A method for characterizing and measuring the disaster resilience of urban public services

Abstract

This invention discloses a method for characterizing and measuring the disaster resilience of urban public services. The steps are as follows: A composite urban spatial network of residence, services, and transportation under normal conditions is constructed based on the urban road network; through disaster simulation, failed road sections and functional nodes under different disaster intensities are removed to construct a damaged composite urban spatial network of residence, services, and transportation; the per capita access to public services for residents at each residential node is calculated to form a performance model of the urban spatial network based on residents' access to public services; the rate of change in the per capita access to public services before and after the disaster is calculated; and the relationship curve between this rate of change and the disaster intensity is plotted to measure the disaster resilience of urban public services. This invention characterizes the dynamic evolution of the supply and demand matching performance of urban public services under multi-system collaboration by using changes in residents' access to public services, and establishes a measurement model for the disaster resilience of urban public services, providing a technical tool for enhancing urban resilience in planning practice.

Description

Technical Field

[0001] This invention relates to the field of urban disaster resilience analysis technology, and specifically to a method for characterizing and measuring the disaster resilience of urban public services. Background Technology

[0002] Various disasters and disturbances are significant obstacles to urban safety and sustainable development, severely impacting the normal lives of urban residents and potentially causing catastrophic consequences such as loss of life and property and social disorder. Therefore, urban resilience has emerged as a new paradigm for urban risk governance.

[0003] From a people-centered perspective, the core requirement of urban resilience is to promptly recognize the changing characteristics of urban residents' lives and their living spaces under emergency conditions during disasters, ensuring the efficient and stable operation of the city and minimizing the impact of disturbances on residents' access to public services. Therefore, measuring the disaster resilience level of urban public services and analyzing the relationship between urban spatial systems and disaster resilience can provide new technical tools for urban disaster management and the construction of resilient cities.

[0004] Existing urban resilience measurement methods mostly focus on the sustainability of single systems such as water supply, power transmission, and communications, characterizing resilience levels by changes in the supply-side service level or network structure characteristics of urban systems during disturbances. For example:

[0005] (1) "A method for assessing the resilience of urban subway flooding disasters" (CN114169781A) assesses urban resilience by starting with subway flooding;

[0006] (2) The "Urban Resilience Assessment Method for Emergency Management" (CN113191647A) assesses urban resilience from three different aspects: the pressure a city faces in the face of possible emergencies, the city's own state when an emergency occurs, and the emergency response made by the city after an emergency occurs.

[0007] (3) "An Urban Flood Resilience Assessment System and Method" (CN113869807A) assesses urban resilience by starting with urban floods;

[0008] (4) "A method for assessing urban ecological resilience based on the Internet of Things and big data" (CN114021866A) assesses urban resilience from the perspective of urban ecology;

[0009] (5) "An Assessment Method for the Resilience of Urban Rail Transit Networks" (CN111882241A) assesses urban resilience by starting with rail transit networks;

[0010] (6) "A quantitative measurement method for urban street network resilience" (CN114037199A) assesses urban resilience by starting with the structure of urban street network.

[0011] However, due to the complex interrelationships and coordination mechanisms among the various elements of an urban complex system, functional failures caused by disasters pose a risk of cross-system transmission and disrupt the original supply-demand interaction of public services. Therefore, the stability and resilience of the service level or network structure of a single system cannot effectively guarantee that urban residents can still access public services normally under disaster scenarios. Currently, there is a lack of methods to characterize the matching process from the supply side to the demand side of urban services under the support of multiple systems from the perspective of urban system interrelation and coordination, to calculate the impact intensity of disaster processes on the overall operational status of the urban complex system and the normal lives of residents, and to propose a method for characterizing and measuring the disaster resilience level of urban public services. Summary of the Invention

[0012] To address the aforementioned problems in existing technologies, this invention provides a method for characterizing and measuring the disaster resilience of urban public services. From the perspective of urban system correlation and coordination, the urban street network is regarded as the basic framework of urban spatial form. It integrates urban functions such as residence, public services, and transportation to construct a complex urban spatial network, characterizes the matching process from the supply side to the demand side of urban services under the support of multiple systems, and calculates the intensity of the impact of disaster processes on the overall operation of the urban complex system and the normal life of residents.

[0013] The technical solution of the present invention is as follows:

[0014] A method for characterizing and measuring the disaster resilience of urban public services includes the following steps:

[0015] S1. Collect raw spatial vector data of urban roads and areal data of public service facilities and residential communities; based on urban roads, map urban space into a power-oriented and oriented urban basic spatial network; on this basis, map public service facilities and residential communities into functional nodes in the network, and construct a normal urban spatial composite network of residence, service and transportation.

[0016] S2. Using the urban spatial composite network of residential-service-transportation described in step S1 as the initial scenario, through experimental simulation, remove the inaccessible road sections under different intensity of disaster disturbances, and construct the urban spatial composite network of residential-service-transportation after damage under different disaster intensities.

[0017] S3. Based on the flow cost and supply and demand scale between residence and service point pairs, the service level of public service facilities is allocated to each residence node, the per capita access to public services in each residence node is calculated, and an urban spatial network performance model based on residents' access to public services is formed.

[0018] S4. Based on the urban spatial network performance model for residents' access to public services described in step S3, calculate the rate of change of per capita access to public services in each statistical unit before and after the disaster, and characterize the performance change of urban public services.

[0019] S5. Plot the relationship curve between the rate of change in per capita available public services and disaster intensity to measure the disaster resilience of urban public services.

[0020] Furthermore, the method for constructing a residential-service-transportation urban spatial composite network in step S1 includes the following steps:

[0021] S1-1. Perform topological processing on the original spatial vector data of urban roads, abstracting each road intersection, ramp, etc., into a point set N. s ={n1,n2,…,n k The road segments connecting intersections and ramps are abstracted as edge set E. s ={l1,l2,…,l m}, edge l m Euclidean distance d m As its weights, it forms the urban basic spatial network diagram G(N) s E s );

[0022] S1-2. Extract the centroids of the residential area areal data, and use population data as the weight for each centroid to form a set N of residential nodes. r ={r1,r2,…,r i Find the nearest road intersection to each residential node and connect them to form a set of connecting edges E that connect the residential nodes to the urban basic spatial network. r ={l r1 ,l r2 ,…,l ri}, edge l ri Euclidean distance d ri As its weight, it abstractly represents the travel distance of residents from their residential community to the city road, forming a residential-transportation integrated urban spatial network diagram G(N). s ∪N r E s ∪E r );

[0023] S1-3. Extract the geographical locations of each public service facility, and use the service level of the facility as the weight of each point to form a set N of public service nodes. f ={f1,f2,…,f jFind the road intersections closest to each public service node and connect them to form a set of connecting edges E that connect the facilities to the urban basic spatial network. f ={l f1 ,l f2 ,…,l fj}. Move edge l fj Euclidean distance d fj As its weight, it abstractly represents the distance from public service facilities to urban roads, forming a typical urban spatial composite network diagram G(N) of residence-service-transportation. s ∪N r ∪N f E s ∪E r ∪E f ).

[0024] Furthermore, step S2, which involves constructing a composite urban spatial network of residential, service, and transportation areas damaged under different disaster intensities, includes:

[0025] S2-1. Through urban disaster experiment simulation, identify the road sections and urban land affected by disasters under different disaster intensities;

[0026] S2-2. Overlay the identification results with the urban spatial composite network diagram of residence-service-transportation under normal conditions, and then overlay the results onto the set E of the composite network edges. s ∪E r ∪E f In the process, remove the edges mapped to road segments that failed due to disasters, and then add them to the set N of the composite network points. s ∪N r ∪N f In this process, road network nodes and public service nodes that are no longer effective for travel are removed, resulting in a composite urban spatial network of residence, services, and transportation damaged under different disaster intensities.

[0027] Furthermore, step S3, based on the urban spatial network performance model for residents' access to public services, includes the following steps:

[0028] S3-1. Based on the edge weights in the urban spatial composite network of residence-service-transportation, and according to the theoretical service scope of public services, calculate the directed travel cost matrix A between residence-service point pairs. rf ;

[0029] S3-2, Based on the directed travel cost matrix A between residence-service point pairs rf The scale of residential service points determines the allocation of public service facilities' service levels to each residential node; the service allocation ratio of public service node j to resident node i is calculated using the following formula:

[0030]

[0031] Among them, P ij M is the service allocation ratio from public service node j to resident node i. j D represents the service level of public service node j. i Let i represent the demand scale of residential community i, i.e., the number of permanent residents, n be the number of residential nodes, α be the distance attenuation coefficient, and A be the distance attenuation coefficient. rf (i,j) is the directed travel cost matrix A between residence-service point pairs. rf The value in the i-th row and j-th column;

[0032] S3-3. In the urban spatial composite network of residence, services, and transportation, the per capita access to public services for residents within each residential node is calculated using the following formula:

[0033]

[0034] Among them, A i Q represents the per capita access to public services for residents at resident node i. i P represents the level of public services that resident node i obtains from all public service nodes. ij M is the service allocation ratio from public service node j to resident node i. j D represents the service level of public service node j. i Let i represent the demand scale of residential community i, i.e., the number of permanent residents, and k be the number of public service nodes.

[0035] Furthermore, step S3-1, based on the theoretical service scope of public services, involves calculating the directed travel cost matrix A between residence-service point pairs under different scenarios. rf The calculation method is as follows:

[0036]

[0037] Among them, A rf (i,j) is the directed travel cost matrix A between residence-service point pairs. rf The value in the i-th row and j-th column, In the urban spatial complex network of residence, service, and transportation, d0 is the shortest path length from residential node i to public service facility j, and d0 is the theoretical maximum service range of the public service.

[0038] Furthermore, step S4 calculates the rate of change in per capita access to public services within each statistical unit before and after the disaster. The steps characterizing the performance changes of urban public services include:

[0039] S4-1. Summarize the per capita access to public services (Q) within each statistical unit.pre The per capita access to public services Q after a disaster post Q pre Q post Calculate using the following formula:

[0040]

[0041]

[0042] Where i is the residential node, N r A represents the set of residential nodes in the urban spatial complex network of residence, service, and transportation within the statistical unit. i A' represents the per capita access to public services at residential node i under normal circumstances. i D represents the per capita access to public services at residential node i after a disaster. i This represents the demand scale of residential community i, i.e., the number of permanent residents.

[0043] S4-2. Calculate the rate of change P of per capita access to public services in cities under different disaster intensities; P is calculated using the following formula:

[0044]

[0045] Among them, Q pre This indicates the normal level of public services available to each person. This represents the per capita level of public services available after a disaster of intensity 'a'.

[0046] Furthermore, step S5 involves plotting the relationship curve between the rate of change in per capita available public services and disaster intensity. The steps for measuring the disaster resilience of urban public services include:

[0047] S5-1. Plot the curve showing the relationship between the rate of change P of per capita access to public services in the city and the intensity of disaster. The horizontal axis represents the intensity of disaster, and the vertical axis represents the degree of change in the performance of public services.

[0048] S5-2. Solve the network of each connected subgraph of the urban spatial composite network of residential-service-transportation under different disaster intensities, sort them in descending order of the number of nodes, and extract the size of the second largest connected subgraph.

[0049] S5-3. Identify the maximum value of the second largest connected subgraph of the urban spatial composite network of residential-service-transportation during the change of disaster intensity. This value is considered as the critical state in which the network structure reaches fragmentation and serves as the threshold point for the disaster-bearing capacity of the network structure.

[0050] S5-4. Calculate the integral value of the rate of change of public service level P against disaster intensity before the threshold point of collapse of the urban spatial composite network structure of residence-service-transportation. This value characterizes the disaster resilience of urban public services and is calculated using the following formula:

[0051]

[0052] Among them, Q pre Q represents the average level of public services available to each person under normal circumstances. post This indicates the per capita access to public services after a disaster, a max This represents the threshold point at which the complex urban spatial network structure of residence, services, and transportation collapses.

[0053] The beneficial technical effects of this invention are as follows:

[0054] (1) This invention takes maintaining the level of access to public services for residents as the core objective of urban resilience. It integrates complex network theory and urban public service supply and demand distribution model, and generalizes the flow process and travel cost of residents to access public services into connection paths and weights in complex networks. It proposes a basic conceptual model for measuring resilience level by abstractly expressing the relationship between multiple systems and analyzing the interaction mechanism of disaster disturbance, spatial structure and access to services on the demand side of residents.

[0055] (2) Compared with the current common methods for measuring resilience level by proxy indicators to characterize the performance of a single system, this invention regards the city as a complex system, depicts the matching process from the supply side to the demand side of urban services in the experiment, and characterizes the dynamic evolution of the overall performance of the urban complex system by the degree of change in the public services available to residents, and establishes a method and calculation model for measuring the disaster resilience of urban public services, and clearly characterizes the level of urban resilience.

[0056] (3) This invention can reveal the changes in the performance of the urban residential-service-transportation complex system during disturbance, analyze the differences in resilience levels in different regions and time periods, identify disaster-bearing intensity and key structural elements, and provide technical tools for targeted enhancement of urban system resilience in planning practice. Attached Figure Description

[0057] Figure 1 This is a flowchart of the present invention;

[0058] Figure 2 This is a curve showing the relationship between the rate of change in urban public service levels and disaster intensity in an example. Detailed Implementation

[0059] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0060] The flowchart of this invention is as follows Figure 1 As shown. This invention is used to measure the disaster resilience of comprehensive medical service facilities in the central urban area of ​​Shanghai under a rainstorm and flooding scenario. The specific steps are as follows:

[0061] S1. Based on urban roads, urban space is mapped into a empowered and oriented urban basic spatial network. On this basis, public service facilities and residential communities are mapped as functional nodes in the network, constructing a normalized urban spatial composite network of residence, services, and transportation. The specific steps are as follows:

[0062] S1-1. Using the OSMnx and NetworkX model libraries in Python, collect the original spatial vector data of all urban road networks above the branch road level on the surface, including rapid interchange systems, from open-source maps; use the 2000 National Geodetic Coordinate System (CGCS2000) projection for ArcGIS visualization; record basic information such as road name, direction of travel, road segment length, whether it is an elevated bridge, and whether it is a tunnel in the attribute table of its corresponding road segment.

[0063] S1-2. Collect isometric data of public service facilities and residential areas by calling open-source map APIs, and use the 2000 National Geodetic Coordinate System (CGCS2000) projection for ArcGIS visualization; extract the centroids of the isometric data of public service facilities and residential areas to represent their relative position in the city; based on the statistical data of public service facilities and the population census data, record the service level of public service facilities and the population of residential areas in the attribute table of their corresponding centroids.

[0064] S1-3. Using the Geopandas model library in Python, the spatial location and attribute tables of the above road network, public service facilities, and residential areas are transformed into a DataFrame data structure that can be computed.

[0065] S1-4. Perform topological processing on the urban road network, abstracting each road intersection, ramp, etc., into a set of points N. s ={n1,n2,…,n k The road segments connecting intersections and ramps are abstracted as edge set E. s ={l1,l2,…,l m}, edge lm Euclidean distance d m As its weights, it forms the urban basic spatial network diagram G(N) s E s );

[0066] S1-5. Perform topological processing on the points corresponding to the residential communities, using the population of the residential community as its weight, to form a set N of residential nodes. r ={r1,r2,…,r i Find the nearest road intersection to each residential node and connect them to form a set E of connecting edges E linking the residential nodes to the urban basic spatial network. r ={l r1 ,l r2 ,…,l ri}, edge l ri Euclidean distance d ri As its weight, it abstractly represents the travel distance of residents from their residential community to the city road, forming a residential-transportation integrated urban spatial network diagram G(N). s ∪N r E s ∪E r );

[0067] S1-6. Perform topological processing on the points corresponding to public service facilities, and use the service level of the facility as the weight of the point to form a set N of public service nodes. f ={f1,f2,…,f j Find the road intersections closest to each public service node and connect them to form a set E of connecting edges connecting facilities to the urban basic spatial network. f ={l f1 ,l f2 ,…,l fj}. Move edge l fj Euclidean distance d fj As its weight, it abstractly represents the distance from public service facilities to urban roads, forming a typical urban spatial composite network diagram G(N) of residence-service-transportation. s ∪N r ∪N f E s ∪E r ∪E f ).

[0068] S2. Using the urban spatial composite network of residential-service-transportation described in step S1 as the initial scenario, through experimental simulation, remove the impassable road sections under different intensities of disaster disturbance, and construct the urban spatial composite network of residential-service-transportation damaged under different disaster intensities. The specific steps are as follows:

[0069] S2-1. Through urban disaster experiment simulation, identify the road sections and urban land affected by disasters under different disaster intensities;

[0070] S2-2. Overlay the identification results with the urban spatial composite network diagram of residential-service-transportation under normal conditions to determine the nodes and edges that have failed in the urban spatial composite network of residential-service-transportation under disaster scenarios; use the Remove function in Python to process the urban spatial composite network of residential-service-transportation under normal conditions, and determine the nodes and edges that have failed in the set E of the composite network edges. s ∪E r ∪E f In the process, remove the edges mapped to road segments that failed due to disasters, and then add them to the set N of the composite network points. s ∪N r ∪N f In this process, road network nodes and public service nodes that are no longer effective for travel are removed, resulting in a composite urban spatial network of residence, services, and transportation damaged under different disaster intensities.

[0071] S3. Based on the flow cost and supply-demand scale between residences and service points, allocate the service level of public service facilities to each residence node, calculate the per capita access to public services within each residence node, and form an urban spatial network performance model based on residents' access to public services. The specific steps are as follows:

[0072] S3-1. Based on the Dijkstra shortest path algorithm provided in the NetworX model package, calculate the shortest path length between residential-service point pairs in different scenarios based on the edge weights in the urban spatial composite network of residential-service-transportation. If there is no path between the two, it is denoted as ∞.

[0073] S3-2. Based on the theoretical service scope of public services, form a directed travel cost matrix A between residence-service point pairs under different scenarios. rf Directed travel cost matrix A rf The calculation method is as follows:

[0074]

[0075] Among them, A rf (i,j) is the directed travel cost matrix A between residence-service point pairs. rf The value in the i-th row and j-th column, In the urban spatial complex network of residence, service, and transportation, d0 is the shortest path length from residential node i to public service facility j, and d0 is the theoretical maximum service range of the public service.

[0076] S3-3, Based on the directed travel cost matrix A between residence-service point pairs rf The scale of residential service points determines the allocation of public service facilities' service levels to each residential node. The service allocation ratio of public service node j to resident node i is calculated using the following formula:

[0077]

[0078] In the formula, P ij M is the service allocation ratio from public service node j to resident node i. j D represents the service level of public service node j. i Let i represent the demand scale of residential community i, i.e., the number of permanent residents, n be the number of residential nodes, α be the distance attenuation coefficient, and A be the distance attenuation coefficient. rf (i,j) is the directed travel cost matrix A between residence-service point pairs. rf The value in the i-th row and j-th column;

[0079] S3-4. Calculate the per capita access to public services for residents within each residential node in the urban spatial composite network of residence, services, and transportation, using the following formula:

[0080]

[0081] In the formula, A i Q represents the per capita access to public services for residents at resident node i. i P represents the level of public services that resident node i obtains from all public service nodes. ij M is the service allocation ratio from public service node j to resident node i. j D represents the service level of public service node j. i Let i represent the demand scale of residential community i, i.e., the number of permanent residents, and k be the number of public service nodes.

[0082] S4. Based on the urban spatial network performance model for residents' access to public services described in step S3, calculate the rate of change of per capita access to public services within each statistical unit before and after the disaster, characterizing the performance change of urban public services. The specific steps are as follows:

[0083] S4-1. Summarize the per capita access to public services (Q) within each statistical unit. pre The per capita access to public services Q after a disaster post Q pre Q post Calculate using the following formula:

[0084]

[0085]

[0086] Where i is the residential node, N r A represents the set of residential nodes in the urban spatial complex network of residence, service, and transportation within the statistical unit. i A' represents the per capita access to public services at residential node i under normal circumstances. i D represents the per capita access to public services at residential node i after a disaster. i This represents the demand scale of residential community i, i.e., the number of permanent residents.

[0087] S4-2. Calculate the rate of change P of per capita access to public services in cities under different disaster intensities; P is calculated using the following formula:

[0088]

[0089] In the formula, Q pre This indicates the normal level of public services available to each person. This represents the per capita level of public services available after a disaster of intensity 'a'.

[0090] S5. Plot the relationship curve between the rate of change in per capita available public services and disaster intensity to measure the disaster resilience of urban public services. Specific steps are as follows:

[0091] S5-1. Using the Matplotlib module, plot the curve showing the relationship between the rate of change P of the per capita available public services in the city and the intensity of the disaster. The horizontal axis represents the intensity of the disaster, and the vertical axis represents the degree of change in the performance of public services.

[0092] S5-2. Call the connected_components function in the NetworkX model library to solve the network of each connected subgraph of the urban spatial composite network of residential-service-transportation under different disaster intensities. Sort the subgraphs in descending order of the number of nodes and extract the size of the second largest connected subgraph.

[0093] S5-3. Using the Matplotlib module, identify the maximum value of the second largest connected subgraph of the urban spatial composite network of residential-service-transportation during the change of disaster intensity. This value is considered as the critical state in which the network structure reaches fragmentation and is used as the threshold point for the disaster-bearing capacity of the network structure.

[0094] S5-4. Calculate the integral value of the rate of change of public service level P against disaster intensity before the threshold point of collapse of the urban spatial composite network structure of residence-service-transportation. This value characterizes the disaster resilience of urban public services and is calculated using the following formula:

[0095]

[0096] Among them, Q pre Q represents the average level of public services available to each person under normal circumstances. post This indicates the per capita access to public services after a disaster, a max This represents the threshold point at which the complex urban spatial network structure of residence, services, and transportation collapses.

[0097] Figure 2 The figure shows the relationship between the per capita comprehensive medical service level change rate P and disaster intensity under a rainstorm and flooding scenario in the central urban area of ​​Shanghai and its 10 districts. The comprehensive medical service level is represented by the number of hospital beds, and the disaster intensity is represented by the recurrence interval of the rainstorm and flooding. Calculating the area enclosed by the curves and the horizontal and vertical axes allows for the measurement of the disaster resilience level of urban comprehensive medical services in different regions.

[0098] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, and for those of ordinary skill in the art, various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. Therefore, the present invention is not limited to the specific details without departing from the general concept defined by the claims and their equivalents.

Claims

1. A method for characterizing and measuring the disaster resilience of urban public services, characterized in that, Includes the following steps: S1. Collect raw spatial vector data of urban roads and areal data of public service facilities and residential communities; based on urban roads, map urban space into a power-oriented and oriented urban basic spatial network; on this basis, map public service facilities and residential communities into functional nodes in the network, and construct a normal urban spatial composite network of residence, service and transportation. S2. Using the urban spatial composite network of residential-service-transportation described in step S1 as the initial scenario, through experimental simulation, remove the inaccessible road sections under different intensity of disaster disturbances, and construct the urban spatial composite network of residential-service-transportation after damage under different disaster intensities. S3. Based on the flow cost and supply and demand scale between residences and service points, allocate the service level of public service facilities to each residence node, calculate the per capita access to public services for residents within each residence node, and form an urban spatial network performance model based on residents' access to public services; the steps include: S3-1. Based on the edge weights in the urban spatial composite network of residence-service-transportation, and according to the theoretical service scope of public services, calculate the directed travel cost matrix A between residence-service point pairs. rf Directed travel cost matrix A rf The calculation method is as follows: Among them, A rf (i,j) is the directed travel cost matrix A between residence-service point pairs. rf The value in the i-th row and j-th column, In the urban spatial complex network of residence, service, and transportation, d0 is the shortest path length from residential node i to public service facility j, and d0 is the theoretical maximum service range of the public service. S3-2, Based on the directed travel cost matrix A between residence-service point pairs rf The scale of residential service points determines the allocation of public service facilities' service levels to each residential node; the service allocation ratio of public service node j to resident node i is calculated using the following formula: Among them, P ij M is the service allocation ratio from public service node j to resident node i. j D represents the service level of public service node j. i Let i represent the demand scale of residential community i, i.e., the number of permanent residents, n be the number of residential nodes, α be the distance attenuation coefficient, and A be the distance attenuation coefficient. rf (i,j) is the directed travel cost matrix A between residence-service point pairs. rf The value in the i-th row and j-th column; S3-3. In the urban spatial composite network of residence, services, and transportation, the per capita access to public services for residents within each residential node is calculated using the following formula: Among them, A i Q represents the per capita access to public services for residents at resident node i. i P represents the level of public services that resident node i obtains from all public service nodes. ij M is the service allocation ratio from public service node j to resident node i. j D represents the service level of public service node j. i Let i represent the demand scale of residential community i, i.e., the number of permanent residents, and k be the number of public service nodes; S4. Based on the urban spatial network performance model for residents' access to public services described in step S3, calculate the rate of change of per capita access to public services in each statistical unit before and after the disaster, and characterize the performance change of urban public services. S5. Plot the relationship curve between the rate of change in per capita available public services and disaster intensity to measure the disaster resilience of urban public services; steps include: S5-1. Plot the curve showing the relationship between the rate of change P of per capita access to public services in the city and the intensity of disaster. The horizontal axis represents the intensity of disaster, and the vertical axis represents the degree of change in the performance of public services. S5-2. Solve the network of each connected subgraph of the urban spatial composite network of residential-service-transportation under different disaster intensities, sort them in descending order of the number of nodes, and extract the size of the second largest connected subgraph. S5-3. Identify the maximum value of the second largest connected subgraph of the urban spatial composite network of residential-service-transportation during the change of disaster intensity. This value is considered as the critical state in which the network structure reaches fragmentation and serves as the threshold point for the disaster-bearing capacity of the network structure. S5-4. Calculate the integral value of the rate of change of public service level P against disaster intensity before the threshold point of collapse of the urban spatial composite network structure of residence-service-transportation. This value characterizes the disaster resilience of urban public services and is calculated using the following formula: Among them, Q pre Q represents the average level of public services available to each person under normal circumstances. post This indicates the per capita access to public services after a disaster, a max This represents the threshold point at which the complex urban spatial network structure of residence, services, and transportation collapses.

2. The method for characterizing and measuring the disaster resilience of urban public services according to claim 1, characterized in that, The method for constructing a composite urban spatial network of residence, services, and transportation in step S1 includes the following steps: S1-1. Perform topological processing on the original spatial vector data of urban roads, abstracting each road intersection and ramp entrance into a point set N. s ={n1,n2,…,n k The road segments connecting intersections and ramps are abstracted as edge set E. s ={l1,l2,…,l m }, edge l m Euclidean distance d m As its weights, it forms the urban basic spatial network diagram G(N) s E s ); S1-2. Extract the centroids of the residential area areal data, and use population data as the weight for each centroid to form a set N of residential nodes. r ={r1,r2,…,r i Find the nearest road intersection to each residential node and connect them to form a set of connecting edges E that connect the residential nodes to the urban basic spatial network. r ={l r1 ,l r2 ,…,l ri }, edge l ri Euclidean distance d ri As its weight, it abstractly represents the travel distance of residents from their residential community to the city road, forming a residential-transportation integrated urban spatial network diagram G(N). s ∪N r E s ∪E r ); S1-3. Extract the geographical locations of each public service facility, and use the service level of the facility as the weight of each point to form a set N of public service nodes. f ={f1,f2,…,f j Find the road intersections closest to each public service node and connect them to form a set of connecting edges E that connect the facilities to the urban basic spatial network. f ={l f1 ,l f2 ,…,l fj }; Place edge l fj Euclidean distance d fj As its weight, it abstractly represents the distance from public service facilities to urban roads, forming a typical urban spatial composite network diagram G(N) of residence-service-transportation. s ∪N r ∪N f E s ∪E r ∪E f ).

3. The method for characterizing and measuring the disaster resilience of urban public services according to claim 1, characterized in that, Step S2 involves constructing a composite urban spatial network of residential, service, and transportation areas after damage under different disaster intensities. This includes: S2-1. Through urban disaster experiment simulation, identify the road sections and urban land affected by disasters under different disaster intensities; S2-2. Overlay the identification results with the urban spatial composite network diagram of residence-service-transportation under normal conditions, and then overlay the results onto the set E of the composite network edges. s ∪E r ∪E f In the process, remove the edges mapped to road segments that failed due to disasters, and then add them to the set N of the composite network points. s ∪N r ∪N f In this process, road network nodes and public service nodes that are no longer effective for travel are removed, resulting in a composite urban spatial network of residence, services, and transportation damaged under different disaster intensities.

4. The method for characterizing and measuring the disaster resilience of urban public services according to claim 1, characterized in that, Step S4 calculates the rate of change of per capita access to public services within each statistical unit before and after the disaster. The steps characterizing the performance changes of urban public services include: S4-1. Summarize the per capita access to public services (Q) within each statistical unit. pre The per capita access to public services Q after a disaster post Q pre Q post Calculate using the following formula: Where i is the residential node, N r A represents the set of residential nodes in the urban spatial complex network of residence, service, and transportation within the statistical unit. i A' represents the per capita access to public services at residential node i under normal circumstances. i D represents the per capita access to public services at residential node i after a disaster. i This represents the demand scale of residential community i, i.e., the number of permanent residents. S4-2. Calculate the rate of change P of per capita access to public services in cities under different disaster intensities; P is calculated using the following formula: Among them, Q pre This indicates the normal level of public services available to each person. This represents the per capita level of public services available after a disaster of intensity 'a'.

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