Method for evaluating community life toughness based on multi-dimensional seepage

Abstract

The invention discloses a method for evaluating community life toughness based on multi-dimensional seepage. The method comprises the following steps: acquiring road data and POIs data of a research area; calculating the midpoint of each road, and connecting the POIs to the midpoint of the nearest road; establishing a road network G1 by using an initial modeling method; establishing a road network G2 by using a dual modeling method; constructing a network sub-graph Gi (v, e); executing a seepage process on the network sub-graph Gi (v, e); and drawing a certain type of service proportion curve, and calculating the integral area of the curve as the community life toughness. According to the invention, based on a network seepage method, a traditional accessibility index is expanded to a community toughness dimension by using a method for measuring life toughness through multi-dimensional seepage; the multi-dimensional attribute of the community road is used as the seepage weight, a disturbance scene and a preposed environment for data input are prevented from being preset in advance, a physical space system of the community is returned to carry out dynamic multi-process simulation, and the requirement for identifying key elements and threshold values of system toughness can be directly responded.

Description

Technical Field

[0001] The present invention relates to the research field of urban community public services, and in particular to a method for evaluating community life resilience based on multidimensional seepage. Background Art

[0002] As the basic unit of the city and the core spatial category for the configuration of public service facilities, the accurate representation of the service level of the community is crucial to improving the quality of community life and optimizing resource allocation. The evaluation of the existing community public service level usually relies on single-dimensional accessibility indicators, which are mostly based on service scale and distance. However, the main limitation of this approach is that they cannot fully reflect the resilience and adaptability of the community in the face of systemic disturbances. Community life resilience can be understood as the ability of the community to obtain and effectively use the public service facilities needed for daily life under certain conditions.

[0003] At present, research and methods for accurately describing life resilience at the community scale are still relatively scarce. First, traditional accessibility indicators fail to fully consider the complex responses of communities in actual disturbance scenarios, such as the dynamic changes in service acquisition and the actual situation of resource allocation. Second, due to the lack of high-precision disturbance data, accessibility indicators cannot be applied to specific disturbance scenarios, which limits the practical application of the evaluation results. In addition, community life resilience is driven by multiple factors of the internal system, including the combined influence of spatial layout, functional configuration, and service network.

[0004] Therefore, there is an urgent need for a measurement method to accurately assess community resilience and identify key elements and core protection areas, thereby providing a scientific basis and important reference for the configuration of public service facilities and spatial structure adjustment in urban communities. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention provides a method for evaluating community life resilience based on multidimensional percolation, which introduces a network percolation model to comprehensively consider the multidimensional factors of the community road system (including width, angle, length, height and connectivity), thereby simulating the percolation process of the gradual removal of network edges under different restrictive conditions. This process effectively characterizes the change in the capacity of the community road system under obstruction, thereby reflecting the ability of residents to obtain various public service facilities in the face of systemic disturbances, and thus accurately assessing the level of community life resilience.

[0006] The technical solution of the present invention is as follows:

[0007] A method for evaluating community living resilience based on multidimensional percolation includes the following steps:

[0008] S1. Collect road spatial vector data of the study area and POIs spatial vector data representing various public service facilities;

[0009] The roads in the study area are simplified into line segments, which are called road segments; the road space vector data is a collection of road segments; the road segment data includes width W, angle A, length L, height H, connectivity K and spatial position information; the connectivity K refers to the number of other road segments connected to the road segment;

[0010] The POIs refer to Point of Interest, which are obtained by screening and recombining according to the POI types of Amap, including shopping, catering, accommodation, business office, school, culture, medical care, sports and leisure, social institutions, scenic spots and transportation facilities; the POIs spatial vector data is a collection of the above POIs;

[0011] S2, calculating the midpoint of each road segment in the road space vector data, called the road midpoint; connecting the POIs in the POI space vector data to the nearest road midpoint; for each road midpoint, recording the number of POIs connected to the point;

[0012] S3. Use the initial modeling method to establish a road network G1(V,E), where V is a set of network nodes, each of which corresponds to a road intersection; E is a set of network edges, each of which corresponds to a road segment; the specific method is as follows: set the width W, length L, height H and connectivity K of the road segment as the edge weights of the corresponding network edges in E, and set the number of POIs connected by the road segment as the edge attributes of the corresponding network edges in E;

[0013] S4. Use the dual modeling method to establish a road network G2(V,E), where V is a set of network nodes, each network node corresponds to a road intersection; E is a set of network edges, each network edge corresponds to a road segment; the specific method is as follows:

[0014] S4-1. Extract each network edge in G1(V,E) and mark it as edge i; obtain other network edges connected to the starting point of edge i, and mark the set composed of these network edges as i_link;

[0015] S4-2, select a network edge in i_link, mark it as edge j; calculate the angle between edge j and edge i, and select an angle less than 180 degrees, and use the complementary angle of the angle to represent the turning angle a_ij between the roads;

[0016] S4-3, input edge i and edge j as network nodes into G2(V,E), and set the turning angle a_ij as the edge weight;

[0017] S4-4, repeat S4-1 to S4-3 until all network edges of G1(V,E) are processed as edge i;

[0018] S4-5. Delete the repeated network nodes and network edges in G2(V,E) to obtain the final G2(V,E);

[0019] S5. For each network node in G2(V,E), calculate all reachable network nodes within 1000m, including the network node itself, to obtain a reachable node set; use the reachable node set to construct a network subgraph Gi(v,e);

[0020] S6. Set the width W, angle A, length L, height H and connectivity K as the percolation dimension; select a percolation dimension and perform the following percolation process for each network subgraph Gi(v,e):

[0021] S6-1. If the percolation dimension is angle A, use Gi(v,e) percolation; otherwise, find the network edges and network nodes in G1(V,E) corresponding to the network nodes and network edges in Gi(v,e), and construct these node sets to obtain a new network subgraph Gi(v,e);

[0022] S6-2. Calculate the maximum value PCmax and the minimum value PCmin of the percolation dimension in the network G1(V, E) or G2(V, E), that is, if the percolation dimension is angle A, calculate the maximum value and the minimum value of the percolation dimension attribute of all network edges in G2(V, E), the former is PCmax, and the latter is PCmin; otherwise, calculate the maximum value and the minimum value of the percolation dimension attribute of all network edges in G1(V, E), the former is PCmax, and the latter is PCmin;

[0023] S6-3, divide the interval formed by PCmax and PCmin into 100 equal intervals to obtain the interval [PCmin, PC1, PC2, …, PCmax];

[0024] S6-4. Select the parameters of the seepage process, i.e., the seepage scale PC, according to the following rules:

[0025] (3) The order of selection of percolation dimensions is: width W, angle A, length L, height H, and connectivity K;

[0026] (4) The selection rule of width W is the narrowest, the selection rule of angle A is the largest turning angle, the selection rule of length L is the longest, the selection rule of height H is the lowest, and the selection rule of connectivity K is the one with the most connected roads, that is, for angle A, length L and connectivity K, the percolation scale PC is selected from PCmax to the smallest; for width W and height H, the percolation scale PC is selected from PCmin to the largest;

[0027] S6-5. Delete network edges according to the following rules to obtain Gi':

[0028] If the percolation dimension is angle A, length L, or connectivity K, delete the edges in the network Gi(v,e) whose edge weight is greater than or equal to PC;

[0029] If the percolation dimension is width W or height H, remove the edges in the network Gi(v,e) whose edge weights are less than or equal to PC.

[0030] S6-6, if the percolation dimension is not angle A, then perform step S4 on Gi', and change the modeling method of the network from initial modeling to dual modeling; based on the network Gi', obtain all network nodes reachable within 1000m from each network node, obtain a reachable node set, and summarize the number of POIs on the reachable node set, which is the number of various POIs reachable by the current network subgraph Gi(v,e) under the percolation scale PC;

[0031] S6-7, repeat S6-4 to S6-6 for each network subgraph Gi(v,e) to obtain the number of POIs of each type that can be reached by the subgraph at each percolation scale PC; normalize the number according to the maximum number of POIs of each type that can be reached by each network subgraph Gi(v,e), that is:

[0032]

[0033] Among them, α i (PC) is the proportion of services that network node i can obtain in its own subgraph Gi when the percolation scale is PC, N PC,i is the number of services that network node i can obtain in its subgraph when the percolation scale is PC, N max,i It is the maximum number of services that the network node can obtain at each percolation scale;

[0034] S7. Based on the results of network percolation, a box plot is drawn showing the proportion curves of various POIs that can be obtained by the community during the percolation process in a specific dimension, where the X-axis represents the PC value from PCmin to PCmax, and the Y-axis represents the proportion of services that can be obtained when the percolation scale is PC, i.e., α i (PC);

[0035] Draw a scale ratio diagram of the network subgraph scale of G2(V,E) during the percolation process, where the X-axis represents the PC value from PCmin to PCmax, and the Y-axis represents the ratio of the network subgraph scale of G2(V,E) to the initial scale;

[0036] S8. The point where the scale of the second largest subgraph of the network subgraph scale ratio graph drops sharply represents the critical threshold 1, and the point where the community can obtain services in the box plot represents the critical threshold 2. Based on the critical threshold 1 or critical threshold 2, the integral area of ​​the proportion curve of each community's access to a certain type of service is calculated as the life resilience of the community under the percolation of a specific dimension, that is:

[0037] where ξ=PCmax or PCmin

[0038] Among them, R i,ρ It indicates that at the critical threshold ρ, road node i can obtain the integral of the service ratio.

[0039] Furthermore, the connectivity K in S1 is calculated using Depthmap software.

[0040] Furthermore, when S2 calculates the midpoint of the road, it calls the geometry.centroid function of the Geopandas library in the Python language.

[0041] Furthermore, when S2 connects the POIs of the POIs spatial vector data to the nearest road midpoint, it calls the sjoin_nearest function of the Geopandas library in the Python language.

[0042] Furthermore, when S5 uses the initial modeling method to establish the road network G1(V,E), it calls the Networkx library in the Python language.

[0043] Furthermore, when S6 uses the dual modeling method to build the road network G2(V,E), it calls the igraph library in the Python language.

[0044] The beneficial technical effects of the present invention are:

[0045] (1) Based on the network percolation method, this paper proposes a method to measure life resilience using multidimensional percolation at the community scale, expanding the traditional accessibility index to the dimension of community resilience.

[0046] (2) The present invention uses the multi-dimensional attributes of community roads as percolation weights, avoiding the need to pre-set disturbance scenarios and data input pre-conditions, and returns to the community’s own physical space system to perform dynamic multi-process simulations, which can directly respond to the requirements for key elements and threshold identification of system resilience.

[0047] (3) The life resilience measurement method proposed in this paper is a basic framework with scalability and flexibility. The percolation dimension can input corresponding data according to usage requirements; the accessibility index can be replaced by other accessibility indicators that are more complex and consider behavioral simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of deleting network edges from a network subgraph;

[0049] Figure 2 It is a diagram of the scale of the network subgraphs with the change of PC drawn according to the percolation of the length dimension and a box plot of the proportion of services available in each community and the average value of the whole community;

[0050] Figure 3 It is the integral calculation result of the service proportion curve available to each community. DETAILED DESCRIPTION

[0051] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] The embodiment adopts the present invention to measure the community life resilience in the area within the outer ring of Shanghai. The specific steps are as follows:

[0053] S1. Collect the road network data of the area within the outer ring of Shanghai in 2023, simplify the roads into line segments, called road segments; the data of road segments include width W, angle A, length L, and height H; convert the coordinate system to EPSG=32651; use Depthmap software to calculate the connectivity K, and use the road segments as indexes to map the connectivity K back to the road network data to obtain shapefile spatial data, namely road network shapefile data.

[0054] S2. Collect POIs data within the outer ring of Shanghai in 2023, merge and combine them according to the classification in Amap (see Table 1 for specific category combinations) to obtain the 11 categories of POIs used in the end; convert the coordinate system to EPSG=32651, and process the POIs into shapefile spatial data to obtain POIs shapefile data.

[0055] Table 1 Combination of POI types in Amap

[0056]

[0057] S3. Use the Geopandas library in Python to read the road network shapefile data obtained in S1 and the POIs shapefile data obtained in S2. Use the geometry.centroid function to calculate the midpoint of each road segment in the road network shapefile data, that is, the midpoint of the road. Index the coordinates of the midpoint of the road with the corresponding road, create a GeoDataFrame of the Point class, and obtain the road data.

[0058] S4. Use the sjoin_nearest function in the Geopandas library to join the POIs shapefile data to the nearest road midpoint, and use the same road midpoint as the index to perform a pandas groupby operation to summarize the number of various POIs connected to the road midpoint and connect to the road data according to the index.

[0059] S5. Call the Networkx library to use the initial modeling method to establish a road network G1(V,E), where V is a set of network nodes, each of which corresponds to a road intersection; E is a set of network edges, each of which corresponds to a road segment. The specific method is as follows: traverse the road data line by line, add road segments as network edges, and set the width W, length L, height H and connectivity K of the road segments as the edge weights of the network edges, set the number of various POIs connected by the road segments as the edge attributes of the network edges, and set the starting point of the road segments as network nodes.

[0060] S6. Call the igraph library to use the dual modeling method to build a road network G2(V,E), where V is a set of network nodes, each network node corresponds to a road intersection; E is a set of network edges, each network edge corresponds to a road segment. The specific method is as follows:

[0061] S6-1. Extract each network edge in G1(V,E) and mark it as edge i; obtain other network edges connected to the starting point of edge i, and mark the set composed of these network edges as i_link.

[0062] S6-2. Select a network edge in i_link and mark it as edge j; calculate the angle between edge j and edge i, and select an angle less than 180 degrees, and the complementary angle of the angle represents the turning angle a_ij between the roads.

[0063] S6-3. Input edge i and edge j as network nodes into G2(V,E), and set the turning angle a_ij as the edge weight.

[0064] S6-4. Repeat S6-1 to S6-3 until all network edges of G1(V,E) are processed as edge i.

[0065] S6-5. Use the simplify function of the igraph library to delete the duplicate network nodes and network edges in G2(V,E) to obtain the final G2(V,E).

[0066] S7. For each network node i in G2(V,E), use the distances function of the igraph library to obtain all reachable network nodes (including itself) within a range of 1000m according to the length L weight, and obtain the reachable node set; use the reachable node set to construct the network subgraph Gi(v,e), Figure 1 The left figure is the network subgraph Gi(v,e) consisting of the reachable nodes within 1000m selected by network node i.

[0067] S8. Set the width W, angle A, length L, height H and connectivity K as the percolation dimension; select a percolation dimension and perform the percolation process on each network subgraph Gi(v,e). The percolation process is as follows: Figure 1 As shown:

[0068] S8-1. If the percolation dimension is angle A, use Gi(v,e) percolation; otherwise, find the network edges and network nodes in G1(V,E) corresponding to the network nodes and network edges in Gi(v,e), and construct these node sets to obtain a new network subgraph Gi(v,e).

[0069] S8-2. Calculate the maximum value PCmax and minimum value PCmin of the percolation dimension in the network G1(V,E) or G2(V,E), that is: if the percolation dimension is angle A, calculate the maximum and minimum values ​​of the turning angles of all edges in G2(V,E); otherwise calculate the maximum and minimum values ​​of the percolation dimension of all edges in G1(V,E).

[0070] S8-3. Divide the interval formed by PCmax and PCmin into 100 equal parts to obtain the interval [PCmin, PC1, PC2, …, PCmax].

[0071] S8-4. Select the parameters of the seepage process, i.e., the seepage scale PC, according to the following rules:

[0072] (5) The order of selection of percolation dimensions is: width W, angle A, length L, height H, and connectivity K;

[0073] (6) The selection rule of width W is the narrowest, the selection rule of angle A is the largest turning angle, the selection rule of length L is the longest, the selection rule of height H is the lowest, and the selection rule of connectivity K is the one with the most connected roads. That is, for angle A, length L, and connectivity K, the percolation scale PC is selected from PCmax to the smallest; for width W and height H, the percolation scale PC is selected from PCmin to the largest.

[0074] S8-5, such as Figure 1 As shown, delete the network edges according to the following rules to obtain Gi':

[0075] If the percolation dimension is angle A, length L, or connectivity K, use the remove function to delete the edges in the network Gi(v,e) whose edge weights are greater than or equal to PC.

[0076] If the percolation dimension is width W or height H, use the remove function to delete the edges in the network Gi(v,e) whose edge weights are less than or equal to PC.

[0077] Figure 1 The second and third figures show the results of deleting different network edges when the PC values ​​are different (PC = PC1 or PC2).

[0078] S8-6. If the percolation dimension is not angle A, execute step S6 for Gi', and modify the initial modeling network into a dual modeling network; based on the network Gi', use the distances function of the igraph library again to obtain all reachable nodes within 1000m from node i according to the length L weight, and summarize the number of POIs on the node set.

[0079] S8-7, repeat S8-4 and S8-5 for each network subgraph Gi(v,e) to obtain the number of POIs of each type that can be reached by the subgraph at each percolation scale PC; normalize the number according to the maximum number of POIs of each type that can be reached by each network subgraph Gi(v,e), that is:

[0080]

[0081] Among them, α i (PC) is the proportion of services that road node i can obtain in its own subgraph Gi when the percolation scale is PC, N PC,i is the number of services that road node i can obtain in its subgraph when the percolation scale is PC, N max,i is the maximum number of services that the node can obtain at each percolation scale.

[0082] S9. Based on the results of network percolation, draw a box plot of the proportion curves of various POIs that can be obtained by the community during the percolation process in a specific dimension, such as Figure 2 As shown in the right figure of , the X-axis represents the PC value of 100 intervals from PCmin to PCmax, and the Y-axis represents the proportion of available services when the percolation scale is PC, that is, α i (PC).

[0083] Draw a subgraph scale diagram of the network G2(V,E) during the percolation process, such as Figure 2 As shown in the left figure, the X-axis represents the PC value of 100 intervals from PCmin to PCmax, and the Y-axis represents the ratio of the G2 network subgraph scale to the initial scale.

[0084] S10, the point where the second largest sub-graph scale drops sharply in the sub-graph scale ratio diagram represents the critical threshold 1, and the point where the community accessible services change in the box plot represents the critical threshold 2. Figure 2 As shown in the figure, according to the principle that the sudden drop of the second largest subgraph represents network collapse, the critical threshold 1 = 434m is identified, and according to the lower line of the box plot (25% quantile), the critical threshold 2 = 283m is identified. Based on critical threshold 1 or critical threshold 2, the integral area of ​​the proportion curve of each community that can obtain a certain type of service can be calculated as the life resilience of the community under the percolation of a specific dimension, that is:

[0085] where ξ=PCmax or PCmin

[0086] Among them, R i,ρ It indicates that at the critical threshold ρ, road node i can obtain the integral of the service ratio.

[0087] R of critical threshold 1 i,ρ The calculation results are as follows Figure 3 As shown in the left figure, the critical threshold 2 R i,ρ The calculation results are as follows Figure 3 These two results are the community life resilience within the Shanghai Outer Ring Road in 2023.

[0088] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes, and can be fully applicable to various fields suitable for the present invention. For those familiar with the art, for those of ordinary skill in the art, various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to specific details.

Claims

1. A method for evaluating community life resilience based on multidimensional percolation, comprising the following steps: S1. Collect road spatial vector data of the study area and POIs spatial vector data representing various public service facilities; The roads in the study area are simplified into line segments, which are called road segments; the road space vector data is a collection of road segments; the road segment data includes width W, angle A, length L, height H, connectivity K and spatial position information; the connectivity K refers to the number of other road segments connected to the road segment; The POIs refer to Point of Interest, which are obtained by screening and recombining according to the POI types of Amap, including shopping, catering, accommodation, business office, school, culture, medical care, sports and leisure, social institutions, scenic spots and transportation facilities; the POIs spatial vector data is a collection of the above POIs; S2, calculating the midpoint of each road segment in the road space vector data, called the road midpoint; connecting the POIs in the POI space vector data to the nearest road midpoint; for each road midpoint, recording the number of POIs connected to the point; S3. Use the initial modeling method to establish a road network G1(V,E), where V is a set of network nodes, each of which corresponds to a road intersection; E is a set of network edges, each of which corresponds to a road segment; the specific method is as follows: set the width W, length L, height H and connectivity K of the road segment as the edge weights of the corresponding network edges in E, and set the number of POIs connected by the road segment as the edge attributes of the corresponding network edges in E; S4. Use the dual modeling method to establish a road network G2(V,E), where V is a set of network nodes, each network node corresponds to a road intersection; E is a set of network edges, each network edge corresponds to a road segment; the specific method is as follows: S4-1. Extract each network edge in G1(V,E) and mark it as edge i; obtain other network edges connected to the starting point of edge i, and mark the set of these network edges as i_link; S4-2, select a network edge in i_link, mark it as edge j; calculate the angle between edge j and edge i, and select an angle less than 180 degrees, and use the complementary angle of the angle to represent the turning angle a_ij between the roads; S4-3, input edge i and edge j as network nodes into G2(V,E), and set the turning angle a_ij as the edge weight; S4-4, repeat S4-1 to S4-3 until all network edges of G1(V,E) are processed as edge i; S4-5. Delete the repeated network nodes and network edges in G2(V,E) to obtain the final G2(V,E); S5. For each network node in G2(V,E), calculate all reachable network nodes within 1000m, including the network node itself, to obtain a reachable node set; use the reachable node set to construct a network subgraph Gi(v,e); S6. Set the width W, angle A, length L, height H and connectivity K as the percolation dimension; select a percolation dimension and perform the following percolation process for each network subgraph Gi(v,e): S6-1. If the percolation dimension is angle A, use Gi(v,e) percolation; otherwise, find the network edges and network nodes in G1(V,E) corresponding to the network nodes and network edges in Gi(v,e), and construct these node sets to obtain a new network subgraph Gi(v,e); S6-2. Calculate the maximum value PCmax and the minimum value PCmin of the percolation dimension in the network G1(V, E) or G2(V, E), that is, if the percolation dimension is angle A, calculate the maximum value and the minimum value of the percolation dimension attribute of all network edges in G2(V, E), the former is PCmax, and the latter is PCmin; otherwise, calculate the maximum value and the minimum value of the percolation dimension attribute of all network edges in G1(V, E), the former is PCmax, and the latter is PCmin; S6-3, divide the interval formed by PCmax and PCmin into 100 equal intervals to obtain the interval [PCmin, PC1, PC2, …, PCmax]; S6-4. Select the parameters of the seepage process, i.e., the seepage scale PC, according to the following rules: (1) The order of selection of percolation dimensions is: width W, angle A, length L, height H, and connectivity K; (2) The selection rule of width W is the narrowest, the selection rule of angle A is the largest turning angle, the selection rule of length L is the longest, the selection rule of height H is the lowest, and the selection rule of connectivity K is the one with the most connected roads, that is, for angle A, length L, and connectivity K, the percolation scale PC is selected from PCmax to the smallest; for width W and height H, the percolation scale PC is selected from PCmin to the largest; S6-5. Delete network edges according to the following rules to obtain Gi': If the percolation dimension is angle A, length L, or connectivity K, delete the edges in the network Gi(v,e) whose edge weight is greater than or equal to PC; If the percolation dimension is width W or height H, remove the edges in the network Gi(v,e) whose edge weights are less than or equal to PC. S6-6, if the percolation dimension is not angle A, then perform step S4 on Gi', and change the modeling method of the network from initial modeling to dual modeling; based on the network Gi', obtain all network nodes reachable within 1000m from each network node, obtain a reachable node set, and summarize the number of POIs on the reachable node set, which is the number of various POIs reachable by the current network subgraph Gi(v,e) under the percolation scale PC; S6-7, repeat S6-4 to S6-6 for each network subgraph Gi(v,e) to obtain the number of POIs of each type that can be reached by the subgraph at each percolation scale PC; normalize the number according to the maximum number of POIs of each type that can be reached by each network subgraph Gi(v,e), that is: Among them, α i (PC) is the proportion of services that network node i can obtain in its own subgraph Gi when the percolation scale is PC, N PC,i is the number of services that network node i can obtain in its subgraph when the percolation scale is PC, N max,i It is the maximum number of services that the network node can obtain at each percolation scale; S7. Based on the results of network percolation, a box plot is drawn showing the proportion curves of various POIs that can be obtained by the community during the percolation process in a specific dimension, where the X-axis represents the PC value from PCmin to PCmax, and the Y-axis represents the proportion of services that can be obtained when the percolation scale is PC, i.e., α i (PC); Draw a scale ratio diagram of the network subgraph scale of G2(V,E) during the percolation process, where the X-axis represents the PC value from PCmin to PCmax, and the Y-axis represents the ratio of the network subgraph scale of G2(V,E) to the initial scale; S8. The point where the scale of the second largest subgraph of the network subgraph scale ratio graph drops sharply represents the critical threshold 1, and the point where the community can obtain services in the box plot represents the critical threshold 2. Based on the critical threshold 1 or critical threshold 2, the integral area of ​​the proportion curve of each community's access to a certain type of service is calculated as the life resilience of the community under the percolation of a specific dimension, that is: where ξ=PCmax or PCmin Among them, R i,ρ It indicates that at the critical threshold ρ, road node i can obtain the integral of the service ratio.

2. The method for evaluating community living resilience based on multidimensional seepage according to claim 1, characterized in that: The connectivity K described in S1 was calculated using Depthmap software.

3. The method for evaluating community living resilience based on multidimensional seepage according to claim 1, characterized in that: When S2 calculates the midpoint of a road, it calls the geometry.centroid function of the Geopandas library in Python.

4. The method for evaluating community living resilience based on multidimensional seepage according to claim 1, characterized in that: When S2 connects POIs in POIs spatial vector data to the nearest road midpoint, it calls the sjoin_nearest function of the Geopandas library in the Python language.

5. The method for evaluating community living resilience based on multidimensional seepage according to claim 1, characterized in that: When S5 uses the initial modeling method to build the road network G1(V,E), it calls the Networkx library in the Python language.

6. The method for evaluating community living resilience based on multidimensional seepage according to claim 1, characterized in that: When S6 uses the dual modeling method to build the road network G2(V,E), it calls the igraph library in the Python language.

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