Evaluation of Urban Infrastructure Resilience Based on Risk–Resilience Coupling: A Case Study of Zhengzhou City

Abstract

The frequent occurrence of disasters has brought significant challenges to increasingly complex urban systems. Resilient city planning and construction has emerged as a new paradigm for dealing with the growing risks. Infrastructure systems like transportation, lifelines, flood control, and drainage are essential to the operation of a city during disasters. It is necessary to measure how risks affect these systems’ resilience at different spatial scales. This paper develops an infrastructure risk and resilience evaluation index system in city and urban areas based on resilience characteristics. Then, a comprehensive infrastructure resilience evaluation is established based on the risk–resilience coupling mechanism. The overall characteristics of comprehensive infrastructure resilience are then identified. The resilience transmission level and the causes of resilience effects are analyzed based on the principle of resilience scale. Additionally, infrastructure resilience enhancement strategies under different risk scenarios are proposed. In the empirical study of Zhengzhou City, comprehensive infrastructure resilience shows significant clustering in the city area. It is high in the central city and low in the periphery. Specifically, it is relatively high in the southern and northwestern parts of the airport economy zone (AEZ) and low in the center. The leading driving factors in urban areas are risk factors like flood and drought, hazardous materials, infectious diseases, and epidemics, while resilience factors include transportation networks, sponge city construction, municipal pipe networks, and fire protection. This study proposes a “risk-resilience” coupling framework to evaluate and analyze multi-hazard risks and the multi-system resilience of urban infrastructure across multi-level spatial scales. It provides an empirical resilience evaluation framework and enhancement strategies, complementing existing individual dimensional risk or resilience studies. The findings could offer visualized spatial results to support the decision-making in Zhengzhou’s resilient city planning outline and infrastructure special planning and provide references for resilience assessment and planning in similar cities.

1. Introduction

Cities are constantly facing disruption due to several natural and man-made calamities during their growth and operation, including natural disasters like heat waves, torrential rains, earthquakes, and mudslides; public health issues like epidemics; and unintentional disasters like fires and transportation accidents. A variety of disasters happen frequently as a result of the city’s ongoing social and economic development and concentration, as well as the increasing conflict between human activity and the environment. Internal and external disturbances are becoming more frequent, intense, and unpredictable in cities [1]. The population, scale, functional agglomeration, and inherent structural complexity of cities all increase during the urban development process, which makes them more convenient and prosperous while also increasing the impact of disasters on cities [2]. The idea of resilient cities has drawn tremendous attention from governments and organizations in order to address the growing risks and disruptions both inside and outside of cities. Resilient city planning and construction has emerged as a new paradigm for dealing with environmental issues and disasters. The term “resilience” originally meant “to return to the original state”. It has since been used in a variety of fields. In order to handle the uncertainty of disruptions, the resilient city forgoes the single concept of resistance under the conventional concept of disaster prevention. Instead, it stresses the comprehensive enhancement of the city’s ability to resist, absorb, recover, adapt, and transform based on a reasonable establishment of defense standards [3,4]. The conceptual framework [5,6] and evaluation methods [7,8] for urban resilience have been extensively studied worldwide. Following the COVID-19 pandemic, increased focus has been placed on both overall urban resilience and subsystem resilience, particularly in addressing public health challenges [9,10] and multi-hazard scenarios [11], to improve urban resilience and sustainability.

At the G20 Summit in November 2020, China presented a series of sustainable development projects aimed at creating a resilient, sustainable, and inclusive future [12]. Enhancing territorial spatial resilience was one of the guiding requirements in the 2020 Guidelines for the Preparation of Provincial Territorial Spatial Planning. The development of resilient cities was suggested as a visionary target for China’s 14th Five-Year Plan for the Development of the National Economy and Society in 2021, making resilient cities a national strategy. Since then, Chinese scholars have increasingly focused on urban resilience [13,14,15]. The efficient functioning of urban infrastructure systems is essential to the cities. The normal operation of the urban system’s activities will be directly impacted by the speed of recovery and the integrity of the infrastructure functions when the city experiences unknown shocks. Most studies on infrastructure resilience have focused on individual subsystems, such as transportation [16], or examined it within the context of urban resilience [13]. Recently, research studies have also investigated the cascading effects of infrastructure systems under disaster conditions [17]. The “7.20” disaster of torrential rainstorms that struck Zhengzhou City in 2021 caused significant economic losses and fatalities, seriously devastated the city’s infrastructure system, and presented significant challenges for the creation of resilient municipal infrastructure. Resilience planning is a critical approach for more resistant, adaptive, and sustainable cities in the face of multiple internal and external risks. This process must be grounded in a thorough evaluation of existing risk resistance and resilience capacities.

However, in terms of evaluation scales, the current resilience evaluations mostly focus on urban agglomerations, provincial regions, or entire cities. Examples include the resilience evaluation of the Yangtze River Delta city cluster [13], the vulnerability assessment of climate change hotspots in the Eastern Mediterranean and Middle East region [18], and the Shanghai resilient city planning index system, which primarily targets the city as a scale-free network [12]. Because there are few resilience evaluations based on urban districts, counties, streets, and even communities as the basic unit, it is challenging to provide spatial analysis support for urban resilience planning when it comes to the spatial characterization of urban resilience under the influence of urban disasters and risks. In order to support urban infrastructure resilience planning, thorough research is required for indicator selection, level assessment, and coupling analysis.

In constructing infrastructure resilience evaluation systems, most frameworks are designed based on resilient city characteristics, infrastructure system components, and urban disaster phases, with indicators tailored to the study area’s scale and specific features. For example, studies have assessed infrastructure resilience in Northeast China using economic, social, ecological, and infrastructural dimensions [19,20], while others have focused on energy, transportation, drainage, telecommunications, and medical centers within urban agglomerations [21] and provincial areas [22]. When integrated into urban resilience evaluation systems, infrastructure typically includes water, energy, and transportation sectors [23,24]. Additionally, some scholars have incorporated the network characteristics of infrastructure systems into their analyses [25]. Based on the aforementioned, the pertinent research on infrastructure resilience has been broadly categorized into the following focus areas in recent years: research on flood and rain resilience; research on municipal infrastructure resilience, including the risk of secondary disasters like cascading failures of municipal infrastructure brought on by earthquakes, hurricanes, and other disasters; and research on transportation resilience, which is also more closely related to spatial planning.

Regarding infrastructure resilience evaluation methods, traditional municipal engineering approaches can be categorized into the basic calculation method, the expansion of the basic model, and the interrelated infrastructure resilience calculation method [26,27]. For specific infrastructure resilience evaluations, scholars have employed complex network methods to measure transportation network resilience [16,28]. In resilience planning, normalized resilience assessments [29] mostly use the comprehensive index method, weighted summation, TOPSIS [30], and system dynamics [31]. During the evaluation stage of resilient city planning, it is challenging to conduct systematic spatial research to normalize dimensionless infrastructure resilience with other systems, such as urban public service facilities or land use resilience, and to suggest countermeasures from the perspective of spatial planning.

In urban disaster resilience research, some scholars focus on single hazards, while others adopt multi-scale and multidisciplinary approaches to study multi-hazard composite resilience. Examples of single-hazard studies include assessments of geologic hazard resilience in Beijing [32], flood resilience in Xi’an [33], and flood resilience of critical infrastructure in Torbay [34]. However, research on coupled multi-hazard risk and resilience assessment remains limited. For instance, Beijing’s multi-hazard urban risk assessment is based on the superposition of hazard factors and the vulnerability matrix of disaster-affected entities [2], similar to Austin’s study combining multiple natural hazard exposure with social vulnerability [35], where the focus is primarily on vulnerability rather than resilience. Although Hefei City’s resilience evaluation of municipal infrastructure employed cross-scenario analysis of risk and system vulnerability, the methodology was largely qualitative rather than quantitative [29].

In summary, existing infrastructure resilience research predominantly focuses on single-hazard assessments, limited scales, and specific perspectives, lacking multi-hazard, multi-scale, and multi-system coupled evaluations. Additionally, there is insufficient analysis at spatial scales such as districts, counties, and streets. In contrast to previous studies, this paper focuses on the coupled relationship between multi-hazard risks and the internal multi-system resilience of infrastructure at different spatial scales. This approach reflects the complex risk environment and overall resilience level of urban infrastructure in a more comprehensive way, providing a scientific basis and theoretical foundation for enhancing its resilience and sustainability.

Therefore, as part of the resilient city assessment of Zhengzhou, this study incorporates multi-hazard risks and conducts a coupled assessment of multiple hazards and resilience to establish an infrastructure risk and resilience evaluation index system at two distinct scales and conducts a comprehensive infrastructure resilience evaluation using the “risk-resilience” coupling framework. In order to support future urban resilience planning outlines, infrastructure resilience special plans, and action plans, strategies to increase the resilience of infrastructure systems under various risk disturbance scenarios are proposed based on the empirical analysis of Zhengzhou City. Due to their ease of planning transmission, urban districts, counties, and streets are considered the fundamental units of evaluation in terms of scale. The comprehensive indicator evaluation approach, which makes it conducive to spatial systematic research with other urban systems, is used as the evaluation method. In terms of its structure, the evaluation system is separated into three infrastructure resilience research target areas: transportation, flood control and drainage, and lifelines. This approach provides a comprehensive understanding of the complex risk environment and overall resilience level of infrastructure.

2. Materials and Methods

In order to achieve the comprehensive resilience evaluation results of infrastructure, this study first establishes the infrastructure risk and resilience evaluation index system. Next, it builds the “risk-resilience” coupling grading matrix (Figure 1). To more accurately reflect the state of the infrastructure and external pressure, different indicators must be used based on the differences between the external environment and the internal system of infrastructure at different scales. At the same time, a differentiated evaluation system for the city area and the urban area must be built, taking into account the spatial distribution of the data and the challenges of obtaining it. At the spatial scale, districts and counties are adopted as the minimum analysis units for the metropolitan area, and streets are the minimum units for urban areas. This approach facilitates the implementation of subsequent planning while, to some extent, reducing data acquisition challenges.

2.1. Selection of Indicators

The criteria of operability, typicality, and comprehensiveness are used in the selection of indicators for the infrastructure resilience assessment system. Resilience theory, relevant research, and national standards serve as the foundation for the initial screening and summary processes. Following that, the indicators are regularly improved by field research, expert validation meetings, and conversations with government agencies and planning technicians.

In order to choose the indicators for the risk evaluation system, the disaster factor database, the “China’s Comprehensive Natural Hazard Risk Classification System” classification standards, and other literature are first considered. Then, taking into account the disaster’s frequency, intensity, scope, and correlation with urban and rural planning, among other factors, a variety of disaster-causing factors are compiled. Second, the disaster-causing elements with a danger rating of 4 or 5 are used as the evaluation indices of infrastructure risk utilizing the expert-assigned questionnaire approach. Lastly, the risk evaluation system for the transportation, flood control and drainage, and lifelines subsystems is built based on the degree of association between the indicators and the subsystems.

2.2. Calculation of Indicator Weights

The weights of the resilience evaluation indicator system are established by combining the Analytic Hierarchy Process (AHP) with the Entropy Weight Method (EWM). Although the weights of the indicators are consistent with their actual importance due to the subjective judgment and experience of the decision maker, the subjective weighting approach is susceptible to the subjective randomness of the decision maker. Although the objective assignment approach is based on original data and makes full use of objective actual information, it is susceptible to interference from sample data and may have indicator weights that are out of proportion to the indicators’ true significance. The combined assignment method integrates subjective and objective weights through mathematical optimization, balancing expert experience with objective data. This approach addresses the limitations of single-method weighting systems, offering enhanced robustness and reliability in comprehensive evaluations [36,37]. However, the effect of the combined assignment method depends on the rationality of the weight combination method and the suitability of the application scene. This paper adopts the principle of minimum information entropy after Lagrange multiplier optimization to balance subjective and objective information by constraining optimization and minimizing information loss. Applied to infrastructure resilience evaluation, where it effectively integrates expert experience with objective data to establish a scientifically sound basis for weight allocation. The formula is shown in Equation (1).

(1)$\omega \left(j\right)={\displaystyle \frac{{\left[{\omega }_1\left(j\right){\omega }_2\left(j\right)\right]}^{0.5}}{{\sum }_{j=1}^n{\left[{\omega }_1\left(j\right){\omega }_2\left(j\right)\right]}^{0.5}}}$

where $n$ is the number of indicators, and the subjective weight ${\omega }_1\left(j\right)$ from AHP and objective weight ${\omega }_2\left(j\right)$ from EWM are combined to optimize combined weight $\omega \left(j\right)$.

In the risk evaluation index system, the weights for risk indicators in the transportation, flood control and drainage, and lifeline subsystems are assigned based on their hazard levels, given the intuitive nature of disaster-causing factors. Specifically, each indicator’s weight is proportional to its hazard level, maintaining the same ratio as the hazard level ratio. The average of the weights of each risk indicator in the three systems is used to calculate the total weight of the infrastructure.

2.3. Evaluation Methodology

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a widely used multi-attribute decision analysis method known for its intuitive concept of positive and negative ideal solution distances, flexible weight assignment, and efficient computational process, with applications across various fields [13,30]. TOPSIS is utilized to evaluate and rank the resilience states in this study. The method requires using the matrix $D$ composed of $n$ indicators of $m$ solutions, constructing the positive ideal solution $z^{+}$ and negative ideal solution $z^{-}$, which are the optimal and the worst solutions. Then, the Euclidean distances $D_i^{+}$ and $D_i^{-}$ between each solution and the positive and negative ideal solutions are calculated in order to obtain the relative affinity close to the positive ideal point and far from the negative ideal point $C_i$, which is used as the basis for evaluating the advantages and disadvantages. The main calculation formula is as follows:

(2)$D_i^{+}=\sqrt{{\sum }_{j=1}^n\omega \left(j\right){\left(z_j^{+}-z_{ij}\right)}^2}$

(3)$D_i^{-}=\sqrt{{\sum }_{j=1}^n\omega \left(j\right){\left(z_j^{-}-z_{ij}\right)}^2}$

(4)$C_i={\displaystyle \frac{D_i^{-}}{D_i^{+}+D_i^{-}}}$

2.4. Risk–Resilience Coupling Matrix

To visualize the coupling between infrastructure risk and resilience, this study develops a risk–resilience coupling matrix, adapting the widely used risk matrix framework from risk management. The “risk-resilience” coupling grading matrix in Figure 2 is built for the comprehensive resilience evaluation based on the four-grade categorization evaluation results of infrastructure risk and resilience. The “resilience” score ranges from 1 to 4 points; the higher the score, the better the resilience. The “risk” score ranges from 1 to 4 points; the higher the score, the lower the risk. Consistent with the risk matrix consistency axioms [38], results are classified into levels I to IV, with higher levels indicating greater comprehensive resilience.

2.5. Spatial Autocorrelation

Spatial autocorrelation analysis is a spatial statistical method for examining the distribution characteristics of geospatial data. In this study, spatial correlation analysis is conducted using ArcGIS 10.8. Global spatial autocorrelation was assessed through the global Moran’s I index and the General G index, with significance tests indicating the overall spatial correlation. For local spatial autocorrelation, we employed Getis-Ord G* and Anselin Local Moran’s I. The former identifies statistically significant hotspot areas (higher-than-expected values) and coldspot areas (lower-than-expected values), while the latter evaluates the spatial correlation between a region and its neighboring areas [39].

2.6. Geo Detector

Geo Detector is a statistical method for detecting spatial heterogeneity and determining the driving factors [40,41]. This study utilizes Geo Detector to analyze the driving causes of geographic variability of infrastructure resilience in the Zhengzhou city region, with a focus on factor detection and interaction detection outcomes. Factor detection can determine each factor’s explanatory power for spatial differentiation of resilience. Interaction detection can determine the interaction between different factors. The main calculation formula is as follows:

(5)$q=1-{\displaystyle \frac{{\sum }_{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}}$

where $q$ is the explanatory power of the driving factor—taking the value range [0, 1], a larger value indicates that the factor has higher explanatory power for resilience; $L$ is the number of classifications; $N_h$ and $N$ are the number of sample units of the stratum $h$ and the whole region; and ${\sigma }_h^2$ and ${\sigma }^2$ are the variance of the stratum $h$ and the whole region [41].

2.7. Data Sources

The objective data come from various special planning and statistical documents, Zhengzhou City Statistical Yearbook (2011–2017), and the open platform of Amap. The great majority of the indicator data are gathered through data processing and calculation. Objective data were collected primarily from official sources for the same or similar years to maximize accuracy. Subjective data come from the expert questionnaire of the resilience indicator system and the hazard evaluation of risk indicators. The structured questionnaire of resilience indicator system employed a nine-level pairwise comparison scale. It was completed by ten experts, including six senior planning technicians and four university professors in the urban planning field. The questionnaire of risk–hazard indicators used a rank evaluation method, inviting eight experts familiar with the context of Zhengzhou to provide five-level rankings for each risk indicator, comprising six senior planning technicians and two university professors. In both of the stages, expert responses from valid questionnaires were aggregated using weighted averaging to enhance data reliability and minimize sampling variability.

3. Results

3.1. Evaluation Indicator System

The first is the initial establishment phase of the indicators. In the establishment of the risk indicator system, 52 disaster factors are derived from consulting the disaster factor database, the classification criteria of “Construction of China’s Comprehensive Classification System for Natural Disaster Risk” and other literature, as well as by carefully taking into account the disaster’s frequency of occurrence, intensity, scope of influence, and relevance to urban and rural planning (Figure 3). In the establishment of the resilience indicator system, 64 resilience factors were derived from the resilience theory and related research, national standards and norms like the guide for safety resilient city evaluation [42], and the five resilience characteristics—robustness, rapidity, redundancy, resourcefulness, and adaptability—that were separated into the three main systems of transportation, flood control and drainage, and lifeline for the initial indicator selection (Figure 4).

In order to make the evaluation index system more in line with the actual situation of Zhengzhou City and the requirements of drafting the resilience planning outline, it has undergone an optimization process that has involved adding, removing, and replacing indicators through discussions with planning technicians, conversations with government departments, expert validation meetings, and field research. The resilience evaluation system of Zhengzhou was then established, comprising 23 indicators across the city and urban areas (Table 1 and Table 2). Based on the AHP method, 10 questionnaires were distributed, with eight valid responses obtained on both a city and urban scale. The weighted average judgment matrix successfully passed the consistency test (Table 3 and Table 4). Concurrently, based on the risk–hazard questionnaire (eight distributed, seven valid responses), two risk evaluation systems were finalized: one for the city area with 15 indicators and another for the urban area with 14 indicators, all selected based on their high risk levels (levels 4 and 5). The weighting of both risk and resilience indicator systems for the city and urban areas was determined using the methodology outlined in Section 2.2 (Table 1, Table 2, Table 5 and Table 6).

3.2. Evaluation Results for Zhengzhou City Area

Based on the TOPSIS and weighting method, the resilience and risk evaluation of the infrastructure in the Zhengzhou city area is carried out, respectively. The “risk-resilience” coupling matrix is used to conduct a comprehensive evaluation of the infrastructure resilience in the Zhengzhou city area (Figure 5).

In the Zhengzhou city area, the overall infrastructure risk shows a spatial pattern of high in the east and west and low in the center, with Gongyi City, Xingyang City, Dengfeng City, Xinmi City, Xinzheng City, Jinshui District, and Zhengdong New District having a higher risk and AEZ (Airport Economy Zone), NHTDZ (National High and New Technology Industries Development Zone), Shangjie District, and ETDZ (Economic and Technological Development Zone) having a lower risk. The overall infrastructure resilience shows a pattern of high urban areas and low surrounding areas, with Shangjie District, Zhongyuan District, and Jinshui District being better; NHTDZ, Erqi District, Guancheng District, ETDZ, and Zhengdong New District being average; and Huiji District, Zhongmou County, Gongyi City, Dengfeng City, Xinmi City, Gongyi City, Xingyang City, Xinzheng City, and AEZ being worse. The overall comprehensive infrastructure resilience shows a spatial pattern of high resilience in the central urban area and low resilience in the periphery, with Xingyang City, Dengfeng City, and Gongyi City being poor, and Zhongyuan District, Shangjie District, NHTDZ, and ETDZ being good.

An interesting observation is that local professionals frequently expect Zhengdong New District to demonstrate higher resilience. However, the combined resilience results are generally moderate due to significant risks in the area, such as high population density, proximity to the Yellow River, and the presence of 500 KV high-voltage stations. In contrast, although the AEZ is less developed, and its lower risk sources and population density make the comprehensive resilience better than expected.

The risk evaluation results in this study show certain differences with the previous dual-evaluation results of Zhengzhou, which are based on a resource and environmental carrying capacity evaluation and a suitability evaluation for territorial spatial development. The dual evaluation emphasized risk control of natural disasters and conservation of natural resources, whereas the assessment in this paper expands the scope to include accidental disasters and public health risks, resulting in the higher-risk zones in the central city where hazardous facilities are concentrated.

The integrated resilience of the three systems—transportation, flood control and drainage, and lifeline—was computed in order to better investigate the spatial distribution of resilience within the resilience system for each infrastructure specialization (Figure 6). In the city area, the comprehensive resilience of the transportation system as a whole presents a spatial pattern of higher urban area and lower periphery area and higher south and lower north; the comprehensive resilience of the flood control and drainage system as a whole presents a spatial pattern of high urban, low periphery, high central, and low east and west; and the comprehensive resilience of the lifeline system as a whole presents a spatial pattern of higher urban and lower periphery.

3.3. Evaluation Results for Zhengzhou Urban Area

3.3.1. Spatial Patterns of Resilience

The resilience and risk evaluation of infrastructure in the Zhengzhou urban area is also carried out. The urban area infrastructure risk, resilience, and risk–resilience coupling evaluation results are shown in Figure 7. Further, the comprehensive resilience evaluation results of the three systems of urban transportation, flood control and drainage, and lifeline are shown in Figure 8.

In the Zhengzhou urban area, the overall infrastructure risk presents a spatial pattern that is high in the urban area and low in the aerial port area. The transport systems in Zhengdong New District, the north and east of ETDZ, the south of Guancheng District, the middle of Erqi District, the west of NHTDZ, the south of Zhongyuan District, the north of Huiji District, and the middle of Jinshui District have higher risks; the transport systems in the AEZ and the center of the downtown area have lower risks. The overall infrastructure resilience presents a spatial pattern that is high in the central part of the city, low in the periphery, relatively high in the south and northwest of the aerial port area, and low in the center of the city. The western part of Zhengdong New District, the southern part of Jinshui District, the eastern part of Huiji District, the eastern part of NHTDZ, the central part of Zhongyuan District, the northern part of Guancheng District, ETDZ, and the northern part of AEZ are better; the northeastern part of Zhengdong New District, the central part of Erqi District, the western part of Zhongyuan District, the western part of Huiji District, and the southern and central parts of AEZ are worse.

Infrastructure risk—the overall resilience coupling result shows that the central part of the city is high, the periphery is low, the southern and northwestern parts of the AEZ are relatively high, and the central part is low. The south and north of the AEZ, the northeastern part and the northwestern corner of the Zhongyuan District, the Erqi District, the northern part of the Guancheng District, the southern part of the Jinshui District, the northwestern part of Zhengdong New District, and the northwestern part of the ETDZ are more resilient. The central part of Erqi District, the southern part of Guancheng District, the northern part of Huiji District, the central part of Jinshui District, the periphery of the main city, and the central part of the AEZ are less resilient.

In the urban area, the comprehensive resilience of the transportation system and lifeline system as a whole presents the spatial pattern of the central city as being low and the AEZ as being high; the flood control and drainage system comprehensive resilience as a whole presents the spatial pattern of the central city as being high and the periphery as being low.

3.3.2. Spatial Correlation Analysis

To further evaluate the clustering characteristics of the comprehensive resilience evaluation results in the Zhengzhou city district, we use ArcGIS 10.8 to perform spatial correlation analysis, including global and local spatial autocorrelations.

The global spatial autocorrelation shows a positive Moran’s I index of 0.2655, which passes the 1% significant test (p-Value < 0.01, Z-Score > 2.58). The Getis-Ord General G provides a z-score of 4.17, indicating that Zhengzhou urban area comprehensive resilience has a significant positive correlation agglomeration in the global space and a high/high clustering pattern.

The local spatial autocorrelation of Getis-Ord G* provides a distribution map of hot and cold spot clusters (Figure 9a). The hotspot clusters are located in the central city core and the northern portion of the AEZ, while the coldspot clusters are located in the central city’s north and south. The findings show that there is a considerable spatial disparity and that the comprehensive resilience of the infrastructure in the central city’s core area is strong, while that of the periphery is poor.

Additionally, we acquired Anselin Local Moran I’s high/low agglomeration distribution map (Figure 9b). The “High-High” and “Low-High” agglomerations are staggered in the core area of the city. The “Low-Low” agglomerations are distributed in a small number of areas in the middle of Huiji District and the western part of Erqi and Guancheng Districts. The “High-Low” agglomerations are distributed in the middle of Huiji District and the western part of Erqi District. The “High-Low” agglomerations are scattered throughout the eastern section of Huiji District. The findings indicate that while the integrated resilience of infrastructure in the central city’s core area is high, there are notable spatial differences with the surrounding areas, whereas there are no significant spatial differences with the integrated resilience of infrastructure in the aerodrome area or the central city’s periphery.

3.3.3. Driving Factor Analysis

Using factor detection with Geo Detector, the infrastructure risk, resilience, and risk–resilience coupling results are analyzed in the Zhengzhou urban area (Table 7). For infrastructure resilience, overall emergency road density (A7), main network density (A2), road network density (A3), 800 m radius coverage of rail transit stations (A5), and hub accessibility (A6) affect transportation system resilience. Water supply network, power grid, medium-pressure gas network density (C1, C3, C4, C8), and fire coverage (C11) influence lifeline resilience. Flood control and drainage factors have little impact on the spatial divergence of infrastructure resilience. For infrastructure risk, heavy rainstorms (D8), traffic accidents (D9), electric utility accidents (D10), diseased reservoirs (D3), hazardous chemical accidents (D12), fires (D13), and infectious disease outbreaks (D14) have a large impact on the spatial divergence of infrastructure risk. Single factors have less significance and explanatory power for comprehensive infrastructure resilience, and the spatial divergence of integrated resilience is more affected by 5 min coverage of fire and rescue (C11) in resilience, diseased reservoirs (D3), and hazardous chemical accidents (D12) in risk.

Interaction detection of Geo Detector is used to undertake an interaction analysis of the driving variables of urban infrastructure comprehensive resilience (Figure 10). The findings show that the factors exhibit varying degrees of two-factor enhancement and nonlinear enhancement, and the explanatory power of the factors after the interaction is significantly increased, implying that the spatial heterogeneity of the comprehensive resilience of urban area infrastructures is the result of multiple factors acting together. Among these, the mean value of the explanatory power of multiple components after interaction with other factors is greater than 0.3, indicating a significant driving factor for the geographic variation of infrastructure resilience in the Zhengzhou urban area. These risk indicators include urban flood-prone water points (D1), dangerous reservoirs (D3), hazardous chemical accidents (D12), and infectious disease outbreaks (D14), while resilience indicators include transportation networks (A1, A2, A3, A4, and A5), the proportion of the areas of sponge city up to the standard drainage zoning (B1), the density of pipeline networks of each municipal facility (C1, C4, C7, and C8), and 5 min coverage of the fire rescue services (C11). These findings share similarities with previous research on resilience factors, such as electricity consumption and gas supply [13], while also incorporating additional considerations, including risk factors, sponge city infrastructure, and fire safety facilities.

3.4. Individual Case Study

In order to examine the degree of resilience transmission and the reasons behind resilience effects in the city and urban area units based on the resilience scaling principle, the following case study examines Huiji District, which is situated in the center of the urban area.

Huiji District, a crucial development sector in the Central Plains Economic Zone’s core growth pole, is situated on the south bank of the Yellow River and north of Zhengzhou city center. Huiji District has low risk (Score 3), weak resilience (Score 2), and low comprehensive resilience (Level II), and its resilience and comprehensive resilience are ranked low in the central urban area. The analysis is as follows:

Heavy rains (Score 1) and traffic accidents (Score 1) are the primary risk factors in Huiji District (Figure 11). Other risk factors include floodplains, hazardous materials, fires, and infectious disease outbreaks (Score 2). The main metropolitan region is in great danger of severe rainfall due to Zhengzhou’s excessive localized precipitation and the “urban rain island” phenomenon. Huiji is more vulnerable to flooding than the districts and counties in the south because of the Yellow River to the north and a lengthy embankment line. In comparison to the county area, Huiji District has a higher risk of hazardous materials, fires, and infectious disease outbreaks due to its oil depots, flammable and explosive production and storage units, urban villages, warehouses, and other fire accidents with poor fire prevention awareness. Additionally, this district also has vegetable markets, health stations, and other areas infectious disease may spread.

Huiji District has high flood control and drainage resilience (Score 3) but poor lifeline and transportation resilience (Scores 1 and 2). Nearly fifty percent of Scores 1 and 2 are single-indicator grades. In the transportation system, notable weaknesses include the high 10,000 traffic accident death rate (Score 1), insufficient highway exit coverage within a 5 km radius (Score 2), and a limited number of external corridors (Score 2). The percentage of river channels in the drainage and flood control system that satisfy flood control requirements is low (Score 2). Within the lifeline system, significant shortcomings include low per capita water resources (Score 1), high dependence on external water sources (Score 1), sparse high-voltage power grid and heating pipeline networks (Score 2), poor service capacity of water plants (Score 2), and inadequate coverage of municipal fire hydrant within a 150 m radius (Score 2).

As a result, Huiji District needs to strengthen the construction of flood control and drainage systems; improve the early warning capacity for storms, floods, fires, and other disasters in the area of risk prevention and mitigation; and improve the management of infectious agents and hazardous materials in the area. Transportation and lifelines must be prioritized in order to increase resilience. Strengthening the development of safe transportation and continuing to encourage the building of road networks are essential in the transportation sector. River flood control construction needs to be strengthened in the drainage and flood control areas. Promoting water resources, enhancing water safety, and advancing the development of municipal pipeline networks and firefighting facilities are all essential in the lifelines field.

3.5. Infrastructure Resilience Enhancement Strategies

Based on the findings of the complete resilience evaluation, a comprehensive enhancement strategy for infrastructure transportation, flood control and drainage, and lifeline systems is suggested (Figure 12). Following that, the risk indicators and weights from the infrastructure risk evaluation index system derived from the survey in this study are used to identify flooding, geologic disasters, fire accidents, infectious diseases, and epidemics as the primary stress risk scenarios. The potential state of transportation, flood control and drainage, and lifeline systems under each disaster risk is stated separately, with matching resilience enhancement methods provided (Figure 13, Figure 14, Figure 15 and Figure 16).

4. Discussion

This study develops an evaluation index system for risk and resilience of infrastructure in Zhengzhou’s city and urban areas, uses a combination of AHP and EWM to determine index weights, and evaluates the comprehensive resilience of infrastructure using the TOPSIS method and the coupling matrix of risk–resilience. This study’s findings can help to depict the geographical characteristics of Zhengzhou’s resilience planning outline and special infrastructure planning. It is beneficial for planning to evaluate at the district, county, and street levels in order to provide ideas and experiences for evaluating safety resilience and resilience planning in similar cities in China, as well as to promote the construction of resilient cities in Zhengzhou City and elsewhere.

Based on Conzen’s urban form theory, which emphasizes planning units, this paper employs districts and streets as assessment units to develop unitized resilience enhancement and transmission. The difference in resilience findings between city and urban areas is a spatial manifestation of the resilience scaling principle, which states that infrastructure systems must be resilient at different scales. The influencing factors and their interactions reflect the importance of transportation, flood control and drainage, and lifeline subsystem synergies for resilience enhancement, as well as the principle of dependent coexistence of resilience systems. Finally, resource allocation is adjusted to improve resilience using the resilience augmentation approach.

The limitations of this study are as follows: While the risk–resilience coupling matrix offers an intuitive analytical framework, its limitations may introduce interpretive biases due to the complex interdependence between risk and resilience. The simplified structure of the matrix may also obscure the nuanced dynamics of their relationship. A standardized and scientifically robust urban resilience assessment system is still absent. The current approach relies on static composite indices, which fail to capture the dynamic nature of resilience indicators. Although the AHP-EWM combination weighting method balances subjective expertise with objective data, its accuracy depends on the precision of these methods and may introduce additional uncertainties. Furthermore, the effect of the combined assignment method depends on its rationality and the suitability of the application scenario. Data collection and processing are also constrained, as the timeliness and accuracy of indicator data cannot be fully guaranteed. Data derived from planning documents may only partially reflect real conditions and big data platforms may contain inherent biases. Furthermore, the reliance on relative standards for evaluation is due to limited spatial indicators in national standards and weak inter-city applicability. Additionally, the risk and resilience indicators tailored to Zhengzhou’s specific characteristics may not be directly applicable to cities with differing natural environments, economic structures, or risk profiles.

In future research, dynamic system modeling and network analysis will be adopted to explore the complex interactions and intrinsic linkages between risk and resilience, extending the coupling matrix framework. Dynamic evaluation methods that reflect the temporal changes of resilience indicators, as well as differentiated measures for special resilience assessments based on the characteristics of various infrastructure systems, should be introduced, and more efficient data processing and analysis methods should be used. Diverse weighing algorithms will be adopted to improve the scientific and practical relevance of optimization techniques, with additional analysis on the stability of weighting results. Data using the natural resources census should be estimated to be collected, which will broaden data collection methods and procedures. Data from numerous sources should be collected to cross-validate and increase data quality. The establishment of local resilience standards will be promoted based on differences in urban space and metropolitan areas. Further support for the transmission and implementation of resilient city planning will be proposed. Considering the application of this framework on different types of cities, classified resilience evaluation frameworks need to be developed using cross-case study and validation methods, providing more adaptable indicators and approaches to improve its generalizability.

5. Conclusions

The risk and resilience evaluation index system of Zhengzhou’s city and urban areas is constructed in this paper using the risk–resilience coupling comprehensive evaluation framework and resilience-related basic theories. The indexes are continuously optimized through discussions with planning technicians, governmental departmental symposiums, expert demonstration meetings, and field research. The weights of the indexes are determined using the combination of the AHP and the EWM. The TOPSIS method and the risk–resilience coupling matrix are used to conduct comprehensive resilience evaluation of infrastructure, and driving factor analysis is carried out using Geo Detector.

The multi-scale resilience of infrastructure exhibits spatial imbalances. In the Zhengzhou city area, the comprehensive infrastructure resilience is shown to have a spatial pattern of high resilience in the city center and low resilience in the city’s periphery overall, with higher resilience in Zhongyuan, Shangjie, NHTDZ, and ETDZs and lower resilience in Xingyang, Dengfeng, and Gongyi.

In the Zhengzhou urban area, the overall comprehensive infrastructure resilience result shows that the central part of the city is high, the periphery is low, the southern and northwestern parts of the AEZ are relatively high, and the central part is low. The south and north of the AEZ, the northeastern part and the northwestern corner of the Zhongyuan District, the Erqi District, the northern part of the Guancheng District, the southern part of the Jinshui District, the northwestern part of Zhengdong New District, and the northwestern part of the ETDZ are more resilient. The central part of Erqi District, the southern part of Guancheng District, the northern part of Huiji District, the central part of Jinshui District, the periphery of the main city, and the central part of the AEZ are less resilient.

The results of the spatial autocorrelation analysis show that the comprehensive infrastructure resilience in urban areas is highly concentrated in spatial distribution. According to the Geo Detector results, risk factors like floods and droughts, dangerous chemicals, and infectious disease outbreaks, as well as resilience factors like transportation networks, sponge city development, pipeline networks of various municipal facilities, and fire protection, are the primary factors of the formation of spatial differentiation of resilience. Therefore, enhancing urban infrastructure resilience requires spatial planning tools, including optimized land use, transportation networks, and municipal facility layouts. Concurrently, risks should be reduced through improved risk management and sponge city initiatives, fostering resource efficiency and sustainable urban development.

Huiji District is examined in depth as an example based on the results of the comprehensive evaluation in order to determine the reasons behind the districts’ resilience evaluation results in terms of the evaluation indexes, and the corresponding solutions for enhancing resilience and the following suggestions are proposed:

• (1). Enhancing infrastructure development in peripheral areas to mitigate regional disparities. The resilience evaluation results of the city and urban areas as a whole show the characteristics of a high center and low periphery, and it is necessary to strengthen the construction of infrastructure in areas with weak resilience, especially in terms of the transportation network, sponge city construction, and municipal facility pipeline network, and to promote the overall coordination of the city and county areas;

• (2). Refining disaster risk management and emergency response systems while implementing more stringent environmental conservation measures. Priority should be given to managing critical risk factors, including hydrological extremes, hazardous materials, and infectious disease outbreaks. This requires establishing comprehensive monitoring networks, early warning systems, and contingency plans. Long-term environmental strategies should be emphasized to minimize risk probability and impact, thereby supporting sustainable urban growth;

• (3). Facilitating information exchange and cross-sectoral coordination. Using digital technologies such as big data analytics and artificial intelligence can enhance infrastructure resilience monitoring and information sharing, breaking down existing data silos. Strengthening interdepartmental collaboration among transportation, telecommunications, public health, and cooperative enterprises is crucial for improving systemic resilience.

The results reveal the spatial distribution characteristics of infrastructure resilience in Zhengzhou, proposing multi-hazard resilience enhancement strategies. The findings provide a spatial framework and targeted areas for subsequent resilience planning, including risk mitigation zones and resilience improvement districts, while establishing differentiated planning guidelines for local implementation. Furthermore, the research highlights the influence of environmental risks and blue–green infrastructure on system resilience, offering empirical support for stricter environmental regulations and sustainable urban development policies.

Author Contributions

Conceptualization, W.D.; Methodology, W.D. and J.W.; Software, Y.Z. and Z.C.; Validation, Y.Z.; Formal analysis, W.D.; Investigation, W.D., Y.Z., D.G., Z.C. and J.W.; Resources, D.G.; Data curation, Y.Z. and Z.C.; Writing – original draft, W.D. and Y.Z.; Writing – review & editing, W.D. and Y.Z.; Visualization, Y.Z. and Z.C.; Supervision, W.D., D.G. and J.W.; Project administration, W.D., D.G. and J.W.; Funding acquisition, W.D. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

Author Dongliang Guo was employed by the company Zhengzhou Urban Planning Design and Survey Research Institute Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Research design.

Figure 2. (a) Result matrix of risk–resilience coupling. (b) Corresponding color legends on the map [2].

Figure 3. Initial indicator system for infrastructure risk evaluation in Zhengzhou City.

Figure 4. Initial indicator system for infrastructure resilience evaluation in Zhengzhou City.

Figure 5. Evaluation results of infrastructure resilience, risk, and comprehensive resilience in the Zhengzhou city area.

Figure 6. Evaluation results of resilience, risk, and comprehensive resilience of infrastructure subsystems in the Zhengzhou city area (transportation, flood control and drainage, and lifelines).

Figure 7. Evaluation results of infrastructure resilience, risk, and comprehensive resilience in the Zhengzhou urban area.

Figure 8. Evaluation results of resilience, risk, and comprehensive resilience of infrastructure subsystems in the Zhengzhou urban area (transportation, flood control and drainage, and lifelines).

Figure 9. Spatial autocorrelation analysis of comprehensive infrastructure resilience in the Zhengzhou urban area ((a) Getis-Ord G*, (b) Anselin Local Moran’s I).

Figure 10. Heat map of the interaction of factors affecting comprehensive infrastructure resilience in the Zhengzhou urban area.

Figure 11. Examples of risks faced in Huiji District.

Figure 12. Resilience enhancement strategies for each system.

Figure 13. Resilience enhancement strategies in flooding risk scenarios.

Figure 14. Resilience enhancement strategies in geologic disaster risk scenarios.

Figure 15. Resilience enhancement strategies in fire accident risk scenarios.

Figure 16. Resilience enhancement strategies in infectious diseases and epidemic risk scenarios.

Table 1

Evaluation index system of infrastructure resilience in the Zhengzhou city area.

Target Layer—Infrastructure Resilience		Unit	Resilience Features	Weight
Criterion Layer—Transportation Resilience
Sub-Criterion Layer	Index Layer			AHP Weight	EWM Weight	Combined Weight
Transportation network	5 km radius coverage of highway entrances and exits	%	Robustness	0.0638	0.0385	0.0546
	Number of external access points	Pcs	Robustness	0.0780	0.0179	0.0412
	Transportation connectivity	%	Redundancy	0.0403	0.0180	0.0297
Transportation hub	Hub accessibility	1/h	Redundancy	0.0727	0.0164	0.0381
Traffic safety	Traffic accident fatality rate per 10,000	%	Robustness	0.0562	0.0157	0.0327
Criterion layer—flood control and drainage resilience
Flood control and drainage system construction	Drainage network capacity	km/mm	Robustness	0.0339	0.0587	0.0492
	Proportion of stagnant space area	%	Robustness	0.0280	0.0589	0.0448
	Proportion of floodway area	%	Robustness	0.0221	0.0435	0.0342
	Proportion of rivers with flood protection standards	%	Robustness	0.0383	0.0256	0.0345
Criterion layer—lifeline resilience
Water supply	Water resources per capita	L	Robustness	0.0461	0.1034	0.0761
	Percentage of outgoing water sources	%	Redundancy	0.0247	0.0420	0.0355
	Density of water supply network	km/km2	Robustness	0.0491	0.0234	0.0374
	Waterwork service capacity	L/person	Redundancy	0.0727	0.0939	0.0911
Electricity supply	Substation service capacity	Pcs/person	Redundancy	0.0578	0.0386	0.0520
	Density of high-voltage grid	km/km2	Robustness	0.0770	0.0306	0.0535
	Density of medium-voltage grid	km/km2	Robustness	0.0578	0.0234	0.0405
Heat supply	Density of heating network	km/km2	Redundancy	0.0125	0.0718	0.0265
Gas supply	Residential gas penetration rate	%	Robustness	0.0102	0.0564	0.0256
	Gas utility service capacity	Pcs/person	Robustness	0.0123	0.0439	0.0170
	Density of high-pressure and sub-high-pressure gas pipeline network	km/km2	Robustness	0.0102	0.0234	0.0566
	Density of medium-pressure gas network	km/km2	Robustness	0.0397	0.0664	0.0331
Firefighting	Coverage of 150 m radius of municipal fire hydrants	%	Robustness	0.0265	0.0557	0.0423
	Coverage of fire and rescue 5 min	%	Robustness	0.0701	0.0341	0.0539

Table 2

Evaluation index system of infrastructure resilience in the Zhengzhou urban area.

Target Layer—Infrastructure Resilience		Unit	Resilience Features	Weight
Criterion Layer—Transportation Resilience
Sub-Criterion Layer	Index Layer			AHP Weight	EWM Weight	Combined Weight
Transportation network	Road area per capita (A1)	m2	Redundancy	0.0105	0.0578	0.0307
	Density of the backbone network (A2)	km/km2	Robustness	0.0596	0.0094	0.0295
	Road network density (A3)	km/km2	Robustness	0.0397	0.0096	0.0244
	Density of rail transit network (A4)	km/km2	Robustness	0.0182	0.0511	0.038
	800 m radius coverage of rail transit stations (A5)	%	Robustness	0.0105	0.0307	0.0224
Transportation hub	Hub accessibility (A6)	1/h	Redundancy	0.0835	0.0020	0.0161
Emergency transportation	Density of overall emergency road (A7)	km/km2	Resourcefulness	0.0807	0.0121	0.039
	Proportion of disaster-prone emergency roads (A8)	%	Resourcefulness	0.0322	0.0008	0.0063
Criterion layer—flood control and drainage resilience
Sponge city construction	Proportion of drainage zoning area in sponge cities meeting standards (B1)	%	Robustness	0.0392	0.0961	0.0765
Flood control and drainage system construction	Proportion of stagnant space area (B2)	%	Robustness	0.0549	0.0349	0.0546
	Proportion of floodway area (B3)	%	Robustness	0.0401	0.0418	0.0511
	Proportion of rivers with flood protection standards (B4)	%	Robustness	0.0568	0.0918	0.09
Criterion layer—lifeline resilience
Water supply	Density of water supply network (C1)	km/km2	Robustness	0.1330	0.0111	0.0479
Electricity supply	Substation service capacity (C2)	Pcs/person	Redundancy	0.0425	0.0662	0.0662
	Density of high-voltage grid (C3)	km/km2	Robustness	0.0567	0.0293	0.0508
	Density of medium-voltage grid (C4)	km/km2	Robustness	0.0425	0.0111	0.0271
Heat supply	Density of heating network (C5)	km/km2	Robustness	0.0374	0.0563	0.0573
Gas supply	Gas utility service capacity (C6)	Pcs/person	Redundancy	0.0100	0.1086	0.0412
	Density of high-pressure and sub-high-pressure gas pipeline network (C7)	km/km2	Robustness	0.0134	0.0862	0.0424
	Density of medium-pressure gas network (C8)	km/km2	Robustness	0.0100	0.0111	0.0132
Comprehensive pipeline corridor	Density of comprehensive corridors built (C9)	km/km2	Robustness	0.0361	0.1126	0.0795
Firefighting	Coverage of 150 m radius of municipal fire hydrants (C10)	%	Robustness	0.0317	0.0425	0.0457
	Coverage of fire and rescue 5 min (C11)	%	Robustness	0.0607	0.0267	0.0502

Table 3

Consistency ratios (CRs) of AHP for infrastructure resilience index system in the Zhengzhou city area (‘/’ means no need for consistency test).

Target Layer	CR	Criterion Layer	CR	Sub-Criterion Layer	CR
Infrastructure resilience	0.036	Transportation resilience	0.051	Transportation network	0.009
				Traffic safety	/
				Emergency transportation	/
		Flood control and drainage resilience	/	Flood control and drainage system construction	0.013
		Lifeline resilience	0.016	Water supply	0.014
				Electricity supply	0.011
				Heat supply	/
				Gas supply	0.021
				Firefighting	/

Table 4

Consistency ratios (CR) of AHP for infrastructure resilience index system in the Zhengzhou urban area (‘/’ means no need for consistency test).

Target Layer	CR	Criterion Layer	CR	Sub-Criterion Layer	CR
Infrastructure resilience	0.008	Transportation resilience	0.045	Transportation network	0.018
				Transportation hub	/
				Emergency transportation	/
		Flood control and drainage resilience	/	Sponge city construction	/
				Flood control and drainage system construction	0
		Lifeline resilience	0.023	Water supply	/
				Electricity supply	0.015
				Heat supply	/
				Gas supply	0.012
				Comprehensive pipeline corridor	/
				Firefighting	/

Table 5

Evaluation index system of infrastructure risk in the Zhengzhou city area.

Target Layer—Infrastructure Resilience		Unit	Risk Level	Weight
Criterion Layer—Transportation Resilience
Sub-Criterion Layer	Index Layer
Flood and drought	Urban flood-prone water points	Pcs	Level 4	0.1141
	Flooding	km2	Level 5	0.1426
	Dangerous reservoirs	Pcs	Level 5	0.1426
Geologic disaster	Landslides	Pcs	Level 4	0.0439
	Crumble	Pcs	Level 4	0.0439
	Subside	Pcs	Level 4	0.0439
Meteorological disaster	Blizzard	mm	Level 4	0.0439
	Rainstorm	mm	Level 5	0.1426
Criterion layer—accidental disaster
Mineral accident	Mineral accident	km2	Level 4	0.0439
Transportation accident	Transportation accident	Pcs	Level 4	0.0205
Facility accident	Electricity facility accident	Pcs	Level 5	0.0549
	Gas facility accident	Pcs	Level 5	0.0549
Hazardous chemical incident	Hazardous chemical incident	Pcs	Level 4	0.0439
Fire accident	Fire accident	m	Level 4	0.0439
Criterion layer—public health
Infectious diseases and epidemic	Infectious diseases and epidemic	Pcs	Level 4	0.0205

Table 6

Evaluation index system of infrastructure risk in the Zhengzhou urban area.

Target Layer—Infrastructure Resilience		Unit	Risk Level	Weight
Criterion Layer—Transportation Resilience
Sub-Criterion Layer	Index Layer
Flood and drought	Urban flood-prone water points (D1)	Pcs	Level 4	0.1172
	Flooding (D2)	km2	Level 5	0.1465
	Dangerous reservoirs (D3)	Pcs	Level 5	0.1465
Geologic disaster	Landslides (D4)	Pcs	Level 4	0.047
	Crumble (D5)	Pcs	Level 4	0.047
	Subside (D6)	Pcs	Level 4	0.047
Meteorological disaster	Blizzard (D7)	mm	Level 4	0.047
	Rainstorm (D8)	mm	Level 5	0.1465
Criterion layer—accidental disaster
Transportation accident	Transportation accident (D9)	Pcs	Level 4	0.0219
Facility accident	Electricity facility accident (D10)	Pcs	Level 5	0.0588
	Gas facility accident (D11)	Pcs	Level 5	0.0588
Hazardous chemical incident	Hazardous chemical incident (D12)	Pcs	Level 4	0.047
Fire accident	Fire accident (D13)	m	Level 4	0.047
Criterion layer—public health
Infectious diseases and epidemic	Infectious diseases and epidemic (D14)	Pcs	Level 4	0.0219

Table 7

Detection of spatial heterogeneity of infrastructure resilience in the Zhengzhou urban area (* means q-value < 0.05).

Resilience Index	Resilience q-Value	Comprehensive Resilience q-Value	Risk Index	Risk q-Value	Comprehensive Resilience q-Value
A1	0.029	0.156	D1	0.14	0.135
A2	0.382 *	0.197	D2	0.107	0.168
A3	0.33 *	0.201	D3	0.313 *	0.204 *
A4	0.278	0.176	D4	0.08	0.075 *
A5	0.247 *	0.141	D5	0.105	0.119
A6	0.215 *	0.109	D6	0.08	0.075 *
A7	0.448 *	0.114	D7	0.063	0.01
A8	0.036	0.071	D8	0.512 *	0.023
B1	0.133	0.194	D9	0.428 *	0.068
B2	0.138	0.064	D10	0.293 *	0.105
B3	0.178	0.115	D11	0.106	0.045
B4	0.192	0.145	D12	0.424 *	0.352 *
C1	0.352 *	0.255	D13	0.192 *	0.03
C2	0.02	0.079	D14	0.313 *	0.091
C3	0.242 *	0.075
C4	0.352 *	0.255
C5	0.078	0.069
C6	0.008	0.034
C7	0.084	0.199
C8	0.352 *	0.255
C9	0.255	0.138
C10	0.337 *	0.241
C11	0.249 *	0.208 *

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