A Systematic Literature Review on Urban Resilience Enabled with Asset and Disaster Risk Management Approaches and GIS-Based Decision Support Tools

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This paper presents a review of literature on urban resilience, highlighting research gaps and suggesting solutions such as using asset and disaster risk management methods combined with GIS-based decision-making tools to improve resilience in urban areas. This can be applied in the field of urban planning and design, disaster risk management and asset management planning decisions to enhance the ability of cities and communities to optimally withstand and recover from disruptions.

Abstract

Urban Resilience (UR) enables cities and communities to optimally withstand disruptions and recover to their pre-disruption state. There is an increasing number of interdisciplinary studies focusing on conceptual frameworks and/or tools seeking to enable more efficient decision-making processes that lead to higher levels of UR. This paper presents a systematic review of 68 Scopus-indexed journal papers published between 2011 and 2022 that focus on UR. The papers covered in this study fit three categories: literature reviews, conceptual models, and analytical models. The results of the review show that the major areas of discussion in UR publications include climate change, disaster risk assessment and management, Geographic Information Systems (GIS), urban and transportation infrastructure, decision making and disaster management, community and disaster resilience, and green infrastructure and sustainable development. The main research gaps identified include: a lack of a common resilience definition and multidisciplinary analysis, a need for a unified scalable and adoptable UR model, margin for an increased application of GIS-based multidimensional tools, stochastic analysis of virtual cities, and scenario simulations to support decision making processes. The systematic literature review undertaken in this paper suggests that these identified gaps can be addressed with the aid of asset and disaster risk management methods combined with GIS-based decision-making tools towards significantly improving UR.

1. Introduction

Urban settlements are expected to house more than 60% of the world’s population by 2030. According to UN forecasts, there are already over 4 billion urban inhabitants worldwide, with more than 863 million unofficial residents in urban settlements. This number is projected to grow at a rate of over 1 million every 10 days [1]. Urban areas produce more than 75% of the global GDP and account for the majority of global energy consumption. Cities also contribute to 70% of global greenhouse gas emissions. Additionally, 90% of metropolitan areas are located on coasts, exposing a large portion of the worldwide population to disaster risks arising from climate change [2].

As urbanization continues to increase, tackling the problems associated with urbanization and climate change requires innovative sustainable solutions to enhance Urban Resilience (UR). UR is a concept that addresses the issues of urbanization and climate change in all its facets.

The study’s relevance and significance can be found in the fact that natural hazards such as earthquakes, floods, windstorms, tsunamis, and volcanic eruptions pose a perpetual threat to the safe and effective functioning of critical infrastructures in a critical public service context [3]. These natural disasters have the potential to disrupt the flow of information and trade, as well as compromise security and safety [4]. This is particularly true in the current global economy, where supply chain interruptions are becoming increasingly common [5,6].

Implementing efficient urban resilience (UR) concepts requires a multidisciplinary approach that involves all relevant stakeholders. A long-term strategy is essential for achieving sustainable UR. To enhance resilience and prepare for natural disasters, cities must focus on building early warning systems, developing emergency operations plans, and implementing risk mitigation measures within their communities [7,8,9].

Enhancing Urban Resilience (UR) requires a range of solutions that can be implemented at different levels and by various stakeholders [10,11]. These solutions can include regulations, legislation, guidelines on technical issues such as building codes or land use planning, financing for services and critical infrastructure assets, and urban planning tools such as zoning plans. Additionally, partnerships between local authorities and various organizations can play an important role in implementing UR strategies [12,13].

In recent years, experts and politicians have been focusing on identifying the most effective techniques for dealing with natural disasters in cities. This has been driven by the increased frequency and severity of natural disasters due to climate change, and the need to better understand how cities can withstand these events and prepare for them [14,15,16].

The purpose of this review paper is to examine the major trends in Urban Resilience (UR) research and explore how management approaches, decision science methods and tools can support the achievement of the United Nations (UN) Agenda for Sustainable Development by increasing resilience in cities and communities. Additionally, the paper aims to identify research gaps and potential opportunities to enhance multidisciplinary UR decision-making processes.

This paper is divided into six sections. The introduction provides an overview of the motivation and scope of the review, as well as the objectives of the paper. The second section examines the background knowledge and relevant approaches and techniques that can impact UR, including how UR and sustainability can be enhanced through asset management and risk management approaches and decision science and support tools, specifically GIS-based tools, to improve the performance of assets and asset systems in cities during natural and man-made disasters. The third section details the methodology used to conduct the systematic review, including the PRISMA protocol, keywords, and selection of studies. The fourth section presents a bibliometrics and results analysis, including data visualization. The fifth section discusses the findings of the study and highlights research gaps and current trends, as well as uses natural language-processing techniques. The final section concludes the study and suggests areas for future research.

2. Background Knowledge

This section presents the background knowledge of two key conceptual constructs for maximizing and protecting the value of constructed assets during disaster risk events: asset management and risk management. Additionally, it highlights relevant decision-making support tools and analytical solutions that can be integrated to support the achievement of the United Nations 2030 Agenda for Sustainable Development’s twin goals of creating resilient and sustainable cities. Figure 1 illustrates a conceptual framework for improving multidisciplinary decision-making towards sustainability and urban resilience. This framework is further explored in the following three sections: (1) resilience and sustainability of urban infrastructure and buildings; (2) asset and disaster risk management; (3) decision science support mechanisms and tools.

2.1. Asset and Disaster Risk Management

An asset is a tangible or intangible item that has value or potential value to a person or organization [17]. Asset management, as defined by international standards, is the coordinated effort of an organization to maximize value from its assets by balancing risk, cost, opportunity, and performance throughout their lifecycles [18]. In the context of urban resilience, asset management is critical for preventing future unfavorable events and ensuring assets are prepared for them. Public and private sectors, as well as regional and state governments, must invest in asset resilience to achieve this [19,20].

The asset management approach plays a crucial role in allocating limited resources (people, money, time, natural resources, etc.) to initiatives that yield the greatest value for all stakeholders throughout the lifecycle of urban assets and systems. Many organizations worldwide have implemented Asset Management Systems (AMS) that comply with the ISO 55000 family of standards to develop consistent strategies and coordinate the delivery of resources and tasks to maximize profitability [21,22,23].

Asset management is a crucial component of risk management, as it addresses the financial and reputational risks associated with speculation. According to international standards on risk management (ISO 31000), risk is defined as “the effect of uncertainty on objectives” and risk management involves “coordinated activities to direct and control an organization with regard to risk.” These standards provide guidelines for designing, implementing, and continually improving risk management processes throughout an organization [24].

The decision-making process in risk management involves assessing the appropriate level of risk for a certain choice and determining the steps to be taken in case of a risk event. Both risk management and asset management aim to ensure that resources are allocated to initiatives that benefit the community [25,26,27].

To build urban resilience, national and municipal governments must establish local disaster risk-management strategies to mitigate the impact of climate change [28]. This includes regularly reporting on small-scale onset hazardous occurrences that are not recorded in global catastrophe loss databases [29]. It is also crucial to acquire consistent data on losses from all dangers and underlying concerns.

However, the implementation of findings from the Habitat III Urban System Model may face obstacles due to a lack of transparency, flaws in urban governance, and constraints in financial and human resources. These factors can lead to socioeconomic evaluation biases and lower performance of urban resilience.

Vulnerability assessment is an important aspect of the climate risk assessment process, as it identifies potential disruption to the community caused by climatic impacts. Urban risk governance involves the diverse roles and responsibilities of different players in minimizing urban risks. The government plays a crucial role in developing national policies, implementing mitigation measures, and establishing emergency response procedures. Local governments also play a role in urban risk management through land-use planning, construction rules, disaster preparedness programs, and evacuation plans. Community members, including households and individuals, can also improve resilience by implementing disaster preparedness measures [30].

Private sector organizations play an important role in urban risk management, as they develop buildings or infrastructure projects that are sensitive to natural disasters. Civil society organizations provide input into public decision-making processes about policy implementation targeted at decreasing dangers for communities living in high-risk areas. International organizations, such as the United Nations, may also help countries with limited resources implement their policy agendas by providing financial assistance or technical expertise.

Both asset and risk management approaches offer critical processes for controlling and minimizing hazards in urban systems, and for improving the safety, reliability, and efficiency of assets and asset systems [31,32]. Resilient systems are built and utilized for recovery and adaptation rather than just resistance to the initial disturbance. Resilience thinking supports asset and disaster risk management by accelerating system recovery, especially when common risk management measures struggle to mitigate a disruption [33]. The importance of resilience as applied to urban infrastructure and buildings, and its role in achieving sustainable development goals, will be discussed in the next section.

2.2. Resilience and Sustainability of Urban Infrastructure and Buildings

Cities currently house more than half of the world’s population, and this figure is expected to increase by 2.5 billion people by 2050, with the majority of this growth occurring in emerging nations [34]. While cities have traditionally been associated with wealth, progress, and opportunity, they are also facing unprecedented levels of inequality and poverty. Urbanization also has an impact on natural resources and ecosystems, as well as climate change mitigation efforts, due to the heavy reliance on fossil fuels for electricity in cities.

Natural catastrophes pose a significant threat to cities, as seen in the examples of Hurricane Katrina in 2005 and Hurricane Harvey in 2017, which resulted in significant loss of life and damage [35]. In order to improve resilience and sustainability in coastal regions, it is crucial to understand the vulnerability concepts and existing definition of vulnerability [36]. This section will focus on the importance of resilience and sustainability in urban infrastructure and buildings and will highlight measures that can be taken to enhance resilience in the face of natural disasters and climate change.

Beyond risk management, resilience management addresses the complexity of large interconnected systems and the unpredictability of future risks, particularly those related to climate change [33]. Resilience management includes: performing preparation planning and training, adhering to inspection and maintenance procedures and improving them (asset management), developing, executing, and upgrading risk management processes, revising design requirements in response to varied feedbacks, participating in various industrial associations, as well as standard committees and regulatory bodies, adopting resilience-based asset management principles and techniques in the face of deep uncertainty and different disruptive occurrences, and preparing for foreseeable global shocks to maintain economic sustainability and provide a sufficient service level to clients.

Recognizing the significance of resilience and sustainability in buildings and infrastructure is crucial, as both resilience and sustainability are essential in the face of climate change and its effects on the built environment. In this context, resilience refers to a structure’s ability to survive disruptions such as floods, fire [37], and earthquakes and other natural disasters, whereas sustainability relates to the capacity of buildings and infrastructures to be environmentally sustainable [38,39].

Resilience is a system’s ability to adapt to change while maintaining its fundamentally specified performance [40]. Resilient communities are able to endure, absorb, or recover quickly from catastrophic events such as floods [41,42,43], earthquakes [44], hurricanes [45] or heat waves [46] because they were constructed with hazard risks in consideration through integrated planning methods that handle several hazards concurrently.

UR refers to the quantifiable capacity of any urban system, together with its residents, to preserve continuity despite all shocks and pressures while constructively adapting and reforming toward sustainability [47]. A resilient city is one that evaluates, plans for, and takes action to cope to natural and man-made disasters, both predicted and unforeseen [48]. Resilient cities are better prepared to preserve development achievements and improve the lives of citizens.

Urban resilience’s ultimate goal is to increase cities’ capacities to recover from natural disasters. Efforts to achieve this goal are being made by various prominent actors, such as The World Bank Group, Global Facility for Disaster Reduction and Recovery, 100 Resilient Cities, UNISDR, C40, Inter-American Development Bank, Rockefeller Foundation, ICLEI, and Cities Alliance. The 7th World Urban Forum session in Medellin, Colombia in 2014 at UN-Habitat, known as the Medellin Collaboration, brought together influential players focused on developing resilience globally [49].

UN-Habitat, the Global Covenant of Mayors, and the Intergovernmental Panel on Climate Change also hosted the Cities and Climate Science Innovate4Cities Conference, which brought together approximately 200 gatherings and approximately 7000 participants from 159 countries to promote understanding and technology for urban climate policy [50].

The Medellin Collaboration developed a platform to assist regional authorities and relevant municipal experts in understanding the fundamental purpose of the wide range of tools and diagnostics created to test, evaluate, track, and enhance city-level resilience. These tools range from self-deployable quick evaluations to create an overall understanding and benchmark of a city’s resilience, to action-oriented tools that require more advanced institutional, technical, and economic capacities to implement, and others that are designed to pinpoint and prioritize budget allocation.

The Rockefeller Foundation has developed the 100 Resilient Cities program to promote urban resilience, which is defined as the ability of individuals, communities, institutions, enterprises, and systems within a city to endure, adapt, and thrive in the face of recurrent pressures and severe disruptions [51,52,53]. The City Resilience Index (CRI), created by Arup and financed by The Rockefeller Foundation, is the result of five years of study and testing. It is a tool that helps cities understand and address these concerns in a systematic manner. The CRI has four main dimensions: (1) health and well-being, including minimum human vulnerability, a variety of livelihoods and job opportunities, and strong safeguards for human health and life; (2) economy and society, including economic sustainability, total security and the rule of law, and shared identity and citizen involvement; (3) infrastructure and environment, including decreased vulnerability and fragility, efficient delivery of key services, and reliable transportation and connectivity; (4) leadership and strategy, including integrated development planning, empowered actors, and efficient management and leadership.

Information interchange among critical infrastructures is essential for identifying interdependencies and enhancing their resilience. For example, DOMINO is a tool developed by the Centre Risque & Performance, Polytechnique Montréal (Québec, Canada) that enables multi-organizational collaboration and can aid in solving complex problems through knowledge sharing [54]. This tool can recognize the interrelations among critical infrastructures and simulate potential domino effects of their failure. This means that upstream work is done within major infrastructure organizations to encourage them to implement more strategic, holistic, and integrated asset, risk, and resilience management methods. Only then can successful and long-term collaboration among critical infrastructures be possible [55].

It is difficult or impossible to regulate highly interconnected systems, which are prone to breakdowns at all scales, posing major hazards to civilization even in the absence of external shocks. New vulnerabilities are emerging as a result of the growing interdependence of our energy, food, and water infrastructure, global supply chains, financial and communication systems, ecosystems, and climate [56].

However, it has also been argued that cities, despite being highly interconnected systems, are also resilient complex systems. For many years, cities have endured natural and man-made disasters and, in some cases, have even become more robust and resilient in the face of disasters [57]. However, there are new hazards and concerns for cities [58] that are expressed in Goals 9 and 11 of the 2030 Agenda for Sustainable Development of the United Nations (UN).

Urban sustainability and resilience are integral to achieving the United Nations’ Sustainable Development Goals (SDGs) [59,60]. With an increasing global population and complex urban development demands, revolutionary solutions are needed to meet the challenges of urbanization and climate change [58,61,62,63].

Goal 9 of the 2030 Agenda for Sustainable Development of United Nation (UN) refers to “Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation”. This goal is a reminder that, when natural catastrophes strike, urban regions suffer more mortality and economic losses than rural areas because of the influx of population, structures, industries, and assets, including the densely interwoven infrastructures [64]. Megacities’ interconnected infrastructures are vulnerable to cascading system failures such as in roads and railways, water and energy supply networks, telecommunication systems, sewage systems, and green infrastructures [65]. Governments and companies are being forced to recognize and handle the larger and more rapidly altering environment. One can for example consider the risks arising from the failure of energy, or communication, systems. Cascading failures introduce a new hazard potential that cannot be fully addressed by minimizing risks in single system components [66].

Furthermore, a significant portion of the global population explosion is concentrated in low-lying coastal cities, which are susceptible to urbanization and the effects of sea level rise and storm surge [67,68]. Goal 11 aims to make cities and settlements inclusive, safe, resilient, and sustainable to address the reality that over half of the world’s population now resides in urban areas and to decrease the threat of natural disasters caused by urbanization. Climate change impacts such as extreme weather events can cause significant damage and economic loss across many locations. Smart city design can help reduce vulnerability to these disasters and the need for international collaboration on this issue is more important than ever.

Millions of people live in cities, which are complex asset systems. They are sources of economic development and job prospects, but are also some of our planet’s most vulnerable areas in terms of climate change implications. As a result, this objective seeks to enhance people’s lives by ensuring the sustainable management and control of cities’ resources while lowering their environmental impact. This includes safeguarding human settlements against natural disasters (such as earthquakes or floods), reducing their vulnerability to disasters through risk reduction measures (such as better housing construction), ensuring access to clean water supply systems by promoting proper sanitation facilities (such as toilets), improving waste management services (including recycling), and making urban environments more resilient to extreme heat events such as droughts or floods [69].

Goal 11.1 covers Disaster Risk Reduction (DRR) as an essential component of social and economic development if growth is to be long-term. Several worldwide documents on disaster risk reduction and sustainable development have acknowledged this. As the first major worldwide framework for disaster risk reduction, the Yokohama Strategy and Plan of Action for a Safer World (1994) acknowledged the interdependence of sustainable development and disaster risk reduction [70]. Since then, this close interdependence has been continuously reinforced within key global agreements, ranging from the Millennium Development Goals to the Johannesburg Plan of Implementation (Johannesburg, September 2002), the “Hyogo Framework for Action (2005–2015)” and the “Future We Want” [71], the Sendai Framework for Disaster Risk Reduction [72], and the 2030 Agenda for Sustainable Development.

According to the United Nations International Strategy for Disaster Reduction, communities are becoming increasingly vulnerable to global climatic change consequences, particularly drought, floods, heat stress, severe rainfall events, and other natural disasters [61,73,74].

Hydro Quebec provides the example of an ice storm case study that motivated improvements in the mechanical strength of the grid infrastructure. New construction standards were established and vegetation around transmission and distribution lines was better controlled; the transmission and distribution system was reconfigured to increase the security of the energy sources and include backup sources of supply in the event of line failures [75].

Goal 11.2 relates to sustainable cities and human settlements. Cities currently house more than half of the world’s population. This figure is predicted to expand by 2.5 billion people by 2050, with the majority of this expansion occurring in emerging nations.

In order to show that the UN sustainable development goals can only be achieved if the elements and processes of geodiversity are unquestionably taken into account in the global agenda, a review studied the geodiversity concept and draws connections with well-established concepts and strategies, specifically the ones related with natural capital and ecosystem services [76].

Cities have long been associated with riches, growth, and opportunity, but they are also experiencing unprecedented levels of inequality and poverty. Urbanization has an impact on natural resources and ecosystems, as well as climate change mitigation efforts, because cities rely heavily on fossil fuels for energy. Natural catastrophes pose a threat to cities. We have seen some of the biggest disasters caused by catastrophic weather occurrences during the last few decades. Cities they may be strengthened using an effective UR strategy to cut losses and enhance the effectiveness of the present asset systems.

2.3. Decision Science Support Mechanism and/or Tools

Decision science has applications in various fields of study and is recognized to provide supportive tools for different types of decision makers to make a concise and unbiased decision [77]. With regards to UR, there are few decision-making studies employing hard data in the post-disaster area, although this is critical to examine observable environmental aspects rather than depending simply on expert opinion. Employing only tacit knowledge is unproductive [78].

Disaster Risk Management (DRM) is a complex process that involves evaluating and mitigating the potential impacts of natural and man-made hazards on communities and infrastructure. Multicriteria Decision Making (MCDM) methods can be useful in DRM by helping decision makers to evaluate and compare alternative options for risk reduction and response [79,80].

Some of the most widely used MCDM [81] methods in DRM include (i) Analytic Hierarchy Process (AHP): AHP is a method that breaks down a complex decision problem into a hierarchy of smaller, more manageable sub-problems. It is particularly useful in DRM for evaluating and comparing alternative options for risk reduction and response, and for prioritizing response strategies; (ii) Multi-attribute Utility Theory (MAUT): MAUT is a method that allows the decision maker to assign numerical values to each criterion and then combine these values to form a single overall score for each alternative. It is useful in DRM for evaluating and comparing alternative options for risk reduction and response; (iii) Multi-Criteria Decision Analysis (MCDA): MCDA is a generic term that refers to a wide range of methods used to evaluate and compare alternatives based on multiple criteria. It includes methods such as AHP and MAUT, as well as other methods such as the Electre and Promethee methods [81,82,83].

In addition to these methods, GIS (Geographic Information System) is also widely used in DRM. GIS can provide spatial context to the data and can be used to display and analyze data in a spatial context, which can help decision makers to understand the problem and evaluate alternatives in a more comprehensive way. GIS can also be used to create hazard and vulnerability maps, which can be used to identify areas that are most at risk and to target risk reduction and response efforts. GIS can also be used to support decision-making by providing real-time information during an emergency response and can be used to analyze the effectiveness of response strategies after a disaster [84].

MCDM methods, such as AHP, MAUT and MCDA, are widely used in disaster risk management to evaluate and compare alternative options for risk reduction and response. GIS is also widely used in DRM as it can provide spatial context to the data and can be used to display and analyze data in a spatial context, support decision-making during an emergency response and can be used to analyze the effectiveness of response strategies after a disaster. With the integration of GIS and MCDM methods, decision makers can have a better understanding of the problem and can evaluate alternatives in a more comprehensive way.

The need, potential, and challenges for incorporating Life Cycle Assessment into traditional approaches to decision problems, as well as its application areas on transportation planning, flood management, and food production and consumption, are explored in a study that examines how environmental impacts are taken into account in various fields of interest for decision makers [85]. However, decision support systems alone are not sufficient. These can also benefit from various statistical analysis tools, such as bi-variate correlation, agglomerative hierarchical and non-hierarchical clustering (K-mean), principal component analysis, and multivariate regression models [86].

Urban resilience techniques may be implemented at various stages of the hazard chain, including disaster risk reduction, disaster preparation, and disaster response. Building UR strategies may strive to alleviate the consequences of catastrophes or avoid them from happening.

A significant aspect of asset or risk management systems is their decision-making function, by ensuring that activities are taken in a methodical and precise way and lead to intended results. The decision making role for classifying and assessing risks is the most essential aspect of risk management or the most significant control function in risk management, and is frequently emphasized in the discussion on risk management decisions [87].

The concept of asset management and how it can be integrated with risk management to improve decision making for urban resilience has been previously explored. There is an increasing awareness that asset management can be aligned with risk management strategies to improve decision making for UR [88].

The process of decision-making in asset management is a highly intricate undertaking that encompasses not only technical elements such as modeling and data analysis, but also human factors such as bias, uncertainty, and perception. In an era of Big Data, artificial intelligence, IoT, and machine learning, it is essential to recognize and factor in the effects of these human factors in order to make sound and effective decisions [89]. It is necessary to combine ‘soft’ and ‘hard’ issues in the decision-making process [90].

Ensuring that organizations adopt consistent approaches based on established best practices, rather than relying on disparate individual methods or a lack of auditable methods, poses a significant challenge. This is particularly true for the multitude of smaller decisions that can have a significant impact on asset management. Technical solutions that are highly advanced can often be difficult to comprehend and explain, resulting in the “black box” syndrome where the complexity of the model obscures the rationale behind the decision.

Risk-Informed Decision-Making (RIDM) is a methodology that provides a formalized, rational, and systematic approach to identifying, assessing, and communicating the various factors that support making a risk-informed decision [91,92]. Developed in collaboration between IREQ/Hydro-Quebec and the University of Quebec (UQTR), the RIDM process involves considering, appropriately weighting, and integrating a range of often complex inputs and insights into decision making [89,91].

In order to arrive at an appropriate decision, high-quality engineering analyses are necessary but not sufficient. It is crucial to adopt a comprehensive approach that integrates the outcomes of various quantitative analyses and other relevant, intangible and hardly quantifiable influence factors. Methods of Multi-Attribute Decision-Making (MADM) such as AHP, Fuzzy AHP, PROMETHE, TOPSIS, ELECTRE, and MAUT, can be considered to support the final decision-making. In this process, the decision maker, supported by subject matter experts, analysts, and stakeholders, must engage in a high-level analysis and deliberation, taking into account all relevant insights for a satisfactory decision-making [89].

The decision-making process in asset management is a multifaceted endeavor that necessitates a structured methodology for balancing various competing priorities, managing external and internal factors, and achieving a harmonious equilibrium between short-term needs and long-term benefits. Organizations can accomplish this by implementing a well-designed asset management system in accordance with the ISO 55000 family of standards [17,93]. However, organizations must also be prepared to address the risks and uncertainties associated with extreme and large-scale disruptive events in their strategic and asset management decisions. As such, it is crucial to integrate the concepts of resilience and asset management to achieve sustainable development, optimal service levels, and economic sustainability [94].

In the decision making process, it is imperative to strike a delicate balance between multiple competing interests and factors such as performance, risks, benefits, costs, opportunities, short-term goals, and long-term sustainability. Modern electrical utilities employ a variety of models and tools to mitigate uncertainties and better quantify risks within their asset management decision-making processes. However, it is essential to link the information and insights obtained from these quantitative models to the decision maker’s needs and take into account other intangible factors that may have a significant impact on final decisions[92].

Geographic Information Systems (GIS), spatial data and maps are generally applied to better assess and control threats in the built environment. GIS has proven to be a useful tool for presenting and analyzing layers of information in a spatial manner since the 1990s. It offers decision makers with information that is simple to grasp and process. GIS-based decision-support systems promote communication between researchers and decision-makers and provide a platform for multidisciplinary research [95].

UR has been increasingly discussed and incorporated into policymaking in view of controlling hazards in cities/urban areas. Consequently, it became relevant to investigate methods for visualizing and mapping UR and to comprehend the added value deriving from these types of efforts. Previous research has shown that adaptive resilience is mapped after a disaster mostly through recovery measures, and that top-down techniques are commonly used to map inherent resilience. However, resilience maps do not examine the topic of resilience completely, resilience maps do not depict the ability of systems to adapt or evolve, nor do they reflect the systemic attribute of resilience [96].

This lends credence to the idea of strengthening urban resilience to ensure risk-aware spatial planning strategies for the built environment and key infrastructure, bringing a fresh perspective in the settings of socio-ecological reconstruction and the cultural vitality of civil society [29,97].

The relevance of assets and risk in the context of urban sustainability and resilience is emphasized in this section, where the management of assets and asset systems will be discussed in relation to our cities’ infrastructure and buildings. To this extent, decision makers require a variety of tools and approaches to improve the decision making process in order to manage these asset systems that are vulnerable to diverse risks, such as tangible or intangible, natural, or man-made disasters. To arrive at a unified interdisciplinary solution for sustainable UR, a combination of data-driven and stochastic analysis will be needed. To that aim, this study attempted to identify the current trend in UR as well as potential research prospects that should be pursued in future research initiatives. These trends and gaps were retrieved via a rigorous process that included subjective and objective assessments to produce accurate and all-inclusive results.

3. Methodology

3.1. Rationale

This review article investigates the present state of UR research and implementation to constructed assets such as buildings and infrastructures. The authors performed a systematic literature review to ensure that the study results conform to a pre-defined and reproducible methodology and that the research quality is not impacted by a priori assumptions or the researcher’s expertise, which is a typical feature of narrative literature reviews.

3.2. Protocol and Registration

The systematic literature review uses the Preferred Reporting Criteria for Systematic Reviews and Meta-Analyses format (PRISMA). PRISMA is a broadly accepted literature review process. It was established by a group of medical authors [98] to improve the clarity, dependability, and precision of systematic literature reviews. For more reliable reporting in a systematic review, these authors presented a 27 item checklist and a related flow diagram. Because of its transparency, reliability, and conciseness, the authors chose PRISMA to perform the systematic literature review of UR of buildings and infrastructures.

The identification step of the systematic study was followed by a paper screening, eligibility, and the final selection of the records to be included in the content analysis (Figure 2). The review process began with setting up the eligibility criteria, the information sources, and the search query. The first set of results was then filtered according to the eligibility criteria, the remaining articles are joined into a single set. Next, the papers were analyzed according to their title, abstract and keywords, and the papers out of scope were excluded. Finally, the texts of the remaining papers were fully read, and some additional and relevant references were included in this step. Again, the articles out of scope were removed and the final list of papers was obtained.

3.3. Eligibility Criteria

The evaluated papers in this study all meet three predefined qualifying criteria. First, because English has the most published and peer-reviewed papers, it was chosen as the publication language. The second criterion was to narrow down the keywords so that authors could gain insights on a specific focus of UR, namely infrastructure and building asset and risk management approaches supported with GIS-based decision tools.

Only peer-reviewed published records are considered to provide an additional level of quality assurance. There were no restrictions on the year of publication, the title of the journal or the number of citations.

3.4. Information Sources

The data for the bibliometric search, as well as the information sources used in the search, came from the Scopus database. The academic sector recognizes this database for its stringent quality requirements and absolute higher coverage in all fields including engineering than Web of Science [99], extensive article coverage, considerable citation, and abstract sources [100]. The Scopus search engine also employs a Boolean syntax, which enables the application of precise constraints and the generation of more refined results. Furthermore, this search engine enables a real-time bibliometric analysis of the results (distribution of publications by author, country, year, and so on), which adds value to the search and facilitates the iterative process of selecting an appropriate search phrase. The most recent search was conducted on 28 June 2022.

3.5. Search

Figure 2 shows the query structure and keywords utilized for this literature review. The authors cite the Scopus Search Guide for further information on this syntax [100]. Choosing the best structure and keywords for the search was an iterative process that began with a preliminary keyword search and was followed by a refining process based on the findings. The search string is divided into three sections: (1) the Urban Resilience (UR) domain; (2) the GIS and spatial analysis; (3) the various disasters infrastructures were subjected to.

3.6. Study Selection

The phrases used when searching the Scopus database, in view of the systematic literature review, are presented below. They was properly combined and crafted to cover the topic while applying adequate restrictions to avoid producing a large number of results. This is a crucial component of the systematic literature review study with impact on the final outcomes. Defining the search term, on the other hand, might make it clearer and more reproducible, which is an important aspect of a research article.

TITLE-ABS-KEY ((urban OR city OR cities) AND (map OR gis OR spatial) AND (resilien*) AND (natural OR manmade) AND (hazard OR disaster) AND (infrastructure)) AND (LIMIT-TO (LANGUAGE, “English”)).

The first part of the research string covers the study domain of asset and risk management. The second part covers Urban Resilience (UR) and is subdivided into two components. The first component is the primary keyword “urban” and possible synonyms, such as “cities” or “city” included in combination with the “OR” operator. The third part of the string covers the domain of resilience by using “resilien*” with the “AND” operator to cover potential variations. The next phrase in the search is “natural” OR “man*made” AND “hazard” OR “disaster,” and their synonyms to encompass different types of disaster risks that are important to UR. This is to follow to next term subjecting to infrastructure and buildings. The fourth part includes “map*” OR “GIS” OR “spatial” to consider studies dealing with spatial analysis and visualization tools to enhance UR decision-making.

The authors opt to employ more search phrases while searching in all titles, abstracts, and keywords to limit down the quantity of results to make them more useable and to avoid having too many that make proper analysis hard. As a result of the initial search query, which contained 511 papers, it was then reduced to 96 scientific papers, 67 of which brought insights into the conclusion of this study.

3.7. Data Collection Process

The Scopus search engine records were exported to a spreadsheet and processed according to the PRISMA flow diagram with creating some extra columns on a spreadsheet to segregate and organize the articles based on their different characteristics (e.g., justification for exclusion, paper objectives, achievements, relevance, etc.). Each screened paper was downloaded and studied for the complete paper review step.

3.8. Risk of Bias

This study of the literature identified a few factors of bias risk. First, because there is no redundancy for dispute resolution, the reviewing process was handled by a single individual, which raises the possibility of compromising the overall quality of the study. The number of publications to be evaluated is another potential danger factor. Because of the large number of papers examined, the reviewer had to put in a lot of reading time throughout the screening process. This may cause reading fatigue and bias in the categorization of article relevance. To compensate for this situation, the reviewer set a daily limit of articles to screen.

Another possible source of bias is not including article restrictions. Choosing just journal articles for quality assurance was a trade-off that may have resulted in the removal of relevant and high-quality conference papers, which authors chose not to do.

One last example of a potential bias risk might be found in the publishing wording. Despite the fact that English is the most often used language in academia, certain publications were excluded owing to this limitation. Some of those publications, particularly those from countries where UR apps have a relevant degree of implementation, may give helpful information regarding the research issue (e.g., Germany and China).

4. Bibliometric Analysis Results

In comparison to prior UR reviews (e.g., [101,102,103,104], this review presents novelties as it offers a bibliometric assessment of the study trend using statistical analysis and Natural Language Processing (NLP) to extract the current trend of urban resilience using GIS-based decision-making tools. This systematic literature review is a direct result of the implementation of a systematic literature review (PRISMA), which allowed us to screen out articles that were out of scope and work mainly with those that were within the specified scope. Furthermore, the lack of research on the application of UR using GIS for cities facing natural disasters lessened the numbers throughout the screening phase (see flowchart in Figure 2), resulting in a bibliometric evaluation of just 67 papers. As a result, the various bibliometric analysis approaches (such as Natural Language Processing (NLP) keyword co-occurrence) gave inconsequential findings in this case, and the authors picked only those with relevant results to offer. The findings also revealed that there were no significant articles addressing UR research in collaboration with a GIS decision support system as a high-level system in the asset and risk management of cities.

4.1. Natural Language Processing of the Word Trend

In order to identify the key word trends in the 67 chosen articles, we used a natural language processing word cloud that is powered by artificial intelligence [105]. As shown in

Table 1. Keyword co-occurrence based on Monkey Learn.

Word	Count	Relevance
climate change	30	0.998
risk assessment	19	0.606
geographic information system	11	0.588
urban infrastructure	10	0.321
decision making	11	0.285
community resilience	8	0.285
disaster management	8	0.285
flood/flooding	74	0.285
baseline resilience indicators	5	0.267
flood risk management	5	0.267
green infrastructure	9	0.250
transportation infrastructure	8	0.250
land use	8	0.250
disaster resilience	14	0.214
infrastructural development	6	0.214
sustainable development	6	0.214
urban planning	6	0.214
risk management	13	0.178
critical infrastructure	8	0.178
infrastructure resilience	6	0.178
urban development	5	0.178
disaster prevention	5	0.178
disaster risk reduction	3	0.160
resilience knowledge system	3	0.160
electric network analysis	3	0.160
urban resilience knowledge	3	0.160
analytic hierarchy process	3	0.160
electric power network	3	0.160
principal components analysis	3	0.160
vulnerability	25	0.147
transportation system	5	0.143
spatial analysis	4	0.143
complex network	4	0.143
spatial planning	4	0.143

and Figure 3, this method resulted in a word cloud and graph showing the phrases that appeared the most frequently when the titles, author keywords, and index keywords were combined. In the process of creating the illustrations, we eliminated some of the highly obvious terms that serve as the research’s single keyword, such as “resilience”, “urban”, “disaster”, “natural disasters”, and specific names used to identify the countries under study. This has allowed us to develop a more related, interdisciplinary perspective on the subject.

4.2. Annual Publications

In a recent period of five years, from 2017 to 2021, more than 75% of all selected papers (67) were published, according to a bibliometric analysis. The number of yearly publications has increased, particularly in 2021, and is expected to reach more than 15 publications in 2022 (Figure 4).

This development pattern fits the findings of previous UR-related reviews [96,102,106,107], corroborating the notion of UR as an important topic with expanding academic interest. The findings also revealed that there were no prominent publications in terms of UR research in combination with GIS decision support tools.

4.3. Subject Areas and Resource Type

The Scopus search engine’s publication pattern of the 96 unscreened papers is indicated by topic area in Figure 5. To better emphasize the impact of each discipline area, authors choose to utilize percentages rather than numbers in this pie graphic. The second factor is that the articles are interdisciplinary, meaning that 96 of them span a total of 185 fields. The graph shows that 24, 19, and 17% (a total of 60 percent) of the results are related to environmental science, social science, and engineering fields, respectively. This suggest that all three of these disciplines contribute equally to UR, and any UR research projects should pay particular attention and integrate all three disciplines. The remaining 40 percent is generally distributed across earth and planetary sciences (12%), energy (5%), and computer science (4%), all of which are vital for use in upcoming research.

Figure 6 illustrates the different types of papers, with articles accounting for 63% of the total, conference papers for 21%, and book chapters and reviews for 8% apiece. The majority of the papers are conceptual and analytical research that look for methods to structure UR and use such models and frameworks in real-world case studies. There are many different one-dimensional and multi-dimensional analyses of articles. For instance, the majority of them just examine floods, earthquakes, or other natural disasters as a single natural disaster, while some examine groups of them and how they interact.

5. Discussion

This systematic literature review had two main objectives. The first objective was to highlight the major areas of discussion in UR publications. The second objective was to explore the knowledge gaps and future study opportunities for UR in decision science. The following sections discuss the extent to which these two objectives of the proposed systematic literature review are met. In Appendix A, a detailed bibliometric analysis is presented in the form of a table that includes the title, reference, research gap/motivation, objective/purpose, and result/output of all studies used.

5.1. Major Areas of Discussion in UR Publications

5.1.1. Climate Change

In terms of its effects on regional and temporal climatic variability and change rates, climate change is a long-term global change that neither happens by coincidence nor by design [33]. Urban climate change resilience acknowledges the complexities of rapidly expanding urban regions and the uncertainties related to climate change while embracing climate change adaptation, preventive activities, and disaster risk reduction [108].

The use of unsustainable resources, a shortage of housing and infrastructure, the prevalence of poverty, rapid urbanization, crime, natural disasters, and the effects of climate change are just a few of the problems that cities face. The concept of “excellent urban governance” is necessary for countries to successfully plan and implement sustainable development efforts [109]. Urban resilience is a holistic term that contributes to a city’s capacity to manage unpredicted and foreseeable risk-related events in a sustainable manner. This has led researchers to investigate the significance of urban management governance and the link between strong urban governance and city resilience by document analysis.

For example, flood hazard modeling was developed as a methodology to help in assessing community resilience, because the Emergency Management Agency’s Flood Insurance Rate Maps are insufficient for the changing requirements for public resilience evaluation and decision-making [110]. This methodology demonstrates the likely effects of climate change on civil infrastructure in the twenty-first century and argues that these effects are not insignificant but can be controlled with the appropriate engineering.

5.1.2. Disaster Risk Assessment and Treatment

The United Nations office for Disaster Risk Reduction (UNDRR) promotes the analysis of possible hazards and the assessment of current exposure and susceptibility circumstances that collectively potentially affect people, property, services, livelihoods, and the environment over which they rely. This can be done using qualitative or quantitative techniques [111]. Disaster risk assessments involve the following steps: (i) identifying hazards; reviewing technical aspects of hazards, such as their location, intensity, frequency, and probability; (ii) analyzing exposure and vulnerability, along with the physical, social, health, environmental, and economic dimensions; (iii) assessing the efficacy of existing and alternative coping mechanisms in light of likely risk scenarios.

UNDRR also discussed disaster risk management as the use of policies and techniques for reducing disaster risk in order to avoid new disaster risks, lower current disaster risks, and manage residual risks. This helps to increase disaster resilience and cut down on disaster losses [111]. It is possible to distinguish among prospective, corrective, and compensating disaster risk management—also known as residual risk management—actions in disaster risk management.

Many communities are vulnerable to natural disasters, resulting in economic, social, and environmental damages as a result of insufficient investment and planning. Cities must alter their institutional frameworks in order to foster a culture of Disaster Risk Reduction (DRR) and collect and distribute knowledge for sound decision-making [112]. Investing in early warning systems, developing risk assessments and vulnerability maps through financing for social services and infrastructure, and developing and enforcing land use policies to reduce hazards and regulate construction rules for safer human settlements are all important steps toward improving UR.

Risks and vulnerabilities are considered in urban planning, considering human habitation of hazard zones, hazard analysis and the creation of hazard maps, control over unauthorized development, scenario-based planning, the use of action and reaction characteristics, stakeholder engagement, proactive planning, level of flexibility, land, and appropriate acquisition [97,113].

The key results of the world energy council are that (1) for market tools, technology and data solutions, collaborations and partnerships, and communications, short-term agility is crucial; (2) lack of coordination, complicated backup plans, underused communication, and escalating failure costs are major obstacles to the dynamic resilience of whole energy systems in transition. The primary facilitators of dynamic resilience are improved climate change scenario modeling and weather forecasts in determining long-term adaptation needs; (3) Building resilience across more intricate and embedded energy systems requires a larger role for simulated and shared experiences, participatory preparation planning, and other best-practice learning methods [66].

5.1.3. Geographic Information System (GIS)

The growing availability of ‘big data’ has prompted hopes that the world can be more predictable and controllable. Real-time management has the potential to overcome instabilities induced by delayed input or a lack of knowledge. However, there are significant limitations to this: having too much data might make it impossible to distinguish between accurate and ambiguous or wrong information, resulting in poor decision-making. Having too much information may result in a more obscure rather than a more truthful image [56].

GIS is a digital ability to collect, store, verify, and display data about locations on the land surface [114]. GIS can offer more accurate and meaningful information about the UR indicators of cities to urban policy makers and high-level decision maker [115]. It is possible to transform raw data into a more tangible and understandable tool that researchers and practitioners can use more frequently while spending less time digesting and generating new insights in this broad field of study by analyzing and visualizing UR dimensions, indicators, and parameters.

Multi-hazard spatial and geographical scales analysis is essential for improving resilience and disaster response in rural towns and cities vulnerable to severe seasonal weather [116].

Based on a cooperative geographical resilience assessment technique that includes three resilience evaluation methods and the use of geo-visualization techniques, including the use of GIS for data processing, assessment, visualization, mapping, and model processing, spatial decision-support tools can be developed. This approach integrates the territory’s technical, urban, and social components while emphasizing the multiple alternatives available to promote regional resilience through collaboration and the use of a visual tool [117]. There are various services such as Google Maps, Google Earth, and free and/or open-source tools such as QGIS (Quantum GIS), GRASS, SAGA, Monteverdi, Sextante GIS, and Orfeo Toolbox, which can help to develop multiple GIS-based models [118].

5.1.4. Urban and Transportation Infrastructure

A coordinated infrastructure resilience evaluation and planning process should consider infrastructure interconnection and the impacts of cascading failures. Socioeconomic aspects and land use characteristics should be incorporated in the interdependent resilience assessment for a more full and equitable resilience planning process [119]. Findings in this area also emphasized the importance of having a strong and developed economy, excellent education, and training programs to raise public awareness of disaster prevention and mitigation, adequate funding for vital infrastructure, particularly in the areas of transportation and communication, sound environmental policies to safeguard ecosystems and water resources, and extra care and budgets for disaster risk for vulnerable groups [120].

It is necessary to analyze how the availability and distribution of transportation infrastructure might affect the disaster resilience of human-infrastructure systems in metropolitan settings since disaster resilience is viewed as a dynamic process before, during, and after catastrophes in different communities. For example, areas with more transportation diversity show greater resilience in terms of their mobility both during and after the storm [121].

5.1.5. Decision Making and Disaster Management

It can be argued that some important safety procedures against man-made disasters are not performed today due to a lack of theoretical knowledge and, as a result, incorrect policy actions. Some authors advocate that there a common misunderstanding about complex systems is to consider that these can be adequately governed or that socioeconomic systems self-correct without significant threats to society. Due to the systemic character of man-made catastrophes, it is difficult to make someone accountable for the harm inflicted. As a result, traditional self-adjustment and feedback processes fail to assure responsible behavior to prevent potential tragedies [56]. Because the world’s interconnect assets and risk management strategies are too complicated to be optimized by top-down management in real time, the notion of a sole dictator would not work efficiently. Decentralized cooperation with impacted system components can produce better results that are tailored to local requirements. This implies that a participative strategy that makes use of local resources might be more effective. This method is also more resilient to disruptions.

The Sendai Framework for Disaster Risk Reduction applies to the risk of small-scale and large-scale, frequent, and rare, unexpected and gradual disasters caused by natural or manmade disasters, and environment related, technological, and biological associated risks, with the goal of significantly reducing disaster risk and risks in lives, livelihoods, and health, and economic, physical, social, cultural, and environmental assets of individuals, organizations, societies, and governments [72]. It aims to “prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience”.

There are various frameworks that can be support decision making and enhance UR, namely, action plans for future vigilance to lessen the increasing effects of risks on cities. These have been devised as a road map for establishing an UR knowledge system for practitioners, decision-makers, and local authorities [122].

UR decision-making tools are built in response to the needs of the urban environment, considering many dimensions and indicators, functioning alone or in conjunction, both with and without weighting of MCDM approaches, and can be subjective (expert-based) or objective (data-driven/stochastic). Choosing the appropriate mix of techniques is context-dependent and is a challenge in itself. This is something that needs further exploration and future research work.

5.1.6. Community and Disaster Resilience

Many communities are vulnerable to natural disasters, resulting in economic, social, and environmental damages [112]. Due to the loss of lives and livelihoods caused by flood dangers, the government began to think about the need for research aimed at reducing flood impacts and raising awareness to build more adaptable and resilient communities [123].

There are various tools such as the Baseline Resilience Indicators for Community (BRIC), which examines the baseline resilience to natural hazards [124]. A study finding also highlighted the value of having a robust and developed economy, excellent education, and training programs to increase public awareness of disaster prevention and mitigation, adequate funding for crucial infrastructure, particularly in the areas of transportation and communication, sound environmental policies to safeguard ecosystems and water resources, and extra care and budgets for disaster risk for vulnerable groups [120].

5.1.7. Green Infrastructure and Sustainable Development

Actions that work with and improve natural environments are examples of nature-based solutions [125]. There are several instances of nature-based approaches. Soil erosion and flood danger can be reduced by afforestation, reforestation, and the preservation of current forestland. Recovering marshlands and natural wetlands helps coastal communities protect themselves against severe storms [126]. The urban heat island impact is decreased by creating green space in neighborhoods. Such nature-based solutions have various co-benefits in addition to protecting communities from the worst impacts of extreme weather [127].

Neighborhood parks and street trees boosted the advantages in residential areas. Paddy fields have also been proven to be particularly efficient in reducing local climate, which is especially relevant where agricultural grounds border residential areas [128]. It also was discovered that green infrastructure needs a thorough grasp of the political, social, economic, and environmental elements of the poor urban population [129]. The key is cohesive collaboration and full engagement of urban stakeholders [130].

5.2. Knowledge Gaps and Future Study Opportunities on UR and Decision Science

5.2.1. Resilience Definition and Multidisciplinary Analysis

Resilience is often characterized as a system’s capacity to resist a substantial shock and sustain or promptly continue at normal performance in UR literature. However, there is dispute over both the traits that define resilience and the proper analytical unit for resilience assessment. Because of the many intellectual traditions and lineages represented in the various study fields, there is heterogeneity in how the term of resilience is used [131]. As a result, the context in which it is used may define urban resilience as anything from the capability of the system to adjust to changing environmental conditions to the degree of endurance to maintain functional performance and the ability to sprint back.

5.2.2. Unified Scalable and Adoptable UR Model

Predictions appear conceivable over the short-term and in a probabilistic perspective for today’s build environment. Even with all the facts in the world, one cannot predict the future; nonetheless, one can establish if systems are prone to cascades or not. Furthermore, faulty system components can be leveraged to provide early warning signals. However, if safety procedures are not taken, spontaneous cascades may become uncontrollable and devastating. To put it another way, predictability and controllability are a result of effective system operation and design. Learning how to put this into effective approaches and how to exploit the good aspects of cascade effects will be a twenty-first-century problem [56].

There are certain multi-dimensional UR models and frameworks that operate rather well in their intended applications, but by considering the particular needs of different cities and catastrophes, these models must be rebuilt each time by researchers. To that end, a more advanced model that is scalable and adaptive for different disasters and cities based on their demands and priorities is required.

5.2.3. Geographic Information System (GIS) UR Multidimensional Tools

There is a requirement to transform all data into geo-tagged transferrable data to enable breaking their information into statistical models and making evaluation by decision support systems possible, in order for high level decision makers to better understand the problem and solution. To improve the model, the GIS-based model should be worked alongside raw data in a cloud-based environment.

5.2.4. Stochastic Analysis of Virtual Cities

Because data acquisition is costly and time demanding, extending the acquired data to a broader ecosystem would be extremely valuable. To that aim, if the acquired data do not cover all characteristics of the concept, they can be expanded using inverse distribution employing local or global reverse sampling methods for continuous data and discrete variables dependent on their application. Then, to establish a larger prospective and save survey time and expense, expand this amplified data to all available locations in the city. This solution may not be the most accurate and may be biased in certain circumstances, but it may be used as a tool in research to provide preliminary insight into how to enhance indicators before making final decisions on final dimensions and indicators.

5.2.5. Scenario-Based Decision Making Mechanism for UR

Cities require a completely novel comprehensive and inclusive framework for recognizing and adopting disruptions, integrating multiple objectives and goals, and proactively preparing towards enhanced urban futures in policy and planning [58,132].

6. Conclusions

Natural and man-made disasters caused by climate change, natural disasters, and technology advancement can cause major disruptions and damage to built environment components, which are crucial for functioning modern society. Because of direct exposure to several climatic risks such as high temperature and precipitation, and sea-level rises, the built environment is more exposed to climate change consequences than ever before. As a result, implementing UR measures into the built environment is critical for asset systems to endure significantly and avoid failure or breakdown, and adapt quickly as a result of various mentioned disruptions. Efficient decision making in the UR domain enables public and private authorities to evolve into resilient spots capable of withstanding and adapting to disruptions. This is accomplished by utilizing the concept of fuzzy bounded and unbounded rationality, where the decision-maker may choose the best course of action based on the facts at hand.

This paper presents a systematic literature review of the past studies conducted on the UR and decision science perspective. The systematic literature review is organized under five main headings: The first section of this article examines background information and adjacent disciplines that can have a favorable influence on the subject of UR. The second section goes about the technique (PRISMA) and how it was employed in this study. The third section goes through bibliometrics and results analysis, while the fourth section goes over the study’s findings and supports both objectives. The conclusion and discussion of future research constitute the study’s last component.

Objective one was to highlight the major areas of discussion in UR publications: (1) climate change; (2) disaster risk assessments and management; (3) geographic information system; (4) urban and transportation infrastructure; (5) decision making and disaster management; (6) community and disaster resilience; (7) green infrastructure and sustainable development.

For the second objective, the main research gaps are identified as (1) resilience definition and multidisciplinary analysis; (2) unified scalable and adoptable UR model; (3) geographic information system (GIS) UR multidimensional tools; (4) stochastic analysis of virtual cities; (5) scenario-based decision-making mechanism for UR. All of these identified aspects can be significantly improved for further analysis of the UR and disaster risks, and the authors will try to resolve these gaps in their future research.