JP6610345B2 - Apparatus, method, and program for predicting area where manager is absent - Google Patents

Description

  The present invention relates to an apparatus, a method, and a program for predicting an administrator absent area.

  In this specification, an “administrator” in a region refers to, for example, an owner, a user, a land leaseholder, or the like who manages real estate such as land and buildings in the region. The “manager absent area” means an area where the manager is absent, and includes an area where no manager exists, an area where the manager is unknown, and the like.

  In environmental design, regional redevelopment, city evaluation, real estate transactions, etc., it is desirable to grasp the areas where managers are absent. Conventionally, for example, whether or not there is a land owner is confirmed by an administrative visit, mail, or telephone survey using the land registry. However, it is difficult for the government to know in advance or immediately that the owner will be absent unless there is a report regarding the land owner to the government. For example, after a house becomes a vacant house, the land becomes abandoned farmland, etc., and the house or land begins to be damaged, complaints from local residents of the land are sent to the government, so the owner of the house or land There are many cases where it can be found that is absent.

  For example, in Patent Document 1, it is proposed that a candidate for abandoned cultivation is presented according to the age of the owner for the input lot number. However, this proposed method does not judge abandoned farmland candidates by comprehensively using a plurality of information such as changes in the natural environment and economic information. For this reason, it is impossible to grasp the situation such as whether or not the land of the input lot number is isolated compared to the surrounding area. That is, it is difficult to accurately predict an area where there is no manager who may become a vacant house, an abandoned farmland, or the like, and accurately predict when the area will be absent. For this reason, it is difficult to formulate comprehensive measures for areas where managers are absent in administrative and development projects.

JP 2012-208711 A

  Conventionally, it is difficult to accurately predict candidates for an area where there is no manager and accurately predict the time when the area will be absent.

  Therefore, in one aspect, an object is to provide an apparatus, a method, and a program for predicting an administrator absent area that can accurately predict an administrator absent area and accurately predict a time when the manager is absent. .

  According to one proposal, each second region is defined based on means for dividing the first region to be evaluated into a plurality of second regions and natural environment information representing the natural environment of the first region. Extract a second area including a third area where the third area, which is an urban area or a developed area, has increased by a certain percentage or more in a past first certain period from a plurality of third areas to be formed Based on the means for setting a candidate for an administrator absent region and economic information representing the economic status of the candidate, the candidate is a candidate whose economic status has deteriorated by a certain percentage or more in the past second fixed period. Means for narrowing down the candidates, environmental assessment information representing records of environmental maintenance of the candidates, map information representing owners on a real estate registry corresponding to the candidate maps, and land use of the candidates At least one of the social information Further refine based on multicast, apparatus for predicting the administrator absence region provided with a means for predicting the timing predicted administrator absent area is generated is provided.

  According to one aspect, it is possible to accurately predict the area where the manager is absent and accurately predict the time when the area is absent.

It is a figure which shows an example of an information processing system. It is a block diagram which shows an example of a computer. It is a flowchart explaining an example of the process which estimates an administrator absent area. It is a figure which shows an example of the relationship between a vegetation map and vegetation natural degree distribution. It is a figure which shows the outline | summary of a vegetation natural degree. It is a figure which shows the detail of a vegetation natural degree. It is a figure explaining an example of the change of the occupation ratio of the vegetation natural degree in a mesh. It is a figure which shows an example of distribution of the mesh in which the area of the secondary forest or the natural forest decreased by 8% or more. It is a flowchart explaining an example of a prediction process of an administrator absent area based on the data regarding economic information. It is a figure which shows an example of an economic information database. It is a figure which shows the other example of an economic information database. It is a flowchart explaining an example of the narrowing-down process of an administrator absent area based on the data regarding environmental assessment information. It is a flowchart explaining an example of the narrowing-down process of an administrator absent area based on the data regarding map information. It is a figure which shows an example of the relationship between the conditions used in the process of FIG. 13, and a parameter. It is a flowchart explaining an example of the narrowing-down process of an administrator absent area based on the data regarding map information. It is a figure which shows an example of the relationship between the conditions used in the process of FIG. 15, and a parameter. It is a figure which shows an example of the land price information of Atsugi-shi which is an upward trend. It is a figure which shows an example of the land price information of Yokosuka City which is a downward trend. It is a flowchart explaining an example of the narrowing-down process of an administrator absent area based on the data regarding social information. It is a figure which shows an example of the basic parameter | index contained in social information. It is a figure which shows an example of the condition of the farmer contained in social information. It is a figure which shows an example of the ownership situation and borrowed cultivated land ratio of the Tabata orchard of the village W1. It is a figure which shows an example of the basic parameter | index contained in social information. It is a figure which shows an example of the condition of the farmer contained in social information. It is a figure which shows an example of the ownership situation and borrowed cultivated land rate of the Tabata orchard of village W2. It is a figure which shows an example of output data. It is a figure which shows an example of the output data containing a map.

  In the apparatus, method, and program for predicting an administrator-absent area of disclosure, a first area (for example, a region) to be evaluated is divided into a plurality of second areas (for example, meshes), and the natural area of the first area is divided. Based on the natural environment information representing the environment, among the plurality of third areas (for example, areas) forming each second area, the third area that is an urban area or a developed area in the past first predetermined period. The second area including the third area where the area has increased by a certain rate or more is extracted and set as a candidate for an administrator absent area. Further, the candidates are narrowed down to candidates whose economic situation has deteriorated by a certain percentage or more in the past second fixed period based on the economic information representing the economic situation of the region. In addition, for the narrowed candidates, environmental assessment information that represents the candidate's environmental maintenance record, map information that represents the owner of the real estate corresponding to the candidate map, and social information that represents the status of the land use of the candidate Based on at least one piece of information, it narrows down further and predicts the time when the predicted manager absent area occurs.

  Embodiments of a device, a method, and a program for predicting an area where there is no manager will be described below with reference to the drawings.

  In environmental design, regional redevelopment, city evaluation, real estate transactions, etc., it is desirable to grasp the areas where managers are absent. Therefore, in the embodiment described below, a region where the manager is absent is predicted, and a period until the manager of the region is absent, that is, a time when the region where the manager is absent is predicted.

  First, a region to be evaluated is specified, and the specified region is divided into a plurality of meshes. In addition, based on the natural environment information representing the specified local natural environment, the area that is the urban area or the development area has increased by a certain percentage or more in the past first fixed period among the plurality of areas forming each mesh. Extract a mesh that includes the current area and set it as a candidate for an administrator absent area. Then, based on the economic information representing the economic status of the candidates, the candidates are narrowed down to candidates whose economic status has deteriorated by a certain percentage or more in the past second fixed period. The narrowed candidates are environmental assessment information that represents the candidate's environmental maintenance record, map information that represents the candidate's map and the owner of the corresponding real estate registry, and social information that represents the candidate's land use status. Further narrowing down based on at least one piece of information, and predicting the time when the predicted manager absent area occurs.

  The natural environment information is information representing the natural environment of the region, and may include, for example, a vegetation natural degree based on a vegetation survey conducted by the Ministry of the Environment. Specifically, if the designated region is divided into a plurality of meshes, and the percentage of the area that is an urban area or a built-up area occupying one mesh in a certain period in the past has increased, The mesh is extracted as one candidate. On the other hand, the ratio of areas that are either farmland, secondary forests of compensation vegetation, secondary forests close to natural vegetation, or natural forests occupy one mesh has decreased by a certain percentage (for example, 10%) or more. Then, the mesh is extracted as the second candidate for the manager absent area. The mesh is, for example, a square region having a minimum unit of 1 km × 1 km, but the shape of the mesh and the area corresponding to the minimum unit are not particularly limited. Further, the shape of the plurality of areas forming the mesh is not particularly limited.

  Next, the first candidate and the second candidate are narrowed down based on the designated local economic information, thereby predicting the area where the manager is absent. The economic information is information representing the economic situation of the candidate area, and includes, for example, the area where the number of applications for regional tax revenues and high medical expenses is increasing.

  In addition, candidates for the absence of managers narrowed down based on the economic information of the candidate area are further narrowed down based on at least one of environmental assessment information, map information, and social information of the candidate area. , Predict when the predicted absence of managers will occur. The environmental assessment information is information that represents a record of regional environmental maintenance, and includes, for example, the region, year, etc. in which environmental assessment was performed in a new housing development project or residential land urban infrastructure development. The map information is information representing the owner of the real estate corresponding to the map of the area, and includes, for example, the time of owner transfer due to the inheritance of the register, real estate information related to the property tax or the inheritance tax, and the like. Further, the social information is information representing the state of land use in the area, and includes, for example, increase / decrease in the number of farmers over 65 years old, borrowed cultivated land ratio or loan cultivated land ratio of farmland or orchard.

  As described above, the region where the manager is absent is predicted by comprehensively using at least one information among environmental assessment information, map information, and social information, that is, a plurality of information, in addition to changes in natural environment and economic information. Therefore, it is possible to accurately predict the area where the manager is absent and accurately predict the time when the area is absent.

  It should be noted that natural environment information, economic information, environmental assessment information, map information, and social information are all generally public information (that is, public data), but are generally non-public information (that is, private information). Data). Public data is data that is open to the public. On the other hand, non-public data is open only to a specific organization (or specific object) such as an administration, and is non-public data for the general public. Applications for data collection, evaluation analysis, and result display using public data include, for example, Eva Shiva (EvaCva, registered trademark) equipped with Linked Open Data (LOD) technology It is.

  FIG. 1 is a diagram illustrating an example of an information processing system. In the information processing system 1 shown in FIG. 1, a plurality of computers 2-1 to 2-n (n is a natural number of 2 or more) are communicably connected via a network 4. Each computer 2-i (i = 1 to n) has, for example, a well-known hardware configuration and can manage at least one database (DB: Data-Base) 3-i. In FIG. 1, for convenience, the database 3-i is shown in the computer 2-i. However, the database 3-i is connected to the computer 2-i via the network 4 even if it is externally connected to the computer 2-i. It may be connected to i. In FIG. 1, a database 3-0 is an example of a database connected to the network 4 and accessible from at least one computer 2-i. The information processing system may include a computer that does not manage the database. The databases 3-0 to 3-n include, for example, a natural environment information database, an economic information database, an environmental assessment database, a map information database, a social information database, and the like which will be described later. The network 4 can be formed by one or a plurality of networks, and may include the Internet.

  The information processing system 1 illustrated in FIG. 1 may form a cloud computing system. In this case, the computer on the cloud computing system can access the database on the cloud computing system.

  The information processing system 1 shown in FIG. 1 is assumed to be used by connecting to the Internet, for example, but may be used in a stand-alone manner.

  FIG. 2 is a block diagram illustrating an example of a computer. A computer 2-i illustrated in FIG. 2 includes a processor 21, a memory 22, an input device 23, a display device 24, and an interface (or communication device) 25 that are connected to each other via a bus 20. The processor 21 can be formed by a central processing unit (CPU), for example, and executes a program stored in the memory 22 to control the entire computer 2-i. The memory 22 can be formed by, for example, a semiconductor storage device, a magnetic recording medium, an optical recording medium, a magneto-optical recording medium, or the like. The memory 22 stores various programs including a program for predicting the area where the manager is absent, various data, and the like executed by the processor 21. The memory 22 can form one or a plurality of databases. The memory 22 may be externally connected to the computer 2-i or may be connected via the network 4.

  The input device 23 can be formed by, for example, a keyboard operated by a user (or an operator), and is used to input commands and data to the processor 21. The display device 24 displays a message for the user, a result of a process for predicting an administrator absent area, and the like. The interface 25 connects the computer 2-i to the network 4 so that it can communicate with other computers.

  The computer 2-i is not limited to a hardware configuration in which components of the computer 2-i are connected via the bus 20. Further, even though the computers 2-1 to 2-n all have the same hardware configuration, for example, at least one computer 2-1 has a hardware configuration different from the other computers 2-2 to 2-n. You may have. For example, general-purpose computers may be used as the computers 2-1 to 2-n.

  Next, an example of an apparatus, a method, and a program for predicting an administrator absent area in an embodiment will be described. In this example, for convenience of explanation, the user operates the computer 2-1 to execute a process of predicting the area where the manager is absent, and is managed by one or more other computers, for example, the computers 2-2 and 2-3. Data is acquired from the databases 3-2 and 3-3. As an example, a case will be described in which data stored in the databases 3-2 and 3-3 is public data, and non-public data can be acquired from the database 3-1 managed by the computer 2-1.

  When the information processing system 1 shown in FIG. 1 forms a cloud computing system, the process of predicting the area where the manager is absent may be executed by one or a plurality of computers, that is, one or a plurality of processors. .

  FIG. 3 is a flowchart for explaining an example of a process for predicting an administrator absent area. The process shown in FIG. 3 can be executed by, for example, the processor 21 of the computer 2-1 executing a program for predicting an administrator absent area stored in the memory 22 of the computer 2-1. The computer 2-1 that executes the process of predicting the manager absent area forms an example of a device that predicts the manager absent area.

  In FIG. 3, in step S <b> 1, the processor 21 acquires a designation of a prefecture that is an example of a region to be evaluated. For example, the processor 21 acquires the prefecture designated by the user operating the input device 23.

  In step S2, the processor 21 acquires at least data related to the natural environment information of the designated prefecture from, for example, a database (for example, a natural environment information database) 3-2 in the memory 22 of the computer 2-2. Based on the data related to the natural environment information, a municipality, which is an example of an area (first area) that may become an administrator absent area in the designated prefecture, is extracted. The data related to the natural environment stored in the natural environment information database may include, for example, the vegetation natural degree based on the vegetation survey conducted by the Ministry of the Environment, for example, geographic information system (GIS) data (ie, map). It may include the vegetation map and vegetation naturalness in the above. In this case, in step S2, the processor 21 extracts the area of the vegetation community associated with the vegetation naturalness using the vegetation map in the designated prefecture, calculates the area of the extracted area, To determine the occupancy ratio of each vegetation naturalness area. In this case, in step S2, the processor 21 further has an area (also referred to as a polygon) having a natural vegetation level of “1” (a city area or a developed area) in the mesh (second area smaller than the first area). The third area smaller than the area of 2) extracts municipalities that are 50% or more, which is an example of a certain ratio, compared to, for example, 20 years ago, which is an example of a certain period before. Assuming that the useful life of a house is about 20 years, the change in vegetation naturalness has been in the past 20 years, for example, from an agricultural land with a vegetation naturalness of “2” or “3” to an urban area or an established land with a vegetation naturalness of “1” A mesh with a high ratio of 50% or more occupied by the changed area may become a manager absent area.

  Note that if the region to be evaluated is, for example, a municipality, the region where the manager is absent may be, for example, a district within the municipality.

  FIG. 4 is a diagram illustrating an example of a relationship between a vegetation map of one mesh and a vegetation naturalness distribution when GIS information (that is, map information) nationwide is divided by a 1 km × 1 km mesh, for example. As shown in FIG. 5, the vegetation naturalness is classified into outlines of vegetation naturalness “1” to “10”. Further, the details of the natural vegetation degree based on the vegetation survey conducted by the Ministry of the Environment are classified as shown in FIG.

Vegetation naturalness was originally defined by the Ministry of the Environment as an indicator of how much the naturalness of a community remains from a phytosociological perspective. Nature is an artificial measure for evaluating nature. It is divided into certain ranks (classifications) according to the degree of strength and resilience. The nature of vegetation is classified into 10 levels, with “1” indicating that there is almost no vegetation as an urban area or industrial site, and “10” indicating extremely natural nature. As shown in FIG. 5 and FIG. Divided into streets. The natural vegetation level “9” and the natural vegetation level “10” have the same rank in terms of naturalness.
“1”: Urban area or newly developed area (area where almost no vegetation remains)
“2”: Agricultural land (including paddy fields, upland fields, residential areas with green coverage of 60% or more)
“3”: Agricultural land (orchard such as orchard, mulberry field, tea plantation, nursery)
"4": A short secondary grassland (a grassland with a short height such as a shiba community)
“5”: tall secondary grassland (heavy grassland such as Sasa community, Susuki community)
“6”: Afforestation (Afforestation sites for evergreen conifers, deciduous conifers, evergreen broadleaf trees, etc.)
“7”: Secondary forest of compensation vegetation (Kurimizunara community, Kunugi-Konara community, etc.)
“8”: Secondary forests close to natural vegetation (eg beech, Mizunara reforestation forest, shii oak sprout forest)
“9”: Natural forests (extreme or natural forests such as Scots pine-Todomatsu communities, beech communities)
“10”: Natural grasslands / wetlands (Takayama Heide, wind-powered grasslands, natural grasslands, etc.)
Unlike the method of determining the vegetation naturalness degree map information of the Ministry of the Environment, which assigns one natural degree to the map mesh ID, in this embodiment, the vegetation natural degree “1” to “10” corresponds to each mesh. It is used to indicate the ratio (occupation ratio) occupied by the area (area) of vegetation naturalness. In other words, the vegetation community area linked to the natural vegetation level using the vegetation map is utilized. A plurality of vegetation communities having different vegetation naturalness exist in one section of each map mesh ID, and these plurality of vegetation communities are regarded as areas (polygons). In addition, it is possible to determine which vegetation naturalness each vegetation community area has by associating vegetation communities with vegetation naturalness using data from the Ministry of the Environment, for example. Further, an occupation ratio is calculated as to what area the area (polygon) of the vegetation community having each vegetation natural degree occupies in the mesh. That is, in this embodiment, an occupation ratio is used in which the area having the natural vegetation level is extracted from how much area is occupied in the mesh.

  The vegetation map shown in FIG. 4A is converted into the vegetation natural degree distribution shown in FIG. 4B based on the details of the vegetation natural degree shown in FIG. 5 or the vegetation natural degree shown in FIG. In FIG. 4, the vegetation map shown in FIG. 4A shows the occupation ratios of polygons (areas) a to e in which the vegetation communities in the mesh are collected. On the other hand, in FIG. 4, the vegetation natural degree distribution shown in (b) shows the occupation ratio of the area having each vegetation natural degree in the mesh. In this example, the occupation ratio of the area N7 having a vegetation natural degree of “7” is 50%, the occupation ratio of the area N5 having a vegetation natural degree of “5” is 13%, and the occupation ratio of the area N3 having a vegetation natural degree of “3” The occupation ratio of the area N2 with the ratio of 5%, the vegetation naturalness level “2” is 28%, and the occupation ratio of the area N1 with the vegetation naturalness degree “1” (city area or land development) is 4%.

  Next, based on the change in the vegetation naturalness distribution over a certain period as shown in FIG. 4 (b), the area where the vegetation natural degree is “1” (city area or land development) is 50% or more than before the certain period. Extract the increasing mesh. FIG. 7 is a diagram illustrating an example of a change in the occupation ratio of the vegetation naturalness within the mesh. In FIG. 7, (a) shows the proportion of vegetation naturalness in the mesh in 2015, (b) shows the proportion of vegetation naturalness in the mesh in 1995, and (c) is 20 years in 2015. The difference of the ratio of the natural degree of vegetation in the mesh which changed compared with before is shown. In the example shown in FIG. 7, the area where the mesh ID number 2015 of (a) is ID1 and ID2 and the vegetation natural degree is “1” (city area or land development) is compared to (c) 1995 (c). As can be seen from the graph, the increase is 5.4% and 50.3%, respectively. In this case, since the proportion of the area where the vegetation natural degree of mesh ID number ID2 is “1” (city area or land development) has increased by 50% or more in 2015 compared to 1995, other mesh ID numbers There is a possibility that the percentage of increase will be large compared to, and there will be a region where there is no manager.

  In the example shown in FIG. 7, the area of the vegetation natural degree “8” (secondary forest close to natural vegetation) whose mesh ID number is ID2 in 2015 in (a) is compared to 1995 in (b). As shown in FIG. 6C, it is found that there is a decrease of 72.8%. Further, in the example shown in FIG. 7, the area of the vegetation naturalness “2” and “3” (agricultural land) whose mesh ID number is ID3 in 2015 in (a) is compared to 1995 in (b). As shown in (c), it has decreased by 15.4% and 1.8% respectively, and the farmland has been reduced by 17.2% (= 15.4% + 1.8%) in total I understand.

  Next, in step S3, the processor 21 determines whether or not at least one area having a vegetation naturalness level of “1” (city area or creation area) has increased in the extracted vegetation naturalness degree mesh of the municipalities. If the determination result is Yes, the process proceeds to step S4. If the determination result is No, the process proceeds to step S5. In step S4, the processor 21 extracts the municipality extracted in step S2 as a first candidate A1 that may be an administrator absent area, and the process proceeds to step S7 described later. The processor 21 may extract a plurality of first candidates A1, that is, a first candidate group in step S4.

  In step S5, the processor 21 determines that the vegetation naturalness is “2” or “3” (agricultural land) or the vegetation naturalness is “7”, “8” in the extracted mesh of vegetation naturalness of the municipalities. Or, it is determined whether or not there is a mesh in which the ratio of the area occupied by “9” (secondary forest of compensation vegetation, secondary forest close to natural vegetation, or natural forest) is reduced by, for example, 10% or more, If the determination result is No, the process returns to step S1, and if the determination result is Yes, the process proceeds to step S6. In step S6, the processor 21 extracts the municipality extracted in step S2 as the second candidate A2 that may be an administrator absent area, and the process proceeds to step S7. The processor 21 may extract a plurality of second candidates A2, that is, a second candidate group in step S6.

  As described above, by the processes in steps S2 to S6, areas that are urban areas or creation areas in the past certain period among a plurality of areas forming each mesh based on the natural environment information representing the natural environment of the area. It is possible to extract a mesh including an area that has increased by a certain percentage or more and set it as a candidate for an administrator absent area.

  For example, in the example shown in FIG. 7, the vegetation naturalness which is a region where the vegetation with mesh ID numbers 2015 and ID2 of 2015 in FIG. It can be seen that the area of the urban area or the developed area increased by 5.4% and 50.3%, respectively, compared with 1995 of (b). In this case, the increase in the area of the mesh ID number ID2 with a vegetation natural degree of “1” (city area or land development) is 50% or more, and it can be seen that the increase rate is larger than other meshes. Extracted as one candidate A1. On the other hand, in order to judge the devastation of farmland or forests, the vegetation naturalness is “8” (secondary forest close to natural vegetation) for the mesh ID numbers ID2 and ID2 in FIG. Area decreased by 72.8%. On the other hand, the latter is devastated because the area of vegetation naturalness “2”, “3” (agricultural land) has decreased by a total of 17.2% (= 15.4% + 1.8%). I understand that. In this case, the increase in the area of the vegetation natural degree “8” (secondary forest close to natural vegetation) whose mesh ID number is ID2 is 10% or more. In this example, the area where the mesh ID number is ID2 is already It is extracted as a first candidate A1 of an area that may be an administrator absent area. On the other hand, the increase in the total area of the vegetation naturalness level “2” and “3” (agricultural land) with the mesh ID number ID3 is 10% or more, so the vegetation naturalness level with the mesh ID number ID3 is “2”. , “3” (agricultural land) is extracted as a second candidate A2 of an area that may become an administrator absent area.

  Thus, for example, assuming 2015 as the base, assuming that the useful life of the house is about 20 years, the difference from the occupation ratio of the area having a specific natural vegetation in the mesh in 1995 20 years ago Calculate Collecting meshes with an occupancy ratio of “1” vegetation naturalness (city area or creation area) increased compared to 20 years ago, and the rate of increase is 50, for example, among the meshes in the municipalities to be evaluated The mesh with the largest increase rate of at least% is extracted as a first candidate A1 of an area that may be an administrator absent area. Similarly, if the percentage of increase in the occupancy ratio of the area where the natural vegetation level is “1” (city area or reclaimed land) is 50% or more and there is one or more meshes that are increasing secondly, similarly Is extracted as a first candidate A1 of an area that may be a person absent area.

  In addition, even if there is not more than one mesh where the area of vegetation naturalness “1” (city area or reclaimed land) is increasing, the area of vegetation naturalness “2”, “3” (agricultural land) When there is one or more meshes where the area of naturalness is "7", "8", "9" (secondary forest of compensation vegetation, secondary body close to natural vegetation, and natural forest) is reduced by 10% or more, for example Is extracted as a second candidate A2 of an area that may become an administrator absent area. If there is no increase / decrease in the occupation ratio of the area of the vegetation natural degree in the mesh, the designation of the next region to be evaluated is acquired.

  Fig. 8 shows areas where the natural vegetation levels in 2015 are "7", "8", and "9" (secondary forest of compensation vegetation, secondary bodies close to natural vegetation, and natural forests) compared to 1995. It is a figure which shows an example of distribution of the mesh which decreased by 8% or more.

  In step S7, the processor 21 obtains at least data relating to the extracted economic information of the municipality from, for example, a database (eg, economic information database) 3-1 in the memory 22 of the computer 2-1, and relates to the obtained economic information. Based on the data, the manager-absent area is predicted from the first candidate A1 or the second candidate A2. Data on economic information stored in the economic information database includes, for example, areas where tax revenues from municipalities, applications for high medical expenses are increasing, areas where complaints about natural creatures such as damage to birds and animals are increasing, and birds and animals It may also include costs for countermeasures against damage caused by natural creatures such as For example, the processor 21 has a future burden rate exceeding 100% with respect to the tax revenue of municipalities included in the acquired economic information (that is, the future will be in the red), and the number of applications for high medical expenses has been in the past for a certain period For example, an area that has increased in the past 20 years, for example, is determined as an area that is highly likely to be an administrator absent area. In addition, the number of complaints regarding natural creatures such as wildlife damage in municipalities included in the acquired economic information has increased over the past 20 years, for example, in the past 20 years, and the natural creatures such as birds and beasts. For example, an area where the cost of countermeasures against damage due to damage has increased in the past 20 years, which is an example of a certain period in the past, is determined to be an area where there is a high possibility of being an administrator absent area. On the other hand, although the future burden rate of the tax revenue of municipalities included in the acquired economic information exceeds 100% (that is, the future will be in the red), the number of applications for high medical expenses has been in the past for a certain period For example, an area that has not increased in the past 20 years, for example, is determined as an area that has a medium possibility of becoming an administrator absent area. In addition, although the number of complaints regarding natural creatures such as wildlife damage in municipalities included in the acquired economic information has increased in the past 20 years, which is an example of a certain period in the past, for example, For example, an area where the cost of countermeasures against damage is an example of a past fixed period and has not increased in the past 20 years is determined as an area where the possibility of becoming an administrator absent area is moderate. The number of applications for high-priced medical expenses may be the number of applications for high-priced medical expenses that are an example of a certain age group, for example, 65 years old or older and less than 75 years old. After step S7, the process proceeds in parallel to steps S8, S9, and S11 described later. Steps S8 and S9 are procedures mainly for extracting an administrator absent area that may become a vacant house in an urban area, and step S11 is mainly for extracting an administrator absent area by abandoning cultivation. It is a procedure to do.

  Thus, the process of step S7 can narrow down candidates to candidates whose economic situation has deteriorated by a certain percentage or more in a certain past period based on the economic information representing the candidate economic situation.

  In this example, the data related to economic information includes the area where the number of complaints about natural creatures such as damage to birds and beasts and the cost of measures against damage caused by natural creatures such as birds and beasts. Areas where the number of complaints related to the increase is included in the data related to natural environment information.

  FIG. 9 is a flowchart for explaining an example of a process for predicting an area where an administrator is absent based on data on economic information, which is executed by the processor 21 in step S7. In FIG. 9, step S71 and step S72 are executed in parallel in this example. In step S71, the processor 21 determines whether or not the future burden rate exceeds 100% with respect to the tax revenue of the municipality included in the acquired economic information (that is, the future will be in the red), and the determination result is Yes. If YES, the process proceeds to step S73 described later, and if the determination result is NO, the process returns to the process of FIG. FIG. 10 is a diagram illustrating an example of an economic information database including economic information used in step S71.

  In step S <b> 73, the processor 21 extracts municipalities whose future burden rate exceeds 100% with respect to tax revenue. In step S74, the processor 21 determines whether or not there is a municipality in which the number of applications for high-priced medical expenses included in the acquired economic information is an example of a past fixed period, for example, has increased in the past 20 years. If the result is Yes, the process proceeds to step S75, and if the determination result is No, the process proceeds to step S77 described later. FIG. 11 is a diagram showing another example of an economic information database including economic information used in step S74. The economic information database shown in FIG. 11 may be integrated with or separate from the economic information database shown in FIG.

  In step S75, the processor 21 extracts municipalities in which the number of applications for high-priced medical expenses has increased over the past 20 years. In step S76, the processor 21 determines that the health condition of the elderly person is poor and the number of elderly persons requiring care is increasing, and the extracted municipalities are set as candidates B1 that are likely to be in areas without managers. Then, the process returns to the process of FIG. In step S77, the processor 21 sets the extracted municipality as a candidate B2 having a medium possibility of becoming an administrator absent area, and the process returns to the process of FIG.

  That is, in the first candidate A1 or the second candidate A2, in the municipal revenue (tax revenue), the municipalities in which the future burden exceeds 100% or the municipalities with the deficit ratio are acquired from the economic information database shown in FIG. Judging from the economic information. In addition, regarding the status of high-cost medical expenses, among municipalities, for example, the proportion of certified seniors requiring long-term care is 10% or more of the proportion of senior citizens. It is judged from the economic information acquired from the economic database shown in FIG. Such a municipality is regarded as an increase in the number of elderly people requiring care, that is, the health status of the elderly is poor, and is set as a candidate B1 that is likely to increase the number of houses or farmland in the absence of managers in the future.

  On the other hand, in step S72, the processor 21 determines whether or not the number of complaints regarding natural creatures such as damage to birds and beasts in municipalities included in the acquired economic information has increased over the past 20 years, which is an example of a certain period in the past. If the determination result is Yes, the process proceeds to step S78 described later, and if the determination result is No, the process returns to the process of FIG. The economic information used in step S72 may be acquired from the economic information database shown in FIG.

  In step S78, the processor 21 extracts municipalities in which the number of complaints regarding natural creatures such as bird and beast damage has increased in the past 20 years. In step S79, the processor 21 determines whether or not there is a municipality in which the cost of countermeasures against damage caused by natural creatures such as birds and beasts included in the acquired economic information has increased in the past 20 years, which is an example of the past certain period. If the determination result is Yes, the process proceeds to step S80-1, and if the determination result is No, the process proceeds to step S77. The economic information used in step S79 may be acquired from the economic information database shown in FIG.

  In step S80-1, the processor 21 extracts cities, towns and villages where the cost of countermeasures against damage from natural creatures such as birds and beasts has increased in the past 20 years. In step S80-2, the processor 21 determines that the population that goes out to the farmland decreases and the population that loses the energy to continue the farmhouse increases, and the extracted municipalities are regarded as manager absent areas. It sets to candidate B1 with high possibility that it will become, and a process returns to the process of FIG.

  That is, the number of municipalities that have increased in the past 20 years with respect to the number of complaints regarding damage to birds and beasts is determined from the economic information acquired from the economic information database shown in FIG. For example, it is assumed that the number of complaints regarding damage to birds and beasts has rapidly increased in the past five years in the town R1, and last year there were 500 complaints. On the other hand, in town R2, there are about 90 complaints every year, and the number of complaints has gradually increased over the past 10 years. In town R3, there were about 300 cases 10 years ago, but recently it has been reduced to 20. In such a case, it is considered that there has been some change in land use or change in the natural environment in town R1 and town R2, so these areas are set as candidates B2. On the other hand, in the town R1, the town R2, and the town R3, an area where the cost of bird and animal damage countermeasures has increased in the past 20 years is set as the candidate B1. This is because there is a high possibility that the invasion of farmland due to an increase in abandoned cultivated land, or that there is a possibility that a house without an administrator is left and the grass in the garden grows and becomes a path for the beast. is there. In particular, the damage to birds and beasts on farmland means that the population going out to farmland has decreased, and it can be understood that there are many people who lose the energy to continue farming at an older age. In this way, in step S7, the possibility of becoming an administrator absent area is prioritized with high or medium degree.

  Returning to the description of the processing in FIG. 3, in step S <b> 8, the processor 21 obtains at least data related to the environmental assessment information of the candidate B from the database (for example, environmental assessment database) 3-3 in the memory 22 of the computer 2-3. Based on the data related to the acquired environmental assessment information, the candidate B (B1 or B2) of the manager absent area predicted in step S7 is narrowed down to the areas where the environmental assessment has been performed. The environmental assessment information stored in the environmental assessment database may include, for example, the area and year in which the environmental assessment was performed in a new housing development project or residential land urban infrastructure development.

  FIG. 12 is a flowchart for explaining an example of the narrowing-down process of the manager absent area based on the data related to the environmental assessment information, which is executed by the processor 21 in step S8. In FIG. 12, in step S81, based on the data related to the acquired environmental assessment information, the processor 21 is an area where the environmental assessment has been performed on the candidate B1 or B2 of the manager absent area predicted in step S7. , Narrow down to candidate C. In step S <b> 82, the processor 21 obtains an implementation year t <b> 1 in which the environmental assessment has been performed for an area where the environmental assessment has been performed, that is, for the candidate C. In step S83, the processor 21 obtains the elapsed period t = t1-t2 from the residential land creation to the present time using the year (hereinafter referred to as “survey year”) t2 in which the survey is performed. In step S84, the processor 21 determines whether t ≧ 30, for example. If the determination result is Yes, the process proceeds to step S85, and if the determination result is No, the process proceeds to step S86.

  If, for example, it exceeds 30 years from the time of residential land creation, the house will become obsolete assuming that the life of the house is 30 years. Assuming that a home purchaser purchases a home at an average of 30 years old, for example, a resident who lives in a construction site that has passed t = 48 years is t + 30 = 78 years old, and the average age of owners of this home (property) is It is likely that you are over 78 years old. Therefore, in step S85, the processor 21 sets the average estimated age s of owners in the survey year t2 to 78 years, sets the candidate C to the first candidate C1, and the processing returns to the processing in FIG.

  In step S86, the processor 21 determines whether or not 20 ≦ t <30. If the determination result is Yes, the process proceeds to step S87, and if the determination result is No, the process proceeds to step S88. For example, a resident who lives in a construction site that has passed t = 27 years is t + 30 = 57, and the average age of owners of this house (property) is likely to be 57 or older. Therefore, in step S87, the processor 21 sets the average estimated age s of owners in the survey year t2 to 57 years old, sets the candidate C to the second candidate C2, and the process returns to the process of FIG.

  In step S88, the processor 21 determines whether or not t ≦ 20. If the determination result is Yes, the process proceeds to step S89, and if the determination result is No, the process returns to the process in FIG. In step S89, the processor 21 sets the candidate C as the third candidate C3, and the process returns to the process of FIG.

  In this manner, the candidate site C that is further narrowed down is extracted from the municipalities of the candidates B1 or B2 using the history in which the assessment has been performed based on the environmental assessment system in the past. Environmental assessments can be obtained from public information because they are conducted with large-scale land development. First, in a new housing development project or residential land urban infrastructure development a certain period before a certain evaluation year, we selected a property that had undergone an environmental assessment, and conducted an environmental assessment from the implementation year t1 and the survey year t2. The elapsed period t from the time is obtained. In this example, the environmental assessment information database stores information indicating that a large-scale housing development was performed in town R1 in 1957, in town R2 in 1988, and in town R3 in 1990. ing. If the current survey year t2 is 2015, the elapsed period t since the implementation of the environmental assessment is 48 years for town R1, 27 years for town R2, and 25 years for town R3. . In this case, it is town R1 that has passed 30 years or more since the implementation of the environmental assessment, and if it exceeds 30 years from the creation of the residential land, the life of the house is 30 years. It is thought that there is. Also, home purchasers are presumed to purchase at the average age of 30 at the time, so adding 30 to t = 48 gives 78, and the average age of owners of this property is likely to be around 78 years old I understand that. Therefore, the average estimated age s of the property owners in the town R1 in the survey year t2 is set to 78 years old. Thereby, this area (town R1) is set as the first candidate C1, and the owner average age s is set to 78 years. On the other hand, in the town R2, for example, a resident who has lived in a construction site that has passed t = 27 years, adds 30 to t = 27, and becomes 57, and the average age of owners of this property may be 57 or older I understand that it is expensive. Therefore, the average estimated age s of the property owners in the town R2 in the survey year t2 is set to 57 years old. Thereby, this area (wait R2) is set as the second candidate C2, and the owner average age s is set to 57 years old.

  Returning to the description of the processing in FIG. 3, in step S <b> 9, the processor 21 obtains at least data related to the map information of the candidate C from the database (for example, map information database) 3-1 in the memory 22 of the computer 2-1. Areas overlapped by overlapping areas that meet the conditions among the candidate C (C1, C2, or C3) of the manager absent area predicted in step S8 based on the acquired map information data Therefore, the time when the manager is absent is predicted from the time of owner transfer by inheritance. Map information stored in the map information database, for example, the time of ownership transfer by inheritance of the registry, the published price that is an example of real estate information related to property tax or inheritance tax, the published price has declined over a period of time It may include regions.

  In step S9, the processor 21 may acquire at least data related to the map information of the candidate B from, for example, a database (for example, a map information database) 3-1 in the memory 22 of the computer 2-1. In this case, based on the acquired data relating to the map information, in the candidate B (B1 or B2) of the manager absent area predicted in step S7, the areas that meet the conditions are overlapped, and the overlapping areas are inherited. The time when the manager is absent may be predicted from the time of owner transfer.

  FIG. 13 is a flowchart for explaining an example of the narrowing-down process of the manager absent area based on the data related to the map information, which is executed by the processor 21 in step S9. In FIG. 13, in step S91, the processor 21 records ownership transfer by inheritance based on the information in the registry for the candidate C (C1, C2, or C3) of the manager absent area included in the map information. To extract. In step S92, the processor 21 determines whether or not the year t3 of the owner transfer by inheritance is a property of 30 years or more from the survey year t2, that is, whether t3 ≧ 50, and the determination result is Yes. If so, the process proceeds to step S93-1, and if the determination result is No, the process proceeds to step S94. In step S93-1, the processor 21 determines that the owner is a late elderly person, has a healthy lifespan or more, and has a barely old age capable of talking with the land owner himself, and determines candidate C as candidate D1. To do. In addition, the average healthy life expectancy w of men is 70.42 years old, and the period V = w−s in which the owner of the land can talk with the owner himself is 70.42−78 = −7.58. Since the year is already a minus (-), it can be seen that there is a high possibility that the owner cannot already have a conversation. In step S93-2, the processor 21 calculates the number of years u = T−s obtained by subtracting the average estimated age (s = 78 years old) of the current owner of the land from the average lifespan T years of males in the corresponding prefecture. The period u for which the land can be managed is set, and the processing returns to the processing of FIG. When the land owner is a male, the average life span T of the male is 79.55 years old. Therefore, when the average estimated age s = 78 years old is used, the period u in which the land can be managed is 79.55−78 = 1. It will be 5 years, and it will be 1.5 years until there is no land owner (manager).

  In step S94, the processor 21 determines whether or not the year t3 of ownership transfer by inheritance is a property that is 30 years or more and less than 50 years from the survey year t2, that is, 30 ≦ t3 <50. If the determination result is Yes, the process proceeds to step S95-1, and if the determination result is No, the process proceeds to step S96. In step S95-1, the processor 21 determines that the owner is an elderly person (for example, 70 years old for men and 73 years old for women) and is at a level that allows conversation with the owner himself. Confirm as candidate D2. The average healthy life span w of men is 70.42 years old, and when the average estimated age s = 67 years old is used, the period V = w−s during which conversation with the owner himself is possible is 70.42−67 = 3.42. It will be a year. In step S93-2, the processor 21 sets the period during which the land can be managed (remaining management years) as u = (average healthy life span w of prefecture) − (average estimated age s of owners in survey year t2) +3 years. The process returns to the process of FIG. For example, when the year t3 of owner transfer by inheritance is 37 years and the owner has not changed since 37 years ago, the average estimated age s = 67 years. Since the healthy life expectancy w of men is 70.42 years old, u = (70.42−67) + 3 = 6.42 years, up to the age three years after the end of the healthy life expectancy w, 6.42 years Therefore, it can be seen that the period u in which the land can be managed is 6.42 years.

  In step S96, the processor 21 determines whether or not the year t3 of the owner transfer by inheritance is a property that is 20 years or more and less than 30 years from the survey year t2, that is, 20 ≦ t3 <30. If the determination result is Yes, the process proceeds to step S97-1, and if the determination result is No, the process proceeds to step S98. In step S97-1, the processor 21 is an elderly person (for example, 70 years old for men and 73 years old for women), can talk with the owner himself, and the owner himself manages the land for the future. The candidate C is determined as a candidate D1. The average healthy life expectancy w of men is 70.42 years old, and when the average estimated age s = 65 years old is used, the period V = w−s during which the owner can talk with himself is 70.42−65 = 5.42. It will be a year. In step S97-2, the processor 21 sets the period during which the land can be managed (remaining management years) as u = (average health life w of prefecture) − (average estimated age s of owners in the survey year) +5 years, The process returns to the process of FIG. Since the healthy life expectancy w of men is 70.42 years old, u = (70.42-65) + 5 = 10.42 years. Until the age five years after the end of the healthy life expectancy w, 10.42 years left Therefore, it can be seen that the period u during which the land can be managed is 10.42 years.

  In step S98, the processor 21 determines whether or not the year t3 of owner transfer by inheritance is a property less than 20 years from the survey year t2, that is, whether t3 <20, and the determination result is Yes. If there is, the process proceeds to step S99, and if the determination result is No, the process returns to the process in FIG. In step S99, the processor 21 excludes the candidate C from the candidates D1 to D3, and the process returns to the process of FIG.

  FIG. 14 is a diagram illustrating an example of the relationship between conditions and parameters used in the process of FIG. As shown in FIG. 14, when t ≧ 50, an example of a parameter, the period in which land can be managed (remaining management years) u is the land owner in the survey year t2 from the average life T of the corresponding prefecture. The number of years minus the average estimated age s. In the case of 30 ≦ t <50, the period u in which the land can be managed is the number of years obtained by subtracting the average estimated age s of the owner of the land in the survey year t2 from the average life expectancy T of the corresponding prefecture and adding 3 years. In addition, in the case of t ≦ 30, the period u in which the land can be managed is the number of years obtained by subtracting the average estimated age s of the owner of the land in the survey year t2 from the average life T of the corresponding prefecture and adding 5 years.

  Returning to the description of the processing in FIG. 3, in step S <b> 10, the processor 21 obtains at least data relating to the map information of the candidate B from the database (for example, map information database) 3-1 in the memory 22 of the computer 2-1. Based on the data related to the acquired map information, in the candidate B (B1 or B2) of the manager absent area predicted in step S7, the areas that meet the conditions are overlapped, The land use diversion price after the acquisition is judged and prioritized in descending order of diversion value.

FIG. 15 is a flowchart for explaining an example of the narrowing-down process of the manager absent area based on the data regarding the map information, which is executed by the processor 21 in step S10. In FIG. 15, in step S101, the processor 21 obtains the secular change of the land transaction price based on the published price for the candidate B (B1 or B2) in the manager absent area, which is included in at least the candidate B map information. Therefore, the median x (y) of each year (x) is obtained. In step S102, the processor 21 obtains, for example, a slope (a) of the change over time of the median land price (yen / m ² ) from a certain past period (for example, five years) to the present. Thereby, the equation y = ax + b can be derived. Here, b is an intercept, and is the land price (yen / m ² ) in the first year when the land price is evaluated in a certain period. In step S103, the processor 21 determines whether or not a ≧ 1, and if the determination result is Yes, the process proceeds to step S104-1, and if the determination result is No, the process proceeds to step S105 described later. move on. In step S104-1, the processor 21 has a tendency that the land price is on the rise, and even if there is a land where there is no manager, there is a utilization example in which the land has been obtained. Therefore, it is judged that the value of future land use is high. In step S104-2, the processor 21 determines that the land value is high and the diversion possibility is high, determines the candidate B as the candidate D1, and the process returns to the process of FIG.

  In step S105, the processor 21 determines whether or not 0 ≦ a <1, and if the determination result is Yes, the process proceeds to step S106-1, and if the determination result is No, the process is described later. The process proceeds to S107. In step S106-1, the processor 21 determines that the value of future land use is moderate. In step S106-2, the processor 21 determines that the land value is medium and the diversion possibility is medium, determines the candidate B as the candidate D2, and the process returns to the process of FIG.

  In step S107, the processor 21 determines whether or not a <0. If the determination result is Yes, the process proceeds to step S108-1, and if the determination result is No, the process proceeds to the process in FIG. Return. In step S108-1, the processor 21 determines that the value of future land use is low. In step S108-2, the processor 21 determines that the land value is low and the diversion possibility is low, determines the candidate B as the candidate D3, and the process returns to the process of FIG.

  FIG. 16 is a diagram illustrating an example of a relationship between conditions and parameters used in the process of FIG. As shown in FIG. 16, when a ≧ 1, the value of future land use, which is an example of a parameter, is determined to be high. If 0 ≦ a <1, the value of future land use is judged to be moderate. If a <0, the value of future land use is judged to be low.

For example, the example of the city U1 and the city U2 which went up as candidate B1, B2 is given. The land transaction price is determined from the slope of the secular change of the median of the past (for example, five years) land price (yen / m ² ). FIG. 17 is a diagram illustrating an example of land price information of the city U1 where a ≧ 1 (upward trend). FIG. 18 is a diagram illustrating an example of land price information of the city U2 where a <0 (downward trend). 17 and 18, the vertical axis represents the land transaction price, and the horizontal axis represents the year at the time of land transaction. In FIG. 17 and FIG. 18, square marks indicate average values, circle marks indicate median values, x marks indicate third quartiles, and triangle marks indicate first quartiles.

If the median value of land prices (yen / m ² ) in the past (for example, five years) in the city U1 and city U2 pays attention to the slope over time, the value of future land use is determined as follows: can do.

  For example, when the land transaction price is in an upward trend (slope a ≧ 1) as in the city U1, even if there is a land where there is no manager, it is determined that there is a high possibility of selling high thereafter. On the other hand, when the land transaction price is in a decreasing trend (a <0) as in the city U2, it is determined that it is difficult to sell even if the land without the manager is acquired. That is, the city U1 is determined as a candidate D1 having a high value for future land use and a high possibility of diversion. On the other hand, the city U2 is determined as a candidate D3 whose future land use value is low and the diversion possibility is low. In addition, although illustration is abbreviate | omitted, in the case of a city where the land transaction price tends to be flat (0 ≦ a <1), it is determined that there is a possibility that the land will be sold even if the land is not in the manager. That is, such a city has a medium value for future land use, and the possibility of diversion is determined as a medium candidate D2.

  In step S10, the processor 21 obtains land from the overlapping areas by overlapping the areas that meet the conditions in the candidate D (D1, D2, D3) of the manager absent area predicted in step S9. It is also possible to determine the diversion price for land use after the completion and prioritize in descending order of diversion value.

  In step S10, the processor 21 overlaps the areas that meet the conditions in the candidate E (E1, E2) of the manager absent area predicted in step S11 described later, and acquires the land from the overlapping areas. It is also possible to determine the diversion price for land use after the completion and prioritize in descending order of diversion value. In this case, in step S10, the processor 21 may acquire at least data relating to the map information of the candidate E from, for example, a database (eg, social information database) 3-1 in the memory 22 of the computer 2-1.

  Returning to the description of the processing in FIG. 3, in step S <b> 11, the processor 21 obtains at least data regarding social information of the candidate B from the database (for example, social information database) 3-1 in the memory 22 of the computer 2-1. Based on the data related to the acquired social information, among the candidate B (B1 or B2) of the manager absent area predicted in step S7, there is a secular change (that is, increase / decrease) of the social information within a certain period. Narrow down to large areas and prioritize in descending order of social information. In particular, here, it is characterized by narrowing down high priority areas that have land that has become abandoned farmland due to devastation caused by neglected farmland. Social information stored in the social information database includes, for example, an increase or decrease in the number of farmers over 65 years of age, an example of a certain age, a borrowed cultivated land ratio or a rented cultivated land ratio of a farmland or an orchard, a farmer family It may include the number, the number of family members of a two-generation family business, the number of family members of a three-generation family business.

  In a village in the municipality where Candidate B is located, for example, increase or decrease in the number of farmers over 65 years old, rate of borrowed cultivated land or rented farmland, or number of cultivated land, number of family members of a first-generation family farmer, number of family members Using social information such as the number of family members of a three-generation family business, the secular change of social information over a certain period is calculated, and areas with high fluctuations are extracted. Further, for example, there is a farmer who is 65 years or older, and an area where the number of farmhouses corresponding to this condition is reduced in the evaluation year is extracted.

  Borrowed arable land refers to cultivated land borrowed from other people for cultivation purposes. Loan cultivated land refers to self-owned cultivated land lent to others. In addition, abandoned farmland refers to land that was previously cultivated and has not been cultivated over the past year, and that has not been clearly cultivated over the past few years.

  As described above, the candidates narrowed down based on the economic information by the processes of steps S8 to S11 are changed to the environmental assessment information indicating the candidate environmental maintenance record, the owner on the real estate registry corresponding to the candidate map, and so on. Further refinement can be made based on at least one of the map information to be represented and the social information representing the status of the candidate land use.

  FIG. 19 is a flowchart for explaining an example of the narrowing-down process of the manager absent area based on the data related to social information, which is executed by the processor 21 in step S11. In FIG. 19, in step S <b> 111, the processor 21 extracts an area where there are many farmers who cannot manage land from social information. In step S112, the processor 21 determines whether or not there is an area where the extracted number of farmers has decreased by 50% or more in a certain period in the past, for example, the past 30 years. If the determination result is Yes, the process is step. The process proceeds to S112, and if the determination result is No, the process returns to the process of FIG. In step S113, the processor 21 extracts an area that has decreased by 50% or more in the past 30 years.

  In step S114, the processor 21 extracts regions with high land use value from the social information and assigns priorities. For example, it is considered that there are more borrowers in areas where the ratio of borrowed arable land is higher than in the surrounding area. In step S115, the processor 21 extracts, as a candidate E1, a village that is an example of an area having high land use value and high priority. In step S116, the processor 21 determines from the social information a village where the number of first-generation farms (L) is larger than the number of second-generation farms (M) and the number of third-generation farms (N). In step S116-1, the processor 21 determines whether or not L ≧ M + N. If the determination result is Yes, the process proceeds to step S116-2, and if the determination result is No, the process is described later. Proceed to S116-4. In step S116-2, the processor 21 determines from the social information that the village is likely to have an administrator who abandons the cultivated land. In step S116-3, the processor 21 extracts, as a candidate E1, a village that is determined to have a high possibility that there is an administrator who abandons the cultivated land, and the process proceeds to step S117 described later.

  On the other hand, in step S116-4, the processor 21 determines whether or not L <M + N. If the determination result is Yes, the process proceeds to step S116-5. If the determination result is No, the process is illustrated in FIG. Return to step 3. In step S116-5, the processor 21 determines from the social information that there are few managers who abandon the cultivated land. In step S116-6, the processor 21 extracts, as candidates E2, villages that are judged to have few managers who abandon the cultivated land, and the process proceeds to step S117.

  In step S117, the processor 21 sets a period (u) in which the land can be managed. In step S117-1, the processor 21 calculates the healthy life expectancy w of a male in the target prefecture from the average age (x) of a farmer who is operating one generation of the land of the candidate E1 (the national average is 70. A value obtained by subtracting 42 years old and 73.62 years old for women is set as a period (u) in which the land can be managed. That is, the candidate E1 is set to u = w− (average age x of 1 generation farmer). When the average healthy life expectancy of a prefecture is 74 years old and the average age of a 1st generation farmer in the area is 72 years old, the period (u) during which the land can be managed is set to 74-72.5 = 2.5 years. In step S117-2, the processor 21 calculates a value obtained by subtracting the healthy age w of the male in the target prefecture from the average age (x) of the farmer who is managing the first generation of the land of the candidate E2. The period (u) during which the land can be managed is set. That is, for the candidate E2, u = w− (average age x of one-generation farmhouses) +3 years. When the average healthy life expectancy of a prefecture is 74 years old and the average age of a 1st generation farmer in the area is 71.5 years old, the period (u) during which the land can be managed is set to 74-71.5 + 3 = 5.5 years The After step S117, the process returns to the process of FIG.

  FIG. 20 is a diagram illustrating an example of a basic index included in social information. FIG. 20 shows a basic index of the village W1 of the town of the candidate E1. The basic index shown in FIG. 20 includes a prefecture code, a municipality code, an old municipality code, and an industrial settlement code that include the candidate E1.

  FIG. 21 is a diagram illustrating an example of a farmer's situation included in social information. FIG. 21 shows the situation of the farmhouse in the village W1 of the candidate E1 town. In FIG. 21, it can be seen from the information enclosed by the ellipse w11 that L (2) ≧ M (0) + N (0) is established, and the rate of the 1st generation family management is 100% for the village W1.

  FIG. 22 is a diagram illustrating an example of the ownership situation and the borrowed cultivated land rate of the Tabata Orchard in the village W1. In FIG. 22, the vertical axis indicates the ownership status and the borrowed cultivated land ratio (%) of the village W1 in the village W1 obtained from the information indicated by the ellipse w12 in FIG. In FIG. 22, the horizontal axis represents the year.

  FIG. 23 is a diagram illustrating an example of a basic index included in social information. FIG. 23 shows basic indices of the village W2 of the town of the candidate E1. The basic index shown in FIG. 23 includes a prefecture code, a municipality code, an old municipality code, and an industrial settlement code that include the candidate E1.

  FIG. 24 is a diagram illustrating an example of a farmer's situation included in social information. FIG. 24 shows the situation of the farmhouse in the village W2 in the town of the candidate E1. In FIG. 24, it is understood from the information enclosed by the ellipse w21 that L (26) ≧ M (5) + N (0) is established, and for the village W2, the ratio of the first generation family management is 84%.

  FIG. 25 is a diagram illustrating an example of the ownership situation and the borrowed cultivated land rate of the Tabata Orchard in the village W2. In FIG. 25, the vertical axis indicates the ownership status and the borrowed cultivated land ratio (%) of the village W2 in the village W2, which is obtained from the information surrounded by the ellipse w22 in FIG. In FIG. 25, the horizontal axis indicates the year.

  From FIG. 20 to FIG. 25, the land use or land ownership rate of the village W1 in the village W1 is higher than the land ownership or borrowing land rate in the village W2 and the land use rate. It is understood that this is a high priority area. In other words, the village W1 is abandoned as it is because the next generation has no hand over to the next generation, but because of the high value of land use, the land transaction taking into account the takeover of the land manager is now under consideration. If done, it means that there is a high possibility of being handed over to an administrator for effective land use avoiding devastation.

  Returning to the description of the processing in FIG. 3, after step S10, the processing proceeds to step S12. In step S12, the processor 21 converts the condition based on the environmental assessment information, the condition based on the map information, and the condition based on the social information into a star chart using the address information extracted as the area where the manager is absent. In addition, the address where the application for high-priced medical expenses applicant is applied is added, and the area with many corresponding points is determined. Then, the remaining management years derived from the results are output.

  In this way, it is possible to predict the time when the predicted manager absent area occurs by the process of step S12.

  Note that the process of step S12 may be executed after any of steps S8 to S10.

  In this way, candidates for manager absent areas are extracted under conditions based on natural environment information, and candidates are narrowed down under conditions based on economic information. Further, candidates for manager absent areas are further narrowed down by a condition based on at least one of the conditions based on environmental assessment information, the conditions based on map information, and the conditions based on social information. In this case, by overlapping the areas that meet the conditions, narrow down the candidates for areas that are absent from the manager, and from the overlapping areas, determine the diversion price of land use after acquiring the land, in order of high diversion value. Prioritization can also be performed. In addition, in order to further narrow down the candidates for the absence of managers narrowed down by conditions based on natural environment information and economic information using conditions based on at least one of environmental assessment information, map information, and social information, The increase can be suppressed.

  That is, by adding the address for which, for example, high-priced medical fee applicant information (address) is applied, using a star chart with the candidate address information of the manager absent area extracted by the processing of steps S7 to S11, You may determine the area with many applicable places as a final candidate. Further, the remaining management years derived from the final candidate may be output.

  FIG. 26 is a diagram illustrating an example of output data. FIG. 26 shows an example of the output data in the star chart format displayed on the display device 24 by the processor 21 in step S11, for example. In this example, in the city U1, information indicating that the address name 1-20-20 is the first candidate C1 in the manager absent area and the remaining management years are 1.5 years is displayed. In the city U3, information indicating that the village W1 is the first candidate in the manager absent area and the remaining management years are 1.5 years is also displayed.

  FIG. 27 is a diagram illustrating an example of output data including a map. FIG. 27 shows an example of graphic format output data displayed on the display device 24 by the processor 21 in step S11, for example. In this example, in the city U1, the address name 1-20-20 is the first candidate C1 of the manager absent area, and in addition to the information indicating that the remaining management years are 1.5 years, the corresponding first Image information indicating the position of the one candidate C1 on the map is displayed.

  In addition, the format of displaying the candidate of the manager absent area, the remaining management years, and the like on the display device 24 is not particularly limited.

  According to the above embodiment, the manager absent area is predicted comprehensively using a plurality of information such as natural environment information, economic information, environmental assessment information, map information, and social information. Therefore, it is possible to accurately predict the time when the manager is absent. Therefore, in addition to changes in the natural environment and economic information, collect environmental assessment information, map information, social information, etc., including personal records, and select a region that will be a reserve army such as vacant houses, abandoned farmland, It becomes possible to predict when the owner will be absent.

  Next, an example of the utilization scene of the above embodiment will be described.

  In the case of utilization scenes at financial institutions, the real estate industry or financial institution uses land mortgage, etc. by reverse mortgage, etc. by predicting candidates for areas without managers as in the above example and predicting when they will be areas without managers. Can keep the value of. At the same time, senior citizens can use their funds as collateral for land without relying on pensions alone.

  In the case of utilization scenes in local governments, by predicting candidates for areas where managers are absent as in the above example, the local governments can create smart communities, smart cities, and towns rich in nature, etc. When it is implemented, it can be used for redevelopment such as urban greening by securing land to be devastated without the owner. Thereby, greening of an urban area is attained.

  In recent years, an increasing number of companies are moving to agricultural corporations, and securing farmland that has been maintained so far has become a challenge. In the case of a utilization scene in an agricultural corporation, it is possible to predict a candidate for a manager-absent area as in the above example and predict when it will be a manager-absent area. You can know where you can rent farmland.

  For example, depending on the region, there is a possibility that the population living in the suburbs may decrease as the population decreases or the population concentrates in urban areas. In the case of utilization in railway real estate, it is possible to predict candidates for the absence of managers as in the above example and predict when they will be in the absence of managers. By considering the development, you can promote the creation of a more attractive city.

  According to the utilization scene as described above, it is possible to improve the value of the land and to obtain a rich life for the elderly by obtaining funds through reverse mortgage. In addition, as a ripple effect, it is possible to improve the comfort and sense of security in urban areas. In urban areas, ruined empty houses become a problem from the viewpoint of the landscape and the safety of the surrounding residents, so the removal of heavy ruins is clearly visible from the viewpoint of the landscape. On the other hand, it will prevent the beautiful landscape from devastation in rural areas, connect the land that has been maintained by farmers who have no farmland bearers to the next generation, and maintain the landscape and improve the functions of the farmland. In addition, it can be expected to secure and preserve the habitats of animals and plants that inhabit the natural environment where Satochi Satoyama is maintained.

The following additional notes are further disclosed with respect to the embodiment including the above examples.
(Appendix 1)
Means for dividing the first region to be evaluated into a plurality of second regions;
Based on the natural environment information representing the natural environment of the first area, a third area that is an urban area or a developed area in the past first predetermined period among the plurality of third areas forming each second area. Means for extracting a second area including a third area where the area of the area is increased by a certain percentage or more and setting it as a candidate for an administrator absent area;
Means for narrowing the candidates to candidates whose economic conditions have deteriorated by a certain percentage or more in the past second fixed period based on economic information representing the economic conditions of the candidates;
The candidates that have been narrowed down are environmental assessment information that represents a record of the environmental improvement of the candidate, map information that represents the owner of the real estate corresponding to the candidate map, and a society that represents the status of land use of the candidate. Means for further narrowing down based on at least one of the information and predicting when the predicted absence of the administrator area occurs;
A device for predicting an area where an administrator is absent, characterized by comprising:
(Appendix 2)
The said natural environment information contains the vegetation natural degree of the said 1st area | region, The apparatus which estimates the manager absent area of Additional remark 1 characterized by the above-mentioned.
(Appendix 3)
The means for setting the candidate is a second area where the third area where the vegetation natural degree is “1” indicating an urban area or a developed area is increased by at least one, or the vegetation natural degree is farmland. There is one third region which is “2” or “3” indicating “,” or “7”, “8” or “9” indicating a secondary forest, a secondary forest close to nature, or a natural forest, respectively. The apparatus for predicting an administrator absent area according to appendix 2, wherein a second area in which a ratio of occupying the second area is decreased by a certain ratio or more is set as the candidate.
(Appendix 4)
The means for narrowing down the candidate against damages caused by natural organisms, tax revenues of municipalities included in the economic information, areas where the number of applications for high medical expenses is increasing, areas where complaints about natural organisms are increasing, and Prediction of an administrator absent area according to any one of appendices 1 to 3, characterized in that at least one of the countermeasure costs is narrowed down to candidates that have deteriorated by a certain percentage or more in the second predetermined period in the past. Device to do.
(Appendix 5)
The predicting means executes the area and year in which the narrowed candidates are subjected to environmental assessment in the new housing development project or residential land city infrastructure included in the environmental assessment information in the second predetermined period in the past. The apparatus for predicting an administrator absent area according to any one of appendices 1 to 4, characterized in that the candidates are narrowed down to candidates.
(Appendix 6)
The means for predicting the narrowed candidates, the time of owner transfer by inheritance of the registry included in the map information, real estate information related to property tax or inheritance tax, and the area where the published price has decreased for a certain period 6. The appendix 1 to 5, wherein at least one of the managers is limited to candidates whose managers are absent within a certain period or whose diversion value is increased by a certain percentage or more within a certain period. A device that predicts areas where managers are absent.
(Appendix 7)
The means for predicting the increase or decrease in the number of farmers over a certain age included in the social information, the borrowed cultivated land ratio or the rented cultivated land ratio of the farmland or the orchard, the number of family members of a first-generation family farmer Appendices 1 to characterized in that at least one of the number of family members of a two-generation family business and the number of family members of a three-generation family business is narrowed down to candidates that have changed more than a certain percentage in the second predetermined period in the past. The apparatus which predicts the manager absent area of any one of 6.
(Appendix 8)
Dividing the first region to be evaluated into a plurality of second regions;
Based on the natural environment information representing the natural environment of the first area, among the plurality of third areas forming each second area, the first area that is a city area or a developed area in the past first predetermined period. The second area including the third area where the area of 3 has increased by a certain percentage or more is extracted and set as a candidate for an administrator absent area,
Based on the economic information representing the economic status of the candidates, the candidates are narrowed down to candidates whose economic status has deteriorated by a certain percentage or more in the past second fixed period,
The candidates that have been narrowed down are environmental assessment information that represents a record of the environmental improvement of the candidate, map information that represents the owner of the real estate corresponding to the candidate map, and a society that represents the status of land use of the candidate. Further refine based on at least one piece of information,
Predict when the predicted admin absent area will occur,
A method for predicting an area where an administrator is absent, characterized in that the processing is executed by a computer.
(Appendix 9)
The method for predicting an administrator absent area according to appendix 8, wherein the natural environment information includes a vegetation natural degree of the first area.
(Appendix 10)
The candidate is set in the second region where the third region where the vegetation natural degree is “1” indicating an urban area or a developed land is increased by at least one, or the vegetation natural degree indicates an agricultural land. “2” or “3”, or a third region that is “7”, “8”, or “9” indicating a secondary forest, a secondary forest close to nature, or a natural forest, respectively. The method for predicting an administrator absent area according to appendix 9, wherein the second area in which the ratio of occupying the area is reduced by a certain ratio or more is set as the candidate.
(Appendix 11)
The narrowing down is a measure against the damage caused by natural organisms, the tax revenue of municipalities included in the economic information, the region where the number of applications for high medical expenses is increasing, the number of complaints about natural organisms is increasing, and The manager-absent area according to any one of appendices 8 to 10, wherein at least one of the expenses is narrowed down to candidates that have deteriorated by a certain percentage or more in the second predetermined period in the past. Method.
(Appendix 12)
In the further narrowing down, the area and year in which the environmental assessment was conducted in the new second period of the past in the new housing development project or residential land urban infrastructure development included in the environmental assessment information. 12. The method for predicting an administrator absent area according to any one of appendices 8 to 11, wherein the candidate is narrowed down to candidates.
(Appendix 13)
In the further narrowing down, the narrowed candidates are classified into the time of ownership transfer by inheritance of the registry included in the map information, real estate information related to property tax or inheritance tax, and the area where the published price has fallen for a certain period of time. The management according to any one of appendices 8 to 12, characterized in that at least one of the managers is absent within a certain period or is narrowed down to candidates whose diversion value is increased by a certain percentage or more within a certain period. A device that predicts areas where people are absent.
(Appendix 14)
In the further narrowing down, the narrowed candidates are obtained by increasing / decreasing the number of farmers over a certain age included in the social information, the borrowed cultivated land ratio or the rented cultivated land ratio of farmland or garden, Additional remarks 8 to 13 characterized in that at least one of the number of generation family-managed families and the number of family members of three-generation family-run is narrowed down to candidates that have changed more than a certain percentage in the past second certain period. A method for predicting an administrator absent area according to any one of the above.
(Appendix 15)
A program for causing a computer to execute a process for predicting an area where an administrator is absent,
Dividing the first region to be evaluated into a plurality of second regions;
Based on the natural environment information representing the natural environment of the first area acquired from the first database, among a plurality of third areas forming each second area, in the past first fixed period. , Extract the second area including the third area where the third area that is an urban area or a developed area has increased by a certain percentage or more and set it as a candidate for an administrator absent area,
Based on the economic information representing the economic situation of the region obtained from the second database, the candidates are narrowed down to candidates whose economic situation has deteriorated by a certain percentage or more in the past second fixed period,
Environmental assessment information representing the candidate's environmental maintenance records obtained from the third database for the narrowed candidates, owners on the real estate registry corresponding to the candidate map obtained from the fourth database Further narrowing down based on at least one piece of information that is obtained from the map information that represents and the social information that represents the status of land use of the candidate obtained from the fifth database,
Predict when the predicted admin absent area will occur,
A program for causing a computer to execute processing.
(Appendix 16)
The program according to claim 15, wherein the natural environment information includes a vegetation natural degree of the first region.
(Appendix 17)
The candidate is set in the second region where the third region where the vegetation natural degree is “1” indicating an urban area or a developed land is increased by at least one, or the vegetation natural degree indicates an agricultural land. “2” or “3”, or a third region that is “7”, “8”, or “9” indicating a secondary forest, a secondary forest close to nature, or a natural forest, respectively. The program according to appendix 16, wherein the second area in which the ratio of occupying the area is reduced by a certain ratio or more is set as the candidate.
(Appendix 18)
The narrowing down is a measure against the damage caused by natural organisms, the tax revenue of municipalities included in the economic information, the region where the number of applications for high medical expenses is increasing, the number of complaints about natural organisms is increasing, and 18. The program according to any one of appendices 15 to 17, wherein at least one of the expenses is narrowed down to candidates that have deteriorated by a certain percentage or more in the second predetermined period in the past.
(Appendix 19)
In the further narrowing down, the area and year in which the environmental assessment was conducted in the new second period of the past in the new housing development project or residential land urban infrastructure development included in the environmental assessment information. The program according to any one of appendices 15 to 18, wherein the program is narrowed down to candidates.
(Appendix 20)
In the further narrowing down, the narrowed candidates are classified into the time of ownership transfer by inheritance of the registry included in the map information, real estate information related to property tax or inheritance tax, and the area where the published price has fallen for a certain period of time. 20. The program according to any one of appendices 15 to 19, wherein at least one of the managers is limited to candidates whose manager is absent within a certain period or whose diversion value is increased by a certain percentage or more within a certain period. .
(Appendix 21)
In the further narrowing down, the narrowed candidates are obtained by increasing / decreasing the number of farmers over a certain age included in the social information, the borrowed cultivated land ratio or the rented cultivated land ratio of farmland or garden, Additional remarks 15 to 20 characterized in that at least one of the number of generation family-managed families and the number of family members of three-generation family-run is narrowed down to candidates that have changed by a certain percentage or more in the second predetermined period in the past. The program according to any one of the above.

  As mentioned above, although the apparatus, method, and program which estimate the area where the manager is absent of the disclosure have been described by the embodiments, the present invention is not limited to the above embodiments, and various modifications and improvements can be made within the scope of the present invention. It goes without saying that it is possible.

1 Information Processing System 2-1 to 2-n Computer 3-0 to 3-n Database (DB)
4 Network 20 Bus 21 Processor 22 Memory 23 Input device 24 Display device 25 Interface

Claims (5)

Means for dividing the first region to be evaluated into a plurality of second regions;
Based on the natural environment information representing the natural environment of the first area, among the plurality of third areas forming each second area, the first area that is a city area or a developed area in the past first predetermined period. Means for extracting a second area including the third area in which the area of 3 is increased by a certain percentage or more and setting it as a candidate for an administrator absent area;
Means for narrowing the candidates to candidates whose economic conditions have deteriorated by a certain percentage or more in the past second fixed period based on economic information representing the economic conditions of the candidates;
The candidates that have been narrowed down are environmental assessment information that represents a record of the environmental improvement of the candidate, map information that represents the owner of the real estate corresponding to the candidate map, and a society that represents the status of land use of the candidate. Means for further narrowing down based on at least one of the information and predicting when the predicted absence of the administrator area occurs;
A device for predicting an area where an administrator is absent, characterized by comprising:   The said natural environment information contains the vegetation natural degree of the said 1st area | region, The apparatus which estimates the manager absence area of Claim 1 characterized by the above-mentioned.   The means for extracting the candidate is a second area where the third area where the vegetation natural degree is “1” indicating an urban area or a developed area is increased by at least one, or the vegetation natural degree is farmland. There is one third region that is “2” or “3” indicating “,” or “7”, “8”, or “9” indicating a secondary forest, a secondary forest close to nature, or a natural forest, respectively. The apparatus for predicting an administrator absent area according to claim 2, wherein a second area in which a ratio of occupying the second area is decreased by a certain ratio or more is set as the candidate. Dividing the first region to be evaluated into a plurality of second regions;
Based on the natural environment information representing the natural environment of the first area, among the plurality of third areas forming each second area, the first area that is a city area or a developed area in the past first predetermined period. The second area including the third area where the area of 3 has increased by a certain percentage or more is extracted and set as a candidate for an administrator absent area,
Based on the economic information representing the economic status of the candidates, the candidates are narrowed down to candidates whose economic status has deteriorated by a certain percentage or more in the past second fixed period,
The candidates that have been narrowed down are environmental assessment information that represents a record of the environmental improvement of the candidate, map information that represents the owner of the real estate corresponding to the candidate map, and a society that represents the status of land use of the candidate. Further refine based on at least one piece of information,
Predict when the predicted admin absent area will occur,
A method for predicting an area where an administrator is absent, wherein the process is executed by a computer. A program for causing a computer to execute a process for predicting an area where an administrator is absent,
Dividing the first region to be evaluated into a plurality of second regions;
Based on the natural environment information representing the natural environment of the first area acquired from the first database, among a plurality of third areas forming each second area, in the past first fixed period. , Extract the second area including the third area where the third area that is an urban area or a developed area has increased by a certain percentage or more and set it as a candidate for an administrator absent area,
Based on the economic information representing the economic status of the candidate obtained from the second database, the candidates are narrowed down to candidates whose economic status has deteriorated by a certain percentage or more in the past second fixed period,
Environmental assessment information representing the candidate's environmental maintenance records obtained from the third database for the narrowed candidates, owners on the real estate registry corresponding to the candidate map obtained from the fourth database Further narrowing down based on at least one piece of information that is obtained from the map information that represents and the social information that represents the status of land use of the candidate obtained from the fifth database,
Predict when the predicted admin absent area will occur,
A program for causing a computer to execute processing.