A linear inheritance space correlation analysis method and system based on a network topology model

Abstract

This invention discloses a method and system for analyzing the spatial correlation of linear heritage sites based on a network topology model. First, the invention acquires and weights parameterizes multi-source data on linear heritage elements and transportation networks, generating transportation nodes and constructing a coupled weighted network topology model that includes heritage sites, real transportation nodes, virtual access nodes, transportation paths, connecting edges, and symbiotic edges. Then, based on this model, multi-dimensional indicators such as spatial clustering, network influence, and structural importance are calculated. An entropy weight method is used to synthesize a spatial correlation index that quantifies the coupling degree between heritage sites, real transportation nodes, and the road network. Finally, the index is mapped back to a geographic information system for hierarchical visualization to identify core hub nodes and peripheral nodes. This invention overcomes the subjectivity of traditional qualitative assessments and the shortcomings of homogenized processing in existing network models, effectively achieving the objective quantification of the interdependent interaction between linear heritage points and lines, and has broad application prospects.

Description

Technical Field

[0001] This invention relates to a technical method for quantitative calculation of the spatial keyness of linear heritage, belonging to the field of geospatial analysis technology. Background Technology

[0002] Linear cultural heritage, as a special type of heritage, consists of linear transportation corridors (Routes) and point-like heritage sites, forming a dependent system of points and lines. It is characterized by its large cross-regional scale, diverse element types, complex spatial structure, and strong interactive connections. Unlike conventional cultural heritage, the core value of linear heritage lies in its flow and connection. The rise and fall of heritage sites depends on the accessibility of the road network, while the cultural significance of the road network is reflected through the heritage sites along the route. There is a deep spatial dependence between the two in terms of material exchange (such as commerce and post stations), information exchange (such as religion and culture), or functional exchange (such as beacon towers and water conservancy facilities). However, current geospatial analysis, heritage protection, and urban planning practices still have the following limitations in analyzing this complex relationship:

[0003] First, there is insufficient emphasis on interactive relationships. The essential characteristic of this type of linear heritage lies in the strong transmission mechanism within it. Existing static spatial analysis methods often only focus on the geographical proximity of heritage sites, neglecting the dynamic functional dependencies within the heritage system caused by trade, transportation, resource allocation, and other factors. For such complex heritage systems that rely on functional transmission, without introducing a network topology perspective for weighted modeling, it will be impossible to reveal their deep structure and core hubs.

[0004] Secondly, the spatial connections between heritage sites are difficult to quantify, lacking objective evaluation criteria. Existing conservation efforts largely rely on historical document verification or expert judgment to describe the relationship between heritage sites and routes. This approach is qualitative and lacks the support of mathematical models, making it difficult to transform implicit historical and geographical relationships into calculable numerical indicators. Although existing GIS spatial analysis technology can handle physical distances, it struggles to measure deep connections in topological structures. This prevents decision-makers from accurately grasping the actual strength of the connection between each heritage site and the road network, resulting in fragmented conservation decisions that prioritize material aspects while neglecting spatial connections.

[0005] Third, existing network analysis models suffer from homogenization defects, leading to assessment results that are out of touch with reality. Although some studies have introduced social network analysis (SNA) tools, when applied to the field of linear heritage, existing techniques typically simplify all heritage sites into homogeneous nodes and treat all transportation paths as equally weighted edges. This simplification ignores the differences in heritage status, historical importance, and accessibility of different roads in the real world, resulting in overly idealized network models. The calculated network structure fails to accurately reflect historical and social realities, thus distorting the correlation assessment results.

[0006] Finally, the lack of diagnostic composite quantitative indicators makes it difficult to directly assist planning decisions. Existing research mostly stops at the static visualization of heritage site attributes, failing to reflect the dynamic interaction between heritage sites and linear heritage, and making it difficult to identify hidden key nodes. This results in a certain disconnect between the analyzed data and planning decisions such as hierarchical protection and revitalization, and fails to provide a scientific basis for differentiated resource allocation. Summary of the Invention

[0007] Purpose of the invention: The purpose of this invention is to provide a linear heritage spatial correlation analysis method and system based on network topology model, in order to solve the problems in the existing technology that the assessment of spatial correlation between heritage sites relies too much on the subjective experience of experts and lacks objective quantitative calculation standards, and that the homogenization defect in network modeling leads to the distortion of assessment results.

[0008] Technical Solution: To achieve the above-mentioned objectives, in a first aspect, this invention provides a method for analyzing the spatial correlation of linear heritage based on a network topology model, comprising the following steps:

[0009] Step 1: Obtain the geospatial coordinates and multidimensional attribute data of heritage sites within the target area, obtain the vector data and attribute data of the transportation network, and construct a basic geospatial dataset of linear heritage sites;

[0010] Step 2: Perform weighted parameterization on heritage sites and traffic routes, set initial weight parameters, and process the road network to generate weighted real traffic nodes.

[0011] Step 3: Establish the physical adjacency relationships between heritage points, real traffic nodes, and virtual access nodes, and construct a coupled weighted network topology model that includes heritage points, real traffic nodes, virtual access nodes, traffic paths, connecting edges, and symbiotic edges. The virtual access point is the perpendicular point of the heritage point on the traffic path closest to it, the connecting edge is the edge connecting the heritage point to its corresponding virtual access node, and the symbiotic edge is the edge connecting two heritage points with a distance less than a preset threshold.

[0012] Step 4: Construct a hierarchical evaluation index system for the spatial relevance of linear heritage, calculate the kernel density value index reflecting spatial clustering, the weighted degree centrality index reflecting network influence, the eigenvector centrality index and the harmonic proximity centrality index, and the betweenness centrality index reflecting structural importance, and perform dimensionless processing on the index values.

[0013] Step 5: Calculate the objective weights of each evaluation index for heritage sites and actual traffic nodes using the entropy weight method, and synthesize the spatial correlation index for each heritage site and actual traffic node based on the weighted summation formula.

[0014] Step 6: Map the calculated spatial correlation index back to the heritage point or real traffic node layer in the geographic information system, classify the heritage points and real traffic nodes respectively using clustering algorithms, and render the heritage point and real traffic node layers respectively using hierarchical symbols.

[0015] Furthermore, step 1 specifically includes:

[0016] Step 1.1: Collect the geospatial coordinates of heritage sites and conduct preliminary correlation screening based on the transmission mechanism characteristics of linear heritage systems. Obtain attribute data of heritage sites through historical document mining or field research, including one or more of the following: cultural relic level, word frequency popularity, or expert scores. Import the collected heritage sites into the geographic information data platform.

[0017] Step 1.2: Collect historical or modern traffic network vector data and obtain attribute data, including one or more of road grade, historical importance, or road length, and import the collected traffic network vector data into the geographic information data platform.

[0018] Furthermore, step 2 specifically includes:

[0019] Step 2.1: Based on the heritage point attribute data obtained in Step 1, assign initial weight parameters to the heritage points to characterize the differences in the basic status of the heritage points in the network;

[0020] Step 2.2: Based on the road network attribute data obtained in Step 1, assign initial weight parameters to each traffic path to characterize the ease or difficulty of material and information flow between heritage sites;

[0021] Step 2.3: Perform line-to-point and topology checks on the input real traffic network vector data, extract the geometric intersections and line endpoints in the network, define the geometric intersections and line endpoints as real traffic nodes in the network, and assign them values ​​based on the highest weight of the connected traffic paths.

[0022] Furthermore, step 3 specifically includes:

[0023] Step 3.1: Using GIS spatial analysis technology, draw a perpendicular line from the heritage point to the nearest transportation path, and define the resulting perpendicular point as a virtual access node, and assign values ​​according to the weight of the transportation path it belongs to; construct connecting line segments with the coordinates of the heritage point as the starting point and the coordinates of the corresponding virtual access node as the ending point, and define them as connecting edges; for heritage points with a distance less than a preset threshold, establish connection relationships between heritage points, and define them as symbiotic edges; set the weights of connecting edges and symbiotic edges to fixed values ​​lower than the minimum value of the path weight; construct a physical adjacency network of heritage points, real transportation nodes, and virtual access nodes.

[0024] Step 3.2: Merge the heritage points, real traffic nodes, and virtual access nodes into a new point feature dataset, which will be used as network nodes. Merge the traffic paths, connecting edges, and symbiotic edges into a new line feature dataset, which will be used as network edges. Combine the node weights and edge weights to construct the coupled weighted topology network model.

[0025] Furthermore, step 4 specifically includes:

[0026] Step 4.1 Calculate the spatial clustering index, including: calculating the kernel density value, wherein the kernel density value is the element density value of heritage points and real traffic nodes per unit area calculated using the Gaussian kernel function;

[0027] Step 4.2: Calculate network influence metrics, including: calculating weighted degree centrality, which is the sum of the weights of the edges directly connected to the node; calculating eigenvector centrality, which is used to measure the quality of the node based on the importance of its neighboring nodes; and calculating harmonic proximity centrality, which is the sum of the reciprocals of the shortest path distances from the harmonic proximity centrality node to all other nodes in the network.

[0028] Step 4.3: Calculate the structural importance index, including: calculating betweenness centrality, where betweenness centrality is the frequency with which a node appears on all shortest paths in the network;

[0029] Step 4.4: Map each basic index to the [0, 1] interval using the range standardization method. The formula is as follows:

[0030]

[0031] in, This represents the original value of the i-th network node for the j-th metric. The values ​​are standardized. Let j be the maximum value of the j-th index across all nodes. Let j be the minimum value of the j-th index across all nodes.

[0032] Furthermore, step 5 specifically includes:

[0033] Step 5.1: Calculate the weight of the j-th indicator. :

[0034]

[0035] Step 5.2: Calculate the information entropy of the j-th index using the Shannon entropy formula. :

[0036]

[0037] Where m is the number of heritage sites or actual traffic nodes, a constant. ;

[0038] Step 5.3: Calculate the objective weight of the j-th indicator.

[0039]

[0040] Where n is the number of indicators;

[0041] Step 5.4: Based on the weighted summation formula, synthesize the spatial correlation index for each heritage site and actual traffic node:

[0042] .

[0043] Furthermore, step 6 specifically includes:

[0044] Step 6.1: Map the spatial correlation index calculated in Step 5 back to the geographic information data platform for visualization.

[0045] Step 6.2: Using Jenks' natural discontinuity method, based on the numerical characteristics of the spatial correlation index, heritage sites and real transportation nodes are automatically clustered into multiple categories, and then displayed intuitively in the geographic information data platform through node color and size. Based on this, core hub nodes and edge nodes in the network are identified, so as to formulate differentiated protection and revitalization strategies.

[0046] Secondly, the present invention provides a linear heritage spatial correlation analysis system based on a network topology model, used to implement the linear heritage spatial correlation analysis method based on a network topology model described in the first aspect. The system includes:

[0047] The dataset construction module is used to obtain the geospatial coordinates and multidimensional attribute data of heritage sites within the target area, obtain the vector data and attribute data of the transportation network, and construct a linear heritage basic geospatial dataset.

[0048] The weight generation module is used to perform weighted parameterization processing on heritage sites and traffic routes, set initial weight parameters, and process the road network to generate weighted real traffic nodes.

[0049] The network model construction module is used to establish the adjacency relationship between heritage points, real traffic nodes, and virtual access nodes in physical space, and to construct a coupled weighted network topology model that includes heritage points, real traffic nodes, virtual access nodes, traffic paths, connecting edges, and symbiotic edges. The virtual access point is the perpendicular point of the heritage point on the traffic path that is closest to it. The connecting edge is the edge that connects the heritage point to its corresponding virtual access node. The symbiotic edge is the edge that connects two heritage points whose distance is less than a preset threshold.

[0050] The evaluation index calculation module is used to construct a hierarchical evaluation index system for the spatial relevance of linear heritage. It calculates the kernel density value index reflecting spatial clustering, the weighted degree centrality index reflecting network influence, the eigenvector centrality index and the harmonic proximity centrality index, as well as the betweenness centrality index reflecting structural importance, and performs dimensionless processing on the index values.

[0051] The spatial correlation index calculation module is used to calculate the objective weights of each evaluation index of heritage sites and real traffic nodes using the entropy weight method, and synthesize the spatial correlation index of each heritage site and real traffic node based on the weighted summation formula.

[0052] The classification and display module is used to map the calculated spatial correlation index back to the heritage point or real traffic node layer in the geographic information system. It uses clustering algorithms to classify heritage points and real traffic nodes respectively, and renders the heritage point and real traffic node layers respectively through hierarchical symbols.

[0053] Thirdly, the present invention provides a computer system including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the steps of the linear heritage spatial correlation analysis method based on a network topology model as described in the first aspect.

[0054] Fourthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the linear heritage spatial correlation analysis method based on a network topology model as described in the first aspect.

[0055] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0056] 1. This invention constructs a coupled weighted network topology model that includes heritage sites, real traffic nodes, and virtual access nodes, and introduces connecting edges and symbiotic edges to transform the adjacency relationship between heritage sites and traffic nodes in physical space into a computable network structure. It proposes a quantitative calculation method based on spatial correlation index, which transforms fuzzy historical and geographical correlations into computable numerical indicators, providing scientific and objective data support for studying the interaction between points and lines in linear heritage, and for formulating protection lists and hierarchical management.

[0057] 2. This invention constructs a coupled network that includes heritage sites and real traffic nodes, and performs weighted modeling of network nodes and paths, fully taking into account the huge differences between different heritage sites and different traffic paths in reality, so that the model can more accurately reflect the actual status of heritage sites in complex geographical and social environments.

[0058] 3. This invention uses the entropy weight method to determine the objective weights of each evaluation index, and synthesizes the spatial correlation index by weighted summation. The weights are allocated entirely based on the dispersion of the index data itself, eliminating human subjectivity and enabling the final spatial correlation index to truly reflect the relative position of each heritage site and transportation node in the coupled network.

[0059] 4. This invention maps the spatial correlation index back to the geographic information system and uses clustering algorithms and hierarchical symbols to classify and render heritage sites and real traffic nodes. It can intuitively identify core hub nodes and edge nodes in the network, providing a clear decision-making basis for the hierarchical protection, revitalization and utilization and differentiated resource allocation of linear heritage. Attached Figure Description

[0060] 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.

[0061] Figure 1 This is a flowchart of the linear heritage spatial correlation analysis based on the network topology model in this invention;

[0062] Figure 2 This is a diagram showing the results of the linear heritage spatial correlation analysis within the target area in this embodiment of the invention. Detailed Implementation

[0063] 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.

[0064] The following detailed explanation of the technical solution of the present invention is illustrated through a specific embodiment using linear heritage spatial correlation analysis within the target area. Figure 1 As shown in the figure, an embodiment of the present invention provides a method for analyzing the spatial correlation of linear heritage based on a network topology model, comprising the following steps:

[0065] Step 1: Obtain the geospatial coordinates and multidimensional attribute data of heritage sites within the target area, and simultaneously obtain the vector data and attribute data of the transportation network to construct a linear heritage basic geospatial dataset.

[0066] In practice, this step specifically includes:

[0067] Step 1.1: Collect the geospatial coordinates of heritage sites and conduct preliminary correlation screening based on the transmission mechanism characteristics of linear heritage systems. Then, obtain attribute data such as the cultural relic level, word frequency popularity, or expert scores of heritage sites through historical document mining or field research, and import the collected heritage sites into the geographic information data platform.

[0068] The specific method involves identifying the interactive characteristics of the target linear heritage sites based on historical and geographical data, confirming that they possess at least one of the following interactive characteristics: (1) Material exchange type: There is a definite material transportation route or relay system, such as the relay stations on the Tea Horse Road; (2) Functional collaboration type: There are interactive facilities for upstream and downstream collaboration, such as the water conservancy facilities of the Grand Canal or the defensive works for the transmission of beacon fires on the Great Wall; (3) Information transmission type: There is a religious propagation route or cultural radiation network, such as the Guan Yu faith sites on the Tea Road. After confirming that they meet the above characteristics, the Web service API interface of electronic maps (such as Gaode / Baidu / Google Maps) is called. The coverage area of ​​the linear heritage is set as the search scope. Multiple rounds of searches are conducted using keywords in the keyword library to obtain the names, addresses, administrative divisions, and latitude and longitude coordinates (such as GCJ-02 or WGS-84 coordinate systems) of heritage sites that meet the interactive characteristics of linear heritage, and export them in CSV or Excel format. Historical geographic information databases and relevant historical documents were consulted to manually extract information on heritage sites that existed in the past but have since disappeared or been renamed. Historical data was then backtracked and compared with the crawled data. Duplicates and irrelevant place names were manually removed, and questionable sites were verified through on-site investigations to ultimately select valid heritage site data. Attribute information was mined and supplemented for the acquired heritage sites. This could be done by consulting cultural relic survey archives, local chronicles, or relevant protection lists to obtain the cultural relic protection level (e.g., national, provincial, municipal / county level) or historical existence period of each heritage site; text mining techniques could be used to perform word frequency statistics on relevant historical documents to obtain the document word frequency popularity value of each heritage site; or raw expert scores on the value of heritage sites could be obtained through questionnaires. Finally, a basic dataset of heritage sites containing names, coordinates, and the aforementioned raw attribute data was formed and imported into the ArcMap 10.8 geographic information data platform.

[0069] Step 1.2: Collect historical or modern traffic network vector data, and obtain attribute data such as road grade, historical importance or road length, and import the collected traffic network vector data into the geographic information data platform.

[0070] The specific method involves obtaining road network data (OSM files) within the target area from the OSM map data platform, inputting them into the ArcMap 10.8 geographic information data platform, and using the LoadOSM File tool in the open-source plugin ArcGIS Editor for OSM to convert the OSM file to a Shapefile for further data processing. The Shapefile-formatted road network data is then imported, and the "Buffer" tool under the "Analysis Tools - Neighborhood Analysis" category is used, setting the buffer width to 30m, to process and export it as a 1-bit monochrome threshold TIFF file for further data processing. Finally, the TIFF-formatted road network data is imported, the import symbology is set to "Binary Classification," and the "Raster Conversion Line" tool under the "Conversion Tools - From Raster" category is used to integrate and process the data into digital road network spatial data that supports spatial structure extraction. This processed data is then exported as a Shapefile named "Traffic Path." The relevant fields are then completed in the attribute table of the road network vector data. Based on historical geographical data or modern transportation planning documents, the original attribute data such as road grade (e.g., official road / national highway, commercial road / provincial road, rural road), historical importance (e.g., whether it is a major commercial artery or a post road) and road geometric length, which are automatically obtained through GIS calculation tools, are entered to form a traffic network dataset with complete geometric and attribute information.

[0071] Step 2: Perform weighted parameterization on heritage sites and traffic routes, set initial weight parameters, and process the road network to generate weighted real traffic nodes.

[0072] In practice, this step specifically includes:

[0073] Step 2.1: Based on one or more attribute data obtained in Step 1, such as cultural relic grade, word frequency popularity, or expert score, assign initial weight parameters to the heritage site. ( ), to characterize the differences in the fundamental status of heritage sites within the network;

[0074] Specifically, the method involves opening the attribute table of heritage point vector data in the ArcMap 10.8 geographic information data platform, creating a new double-precision field named "Node_Weight". Based on the attribute data obtained in step 1 and the preset quantification assignment standards, initial weight parameters are assigned to the heritage points. For example, in this embodiment, heritage points are weighted according to their cultural relic level: national-level cultural relic protection units have a weight of 1.0, provincial-level cultural relic protection units have a weight of 0.8, municipal-level cultural relic protection units have a weight of 0.7, county-level cultural relic protection units have a weight of 0.6, and heritage points without cultural relic registration have a weight of 0.5, forming a heritage point attribute table containing "ID-Name-Longitude-Latitude-Node Weight". In some embodiments, weights can also be assigned based on word frequency classification, or by comprehensively considering weighted values ​​from different dimensions.

[0075] Step 2.2: Based on one or more of the following attributes obtained in Step 1: road grade, historical importance, or road length, assign initial weight parameters to each traffic path. ( ), to characterize the ease or difficulty of material and information flow between heritage sites;

[0076] Specifically, in the ArcMap 10.8 geographic information data platform, open the attribute table of the traffic path vector data and create a new double-precision field named "Edge_Weight". Based on the road attributes obtained in step 1 and the preset quantization assignment standard, assign initial weight parameters to the traffic paths. For example, in this embodiment, the weights are assigned based on road grade: the weight of main roads in linear heritage is 1.0, secondary roads are 0.8, and ordinary roads are 0.6. In some embodiments, weighted values ​​from different dimensions can also be considered comprehensively.

[0077] Step 2.3: Perform feature-to-point and topology checks on the input real traffic network vector data, extract the geometric intersections and line endpoints in the network, define the geometric intersections and line endpoints as real traffic nodes in the network, and assign them values ​​based on the highest weight of the connected traffic paths.

[0078] The specific implementation method involves using the "Feature to Line" tool on the road network vector data in the ArcMap 10.8 geographic information data platform, setting a tolerance value to ensure that all intersecting line segments are broken at the intersection points, forming independent line features. The Topology Checker tool is used to repair topological errors such as "dangles" and "pseudo nodes" to ensure the geometric connectivity of the road network. The "Feature Vertices to Points" tool in the data management module of the ArcMap 10.8 geographic information data platform is used, selecting the parameter "Both Ends" (extracting the start and end points of line segments) to automatically extract the endpoints of all line segments in the road network. Alternatively, the "Intersect" tool can be used to extract the geometric intersection points of all line segments (including crossroads, T-junctions, and multi-way intersections). Finally, the generated point features are subjected to a "Delete Identical" operation to remove duplicate points with overlapping coordinates. Define the cleaned point feature layer as "True Traffic Nodes (TTN)" and create a new "Type" field in its attribute table labeled "TTN", then assign values ​​based on the highest weight of the connected traffic paths.

[0079] Step 3: Establish the physical adjacency relationships between heritage sites, real traffic nodes, and virtual access nodes, and construct a coupled weighted network topology model that includes heritage sites, real traffic nodes, virtual access nodes, traffic paths, connecting edges, and symbiotic edges.

[0080] Wherein, the virtual access point is the perpendicular point of the heritage point on the traffic path closest to it, the connecting edge is the edge connecting the heritage point and its corresponding virtual access node, and the symbiotic edge is the edge connecting two heritage points with a distance less than a preset threshold.

[0081] In practice, this step specifically includes:

[0082] Step 3.1: Using GIS spatial analysis technology, draw a perpendicular line from the heritage point to the nearest transportation path, and define the resulting perpendicular point as a virtual access node, and assign values ​​according to the weight of the transportation path it belongs to; construct connecting line segments with the coordinates of the heritage point as the starting point and the coordinates of the corresponding virtual access node as the ending point, and define them as connecting edges; for heritage points with a distance less than a preset threshold, establish connection relationships between heritage points, and define them as symbiotic edges; set the weights of connecting edges and symbiotic edges to fixed values ​​lower than the minimum value of the path weight; construct a physical adjacency network of heritage points, real transportation nodes, and virtual access nodes.

[0083] The specific implementation method involves using the "Near" tool under "Analysis Tools - Neighborhood Analysis" in the ArcMap 10.8 geographic information data platform. The input feature is "Heritage Points," and the neighboring features are "Traffic Routes." The "Location" option is selected to calculate the vertical distance from each heritage point to the nearest traffic route, and the precise coordinates of the perpendicular's foot on the route (NEAR_X, NEAR_Y) are obtained. Subsequently, based on these perpendicular coordinates, a new point layer is generated using the "Point Set to Feature" or "Create Feature Class" tools under "Data Management Tools - Features," defined as "Virtual Access Nodes (VANs)," and assigned values ​​according to the weight of their respective traffic routes. These nodes represent the actual entrances and exits of heritage points on the road network. Next, the "Split Lineat Point" tool under "Data Management Tools - Features" is used. The generated virtual access nodes are used as splitting features to break the "Traffic Network" line features obtained in step 1.2. This operation physically divides the originally continuous road segment into two independent line elements at the perpendicular point, transforming the "virtual access node" from a simple geometric point into an endpoint of the road network path, thus giving it the node attributes in the network topology. Using the "XY to Line" tool, a connecting line segment is generated with the heritage point coordinates as the starting point and the corresponding virtual access node coordinates as the ending point, defined as a "connection edge," realizing spatial modeling of the heritage point perpendicular to the traffic route. The "Point Distance" tool is then used, with both input and neighboring elements set to the "Heritage Point" layer, and the "Search Radius" set to 500 meters (this threshold is set based on the visible or walkable range). After calculation, point pairs with a distance greater than 0 and less than 500 meters are selected, and connecting lines are generated using the same "XY to Line" tool, defined as "co-existing edges." For example, in this embodiment, the weights of the connection edge and the co-existing edge are set to a fixed value of 0.5, lower than the minimum path weight. Through these two steps, a physical adjacency network interconnecting heritage points, real traffic nodes, and virtual access nodes is constructed.

[0084] Step 3.2: Based on complex network theory, the heritage point, real traffic node and virtual access node layers are merged into a new point feature dataset and uniformly used as network nodes. The traffic path, connecting edge and symbiotic edge layers are merged into a new line feature dataset and used as network edges. The coupled weighted topology network model is constructed by combining node weights and edge weights.

[0085] The specific implementation method involves using the "Merge" tool in the ArcMap 10.8 geographic information data platform to merge the "Heritage Points" layer, the "Real Traffic Nodes" layer, and the "Virtual Access Nodes" layer into a new point feature dataset, serving as network nodes. The attribute table uniformly retains the ID (unique identifier), Type (node ​​type), Weight (node ​​weight), and Lat / Lng (geographic coordinates latitude and longitude). The weight of the virtual access nodes can be set to a default value or inherit the attribute weights of their respective road segments. Using the "Merge" tool, the "Traffic Path" layer (broken and reconstructed in step 2.3), the "Connection Edge" layer generated in step 3.1, and the "Symbiotic Edge" layer are merged into a new line feature dataset, serving as network edges. The attribute table uniformly retains the Source (starting point ID), Target (ending point ID), Type (connection type), and Weight (edge ​​weight) fields. The above node and edge tables are then imported into the social network analysis software Gephi. During the import process, the Weight field is specified as the weight parameter for network analysis, constructing a point-line hybrid undirected weighted network model. This model preserves the topological structure of geospatial space and incorporates social functional attributes through weight parameters, laying the foundation for subsequent centrality calculations.

[0086] Step 4: Construct a hierarchical evaluation index system for the spatial relevance of linear heritage, calculate the kernel density value index reflecting spatial clustering, the weighted degree centrality index reflecting network influence, the eigenvector centrality index and the harmonic proximity centrality index, and the betweenness centrality index reflecting structural importance, and perform dimensionless processing on the index values.

[0087] In practice, this step specifically includes:

[0088] Step 4.1 Calculate the spatial clustering index, including: calculate the kernel density value (I1), that is, use the Gaussian kernel function to calculate the element density value of heritage sites and traffic nodes per unit area;

[0089] The specific implementation method involves using the "Kernel Density" tool under the "Spatial Analyst" module in the ArcMap 10.8 geographic information data platform. The "heritage points" constructed in step 3.2 and the "real traffic nodes" vector layers constructed in step 2 are used as input elements. The "Population" field is set to the node weight parameter assigned in step 2 to reflect the differences in the contribution of different levels of heritage points to clustering; the "Bandwidth" is set to a threshold determined based on the scale of the study area; and the output cell size is set. The tool is run to calculate and generate a continuous raster density surface using the Gaussian kernel function. The "Extract Values ​​to Points" tool is used to extract the generated raster density values ​​back into the attribute table of each heritage point and real traffic node, generating a new field "Kernel_Density" as the spatial clustering index value I1 for that node.

[0090] Step 4.2: Calculate network influence metrics, including: calculating weighted degree centrality (I2), which is the sum of the weights of edges directly connected to a node; calculating eigenvector centrality (I3), which measures the quality of a node based on the importance of its neighboring nodes; and calculating harmonic proximity centrality (I4), which is the sum of the reciprocals of the shortest path distances from a node to all other nodes in the network.

[0091] The specific implementation method is as follows: In the "Statistics" panel on the right side of Gephi, find the "Average Weighted Degree" function and click "Run". The software will calculate the sum of the weights of all edges directly connected to the node, and the result will be automatically written to the "Weighted Degree" column in the node attribute table as I2. In the "Statistics" panel, find the "Eigenvector Centrality" function and click "Run". This algorithm iteratively calculates the importance relationship between a node and its neighboring nodes based on the eigenvalues ​​of the adjacency matrix (i.e., nodes connected to higher-weight nodes score higher), and the result will be written to the "Eigenvector Centrality" column as I3. In the "Statistics" panel, run the proximity centrality algorithm to calculate the reciprocal sum of the shortest path distances from the node to all other nodes in the network. This metric can effectively handle distance measurement problems in disconnected networks, and the result will be written to the "Harmonic Closeness Centrality" column as I4.

[0092] Step 4.3: Calculate the structural importance index, including: calculating the betweenness centrality (I5), which is the frequency with which the node appears on all shortest paths in the network;

[0093] The specific implementation method involves locating the "Network Diameter" or "Betweenness Centrality" function in the "Statistics" panel of the Gephi software and clicking "Run". The software uses the Brandes algorithm to traverse the shortest paths of all node pairs in the network and counts the number of times each node appears as a "bridge" on these shortest paths. After the calculation is complete, the result is automatically written to the "Betweenness Centrality" column in the node attribute table as the structural importance index value I5.

[0094] Step 4.4: Dimensionless processing. The range standardization method is used to map each basic index to the [0, 1] interval. The formula is:

[0095]

[0096] in, This represents the original value of the i-th network node for the j-th metric. The values ​​are standardized. Let j be the maximum value of the j-th index across all nodes. Let j be the minimum value of the j-th index across all nodes.

[0097] The specific implementation method involves exporting the node attribute table calculated by Gephi into CSV or Excel format, containing the five indicator columns I1 to I5 mentioned above, with a partial view of the legacy point data shown in Table 1. Using data processing software (such as Excel), the range standardization formula is applied to transform the data in each column, with a partial view of the legacy point data shown in Table 2. The calculated standardized values ​​are saved as a new dataset for subsequent weight calculations and index synthesis.

[0098] Table 1: Actual Results of Heritage Site Index Calculation (Partial Schematic)

[0099]

[0100] Table 2: Standardized results of the range calculation for heritage site indicators (partial illustration)

[0101]

[0102] Step 5: Calculate the objective weights of each evaluation index for heritage sites and actual traffic nodes using the entropy weight method, and synthesize the spatial correlation index for each heritage site and actual traffic node based on the weighted summation formula.

[0103] In practice, this step specifically includes:

[0104] Step 5.1: Calculate the weight of the j-th indicator. :

[0105]

[0106] The specific implementation method involves importing the standardized data matrix obtained in step 4.4 into data analysis software (such as Excel). For each column in the matrix (i.e., each evaluation indicator), the proportion of the value of the i-th node on that indicator to the sum of the values ​​of all nodes of that indicator is calculated. The data of the heritage points are shown in Table 3.

[0107] Table 3: Weighting of Indicators for Heritage Sites (Partial Schematic)

[0108]

[0109] Step 5.2: Calculate the information entropy of the j-th index using the Shannon entropy formula. :

[0110]

[0111] Where m is the number of heritage sites or actual traffic nodes, a constant. .

[0112] The specific implementation method is as follows: Based on the weight matrix calculated in step 5.1, the information entropy of the j-th indicator is calculated using the Shannon Entropy formula to measure the dispersion of the indicator data. Note: In the specific calculation, to avoid calculation errors caused by ln(0), it is usually necessary to... A minimum value (e.g., 0.00001) is added to the base value for smoothing. Calculation results The value is between 0 and 1, and the heritage point in this embodiment The calculation results are shown in Table 4. The smaller the entropy value, the greater the data difference of the indicator and the more information it carries.

[0113] Table 4: Information Entropy of Various Indicators for Heritage Sites

[0114]

[0115] Step 5.3: Calculate the objective weight of the j-th indicator.

[0116] .

[0117] Where n represents the number of indicators, which is 5 in this example. The specific implementation method is to calculate the weight of each indicator based on the information entropy of each indicator obtained in section 5.2. The calculation results of the indicator weights for the heritage points in this embodiment are shown in Table 5.

[0118] Table 5: Weights of various indicators for heritage sites

[0119]

[0120] Step 5.4: Based on the weighted summation formula, synthesize the spatial correlation index for each heritage site and actual traffic node:

[0121] .

[0122] Specifically, the implementation method involves using a weighted summation model to convert the standardized values ​​from step 4.4 into a single value. Compared with the weights determined in step 5.3 By performing product summation, the final spatial connectivity index comprehensive score for each heritage site and actual transportation node is calculated. The spatial correlation index of heritage sites in this embodiment is partially shown in Table 6.

[0123] Table 6: Spatial Relevance Index of Heritage Sites (Partial Illustration)

[0124]

[0125] Step 6: Map the calculated spatial correlation index back to the heritage point or real traffic node layer in the geographic information system, classify the heritage points and real traffic nodes respectively using clustering algorithms, and render the heritage point and real traffic node layers respectively using hierarchical symbols.

[0126] In practice, this step specifically includes:

[0127] Step 6.1: Map the spatial correlation index calculated in Step 5 back to the GIS geographic information system for visualization.

[0128] The specific implementation method involves importing the data table (CSV or Excel format) containing the "Index" field, calculated and saved in step 5, into the Arcmap 10.8 geographic information data platform. Using the "Join Field" tool under "Data Management Tools - Connections and Associations," and with the unique identifier Id as the connection key, the spatial correlation index values ​​from the external data table are precisely linked to the attribute tables of the "Heritage Points" and "Real Traffic Nodes" vector layers, thus achieving the reintegration of attribute data and spatial data.

[0129] Step 6.2: Using Jenks' natural discontinuity method, based on the numerical characteristics of the spatial correlation index, heritage sites and real transportation nodes are automatically clustered into multiple categories, and then displayed intuitively in the Arcmap 10.8 geographic information data platform by node color and size. Based on this, core hub nodes and edge nodes in the network are identified, so as to formulate differentiated protection and revitalization strategies.

[0130] The specific implementation method is as follows: Open the Layer Properties panel, go to the "Symbolic System" tab, and select "Graded Color" or "Graded Symbol" mode under "Quantity". In the "Value" field, select Index; in the "Classification" method, select "Jenks Natural Discontinuity Method" and set the number of classifications. In this example, the number of heritage points is set to 5, and the result is as follows... Figure 2 As shown.

[0131] Based on the same inventive concept, this invention provides a linear heritage spatial correlation analysis system based on a network topology model, used to implement the aforementioned linear heritage spatial correlation analysis method based on a network topology model. The system includes:

[0132] The dataset construction module is used to obtain the geospatial coordinates and multidimensional attribute data of heritage sites within the target area, obtain the vector data and attribute data of the transportation network, and construct a linear heritage basic geospatial dataset.

[0133] The weight generation module is used to perform weighted parameterization processing on heritage sites and traffic routes, set initial weight parameters, and process the road network to generate weighted real traffic nodes.

[0134] The network model construction module is used to establish the adjacency relationship between heritage points, real traffic nodes, and virtual access nodes in physical space, and to construct a coupled weighted network topology model that includes heritage points, real traffic nodes, virtual access nodes, traffic paths, connecting edges, and symbiotic edges. The virtual access point is the perpendicular point of the heritage point on the traffic path that is closest to it. The connecting edge is the edge that connects the heritage point to its corresponding virtual access node. The symbiotic edge is the edge that connects two heritage points whose distance is less than a preset threshold.

[0135] The evaluation index calculation module is used to construct a hierarchical evaluation index system for the spatial relevance of linear heritage. It calculates the kernel density value index reflecting spatial clustering, the weighted degree centrality index reflecting network influence, the eigenvector centrality index and the harmonic proximity centrality index, as well as the betweenness centrality index reflecting structural importance, and performs dimensionless processing on the index values.

[0136] The spatial correlation index calculation module is used to calculate the objective weights of each evaluation index of heritage sites and real traffic nodes using the entropy weight method, and synthesize the spatial correlation index of each heritage site and real traffic node based on the weighted summation formula.

[0137] The classification and display module is used to map the calculated spatial correlation index back to the heritage point or real traffic node layer in the geographic information system. It uses clustering algorithms to classify heritage points and real traffic nodes respectively, and renders the heritage point and real traffic node layers respectively through hierarchical symbols.

[0138] This invention also provides a computer system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the linear heritage spatial correlation analysis method based on a network topology model.

[0139] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the linear heritage spatial correlation analysis method based on a network topology model.

[0140] The program code used to implement the method of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the steps of the method of the present invention to be performed. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a standalone software package, or entirely on a remote machine or server. All aspects not detailed in this invention are well-known to those skilled in the art.

Claims

1. A method for analyzing the spatial correlation of linear heritage based on a network topology model, characterized in that, Includes the following steps: Step 1: Obtain the geospatial coordinates and multidimensional attribute data of heritage sites within the target area, obtain the vector data and attribute data of the transportation network, and construct a basic geospatial dataset of linear heritage sites; Step 2: Perform weighted parameterization on heritage sites and traffic routes, set initial weight parameters, and process the road network to generate weighted real traffic nodes. Step 3: Establish the physical adjacency relationships between heritage points, real traffic nodes, and virtual access nodes, and construct a coupled weighted network topology model that includes heritage points, real traffic nodes, virtual access nodes, traffic paths, connecting edges, and symbiotic edges. The virtual access point is the perpendicular point of the heritage point on the traffic path closest to it, the connecting edge is the edge connecting the heritage point to its corresponding virtual access node, and the symbiotic edge is the edge connecting two heritage points with a distance less than a preset threshold. Step 4: Construct a hierarchical evaluation index system for the spatial relevance of linear heritage, calculate the kernel density value index reflecting spatial clustering, the weighted degree centrality index reflecting network influence, the eigenvector centrality index and the harmonic proximity centrality index, and the betweenness centrality index reflecting structural importance, and perform dimensionless processing on the index values. Step 5: Calculate the objective weights of each evaluation index for heritage sites and actual traffic nodes using the entropy weight method, and synthesize the spatial correlation index for each heritage site and actual traffic node based on the weighted summation formula. Step 6: Map the calculated spatial correlation index back to the heritage point or real traffic node layer in the geographic information system, classify the heritage points and real traffic nodes respectively using clustering algorithms, and render the heritage point and real traffic node layers respectively using hierarchical symbols.

2. The method for analyzing the spatial correlation of linear heritage based on a network topology model according to claim 1, characterized in that, Step 1 specifically includes: Step 1.1: Collect the geospatial coordinates of heritage sites and conduct preliminary correlation screening based on the transmission mechanism characteristics of linear heritage systems. Obtain attribute data of heritage sites through historical document mining or field research, including one or more of the following: cultural relic level, word frequency popularity, or expert scores. Import the collected heritage sites into the geographic information data platform. Step 1.2: Collect historical or modern traffic network vector data and obtain attribute data, including one or more of road grade, historical importance, or road length, and import the collected traffic network vector data into the geographic information data platform.

3. The method for analyzing the spatial correlation of linear heritage based on a network topology model according to claim 1, characterized in that, Step 2 specifically includes: Step 2.1: Based on the heritage point attribute data obtained in Step 1, assign initial weight parameters to the heritage points to characterize the differences in the basic status of the heritage points in the network; Step 2.2: Based on the road network attribute data obtained in Step 1, assign initial weight parameters to each traffic path to characterize the ease or difficulty of material and information flow between heritage sites; Step 2.3: Perform line-to-point and topology checks on the input real traffic network vector data, extract the geometric intersections and line endpoints in the network, define the geometric intersections and line endpoints as real traffic nodes in the network, and assign them values ​​based on the highest weight of the connected traffic paths.

4. The method for analyzing the spatial correlation of linear heritage based on a network topology model according to claim 1, characterized in that, Step 3 specifically includes: Step 3.1: Using GIS spatial analysis technology, draw a perpendicular line from the heritage point to the nearest transportation path, and define the resulting perpendicular point as a virtual access node, and assign values ​​according to the weight of the transportation path it belongs to; construct connecting line segments with the coordinates of the heritage point as the starting point and the coordinates of the corresponding virtual access node as the ending point, and define them as connecting edges; for heritage points with a distance less than a preset threshold, establish connection relationships between heritage points, and define them as symbiotic edges; set the weights of connecting edges and symbiotic edges to fixed values ​​lower than the minimum value of the path weight; construct a physical adjacency network of heritage points, real transportation nodes, and virtual access nodes. Step 3.2: Merge the heritage points, real traffic nodes, and virtual access nodes into a new point feature dataset, which will be used as network nodes. Merge the traffic paths, connecting edges, and symbiotic edges into a new line feature dataset, which will be used as network edges. Combine the node weights and edge weights to construct the coupled weighted topology network model.

5. The method for analyzing the spatial correlation of linear heritage based on a network topology model according to claim 1, characterized in that, Step 4 specifically includes: Step 4.1 Calculate the spatial clustering index, including: calculating the kernel density value, wherein the kernel density value is the element density value of heritage points and real traffic nodes per unit area calculated using the Gaussian kernel function; Step 4.2: Calculate network influence metrics, including: calculating weighted degree centrality, which is the sum of the weights of the edges directly connected to the node; calculating eigenvector centrality, which is used to measure the quality of the node based on the importance of its neighboring nodes; and calculating harmonic proximity centrality, which is the sum of the reciprocals of the shortest path distances from the harmonic proximity centrality node to all other nodes in the network. Step 4.3: Calculate the structural importance index, including: calculating betweenness centrality, where betweenness centrality is the frequency with which a node appears on all shortest paths in the network; Step 4.4: Map each basic index to the [0, 1] interval using the range standardization method. The formula is as follows: ； in, This represents the original value of the i-th network node for the j-th metric. The values ​​are standardized. Let j be the maximum value of the j-th index across all nodes. Let j be the minimum value of the j-th index across all nodes.

6. The method for analyzing the spatial correlation of linear heritage based on a network topology model according to claim 1, characterized in that, Step 5 specifically includes: Step 5.1: Calculate the weight of the j-th indicator. : ； Step 5.2: Calculate the information entropy of the j-th index using the Shannon entropy formula. : ； Where m is the number of heritage sites or actual traffic nodes, a constant. ; Step 5.3: Calculate the objective weight of the j-th indicator. : ； Where n is the number of indicators; Step 5.4: Based on the weighted summation formula, synthesize the spatial correlation index for each heritage site and actual traffic node: 。 7. The method for analyzing the spatial correlation of linear heritage based on a network topology model according to claim 1, characterized in that, Step 6 specifically includes: Step 6.1: Map the spatial correlation index calculated in Step 5 back to the geographic information data platform for visualization. Step 6.2: Using Jenks' natural discontinuity method, based on the numerical characteristics of the spatial correlation index, heritage sites and real transportation nodes are automatically clustered into multiple categories, and then displayed intuitively in the geographic information data platform through node color and size. Based on this, core hub nodes and edge nodes in the network are identified, so as to formulate differentiated protection and revitalization strategies.

8. A linear heritage spatial correlation analysis system based on a network topology model, used to implement the linear heritage spatial correlation analysis method based on a network topology model according to any one of claims 1-7, characterized in that, include: The dataset construction module is used to obtain the geospatial coordinates and multidimensional attribute data of heritage sites within the target area, obtain the vector data and attribute data of the transportation network, and construct a linear heritage basic geospatial dataset. The weight generation module is used to perform weighted parameterization processing on heritage sites and traffic routes, set initial weight parameters, and process the road network to generate weighted real traffic nodes. The network model construction module is used to establish the adjacency relationship between heritage points, real traffic nodes, and virtual access nodes in physical space, and to construct a coupled weighted network topology model that includes heritage points, real traffic nodes, virtual access nodes, traffic paths, connecting edges, and symbiotic edges. The virtual access point is the perpendicular point of the heritage point on the traffic path that is closest to it. The connecting edge is the edge that connects the heritage point to its corresponding virtual access node. The symbiotic edge is the edge that connects two heritage points whose distance is less than a preset threshold. The evaluation index calculation module is used to construct a hierarchical evaluation index system for the spatial relevance of linear heritage. It calculates the kernel density value index reflecting spatial clustering, the weighted degree centrality index reflecting network influence, the eigenvector centrality index and the harmonic proximity centrality index, as well as the betweenness centrality index reflecting structural importance, and performs dimensionless processing on the index values. The spatial correlation index calculation module is used to calculate the objective weights of each evaluation index of heritage sites and real traffic nodes using the entropy weight method, and synthesize the spatial correlation index of each heritage site and real traffic node based on the weighted summation formula. The classification and display module is used to map the calculated spatial correlation index back to the heritage point or real traffic node layer in the geographic information system. It uses clustering algorithms to classify heritage points and real traffic nodes respectively, and renders the heritage point and real traffic node layers respectively through hierarchical symbols.

9. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the linear heritage spatial correlation analysis method based on network topology model according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the linear heritage spatial correlation analysis method based on network topology model according to any one of claims 1-7.

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