Sustainability 15 09698

Article
Research on the Evaluation and Spatial–Temporal Evolution of

Safe and Resilient Cities Based on Catastrophe Theory—A
Case Study of Ten Regions in Western China
Yong Xiang 1, Yonghua Chen 1, Yangyang Su 1,*, Zeyou Chen 1 and Junna Meng 2
1 School of Civil Engineering, Architecture, Environment, Xihua University, Chengdu 610039, China;
0120010008@mail.xhu.edu.cn (Y.X.); chenyonghua@stu.xhu.edu.cn (Y.C.);
0119940033@mail.xhu.edu.cn (Z.C.)
2 College of Management and Economics, Tianjin University, Tianjin 300072, China; mengjunna@tju.edu.cn
* Correspondence: suyangyang@stu.xhu.edu.cn
Abstract: In today’s highly complex world, urban security has become a focus of attention for people
in various positions due to its enormous uncertainty. As an essential path towards urban safety,
resilient development can effectively provide emergency management capability for cities when
they are exposed to unknown risks. In this study, an evaluation-index system for urban-safety re-
silience was constructed from the perspective of sustainable urban development. The urban-safety-
resilience evaluation model was established with the help of catastrophe theory to study and ana-
lyze urban-safety resilience. The corresponding spatial–temporal-evolution analysis used the geo-
graphic information system (GIS) and Moran index to evaluate the urban-security resilience of 10
regions in western China. Finally, it was concluded that (1) the urban-safety resilience of most re-
gions in western China showed an increasing trend over time in 2017, 2019, and 2021; (2) the urban-
safety resilience of Chongqing, Sichuan, and Shaanxi provinces is at a relatively high level compared
to the western region overall; and (3) regions such as Ningxia and Gansu are disaster-prone, and
urban infrastructure conditions are relatively backward. Therefore, urban planning and governance
Citation: Xiang, Y.; Chen, Y.; Su, Y.; should be flexibly transformed to explore and apply appropriate urban-safety-resilience models,
Chen, Z.; Meng, J. Research on the with sustainable development as the cornerstone.
Evaluation and Spatial–Temporal
Evolution of Safe and Resilient Cities
Keywords: urban-safety resilience; spatial–temporal-evolution analysis; western China; catastro-
Based on Catastrophe Theory—A
phe theory
Case Study of Ten Regions in
Western China. Sustainability 2023,
15, 9698. https://doi.org/10.3390/
su15129698
1. Introduction
Academic Editors: Guijun Li and
As giant and complex systems comprising social, economic, and ecological factors
Daohan Huang
[1], cities’ uncertainty and unknown risks will increase with the acceleration of the urban-
Received: 7 May 2023 ization process. The vulnerability of urban systems to unknown risk factors, such as cli-
Revised: 15 June 2023 mate change, energy crises, natural disasters, international situations, food security, and
Accepted: 15 June 2023
financial emergencies, is particularly pronounced, and this is one of the critical issues con-
Published: 16 June 2023
straining sustainable urban development [2]. In recent years, the term “urban resilience”
has emerged with particular frequency in academic and policy contexts [3]. As a new
pathway for urban development, “urban resilience” has multiple capabilities in urban
Copyright: © 2023 by the authors. Li- systems and their associated socio-technical and socio-ecological networks within dy-
censee MDPI, Basel, Switzerland. namic boundaries. These include the capacity to preserve or swiftly reinstate essential
This article is an open access article
operations in response to disruptions, the capacity to adapt to variation, and the capacity
distributed under the terms and con-
to enhance constrained adaptive and resilient systems [4].
ditions of the Creative Commons At-
To achieve the transition from high-speed to high-quality urban development, the
tribution (CC BY) license (https://cre-
Chinese government has focused on consolidating the concept of safe development, en-
ativecommons.org/licenses/by/4.0/).
couraging people-oriented urbanization, and building resilient cities in the 14th Five-Year
Sustainability 2023, 15, 9698. https://doi.org/10.3390/su15129698 www.mdpi.com/journal/sustainability

Sustainability 2023, 15, 9698 2 of 50
Plan and the 20th National Congress. From the perspective of enhancing urban resilience
to ensure urban safety, resilience and safety are inextricably linked; therefore, urban-
safety resilience has received close attention as a new topic [5]. Based on the urban resili-
ence theory, urban-safety resilience focuses on a broader, integrated, and flexible analysis
of urban-public-safety events and has been identified as a new paradigm for urban-safety
development [6], which helps urban systems to deal with the impact of continuous dy-
namic changes in internal and external risks in the face of the uncertainty of unknown
risks [7]. In addition, as a model for sustainable urban development, the strategic, for-
ward-looking, and integrated nature of urban-security resilience offers an additional prac-
tical quality to mega-urban systems. Therefore, urban-security-resilience assessment will
become a strategic direction and new model for urban security, and an effective solution
for unknown-risk management in sustainable urban development.
The current spatial layout of China’s overall urban resilience shows a highly remark-
able east–high and west–low divergence pattern that fits the Hu Huanyong line [8]. The
western part of China, which accounts for 70.6% of the country’s total territory, is over-
whelmingly in need of enhanced construction [9]. Moreover, the western region is in the
northwest continental disaster zone. The plateau zone and frequent natural disasters have
created sensitivity and fragility in the ecosystem of the region [10]. To investigate the cur-
rent state of urban-safety management and urban-resilience construction in western
China, this study analyzed the level and spatial and temporal evolution of urban-safety
resilience in 10 regions in western China by constructing an urban-safety-resilience eval-
uation-index system and quantitatively measured the impact of urban people, urban fa-
cilities, and urban management on urban-safety resilience. The contributions of this paper
are as follows. Firstly, this paper introduces the catastrophe-level method to analyze the
macro-level indicator system, which can not only be used to obtain more accurate evalu-
ations of urban-safety resilience, but can also provide assessment tools for urban safety
and resilience development in other regions. Secondly, this paper assesses the urban-
safety resilience of 10 regions in western China in three dimensions: personnel, facilities,
and management. The findings will help to better understand the variability in the urban-
safety-resilience components of the respective regions, as well as helping to provide rec-
ommendations for sustainable development in these regions. Finally, in addition to eval-
uating urban-safety resilience, this paper also uses Moran-index analysis to further ex-
plore the spatial and temporal evolution of urban-safety resilience and discusses compre-
hensive development strategies for cities in western regions from multiple perspectives,
to provide policy recommendations for integrated regional urban development.
The remaining sections of this paper are organized as follows. Section 2 presents a
literature review and analysis on urban resilience and urban security resilience. Section 3
explains the methodology used for the urban-security-resilience evaluation-index system
and the spatial–temporal evolution analysis. Section 4 presents the urban-security-resili-
ence evaluation and spatial–temporal evolution analysis for the 10 regions in western
China, and discusses the results. Section 5 presents the conclusions and recommendations.
2. Literature Review
2.1. Related Research on Urban Resilience
Urban safety is essential to maintain social stability and enhance sustainable devel-
opment in the growing urbanization process. Different scholars have defined the scope of
the research on urban safety in varying ways. Krimsky proposed that polycentric theory
can be applied to security control in most cities [11]. In contrast, Brugmann argued that
research on urban safety should start with resilience, which is the key to sustaining the
essential form of sustainable cities [12].
With the formal introduction of the urban-disaster-emergency-response system, ur-
ban resilience has gradually become a significant research component in new urban con-
structions. However, there are differences in its definition in academic circles at present.
Jean-Marie and other scholars believed that resilient cities mainly use their internal re-
sources and urban systems to resist different levels of disaster crises through technical
and human means and recover quickly after disasters [13]. Asadzadeh et al. argue that a
city’s security resilience depends on its ability to withstand and recover from the massive
damage and unstable chaos caused by disasters [14]. However, Patricia’s notion of urban
resilience emphasizes the systematic nature of urban resilience, arguing that it should be
biased more towards temporal variables, i.e., expressing the process quantity rather than
only the outcome quantity of urban resilience. Therefore, urban-resilience management
needs to be implemented from the pre-disaster, disaster, and post-disaster perspectives,
with a complete cycle of disaster events and response processes considered accordingly
[15].
The current body of urban-resilience research covers a wide range of topics, and there
are many ways to measure urban resilience. For instance, Lu Hao et al. used the BP neural
network with a genetic algorithm to build an urban-resilience-measurement model and
used the convergence model to analyze spatial–temporal evolution of the Chengdu–
Chongqing urban agglomeration [16]. Ma Fei et al. used the extreme-entropy method to
calculate the resilience level of urban agglomerations in China and explored the factors
influencing the spatial–temporal evolution using gray correlation analysis [17]. Ma Xuefei
et al. assessed the factors affecting urban spatial resilience in the Harbin–Changchun ur-
ban agglomeration and the impact of its spatial differentiation using the Geodetector
method and obtained the spatial evolutionary characteristics of urban resilience by apply-
ing a Jenks inter-natural fault classification [18]. Liu et al. dissected the spatial and tem-
poral evolution characteristics of the urban resilience in 18 cities in Henan province using
the entropy method, the Thiel index, and an exploratory spatial data analysis (ESDA), and
explored the influencing factors with a spatial econometric model [19]. Furthermore, some
scholars and experts have also investigated and studied the framework and application
of urban resilience. Many scholars’ investigations of urban-resilience frameworks can be
roughly divided into two categories: the first is the establishment of an urban-resilience
framework in the social, economic, engineering, policy, and organizational dimensions.
For example, Shi Yijun and other scholars determined the essential features of sophisti-
cated urban systems by exploring the principles of urban-system operations. They con-
structed a structure for complicated urban systems with three dimensions, system envi-
ronment, system elements, and system structure, and explored various aspects of the
methodology to evaluate the resilience of urban systems. This method can be used in con-
sultations on the development of urban safety and resilience enhancement [20]. Rina et al.
artificially measured the impact of various natural hazards on urban resilience at the
coastal scale. They measured the layouts of urban structures in five dimensions: engineer-
ing resilience, economic resilience, management resilience, environmental resilience, and
social resilience [21]. Mahsa et al. divided the urban-resilience framework into six do-
mains: societal, infrastructure, economy, environment, neighborhood capital, and institu-
tion. The performance of the community-tracking model in terms of various aspects of the
urban-resilience framework was used to provide an additional reference for city managers
on the planning and construction of resilient cities [22]. The second type of framework
aims to develop revelatory tools using nonlinear cross-domain knowledge and geograph-
ical characteristics by combining sensitivity and vulnerability to guide the development
of multi-scale resilience-assessment-indicator systems. Manyena clarified the correlation
between the components of resilience, integrated capacity, and the implementation pro-
cedure through a comprehensive analysis of the urban-resilience framework and devel-
oped an indicator-measurement tool for regional urban resilience [23]. Karen conducted
a statistical validation to determine the timeliness of an urban-resilience-measurement
tool for flooding [24].
The notion of urban resilience has progressively appeared in national macro-policy
and local urban-development-strategy reports, along with studies related to safety-resili-
ence frameworks. For example, the Rockefeller Foundation developed a series of urban-
safety-resilience-indicator frameworks, mainly focusing on urban systems and services,

the health and well-being of urban residents, government leadership and strategy, and
economic and social organization [15]. The United Nations developed the Sendai Frame-
work and the Hyogo Framework, which are composed of four areas: understanding dis-
aster risk, improving disaster-risk management, strengthening disaster-risk prepared-
ness, and building urban resilience [25]. The new European Resilience Management
Guidelines (ERMG) encourages the participation of all urban actors in urban governance
by developing urban safety strategies. This is intended to achieve a resilient management
capacity for cities and an integrated response capacity to strengthen urban-resilience man-
agement at all stages.
2.2. Related Research on Urban-Safety Resilience

At present, urban resilience is still a common international academic concept. Urban-
safety resilience is based on urban resilience, focusing on the new situation faced by urban
safety development. Research on urban-safety resilience is still limited to the theoretical
results related to urban resilience, and there is a lack of targeted urban-safety-resilience
research. In recent years, with the deepening of urban-safety-resilience research, led by
China, scholars and experts in related fields have gradually achieved results in exploring
the application strategies and evaluation schemes of urban-safety-resilience practices.
The current research on the evaluation methods for urban-safety resilience is quali-
tative. This qualitative research primarily focuses on establishing a theoretical evaluation
frame for urban-safety resilience and highlighting the core components of urban-safety
resilience.
In their evaluation study of urban-safety resilience, Huang Lang et al. built a specific
conceptual model of urban-system-safety resilience. They determined key considerations
in urban-system-safety resilience, such as the link between the elements of the municipal
system, covering characteristics and functional characteristics, etc. [26]. Based on the con-
text of urban-emergency and disaster prevention and safety development, scholars such
as Fang proposed four points of urban-safety resilience: social resilience, institutional re-
silience, technological resilience, and project resilience [27]. Chen An et al. established a
dynamic evolution mechanism of urban-safety resilience in the context of external risk-
based perturbations, providing a new method of thinking for subsequent research on ur-
ban-safety-resilience evaluation [28]. Fan Weicheng established a urban-safety-resilience-
system framework from the public-security perspective and argued for the strengthening
of urban-safety resilience through science and technology, culture and management, the
consolidation of the urban public-security-governance system, and the modernization of
urban governance [7]. Based on the perspective of public health security, Julia Wang ex-
plored the disaster-response ability of China’s urban-safety-resilience construction in
terms of management mechanisms, spatial planning, culture and education, and material
security [29]. Hu et al. found that China’s urban-safety resilience suffers from a lack of a
complete organizational structure and institutional system, insufficient innovation and
digital transformation, a lack of regional differences in spatial planning, and a lack of re-
dundancy in urban infrastructure. They proposed an architectural system for the whole-
cycle resilience management of large urban systems that includes the dimensions of sub-
ject, space, mode, and process [30]. Some scholars believed that the urban-security-resili-
ence framework should be further studied quantitatively using both quantitative and
qualitative indicators. That is, an evaluation model of the urban-security-resilience-index
system should be established. This research method is China’s most widely used tool for
urban-safety-resilience evaluation [31]. Guo Yuyu et al. believed that we should combine
the implication of urban-safety resilience and the characteristics of drawing power, resil-
ience, and adaptability and build an evaluation-index system for urban-safety resilience
in four dimensions: social resilience, economic resilience, environmental resilience, and
infrastructure resilience [32]. By analyzing the concept and connotations of urban resili-
ence, Chen Changchun et al. established an evaluation-index system for urban-safety
resilience using the three dimensions of resilience, recovery, and adaptation during flood
and rainstorm disasters [31]. In the framework of the evaluation-index system of urban-
safety resilience, Tian Jiefang et al. used the four dimensions of urban-infrastructure sub-
system, social subsystem, organizational subsystem, and economic subsystem as a basis
on which to assess the urban-safety resilience of all subjects and dimensions in the face of
various disasters [33]. In specific studies on urban-safety-resilience-index systems, schol-
ars mainly focused on natural, social, economic, institutional, and infrastructural aspects.
From the perspective of nature, Zhang Hongmei et al. selected 11 indicators in terms of
disasters, resources, and socio-economics and measured the level of protection of envi-
ronmental ecology in Fuzhou [34]. Pang Sha et al. provided and selected 16 indicators
from four dimensions, ecological sensitivity, adaptive capacity, natural ecology, and hu-
man disturbance, and adopted the comprehensive index-evaluation method to derive the
corresponding values of these indicators based on specific environmental problems. They
then established the environmental-resilience-indicator-evaluation system [35]. From the
perspective of society, scholars such as Ge Lingling used social structure, demographics,
and culture as dimensions to construct a social-resilience-evaluation system, focusing on
social resilience [36]. From an economic perspective, Zhao Guojie et al. selected indicators
from four perspectives: carrying capacity, resilience, sensitivity, and stability. These were
used to evaluate the economic resilience of the coastal zone in Hebei Province [37]. Su Fei
measured economic resilience by dividing the corresponding indicators into sensitivity,
exposure, and coping capacity [38]. From the perspective of urban systems, Na Wei et al.
selected indicators with which to establish an urban-system-resilience-index systemin
three dimensions: sensitivity, loss, and stability [39]. By exploring the connotations of ur-
ban-safety resilience and the sustainable development model, Li Bo et al. considered the
issue of urban-safety resilience. They selected 20 indicators to qualitatively and quantita-
tively analyze the three aspects of sensitivity, exposure, and resilience [40]. Liu Hui et al.
created an indicator-evaluation system of firefighting resilience based on the following
indicators: disaster resistance, disaster recovery, and disaster resilience [41].
Some scholars studied different types of city in terms of their urban-safety resilience
because the scope of urban-safety resilience differs for different city types. Inspired by the
experiences of such mega-cities as New York, Tao Xidong believed that domestic mega-
cities should organize innovations in urban-resilience construction, implement a financial
investment system to support infrastructure renewal and maintenance, optimize the de-
sign of the urban energy-supply chain and its spatial layout, and create a framework for
urban-community safety and resilience [42]. By setting up an indicator system for the rat-
ing of urban-safety resilience in the Pearl River Delta during tropical cyclone disasters,
Du Jinying suggested the development of urban-safety resilience in the Pearl River Delta
by targeting ecological conditions, development levels, and organizational safeguards
[43]. Focusing on a coastal city cluster with a high risk of rainfall and flooding, Tian Jian
et al. used multi-source data and the intelligent analysis of rain- and flooding-hazard-
identification technology to build a multi-faceted collaborative urban-safety-and-resili-
ence layout plan [44].
2.3. Methods, Scales, and Gaps in Current Urban-Safety Resilience

Urban resilience continues to be a major topic internationally. Furthermore, many
scholars have conducted a series of studies on the concept, definition, and evaluation of
urban resilience. Most of these scholars used methods such as conceptual definition and
connotation definition to argue that urban resilience is urban systems’ ability to prevent,
respond, and recover from disasters by working with residents, organizations, and gov-
ernments [13–15]. Meanwhile, some scholars use a variety of different methods to evalu-
ate urban resilience and urban-security resilience, including the entropy method
[16,18,32], improved entropy method [8], TOPSIS method [45,46], and extreme-entropy
method [17]. They assess the security resilience of each city from social, economic, engi-
neering, environmental, management, infrastructure, and institutional perspectives to
effectively analyze different cities and improve the construction of their urban-security
resilience through appropriate measures. However, the vast majority of previous research
focused on assessing urban resilience from a static perspective, with less research on the
spatial and temporal evolutionary characteristics of urban resilience. From the literature
on the spatial-temporal evolution of urban resilience [16–19], it can be concluded that the
assessment of urban resilience from a dynamic perspective can provide a more compre-
hensive analysis of the factors influencing the differences in different cities’ resilience lev-
els.
Some countries, led by China, have conducted a series of studies on urban-safety re-
silience in recent years. Most of these studies focused on the evaluation of urban-safety
resilience in mega-cities and developed economic zones, and most of the evaluated di-
mensions were reviewed in terms of economic, social, and institutional aspects. The com-
prehensiveness of the coverage needs to be improved, and more potential urban-safety
resilience dimensions need to be comprehensively evaluated.
By combining studies from the literature related to urban resilience and urban-secu-
rity resilience, the following problems were found to still exist in the current research on
urban-safety resilience in China.
First, most of the studies on urban-safety resilience in China were based on examples
from developed regions, such as the eastern areas. Few explored urban-safety resilience
in China’s western or northwestern areas.
Second, the application of urban-safety resilience is comprehensive and discipline-
spanning. It is often difficult for relevant studies to effectively support the decision assess-
ment, making it difficult for urban-planning decision makers to properly understand the
relevant urban-safety-resilience evaluation results.
Third, urban-safety resilience is highly susceptible to coercive factors, systemic struc-
tural and chronic stresses, and perturbations from various natural and social perspectives.
Previous studies did not offer progress on the issue of urban-safety resilience caused by
the impact of such multiple stresses.
Therefore, based on the perspective of sustainable development, this paper evaluates
and analyzes the urban-safety resilience and spatial–temporal evolution of 10 regions in
western China from a macro perspective. In terms of evaluation objects, most scholars
choose cities and regions with high economic levels for evaluation, and relatively little
attention is paid to western China. This study makes up for the current lack of a wide
range of urban-security-resilience studies to a certain extent and uses GIS, space weights,
and Moran’s index to analyze the spatial–temporal-evolution characteristics of the regions
and to explore the changes in the evolution patterns and the correlations between patterns
of urban-security resilience in the past.
3. Methodology
3.1. Urban-Safety Resilience
3.1.1. Concept and Connotation
Since the 14th Five-Year Plan, building of resilient cities based on the concept of
safety development in China has been a new aim. Urban-safety resilience adds the concept
of urban-safety development to urban resilience. Few scholars have studied urban-safety
resilience because it has been proposed for a relatively short time. The definition and con-
notations of the concept have not been unified. The definition and connotations of urban-
safety resilience are defined through two aspects: the first is based on the definition of
urban resilience, and the second is based on the connotations of urban safety.
The main studies using the first perspective are as follows. Rina et al. differentiated
urban resilience into engineering resilience, economic resilience, management resilience,
environmental resilience, and social resilience to improve the structural layouts of cities
[21]. Mahsa, on the other hand, made additional references to urban-resilience building in
terms of social resilience, infrastructure resilience, economic resilience, environmental re-

silience, community-capital resilience, and physical resilience [22].
The main studies conducted from the perspective of the second category are as fol-
lows. Against the background of urban-emergency and disaster prevention and safety de-
velopment, scholars such as Fang believed that the connotations of urban safety resilience
should include social personnel safety, engineering safety, and technical safety [27]. Fan
et al. believed that urban-security resilience should be strengthened from the perspective
of public security in terms of science and technology, culture and management, and
strengthening of the urban public-security-governance system [6]. Ge et al. believed that
the connotations of urban-security resilience should include social structure, social de-
mographics, and social culture [36].
Using the research reference Guide for safety resilient city evaluation [47], and systema-
tizing the literature review, this paper defines urban-security resilience as cities’ ability to
cope with and recover from disasters throughout the whole process, with the concept of
security development as the guiding principle and population, facilities, and management
as the basis of development. Its connotations should include the following elements.
(1) Resilience of urban-personnel safety
Urban-personnel-security resilience is the ability of the urban population to secure
basic livelihood security before and after a disaster. It is based on a people-oriented con-
cept, involving judgments on the primary livelihood conditions of the city. It aims to en-
sure that urban populations can cope with unknown risks before facing disasters, mitigate
the damage caused when disasters occur, and recover quickly after experiencing such dis-
asters.
(2) Resilience of urban-facility safety
Urban-facility-safety resilience refers to urban facilities’ ability to maintain basic op-
erations in response to disasters. In studies on urban-safety resilience, the urban-infra-
structure network is a prerequisite for a city’s ability to resume operations after a disaster.
Its loss of function can lead to significant social effects and economic losses [48]. The ur-
ban-infrastructure network mainly includes the water-distribution network, power-dis-
tribution network, transportation network, and communication-distribution network. In
addition, infrastructure that can prepare for construction works, urban-disaster-emer-
gency prediction, and emergency security also belongs to the urban-facility-safety-resili-
ence category. As noted by Fang et al, cities should aim to enhance their urban security
and resilience, urban-disaster-risk monitoring and early-warning systems, urban infra-
structure, lifeline-engineering security, emergency-communication-security-project con-
struction, etc., and strengthen their disaster-emergency response and rescue, as well as
their material and equipment security [27].
(3) Resilience of urban-management safety
Urban-management-security resilience refers to urban systems’ ability to prevent
and respond to disasters. Urban governance plays a pivotal role in urban-safety resilience
[49]. The resilience of urban-management safety requires the ability to provide basic se-
curity and emergency supplies in the event of a disaster and offer certain supplies and
medical support in the event of an unknown risk. In the security-management process,
the type, frequency, and extent of regional risks should be assessed and controlled to pre-
vent unpredictable damage and losses to cities from disasters.
3.1.2. Principles for Constructing the Indicator System

The reasonable degree of the index system of urban-security resilience directly affects
the objective accuracy of the evaluation results, and the construction of a reasonable index
system is a key step in the evaluation. The construction of the urban-security-resilience-
evaluation-index system follows the following principles:
(1) Objectivity
In addition to serving as a basis for evaluation, the evaluation-index system of urban-
security resilience can also be used as the basis for regional planning and urban-security-
resilience-information platforms in subsequent research. At the same time, the construc-
tion of an urban-security-resilience-evaluation-index system should be based on profound
analysis and scientific research on the scope and characteristics of urban-security resili-
ence to produce a practical and scientific index system. By analyzing the connotations of
urban-security resilience, this study establishes the mechanism of urban-security resili-
ence and uses it as a guarantee of the objectivity of the index system.
(2) Quantitative
When selecting the indicators which can be reflected by data directly or by data after
quantification, the objectivity of the indicators should be emphasized. This paper uses
spatial and temporal evolution analyses, and strictly considers the changes in the indica-
tors in each year. Since it is difficult to quantify qualitative indicators, in this research,
qualitative indicators were avoided as much as possible when constructing the indicators.
(3) Systemic
When selecting urban-safety-resilience-evaluation indicators, various factors influ-
encing urban-safety resilience should be considered comprehensively in terms of the spe-
cific purpose and the definition of relevant resilience levels at each stage. Moreover, when
selecting indicators, not only should the safety-resilience-related indicators of the city it-
self be considered, but also the ecological, economic, and development issues of the city.
At the same time, there should be a certain logical relationship among the indicators in
order to ensure that each indicator has a specific role and to avoid duplication.
(4) Authoritativeness
With the growing demand for urban-safety-resilience development, the need to en-
hance the capacity for rapid recovery from urban disasters and risk-response capacity has
become increasingly urgent. The evaluation indexes should be authoritative. If some data
are missing, reasonable mathematical methods should be used to make reasonable pre-
dictions.
3.1.3. Establishment of an Urban-Safety-Resilience-Index System

The evaluation system requires not only a definition of the connotations of indicators
as a theoretical basis, but also the construction of a practical set of indicators to achieve a
specific evaluation. The structure of the index system in this paper mainly combines the
definition of connotations and draws on the Guide for safety resilient city evaluation [47] and
some of the related research to assess urban-security resilience in terms of people, facili-
ties, and management in three areas: the security resilience of urban people mainly reflects
the preparedness of urban populations to cope with disasters. After the challenges of
COVID-19, the importance of population resilience is obvious [50], so in this paper, we
selected three Tier 2 indicators and nine Tier 3 indicators to reflect the carrying capacity
of the city and the ability to guarantee safety [16,17,51]; the security resilience of urban
facilities mainly reflects the ability of a city to maintain the normal operation of its facili-
ties. Most of the evaluation studies in this field have taken the facility dimension as the
assessment criterion [16,33], so in this paper, we selected five Tier 2 indicators and fifteen
Tier 3 indicators to reflect the city’s supply capacity, disaster-warning capacity, and facil-
ity-guarantee capacity [31,32]; urban-management-security resilience mainly reflects the
coordination ability of a city’s regional disaster scale and input guarantee, the ability of
the economic input to correctly respond to the risk of shock and to reduce the losses to a
certain extent [52], and the balance between urban disasters. Therefore, this paper selects
2 Tier 2 indicators and 6 Tier 3 indicators to reflect the economic strength and disaster
resistance needs of cities [16,32].
To ensure that the differences between urban and rural spaces were clearly described,
the selection of indicators in this paper took into consideration the fact that the urban
population has a high aggregation, as well as the observation that the rural population is
older than the urban population [53], which affects the degree of differentiation of human
security. Therefore, the indicators with the greatest impact on urban resilience were cho-
sen [54]. Urban areas are complex and giant systems compared with rural areas, and their
disaster-prevention facilities and management systems are more extensive and have a
more significant impact than those in rural areas. The indicators in this paper were chosen
to ensure the urban characteristics and achieve as much of an urban–rural distinction as
possible. The evaluation-index system in this paper is shown in Table 1. The correspond-
ing indicator descriptions are shown in Appendix A.
Table 1. Evaluation-index system of urban-safety resilience.
General Goal Tier 1 Indicators Tier 2 Indicators Tier 3 Indicators

Basic population The resident-population density in built-up areas (D1)
attributes Level of basic medical insurance for urban workers (D2)
(C1) Percentage of transient population (D3)
The resilience of Social participation Level of urban-health-technology talent pool (D4)
urban-personnel preparation Number of hospitals (D5)
safety (C2) Social-organization-unit level (D6)
(B1) Sense of security Personal-accident-insurance income (D7)
and security Urban commercial-insurance income (D8)
culture Number of employees involved in work-related-injury-insurance
(C3) coverage (D9)
Land-development intensity (D10)
Construction
The proportion of land area in security-vulnerable areas (D11)
(C4)
Number of employees in construction-industry enterprises (D12)
Traffic facilities Road-network density (D13)
(C5) Level of urban traffic-lighting facilities (D14)
Lifeline project Cell-phone penetration rate (D15)
Urban-safety amenities Number of fixed-broadband households (D16)
resilience (A) The resilience of (C6) Level of gas-supply facilities (D17)
urban-facility safety Level of seismic-monitoring facilities (D18)
Monitoring and
(B2) The public reach of meteorological hazard monitoring and anticipation
warning facilities
of early alert messages (D19)
(C7)
Urban intelligent-pipe-network density (D20)
Shelter area per capita (D21)
Emergency- Storage area of disaster-relief-reserve institutions per 10,000 people
security facilities (D22)
(C8) Number of beds in healthcare facilities for 10,000 people (D23)
Greenery coverage (D24)
The hazard-related mortality rate per million population (D25)
Risk-control level Annual direct financial damage resulting from catastrophes as a
The resilience of
(C9) percentage of area GDP (D26)
urban-management
Annual percentage of people affected (D27)
safety
Support-security Public-security financial expenditure (D28)
(B3)
input Healthcare financial expenditure (D29)
(C10) Transportation financial expenditure (D30)
3.1.4. Study Area and Time

In this paper, to explore the level of development of safe and resilient cities in western
China, the 10 regions of Chongqing, Sichuan, Yunnan, Guizhou, Shaanxi, Gansu, Qinghai,
Ningxia, Guangxi, and Inner Mongolia were selected according to the preferability and
feasibility of regional development and due to the unavailability of some indicator data
in Xinjiang and Tibet. At present, China’s development is still unbalanced, but there is a
significant focus on the development of the western district at the national level. Many
financial and material resources have been invested in urban construction, and its devel-
opment potential is enormous. However, there is scant research on urban-safety resilience
in China’s western region. Therefore, in this paper, we study the security resilience of
cities in western China.
When selecting the research time, to prevent the data obtained by selecting consecu-
tive years from not significantly reflected the changing trends, we studied the relevant
data by selecting intervals of years, which made the changes in the data more accurate
and the changes in the trend more intuitive. Since relevant statistics for 2022 have not yet
been published, the most recent year in this study is 2021. The concept of a safe and resil-
ient city is relatively recent, so the primary data from three years, 2017, 2019, and 2021,
were selected for the evaluation of safe and resilient city development in western China
based on careful consideration and reasonable verification.
3.2. Catastrophe-Progression Method

The main evaluation methods included the ANP network, fuzzy comprehensive
evaluation, hierarchical analysis, and catastrophe progression. To distinguish the ad-
vantages and disadvantages of each evaluation approach and the suitability of each
method for this study, the primary evaluation methods’ advantages, weaknesses, and ap-
plicability are summarized in Table 2.
Table 2. Comparison of evaluation methods.
Evaluation
Advantages Disadvantages Scope of Application
Methodology
Insufficient use of the volatility of The nature of the
Explicit and systematic the change in its evaluation influential parameters
Fuzzy integrated results that translate results when the affiliation and the difficulty in
evaluation qualitative metrics into degree changes, mainly because quantifying the
quantitative metrics of the considerable subjectivity in assessment of the
the evaluation process activity
Failure to consider the interplay
Translation of qualitative No cross-talk between
between different levels of
Hierarchical analysis metrics into quantitative factors and between
decision-making or the same
metrics levels
level
Superior flexibility,
Less computationally
considering both the factors More cumbersome to use in
ANP-network intensive and relatively
and the dependencies complex decision-making
analysis deterministic risk-
between the superordinate processes
evaluation problem
factors of each factor
Multiple phases of
Flexible indicators, simple
Object element The hand must be a relatively evaluation of numerous
process, more systematic and
model definite value evaluation objects
refined results
identified by indicators
Errors and problems are Inability to fully reflect the Evaluation questions are
Monte Carlo independent of the number interactions between project risk relatively simple and
of dimensions; factors defined
problems with statistical Deterministic problems need to

properties can be solved transform into stochastic
directly; problems
Issues of a continuous nature
do not need to discretize
It can be used to analyze
Study of systems with
indicator systems with
complex internal
complex influencing factors Due to its model characteristics,
Catastrophe structures or unknown
that produce unclear the definition of the risk level
progression mechanisms of
catastrophe points; interval is more complicated
interaction of internal
Not overly dependent on
factors
weights
The catastrophe theory has the following main advantages. ① The catastrophe-level
method is systematic and can better cover all aspects of urban-safety-resilience evaluation.
② The catastrophe-level method is organized and can better protect all aspects of urban-
safety-resilience evaluation. ③ A good evaluation of complex structures can lead to a
more accurate evaluation of the more complex evaluation-index system of urban-safety
resilience. ④ With significant hysteresis, the catastrophe-level method can allow the more
precise evaluation of changing trends in urban-safety resilience. Therefore, this paper
mainly uses the catastrophe theory for urban-safety-resilience evaluation.
Catastrophe theory, as a general theoretical approach that acts specifically to repre-
sent changes in the state of a system, usually simulates changes in the system’s state in
various periods with the help of constructive function models [55]. The function model in
the catastrophe theory is a latent feature, which reflects the system’s state. The possible
positions corresponding to different models represent very different meanings.
Before applying the catastrophe theory, an evaluation pattern needs to be constructed
based on the catastrophe-progression approach for the urban-safety-and-resilience-index
system, as follows.
(1) Data Sources
In this study, to strictly ensure that original data sources could be supported for an
evaluation of urban-safety resilience in western China, raw data were collected from the
National Bureau of Statistics, Chongqing Municipal Bureau of Statistics, Sichuan Provin-
cial Bureau of Statistics, Yunnan Provincial Bureau of Statistics, Guizhou Provincial Bu-
reau of Statistics, Shaanxi Provincial Bureau of Statistics, Gansu Provincial Bureau of Sta-
tistics, Qinghai Provincial Bureau of Statistics, Ningxia Hui Autonomous Region Bureau
of Statistics, Guangxi Zhuang Autonomous Region Bureau of Statistics, Inner Mongolia
Autonomous Region Bureau of Statistics, China Urban Construction Statistics Yearbook,
and health statistics yearbooks of each province, as well as other official channels. After
the data for 2017, 2019, and 2021 were obtained, they were collected and organized, lead-
ing to the initial data shown in Appendix B.
(2) Data Processing
Before calculating the data related to the indicator system of urban-safety resilience
constructed in the paper, the data were normalized and transformed into the same range
by dimensionless transformation because of their different units, different numerical
sizes, and significant differences in the forward and backward directions. In dimension-
less conversion, the range-transform method is generally used to process all data into di-
mensionless and comparable values in the interval of [0, 1] [56].
The range-transform method can divide data into three categories, according to the
positive and negative nature of their indicators:
Positive indicators correspond to the formula:
𝑥𝑥𝑗𝑗 − 𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚

𝑦𝑦𝑗𝑗 = (1)
𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 − 𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚
Inverse indicators have the corresponding formula:
𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 −𝑥𝑥𝑗𝑗
𝑦𝑦𝑗𝑗 =
𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 −𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚
(2)
Interval optimal indicators have the corresponding formula:

𝑎𝑎 − 𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚
⎧ 1 − 𝑎𝑎 − 𝑥𝑥 𝑚𝑚𝑚𝑚𝑚𝑚 , �𝑥𝑥𝑗𝑗 ≤ 𝑥𝑥𝑗𝑗 < 𝑎𝑎�
⎪ 𝑗𝑗
𝑦𝑦𝑗𝑗 = 1, �𝑎𝑎 ≤ 𝑥𝑥𝑗𝑗 ≤ 𝑏𝑏� (3)

⎨ 𝑥𝑥𝑗𝑗 − 𝑏𝑏
⎪1 − , �𝑏𝑏 < 𝑥𝑥𝑗𝑗 ≤ 𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 �
⎩ 𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 − 𝑏𝑏
Equation: 𝑥𝑥𝑗𝑗 —the raw index data; 𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 —the maximum value of the index data;
𝑥𝑥𝑗𝑗 —the minimum value of the indicator data; 𝑦𝑦𝑗𝑗 —the transformed indicator value.
𝑚𝑚𝑚𝑚𝑚𝑚
The optimal interval for the indicator data is [𝑎𝑎, 𝑏𝑏].

(3) Model Selection
After completing the dimensionless calculation of the raw data of indicators, the ca-
tastrophe-level values of needles at different levels were calculated by the matching ca-
tastrophe model corresponding to the normalization formula. Net, the catastrophe-level
values of hands at each station were calculated upward and individually based on the
framework of the indicator system. Finally, the catastrophe-level values of the total urban-
safety resilience were obtained.
Since the normalization formula needed to be selected following the principle of
matching the number of control variables and state variables with the corresponding nor-
malization formula, several correspondence methods covered in this paper are shown in
Table 3.
Table 3. This paper involves the normalization formula and the corresponding table of state varia-
bles and control variables.
Catastrophe Status Control

Potential Function Normalized Formula
Type Variable Variable
3
cusp catastrophe 1 2 𝐹𝐹(𝑥𝑥) = 𝑥𝑥 4 + 𝑎𝑎𝑥𝑥 2 + 𝑏𝑏𝑏𝑏 𝑥𝑥𝑎𝑎 = √𝑎𝑎, 𝑥𝑥𝑏𝑏 = √𝑏𝑏
swallowtail 3
𝑥𝑥𝑎𝑎 = √𝑎𝑎, 𝑥𝑥𝑏𝑏 = √𝑏𝑏,
1 3 𝐹𝐹(𝑥𝑥) = 𝑥𝑥 5 + 𝑎𝑎𝑥𝑥 3 + 𝑏𝑏𝑥𝑥 2 + 𝑐𝑐𝑐𝑐
catastrophe 4
𝑥𝑥𝑐𝑐 = √𝑐𝑐
3
butterfly 𝑥𝑥𝑎𝑎 = √𝑎𝑎, 𝑥𝑥𝑏𝑏 = √𝑏𝑏,
1 4 𝐹𝐹(𝑥𝑥) = 𝑥𝑥 6 + 𝑎𝑎𝑥𝑥 4 + 𝑏𝑏𝑥𝑥 3 + 𝑐𝑐𝑥𝑥 2 + 𝑑𝑑𝑑𝑑 5
catastrophe 4
𝑥𝑥𝑐𝑐 = √𝑐𝑐 , 𝑥𝑥𝑑𝑑 = √𝑑𝑑
hut 3 4
𝑥𝑥𝑎𝑎 = √𝑎𝑎, 𝑥𝑥𝑏𝑏 = �𝑏𝑏, 𝑥𝑥𝑐𝑐 = √𝑐𝑐 ,
1 5 𝐹𝐹(𝑥𝑥) = 𝑥𝑥 7 + 𝑎𝑎𝑥𝑥 5 + 𝑏𝑏𝑥𝑥 4 + 𝑐𝑐𝑥𝑥 3 + 𝑑𝑑𝑥𝑥 2 + 𝑒𝑒𝑒𝑒
catastrophe 5 6
𝑥𝑥𝑑𝑑 = √𝑑𝑑 , 𝑥𝑥𝑒𝑒 = √𝑒𝑒
Specifically, 𝑥𝑥 is the state variable, 𝑎𝑎, 𝑏𝑏, c, d, and e are the control variables, and
𝐹𝐹(𝑥𝑥) is the system state and the potential energy condition of the whole system when the
state variable is x. The control variable is determined by the number of similar indicators,
and the normalized formula is determined by the weighting of the indicators.
After determining the corresponding model by the number of control variables and
their state variables, the specific correlation formula was determined by ranking the
weights of indicators within the same level and determining the correlations between the
indicators. The weighted ranking is shown in Table 4.
Table 4. Ranking of the weighting of urban-safety and -resilience indicators.
Indicator Ranking of
Upper-Level Indicators Indicators the Same Higher-
Level Indicators
The resident population’s density in built-up areas (D1) 3
Basic population attributes
Level of basic medical insurance for urban workers (D2) 1
(C1)
Percentage of transient population (D3) 2
Social-participation Level of urban-health-technology talent pool (D4) 1
preparation Number of hospitals (D5) 3
(C2) Social-organization-unit level (D6) 2
Personal-accident-insurance income (D7) 3
Sense of security and
Urban-commercial-insurance income (D8) 1
security culture
Number of employees involved in work-related-injury-insurance
(C3) 2
coverage (D9)
Land-development intensity (D10) 2
Construction
The proportion of land area in security-vulnerable areas (D11) 1
(C4)
Number of employees in construction-industry enterprises (D12) 3
Traffic facilities Road-network density (D13) 1
(C5) Level of urban-traffic-lighting facilities (D14) 2
Cell-phone penetration rate (D15) 3
Lifeline-project amenities
Number of fixed-broadband households (D16) 2
(C6)
Level of gas-supply facilities (D17) 1
Level of seismic-monitoring facilities (D18) 3
Monitoring and warning
The public reach of meteorological hazard monitoring and
facilities 2
anticipation of early alert messages (D19)
(C7)
Urban intelligent-pipe-network density (D20) 1
Shelter area per capita (D21) 1
Emergency security Storage area of disaster-relief-reserve institutions per 10,000 people
2
facilities (D22)
(C8) Number of beds in healthcare facilities for 10,000 people (D23) 3
Greenery coverage (D24) 4
The hazard-related mortality rate per million population (D25) 3
Risk-control level Annual direct financial damage resulting from catastrophes as a
2
(C9) percentage of area GDP (D26)
Annual percentage of people affected (D27) 1
Public-security financial expenditure (D28)
Support-security input 1
Healthcare financial expenditure (D29)
(C10)
Transportation financial expenditure (D30) 2
Basic population attributes
1
(C1)
The resilience of urban- Social-participation preparation
3
personnel safety (B1) (C2)
Sense of security and security culture
2
(C3)
Construction
3
(C4)
The resilience of urban-
Traffic facilities
facility safety (B2) 2
(C5)
Lifeline-project amenities 1
(C6)
Monitoring and warning facilities
4
(C7)
Emergency security facilities
5
(C8)
Risk-control level
2
The resilience of urban- (C9)
management safety (B3) Support-security input
1
(C10)
The resilience of urban-personnel safety (B1) 2
Urban-safety resilience (A) The resilience of urban-facility safety (B2) 1
The resilience of urban-management safety (B3) 3
According to the different catastrophe-level algorithms, there are two types of rela-
tionship between indicators of the same class in terms of correlation: “complementary”
and “non-complementary.” Through consultations with experts, this paper establishes the
internal connections and structural integrity of the indicator-evaluation system for urban-
safety resilience. The relationships between the indicators in the same layer are shown in
Table 5.
Table 5. Internal-relationship table of indicators at the same level.
Indicator Levels Hierarchical Internal Metrics Internal Relations

Tier 1 Indicators B1, B2, B3 Complementary
C1, C2, C3 Complementary
Tier 2 indicators C4, C5, C6, C7, C8 Non-complementary
C9, C10 Complementary
D1, D2, D3 Complementary
D10, D11, D12 Non-complementary
D13, D14 Complementary
Tier 3 indicators
D21, D22, D23, D24 Complementary
Based on the normalized formula, indicator weights, and indicator-relationship prin-

ciples, the overall urban-safety-resilience model is shown in Figure 1.
Figure 1. Catastrophe model of urban-safety resilience.
The total catastrophe level was obtained by calculating each indicator from the bot-
tom to the top.
(3) Interval division
After the catastrophe model obtained the evaluation results, the results needed to be
refined by an interval segmentation of the model. Compared with the mean segmentation,
uniform distribution, and quantile methods, the K-means clustering algorithm can better
obtain the rank variability in evaluation results and is unaffected by the absolute aggre-
gation effect. In contrast, the high aggregation of the catastrophe model’s evaluation re-
sults means that the K-means clustering algorithm can be used as the interval-division
method for catastrophe models.
The main steps in the K-means clustering algorithm are as follows: ascertain the num-
ber of cluster centroids K, group the K-cluster centers and calculate the distance between
each factor to obtain the cluster-center-class group with the shortest distance; next, obtain
the corresponding K-class groups through classification and, finally, iterate and loop this
process until the termination condition is satisfied [57].
According to the K-means clustering algorithm, it is assumed that there is a gradient
category of urban-safety-and-resilience-evaluation results, and its criterion function is:
𝑘𝑘 𝑁𝑁𝑗𝑗
𝐽𝐽 = � � ‖𝑋𝑋𝑖𝑖 − 𝑍𝑍𝑗𝑗 ‖2 , 𝑋𝑋𝑖𝑖 ∈ 𝑆𝑆𝑗𝑗 (4)
𝑗𝑗=1 𝑖𝑖=1
where 𝐽𝐽 is the sum of the squares of the distances from the sample points of each evalu-
ation result in the cluster to the center of the class, 𝑆𝑆𝑗𝑗 is the set of sample points for each
evaluation result, 𝑍𝑍𝑗𝑗 is the set of sample points for each evaluation result, the center point
of the 𝑆𝑆𝑗𝑗 , and 𝑁𝑁𝑗𝑗 is the sample size of the set of sample points for each evaluation result.
The algorithm aims to find the minimum value of the criterion function and the
square’s minimum value. The 𝐽𝐽𝑗𝑗 of the distance from each evaluation-result sample point
to the center of the class. This can be used to solve the following equation:
𝜕𝜕𝐽𝐽𝑗𝑗
=0 (5)
𝜕𝜕𝑍𝑍𝑗𝑗
The meaning of the letters in the formula is the same as in Formula (4).
Substituting Formula (5) into Formula (6):
𝜕𝜕 𝑁𝑁𝑗𝑗 𝜕𝜕
� ‖𝑋𝑋𝑖𝑖 − 𝑍𝑍𝑖𝑖 ‖2 = (𝑋𝑋 − 𝑍𝑍𝑖𝑖 )𝑇𝑇 (𝑋𝑋𝑖𝑖 − 𝑍𝑍𝑖𝑖 ) = 0 (6)
𝜕𝜕𝑍𝑍𝑗𝑗 𝑖𝑖=1 𝜕𝜕𝑍𝑍𝑗𝑗 𝑖𝑖
The letters in the formula mean the same as in Formula (4).
The solution of Formula (6) yields the centroid. The 𝑍𝑍𝑗𝑗 of the sample-point set 𝑆𝑆𝑗𝑗 of
the evaluation results is as follows:
1 𝑁𝑁𝑗𝑗
𝑍𝑍𝑗𝑗 = � 𝑋𝑋𝑖𝑖 , 𝑋𝑋𝑖𝑖 ∈ 𝑆𝑆𝑗𝑗 (7)
𝑁𝑁𝑗𝑗 𝑖𝑖=1
The letters in the formula mean the same as in Formula (4) [58].
The above is the theoretical solution model, which is challenging to operate in the
actual solution, so the exact resolution is mainly calculated using an iterative operation.
The main steps are as follows: ① K samples are arbitrarily selected as clustering centers;
② the distance between the samples, and each cluster center is calculated; ③ the average
value of the samples of each category is determined as the new cluster center; ④ if the
cluster center does not change after an iteration or reaches the maximum iteration num-
ber, the process concludes with the cluster center. Otherwise, it is necessary to return to
step ② [59].
The iterative algorithm above can divide the city-safety-and-resilience-evaluation re-
sults into zones and form an echelon of the development levels in the western region.
3.3. Spatial Statistical Theory

(1) Geographic Information System (GIS)
According to a broad definition, a GIS is an information system used to process geo-
graphic and spatial data [60]. Data collection, processing, storage, and analysis are usually
performed with geographic data as the core [61]. So far, GIS has been adopted in various
domains, and it has become a standard tool for analyzing problems with spatial attributes.
This paper constructs geographic layers by placing the evaluation results as elements in
layers.
(2) Space weights
In this paper, the correlation between levels of urban-safety resilience is mainly es-
tablished using the spatial analysis software, GeoDa. At present, the four main spatial
matrices used in GeoDa software are the queen-contiguity type, rook-contiguity type,
threshold-distance type, and K-nearest-neighbors type. The specific spatial matrix rela-
tionships are shown in Table 6.
Table 6. Spatial matrix relationships.
Spatial Matrix Type Spatial Matrix Relationship

It is generally used to construct a spatial matrix with the
Rook-contiguity type help of the spatial relationship between two factors, that
have joint edges.
It is generally used to construct a spatial matrix using the
Queen-contiguity type spatial relationship between two factors that have typical
edges or common points.
Generally, the spatial matrix is constructed by using the
K-nearest-neighbors type distance between two elements after fixing the number of
adjacent elements.
It is generally used to construct a spatial matrix regarding
Threshold-distance type
the distance between two elements.
The model constructed in ArcGIS in this paper does not involve the location of each
point in space but studies the relationship between the geographic distance between each
local unit and the high-quality development of the construction industry. Therefore, the
spatial matrix should be selected with a fuller consideration of the neighboring relation-
ship between each province and autonomous region in the western area, reflecting the
macro-level spatial-distribution relationship. In addition, the areas selected for this study
were all adjacent to typical edges, and there was no common-point adjacency. The rook-
contiguity type mainly reflects the spatial-distribution relationship through the adjacency
relationship between two factors. It is more appropriate to choose to construct this type
of spatial matrix. The spatial weight matrix corresponding to this matrix is generally de-
termined according to the spatial adjacency function [62], referring to the space-matrix
construction of the queen-contiguity type. The construction of the space matrix in this
paper is as follows:
𝜔𝜔11 𝜔𝜔12 ⋯ 𝜔𝜔1𝑛𝑛
𝜔𝜔21 𝜔𝜔22 ⋯ 𝜔𝜔2𝑛𝑛
𝑊𝑊 = �
⋮ ⋮ ⋮
� (8)
⋮
𝜔𝜔𝑛𝑛1 𝜔𝜔𝑛𝑛2 ⋯ 𝜔𝜔𝑛𝑛𝑛𝑛
1, Two units adjacent to each other
where 𝜔𝜔𝑖𝑖𝑖𝑖 = � .
0, Two units are not adjacent to each other
(3) Moran’s index
The Moran Index is divided into the global Moran Index [63] and the local Moran
index [64].
The global Moran index in this paper portrays the overall trend of the spatial corre-
lations in urban-safety resilience across the study area and is calculated as follows:
𝑛𝑛 ∑𝑛𝑛𝑖𝑖=1 ∑𝑛𝑛𝑗𝑗=1 𝑤𝑤𝑖𝑖,𝑗𝑗 𝑧𝑧𝑖𝑖 𝑧𝑧𝑗𝑗
𝐼𝐼 = (9)
𝑆𝑆0 ∑𝑛𝑛𝑖𝑖=1 𝑧𝑧𝑖𝑖 2
where 𝑧𝑧𝑖𝑖 is the deviation of the attribute value of urban-safety resilience from the mean,
𝑤𝑤𝑖𝑖,𝑗𝑗 is the spatial weight of urban-safety resilience in an area, 𝑛𝑛 is the total number of
areas, and 𝑆𝑆0 is the set of all urban-safety-resilience spatial weights.
The local Moran index measures the spatial correlation between the safety resilience
of a regional city and the safety resilience of its neighboring towns, and its model equation
is as follows:
𝑍𝑍𝑖𝑖 𝑛𝑛
𝐼𝐼𝑛𝑛 = 2
� 𝑤𝑤𝑖𝑖𝑖𝑖 𝑍𝑍𝑗𝑗 (10)
𝑆𝑆 𝑗𝑗≠𝑖𝑖
Next, 𝑍𝑍𝑖𝑖 = yI − y� , 𝑍𝑍𝑗𝑗 = yj − y� , S 2 = 1�n ∑(yi − y� )2 , 𝑤𝑤𝑖𝑖𝑖𝑖 is the spatial weight, and 𝑛𝑛 is
the total number of factors.
Moran’s I provide a correlation basis for the spatial distribution of urban-safety resil-
ience in the western region of this study. When the value is greater than zero, this repre-
sents a positive spatial association; when Moran’s I is less than zero, this represents a neg-
ative spatial association; when Moran’s I is zero, the space is spatially random [65].
4. Results
4.1. Evaluation Results
The raw data of the calculation variables of each year’s index calculated by the cal-
culation method above are shown in Appendix C. The urban-security-resilience catastro-
phe-level values for the 10 regions in western China in 2017, 2019, and 2021 are shown in
Appendix D. The urban-safety-resilience levels of the personnel, facilities, and manage-
ment are showcased in Figures 2–5 below.
Figure 2. Urban-safety-resilience levels in western China in 2017, 2019, and 2021.
Figure 3. Resilience levels of urban-personnel safety in western China in 2017, 2019, and 2021.
Figure 4. Resilience levels of urban-facility safety in western China in 2017, 2019, and 2021.
Figure 5. Resilience levels of urban-management safety in western China in 2017, 2019, and 2021.
In this study, the K-means-cluster analysis was performed using SPSS Statistics 27
software to determine the urban-safety-resilience-grade-evaluation results and the
resilience grade by using the urban-safety-resilience catastrophe-level values for 2017,

2019, and 2021 in the 10 regions in western China as clustering factors. After the iteration,
the cluster centers were obtained, as showcased in Table 7.
Table 7. Assessment results for urban-safety resilience in the western region in 2017, 2019, and
2021.
2017 2019 2021

Evaluation Resilience Evaluation Resilience Evaluation Resilience
Region Region Region
Results Level Results Level Results Level
Chongqing 0.925 High Chongqing 0.932 High Chongqing 0.933 High
Sichuan 0.947 High Sichuan 0.980 High Sichuan 0.981 High
Relatively Relatively Relatively
Guizhou 0.873 Guizhou 0.882 Guizhou 0.893
high high high
Yunnan 0.642 Yunnan 0.837 Yunnan 0.891
low high high
Shaanxi 0.917 High Shaanxi 0.930 High Shaanxi 0.938 High
Relatively Relatively
Gansu 0.767 Moderate Gansu 0.635 Gansu 0.845
low high
Qinghai 0.519 Low Qinghai 0.608 Qinghai 0.594
low low
Ningxia 0.833 Ningxia 0.843 Ningxia 0.841
high high high
Guangxi 0.867 Guangxi 0.887 Guangxi 0.907 High
high high
Inner Relatively Inner Relatively Inner Relatively
0.876 0.889 0.887
Mongolia high Mongolia high Mongolia high
4.2. Spatial Evolution Analysis of Urban-Safety Resilience

For the spatial analysis, we mainly used ArcGIS 10.6 and GeoDa 1.2 software to pro-
duce national basic geographic information data at a scale of 1:1 million using ArcGIS
software. The standard map for 2022 released by the Ministry of Natural Resources of
China was combined with geographic maps of 10 regions in western China and urban-
security-resilience-evaluation data to build a GIS model of urban-security resilience in the
10 regions in western China.
4.2.1. Analysis of the Spatial Distribution

(1) Spatial distribution of urban-safety-resilience-evaluation results
Based on the urban-safety-resilience GIS models of 10 regions in western China in
2017, 2019, and 2021, the urban-safety-resilience-level-division intervals were imported
through ArcGIS software. Figure 6 was collated based on the urban-safety-resilience-
level-distribution map.
Figure 6. Urban-safety-resilience-grade distribution in the western region in 2017, 2019, and 2021
(since data on some indicators for Xinjiang and Tibet in western China could not be collected, these
two regions are not included in this figure).
According to the preliminary analysis in Figure 6, the urban-safety-resilience levels

in the three years of 2017, 2019, and 2021 had a spatial-distribution trend ranging from
low to high roughly from east to west; the provinces with high resilience levels were
mainly in the three regions of Chongqing, Sichuan, and Shaanxi. The three main regions
with lower and low resilience levels were Qinghai, Yunnan, and Gansu, and there was a
trend of weakening in the three core provinces of Chongqing, Sichuan, and Shaanxi com-
pared to the surrounding areas. There were several reasons for this. Firstly, regarding
economic development, the total GDP of Chongqing, Sichuan, and Shaanxi is the highest
in the whole western region, so the economic level of these areas guarantees the construc-
tion of safe and resilient cities. Secondly, regarding geographical location, Chongqing, Si-
chuan, and Shaanxi are in the central–eastern position; they are both hubs of the connec-
tion between the western region connected and the Middle East, and they occupy an ad-
vantageous geographical location within the region generally, so the development of their
urban-security resilience can drive that of the surrounding areas. Thirdly, from the per-
spective of city promotion, Chongqing, Chengdu, and Xi’an are currently the core cities
with the most advanced development in the western region and in the country in general.
This undeniably drives the growth of cities in the whole province and encourages the
development of safety-resilient cities around the entire western region. In conclusion, the
regional variability in western China currently shows a relative imbalance in the develop-
ment of safe and resilient cities.
(2) Spatial distribution of evaluation results of first-level indicators
After the Tier 1 spatial analysis of the urban-safety resilience, the spatial distribution
of specific aspects of urban-safety resilience was analyzed again, using the first-level-in-
dicator evaluation level as a starting point. As in the previous section, we used the ele-
ment-differentiation function in ArcGIS software to classify the urban human-safety resil-
ience, urban-facility-safety resilience, and urban-management-safety resilience in the 10
regions of western China in 2021. In this paper, the natural crack-classification method,
which is similar in principle to K-means cluster analysis, was used to perform the classi-
fication. After forming the spatial distribution maps of the safety resilience of people, the
safety resilience of urban facilities, and the safety resilience of urban management in the
10 western regions in 2021, Figure 7 was produced.
Figure 7. (a) Resilience levels of urban-personnel safety in 2021; (b) resilience levels of urban-facilitiy
safety in 2021; (c) resilience levels of urban-management safety in 2021 (since data on some indica-
tors for Xinjiang and Tibet in western China could not be collected, these two regions are not in-
cluded in this figure).
According to Figure 7, the distribution of each Tier 1 indicator showed an enormous

difference from the others. For example, the safety resilience of urban people in western
China roughly decreased from south to north, which was because the geographical envi-
ronment and habitation conditions in the southwest are better than those in the northwest;
furthermore, the safety resilience of urban facilities in the western region of China de-
creased from the middle to both sides, since the safety resilience of urban facilities is not
only affected by the local financial level. It is evident that the urban facilities’ safety resil-
ience in Sichuan, Chongqing, and Shaanxi was higher than in the rest of the western re-
gion; the urban-management-safety resilience of the territory of the west of China showed
a decreasing trend from the southwest to the northwest, which was due to the frequent
occurrence of natural disasters in the northwest and the lack of disaster-detection facili-
ties, as well as the relatively low investment levels of emergency funds compared to the
southwest. According to the distribution of these three level indicators, it can be seen that
although the overall urban-safety resilience in Chongqing, Sichuan, and Shaanxi was rel-
atively high, it did not significantly drive the development of urban-safety resilience in
some neighboring regions.
4.2.2. Spatial correlation analysis
Because spatial analysis requires macroscopic analysis and quantitative analysis,
which can reflect spatial spillover and aggregation effects, the spatial-distribution analysis
of the urban-safety resilience in the western region needed to be accompanied by spatial-
correlation analyses, including a global spatial-correlation analysis and a local spatial-cor-
relation analysis.
(1) Global correlation
In this paper, the urban-safety resilience of the 10 western regions in 2017, 2019, and
2021 was calculated to obtain the Moran index and its related indexes. The results are
shown in Figures 8–13.
As can be seen in Figure 8, the median value of the Moran index of the spatial-distri-
bution of the safety and resilience of the cities in the western region in 2017 was -0.009,
within the interval from -1 to 1, and not equal to 0, which represents the possibility of
spatial aggregation. However, this value is low; the importance of 0.491 suggests the prob-
ability of the random generation of spatial data, which indicates that the likelihood of data
aggregation is greater than the possibility of random data distribution. However, the null
hypothesis cannot be rejected. A score of 0.688 is more significant than -1.65 and a score
of less than 1.65 indicates that there is no significant spatial correlation, and that the pos-
sibility of aggregation is very low, or even zero.
Figure 8. The results of the global spatial-correlation analysis for 2017.
As can be seen in Figure 9, the median value of the Moran index of the spatial distri-
bution of the safety and resilience of the cities in the western region in 2019 was 0.161,
within the interval from -1 to 1 and not equal to 0, representing the possibility of spatial
aggregation; the value of 0.075 represents the probability of the random generation of spa-
tial data, indicating that the likelihood of data aggregation was greater than the possibility
of random data distribution, but the null hypothesis cannot be significantly rejected. A
score of 1.782 is greater than 1.65; a score of less than 1.96 indicates significant spatial
correlation and the existence of some aggregation, although this aggregation was weak.
Figure 9. The results of the global spatial-correlation analysis for 2019.
Figure 10 shows that the median value of the Moran index of the global distribution
of secure and resilient cities in the western region in 2021 was 0.056, within the interval
from -1 to 1 and not equal to 0, representing the possibility of spatial aggregation. How-
ever, this value is low, and the likelihood of spatial aggregation is small. The value of 0.162
means the probability that the spatial data were randomly generated, indicating that the
likelihood of data aggregation was greater than that of a random distribution of data. The
value of 1.399 is more significant than that of -1.65, and a value of less than 1.65 indicates
that there is no significant spatial correlation, and that the likelihood of aggregation and
non-aggregation is very low.
Figure 10. The results of the global spatial correlation analysis for 2021.
As can be seen in Figure 11, the Moran index of the global distribution of urban-
personnel-safety resilience in the western region in 2021 was 0.035 in the interval, repre-
senting a positive spatial correlation and the possibility of spatial aggregation. The value
of 0.36 represents the probability that the spatial data were randomly generated, indicat-
ing that the spatial data were less random, but the null hypothesis cannot be significantly
rejected. The score of 0.92 was more significant than that of -1.65, and a score of less than
1.65 indicated that there was no spatial correlation and a random distribution.
Figure 11. Moran index of resilience levels of urban-personnel safety for 2021.
As can be seen in Figure 12, the Moran index of the global distribution of the urban-
facility-safety resilience in the western region in 2021 was 0.037 in the interval, represent-
ing a positive spatial correlation and the possibility of spatial aggregation. The value of
0.28 represents the probability that the spatial data were randomly generated, indicating
that the spatial data were less random, but the null hypothesis cannot be significantly
rejected. A score of 1.07 is greater than -1.65 and a score of less than 1.65 indicates that
there is no spatial correlation and a random distribution.
Figure 12. Moran index of resilience levels of urban-facility safety for 2021.
As can be seen in Figure 13, the Moran index of the global distribution of urban-
management-safety resilience in the western region in 2021 was 0.136 in the interval, rep-
resenting a positive spatial correlation and the possibility of spatial aggregation. The value
of 0.11 represents the probability that the spatial data were randomly generated, indicat-
ing that the spatial data were less random, but the null hypothesis cannot be significantly
rejected. A score of 1.62 is greater than −1.65, and a score of less than 1.65 indicates that
there is no spatial correlation and a random distribution.
Figure 13. Moran index of resilience levels of urban-management safety for 2021.
(2) Local Correlations

For the correlation analysis of the local correlations in urban-safety resilience, we im-
ported the shp file obtained through ArcGIS into the GeoDa software. Next, we used the
correlation-spatial-weight matrix to analyze the local spatial correlations. The results ob-
tained after the univariate spatial analysis were as follows.
① Scatter Chart
The first quadrant in the four quadrants of the scatter diagram indicates “high–high”
clustering, suggesting that the urban-safety resilience in this part of the region and the
urban-safety resilience of the surrounding areas were both at a high level; the second
quadrant indicates “low–high” clustering, suggesting that the urban-safety resilience was
at a low level; the third quadrant indicates “low–low” clustering, suggesting that the ur-
ban-safety resilience of this part of the region and the urban-safety resilience of the sur-
rounding regions were both at a low level; the fourth quadrant indicates “high–low” clus-
tering, suggesting that the urban-safety resilience of this part of the region was at a high
level. Nevertheless, the urban-safety resilience in the surrounding area was at a low level.
After the results were obtained through GeoDa software, the local spatial-correlation-
analysis scatterplots of urban-safety resilience in 2017, 2019, and 2021 and the urban-safety
resilience of people, facilities, and management in 2021 were summarized, and the spatial-
correlation scatterplot-analysis table was produced, as shown in Table 8.
Table 8. Spatial-correlation scatter-plot analysis.
2021 2021
Object of 2017 2019 2021 2021 Urban-
Urban- Urban-
Evaluation Urban- Urban- Urban- Manageme
Personnel- Facility-
Safety Safety Safety nt-Safety
Safety Safety
Region Resilience Resilience Resilience Resilience
Resilience Resilience
Chongqing H-H H-H H-H H-H H-H H-H
Sichuan H-L H-L H-L H-H H-L H-L
Guizhou H-H H-H H-H H-H L-H H-H
Yunnan L-H L-H L-H H-H L-H H-H
Shaanxi H-H H-H H-H H-L H-H H-H
Gansu L-H L-H L-L L-L L-L L-L
Qinghai L-H L-L L-H L-H L-H L-H
Ningxia H-H H-L L-H L-L L-H L-H
Guangxi H-L H-H H-H H-H H-L H-H
Inner Mongolia H-H H-L H-H L-L H-L H-L
② Significance plot and clustering plot

The LISA significance map reflected whether the local spatial aggregation of the ur-
ban-safety resilience was significant, as well as the degree of this significance, while the
LISA clustering map explored whether there was a substantial local spatial correlation in
some areas, which was a further analysis that was carried out after the initial analysis of
the local spatial correlations based on the scatter plot.
After analyzing the urban-safety resilience in 2017, 2019, and 2021 and the urban-
safety resilience of the personnel, facilities, and management in western China in 2021
using GeoDa software for the local spatial-correlation analysis, the LISA significance
maps and LISA clustering maps were obtained, and the results are expressed in Table 9
and Figure 14.
Table 9. Spatial-correlation LISA significance-plot analysis.
2021 Urban- 2021 Urban-

Object of 2017 2019 Urban- 2021 Urban- 2021 Urban-
Personnel- Management-
Evaluation UrbanSafety Safety Safety Facility-Safety
Safety Safety
Region Resilience Resilience Resilience Resilience
Resilience Resilience
Relatively Relatively Relatively Relatively Relatively
Chongqing Insignificant
significant significant significant significant significant
Sichuan Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant
Guizhou Insignificant Insignificant Insignificant
significant significant significant
Yunnan Insignificant Insignificant Insignificant Insignificant
significant significant
Relatively
Shaanxi Insignificant Insignificant Insignificant Insignificant Insignificant
significant
Gansu Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant
Qinghai Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant
Ningxia Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant
Guangxi Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant
Inner Mongolia Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant
Figure 14. (a) Spatial clustering map of the western region in 2017; (b) spatial clustering map of the
western region in 2019; (c) spatial clustering map of the western region in 2021; (d) spatial clustering
of security resilience of urban personnel in western cities in 2021; (e) spatial clustering of security
resilience of urban facilities in the western region in 2021; (f) spatial clustering of security resilience
of urban facilities in the western region in 2021 (since data on some indicators for Xinjiang and Tibet
in western China could not be collected, these two regions are not included in this figure).
Table 9 and Figure 14 demonstrate that the spatial aggregation of the cities with se-
curity resilience in the western region was relatively weak; only Chongqing, Yunnan,
Guizhou, and the surrounding areas had a more significant spatial aggregation. Further-
more, the rest of the regions did not form significant aggregation due to the influence of
various factors and their high and low surrounding resilience. However, it can be seen
from the general distribution that cities in Sichuan, Chongqing, and Shaanxi, near the
eastern region, were at relatively high levels of security resilience, while the cities in the
westernmost regions, such as Qinghai, were at relatively low levels of security resilience.
4.3. Analysis of Results

According to the ranking of the urban-security-resilience levels of the 10 regions in
western China, Sichuan, Chongqing, and Shaanxi were the first, second and, third, respec-
tively, while Qinghai and Gansu ranked tenth and ninth, respectively. The main factors
regarding the higher level of urban safety and resilience in Sichuan, Chongqing, and
Shaanxi were as follows. Firstly, the Chengdu–Chongqing urban agglomeration is in Si-
chuan and Chongqing, which is an important platform for the development of the western
area, a strategic support for the Yangtze River economic belt, and an important demon-
stration area for the national promotion of new urbanization. Chengdu and Chongqing
have established a full-chain supervision system regarding energy safety and stable sup-
ply, fire prevention and control, and the prevention of major food and drug risks, as well
as increased penalties for violations to promote service quality and safety and enhance
the happiness of citizens. Due to their high economic levels, cities such as Chengdu and
Chongqing were able to quickly establish temporary defense facilities for natural disasters
during the Yibin earthquake in 2019 and the floods in southern China in 2020, and have
more advanced disaster-warning systems, so they suffer fewer disaster-related losses. As
Hao et al. noted, the Chengdu–Chongqing urban agglomeration will become a new and
important urban agglomeration in China in the next five years, similar to the famous
Yangtze River delta, Guangdong, Hong Kong, Macao, and Beijing–Tianjin–Hebei urban
agglomerations [16]. Therefore, the priority of the policy is significantly higher than that
in other regions. Secondly, from the research by Feng et al., it is known that the develop-
ment trend of new urbanization and ecological environments, and the comprehensive de-
velopment level and coordination level of both in Shaanxi are steadily increasing [66].
During the heavy-rainfall-related flood disaster in 2021, the cities showed a better resili-
ence capacity because of Shaanxi’s more complete disaster-prevention plans and meticu-
lous hidden-danger-investigation work. The main factors in the lower level of urban-
safety resilience in Qinghai and Gansu are as follows. First, the vast geographical areas of
Qinghai and Gansu, the low percentage of developable area, and the low infrastructure
coverage per unit area. Second, as shown by Deng et al., in the disaster-prone agricultural
and pasture areas of Qinghai, the high ratio of losses caused by disasters, as well as the
population and economic vulnerability, are extremely significant; therefore, the construc-
tion of safe and resilient cities is extremely difficult [67]. The research of some scholars
further confirms that the emergency-rescue service capacity in Gansu Province is not com-
prehensive, the construction of the emergency-lifeline system is lagging, and other factors
affect the construction of a safe and resilient city [68]. According to the analysis of the
urban personnel, facilities, and management-safety-resilience results, the following con-
clusions can be drawn:
(1) The security resilience of urban people in the 10 regions of western China is gen-
erally at a high level, and most of the provinces show an upward trend each year, which
indicates that China pays particular attention to security prevention and control at the
social level. As some scholars’ studies have shown, the comprehensive population and
economic development in western China are steadily increasing [69]. However, the secu-
rity and resilience of urban populations in Qinghai and Ningxia are relatively low, for the
following reasons. Firstly, the aging of the population in Qinghai and Ningxia is more
significant, and the age structure of the population in Qinghai and Ningxia is much
weaker than that in other regions due to the large migration of young laborers. Secondly,
the social security, welfare, and medical support services in Qinghai and Ningxia struggle
to cope with the current demographic problems. Finally, the Chinese government is con-
tinuing to strengthen the construction of the western region to ensure the safety and health
of urban residents and to greatly reduce the incalculable damage caused by disasters.
(2) The resilience of urban facilities in the 10 regions in western China varies signifi-
cantly from region to region, such as in the Gansu and Qinghai provinces, which are
mainly located in the northwest of China and have obvious characteristics of sparsely
populated areas. Their urban infrastructure is not updated and maintained promptly due
to the harsh climate and economic backwardness, so their urban facilities have low resili-
ence. Qinghai’s transportation and disaster-warning facilities significantly affect the over-
all resilience level. A further analysis revealed that Qinghai is located in a high-altitude
region, and that the cost of facility construction and maintenance is much higher than
other regions, while the loss of talent makes it difficult for Qinghai to reach a leading level
of development. Gansu’s construction and lifeline-engineering facilities affect its resili-
ence level because most of Gansu is arid or semi-arid, water resources are more scarce
than in other regions, and the urban-network layout has not yet reached the average na-
tional standard, while the complexity of the terrain leads to the slow development of its
construction industry, and the development and utilization of land are still underdevel-
oped.
(3) The level of urban-management security resilience in the 10 regions in western
China is generally on the rise, with some regions displaying lower levels in some years,
such as the Ningxia, Gansu, and Qinghai provinces, which are disaster-prone and struggle
to control risks compared to the other provinces. Specifically, the Qinghai and Ningxia
regions show a lower level of support regarding security inputs, since their financial level
is low, although state subsidies, to a certain extent, relieve local financial pressure, and
the impact of several factors led to a more limited financial investment in public safety
and transportation. In addition, Qinghai and Gansu suffer from a higher frequency of dis-
asters. Therefore, a more significant issue is the high degree of disaster damage in these
areas; accordingly, the direct economic losses in terms of the proportion of the regional
GDP were greater in Qinghai and Gansu, which in turn affected their urban-safety man-
agement.
In addition, the overall spatial aggregation of the urban security resilience in the 10
western regions of China is more general. Specifically, Sichuan, Chongqing, and Shaanxi
have more obvious spatial spillover to the surrounding areas regarding urban manage-
ment and facility-security resilience, which can effectively drive the development of these
factors in these surrounding areas. However, the level of urban-personnel-security resili-
ence does not reflect this obvious spatial spillover. The reason for this is mainly that Si-
chuan, Chongqing, and Shaanxi, in terms of demographic structure, have the same prob-
lems as other regions, such as serious aging. In terms of infrastructure and management,
Shaanxi serves as the center, with its advanced innovation and technological support, as
well as the high-speed development of economic support, which clearly radiates to the
surrounding areas. At the same time, according to this study, Shaanxi has achieved new
progress in ecological construction, and with its advantageous location, bearing east and
opening up to the west, Shaanxi’s social economy and ecological environment both have
a good influence on its surrounding areas [69].
Furthermore, the local spatial correlation of urban security resilience in the 10 west-
ern regions of China is relatively general, and there is “high–high” aggregation between
Chongqing, Guizhou, Shaanxi, and the surrounding areas, which indicates a certain link-
age development in these regions. This mainly occurs in terms of urban personnel and
management-security resilience, which show more obvious linkage development. In some
regions, there is also “high–low” or “low–high” clustering, which indicates that the shar-
ing of urban-security resilience is not strong in these regions; for example, there is a high
level of urban-security resilience in Yunnan in the neighboring regions, but a medium
level in the whole western region. The strength of Yunnan is not sufficient to drive the
development of its surrounding areas.
5. Conclusions and Recommendations

This paper took 10 provinces, autonomous regions, and municipalities directly under
the central government in western China as the research objects and constructed an eval-
uation-index system using three dimensions, the resilience of urban-personnel safety, the
resilience of urban-facility safety, and the resilience of urban-management safety through
research statistics and expert consultations. We analyzed the spatial and temporal evolu-
tion characteristics of urban-safety resilience using ArcGIS software, and drew the follow-
ing conclusions.
(1) From the analysis of the urban-safety-resilience evaluation results, more than half
of the 10 provinces in western China showed a slow increase each year, which indicates
that China is in the initial stage of the construction of safe and resilient cities. Parts of the
institutional system and organizational structure are not yet perfect, while some regions
lack digital transformation. However, overall, its development is progressing, without
major changes. Some regions have fluctuations in their urban-safety-resilience levels due
to changes in some indicators, which may be related to serious disasters or changes in
certain factors over a specific period. For example, some regions experienced a slowdown
in economic development due to the global pandemic’s impact on the construction of safe
and resilient cities, which reduced the urban-safety-resilience levels. In contrast, some re-
gions experienced changes in their construction industries in some years, leading to a de-
crease in urban-safety and resilience levels. For example, in some regions, the economic
development slowdown due to the global pandemic reduced cities’ safety and resilience.
In contrast, in other areas, the urban-safety resilience fluctuated due to construction-in-
dustry changes in some years.
(2) According to the spatial-evolution analysis for each year, the spatial aggregation
of each year was weak. However, the general trend gradually showed Sichuan Province,
Chongqing City, and Shaanxi Province as the centers of this aggregation, which progres-
sively dispersed to the surrounding area. From the current perspective, the safe and resil-
ient cities in the neighboring areas of these three regions are developing more strongly.
They play a particular role in the development of regional linkage. The spatial aggregation
of safe and resilient cities in China’s western region was enhanced each year, but inevita-
ble fluctuations also occurred. During this period, the spatial planning in China’s western
region showed some homogeneity and non-adaptability, meaning that some social syner-
gism was insufficient. Hence, the aggregation effect in 2017 was not apparent, and 2021
was critical in the fight against the pandemic in China. Consequently, the development of
safe and resilient cities in China’s western region slowed down. Some areas were more
affected by the pandemic than others, which led to fluctuations in the aggregation.
In general, there was a specific correlation between the construction of safe and resil-
ient cities and time and space in the western region, and the level of urban-safety resilience
in most of the western territory gradually increased over the years, but the growth rate
was slow. Some regions showed fluctuations in their level of urban-safety resilience. This
could indicate that the development of urban-safety resilience in most of the western re-
gions is in the initial stage. The development changes are insignificant, and their stability
and synergism can improve further.
In this regard, this paper upholds the principle of ecological sustainability and em-
phasizes the quality of development in the construction of safe and resilient cities from
the perspectives of a risk-identification system and resilience-governance planning, con-
struction tasks and objectives, collaborative governance, and the institutional system. The
specific recommendations of this paper are as follows.
Firstly, strengthen the security resilience of facilities at the spatial scale in urban ar-
eas. The construction of infrastructure public safety should be strengthened in terms of
overall design and management. In response to various unknown risks, the design of
emergency-infrastructure disaster-prevention functions should be strengthened, and the
resilience of the corresponding infrastructure should be dynamically controlled using
technologies such as the Internet of Things. The safety and resilience standards for
buildings in different disaster-risk areas should also be studied, potential risk buildings
should be proactively maintained and reinforced, and the new standards should be ap-
plied in the construction of new buildings, such as housing and municipal facilities. Sur-
plus-space resources should be ensured, an open-space skeleton should be built, the flex-
ibility and effectiveness of the integrated transportation system should be enhanced, and
the layouts of emergency-evacuation sites should be optimized.
Secondly, there should be a reasonable increase in public financial investment to im-
prove the level of urban-security facilities. Special funds should be set up for the construc-
tion of safe and resilient cities; the use of these special funds should be coordinated, the
effectiveness of their use should be improved, and the provision of key projects and fund-
ing for safe and resilient cities should be guaranteed. The layout of the urban spatial struc-
ture should be optimized, and cities should be guided to shift from monocentric to poly-
centric and from circled to distributed forms. Overly centralized urban functions should
be prevented. The planning of current urban facilities should be upgraded and strength-
ened, and a multi-selective urban-transportation system should be built. Disaster preven-
tion and emergency planning should be strengthened and the rational use of basic public-
service resources in cities should be increased so that cities can maintain a robust and
sustainable development momentum in various environments.
Thirdly, multi-dimensional synergistic linkage should be developed. Safe and resili-
ent city governance should make use of the collaborative governance theory to achieve
complementary advantages through data collection or practical testing, the participation
of social forces and the synergy of various social stakeholders should be encouraged, and
the overall level of social governance should be improved. In city-related security-and-
resilience public affairs, the government should play the role of coordinator, leader, and
commander, enhance emergency linkage-management capacity through cooperative op-
erations with all social parties, and ultimately improve urban security and resilience-man-
agement capacity.
Fourthly, emergency laws and regulations should be improved, and the social gov-
ernance system should be strengthened. Urban-security resilience should be raised to the
strategic level of maintaining national economic security and development, the relevant
policies and standards for the construction of secure and resilient cities should be im-
proved, local normative documents for rescue and relief should be formulated and re-
vised, comprehensive approaches to disaster mitigation, post-disaster reconstruction, dis-
aster relief, etc., should be established, and a corresponding rule-of-law guarantee should
be provided for the construction of secure and resilient cities. The emergency manage-
ment system should be improved, an up-and-down, responsive urban emergency net-
work should be established, and a practical emergency-disposal model should be built.
An all-weather digital comprehensive risk-prediction-and-management information-
sharing platform should be built, as well as a mechanism for digital urban-security-risk
monitoring and early warning, to enhance the ability to prevent various risks and com-
prehensively improve urban emergency-response and risk-management capabilities.
The evaluation indexes and models selected in this study do not fully cover the ur-
ban-security-resilience system, and the evaluation indexes will be periodically re-evalu-
ated as the cities develop. According to the conclusions of this study, it is necessary to
focus on the security resilience of urban facilities and adjust the urban-security-resilience-
evaluation system according to specific situations. At the same time, as the technical con-
ditions of smart cities become more mature, it is beneficial to explore the security resili-
ence of smart cities. Future research should consider the regional differences and coordi-
nation of various subjects in the construction of security resilience through the develop-
ment of smart cities and focus on the security-resilience characteristics of such cities.
Author Contributions: conceptualization, Y.X. and Z.C.; methodology, Y.X.; software, Y.S.; valida-
tion, Y.X., J.M., and Y.S.; formal analysis, Y.X. and Y.S.; data curation, Y.C.; writing—original draft
preparation, Y.X. and Y.C.; writing—review and editing, Y.X.; visualization, Y.C. All authors have
read and agreed to the published version of the manuscript.
Funding: This research was funded by the General Program of National Natural Science Foundation
of China (NSFC): A Study on Polycentric Governance Mechanism of Resilience of Critical Infrastruc-
ture Systems of Smart Cities (grant no. 72174140).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
authors.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Initial evaluation indicators and description of urban-safety resilience.

Indicator
Tertiary Indicators Unit Indicator Description
Type
10,000 The ratio of the number of the resident population in the built-up zone to the
The resident-population density in built-up
people/k area of the built-up zone, calculated as the number of the resident population in Positive
areas
m2 the built-up zone/area of the built-up zone
Level of basic medical insurance for urban 10,000 Refers to the number of people who participate in corresponding basic medical
Positive
workers people insurance for urban workers according to the relevant national regulations
The number of transient urban population as a percentage of the total urban
Percentage of transient population % population, calculated as the number of urban transient population/(total Reverse
urban resident population + number of transient urban population) × 100%
The number of people with disabilities as a percentage of the total urban
Percentage of the population with
% resident population, calculated as the number of people with disabilities in the Reverse
disabilities
urban resident population/total urban resident population × 100%
Level of urban-health-technology talent
people Refers to the number of health technicians per 10,000 residents in the city Positive
pool
Refers to the ratio of the number of hospitals to the total area of the city’s
Block/100
Number of hospitals administrative zone; calculated as the number of hospitals/total area of the Positive
km2
city’s administrative zone
Social-organization-unit level size Refers to the number of social organization units in the city in a certain period Positive
CNY
Personal-accident-insurance income Refers to the income from urban personal accident insurance in a certain period Positive
10,000
CNY
Urban commercial insurance income Refers to the city’s commercial insurance revenue for a certain period Positive
10,000
Number of people covered by work-injury 10,000 Refers to the number of persons insured against work injuries at the end of the
Positive
insurance people year in a certain period
The ratio of urban-construction-land area to the total area of the urban
Land-development intensity % administrative zone, calculated as urban-construction-land area/total area of Positive
urban administrative zone × 100%
Refers to the weak-security land area as a percentage of the built-up-zone
The proportion of land area in security-
% construction-land area, calculated as weak-security-area land area/built-up- Reverse
vulnerable areas
zone construction-land area × 100%
Number of employees in construction- 10,000 Refers to the number of construction enterprises employed in the city in a
Positive
industry enterprises people certain period.
The ratio of the road area of the municipal district to the total resident
Road-network density % population of the municipal district, calculated as the road area of the Positive
municipal district/the total resident population of the municipal district × 100%
Km/10,000The length of roads per 10,000 residents in a city, calculated as the length of
Road length per 10,000 people Positive
people roads/total resident population in a city
Level of urban traffic-lighting facilities Marigold Refers to the number of street lightis in the city in a certain period. Positive
size/100 Refers to the average number of cell phones per 100 people in the total
Cell-phone penetration rate Positive
people population of the administrative area.
10,000
Refers to the number of households with fixed broadband in a city over a
Number of fixed-broadband households household Positive
certain period.
s
Kilometer
Level of gas-supply facilities Refers to the lengths of urban natural-gas pipelines in a certain period. Positive
s
Refers to the ratio of the number of seismic stations to the total area of the
Block/10,0
Level of seismic-monitoring facilities urban administrative zone, calculated as the number of seismic stations/total Positive
00 km 2
area of the urban administrative zone (10,000 square kilometers)
Public coverage of meteorological disaster Refers to the ratio of automatic weather stations to the total area of the
pcs/100
monitoring and forecasting early-warning administrative zone of the city, calculated as automatic weather stations/total Positive
km 2
information area of the administrative area of the city (hundred square kilometers)
Refers to the ratio of the length of fiber-optic cable lines to the total area of the
Urban intelligent-pipe-network density /km administrative zone of the city; calculated as the length of fiber-optic cable Positive
lines/total area of the administrative zone of the city
The ratio of urban emergency-shelter area to total urban resident population.
Shelter area per capita m2/people calculated as urban emergency-shelter area (square meters)/total urban resident Positive
population (people)
Refers to the ratio of the area of the emergency-supplies reserve to the total
Storage area of disaster-relief-reserve m2/10,000 resident population of the city (10,000 people), calculated as the area of the
Positive
institutions per 10,000 people people emergency-supplies reserve (square meters)/total resident population of the
city (10,000 people)
The ratio of the number of beds in various medical and health institutions to
Number of beds in medical and health pcs/10,000
the total urban resident population (10,000 people), calculated as the number of Positive
institutions per 10,000 people people
beds in various medical and health institutions/total urban resident population
Refers to the percentage of the total area covered by greenery in the built-up
Greenery coverage % Positive
area of the city.
Refers to the ratio of annual disaster-related deaths to the total urban resident
The disaster-related-death rate per million
% population (million), calculated as annual disaster-related deaths/total urban Reverse
population
resident population (million) × 100%
Refers to the ratio of annual direct economic losses due to disaster to regional
Annual direct economic losses due to
% GDP, calculated as annual direct economic losses due to disaster/regional GDP Reverse
disasters as a percentage of regional GDP
× 100%
The ratio of the number of deaths from class A and B infectious diseases to the
The mortality rate of class A and B
total urban resident population (100,000 people), calculated as the number of Reverse
statutory infectious diseases
deaths from class A and B infectious diseases/total urban resident population
Refers to the annual number of fire deaths as a percentage of the total urban
The fire-death rate per 10,000 people % resident population (10,000 people), calculated as the annual number of fire Reverse
deaths/total urban resident population × 100%
The ratio of the annual number of criminal cases to the total urban resident
Incidence of criminal cases per 10,000
population (10,000 people), calculated as the annual number of criminal Reverse
people
cases/total urban resident population
The ratio of the annual disaster population to the total resident population of
Percentage of people affected in a year % the city, calculated as annual disaster population/total resident population of Reverse
the city × 100%
CNY 100
Public-aecurity financial expenditure Refers to the city’s public-safety financial expenditure in a certain period Positive
million
CNY 100
Healthcare financial expenditure Refers to the financial expenditure on urban healthcare in a certain period Positive
million
CNY 100
Transportation financial expenditure Refers to the financial expenditure on urban transportation in a certain period Positive
million
Appendix B
Table A2. Table of raw data for each indicator in 2017.
Inner
Data Name Unit Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Guangxi
Mongolia
The total area of the urban built-up zone Km2 1423 2832 986 1142 1287 869 200 458 1414 1269
Number of permanent residents in the
10,000 people 1121.62 2065.83 624.66 857.69 1000.46 532.31 175.08 233.50 896.60 670.09
built-up zone
The population of urban workers with
10,000 people 640.30 1526.40 410.40 491.30 619.80 320.20 94.00 123.50 556.70 495.10
basic medical insurance
Number of temporary urban residents 10,000 people 504.31 569.72 128.82 131.50 98.79 150.67 23.36 71.04 252.16 236.20
The total urban resident population 10,000 people 2489.92 4127.32 1371.70 1658.18 1740.14 895.88 226.85 341.37 2392.78 982.27
Number of urban health technicians per
people 79 85 156 146 116 87 220 106 91 130
10,000 people
Number of hospitals size 749 2219 1270 1252 1150 526 212 209 589 720
Social organization units size 16,824 42,282 12,700 23,184 24,725 27,079 5291 6548 24,567 15,116
Life-insurance-premium income CNY 100 million 560.13 1441.28 210.05 357.52 654.8 254.06 46.86 109.25 369.13 390.23
City commercial-insurance revenue CNY 100 million 744.00 1937.64 389.31 612.66 869.01 366.38 80.20 165.29 565.11 570.06
Number of worker-compensation-
insurance participants at the end of the 10,000 people 504.61 876.04 332.48 383.67 459.35 198.58 64.85 90.35 388.79 307.76
year
City area Km2 7440 8359 3184 3157 2621 1591 688 2159 5789 4885
The total area of the city’s administrative
Km 2 43,263.10 82,433.06 34,176.60 84,818.32 49,054.71 87,442.07 166,331.50 23,697.42 68,539.76 147,077.45
zone
Area of land in areas with weak security
(industrial land + logistics and storage Km2 275.57 521.67 183.18 159.08 173.12 201.72 26.65 56.08 272.93 209.66
land)
Construction-industry employees 10,000 people 224.79 352.83 77.64 152.72 137.98 56.88 11 12.42 126.15 27.7
Road area in the city 10,000 m2 19,015 33,979 8930 11,856 17,538 10,678 2753 6545 19,821 21,277
City street lighting size 584,172 1,344,947 584,172 500,123 500,123 318,333 131,213 247,589 695,594 584,422
Number of households withfixed
10,000 households 661.40 1430.30 436.70 694.20 662.20 380.00 105.90 140.60 671.80 390.50
broadband
Cell-phone-penetration rate size/100 people 106.49 92.67 97.36 88.08 110.04 96.22 102.09 116.16 89.77 112.36
Length of natural-gas pipeline Km 22,320 49,338 6414 5947 16,567 3140 2244 6279 5513 9680
Total number of seismic stations size 10 112 7 250 93 100 39 17 81 67
Number of automatic weather-station

size 1997 4753 2948 3414 1537 2159 437 893 2399 1688
sites
Fiber-optic cable-line length 10,000 km 93.07 250.61 86.62 108.85 108.67 72.59 21.04 19.86 109.35 98.56
Urban emergency-shelter area Km2 113.77 346.50 110.27 134.62 289.58 131.07 35.66 58.81 156.19 151.71
Disaster-relief-reserve-agency storage
Km2 30.99 70.86 27.94 36.51 27.14 30.37 13.42 13.50 52.48 48.26
area
Number of beds in various types of
10,000 sheets 76.93 79.48 144.13 114.25 87.05 78.18 175.28 81.04 63.53 105.13
medical and health institution
Greening coverage of built-up areas % 40.32 40.00 37.01 38.87 39.88 33.28 32.55 40.41 39.12 40.22
Annual disaster-related deaths People 52 186 68 110 69 22 17 0 92 18
CNY 100 million 24.50 153.90 57.60 76.60 162.90 105.10 17.40 12.00 99.00 126.50
disasters
Gross city product CNY 100 million 20,066.30 37,905.10 13,605.40 18,486.00 21,473.50 7336.70 2465.10 3200.30 17,790.70 14,898.10
Number of people affected in the city 10,000 people 163.87 218.41 253.80 291.16 399.12 321.54 110.17 138.48 180.81 466.86
Public-security financial rxpenditure CNY 100 million 235.91 471.42 268.09 343.26 241.82 170.38 90.04 64.59 283.17 250.09
Healthcare financial expenditure CNY 100 million 353.79 831.46 436.21 546.99 418.27 289.24 125.21 97.98 512.31 323.48
Transportation financial expenditure CNY 100 million 287.97 526.68 336.91 511.24 304.03 285.75 95.38 100.82 244.09 344.38
Inner
Mongolia
The total area of the urban built-up
Km2 1515.41 3054.31 1085.52 1217.60 1357.51 875.72 215.19 489.05 1542.78 1269.74
zone
Number of permanent residents in
10,000 people 1185.60 2214.03 681.49 890.89 1159.30 535.73 180.19 223.96 930.87 678.97
the built-up zone
The population of urban workers
10,000 people 720.60 1778.10 462.00 528.00 712.90 344.30 103.70 141.10 620.50 530.70
with basic medical insurance
Number of temporary urban
10,000 people 478.10 550.99 155.34 173.47 131.87 136.32 19.46 70.10 312.21 256.68
residents
Number of urban health technicians
people 93 95 94 138 110 102 133 111 94 144
per 10,000 people
Social-organization units size 17,553 44,932 13,753 23,640 30,548 24,644 6084 6083 27,118 16,998
insurance participants at the end of 10,000 people 661.67 1177.14 408.51 438.51 577.42 244.10 73.99 119.58 442.23 338.24
the year
City area Km2 7660 8610 3651 3204 2431 1978 696 952 5814 5082
The total area of the city’s
Km2 43,263.52 85,091.09 36,217.91 87,343.65 53,039.80 88,539.17 197,504.60 22,201.63 70,298.38 148,649.07
administrative zone
Area of land in areas of weak
security (industrial land plus Km2 294.09 558.56 200.1 162.29 207.83 242.03 31.28 59.3 253.11 198.61
logistics and storage land)
Construction-industry employees 10,000 people 216.18 351.36 80 141.54 145.21 50.85 8.1 11.25 141.94 20.52
Road area in the city 10,000 m2 22,160 42,936 11,786 15,050 21,039 12,450 3797 7625 26,726 21,571
City-street lighting size 775,469 1,550,215 626,167 623,986 824,590 354,166 146,458 238,104 654,416 576,538
10,000 households 920.40 1830.70 715.60 783.90 878.20 548.70 130.30 204.90 845.90 594.80
broadband
Cell-phone penetration rate size/100 people 117.75 112.76 111.78 100.10 119.72 103.92 110.73 119.24 103.38 118.59
Length of natural-gas pipelines Km 23,613 57,055 7816 7904 21,514 3817 2500 6906 8456 10,145
Number of automatic weather-
size 1805 5079 3026 2583 1575 1575 527 866 2303 1658
station sites
Disaster-relief-reserve-agency
Km2 31.46 76.17 37.78 35.57 33.27 41.56 16.79 13.87 46.65 51.28
storage area
10,000 sheets 87.85 86.02 81.61 104.14 80.46 90.81 105.52 79.31 66.12 113.87
Annual direct economic losses due
CNY 100 million 19.60 340.90 47.00 102.10 58.80 46.50 14.30 2.90 100.50 46.80
to disasters
Gross city product CNY 100 million 23,605.77 46,363.80 16,769.34 23,223.75 25,793.17 8718.30 2941.10 3748.48 21,237.14 17,212.53
Number of people affected in the

10,000 people 145.90 487.60 277.20 949.40 458.80 224.50 86.90 14.60 356.00 220.70
city
Public-security financial
CNY 100 million 268.66 525.64 280.06 382.83 285.90 191.29 89.57 66.74 312.18 249.06
expenditure
Transportation financial
CNY 100 million 292.35 687.83 347.79 542.81 283.94 360.35 172.6 88.87 219.49 403.38
expenditure
Inner
Mongolia
The total area of the urban built-up
Km2 1645 3367 1187 1252 1527 928 249 495 1679 1271
zone
Number of permanent residents in
10,000 people 1322.63 2514.51 726.22 771.19 1255.46 528.60 191.22 230.65 1027.68 682.13
the built-up zone
The population of urban workers
10,000 people 795.90 1945.80 479.40 569.20 783.80 372.30 114.80 159.60 714.80 564.70
with basic medical insurance
Number of temporary urban
10,000 people 429.54 864.43 184.76 193.73 179.90 181.82 19.27 68.31 345.08 276.07
residents
Number of urban health
people 77 100 102 120 102 111 125 104 103 116
technicians per 10,000 people
Social-organization units size 18,561 45,535 14,742 23,011 31,210 21,554 5997 5070 29,485 17,288
insurance participants at the end of 10,000 people 765.73 1472.06 529.94 541.91 629.61 278.74 95.93 143.79 551.31 338.22
the year
City area Km2 7781 9314 4049 3304 2619 2022 739 956 5306 4566
The total area of the city’s
Km2 43,263.52 92,234.06 41,808.54 91,678.06 56,687.11 89,281.63 203,423.45 21,889.03 78,641.38 148,694.54
administrative zone
Area of land in areas of weak

security (industrial land + logistics Km2 358.01 616.55 183.47 170.32 202.2 245.27 36.61 58.83 283.21 158.24
and storage land)
Construction-industry employees 10,000 people 205.54 364.57 71.58 120.21 129.83 46.01 6.01 11.09 118.51 15.49
Road area in the city 10,000 m2 26,320 56,342 20,027 18,536 24,556 15,074 4133 8193 32,100 23,057
City-street lighting size 899,100 2,166,500 820,600 761,700 852,900 434,900 152,600 248,100 831,100 622,300
10,000 households 984.30 2013.90 815.20 989.40 1213.60 661.30 152.10 246.90 1043.90 701.50
broadband
Cell-phone penetration rate size/100 people 116.77 111.55 110.85 107.58 120.83 110.23 114.56 119.46 109.42 125.71
Length of natural-gas pipeline Km 242,66 75,350 9938 9760 28,700 4625 4666 7566 12,985 11,891
Number of automatic weather-
size 1805 5472 2722 2711 1952 1478 549 861 2484 1727
station sites
Disaster-relief-reserve-agency
Km2 36.62 85.97 30.57 41.44 32.33 41.99 13.47 10.05 51.79 34.58
storage area
10,000 sheets 71.03 87 90.73 86.17 83.5 85.8 90.64 67.9 73.91 88.28
Annual direct economic losses due
CNY 100 million 29.80 248.70 30.20 104.90 317.30 67.30 45.70 13.70 22.80 76.40
to disasters
Gross city product CNY 100 million 27,894.00 53,850.80 19,586.40 27,146.80 29,801.00 10,243.30 3346.60 4522.30 24,740.90 20,514.20
Number of people affected in the
10,000 people 140.00 714.30 244.60 791.50 834.50 389.10 49.50 132.20 261.10 232.10
city
Public-security financial
CNY 100 million 273.20 531.89 274.27 375.60 289.72 193.55 93.90 63.95 288.38 249.79
expenditure
Transportation financial
CNY 100 million 277.44 717.35 336.45 601.4 325.83 287.29 189.44 84.35 335.51 345.99
expenditure
Appendix C
Table A5. Table of raw data for 2017 indicator-calculation variables.
Chongqin Inner
Indicators Unit Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Guangxi
g Mongolia
The resident population density in built-up areas 10,000 people/km2 0.79 0.73 0.63 0.75 0.78 0.61 0.88 0.51 0.63 0.53
Level of basic medical insurance for urban workers 10,000 people 640.3 1526.4 410.4 491.3 619.8 320.2 94 123.5 556.7 495.1
Percentage of transient population % 16.84 12.13 8.59 7.35 5.37 14.40 9.34 17.23 9.53 19.38
Level of urban-health-technology talent pool People 79 85 156 146 116 87 220 106 91 130
Number of hospitals Block/100 km2 1.73 2.69 3.72 1.48 2.34 0.60 0.13 0.88 0.86 0.49
Social-organization-unit level size 16,824 42,282 12,700 23,184 24,725 27,079 5291 6548 24,567 15,116
Personal accident insurance income CNY 100 million 560.13 1441.28 210.05 357.52 654.8 254.06 46.86 109.25 369.13 390.23
Urban commercial insurance income CNY 100 million 744 1937.64 389.31 612.66 869.01 366.38 80.2 165.29 565.11 570.06
Number of people covered by work-injury insurance 10,000 people 504.61 876.04 332.48 383.67 459.35 198.58 64.85 90.35 388.79 307.76
Land-development intensity % 17.20 10.14 9.32 3.72 5.34 1.82 0.41 9.11 8.45 3.32
The proportion of land area in security-vulnerable
% 19.37 18.42 18.58 13.93 13.45 23.21 13.33 12.24 19.30 16.52
zones
Number of employees in construction-industry
10,000 people 224.79 352.83 77.64 152.72 137.98 56.88 11 12.42 126.15 27.7
enterprises
Road-network density % 43.95 41.22 26.13 13.98 35.75 12.21 1.66 27.62 28.92 14.47
Level of urban traffic-lighting facilities Size 584,172 1,344,947 584,172 500,123 500,123 318,333 131,213 247,589 695,594 584,422
Department/100
Cell-phone penetration rate 106.49 92.67 97.36 88.08 110.04 96.22 102.09 116.16 89.77 112.36
people
Number of fixed-broadband households 10,000 households 661.4 1430.3 436.7 694.2 662.2 380 105.9 140.6 671.8 390.5
Level of gas-supply facilities Km 22,320 49,338 6414 5947 16,567 3140 2244 6279 5513 9680
Level of seismic-monitoring facilities Block/10,000 km2 2.31 13.59 2.05 29.47 18.96 11.44 2.34 7.17 11.82 4.56
Public coverage of meteorological disaster monitoring
pcs/100 km2 4.62 5.77 8.63 4.03 3.13 2.47 0.26 3.77 3.50 1.15
and forecasting early-warning information
Urban intelligent-pipe-network density /km 21.51 30.40 25.34 12.83 22.15 8.30 1.26 8.38 15.95 6.70
Shelter area per capita m2/people 4.57 8.40 8.04 8.12 16.64 14.63 15.72 17.23 6.53 15.44
Storage area of disaster-relief-reserve institutions per
m2/10,000 people 12,446.18 17,168.53 20,368.89 22,018.12 15,596.45 33,899.63 59,158.03 39,546.53 21,932.65 49,131.09
10,000 people
Number of beds in medical and health institutions per

Size/10,000 people 76.93 79.48 144.13 114.25 87.05 78.18 175.28 81.04 63.53 105.13
10,000 people
Greenery coverage % 40.32 40.00 37.01 38.87 39.88 33.28 32.55 40.41 39.12 40.22
The disaster-related death rate per million population % 0.02 0.05 0.05 0.07 0.04 0.02 0.07 0.00 0.04 0.02
Annual direct economic losses due to disasters as a
% 0.12 0.41 0.42 0.41 0.76 1.43 0.71 0.37 0.56 0.85
percentage of regional GDP
Percentage of people affected in a year % 6.58 5.29 18.50 17.56 22.94 35.89 48.57 40.57 7.56 47.53
Public-security financial expenditure CNY 100 million 235.91 471.42 268.09 343.26 241.82 170.38 90.04 64.59 283.17 250.09
Inner
Indicators Unit Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Guangxi
Mongolia
The resident population density in built-up areas 10,000 people/km2 0.78 0.72 0.63 0.73 0.85 0.61 0.84 0.46 0.60 0.53
Level of basic medical insurance for urban workers 10,000 people 720.6 1778.1 462 528 712.9 344.3 103.7 141.1 620.5 530.7
Level of urban-health-technology talent pool People 93 95 94 138 110 102 133 111 94 144
Urban commercial insurance income CNY 100 million 916.46 2148.66 489.26 742.1 1033.49 444.32 98.44 197.67 664.92 729.82
Number of people covered by work-injury insurance 10,000 people 661.67 1177.14 408.51 438.51 577.42 244.1 73.99 119.58 442.23 338.24
% 19.41 18.29 18.43 13.33 15.31 27.64 14.54 12.13 16.41 15.64
zones
10,000 people 216.18 351.36 80 141.54 145.21 50.85 8.1 11.25 141.94 20.52
enterprises
Department/100
people
Number of fixed-broadband households 10,000 households 920.4 1830.7 715.6 783.9 878.2 548.7 130.3 204.9 845.9 594.8
Level of gas-supply facilities Km 23,613 57,055 7816 7904 21,514 3817 2500 6906 8456 10,145
pcs/100 km2 4.17 5.97 8.35 2.96 2.97 1.78 0.27 3.90 3.28 1.12
Urban intelligent-pipe-network density /km 27.78 39.12 31.78 22.93 28.89
10.03 1.65 10.82 25.01 8.81
Shelter area per capita m2/people 4.94 9.12 7.22 9.82 11.47
12.49 17.09 23.78 10.28 15.14
Storage area of disaster-relief-reserve institutions per 45,693.4
m2/10,000 people 12,257.89 18,257.87 25,881.50 20,680.23 16,732.80 72190.21 40,166.81 19,294.80 51,379.16
10,000 people 3
Size/10,000 people 87.85 86.02 81.61 104.14 80.46 90.81 105.52 79.31 66.12 113.87
10,000 people
The disaster-related death rate per million population % 0.01 0.04 0.05 0.04 0.03 0.02 0.04 0.01 0.04 0.01
% 0.08 0.74 0.28 0.44 0.23 0.53 0.49 0.08 0.47 0.27
Inner
Indicators Unit Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Guangxi
Mongolia
The resident population density in
10,000 people/km2 0.80 0.75 0.61 0.62 0.82 0.57 0.77 0.47 0.61 0.54
built-up areas
Level of basic medical insurance for
10,000 people 795.9 1945.8 479.4 569.2 783.8 372.3 114.8 159.6 714.8 564.7
urban workers
Level of urban-health-technology talent
People 77 100 102 120 102 111 125 104 103 116
pool
Urban commercial insurance income CNY 100 million 965.5 2204.91 496.26 690.2 1052.37 490.32 106.89 211.14 780.6 645.56
Number of people covered by work-
10,000 people 765.73 1472.06 529.94 541.91 629.61 278.74 95.93 143.79 551.31 338.22
injury insurance
The proportion of land area in security-
% 21.76 18.31 15.46 13.60 13.24 26.43 14.70 11.88 16.87 12.45
vulnerable zones
Number of employees in construction-
10,000 people 205.54 364.57 71.58 120.21 129.83 46.01 6.01 11.09 118.51 15.49
industry enterprises
Department/100
people
Number of fixed-broadband
10,000 households 984.3 2013.9 815.2 989.4 1213.6 661.3 152.1 246.9 1043.9 701.5
households
Level of gas-supply facilities Km 24,266 75,350 9938 9760 28,700 4625 4666 7566 12,985 11,891
Public coverage of meteorological
disaster monitoring and forecasting pcs/100 km2 4.17 5.93 6.51 2.96 3.44 1.66 0.27 3.93 3.16 1.16
early-warning information
Urban intelligent-pipe-network density /km 327,134.73 406,357.48 321,967.71 25,8655.12 316,773.95 116,776.54 18,188.66 134,222.48 309,061.21 106,110.15
Shelter area per capita m /people
2 4.81 9.03 6.25 8.51 9.94 12.98 14.85 20.87 10.67 19.46
Storage area of disaster-relief-reserve
m2/10,000 people 13,819.75 18,911.55 18,506.97 22,984.55 15,157.91 46,581.54 45,219.55 22,847.66 19,345.71 37,833.28
institutions per 10,000 people
Number of beds in medical and health
Size/10,000 people 71.03 87.00 90.73 86.17 83.50 85.80 90.64 67.90 73.91 88.28
institutions for 10,000 people
The disaster-related death rate per
% 0.01 0.01 0.00 0.02 0.03 0.00 0.04 0.00 0.00 0.03
million population
disasters as a percentage of regional % 0.11 0.46 0.15 0.39 1.06 0.66 1.37 0.30 0.09 0.37
GDP
Appendix D
Table A8. Catastrophe-level values for each bottom metric in 2017.
Catastrophe-Level Values
Indicators Inner
Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Guangxi
Mongolia
The resident population density in built-up areas 0.790 0.649 0.418 0.701 0.764 0.367 1.000 0.120 0.419 0.164
Level of basic medical insurance for urban workers 0.295 0.774 0.171 0.215 0.284 0.122 0.000 0.016 0.250 0.217
Percentage of transient population 0.357 0.621 0.820 0.889 1.000 0.494 0.778 0.335 0.767 0.214
Level of urban-health-technology talent pool 0.014 0.056 0.552 0.483 0.273 0.070 1.000 0.203 0.098 0.371
Number of hospitals 0.450 0.716 1.000 0.379 0.620 0.137 0.005 0.214 0.208 0.105
Social-organization-unit level 0.290 0.920 0.189 0.448 0.486 0.544 0.005 0.037 0.482 0.248
Personal accident insurance income 0.321 0.871 0.102 0.194 0.380 0.129 0.000 0.039 0.201 0.215
Urban commercial insurance income 0.312 0.874 0.145 0.251 0.371 0.135 0.000 0.040 0.228 0.231
Number of people covered by work-injury insurance 0.313 0.576 0.190 0.227 0.280 0.095 0.000 0.018 0.230 0.173
Land-development intensity 0.955 0.555 0.508 0.191 0.283 0.083 0.003 0.497 0.459 0.168
0.525 0.585 0.575 0.870 0.901 0.281 0.909 0.977 0.529 0.706
zones
0.610 0.967 0.200 0.409 0.368 0.142 0.014 0.018 0.335 0.060
enterprises
Road-network density 0.712 0.666 0.412 0.207 0.574 0.178 0.000 0.437 0.459 0.216
Level of urban traffic-lighting facilities 0.223 0.596 0.223 0.181 0.181 0.092 0.000 0.057 0.277 0.223
Number of fixed-broadband households 0.291 0.694 0.173 0.308 0.292 0.144 0.000 0.018 0.297 0.149
Level of gas-supply facilities 0.275 0.644 0.057 0.051 0.196 0.012 0.000 0.055 0.045 0.102
Level of seismic-monitoring facilities 0.004 0.137 0.001 0.323 0.200 0.111 0.004 0.061 0.116 0.030
Public coverage of meteorological disaster

monitoring and forecasting early-warning 0.521 0.658 1.000 0.450 0.343 0.264 0.000 0.419 0.387 0.106
information
Urban intelligent-pipe-network density 0.514 0.740 0.612 0.294 0.531 0.179 0.000 0.181 0.373 0.138
Shelter area per capita 0.000 0.199 0.181 0.185 0.628 0.524 0.580 0.659 0.102 0.566
Storage area of disaster-relief-reserve institutions
0.003 0.082 0.135 0.163 0.056 0.361 0.783 0.455 0.161 0.615
per 10,000 people
Number of beds in medical and health institutions
0.120 0.143 0.721 0.454 0.210 0.131 1.000 0.157 0.000 0.372
per 10,000 people
Greenery coverage 0.736 0.706 0.423 0.599 0.695 0.069 0.000 0.745 0.623 0.727
The disaster-related death rate per million
0.721 0.399 0.338 0.115 0.471 0.672 0.000 1.000 0.487 0.755
population
0.967 0.757 0.745 0.751 0.497 0.000 0.536 0.780 0.646 0.431
Percentage of people affected in a year 0.954 0.979 0.720 0.738 0.633 0.379 0.130 0.287 0.935 0.150
Public-security financial expenditure 0.367 0.871 0.436 0.597 0.380 0.227 0.056 0.001 0.468 0.398
Healthcare financial expenditure 0.314 0.870 0.410 0.539 0.389 0.239 0.048 0.016 0.498 0.278
Transportation financial expenditure 0.304 0.608 0.366 0.588 0.324 0.301 0.058 0.065 0.248 0.376
Catastrophe-Level Values
Indicators Inner
Mongolia
Number of people covered by work-injury insurance 0.424 0.790 0.244 0.266 0.364 0.127 0.006 0.039 0.268 0.194
0.523 0.594 0.584 0.908 0.783 0.000 0.832 0.985 0.713 0.762
zones
0.586 0.963 0.206 0.378 0.388 0.125 0.006 0.015 0.379 0.040
enterprises
Level of urban traffic-lighting facilities 0.317 0.697 0.243 0.242 0.341 0.110 0.007 0.053 0.257 0.219
0.467 0.682 0.967 0.323 0.324 0.181 0.001 0.435 0.361 0.103
Storage area of disaster-relief-reserve institutions per
0.000 0.100 0.227 0.141 0.075 0.558 1.000 0.466 0.117 0.653
10,000 people
0.218 0.201 0.162 0.363 0.151 0.244 0.376 0.141 0.023 0.450
10,000 people
The disaster-related death rate per million population 0.860 0.491 0.305 0.457 0.651 0.677 0.484 0.884 0.426 0.893
0.996 0.515 0.850 0.733 0.889 0.664 0.698 1.000 0.708 0.856
Catastrophe-level values
Indicators Inner
Mongolia
Number of people covered by work-injury
0.498 1.000 0.331 0.339 0.401 0.152 0.022 0.056 0.346 0.194
insurance
0.373 0.592 0.773 0.891 0.914 0.077 0.821 1.000 0.684 0.964
zones
0.556 1.000 0.183 0.318 0.345 0.112 0.000 0.014 0.314 0.026
enterprises
Level of urban traffic -ighting facilities 0.377 1.000 0.339 0.310 0.355 0.149 0.011 0.057 0.344 0.241
Public coverage of meteorological disaster
monitoring and forecasting early-warning 0.467 0.678 0.747 0.322 0.380 0.167 0.001 0.439 0.346 0.107
information
Storage area of disaster-relief-reserve institutions
0.026 0.111 0.104 0.179 0.048 0.573 0.550 0.177 0.118 0.427
per 10,000 people
Number of beds in medical and health institutions

0.067 0.210 0.243 0.203 0.179 0.199 0.243 0.039 0.093 0.221
per 10,000 people
The disaster-related death rate per million
0.904 0.909 0.960 0.719 0.650 0.985 0.418 0.939 0.965 0.664
population
0.978 0.716 0.943 0.772 0.271 0.572 0.049 0.834 0.989 0.782
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Article

Research on the Evaluation and Spatial–Temporal Evolution of

Sustainability 2023, 15, 9698. https://doi.org/10.3390/su15129698 www.mdpi.com/journal/sustainability

safety-resilience-indicator frameworks, mainly focusing on urban systems and services,

2.2. Related Research on Urban-Safety Resilience

2.3. Methods, Scales, and Gaps in Current Urban-Safety Resilience

terms of social resilience, infrastructure resilience, economic resilience, environmental re-

3.1.2. Principles for Constructing the Indicator System

3.1.3. Establishment of an Urban-Safety-Resilience-Index System

Table 1. Evaluation-index system of urban-safety resilience.

General Goal Tier 1 Indicators Tier 2 Indicators Tier 3 Indicators

3.1.4. Study Area and Time

3.2. Catastrophe-Progression Method

Table 2. Comparison of evaluation methods.

problems with statistical Deterministic problems need to

𝑥𝑥𝑗𝑗 − 𝑥𝑥𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚

Interval optimal indicators have the corresponding formula:

𝑦𝑦𝑗𝑗 = 1, �𝑎𝑎 ≤ 𝑥𝑥𝑗𝑗 ≤ 𝑏𝑏� (3)

The optimal interval for the indicator data is [𝑎𝑎, 𝑏𝑏].

Catastrophe Status Control

Table 4. Ranking of the weighting of urban-safety and -resilience indicators.

Table 5. Internal-relationship table of indicators at the same level.

Indicator Levels Hierarchical Internal Metrics Internal Relations

Based on the normalized formula, indicator weights, and indicator-relationship prin-

Figure 1. Catastrophe model of urban-safety resilience.

3.3. Spatial Statistical Theory

Table 6. Spatial matrix relationships.

Spatial Matrix Type Spatial Matrix Relationship

Figure 2. Urban-safety-resilience levels in western China in 2017, 2019, and 2021.

resilience grade by using the urban-safety-resilience catastrophe-level values for 2017,

2017 2019 2021

4.2. Spatial Evolution Analysis of Urban-Safety Resilience

4.2.1. Analysis of the Spatial Distribution

According to the preliminary analysis in Figure 6, the urban-safety-resilience levels

According to Figure 7, the distribution of each Tier 1 indicator showed an enormous

Figure 8. The results of the global spatial-correlation analysis for 2017.

Figure 9. The results of the global spatial-correlation analysis for 2019.

(2) Local Correlations

Table 8. Spatial-correlation scatter-plot analysis.

② Significance plot and clustering plot

Table 9. Spatial-correlation LISA significance-plot analysis.

2021 Urban- 2021 Urban-

4.3. Analysis of Results

5. Conclusions and Recommendations

Table A1. Initial evaluation indicators and description of urban-safety resilience.

Table A2. Table of raw data for each indicator in 2017.

Number of automatic weather-station

Table A3. Table of raw data for each indicator in 2019.

Number of people affected in the

Table A4. Table of raw data for each indicator in 2021.

Area of land in areas of weak

Table A5. Table of raw data for 2017 indicator-calculation variables.

Number of beds in medical and health institutions per

Table A6. Table of raw data for 2019 indicator-calculation variables.

Table A7. Table of raw data for 2021 indicator-calculation variables.

Table A8. Catastrophe-level values for each bottom metric in 2017.

Public coverage of meteorological disaster

Table A9. Catastrophe-level values for each bottom metric in 2019.

Table A10. Catastrophe-level values for each bottom metric in 2021.