Method for determining the vitality boundary of scenic spots based on circle layer potential domain expansion algorithm

Abstract

The present application belongs to the technical field of space boundary demarcation, and discloses a scenic spot vitality boundary demarcation method based on a circle layer potential field expansion algorithm, which specifically comprises the following steps: based on the two dimensions of human interaction and environmental carrying capacity, a spatial vitality index system of the scenic spot is constructed; multi-source heterogeneous data related to the spatial vitality index system in the scenic spot are collected and preprocessed to construct a vitality information database of the scenic spot; the vitality information database is used to quantize and comprehensively evaluate the vitality indexes in each grid unit to generate a spatial vitality grid map reflecting the vitality level of the scenic spot; the vitality values in the spatial vitality grid map are classified, the vitality contour lines are extracted, and multi-level vitality circle layers are divided; the problems of strong subjectivity, insufficient accuracy and poor dynamic adaptability of the existing vitality boundary demarcation method are solved.

Description

Technical Field

[0001] This invention belongs to the field of spatial boundary delineation technology, specifically relating to a method for delineating the vitality boundary of scenic spots based on a concentric potential domain expansion algorithm. Background Technology

[0002] With economic and social development and the improvement of people's living standards, the public's demand for high-quality tourism and leisure environments is constantly increasing, placing higher demands on the landscape quality and functional optimization of scenic spots. Against this backdrop, scientifically delineating the vibrant boundaries that are highly coupled with the landscape environment has become a key technological foundation for the sustainable development and refined management of scenic areas. However, existing methods largely rely on tourist behavior data, spatial thermal analysis, or empirical judgment, combined with spatial elements such as local topography, accessibility, and attraction distribution for comprehensive analysis. While these methods can initially reflect the spatial distribution of tourist activities, they lack systematic spatial hierarchical modeling and dynamic expansion mechanism analysis, making it difficult to accurately depict the dynamic adaptation relationship between the scope of tourist activities and the environmental conditions of the scenic area. Furthermore, existing vibrant space evaluation systems rely on single indicators, lacking the coordinated integration of multi-dimensional factors such as tourist behavior characteristics, environmental carrying capacity, and the intensity of human interaction. This results in a simplistic indicator system with weak correlations, making it difficult to comprehensively reflect the generation logic and dynamic evolution characteristics of vibrant spaces. The evaluation results are relatively one-sided and cannot effectively support the spatial optimization and refined management needs of scenic spots. Furthermore, traditional methods for delineating activity boundaries often rely on empirically set thresholds, cluster analysis, or contour line extraction. These methods result in coarse spatial delineation, strong subjectivity, and a lack of quantitative extraction mechanisms based on mathematical modeling. Consequently, they fail to effectively fit the changing patterns of visitor space utilization, leading to problems such as ambiguity, significant jumps, and discrepancies with actual spatial activity distribution, thus affecting the objectivity and accuracy of the delineation results. Therefore, existing technologies suffer from problems such as strong subjectivity, insufficient accuracy, and poor dynamic adaptability in activity boundary delineation methods. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method for delineating the vitality boundary of scenic spots based on a concentric potential domain expansion algorithm, which solves the problems of strong subjectivity, insufficient accuracy, and poor dynamic adaptability in existing vitality boundary delineation methods.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] The method for delineating the vitality boundary of scenic spots based on the concentric potential domain expansion algorithm includes the following steps:

[0006] Based on the dual dimensions of human interaction and environmental carrying capacity, a spatial vitality index system for scenic spots is constructed.

[0007] Collect multi-source heterogeneous data related to the spatial vitality index system in scenic spots and perform preprocessing to construct a vitality information database for scenic spots;

[0008] Using a vitality information database, the vitality indicators in each grid cell are quantified and comprehensively evaluated to generate a spatial vitality grid map that reflects the vitality level of scenic spots.

[0009] The vitality values ​​in the spatial vitality raster map are graded, vitality contour lines are extracted, and multiple levels of vitality concentric circles are divided.

[0010] Define the potential domain expansion coefficient and, based on the spatial distribution characteristic that the cumulative value of vitality within each level of vitality concentric circle decays exponentially with the corresponding expansion radius, integrate the concentric circle potential domain expansion algorithm.

[0011] Based on the concentric potential domain expansion algorithm, the spatial utilization of each level of vitality concentric circle is calculated, and a spatial utilization curve is generated by fitting.

[0012] The optimal vector boundary layer of the scenic area is determined based on the space utilization curve and superimposed on the spatial vitality raster map of the scenic area to generate the spatial vitality boundary layer.

[0013] The dimensions of humanistic interaction include cohesion, diversity, and benefit to the people;

[0014] The vitality indicators in clustering include the tourist clustering index;

[0015] Vitality indicators within diversity include activity richness indicators;

[0016] The vitality indicators in the benefit-to-the-people policy include accessibility indicators and point-of-interest density indicators;

[0017] Environmental carrying capacity dimensions include safety and ecology;

[0018] Safety performance indicators include topographic relief, water catchment, and aerosol optical thickness.

[0019] Ecological vitality indicators include vegetation coverage, habitat quality index, and soil conservation index.

[0020] Collect multi-source heterogeneous data related to the spatial vitality index system from each grid cell and preprocess them to construct a vitality information database for scenic spots. The specific steps include:

[0021] Collect multi-source heterogeneous data covering human interaction and environmental carrying capacity characteristics in each grid cell. The multi-source heterogeneous data includes tourist location service data, point of interest distribution data, remote sensing imagery, digital elevation model, aerosol optical thickness data, and road traffic data.

[0022] A preprocessing workflow for data fusion and standardization of multi-source heterogeneous data is provided to unify the spatial reference of various data. The preprocessing workflow includes unified coordinate system projection, resampling to unify spatial resolution, format conversion, invalid value removal and noise filtering, and unified data time window.

[0023] Based on the preprocessed multi-source heterogeneous data, calculate various vitality indicators in the spatial vitality index system;

[0024] All vitality index calculation results in each grid cell are output in a uniform grid format, and each vitality index forms an independent index layer with the same spatial reference.

[0025] Based on the existing management zoning or ecological control boundaries of scenic spots, statistical processing is performed on each indicator layer to extract the statistical parameters of each vitality indicator within each grid cell. The statistical parameters include entropy, maximum value, minimum value and average value.

[0026] By overlaying and integrating all processed indicator layers with spatial references, a vitality information database for scenic spots is constructed.

[0027] The vitality indicators in each grid cell are quantified and comprehensively evaluated to generate a spatial vitality raster map reflecting the vitality level of the scenic area. This process includes the following steps:

[0028] The vitality index data in each grid cell are standardized to generate a dimensionless standardized index matrix.

[0029] By analyzing the information distribution characteristics, the dispersion and discriminative power of each vitality indicator data in the overall data of each indicator within the grid cell are calculated, and the relative importance coefficient of each vitality indicator is determined.

[0030] Based on the relative importance coefficients of each vitality indicator, weights are assigned to each vitality indicator.

[0031] The weights of each vitality indicator are combined with the dimensionless standardized indicator matrix to obtain a comprehensive evaluation matrix.

[0032] The vitality index data are sorted based on their relative distance from the ideal state to generate a comprehensive vitality score.

[0033] The overall vitality score is assigned to the corresponding grid cell to generate a spatial vitality raster map that reflects the vitality level of the scenic spot.

[0034] The vitality values ​​in the spatial vitality raster map are graded, vitality contour lines are extracted, and multiple levels of vitality concentric circles are divided. This process includes the following steps:

[0035] The standard deviation segmentation method is used to divide the vitality value into a preset number of continuous vitality levels, forming a gradient interval from low vitality to high vitality.

[0036] For each vitality level, the corresponding raster region in the spatial vitality raster map is subjected to polygon boundary extraction to generate closed vitality contour lines, which are then converted into vector boundary layers through vectorization.

[0037] Geometric smoothing and topological correction are performed on each vector boundary layer to construct a multi-level vitality concentric circle with continuous spatial hierarchical relationship;

[0038] Assign attribute fields to each level of vitality circle, including circle number, average vitality value, and circle area.

[0039] Based on the concentric potential domain expansion algorithm, the potential domain expansion coefficient is defined, and the space utilization calculation formula is constructed, specifically including the following steps:

[0040] The potential domain expansion coefficient is defined to measure the overall vitality structure characteristics of scenic spots, comprehensively reflecting the spatial aggregation intensity and distribution equilibrium of vitality in scenic spots. The expression for the potential domain expansion coefficient is as follows:

[0041]

[0042] In the formula, c represents the vitality aggregation value; E n Represents the entropy of vitality distribution;

[0043] The vitality clustering value c is used to reflect the degree of vitality clustering in scenic spots and historical sites. The expression for the vitality clustering value c is as follows:

[0044]

[0045] In the formula, f(x) and f1(x) are both curve equations. In the curve equations, each circle number x is the independent variable on the horizontal axis, and the expansion radius corresponding to each level of vitality circle is the dependent variable on the vertical axis; x represents the circle number, x∈[1,Q];

[0046] f(x) represents the curve of the actual expansion radius of the concentric circle; A is the area enclosed by f(x) and the horizontal axis of the coordinate system;

[0047] f1(x) represents the ideal expansion radius curve of the concentric circles; A h Let f1(x) be the area enclosed by f1(x) and the x-axis.

[0048] Q represents the preset number of vitality levels;

[0049] Local vitality centers are identified based on the spatial vitality raster map. Spatial sub-regions corresponding to these local vitality centers are defined, and vitality distribution entropy E is constructed based on these spatial sub-regions. n The calculation formula is:

[0050]

[0051] In the formula, the vitality distribution entropy E n Used to measure the evenness of the distribution of vitality; p i This represents the proportion of the cumulative vitality value within the i-th spatial sub-region to the total vitality value within the scenic area; n is the number of spatial sub-regions.

[0052] Based on the spatial distribution characteristic that the cumulative vitality value within each vitality layer decreases exponentially with the corresponding expansion radius, and combined with the potential domain expansion coefficient, the formula for calculating spatial utilization is constructed as follows:

[0053]

[0054] In the formula, θ x V represents the space utilization of the x-th level vitality zone; x R represents the proportion of the cumulative vitality value within the x-th level vitality zone to the total vitality value within the scenic area; x The expansion radius of the x-th level vitality layer The proportion of the expansion radius of the most dynamic circle; This is an exponentially decaying term, used to reflect the trend of vitality decreasing with increasing distance.

[0055] The calculation of the vitality clustering value c includes the following steps:

[0056] Extract the polygonal boundaries of each level of vitality layer from the spatial vitality raster map;

[0057] Calculate the area S of each vitality zone. x And convert it into the expansion radius.

[0058] A sequence is formed by combining the circle numbers of each level of vitality with the expansion radius of the corresponding vitality circle.

[0059] Sequences corresponding to each level of vitality strata By performing interpolation fitting, the expression for the actual expansion radius curve f(x) of the concentric rings is constructed as follows:

[0060]

[0061] Assuming that scenic spots expand outwards in a fixed-scale manner, the additional area S of each level of vitality zone... x It should remain constant, or expand the radius. As the vitality circle level increases linearly, the expression for the ideal expansion radius curve f1(x) of the circle is:

[0062]

[0063] Integrating f(x) and f1(x) over the interval x∈[1,Q], we obtain A and A'. h ;

[0064] A and A h Substituting into the formula for calculating the vitality aggregation value c, we obtain the vitality aggregation value c.

[0065] Calculate the vitality distribution entropy E n Specifically, it includes the following steps:

[0066] In the spatial vitality raster map, a fixed neighborhood window is set, and the maximum value within the neighborhood of each cell is extracted to construct an extreme value layer R. focal (i,j), the expression is as follows:

[0067] R focal (i,j)=max]R(i′,j′)|(i′,j′)}

[0068] Local peak points are extracted by difference calculation, and the difference calculation formula is as follows:

[0069] D(i,j)=R focal (i,j)-R(i′,j′)

[0070] When the difference D(i,j) is zero, the pixel is the local vitality center point;

[0071] The identified local activity centers are converted into vector points and unified to a consistent spatial reference frame;

[0072] Based on the identified local vitality centers, the Voronoi diagram is formed by generating a spatial influence domain with each local vitality center as the control point through the nearest neighbor dominance principle and the shortest distance assignment rule.

[0073] Each Voronoi polygon region is defined as a spatial sub-region, realizing the spatial segmentation of the local vitality center point within the scenic area;

[0074] The vitality values ​​within each spatial sub-region are aggregated and statistically analyzed to obtain the cumulative vitality value V within the i-th spatial sub-region. i ;

[0075] The total vitality value V is obtained by summing the vitality values ​​within scenic spots and tourist attractions. total ;

[0076] Based on the cumulative vitality value V in the i-th spatial sub-region i With total vitality value V total Calculate p i The specific calculation formula is as follows:

[0077]

[0078] p i Substitute the vitality distribution entropy E n In the calculation formula, the vitality distribution entropy E is obtained. n .

[0079] Based on the concentric potential domain expansion algorithm, the spatial utilization of each level of vitality concentric circle is calculated, and a spatial utilization curve is generated by fitting. The specific steps include:

[0080] The calculated vitality clustering value c and vitality distribution entropy E n Substitute the values ​​into the expression for the potential domain expansion coefficient to calculate the potential domain expansion coefficient γ.

[0081] Based on the spatial vitality raster map, the V corresponding to each level of vitality layer is extracted and calculated. x R x Parameter values;

[0082] Combining γ, V x R x Parameter values ​​are used to calculate the space utilization of each level of the activity zone;

[0083] Relate the circle number x to the space utilization θ of the corresponding vitality circle x All form a sequence (x, θ) x );

[0084] For each set of sequences (x, θ) x Interpolation fitting is performed to generate a space utilization curve.

[0085] The optimal vector boundary layer for scenic spots is determined based on the space utilization curve and then overlaid onto the spatial vitality raster map of the scenic spots to generate a spatial vitality boundary layer. The specific steps include:

[0086] Based on the space utilization curve, extract the maximum point of space utilization;

[0087] The vector boundary layer of the vitality layer corresponding to the concentric circle number of the point with the maximum space utilization is the optimal vector boundary layer.

[0088] The optimal vector boundary layer is overlaid onto the spatial vitality raster map of the scenic area to generate the spatial vitality boundary layer.

[0089] The beneficial effects of this invention are:

[0090] 1. This invention constructs a spatial vitality index system, comprehensively integrating multi-source heterogeneous data such as tourist location service data, point of interest distribution data, and remote sensing imagery, to achieve accurate identification and quantitative evaluation of the vitality space of scenic spots; by introducing a concentric potential domain expansion algorithm, using pixels as the basic unit for concentric division and spatial utilization calculation, it effectively avoids the subjectivity problem caused by relying on experience judgment in traditional methods, thereby achieving high precision and high objectivity in boundary extraction;

[0091] By regularly collecting and updating visitor location service data, point-of-interest heat data, and remote sensing monitoring data, the dynamic characteristics of visitor flow changes and scenic area environmental conditions are reflected in real time. Based on this real-time data, the potential domain expansion coefficient is dynamically adjusted, the vitality index is recalculated periodically, and the spatial vitality raster map is updated, thereby adjusting the calculation of concentric circle division and spatial utilization. In this way, the potential domain expansion coefficient can change dynamically according to the latest data, thus ensuring that the delineation of the vitality boundary always reflects the current vitality distribution status of the scenic area, significantly improving the dynamic adaptability of boundary delineation, and solving the problems of strong subjectivity, insufficient accuracy, and poor dynamic adaptability in existing vitality boundary delineation methods.

[0092] 2. This invention uses a hierarchical processing method to divide the vitality raster map into multiple levels of vitality concentric circles by extracting vitality contour lines. This makes the extraction of vitality boundaries more consistent with the actual spatial expansion patterns, thereby enhancing the matching between the boundary delineation results and the geographic spatial structure. By calculating the spatial utilization of each level of vitality concentric circle and fitting the spatial utilization curve, this invention can provide a scientific basis for the gradient development of scenic spots and the optimized construction of vitality zones, thereby promoting the rational reorganization of the spatial structure of scenic areas and the optimization and upgrading of functional layout. Attached Figure Description

[0093] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0094] Figure 1 This is a schematic diagram of the process for delineating the vitality boundary of scenic spots according to the present invention;

[0095] Figure 2 This is a spatial vitality grid diagram in an embodiment of the present invention;

[0096] Figure 3 This is a schematic diagram of the ten-level vitality layer structure in an embodiment of the present invention;

[0097] Figure 4 This is a schematic diagram of the thirty-level vitality layer structure in an embodiment of the present invention;

[0098] Figure 5 This is a schematic diagram of the twenty-level vitality layer structure in an embodiment of the present invention;

[0099] Figure 6 This is the Voronoi diagram of the activity distribution in this embodiment of the invention;

[0100] Figure 7 This is an expansion radius curve diagram from an embodiment of the present invention;

[0101] Figure 8 This is a diagram showing the change in space utilization in this embodiment;

[0102] Figure 9 It is the vitality boundary map defined by the method of this invention;

[0103] Figure 10 It is the vitality boundary map delineated by the concave polygon method in the prior art;

[0104] Figure 11 It is the vitality boundary map delineated by the Densi-Graph curve method in the existing technology. Detailed Implementation

[0105] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0106] like Figures 1 to 11 As shown, the method for delineating the vitality boundary of scenic spots based on the concentric potential domain expansion algorithm specifically includes the following steps:

[0107] Based on the dual dimensions of human interaction and environmental carrying capacity, a spatial vitality index system for scenic spots is constructed. The spatial vitality index system is shown in Table 1.

[0108] Collect multi-source heterogeneous data related to the spatial vitality index system in scenic spots and perform preprocessing to construct a vitality information database for scenic spots;

[0109] Using a vitality information database, the vitality indicators in each grid cell are quantified and comprehensively evaluated to generate a spatial vitality grid map that reflects the vitality level of scenic spots.

[0110] The vitality values ​​in the spatial vitality raster map are graded, vitality contour lines are extracted, and multiple levels of vitality concentric circles are divided.

[0111] Define the potential domain expansion coefficient and, based on the spatial distribution characteristic that the cumulative value of vitality within each level of vitality concentric circle decays exponentially with the corresponding expansion radius, integrate the concentric circle potential domain expansion algorithm.

[0112] Based on the concentric potential domain expansion algorithm, the spatial utilization of each level of vitality concentric circle is calculated, and a spatial utilization curve is generated by fitting.

[0113] The optimal vector boundary layer of the scenic area is determined based on the space utilization curve and superimposed on the spatial vitality raster map of the scenic area to generate the spatial vitality boundary layer.

[0114] The dimensions of humanistic interaction include cohesion, diversity, and benefit to the people;

[0115] The vitality indicators of clustering include the tourist clustering index (A1);

[0116] Vitality indicators within diversity include activity richness indicators (B1);

[0117] The vitality indicators in the benefit-to-the-people category include the accessibility indicator (C1) and the point of interest (POI) density indicator (C2);

[0118] Environmental carrying capacity dimensions include safety and ecology;

[0119] The safety performance indicators include topographic relief (D1), water catchment (D2), and aerosol optical thickness (D3).

[0120] Ecological vitality indicators include vegetation cover (E1), habitat quality index (E2), and soil conservation index (E3).

[0121] Table 1. Spatial Vitality Index System for Scenic Spots

[0122]

[0123] Collect multi-source heterogeneous data related to the spatial vitality index system from each grid cell and preprocess them to construct a vitality information database for scenic spots. The specific steps include:

[0124] Collect multi-source heterogeneous data covering human interaction and environmental carrying capacity characteristics in each grid cell. The multi-source heterogeneous data includes visitor location service (LBS) data, point of interest (POI) distribution data, remote sensing imagery, digital elevation model (DEM), aerosol optical thickness (AOD) data, and road traffic data.

[0125] A preprocessing workflow for data fusion and standardization of multi-source heterogeneous data is provided to unify the spatial reference of various data. The preprocessing workflow includes unified coordinate system projection, resampling to unify spatial resolution, format conversion, invalid value removal and noise filtering, and unified data time window.

[0126] Preferably, GeoTIFF or Shapefile can be used for format conversion;

[0127] GeoTIFF: A raster file format used to store raster data, such as remote sensing images and digital elevation models;

[0128] Shapefile: A vector file format used to store vector data, such as geographic boundaries, geometric objects like points, lines, and polygons;

[0129] Based on the preprocessed multi-source heterogeneous data, calculate various vitality indicators in the spatial vitality index system;

[0130] All vitality index calculation results in each grid cell are output in a uniform grid format, and each vitality index forms an independent index layer with the same spatial reference.

[0131] Based on the existing management zoning or ecological control boundaries of scenic spots, statistical processing is performed on each indicator layer to extract the statistical parameters of each vitality indicator within each grid cell. The statistical parameters include entropy, maximum value, minimum value and average value.

[0132] By overlaying and integrating all processed indicator layers with spatial references, a vitality information database for scenic spots is constructed.

[0133] The vitality indicators in each grid cell are quantified and comprehensively evaluated to generate a spatial vitality raster map reflecting the vitality level of the scenic area. This process includes the following steps:

[0134] The vitality index data in each grid cell are standardized, and the numerical scale is unified by the interval normalization method. Positive and negative indicators are distinguished to generate a dimensionless standardized index matrix.

[0135] By analyzing the information distribution characteristics, the dispersion and discriminative power of each vitality indicator data in the overall data of each indicator within the grid cell are calculated, and the relative importance coefficient of each vitality indicator is determined.

[0136] Based on the relative importance coefficients of each vitality indicator, weights are assigned to each vitality indicator to ensure that the weighting results are objective and repeatable.

[0137] The weights of each vitality indicator are combined with the dimensionless standardized indicator matrix to obtain a comprehensive evaluation matrix.

[0138] The vitality index data are sorted based on their relative distance from the ideal state to generate a comprehensive vitality score.

[0139] The overall vitality score is assigned to the corresponding grid cell to generate a spatial vitality raster map that reflects the vitality level of the scenic spot.

[0140] In this embodiment of the application, the Taihu Xishan Scenic Area in Suzhou City, Jiangsu Province, is taken as the research area. Multi-source heterogeneous data covering human interaction and environmental carrying capacity characteristics were collected, including: GF-1 satellite remote sensing image acquired on May 9, 2023; 4012 pieces of tourist location service (LBS) data collected through Sina Weibo Open Platform in the past five years; 1793 points of interest (POI) distribution data extracted from Gaode Map Open Platform; 30-meter resolution ASTER GDEM digital elevation model data; and OpenStreetMap road network data combined with local correction information, extracting a total of 3706 road nodes and line segments.

[0141] By analyzing the information distribution characteristics, the dispersion and distinguishing ability of each vitality indicator data in the overall data of each indicator within the grid cell are calculated, the relative importance coefficient of each vitality indicator is determined, and the weight range of the vitality indicators is finally obtained as 0.06 to 0.13, as shown in Table 2.

[0142] In the weighted calculation stage, the weights of each vitality indicator are weighted and synthesized with the dimensionless standardized indicator matrix to obtain a comprehensive evaluation matrix. Further, the data are sorted based on the relative distance between each vitality indicator and the ideal state to generate a comprehensive vitality score. The results show that the comprehensive vitality score range for Xishan Scenic Area is 0.324–0.857, with an average of 0.561. Finally, the comprehensive vitality score is assigned to the corresponding raster cells to generate a spatial vitality raster map reflecting the vitality level of the scenic area (e.g., ...). Figure 2 The relative levels of activity in different grayscale levels correspond to different levels of activity in the corresponding regions.

[0143] Table 2. Weights and Statistical Measures of Spatial Vitality Indicators for Scenic Spots

[0144]

[0145] The vitality values ​​in the spatial vitality raster map are graded, vitality contour lines are extracted, and multiple levels of vitality concentric circles are divided. This process includes the following steps:

[0146] The standard deviation segmentation method is used to divide the vitality value into a preset number of continuous vitality levels, forming a gradient interval from low vitality to high vitality.

[0147] For each vitality level, the corresponding raster region in the spatial vitality raster map is subjected to polygon boundary extraction to generate closed vitality contour lines, which are then converted into vector boundary layers through vectorization.

[0148] Geometric smoothing and topological correction are performed on each vector boundary layer to construct a multi-level vitality concentric circle with continuous spatial hierarchical relationship;

[0149] Assign attribute fields to each level of vitality circle, including circle number, average vitality value and circle area;

[0150] In the vitality circle test of the embodiment, it was found that when the number of circles is less than 20 (such as 15 or 10 levels), it is difficult to accurately depict the local undulation characteristics of the vitality space, and the problem of "oversimplification" of the boundaries is prone to occur. For example, when the number of circles is 10, the schematic diagram of the multi-level vitality circle structure is as follows. Figure 3 As shown; however, when the number of concentric circles exceeds 20 levels (such as 25 or 30 levels), the boundary differences between the concentric circles tend to become blurred, affecting the efficiency of identifying the trend of vitality expansion. For example, when the number of concentric circles is 10, the schematic diagram of the multi-level vitality concentric circle structure is as follows. Figure 4 As shown; therefore, considering both spatial representation accuracy and subsequent computational stability, it was ultimately determined that the vitality layer would be divided into 20 levels, and the corresponding multi-level vitality layer structure diagram is shown below. Figure 5 As shown.

[0151] Based on the concentric potential domain expansion algorithm, the potential domain expansion coefficient is defined, and the space utilization calculation formula is constructed, specifically including the following steps:

[0152] The potential domain expansion coefficient is defined to measure the overall vitality structure characteristics of scenic spots, comprehensively reflecting the spatial aggregation intensity and distribution equilibrium of vitality in scenic spots. The expression for the potential domain expansion coefficient is as follows:

[0153]

[0154] In the formula, c represents the vitality aggregation value; E n Represents the entropy of vitality distribution;

[0155] The vitality clustering value c is used to reflect the degree of vitality clustering in scenic spots and historical sites. The expression for the vitality clustering value c is as follows:

[0156]

[0157] In the formula, f(x) and f1(x) are both curve equations. In the curve equations, each circle number x is the independent variable on the horizontal axis, and the expansion radius corresponding to each level of vitality circle is the dependent variable on the vertical axis; x represents the circle number, x∈[1,Q];

[0158] f(x) represents the curve of the actual expansion radius of the concentric circle; A is the area enclosed by f(x) and the horizontal axis of the coordinate system;

[0159] f1(x) represents the ideal expansion radius curve of the concentric circles; A h Let f1(x) be the area enclosed by f1(x) and the x-axis.

[0160] Q represents the preset number of vitality levels;

[0161] Local vitality centers are identified based on the spatial vitality raster map. Spatial sub-regions corresponding to these local vitality centers are defined, and vitality distribution entropy E is constructed based on these spatial sub-regions. n The calculation formula is:

[0162]

[0163] In the formula, the vitality distribution entropy E n Used to measure the evenness of the distribution of vitality; p i This represents the proportion of the cumulative vitality value within the i-th spatial sub-region to the total vitality value within the scenic area; n is the number of spatial sub-regions.

[0164] Based on the spatial distribution characteristic that the cumulative vitality value within each vitality layer decreases exponentially with the corresponding expansion radius, and combined with the potential domain expansion coefficient, the formula for calculating spatial utilization is constructed as follows:

[0165]

[0166] In the formula, θ x V represents the space utilization of the x-th level vitality zone; x R represents the proportion of the cumulative vitality value within the x-th level vitality zone to the total vitality value within the scenic area; x The expansion radius of the x-th level vitality layer The proportion of the expansion radius of the most dynamic circle; This is an exponential decay term, used to reflect the trend of vitality decreasing with increasing distance;

[0167] The above-mentioned vitality clustering value c and vitality distribution entropy E n The method of calculating space utilization by combining the potential domain expansion coefficient and the potential domain expansion coefficient is integrated into a concentric potential domain expansion algorithm.

[0168] The calculation of the vitality clustering value c includes the following steps:

[0169] Extract the polygonal boundaries of each level of vitality layer from the spatial vitality raster map;

[0170] Calculate the area S of each vitality zone. x And convert it into the (equivalent circle) expansion radius.

[0171] After normalization, a sequence is formed by combining the layer number of each vitality layer with the expansion radius of the corresponding vitality layer.

[0172] Sequences corresponding to each level of vitality strata By performing interpolation fitting, the expression for the actual expansion radius curve f(x) of the concentric rings is constructed as follows:

[0173]

[0174] Assuming that scenic spots expand outwards in a fixed-scale manner, the additional area S of each level of vitality zone... x It should remain constant, or expand the radius. As the vitality circle level increases linearly, the expression for the ideal expansion radius curve f1(x) of the circle is:

[0175]

[0176] Integrating f(x) and f1(x) over the interval x∈[1,Q], we obtain A and A'. h ;

[0177] A and A h Substituting into the formula for calculating the vitality aggregation value c, we obtain the vitality aggregation value c.

[0178] In this embodiment, the Simpson numerical integration method is used to integrate the two curves respectively, and the vitality aggregation value c = A / Ah = 0.75 is calculated.

[0179] Calculate the vitality distribution entropy E n Specifically, it includes the following steps:

[0180] In the spatial vitality raster map, a fixed neighborhood window is set, and the maximum value within the neighborhood of each cell is extracted to construct an extreme value layer R. focal (i,j), the expression is as follows:

[0181] R focal (i,j)=max]R(i′,j′)|(i′,j′)}

[0182] Local peak points are extracted by difference calculation, and the difference calculation formula is as follows:

[0183] D(i,j)=R focal (i,j)-R(i′,j′)

[0184] When the difference D(i,j) is zero, the pixel is the local vitality center point;

[0185] The identified local activity centers are converted into vector points and unified to a consistent spatial reference frame;

[0186] Based on the identified local vitality centers, the nearest neighbor dominance principle is used to generate a spatial influence domain with each local vitality center as a control point, forming a Voronoi diagram, as shown below. Figure 6 As shown;

[0187] Each Voronoi polygon region is defined as a spatial sub-region, realizing the spatial segmentation of the local vitality center point within the scenic area;

[0188] The vitality values ​​within each spatial sub-region are aggregated and statistically analyzed to obtain the cumulative vitality value V within the i-th spatial sub-region. i ;

[0189] The total vitality value V is obtained by summing the vitality values ​​within scenic spots and tourist attractions. total ;

[0190] Based on the cumulative vitality value V in the i-th spatial sub-region i With total vitality value V total Calculate p i The specific calculation formula is as follows:

[0191]

[0192] p i Substitute the vitality distribution entropy E n In the calculation formula, the vitality distribution entropy E is obtained. n ;

[0193] In this embodiment, the final value of the vitality distribution entropy E is obtained. n =2.47.

[0194] Based on the concentric potential domain expansion algorithm, the spatial utilization of each level of vitality concentric circle is calculated, and a spatial utilization curve is generated by fitting. The specific steps include:

[0195] The calculated vitality clustering value c and vitality distribution entropy E n Substitute the values ​​into the expression for the potential domain expansion coefficient to calculate the potential domain expansion coefficient γ.

[0196] In the research experiments of this application's embodiments, c and E n Substituting into the formula for calculating the potential domain expansion coefficient, we obtain the system potential domain expansion coefficient γ≈0.216;

[0197] Based on the spatial vitality raster map, the V corresponding to each level of vitality layer is extracted and calculated. x R x Parameter values;

[0198] Combining γ, V x R xParameter values ​​are used to calculate the space utilization of each level of the activity zone;

[0199] Relate the circle number x to the space utilization θ of the corresponding vitality circle x All form a sequence (x, θ) x );

[0200] For each set of sequences (x, θ) x Interpolation fitting is performed to generate a space utilization curve, such as... Figure 8 As shown;

[0201] γ, V x R x and θ x These parameters collectively constitute the structured parameters of the vitality of the concentric circles. In the research and experiment of this application embodiment, the structured parameters of the vitality of each level of the vitality concentric circle are shown in Table 3.

[0202] The optimal vector boundary layer for scenic spots is determined based on the space utilization curve and then overlaid onto the spatial vitality raster map of the scenic spots to generate a spatial vitality boundary layer. The specific steps include:

[0203] Based on the space utilization curve, extract the maximum point of space utilization;

[0204] The vector boundary layer of the vitality layer corresponding to the concentric circle number of the point with the maximum space utilization is the optimal vector boundary layer.

[0205] The optimal vector boundary layer is overlaid onto the spatial vitality raster map of the scenic area to generate a spatial vitality boundary layer.

[0206] By first fitting and generating a space utilization curve, and then extracting the maximum value point from the space utilization curve, the interference of local fluctuations in the original space utilization data on the identification of the optimal vector boundary layer can be avoided, resulting in higher stability and representativeness.

[0207] Table 3. Structured Parameters of Layer Vitality

[0208]

[0209]

[0210] As shown in Table 3, in the research and experiment of this application embodiment, the space utilization θ x The maximum value point is located in the 9th layer. The vector boundary layer of the 9th layer is extracted and overlaid onto the spatial vitality raster map of the Xishan Scenic Area. Graphic cropping and visual optimization are then performed to obtain the final vitality boundary map of the Xishan Scenic Area (e.g., ...). Figure 9 (as shown);

[0211] The method of this application is compared with the concave polygon method and the Densi-Graph curve method (such as...). Figure 10 , 11 (As shown) The accuracy of the activity boundary delineation is compared, and the boundary accuracy evaluation formula is:

[0212]

[0213] In the formula: This represents the proportion of the cumulative vitality value of the circle containing the vitality boundary to the total vitality value. μ represents the proportion of the area covered by the vitality boundary to the total area; the larger the μ value, the greater the proportion of accumulated vitality can be accommodated within a smaller boundary coverage area, and the higher the fit and accuracy of the boundary morphology.

[0214] The boundary accuracy of the active boundary delineated by the method of this application, the outer concave polygon method, and the Densi-Graph curve method are 2.31, 1.59, and 0.65, respectively, which shows that the delineation accuracy based on the concentric potential domain expansion curve is significantly improved.

[0215] This invention constructs a spatial vitality index system, comprehensively integrating multi-source heterogeneous data such as visitor location service data, point-of-interest distribution data, and remote sensing imagery, to achieve accurate identification and quantitative evaluation of the vitality space of scenic spots. By introducing a concentric potential domain expansion algorithm, using pixels as the basic unit for concentric division and spatial utilization calculation, it effectively avoids the subjectivity problems caused by reliance on experience-based judgment in traditional methods, thus achieving high precision and objectivity in boundary extraction. By dynamically adjusting the potential domain expansion coefficient and regularly collecting and updating visitor location service data, point-of-interest heat data, and remote sensing monitoring data, it reflects the dynamic characteristics of visitor flow changes and scenic area environmental conditions in real time. Based on this real-time data, the vitality index is periodically recalculated and the spatial vitality is updated. The invention utilizes a raster map to adjust the layer division and spatial utilization calculations. This allows the potential domain expansion coefficient to dynamically change based on the latest data, ensuring that the delineation of vitality boundaries always reflects the current distribution of vitality in the scenic area, significantly improving the dynamic adaptability of boundary delineation. By processing the vitality raster map hierarchically and extracting vitality contour lines to divide into multi-level vitality layers, the extraction of vitality boundaries more closely matches the actual spatial expansion patterns, thereby enhancing the matching between boundary delineation results and the geographic spatial structure. By calculating the spatial utilization of each level of vitality layer and fitting spatial utilization curves, this invention can provide a scientific basis for the gradient development of scenic areas and the optimized construction of vitality zones, thereby promoting the rational reorganization of scenic area spatial structure and the optimization and upgrading of functional layout.

[0216] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0217] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for delineating the vitality boundary of scenic spots based on a concentric potential domain expansion algorithm, characterized in that, Specifically, the following steps are included: Based on the dual dimensions of human interaction and environmental carrying capacity, a spatial vitality index system for scenic spots is constructed. Collect multi-source heterogeneous data related to the spatial vitality index system in scenic spots and perform preprocessing to construct a vitality information database for scenic spots; Using a vitality information database, the vitality indicators in each grid cell are quantified and comprehensively evaluated to generate a spatial vitality grid map that reflects the vitality level of scenic spots. The vitality values ​​in the spatial vitality raster map are graded, vitality contour lines are extracted, and multiple levels of vitality concentric circles are divided. Define the potential domain expansion coefficient and, based on the spatial distribution characteristic that the cumulative value of vitality within each level of vitality concentric circle decays exponentially with the corresponding expansion radius, integrate the concentric circle potential domain expansion algorithm. Based on the concentric potential domain expansion algorithm, the spatial utilization of each level of vitality concentric circle is calculated, and a spatial utilization curve is generated by fitting. The optimal vector boundary layer of the scenic area is determined based on the space utilization curve and superimposed on the spatial vitality raster map of the scenic area to generate a spatial vitality boundary layer. Define the potential domain expansion coefficient, and based on the spatial distribution characteristic that the cumulative vitality value within each level of vitality concentric circle decays exponentially with the corresponding expansion radius, integrate the concentric potential domain expansion algorithm, which specifically includes the following steps: The potential domain expansion coefficient is defined to measure the overall vitality structure characteristics of scenic spots, comprehensively reflecting the spatial aggregation intensity and distribution equilibrium of vitality in scenic spots. The expression for the potential domain expansion coefficient is as follows: In the formula, Indicates the vitality aggregation value; Represents the entropy of vitality distribution; Vitality Aggregation Value A vitality aggregation value is used to reflect the degree of vitality aggregation in scenic spots and historical sites. The expression is as follows: In the formula, 、 All are curve equations, in which the independent variable is the number x of each circle, and the dependent variable is the expansion radius corresponding to each level of vitality circle. x Indicates the layer number. x ∈ ; The curve representing the actual expansion radius of the concentric circles; for The area enclosed by the x-axis of the coordinate system; Represents the ideal expansion radius curve of the concentric circles; for The area enclosed by the x-axis of the coordinate system; Q Indicates the preset number of vitality levels; Local vitality centers are identified based on the spatial vitality raster map. Spatial sub-regions corresponding to these local vitality centers are defined, and vitality distribution entropy is constructed based on these spatial sub-regions. The calculation formula is: In the formula, the vitality distribution entropy Used to measure the evenness of the distribution of vitality; Indicates the first The proportion of the cumulative vitality value within a spatial sub-region to the total vitality value within the scenic area; n is the number of spatial sub-regions; Based on the spatial distribution characteristic that the cumulative vitality value within each vitality layer decreases exponentially with the corresponding expansion radius, and combined with the potential domain expansion coefficient, the formula for calculating spatial utilization is constructed as follows: In the formula, For the first Space utilization of the high-level vitality circle; For the first The proportion of the cumulative vitality value within the first-level vitality circle to the total vitality value within the scenic area; For the first Expansion radius of the first-level vitality circle The proportion of the expansion radius of the most dynamic circle; This is an exponentially decaying term, used to reflect the trend of vitality decreasing with increasing distance.

2. The method for delineating the vitality boundary of scenic spots according to claim 1, characterized in that, The dimensions of humanistic interaction include cohesion, diversity, and benefit to the people; The vitality indicators in clustering include the tourist clustering index; Vitality indicators within diversity include activity richness indicators; The vitality indicators in the benefit-to-the-people policy include accessibility indicators and point-of-interest density indicators; Environmental carrying capacity dimensions include safety and ecology; Safety performance indicators include topographic relief, water catchment, and aerosol optical thickness. Ecological vitality indicators include vegetation coverage, habitat quality index, and soil conservation index.

3. The method for delineating the vitality boundary of scenic spots according to claim 2, characterized in that, Collect multi-source heterogeneous data related to the spatial vitality index system from each grid cell and preprocess them to construct a vitality information database for scenic spots. The specific steps include: Collect multi-source heterogeneous data covering human interaction and environmental carrying capacity characteristics in each grid cell. The multi-source heterogeneous data includes tourist location service data, point of interest distribution data, remote sensing imagery, digital elevation model, aerosol optical thickness data, and road traffic data. A preprocessing workflow for data fusion and standardization of multi-source heterogeneous data is provided to unify the spatial reference of various data. The preprocessing workflow includes unified coordinate system projection, resampling to unify spatial resolution, format conversion, invalid value removal and noise filtering, and unified data time window. Based on the preprocessed multi-source heterogeneous data, calculate various vitality indicators in the spatial vitality index system; All vitality index calculation results in each grid cell are output in a uniform grid format, and each vitality index forms an independent index layer with the same spatial reference. Based on the existing management zoning or ecological control boundaries of scenic spots, statistical processing is performed on each indicator layer to extract the statistical parameters of each vitality indicator within each grid cell. The statistical parameters include entropy, maximum value, minimum value and average value. By overlaying and integrating all processed indicator layers with spatial references, a vitality information database for scenic spots is constructed.

4. The method for delineating the vitality boundary of scenic spots according to claim 3, characterized in that, The vitality indicators in each grid cell are quantified and comprehensively evaluated to generate a spatial vitality raster map reflecting the vitality level of the scenic area. This process includes the following steps: The vitality index data in each grid cell are standardized to generate a dimensionless standardized index matrix. By analyzing the information distribution characteristics, the dispersion and discriminative power of each vitality indicator data in the overall data of each indicator within the grid cell are calculated, and the relative importance coefficient of each vitality indicator is determined. Based on the relative importance coefficients of each vitality indicator, weights are assigned to each vitality indicator. The weights of each vitality indicator are combined with the dimensionless standardized indicator matrix to obtain a comprehensive evaluation matrix. The vitality index data are sorted based on their relative distance from the ideal state to generate a comprehensive vitality score. The overall vitality score is assigned to the corresponding grid cell to generate a spatial vitality raster map that reflects the vitality level of the scenic spot.

5. The method for delineating the vitality boundary of scenic spots according to claim 4, characterized in that, The vitality values ​​in the spatial vitality raster map are graded, vitality contour lines are extracted, and multiple levels of vitality concentric circles are divided. This process includes the following steps: The standard deviation segmentation method is used to divide the vitality value into a preset number of continuous vitality levels, forming a gradient interval from low vitality to high vitality. For each vitality level, the corresponding raster region in the spatial vitality raster map is subjected to polygon boundary extraction to generate closed vitality contour lines, which are then converted into vector boundary layers through vectorization. Geometric smoothing and topological correction are performed on each vector boundary layer to construct a multi-level vitality concentric circle with continuous spatial hierarchical relationship; Assign attribute fields to each level of vitality circle, including circle number, average vitality value, and circle area.

6. The method for delineating the vitality boundary of scenic spots according to claim 1, characterized in that, Calculate the vitality clustering value Specifically, it includes the following steps: Extract the polygonal boundaries of each level of vitality layer from the spatial vitality raster map; Calculate the area of ​​each vitality zone S x And convert it into the expansion radius. ; A sequence is formed by combining the circle number of each level of vitality circle with the expansion radius of the corresponding vitality circle. ， ); The sequence corresponding to each level of vitality circle ( Interpolation fitting is performed to construct the actual expansion radius curve of the concentric circles. The expression is: Assuming that scenic areas expand outwards at a fixed scale, the additional area added by each level of the activity zone is... It should remain constant, or expand the radius. As the level of the activity circle increases linearly, an ideal expansion radius curve for the circle is constructed. The expression is: In the interval Internally respectively to 、 Integrating, we get A and A h ; A and A h Substitute vitality aggregation value In the calculation formula, the vitality aggregation value is obtained. .

7. The method for delineating the vitality boundary of scenic spots according to claim 6, characterized in that, Calculate the entropy of vitality distribution Specifically, it includes the following steps: In the spatial vitality raster map, a fixed neighborhood window is set, and the maximum value within the neighborhood of each cell is extracted to construct an extreme value layer. The expression is as follows: Local peak points are extracted by difference calculation, and the difference calculation formula is as follows: When the difference When the value is zero, the pixel is the local vitality center point; The identified local activity centers are converted into vector points and unified to a consistent spatial reference frame; Based on the identified local vitality centers, and utilizing the nearest neighbor dominance principle, a spatial influence domain is generated with each local vitality center as a control point through the shortest distance allocation rule, forming... Voronoi picture; Each Voronoi A polygonal region is defined as a spatial sub-region, realizing the spatial segmentation of the local activity center point within a scenic area; The vitality values ​​within each spatial sub-region are aggregated and statistically analyzed to obtain the first... i Cumulative vitality value within each spatial sub-region ; The total vitality value is obtained by summing the vitality values ​​within scenic spots and attractions. ; According to the i Cumulative vitality value within each spatial sub-region With total vitality calculate The specific calculation formula is as follows: Will Substitute the vitality distribution entropy The calculation formula yields the vitality distribution entropy. .

8. The method for delineating the vibrant boundary of scenic spots according to claim 7, characterized in that, Based on the concentric potential domain expansion algorithm, the spatial utilization of each level of vitality concentric circle is calculated, and a spatial utilization curve is generated by fitting. The specific steps include: Calculated vitality aggregation value and vitality distribution entropy Substituting into the expression for the potential domain expansion coefficient, calculate the potential domain expansion coefficient. ; Based on the spatial vitality raster map, extract and calculate the corresponding vitality layers at each level. Parameter values; Combination , Parameter values ​​are used to calculate the space utilization of each level of the activity zone; Number the circles Space utilization of the corresponding activity circle All constitute sequences ( , ) ; For each group of sequences ( ， ) Interpolation fitting is performed to generate a space utilization curve.

9. The method for delineating the vitality boundary of scenic spots according to claim 8, characterized in that, The optimal vector boundary layer for scenic spots is determined based on the space utilization curve and then overlaid onto the spatial vitality raster map of the scenic spots to generate a spatial vitality boundary layer. The specific steps include: Based on the space utilization curve, extract the maximum point of space utilization; The vector boundary layer of the vitality layer corresponding to the concentric circle number of the point with the maximum space utilization is the optimal vector boundary layer. The optimal vector boundary layer is overlaid onto the spatial vitality raster map of the scenic area to generate the spatial vitality boundary layer.

Images