Geographic information decision support method for territorial space planning

Abstract

The invention relates to the field of decision support, in particular to a geographic information decision support method for territorial space planning. The method comprises the steps that the boundary of a planning area is determined, and the planning area is divided into grid units; multi-source geographic data are collected and subjected to standardization processing, normalized multi-source geographic data are obtained, and the basic ecological bearing capacity of the grid units is calculated; an ecological collaborative network is constructed, the basic ecological bearing capacity of the grid units is dynamically adjusted, and dynamic ecological bearing capacity is generated; and based on the dynamic ecological bearing capacity and the normalized multi-source geographic data, allocating partition types for the grid units. The problem that a traditional method neglects the comprehensive influence of different ecological factors on the bearing capacity of the grid unit is solved; the synergistic effect between the grid units is often neglected, and optimization is usually not carried out in a framework limited by ecological bearing capacity; and only single ecological benefit or economic benefit is considered, and comprehensive evaluation of ecological, economic and social benefits cannot be carried out.

Description

Technical Field

[0001] The present invention relates to the field of decision support, and particularly to a geographic information decision support method for territorial spatial planning. Background Art

[0002] With the acceleration of the global urbanization process and the continuous impact of human activities on the environment, how to scientifically and efficiently plan territorial space and rationally allocate resources has become an important issue faced by decision-makers. Territorial spatial planning not only involves the rational use of land, but also includes multiple aspects such as ecological protection, urban development, transportation, and energy management. Traditional territorial spatial planning methods often rely on manual experience and basic geographical data analysis, which are inaccurate for ecological space planning and lack refined ecological carrying capacity assessment. However, with the rapid development of information technology, especially geographic information system (GIS) technology, modern territorial spatial planning has gradually shifted towards an intelligent, refined, and data-driven decision support system. Summary of the Invention

[0003] The present invention provides a geographic information decision support method for territorial spatial planning to solve the technical problems that traditional methods usually rely on a single source of geographical data, ignoring the comprehensive impact of different ecological factors on the carrying capacity of grid cells; often neglecting the synergy between grid cells (especially in complex ecological service flow processes such as water resource flow and air purification); usually failing to optimize within the framework of ecological carrying capacity constraints, which may lead to overloading of the ecological carrying capacity of some grid cells; and only considering a single ecological benefit or economic benefit, without comprehensively evaluating ecological, economic, and social benefits.

[0004] A geographic information decision support method for territorial spatial planning according to the present invention specifically includes the following technical solutions: A geographic information decision support method for territorial spatial planning includes the following steps: S1. After loading the boundary data of the planning area and determining the boundary, divide the planning area into grid cells; collect multi-source geographical data and perform standardization processing to obtain normalized multi-source geographical data; calculate the basic ecological carrying capacity of the grid cells based on the normalized multi-source geographical data. S2. Construct an ecological synergy network, and dynamically adjust the basic ecological carrying capacity of the grid cells through an ecological synergy network dynamic adjustment algorithm to generate a dynamic ecological carrying capacity. S3. Based on the dynamic ecological carrying capacity and the normalized multi-source geographical data, assign partition types to the grid cells to obtain the optimal partition scheme of the grid cells.

[0005] Preferably, the S1 specifically includes: The normalized multi-source geographic data includes the normalized scores of raster cells on ecological factors and the normalized scores of socioeconomic data; the normalized scores of the socioeconomic data include the normalized scores of ecological benefits, economic benefits, and social benefits.

[0006] Preferably, the S1 specifically includes: By using the weighted overlay method, the normalized scores of raster cells on ecological factors are comprehensively calculated to obtain the basic ecological carrying capacity of the raster cells.

[0007] Preferably, the S2 specifically includes: For the ecological synergy network, each raster cell in the planning area is regarded as a node, and the connection relationship between nodes is established based on the flow of ecological services.

[0008] Preferably, the S2 specifically includes: In the implementation process of the ecological synergy network dynamic adjustment algorithm, based on the ecological synergy network, calculate the degree centrality of the node and the distance between the node and its neighbor nodes; traverse the neighbor nodes of the current node, calculate the logarithmic decay ratio of the basic ecological carrying capacity of the neighbor node to the distance between the current node and the neighbor node, and combine the ecological synergy influence factor to obtain the ecological synergy influence of the neighbor node on the current node; accumulate the ecological synergy influences of all neighbor nodes on the current node to obtain the total synergy effect; based on the basic ecological carrying capacity of the current node and the total synergy effect, obtain the dynamic ecological carrying capacity.

[0009] Preferably, the S2 specifically includes: The ecological synergy influence factor is calculated based on the basic ecological carrying capacities of the current node and its neighbor nodes, combined with the degree centrality of the neighbor node, and introducing the ecological service interaction intensity; the ecological service interaction intensity is calculated based on the normalized multi-source geographic data to quantify the intensity of the flow of ecological services.

[0010] Preferably, the S3 specifically includes: By analyzing the demands of the planning area, obtain the list of partition types, and assign the ecological carrying capacity demand coefficients to each partition; based on the normalized scores of ecological benefits, economic benefits, and social benefits, perform weighted overlay according to the preset partition benefit weights to obtain the comprehensive benefit coefficients when the raster cells are assigned to different partition types.

[0011] Preferably, the S3 specifically includes: Based on the comprehensive benefit coefficients when the raster cells are assigned to different partition types, and combined with the binary decision variables, construct the objective function; by maximizing the sum of the comprehensive benefit coefficients of all raster cells, partition the raster cells to obtain the optimal partition scheme of the raster cells.

[0012] Preferably, the step S3 specifically includes: In the process of maximizing the objective function value, it is necessary to satisfy the dynamic ecological carrying capacity constraint and the uniqueness constraint; for the dynamic ecological carrying capacity constraint, an ecological carrying capacity demand coefficient is introduced, and it is required that the ecological carrying capacity demand required for the zoning type allocated to each grid cell does not exceed the dynamic ecological carrying capacity; for the uniqueness constraint, it is required that each grid cell can only be allocated to one zoning type.

[0013] The beneficial effects of the technical solution of the present invention are: 1. By loading the boundary data of the planning area through GIS software and using a rasterization tool to subdivide the area into regular grid cells, calculating the basic ecological carrying capacity for each grid cell, it is possible to refine the ecological carrying capacity assessment in space, avoid extensive regional division, ensure the accurate assessment of the ecological support capacity of each grid cell, help clarify the ecological sensitivity of each region, and provide a scientific basis for land use and development.

[0014] 2. Based on the complex network theory, an ecological synergy network is constructed to dynamically adjust the basic ecological carrying capacity of grid cells, thereby considering the synergy of ecological services between regions, breaking through the limitations of traditional methods that only consider single factors or spatial distances, comprehensively considering the ecological interaction and topological relationship between regions, being able to truly reflect the cross-regional impact of ecological service flows such as water resource flow and air purification diffusion, and improving the accuracy and applicability of ecological assessment.

[0015] 3. By analyzing the requirements of the planning area to determine the list of zoning types and introducing dynamic ecological carrying capacity and linear programming methods to optimize the functional zoning of grid cells, it not only ensures that ecologically sensitive areas (such as wetlands, forests, etc.) are preferentially used for ecological protection, but also can avoid overdevelopment of areas with high ecological carrying capacity by weighing ecological, economic, and social benefits, and promote the realization of the balance between ecological protection and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flowchart of a geographic information decision support method for territorial spatial planning according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0019] The following specifically describes the specific solution of a geographic information decision support method for territorial spatial planning provided by the present invention in conjunction with the accompanying drawings.

[0020] Refer to the attached Figure 1 , which shows a flowchart of a geographic information decision support method for territorial spatial planning provided by an embodiment of the present invention. The method includes the following steps: S1. After loading the boundary data of the planning area and determining the boundary, divide the planning area into grid cells; collect multi-source geographic data and perform standardization processing to obtain normalized multi-source geographic data; calculate the basic ecological carrying capacity of the grid cells based on the normalized multi-source geographic data; Load the boundary data of the planning area through geographic information system (GIS) software (such as ArcGIS Pro or QGIS); After determining the boundary of the planning area, use the rasterization tool of GIS software (such as the "CreateFishnet" function of ArcGIS) to divide the planning area into regular rectangular grid cells, set the grid resolution to 100m×100m, while ensuring the calculation efficiency and meeting the spatial accuracy requirements of urban and regional planning; each grid cell is assigned a unique identifier (number), and the geographic coordinates and area of the grid cell are recorded; Collect multi-source geographic data through national information platforms (such as the National Earth System Science Data Center, China Meteorological Data Network, etc.) and perform standardization processing to obtain normalized multi-source geographic data; the normalized multi-source geographic data includes the normalized scores of grid cells on ecological factors (such as vegetation coverage rate, water resource availability, etc.) and the normalized scores of socio-economic data; the socio-economic data includes ecological benefit data, economic benefit data, and social benefit data; the standardization processing adopts the linear normalization method, which is used to map the multi-source geographic data to the [0,1] interval, which is a well-known technical means to those skilled in the art and will not be elaborated here; Perform weighted superposition on the normalized scores of grid cells on ecological factors to calculate the basic ecological carrying capacity of each grid cell. The formula is as follows:

[0021] Where represents the basic ecological carrying capacity of grid cell , reflecting the ecological support ability of the grid cell under the comprehensive influence of ecological factors (such as vegetation coverage rate, water resource availability, etc.); Indicates the summation of the weighted contributions to all ecological factors (from 1 to ); Indicates the number of ecological factors; Indicates the ecological factor (such as vegetation coverage rate, water resource availability, etc.) in the relative importance weight in the assessment of basic ecological carrying capacity, and the value range is , and satisfies , determined by the expert scoring method (such as the Delphi method, inviting experts in ecology and geography to evaluate) or the data-driven method (such as the entropy method, calculating based on the information entropy of ecological factor data); Indicates the normalized score of the grid cell on the ecological factor (such as vegetation coverage rate, water resource availability, etc.), reflecting the performance of the ecological factor in the grid cell, from the normalized multi-source geographical data; The basic ecological carrying capacity comprehensively reflects the ecological support ability of the grid cell, can provide ecological constraints for territorial space planning, and guide functional zoning to avoid overdevelopment of ecologically sensitive areas.

[0022] S2. Construct an ecological synergy network, and through the dynamic adjustment algorithm of the ecological synergy network, dynamically adjust the basic ecological carrying capacity of the grid cell to generate a dynamic ecological carrying capacity; Construct an ecological synergy network based on complex network theory, and use the dynamic adjustment algorithm of the ecological synergy network to dynamically adjust the basic ecological carrying capacity of the grid cell to generate a dynamic ecological carrying capacity reflecting the ecological service synergy among regions; The dynamic adjustment algorithm of the ecological synergy network breaks through the limitation of only considering spatial distance in the traditional sense, comprehensively considers the topological relationship (such as connection strength, node importance) and carrying capacity difference among grid cells, quantifies the cross-regional impact of ecological services, and generates a carrying capacity result that conforms to the actual ecological process; For the ecological synergy network mentioned above, each grid cell in the planning area is regarded as a network node, and each node represents a geographical space unit of 100 meters × 100 meters, and the connection relationship between nodes is established through the flow of ecological services (such as water resource flow, air purification diffusion). The connection relationship is based on the normalized multi-source geographical data and is determined depending on the actual ecological process. For example, the water resource flow is determined through the river network, and the air purification diffusion is determined through wind direction and wind speed data. The establishment of the connection relationship is a well-known technical means for those skilled in the art and will not be elaborated here; Furthermore, comprehensively considering the ecological service interaction intensity, degree centrality, and carrying capacity difference, an ecological co-influence factor is calculated for the ecological impact of each node on its neighbor nodes. Specifically, in the ecological co-network, the degree centrality of each node is calculated to reflect the number of connections between the node and its neighbor nodes, demonstrating the hub role of the node in the ecological co-network. The carrying capacity difference is the absolute value of the difference in the basic ecological carrying capacity between the current node and its neighbor nodes, which is used to modulate the co-intensity to avoid unreasonable excessive impacts between nodes with large differences in carrying capacity (such as cities and wetlands). Based on the complex network theory and the ecological principle of ecosystem service flow, a calculation formula for dynamic ecological carrying capacity is constructed. For each grid cell, its neighbor nodes are traversed, and the logarithmic decay ratio of the basic ecological carrying capacity of the neighbor nodes to the distance between the nodes is calculated to simulate the attenuation effect of ecological service flow with distance. Further, an ecological co-influence factor is introduced to obtain the ecological co-influence of neighbor nodes on the current node. The ecological co-influences of all neighbor nodes are accumulated to obtain the total co-effect. Based on the basic ecological carrying capacity of the current node and the total co-effect, the dynamic ecological carrying capacity is obtained. The specific formula for the dynamic ecological carrying capacity is as follows:

[0023] Where represents the dynamic ecological carrying capacity of the grid cell after considering the influence of the ecological co-network, reflecting the co-effect of ecological service flow (such as water resource flow, air purification diffusion) between regions; represents the co-effect term, which is used to comprehensively consider the ecological co-influence of neighboring grid cells (i.e., neighbor nodes) to adjust the basic ecological carrying capacity; represents the summation of the ecological co-influences on all neighboring grid cells (excluding the grid cell itself ); represents the basic ecological carrying capacity of the grid cell ; represents the logarithmic decay function of the Euclidean distance between the grid cell and the grid cell , which is used to simulate the non-linear attenuation of ecological service flow with distance (such as the weakening of water resource flow with distance). Adding 1 is to avoid undefined when the distance is 0; represents the Euclidean distance between the grid cell and the grid cell , which is calculated through the ArcGIS spatial analysis tool; represents the ecological co-influence factor between the grid cell and the grid cell , which is used to quantify the ecological co-influence between the grid cells Importance in the ecological collaborative network, calculated by the formula:

[0024] where represents the ecological service interaction intensity between grid cell and grid cell , which is calculated based on normalized multi-source geographical data and is used to quantify the intensity of ecological service flow, such as water resource flow and air purification diffusion. This is a well-known technical means for those skilled in the art and will not be elaborated here. The value range is , 0 indicates no interaction, and 1 indicates strong interaction; represents the degree centrality of grid cell in the ecological collaborative network, which is used to reflect the number of connections of the grid cell. For example, at the confluence of rivers, due to many connections, the degree centrality is high. The value range is , 0 indicates no connection, and 1 indicates high connectivity; represents the carrying capacity difference modulation term, which modulates the collaborative impact by adding 1 to the absolute value of the difference in the basic ecological carrying capacity between grid cell and grid cell . The greater the carrying capacity difference, the smaller the collaborative impact. Adding 1 is to ensure that the denominator is non-zero; Aiming at the deficiency that traditional basic carrying capacity assessment ignores regional collaboration, an ecological collaborative impact factor is introduced to capture the cross-regional impact of ecological service flow (such as water resource flow and air purification diffusion) to ensure that the planning results conform to the actual ecological process.

[0025] S3. Based on the dynamic ecological carrying capacity and normalized multi-source geographical data, assign partition types to grid cells to obtain the optimal partition scheme for grid cells; By analyzing the requirements of the planning area, determine the list of partition types and assign an ecological carrying capacity requirement coefficient to each partition; the ecological carrying capacity requirement coefficient is determined through expert consultation (such as ecologists, urban planning experts); the ecological carrying capacity requirement coefficient quantifies the degree of occupation of ecological resources by different functional partition types, reflecting the principle of giving priority to ecological protection. The level of the ecological carrying capacity requirement coefficient directly affects the functional partition allocation to ensure that ecologically sensitive areas (such as wetlands) are preferentially allocated as ecological protection areas and reduce the development pressure on areas with high carrying capacity; Meanwhile, for each grid cell, based on the normalized scores of ecological benefits, economic benefits, and social benefits in the normalized scores of socioeconomic data, weighted superposition is performed according to the partition benefit weights to calculate the comprehensive benefit coefficients when the grid cell is assigned to different partition types; the comprehensive benefits include ecological benefits, economic benefits, and social benefits; the comprehensive benefit coefficient reflects the comprehensive value of the grid cell when assigned to different functional partitions, reflecting the trade-off between ecological protection and development; the partition benefit weights are the weights of each benefit type set through the expert experience method according to the goals of the partition type, which are not limited here; Using the linear programming method, construct the objective function. By maximizing the sum of the comprehensive benefit coefficients of all grid cells, determine the optimal partition plan for each grid cell while satisfying the dynamic ecological carrying capacity constraint; The objective function is expressed as follows:

[0026] Where, represents the objective function value, which is used as the optimization goal, and the optimal partition plan is generated by maximizing the objective function value; represents maximization; represents double summation; represents the total number of partition types; represents the grid cell when assigned to the partition type the comprehensive benefit coefficient, which is used to quantify ecological, economic, and social benefits to reflect the benefits of partition selection; represents a binary decision variable, which is used to reflect whether the grid cell is assigned to the partition type (1 means assigned, 0 means not assigned); To ensure that the partition plan does not exceed the ecological carrying capacity, two types of constraint conditions need to be set: Ecological carrying capacity constraint: The ecological carrying capacity demand required for the partition type assigned to each grid cell cannot exceed the dynamic ecological carrying capacity. The formula is expressed as follows:

[0027] Where, represents the ecological carrying capacity demand coefficient of the partition type , which is derived from expert evaluation, and the value range is ; Uniqueness constraint: Each grid cell can only be assigned to one partition type. The formula is as follows:

[0028] The ecological carrying capacity constraint ensures that the zoning plan respects the regional ecological restrictions, and the uniqueness constraint guarantees that the functional allocation of each grid cell is clear, avoiding conflicts or overlaps and enhancing the operability of the zoning plan.

[0029] In summary, a geographic information decision support method for territorial spatial planning has been completed.

[0030] The sequence of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0031] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key points of each embodiment are the differences from other embodiments.

[0032] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A geographic information decision support method for territorial spatial planning, characterized in that, It includes the following steps: S1. After loading the boundary data of the planning area and determining the boundary, divide the planning area into grid cells; collect multi-source geographical data and perform normalization processing to obtain the normalized multi-source geographical data; calculate the basic ecological carrying capacity of the grid cells based on the normalized multi-source geographical data. S2. Construct an ecological collaboration network, and dynamically adjust the basic ecological carrying capacity of the grid cells through the ecological collaboration network dynamic adjustment algorithm to generate the dynamic ecological carrying capacity. S3. Based on the dynamic ecological carrying capacity and the normalized multi-source geographical data, assign partition types to the grid cells to obtain the optimal partition scheme of the grid cells.

2. The geographic information decision-making support method for territorial spatial planning according to claim 1, wherein Specifically, S1 includes: The normalized multi-source geographical data includes the normalized scores of the grid cells on ecological factors and the normalized scores of socio-economic data; the normalized scores of the socio-economic data include the normalized scores of ecological benefits, economic benefits, and social benefits.

3. A geographic information decision support method for territorial spatial planning according to claim 2, characterized in that, Specifically, S1 includes: Calculate the basic ecological carrying capacity of the grid cells by comprehensively considering the normalized scores of the grid cells on ecological factors through the weighted overlay method.

4. A geographic information decision support method for territorial spatial planning according to claim 1, characterized in that, Specifically, S2 includes: In the ecological collaboration network, each grid cell in the planning area is regarded as a node, and the connection relationship between nodes is established based on the flow of ecological services.

5. A geographic information decision support method for territorial spatial planning according to claim 4, characterized in that, Specifically, S2 includes: In the implementation process of the ecological collaboration network dynamic adjustment algorithm, based on the ecological collaboration network, calculate the degree centrality of the node and the distance between the node and its neighbor nodes; traverse the neighbor nodes of the current node, calculate the logarithmic decay ratio of the basic ecological carrying capacity of the neighbor node to the distance between the current node and the neighbor node, and combine the ecological collaboration influence factor to obtain the ecological collaboration influence of the neighbor node on the current node; accumulate the ecological collaboration influences of all neighbor nodes on the current node to obtain the total collaboration effect; based on the basic ecological carrying capacity of the current node and the total collaboration effect, obtain the dynamic ecological carrying capacity.

6. A geographic information decision support method for territorial spatial planning according to claim 5, characterized in that, Specifically, S2 includes: The ecological collaboration influence factor is calculated based on the basic ecological carrying capacities of the current node and its neighbor nodes, combined with the degree centrality of the neighbor node, and introducing the ecological service interaction intensity; the ecological service interaction intensity is calculated based on the normalized multi-source geographical data to quantify the intensity of the flow of ecological services.

7. A geographic information decision support method for territorial spatial planning according to claim 2, characterized in that, Specifically, S3 includes: By analyzing the requirements of the planning area, obtain the list of partition types, and assign ecological carrying capacity demand coefficients to each partition; based on the normalized scores of ecological benefits, economic benefits, and social benefits, perform weighted overlay according to the preset partition benefit weights to obtain the comprehensive benefit coefficients when the grid cells are assigned different partition types.

8. A geographic information decision support method for territorial spatial planning according to claim 7, characterized in that, Specifically, S3 includes: Based on the comprehensive benefit coefficients when the grid cells are assigned different partition types, and combined with binary decision variables, construct the objective function; partition the grid cells by maximizing the sum of the comprehensive benefit coefficients of all grid cells to obtain the optimal partition scheme of the grid cells.

9. A geographic information decision support method for territorial spatial planning according to claim 8, characterized in that, Specifically, S3 includes: In the process of maximizing the objective function value, it is necessary to satisfy the dynamic ecological carrying capacity constraint and the uniqueness constraint; for the dynamic ecological carrying capacity constraint, an ecological carrying capacity demand coefficient is introduced, requiring that the ecological carrying capacity demand required by the partition type assigned to each grid cell does not exceed the dynamic ecological carrying capacity; for the uniqueness constraint, it is required that each grid cell can only be assigned to one partition type.

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