JP2015023842A - Evaluation device, evaluation program, and evaluation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To generate information by which a predetermined evaluation item can be simply evaluated.SOLUTION: An evaluation device 10 has a storage section 13, an extracting section 14a, and a generating section 14b. The storage section 13 stores vegetation data showing vegetation and terrain data showing terrain in a predetermined region. The extracting section 14a extracts a plurality of attribute values on the basis of the vegetation data and the terrain data for each of a plurality of regions formed by dividing the predetermined region. The generating section 14b generates information with which the plurality of attribute values extracted by the extracting section 14a are associated, for each of the plurality of regions.

Description

  The present invention relates to an evaluation apparatus, an evaluation program, and an evaluation method.

  There is a technique for evaluating whether or not a specific organism is likely to survive in a post-development state in a certain region or a planned development region. For example, there is a technique called HEP (Habitat Evaluation Procedure) that evaluates whether a specific organism is likely to survive.

  In addition, there is a technique for evaluating whether various organisms are likely to persist in a post-development state in a certain area or a planned development area. For example, there is a technology called InVEST (Integrated Valuation of Environmental Services) that evaluates threats common to various organisms.

  In addition, there is a technique for evaluating the habitat state of living organisms, taking into consideration the habitat area and perimeter, distance between habitats, roads, water bodies, etc. There is also a technology for predicting the effect of improving biodiversity in urban greening plans from vegetation environment information and biological distribution information.

JP 2003-102326 A JP 2009-136265 A JP 2006-67957 A

  Here, when evaluating whether or not a specific organism is likely to persist or whether or not various organisms are likely to persist, it is conceivable that the region to be evaluated is identified. Then, when specifying what kind of area the evaluation target area is, it is conceivable to create the following map. For example, by superimposing a map showing the slope of the slope of the evaluation target area, a map showing the situation of the water area of the evaluation target area, and a vegetation map showing the vegetation status of the evaluation target area, the evaluation target area It is conceivable to create a map showing what kind of region is. The map created in this way is used to evaluate whether a specific organism is likely to persist or whether various organisms are likely to persist.

  FIG. 18 is a diagram schematically illustrating an example of a conventional method for creating a map that indicates what kind of area is to be evaluated. In the example of FIG. 18, a map 91 indicating the slope of the slope of the evaluation target region, a map 92 indicating the state of the water area of the evaluation target region, and a vegetation map 93 indicating the vegetation status of the evaluation target region are superimposed. Thus, a case where a map 94 indicating what kind of area is to be evaluated is created. However, when performing the various evaluations described above using the map 94, the map 94 has a problem in that the attributes indicated by the maps 91 to 93 are complicatedly overlapped, so that the evaluation cannot be easily performed. is there.

  Note that the above-described problem is not limited to the case of evaluating the viability of a living thing, but similarly occurs when evaluating other evaluation items.

  In one aspect, an object of the present invention is to generate information that can easily evaluate a predetermined evaluation item.

  In one aspect, the evaluation device includes a storage unit, an extraction unit, and a generation unit. The storage unit stores vegetation data indicating vegetation in a predetermined area and terrain data indicating terrain. The extraction unit extracts a plurality of attribute values for each of a plurality of regions obtained by dividing a predetermined region based on the vegetation data and the terrain data. The generation unit generates information in which a plurality of attribute values extracted by the extraction unit are associated with each other for a plurality of regions.

  Information capable of easily evaluating a predetermined evaluation item can be generated.

FIG. 1 is a diagram illustrating an example of a functional configuration of the evaluation apparatus according to the embodiment. FIG. 2 is a diagram for explaining an example of a method for generating map data of a map indicating habitat attributes. FIG. 3A is a diagram for describing an example of processing for generating map data of a map in which an attribute of a slope direction is defined. FIG. 3B is a diagram for explaining an example of processing for generating map data of a map in which an attribute of a slope direction is defined. FIG. 4 is a flowchart illustrating a procedure of processing executed by the extraction unit according to the embodiment. FIG. 5 is a diagram illustrating an example of a data structure of a table included in the copied topographic map data. FIG. 6A is a diagram illustrating an example of a map in which an attribute of a slope direction is defined. FIG. 6B is a diagram illustrating an example of a map in which an attribute of a slope direction is defined. FIG. 7 is a diagram for explaining an example of processing for generating map data of a map in which an attribute of distance from a water area is defined. FIG. 8 is a diagram for explaining an example of processing for generating map data of a map in which an attribute of distance from a water area is defined. FIG. 9A is a flowchart illustrating a procedure of processing executed by the extraction unit according to the embodiment. FIG. 9B is a flowchart illustrating a procedure of processing executed by the extraction unit according to the embodiment. FIG. 10 is a diagram for explaining an example of processing for generating map data of a map in which an attribute of distance from a road is defined. FIG. 11 is a diagram for explaining an example of processing for generating map data of a map in which an attribute of distance from a road is defined. FIG. 12 is a diagram for explaining an example of processing for dividing four maps into a plurality of regions. FIG. 13 is a diagram for explaining an example of processing for extracting attribute values. FIG. 14 is a diagram illustrating an example of the total of four attribute values. FIG. 15 is a diagram illustrating an example of habitat conditions for Asiatic black bears. FIG. 16 is a flowchart illustrating the redistribution processing procedure according to the embodiment. FIG. 17 is a diagram illustrating a computer that executes an evaluation program. FIG. 18 is a diagram schematically illustrating an example of a conventional method for creating a map that indicates what kind of area is to be evaluated.

  Hereinafter, embodiments of an evaluation apparatus, an evaluation program, and an evaluation method disclosed in the present application will be described in detail with reference to the drawings. The embodiments do not limit the disclosed technology.

[Example of functional configuration of evaluation device]
FIG. 1 is a diagram illustrating an example of a functional configuration of the evaluation apparatus according to the embodiment. As illustrated in the example of FIG. 1, the evaluation device 10 includes an input unit 11, an output unit 12, a storage unit 13, and a control unit 14. The evaluation apparatus 10 according to the present embodiment evaluates the viability of a living thing.

  The input unit 11 inputs various information to the control unit 14. For example, the input unit 11 receives an instruction for executing an evaluation process described later from a user of the evaluation apparatus 10 and inputs the received instruction to the control unit 14. The input unit 11 receives information indicating the evaluation target region from the user, and inputs the received information indicating the evaluation target region to the control unit 14. Examples of the device of the input unit 11 include a mouse and a keyboard.

  The output unit 12 outputs various information. For example, the output unit 12 displays the evaluation result under the control of an output control unit 14d described later. An example of the device of the output unit 12 is a liquid crystal display.

  The storage unit 13 stores various information. For example, the storage unit 13 stores a vegetation map database (Data Base) 13a, a topographic map database 13b, a development plan map database 13c, a habitat species database 13d, and a habitat condition database 13e. In the following description, the database may be abbreviated as “DB”. The storage unit 13 stores attribute value information 13f.

  In the vegetation map DB 13a, vegetation map data indicating a vegetation map is registered for each region. Contour lines are described in the topographic map DB 13b, and topographic map data indicating topographic maps having water areas such as ponds and rivers and roads are registered for each region. The development plan DB 13c has vegetation map data showing the vegetation map after development in the planned development area, water areas such as ponds and rivers after development in the planned development area, roads, and contour lines. Development plan map data having registered topographic map data is registered. In the habitat species DB 13d, the types of living creatures are registered for each region. In the inhabiting condition DB 13e, conditions that allow living organisms to inhabit are registered for each type of organism.

  The attribute value information 13f is registered in association with an identifier of an area described later and information associated with four attribute values described later by an extracting unit 14a described later. Details of the attribute value information 13f will be described later.

  The storage unit 13 is, for example, a semiconductor memory device such as a flash memory, or a storage device such as a hard disk or an optical disk.

  The control unit 14 has an internal memory for storing programs defining various processing procedures and control data, and executes various processes using these. As shown in FIG. 1, the control unit 14 includes an extraction unit 14a, a generation unit 14b, an evaluation unit 14c, and an output control unit 14d.

  The extraction unit 14a extracts various information. For example, the extraction unit 14a describes one aspect of the extraction unit 14a. For example, when an instruction to execute the evaluation process is input from the input unit 11, the extraction unit 14 a prompts input of information indicating the evaluation target region (for example, “Please input the evaluation target region”). The output unit 12 is controlled so as to be displayed. Then, the extraction unit 14a determines whether or not information indicating the evaluation target area has been input from the input unit 11.

  When it is determined that the information indicating the evaluation target area is input, the extraction unit 14a acquires the vegetation map data of the evaluation target area indicated by the input information from the vegetation map DB 13a. Moreover, the extraction part 14a acquires the topographic map data of the area to be evaluated from the topographic map DB 13b.

  Then, the extraction unit 14a converts the vegetation in the vegetation map indicated by the acquired vegetation map data into habitat attributes that are attributes such as evergreen trees, deciduous trees, shrubs, and grasslands to which the vegetation belongs. Generate map data of the map showing the attributes. FIG. 2 is a diagram for explaining an example of a method for generating map data of a map indicating habitat attributes. For example, as illustrated in FIG. 2, the extraction unit 14 a converts vegetation such as coniferous forest, cypress, and cedar into an evergreen tree. In addition, as illustrated in FIG. 2, the extraction unit 14 a converts vegetation such as kunugi, konara and oak into deciduous trees. Further, as illustrated in FIG. 2, the extraction unit 14a converts vegetation such as azaleas, orchards, and camellia into shrubs. In addition, as illustrated in FIG. 2, the extraction unit 14 a converts vegetation such as field, sasa, and pampas grass into grassland. In this way, the extraction unit 14a converts various types of vegetation into habitat attributes, and generates map map data indicating habitat attributes. Then, the extraction unit 14 a stores map data indicating the generated habitat attribute in the internal memory of the control unit 14.

  Then, the extracting unit 14a generates map data of a map in which the attribute of the slope direction is defined from the topographic map indicated by the acquired topographic map data. Here, there are five attributes of the direction of the slope, for example, “south-facing gentle slope”, “north-facing gentle slope”, “south-facing steep slope”, “north-facing steep slope”, and “substantially flat”. The attribute of the direction of the slope is listed. FIG. 3A and FIG. 3B are diagrams for explaining an example of processing for generating map data of a map in which an attribute of a slope direction is defined. 3A and 3B show contour lines described in the topographic map indicated by the topographic map data acquired by the extraction unit 14a. FIG. 4 is a flowchart illustrating a procedure of processing executed by the extraction unit according to the embodiment.

  For example, as illustrated in FIGS. 3A and 3B and FIG. 4, the extraction unit 14a copies the acquired topographic map data (S1). Then, the extraction unit 14a draws a straight line 80a from the north to the south at the position moved east from the west end by (A / 2) m in the topographic map indicated by the copied topographic map data (S2).

  Then, the extraction unit 14a selects one unselected intersection as the first intersection from the north side among the plurality of intersections of the drawn straight line and the contour line (S3). For example, the extraction unit 14a selects one unselected intersection 81a as the first intersection from the north side among the intersections 81a to 81d between the straight line 80a and the contour line. Then, the extraction unit 14a selects one of the northernmost intersections as the second intersection among the intersections on the south side of the intersection selected as the first intersection (S4). For example, when the intersection 81a is selected as the first intersection, the extraction unit 14a selects the northernmost intersection 81b as the second intersection among the intersections 81b to 81d on the south side of the intersection 81a.

  Then, the extraction unit 14a divides the height difference indicated by each of the first intersection and the second intersection by the horizontal distance between the first intersection and the second intersection, and the slope of the slope (steepness). Is calculated (S5). For example, when the difference in height is 50 m and the horizontal distance is 1000 m, the extraction unit 14a calculates the inclination “0.05”. The extraction unit 14a uses a value obtained by subtracting the height of the second intersection from the height indicated by the first intersection as the difference in height indicated by each of the first intersection and the second intersection. it can. In this case, when the calculated slope is a positive value, the slope is southward, and when the slope is negative, the slope is northward.

  Then, the extraction unit 14a identifies a rectangular region in which the line segment of the first intersection and the second intersection is a line that bisects the area (S6). For example, the extraction unit 14a specifies a rectangular region 83a in which the line segment of the intersection point 81a and the intersection point 81b is a line that bisects the area. Then, the extraction unit 14a registers the attribute corresponding to the calculated inclination in the copied topographic map data in association with the identified rectangular area (S7). For example, when the calculated inclination is greater than “0.1” and equal to or less than “1.0”, the extraction unit 14a associates the rectangular region with the rectangular region because the rectangular region is a gentle slope facing south. Then, the attribute “slow slope facing south” is registered in the copied topographic map data. In addition, when the calculated inclination is “−1.0” or more and smaller than “−0.1”, the extraction unit 14a determines that the rectangular area is a gentle slope facing north. In association, the attribute “north-facing gentle slope” is registered in the copied topographic map data. In addition, when the calculated inclination is larger than “1.0”, the extraction unit 14a has the attribute “south slope facing south” in association with the rectangular area because the rectangular area is a south facing steep slope. Is registered in the copied topographic map data. In addition, when the calculated inclination is smaller than “−1.0”, the extraction unit 14a associates the rectangular area with the rectangular area, so that the attribute “Northward steep slope” is associated with the rectangular area. Is registered in the copied topographic map data. In addition, when the calculated inclination is “−0.1” or more and “0.1” or less, the extraction unit 14a associates the rectangular region with the attribute “ "Almost flat" is registered in the copied topographic map data.

  FIG. 5 is a diagram illustrating an example of a data structure of a table included in the copied topographic map data. The table shown in the example of FIG. 5 includes items of “identifier” and “attribute”. In the “identifier” item, an identifier for identifying the specified rectangular area is registered. In the “attribute” item, the attribute of the rectangular area indicated by the identifier registered in the “identifier” item is registered. In S7 described above, the extraction unit 14a registers the identifier of the identified rectangular area in the “identifier” item of the table as illustrated in the example of FIG. 5, and the “attribute” item corresponds to the calculated inclination. Registered attributes. In this way, the extraction unit 14a registers the attribute corresponding to the calculated inclination in the copied topographic map data in association with the identified rectangular area.

  Then, the extraction unit 14a determines whether or not there is an intersection on the straight line south of the second intersection (S8). When it is determined that there is no intersection on the straight line south of the second intersection (S8; No), the extraction unit 14a performs the following process. In other words, the extraction unit 14a determines whether or not the distance between the drawn straight line and the eastern end is (A × (3/2)) m or more in the topographic map indicated by the copied topographic map data ( S9). When it is determined that the distance between the drawn straight line and the east end is equal to or greater than (A × (3/2)) m (S9; Yes), the extraction unit 14a performs the following process. That is, the extraction unit 14a draws a line from north to south at a position moved east by Am from the current position in the topographic map indicated by the copied map data (S10), and returns to S3.

  Also, when it is determined that there is an intersection on the straight line south of the second intersection (S8; Yes), the extraction unit 14a returns to S3.

  If it is determined that the distance between the drawn straight line and the east end is not greater than (A × (3/2)) m (S9; No), the extraction unit 14a defines the attribute of the slope direction. The process of generating the map data of the map that has been completed is terminated.

  By the process of generating the map data of the map in which the attribute of the slope direction is defined as described above, the attribute corresponding to the inclination is registered in the copied topographic map data in association with the rectangular area. Therefore, the extraction unit 14a performs the following process by executing the process of generating the map data of the map in which the attribute of the slope direction is defined as described above. That is, the extraction unit 14a generates the copied topographic map data in which the attribute corresponding to the inclination is registered in association with the rectangular area as the map data of the map in which the attribute of the slope direction is defined. 6A and 6B are diagrams illustrating an example of a map in which an attribute of a slope direction is defined. FIG. 6A shows a rectangular region in which the attribute of the south-facing steep slope whose slope is larger than “1.0” is defined, and the attribute of the steep slope facing north where the slope is smaller than “−1.0”. Shows a map having a defined rectangular area. Further, FIG. 6B shows a rectangular region in which the attribute of the gentle slope facing south where the slope is greater than “0.1” and less than or equal to “1.0” is defined, and “−1.0” or greater. A map having a rectangular area defined with a north-facing gentle slope attribute that is smaller than “−0.1”.

  Then, the extraction unit 14 a stores the generated map data of the map in which the attribute of the slope direction is defined in the internal memory of the control unit 14.

  And the extraction part 14a produces | generates the map data of the map in which the attribute of the distance from water areas, such as a river and a pond, was defined from the topographic map which the acquired topographic map data shows. Here, as an attribute of the distance from the water area, for example, when the water area is a river, “the area within the range where the distance from the water area is 0 m or more and less than 50 m”, “the distance from the water area is 50 m or more and less than 100 m "Region within the range". In addition, as an attribute of the distance from the water area, when the water area is a river, “the area within the range of 100 m or more and less than 500 m from the water area”, “the area within the range of 500 m or more and less than 1000 m from the water area And “regions with a distance from the water area of 1000 m or more”. In addition, as an attribute of the distance from the water area, for example, when the water area is a pond, “the area within the range of 0 m or more and less than 50 m from the center of the water area”, “the distance from the center of the water area is 50 m The area | region in the range below 100 m or more "is mentioned. In addition, as an attribute of the distance from the water area, when the water area is a pond, “the area within the range of 100 m or more and less than 500 m from the center of the water area”, “the distance from the center of the water area is 500 m or more and less than 1000 m "Area within range". In addition to these, “regions in which the distance from the center of the water area is 1000 m or more” can be mentioned. FIG. 7 and FIG. 8 are diagrams for explaining an example of processing for generating map data of a map in which an attribute of distance from a water area is defined. FIG. 7 shows water areas such as rivers and ponds in the topographic map indicated by the topographic map data acquired by the extraction unit 14a. FIGS. 9A and 9B are flowcharts illustrating a procedure of processing executed by the extraction unit according to the embodiment.

  For example, as illustrated in FIGS. 9A and 9B, the extraction unit 14a copies the acquired topographic map data (S11). And the extraction part 14a specifies water areas, such as a river and a pond, in the topographical map which the copied topographical map data shows (S12). For example, as illustrated in FIG. 7, the extraction unit 14 a specifies a river water area 70 a and a pond water area 70 b.

  And the extraction part 14a determines whether there exists an unselected water area in the identified water area (S13). When there is an unselected water area (S13; Yes), the extraction unit 14a selects one unselected water area (S14). For example, the extraction unit 14a selects an unselected water area 70a. And the extraction part 14a determines whether the curve which shows the selected water area is an open curve (S15). Here, when the curve indicating the water area is an open curve, the water area is a pond, and when the curve indicating the water area is not an open curve, that is, when it is a closed curve, the water area is considered to be a river. . For example, as shown in FIG. 7, the curve indicating the river water area 70a is an open curve. Moreover, as shown in FIG. 7, the curve which shows the water area 70b of a pond is a closed curve.

  When it is determined that the curve is an open curve (S15; Yes), since the selected water area is considered to be a river, the extraction unit 14a is within a range of less than 50 m from the selected water area on both banks of the selected water area. Is specified (S16). For example, as illustrated in FIG. 8, the extraction unit 14 a identifies regions 71 a and 71 b within a range of less than 50 m from the water area 70 a on both banks of the selected water area 70 a. And the extraction part 14a specifies the area | region in the range of 50 m or more and less than 100 m from the selected water area in the both banks of the selected water area (S17). For example, as illustrated in FIG. 8, the extraction unit 14 a specifies regions 72 a and 72 b within a range of 50 m or more and less than 100 m from the water region 70 a on both banks of the selected water region 70 a. Similarly, the extraction unit 14a specifies a region within a range of 100 m or more and less than 500 m from the selected water region on both banks of the selected water region (S18), and 500 m or more from the selected water region on both banks of the selected water region. An area within a range of less than 1000 m is specified (S19). Furthermore, the extraction part 14a specifies the area | region of 1000 m or more from the selected water area in the both banks of the selected water area (S20).

  And the extraction part 14a matches with the area | region within the range less than 50 m from a water area, and registers into the copied topographic map data the information which shows that it is an area within the range less than 50 m from a water area (S21). And the extraction part 14a matches with the area | region within the range of 50 m or more and less than 100 m from a water area, and registers into the copied topographic map data the information which shows that it is an area | region within the range of 50 m or more and less than 100 m from a water area. (S22). And the extraction part 14a matches with the area | region within the range of 100 m or more and less than 500 m from a water area, and registers into the copied topographic map data the information which shows that it is an area | region within the range of 100 m or more and less than 500 m from a water area. (S23). And the extraction part 14a matches with the area | region within the range of 500 m or more and less than 1000 m from a water area, and registers the information which shows that it is an area | region within the range of 500 m or more and less than 1000 m in the copied topographic map data. (S24). And the extraction part 14a matches the area | region of the range of 1000 m or more from a water area, registers the information which shows that it is an area | region of the range of 1000 m or more from a water area in the copied topographic map data (S25), and to S13 Return.

  On the other hand, when it is determined that the curve is not an open curve (S15; No), since the selected water area is considered to be a pond, the extraction unit 14a identifies an area within a range of less than 50 m from the center of the selected water area. (S26). For example, as illustrated in FIG. 8, the extraction unit 14a identifies a region 73 within a range of less than 50 m from the center of the selected water area 70b. And the extraction part 14a specifies the area | region in the range of 50 m or more and less than 100 m from the center of the selected water area (S27). For example, as illustrated in FIG. 8, the extraction unit 14 a specifies a region 74 within a range of 50 m or more and less than 100 m from the center of the selected water region 70 b. Similarly, the extraction unit 14a specifies a region within a range of 100 m or more and less than 500 m from the center of the selected water area (S28), and specifies a region within a range of 500 m or more and less than 1000 m from the center of the selected water area (S29). ). Furthermore, the extraction part 14a specifies the area | region of the range of 1000 m or more from the center of the selected water area (S30).

  And the extraction part 14a matches with the area | region within the range less than 50m from the center of a water area, and registers into the copied topographic map data the information which shows that it is an area | region within the range less than 50m from the center of a water area. (S31). Then, the extraction unit 14a copies the information indicating that the area is within the range of 50 m or more and less than 100 m from the center of the water area in association with the area within the range of 50 m or more and less than 100 m from the center of the water area. The data is registered (S32). Then, the extraction unit 14a copies the information indicating that the area is within the range of 100 m or more and less than 500 m from the center of the water area in association with the area within the range of 100 m or more and less than 500 m from the center of the water area. The data is registered (S33). Then, the extraction unit 14a copies the information indicating that the area is within the range of 500 m or more and less than 1000 m from the center of the water area in association with the area within the range of 500 m or more and less than 1000 m from the center of the water area. The data is registered (S34). Then, the extraction unit 14a registers, in the copied topographic map data, information indicating that the region is in the range of 1000 m or more from the center of the water area in association with the region in the range of 1000 m or more from the center of the water area (S35). ), Return to S13.

  On the other hand, when there is no unselected water area (S13; No), the extraction part 14a complete | finishes the process which produces | generates the map data of the map in which the attribute of the distance from a water area was defined.

  By the process of generating the map data of the map in which the attribute of the distance from the water area is defined as described above, the copied topographic map data is associated with each area and the attribute corresponding to the distance from the water source is registered. Is done. Therefore, the extraction unit 14a registers the distance attribute from the water area in association with each area by executing the process of generating the map data of the map in which the attribute of the distance from the water area is defined as described above. The copied topographic map data is generated as map data of a map in which the attribute of distance from the water area is defined.

  Then, the extraction unit 14 a stores the generated map data of the map in which the attribute of the distance from the water area is defined in the internal memory of the control unit 14.

  And the extraction part 14a produces | generates the map data of the map in which the attribute of the distance from a road was defined from the topographic map which the acquired topographic map data shows. Here, as attributes of the distance from the road, for example, “an area within a range where the distance from the road is 0 m or more and less than 50 m”, “an area within a range where the distance from the road is 50 m or more and less than 100 m”, “road” The area | region in the range whose distance from is 100 m or more and less than 500 m "is mentioned. Furthermore, examples of the attribute of the distance from the road include “an area within a range where the distance from the road is 500 m or more and less than 1000 m” and “an area where the distance from the road is 1000 m or more”.

  For example, when the water area is a river, the extraction unit 14a defines the attribute of the distance from the road by the same method as described above for generating the map data of the map in which the attribute of the distance from the water area is defined. The map data of the map can be generated. FIGS. 10 and 11 are diagrams for explaining an example of processing for generating map data of a map in which an attribute of distance from a road is defined. FIG. 10 shows a road in the topographic map indicated by the topographic map data acquired by the extraction unit 14a. For the road shown in FIG. 10, the extraction unit 14a uses a method similar to the above-described method for generating map data of a map in which the attribute of the distance from the water area is defined, as shown in the example of FIG. Generate map data of the map with the attribute of the distance from.

  Then, the extraction unit 14 a stores the generated map data of the map in which the attribute of the distance from the road is defined in the internal memory of the control unit 14.

  And the extraction part 14a acquires the following four map data stored in the internal memory of the control part 14. FIG. That is, the extraction unit 14a includes map data indicating a habitat attribute, map data defining a slope orientation attribute, map map data defining a distance attribute from a water area, a road Map data of a map in which the attribute of the distance from is defined is acquired.

  And the extraction part 14a divides | segments each of the map which each of the acquired four map data shows into several area | regions similarly. FIG. 12 is a diagram for explaining an example of processing for dividing four maps into a plurality of regions. As shown in FIG. 12, the extraction unit 14a includes a map 21 indicating habitat attributes, a map 22 defining slope orientation attributes, a map 23 defining distance attributes from water areas, and roads. The map 24 in which the distance attribute is defined is divided into a plurality of regions 30 in the same manner. In the example of FIG. 12, each of the maps 21 to 24 is divided so that the extraction unit 14a can obtain 12 (30 × 15) regions 30 in the north-south direction and 15 in the east-west direction. Show the case. Thus, by dividing each of the four maps 21 to 24 into a plurality of regions 30 in the same manner, a certain region 30 in a certain map has a corresponding region 30 in the other three maps. .

  And the extraction part 14a selects each of the some area | region 30 in the map 21 which shows a habitat attribute one by one. Each time the region 30 is selected, the extraction unit 14a performs the following process. That is, the extraction unit 14a acquires the habitat attribute defined in the selected region 30. In this way, the extraction unit 14a acquires habitat attributes defined in the selected region 30. Each time the region 30 is selected, the extraction unit 14a identifies the region 30 corresponding to the selected region 30 among the plurality of regions 30 in the map 22 in which the attribute of the slope direction is defined, and the identified region The attribute of the direction of the slope defined in 30 is acquired. In this way, the extraction unit 14 a acquires the attribute of the slope direction defined in the region 30 corresponding to the selected region 30. Each time the region 30 is selected, the extraction unit 14a identifies and identifies the region 30 corresponding to the selected region 30 among the plurality of regions 30 in the map 23 in which the attribute of the distance from the water area is defined. The attribute of the distance from the water area defined in the area 30 is acquired. In this way, the extraction unit 14a acquires the attribute of the distance from the water area defined in the area 30 corresponding to the selected area 30. Each time the region 30 is selected, the extraction unit 14a identifies and identifies the region 30 corresponding to the selected region 30 among the plurality of regions 30 in the map 24 in which the attribute of the distance from the road is defined. The attribute of the distance from the road defined in the area 30 is acquired. In this way, the extraction unit 14a acquires the attribute of the distance from the road defined in the region 30 corresponding to the selected region 30.

  Then, the extraction unit 14a includes the habitat attribute defined in the selected area 30, the slope orientation attribute defined in the area 30 corresponding to the selected area 30, the distance attribute from the water area, and the distance from the road. When the attribute is acquired, the following processing is performed. That is, the extraction unit 14a extracts an attribute value according to the acquired habitat attribute, extracts an attribute value according to the acquired attribute of the slope direction, and acquires an attribute value according to the acquired attribute of the distance from the water area And an attribute value corresponding to the acquired attribute of the distance from the road is extracted.

  For example, when the acquired habitat attribute is an evergreen tree, the extraction unit 14a extracts an attribute value “1000” corresponding to the evergreen tree. Further, when the acquired habitat attribute is a deciduous species, the extracting unit 14a extracts an attribute value “2000” corresponding to the deciduous species. Further, when the acquired habitat attribute is a shrub, the extraction unit 14a extracts an attribute value “3000” corresponding to the shrub. Further, when the acquired habitat attribute is grassland, the extraction unit 14a extracts an attribute value “4000” corresponding to the grassland.

  In addition, when the acquired attribute of the slope direction is the south-facing gentle slope, the extracting unit 14a extracts the attribute value “100” corresponding to the south-facing gentle slope. Further, when the acquired attribute of the slope direction is the north-facing gentle slope, the extracting unit 14a extracts the attribute value “200” corresponding to the north-facing gentle slope. In addition, when the acquired attribute of the slope direction is a steep slope facing south, the extraction unit 14a extracts the attribute value “300” corresponding to the steep slope facing south. If the acquired attribute of the slope direction is a steep slope facing north, the extraction unit 14a extracts the attribute value “400” corresponding to the steep slope facing north. In addition, when the acquired attribute of the orientation of the slope is substantially a plane, the extraction unit 14a extracts an attribute value “500” that substantially corresponds to the plane.

  In addition, the extraction unit 14a is configured such that the acquired distance attribute from the water area is an area where the distance from the water area is 0 m or more and less than 50 m, or an area where the distance from the center of the water area is 0 m or more and less than 50 m. If so, the attribute value “10” corresponding to these attributes is extracted. In addition, the extraction unit 14a may have an attribute of a distance from the water area that is “an area in the range where the distance from the water area is 50 m or more and less than 100 m” or a distance from the center of the water area that is 50 m or more and less than 100 m. In the case of the region, the attribute value “20” corresponding to these attributes is extracted. In addition, the extraction unit 14a is configured such that the acquired attribute of the distance from the water area is an area where the distance from the water area is within a range of 100 m or more and less than 500 m, or an area where the distance from the center of the water area is 100 m or more and less than 500 m. If so, the attribute value “30” corresponding to these attributes is extracted. In addition, the extraction unit 14a is configured such that the acquired attribute of the distance from the water area is an area where the distance from the water area is within a range of 500 m or more and less than 1000 m, or an area where the distance from the center of the water area is 500 m or more and less than 1000 m. If so, the attribute value “40” corresponding to these attributes is extracted. Further, when the attribute of the distance from the acquired water area is an area having a distance from the water area of 1000 m or more, or an area having a distance from the center of the water area of 1000 m or more, the extraction unit 14a The attribute value “50” corresponding to the attribute is extracted.

  Further, when the acquired attribute of the distance from the road is an area within the range where the distance from the road is 0 m or more and less than 50 m, the extraction unit 14a extracts the attribute value “1” corresponding to this attribute. . Further, when the acquired attribute of the distance from the road is an area within the range where the distance from the road is 50 m or more and less than 100 m, the extraction unit 14a extracts the attribute value “2” corresponding to this attribute. . Further, when the acquired attribute of the distance from the road is an area within the range of the distance from the road of 100 m or more and less than 500 m, the extraction unit 14a extracts the attribute value “3” corresponding to this attribute. . Further, when the acquired attribute of the distance from the road is an area within the range of the distance from the road of 500 m or more and less than 1000 m, the extraction unit 14a extracts the attribute value “4” corresponding to this attribute. . Further, when the acquired attribute of the distance from the road is an area having a distance from the road of 1000 m or more, the extracting unit 14a extracts the attribute value “5” corresponding to this attribute.

  FIG. 13 is a diagram for explaining an example of processing for extracting attribute values. For example, as illustrated in the example of FIG. 13, when the region 30 at the position (7, 7) in the seventh row and the seventh column is selected, the extraction unit 14a determines that the acquired habitat attribute is a deciduous species. The attribute value “2000” corresponding to the fallen leaf type is extracted. In addition, when the acquired attribute of the slope direction is the south-facing gentle slope, the extracting unit 14a extracts the attribute value “100” corresponding to the south-facing gentle slope. Further, when the acquired attribute of the distance from the water area is an area within the range of the distance from the water area of 100 m or more and less than 500 m, the extraction unit 14a extracts the attribute value “30” corresponding to this attribute. . Further, when the acquired attribute of the distance from the road is an area having a distance from the road of 1000 m or more, the extracting unit 14a extracts the attribute value “5” corresponding to this attribute.

  The extraction unit 14a selects all the regions 30 one by one and performs the above-described processing. Thereby, the extraction unit 14a can extract the attribute values of the four types of attributes described above for each of all the regions 30.

  The generation unit 14b generates information in which the four attribute values extracted by the extraction unit 14a are associated with each other for each of the plurality of regions 30. One aspect of the generation unit 14b will be described. For example, when four types of attribute values are extracted for each of all the areas 30 by the extraction unit 14a, the generation unit 14b performs the following processing. That is, the generation unit 14b calculates the sum of the four attribute values extracted by the extraction unit 14a for each region 30. For example, when four attribute values “2000”, “100”, “30”, “5” as shown in the example of FIG. 13 are extracted by the extraction unit 14a, the generation unit 14b The sum “2135” of attribute values is calculated.

  Here, what is meant by the sum of the four attribute values will be described. For example, the attribute value of the habitat attribute is a four-digit numerical value as described above, but the habitat attribute can be identified by a value of a thousand digit of the four-digit numerical value. Further, the attribute value of the attribute of the slope direction is a 3-digit numerical value as described above, but the attribute of the slope direction can be identified by the value of the hundreds of the 3-digit numerical value. Further, the attribute value of the attribute of the distance from the water area is a two-digit numerical value as described above, but the attribute of the distance from the water area can be identified by the tens place value of the two-digit numerical value. Further, the attribute value of the attribute of the distance from the road is a single-digit numerical value as described above, and the attribute of the distance from the road can be identified by the value of the first digit of the single-digit numerical value. Therefore, the sum of the four attribute values is a 4-digit number, but the habitat attribute can be identified by the four-digit numerical value of the thousandth place, and the slope value attribute by the hundredth place value. Can be identified. The total of the four attribute values is a four-digit number, but the attribute of distance from the water area can be identified by the ten-digit value of the four-digit value, and from the road by the one-digit value. The distance attribute can be identified.

  FIG. 14 is a diagram illustrating an example of the total of four attribute values. The example of FIG. 14 shows a case where the sum of the four attribute values is “V × 1000 + W × 100 + X × 10 + Y”. In the example of the total of the four attribute values shown in FIG. 14, the habitat attribute can be identified by the value of V, and the attribute of the slope direction can be identified by the value of W. Further, in the total of the four attribute values shown in the example of FIG. 14, the attribute of the distance from the water area can be identified by the value of X, and the attribute of the distance from the road can be identified by the value of Y.

  Therefore, the generation unit 14b can generate information that is a total of four attribute values that can easily identify the attribute. Further, the total of the four attribute values is compared with the attribute value of the organism when evaluating the survivability of the organism. Therefore, the production | generation part 14b can produce | generate the information which can evaluate the viability of a living body simply.

  Then, the generation unit 14b registers the total of the four attribute values and the identifier of the corresponding region 30 in the attribute value information 13f in association with each other. As described above, the generation unit 14 b performs the process of associating the total of the four attribute values with the identifier of the corresponding area 30 and registering them in the attribute value information 13 f for all the areas 30. As a result, in the attribute value information 13f, a total of four attribute values that can easily evaluate the survivability of a living organism is registered for each region 30. Further, the generation unit 14b generates the attribute value information 13f by registering the attribute value information 13f in association with the sum of the four attribute values and the identifier of the corresponding region 30.

  The evaluation unit 14c compares the attribute value information 13f generated by the generation unit 14b with the attribute value of the organism to be evaluated for each region 30, and performs a predetermined evaluation based on the comparison result. For example, the evaluation unit 14c evaluates whether various organisms are likely to persist. One aspect of the evaluation unit 14c will be described.

  For example, when the total of the four attribute values described above is registered in the attribute value information 13f for all regions 30 by the generation unit 14b, the evaluation unit 14c performs the following process. That is, the evaluation unit 14c acquires all information related to living organisms inhabiting the evaluation target area from the habitat DB 13d.

  Then, the evaluation unit 14c selects 12 types of representative organisms from the organisms that live in the evaluation target area. For example, the evaluation unit 14c includes two species of herbivorous and carnivorous organisms from birds, two species of herbivorous and carnivorous from mammals, one species of amphibians, and one species of reptiles. Two species of herbivorous and carnivorous are selected from one organism, freshwater fish. In addition, the evaluation unit 14c selects two kinds of herbivorous and carnivorous organisms from among the insects, and two kinds of organisms such as trees and grasses from the plants. For example, the evaluation unit 14c selects a photogiant and a kingfisher from birds, a black bear and a deer from mammals, a lizard from amphibians, a snake from reptiles, a yamame and a zebrafish from freshwater fish. . In addition, for example, the evaluation unit 14c selects swordfish and Akaekane from among the insects, and beech and Miyamakusa from among the plants.

  Here, an example of a process in which the evaluation unit 14c selects 12 kinds of representative organisms will be described. First, an example of processing in which the evaluation unit 14c selects two species of herbivorous and carnivorous organisms from birds will be described. For example, the evaluation unit 14c specifies all herbivorous and carnivorous organisms of birds from the organisms that live in the evaluation target area. And the evaluation part 14c excludes living things, such as a crow and a rat which can survive with a human being in an urban area, from the specified living things. This is because various creatures become extinct by human development, so creatures such as crows and rats that coexist with humans in urban areas are inappropriate as indicators of biological diversity. . Furthermore, the evaluation unit 14c excludes organisms such as sweetfish that are beneficial to humans and are targets of capture, and organisms such as cormorants that are harmful to humans and are targets of extermination. This is due to the effects of human capture and extermination. After the exclusion described above is performed, the evaluation unit 14c identifies the average bird herbivorous and carnivorous organisms among the organisms that live in the evaluation target region from the organisms that were not excluded. . Here, when the herbivorous organism and the carnivorous organism of birds become one species, the evaluation unit 14c determines the herbivorous and carnivorous organisms of the identified birds as the herbivorous and Select as a carnivorous organism. On the other hand, when the herbivorous organism and the carnivorous organism of the birds are not one, the evaluation unit 14c performs the following process. In other words, the evaluation unit 14c selects, from the average bird herbivorous and carnivorous organisms, one species each of which is sensitive to environmental changes, such as an endangered species, so that the herbivorous nature of the representative bird And select carnivorous organisms.

  Moreover, the evaluation part 14c can select other 10 types of living things by the method similar to the method of selecting the 2 types of avian herbivorous and carnivorous organisms mentioned above.

  After selecting 12 representative organisms, the evaluation unit 14c determines whether each of the 12 representative organisms is suitable for inhabiting in the region to be evaluated. Here, an example of processing for determining whether or not each of the 12 species of representative organisms is suitable for inhabiting in the area to be evaluated will be described. Hereinafter, an example of a process for determining whether or not a black bear among 12 representative organisms is suitable for inhabiting in a region to be evaluated will be described. In addition, it is possible to determine whether or not other organisms are suitable for inhabiting in the evaluation target area by the same method.

  First, the evaluation unit 14c acquires the living conditions of the black bear from the living condition DB 13e. FIG. 15 is a diagram illustrating an example of habitat conditions for Asiatic black bears. The habitat conditions shown in the example of FIG. 15 indicate that the black bears eat nuts and the like, so that the habitat of black bears is an area where there are deciduous trees that drop many nuts and the like. Further, the habitat condition shown in the example of FIG. 15 indicates that the attribute of the black bear is “deciduous tree” and the attribute value of the black bear is “2000”.

  In addition, the habitat conditions shown in the example of FIG. 15 indicate that black bears live on slopes facing south rather than facing north, and are difficult to live on steep slopes. In addition, the habitat conditions shown in the example of FIG. 15 indicate that the attributes of black bears are “slope facing south” and “substantially flat”, and the attribute values of black bears are “100” and “500”.

  In addition, the habitat condition shown in the example of FIG. 15 indicates that the black bear lives at a distance within 1000 m from the water area. Further, the habitat condition shown in the example of FIG. 15 indicates that the attribute of the black bear is an attribute corresponding to each of the attribute values “10”, “20”, “30”, and “40” described above. Further, the habitat condition shown in the example of FIG. 15 indicates that the attribute values of black bears are “10”, “20”, “30”, and “40”.

  In addition, the habitat condition shown in the example of FIG. 15 indicates that the black bear lives in a place away from the road by 100 m or more. In addition, the habitat conditions shown in FIG. 15 indicate that the attributes of Asiatic black bears are “regions in the range where the distance from the road is not less than 100 m and less than 500 m”, “regions in the range where the distance from the road is not less than 500 m and less than 1000 m”, “ It indicates that the area is a range whose distance from the road is 1000 m or more. The habitat conditions shown in the example of FIG. 15 indicate that the attributes of the black bear are “3”, “4”, and “5”.

  Then, the evaluation unit 14c refers to the living conditions of the black bear, and when the total of the four types of attribute values is calculated for the black bear, the value of the thousands of the total of the four types of attribute values, the value of the hundreds , Decimal value, Range of possible ones value is specified. For example, when referring to the habitat condition shown in the example of FIG. 15, the evaluation unit 14c determines that the total value of the four types of attribute values is “2” and the value of the hundreds is “1”. It is specified that “5”, the value of the tens place can be “1”, “2”, “3”, and “4”, and the value of the first place can be “3”, “4”, and “5”.

  Then, the evaluation unit 14c refers to the attribute value information 13f and performs the following process on each of all the areas 30. That is, the evaluation unit 14 c specifies the total of the four attribute values associated with the region 30 for each region 30. Then, for each region 30, the evaluation unit 14c compares the total of the four specified attribute values with a value that can be taken by the total of the four types of attribute values for the black bear. For example, for each region 30, the evaluation unit 14c has “2” as the thousandth place of the total of the four specified attribute values, and “1” or “5” as the hundredth place. It is determined whether the value of “1”, “2”, “3” or “4” is “1”, “3”, “4” or “5”. Then, the thousandth place of the total of the four attribute values is “2”, the hundredth place value is “1” or “5”, and the tenth place value is “1” or “2”. For the region 30 determined to be “3” or “4” and the first place is “3”, “4”, or “5”, the evaluation unit 14c performs the following determination. That is, the evaluation unit 14c determines that the region 30 is a region where the black bear can live. On the other hand, when the result of the above-described determination with respect to the total of the four attribute values is negative, the evaluation unit 14c determines that the area 30 for which negative determination has been made is a region where it is difficult for black bears to inhabit. . The evaluation unit 14 c performs the process as described above for each of all the areas 30.

  And when the process mentioned above was performed with respect to each of all the area | regions 30, the evaluation part 14c calculates the number of the area | regions 30 which a black bear can inhabit. And the evaluation part 14c determines whether the calculated number of the area | region 30 in which the black bear can inhabit is more than predetermined number. When it is determined that the calculated number of areas 30 in which black bears can live is greater than or equal to the predetermined number, the evaluation unit 14c determines that black bears can live in the evaluation target area. On the other hand, when the calculated number of areas 30 in which black bears can live is not a predetermined number or more, the evaluation unit 14c determines that the black bears cannot live in the evaluation target area.

  By the method as described above, the evaluation unit 14c can determine whether the black bear is suitable for inhabiting in the area to be evaluated. Further, the evaluation unit 14c can determine whether or not other species of organisms are suitable for inhabiting in the area to be evaluated by the same method.

  Then, when the evaluation unit 14c determines whether each of the 12 species of organisms is suitable for inhabiting in the region to be evaluated, the number of organisms suitable for inhabiting in the region to be evaluated among the 12 species of organisms. Is identified. Then, when the number of organisms suitable for inhabiting in the evaluation target area is “12”, the evaluation unit 14c determines that biodiversity can be maintained in the evaluation target area. In addition, when the number of organisms suitable for inhabiting in the evaluation target area is “10” or “11”, the evaluation unit 14c generally excludes some species in the evaluation target area, except for some species. Judge that diversity can be maintained. In addition, when the number of organisms suitable for inhabiting in the evaluation target area is any one of “1” to “9”, the evaluation unit 14c can maintain biodiversity in the evaluation target area. judge.

  The output control unit 14d controls output of various types of information. For example, the output control unit 14d performs the following process when it is determined by the evaluation unit 14c that biodiversity can be maintained in the evaluation target area. In other words, the output control unit 14d controls the output unit 12 to display a message that “the biodiversity can be maintained in the evaluation target area”.

  Further, the output control unit 14d performs the following processing when it is determined by the evaluation unit 14c that the biodiversity can be generally maintained except for some species in the evaluation target region. In other words, the output control unit 14d controls the output unit 12 so as to display a message that “in the region to be evaluated, except for some species, the biodiversity can be generally maintained”.

  The output control unit 14d performs the following process when the evaluation unit 14c determines that biodiversity cannot be maintained in the evaluation target area. In other words, the output control unit 14d controls the output unit 12 to display a message that “the biodiversity cannot be maintained in the evaluation target area”.

  The control unit 14 is a circuit such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a central processing unit (CPU), or a micro processing unit (MPU).

  Here, the evaluation apparatus 10 performs the same process as the process described above using the development plan map data registered in the development plan map DB 13c, thereby evaluating the biodiversity after development in the development planned area. It can also be done.

[Process flow]
Next, the flow of processing executed by the evaluation apparatus 10 according to the present embodiment will be described. FIG. 16 is a flowchart illustrating the procedure of the evaluation process according to the embodiment. The evaluation process according to the embodiment is executed by the control unit 14 when, for example, an instruction for executing the evaluation process is input from the input unit 11 to the control unit 14.

  As illustrated in FIG. 16, the extraction unit 14 a controls the output unit 12 to display a message prompting input of information indicating the evaluation target area (for example, “Please input the evaluation target area”). (S101). Then, the extraction unit 14a determines whether or not information indicating the evaluation target area has been input from the input unit 11 (S102). When the information indicating the evaluation target area is not input (S102; No), the extraction unit 14a performs the determination of S102 again.

  On the other hand, when the information indicating the evaluation target area is input (S102; Yes), the extraction unit 14a acquires the vegetation map data of the evaluation target area indicated by the input information from the vegetation map DB 13a. In addition, the extraction unit 14a acquires the topographic map data of the evaluation target region from the topographic map DB 13b (S103).

  Then, the extraction unit 14a converts the vegetation in the vegetation map indicated by the acquired vegetation map data into habitat attributes that are attributes such as evergreen trees, deciduous trees, shrubs, and grasslands to which the vegetation belongs. Map data of the map indicating the attribute is generated (S104).

  And the extraction part 14a produces | generates the map data of the map in which the attribute of the direction of a slope was defined from the topographic map which the acquired topographic map data shows (S105).

  And the extraction part 14a produces | generates the map data of the map in which the attribute of the distance from water areas, such as a river and a pond, was defined from the topographic map which the acquired topographic map data shows (S106).

  And the extraction part 14a produces | generates the map data of the map in which the attribute of the distance from a road was defined from the topographic map which the acquired topographic map data shows (S107).

  And the extraction part 14a acquires four map data stored in the internal memory of the control part 14, and divides | segments each of the map which each of the acquired four map data shows into the some area | region 30 similarly. (S108).

  The extraction unit 14a extracts the attribute values of the four types of attributes for each of all the areas 30 (S109).

  The generation unit 14b generates the attribute value information 13f by registering the total of the four attribute values and the identifier of the corresponding region 30 in association with the attribute value information 13f for all the regions 30 ( S110).

  The evaluation unit 14c performs evaluation on biodiversity in the evaluation target area (S111). Then, the output control unit 14d controls the output unit 12 to display the result of the evaluation on the biodiversity in the evaluation target area (S111), and ends the process.

  As described above, the evaluation apparatus 10 according to the embodiment includes 4 for each of the plurality of areas 30 obtained by dividing the evaluation target area based on the vegetation data indicating the vegetation in the evaluation target area and the topographic data indicating the topography. Extract attribute value of type. Then, for each of the plurality of regions 30, the evaluation device 10 associates the information that associates the extracted four types of attribute values with the identifier of the region 30 and registers the information in the attribute value information 13f. As described above, the evaluation device 10 generates information in which four attribute values that can easily identify the attribute are associated with each other. Therefore, the evaluation apparatus 10 can generate information that can easily evaluate biodiversity in the region to be evaluated.

  Moreover, the evaluation apparatus 10 according to the embodiment includes compact and easy-to-handle information called information in which the four attribute values registered in the attribute value information 13f are associated with each other, and the sum of the four attribute values of the organism to be evaluated. The biodiversity is evaluated in comparison with each region 30. Therefore, according to the evaluation apparatus 10, it is possible to evaluate biodiversity accurately and quickly.

  Although the embodiments related to the disclosed apparatus have been described above, the present invention may be implemented in various different forms other than the above-described embodiments.

  For example, all or some of the processes described as being automatically performed among the processes described in the embodiments may be performed manually. In addition, among the processes described in the embodiments, all or a part of the processes described as being performed manually can be automatically performed by a known method.

  In addition, the processes in each process described in each embodiment can be arbitrarily finely divided or combined according to various loads and usage conditions. Also, can be omitted.

  In addition, the order of processes in each process described in each embodiment can be changed according to various loads and usage conditions.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific state of distribution / integration of each device is not limited to the one shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

[Evaluation program]
Various processes of the evaluation apparatus 10 described in the above embodiment can be realized by executing a program prepared in advance on a computer system such as a personal computer or a workstation. In the following, an example of a computer that executes an evaluation program having the same function as that of the evaluation apparatus 10 described in the above embodiment will be described with reference to FIG. FIG. 17 is a diagram illustrating a computer that executes an evaluation program.

  As illustrated in FIG. 17, the computer 300 includes a CPU 310, a ROM 320, an HDD (Hard Disk Drive) 330, and a RAM 340. These devices 310 to 340 are connected via a bus 350.

  The ROM 320 stores a basic program such as an OS (Operating System). In addition, the HDD 330 stores in advance an evaluation program 330a that exhibits the same functions as the units 14a to 14d shown in the above-described embodiment. The HDD 330 stores various DBs and attribute value information stored in the storage unit 13.

  Then, the CPU 310 reads the evaluation program 330a from the HDD 330 and executes it.

  Then, the CPU 310 reads various DBs and attribute value information from the HDD 330 and stores them in the RAM 340. Further, the CPU 310 executes the evaluation program 330 a using various DBs and attribute value information stored in the RAM 340. Note that the data stored in the RAM 340 may not always be stored in the RAM 340. Data used for processing may be stored in the RAM 340.

  Note that the above-described evaluation program 330a is not necessarily stored in the HDD 330 from the beginning.

  For example, the program is stored in a “portable physical medium” such as a flexible disk (FD), a CD-ROM, a DVD disk, a magneto-optical disk, or an IC card inserted into the computer 300. Then, the computer 300 may read and execute the program from these.

  Furthermore, the program is stored in “another computer (or server)” connected to the computer 300 via a public line, the Internet, a LAN, a WAN, or the like. Then, the computer 300 may read and execute the program from these.

10 Evaluation Device 13 Storage Unit 13a Vegetation Map DB
13b Topographic map DB
13c Development plan DB
13d Habitat DB
13e Habitation condition DB
13f Attribute value information 14 Control unit 14a Extraction unit 14b Generation unit 14c Evaluation unit 14d Output control unit

Claims (7)

A storage unit for storing vegetation data indicating vegetation in a predetermined area and terrain data indicating terrain;
An extraction unit that extracts a plurality of attribute values for each of a plurality of regions obtained by dividing the predetermined region based on the vegetation data and the terrain data;
For each of the plurality of regions, a generation unit that generates information in which a plurality of attribute values extracted by the extraction unit are associated;
The evaluation apparatus characterized by having. 2. The information processing apparatus according to claim 1, further comprising: an evaluation unit that compares the information generated by the generation unit with an attribute value of an organism to be evaluated for each region and performs a predetermined evaluation based on the comparison result. The evaluation apparatus as described in. The evaluation device according to claim 1, wherein the extraction unit extracts an attribute value corresponding to a distance from a water area for each of the areas based on the topographic data. The evaluation device according to claim 1, wherein the extraction unit extracts an attribute value corresponding to a slope of the slope for each region based on the topographic data. . The extraction unit, for each region based on the vegetation data and the terrain data, an attribute value according to the tree that inhabits, an attribute value according to the slope of the slope, an attribute value according to the distance from the water area, and , Extract the attribute value according to the distance from the road,
The generation unit, for each region, an attribute value according to the tree inhabiting, an attribute value according to the slope of the slope, an attribute value according to the distance from the water area, and a distance from the road The evaluation apparatus according to any one of claims 1, 2, 3, and 4, wherein information that associates the corresponding attribute value is generated. On the computer,
Based on the vegetation data indicating the vegetation in the predetermined area stored in the storage unit and the terrain data indicating the terrain, a plurality of attribute values are extracted for each of the plurality of areas obtained by dividing the predetermined area,
An evaluation program for generating information in which the plurality of attribute values extracted are associated with each other for each of the plurality of regions. Computer
Based on the vegetation data indicating the vegetation in the predetermined area stored in the storage unit and the terrain data indicating the terrain, a plurality of attribute values are extracted for each of the plurality of areas obtained by dividing the predetermined area,
An evaluation method comprising: generating information in which the plurality of extracted attribute values are associated with each other for each of the plurality of regions.

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