A Geographic Information Decision Support Method for Territorial Spatial Planning

Abstract

This invention relates to the field of decision support, and more particularly to a geographic information decision support method for land spatial planning. The method includes: determining the boundaries of the planning area and dividing the planning area into grid cells; collecting multi-source geographic data and performing standardization processing to obtain normalized multi-source geographic data, and calculating the basic ecological carrying capacity of the grid cells; constructing an ecological collaboration network to dynamically adjust the basic ecological carrying capacity of the grid cells, generating dynamic ecological carrying capacity; and assigning zoning types to the grid cells based on the dynamic ecological carrying capacity and the normalized multi-source geographic data. This invention addresses the technical problems of traditional methods that neglect the comprehensive impact of different ecological factors on the carrying capacity of grid cells; often overlook the synergistic effects between grid cells and typically fail to optimize within the framework of ecological carrying capacity constraints; and only consider single ecological or economic benefits, failing to comprehensively evaluate ecological, economic, and social benefits.

Description

Technical Field

[0001] This invention relates to the field of decision support, and more particularly to a geographic information decision support method for land spatial planning. Background Technology

[0002] With the acceleration of global urbanization and the increasing impact of human activities on the environment, how to scientifically and efficiently plan national land space and rationally allocate resources has become a crucial issue for policymakers. National land space planning not only involves the rational use of land but also encompasses ecological protection, urban development, transportation, energy management, and many other aspects. Traditional national land space planning methods often rely on manual experience and basic geographic data analysis, resulting in inaccuracies in ecological space planning and a lack of refined ecological carrying capacity assessments. However, with the rapid development of information technology, especially Geographic Information Systems (GIS) technology, modern national land space planning has gradually shifted towards intelligent, refined, and data-driven decision support systems. Summary of the Invention

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

[0004] The present invention provides a geographic information decision support method for land spatial planning, which specifically includes the following technical solutions:

[0005] A geographic information decision support method for land spatial planning includes the following steps:

[0006] S1. After loading the boundary data of the planning area and determining the boundaries, the planning area is divided into grid cells; multi-source geographic data is collected and standardized to obtain normalized multi-source geographic data; based on the normalized multi-source geographic data, the basic ecological carrying capacity of the grid cells is calculated.

[0007] S2. Construct an ecological collaborative network, and dynamically adjust the basic ecological carrying capacity of the grid unit through the ecological collaborative network dynamic adjustment algorithm to generate dynamic ecological carrying capacity;

[0008] S3. Based on dynamic ecological carrying capacity and normalized multi-source geographic data, assign partition types to raster cells to obtain the optimal partitioning scheme for raster cells.

[0009] Preferably, S1 specifically includes:

[0010] 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 socioeconomic data include the normalized scores of ecological benefits, economic benefits, and social benefits.

[0011] Preferably, S1 specifically includes:

[0012] By using a weighted overlay method, the basic ecological carrying capacity of the grid cells is calculated by combining the normalized scores of the grid cells on ecological factors.

[0013] Preferably, S2 specifically includes:

[0014] The ecological collaborative network treats each grid unit in the planning area as a node and establishes connections between nodes based on the flow of ecological services.

[0015] Preferably, S2 specifically includes:

[0016] In the implementation of the dynamic adjustment algorithm for the ecological collaborative network, based on the ecological collaborative network, the degree centrality of the nodes and the distance between the nodes and their neighbors are calculated; the neighbors of the current node are traversed, and the logarithmic decay ratio of the basic ecological carrying capacity of the neighbors to the distance between the current node and its neighbors is calculated. Combined with the ecological collaborative influence factor, the ecological collaborative influence of the neighbors on the current node is obtained; the ecological collaborative influence of all neighbors on the current node is accumulated to obtain the total collaborative effect; based on the basic ecological carrying capacity of the current node and the total collaborative effect, the dynamic ecological carrying capacity is obtained.

[0017] Preferably, S2 specifically includes:

[0018] The ecological synergy impact factor is calculated based on the basic ecological carrying capacity of the current node and its neighboring nodes, combined with the degree centrality of the neighboring nodes, and by incorporating the ecological service interaction intensity. The ecological service interaction intensity is calculated based on normalized multi-source geographic data to quantify the intensity of ecological service flow.

[0019] Preferably, S3 specifically includes:

[0020] By analyzing the needs of the planning area, a list of zoning types is obtained, and an ecological carrying capacity demand coefficient is assigned to each zoning. Based on the normalized scores of ecological benefits, economic benefits, and social benefits, the grid cells are weighted and superimposed according to the preset zoning benefit weights to obtain the comprehensive benefit coefficient when the grid cells are assigned to different zoning types.

[0021] Preferably, S3 specifically includes:

[0022] Based on the comprehensive benefit coefficients of grid cells when they are assigned to different partition types, and combined with binary decision variables, an objective function is constructed; by maximizing the sum of the comprehensive benefit coefficients of all grid cells, the grid cells are partitioned to obtain the optimal partitioning scheme for the grid cells.

[0023] Preferably, S3 specifically includes:

[0024] In the process of maximizing the objective function value, dynamic ecological carrying capacity constraints and uniqueness constraints need to be satisfied. The dynamic ecological carrying capacity constraint introduces an ecological carrying capacity demand coefficient, requiring that the ecological carrying capacity demand required for each grid cell to be assigned a partition type cannot exceed the dynamic ecological carrying capacity. The uniqueness constraint requires that each grid cell can only be assigned to one partition type.

[0025] The beneficial effects of the technical solution of the present invention are:

[0026] 1. By loading the boundary data of the planning area into GIS software and using rasterization tools to subdivide the area into regular raster units, the basic ecological carrying capacity of each raster unit is calculated. This can refine the ecological carrying capacity assessment in space, avoid extensive regional division, and ensure that the ecological support capacity of each raster unit is accurately assessed. This helps to clarify the ecological sensitivity of each region and provide a scientific basis for land use and development.

[0027] 2. Based on complex network theory, an ecological collaborative network is constructed, and the basic ecological carrying capacity of grid units is dynamically adjusted to take into account the synergistic effect of ecological services between regions. This breaks through the limitations of traditional methods that only consider a single factor or spatial distance. It comprehensively considers the ecological interaction and topological relationship between regions, and can truly reflect the cross-regional impact of ecological service flows such as water resource flow and air purification diffusion, thereby improving the accuracy and applicability of ecological assessment.

[0028] 3. By analyzing the needs of the planning area, a list of zoning types is determined. By introducing dynamic ecological carrying capacity and linear programming methods, the functional zoning of grid units is optimized. This not only ensures that ecologically sensitive areas (such as wetlands and forests) are given priority for ecological protection, but also avoids over-development of areas with high ecological carrying capacity by balancing ecological, economic and social benefits, thus promoting a balance between ecological protection and development. Attached Figure Description

[0029] Figure 1 This is a flowchart of a geographic information decision support method for land spatial planning according to the present invention. Detailed Implementation

[0030] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0032] The following description, in conjunction with the accompanying drawings, details a specific scheme of the geographic information decision support method for territorial spatial planning provided by this invention.

[0033] See attached document Figure 1 The diagram illustrates a flowchart of a geographic information decision support method for land spatial planning provided by an embodiment of the present invention. The method includes the following steps:

[0034] S1. After loading the boundary data of the planning area and determining the boundaries, the planning area is divided into grid cells; multi-source geographic data is collected and standardized to obtain normalized multi-source geographic data; based on the normalized multi-source geographic data, the basic ecological carrying capacity of the grid cells is calculated.

[0035] Load the boundary data of the planning area using Geographic Information System (GIS) software (such as ArcGIS Pro or QGIS);

[0036] After determining the boundaries of the planning area, the planning area is divided into regular rectangular raster cells using the rasterization tools of GIS software (such as ArcGIS's "CreateFishnet" function). The raster resolution is set to 100m×100m to ensure computational efficiency while meeting the spatial accuracy requirements of urban and regional planning. Each raster cell is assigned a unique identifier (number) and the geographic coordinates and area of ​​the raster cell are recorded.

[0037] Multi-source geographic data is collected through national-level information platforms (such as the National Earth System Science Data Center and the China Meteorological Data Network) and standardized to obtain normalized multi-source geographic data. The normalized multi-source geographic data includes the normalized scores of raster cells on ecological factors (such as vegetation coverage and water resource availability) 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 process uses a linear normalization method to map the multi-source geographic data to the [0,1] interval, which is a well-known technique and will not be described in detail here.

[0038] The basic ecological carrying capacity of each grid cell is calculated by weighting and summing the normalized scores of the grid cells on ecological factors, as shown in the following formula:

[0039]

[0040] in, Represents grid cells The basic ecological carrying capacity reflects the ecological support capacity of a grid unit under the comprehensive influence of ecological factors (such as vegetation coverage, water availability, etc.). This indicates the application of all ecological factors. (from 1 to Sum the weighted contributions of ) Indicates the quantity of ecological factors; Indicates ecological factors The relative importance weights of factors such as vegetation cover and water availability in the basic ecological carrying capacity assessment are given, with values ​​ranging from [value range missing]. And satisfy The value can be determined through expert scoring methods (such as the Delphi method, which invites experts in ecology and geography to evaluate) or data-driven methods (such as the entropy method, which calculates the information entropy based on ecological factor data). Represents grid cells In ecological factors The normalized scores on (such as vegetation coverage, water availability, etc.) reflect the performance of ecological factors in the raster cells and are derived from normalized multi-source geographic data.

[0041] The basic ecological carrying capacity comprehensively reflects the ecological support capacity of the grid unit, and can provide ecological constraints for national spatial planning, guide functional zoning, and avoid over-development of ecologically sensitive areas.

[0042] S2. Construct an ecological collaborative network, and dynamically adjust the basic ecological carrying capacity of the grid unit through the ecological collaborative network dynamic adjustment algorithm to generate dynamic ecological carrying capacity;

[0043] An ecological synergy network is constructed based on complex network theory. An ecological synergy network dynamic adjustment algorithm is used to dynamically adjust the basic ecological carrying capacity of grid units, thereby generating a dynamic ecological carrying capacity that reflects the synergistic effect of ecological services among regions.

[0044] The proposed dynamic adjustment algorithm for the ecological collaborative network breaks through the limitations of traditional methods that only consider spatial distance. It comprehensively considers the topological relationships between grid cells (such as connection strength and node importance) and carrying capacity differences, quantifies the cross-regional impact of ecological services, and generates carrying capacity results that conform to actual ecological processes.

[0045] The ecological collaborative network treats each grid unit of the planning area as a network node, with each node representing a 100m × 100m geographic spatial unit. The connection between nodes is established through the flow of ecological services (such as water resource flow and air purification diffusion). The connection is based on normalized multi-source geographic data and depends on actual ecological processes. For example, water resource flow is determined through the river network, and air purification diffusion is determined through wind direction and wind speed data. The establishment of the connection is a technical means well known to those skilled in the art and will not be described in detail here.

[0046] Furthermore, taking into account the intensity of ecosystem service interactions, degree centrality, and carrying capacity differences, an ecological synergy impact factor is calculated for the ecological impact of each node on its neighboring nodes. Specifically, in the ecological synergy network, the degree centrality of each node is calculated to reflect the number of connections between the node and its neighboring nodes, thus demonstrating the node's pivotal role in the ecological synergy network. The carrying capacity difference is the absolute value of the difference in basic ecological carrying capacity between the current node and its neighboring nodes, used to modulate the synergy intensity to avoid unreasonable excessive impacts between nodes with excessively large carrying capacity differences (such as cities and wetlands).

[0047] Based on complex network theory and the ecological principles of ecosystem service flows, a formula for calculating dynamic ecological carrying capacity is constructed. For each grid cell, its neighboring nodes are traversed, and the logarithmic decay ratio of the basic ecological carrying capacity of the neighboring nodes to the distance between nodes is calculated to simulate the decay effect of ecosystem service flows with distance. Furthermore, an ecological synergy impact factor is introduced to obtain the ecological synergy impact of neighboring nodes on the current node. The ecological synergy impacts of all neighboring nodes are summed to obtain the total synergy effect. Based on the basic ecological carrying capacity of the current node and the total synergy effect, the dynamic ecological carrying capacity is obtained. The specific formula for dynamic ecological carrying capacity is as follows:

[0048]

[0049] in, Represents grid cells Dynamic ecological carrying capacity, taking into account the impact of ecological synergy networks, reflects the synergistic effect of inter-regional ecological service flows (such as water resource flow and air purification and diffusion). This represents the synergistic effect term, used to synthesize neighboring grid cells (i.e., neighboring nodes). The ecological synergistic impact, in order to adjust the basic ecological carrying capacity; This indicates all neighboring grid cells. (excluding its own grid cells) The summation of ecological synergistic impacts; Represents grid cells Basic ecological carrying capacity; Represents grid cells With grid cells The logarithmic decay function of Euclidean distance is used to simulate the nonlinear decay of ecosystem service flows with distance (such as the weakening of water resource flows with distance). Adding 1 is to avoid the undefined condition when the distance is 0. Represents grid cells With grid cells The Euclidean distance between them was calculated using ArcGIS spatial analysis tools. Represents grid cells With grid cells Ecological synergy influencing factors are used to quantify raster units. Its importance in the ecological collaborative network is calculated using the following formula:

[0050]

[0051] in, Represents grid cells With grid cells The intensity of ecosystem service interactions between different regions is calculated based on normalized multi-source geographic data. It is used to quantify the intensity of ecosystem service flows, such as water resource flow and air purification diffusion. This is a well-known technique in the field and will not be elaborated upon here. The value range is [value range missing]. 0 indicates no interaction, and 1 indicates strong interaction; Represents grid cells In ecological collaborative networks, degree centrality reflects the number of connections in a grid cell. For example, river confluences have high degree centrality due to numerous connections, and its value ranges from [value missing]. 0 indicates no connectivity, and 1 indicates high connectivity; This indicates the load-bearing capacity difference modulation term, expressed through the grid unit. With grid cells The absolute value of the difference in basic ecological carrying capacity is added by 1 to modulate the synergistic effect. The greater the difference in carrying capacity, the smaller the synergistic effect. Adding 1 is to ensure that the denominator is non-zero.

[0052] To address the shortcomings of traditional basic carrying capacity assessments that neglect regional collaboration, ecological synergy influencing factors are introduced to capture the cross-regional impacts of ecosystem service flows (such as water resource flow and air purification diffusion) to ensure that planning results conform to actual ecological processes.

[0053] S3. Based on dynamic ecological carrying capacity and normalized multi-source geographic data, assign partition types to raster units to obtain the optimal partitioning scheme for raster units.

[0054] By analyzing the needs of the planning area, a list of zoning types is determined, and an ecological carrying capacity demand coefficient is assigned to each zoning. The ecological carrying capacity demand coefficient is determined through expert consultation (such as ecologists and urban planning experts). The ecological carrying capacity demand coefficient quantifies the degree of ecological resource occupation by different functional zoning types, reflects the principle of prioritizing ecological protection, and the level of the ecological carrying capacity demand coefficient directly affects the allocation of functional zoning to ensure that ecologically sensitive areas (such as wetlands) are given priority as ecological protection zones, thereby reducing development pressure on high-carrying-capacity areas.

[0055] Simultaneously, for each grid cell, the normalized scores of ecological benefits, economic benefits, and social benefits from the normalized scores of socioeconomic data are weighted and superimposed according to the zoning benefit weights to calculate the comprehensive benefit coefficient when the grid cell is assigned to different zoning 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 it is assigned to different functional zones, embodying the trade-off between ecological protection and development. The zoning benefit weights are set according to the objectives of the zoning type using expert experience, and are not limited here.

[0056] Using linear programming, an objective function is constructed. By maximizing the sum of the comprehensive benefit coefficients of all grid cells, the optimal zoning scheme for each grid cell is determined, while simultaneously satisfying dynamic ecological carrying capacity constraints.

[0057] The objective function is expressed as follows:

[0058]

[0059] in, The objective function value is used as the optimization objective, and the optimal partitioning scheme is generated by maximizing the objective function value. Indicates maximization; This indicates a double summation; Indicates the total number of partition types; Represents grid cells Assigned as partition type The comprehensive benefit coefficient at that time is used to quantify ecological, economic and social benefits to reflect the benefits of zoning selection; Represents a binary decision variable used to reflect the grid cell. Is it assigned as a partition type? (1 indicates allocation, 0 indicates no allocation);

[0060] To ensure that the zoning plan does not exceed the ecological carrying capacity, two types of constraints need to be set:

[0061] Ecological carrying capacity constraint: The ecological carrying capacity requirement for each grid cell's assigned partition type cannot exceed the dynamic ecological carrying capacity, as expressed by the formula below:

[0062]

[0063] in, Indicates partition type The ecological carrying capacity demand coefficient is derived from expert assessment, and its value range is [value range missing]. ;

[0064] Uniqueness constraint: Each raster cell can only be assigned to one partition type, as shown in the following formula:

[0065]

[0066] Ecological carrying capacity constraints ensure that the zoning scheme respects regional ecological limitations, while uniqueness constraints ensure that the functional allocation of each grid unit is clear, avoiding conflicts or overlaps and improving the operability of the zoning scheme.

[0067] In summary, a geographic information decision support method for land spatial planning has been developed.

[0068] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0069] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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 within the protection scope of the present invention.

Claims

1. A geographic information decision support method for land spatial planning, characterized in that, Includes the following steps: S1. After loading the boundary data of the planning area and determining the boundaries, the planning area is divided into grid cells; multi-source geographic data is collected and standardized to obtain normalized multi-source geographic data; based on the normalized multi-source geographic data, the basic ecological carrying capacity of the grid cells is calculated. S2. Construct an ecological collaboration network. Through the dynamic adjustment algorithm of the ecological collaboration network, dynamically adjust the basic ecological carrying capacity of the grid unit to generate dynamic ecological carrying capacity. In the dynamic adjustment algorithm of the ecological collaboration network, each grid unit in the planning area is regarded as a node, and the connection relationship between nodes is established based on the flow of ecological services. Based on the normalized multi-source geographic data, an ecological collaboration impact factor is generated, and the ecological collaboration impact of neighboring nodes on the current node is calculated. The total synergistic effect is obtained by summing the ecological synergistic effects of all neighboring nodes; the dynamic ecological carrying capacity is obtained based on the basic ecological carrying capacity of the current node and the total synergistic effect. S3. Based on dynamic ecological carrying capacity and normalized multi-source geographic data, an ecological carrying capacity demand coefficient is introduced to assign partition types to raster units, thereby obtaining the optimal partitioning scheme for raster units.

2. The geographic information decision support method for land spatial planning according to claim 1, characterized in that, 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 socioeconomic data include the normalized scores of ecological benefits, economic benefits, and social benefits.

3. The geographic information decision support method for land spatial planning according to claim 2, characterized in that, S1 specifically includes: By using a weighted overlay method, the basic ecological carrying capacity of the grid cells is calculated by combining the normalized scores of the grid cells on ecological factors.

4. The geographic information decision support method for land spatial planning according to claim 1, characterized in that, S2 specifically includes: In the implementation of the dynamic adjustment algorithm of the ecological collaborative network, based on the ecological collaborative network, the degree centrality of the node and the distance between the node and its neighboring nodes are calculated; the neighboring nodes of the current node are traversed, and the logarithmic decay ratio of the basic ecological carrying capacity of the neighboring nodes to the distance between the current node and its neighboring nodes is calculated. Combined with the ecological collaborative influence factor, the ecological collaborative influence of the neighboring nodes on the current node is obtained.

5. A geographic information decision support method for land spatial planning according to claim 4, characterized in that, S2 specifically includes: The ecological synergy impact factor is calculated based on the absolute value of the difference in basic ecological carrying capacity between the current node and its neighboring nodes, combined with the degree centrality of the neighboring nodes, and by incorporating the ecological service interaction intensity. The ecological service interaction intensity is calculated based on normalized multi-source geographic data to quantify the intensity of ecological service flow.

6. A geographic information decision support method for land spatial planning according to claim 2, characterized in that, S3 specifically includes: By analyzing the needs of the planning area, a list of zoning types is obtained, and an ecological carrying capacity demand coefficient is assigned to each zoning. Based on the normalized scores of ecological benefits, economic benefits, and social benefits, the grid cells are weighted and superimposed according to the preset zoning benefit weights to obtain the comprehensive benefit coefficient when the grid cells are assigned to different zoning types.

7. A geographic information decision support method for land spatial planning according to claim 6, characterized in that, S3 specifically includes: Based on the comprehensive benefit coefficients of grid cells when they are assigned to different partition types, and combined with binary decision variables, an objective function is constructed; by maximizing the sum of the comprehensive benefit coefficients of all grid cells, the grid cells are partitioned to obtain the optimal partitioning scheme for the grid cells.

8. A geographic information decision support method for land spatial planning according to claim 7, characterized in that, S3 specifically includes: In the process of maximizing the objective function value, dynamic ecological carrying capacity constraints and uniqueness constraints need to be satisfied. The dynamic ecological carrying capacity constraint introduces an ecological carrying capacity demand coefficient, requiring that the ecological carrying capacity demand required for each grid cell to be assigned a partition type cannot exceed the dynamic ecological carrying capacity. The uniqueness constraint requires that each grid cell can only be assigned to one partition type.

Images