A method for modifying evaluation of land space utilization based on terrain constraints

Abstract

The application discloses a land space utilization evaluation correction method based on terrain constraint, relates to the technical field of land space analysis and spatial data processing, and solves the technical problems of low evaluation accuracy and large result deviation of an existing model in a complex terrain area; the method comprises the following steps: 1, data acquisition and preprocessing; the data comprises land space data and DEM data; 2, constructing an evaluation index system according to the land space data and calculating the system evaluation value of land space utilization; 3, constructing a terrain constraint coefficient based on the DEM data; steps 2 and 3 have no sequence; 4, correcting the system evaluation value of land space utilization by using the terrain constraint coefficient; and the application significantly improves the evaluation accuracy of the complex terrain area.

Description

Technical Field

[0001] This invention relates to the field of land space analysis and spatial data processing technology, specifically to a land space use evaluation and correction method based on topographic constraints. Background Technology

[0002] Currently, the coupling and coordination relationship between territorial space and economic development has gradually become an important evaluation criterion for spatial governance. Existing methods typically construct indicator systems for the economic subsystem and the territorial space subsystem, and use coupling and coordination degree models to quantitatively analyze the relationship between the two.

[0003] Existing coupling coordination models are widely used in regional economic-spatial collaborative evaluation. These models are based on an implicit assumption: that the evaluation units are homogeneous, and that the impact of topographical differences on the evaluation results is negligible. However, with the application of these models in complex terrain areas such as mountains and hills, research has revealed fundamental problems with this assumption: (1) The model's homogeneity assumption does not match geographical reality: Topographical differences directly affect the feasibility and efficiency of land use—plains allow for large-scale mechanized operations, while mountainous and hilly areas are limited by slope and undulation, resulting in naturally lower spatial utilization efficiency. Existing calculation methods do not consider the physical limitations of topographical ruggedness on the actual usable area and spatial heterogeneity of the land surface, leading to severe nonlinear distortions in spatial geometric calculations and utilization efficiency indicators in areas with drastic elevation changes, thus causing a systematic overestimation of spatial utilization efficiency in mountainous areas.

[0004] (2) Unclear object of correction: Some studies attempt to introduce correction factors, but the object of correction is usually a comprehensive index or economic subsystem, rather than the land use subsystem directly constrained by topography. This approach of correcting the wrong object not only fails to correct the bias, but may also introduce new errors.

[0005] (3) Lack of feasible quantification methods: Even if the impact of terrain differences is recognized, there is a lack of systematic quantification methods for converting terrain factors into mathematical coefficients that can be embedded in the model. Existing solutions are mostly qualitative descriptions or simple linear processing, which are difficult to accurately characterize the nonlinear characteristics of terrain constraints. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a land space use evaluation correction method based on terrain constraints, which solves the technical problems of low evaluation accuracy and large result deviation in existing models in complex terrain areas.

[0007] A method for correcting land use evaluation based on topographic constraints includes the following steps: Step 1: Data acquisition and preprocessing; the data includes land spatial data and DEM data; Step 2: Construct an evaluation index system based on land space data and calculate the system evaluation value of land space utilization; Step 3: Using GIS spatial analysis technology, extract slope, aspect and topographic relief from DEM data, and construct a nonlinear continuous function to simulate the constraints of natural geographical terrain as the terrain constraint coefficient; Steps 2 and 3 are not in any particular order; Step 4: Use the terrain constraint coefficient to perform spatial heterogeneity physical correction on the system evaluation value of land space use, and eliminate the geometric and spatial use efficiency calculation bias in complex terrain areas.

[0008] Further, step 2 includes: Step 2.1: Construct land use indicators based on land space data, including the proportion of construction land, the proportion of ecological land, the rate of reduction of cultivated land, and the added value of secondary and tertiary industries per unit area of ​​construction land; Step 2.2: Standardize each indicator using the range method according to its direction; Step 2.3: Determine the weights of each indicator using the entropy method; Step 2.4: Integrate the land space use indicators into a systematic evaluation value of land space use based on the indicator weights.

[0009] Further, step 2.3 includes: The data from n evaluation units and t evaluation periods within the study area are merged to construct a global decision matrix, resulting in N evaluation records, where N = n * t. The weight of the q-th record (q=1,2....N) under the j-th indicator is calculated as follows: , This represents the standardized value of the q-th evaluation record and the j-th indicator; Calculate the information entropy of the j-th indicator: ,in ; Calculate the coefficient of variation for the j-th indicator: ; Calculate the weight of the j-th indicator: m represents the total number of indicators.

[0010] Further, step 3 includes: Step 3.1: Based on DEM data, calculate terrain factors through GIS spatial analysis. The terrain factors include slope, aspect, and topographic relief. Slope represents the steepness of a surface unit, and the unit is degrees. Slope aspect, representing the orientation of a surface unit, is converted into a dimensionless slope aspect index; Topographic relief, representing the relative change in surface elevation within the i-th evaluation unit, reflects the depth of surface incision and the degree of topographic fragmentation. The formula is as follows:

[0011] In the formula, This represents the maximum elevation value within the i-th evaluation unit. The minimum elevation value within the i-th evaluation unit. This represents the total area of ​​the i-th evaluation unit; Step 3.2: Construct a nonlinear continuous function to characterize the continuous variation of terrain constraints, as shown in the following expression:

[0012] In the formula, Let be the average slope of the i-th evaluation unit; This refers to the degree of topographic relief. The aspect index is a dimensionless value converted from the original aspect angle, with a value range of [0,1]. The larger the value, the smaller the constraint of the aspect on the use of land space. a, b, and c are topographic influence parameters, which are determined based on empirical values ​​in topography.

[0013] Furthermore, step 3 also includes: determining the applicability of the terrain constraint coefficient; Before calculation, determine whether terrain constraints are needed: the coefficient of variation (CV) is used to measure the dispersion of terrain within the study area, and the calculation formula is as follows:

[0014] This represents the standard deviation of the slope of all evaluation units within the study area. The slope is expressed as the arithmetic mean, and CV is a dimensionless number. The larger the value, the more significant the terrain difference. Specifically, a difference threshold is set. If the CV is greater than the difference threshold, the terrain difference is judged to be significant, and terrain constraint correction is adopted. When the CV is less than the difference threshold, the terrain is judged to be flat, and there is no need to correct the system evaluation value of land space use.

[0015] Furthermore, the corrected expression for step 4 is as follows:

[0016] In the formula, This represents the revised systematic evaluation value of land space use. The system evaluation value representing the use of national land space It represents a nonlinear continuous function.

[0017] The beneficial effects of this invention include: 1. Introducing a multi-factor terrain constraint mechanism: It not only considers slope, but also integrates multi-dimensional terrain factors such as slope aspect and terrain relief, which significantly improves the evaluation accuracy of complex terrain areas; 2. Introduce an applicability judgment mechanism: automatically determine whether terrain constraint correction is needed based on regional terrain characteristics to avoid unnecessary computational overhead; 3. Spatiotemporal evolution analysis capability: Supports comparative analysis of data from multiple periods to identify the spatiotemporal evolution patterns of coupling coordination. Attached Figure Description

[0018] Figure 1 This is a flowchart of a land use evaluation and correction method based on terrain constraints, which is involved in an embodiment of this application.

[0019] Figure 2 This is a schematic diagram showing the modified 2017 coordination level distribution of an evaluation unit in a certain area of ​​a certain city, as described in the embodiments of this application.

[0020] Figure 3 This is a schematic diagram showing the modified 2022 coordination level distribution of an evaluation unit in a certain area of ​​a certain city involved in the embodiments of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0022] Example 1 The following is in conjunction with the appendix Figure 1 Specific embodiments of the present invention will be described in detail; A method for revising land use evaluation based on topographic constraints, such as Figure 1 As shown, it includes: Step 1: Data acquisition and preprocessing; the data includes land spatial data, socio-economic data, and DEM data.

[0023] The land and space data are derived from the results of the National Land Survey and Annual Change Survey, and are in the form of vector plot data, including land type information such as cultivated land, forest land, and construction land. The socio-economic data are derived from statistical yearbooks and district and county statistical bulletins, with district and county-level administrative units as the statistical scale. The DEM data are derived from SRTM, with a spatial resolution of not less than 30m.

[0024] The preprocessing includes unifying the spatial coordinate system and using the district / county-level administrative boundaries as the evaluation unit to unify the evaluation unit boundaries.

[0025] Step 2: Construct an evaluation index system and calculate the index.

[0026] Step 2.1: Construct land use indicators based on land space data, including the proportion of construction land, the proportion of ecological land, the rate of reduction of cultivated land, and the added value of secondary and tertiary industries per unit area of ​​construction land; a specific example of the indicator system is shown in Table 1.

[0027] Table 1 Example of an indicator system

[0028] The direction of the indicators in the table represents the evaluation direction: 1) Positive indicators: The higher the indicator value, the better the evaluation result; for example, the higher the GDP per capita, the better. 2) Negative indicators: The smaller the indicator value, the better the evaluation result. For example, the lower the proportion of construction land and the more intensive the space utilization, the better the evaluation.

[0029] Perform natural logarithmic transformation on indicators with significant differences in magnitude, such as the added value of the secondary and tertiary industries per unit of construction land:

[0030] The term "significant difference in magnitude" refers to a situation where the ratio of the maximum to the minimum value (extreme value ratio) of a certain indicator within the study area is greater than 10, or the coefficient of variation (the ratio of the standard deviation to the mean) is greater than 1.0. This indicates that the data has a significant discrete distribution and requires logarithmic transformation to reduce the data difference and meet the requirements of the entropy method for data distribution.

[0031] Step 2.2: Standardize each indicator using the range method based on its direction: Positive indicators:

[0032] Negative indicators:

[0033] In the formula, This represents the original value of the i-th evaluation unit and the j-th indicator. This represents the standardized value of the i-th evaluation unit and the j-th indicator, with a value range of [0, 1]. This represents the maximum value of the j-th indicator among all evaluation units. This represents the minimum value of the j-th indicator among all evaluation units; the j-th indicator refers to the indicator number determined after numbering the four economic development indicators and the four land space utilization indicators.

[0034] The evaluation unit is the basic spatial unit of evaluation, and in this embodiment, it is a district or county-level administrative unit.

[0035] Step 2.3: Determine the weights of each indicator using the entropy method; Step 2.3 includes: The data from n evaluation units and t evaluation periods within the study area are merged to construct a global decision matrix, resulting in N evaluation records, where N = n * t. The weight of the q-th record (q=1,2....N) under the j-th indicator is calculated as follows: , This represents the standardized value of the q-th evaluation record and the j-th indicator; Calculate the information entropy of the j-th indicator: ,in ; Calculate the coefficient of variation for the j-th indicator: ; Calculate the weight of the j-th indicator: m represents the total number of indicators.

[0036] Step 2.4: Calculate the system evaluation value of the indicators; Systematic evaluation value of land space use:

[0037] In the formula, Corresponding land space utilization evaluation indicators.

[0038] Step 3: Construct terrain constraint coefficients; Step 3.1: Extract terrain factors; (1) Acquire basic data: Acquire 30m resolution SRTM digital elevation model (DEM) data of the study area, which records the elevation value of each cell; (2) Extracting topographic factors: Based on DEM data, the following topographic factors are calculated through GIS spatial analysis: Slope, which indicates the steepness of a unit of land surface, is measured in degrees (°). Slope aspect, representing the orientation of a surface unit, is converted into a dimensionless slope aspect index.

[0039] Topographic relief represents the relative change in surface elevation within the i-th evaluation unit, reflecting the depth of surface incision and the degree of topographic fragmentation. A larger value indicates more rugged terrain. The formula is as follows:

[0040] In the formula, This represents the maximum elevation value within the i-th evaluation unit. The minimum elevation value within the i-th evaluation unit. This represents the total area of ​​the i-th evaluation unit, such as the area of ​​a district or county-level administrative unit.

[0041] Step 3.2: Construct a nonlinear continuous function to characterize the continuous variation of the terrain constraint effect, as shown in the following expression:

[0042] In the formula, Let be the average slope of the i-th evaluation unit; This refers to the degree of topographic relief. The aspect index is a dimensionless value converted from the original aspect angle, with a value range of [0,1]. The larger the value, the smaller the constraint of the aspect on the use of land space. a, b, and c are topographic influence parameters, which are determined based on empirical values ​​in topography.

[0043] The conversion of the slope aspect index is based on the slope aspect classification standard in the "National Agricultural Zoning Technical Regulations". Combined with the measured land use efficiency data in Southwest China, the slope aspect is divided into five levels: 0.2 for north slope, 0.6 for east slope, 0.8 for south slope, 0.6 for west slope, and 1.0 for flat land. For slope aspects between the main directions, the slope aspect index can be determined by interpolation.

[0044] The main pathways of slope aspect influence are shown in Table 2. Based on long-term observation data and land use efficiency research in Southwest China, a simplified five-level classification is shown in Table 3. Table 2. Various influencing mechanisms of slope aspect

[0045] Table 3 Slope Aspect Classification Diagram

[0046] Step 3.3: Determine the applicability of the terrain constraint coefficient; To improve computational efficiency, it is first determined whether terrain constraints need to be introduced before calculation: the coefficient of variation (CV) is used to measure the dispersion of terrain (slope) within the study area, and the calculation formula is as follows:

[0047] This represents the standard deviation of the slope of all evaluation units within the study area. The slope is expressed as the arithmetic mean, and CV is a dimensionless number; a larger value indicates a more significant terrain difference. Specifically, a difference threshold is set. If the CV is greater than the threshold, the terrain difference is considered significant, and terrain constraint correction is applied; if the CV is less than the threshold, the terrain is considered flat, and the uncorrected value is directly applied. Calculate the coupling coordination degree.

[0048] Step 4: Correct the system evaluation value of land space use using the terrain constraint coefficient, as shown in the following expression:

[0049] Example 2 This embodiment is an application scenario of the method proposed in Embodiment 1. It includes calculating the coupling coordination degree by combining the corrected system evaluation value of land space utilization calculated in Embodiment 1 with the system evaluation value of economic development. The economic development system evaluation value is calculated by analyzing socio-economic data using the method proposed in Embodiment 1. The socio-economic data includes per capita GDP, GDP per unit area, the proportion of the tertiary industry, and per capita disposable income of urban residents. The coupling degree C is calculated using the following formula:

[0050] The coupling degree C ∈ [0,1], and the larger the value, the stronger the interaction between the two subsystems; The formula for calculating the overall coordination degree is as follows:

[0051] In the formula, α and β are weighting coefficients, reflecting the importance of the two subsystems; the values ​​are taken in light of the importance of economic development and land use in regional sustainable development.

[0052] The coupling coordination degree is calculated using the following formula:

[0053] The coupling coordination degree D∈[0,1], and the larger the value, the higher the level of coordinated economic and spatial development.

[0054] Coordination levels are categorized based on the numerical range of the coupling coordination degree. The coordination level increases with the increase of the coupling coordination degree D, ranging from low to high as follows: mild disharmony, near disharmony, barely coordinated, primary coordination, intermediate coordination, and good coordination.

[0055] Output the spatial distribution map of coupling coordination degree of each evaluation unit, and the time series variation map of coupling coordination degree of each evaluation unit in multiple periods; Based on the results of coupling coordination degree calculation, imbalanced regions are identified and imbalance types are classified; the imbalance types include spatial urbanization faster than economic urbanization, economic agglomeration but spatial overdraft, and low-growth and low-economy types. The criteria for determining that spatial urbanization is faster than economic urbanization are: a high proportion of construction land (> the average), low added value of secondary and tertiary industries per unit of construction land (< the average), and a high rate of reduction of arable land (> the average). The criteria for determining economic agglomeration but spatial overdraft are: high GDP per unit area (> average), low proportion of ecological land (< average), and high proportion of construction land (> average). The criteria for identifying a "double-low" type are: low per capita GDP (< average), low added value of the secondary and tertiary industries per unit area of ​​construction land (< average), and low proportion of construction land (< average).

[0056] Specifically, this embodiment uses the central urban area of ​​a certain city as the implementation object to explain in detail the application process of the application scenario described in this embodiment. The research period is from 2017 to 2022, which can effectively capture the synergistic evolution characteristics of land spatial pattern and economic development. This embodiment takes the central urban area of ​​a certain city as the research object, and the evaluation unit includes: the nine districts and counties of the central urban area of ​​the city.

[0057] Step 1: Data preparation and preprocessing, specifically using the following data sources: The land space data comes from the results of the Second National Land Survey Update (2017) and the results of the Third National Land Survey Change Survey (2022). Socioeconomic data are sourced from the "Statistical Yearbook of a Certain City" (2018 and 2023), with 2018 corresponding to the statistical results of 2017, 2023 corresponding to the statistical results of 2022, and statistical bulletins of various districts and counties. The digital elevation model data comes from SRTM (Shuttle Radar Topography Mission).

[0058] The preprocessing includes the following steps: The land spatial data, socio-economic data and DEM data are unified into the same spatial coordinate system (CGCS2000), with the district and county-level administrative boundaries as the evaluation unit boundaries; Based on the national land survey classification system, land use types are categorized into eight primary categories. On this basis, indicators such as the proportion of construction land, the proportion of ecological land, the rate of reduction of cultivated land, and the added value of secondary and tertiary industries per unit of construction land are calculated for each evaluation unit.

[0059] Finally, a natural logarithmic transformation was applied to GDP per unit area and the added value of the secondary and tertiary industries per unit area of ​​construction land to smooth out data fluctuations.

[0060] Step 2: Construct an indicator system and calculate indicator values; Step 2.1: Let the number of evaluation units n=9, the evaluation period t=2 (2017, 2022), and the number of indicators m=8. Each evaluation unit corresponds to 8 indicator values ​​in each period. Taking one evaluation unit (a district in a city) as an example, the indicator system data calculation in 2017 is shown in Tables 4 and 5. Table 4 shows the directly obtained indicators, where the original data is directly used as indicator data. Table 5 shows the derived indicators and their calculation methods, where the original data is processed through the corresponding calculation methods to obtain the indicator data.

[0061] Table 4 Directly Obtained Indicators

[0062] Table 5 Calculation of Derivative Class Indicators

[0063] Indicator Description: The rate of reduction in arable land is a periodic indicator (the others are static indicators), reflecting the cumulative change during the observation period from 2017 to 2022. This value is used for calculations in both 2017 and 2022.

[0064] Physical meaning: The rate of reduction in arable land reflects the "intensity of change in arable land between 2017 and 2022." This intensity is constant within the cycle and can be considered an attribute of that cycle, rather than a state at a specific point in time. Similar to the "average annual growth rate," it can be attributed to both the beginning and end of the period.

[0065] Step 2.2: Standardize the indicator data of each evaluation unit sequentially using the range method; taking the per capita GDP indicator (11.199) of the evaluation unit in Step 2.1 as an example, j=1, the standardization process is as follows: 1. Calculate the global maximum and minimum values ​​based on the per capita GDP data of all evaluation units: =26.77 =5.689 2. Standardize the target indicator data based on the global maximum and minimum values: = (11.199 - 5.689) / (26.77 - 5.689) = 0.261 3. For negative indicators, a reverse standardization formula is used. After standardization, all indicator values ​​are converted to the [0,1] interval.

[0066] Step 2.3: Determine the weights of each indicator using the entropy method; Step 2.31: Construct the global decision matrix; By merging the data from 9 evaluation units and 2 periods, a global decision matrix with 18 rows and 8 columns is constructed:

[0067] Step 2.32: Calculate the information entropy of each indicator; Taking GDP per capita as an example: (1) Calculate the weight of each record under the first indicator (GDP per capita):

[0068] Step 1: Calculate the sum of standardized values =3.928 get:

[0069] (2) Calculate information entropy:

[0070] Specifically, taking the per capita GDP of the aforementioned evaluation unit in 2017 as an example: =0.261 / 3.928=0.0665 =-2.3267, = =0.3460, 0.805 Step 2.33: Calculate the difference coefficient and weights; Calculate the coefficient of variation of GDP per capita: = =1-0.805=0.195; Repeat the above steps to calculate the difference coefficients for the other indicators; Calculate the weight of each indicator: The results are shown in Table 6.

[0071] Table 6: Results of Indicator Weight Calculation

[0072] Step 2.4: Calculate the system evaluation value; Step 2.41: Systematic evaluation value of economic development ;

[0073] Taking a certain district in a certain city in 2017 as an example:

[0074] Step 2.42: Systematic evaluation value of land space use ;

[0075] Taking a certain district in a certain city in 2017 as an example:

[0076] Step 3: Construct terrain constraint coefficients; Step 3.1: Extract topographic factors. Based on 30m resolution SRTM DEM data, extract topographic factors for each district / county: Taking a certain district in a certain city as an example, the following is included: average slope: =5.2°; Topographic relief: =0.28m / km²; Aspect Index: =0.6 (Dongpo); Step 3.2: Construct a nonlinear continuous function:

[0077] In the formula, Let be the average slope of the i-th evaluation unit; This refers to the degree of topographic relief. The aspect index converts the original aspect angle into a dimensionless value, with a range of [0, 1]. The larger the value, the smaller the constraint that the aspect has on the use of land space.

[0078] The conversion is based on the slope aspect classification standard in the "National Agricultural Zoning Technical Regulations". Combined with measured land use efficiency data in Southwest China, the slope aspect is divided into five levels: 0.2 for north slope, 0.6 for east slope, 0.8 for south slope, 0.6 for west slope, and 1.0 for flat land. a, b, and c are topographic influence parameters, determined based on empirical topographic values. In this embodiment: a=0.05, reflecting that for every 1° increase in slope, the space utilization efficiency decreases by about 5%; b=0.01, reflecting that for every 10 m / km² increase in topographic relief, the efficiency decreases by about 1%; c=0.02, reflecting that the efficiency of the north slope decreases by about 2% relative to the south slope.

[0079] Taking a certain district in a certain city as an example: 1 / (1 + 0.05 * 5.2 + 0.01 * 0.28 + 0.02 * 0.6) = 0.784 Step 3.3: Calculate the terrain variation coefficient; Calculate the mean and standard deviation of the slope of all evaluation units in the study area (9 districts and counties of a city): =3.8° =8.3° =0.46>0.3.

[0080] This represents the standard deviation of the slope of all evaluation units within the study area. The slope is expressed as the arithmetic mean, and CV is a dimensionless number. A larger value indicates more significant terrain differences. In this embodiment, the difference threshold is 0.3. When CV > 0.3, the terrain differences are significant, and terrain constraint correction is applied. When CV ≤ 0.3, the terrain is flat, and the uncorrected U-shape is directly used. _SPACE Calculate the coupling coordination degree.

[0081] In this embodiment, the difference threshold is set to 0.3, and its determination is based on the following criteria: (1) Statistical basis The coefficient of variation (CV) is a dimensionless indicator that measures the dispersion of data. According to statistical grading standards for the degree of variation: CV < 0.1: Weak mutation 0.1 ≤ CV < 0.3: Moderate variation CV ≥ 0.3: Strong mutation When CV ≥ 0.3, it indicates that there is strong variability in the data, that is, the slope difference within the study area is significant.

[0082] (2) Geographical basis In geomorphological classification and land use suitability assessment, when the coefficient of variation of slope in a region exceeds 0.3, it is generally considered that there is obvious geomorphological differentiation in the region (such as the coexistence of plains and mountains), and the influence of topographic factors on land use cannot be ignored.

[0083] Step 4: Revise the systematic evaluation values ​​of land space use;

[0084] Taking a certain district in a certain city as an example: =0.186 * 0.784 = 0.146 Step 5: Calculate the coupling coordination degree; Step 5.1: Coupling degree calculation;

[0085] Step 5.2: Calculation of overall coordination degree;

[0086] In the formula, α and β are weighting coefficients, reflecting the importance of the two subsystems. In this embodiment, given that economic development and spatial utilization are equally important in regional sustainable development, α = β = 0.5 is chosen.

[0087] Step 5.3: Calculate the coupling coordination degree;

[0088] Taking a certain district in a certain city as an example: C=2 =0.9987 0.5 × 0.1617 + 0.5 × 0.146 = 0.1539 =0.3920 Step 5.4: Repeat steps 5.1-5.3 until the coupling coordination degree of all evaluation units in the study area is calculated; Step 5.5: Divide the coordination level. According to the numerical range of the coupling coordination degree, the coordination level is upgraded as the coupling coordination degree D increases. From low to high level, it includes: mild disharmony, near disharmony, barely coordinated, primary coordination, intermediate coordination, and good coordination.

[0089] Taking data from a certain district in a certain city in 2017 as an example, the comparison with and without terrain constraint correction is shown in Table 7: Table 7 Comparison of Corrections with and without Terrain Constraints

[0090] After the correction, the coupling coordination degree of mountainous areas is reduced, which more accurately reflects the actual level of spatial utilization efficiency constrained by terrain.

[0091] Step 6: Output the final evaluation results.

[0092] Step 6.1: Output the final evaluation results as follows Figures 2-3 As shown, where Figure 2 This represents the revised distribution of coordination levels for a certain area in a city in 2017. Figure 3 This figure represents the revised coordination level distribution of a certain area in a certain city in 2022. The legend in the figure, from top to bottom, represents 0.3-0.4 slight imbalance, 0.4-0.5 near imbalance, 0.5-0.6 barely coordinated, 0.6-0.7 primary coordinated, and 0.7-0.8 intermediate coordinated.

[0093] Step 6.2: Identify imbalanced regions; regions where D < 0.5 are identified as imbalanced regions. Step 6.3: Diagnostic determination of the disorder type is shown in Table 8; Table 8. Diagnosis of Dysfunction Type

[0094] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

Claims

1. A method for correcting land use evaluation based on topographic constraints, characterized in that, Includes the following steps: Step 1: Data acquisition and preprocessing; the data includes land spatial data and DEM data; Step 2: Construct an evaluation index system based on land space data and calculate the system evaluation value of land space utilization; Step 3: Using GIS spatial analysis technology, extract slope, aspect and topographic relief from DEM data, and construct a nonlinear continuous function to simulate the constraints of natural geographical terrain as the terrain constraint coefficient; Steps 2 and 3 are not in any particular order; Step 4: Use the terrain constraint coefficient to perform spatial heterogeneity physical correction on the system evaluation value of land space use, and eliminate the geometric and spatial use efficiency calculation bias in complex terrain areas; Step 3 includes: Step 3.1: Based on DEM data, calculate terrain factors through GIS spatial analysis. The terrain factors include slope, aspect, and topographic relief. Slope represents the steepness of a surface unit, and the unit is degrees. Slope aspect, representing the orientation of a surface unit, is converted into a dimensionless slope aspect index; Topographic relief, representing the relative change in surface elevation within the i-th evaluation unit, reflects the depth of surface incision and the degree of topographic fragmentation. The formula is as follows: In the formula, This represents the maximum elevation value within the i-th evaluation unit. The minimum elevation value within the i-th evaluation unit. This represents the total area of ​​the i-th evaluation unit; Step 3.2: Construct a nonlinear continuous function to characterize the continuous variation of terrain constraints, as shown in the following expression: In the formula, Let be the average slope of the i-th evaluation unit; This refers to the degree of topographic relief. The aspect index is a dimensionless value converted from the original aspect angle, with a value range of [0,1]. The larger the value, the smaller the constraint of the aspect on the use of land space. a, b, and c are topographic influence parameters, which are determined based on topographic empirical values. Step 3 also includes: determining the applicability of the terrain constraint coefficient; Before calculation, determine whether terrain constraints are needed: the coefficient of variation (CV) is used to measure the dispersion of terrain within the study area, and the calculation formula is as follows: This represents the standard deviation of the slope of all evaluation units within the study area. The slope is expressed as the arithmetic mean, and CV is a dimensionless number. The larger the value, the more significant the terrain difference. Specifically, a difference threshold is set. If the CV is greater than the difference threshold, the terrain difference is judged to be significant, and terrain constraint correction is adopted. When the CV is less than the difference threshold, the terrain is judged to be flat, and there is no need to correct the system evaluation value of land space use. The corrected expression for step 4 is as follows: In the formula, This represents the revised systematic evaluation value of land space use. The system evaluation value representing the use of national land space It represents a nonlinear continuous function.

2. The method for correcting land use evaluation based on topographic constraints according to claim 1, characterized in that, Step 2 includes: Step 2.1: Construct land use indicators based on land space data, including the proportion of construction land, the proportion of ecological land, the rate of reduction of cultivated land, and the added value of secondary and tertiary industries per unit area of ​​construction land; Step 2.2: Standardize each indicator using the range method according to its direction; Step 2.3: Determine the weights of each indicator using the entropy method; Step 2.4: Integrate the land space use indicators into a systematic evaluation value of land space use based on the indicator weights.

3. The method for correcting land use evaluation based on topographic constraints according to claim 2, characterized in that, Step 2.3 includes: The data from n evaluation units and t evaluation periods within the study area are merged to construct a global decision matrix, resulting in N evaluation records, where N = n * t. The weight of the q-th record (q=1,2....N) under the j-th indicator is calculated as follows: , This represents the standardized value of the q-th evaluation record and the j-th indicator; Calculate the information entropy of the j-th indicator: ,in ; Calculate the coefficient of variation for the j-th indicator: ; Calculate the weight of the j-th indicator: m represents the total number of indicators.

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