Information generation device, information generation system, information generation method, and information generation program

Abstract

According to the present invention, an information generation device acquires: past event information that includes information about the location at which a predetermined event occurred; and supplemental information about the location. Then the information generation device generates simulated event information that simulates the occurrence of an event within a prescribed area at a location within the prescribed area that is based on the location included in the past event information and the supplemental information about the location.

Description

[Technical field]

[0001] The disclosed technology relates to an information generating device, an information generating system, an information generating method, and an information generating program. [Background technology]

[0002] There is a technology that calculates the occurrence probability of a predetermined event for each latitude and longitude, estimates a two-dimensional distribution of the occurrence probability of the event, creates multiple pseudo occurrence patterns of the event from the estimated two-dimensional distribution of occurrence probability, and uses the results in a simulation.

[0003] As a technique for estimating the two-dimensional distribution of the probability of occurrence of an event as described above, a technique using kernel density estimation has been proposed. For example, Non-Patent Document 1 discloses a technique for visualizing piracy hotspots occurring in the waters around Somalia by constructing a spatial database of piracy incidents that occurred in the waters around Somalia, which is the subject of analysis, and then performing spatial interpolation using kernel density estimation to grasp the geographical distribution characteristics of pirate sightings. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Yasuhiro Nagata, Daisuke Watanabe, Shigeki Toriumi, "Spatial Analysis of Piracy in the Waters around Somalia Using Kernel Density Estimation," Proceedings of the Geographic Information System Association Conference (CD-ROM), 2013. Summary of the Invention [Problem to be solved by the invention]

[0005] As described above, in the case of creating a pseudo occurrence pattern of an event, it is possible to apply kernel density estimation as described in Non-Patent Document 1 when estimating a two-dimensional distribution of the occurrence probability of the event. In this case, generally, kernel density estimation is performed on a data string of past event occurrence positions under the relevant conditions, and the density for each coordinate is calculated to estimate a two-dimensional distribution of the occurrence probability of the event. Then, a pseudo occurrence pattern of the event is created according to the estimated two-dimensional distribution. In this case, there is a problem that the calculation of kernel density estimation takes time.

[0006] Furthermore, in kernel density estimation, for example, when determining the occurrence probability between past event occurrence points, interpolation processing is often performed by applying a single interpolation method such as Gaussian distribution. However, if the occurrence probability between event occurrence points is uniformly interpolated using a Gaussian distribution, reality may not be appropriately reflected, and an inaccurate two-dimensional distribution of occurrence probability may be estimated.

[0007] The disclosed technology has been made in consideration of the above-mentioned points, and aims to provide an information generation device, an information generation system, an information generation method, and an information generation program that can generate pseudo-event information that simulates the occurrence of a predetermined event more quickly and in a manner that more accurately reflects the occurrence of a real event. [Means for solving the problem]

[0008] A first aspect of the present disclosure is an information generating device that includes an acquisition unit that acquires past event information including information on a location where a predetermined event occurred and associated information related to the location, and a generation unit that generates pseudo-event information that simulates the occurrence of the event within a specified area, the pseudo-event information having an occurrence location that is a location included in the past event information and a location within the specified area based on the associated information related to the location.

[0009] A second aspect of the present disclosure is an information generation system including an acquisition unit that acquires past event information including information on a location where a predetermined event occurred and associated information related to the location, and a generation unit that generates pseudo-event information that simulates the occurrence of the event within a specified area, the pseudo-event information having an occurrence location that is a location included in the past event information and a location within the specified area based on the associated information related to the location.

[0010] A third aspect of the present disclosure is an information generating method including: an acquisition unit acquires past event information including information on a location where a predetermined event occurred and associated information related to the location; and a generation unit generates pseudo-event information that simulates the occurrence of the event within a specified area, the pseudo-event information having an occurrence location that is a location included in the past event information and a location within the specified area based on the associated information related to the location.

[0011] A fourth aspect of the present disclosure is an information generation program for causing a computer to function as each unit of the information generation device of the first aspect. Effect of the Invention

[0012] According to the disclosed technology, simulated event information that simulates the occurrence of a predetermined event can be generated more quickly and in a manner that more closely reflects the occurrence of a real event. [Brief description of the drawings]

[0013] [Figure 1] FIG. 2 is a schematic diagram illustrating an example of a hardware configuration of an information generating system according to an embodiment. [Diagram 2] 1 is a block diagram showing an example of a functional configuration of an information generating device according to an embodiment. [Diagram 3] FIG. 2 is a schematic diagram showing an example of a configuration of an event database according to the embodiment. [Figure 4] 4A and 4B are schematic diagrams illustrating an example of a region including a predetermined region according to the embodiment. [Diagram 5]FIG. 13 is a schematic diagram showing an example of calculating the bias in distribution of the probability of injury or illness by kernel density estimation; [Figure 6] 11A and 11B are schematic diagrams illustrating an example of a case where a selection position is selected according to the embodiment. [Figure 7] 11 is a schematic diagram showing an example of a case where simulated event information is generated depending on the type of facility related to a selected position according to the embodiment. FIG. [Figure 8] 11 is a flowchart illustrating an example of information generation processing in the information generating device according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, an example of an embodiment of the disclosed technology will be described with reference to the drawings. In each drawing, the same or equivalent components and parts are given the same reference numerals. In addition, the dimensional ratios of the drawings are exaggerated for the convenience of explanation and may differ from the actual ratios.

[0015] 1, the information generating system 1 includes an information generating device 10. The information generating device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, and a communication I / F (Interface) 17. Each component is connected to each other via a bus 19 so as to be able to communicate with each other.

[0016] The CPU 11 is a central processing unit, and executes various programs and controls each part. That is, the CPU 11 reads a program from the ROM 12 or the storage 14, and executes the program using the RAM 13 as a working area. The CPU 11 controls each of the above components and performs various arithmetic processing according to the program stored in the ROM 12 or the storage 14. In this embodiment, an information generating program is stored in the ROM 12 or the storage 14. The information generating program may be one program, or may be a group of programs composed of multiple programs or modules.

[0017] The ROM 12 stores various programs and various data. The RAM 13 temporarily stores programs or data as a working area. The storage 14 is configured with an HDD (Hard Disk Drive) or an SSD (Solid State Drive) and stores various programs including an operating system and various data.

[0018] The communication I / F 17 is an interface for communicating with other devices, and uses standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark).

[0019] Next, a description will be given of the functional configuration of the information generating device 10. As shown in Fig. 2, the information generating device 10 includes an acquiring unit 101 and a generating unit 102. Furthermore, the information generating device 10 includes an event database 120.

[0020] The acquiring unit 101 acquires past event information including information on a location where a predetermined event occurred in a specified area, and associated information related to the location, from the event database 120. The acquiring unit 101 may acquire past event information including information on a location where a predetermined event occurred in a specified area and in a specified time period, and associated information related to the location. In this embodiment, the latitude and longitude of the location where the injured person occurred, and the date and time when the injured person occurred are applied as the event information. In addition, in this embodiment, the type of facility where the injured person occurred is applied as the associated information. Hereinafter, the event information and the associated information are also referred to as "occurrence information".

[0021] As shown in FIG. 3, the event database 120 is a database in which the latitude and longitude of the location where the injured person occurred and the associated information are stored for each date and time when the injured person occurred. In FIG. 3, each row represents the occurrence information. In the event database 120 shown in FIG. 3, information indicating whether the type of the facility where the injured person occurred is a first type or a second type in which the probability of the injured person occurring around the location where the injured person occurred is lower than the first type is stored as the associated information. In this embodiment, a house is applied as the first type, and a hospital is applied as the second type. However, the present invention is not limited to these examples. For example, a building may be applied as the first type, and a school or the like may be applied as the second type. In addition, the types of facilities are not limited to two types, and three or more types may be set.

[0022] The acquisition unit 101 also acquires the occurrence probability of an injured person meeting a specific condition in a predetermined area based on the event information stored in the event database 120. Specifically, the acquisition unit 101 acquires the number of injured people meeting the specific condition among the injured people who occurred in a predetermined area during a predetermined period from the event database 120. For example, the specific condition is that the occurrence date and time is a specific time period. In this case, the acquisition unit 101 counts the number of event information whose date and time are included in the specific time period and whose position represented by latitude and longitude is included in the predetermined area among the event information for the predetermined period stored in the event database 120. Note that the specific condition is not limited to the above example, and may be that the occurrence date and time is a specific day of the week. The acquisition unit 101 then acquires the occurrence probability of an injured person by dividing the number of injured people by the above-mentioned predetermined period. For example, it is assumed that the predetermined period is the past 100 days, the specific area is a specific district represented by a rectangular area, and the specific time period is between 9:00 and 9:00, and there are 10 pieces of corresponding event information in the event database 120. In this case, the probability of an injury occurring in a particular area between 9:00 and 9:00 is 0.1.

[0023] The acquisition unit 101 passes the occurrence information and the probability of occurrence of an injured person in a predetermined area acquired from the event database 120 to the generation unit 102 .

[0024] The generation unit 102 generates pseudo-event information that simulates the occurrence of an injury or illness within a specified area, and generates pseudo-event information with the occurrence position being a position within the specified area based on the occurrence information within the specified area acquired by the acquisition unit 101.

[0025] Specifically, in order to determine the number of pieces of simulated event information to be generated, the generating unit 102 uses the occurrence probability of the injured or sick person acquired by the acquiring unit 101 to construct an occurrence prediction model of the injured or sick person assuming that the probability distribution of the number of injured or sick person occurrences follows, for example, a Poisson distribution. The generating unit 102 uses this occurrence prediction model to generate a simulated number of injured or sick person occurrences that meet specific conditions in a specified area. It is assumed that the occurrence prediction model constructed based on the occurrence probability of the injured or sick person in the 9 o'clock hour in the above-mentioned specific area simulates multiple cases of the occurrence of the injured or sick person in the 9 o'clock hour in the area. In this case, in many cases, the number of injured or sick person occurrences is 0, but in rare cases, one occurrence is simulated, and even more rarely, two occurrences are simulated in one case.

[0026] Furthermore, in order to determine the occurrence position of each piece of pseudo-event information to be generated, the generating unit 102 randomly selects one piece of occurrence information from the occurrence information within the predetermined area acquired by the acquiring unit 101. Hereinafter, the occurrence information selected by the generating unit 102 is referred to as selected information. Then, when the type of facility indicated by the incidental information in the selected information is a house, the generating unit 102 generates pseudo-event information having a position where noise is added to a position corresponding to the selected information (hereinafter referred to as a "selected position"). Specifically, the generating unit 102 generates pseudo-event information having a position where a occurrence position is represented by latitude and longitude obtained by adding a predetermined value to at least one of the latitude and longitude of the selected position. More specifically, the generating unit 102 adds a value generated by random numbers within a numerical range equivalent to a certain range (for example, from -200m to +200m) of latitude and longitude to at least one of the latitude and longitude of the selected position. When the type of facility indicated by the supplementary information in the selected information is a house, the occurrence location is highly likely to be a residential area, and therefore the occurrence of injured or ill persons is not limited to the past occurrence location, but may occur from other houses in the residential area. In other words, there is a high possibility that injured or ill persons will occur in the vicinity of the selected location as well. This process increases the possibility that occurrence information from locations with a large number of injured or ill persons will be selected at the stage when the generating unit 102 selects the selected information, and furthermore, a process is performed to shift the occurrence location from the selected location, so that pseudo occurrence of injured or ill persons in other houses can be appropriately simulated.

[0027] On the other hand, when the type of facility indicated by the incidental information in the selection information is a hospital, the generating unit 102 generates pseudo-event information with the selected position as the occurrence position. When the type of facility indicated by the incidental information in the selection information is a hospital, it is unlikely that injured or ill people are likely to occur in facilities other than the hospital around the hospital. In other words, the possibility of injured or ill people occurring around the selected position is low. This process makes it possible to more accurately simulate the occurrence of injured or ill people in hospitals. In particular, the proportion of the number of injured or ill people occurring in hospitals is high among the total number of injured or ill people. Therefore, by storing the incidental information indicating the facility where the injured or ill person occurred in the event database 120 as described above, distinguishing between hospitals and other facilities, it is possible to more accurately simulate the occurrence of injured or ill people in hospitals.

[0028] Here, if the occurrence probability is distributed uniformly within a predetermined region, the occurrence position of the pseudo event information may be generated, for example, randomly within the predetermined region. However, in reality, it is difficult to imagine that the occurrence probability is distributed uniformly within the predetermined region, and the occurrence probability should be biased depending on the position within the predetermined region. For comparison, a case where the bias of the occurrence probability of an event depending on the position is calculated by kernel density estimation will be described with reference to Figs. 4 and 5. Fig. 4 is a map showing an area including a predetermined region (in the example shown in Fig. 4, the area surrounded by diagonal lines) in which the occurrence of an injured person is simulated. When kernel density estimation is performed, as shown in Fig. 5, kernel density estimation is performed based on the position where an injured person has occurred in the past within the predetermined region shown in Fig. 4 (in the example shown in Fig. 5, the position indicated by a cross mark) to calculate the bias of the distribution of the occurrence probability of an injured person within the predetermined region. In addition, when calculating the bias of the distribution of the occurrence probability, an interpolation process is performed by applying a single interpolation method such as Gaussian distribution, and the occurrence probability between the positions where an injured person has occurred in the past is calculated. In the example of FIG. 5, the occurrence probability of each position within a predetermined area is represented by the density of the hatching, and the darker the density of the hatching, the higher the occurrence probability. When performing kernel density estimation, there is a problem that the more positions where injuries have occurred in the past or the higher the resolution of the two-dimensional plane, the more time it takes. In addition, when a single interpolation method is applied to perform the interpolation process, the bias in the positions of the occurrence probability may not reflect reality. Therefore, there is a problem that the pseudo-event information generated using the occurrence probability may also be inaccurate because it does not reflect reality.

[0029] Next, a case where the pseudo-event information is generated in this embodiment will be described with reference to FIG. 4, FIG. 6, and FIG. 7. In this embodiment, as shown in FIG. 6, first, a selected position (a position represented by a circled cross in the example shown in FIG. 6) is selected from positions where an injured person has previously occurred in the specified area shown in FIG. 4 (positions represented by a cross in the example shown in FIG. 6). Then, as shown in FIG. 7, when the type of facility at the selected position is a house, pseudo-event information is generated for a position where noise is added to the selected position (position represented by a triangle in the example shown in FIG. 7). Note that, in the example shown in FIG. 7, α is added in the longitude direction and β is added in the latitude direction as noise. On the other hand, when the type of facility at the selected position is a hospital, pseudo-event information is generated with the selected position as the occurrence position (position represented by a triangle in the example shown in FIG. 7). This process makes it possible to accurately grasp the bias due to the position of the occurrence probability of an injured person in the specified area and generate pseudo-event information.

[0030] Next, the operation of the information generating device 10 will be described.

[0031] 8 is a flowchart showing the flow of processing by the information generating device 10. The CPU 11 reads out an information generating program from the ROM 12 or the storage 14, loads it into the RAM 13, and executes it, thereby performing the information generating process.

[0032] In step S101, the CPU 11 functioning as the acquisition unit 101 acquires, from the event database 120, occurrence information within a predetermined area.

[0033] In step S102, the CPU 11, as the acquisition unit 101, acquires the occurrence probability of injuries or illnesses meeting specific conditions within a specified area by dividing the number of injuries or illnesses that occurred within a specified area during a specified period and that meet specific conditions by the specified period.

[0034] In step S103, the CPU 11, as the generation unit 102, constructs a prediction model for the occurrence of injuries or illnesses using the probability of occurrence of injuries or illnesses obtained in step S102, and uses the prediction model to artificially generate the number of injuries or illnesses that meet certain conditions within a specified area.

[0035] In step S104, the CPU 11 functioning as the generating unit 102 randomly selects one piece of selection information from the generated information acquired in step S101.

[0036] In step S105, the CPU 11, functioning as the generating unit 102, determines whether the type of facility indicated by the incidental information in the selection information selected in step S104 is a residence. If the type of facility indicated by the incidental information is a residence (step S105: YES), the process proceeds to step S106. On the other hand, if the type of facility indicated by the incidental information is a hospital (step S105: NO), the CPU 11 proceeds to step S107.

[0037] In step S106, the CPU 11, functioning as the generating unit 102, generates pseudo-event information whose occurrence position is a position represented by latitude and longitude obtained by adding a predetermined value to at least one of the latitude and longitude of the selected position.

[0038] In step S107, the CPU 11 functions as the generating unit 102 to generate pseudo-event information having the selected position as the occurrence position.

[0039] In step S108, the CPU 11, as the generating unit 102, determines whether or not the same number of pieces of simulated event information as the number of simulated injured persons generated in the process of step S103 have been generated. If the CPU 11 has generated the same number of pieces of simulated event information as the number of simulated injured persons (step S108: YES), the CPU 11 ends this information generating process. On the other hand, if the CPU 11 has not generated the same number of pieces of simulated event information as the number of simulated injured persons (step S108: NO), the CPU 11 returns to step S104.

[0040] As described above, the information generating device according to this embodiment acquires past event information including information on the position where a predetermined event has occurred and incidental information on the position. The information generating device generates pseudo-event information that simulates the occurrence of an event in a predetermined area, and the pseudo-event information has the occurrence position as the position included in the past event information and the position in the predetermined area based on the incidental information on the position. This makes it possible to create pseudo-event information that simulates the occurrence of a predetermined event more quickly and more accurately reflecting the occurrence of a real event. Specifically, compared to the case where kernel density estimation is performed uniformly using a Gaussian distribution or the like, it is possible to capture the probability distribution of the occurrence position more accurately. In addition, in this embodiment, the case where the occurrence probability is calculated from, for example, 10 pieces of event information has been described, but in reality, in most cases, the amount of past event information used to estimate the occurrence probability is enormous, and the effect of speeding up is higher as the amount of past event information increases.

[0041] <Modification> The present invention is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the spirit and scope of the present invention.

[0042] For example, in the above embodiment, a case where simulated event information is generated that simulates the occurrence of an injury or illness has been described. However, the present invention is not limited to this example. For example, the present invention may be applied to a case where simulated event information is generated that simulates the occurrence of a crime, a traffic jam, or the like.

[0043] In the above embodiment, the latitude and longitude of the location where the injured person became ill and the date and time when the injured person became ill are applied as an example of the event information, but other event information may be used. For example, the address of the location where the injured person became ill and the date and time when the injured person became ill may be applied as the event information. In this case, for example, when the type of facility indicated by the additional information in the selection information is a residence, the generating unit 102 may generate pseudo-event information with the occurrence location being a location indicated by an address having the same postal code as the postal code of the selection location.

[0044] In the above embodiment, the type of facility where the injured person occurred is used as an example of the incidental information, but other incidental information may be used. For example, information on an event held at the location where the injured person occurred may be used as the incidental information.

[0045] In addition, various processes that the CPU reads and executes software (programs) in the above embodiment may be executed by various processors other than the CPU. Examples of processors in this case include PLDs (Programmable Logic Devices) such as FPGAs (Field-Programmable Gate Arrays) whose circuit configuration can be changed after manufacture, and dedicated electric circuits such as ASICs (Application Specific Integrated Circuits) that are processors having circuit configurations designed exclusively for executing specific processes. The information generation process may be executed by one of these various processors, or may be executed by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, and a combination of a CPU and an FPGA). The hardware structure of these various processors is, more specifically, an electric circuit that combines circuit elements such as semiconductor elements.

[0046] In the above embodiment, the information generating program is stored (installed) in advance in the storage 14, but the present invention is not limited to this. The program may be provided in a form stored in a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. The program may also be downloaded from an external device via a network.

[0047] The following supplementary notes are further disclosed regarding the above embodiment.

[0048] (Additional note 1) An information generating device, Memory, at least one processor coupled to the memory; Including, The processor, Acquiring past event information including information on a location where a predetermined event has occurred and supplementary information related to the location; A pseudo-event information is generated by simulating the occurrence of the event in a predetermined area, the pseudo-event information being generated with a position included in the past event information and a position in the predetermined area based on additional information related to the position. The information generating device configured as above.

[0049] (Additional note 2) A non-transitory storage medium storing a program executable by a computer to execute an information generation process, The information generation process includes: Acquiring past event information including information on a location where a predetermined event has occurred and supplementary information related to the location; A pseudo-event information is generated by simulating the occurrence of the event in a predetermined area, the pseudo-event information being generated with a position included in the past event information and a position in the predetermined area based on additional information related to the position. Non-transitory storage medium. [Explanation of symbols]

[0050] 1 Information Generation System 10 Information generation device 11 CPU 12 ROM 13 RAM 14. Storage 17 Communication I / F 19 Bus 101 Acquisition Department 102 Generation part 120 Event Database

Claims

1. an acquisition unit that acquires past event information including information on a location where a predetermined event has occurred and supplementary information related to the location; a generator that generates simulated event information that simulates an occurrence of the event within a predetermined area, the simulated event information having an occurrence position that is a position within the predetermined area based on a position included in the past event information and additional information related to the position; Equipped with the generation unit generates the number of pieces of pseudo-event information based on an occurrence probability of the event calculated from the past event information. Information generation device.

2. The information generating device according to claim 1 , wherein the additional information is a type of facility in which the event occurred.

3. The generation unit is When the type of the facility indicated by the incidental information related to a position selected from the positions included in the past event information is a first type, a position obtained by adding noise to the position included in the past event information is set as the occurrence position; When the type of the facility indicated by the incidental information regarding the selected location is a second type in which the probability of the event occurring around the selected location is lower than that of the first type of facility, the location included in the past event information is determined as the occurrence location. The information generating device according to claim 2 .

4. The information generating device of claim 3, wherein when the generating unit adds noise to a position included in the past event information, the generating unit sets the occurrence position to a position represented by latitude and longitude obtained by adding a predetermined value to at least one of the latitude and longitude of the position included in the past event information.

5. The number based on the occurrence probability of the event calculated from the past event information is obtained based on the occurrence probability of the event, assuming that the probability distribution of the number of occurrences of the event follows a Poisson distribution. The information generating device according to claim 1 .

6. an acquisition unit that acquires past event information including information on a location where a predetermined event has occurred and supplementary information related to the location; a generator that generates simulated event information that simulates an occurrence of the event within a predetermined area, the simulated event information having an occurrence position that is a position within the predetermined area based on a position included in the past event information and additional information related to the position; Equipped with the generation unit generates the number of pieces of pseudo-event information based on an occurrence probability of the event calculated from the past event information. Information generating system.

7. The acquisition unit acquires past event information including information on a location where a predetermined event has occurred and supplementary information related to the location; a generation unit generates simulated event information that simulates an occurrence of the event in a predetermined area, the simulated event information having an occurrence position that is a position included in the past event information and a position within the predetermined area based on auxiliary information related to the position.

1. A method for generating information, comprising: the generation unit generates the number of pieces of pseudo-event information based on an occurrence probability of the event calculated from the past event information. Information generation method.

8. An information generating program for causing a computer to function as each unit of the information generating device according to any one of claims 1 to 5.

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