Urban and rural crisscross zone evaluation method and system based on intermediary effect intensity model

Abstract

The invention provides an intermediary effect intensity model-based urban and rural ecotone evaluation method, which comprises the following steps of: S1, acquiring and preprocessing urbanization data and land utilization data, delimiting an urban and rural ecotone and acquiring urban and rural ecotone data; s2, constructing an urban and rural interlaced zone distribution diagram based on the delimited urban and rural interlaced zone, and carrying out grid division on the urban and rural interlaced zone distribution diagram; s3, acquiring a PLS-SEM model analysis result for the urbanization data and the urban and rural crisscross zone data on the basis of a PLS-SEM intermediary effect intensity model; S4, constructing a new intermediary effect intensity model on the basis of the PLS-SEM model analysis result; s5, calculating an intermediary effect intensity value based on the new intermediary effect intensity model; and S6, evaluating the urban and rural interlaced zone based on the intermediary effect intensity value. According to the method, the intermediary effect of the urban-rural interlaced zone is analyzed to explore the interaction relationship between urbanization and the urban-rural interlaced zone, and then a new intermediary effect intensity model is proposed to evaluate the urban-rural interlaced zone.

Description

Technical Field

[0001] The present invention belongs to the technical field of evaluation of the urban-rural fringe, and particularly relates to an evaluation method and system for the urban-rural fringe based on a mediation effect intensity model. Background Art

[0002] Numerous scholars have studied and found that there is a mutual relationship between urbanization and the urban-rural fringe, and used spatial autocorrelation analysis and structural equation models to explore its driving factors. However, most of the driving factors are analyzed from the perspective of urbanization (in terms of population, society, economy, and land use). Few scholars have explored the mediation effect of the relationship between urbanization and the urban-rural fringe. However, some studies on urbanization indicate that the urban-rural fringe (URF) is the area with the most prominent contradictions, and urbanization and the urban-rural fringe are closely related and interact with each other. Recent studies have proposed that the interaction between urbanization and the urban-rural fringe has obvious spatial heterogeneity and distribution differences in the urban-rural gradient. This indirectly proves the mediation effect in the interaction between urbanization and the urban-rural fringe. Therefore, analyzing the formation, evolution, and mediation effect of the urban-rural fringe during the process of urban expansion is not only the basis for solving these urban problems, but also provides a scientific basis for the optimization of urban spatial structure and function.

[0003] The urban-rural fringe is a unique region, quite different from urban and rural areas, and its spatial structure is dynamic. The evolution of the spatial structure of the urban-rural fringe is affected by social and economic development, land use, transportation, and various regional activities. Scholars have focused on the changes in land use / cover and spatial morphological characteristics during urban sprawl. Some scholars believe that as the city expands, the urban fringe gradually shows irregularity and looseness, which increases the vulnerability of agricultural land. Some scholars have further discussed the climate and environmental impacts of land use changes during urban sprawl. Relevant scholars have proved that the urban-rural fringe makes the greatest contribution among the influencing factors of the deterioration of the urban microclimate. However, more studies focus on land use changes. The urban-rural fringe is the result of stages of urbanization, and urbanization is the greatest factor affecting the urban-rural fringe. The mediation effect in the interaction between urbanization and the urban-rural fringe must exist and is worthy of exploration. However, few relevant scholars have conducted similar explorations at present.

[0004] Structural equation modeling (SEM) is divided into Bayesian, hierarchical, or partial least squares SEM (PLS-SEM). The PLS-SEM method has high predictability. Another characteristic of SEM is using measured variables to construct latent variables. Therefore, SEM can evaluate the interconnections between different components in a complex system. In addition, SEM can simultaneously capture the effects of the relationships among numerous variables. Different from traditional multivariate statistical techniques such as multiple regression, principal component analysis, and cluster analysis, SEM can include multiple variables simultaneously to examine the correlations between structures, clearly indicating the strength of each correlation. SEM is often used to deal with multi-factor causal relationships. SEM is used to estimate latent variables and create a complex variable prediction model. This method provides a better understanding of the direct and indirect interactions between factors. Compared with the SEM model, PLS-SEM relaxes the assumption of multivariate normal distribution in the parameter estimation process and is suitable for exploratory research. However, the PLS-SEM model only verifies whether the mediation effect coefficient (path coefficient) and its path hold, and cannot further construct a perfect mediation effect strength model to specifically calculate the mediation effect strength and guide urban development, so it has no practical guiding significance. Based on the PLS-SEM model, the present invention further proposes a mediation effect strength model considering human disturbance terms, which can evaluate the urban-rural fringe and guide urban development by calculating and specifically showing the mediation effect strength, and has practical guiding significance.

[0005] Currently, different research methods have their respective limitations when dealing with the interaction relationship between urbanization and the urban-rural fringe. The structural equation modeling (SEM) has more advantages. For example, different from traditional multivariate statistical techniques such as multiple regression, principal component analysis, and cluster analysis, SEM can include multiple variables simultaneously to examine the correlations between structures, clearly indicating the strength of each correlation, and this method can have a better understanding of the direct and indirect interactions between influencing factors. Currently, the research on the urban-rural fringe mainly focuses on the delineation method, and few scholars have explored the evaluation research. Summary of the Invention

[0006] The present invention proposes an evaluation method for the urban-rural fringe based on the mediation effect strength model. By analyzing the mediation effect of the urban-rural fringe, it explores the interaction relationship between urbanization and the urban-rural fringe, and then proposes a new mediation effect strength model to evaluate the urban-rural fringe. The present invention proposes a new mediation effect strength model to evaluate the urban-rural fringe, and guides the urban development direction and its reasonable layout from the perspective of the mediation effect strength.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions:

[0008] An evaluation method for the urban-rural fringe based on the PLS-SEM mediation effect strength model, comprising the following steps:

[0009] Step S1. Obtain urbanization data and land use data and perform preprocessing. Based on the three-dimensional index system of land development intensity in the preprocessed land use data, delimit the urban-rural fringe and obtain urban-rural fringe data;

[0010] Step S2. Construct a distribution map of the urban-rural fringe based on the delimited urban-rural fringe and perform grid division;

[0011] Step S3. Obtain the PLS-SEM model analysis results for the urbanization data and the urban-rural fringe data based on the PLS-SEM mediation effect intensity model:

[0012] Step S4. Construct a new mediation effect intensity model based on the PLS-SEM model analysis results;

[0013] Step S5. Calculate the mediation effect intensity value based on the new mediation effect intensity model;

[0014] Step S6. Evaluate the urban-rural fringe based on the mediation effect intensity value.

[0015] Furthermore, the urbanization data in Step S1 includes population urbanization data and multiple other urbanization data;

[0016] The preprocessing includes: performing gridization and normalization preprocessing on the urbanization data and the urban-rural fringe data.

[0017] Furthermore, the delimitation of the urban-rural fringe based on the three-dimensional index system of land development intensity in the preprocessed land use data in Step S1 includes:

[0018] Obtain the scale data, vitality data, and density data of the land development intensity in the land use data;

[0019] Based on the scale data, vitality data, and density data of the land development intensity, use the K-means clustering method to delimit the urban-rural fringe;

[0020] Use the silhouette coefficient and the consistency ratio to evaluate the accuracy of clustering and identification.

[0021] Furthermore, Step S3 includes:

[0022] Step S3.1 Set the population urbanization data as the independent variable, multiple other urbanization data as the mediating variables, and the urban-rural fringe data as the dependent variable, and set the mediation effect path based on the population urbanization data, multiple other urbanization data, and the urban-rural fringe data;

[0023] Step S3.2 Calculate the path coefficients of each urbanization data for the next urbanization data or the urban-rural fringe in the mediation effect path based on the PLS-SEM mediation effect intensity model, and obtain the PLS-SEM model analysis results.

[0024] Further, the step S4 includes:

[0025] Step S4.1 normalizes the path coefficients in the analysis results of the PLS-SEM model to obtain normalized path coefficients;

[0026] Step S4.2 obtains the human interference coefficient of each grid based on the area to which the grid in the urban-rural fringe distribution map belongs;

[0027] Step S4.3 constructs a new mediating effect intensity model based on the normalized path coefficients and human interference coefficients.

[0028] Further, the new mediating effect intensity model is:

[0029] M = p x [a'X × ((b 1 'm 1 ) × (b 2 'm 2 ) × …(b n 'm n ))] + ε 1

[0030] where M is the mediating effect intensity value, p x is the human interference coefficient of the xth grid, m 1 , m 2 , m n are all mediating variables, a' is the normalized path coefficient of the population urbanization data for the first mediating variable m 1 in the mediating effect path; b 1 ' is the normalized path coefficient of the mediating variable m 1 for the second mediating variable m 2 in the mediating effect path, b 2 ' is the normalized path coefficient of the mediating variable m 2 for the next mediating variable in the mediating effect path, b n ' is the normalized path coefficient of the mediating variable m n for the urban-rural fringe data, and ε 1 is the mediating effect regression residual.

[0031] Further, the human interference coefficient p x of the xth grid is:

[0032]

[0033] where x represents the xth grid, x ∈ N 1 represents that the xth grid is within the development zone, x ∈ N 2Indicates that the x-th grid is within the ecological protection area, where x ∈ N 3 Indicates that the x-th grid is outside the development zone or the ecological protection area but within the set enhancement or attenuation range; x ∈ N 4 Indicates that the x-th grid is outside the set enhancement or attenuation range; N 1 Indicates the development zone range, N 2 Indicates the ecological protection area range, N 3 Indicates the enhancement or attenuation range outside the development zone or the ecological protection area, N 4 Indicates outside the enhancement or attenuation range outside the development zone or the ecological protection area; i txy Is the planning coefficient of the development zone or the ecological protection area.

[0034] Furthermore, the planning coefficient of the development zone or the ecological protection area is:

[0035]

[0036] where e is the base of the natural logarithm function, d xy Is the spatial distance between the x-th grid and the grid cell y in the interference source t, d tmax Is the maximum influence distance of the interference source t.

[0037] Furthermore, the step S6 includes:

[0038] Calculating the mediation effect intensity value of each grid in the urban-rural fringe based on the new mediation effect intensity model;

[0039] Displaying the mediation effect intensity value of each grid in the urban-rural fringe using the grid method and obtaining the mediation effect intensity value distribution map;

[0040] Dividing the total effect intensity value into several grades and evaluating the development intensity of the urban-rural fringe based on the mediation effect intensity value distribution map.

[0041] On the other hand, the present invention provides an evaluation system for the urban-rural fringe based on the PLS-SEM mediation effect intensity model, including:

[0042] A data acquisition and preprocessing module, which is used to acquire urbanization data and land use data and perform preprocessing, delimit the urban-rural fringe based on the three-dimensional index system of land development intensity in the preprocessed land use data, and obtain urban-rural fringe data;

[0043] An urban-rural fringe distribution map construction module, which is used to construct an urban-rural fringe distribution map based on the delimited urban-rural fringe and perform grid division;

[0044] An analysis result acquisition module, which is used to obtain the PLS-SEM model analysis result for the urbanization data and the urban-rural fringe data based on the PLS-SEM mediation effect intensity model;

[0045] A new mediation effect intensity model construction module, which is used to construct a new mediation effect intensity model based on the PLS-SEM model analysis result;

[0046] A mediation effect intensity value acquisition module, which is used to calculate the mediation effect intensity value based on the new mediation effect intensity model;

[0047] An evaluation module, which is used to evaluate the urban-rural fringe based on the mediation effect intensity value;

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention proposes an evaluation method for the urban-rural fringe based on the mediation effect intensity model, analyzes the mediation effect in the interaction relationship between urbanization and the urban-rural fringe, reveals their mutual relationship and potential influence paths, and makes up for the insufficient problem of the current academic evaluation method for the urban-rural fringe.

[0050] 2. The present invention constructs a PLS-SEM model by extracting effective influencing factors, quantifies the interaction and its contribution degree between urbanization and the urban-rural fringe, hypothesizes and verifies the mediation effect path in the interaction relationship between urbanization and the urban-rural fringe, comprehensively evaluates the mediation effect of the urban-rural fringe, and then proposes a new mediation effect intensity model to evaluate the urban-rural fringe, guiding the urban development direction and its reasonable layout by integrating urbanization factors and policy factors. The new mediation effect intensity model has strong interpretability, a wide application range, takes into account policy factors, and is easy to use and implement.

[0051] 3. The present invention studies the interaction relationship between urbanization and the urban-rural fringe and the interaction of their potential driving factors, focuses on the quantitative evaluation of the mediation effect jointly affected by multiple factors, reveals the co-regulation of each factor in the relationship between urbanization and the urban-rural fringe, and proposes a quantitative evaluation method for the urban-rural fringe based on the mediation effect. Studying these objectives is expected to provide important support for a deeper understanding of the co-development mechanism between urbanization and the urban-rural fringe and for the sustainable development of cities.

[0052] 4. The evaluation method proposed by the present invention has universality and applicability for promotion to regional and large-scale ranges. Description of the Drawings

[0053] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 This is the flow chart of the present invention.

[0055] Figure 2 This is the distribution map of the urban-rural fringe constructed by the present invention.

[0056] Figure 3 This is the schematic diagram of the mediating effect path of the PLSSEM model of the present invention Figure 1 。

[0057] Figure 4 This is the schematic diagram of the mediating effect path of the PLSSEM model of the present invention Figure 2 。

[0058] Figure 5 This is the distribution of the mediating effect intensity of the present invention. Detailed implementation manners

[0059] To make the above objects, features, and advantages of the present application more apparent and understandable, the following will make a detailed description of the specific implementation manners of the present application with reference to the drawings. Many specific details are set forth in the following description in order to fully understand the present application. However, the present application can be implemented in many other ways different from those described herein. Those skilled in the art can make similar improvements without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0060] Example 1

[0061] As Figure 1 shown, this embodiment proposes an evaluation method for the urban-rural fringe based on the mediating effect intensity model, including the following steps:

[0062] Step S1. Obtain urbanization data and land use data and perform preprocessing. Based on the three-dimensional index system of land development intensity in the preprocessed land use data, delimit the urban-rural fringe and obtain urban-rural fringe data;

[0063] The urbanization data in Step S1 includes population urbanization data and multiple other urbanization data;

[0064] The preprocessing includes: performing grid and normalization preprocessing on the urbanization data and the urban-rural fringe data.

[0065] In step S1, the delineation of the urban-rural fringe based on the three-dimensional index system of land development intensity in the preprocessed land use data includes:

[0066] Obtain the scale data, vitality data, and density data of the land development intensity in the land use data;

[0067] Based on the scale data, vitality data, and density data of the land development intensity, use the K-means clustering method to delineate the urban-rural fringe;

[0068] Use the silhouette coefficient and the consistency ratio to evaluate the accuracy of clustering and identification.

[0069] Step S2. Based on the delineated urban-rural fringe, construct a distribution map of the urban-rural fringe and conduct grid division;

[0070] In this embodiment, a certain city is selected as the study area, and the urbanization data is divided into three parts: population urbanization data (population data), economic urbanization data (nighttime light and point of interest (POI) data), and spatial urbanization data (main traffic and built-up area data); all data is finally standardized into grid data of 1Km×1Km, and the value is the mean of the proportion of each density data in the grid cell, and the value is divided into 5 levels.

[0071] 1. Obtain population urbanization data: Use the distribution data of population density;

[0072] 2. Obtain economic urbanization data: Calculate the nighttime light and point of interest (POI) data in the ArcGIS software using the raster calculator at a ratio of 50% each;

[0073] 3. Obtain spatial urbanization data: Calculate the main traffic and built-up area distribution data in the ArcGIS software using the raster calculator at a ratio of 50% each.

[0074] Based on the three-dimensional index system of land development intensity, use the K-means clustering method to delineate the urban-rural fringe, use the silhouette coefficient (SC) and the consistency ratio (CR) to evaluate the accuracy of clustering and identification, and set a suitable value of K. Finally, determine a reasonable urban-rural fringe, and obtain the distribution data of the urban-rural fringe with K = 3 and K = 5, as Figure 2 shown; where Figure 2 (a) is a schematic diagram of the distribution data of the urban-rural fringe with K = 3 based on the land use data in 2010;

[0075] Figure 2 (b) is a schematic diagram of the distribution data of the urban-rural fringe with K = 5 based on the land use data in 2010; Figure 2 (c) is a schematic diagram of the distribution data of the urban-rural fringe with K = 3 based on the land use data in 2015; Figure 2(d) Schematic diagram of the distribution data of the urban-rural fringe with K = 5 based on the land use data in 2015; Figure 2 (e) Schematic diagram of the distribution data of the urban-rural fringe with K = 3 based on the land use data in 2020; Figure 2 (f) Schematic diagram of the distribution data of the urban-rural fringe with K = 5 based on the land use data in 2020.

[0076] 1. First, use the scale data of land development intensity (the proportion of the construction land area in the unit), vitality data (the mean value of the POI distribution intensity in the unit), and density data (the mean value of the night light brightness distribution intensity in the unit) to comprehensively quantify the differences in land development intensity among urban areas, urban-rural fringe areas, and rural areas;

[0077] 2. Use the K-means clustering method to delimit the urban-rural fringe: Based on the scale, vitality, and density data of land development intensity, set the range of K values (3, 4, 5, and 6) in Python and perform clustering calculations, and preferably select the urban-rural fringes with K = 3 and 5 for further discussion ( Figure 2 );

[0078] 3. Evaluate the accuracy of the urban-rural fringe and determine the appropriate K value: Calculate the silhouette coefficient (SC) values for different years in Python. The silhouette coefficient (SC) values for all years are greater than 0.7 and are within the acceptable range. When K = 5, the consistency ratio (CR) values are all above 77%, but when K = 3, the CR values are not very ideal. Therefore, in this embodiment, the classification with K = 5 is used as the standard.

[0079] Step S3. Obtain the PLS-SEM model analysis results for the urbanization data and the urban-rural fringe data based on the PLS-SEM mediation effect intensity model: including:

[0080] Step S3.1 Set the population urbanization data as the independent variable, multiple other urbanization data as the mediating variables, and the urban-rural fringe data as the dependent variable, and set the mediation effect path based on the population urbanization data, multiple other urbanization data, and the urban-rural fringe data;

[0081] Step S3.2 Calculate the path coefficients of each urbanization data for the next urbanization data or the urban-rural fringe in the mediation effect path based on the PLS-SEM mediation effect intensity model to obtain the PLS-SEM model analysis results.

[0082] Step S4. Construct a new mediation effect intensity model based on the PLS-SEM model analysis results; including:

[0083] Step S4.1 Perform normalization processing on the path coefficients in the PLS-SEM model analysis results to obtain the normalized path coefficients;

[0084] Step S4.2 obtains the anthropogenic disturbance coefficient of each grid by using the grid method in the ArcGIS software based on the area to which the grid in the urban-rural fringe distribution map belongs;

[0085] Step S4.3 constructs a new mediating effect intensity model based on the normalized path coefficient and the anthropogenic disturbance coefficient.

[0086] The PLS-SEM model is used to estimate the causal network between latent variables and manifest variables, and usually includes a measurement model and a structural model:

[0087] X = Λ x ξ + δ

[0088] Y = Λ y η + ε

[0089] The above equations are the exogenous indicators (X) and endogenous indicators (Y) respectively. Λ represents the relationship between the manifest variable and the latent variable; δ and ε refer to the measurement errors.

[0090] η = βη + Γξ + ξ

[0091] Among them, η is the endogenous latent variable, ξ is the exogenous latent variable, β represents the influence of the exogenous latent variable on the endogenous latent variable. Γ represents the influence of some endogenous latent variables on other endogenous latent variables, and ζ is the regression residual.

[0092] T = W(P'P) -1 P'X

[0093] Among them, T is the factor score matrix of the latent variable, W is the weight matrix, and P is the loading matrix.

[0094] Calculation formulas for the weight (W) and the loading (P):

[0095] W = X(P'P) -1

[0096] P = X'W(W'W) -1

[0097] Compared with the CB-SEM model, PLS-SEM relaxes the multivariate normal distribution assumption in the parameter estimation process and is suitable for exploratory research.

[0098] In this embodiment, research hypotheses are proposed: (1) It is assumed that there is a mediating effect in the interaction relationship between urbanization data and urban-rural fringe data. (2) It is assumed that there are two mediating effect paths:

[0099] Population urbanization has an impact on the urban-rural fringe with economic urbanization and spatial urbanization as mediating variables (PU-EU-SU-UR). Population urbanization has an impact on the urban-rural fringe with economic urbanization as the mediating variable (PU-EU-UR).

[0100] Step S5. Calculate the mediating effect strength value based on the new mediating effect strength model;

[0101] Based on the analysis results of the research hypotheses by the PLS-SEM model, this embodiment proposes a new mediating effect strength model to evaluate the urban-rural fringe.

[0102] According to the processed urbanization and urban-rural fringe data, combined with the research hypotheses and theoretical basis, determine the research framework and construct the model; use the partial least squares method to estimate the model parameters, including the weight coefficient and loading coefficient, and establish the mediating effect model of the urban-rural fringe.

[0103] Next, evaluate the basic model, including model fit, significance test of path coefficients, and reliability test of latent variables (reliability and validity evaluation); modify and optimize the model to generate a new mediating effect model with the highest prediction accuracy.

[0104] The new mediating effect strength model is:

[0105] M = p x [a'X × ((b 1 'm 1 ) × (b 2 'm 2 ) × …(b n 'm n ))] + ε 1

[0106] where M is the mediating effect strength value, p x is the anthropogenic interference coefficient of the xth grid, m 1 , m 2 , m n are all mediating variables, a' is the normalized path coefficient of the population urbanization data for the first mediating variable m 1 in the mediating effect path; b 1 ' is the normalized path coefficient of the mediating variable m 1 for the second mediating variable m 2 in the mediating effect path, b 2 ' is the normalized path coefficient of the mediating variable m 2 for the next mediating variable in the mediating effect path, b n ' is the normalized path coefficient of the mediating variable m n for the urban-rural fringe data, and ε 1 is the mediating effect regression residual.

[0107]

[0108] Among them, a is the path coefficient of the population urbanization data X to the first mediating variable m in the mediating effect path 1 ; a min is the minimum value of the path coefficient of the population urbanization data X to the first mediating variable m 1 in the mediating effect path; a max is the maximum value of the path coefficient of the population urbanization data X to the first mediating variable m 1 in the mediating effect path. Similarly, normalization processing is also performed on other path coefficients and they are transformed to the range of [0, 1].

[0109] The anthropogenic interference coefficient p of the x-th grid is x as follows

[0110]

[0111] where x represents the x-th grid, and x ∈ N 1 indicates that the x-th grid is within the development zone, and x ∈ N 2 indicates that the x-th grid is within the ecological protection area, and x ∈ N 3 indicates that the x-th grid is outside the development zone or ecological protection area but within the set enhancement or attenuation range; x ∈ N 4 indicates that the x-th grid is outside the set enhancement or attenuation range; N 1 represents the development zone range, N 2 represents the ecological protection area range, N 3 represents the enhancement or attenuation range outside the development zone or ecological protection area, N 4 represents outside the enhancement or attenuation range outside the development zone or ecological protection area; i txy is the planning coefficient of the development zone or ecological protection area

[0112] The intensity of the mediating effect is affected by uncertain factors of anthropogenic interference. Among them, the largest uncertain influencing factor is the policy factor. How to quantify the policy factor has always been a major difficulty in the academic field. The policy factors mainly include the development zone planning and the ecological protection planning. The development zone planning will promote urbanization and its urban-rural fringe, and the construction of the ecological protection area will restrict urbanization and its urban-rural fringe

[0113] In this embodiment, the policy factor is quantified, and the anthropogenic interference coefficient p is substituted into the mediating effect intensity value x , and the value range is [0, 1.2] or [0, 0.8]

[0114] p x is divided into three regions and calculated separately in three scenarios

[0115] Scenario 1, when the grid is within the development zone or ecological protection area, let p x = 1.2 or p x= 0.8, indicating a certain degree of enhancement or attenuation of the mediating effect strength value;

[0116] Scenario 2, when the grid is outside the development zone or ecological protection area, if it is within the set enhancement or attenuation range, set p x = i txy ;

[0117] In this embodiment, the set enhancement or attenuation range is within 5 km outside the development zone or ecological protection area;

[0118] Scenario 3, when the grid is outside the development zone or ecological protection area, if it is outside the 5 km range, set p x = 0.

[0119] i txy is the development zone planning coefficient, a positive index. The closer to the development zone, the larger the coefficient and the greater the mediating effect strength value; i txy is the ecological protection area planning coefficient, which is a negative index. The closer to the ecological protection area, the smaller the coefficient and the smaller the mediating effect strength value.

[0120] In this invention, the grid method is used to count data in ArcGIS software. A total of 21,176 1×1 km grid cells are generated in the study area, as Figure 2 and Figure 5 shown.

[0121]

[0122] As the distance from the development zone or ecological protection area increases, the influence of the development zone or ecological protection area will gradually weaken or strengthen. In this embodiment, the maximum influence radius is selected as 5 km. Referring to the INVEST model, a power function is used to represent the weakening or strengthening process of the mediating effect strength with the increase of distance.

[0123] The development zone or ecological protection area planning coefficient is:

[0124]

[0125] where e is the base of the natural logarithm function, and d xy is the spatial distance between the xth grid and the grid cell y in the interference source t, and d tmax is the maximum influence distance of the interference source t.

[0126] According to the analysis results of the mediating effect model in the urban-rural fringe, as shown in Table 1, the hypothesis holds. There are two mediating effect paths: Population urbanization affects the urban-rural fringe through economic urbanization and spatial urbanization as mediating variables (PU-EU-SU-UR). Population urbanization affects the urban-rural fringe through economic urbanization as a mediating variable (PU-EU-UR), as Figure 3 andFigure 4 as shown

[0127] For the path coefficients a (0.659), b 1 (0.669) and b 2 (0.464), linear normalization is performed to obtain a' (0.9512), b 1 '(1), b 2 '(0). Let m 1 be the data of economic urbanization (EU); m 2 be the data of spatial urbanization (SU). According to the calculation formula of the mediation effect strength value and the analysis results in Table 1, there is no direct effect, which is a complete mediation effect.

[0128] Table 1 Analysis Results of Research Hypotheses of PLSSEM Model

[0129]

[0130] Note: Urban-rural fringe (UR); Spatial urbanization (SU); Economic urbanization (EU); Population urbanization (PU).

[0131] Taking the path (PU - EU - SU - UR) as an example, the mediation effect strength value is calculated. The mediation effect strength value is divided into five grades: high (0.1129 - 0.2149), relatively high (0.0650 - 0.1129), general (0.0297 - 0.0649), relatively low (0.0098 - 0.0297), low (0.0000 - 0.0098), and the urban-rural fringe is evaluated.

[0132] Based on the mediation strength data, the mediation effect strength distribution map is displayed and classified by the grid method in ArcGIS software, as Figure 5 shown

[0133] Step S6. Evaluate the development strength of the mediation effect of the urban-rural fringe based on the mediation effect strength value, and summarize the mediation effects and urban development strategies of different cities, as shown in Table 2.

[0134] Table 2 Mediation Effects and Urban Development Strategies of Different Cities

[0135]

[0136] Finally, interpret the model estimation results, analyze and evaluate the urban-rural fringe, summarize the mediation effect strengths and urban development strategies of different cities, and write a research report or paper to provide theoretical and technical support for the coordinated development of urbanization and the urban-rural fringe in Xiangyang City.

[0137] Example 2

[0138] This embodiment provides an evaluation system for the urban-rural fringe based on the mediating effect intensity model, including:

[0139] A data acquisition and preprocessing module, which is used to acquire urbanization data and land use data and perform preprocessing, delimit the urban-rural fringe based on the three-dimensional index system of land development intensity in the preprocessed land use data, and obtain urban-rural fringe data;

[0140] An urban-rural fringe distribution map construction module, which is used to construct an urban-rural fringe distribution map based on the delimited urban-rural fringe and perform grid division;

[0141] An analysis result acquisition module, which is used to obtain the PLS-SEM model analysis result based on the urbanization data and the urban-rural fringe data using the PLS-SEM mediating effect intensity model;

[0142] A new mediating effect intensity model construction module, which is used to construct a new mediating effect intensity model based on the PLS-SEM model analysis result;

[0143] A mediating effect intensity value acquisition module, which is used to calculate the mediating effect intensity value based on the new mediating effect intensity model;

[0144] An evaluation module, which is used to evaluate the urban-rural fringe based on the mediating effect intensity value.

[0145] As mentioned above, the above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

[0146] It should be understood that the parts not detailed in this specification belong to the prior art.

[0147] It should be understood that the above description of the preferred embodiment is relatively detailed, and it should not be considered as a limitation to the protection scope of the present invention patent. Under the inspiration of the present invention, those of ordinary skill in the art can also make substitutions or deformations without departing from the scope protected by the claims of the present invention, and all fall within the protection scope of the present invention. The scope of protection claimed by the present invention shall be subject to the appended claims.

Claims

1. A method for evaluating the urban-rural transition zone based on the mediating effect intensity model, characterized in that: The steps include: Step S1. Obtaining urbanization data and land use data and preprocessing them, delineating the urban-rural transition zone based on the three-dimensional indicator system of land development intensity in the preprocessed land use data and obtaining the urban-rural transition zone data; Step S2. constructing an urban-rural transition zone distribution map based on the delineated urban-rural transition zone and performing grid division; Step S3. Obtain the PLS-SEM model analysis results based on the PLS-SEM mediation effect intensity model for the urbanization data and the urban-rural transition zone data: Step S4. Construct a new mediation effect strength model based on the PLS-SEM model analysis results; Step S5. Calculate the mediation effect strength value based on the new mediation effect strength model; Step S6. Evaluate the urban-rural transition zone based on the mediating effect intensity value.

2. According to claim 1, the method for evaluating the urban-rural transition zone based on the mediating effect intensity model is characterized in that: The urbanization data in step S1 includes population urbanization data and a plurality of other urbanization data; The preprocessing includes: gridding and normalizing the urbanization data and the urban-rural transition zone data.

3. The method for evaluating the urban-rural transition zone based on the mediating effect intensity model according to claim 2 is characterized in that: In step S1, the three-dimensional index system of land development intensity in the pre-processed land use data is used to delineate the urban-rural transition zone, which includes: Obtain the scale data, vitality data and density data of land development intensity in land use data; Based on the scale data, vitality data and density data of land development intensity, the K-means clustering method is used to delineate the urban-rural transition zone; Silhouette coefficient and consistency ratio were used to evaluate the accuracy of clustering and recognition.

4. The method for evaluating the urban-rural transition zone based on the mediating effect intensity model according to claim 3 is characterized in that: The step S3 comprises: Step S3.1 sets the population urbanization data as the independent variable, multiple other urbanization data as the mediating variables, and the urban-rural transition zone data as the dependent variable, and sets the mediation effect path based on the population urbanization data, multiple other urbanization data, and the urban-rural transition zone data; Step S3.2 calculates the path coefficient of each urbanization data on the next urbanization data or urban-rural transition zone in the mediation effect path based on the PLS-SEM mediation effect strength model to obtain the PLS-SEM model analysis results.

5. The method for evaluating the urban-rural transition zone based on the mediating effect intensity model according to claim 4 is characterized in that: The step S4 comprises: Step S4.1 normalizes the path coefficients in the PLS-SEM model analysis results to obtain normalized path coefficients; Step S4.2 obtains the human interference coefficient of each grid based on the area to which the grid belongs in the urban-rural transition zone distribution map; Step S4.3 constructs a new mediation effect strength model based on the normalized path coefficient and the human interference coefficient.

6. The method for evaluating the urban-rural transition zone based on the mediating effect intensity model according to claim 5 is characterized in that: The new mediation effect strength model is: M=p x [a'X×((b1'm1)×(b2'm2)×…(b n 'm n ))]+ε1 Among them, M is the mediating effect strength value, p x is the human interference coefficient of the xth grid, m1, m2, m n are all mediating variables, a' is the normalized path coefficient of the first mediating variable m1 in the mediating effect path of population urbanization data; b1' is the normalized path coefficient of the mediating variable m1 on the second mediating variable m2 in the mediating effect path, b2' is the normalized path coefficient of the mediating variable m2 on the next mediating variable in the mediating effect path, b n ' is the mediating variable m n For the normalized path coefficient of the urban-rural transition zone data, ε1 is the regression residual of the mediating effect.

7. The method for evaluating the urban-rural transition zone based on the mediating effect intensity model according to claim 6 is characterized in that: The artificial interference coefficient p of the x-th grid x for: Where x represents the xth grid, x∈N1 represents that the xth grid is within the development zone, x∈N2 represents that the xth grid is within the ecological protection zone, x∈N3 represents that the xth grid is outside the development zone or ecological protection zone but within the set enhancement or attenuation range; x∈N4 represents that the xth grid is outside the set enhancement or attenuation range; N1 represents the development zone range, N2 represents the ecological protection zone range, N3 represents the enhancement or attenuation range outside the development zone or ecological protection zone, and N4 represents the enhancement or attenuation range outside the development zone or ecological protection zone; i txy It is the planning coefficient of the development zone or ecological protection zone.

8. The method for evaluating the urban-rural transition zone based on the mediating effect intensity model according to claim 7 is characterized in that: The planning coefficient of the development zone or ecological protection zone is: Where, e is the base of the natural logarithm function, d xy is the spatial distance between the xth grid and the grid unit y in the interference source t, d tmax is the maximum impact distance of the interference source t.

9. The method for evaluating the urban-rural transition zone based on the mediating effect intensity model according to claim 8 is characterized in that: The step S6 comprises: The mediating effect intensity value of each grid in the urban-rural transition zone is calculated based on the new mediating effect intensity model; The grid method is used to display the mediating effect intensity value of each grid in the urban-rural transition zone, and the distribution map of the mediating effect intensity value is obtained; The total effect intensity value is divided into several levels and the development intensity of the urban-rural transition zone is evaluated based on the mediating effect intensity value distribution map.

10. An urban-rural transition zone evaluation system based on the mediating effect intensity model, characterized in that: include: A data acquisition and preprocessing module, which is used to acquire and preprocess urbanization data and land use data, delineate the urban-rural transition zone based on the three-dimensional indicator system of land development intensity in the preprocessed land use data, and acquire the urban-rural transition zone data; A module for constructing an urban-rural transition zone distribution map is used to construct an urban-rural transition zone distribution map based on the delineated urban-rural transition zone and perform grid division; An analysis result acquisition module, which is used to obtain PLS-SEM model analysis results based on the PLS-SEM mediation effect intensity model for the urbanization data and the urban-rural transition zone data; A new mediation effect strength model construction module, which is used to construct a new mediation effect strength model based on the PLS-SEM model analysis results; A mediation effect strength value acquisition module, which is used to calculate the mediation effect strength value based on the new mediation effect strength model; An evaluation module, which is used to evaluate the urban-rural transition zone based on the mediating effect intensity value; The urban-rural transition zone evaluation system based on the mediating effect intensity model is used to execute the steps in the urban-rural transition zone evaluation method based on the mediating effect intensity model described in any one of claims 1-9.

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