between individuals by sharing a subjective map with each other or creating a subjective map for group sharing. For example, in the case of a mail group or chat group, a subjective map for the group can be created using mail data and chat data sent and received within the group.

V. Walk information support (walk navigator, etc.)
A person who is familiar with a certain area publishes the subjective map to help other unfamiliar people take a walk with reference.

VI. Analysis of individual differences in spatial cognition (effects of physicality, etc.) Compare and classify subjective maps of various people with different ages, genders, interests, physical characteristics, etc. in a certain region, and use the results as a product advertising strategy And used for city design strategy.

Of course, the invention of this application is not limited to the above embodiments and examples, and various details are possible for the details.

It is a flowchart for demonstrating invention of this application. It is a functional block diagram for demonstrating invention of this application. It is another functional block diagram for demonstrating invention of this application. It is another functional block diagram for demonstrating invention of this application. It is a functional block diagram for demonstrating 1st Embodiment of invention of this application. It is a processing flow figure for explaining a 1st embodiment of the invention of this application. It is a functional block diagram for demonstrating 2nd Embodiment of invention of this application. It is a processing flow figure for explaining a 2nd embodiment of the invention of this application. It is a processing flowchart for demonstrating the 1st Example of invention of this application. It is another processing flowchart for demonstrating the 2nd Example of invention of this application. It is another processing flowchart for demonstrating the 3rd Example of invention of this application. It is another process flowchart for demonstrating the 4th Example of invention of this application. It is another process flowchart for demonstrating the 5th Example of invention of this application. It is a processing flowchart for demonstrating the 6th Example of invention of this application. It is a processing flowchart for demonstrating the 7th Example of invention of this application. It is a processing flowchart for demonstrating the 8th Example of invention of this application. It is a processing flow figure for explaining the 9th example of the invention of this application. It is a processing flowchart for demonstrating the 10th Example of invention of this application.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Subjective map creation system 11 Information collection means 12 Information storage means 13 Existing map storage means 14 Area division means 15 Information distribution means 16 Information extraction means 17 Feature selection means 18 Data embedding means 2 Subjective map creation system 21 Information collection means DESCRIPTION OF SYMBOLS 22 Information storage means 23 Existing map storage means 24 Area division means 25 Movement frequency count means 26 Movement network creation means 27 Data embedding means 3 Subjective map creation system 31 Information collection means 32 Information storage means 33 Existing map storage means 34 Area division Means 35 Information distribution means 36 Information extraction means 37 Feature selection means 38 Movement frequency counting means 39 Movement network creation means 310 Data embedding means 4 Subjective map creation system 41 Information collection device 411 Action information collection part 412 Location information collection part 413 Time Information collection unit 414 Information output unit 42 Information database 421 Information input unit 422 Information storage unit 43 Map database 431 Map input unit 432 Map storage unit 44 Area division device 441 Region division unit 45 Information distribution device 451 Position matching unit 452 Action information allocation unit 46 Information extraction device 461 Information extraction unit 47 Feature selection device 471 High reproducibility judgment unit 472 Rareness judgment unit 473 User designation judgment unit 48 Data embedding device 481 Data embedding unit 400a, 400b, 400c ... region database 5 subjectivity Automatic map creation system 51 information collection device 511 position information collection unit 512 time information collection unit 513 information output unit 52 information database 521 information input unit 522 information accumulation unit 53 map database 531 map input unit 532 map accumulation unit 54 area Dividing device 541 Area dividing unit 55 Movement number counting device 551 Movement number counting unit 56 Moving network creation device 561 Network creation unit 562 Network weighting unit 57 Data embedding device 571 Data embedding unit 500a, 500b, 500c... Region database

Claims (3)

Information collecting means for collecting and collecting behavior information, which is information born from behavior performed at an arbitrary point, positional information, which is information on the position of the arbitrary point, and time information;
Information storage means for associating and storing behavior information, position information and time information collected by the information collection means;
Existing map storage means for storing existing map data;
Area dividing means for dividing the map data stored in the existing map storage means into a plurality of areas;
When counting the number of movements between areas of the map data divided by the area dividing means based on the position information and time information stored in the information storage means, a pair with time information indicating the oldest time is paired. An area corresponding to the position information to be made is a start area, and a movement number counting means for counting the number of movements with respect to the surrounding area moved thereafter;
Create a network that connects all the areas that have moved in order of movement time, determine a weight value according to the number of movements counted by the movement number counting means for each area, and weight the mobile network with the weight value A mobile network creation means to
A subjective cartography system characterized by comprising: Data embedding means for embedding the mobile network created by the mobile network creating means in the map data stored in the existing map storage means;
The subjective mapping system according to claim 1, further comprising:   2. The subjective map creation system according to claim 1, wherein the weighting value is a movement frequency value indicating how much the number of movements between the regions is relative to the total number of movements.