JP2004302262A - Subjective map preparing method and subjective map preparation system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a subjective map preparing method capable of easily preparing a subjective map whose utility value is very high for a user in every user's viewpoint and a subjective map preparation system. <P>SOLUTION: A subjective map is prepared in such a manner that a processing means collects action information and location information while associating them with each other (S1), extracts subjective information out of the collected action information (S2) and embeds the extracted subjective information in an existing objective map data stored in a storage means based on the location information (S3). <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The invention of this application relates to a subjective map creation method and a subjective map creation system. More specifically, the invention of this application relates to a new subjective map creation method and a subjective map creation system capable of easily creating a map having a high degree of subjectivity from the viewpoint of a user.
[0002]
[Prior art]
A map that has been used by society in general is a collection of data that is objective for the user, that is, latitudes, longitudes, azimuths, distances, and the like, which are measured independently of the user's intention, and predetermined area names and route names. -It is only a collection of landmark names and the like, and the user can only refer to those objective data shown on the map.
[0003]
On the other hand, in recent years, a town information map called “town navigator” that introduces various shops and famous places in each region on an existing map has also appeared. It is considered that information collected by electronic bulletin boards, electronic mail announcements, and the like is often used, and is a somewhat subjective collection of data for each user. This is because a questionnaire or the like provides subjective information that reflects preferences, feelings, ideas, and the like obtained by the user's own behavior.
[0004]
However, creation of a subjective map composed of such subjective data requires a user to bother to provide information, to compile statistics of collected information, to adapt statistical data to map data, and the like, which is very complicated. It involves work.
[0005]
In addition, although the town navigator is a subjective map, it can be said that it is still lacking in subjectivity and information because a questionnaire response from a user and text information representing the same are considered to be the main information sources.
[0006]
In addition, various techniques described in Patent Literatures 1 to 5 below have been proposed as techniques for supporting map creation. However, none of them relate to a subjective map, and at present, no technology for facilitating creation of a subjective map has been realized.
[0007]
Although it is not a map creation support, a database for acquiring information on the position of the point and multimedia information such as video and audio relating to the situation of the point at an arbitrary point outdoors and creating a database that stores them in association with each other As a support technology, a technology described in Patent Document 6 below has been proposed, and in this Patent Document 6, mapping of the multimedia information extracted from a database on a map has also been proposed. However, this also lacks the subjectiveness of the multimedia information to be collected and merely displays the multimedia information beside the map display, so this technology is temporarily applied to the creation of a subjective map. Even if it can be done, it is considered that only a subjectivity and information property of the same level as the above-mentioned town information map can be realized.
[0008]
[Patent Document 1]
JP-A-2002-318533
[Patent Document 2]
JP-A-2002-15331
[Patent Document 3]
JP 2001-338305 A
[Patent Document 4]
JP-A-10-332391
[Patent Document 5]
JP-A-10-63181
[Patent Document 6]
JP 2000-235576 A
[0009]
[Problems to be solved by the invention]
In view of the circumstances described above, the invention of this application is based on the viewpoint of each user and can easily create a highly useful subjective map for the user. It is an object to provide a subjective mapping system.
[0010]
[Means for Solving the Problems]
In order to solve the above-described problems, the invention of this application firstly involves a step in which the processing means collects the action information and the position information in association with each other as illustrated in FIG. S1), a step in which the processing means extracts subjective information from the collected behavior information (S2), and a processing means stores the extracted subjective information based on the collected position information. Embedding into existing map data stored in the means (S3).
[0011]
Second, an information collecting means (11) for associating and collecting action information and position information as illustrated in a functional block diagram in FIG. 2, and the action information and information collected by the information collecting means (11). An information storage means (12) for storing position information in association with the location information; an existing map storage means (13) for storing existing map data; and a map data stored in the existing map storage means (13) in a plurality of areas. Area dividing means (14), and information for allocating the action information stored in the information storage means (12) to each area of the map data divided by the area dividing means (14) based on the position information. Information for extracting geographical information from the behavior information allocated by the information distributing means (15) for each area of the map data divided by the distributing means (15) and the area dividing means (14) Output means (16) and feature selection means for selecting a geographic feature from the geographic information extracted by the information extraction means (16) for each area of the map data divided by the area division means (14). And (17) a subjective map creation system (1) characterized by comprising: (3) a geographical feature selected by the feature selecting means (17); A subjective map creation system (1) further comprising data embedding means (18) for embedding the map data stored in (13).
[0012]
Fourth, an information collection unit (21) for collecting action information, position information, and time information in association with each other and an information collection unit (21) as illustrated in the functional block diagram of FIG. Information storage means (22) for storing the action information, the position information and the time information in association with each other, an existing map storage means (23) for storing existing map data, and an existing map storage means (23). The information storage means (22) stores an area dividing means (24) for dividing the existing map data into a plurality of areas, and the number of movements between each area of the map data divided by the area dividing means (24). Between the number-of-movements counting means (25) for counting based on the position information and the time information, and between the respective regions weighted based on the number of movements counted by the number-of-movements counting means (25) And a mobile network creating means (26) for creating a mobile network. A fifth aspect of the present invention provides a subjective map creating system (2), which is provided with a mobile network creating means (26). And a data embedding means (27) for embedding the mobile network in the map data stored in the existing map storage means (23).
[0013]
Sixth, as shown in the functional block diagram of FIG. 4, the information collecting means (31) for collecting the action information, the position information and the time information in association with each other, and the information collected by the information collecting means (31). Information storage means (32) for storing action information, position information and time information in association with each other, existing map storage means (33) for storing existing map data, and existing map storage means (33). An area dividing means (34) for dividing the map data into a plurality of areas; and, in each area of the map data divided by the area dividing means (34), the action information stored in the information storage means (32) is stored in the area. Information distributing means (35) for allocating based on the information; and, for each of the regions of the map data divided by the region dividing means (34), the geographical information based on the behavior information allocated by the information distributing means (35). Information extracting means (36) for extracting physical information; and, for each area of the map data divided by the area dividing means (34), geographical information from the geographical information extracted by the information extracting means (36). The number of movements between each area of the map data divided by the area selecting means (34) and the characteristic selecting means (37) for selecting a characteristic are stored in the position information and time information stored in the information storing means (32). Movement number counting means (38) for counting based on the number of movements counted by the movement number counting means (38), and movement network creation means (39) for creating a movement network between each area weighted based on the movement number counted by the movement number counting means (38). Seventh, a subjective map creation system (3) is provided. Seventh, the geographic feature selected by the feature selection means (37) and the migration are provided. Subjectively further comprising data embedding means (310) for embedding the mobile network created by the network creation means (39) into the map data stored in the existing map storage means (33). A map creation system (3) is provided.
[0014]
BEST MODE FOR CARRYING OUT THE INVENTION
When observing the streets of recent times, many people like to carry various electronic devices such as mobile phones, mobile terminals, digital cameras, etc., in various situations such as walking, stopping, sitting in shops, etc. You can see that they frequently perform various actions that touch the electronic information, such as making a phone call, sending an e-mail, taking a digital image, etc., whenever they like. Naturally, various types of electronic information generated from such actions represent the subjectivity of the person who performed the action, such as intention, ideas, emotions, and preferences. For example, the sending action of an e-mail is in accordance with one's own intention, and the content of the sent e-mail also expresses one's own feelings. The act of capturing a digital image is also in accordance with one's own intention, and the captured image also represents one's own preference. On the other hand, it is considered that these various types of electronic information often include various pieces of information regarding a place. For example, an email might include words that describe the location of the message, words related to the location, or locations that you actually visited but not the location of the message (for example, if the message itself was sent away from that location. In many cases, words related to the shooting location are input, and the image often shows a scenery of the shooting location or a characteristic object at the shooting location. That is, electronic information generated by an action performed at a certain point on the earth is subjective information of the person who performed the action, and information on a place included therein is also subjective information.
[0015]
In view of the above, the inventor of the present invention collectively treats heterogeneous electronic information resulting from the user's behavior as behavior information, collects and accumulates the information together with location information (FIG. 1: step S1), In addition, by extracting the subjective information of the actor regarding the place from the information (FIG. 1: step S2) and mapping it to an existing objective map (FIG. 1: step S3), the user can act in the real world. They found that an entirely new map could be created, which was rich in subjectivity and information, consisting of the subjective information of some people, and based on this, came up with the invention of this application. Then, the map created in this way is called a "subjective map".
[0016]
Therefore, a town information map such as a town navigator is referred to as a subjective map in the description of the related art. However, in practice, the concept of the concept is far from the "subjective map" considered by the inventor of the present invention. It seems that there is no "subjective map" of the same concept in the past.
[0017]
Hereinafter, embodiments of the invention of the present application having the features as described above will be described in detail for each of the above various means. What are called processing means and storage means correspond to the various means described here.
[0018]
<< First Embodiment >>
[Information collection means]
The information collecting means (FIG. 2: (11)) is, for example, an action information collecting unit (411) and a position information collecting unit (412) for collecting the action information and the position information at an arbitrary point in association with each other as illustrated in FIG. ) Can be considered (FIG. 6: “Information collection process”), for example, a remote-operated type or a program provided with an action information collection unit (411) and a position information collection unit (412). Self-propelled robots, mobile phones and mobile terminals. In the description of the concept of the "subjective map", the subjective behavior information of a human moving in the real world is used. However, a device such as a robot that moves and acts according to a remote operation based on a human subject or a program can also be used. is there.
[0019]
Here, “behavior information” means information generated by an action performed at an arbitrary point, for example, text data such as e-mail and diary data input at an arbitrary point, recorded voice and the like. Various types of heterogeneous 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. Also, physical information measured at an arbitrary point (for example, body temperature, pulse, blood pressure, myoelectric potential, respiratory state, brain wave, etc. for a human, battery voltage, motor current, internal temperature, motor temperature for a robot) , Circuit temperature, torque addition, etc.) can also be included in the “action information”. This is because such physical information can be said to be information generated by various actions, such as walking, climbing, and descending, performed by a human or a robot at an arbitrary point. Since these are measured by a detection device such as various sensors (see “behavior information collection unit (411)” described later), they can be called “behavior sensing information”. Furthermore, information on the behavior of an individual user included in a diary, a bulletin board, a travel diary, or the like published on the Internet Web can also be included in the “behavior information” here. In other words, as described above, not only information (including action sensing data) generated by the action at the destination where the user actually moved, but also the past destination point where input / transmission was performed after moving to another place It is possible to take into account the behavior information that has been used. For example, if you go to a park but do not send e-mail, etc., and move to another place such as a shop or home, and then send e-mail or publicize information about the park, By using the invention to create a subjective map intended by the invention of the application, it becomes possible to cover the subjectivity of the subject. Of course, all of the above-mentioned heterogeneous behavior information may be used for map creation, or may be used in one or a plurality of combinations. A subjective map can be created by using at least one type of action information, but it goes without saying that a plurality of types of action information are more subjective and informative.
[0020]
"Position information" means information on the position of an arbitrary point, and for example, latitude and longitude, altitude, and address can be considered. The position information may be any data that can associate the occurrence 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, there are mapping using only longitude and latitude, mapping using only address, and mapping combining them (this will be described in more detail in the description of the information distribution means). If the behavior information is not the actual destination as described above but has been moved to another place, the position of the past destination point included in the latter behavior information is The information about the location is also included in the “location information” here. For example, in the case of the above-mentioned park, for example, the place where the input / transmission was performed is a shop or a home. However, even if the location information is obtained, it does not have any relation to the location of the park described in the mail or the Web. In this case, words related to the position of the park (for example, the name of the park) included in the text information are used as the position information used for mapping.
[0021]
The behavior information collecting unit (411) has a function of collecting the behavior information. For example, a keyboard or the like for text data, a microphone or the like for auditory data, a camera or the like for visual data, and various types for behavior sensing data. A search engine or the like can be considered for Web public data such as a sensor. Apparatuses applied differ depending on the type of action information. What is necessary is just to provide these devices to the said self-propelled robot, a mobile telephone, and a portable terminal. For various sensors for action sensing data, for example, in the case of a human, the sensor is incorporated in a wristwatch to automatically measure a pulse or the like. It is also conceivable to automatically measure the distance. In addition, as for a search engine for public Web data, even if it is not equipped with a moving human or robot, for example, a behavior information collection center is constructed and various search engines are used in the center when creating a subjective map. A diary or a bulletin board.
[0022]
The position information collection unit (412) has a function of collecting the above-mentioned position information. For example, the position information of the place where the user has actually acted can consider GPS or the like. What is necessary is just to provide a GPS function etc. to the said self-propelled robot, a mobile telephone, and a portable terminal. Also, for example, a point where an e-mail or the like is transmitted on a PHS telephone is recognized as a position of a nearest base station, and if the position is within an allowable range of position accuracy at the time of creating a map, the recognition device also has a position collecting function. Can be considered as As for the position information included in the behavior information after moving to another place (in the example of the park, the name of the park, etc.), an extraction device having a function of executing various word extraction methods can be considered.
[0023]
As described above, the relationship between the collected behavior information and the position information is such that one piece of position information always corresponds to one piece of behavior 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 continuously collecting GPS data. Extracts GPS data at the same time as, or collects position information at the time of measurement of physical information such as a person or a robot, and also acts on a certain place in the Web public data For example, information and position information are extracted as a pair.
[0024]
As can be seen from these examples, the collection of the behavior information and the position information always involves time information connecting the two. Therefore, the information collection device (41) collects time information, which can also be called link information for associating the behavior information and the position information, at the same time as the collection of both, and the time information collection unit (413) (see FIG. 5) for that. It can be said that it has. For example, the transmission time of an e-mail is recorded and collected by a cellular phone / portable terminal built-in clock, or the measurement time of physical information is recorded and collected by a built-in clock of a measuring device. A mode of extracting and collecting the information to be represented can be considered. As information indicating time, information using numerical values such as date, hour, minute, second included in text data, various word information such as morning and afternoon, morning, day, night, morning, evening, around noon, early morning, midnight, and various other Any information that can grasp the time, such as the 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.
[0025]
The above information collecting device (41) needs to provide the collected behavior information, position information, and time information to the following information storage means, and transmits the collected information wirelessly or by wire using radio waves, light, or the like. Or an information output unit (414) for giving information in a form other than communication (see FIG. 5). For forms other than communication, for example, a magnetic or optical recording medium that can extract collected information from each collection unit and pass it to the information recording unit can be considered.
[0026]
[Information storage means]
The information storage means (FIG. 2: (12)) includes an information input unit (421) for receiving collected information from the information collection device (41) and an information input unit (421), as illustrated in FIG. An information database (42) having an information storage unit (422) for storing the input collected information in association with each other can be considered (FIG. 6: “information storage processing”).
[0027]
This information database (42) may be separate from the information collection device (41) or may be incorporated in itself.
[0028]
In the former case, for example, an information collection / accumulation center or the like can be constructed and a database device installed therein can be considered, and the collected information from the information output unit (414) of the information collection device (41) is input to the information input unit (421). The information is received or absorbed, and is sequentially stored in the information storage unit (422) (FIG. 5 shows this mode).
[0029]
In the latter case, for example, a form in which a database device is incorporated in the information collection device (41) itself or a form in which a database is constructed in its built-in storage medium can be considered, and reception of collected information in the information collection device (41) can be considered. (The information output unit (414) and the information input unit (421) are both input / output functions inside the device). In this latter mode, an aggregate of the information collection devices (41) may be further realized as a distributed database on a network, and according to this, for example, each program (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 map creation processing.
[0030]
[Existing map storage means]
The existing map storage means (FIG. 2: (13)) includes a map input unit (431) for inputting existing map data and a map data input by the map input unit (431) as exemplified in FIG. And a map database (43) having a map storage unit (432) for storing the map data.
[0031]
"Existing map data" means objective map data consisting of a set of measured latitude, longitude, azimuth, distance, etc., and a predetermined area name, route name, landmark name, etc. It is a map that is the basis of a subjective map to be created by the invention of this application. In addition, this objective map may be completed or undeveloped.
[0032]
[Region dividing means]
The region dividing means (FIG. 2: (14)) divides the objective map data stored in advance in the map database (43) into a plurality of regions as illustrated in FIG. 5, for example. ) Can be considered.
[0033]
This region dividing device (44) can be realized as a device having a region dividing section (441) as a program, or as a program itself.
[0034]
For specific processing, for example, the objective map data is divided into a grid, or administrative divisions and addresses included in the objective map data in advance, or landmarks such as buildings, roads, intersections, green spaces, and rivers. (FIG. 6: “region division processing”). In the former case, grid division may be uniformly performed with the same size. However, a process of reading position information from the information database (42) and dividing a frequently occurring position into a smaller size may be considered. Alternatively, the data amount of the behavior information distributed by the information distributing means described later is fed back, and the size of each area can be automatically adjusted according to the data distribution, that is, the amount of data (FIG. 6: “feedback”).
[0035]
[Information distribution means]
The information distributing means (FIG. 2: (15)) reads out paired data of action information and position information from the information database (42) as exemplified in FIG. 5, for example, and reads the position information and the area dividing device (44). )), A position matching unit (451) for matching with the position of each area in the objective map data divided by the above, and action information paired with the position information in a region that has been matched by the position matching unit (451). And an information distributing device (45) that has the behavior information allocating unit (452) that performs the position matching and the behavior allocation for all the behavior / position information.
[0036]
The information distribution device (45) can be realized as a device having a position matching unit (451) and an action information allocating unit (452) as a program, or as a program itself.
[0037]
For specific processing, for example, when the position information is longitude and latitude, it is detected which of the regions falls into the longitude and latitude range, and the action information is assigned to the corresponding area. The same applies to the case of an address, a combination of latitude and longitude and an address, and other positional information. In this way, the area where the actual action is performed on the objective map is associated.
[0038]
In order to accumulate the action information assigned to each area, an area information database (400a, 400b, 400c...) May be constructed for each area (FIG. 6: “Information distribution processing”). ).
[0039]
[Information extraction means]
The information extracting means (FIG. 2: (16)) is, for example, as shown in FIG. 5, for each area of the objective map data divided by the area dividing apparatus (44). An information extraction device (46) having an information extraction unit (461) for extracting geographical information from the behavior information assigned by (1) can be considered.
[0040]
The information extraction device (46) can be realized as a device having an information extraction section (461) as a program, or as a program itself.
[0041]
For specific processing, for example, geographic information is extracted from each piece of behavior information stored in each of the above-mentioned area databases (400a, 400b, 400c,...) Of each area (FIG. 6: “ Information extraction process "). For example, when the action information is text data, words are extracted by morphological analysis or words are extracted by dictionary matching, and when the information is image data, objects are extracted by image recognition processing, and colors and textures are obtained by various image processing. Extraction of figures, extraction of character information by image character recognition processing, in the case of voice data, identification and extraction of sound sources by voice recognition processing and extraction of main voice frequency components by frequency analysis, etc. Various extraction techniques and recognition techniques can be applied.
[0042]
Here, "geographical information" refers to the mountains, rivers, land and sea, climate, population, settlements, industry, traffic, land conditions and conditions, land heights, wide and narrow, shapes, terrain, topography, buildings, and architecture. It refers to all kinds of geographic information, such as objects and landscapes, and also includes information about geographic human life and culture. That is, all 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 the extracted information can be updated at any time.
[0043]
The extracted geographic information of each area may be stored in the corresponding area database (400a, 400b, 400c,...).
[0044]
[Feature selection means]
The feature selecting means (FIG. 2: (17)) is provided for each of the regions of the objective map data divided by the region dividing device (44) as exemplified in FIG. 5, for example, by the information extracting device (46). The feature selection device (47) for selecting a geographic feature from the geographic information extracted by the above can be considered.
[0045]
The “geographical feature” refers to, in the geographical information extracted from the activity information in a certain area, particularly representative or typical information (an important feature represented by) in the area.
[0046]
More specifically, in order for certain geographic information i to be a “geographic feature” in the region R, one or both of the following conditions (a) and (b) must be satisfied or the condition (c) must be satisfied. Need to meet.
[0047]
(A) In the behavior information obtained in the region R, the appearance frequency of the geographical information i is high (“high reproducibility in the same region”).
[0048]
(B) The appearance frequency of the geographical information i is low in the behavior information obtained in an area other than the area R. Alternatively, the number of regions in which the appearance frequency of the geographic information i is as high as the region R is small (“rareness in other regions”.
[0049]
(C) The user directly designates the geographic information i as an important feature in the region R (“importance by user”).
[0050]
The following are some specific examples.
[0051]
Example 1 (when the conditions (a) and (b) are satisfied): In 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 at the same high frequency. At this time, the geographic information i is a geographic feature in the region R.
[0052]
-Example 2 (when the condition (a) is satisfied but the condition (b) is not satisfied): In the geographical information in the region R, the frequency of occurrence of the geographical information i (= "intersection") is high. There were many other areas where the target information i was filed at the same high frequency. At this time, the geographic information i is not a geographic feature in the region R.
[0053]
Example 3 (when the condition (b) is satisfied but the condition (a) is not satisfied): the geographical information i (= “fire”) included in the geographical information in the region R has a very high appearance frequency in other regions. Although small, it appeared only once in the region R. At this time, the geographic information i is not a geographic feature.
[0054]
-Example 4 (when the conditions (a) and (b) are not satisfied but the condition (c) is satisfied): the appearance frequency of the geographical information i (= "shrine") in the geographical information in the region R is compared with that in other regions. The user designates the geographical information i as the geographical feature of the region R.
[0055]
In Examples 1 to 3, the case where both of the conditions (a) and (b) are simultaneously satisfied is used as a criterion, but only one of them may be used as a criterion. In that case, the geographic information in Examples 2 and 3 is used. i is also a geographic feature. However, geographic information in which the former has a higher characteristic degree (importance) is selected as the geographic characteristic. The condition (c) as in Example 4 is an optional mode, but is preferable when the user's subjectivity is more strongly extracted.
[0056]
Therefore, the feature selecting device (47), for example, determines whether or not the conditions (a), (b), and (c) are satisfied with respect to the geographical information extracted for each region, by the high reproducibility determining unit (471). , A rarity judging unit (472) and a user specifying judging unit (473), and statistically comparing the frequency of appearance of geographical information between the regions or according to a designation from the user. Geographical features that should also be called landmarks are selected (FIG. 6: “feature selection processing”).
[0057]
Various statistics such as “tf (term frequency) / idf (inverted document frequency) method”, “statistical test”, and “method based on total information amount” are used for the high reproducibility determination unit (471) and the rarity determination unit (472). It is possible to execute a strategic method.
[0058]
Regarding the user specification determined by the user specification determination unit (473), for example, the information collection device (41) also collects user specification information for the geographical information i in the behavior information when collecting the behavior information. By storing the specified information together with the behavior information in the information database (42), the geographic information i is determined to be specified by the user, and is selected as the geographic feature in the corresponding region R. Alternatively, input from a user may be separately accepted.
[0059]
The extracted geographical features of each area may be stored in the corresponding area database (400a, 400b, 400c ...).
[0060]
This feature selection device (47) has a device form having the high reproducibility judgment section (471), the rarity judgment section (472) and the user designation judgment section (473) as a program, or a form that is itself a program. realizable.
[0061]
The geographical features selected as described above are not merely subjective data of the user, but should also be referred to as subjective landmark data for each point obtained by statistically processing the data. By using it, a highly subjective and highly informative subjective map can be created.
[0062]
[Data embedding means]
The data embedding means (FIG. 2: (18)) stores the geographical feature data of each area selected by the feature selecting device (47) into the map database (43) as exemplified in FIG. A data embedding device (48) having a data embedding unit (481) for mapping and embedding in a corresponding area in the stored existing objective map data can be considered.
[0063]
The data embedding device (48) can be realized as a device having a data embedding section (481) as a program, or as a program itself.
[0064]
In addition to embedding the selected geographical feature in the objective map to obtain a subjective map, only the geographical feature may be used as the subjective map. In this case, for example, geographical features stored in the area database (400a, 400b, 400c...) Are used as map-like data positioned in each area, 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 separately referred to.
[0065]
Further, subjective map data may be created by superimposing this feature map data on objective map data. FIG. 6 shows this mode, in which “feature map creation processing” and “superposition processing” are sequentially performed.
[0066]
Through the above-described processing, a subjective map with a high degree of subjectivity and information, which is composed of subjective landmark data for each point, is created. You can easily support the creation of a subjective map by just using the actions you take.
[0067]
In addition, it is possible to easily create a subjective map that can be called a private map based on the subjective information of one's own. In this case, for example, if the behavior information is collected and stored as data with user ID information, a subjective map of only the user recognized by the ID information can be created.
[0068]
<< Second Embodiment >>
In the first embodiment described above, a subjective map created by converting a subjective action caused by a user at an arbitrary point into map data and embedding it in an objective map is described. A subjective map is created by using the movement history of the user who moves.
[0069]
In the present embodiment, an information collecting unit (FIG. 3: (21)) for collecting the action information, the position information and the time information in association with each other, and the action information, the position information and the time information collected by the information collecting unit (21) And an information storage unit (FIG. 3: (22)) for storing existing map data, and an existing map storage unit (FIG. 3: (23)) for storing existing map data. An area dividing means (FIG. 3: (24)) for dividing existing map data into a plurality of areas and each means described below are required. The former four means are, for example, as shown in FIG. It can be provided as an information collecting device (51), an information database (52), a map database (53), and a region dividing device (54) having functions substantially similar to those in the first embodiment.
[0070]
Each means other than the above will be described in detail below with reference to FIGS. 7 and 8 as appropriate.
[0071]
[Moving frequency counting means]
The number-of-movements counting means (FIG. 3: (25)) counts the number of movements between the respective regions of the objective map data divided by the region dividing device (54) as exemplified in FIG. ) Can be considered (FIG. 8: "number-of-movements counting process"), which has a counting unit (55) having a counting unit (551) that counts based on the position information and the time information stored in ().
[0072]
The number-of-movements counting device (55) can be realized as a device having a counting section (551) as a program, or as a program itself.
[0073]
Regarding specific processing, first, an area to which position information corresponding to time information indicating the oldest time corresponds is determined as a start area (in FIG. 8, the area R _A ), Based on that, the area R around which it has moved thereafter _B ~ _I Count the number of moves to.
[0074]
The counted number of movements of each area may be accumulated in the corresponding area database (500a, 500b, 500c,...).
[0075]
[Mobile network creation means]
The moving network creating means (FIG. 3: (26)) includes a network creating unit (561) for creating a network connecting the moved areas, as illustrated in FIG. 7, for example, and a movement count device for counting the created network. It has a network weighting unit (562) for weighting based on the number of times of movement counted by (55), so that a mobile network creation device (56) for creating a weighted inter-area mobile network can be considered (FIG. 8: "Mobile network creation process").
[0076]
The mobile network creation device (56) can be realized as a device having a network creation unit (561) and a network weighting unit (562) as a program, or as a program itself.
[0077]
For specific processing, for example, first, a network is created that connects all the moved areas, that is, all the areas corresponding to the position information in the order of the movement time, and then a weighting value according to the number of movements between the respective areas is created. To determine. As this weighting value, a moving frequency value indicating the ratio of each moving frequency to the total moving frequency can be used, and weighting is performed using this moving frequency value. For example, if the total number of movements is 100, the region R _A To region R _B 0.1 if region 10 _C If the number of movements to the area is 5, the movement frequency value is calculated as 0.05, and this value is used to calculate the area R _A To region R _B , Region R _C What is necessary is to weight the network to.
[0078]
According to this, the relationship between the areas can be visualized by the weighted inter-area movement network, and the use of this allows realization of a subjective map.
[0079]
[Data embedding means]
The data embedding means (FIG. 3: (27)) stores the weighted inter-area mobile network created by the mobile network creating device (56) as illustrated in FIG. 7, for example, 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.
[0080]
The data embedding device (57) can be realized as a device having a data embedding section (571) as a program, or as a program itself.
[0081]
Note that, in addition to embedding the created mobile network in an objective map to obtain a subjective map, only the mobile network may be used as the subjective map. In this case, for example, the weighted inter-region movement network can be displayed as map data, and this weighted inter-region movement map is used as it is as a subjective map. According to this, the moving map as the subjective map and the existing objective map can be separately referred to.
[0082]
Further, subjective map data may be created by superimposing the moving map data on the objective map data. FIG. 8 shows this mode, in which “moving map creation processing” and “superposition processing” are sequentially performed.
[0083]
Through the above-described processes, a subjective map with a high degree of subjectivity and information, which is composed of subjective movement data between each point, is created, and the user normally performs e-mail transmission, digital image capturing, and the like. It is possible to easily support the creation of a subjective map simply by performing a certain action, and it is also possible to create a private map that shows your own travel history.
[0084]
In the above-described movement map creation processing, latitude and longitude data, altitude data, address data, and the like are used as position information. However, in order to create a more precise movement map, various movements such as the distance, direction, and speed of movement are performed. Information may also be collected and accumulated as a type of location information and used when creating a mobile network. For example, various modes can be considered, such as performing weighting in consideration of the moving speed and the like in addition to the number of times of movement between certain areas.
[0085]
<< Third Embodiment >>
According to the invention of this application, a subjective map including subjective landmark data and subjective movement data can be created by combining the first and second embodiments described above.
[0086]
In this case, the information collection means (FIG. 4:31), the information storage means (FIG. 4:32), the existing map storage means (FIG. 4:33), the area dividing means (FIG. 4:34), the information distribution means (FIG. 4:34) 4:35), information extraction means (FIG. 4:36), feature selection means (FIG. 4:37), movement count means (FIG. 4:38), and mobile network creation means (FIG. 4:39). Although not shown in the figure, the information collection device (FIG. 5: 41), the information database (FIG. 5: 42), the map database (FIG. 5: 43), the area dividing device (FIG. 5: 44), the information distribution device ( (Fig. 5: 45), information extraction device (Fig. 5: 46), feature selection device (Fig. 5: 47), movement count device (Fig. 7: 55), mobile network creation device (Fig. 7: 56). The data embedding means (FIG. 4: 310) is described above. The data embedding apparatus (Fig. 5:48) and data embedding device can be considered a functional combination of (Fig. 7:57). For data embedding, both the geographical feature or geographical map obtained by the feature selecting device (47) and the mobile network or mobile map obtained by the mobile network creating device (56) are embedded in the objective map data. Good.
[0087]
The subjective map thus obtained can grasp the subjective landmark data and the subjective movement data at a time, and can realize an extremely useful town navigation and the like.
[0088]
【Example】
Here, an example according to the above embodiment of the invention of this application will be described.
[0089]
<< 1st Example >>
As a first embodiment, a case is considered in which an e-mail transmitted from a portable terminal with GPS carried by an individual at an outdoor location is used for subjective map creation. FIG. 9 is a processing flow chart for explaining the present embodiment.
[0090]
First, the existing objective map data is stored, and the text data of the transmitted e-mail (behavior information), the latitude and longitude data of the point where it was transmitted (position information by GPS), and the transmitted Time data (time information by a mobile terminal built-in clock) is stored in association with the data (FIG. 5: information collection device (41), information database (42), map database (43)).
[0091]
Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In this embodiment, a grid-like division of a fixed size is performed.
[0092]
Next, text data is allocated to each area based on latitude and longitude data (FIG. 5: information distribution device (45)). More specifically, it is detected which latitude / longitude range the latitude / longitude data falls into, and text data forming a pair with the latitude / longitude data is assigned to the detected region.
[0093]
Next, a geographic word is extracted from the text data for each area (FIG. 5: information extraction device (46)). For example, if the text data "This shrine is on a hill and Mt. Fuji is ..." is in the upper left corner area in the illustration, the geographical words "Shrine", "Slope" and "Mt. It is cut out.
[0094]
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, “Shrine” of the geographic word data “Shrine” “Saka” and “Mt. Fuji” is selected as a geographic feature word by statistical processing such as the tf / idf method. That is, "shrine" is a landmark keyword in this area.
[0095]
A feature map is created by performing the above extraction processing and selection processing for all areas, and a subjective map is created by superimposing the feature map on an objective map (FIG. 5: data embedding device (48)).
[0096]
<< Second embodiment >>
As a second example, a case where a person visits a certain place outdoors and then moves to another place and uses an e-mail including information on the visited place sent from a mobile phone for use in creating a subjective map. Think. FIG. 10 is a process flowchart for explaining the present embodiment.
[0097]
First, existing objective map data is stored, and text data (behavior information) of the transmitted e-mail and data indicating the name of a visited place included in the text data (position information by word recognition), In addition, time data (time information based on word recognition) indicating the time of visit included in the text data is stored in association with the data (FIG. 5: information collection device (41), information database (42), map database ( 43)).
[0098]
Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In the present embodiment, division is performed based on geographic data.
[0099]
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 a place in the area, and text data paired with the place name data is assigned to the detected area. For example, if the name of a certain temple is included, it is extracted, and the text data is distributed to the right middle area in the illustration, which is the area where the temple exists.
[0100]
Next, a geographic word is extracted from the text data for each area (FIG. 5: information extraction device (46)). For example, if there is text data on the temple that says "It felt dark and cold. It was very quiet.", The geographical words "dark,""cold," and "quiet" would be cut out of the text data.
[0101]
Next, a geographic feature word is selected from the geographic words for each region (FIG. 5: feature selection device (47)). For example, in the middle area on the right, “quiet” among the geographic word data “dark”, “cold”, and “quiet” is selected as the geographic feature word. Thereby, "quiet" becomes a landmark keyword in this area, and subjective information that the temple is quiet is obtained.
[0102]
Thereafter, a feature map is created by performing the above-described extraction processing and selection processing for all regions, and a subjective map is created by overlapping the feature map with an objective map (FIG. 5: Data embedding device (48) )).
[0103]
<< Third embodiment >>
As a third example, a person visits a certain place outdoors and then goes home and uses a diary published on the Web containing information on the visited place written from a personal computer for use in creating a subjective map. Consider the case. FIG. 11 is a processing flowchart for explaining the present embodiment.
[0104]
First, existing objective map data is stored, and text data (action information) of a public Web diary and data indicating the name of a place included in the text data (position information by word recognition), and Time data (time information by word recognition) indicating the time of visit included in the text data is stored in association with the data (FIG. 5: information collection device (41), information database (42), map database (43). )).
[0105]
Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In this embodiment, division (with feedback) is performed based on the data distribution.
[0106]
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 a place in the area, and text data paired with the place name data is assigned to the detected area. For example, if a park name is included, it is extracted and the text data is distributed to the upper left corner area in the illustration where the park exists.
[0107]
Next, a geographic word is extracted from the text data for each area (FIG. 5: information extraction device (46)). For example, if there is text data of “the cherry blossoms are in full bloom and they are very beautiful” for the above-mentioned park, the geographical words “cherry blossom”, “flower”, and “beautiful” are cut out of the text data.
[0108]
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, “Sakura” of the geographic word data “Sakura”, “Flower”, and “Pretty” is selected as the geographic feature word. As a result, "cherry blossom" becomes a landmark keyword in this area, and subjective information that the park is famous for cherry blossoms can be obtained.
[0109]
Thereafter, a feature map is created by performing the above-described extraction processing and selection processing for all regions, and a subjective map is created by overlapping the feature map with an objective map (FIG. 5: Data embedding device (48) )).
[0110]
Although the above-described first to third embodiments are divided into cases by e-mail or Web publication diary, it goes without saying that they can be used collectively. For example, when both the text data of an e-mail and the text data of a Web publication diary are allocated in the upper left corner area, various geographical words are extracted from each of them, and one or more geographical words are statistically extracted from all the words. Features are selected. Such processing is performed for all areas.
[0111]
<< Fourth embodiment >>
As a fourth embodiment, a case is considered in which image data taken at a certain point outdoors from a digital camera and a portable terminal with GPS is used for creating a subjective map. FIG. 12 is a processing flowchart for explaining the present embodiment.
[0112]
First, existing objective map data is stored, and the image data (behavior information), the latitude and longitude data of the point where the image data was captured (position information by GPS), and the time data at which the image data was captured (portable data). (Time information by a terminal internal clock) and accumulate them (FIG. 5: information collection device (41), information database (42), map database (43)).
[0113]
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)).
[0114]
Next, a geographic object is extracted from the image data for each region (FIG. 5: information extraction device (46)). For example, when image data as shown in FIG. 12 is in the upper left corner area, geographical objects "house", "tree", "red roof", and "car" are cut out by image recognition processing or the like.
[0115]
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, “red roof” and “car” among the geographical objects are selected as the geographical characteristic objects by statistical processing. That is, these become landmark objects in this area.
[0116]
Thereafter, a feature map is created by performing the above-described extraction processing and selection processing for all regions, and a subjective map is created by overlapping the feature map with an objective map (FIG. 5: Data embedding device (48) )).
[0117]
<< Fifth Embodiment >>
As a fifth embodiment, a case will be considered in which image data captured from a portable terminal with a digital camera at a certain point outdoors is used for creating a subjective map. FIG. 13 is a processing flowchart for explaining the present embodiment.
[0118]
First, existing objective map data is stored, and image data (behavior information) and image data (position information by image object recognition) indicating a shooting location included in the image data, and time indicating an imaging time are stored. Data (time information by a clock built in the portable terminal) is stored in association with the data (FIG. 5: information collection device (41), information database (42), map database (43)).
[0119]
Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In this embodiment, a grid-like division of a fixed size is performed.
[0120]
Next, image data is allocated to each area based on the image data of the photographing place (FIG. 5: information distribution device (45)). More specifically, it is detected in which region the shooting location image data indicates a location, and image data forming a pair with the shooting location image data is assigned to the detected area. For example, if Mt. Fuji is displayed, it is extracted and text data is distributed to the area where Mt. Fuji exists.
[0121]
Next, a geographic object is extracted from the image data for each region (FIG. 5: information extraction device (46)). For example, when the image data as shown in FIG. 12 is in the upper left corner area, geographical objects “shrine”, “hill”, and “Mt. Fuji” are cut out by image recognition processing or the like.
[0122]
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” of the above geographical objects is selected as a geographical feature object by statistical processing. That is, this is a landmark object in this area.
[0123]
Thereafter, a feature map is created by performing the above-described extraction processing and selection processing for all regions, and a subjective map is created by overlapping the feature map with an objective map (FIG. 5: Data embedding device (48) )).
[0124]
<< Sixth Embodiment >>
As a sixth embodiment, a case is considered in which sound data recorded at a certain point outdoors from an IC recorder + a mobile terminal equipped with GPS is used for creating a subjective map. FIG. 14 is a process flowchart for explaining the present embodiment.
[0125]
First, existing objective map data is stored, and sound data (behavior information), latitude and longitude data of the point where the sound data was recorded (positional information by GPS), and time data at which the sound data was recorded (portable data) are stored. (Time information by a terminal internal clock) and accumulate them (FIG. 5: information collection device (41), information database (42), map database (43)).
[0126]
Next, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)). In this embodiment, a grid-like division of a fixed size is performed.
[0127]
Next, sound data is allocated to each area based on the latitude and longitude data (FIG. 5: information distribution device (45)). More specifically, it is detected which latitude / longitude range the latitude / longitude data falls into, and image data forming a pair with the latitude / longitude data is assigned to the detected region.
[0128]
Next, a geographic sound source is extracted from the sound data for each region (FIG. 5: information extraction 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 “Gyu”, “Gau”, “Hugh”, and “Grumbling” in the upper right corner area, Through a voice recognition process or the like, “railroad crossing”, “busy”, “children's voice”, “dog's cry”, “wind”, and “lightning” are extracted as geographic sound sources.
[0129]
Next, a geographic feature sound source is selected from the geographic sound sources for each region (FIG. 5: feature selection device (47)). For example, in the upper left corner area, “railroad crossing” of the above geographical sound source is selected as the geographic feature object in the statistical processing, and in the upper right corner area, “child's voice”. That is, these become landmark objects in the corresponding area.
[0130]
Thereafter, a feature map is created by performing the above-described extraction processing and selection processing for all regions, and a subjective map is created by overlapping the feature map with an objective map (FIG. 5: Data embedding device (48) )).
[0131]
<< 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 certain place outdoors, body temperature data which is the body information of the person measured at the place and Consider a case where pulse data is used for creating a subjective map. FIG. 15 is a processing flow chart for explaining the present embodiment.
[0132]
First, the existing objective map data is stored, and the measured body temperature data and pulse data (behavior sensing information), the latitude and longitude data of the place where it was measured (position information by GPS), and the measured data are measured. And associated time data (time information from a mobile phone built-in clock) and store them (FIG. 5: information collection device (41), information database (42), map database (43)).
[0133]
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 the latitude and longitude data (FIG. 5: information distribution device (45)). )).
[0134]
Next, geographical physical information is extracted from the body temperature data and the pulse data for each region (FIG. 5: information extraction device (46)). For example, as shown in FIG. 15, geographical body information such as “body temperature rise” when body temperature is higher than normal and “pulse increase” when pulse is higher than normal is recognized and extracted. .
[0135]
Next, geographical feature physical information is selected from the geographical body information for each region (FIG. 5: feature selecting device (47)). For example, in the upper right corner area, “pulse increase” of the geographical physical information is statistically selected as geographical characteristic physical information. As a result, "pulse increase" becomes the landmark physical information in this area, and subjective information that the pulse increases in this area can be obtained.
[0136]
Thereafter, a feature map is created by performing the above-described extraction processing and selection processing for all regions, and a subjective map is created by overlapping the feature map with an objective map (FIG. 5: Data embedding device (48) )).
[0137]
<< Eighth embodiment >>
As an eighth embodiment, when a remote-controlled robot having a temperature / torque sensor function and a GPS function is moved to a certain place outdoors, internal temperature data and torque as body information of the robot measured at the place are measured. Consider a case where load data is used for creating a subjective map. FIG. 16 is a processing flow chart for explaining the present embodiment.
[0138]
First, existing objective map data is stored, and the measured internal temperature data and torque load data (behavior sensing information), the latitude and longitude data of the location where it was measured (position information by GPS), and The measured time data (time information by the robot built-in clock) is stored in association with the time data (FIG. 5: information collection device (41), information database (42), map database (43)).
[0139]
Subsequently, the objective map data is divided into a plurality of regions (FIG. 5: region dividing device (44)), and the internal temperature data and the torque load data are allocated to each region based on the latitude and longitude data (FIG. 5: information distribution device). (45)).
[0140]
Next, geographical physical information is extracted from the internal temperature data and the torque load data for each region (FIG. 5: information extraction device (46)). For example, as shown in FIG. 16, geographical body information that “internal temperature rises” when the internal temperature is higher than normal and “torque load increases” when the torque load is higher than normal is recognized.・ Extracted.
[0141]
Next, geographical feature physical information is selected from the geographical body information for each region (FIG. 5: feature selecting device (47)). For example, in the upper right corner area, “increased torque load” in the geographical physical information is statistically selected as geographical characteristic physical information. As a result, “torque load increase” becomes landmark robot body information in this area, and subjective information that the torque load increases in this area can be obtained.
[0142]
Thereafter, a feature map is created by performing the above-described extraction processing and selection processing for all regions, and a subjective map is created by overlapping the feature map with an objective map (FIG. 5: Data embedding device (48) )).
[0143]
In the first to ninth embodiments, cases are classified according to text data, image data, and sound data. Needless to say, these can be collectively used. For example, when different types of behavior information exist in the same area, various types of geographic information are extracted from each piece of information, and one or more geographic features that are statistically selected from all the pieces of information are selected. Such processing is performed for all areas.
[0144]
<< Ninth Embodiment >>
As a ninth embodiment, a case is considered in which a movement map is created using the latitude and longitude data and time data of the transmission point of the e-mail collected in the first embodiment.
[0145]
As illustrated in FIG. 17, first, existing objective map data is stored, and text data of the transmitted e-mail is stored in association with latitude and longitude data of the point where the text was transmitted, and time data. (FIG. 7: information collection device (51), information database (52), map database (53)), while dividing the objective map data into a plurality of regions (125: region division device (54)). The operation up to this point is the same as in the first embodiment.
[0146]
Next, the number of times of movement between each area is counted, and the movement frequency is further calculated (FIG. 7: movement number counting device (55)). For example, region R _A To region R _B To move to 0.1 = area R _C Movement frequency to 0.05 = area R _D Is calculated as follows.
[0147]
Next, the mobile network between the areas is weighted by the calculated mobile frequency (FIG. 7: mobile network creating device (56)). For example, the weighted network is made thicker as the movement frequency is larger, and thinner as the movement frequency is smaller.
[0148]
The mobile network created as described above can be used as a moving map in which the relationship between regions is clearly visualized. Even if this is used alone as a subjective map, it can be used as a subjective map created by superimposing it on an objective map. It may be a map (FIG. 7: data embedding device (57)).
[0149]
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-mails and the Web publication diaries collected in the second and third embodiments, Alternatively, it is needless to say that the mobile network and the mobile map can be similarly created by using the position data and the time data collected in the fourth to eighth embodiments.
[0150]
<< 10th Embodiment >>
FIG. 18 shows a case where a subjective map is created by superimposing both the feature map and the movement map created as described above on an objective map.
[0151]
As is clear from FIG. 18, this subjective map accurately displays landmarks that the user himself noticed, felt, and felt on a conventional objective map, and also showed the movement history between them. It is clearly displayed so that objective information and subjective information can be easily grasped at once.
[0152]
<< Eleventh embodiment >>
By using the above subjective map, it is considered that various applications such as the following are possible.
[0153]
I. "Soft" navigation (location estimation and route search based on words and perceptual information)
Route guidance is performed using information such as landmark keywords, key objects, and key sound sources embedded in the subjective map.
[0154]
II. Creating a “schematic map” suitable for individuals
By emphasizing the landmark objects on the subjective map more than others, a schematic map is created that is specialized for the individual's interest and physicality.
[0155]
III. Support for acquiring and organizing spatial knowledge of each individual (such as treatment for dysthesia)
By presenting the landmark keywords, key objects, key sound sources, and the like embedded in the subjective map to the user in a timely manner, the user is strongly conscious and supports the storage of the geographic information of the land.
[0156]
IV. Promote sharing and exchange of spatial information (community support, etc.)
Within a group that shares interests, residential areas, etc., each individual supports the interaction between individuals by sharing a subjective map with each other or creating a subjective map shared by the group. For example, in the case of a mail group or a chat group, a subjective map for the group can be created using mail data and chat data transmitted and received within the group.
[0157]
V. Walking information support (walking navigation, etc.)
By publishing a subjective map of a person familiar with an area, we help other people unfamiliar with the area to take a walk through it.
[0158]
VI. Analysis of individual differences (effects of physicality, etc.) on spatial cognition
Compare and classify subjective maps of various people with different ages, genders, interests, physical characteristics, etc. in a certain area, and use the results for product advertising strategies and city planning strategies.
[0159]
Of course, the invention of this application is not limited to the above-described embodiments and examples, and various aspects are possible in details.
[0160]
【The invention's effect】
As described in detail above, according to the invention of this application, a subjective map creation method and a subjective map creation method capable of easily creating a subjective map having extremely high utility value from the viewpoint of each user. A system is provided.
[Brief description of the drawings]
FIG. 1 is a flowchart for explaining the invention of this application.
FIG. 2 is a functional block diagram for explaining the invention of this application.
FIG. 3 is another functional block diagram for explaining the invention of this application.
FIG. 4 is still another functional block diagram for explaining the invention of this application.
FIG. 5 is a functional block diagram for explaining a first embodiment of the present invention;
FIG. 6 is a processing flowchart for explaining a first embodiment of the present invention;
FIG. 7 is a functional block diagram for explaining a second embodiment of the present invention;
FIG. 8 is a processing flowchart for explaining a second embodiment of the present invention;
FIG. 9 is a processing flow chart for explaining a first embodiment of the invention of this application.
FIG. 10 is another processing flowchart for explaining the second embodiment of the present invention;
FIG. 11 is another processing flowchart for explaining a third embodiment of the present invention;
FIG. 12 is still another processing flowchart for explaining the fourth embodiment of the present invention;
FIG. 13 is still another processing flowchart for explaining the fifth embodiment of the invention of this application.
FIG. 14 is a processing flow chart for explaining a sixth embodiment of the present invention;
FIG. 15 is a processing flowchart for explaining a seventh embodiment of the present invention;
FIG. 16 is a processing flowchart for explaining an eighth embodiment of the present invention;
FIG. 17 is a processing flowchart for explaining a ninth embodiment of the present invention;
FIG. 18 is a processing flowchart for explaining a tenth embodiment of the present invention;
[Explanation of symbols]
1 subjective mapping system
11 Information collection means
12 Information storage means
13 Existing map storage means
14 area dividing means
15 Information distribution means
16 Information extraction means
17 Feature selection means
18 Data embedding means
2 subjective mapping system
21 Information collection means
22 Information storage means
23 Existing map storage means
24 area dividing means
25 Movement count means
26 Mobile network creation means
27 Data embedding means
3 subjective mapping system
31 Information collection means
32 Information storage means
33 Existing map storage means
34 area dividing means
35 Information distribution means
36 Information extraction means
37 Feature selection means
38 Movement count means
39 Mobile network creation means
310 Data embedding means
4 subjective mapping system
41 Information collection device
411 Behavior information collection unit
412 Location information collection unit
413 Time Information Collection Unit
414 Information output unit
42 Information Database
421 Information input section
422 Information storage unit
43 Map Database
431 Map input section
432 Map storage unit
44 area dividing device
441 area division unit
45 Information distribution device
451 Position matching unit
452 Behavior information allocator
46 Information extraction device
461 Information extraction unit
47 Feature selection device
471 High reproducibility judgment unit
472 Rarity judgment unit
473 User designation judgment unit
48 Data embedding device
481 Data embedding part
400a, 400b, 400c ... area database
5 subjective mapping system
51 Information collection device
511 Location information collection unit
512 Time information collection unit
513 Information output unit
52 Information Database
521 Information input section
522 Information storage unit
53 Map Database
531 Map input section
532 Map storage unit
54 area dividing device
541 area division unit
55 Movement count device
551 Movement count section
56 Mobile network creation device
561 Network Creation Department
562 Network weighting part
57 Data Embedding Device
571 Data embedding part
500a, 500b, 500c ... area database

Claims (7)

Processing means for collecting action information and location information in association with each other;
Processing means for extracting subjective information from the collected behavior information;
Processing means for embedding the extracted subjective information into existing map data stored in storage means based on the collected position information;
A subjective map creation method comprising: Information collecting means for collecting the behavior information and the location information in association with each other;
Information storage means for storing the action information and the position information collected by the information collection means in association with each other;
Existing map storage means for storing existing map data;
Area dividing means for dividing map data stored in the existing map storage means into a plurality of areas;
Information distribution means for allocating action information stored in the information storage means to each area of the map data divided by the area division means based on the position information;
Information extracting means for extracting geographical information from the action information allocated by the information distributing means for each area of the map data divided by the area dividing means;
Feature selecting means for selecting a geographic feature from the geographic information extracted by the information extracting means for each area of the map data divided by the area dividing means;
A subjective mapping system comprising: Data embedding means for embedding the geographical feature selected by the feature selecting means in the map data stored in the existing map storage means,
The subjective map creation system according to claim 2, further comprising: Information collecting means for collecting the action information, the location information and the time information in association with each other;
Information storage means for storing the action information, position information and time information collected by the information collection means in association with each other;
Existing map storage means for storing existing map data;
Area dividing means for dividing map data stored in the existing map storage means into a plurality of areas;
Movement number counting means for counting the number of movements between each area of the map data divided by the area division means based on the position information and the time information stored in the information storage means;
Moving network creating means for creating a moving network between the areas weighted based on the number of movements counted by the number of movements counting means,
A subjective mapping system comprising: Data embedding means for embedding the mobile network created by the mobile network creation means into map data stored in the existing map storage means,
The subjective map creation system according to claim 4, further comprising: Information collecting means for collecting the action information, the location information and the time information in association with each other;
Information storage means for storing the action information, position information and time information collected by the information collection means in association with each other;
Existing map storage means for storing existing map data;
Area dividing means for dividing map data stored in the existing map storage means into a plurality of areas;
Information distribution means for allocating action information stored in the information storage means to each area of the map data divided by the area division means based on the position information;
Information extracting means for extracting geographical information from the action information allocated by the information distributing means for each area of the map data divided by the area dividing means;
Feature selecting means for selecting a geographic feature from the geographic information extracted by the information extracting means for each area of the map data divided by the area dividing means;
Movement number counting means for counting the number of movements between each area of the map data divided by the area division means based on the position information and the time information stored in the information storage means;
Moving network creating means for creating a moving network between the areas weighted based on the number of movements counted by the number of movements counting means,
A subjective mapping system comprising: Data embedding means for embedding the geographical feature selected by the feature selecting means and the mobile network created by the mobile network creating means in map data stored in the existing map storage means;
7. The subjective map creation system according to claim 6, further comprising:

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