JP6703390B2 - Unoccupied house information generation program - Google Patents

Description

The present invention relates to a vacant house information generation program that performs a process of calculating information about an vacant house.

Conventionally, a technique of collecting and processing unoccupied house information has also been proposed. (For example, Patent Document 1)
Patent Document 1 describes a technique for periodically checking a telephone number of resident information by using a telephone number sending computer device and setting an empty house flag when the telephone number is unused. There is.

JP, 2005-338962, A

With the technique described in Patent Document 1, it is possible to know only the possibility that there is an empty house, and it is not possible to know appropriate information about the empty house.

According to an embodiment of the present invention, a processing unit having a calculation function for calculating vacant house information indicating the possibility of deterioration of an vacant house causes the processing unit to implement a reference function for referring to map data stored in the storage unit. An empty house information generation program is provided. In this program, the map data includes survey information indicating the survey status of each of the plurality of buildings and history information indicating the time since the survey information was acquired. The calculation function includes the function of referring to the survey information and history information stored in, and the longer the time after the survey information is acquired is based on the survey information and history information, the higher the possibility of deterioration of the vacant house. The empty house information is calculated as follows .

In the above embodiment, the map data includes height information about the height of the building and coefficient information according to the height of the building, and the reference function is the height information stored in the storage unit by the processing unit. includes a function of referring to the coefficient information, calculation functions, when engaged number information is the first coefficient information, calculates a first peripheral impact with the height information and the first coefficient information, engaging When the numerical information is the second coefficient information, the second peripheral influence degree is calculated using the height information and the second coefficient information .

The present invention can be realized in various modes. For example, it can be realized in the form of an vacant house information generating device, an vacant house information searching device, an vacant house information searching method, an vacant house information searching system, and the like.

It is a block diagram showing an unoccupied house information generation system. It is explanatory drawing which shows an example of a building attribute. It is an explanatory view showing an example of survey data. It is a flow chart which shows an example of calculation processing of information showing aging deterioration of an unoccupied house. It is an explanatory view showing an example of peripheral situation data. It is a flow chart which shows peripheral influence degree 1 calculation processing. It is a figure which shows the area coefficient data which matched the area of a building and the area coefficient. It is a figure which shows the height coefficient data which matched the height of a building and a height coefficient. It is a figure which shows the example of a display which set the influence area which used the surrounding influence degree 1 of the area of each building. It is a figure which shows the example of a display which set the influence area which used the surrounding influence degree 1 of the height of each building. It is a flow chart which shows an example of peripheral influence degree 2 calculation processing. It is a flow chart which shows an example of peripheral influence degree 3 calculation processing. It is explanatory drawing which shows an example of a crime prevention coefficient and a landscape coefficient. It is an explanatory view showing an example of a crime prevention influence degree and a landscape influence degree about each building. It is explanatory drawing which shows the example of a display of an unoccupied house evaluation value. It is explanatory drawing which shows the example of a display which set the surrounding influence degree 2. It is explanatory drawing which shows the example of a display which set the surrounding influence degree 3. It is explanatory drawing which shows another example of investigation data. It is explanatory drawing which shows another example of investigation data. It is explanatory drawing which shows another example of investigation data.

First embodiment:
The vacant house information generation system and the vacant house evaluation calculation process as the first embodiment of the present invention will be described. FIG. 1 is an explanatory diagram illustrating the configuration of the unoccupied house information generation system of the present embodiment. The unoccupied house information generation system includes a terminal 20 and a server 30. The terminal 20 and the server 30 are connected via the Internet INT in a state where bidirectional communication is possible.

The terminal 20 is a computer and includes a CPU 21, a ROM 22, a RAM 23, a storage unit 24, an input unit 25, and a display unit 27.
The CPU 21 controls the overall operation of the terminal 20 by reading the control program stored in the ROM 22, loading it in the RAM 23 and executing it. The storage unit 24 stores a program (hereinafter, referred to as “vacant house evaluation calculation program”) for performing an empty house evaluation calculation process described later. The CPU 21 functions as a processing unit 26 that performs information calculation processing and the like by reading and executing the unoccupied house evaluation calculation program stored in the storage unit 24. The input unit 25 is used by the user to input various instructions such as geographical information into the terminal 20. The display unit 27 is composed of, for example, a liquid crystal display and displays various information. In addition, in this embodiment, an example in which a computer is used as the terminal 20 has been described, but a mobile phone, a PDA, a smartphone, a navigation device, or the like may be used instead of the computer.

The server 30 includes a CPU 31, a ROM 32, a RAM 33, and a storage unit 34.
The CPU 31 controls the operation of the server 30 by expanding a predetermined program stored in the ROM 32 into the RAM 33 and executing the program. The storage unit 34 includes map data 341 and peripheral situation data 342. The map data 341 and the surrounding situation data 342 will be described below.

The map data 341 relates to road network data composed of links representing passages and nodes representing intersections, polygon data representing the shape of each building or road for drawing a map including buildings and roads, and each building. And feature data including building attribute data indicating attributes.
FIG. 2 is an explanatory diagram showing an example of building attributes.
The building attribute is included in the map data 341, and is an example of data including ID of each building, building name, unoccupied house flag, address, and information indicating the possibility of deterioration of unoccupied house, history, and type. It is configured to include.
The ID is an identifier for identifying each building. The building name is information expressing the name given to each building. The empty house flag is a flag indicating whether each building is an empty house. In the present embodiment, ◯ is given to a building that corresponds to an empty house, and − is given to a building that does not correspond to an empty house.
The address is information expressing the address of each building.
The survey data includes the information that the surveyor has conducted on-site survey for each building. The survey data is composed of the values acquired by the survey of each item from item No. 1 to item No. 6 for each building. Details of each item number will be described later in detail with reference to FIG.
The history is information indicating the number of years since the survey data was acquired. For example, when the history is 0, it indicates that the survey data was acquired within one year. When the history is 1, it indicates that the survey data was acquired one year ago. Thus, the lower the historical number, the fewer years the survey data has been acquired (that is, the newer the survey data), and the higher the historical number, the more years the survey data has been acquired. (That is, the survey data is old). The type is information indicating the structural type of each building such as reinforcing bars and wooden structures. In the present embodiment, when the structural type of the building is a reinforcing bar, the reinforcing bar is used, when the structural type of the building is wooden, it is wooden, and when the structural type of the building is unknown, it is −. In the present embodiment, the structural type of the building is reinforced, wooden, or unknown, but other structural types such as steel or reinforced concrete may be used.

In addition, in the present embodiment and other embodiments, the vacant house is intended to be a building or a work attached to the building, which is normally not used for living or other uses, but is not limited to this. Not done. For example, a vacant house may include a building such as a villa which is seasonally or temporarily located. Further, the history is information indicating the number of years since the survey data was acquired, but the history may be information indicating the date and time after the survey data is acquired.

FIG. 3 is an explanatory diagram showing survey data. The item numbers in FIG. 3 correspond to the survey data 1 to 6 in FIG. As an example, the information on the mailbox in item No. 1 in FIG. 3 corresponds to the survey data 1 in FIG.
The survey data is the information surveyed by multiple investigators during the field survey, and the status of each building (No. 2 to No. 4, No. 6) and the usage status of each building (No. 1 and No. 5) It is composed of and. The higher the score of each survey item, the older the condition of each building or the lower the usage of each building. In other words, the higher the score of each survey item, the more severe the deterioration of an empty house building. In the present embodiment, the vacant house flag is given to the building corresponding to the vacant house and the deterioration state of the building is represented by the score of each survey item, but the vacant house flag may be given using the survey data. That is, for example, when the total of the survey data is equal to or larger than the predetermined value, the unoccupied house flag may be added.
In the survey data in FIG. 3, survey items are associated with each item number. The survey items for each item number are explained below.
Item No. 1 is a survey on whether or not leaflets and DM (direct mail) are stored in the mailboxes installed in each building. A higher score is given as the number of leaflets/DM accumulates, and a lower score is given as the number of leaflets/DM decreases.
Item No. 2 is an investigation of whether the window glass installed in each building is broken. A higher score is given as the degree of breakage of the window glass is increased, and a lower score is given as the degree of breakage of the window glass is decreased.
Item No. 3 is a survey of the presence or absence of curtains installed in each building. More windows without curtains will give a higher score, and more windows with curtains will give a lower score.
Item No. 4 is a survey on whether or not there is an electric meter installed in each building and whether the electric meter is stopped. If there is no electricity meter, 100 points will be awarded. If the electricity meter is stopped, 50 points will be awarded. On the other hand, when there is an electric meter or when the electric meter is operating, a low score is given.
Item 5 is a survey on whether or not people at the gates and entrances installed in each building can pass through. A higher score is given to the person who cannot pass the gate or the entrance, and a lower score is given to the person who can easily pass the gate or the entrance.
Item No. 6 is an investigation of whether the walls installed in each building are deformed, damaged, corroded, or the like. A higher score is given as the wall is deformed, damaged, corroded, etc., and a lower score is given as the wall is not deformed, damaged, corroded, etc.

In the present embodiment, a score of 100 is given according to the degree of applicability of each survey item, but the presence or absence of each survey item may be simply given, and the specific survey item is For example, the survey items may be weighted, for example, with a maximum of 200 points. Further, although the survey data are item numbers 1 to 6, the survey data includes information indicating aged deterioration state of the building and the usage status of the building. The contents of the survey data may be finely subdivided, and may be appropriately added or changed. For example, if it is left as it is, there is a danger that it will be extremely dangerous for safety, such as collapse, as the slope of the building or the foundation of the building is damaged or deformed, the roof or outer wall is damaged or deformed, the retaining wall surface However, the degree of water seeping out due to deterioration may be used as a survey item. In addition, if left as it is, there is a danger that it will be extremely harmful to the sanitary condition. The condition of leaving or illegal dumping may be used as the survey item. Also, assuming that the landscape is significantly impaired due to the lack of proper management, whether or not the roof and outer walls etc. meet the rules of landscape conservation established in the area where the building under study is located. Top Items to be investigated include whether or not they have been left severely damaged or dirty, whether or not many windowpanes have been left broken, and whether or not standing trees have grown to the extent that they cover the entire surface of the building. May be In addition, if it is inappropriate to leave it for the purpose of preserving the surrounding living environment, whether the branches of standing trees are sticking out on nearby roads, etc. Whether there is a large amount of termites that may adversely affect the living environment of local residents, whether unspecified people are left in a state where they can easily enter, etc. Good.

<Unoccupied house evaluation calculation processing>
The processing unit 26 performs a calculation process of calculating information indicating aged deterioration of the plurality of vacant houses, based on data including information indicating the possibility of deterioration of the vacant houses for each of the plurality of vacant houses.
FIG. 4 is a flowchart showing an example of a calculation process of information indicating the deterioration over time of an empty house. The unoccupied house evaluation calculation process is a process of requesting survey data (step S110), a process of obtaining survey data (step S120), a process of calculating the total survey data (step S130), and determining whether the survey data is the latest. It is calculated through processing (step S140), processing for calculating the history coefficient of the total of the survey data (step S150), processing for calculating the type coefficient (step S160), and processing for storing the vacant house evaluation value (step S170). It Here, it is data showing an example of the survey data obtained by the processing of steps S120 and S130, and data including information indicating the status of the vacant house for each of the plurality of vacant houses described above. The processing from step S140 to step S170 is an example of the above calculation processing.

When the unoccupied house evaluation calculation process is started, the processing unit 26 performs a survey data request process (step S110). The specific process of step S110 will be described below. First, the processing unit 26 displays on the display unit 27 a screen for inputting geographical information such as prefectures and municipalities (hereinafter referred to as “geographical information”). Then, the processing unit 26 stores the geographical information of the user input by the user using the input unit 25 in the RAM 23.

When the user inputs the geographical information into the input unit 25, the processing unit 26 requests the inputted geographical information from the server 30. Here, step S110 is completed. Upon receiving the request for geographical information from the processing unit 26, the CPU 31 (server 30) extracts the ID corresponding to the requested geographical information from the building attribute data of the map data 341. Then, the CPU 31 performs a process of extracting data (hereinafter referred to as “vacant house data”) corresponding to the ID having the unoccupied house flag from the IDs, and transmits the data to the processing unit 26. Then, the processing unit 26 acquires unoccupied house data corresponding to the requested geographical information. If the requested geographical information is Fukuoka prefecture, the vacant house data in Fukuoka prefecture is extracted. Further, if the requested geographical information is Kokurakita Ward, Kitakyushu City, Fukuoka Prefecture, vacant house data in Kokurakita Ward, Kitakyushu City, Fukuoka Prefecture is extracted. In this way, unoccupied house data according to the content of the requested geographical information is extracted.

In addition, in the present embodiment, the requested geographical information is the prefecture or the municipality, but the present invention is not limited to this, and the postal code, telephone number, latitude and longitude may be included in the geographical information. For example, if the requested zip code is 102-0072, the vacant house data of Iidabashi, Chiyoda-ku, Tokyo may be extracted. If the requested telephone number is 045, unoccupied house data in Yokohama City, Kanagawa Prefecture may be extracted. Further, the geographical information may adopt the current position acquired by the Global Positioning System (GPS). Of course, when the user does not input the geographical information into the input unit 25, the vacant house data in all over Japan may be extracted.

FIG. 2 shows building attribute data included in the map data corresponding to the geographical information input by the user. Among the building attributes ID1 to ID7, ID1 to ID6 having an empty house flag of ◯ are extracted. An ID7 having an unoccupied house flag of − is not an unoccupied house and is not extracted. That is, the data corresponding to ID1 to ID6 is empty house data.

When the unoccupied house data extraction process is completed, the CPU 31 performs a survey data acquisition process from the unoccupied house data. In FIG. 2, when the CPU 31 acquires the investigation data of ID1, item number 0, item number 20, item number 3, 40, item number 4, 60, item number 80, item number 6, 100. To get. In this way, the CPU 31 acquires the survey data of ID1 to ID6. After that, the CPU 31 transmits the acquired survey data to the processing unit 26. Then, the processing unit 26 stores the received survey data in the RAM 23, thereby completing the survey data acquisition process (step S120).

When the survey data acquisition process is completed, the processing unit 26 performs a survey data total calculation process (step S130). Specifically, in FIG. 2, when calculating the total of the survey data of ID1, the processing unit 26 determines that the item number 0, the item number 20, the item number 40, and the item number 40 are 60. The addition process of 80 of No. 5 and 100 of No. 6 is performed to calculate 300 as the total value. When calculating the total of the survey data of ID2, the processing unit 26 calculates 20 of item No. 1 of item No. 1, 80 of item No. 2 of 60, item No. 60 of item No. 4, 80 of item No. 4 and 100 of item No. 5. The addition processing of item No. 6 90 is performed to calculate 430 as the total value. In this way, the processing unit 26 calculates the total of the survey data of ID1 to ID6 in FIG.

When the process of calculating the total of the survey data is completed, the processing unit 26 uses the history to determine whether the survey data is the latest (step S140). Specifically, in FIG. 2, when the processing unit 26 determines whether or not the survey data of ID1 is the latest, since the history of ID1 is 0 and the survey data is acquired within one year, the latest survey is performed. It is determined to be data. When the processing unit 26 determines whether the survey data of ID2 is the latest, it is determined that the history of ID2 is 3, and the survey data acquired three years ago is not the latest survey data. As described above, the processing unit 26 uses the history to determine whether or not the survey data is the latest, and thus the survey data of ID1 is determined to be the latest among the survey data of ID1 to ID6, and the survey of ID2 to ID6 is performed. The data is determined to be out of date.

When it is determined that the survey data is the latest (step S140: Yes), the processing unit 26 stores the total of the survey data as the vacant house evaluation value in the storage unit 24 (step S170). When it is determined that the survey data is not the latest (step S140: No), the processing unit 26 calculates the history coefficient using the total of the survey data and the history (step S150).
In the present embodiment, the history coefficient is calculated as the number obtained by multiplying the history by the history.
That is, when the history coefficient of ID2 is calculated, 430 is multiplied by 3 to obtain 1290.
When calculating the history coefficient of ID3, 550 is multiplied by 2 to be 1100. When calculating the history coefficient of ID4, 270 is multiplied by 5 to obtain 1350. When calculating the history coefficient of ID5, 150 is multiplied by 7 to obtain 1050. When calculating the history coefficient of ID6, 40 is multiplied by 1 to obtain 40. In this way, a higher history coefficient is calculated as the history number increases.

When the history coefficient is calculated, the processing unit 26 calculates the type coefficient using the history coefficient and the type (step S160). In the present embodiment, the type coefficient is calculated by multiplying the history coefficient by the coefficient for each type. Specifically, it is multiplied by 0.1 when the type is rebar, by 1 when the type is wooden and by 0.5 when the type is unknown. As a result, the type coefficient of ID2 is 1290 multiplied by 1. The type coefficient of ID3 is 110 obtained by multiplying 1100 by 0.1. The type coefficient of ID4 is 675 obtained by multiplying 1350 by 0.5. The type coefficient of ID5 is 1050 multiplied by 1, which is 1050. The type coefficient of ID6 is 40 obtained by multiplying 40 by 1.

When the type coefficient is calculated, the processing unit 26 stores the total of the type coefficients as the vacant house evaluation value in the storage unit 24 (step S170). The vacant house evaluation value is a value obtained by multiplying the total of the survey data by the history and the type, and shows a value in which the aged deterioration corresponding to the type of the vacant house is taken into consideration in the survey data. The vacant house evaluation values of ID1 to ID6 are 300 for ID1, 1290 for ID2, 110 for ID3, 675 for ID4, 1050 for ID5, and 40 for ID6. Since the unoccupied house evaluation value is a value that takes into consideration the degree of aged deterioration according to the type of each unoccupied house, it indicates that the aged deterioration of ID6 is the smallest and the aged deterioration of ID2 is the highest. Then, the processing unit 26 stores the unoccupied house evaluation values of ID1 to ID6 in the storage unit 24, thereby ending the series of processes of the present embodiment.

According to the unoccupied house information generation system as the first embodiment described above, the unoccupied house evaluation value can be calculated based on the survey data including the information in which the situation of the unoccupied house is indexed. The unoccupied house evaluation value is high when the history is large, and is low when the history is small even for the same survey data. The vacant house evaluation value is high if the type is wooden, and the vacant house evaluation value is low if the type is reinforced even if the survey data is the same. In this way, by adding the history and the type to the survey data, it is possible to calculate the evaluation value that reflects the state of aging deterioration of the vacant house.
Then, the user using the terminal 20 can obtain the aging deterioration status of each vacant house as such a specific evaluation value (numerical value), so that the aging deterioration status of the vacant house can be specifically stated. I can figure it out.

Second embodiment:
Next, a second embodiment of the present invention will be described. The system configuration of the unoccupied house information generation system of the second embodiment is such that the surrounding condition data (FIG. 5) is used in addition to the map data, and the processing of the processing unit 26 (surrounding influence degree 1 calculation processing) (FIG. 6) is It differs from the one embodiment (FIG. 4).

<Surrounding situation data>
FIG. 5 is an explanatory diagram showing an example of the surrounding situation data. The surrounding situation data includes information indexing the situation in which an empty house may affect the surroundings, and includes the ID of each building, the building name, the latitude and longitude, the area, the height, the feature status, and the traffic volume. It is configured.
The ID is an identifier for identifying each building, and is the same as the building attribute ID.
The building name is information indicating the name given to each building and is the same as the building name of the building attribute.
The latitude/longitude is information indicating the latitude/longitude of each building. In this embodiment, the latitude and longitude of the center of each building are shown.
The area is information indicating the area of each building, and in this embodiment, each building is shown in square meters.
The height is information indicating the height of each building, and in the present embodiment, each building is indicated in units of meters.
The traffic volume is information indicating the traffic volume for each link, and in the present embodiment, the traffic volume is indicated as large or small in order from the largest traffic volume. Details of the traffic volume will be described later.

<Neighborhood influence degree 1 calculation processing>
The processing unit 26 performs a calculation process of calculating information indicating the influence on the surroundings of each of the plurality of unoccupied houses, based on the surrounding situation data. The surrounding situation data includes area information regarding the area of an empty house and/or height information regarding the height of the empty house. FIG. 6 is a flowchart showing the surrounding influence degree 1 calculation process. The surrounding influence degree 1 indicates the influence degree of each building on the surroundings depending on the area and height of each building. That is, since the area of a building that is an empty house is large, the area that affects security, hygiene, and scenery is wider than when the area of the building is small, and it is given to surrounding buildings and passersby. It is considered that there is a high degree of urgency such as the extent of adverse effects and danger. In addition, since the height of a building that is an empty house is high, compared with the case where the height of the building is low, there is a wider range of danger due to collapse, scattering, etc., and adverse effects on surrounding buildings and passersby etc. It is considered that there is a high degree of urgency such as the degree of The surrounding influence degree 1 calculation process is a process of obtaining an unoccupied house polygon (step S210), a process of obtaining area/height information (step S220), a process of calculating the neighborhood influence degree 1 (step S230), and an influence area setting ( It is calculated through the process of step S240). Here, the area/height information acquired in step S220 is data indicating an example of the above-mentioned surrounding situation data. The processing of steps S230 and S240 is an example of the above calculation processing.

When the peripheral influence degree 1 calculation process is started, the processing unit 26 performs a process of acquiring a polygon (step S210). First, as in step S110 of the first embodiment (see step S110), when the user inputs geographical information to the input unit 25, the processing unit 26 requests the input geographical information from the server 30. .. When the user inputs the geographical information into the input unit 25, the processing unit 26 requests the inputted geographical information from the server 30. The CPU 31 extracts a building that is an empty house from the surrounding situation data among the buildings included in the requested geographical information. As shown in FIG. 5, since the vacant house flags of the buildings A, B, C, D, E, F, and G are ◯, the processing unit 26 sets the buildings A, B, C, D, E, F, and G as vacant house data. To extract. Then, the CPU 31 extracts each polygon data corresponding to the extracted empty house data from the map data 341 as an empty house polygon. The CPU 31 acquires an unoccupied house polygon that is polygon data of an area corresponding to the requested geographical information and a road polygon included in the input geographical information. The CPU 31 transmits the acquired empty house polygon and road polygon to the processing unit 26, and the processing unit 26 stores the received empty house polygon and road polygon in the RAM 23.

When the process of acquiring polygons is completed, the processing unit 26 performs a process of acquiring area and/or height information (step S220). The processing unit 26 requests the CPU 31 for information on the area and/or height corresponding to the acquired empty house (buildings A, B, C, D, E, F, G), and the CPU 31 changes the surrounding situation data to the empty house. Obtain corresponding area and/or height information. The CPU 31 transmits the acquired area and/or height information corresponding to the vacant house to the processing unit 26, and the processing unit 26 stores the received area and/or height in the RAM 23.

When the process of acquiring the area and/or height information is completed, the processing unit 26 performs a peripheral influence degree 1 calculation process (step S230). Here, an example will be described in which the processing unit 26 uses the areas of the buildings A and D in FIG. 5 to perform the calculation process of the surrounding influence degree 1 regarding the area of each building. Here, as shown in FIG. 5, the area of the building A is 200 square meters, and the area of the building D is 100 square meters.

The processing unit 26 reads the area coefficient corresponding to the area of each building stored in the storage unit 24 based on the area of each building. FIG. 7 is a diagram showing area coefficient data in which the area of the building and the area coefficient stored in the storage unit 24 are associated with each other.
For example, since the area of the building A is 200 square meters, the processing unit 26 reads the area coefficient 5 corresponding to 200 square meters from the area coefficient data. Further, since the area of the building D is 100 square meters, the processing unit 26 reads the area coefficient 3 corresponding to 100 square meters from the area coefficient data.
Then, the processing unit 26 calculates a value (in square meters) by multiplying the area of each building by the area coefficient. For example, in the case of the building A, the processing unit 26 multiplies 200 square meters by the area coefficient 5 to calculate 1000 square meters. In the case of the building D, the processing unit 26 multiplies 100 square meters by the area factor 3 to calculate 300 square meters. Thus, the larger the building area is, the greater the influence on the surroundings is, as compared with the case where the area is small.
It should be noted that although an example of using the area of the building is shown in the present embodiment, the area may be not only the area of the building but also the area of the site. The area of the building may be the total floor area of the building or the area of the 1F portion.

Next, an example will be described in which the processing unit 26 uses the heights of the building A and the building E of FIG. 5 to perform the calculation process of the surrounding influence degree 1 regarding the height of each building. Here, as shown in FIG. 5, the height of the building A is 10 meters and the height of the building E is 20 meters.

The processing unit 26 reads the height coefficient corresponding to the height of each building stored in the storage unit 24 based on the height of each building. FIG. 8 is a diagram showing height coefficient data in which the heights of buildings and height coefficients stored in the storage unit 24 are associated with each other. For example, since the height of the building A is 10 meters, the processing unit 26 reads the height coefficient 30 corresponding to 10 meters from the height coefficient data. Since the height of the building E is 20 meters, the processing unit 26 reads the height coefficient 50 corresponding to 20 meters from the height coefficient data.
Then, the processing unit 26 calculates a value (in square meters) by multiplying the height of each building by the height coefficient. For example, in the case of the building A, the processing unit 26 multiplies 10 meters by the height coefficient 30 to calculate 300 square meters. In the case of the building E, the processing unit 26 multiplies 20 meters by the height coefficient 50 to calculate 1000 square meters. As described above, the higher the height of the building is, the greater the influence on the surroundings is, as compared with the case where the height of the building is low. Further, there is a possibility that roofs, walls, and the like will fall and scatter in the vacant house depending on the deterioration state, and the higher the height of the vacant house, the greater the effect.
The value obtained by multiplying the area of the building by the area coefficient or the value of the height of the building by the height coefficient is hereinafter referred to as a surrounding influence degree 1 calculated value.

When the calculation process of the surrounding influence degree 1 of the area and/or height of each building is completed, the processing unit 26 performs the influence region setting process (step S240). In the affected area setting process, a polygon based on the peripheral influence degree 1 calculation value is overlapped with the vacant house polygon of each building of the map including the road polygon and the vacant house polygon acquired in step S210, and the map in which the polygon is superimposed is displayed on the display unit 27. Perform the processing to be displayed on. In the present embodiment, the polygon based on the peripheral influence degree 1 calculated value is a polygon having a predetermined shape having the peripheral influence degree 1 calculated value as an area (hereinafter, referred to as “surrounding influence polygon”). The specific processing of the affected area setting processing will be described below. The processing unit 26 sets a reference point of an empty house polygon of each building. The reference point of the unoccupied house polygon can be set variously. For example, the center of gravity of an unoccupied house polygon may be set as a reference point, or the end points or sides of each unoccupied house polygon may be set as a reference point. Next, the surrounding influence polygon is superimposed on the reference point of the vacant house polygon of the building. This process can be set in various ways. For example, the center of gravity of the surrounding influence polygon may be superposed on the reference point of the vacant house polygon of the building, or the end points or some of the sides of the peripheral influence polygon may be superposed on the reference point of the vacant house polygon of the building. That is, any form may be adopted as long as the reference point of the unoccupied house polygon of the building and the surrounding influence polygon overlap. In the present embodiment, the reference point of the vacant house polygon is set to the center of gravity of the vacant house polygon, and the center of gravity of the vacant house polygon is overlapped with the center of gravity of the peripheral influence polygon. That is, when setting the influence area of the building A, the processing unit 26 superimposes the center of gravity of the surrounding influence polygon on the center of gravity of the empty house polygon of the building A. FIG. 9 is a diagram showing an example of display on the display unit 27 when the influence area is set using the peripheral influence degree 1 of the area of each building.
In FIG. 9, the seven buildings from Building A to Building G are buildings showing empty houses drawn on the display unit 27 by the processing unit 26 using the building polygons, and each empty house polygon has position information. ing. The road edge line is a road drawn by the processing unit 26 on the display unit 27 using road polygons. The links L1, L2, L3, L4, L5, L6, and L7 correspond to roads and include position information of the roads. Circular polygons that surround the buildings A, D, E, and F indicate the affected areas. In the present embodiment, the affected area is shown as a circle, but the shape of the affected area is not limited to a circle, and may be a quadrangle, a polygon, or the like.

Further, in the present embodiment, when performing the setting process of the influence area of the surrounding influence degree of the height of each building, the processing unit 26 superimposes the center of gravity of the surrounding influence polygon of the height of each building on the reference point of the empty house polygon. ing. That is, when setting the influence area of the building A, the processing unit 26 superimposes the center of gravity of the empty influence polygon of the building A on the center of gravity of the peripheral influence polygon of 300 square meters.
FIG. 10 is a diagram showing an example of display on the display unit 27 when the influence area is set using the peripheral influence degree 1 of the height of each building.
In FIG. 10, the seven buildings from building A to building G are buildings showing empty houses drawn by the processing unit 26 using empty house polygons, and each empty house polygon has position information. The road edge line is a road drawn by the processing unit 26 using road polygons. The links L1, L2, L3, L4, L5, L6, and L7 correspond to roads and include position information of the roads. Furthermore, the circles surrounding each of the buildings A, D, E, and F indicate the affected area.

As described above, in the present embodiment, the influence of each building on the surroundings can be calculated by calculating the surrounding influence degree 1 using the coefficient according to the area and/or height of each building.

In the present embodiment, the surrounding influence degree 1 is calculated by adding square meters to the value obtained by multiplying the area of each building or the height of each building by the area coefficient or the height coefficient. However, the area coefficient and the height coefficient are not limited to this, and the area coefficient and the height coefficient may be corrected in consideration of the population density and the building density. For example, the area coefficient may be corrected in consideration that the area of each building is small in the urban area and large in the rural area, and the area coefficient may be corrected. The area factor may be corrected to 4 for 80 square meters, 5 for 130 square meters, and 6 for 180 square meters. In urban areas such as Tokyo and Osaka, the height coefficient may be corrected in consideration of the tendency that there are many buildings with heights of three or more floors. For example, in urban areas, the height of buildings is 10 m. The height coefficient may be corrected to 20 in the case, 30 in the case of 15 m, and 40 in the case of 20 m. Furthermore, the area coefficient and the height coefficient may be corrected based on the value input by the input unit 25 by the user.

Further, in the present embodiment, the surrounding influence degree 1 is calculated using the area and/or height of the building, but the surrounding influence degree 1 may be calculated using the evaluation value of the vacant house. Specifically, when the evaluation value of an empty house is high, the surrounding influence degree 1 is also high, and when the evaluation value of an empty house is low, the surrounding influence degree 1 may be low.

Third embodiment:
Next, a third embodiment of the present invention will be described. The system configuration of the unoccupied house information generation system of the third embodiment is the same as that of the unoccupied house information generation system of the first embodiment.
The third embodiment is different from the first embodiment (FIG. 4) in that peripheral condition data (FIG. 5) is used in addition to map data, and peripheral influence degree 2 calculation processing (FIG. 11).

<Neighborhood influence degree 2 calculation process>
The processing unit 26 determines the vicinity of each of the plurality of empty houses based on the surrounding situation data, which is data including information indicating the situation in which the empty house may affect the surroundings of each of the plurality of empty houses. A calculation process is performed to calculate the information indicating the influence. The peripheral influence degree 2 process, which is an example of the calculation process, is defined based on the number of features in the affected area of the vacant house. The peripheral influence degree 2 indicates the influence degree of the peripheral influence degree 1 on features such as buildings and roads existing around each empty house. In the present embodiment, specifically, when the number of features existing in the affected area of the vacant house is large, the number of buildings that are adversely affected in the surroundings is large. Becomes On the other hand, when the number of features existing in the affected area of the unoccupied house is small, the number of buildings that are adversely affected in the surroundings is small, and thus the surrounding influence degree 2 of the unoccupied house becomes a low value.
FIG. 11 is a flowchart showing an example of the above calculation processing.
The surrounding influence degree 2 calculation process is a process of acquiring an unoccupied house polygon (step S210), a process of acquiring a predetermined distance range (step S250), a process of determining whether there is a surrounding feature (step S260), and a surrounding feature acquisition. Calculation (step S270) and processing for calculating the peripheral influence degree 2 (step S280).
Here, the area/height information used for acquiring the predetermined distance range in step S250 represents an example of the surrounding situation data. Further, the processing from step 260 to step S280 is an example of the above calculation processing.

The surrounding influence degree 2 calculation process will be specifically described. The processing unit 26 performs a process of acquiring the predetermined distance range (step S250). In this embodiment, step S250 is the same as the processing from step S210 to step 240 described in the second embodiment, and the predetermined distance range is the same as the affected area (see circles in FIGS. 9 and 10). The predetermined distance range may be set by the user using the value input by the input unit 25.

When the processing of acquiring the predetermined distance range is completed, the processing unit 26 determines whether there is a feature included in the predetermined distance range of each unoccupied house polygon (step S260). Specifically, when the user inputs geographical information into the input unit 25, the processing unit 26 requests the inputted geographical information from the server 30. The CPU 31 performs extraction processing of the feature information included in the requested geographical information, and transmits the feature information to the processing unit 26. The processing unit 26 stores the received feature information in the RAM 23. Then, the processing unit 26 determines the features included in the polygon within the predetermined distance range (that is, the influence region) from the feature information.
In the case of the building E of FIG. 9, the predetermined distance range of the vacant house polygon of the building E overlaps with the buildings C, G, and the link L4. It is determined that the object has the building C, the building G, and the link L4.
In the case of the building E of FIG. 10, the predetermined distance range of the vacant house polygon of the building E overlaps with the buildings C, D, F, G, and the links L2, L3, L4, L5, L6, and L7. It is determined that the features included in the predetermined distance range of the vacant house polygon of the building E include the buildings C, D, F, G and the links L2, L3, L4, L5, L6, L7.

When the processing for determining whether there is a feature included in the predetermined distance range of each unoccupied house polygon is completed, the processing unit 26 acquires a surrounding feature (step S270).
For example, in the case of the building E in FIG. 9 described above, the processing unit 26 acquires the building C, the building G, and the link L4. In the case of the building E of FIG. 10 described above, the processing unit 26 acquires the buildings C, D, F, G and the links L2, L3, L4, L5, L6, L7.

When the process of acquiring the surrounding features is completed, the processing unit 26 performs the surrounding influence degree 2 calculation process (step S280).
In the present embodiment, the processing unit 26 calculates the surrounding influence degree 2 from the number of features included in the range of the predetermined distance range. Specifically, the processing unit 26 calculates the value of the surrounding influence degree 2 in proportion to the number of features. For example, in the case of the building E in FIG. 9 described above, since the building C, the building G, and the link L4 are present, the processing unit 26 calculates the surrounding influence degree 2 as 3. In the case of the building E in FIG. 10 described above, there are buildings C, D, F, and G and links L2, L3, L4, L5, L6, and L7, so the processing unit 26 calculates the surrounding influence degree 2 as 10.
The processing unit 26 may also extract features by distinguishing them into buildings, roads, etc., and calculate the surrounding influence degree 2 for each. For example, in the case of the building E in FIG. 9 described above, since the building C, the building G, and the link L4 are included, the processing unit 26 calculates the surrounding influence degree 2 regarding the building as 2, and the surrounding influence degree 2 regarding the road as 1. May be. In the case of the building E of FIG. 10 described above, since there are buildings C, D, F, G and links L2, L3, L4, L5, L6, L7, the processing unit 26 sets the surrounding influence degree 2 of the building to 4, roads. The peripheral influence degree 2 regarding may be calculated as 6. Further, for example, the processing unit 26 may separately extract the building into a residential building, a business building, a condominium, a target building, and the like, and weight each to extract the surrounding influence degree 2. Specifically, the processing unit 26 may calculate the peripheral impact degree 2 by weighting the residential building in which children and women live day and night more than the corporate building, and weighting the residential building more than the corporate building. In addition, the processing unit 26 may calculate the surrounding influence degree 2 by weighting according to the number of condominium households and the number of employees of business office buildings. In addition, the processing unit 26 may weight the features around a specific feature, such as a school, to calculate the surrounding influence degree 2. In addition, when the feature is a road, the processing unit 26 may add the weight according to the traffic volume and calculate the surrounding influence degree 2.

As described above, since the features included in the predetermined distance range are extracted and calculated as the target of the surrounding influence degree 2 in the present embodiment, the influence of each building on the surrounding features can be calculated.

Fourth Embodiment:
Next, a fourth embodiment of the present invention will be described. The system configuration of the unoccupied house information generation system of the fourth embodiment is the same as that of the unoccupied house information generation system of the first embodiment.
In the fourth embodiment, the points that use the surrounding situation data (FIG. 5) in addition to the map data and the surrounding influence degree 3 calculation processing (FIGS. 12, 13, and 14) are the same as those in the first embodiment (FIG. 4). Different.

<Neighborhood influence degree 3 calculation processing>
The surrounding influence degree 3 calculation is based on the surrounding situation data, which is data including information indicating the situation in which the vacant house may affect the surroundings of each of the plurality of vacant houses, based on the surrounding situation data. A calculation process for calculating the information indicating the influence on is included. The surrounding situation includes the situation of the features surrounding the empty house and/or the traffic volume of the passage that contacts the empty house. FIG. 12 is a flowchart showing the surrounding influence degree 3 calculation processing which is an example of the above calculation processing. The surrounding influence degree 3 indicates the degree of influence on crime prevention and landscape of each building. The surrounding influence degree 3 calculation process is a process of acquiring an unoccupied house polygon (step S210), a process of acquiring the feature status and/or the amount of traffic (step S280), a process of acquiring a crime prevention coefficient and a landscape coefficient (step S285), A process of determining the presence or absence of a feature situation (step S290), a process of adding a crime prevention coefficient (step S300), a process of adding a landscape coefficient (step S305), and a process of determining whether or not the traffic amount is equal to or more than a predetermined value (step S310). , The crime prevention coefficient is added (step S320), the landscape coefficient is added (step S325), and the crime prevention degree and the landscape influence degree are calculated (step 330). Here, the feature status and/or the amount of traffic in step S280 represents an example of the above-mentioned surrounding situation data. The processing from step S285 to step S330 is an example of the above calculation processing. The process of obtaining an unoccupied house polygon (step S210) is the same as in the second embodiment. Hereinafter, description will be made with reference to FIGS. 5, 12, 13 and 14.

The surrounding influence degree 2 calculation process will be specifically described.
When the process of acquiring an unoccupied house polygon is completed (step S210), the processing unit 26 requests the server 30 for the surrounding situation data of FIG. The CPU 31 performs a process of acquiring the feature status and/or the traffic volume from the surrounding condition data of FIG. 5 and transmits the feature status and/or the traffic volume to the processing unit 26 (step S280). Here, the feature status and/or the traffic volume is information indicating an example of the above-mentioned peripheral status.
In the present embodiment, the feature status features are features surrounding each building such as a fence, a tree, a wall, and a retaining wall, and the feature status is information indicating the presence or absence of the feature and the height of the feature. Is. Next, the traffic volume is information indicating the traffic volume of pedestrians and the traffic volume of vehicles etc. for each link adjacent to the building, and is set to be large or small according to the traffic volume.
As shown in FIG. 5, buildings with feature status are A, C, E, and F. That is, the height of the feature situation of A is 5 m, the height of the feature situation of C is 2 m, the height of the feature situation of E is 3 m, and the height of F is 1.5 m of the feature situation. Further, regarding the traffic volume of the links L1 to L6, L1, L2, L3, and L4 are small, L5 is large, L6 is large, and L7 is small. Therefore, the processing unit 26 extracts the feature statuses of A, C, E, and F and the traffic volume from L1 to L7. The processing unit 26 stores in the RAM 23 the feature states of A, C, E, and F and the traffic volume up to L1, L2, L3, L4, L5, L6, and L7.

When the process of acquiring the feature status and/or the traffic volume is completed (step S285), the processing unit 26 performs the process of acquiring the crime prevention coefficient and the landscape coefficient stored in the storage unit 24 (step S285).
FIG. 13 is an explanatory diagram showing an example of the crime prevention coefficient and the landscape coefficient. In the present embodiment, the crime prevention coefficient indicates the possibility of a suspicious person hiding in an empty house, and the value of the crime prevention coefficient increases as the possibility of a suspicious person hiding in an empty house increases. The landscape coefficient indicates that there is a high possibility that an unoccupied house will be exposed to the public more likely to deteriorate the scenery, and the value of the scenic coefficient increases as the unoccupied house becomes more visible. The crime prevention coefficient and the landscape coefficient are stored in the storage unit 24, and the crime prevention coefficient and the landscape coefficient are associated with each other depending on the presence/absence of a feature state and the size of the traffic volume. That is, when there is a feature situation, the crime prevention coefficient is 100 and the landscape coefficient is 0. If there is no feature status, the crime prevention coefficient is 0 and the landscape coefficient is 100. When the traffic volume is large, the crime prevention coefficient is 100 and the landscape coefficient is 0. When the traffic volume is small, the crime prevention coefficient is 0 and the landscape coefficient is 100. Note that the crime prevention coefficient and the landscape coefficient may be values set by the user in the input unit 25.

When the process of acquiring the feature status and/or the traffic volume is completed, the processing unit 26 performs a process of determining the presence or absence of the feature status (step S290).
The processing unit 26 determines that the buildings A, C, E, and F having the feature situation have the feature situation (step S290: Yes), while the processing unit 26 causes the buildings B, D having no feature situation to exist. , G is determined to have no feature status (step S290: No).

When the process of determining the presence/absence of a feature state is completed, the processing unit 26 performs a crime prevention coefficient addition process (step S300) and a landscape coefficient addition process (step S305). For the buildings A, C, E, and F determined to have the feature status (step S290: Yes), the processing unit 26 adds the crime prevention coefficient (step S300). Specifically, since the buildings A, C, E, and F have feature conditions and the crime prevention coefficient is 100, the processing unit 26 adds 100 to the crime prevention coefficient for the buildings A, C, E, and F. On the other hand, for the buildings B, D, and G that are determined to have no feature status (step S290: No), the processing unit 26 adds the landscape coefficient (step S305). Specifically, the processing unit 26 adds 100 landscape factors to the buildings B, D, and G because the buildings B, D, and G have no feature status and the landscape factor is 100.

When the addition process of the crime prevention coefficient and the landscape coefficient is completed, the processing unit 26 performs a process of determining whether the traffic amount is equal to or more than a predetermined value (step S310). Specifically, the processing unit 26 determines a link with a large traffic volume of each link and a link with a small traffic volume. Therefore, the processing unit 26 determines L5 and L6 as the links having a large traffic volume. Further, the processing unit 26 determines L1, L2, L3, L4, and L7 as the links having a small traffic amount.

When the process of determining whether the traffic amount is the predetermined value or more is completed, the processing unit 26 performs the crime prevention coefficient addition process (step S320) and the landscape coefficient addition process (step S325). For the links L5 and L6 determined to have a large traffic volume (step S310: Yes), the processing unit 26 adds the crime prevention coefficient (step S320). Specifically, since the links L5 and L6 have a large traffic volume and the crime prevention coefficient is 100, the processing unit 26 adds 100 to the crime prevention coefficients of the buildings B and C in contact with L5 and the buildings D and E in contact with L6. To do.
On the other hand, for L1, L2, L3, L4, and L7 as links with a small traffic volume (step S310: No), the processing unit 26 adds the landscape coefficient (step S325). Specifically, since the links L1, L2, L3, L4, and L7 have a small traffic volume and the landscape coefficient is 100, the processing unit 26 includes a building B in contact with the link L1 and a building G in contact with the link L2. 100 is added to the landscape coefficient of the building F in contact with the link L3 and the buildings C, D and F in contact with the link L4.

When the addition process of the crime prevention coefficient and the landscape coefficient is completed, the processing unit 26 performs a process of calculating the crime prevention influence degree and the landscape influence degree (step S330). FIG. 14: is explanatory drawing which shows an example of the crime prevention influence degree and the landscape influence degree about each building. In the present embodiment, the crime prevention impact degree is the total value of the crime prevention coefficients calculated by the processing unit 26 by the above processing, and the landscape impact degree is the total value of the landscape coefficient calculated by the processing unit 26 by the above processing. First, as a specific example of the crime prevention degree, the total value of the crime prevention coefficients added by the processing unit 26 for the building A is 100 (see step S300), and thus the crime prevention degree of the building A is 100. Since the total value of the crime prevention coefficients added by the processing unit 26 for the building C is 200 (see steps S300 and S320), the crime prevention degree of the building C is 200.
Next, to give a specific example of the landscape influence degree, the total value of the landscape coefficients added by the processing unit 26 for the building B is 200 (see steps S305 and S325), and thus the landscape influence degree of the building B is 200. Become. For the building C, the total value of the landscape coefficients added by the processing unit 26 is 100 (see step S325), so the landscape influence degree of the building C is 100.

The crime prevention coefficient indicates the possibility of a suspicious person lurking in an empty house.The higher the possibility of a suspicious person hiding in an empty house, the higher the crime prevention coefficient. Building C and building E (see FIG. 14) whose degree is 200 indicate that a suspicious person is more likely to hide in an empty house than other buildings.
The landscape coefficient indicates that there is a high possibility that an unoccupied house will be exposed to the public more likely to deteriorate the scenery. As the unoccupied house has more chances to be exposed to the public, the value of the scenery coefficient increases. The building B, the building D, the building F, and the building G (see FIG. 14) having a landscape influence degree of 200, which is the total value of the coefficients, are more likely to deteriorate the landscape than other buildings. In this way, the processing unit 26 ends a series of processes when the process of calculating the crime prevention influence coefficient and the landscape influence degree of each building is completed.

As described above, in the present embodiment, since the crime prevention impact degree and the landscape influence degree of each building can be calculated, the influence of each building on the surroundings can be calculated.

Fifth embodiment:
Next, a fifth embodiment of the present invention will be described. In the fifth embodiment, a usage application of the device 20 having the unoccupied house evaluation value calculated in the first embodiment will be described.

First, the user stores in the RAM 23 the geographical information of the user input using the input unit 25. The processing unit 26 extracts the vacant house data corresponding to the geographical information stored in the RAM 23, and extracts the vacant house evaluation value corresponding to each vacant house data. Then, the processing unit 26 displays the extracted vacant house evaluation value on the display unit 27.

FIG. 15 is an explanatory diagram showing an example of the display of the unoccupied house evaluation value. As illustrated in FIG. 15, the processing unit 26 displays the vacant house evaluation value of each ID on the display unit 27 in association with the building on the map.
The display method of the display unit 27 is not limited to this method, and the unoccupied house evaluation values of the respective IDs may be displayed in a list format in descending order of numerical value or in decreasing numerical value. Further, the input unit 25 may receive the input of the vacant house evaluation value by the user, and the processing unit 26 may display the building satisfying the vacant house evaluation value input to the input unit 25 on the display unit 27. For example, when the user inputs the vacant house evaluation value 1000 to the input unit 25, the display unit 27 may display the buildings B and E that satisfy the vacant house evaluation value 1000. In addition, the processing unit 26 may display the survey data of each vacant house and/or the information on the secular change in association with the building on the map.

As described above, since each building is displayed together with the vacant house evaluation value in the present embodiment, the user can know the vacant house evaluation value of each building.

Sixth embodiment:
Next, a sixth embodiment of the present invention will be described. In the sixth embodiment, a usage application of the device 20 having the surrounding influence degree 1 calculated in the second embodiment and the surrounding influence degree 2 calculated in the third embodiment will be described.

First, the user stores in the RAM 23 the geographical information of the user input using the input unit 25. The processing unit 26 performs the extraction process of the predetermined distance range of the peripheral influence degree 1 corresponding to the geographical information stored in the RAM 23, and the processing unit 26 displays the extracted predetermined distance range on the display unit 27 on the map. Explaining with reference to FIG. 9, the processing unit 26 extracts a predetermined distance range and displays the extracted predetermined distance range on the map in a circular shape on the display unit 27.

Next, the processing unit 26 determines whether there is a feature included in the predetermined distance range of each unoccupied house polygon (see step S260) and acquires a surrounding feature (see step S270). In the case of the building E in FIG. 9, the processing unit 26 acquires the building C, the building G, and the link L4. Then, the processing unit 26 calculates the surrounding influence degree 2 from the number of features included in the range of the surrounding influence degree 1. Then, the processing unit 26 displays the calculated surrounding influence degree 2 in association with each building. FIG. 16 is an explanatory diagram showing a display example in which the degree of peripheral influence 2 is set. In the case of the building E in FIG. 16, since there are three features of the building C, the building G, and the link L4, the processing unit 26 displays the peripheral influence degree 2 of 3 on the building E on the display unit 27.

In this embodiment, by displaying the peripheral influence degree 1 and/or the peripheral influence degree 2 as described above, the peripheral influence degree 1 and/or the peripheral influence degree 2 can be used for various purposes. For example, the peripheral influence degree 1 and/or the peripheral influence degree 2 can be used for area marketing in store development work. Specifically, when a restaurant operator selects a candidate site for new opening, whether or not to open a restaurant business by referring to the surrounding influence degree 1 and/or the surrounding influence degree 2 together with the presence or absence of a vacant house around the candidate place It becomes easier to make a decision. Further, for example, an allowable range value regarding the peripheral influence degree 1 and/or the peripheral influence degree 2 may be set for each business type, and only the candidate store locations below the allowable range value may be displayed. Further, when the peripheral influence level 1 is displayed on the map, an area where the peripheral influence level 1 overlaps may be displayed in a color corresponding to the number of overlaps.
Further, the vacant house evaluation value, the surrounding influence degree 1 and the surrounding influence degree 2 can also be used, for example, for measures against vacant houses in administrative agencies. That is, this information may be used when selecting a vacant house to which the government should be involved as a measure against the vacant house and when determining the priority order of taking measures for the vacant house. For example, it is possible to extract vacant houses whose deterioration status is equal to or higher than a predetermined value from the vacant house evaluation value and select a vacant house that is considered to be adversely affecting the surrounding residents etc. as a vacant house with a high priority based on the surrounding impact level 2. it can. Note that, for example, depending on the administrative organization, each item of the survey data and the element for calculating the degree of influence on the surroundings may be weighted according to regional characteristics.
Further, the vacant house evaluation value, the surrounding influence degree 1, and the surrounding influence degree 2 can be used, for example, when persuading a target destination for a renovation proposal by a real estate company. Specifically, the real estate company can present the necessity of renovation to the owner of the vacant house as objective numerical information by showing the vacant house evaluation value, the peripheral influence degree 1, and the peripheral influence degree 2.

Further, the processing unit 26 may add a cost that defines the degree of importance used for route search for a link having at least one of the peripheral influence degree 1, the peripheral influence degree 2, and the peripheral influence degree 3 of each building. For example, in FIG. 9, the cost of the link L4, which is assumed to have the influence level 1 around the building E, may be increased. Then, the link cost may be added and then used in the route search to avoid the costly link route.
Specifically, for example, when an earthquake occurs, a dangerous road that is dangerous due to the collapse of an empty house is identified from the surrounding impact level 1, and the link cost is increased to perform a route search that avoids this dangerous road. be able to. Further, since it is considered that restoration work or rescue work is preferentially performed on this dangerous road when an earthquake occurs, fire departments and police may search for a route using this dangerous road as a destination.
Further, the surrounding influence degree 3 can be used when searching for a safe route. Specifically, by using the crime prevention degree, for example, by increasing the cost of the link in front of an unoccupied house with high crime prevention degree at night, it is possible to search for a route avoiding the front of an unoccupied house with high crime prevention degree. it can. In addition, by using the landscape impact level, for example, when creating a tourist route, by increasing the cost of the link in front of an unoccupied house with high landscape impact, it is possible to perform a route search that avoids the unoccupied house with high tourism impact. it can.
In addition, the peripheral impact degree 3 can be used for intensive patrol of a dangerous place. Specifically, police and security companies can focus on patrol at dangerous points for crime prevention by conducting a route search using an unoccupied house with a high degree of crime prevention as a destination or waypoint.

As described above, in the present embodiment, the processing unit 26 displays the peripheral influence degree 1 and the peripheral influence degree 2 extracted on the display unit 27 in association with the buildings on the map. 1 and surrounding influence degree 2 can be known.

Seventh embodiment:
Next, a seventh embodiment of the present invention will be described. In the seventh embodiment, the usage of the device 20 having the surrounding influence degree 3 calculated in the fourth embodiment will be described.

FIG. 17 is an explanatory diagram showing a display example in which the peripheral influence degree 3 is set. As shown in FIG. 17, the processing unit 26 causes the display unit 27 to display the surrounding influence degree and the landscape influence degree of each building in association with each other on the map building.
First, the user stores in the RAM 23 the geographical information of the user input using the input means 25. The processing unit 26 performs a peripheral influence degree 3 extraction process corresponding to the geographical information stored in the RAM 23. Then, as shown in FIG. 17, the processing unit 26 displays the peripheral influence degree 3 extracted on the display unit 27 in association with the building on the map. For example, it is possible to take appropriate measures against unoccupied houses based on tourism and crime prevention policies of government agencies. Specifically, if the landscape is emphasized as a measure for tourists, if an unoccupied house showing a high degree of landscape influence is highlighted on the map, or if crime prevention is emphasized as a measure against crime, etc. Unoccupied houses showing high values for crime prevention may be highlighted on the map.

In this way, since each building is displayed together with the surrounding influence degree 3 in the present embodiment, the user can know the surrounding influence degree 3 of each building.

It should be noted that the display method of the peripheral influence degree 3 on the map can adopt various aspects. Specifically, the processing unit 26 may highlight a building having a high crime prevention degree or a building having a low crime prevention degree on the display unit 27. Similarly, the processing unit 26 may highlight on the display unit 27 a building having a high degree of landscape influence or a building having a low degree of landscape influence.

Further, the input unit 25 receives the value of the crime prevention degree and the value of the landscape influence degree by the user, and the processing unit 26 displays the buildings which satisfy the value of the crime prevention influence degree and the landscape influence degree input by the input unit 25. It may be displayed on 27. For example, when the user inputs the landscape influence degree 200 to the input unit 25, the processing unit 26 may display only the buildings B, D, F, and G that satisfy the landscape influence degree 200 on the display unit 27.
Further, the input unit 25 accepts the input of personal information such as the height and occupation of the user, and the processing unit 26 displays the building satisfying the crime prevention impact and the landscape impact on the display unit 27 based on the personal information input in the input unit. The display of may be controlled. For example, when the user inputs his/her height of 170 cm as personal information in the input unit 25, the feature status of the building F is 1.5 m, which is lower than 170 cm. The crime prevention degree may not be displayed. When the user inputs his/her occupation as a police officer in the input unit 25 as personal information, the processing unit 26 may display the buildings C and E having the crime prevention impact degree of 200 on the display unit 27.

Modification:
The present invention is not limited to the above-described embodiments and embodiments, and can be implemented in various modes without departing from the scope of the invention, and the following modifications are possible, for example.
-Modification 1:
In the above embodiment, item numbers 1 to 6 are used as the survey data of the building attribute, but the survey data is not limited to this, and various information regarding individual buildings may be acquired. For example, as shown in FIG. 18, as the survey data of the building irrespective of whether it is an empty house or not, information on the state and shape of the roof and the outer wall of the house and the building structure, material and type may be included. By displaying these survey data on the map, for example, the remodeling company can grasp the distribution of customers who are the target of the proposed sales of replacement of the roof or the outer wall from the state where the roof is the outer wall. Further, for example, the administrative agency can grasp the distribution of target buildings to which administrative guidance should be given to buildings using a specific material or buildings in poor condition. Further, for example, an insect eradication company can grasp the distribution of customers in a building whose building structure is wooden.
Further, as shown in FIG. 19, the survey data around the building may include information such as states and types of gates and fences, parking lots, and gardens existing around the house. By displaying these survey data on a map, for example, a remodeling company can grasp the distribution of customers who are the target of the proposed replacement sales of the gate or the fence from the state of the gate or the fence. Further, for example, an administrative institution or the like can be used for predetermined administrative guidance or the like by grasping the distribution of fences with a high risk of collapse from the state or type of the fence. Further, for example, a car sales company or the like can grasp the distribution of customers from the information on the number of cars that can be parked in the parking lot, the type of car, and the carport. Further, for example, a remodeling company or a landscape gardening company can grasp the distribution of customers who can make a proposal business from the information such as the state and size of the garden. Further, for example, the gas company can grasp the distribution of customers from the number of gas cylinders and the information of gas cylinder dealers. Further, for example, a company that sells or lends a warehouse can grasp the distribution of customers from the state of the warehouse and information about the warehouse.
Further, as shown in FIG. 20, the building use survey data may include the type of building use. These survey data can be used in area marketing for store development, for example. Specifically, the environment around the proposed site for opening a store can be grasped by displaying it on a map for each purpose of use such as a house, a shared house, an office, a store, a building, a villa, and a factory/workplace. Further, for example, a building maintenance company can grasp the distribution of customers from the information on the date of completion of the building.
Further, by comparing the survey data of FIGS. 18 to 20 with past survey data, the change point of each survey item can be grasped. By displaying the change points on the map, for example, the trader who handles the survey items can grasp the change distribution of the customers.

20... Terminal, 21... CPU, 22... ROM, 23... RAM, 24... Storage section, 25... Input section, 26... Processing section, 27... Display section, 30... Server, 31... CPU, 32... ROM,
33... RAM, 341... Map data, 342... Surrounding situation data, A... Building A, B... Building B, C... Building C, D... Building D, E... Building E, F... Building F, G... Building G, L1... Link L1,
L2... Link L2, L3... Link L3, L4... Link L4, L5... Link L5,
L6... Link L6, L7... Link L7, INT... Internet

Claims (2)

A vacant house information generation program for causing the processing unit to realize a reference function for referring to map data stored in a storage unit, in a processing unit having a calculation function for calculating vacant house information indicating the possibility of deterioration of an vacant house,
The map data includes survey information indicating a survey status regarding each of a plurality of buildings, and history information indicating time since the survey information was acquired,
The reference function includes a function of causing the processing unit to refer to the survey information and the history information stored in the storage unit,
Based on the survey information and the history information, the calculation function calculates the vacant house information such that the longer the time from the acquisition of the survey information, the higher the possibility of deterioration of the vacant house. program. The vacant house information generation program according to claim 1,
The map data includes height information about the height of the building, and coefficient information according to the height of the building,
The reference function includes a function of causing the processing unit to refer to the height information and the coefficient information stored in the storage unit,
The calculation function, if the previous SL coefficient information is the first coefficient information, calculates a first peripheral impact with said height information of the first coefficient information,
If the previous SL coefficient information is the second coefficient information, vacant information generation program for calculating a second peripheral impact with said with said height information the second coefficient information.

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