Processing device and program

Abstract

A processing device 1 comprises: a generation unit 21 that generates integrated data 13 by superimposing a boundary between lands managed by different entities on land use form data 11 indicating a land use form for each land position; and a calculation unit 22 that quantifies and calculates a relationship between a distance from the boundary and the land use form in the integrated data 13.

Description

Processing device and program

[0001] The present disclosure relates to a processing device and a program.

[0002] The existence of human society and the natural environment is based on the benefits obtained from ecosystems, known as ecosystem services, such as providing habitat for birds and animals and a source of food.

[0003] One example is Payment for Ecosystem Services (PES). PES is an approach that promotes environmental conservation activities by recognizing the economic value of the various benefits provided by nature and providing compensation based on that value. PES not only ensures the sustainability of services, but also contributes to improving the independence of local economies.

[0004] From the perspective of ecosystem services, it is important that natural capital is continuous and not spatially fragmented. In particular, interference between ecosystems caused by forest fragmentation is sometimes called the edge effect (see Non-Patent Document 1). Here, an edge is the boundary in ecology where heterogeneous ecosystems, such as forests and non-forests, are adjacent to each other.

[0005] There is a method for assessing the amount of natural capital using land use patterns obtained from remote sensing (see Non-Patent Document 2). Non-Patent Document 2 calculates the market value of the nature contained within each grid.

[0006] Generally, methods for performing spatial correlation analysis include those disclosed in Non-Patent Document 3 and Non-Patent Document 4.

[0007] Carolina Murcia, "Edge effects in fragmented forests: implications for conservation," [online], February 2, 1995, ScienceDirect, [Retrieved July 1, 2024], Internet <URL: https: / / www.sciencedirect.com / science / article / abs / pii / S0169534700889776> Kyushu University (National University Corporation), Nanzan University (Nanzan Gakuen Educational Corporation), "Reiwa 2 (2020) Environmental Economics Policy Research: Research on Comprehensive Assessment of Environmental, Economic, and Social Sustainability and Assessment of Wealth: Research Report," [online], March 2021. PAP MORAN, "NOTES ON CONTINUOUS STOCHASTC PHENOMENA," 1950. AOKI Yoshitsugu, "Derivation of Spatial Correlation Functions from Stochastic Urban Models," City Planning Institute of Japan, Journal of Urban Planning No. 39-3, October 2004.

[0008] Regarding the edge effect, it is possible that land use patterns may change as management entities change. However, none of the non-patent literature discloses how to evaluate ecosystem changes in areas that are straddled by different management entities.

[0009] The present disclosure has been made in consideration of the above circumstances, and an object of the present disclosure is to provide a technology that can evaluate changes in ecosystems in areas that are managed by different management entities.

[0010] A processing device according to one aspect of the present disclosure includes a generation unit that generates integrated data by superimposing boundaries managed by different land management entities onto land use type data indicating the land use type for each land location, and a calculation unit that quantifies and calculates the relationship between the distance from the boundary in the integrated data and the land use type.

[0011] A program according to one aspect of the present disclosure causes a computer to function as a generation unit that generates integrated data by overlaying boundaries managed by different land management entities onto land use type data indicating the land use type for each land location, and as a calculation unit that quantifies and calculates the relationship between the distance from the boundary in the integrated data and the land use type.

[0012] According to the present disclosure, it is possible to provide a technology that can evaluate changes in ecosystems in areas that are managed by different management entities.

[0013] FIG. 1 is a diagram illustrating functional blocks of a processing device according to the present disclosure. FIG. 2 is a diagram illustrating an example of a process in which a generating unit generates integrated data (part 1). FIG. 3 is a diagram illustrating an example of a process in which a generating unit generates integrated data (part 2). FIG. 4 is a diagram illustrating an example of a process in which a generating unit generates integrated data (part 3). FIG. 5 is a diagram illustrating an example of a process in which a calculating unit identifies a target portion to be quantified (part 1). FIG. 6 is a diagram illustrating an example of a process in which a calculating unit identifies a target portion to be quantified (part 2). FIG. 7 is a diagram illustrating an example of quantified data output by a calculating unit. FIG. 8 is a flowchart illustrating an example of a process in which the processing device is performed. FIG. 9 is a diagram illustrating the hardware configuration of a computer used in the processing device.

[0014] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the description of the drawings, the same parts are designated by the same reference numerals and the description thereof will be omitted.

[0015] (Processing Device) A processing device 1 according to the present disclosure evaluates changes in ecosystems in areas that are shared by different management entities. The processing device 1 includes land use pattern data 11, boundary data 12, integrated data 13, and quantified data 14, as well as functions of a generation unit 21 and a calculation unit 22. Each piece of data is stored in a storage device such as a memory 902 or a storage 903. Each function is implemented in a CPU 901.

[0016] The land use type data 11 is data indicating the land use type for each land location. The land use type specifies the land use type, such as residential land, farmland, forest, factory, etc. The land use type data 11 includes land use types estimated from images using remote sensing data. The land use type data 11 may be in any format as long as it can specify the location of the land and the land use type at that location.

[0017] The land use type specified by the land use type data 11 may be set according to the processing purpose of the processing device 1. For example, when focusing on land that has traces of the environment and human activity, the processing device 1 uses data that can classify the land use type into natural and non-natural. When focusing on ecosystems that differ depending on the habitat environment, the processing device 1 uses data that can classify the land use type into differences in tree species such as broad-leaved trees or conifers, and differences in living organisms that inhabit the land such as rice fields or paddy fields.

[0018] The boundary data 12 is data that identifies boundaries between different land management entities. The land management entity may be, for example, an administrative identifier of the country, local government, or other entity that manages the land, or an association, organization, or the like. Alternatively, the management entity may be a land type, such as an environmental protection area or a mountainous region. The boundary data 12 is, for example, data that associates an identifier of a management entity with each location of land, and boundaries between different management entities may be identified from the boundary data 12. The boundary data 12 may also be polygon data that can identify boundaries.

[0019] The integrated data 13 is data in which boundaries between different land management entities are superimposed on land use type data indicating the land use type for each land location. The integrated data 13 includes the land use type for each land location specified by a predetermined land granularity, as well as the positions of boundaries between different land management entities specified by the land granularity. The integrated data 13 is generated by the generation unit 21.

[0020] The quantified data 14 is data that quantifies changes in ecosystems in areas that span different management entities. In the present disclosure, one of the divided portions of the target area to be quantified is referred to as an area. The quantified data 14 is, for example, data that quantifies the relationship between the distance from a boundary in the integrated data 13 and the land use type. The distance from the boundary is the shortest distance between the boundary and the area to which the land use type is associated. The quantified data 14 associates the distance from the boundary with the correlation strength in the relationship between the change in land use type at that distance. The quantified data 14 is calculated by the calculation unit 22. The data format of the quantified data 14 may differ depending on the quantification method used by the calculation unit 22.

[0021] The generation unit 21 generates integrated data 13 from the land use type data 11 and the boundary data 12. For example, the generation unit 21 generates the integrated data 13 by superimposing boundaries of land managed by different entities on land use type data indicating the land use type for each land location.

[0022] The generation unit 21 specifies the granularity of land positions to be represented in the integrated data 13, such as the granularity of land positions specified by the land use type data 11, the granularity of land positions specified by the boundary data 12, or the granularity of any land position. The generation unit 21 converts the land use type for each land position in the land use type data 11 and associates a land use type identifier with each land position in the specified land position granularity. The generation unit 21 converts the identifier of the management entity for each land position in the boundary data 12 and specifies the boundary position in the specified land position granularity. The generation unit 21 associates a land use type identifier and a boundary with each land position in the specified land position granularity.

[0023] The generation unit 21 may use any other processing method as long as it can superimpose the land use type and the boundary at a predetermined land granularity. For example, the generation unit 21 acquires the geodetic system information and latitude and longitude information contained in the land use type data 11, and specifies the scale of the boundary data 12 and the coordinates that specify the boundary. The generation unit 21 converts the land use type data 11 and the boundary data 12 according to the scale represented in the integrated data 13, and superimposes the converted land use type data 11 and the boundary data to generate the integrated data 13.

[0024] An example of the process in which the generating unit 21 generates the integrated data 13 will be described with reference to FIGS.

[0025] 2 shows an example of the land use type data 11. The land use type data 11 associates the location of land with the use type of the land. In FIG. 2, the land use type is type A, type B, or type C.

[0026] The generation unit 21 refers to the geodetic system information and latitude and longitude information of the land use type data 11, converts the land use type data 11 into data at the scale and coordinates of the boundary data 12, and generates the data shown in Fig. 3. The diagram shown in Fig. 3 associates land use types with each area formed by dividing the land into a grid. Each area in Fig. 3 is hatched to indicate the type of use.

[0027] The generation unit 21 generates integrated data 13 by superimposing the boundaries identified by the boundary data 12 on the data shown in Fig. 3. The data shown in Fig. 4 is an example of the integrated data 13. From the integrated data 13 shown in Fig. 4, the land use type and boundaries at each land location are confirmed.

[0028] The calculation unit 22 quantifies and calculates the relationship between the distance from the boundary and the land use type in the integrated data 13 .

[0029] The calculation unit 22 may quantify the relationship between the distance from the boundary and the land use type by, for example, using spatial correlation analysis, but any method may be used.

[0030] This disclosure describes a method using spatial correlation analysis. Spatial correlation analysis is a technique for obtaining the correlation strength of variables in a space to be analyzed as a function of distance by evaluating a correlation function that represents the correlation between different variables in a two-dimensional space. In this disclosure, the "variable" is a "land use type identifier," and the "different variable" is a "change in land use type." Specific techniques for spatial correlation analysis are described in Non-Patent Documents 3 and 4.

[0031] The calculation unit 22 calculates the correlation strength between the area where land use changes and the distance from the boundary of the area, for example, by spatial correlation analysis. Here, the calculation unit 22 calculates the correlation strength as a value that has a positive correlation with the number of areas for each type of land use change.

[0032] A land use change type is a combination of adjacent land use types, and in the present disclosure, is type A and type B, type B and type C, or type A and type C. The calculation unit 22 counts the number of areas for each land use change type, for example, for each predetermined distance from the boundary. The calculation unit 22 calculates the correlation strength for each predetermined distance from the boundary and for each land use change type so that the correlation strength is higher as the number of areas increases and lower as the number of areas decreases. The calculation unit 22 outputs the correlation strength for each predetermined distance from the boundary and for each land use change type as quantification data 14.

[0033] To identify the target portion to be quantified this time, the calculation unit 22 first determines that the target portion to be quantified is an area contained within a predetermined distance from the boundary. As shown in Fig. 5, the target portion to be quantified is identified from the integrated data 13. In Fig. 5, the solid line indicates the boundary, and the dashed line indicates a position a predetermined distance from the boundary. In the present disclosure, the portion surrounded by the two dashed lines is the target portion to be quantified.

[0034] Here, when a position at a predetermined distance from the boundary separates an area, whether or not the area is included in the target portion is determined according to a predetermined rule. When separating an area, the area may or may not be included in the target portion, and may or may not be included in the target portion depending on the separating position.

[0035] For each area within a predetermined distance from the boundary, the calculation unit 22 identifies adjacent areas and areas where the land use pattern will change, as shown in Fig. 6. Fig. 6 includes cases where the target area and the areas adjacent to the target area are of type A and type B, type B and type C, and type A and type C, respectively.

[0036] The calculation unit 22 identifies the distance from the boundary for each area where the land use changes with respect to the adjacent area, and calculates the correlation between the distance from the boundary and the change in land use. Note that in Fig. 6, the target area to be quantified this time is one square of the area, but by setting it to multiple squares, the correlation between the number of squares (distance) from the boundary and the change in land use may be identified.

[0037] FIG. 7 shows an example of the quantified data 14 calculated by the calculation unit 22. FIG. 7 shows the relationship between the correlation strength and the distance. In FIG. 7, the origin indicates the boundary, the horizontal axis indicates the distance from the boundary, and the vertical axis indicates the correlation strength. FIG. 7 shows that the areas where land use types A and B are adjacent and the areas where land use types B and C are adjacent are most common near the boundary and gradually decrease as the distance from the boundary increases. The areas where land use types A and C are adjacent are most common in areas slightly away from the boundary.

[0038] The calculation unit 22 may output a relational expression of correlation strength with distance from the boundary, as shown in FIG. 7, as quantified data 14 indicating changes in ecosystems in areas straddling different management entities.

[0039] (Processing Method) A processing method according to the present disclosure will be described with reference to FIG.

[0040] In step S101 , the processing device 1 generates integrated data 13 by superimposing the boundaries of land management entities on the land use pattern data 11 .

[0041] In step S102, the processing device 1 identifies an area where land use changes. In step S103, the processing device 1 quantifies the relationship between the area identified in step S102 and the distance from the boundary, and outputs quantified data 14.

[0042] The processing device 1 according to the present disclosure can evaluate ecosystem changes in areas that are managed by different management entities. By evaluating spatial correlation, the processing device 1 can understand the geographical patterns of ecosystem services, which can facilitate appropriate planning for land conservation areas.

[0043] Furthermore, conventional natural capital measurement technology has been limited to calculating the total amount of natural capital, as disclosed in Non-Patent Document 2. In contrast, the processing device 1 according to the present disclosure can quantify the correlation between differences in land management entities and differences in land use patterns. This makes it possible to quantitatively evaluate whether or not changes in land use patterns due to differences in management entities have occurred in the spatial distribution of natural capital, based on similarities or dissimilarities due to differences in land management entities.

[0044] The processing device 1 associates land use patterns identified by remote sensing with the boundaries of management entities by integrating them. This makes it possible to identify areas where multiple management entities work together to conserve natural capital, but where the conservation of natural capital is fragmented due to differences in management entities and the supply of ecosystem services is impaired. By developing measures intensively for the identified areas, unified conservation of natural capital becomes possible even if the management entities are different.

[0045] The processing device 1 according to the present disclosure described above is, for example, a general-purpose computer system including a CPU (Central Processing Unit, processor) 901, a memory 902, a storage 903 (HDD: Hard Disk Drive, SSD: Solid State Drive), a communication device 904, an input device 905, and an output device 906. In this computer system, the CPU 901 executes a program loaded on the memory 902, thereby realizing each function of the processing device 1.

[0046] The processing device 1 may be implemented by one computer or by multiple computers, or may be a virtual machine implemented on a computer.

[0047] The program of the processing device 1 can be stored in a computer-readable recording medium such as a HDD, SSD, USB (Universal Serial Bus) memory, CD (Compact Disc), DVD (Digital Versatile Disc), or can be distributed via a network. The computer-readable recording medium is, for example, a non-transitory recording medium.

[0048] The present disclosure is not limited to the above-described embodiments, and various modifications are possible within the scope of the present disclosure.

[0049] REFERENCE SIGNS LIST 1 Processing device 11 Land use form data 12 Boundary data 13 Integrated data 14 Quantified data 21 Generation unit 22 Calculation unit 901 CPU 902 Memory 903 Storage 904 Communication device 905 Input device 906 Output device

Claims

1. A processing device comprising: a generation unit that generates integrated data by superimposing boundaries of different land management entities onto land use type data that indicates the land use type for each land location; and a calculation unit that quantifies and calculates the relationship between the distance from the boundary in the integrated data and the land use type.

2. The processing device according to claim 1, wherein the calculation unit calculates the correlation strength between the area where the land use type changes and the distance from the boundary of the area by spatial correlation analysis.

3. The processing device according to claim 2, wherein the calculation unit calculates the correlation strength as a value having a positive correlation with the number of areas for each type of change in land use form.

4. A program that causes a computer to function as a generation unit that generates integrated data by overlaying boundaries where the land management entities differ onto land use type data that indicates the land use type for each land location, and a calculation unit that quantifies and calculates the relationship between the distance from the boundary in the integrated data and the land use type.

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