JPWO2011055601A1 - Information analysis apparatus and information analysis method - Google Patents

Abstract

For the purpose of more accurately obtaining the number of people who have difficulty returning home at the time of a disaster, the information analysis apparatus (700) includes an acquisition unit (702) for obtaining subscriber position information in a target time zone including a subscriber ID and an area ID, A unifying unit (703) for narrowing down a plurality of subscriber location information to a single subscriber location information, a first association unit (704) for associating subscriber location information and attribute information, and each mobile device A distance calculation unit that calculates the distance between the location of the mobile subscriber and the address based on the subscriber location information equivalent and the address information in the attribute information associated with the subscriber location information equivalent for the subscriber ( 706), an extraction unit (707) for extracting subscriber position information equivalent whose distance is equal to or greater than a reference value, and classifying and weighting the number corresponding to subscriber position information for each distance and totaling it for each area. And a deriving unit (708) for obtaining the number of people having difficulty returning home for each area.

Description

  The present invention relates to an information analysis apparatus and an information analysis method for analyzing an evacuation situation at the time of a disaster based on location information of a mobile device subscriber.

  Evacuation status at the time of disaster based on various information such as road location, shelter location, evacuation route, moving speed at evacuation, normal population distribution and evacuation start time to prepare for evacuation at the time of disaster A technique for predicting the above is proposed in Patent Document 1.

JP 2009-31461 A

  However, in the above technique, since the evacuation situation at the time of disaster is predicted based on the normal population distribution information stored in advance instead of the actual population distribution information, in order to make a more accurate evacuation situation prediction There was room for improvement. Especially in recent years, there are a large number of people in metropolitan areas who are difficult to go home on foot during disasters (so-called people who are unable to return home) because their daytime places (for example, work place) are far away from their homes. As an evacuation situation at the time of a disaster, it is an extremely important issue to accurately determine the number of people who have difficulty returning home.

  Of course, the work-home distance is calculated from the work address information and home address information for each worker obtained by the conventional census, and the number of workers whose work-home distance meets the prescribed criteria. Based on this, it is possible to calculate an approximate value for the number of people who have difficulty returning home. However, the above method cannot take into account the number of people who were at a place different from the place of work at the time of a disaster and the number of people who visited for shopping or sightseeing purposes. It is difficult to accurately obtain.

  Therefore, the present invention has been made to solve the above-described problems, and an object of the present invention is to more accurately determine the number of people who have difficulty returning home at the time of a disaster.

  An information analysis apparatus according to an aspect of the present invention is a subscriber attribute information storage unit that stores attribute information including address information of a mobile subscriber and the mobile subscriber's subscriber identification information in association with each other. Location information indicating the location of the mobile subscriber, time information at which the location is specified, and subscriber location information including subscriber identification information of the mobile subscriber, wherein the time is the target time zone. An acquisition unit that acquires the subscriber location information within a range, and when the acquisition unit acquires a plurality of subscriber location information for the same mobile station subscriber, the plurality of subscriber locations based on a predetermined rule The unification unit that narrows down the information to the equivalent of the single subscriber location information, and the subscriber identification information equivalent to the subscriber attribute information and the subscriber attribute information in common with the subscriber identification information for each mobile subscriber The attribute information stored in the storage unit. A first associating unit to be attached, a boundary information storing unit storing area boundary information for each area, and for each mobile subscriber, corresponding to subscriber location information and attribute information associated with the subscriber location information A distance calculating unit that calculates a distance between the location of the mobile subscriber and the address based on the address information in the mobile phone, and the subscription in which the calculated distance is equal to or greater than a first reference value for determining a person who has difficulty returning home An extraction unit for extracting the equivalent of the subscriber location information, and classifying and weighting the number corresponding to the extracted subscriber location information for each distance, and based on the location information equivalent to the subscriber location information and the area boundary information And a deriving unit that counts the number corresponding to the subscriber location information for each area to be calculated and derives the number of people who have difficulty returning home based on the total value.

  In the information analyzing apparatus, the acquisition unit is subscriber location information including location information indicating a location of the mobile subscriber, time information at which the location is specified, and subscriber identification information of the mobile subscriber. When the subscriber location information whose time is within the range of the target time zone is acquired, and the unifying unit acquires a plurality of subscriber location information for the same mobile station subscriber by the acquisition unit, the predetermined rule The plurality of subscriber location information is narrowed down to a single subscriber location information. Then, the first associating unit associates, for each mobile station subscriber, the subscriber position information after unification and the attribute information stored in the subscriber attribute information storage unit, in which the subscriber identification information is common. And, for each mobile subscriber, the distance calculation unit, based on the subscriber location information equivalent and the address information in the attribute information associated with the subscriber location information equivalent, the location and address of the mobile subscriber Calculate the distance between them. Further, the extraction unit extracts the subscriber position information equivalent that the calculated distance is equal to or greater than the first reference value for the person who has difficulty in returning home, and the derivation unit calculates the number corresponding to the extracted subscriber position information. Based on the location information equivalent to the subscriber location information and the area boundary information, the number corresponding to the subscriber location information is totaled for each area in the area, and the area based on the total value is classified and weighted for each distance. Deriving the number of people who have difficulty returning home. In this way, the information analysis device calculates the so-called return distance based on the subscriber location information equivalent of the mobile subscriber and the address information, and extracts the equivalent of the subscriber location information whose return distance is a reference value or more, In order to derive the number of people who are unable to go home from the number corresponding to the extracted subscriber location information, the number of people who were at a place different from the place of work at the time of disaster and the number of people who visited for shopping or sightseeing purposes were considered In addition, it is possible to obtain the number of people who have difficulty in returning home, which is much more accurate than before, and to grasp the distribution situation of the number of people who have difficulty returning home in a timely and accurate manner. The above “equivalent to subscriber location information” is not only the subscriber location information itself but also information corresponding to the subscriber location information (for example, a weight value obtained by weight addition used in the current population distribution, etc.) It is a concept that includes The weight values exemplified here will be described later.

  In the information analysis apparatus, the derivation unit aggregates the number corresponding to the subscriber location information for each area in the area based on the attribute information associated with the extracted subscriber location information equivalent, and the aggregate value Based on the above, the number of people having difficulty returning home for each area may be derived for each attribute. In this case, it is possible to derive the number of difficult-to-return homes by attribute with high accuracy and grasp the distribution situation of the number of home-coming people by attribute.

  In the information analysis apparatus, the derivation unit derives the number of people who have difficulty returning home by residence based on the address information in the attribute information associated with the subscriber position information corresponding to the disaster occurrence. May be. In this case, it is possible to obtain the number of people who have difficulty returning home by residence area. In addition, the deriving unit counts the number equivalent to the subscriber location information for each area where the person who has difficulty returning home is located at the time of the disaster, based on the subscriber location information equivalent at the time of the disaster for those who have difficulty returning home in a certain residence By doing so, you may derive | lead-out the number of persons who have difficulty returning home according to the area where the disaster occurred regarding the persons having difficulty returning home in the certain residence. In this case, it is possible to obtain the number of people who are unable to return home by area in the event of a disaster related to those who have difficulty returning home in a certain residence.

  In the information analysis apparatus, the deriving unit may derive the number of people who are unable to return home by area where the mobile subscriber is located when the disaster occurs, based on the subscriber position information corresponding to the disaster. In this case, it is possible to obtain the number of people who are unable to return home according to the location where the people who have difficulty returning home (areas where the disaster occurred). In addition, the deriving unit, based on the subscriber location information equivalent at the time of the disaster for a person who is difficult to return home who was in a certain area at the time of the disaster, the address in the attribute information associated with the subscriber location information equivalent By referring to the information, the number of people who have difficulty returning home by residence area regarding those who have difficulty returning home in the area at the time of the disaster may be derived. In this case, it is possible to obtain the number of people who have difficulty returning home by residence area regarding those who have been in a certain area at the time of the disaster.

  Here, the derivation unit derives the number of people who have difficulty returning home based on the address information in the attribute information associated with the subscriber position information corresponding to the occurrence of the disaster, Deriving the number of people who have difficulty returning home by area based on the location information of the subscriber, and the number of people who have difficulty returning home by area You may derive | lead-out the area where both the number of difficult persons is more than predetermined conditions. In this case, it is possible to accurately analyze the number of people who are unable to return home under the conditions of both the places where the people who have difficulty returning home and the conditions of residence.

  In the information analysis apparatus, the deriving unit may be a subscriber whose calculated distance is equal to or less than a second reference value for determining a support activity candidate and whose attribute information satisfies a predetermined condition for determining a support activity candidate. The number corresponding to the subscriber position information may be totaled for each area, and the number corresponding to the subscriber position information for each area may be derived as the number of support activity candidates based on the total value. In this case, the number of support activity candidates can be obtained.

  By the way, as a term related to “difficult to return home”, “short distance” corresponding to a person who is going out (person who is not at home or in the vicinity thereof) or a person who is going out but can go home on foot "Walkers" are known.

  Therefore, in the information analysis apparatus, the extraction unit extracts the equivalent of the subscriber position information whose calculated distance is equal to or greater than the third reference value for staying person determination set to be smaller than the first reference value. The derivation unit classifies and weights the number corresponding to the extracted subscriber position information for each distance, and the subscriber for each area in the area based on the position information corresponding to the subscriber position information and the area boundary information. The number corresponding to the position information may be totaled, and the number of stayers for each area may be derived based on the total value. In this case, the number of stayers can be obtained.

  Moreover, in the information analysis apparatus, the deriving unit may derive the number of short-distance walking returnees for each area by subtracting the number of people who have difficulty returning home from the number of stayers for each area. In this case, it is possible to obtain the number of people returning home on foot from a short distance.

  The information analysis apparatus may further include a target area extracting unit that extracts an area to be supported based on the derived number of people who have difficulty returning home and a predetermined selection criterion. In this case, an area to be supported can be obtained.

  The location information indicating the location of the mobile subscriber may be expressed by latitude and longitude, and the first associating unit displays the address information in the attribute information to be associated with the subscriber location information equivalent by latitude and longitude. The attribute information including the converted address information may be associated with the subscriber position information by converting the information into the represented format.

  The information analysis apparatus may further include an output unit that outputs a map indicating the distribution of the number of people having difficulty in returning home based on the number of people having difficulty in returning home for each area. In this case, it is possible to visually grasp the distribution of the number of people who are unable to return home from the output map.

  In addition, as the above “area”, any of the division units for dividing the land including sectors, meshes, and municipalities may be applied.

  When the sector is applied as the above “area”, the information analysis apparatus converts the derived number of people who are difficult to return home to the number of people who are difficult to return home for each mesh divided according to a predetermined division rule. You may further provide the conversion part converted based on the positional relationship of the sector and mesh obtained from boundary information and a division | segmentation rule. In this case, it is possible to obtain the number of people having difficulty in returning home for each mesh from the number of people having difficulty in returning home for each sector.

  An information analysis apparatus according to an aspect of the present invention is a subscriber attribute information storage unit that stores attribute information including address information of a mobile subscriber and the mobile subscriber's subscriber identification information in association with each other. Sector identification information indicating a sector in which the mobile subscriber is located, time information for identifying the sector in which the mobile subscriber is located, and subscriber location information including subscriber identification information of the mobile subscriber, An acquisition unit that acquires the subscriber location information whose time is within the range of the target time zone, and a plurality of subscriber location information for the same mobile station subscriber acquired by the acquisition unit, based on a predetermined rule And a single unit for narrowing down the plurality of subscriber location information to a single subscriber location information, and subscriber location information after unitization, wherein the subscriber identification information is common to each mobile subscriber. And recorded in the subscriber attribute information storage unit. A first associating unit for associating the received attribute information, a sector information storage unit storing centroid position information and sector boundary information for each sector, and for each mobile subscriber, A second associating unit for associating the centroid position information of the sector corresponding to the sector identification information in the person position information, the attribute information associated with the common subscriber position information, and the centroid position information of the sector. Based on the position information and the address information in the attribute information, a distance calculation unit that calculates the distance between the center of gravity position of the sector and the address, and the calculated distance is equal to or greater than a predetermined reference value for determining those who are difficult to return home And extracting and extracting the subscriber location information, and classifying and weighting the number of the extracted subscriber location information for each distance, A sector that aggregates each sector based on the sector identification information, and derives the number of people who are unable to return home for each sector based on the aggregated value; A conversion unit that converts the number of people who have difficulty returning home for each mesh partitioned based on the rules based on the sector boundary information and the positional relationship between the sector and the mesh obtained from the partitioning rules, and And an output unit that outputs a map showing the distribution of the number of people having difficulty returning home, based on the number of people having difficulty returning home for each mesh.

  In the above information analysis apparatus, by assuming the current location of the mobile subscriber as the center of gravity of the sector where the mobile subscriber is located, the subscriber location information including the sector identification information and the mobile subscription Based on the address information of the person, the distance between the center of gravity (current position) of the located sector and the address (so-called return distance) is calculated. Extracts subscriber location information whose calculated return distance is greater than or equal to a predetermined reference value for determining those who have difficulty returning home, and categorizes and weights the number of extracted subscriber location information for each distance and aggregates it for each sector Then, the number of people who are unable to return home for each sector is derived based on the total value, the number of people who are unable to return home for each sector is converted into the number of people who are difficult to return home for each mesh, and the number of people who are difficult to return home for each mesh after conversion. Based on the number, a map showing the distribution of the number of people who have difficulty returning home is output. As described above, in the present invention, the so-called return distance is calculated based on the subscriber location information including the serving sector identification information and the mobile subscriber's address information, and the return location is equal to or greater than the reference value. In order to extract information and derive the number of people who are unable to return home from the number of subscriber location information extracted, the number of people who were at a place different from the place of work at the time of a disaster and those who visited for shopping or sightseeing purposes Taking into account the number and the like, it is possible to obtain the number of people who have difficulty returning home, which is much more accurate than in the past, and it is possible to grasp the distribution situation of the number of people having difficulty returning home in a timely and accurate manner.

  In the information analysis apparatus, the derivation unit aggregates the number of subscriber location information for each sector in the area based on the attribute information associated with the extracted subscriber location information, and based on the aggregated value. Thus, the number of persons who have difficulty returning home for each sector may be derived for each attribute. In this case, it is possible to derive the number of difficult-to-return homes by attribute with high accuracy and grasp the distribution situation of the number of home-coming people by attribute.

  The sector center of gravity position information may be represented by latitude and longitude, and the first association unit converts the address information in the attribute information to be associated with the subscriber position information into a format represented by latitude and longitude. The attribute information including the converted address information may be associated with the subscriber location information.

  The information analysis apparatus described above may further include a designation unit for designating the target time zone.

  The invention relating to the information analysis apparatus described above can also be regarded as an invention relating to an information analysis method, and can be described as follows.

  An information analysis method according to an aspect of the present invention includes a subscriber attribute information storage unit that stores attribute information including address information of a mobile station subscriber and subscriber identification information of the mobile station subscriber in association with each other, and , An information analysis method executed by an information analysis apparatus comprising a boundary information storage unit that stores area boundary information for each area, wherein the position information indicating the position of the mobile subscriber is identified An acquisition step of acquiring the subscriber location information including time information and subscriber identification information of the mobile subscriber, wherein the time is within a target time zone; and When a plurality of pieces of subscriber location information are acquired for the same mobile station subscriber by the step, a unified step of narrowing down the plurality of subscriber location information to a single subscriber location information based on a predetermined rule. And, for each mobile subscriber, a first association step of associating the equivalent subscriber position information equivalent to the attribute information stored in the subscriber attribute information storage unit with the same subscriber identification information For each mobile subscriber, the distance between the mobile subscriber location and the address is calculated based on the subscriber location information equivalent and the address information in the attribute information associated with the subscriber location information equivalent. A distance calculating step, an extracting step for extracting the subscriber position information equivalent to the calculated reference distance being equal to or greater than the first reference value for determining a person who has difficulty in returning home, and a number corresponding to the extracted subscriber position information. Classified and weighted for each area, and based on the position information corresponding to the subscriber position information and the area boundary information, the number corresponding to the subscriber position information is totaled for each area in the area, and the area based on the total value Every Includes a derivation step of deriving the stranded commuters number, the.

  In the derivation step, the information analysis apparatus counts the number corresponding to the subscriber position information for each area in which the information analysis apparatus is associated, based on the attribute information associated with the extracted subscriber position information. Based on the total value, the number of people having difficulty returning home for each area may be derived for each attribute.

  An information analysis method according to an aspect of the present invention includes a subscriber attribute information storage unit that stores attribute information including address information of a mobile station subscriber and subscriber identification information of the mobile station subscriber in association with each other, and An information analysis method executed by an information analysis apparatus comprising a sector information storage unit storing centroid position information and sector boundary information for each sector, and sector identification information indicating a sector in which a mobile subscriber is located The subscriber location information including time information for identifying the sector in which the user is located and subscriber identification information of the mobile subscriber, wherein the time is within the range of the target time zone. An acquisition step of acquiring information, and when a plurality of subscriber location information is acquired for the same mobile station subscriber by the acquisition step, the plurality of subscriber location information is simply set based on a predetermined rule. The unification step for narrowing down to the equivalent of the subscriber location information, and the subscriber identification information common to each mobile station subscriber is stored in the subscriber location information and the subscriber attribute information storage unit. A first associating step for associating the attribute information with a second position, and, for each mobile subscriber, a second associating the centroid position information of the sector corresponding to the sector identification information in the subscriber position information with the subscriber position information. An association step; for the attribute information and sector centroid position information associated by common subscriber position information, based on the centroid position information of the sector and address information in the attribute information; A distance calculating step for calculating a distance between addresses, and the subscriber position information in which the calculated distance is equal to or greater than a predetermined reference value for determining a person who has difficulty in returning home. The extraction step to be issued, and the number of extracted subscriber location information are classified and weighted for each distance, and are aggregated for each sector based on the sector identification information included in the subscriber location information, and based on the aggregate value A derivation step for deriving the number of people who have difficulty returning home for each sector, and the number of people who have difficulty returning home for each sector as the number of people who have difficulty returning home for each mesh divided according to a predetermined division rule. And a map showing the distribution of the number of people having difficulty returning home based on the conversion step for converting based on the positional relationship between the sector and the mesh obtained from the partitioning rules, and the number of people having difficulty returning home for each mesh after conversion. An output step of outputting.

  In the above derivation step, the information analysis apparatus classifies and weights the number of the subscriber location information for each distance based on the attribute information associated with the extracted subscriber location information, and totals by attribute for each sector. Then, the number of persons who are unable to return home by attribute for each sector may be derived based on the total value.

  The information analysis method may be executed every predetermined time by the information analysis apparatus.

  According to the present invention, the number of people who have difficulty returning home at the time of a disaster can be obtained more accurately.

1 is a system configuration diagram of a communication system. It is a block diagram which shows the function structure of RNC and an exchange. It is a sequence diagram which shows the process of the mobile station in a communication system, RNC, an exchange, and a management center. It is a figure which shows an example of the management table of the subscriber information managed in the exchange. It is a functional block block diagram of the information analyzer in 1st, 2nd embodiment. It is a figure which shows an example of subscriber attribute information. It is a flowchart of the information analysis process in 1st Embodiment. It is a figure which shows the information content linked | related in the information analysis process in 1st Embodiment. It is a figure which shows the example of derivation | leading-out, such as the number of those who have difficulty returning home for every sector. It is a flowchart which shows the conversion process to the population distribution for every mesh. It is a figure for demonstrating the synthesis | combination of mesh data and a sector diagram. It is a figure for demonstrating calculation of the area and area ratio of each division | segmentation sector. It is a figure for demonstrating the sum total calculation of the population approximate value of the division sector in a certain mesh. It is an example of a distribution map of the number of people who have difficulty in returning home. It is an example of the distribution map of the number of people having difficulty returning home by attribute. It is a flowchart of the information analysis process in 2nd Embodiment. It is a figure which shows the information content linked | related in the information analysis process in 2nd Embodiment. It is a figure for demonstrating the derivation etc. of the number of people having difficulty in returning by the municipalities where those who have difficulty in returning home. It is a figure for demonstrating the derivation etc. of the number of people having difficulty in returning home by municipality where people who have difficulty in returning home. It is a figure for demonstrating derivation | leading-out of the municipality which is the upper rank of the municipality where the person who has difficulty in returning home is higher than the municipality where the person who has difficulty in returning home, and the person who has difficulty returning home. It is a functional block block diagram of the information analyzer in 4th Embodiment. It is a figure which shows the modification of FIG. It is a functional block block diagram which shows the modification of the information analyzer in 2nd Embodiment. It is a flowchart of the modification of the information analysis process in 2nd Embodiment.

  Embodiments of the present invention will be described with reference to the accompanying drawings. Where possible, the same parts are denoted by the same reference numerals, and redundant description is omitted.

[First Embodiment]
[Configuration of communication system]
FIG. 1 is a system configuration diagram of a communication system 10 according to the present embodiment. As shown in FIG. 1, the communication system 10 includes a mobile device 100, a BTS (base station) 200, an RNC (radio control device) 300, an exchange 400, and a management center 500. The management center 500 includes a social sensor unit 501, a petamining unit 502, a mobile demography unit 503, and a visualization solution unit 504.

  The exchange 400 collects SAI (Subscriber Area ID) that is the sector ID of the sector where the mobile device 100 is located via the BTS 200 and the RNC 300. When the mobile device 100 is located in a certain sector, the RNC 300 can grasp and acquire the SAI of that sector. The exchange 400 can receive the SAI of the mobile device 100 acquired in this way when the mobile device 100 executes communication connection. The exchange 400 stores the received SAI, and outputs the collected SAI to the management center 500 at a predetermined timing or in response to a request from the management center 500. Here, in general, the RNC 300 is composed of about 1,000 pieces and is arranged throughout Japan. On the other hand, about 300 exchanges 400 are arranged in Japan.

  As described above, the management center 500 includes the social sensor unit 501, the petamining unit 502, the mobile demography unit 503, and the visualization solution unit 504. Each unit is used for position information of the mobile device 100. Perform statistical processing.

  The social sensor unit 501 is a server device that collects data including the SAI of the mobile device 100 from each exchange 400. The social sensor unit 501 is configured to receive data periodically output from the exchange 400 or to acquire data from the exchange 400 according to a predetermined timing in the social sensor unit 501.

  The petamining unit 502 is a server device that converts data received from the social sensor unit 501 into a predetermined data format. For example, the petamining unit 502 performs a sorting process using a subscriber ID as a key, or performs a sorting process for each area.

  The mobile demography unit 503 is a server device that performs aggregation processing on the data processed in the petamining unit 502, that is, count processing for each item. For example, the mobile demography unit 503 can count the number of subscribers who are in a certain area, and can total the distribution of the locations.

  The visualization solution unit 504 is a server device that processes the data aggregated in the mobile demography unit 503 so as to be visible. For example, the visualization solution unit 504 can map the aggregated data on a map. It should be noted that the information statistically processed in this way is processed so that individuals are not specified so as not to infringe privacy.

  The social sensor unit 501, the petamining unit 502, the mobile demography unit 503, and the visualization solution unit 504 are all configured by the server device as described above, and although not shown, the basic configuration of a normal information processing device Needless to say, it includes a CPU, a RAM, a ROM, an input device such as a keyboard and a mouse, a communication device that communicates with the outside, a storage device that stores information, and an output device such as a display and a printer.

  Next, the configuration of the RNC 300 and the exchange 400 will be described. FIG. 2 is a block diagram showing functional configurations of the RNC 300 and the exchange 400. The RNC 300 includes a communication control unit 302. The exchange 400 includes a communication control unit 401, a conversion unit 402, and a storage unit 403.

  First, the RNC 300 will be described. The communication control unit 302 is a part that establishes communication connection with the mobile device 100 via the BTS 200. For example, communication connection processing based on outgoing call processing or incoming call processing from the mobile device 100 and communication connection processing based on a location registration request It is a part to do. In the present embodiment, the communication control unit 302 can further add the SAI of the mobile device 100 to the Initial UE Message used for the communication connection process and transmit it to the exchange 400. The Initial UE Message includes instruction information indicating a call origination or location registration request, TMSI (Temporary Mobile Subscriber Identity) that uniquely identifies the mobile device 100, and SAI. The TMSI is ID information issued by the exchange 400 when the mobile device 100 is connected to the network.

  The communication control unit 302 can receive the SAI of the sector where the mobile device 100 is located from the BTS 200. Thereby, the RNC 300 can grasp in which sector the mobile device 100 is located. Further, the RNC 300 can grasp the SAI indicating the serving sector ID, and can notify the exchange 400 of this SAI.

  Next, the exchange 400 will be described. The communication control unit 401 is a part that receives an Initial UE Message transmitted from the RNC 300 and performs a communication connection process using the Initial UE Message. Further, the communication control unit 401 transmits a subscriber ID (subscriber identification information), SAI, and SAI acquisition time to the management center 500 periodically or in response to a request from the management center 500.

  The conversion unit 402 is a part that converts the TMSI included in the Initial UE Message received by the communication control unit 401 into a subscriber ID. In the conversion process, the conversion unit 402 extracts a subscriber ID associated with TMSI from the subscriber profile information storage unit 600 that stores subscriber profile information, and converts the extracted subscriber ID into the extracted subscriber ID. The subscriber profile information storage unit 600 is provided, for example, in an HLR (Home Location Register). Here, TMSI and a subscriber ID are associated and managed and stored.

  The storage unit 403 is a part that stores the subscriber ID, the SAI of the mobile device 100 included in the Initial UE Message, and the time when the SAI is acquired in association with each other. The SAI stored in the storage unit 403 is collected in response to a predetermined timing or a request from the management center 500 in accordance with communication processing by the communication control unit 401. The storage unit 403 is stored, for example, in a format as shown in FIG. FIG. 4 is an explanatory diagram showing an example of a management table of subscriber information managed in the exchange 400. As shown in FIG. 4, a subscriber ID, an HLR number, an SAI, and a service type (such as a communication service that can be provided to a mobile device) are stored in association with each other.

  Next, processing of the communication system 10 configured as described above will be described. FIG. 3 is a sequence diagram showing processing of the mobile device 100, the RNC 300, the exchange 400, and the management center 500 in the communication system 10.

  When a transmission request or a location registration request is output from the mobile device 100, the RNC 200 acquires an SAI (sector ID) indicating the location of the mobile device 100 from the base station 200 according to the request (S101). In the RNC 300, the communication control unit 302 extracts the SAI that is the sector ID of the sector in which the mobile device 100 is located and the TMSI that indicates the mobile device 100 (S103). The acquired SAI and TMSI are added to the Initial UE Message by the communication control unit 302 and transmitted to the exchange 400 (S104).

  In the exchange 400, the communication control unit 401 receives the Initial UE Message, and the TMSI included in the Initial UE Message is converted into the subscriber ID of the mobile device 100 by the conversion unit 402 (S105). Then, the converted subscriber ID, SAI acquisition time, and SAI are stored in the storage unit 403 in association with each other (S106). The subscriber ID, SAI acquisition time, and SAI stored in association with each other are referred to as “subscriber location information”. Thereafter, when a call origination request is made in S101, connection processing for the destination is performed (S107).

  The subscriber location information (subscriber ID, SAI acquisition time and SAI) stored in the storage unit 403 is transmitted to the management center 500 or acquired in accordance with a request from the management center 500 (S108).

  In this way, the SAI of the mobile device can be notified to the exchange 400 and the management center 500 using the Initial UE Message. However, the present invention is not limited to this. For example, as a modification of the present embodiment, the communication control unit 302 notifies the TMSI and SAI of the mobile device 100 using a Location Report signal at the time of handover instead of the Initial UE Message. You may do it. This Location Report signal is a signal defined in 3GPP TS25.413. In this case, naturally, the exchange 400 is configured to be able to receive this Location Report signal.

[About information analysis equipment]
Next, the information analysis apparatus according to the present embodiment will be described. FIG. 5 shows a functional block configuration of the information analysis apparatus 700. As an example, the information analysis apparatus 700 corresponds to the petamining unit 502, the mobile demography unit 503, and the visualization solution unit 504 in FIG. As will be described in detail later, the information analysis apparatus 700 of the present embodiment assumes that the current position of the mobile subscriber is the center of gravity of the sector in which the mobile subscriber is located, and thus the above-described SAI (visited area). The distance between the center of gravity position (current position) and the address (the so-called return distance) of the sector in the area based on the subscriber location information including the sector identification information) and the address information of the mobile subscriber. Is calculated.

  As illustrated in FIG. 5, the information analysis apparatus 700 includes a designation unit 701, an acquisition unit 702, a unification unit 703, a first association unit 704, a second association unit 705, a distance calculation unit 706, an extraction unit 707, A derivation unit 708, a conversion unit 709, an output unit 710, a subscriber attribute information storage unit 711, and a sector information storage unit 712 are provided. Hereinafter, the function of each part will be described.

  The designation unit 701 is a part for the operator of the information analysis apparatus 700 to designate a target time zone related to processing to be described later. However, since it is not essential to designate the target time zone, the designation unit 701 in the information analysis apparatus 700 is not an essential constituent requirement. When the target time zone is not specified, a predetermined default time zone (for example, one hour before the current time to the current time) is set as the target time zone.

  The acquisition unit 702 is within the range of the target time zone in which the time information is specified among the subscriber location information including the subscriber ID, SAI acquisition time, and SAI described above from the data acquired from the exchange 400. Get subscriber location information. When the acquisition unit 702 acquires a plurality of subscriber location information for the same mobile station subscriber, the unification unit 703 converts the plurality of subscriber location information based on a predetermined rule as described below. Narrow down to information corresponding to one subscriber location information (equivalent to single subscriber location information). Here, as a single subscriber location information, for example, among the plurality of subscriber location information, the above-mentioned SAI acquisition time may be narrowed down to the latest subscriber location information. Further, a value (weight value) obtained by dividing 1 by the total number of subscriber position information of the same mobile station subscriber within the target time zone is calculated, and the set of the weight values is obtained as a single subscriber position information. It may be considerable. In this case, the sum of the weight values constituting the single subscriber location information is always 1. For example, the total number of subscriber location information within the target time zone for a certain mobile station subscriber is 4, of which one subscriber location information indicating the presence in sector A and the presence in sector B Assuming that there are three pieces of subscriber location information indicating “1”, the weight value constituting the single subscriber location information is 1/4 for sector A and 3/4 for sector B. In the following embodiments, in order to avoid complication of expression, “information corresponding to subscriber location information (corresponding to subscriber location information)” will be described as “subscriber location information” and the description will proceed.

  For example, as shown in FIG. 6, the subscriber attribute information storage unit 711 stores the subscriber ID of the mobile subscriber and the attribute information (address, sex, age) of the mobile subscriber in association with each other. . The first associating unit 704 associates the subscriber position information after unification and the attribute information with a common subscriber ID for each mobile subscriber. The first associating unit 704 also has a function of converting address information in the associated attribute information into latitude / longitude and obtaining information on address latitude and address longitude. Details thereof will be described later.

  The sector information storage unit 712 stores barycentric position information and sector boundary information for each sector. The second associating unit 705 associates, for each mobile station subscriber, the centroid position information of the sector corresponding to the SAI in the subscriber position information with the subscriber position information.

  The distance calculation unit 706 uses the attribute information and the centroid position information of the sector where the subscriber is located (the resident sector) associated with the common subscriber position information. Based on the address information, the distance between the center of gravity position of the located sector and the address (hereinafter referred to as “returning distance”) is calculated. The extraction unit 707 extracts subscriber position information whose calculated return distance is equal to or greater than a predetermined reference value (for example, 10 km) for determining a person who has difficulty returning home.

  The derivation unit 708 classifies and counts the number of extracted subscriber location information for each unit of return distance (for example, 1 km as an example), and weights in stages (as an example, 10-11 km is 10%, 11-12 km is 20%, ..., 19-20km is multiplied by 100%) and totaled for each sector based on the SAI included in the subscriber location information, and the population conversion count (as an example, the reciprocal of the share rate) ) To derive the number of people who have difficulty returning home for each sector. The conversion unit 709 converts the derived number of people having difficulty in returning home for each sector into the number of people having difficulty in returning home for each mesh. The output unit 710 outputs a map indicating the distribution of the number of people having difficulty in returning home based on the number of people having difficulty returning home for each mesh after conversion (see FIG. 14).

  By the way, the deriving unit 708 classifies and weights the number of the subscriber location information for each distance, based on the attribute information associated with the extracted subscriber location information, and tabulates the attribute for each sector. In addition, it also has a function of deriving the number of people who have difficulty returning home by attribute for each sector by multiplying the total value by the population conversion count (as an example, the reciprocal of the share rate). In that case, the conversion unit 709 converts the derived number of difficult-to-return people by attribute for each sector to the number of difficult-to-return homes by attribute for each mesh, and the output unit 710 outputs the number of attributes for each mesh after conversion. Based on the number of people having difficulty in returning home, a map showing the distribution of the number of people having difficulty returning home by attribute is output (see FIG. 15).

[Information analysis processing]
Next, information analysis processing executed in the information analysis apparatus 700 will be described with reference to FIGS.

  In the information analysis apparatus 700, when the target time zone is designated by the operator from the designation unit 701 and a predetermined process start operation is performed, the information analysis process shown in FIG. It is not essential to specify the target time zone. If a predetermined process start operation is performed without specifying the target time zone, a predetermined default time zone (for example, the current time from one hour before the current time) 1 hour) is the target time zone.

  First, the acquisition unit 702 receives SAI as an ID indicating a sector (a sector where the mobile station subscriber is located) sent from the exchange 400, time information (time stamp) when the SAI was acquired, and the movement Among the subscriber location information including the subscriber ID of the mobile subscriber (see FIG. 8A), the subscriber location information whose time stamp is within the target time zone is acquired (step S201 in FIG. 7). . The unifying unit 703 obtains a plurality of subscriber location information based on a predetermined rule when the acquisition unit 702 acquires a plurality of subscriber location information for the same mobile station subscriber. The position information is narrowed down (step S202). Here, as an example, among the plurality of subscriber location information, the time information is narrowed down to the latest subscriber location information.

  Next, the first associating unit 704 associates the subscriber position information after unification and the attribute information with a common subscriber ID for each mobile station subscriber (step S203). At this time, as the address information included in the attribute information, for example, when an automobile inspection registration office address code of the Ministry of Land, Infrastructure, Transport and Tourism or an address code issued by the Geographical Foundation of Japan is used, the first association unit 704 By referring to the correspondence information between the address code and latitude / longitude stored in advance, the address information in the associated attribute information is converted into latitude / longitude, and information on the address latitude and address longitude is obtained. As a result, information associating the subscriber position information and the attribute information shown in FIG. 8B is obtained, and the information shown in FIG. 8B is sent to the second associating unit 705.

  Next, the second associating unit 705 associates, for each mobile station subscriber, the centroid position information of the sector corresponding to the SAI in the subscriber position information with the subscriber position information (step S204). For example, the second associating unit 705 reads the centroid position information (here, the sector centroid latitude information and the sector centroid longitude information) of the sector corresponding to the SAI from the sector information storage unit 712, and displays the centroid position information of the sector. The information shown in FIG. 8C is obtained in association with the information shown in FIG. The information of FIG. 8C is sent to the distance calculation unit 706.

次に、抽出部７０７は、算出された帰宅距離が帰宅困難者判定のための所定の基準値（一例として１０ｋｍ）以上である加入者位置情報を抽出する（ステップＳ２０６）。そして、導出部７０８は、抽出された加入者位置情報の数を、ある帰宅距離単位ごと（一例として１ｋｍ）に分類・集計し、段階的に重み付け（一例として１０〜１１ｋｍは１０％、１１〜１２ｋｍは２０％、…、１９〜２０ｋｍは１００％）を掛け合わせて、当該加入者位置情報に含まれるＳＡＩに基づいてセクタごとに集計し、該集計値に人口数変換計数（一例としてシェア率の逆数）を掛けることでセクタごとの帰宅困難者数を導出する（ステップＳ２０７）。このとき導出部７０８は、抽出された加入者位置情報に関連付けられた属性情報に基づいて、当該加入者位置情報の数を、上記と同様に距離ごとに分類・重み付けしてセクタごとに属性別に集計し、該集計値に人口数変換計数（一例としてシェア率の逆数）を掛けることでセクタごとの属性別の帰宅困難者数を導出する。これにより、例えば、図９に示すように、セクタごとの帰宅困難者数と、その内訳として、男性の帰宅困難者数、女性の帰宅困難者数、年齢層および性別の帰宅困難者数（２０代男性の帰宅困難者数、２０代女性の帰宅困難者数など）を導出することができる。Next, the distance calculation unit 706 uses the address information (address latitude and address longitude) and sector gravity center position information (sector gravity latitude and sector gravity longitude) associated with each other as shown in FIG. (Distance between the above-mentioned located sector gravity center position and address) is calculated (step S205). For example, the return distance is calculated by the following equation (1). The latitude distance conversion coefficient and the longitude distance conversion coefficient may be uniform throughout the country or may vary depending on the region (for example, in the Tokyo area, the latitude distance conversion coefficient is 25.28 and the longitude distance conversion coefficient is 30.83). Thereby, the calculated return distance is further associated with the information of FIG. 8C, and the information of FIG. 8D is obtained.

Next, the extraction unit 707 extracts subscriber location information whose calculated return distance is equal to or greater than a predetermined reference value (for example, 10 km) for determining a person who has difficulty returning home (step S206). Then, the derivation unit 708 classifies and counts the number of extracted subscriber location information for each unit of home distance (1 km as an example), and weights in stages (10 to 11 km as an example is 10%, 11 to 11 km as an example) 12km is multiplied by 20%, ..., 19-20km is multiplied by 100%) and aggregated for each sector based on the SAI included in the subscriber location information, and the population conversion count (as an example, the share rate) The number of people who have difficulty returning home is derived for each sector (step S207). At this time, the deriving unit 708 classifies and weights the number of subscriber location information for each distance based on the attribute information associated with the extracted subscriber location information, and for each attribute for each sector. The total number is multiplied by the population conversion count (for example, the reciprocal of the share rate) and the number of people having difficulty returning home by attribute for each sector is derived. Thus, for example, as shown in FIG. 9, the number of people who are unable to return home by sector, and the breakdown is the number of men who are difficult to return home, the number of women who are difficult to return home, the number of people who are difficult to return home by age group and gender (20 The number of men in their teens who are difficult to return home, the number of women in their 20s who are difficult to return home, etc.) can be derived.

  Next, the conversion unit 709 converts the derived number of people difficult to return home for each sector to the number of people difficult to return home per mesh (step S208). Hereinafter, this conversion process will be outlined with reference to FIGS. First, the conversion unit 709 is reproduced based on a sector diagram (see FIG. 11A) reproduced based on the sector boundary information of each sector stored in the sector information storage unit 712 and a predetermined division rule. A dimensional mesh (see FIG. 11B) is synthesized to obtain a synthesized diagram as shown in FIG. 11C (step S208A).

Next, the conversion unit 709 divides each sector by the mesh boundary in the composite diagram (step S208B). For example, as shown in FIG. 12, sector A in FIG. 11A is divided into four divided sectors A-1, A-2, A-3, and A-4 by mesh boundaries. Then, the conversion unit 709 calculates the area of each divided sector (step S208C), and calculates the area ratio of each divided sector (step S208D). For example, as shown in FIG. 12, if 10 m ² , 50 m ² , 100 m ² , and 40 m ² are calculated as the areas of the divided sectors A-1, A-2, A-3, and A-4, respectively, the divided sector A As an area ratio (for example, percentage) of -1, A-2, A-3, and A-4, 5%, 25%, 50%, and 20% are calculated.

  Next, the conversion unit 709 calculates the number of people who have difficulty returning home in each divided sector (step S208E). For example, if the number of people who have difficulty returning home in sector A in FIG. 11A is 800, as shown in FIG. 13, the number of people having difficulty returning home in divided sector A-2 is 200 (that is, 800 × 25%). ) Is calculated. Similarly, if the number of people who have difficulty returning home in sectors B and C is 500 and 750, respectively, the number of people who have difficulty returning home in divided sector B-1 with an area ratio of 80% in sector B is 400 (ie 500 (Person × 80%) is calculated, and 600 persons (that is, 750 persons × 80%) are calculated as the number of persons having difficulty returning home in the divided sector C-4 having an area ratio of 80% in the sector C.

  Furthermore, the conversion unit 709 calculates the total number of people who have difficulty returning home by calculating the sum of the number of people who have difficulty returning home in a plurality of divided sectors included in one mesh (step S208F). In the example of FIG. 13, a total of 1200 people (that is, 200 + 400 + 600) of the number of people who have difficulty returning home in the divided sectors A-2, B-1, and C-4 included in one mesh is calculated. Let 1200 be the number of people who have difficulty returning home. As described above, the number of people who have difficulty returning home for each sector is converted into the number of people who have difficulty returning home for each mesh.

  Also, the number of people who have difficulty returning home for each sector by attribute can be converted to the number of people who have difficulty returning home by attribute by mesh.

  Returning to FIG. 7, in the last step S209, the output unit 710 outputs a map showing the distribution of the number of people having difficulty returning home as shown in FIG. 14 based on the number of people having difficulty returning home for each mesh after conversion. Similarly, the output unit 710 outputs a map showing the distribution of the number of people having difficulty returning home by attribute as shown in FIG. FIG. 15 shows the distribution of the number of people who have difficulty returning home for men in their 20s.

  By the information analysis process described above, a map (FIG. 14) showing the distribution of the number of people who have difficulty in returning home is output, so that it is possible to visually grasp the distribution situation of the number of people having difficulty in returning home with high accuracy. In addition, since a map (FIG. 15) showing the distribution of the number of difficult to return home by attribute is output, it is possible to visually grasp the distribution situation of the number of difficult to return home by attribute with high accuracy. Further, the information analysis process described above is executed every predetermined time, whereby the distribution situation of the number of people who have difficulty returning home, which changes every moment, can be grasped in a timely and accurate manner.

  As described above, according to the present embodiment, the number of people who have difficulty in returning home can be obtained more accurately based on the subscriber location information that can be acquired from the exchange 400, and the distribution of the number of people who have difficulty returning home can be obtained. The situation can be grasped in a timely and accurate manner.

[Second Embodiment]
The subscriber location information is not limited to the subscriber location information using the sector ID (SAI) in which the subscriber is located or the sector gravity center location described in the first embodiment. Therefore, in the second embodiment, processing based on subscriber location information (for example, GPS location information, PRACH-PD coordinate information, etc.) different from that in the first embodiment will be described.

  For example, when GPS position information is used, the information analysis process executed by the information analysis apparatus 700 is the process shown in FIG. In FIG. 16, the same processes as those in FIG. 7 are given the same numbers. That is, as shown in FIG. 17A, the subscriber position information acquired in step S201 includes GPS position information indicating the position (latitude, longitude) of the subscriber, and time information (time) when the GPS position information is acquired. Stamp) and the subscriber ID of the mobile subscriber.

  Further, as a result of the association in step S203, as shown in FIG. 17A, the subscriber location information is associated with information such as the subscriber's address information and sex / age. The information shown in FIG. 17B is sent to the distance calculation unit 706.

そして、ステップＳ２０６以降の処理は、第１実施形態と同様である。Next, the distance calculation unit 706 uses address information (address latitude and longitude) associated with each other as shown in FIG. 17B and GPS position information indicating the subscriber's position (latitude and longitude). The return distance (distance between the subscriber's position and address) is calculated (step S205X). For example, the return distance is calculated by the following equation (2). The latitude distance conversion coefficient and the longitude distance conversion coefficient may be uniform throughout the country or may vary depending on the region (for example, in the Tokyo area, the latitude distance conversion coefficient is 25.28 and the longitude distance conversion coefficient is 30.83). Thereby, the calculated return distance is further associated with the information of FIG. 17B, and the information of FIG. 17C is obtained.

And the process after step S206 is the same as that of 1st Embodiment.

  In addition, also when using PRACH-PD coordinate information, the number of those who cannot go home can be derived | led-out by the process similar to the above.

  By the way, when using GPS location information and PRACH-PD coordinate information as subscriber location information, it is not essential to convert the number of people counted in units of sectors to the number of people in units of meshes, without performing such conversion processing, As described below, the number of people in various area units (for example, city units, mesh units, sector units, etc.) can be derived.

  For example, as shown in FIG. 23, the information analysis apparatus 700 does not include the second associating unit 705 and the converting unit 709 that are the components of FIG. 5, but instead of the sector information storage unit 712, the area for each area A configuration including a boundary information storage unit 712A that stores boundary information is conceivable. With this configuration, the derivation unit 708 classifies and weights the number of extracted subscriber location information for each distance, and converts the location information of the subscriber location information into the area boundary information stored in the boundary information storage unit 712A. On the basis of this, the number corresponding to the subscriber position information can be totaled for each area in which the area is located, and the number of people who are unable to return home for each area can be derived based on the total value.

  A processing example in the information analysis apparatus 700 is shown in FIG. As shown in FIG. 24, steps S201 to S206 are the same as the processing of FIG. 16 described above. In step S207X, the derivation unit 708 classifies and weights the number of extracted subscriber location information for each distance, Based on the position information of the subscriber position information and the area boundary information stored in the boundary information storage unit 712A, the number corresponding to the subscriber position information is totaled for each area in the area, and the area based on the total value Deriving the number of people who have difficulty returning home. Then, the derived number of people having difficulty returning home is output, for example, in the form of a population distribution chart (step S209).

  In this way, the number of people who have difficulty returning home in various area units (for example, city units, mesh units, sector units, etc.) can be calculated without performing conversion processing to convert the number of people counted in sector units into the number of mesh units. Can be derived.

[Third Embodiment]
In the third embodiment, as an application example, (a-1) Deriving the number of people who are unable to return home by area (hereinafter referred to as “residence”) because this area corresponds to the address of those who have difficulty returning home. For example, (a-2) An example of deriving the number of people who have difficulty returning home for those who have difficulty returning home (hereinafter referred to as “location where people have difficulty returning home”). -1) An example of deriving the number of people who have difficulty returning home by location where it is difficult to return home, and (b-2) Deriving the number of people who have difficulty returning home in a location where it is difficult to return home An example will be described.

  First, (a-1) Example of deriving the number of people who have difficulty in returning home by location, and (a-2) Number of people having difficulty in returning home by location of people who have difficulty returning home, for those who have a certain location of residence as attribute information. An example of deriving is described. In step S207 of FIG. 7, the derivation unit 708 selects only the mobile unit subscriber in a certain residence from the address information in the attribute information associated with the subscriber location information extracted in step S206 as the distance is greater than or equal to the reference value. Based on the sector ID included in the subscriber location information extracted and acquired at the time of disaster occurrence (for example, the time zone from the disaster occurrence time to 10 minutes after the disaster occurrence time) regarding the extracted mobile station subscriber, The number of subscriber location information acquired at the time of the disaster is totaled for each sector where the disaster occurred. Accordingly, it is possible to derive the number of people who have difficulty in returning home by location where people who have difficulty in returning home who have a certain residence as attribute information (here, as an example, the sector where the disaster occurs). As a result, as shown in FIG. 18, for example, the number of people who have difficulty returning home at the time of the disaster for the mobile station subscriber whose city is A city is sector 3 (“sector The number of people who have difficulty in returning home (indicated by A-3) is 200, the number of in-service sectors is sector 1 (indicated by “sector A-1”) is 180, and so on. Here, as shown in FIG. 18, the number of people who are unable to return home may be rearranged in descending order for each sector in which they are located, so that the number of people who are unable to return home whose sector is sector 3 at the time of the disaster occurs. The number of people who have difficulty in returning home in the city of A, such as the number of people who have difficulty in returning home (180 people), followed by the number of people who have difficulty returning home (180 people). Breakdown of the sector in which the disaster occurred (ranking in order of the places where people have difficulty returning home) can be obtained. In addition, by counting the total number of subscriber location information acquired at the time of the disaster for the mobile subscribers in the above-mentioned certain residence, the total number of people who have difficulty returning home where the residence is the above-mentioned residence is derived. it can. For example, in FIG. 18, the total number of “1000 people” who have difficulty in returning home whose residence is A city is derived. The number of persons who have difficulty in returning home as shown in FIG. 18 can be obtained by executing the above processing for other residences (B city, C city,...). At this time, “1000” is derived as the total number of people who have difficulty returning home, where the residence is A city, and “13,000” is the total number of those who have difficulty returning home, where the residence is B city. By ranking the municipalities of residences in descending order of the total number, as shown on the right end of FIG. 18, the municipality ranking information from the people with the most total number of people who have difficulty returning home by residence is easy. Can get to. Here, an example of application to subscriber location information including a sector ID has been shown, but application to other subscriber location information (for example, subscriber location information including GPS location information) is also possible.

  Next, (b-1) an example of deriving the number of people who are unable to return home by location where people have difficulty returning home, and (b-2) the difficulty of returning home by location for those who have difficulty returning home in a location where they have difficulty returning home. An example of deriving the number of persons will be described. Here, for the sake of simplicity, subscriber location information including GPS location information is adopted, and the municipality (location where people who have difficulty returning home) can be derived from the subscriber location information. Suppose that In step S207 of FIG. 7, the derivation unit 708 determines the time from the occurrence of the disaster (for example, the time from the disaster occurrence time to 10 minutes after the disaster occurrence time) from the subscriber position information extracted as the distance is equal to or greater than the reference value in step S206. Only the subscriber location information acquired in the band) is extracted, and the number of the extracted subscriber location information is totaled for each place where it is difficult to return home derived from the subscriber location information. This makes it possible to derive the number of people who have difficulty in returning home by location where people who have difficulty returning home. In the example of FIG. 19, “120,000 people” are the total number of people who have difficulty returning home, and the number of people who have difficulty returning home are “150,000”. Is derived. Further, the derivation unit 708 refers to the address (residential area) information in the associated attribute information for the subscriber location information that is the municipality (for example, A city) where the difficult-to-return home location is derived. Then, for each residence, the number thereof (that is, the number of subscriber location information that is a city (for example, A city) where a person who has difficulty returning home is located) is totalized. As a result, as shown in FIG. 19, as a breakdown of the total number of “120,000 people” who have difficulty returning home, where the homecoming difficult place is A city, the residence is C city (shown as “AC city”). ) The number of persons who have difficulty in returning home is 25,000, the place of residence is B city (indicated by “AB city”), and the number of persons having difficulty returning home is 22,000. Here, as shown in FIG. 19, the number of people who have difficulty returning home may be rearranged in descending order according to the residential area. Next, the number of people who have difficulty returning home, such as the number of people who have difficulty returning home (22,000 people), whose residence is in B city, continues. Ranking). The number of people who have difficulty in returning home as shown in FIG. 19 can be obtained by executing the above processing for other places where people have difficulty returning home (B city, C city,...). On the other hand, as mentioned earlier, the total number of people who have difficulty returning home is “120,000”, and the total number of people who have difficulty returning home is “150,000”. As shown in the right end of FIG. 19, it is difficult to return home by location where people with difficulty returning home. You can easily obtain city ranking information from the person with the largest number of people. Here, an example of application to subscriber location information including GPS location information has been shown, but application to other subscriber location information (for example, subscriber location information including a sector ID) is also possible.

  Furthermore, from the municipality ranking information as shown in FIGS. 18 and 19, the municipality where the person who has difficulty in returning home (the place where the person who has difficulty returning home) is located and the person who has difficulty in returning home (returning home) The municipalities (for example, B city, D city, G city,...) That are higher in the (difficult place of occurrence of difficult person) can be easily identified, and as shown in FIG. Ranking information can be easily obtained. For example, for each target municipality, based on the value obtained by multiplying the number of people who are difficult to return home obtained in FIG. 18 and the number of people who are difficult to return home obtained in FIG. Thus, it is possible to easily obtain ranking information (for example, the first city is B city, the second city is G city, the third city is D city,...).

  In addition, the factors to be considered when obtaining the above-mentioned municipality ranking information as described above are limited to the number of people who have difficulty in returning home by number of residences and the number of people who have difficulty returning home by location where people have difficulty returning home. For example, the characteristics of each region can be analyzed and classified by further considering the region through which the person who has difficulty returning home passes.

  Also consider not only the areas where people who are unable to return home but also factors such as building damage assumption information (for example, information on hazard maps (fire, earthquake, liquefaction, land subsidence forecast maps)). Is also possible.

[Fourth Embodiment]
4th Embodiment demonstrates the example which extracts the area | region used as assistance object based on the number of persons having difficulty returning home, and a predetermined selection criterion as an application example. Various units (for example, a mesh unit, a sector unit, a municipality unit, etc.) can be employed as the “region” unit here, but an example in which the mesh unit is employed will be described below.

  As shown in FIG. 21, in addition to the configuration of the information analysis apparatus of the first embodiment, the information analysis apparatus 700 of the fourth embodiment has a predetermined number of people who have difficulty returning home for each mesh obtained by conversion by the conversion unit 709. A target mesh extraction unit 713 that extracts a mesh to be supported based on the selection criterion may be provided, or the latitude and longitude included in the mesh information and the subscriber position information may be calculated without using the conversion unit 709. Based on the subscriber position information for each mesh, conversion processing by the conversion unit 709 is not performed, and a mesh to be supported is extracted based on the number of people who have difficulty returning home for each mesh and a predetermined selection criterion. A target mesh extraction unit 713 may be provided. In addition, it is assumed that the information analysis apparatus 700 repeatedly derives the number of people who have difficulty returning home for each mesh every predetermined time.

  Here, the target mesh extraction unit 713 is based on the “maximum value” of the number of people having difficulty returning home for each of a plurality of meshes derived in a certain time zone (for example, a disaster occurrence time to a time zone after 3 hours have passed). You may extract the mesh more than the number of people as a support object mesh.

  In addition, the target mesh extraction unit 713 determines that the “average value” of the number of people who have difficulty returning home for each of a plurality of meshes derived in a certain time zone (for example, a disaster occurrence time to a time zone after 3 hours have passed) The above mesh may be extracted as a support target mesh.

  Also, since the area around the station is generally considered to be a region with high daytime population density, the number of people who have difficulty returning home is compared only for meshes that include stations. A mesh may be extracted as a target mesh.

  It should be noted that the region to be supported as described above may be extracted on a sector basis. In that case, as shown in FIG. 22, in addition to the configuration of the information analysis apparatus of the first embodiment, the information analysis apparatus 700 uses the number of people who have difficulty returning home and the predetermined selection criteria for each sector derived by the derivation unit 708. Based on this, a target sector extraction unit 714 that extracts sectors to be supported is provided. The target sector extraction unit 714 may compare the number of people who have difficulty in returning home only for the sector including the station, and may extract a predetermined number of sectors as the target sector from the number of people having difficulty returning home.

  In addition, based on the municipality ranking information from the people who have difficulty returning home by municipality where people who have difficulty returning home, as described in the third embodiment, the predetermined number of people who have difficulty returning home is predetermined. A mode in which a number of sectors are extracted as target sectors is also conceivable.

  In the embodiment described above, the “difficult to return home” is described as a person whose distance between the subscriber's position and address (residential area) is a predetermined reference value or more. However, such a definition of “difficult to return home” is not universal. For example, it is possible to flexibly determine whether or not the person has difficulty returning home according to the attribute (age group) of the subscriber as follows. May be. As an example, if a person over 65 years old is an elderly person, and a person aged 15-29 years old is a young person, in the case of an elderly person, the distance between the subscriber's location and the address will be more than 5 km, and those who have difficulty returning home will occur. When the distance is 10 km or more, all the persons who are difficult to return home, while in the case of young people, the above-mentioned distance is 20 km or more, those who are difficult to return are generated, and when the distance is 30 km or more, all are difficult to return home. May be defined.

  By the way, as a term related to “difficult to return home”, “short distance” corresponding to a person who is going out (person who is not at home or in the vicinity thereof) or a person who is going out but can go home on foot "Walkers" are known. Here, when the “difficult to return home” is defined as a person whose distance between the location of the subscriber and the address is not less than the first reference value (10 km as an example) for determining the return home difficult, The distance between the subscriber's position and the address can be defined as a person having a reference value (for example, 2 km) or more for the staying person determination set to be smaller than the first reference value. The number of stayers is equivalent to the number obtained by subtracting the number of homers (people at or near home) from the total number of subscribers. In addition, “Near-distance walking returnee” can be defined as a person who is not difficult to return home among the staying persons, and the number of short-distance walking returnees is equivalent to the number obtained by subtracting the number of difficult returnees from the staying number. .

  The number of staying persons can be derived by the same process as the process of deriving the number of persons who have difficulty returning home in the above-described embodiment, and the number of persons who have returned home on a short distance walk can be derived. For example, the extraction unit 707 in the information analysis apparatus 700 of FIG. 5 has a subscriber whose distance between the subscriber's position and the address is equal to or greater than a reference value for staying person determination set smaller than the first reference value. The position information is extracted, and the derivation unit 708 classifies and weights the number of extracted subscriber position information for each distance, and based on the position information and the sector boundary information in the subscriber position information, It is possible to count the number of subscriber location information for each and derive the number of staying persons for each sector based on the total value. In addition, the deriving unit 708 can derive the number of short-distance walking returners for each sector by subtracting the number of people who have difficulty returning home from the number of stayers for each sector.

Now, it is important to know the number of people who are expected to operate as rescue teams (hereinafter referred to as “support activity candidates”) in the event of a disaster. As a determination criterion for the support activity candidate, the distance between the subscriber's position and the address is the second reference value for determining the support activity candidate (for example, the same value as the first reference value for determining the person who has difficulty in returning home). ) Adopt the standard that the attribute information satisfies the predetermined condition for determining the support activity candidate (for example, the condition that the age falls within the age group of young people (for example, 20 to 35 years old)). Can do. The deriving unit 708 can derive the number of persons (support activity candidate) that satisfy the above-described criteria by the same process as the process of deriving the number of people who have difficulty in returning home in the above-described embodiment. Further, if the candidate for support activity is defined as “young people who are not difficult to go home”, the total number of subscribers x, the number y of people who are difficult to go home, and the proportion of young people in the total z are already known. The number of support activity candidates can be easily obtained by the equation (3).
Number of support activity candidates =
(Total number of subscribers x-number of people who have difficulty returning home) x percentage of youth in the total z (3)
The number of people who have difficulty returning home derived from the embodiment described above may be used for disaster prevention training. For example, based on the number of people who have difficulty returning home for each mesh, by placing as many people as necessary for disaster prevention drills and support activity candidates, it is possible to conduct disaster drills after reproducing the situation near the time of the disaster. It becomes possible to carry out.

  DESCRIPTION OF SYMBOLS 10 ... Communication system, 100 ... Mobile station, 200 ... Base station, 300 ... Radio control apparatus, 302 ... Communication control part, 400 ... Switch, 401 ... Communication control part, 402 ... Conversion part, 403 ... Memory | storage part, 500 ... Management Center, 501 ... Social sensor unit, 502 ... Petamining unit, 503 ... Mobile demography unit, 504 ... Visualization solution unit, 600 ... Subscriber profile information storage unit, 700 ... Information analysis device, 701 ... Designation unit, 702 ... Acquisition 703 ... Unification unit, 704 ... First association unit, 705 ... Second association unit, 706 ... Distance calculation unit, 707 ... Extraction unit, 708 ... Derivation unit, 709 ... Conversion unit, 710 ... Output unit 711 ... Subscriber attribute information storage unit 712 ... Sector information storage unit 712A ... Boundary information storage unit 713 ... Target mesh Out section, 714 ... target sector extraction unit.

An information analysis apparatus according to an aspect of the present invention is a subscriber attribute information storage unit that stores attribute information including address information of a mobile subscriber and the mobile subscriber's subscriber identification information in association with each other. Location information indicating the location of the mobile subscriber, time information at which the location is specified, and subscriber location information including subscriber identification information of the mobile subscriber, wherein the time is the target time zone. An acquisition unit that acquires the subscriber location information within a range, and when the acquisition unit acquires a plurality of subscriber location information for the same mobile station subscriber, the plurality of subscriber locations based on a predetermined rule The unification unit that narrows down the information to the equivalent of the single subscriber location information, and the subscriber identification information equivalent to the subscriber attribute information and the subscriber attribute information in common with the subscriber identification information for each mobile subscriber The attribute information stored in the storage unit. A first associating unit to be attached, a boundary information storing unit storing area boundary information for each area, and for each mobile subscriber, corresponding to subscriber location information and attribute information associated with the subscriber location information A distance calculating unit that calculates a distance between the location of the mobile subscriber and the address based on the address information in the mobile phone, and the subscription in which the calculated distance is equal to or greater than a first reference value for determining a person who has difficulty returning home An extraction unit for extracting the equivalent of the subscriber location information, and classifying and weighting the number corresponding to the extracted subscriber location information for each distance, and based on the location information equivalent to the subscriber location information and the area boundary information And a derivation unit that counts the number corresponding to the subscriber location information for each area and derives the number of people who have difficulty returning home based on the total value, and the extraction unit has the calculated distance as follows: From the first reference value The subscriber position information equivalent to a third reference value for determining a staying person set to a small value is extracted, and the derivation unit classifies and weights the number corresponding to the extracted subscriber position information for each distance. Then, based on the position information corresponding to the subscriber position information and the area boundary information, the number corresponding to the subscriber position information is totaled for each area in the area, and the number of stayers per area based on the total value The deriving unit derives the number of people walking home for each area by subtracting the number of people who have difficulty returning home from the number of people staying in each area .

In the information analyzing apparatus, the acquisition unit is subscriber location information including location information indicating a location of the mobile subscriber, time information at which the location is specified, and subscriber identification information of the mobile subscriber. When the subscriber location information whose time is within the range of the target time zone is acquired, and the unifying unit acquires a plurality of subscriber location information for the same mobile station subscriber by the acquisition unit, the predetermined rule The plurality of subscriber location information is narrowed down to a single subscriber location information. Then, the first associating unit associates, for each mobile station subscriber, the subscriber position information after unification and the attribute information stored in the subscriber attribute information storage unit, in which the subscriber identification information is common. And, for each mobile subscriber, the distance calculation unit, based on the subscriber location information equivalent and the address information in the attribute information associated with the subscriber location information equivalent, the location and address of the mobile subscriber Calculate the distance between them. Further, the extraction unit extracts the subscriber position information equivalent that the calculated distance is equal to or greater than the first reference value for the person who has difficulty in returning home, and the derivation unit calculates the number corresponding to the extracted subscriber position information. Based on the location information equivalent to the subscriber location information and the area boundary information, the number corresponding to the subscriber location information is totaled for each area in the area, and the area based on the total value is classified and weighted for each distance. Deriving the number of people who have difficulty returning home. In this way, the information analysis device calculates the so-called return distance based on the subscriber location information equivalent of the mobile subscriber and the address information, and extracts the equivalent of the subscriber location information whose return distance is a reference value or more, In order to derive the number of people who are unable to go home from the number corresponding to the extracted subscriber location information, the number of people who were at a place different from the place of work at the time of disaster and the number of people who visited for shopping or sightseeing purposes were considered In addition, it is possible to obtain the number of people who have difficulty in returning home, which is much more accurate than before, and to grasp the distribution situation of the number of people who have difficulty returning home in a timely and accurate manner. The above “equivalent to subscriber location information” is not only the subscriber location information itself but also information corresponding to the subscriber location information (for example, a weight value obtained by weight addition used in the current population distribution, etc.) It is a concept that includes The weight values exemplified here will be described later. By the way, as a term related to “difficult to return home”, “short distance” corresponding to a person who is going out (person who is not at home or in the vicinity thereof) or a person who is going out but can go home on foot "Walkers" are known. Therefore, in the information analysis apparatus, the extraction unit extracts the equivalent of the subscriber position information whose calculated distance is equal to or greater than the third reference value for staying person determination set to be smaller than the first reference value. The derivation unit classifies and weights the number corresponding to the extracted subscriber position information for each distance, and the subscriber for each area in the area based on the position information corresponding to the subscriber position information and the area boundary information. The number corresponding to the position information is totaled, and the number of stayers for each area is derived based on the total value. In this case, the number of stayers can be obtained. Moreover, in the information analysis apparatus, the deriving unit derives the number of short-distance walking walkers for each area by subtracting the number of people who have difficulty in returning from the number of staying persons for each area. In this case, it is possible to obtain the number of people returning home on foot from a short distance.

An information analysis method according to an aspect of the present invention includes a subscriber attribute information storage unit that stores attribute information including address information of a mobile station subscriber and subscriber identification information of the mobile station subscriber in association with each other, and , An information analysis method executed by an information analysis apparatus comprising a boundary information storage unit that stores area boundary information for each area, wherein the position information indicating the position of the mobile subscriber is identified An acquisition step of acquiring the subscriber location information including time information and subscriber identification information of the mobile subscriber, wherein the time is within a target time zone; and When a plurality of pieces of subscriber location information are acquired for the same mobile station subscriber by the step, a unified step of narrowing down the plurality of subscriber location information to a single subscriber location information based on a predetermined rule. And, for each mobile subscriber, a first association step of associating the equivalent subscriber position information equivalent to the attribute information stored in the subscriber attribute information storage unit with the same subscriber identification information For each mobile subscriber, the distance between the mobile subscriber location and the address is calculated based on the subscriber location information equivalent and the address information in the attribute information associated with the subscriber location information equivalent. A distance calculating step, an extracting step for extracting the subscriber position information equivalent to the calculated reference distance being equal to or greater than the first reference value for determining a person who has difficulty in returning home, and a number corresponding to the extracted subscriber position information. Classified and weighted for each area, and based on the position information corresponding to the subscriber position information and the area boundary information, the number corresponding to the subscriber position information is totaled for each area in the area, and the area based on the total value Every It includes a derivation step of deriving the stranded commuters number, and in the extraction step, the information analyzer the calculated distance is, for Sojourners determination that is set smaller than the first reference value the The subscriber position information corresponding to the reference value of 3 or more is extracted, and in the derivation step, the information analyzing apparatus classifies and weights the number corresponding to the extracted subscriber position information for each distance, Based on the position information corresponding to information and the area boundary information, the number corresponding to the subscriber position information is totaled for each area in which the area is located, and the number of staying persons for each area is derived based on the total value, In the derivation step, the information analysis device derives the number of short-distance walking homes for each area by subtracting the number of people who have difficulty returning home from the number of people staying in each area .

Claims (24)

A subscriber attribute information storage unit that stores attribute information including address information of mobile subscribers and subscriber identification information of the mobile subscribers in association with each other;
Location information indicating the location of the mobile subscriber, time information at which the location is specified, and subscriber location information including subscriber identification information of the mobile subscriber, wherein the time is within a target time range. An acquisition unit for acquiring the subscriber location information in
A single unit for narrowing down the plurality of subscriber location information to a single subscriber location information based on a predetermined rule when a plurality of subscriber location information is acquired for the same mobile station subscriber by the acquisition unit When,
A first associating unit that associates the subscriber position information after unification with the attribute information stored in the subscriber attribute information storage unit, for each mobile subscriber, with the same subscriber identification information;
A boundary information storage unit that stores area boundary information for each area;
For each mobile subscriber, the distance for calculating the distance between the location of the mobile subscriber and the address based on the subscriber location information equivalent and the address information in the attribute information associated with the subscriber location information equivalent A calculation unit;
An extraction unit for extracting the equivalent of the subscriber location information whose calculated distance is equal to or greater than a first reference value for determining a person who has difficulty returning home;
The number corresponding to the extracted subscriber location information is classified and weighted for each distance, and based on the location information equivalent to the subscriber location information and the area boundary information, the subscriber location information equivalent A derivation unit that counts the number and derives the number of people who have difficulty returning home based on the total value;
An information analysis apparatus comprising: The derivation unit includes:
Based on the attribute information associated with the extracted subscriber location information equivalent, the number corresponding to the subscriber location information for each area in the area is aggregated for each attribute, and it is difficult to return home for each area based on the aggregate value Deriving the number of people by attribute,
The information analysis apparatus according to claim 1. The derivation unit includes:
Based on the address information in the attribute information associated with the subscriber location information equivalent at the time of the disaster, derive the number of people who have difficulty returning home by residence.
The information analysis apparatus according to claim 2. The derivation unit includes:
Based on the equivalent of subscriber location information at the time of disaster for a person who has difficulty returning home in a certain residence, the number corresponding to subscriber location information is counted for each area where the person who has difficulty returning home is located, Deriving the number of people who are unable to return home in a certain residential area,
The information analysis apparatus according to claim 3. The derivation unit includes:
Based on the subscriber location information equivalent at the time of the disaster, derive the number of people who have difficulty returning home by area where the mobile subscribers were located when the disaster occurred.
The information analysis apparatus according to claim 2. The derivation unit includes:
Based on the subscriber location information equivalent at the time of the disaster for a person who has difficulty returning home in the area at the time of the disaster, refer to the address information in the attribute information associated with the subscriber location information equivalent, Deriving the number of people who are unable to return home by location of those who have difficulty returning home in the area at the time of the disaster,
The information analysis apparatus according to claim 5. The derivation unit includes:
Based on the address information in the attribute information associated with the subscriber location information equivalent at the time of the disaster, the number of people who are unable to return home is derived by location, and the location information equivalent to the subscriber location information at the time of the disaster Based on the number of people who have difficulty returning home by area,
Deriving areas where both the number of people who have difficulty returning home and the number of people who have difficulty returning home by area of residence and the number of people who have difficulty returning home by area where the disaster occurred
The information analysis apparatus according to claim 2. The derivation unit includes:
For each area, the calculated distance is equal to or less than a second reference value for support activity candidate determination, and the attribute information corresponds to subscriber location information that satisfies a predetermined condition for support activity candidate determination. And the number corresponding to the subscriber location information for each area is derived as the number of support activity candidates based on the total value.
The information analysis apparatus according to claim 2. The extraction unit includes:
The calculated distance is equal to or more than the third reference value for staying person determination set smaller than the first reference value, and the subscriber position information equivalent is extracted,
The derivation unit includes:
The number corresponding to the extracted subscriber location information is classified and weighted for each distance, and based on the location information equivalent to the subscriber location information and the area boundary information, the subscriber location information equivalent Count the number and derive the number of stayers for each area based on the total value,
The information analysis apparatus according to any one of claims 1 to 8, wherein: The derivation unit includes:
By subtracting the number of people who are unable to return home from the number of people staying for each area, the number of people returning home walking on a short distance basis for each area is derived.
The information analysis apparatus according to claim 9. The information analyzer is
A target area extraction unit that extracts areas to be supported based on the derived number of people who have difficulty returning home and a predetermined selection criterion;
The information analysis apparatus according to any one of claims 1 to 10, further comprising: The location information indicating the location of the mobile subscriber is represented by latitude and longitude,
The first association unit converts the address information in the attribute information to be associated with the subscriber position information equivalent into a format represented by latitude and longitude, and converts the attribute information including the converted address information into the format Associate with subscriber location information equivalent,
The information analysis apparatus according to any one of claims 1 to 11, wherein: The information analyzer is
An output unit that outputs a map showing the distribution of the number of people having difficulty returning home, based on the number of people having difficulty returning home in each area,
The information analysis apparatus according to any one of claims 1 to 12, further comprising: The area is
The information analysis apparatus according to any one of claims 1 to 13, wherein the information analysis apparatus is one of division units for dividing land, including sectors, meshes, and municipalities. The area is a sector;
The information analyzer is
The derived number of people who are unable to return home for each sector is converted into the number of people who are difficult to return home for each mesh that is partitioned based on a predetermined partitioning rule. A conversion unit for conversion based on the positional relationship of
The information analysis device according to any one of claims 1 to 14, further comprising: A subscriber attribute information storage unit that stores attribute information including address information of mobile subscribers and subscriber identification information of the mobile subscribers in association with each other;
Sector identification information indicating a sector in which the mobile station subscriber is located, time information for identifying the sector in which the mobile station is located, and subscriber location information including subscriber identification information of the mobile station subscriber, An acquisition unit for acquiring the subscriber location information whose time is within a target time zone; and
A single unit for narrowing down the plurality of subscriber location information to a single subscriber location information based on a predetermined rule when a plurality of subscriber location information is acquired for the same mobile station subscriber by the acquisition unit When,
A first associating unit that associates the subscriber location information after unification and the attribute information stored in the subscriber attribute information storage unit, with the same subscriber identification information, for each mobile subscriber;
A sector information storage unit storing the gravity center position information and sector boundary information for each sector;
For each mobile station subscriber, a second associating unit associating the subscriber location information with the gravity center location information of the sector corresponding to the sector identification information in the subscriber location information;
For the attribute information and sector centroid position information associated by common subscriber position information, based on the sector centroid position information and the address information in the attribute information, the distance between the sector centroid position and the address A distance calculation unit for calculating
An extraction unit for extracting the subscriber location information whose calculated distance is equal to or greater than a predetermined reference value for determining a person who is difficult to return;
The number of extracted subscriber location information is classified and weighted for each distance, aggregated for each sector based on sector identification information included in the subscriber location information, and those who have difficulty returning home for each sector based on the aggregate value A derivation unit for deriving a number;
The derived number of people who have difficulty in returning home for each sector is converted into the number of people who have difficulty in returning home for each mesh partitioned based on a predetermined partitioning rule, and the sector and the mesh obtained from the sector boundary information and the partitioning rule. A conversion unit for conversion based on the positional relationship of
An output unit that outputs a map showing the distribution of the number of people having difficulty returning home based on the number of people having difficulty returning home for each mesh after conversion,
An information analysis apparatus comprising: The derivation unit includes:
Based on the attribute information associated with the extracted subscriber location information, the number of the subscriber location information is classified and weighted for each distance and aggregated for each sector, and for each sector based on the aggregate value Deriving the number of people who have difficulty returning home by attribute
The information analysis apparatus according to claim 16. The gravity center position information of the sector is represented by latitude and longitude,
The first association unit converts address information in attribute information to be associated with the subscriber location information into a format represented by latitude and longitude, and converts the attribute information including the converted address information into the subscription Associated with the location information,
The information analysis apparatus according to claim 16 or 17, characterized in that: The information analyzer is
A designating unit for designating the target time zone;
The information analysis apparatus according to any one of claims 1 to 18, further comprising: Subscriber attribute information storage unit storing attribute information including address information of mobile unit subscriber and subscriber identification information of mobile unit subscriber in association with each other, and boundary storing area boundary information for each area An information analysis method executed by an information analysis device comprising an information storage unit,
Location information indicating the location of the mobile subscriber, time information at which the location is specified, and subscriber location information including subscriber identification information of the mobile subscriber, wherein the time is within a target time range. Obtaining the subscriber location information within;
If a plurality of subscriber location information is acquired for the same mobile station subscriber by the acquisition step, a single step for narrowing down the plurality of subscriber location information to a single subscriber location information based on a predetermined rule When,
A first associating step for associating the subscriber position information equivalent to the unified subscriber information and the attribute information stored in the subscriber attribute information storage unit, with the same subscriber identification information, for each mobile subscriber;
For each mobile subscriber, the distance for calculating the distance between the location of the mobile subscriber and the address based on the subscriber location information equivalent and the address information in the attribute information associated with the subscriber location information equivalent A calculation step;
An extraction step of extracting the equivalent of the subscriber location information, wherein the calculated distance is equal to or greater than a first reference value for determining a person who has difficulty in returning home;
The number corresponding to the extracted subscriber location information is classified and weighted for each distance, and based on the location information equivalent to the subscriber location information and the area boundary information, the subscriber location information equivalent A derivation step of counting the number and deriving the number of people having difficulty returning home for each area based on the total value;
An information analysis method comprising: In the derivation step, the information analysis device includes:
Based on the attribute information associated with the extracted subscriber location information equivalent, the number corresponding to the subscriber location information for each area in the area is aggregated for each attribute, and it is difficult to return home for each area based on the aggregate value Deriving the number of people by attribute,
The information analysis method according to claim 20, wherein: Subscriber attribute information storage unit storing attribute information including address information of mobile station subscribers and subscriber identification information of the mobile station subscribers, and gravity center position information and sector boundary information for each sector An information analysis method executed by an information analysis apparatus comprising a sector information storage unit storing
Sector identification information indicating a sector in which the mobile station subscriber is located, time information for identifying the sector in which the mobile station is located, and subscriber location information including subscriber identification information of the mobile station subscriber, An acquisition step of acquiring the subscriber location information whose time is within the range of the target time zone;
If a plurality of subscriber location information is acquired for the same mobile station subscriber by the acquisition step, a single step for narrowing down the plurality of subscriber location information to a single subscriber location information based on a predetermined rule When,
A first associating step for associating the subscriber location information after unification and the attribute information stored in the subscriber attribute information storage unit, with the same subscriber identification information, for each mobile subscriber;
For each mobile station subscriber, a second associating step associating the subscriber location information with the centroid location information of the sector corresponding to the sector identification information in the subscriber location information;
For the attribute information and sector centroid position information associated by common subscriber position information, based on the sector centroid position information and the address information in the attribute information, the distance between the sector centroid position and the address A distance calculating step for calculating
An extraction step of extracting the subscriber location information whose calculated distance is equal to or greater than a predetermined reference value for determining a person who is difficult to return; and
The number of extracted subscriber location information is classified and weighted for each distance, aggregated for each sector based on sector identification information included in the subscriber location information, and those who have difficulty returning home for each sector based on the aggregate value A derivation step for deriving a number;
The derived number of people who are unable to return home for each sector is converted into the number of people who are difficult to return home for each mesh that is partitioned based on a predetermined partitioning rule. A conversion step for converting based on the positional relationship of
An output step for outputting a map showing the distribution of the number of people having difficulty returning home based on the number of people having difficulty returning home for each mesh after conversion;
An information analysis method comprising: In the derivation step, the information analysis device includes:
Based on the attribute information associated with the extracted subscriber location information, the number of the subscriber location information is classified and weighted for each distance and aggregated for each sector, and for each sector based on the aggregate value Deriving the number of people who have difficulty returning home by attribute
The information analysis method according to claim 22. The information analysis method includes:
The information analysis method according to claim 20, wherein the information analysis method is executed every predetermined time by the information analysis apparatus.

Images