Natural hazards like floods, storms, or earthquakes turn into disasters if they hit vulnerable communities and societies. In policy and academia, this understanding has led to a surge of models and risk reduction policies that aim to reduce vulnerability and strengthen resilience. However, it remains unclear which vulnerabilities are the most important, and what stakeholders in different contexts prioritize. To address this gap, this article identifies critical exposure, vulnerability, and coping capacity factors, elicits their priority among emergency responders from different contexts, and analyses their perceived interdependences to understand their cascading potentials. To do that, we conducted a stakeholder survey with experienced disaster and emergency management professionals around the world. The results are used to analyze the perceived relationships between the priority factors via a fuzzy cognitive map. The professionals identified the level of preparedness, exposure to hazard, risk and crisis communication, community engagement, and disaster risk financing as the most important factors. The results show that the most catastrophic disasters are perceived to be caused by a combination of multiple factors and their interdependences. It was also found that practitioners thought that active civil protection agencies and available disaster risk financing have the greatest potential to prevent disasters.

Societies are increasingly confronted with High-Impact Low-Probability (HILP) events. These events pose important challenges to societies as they disrupt critical infrastructures (CIs), the backbone of modern societies, leading to cascading and systemic disruptions across interconnected systems. Stress testing has emerged as a prominent approach for assessing system performance under adverse conditions. However, its suitability for addressing characteristics of HILP events, such as uncertainty, urgency, and complexity, remains unclear. This paper presents a scoping review of stress testing methodologies developed to date for terrestrial CIs, with the aim of identifying key methodological elements, with particular attention to the context of HILP events. The review reveals that existing stress testing approaches remain largely sector-specific and domain-specific, rely predominantly on hazard-centric scenarios, and insufficiently account for multi-sectoral interdependencies, dynamic system behavior, and the recovery and adaptation phases of resilience. Moreover, current methodologies tend to emphasize quantitative modeling, involve limited stakeholder participation, and lack mechanisms for iterative learning and adaptation, thereby constraining their relevance in rapidly evolving HILP contexts. In response to these gaps, this study proposes a conceptual framework for stress testing structured around three main stages of pre-assessment, assessment, and treatment. The framework emphasizes cross-sectoral and multi-domain analysis, stakeholder-inclusive and participatory approaches, and explicit consideration of recovery and adaptation processes. This study provides a foundation for advancing stress testing practices that are specifically tailored to HILP events and fosters the resilience of CIs.

Conventional disaster recovery often prioritizes rapid restoration to a baseline state but potentially perpetuates pre-existing vulnerabilities that neglect long-term resilience. Unlike current approaches that emphasize physical and economic rebuilding with a blind eye to the previous context of disasters, Reformative Recovery (RR) highlights the need to intervene in social and environmental long-term processes leading to vulnerability. This paper advances the concept of ‘Reformative Recovery’ and proposes an analytical framework as a sense-making tool to guide interpretation of recovery dynamics by foregrounding the conditions that foster vulnerability and impede long-term resilience. RR reframes recovery as a continuous and non-linear process shaped by social and institutional dynamics. The analytical framework is developed through a systematic scoping review and structured around six dimensions relevant for RR: 1) social justice, 2) governance arrangements, 3) community and culture, 4) financial mechanisms, 5) built environment, and 6) critical services. As an extension to the state of the art, a social justice dimension is proposed as an overarching component. Furthermore, the concept of critical services is extended beyond networked infrastructures to include locally defined essential services. These dimensions reflect the complex and interconnected nature of disaster impacts. For each dimension, we propose diagnostic prompts to support critical assessment of conditions and potential for inclusive and just post-disaster recovery by means of reformation. Rather than prescriptive indicators, these prompts serve as deliberative tools to support reflective and context-sensitive use by decision-makers and researchers. RR can be used to bridge short-term actions with long-term resilience-building in diverse contexts.

Accelerating urbanization and the inherent uncertainty in urban planning are increasing the demand for approaches that meaningfully integrate qualitative insights with quantitative analysis. While scenarios are widely used to explore multiple urban futures, existing methods that combine narrative storylines with computational models face persistent challenges: narrative assumptions are often oversimplified during translation; model structures frequently lack transparency regarding their underlying assumptions; and integrative processes tend to prioritize consensus, often sidelining the specialized insights of practitioners essential for urbanization strategies. Design Thinking (DT) offers a promising framework to address these limitations through its iterative, non-linear structure that bridges creative and analytical reasoning. Yet, a systematic, reproducible workflow that operationalizes DT for urban scenario development remains underdeveloped. This paper introduces FutureScapes (FS), a stepwise Design Thinking methodology for blending computational models and scenario narratives that embeds expert feedback into the modelling process. FS centers the spatial reasoning of expert stakeholders and introduces semi-quantitative boundary objects—in the form of scenario design maps—to break the traditionally linear sequence from story to simulation. This enables a reflexive process where model outputs actively reshape qualitative scenario assumptions to inform policy-relevant outcomes. The study contributes a generalizable methodology that enhances the contextual relevance, transparency, and strategic utility of computational scenario modelling for metropolitan planning.

Nature-based solutions (NbS) are central to urban resilience efforts, offering climate adaptation benefits alongside social and well-being co-benefits. However, without systematic consideration of socio-spatial factors, NbS implementation may reinforce existing inequalities. This paper adopts a justice-oriented approach to support equitable NbS planning, using Cape Town, South Africa, as a case study. We develop a spatial decision-support framework that integrates ecosystem service demand, social vulnerability, and environmental risk to prioritize NbS types and locations. Results help identify both areas with the greatest need for NbS interventions and the types of NbS most suitable for those areas.

For recovery to be effective and efficient, proactive measures such as strengthening the resilience of important urban assets must be implemented before disaster strikes. However, existing approaches fail to account for potential consecutive disaster impacts, the transformational changes that happen as a result of the disaster, and the shifting role of urban assets in post-disaster environments. This study presents a methodological framework to support pre-disaster recovery planning in urban areas exposed to multi-hazard risks, namely earthquakes followed by floods. In this study, we develop a methodological framework using a graph-based analytical approach to assess the importance of buildings, roads, census blocks, and temporary shelter areas in urban areas. This method focuses on capturing how the importance of urban assets shifts after consecutive disaster events. Applied to Sanremo, Italy, the methodological framework reveals the vulnerabilities associated with centralized urban planning and a notable mismatch between residential density and the distribution of important assets. The findings underscore how network disruptions and consecutive disasters impact urban connectivity, highlighting the urgent need for decentralized planning and adaptable disaster risk reduction strategies.

High Impact Low Probability events (HILPs), often referred to as outliers, are becoming more important in disaster management because they are linked to complex risks and tipping points in interconnected systems. Recent events, such as the cascading effects of the coronavirus pandemic, rising uncertainties from global geopolitical instability, and successive and concurrent extremes driven by climate change, underscore the limitations of relying solely on severe but plausible scenarios for risk practitioners and policymakers. Despite the critical need to integrate HILPs into risk assessment models and emergency preparedness, the field is fragmented, with inconsistent definitions and methodologies. We present a perspective developed under the HORIZON AGILE project (AGnostic risk management for high Impact Low probability Events), which introduces two comprehensive definitions of HILPs and a taxonomy designed to enhance risk assessment, resilience analysis, and crisis management. We provide a validated scientific definition for the academic community and an operational definition tailored for practitioners and stakeholders. Additionally, our taxonomy offers a structured framework to address outlier events that often fall below traditional risk thresholds, ensuring that low-probability, high-impact scenarios with cascading and concurrent dynamics are effectively integrated into risk registers, legislation, and standards development. This study shows how this approach improves methods like stress testing and scenario modelling, especially for the loss of critical services. This empowers policymakers, practitioners, and stakeholders to include more scenarios in their strategies, enhancing resilience and preparedness.

On February 6th, 2023, 11 cities around the southeast of Turkey were hit by a major earthquake with magnitude of 7.9 and 7.6, which resulted in mass destruction and loss of lives in many cities. Antakya, Hatay was one of those cities that lost more than 50 % of the built environment and a death toll of over 50,000. Previously known for its profound historical lineage, rich and diverse cultural heritage, and deep-rooted connection to its cultural identity, Antakya's reconstruction process holds a critical case not only for Turkey but also for the global discourse on urban resilience and post-disaster recovery strategies. Thus, in this study, we deep dive into an ongoing recovery process in Antakya, Turkey. The study's primary objective is to identify the key factors, constraints, opportunities, and challenges involved in reconstructing a city that can withstand future earthquakes. The data used in the study was gathered during fieldwork that was carried out in Hatay, Turkey, in June 2023. The methodology consists of total of 16 focus groups and in-depth interviews with stakeholders, including representatives of professional associations, local government agencies, and public institutions. Our results showed that re-opening educational services plays a crucial role in promoting recovery in Antakya. Furthermore, we examined the complex interplay between cultural ties to place, and urban resilience in the context of disaster recovery. We found that in Antakya, cultural identify may play a crucial role and would have a direct influence on its recovery. These findings hold significance for policymakers, urban planners, and disaster management professionals who must navigate the challenging terrain of post-disaster reconstruction while considering the cultural and emotional ties that bind residents to their cities. Ultimately, this research provides valuable insights into the intricacies of urban recovery and reconstruction processes and contributes to the growing body of knowledge on urban resilience and disaster recovery.

The switch to renewable power generation is promoted aggressively by government policies, growing investments, consumer preferences, and many other factors. However, high renewable penetration can impose significant challenges to designing and employing measures that enhance power grid resilience. Resilience has been posed as a requirement of increased criticality following severe phenomena and events (Texas freeze, California wildfires, India heatwaves, cyberattacks on power plants etc.) that go beyond electrical grid reliability. Dependence of renewables on climate and weather conditions and reliance on information and communication technologies complicate the challenge of accounting for them within grid resilience frameworks. Specifically, the asynchronous and limited-inertia characteristics of inverter-based resources can severely degrade the grid dynamic performance and shrink stability regions. Also, stochastic and intermittent nature of renewables requires the availability and fast response of flexibility resources and increases the computational complexity of decision-making problems, which will make methods for grid resilience even more challenging. Extensive behind-the-meter distributed energy resources further alter the behaviour of both distribution systems and transmission systems. Therefore, this Special Issue aims to address these challenges and key technologies for facilitating grid resilience in the pathway of grid decarbonization, with specific focus on operational and structural resilience of power grids.

As cities grow, the benefits of living in them are increasingly unequally distributed. USA cities, in particular, have experienced rapid suburbanization of poverty and decreased levels of access to jobs for transit-dependent and vulnerable communities. The public transit challenges in suburbs call for innovative forms of transit to turn the tide on urban inequality. On-demand microtransit, a novel type of shared mobility provides efficient, convenient and affordable transportation. Its potential for redressing inequity had yet to be investigated fully in a suburban setting. We presented a case study from the suburbs of Minneapolis-St. Paul in Minnesota, USA. We combined unique datasets of microtransit ridership from two public transit agencies, transit surveys, land use data, and expert interviews, to conduct spatial analysis, accessibility analysis, and equity impact assessments for these suburbs. We found that microtransit enables public transit agencies to reach a larger number of vulnerable riders than fixed-route transit, particularly for commuting and trips to/from commercial areas. Microtransit also provided a cheaper alternative to ride-hailing and a faster alternative to public transit and walking, without cannibalizing ridership from fixed-rout transit alternatives. Finally, microtransit redressed transportation inequities by alleviating access inequality, reaching vulnerable rider groups effectively, and creating travel opportunities that are less spatially concentrated than those provided by traditional, fixed-route public transit. This study provided a framework for further investigations into the impact of microtransit, including in urban core or rural settings, and highlighted the impact of microtransit in reducing access inequity in a suburban environment.

Urban areas are dynamic systems, in which different infrastructural, social and economic subsystems continuously co-evolve. As such, disruptions in one system can propagate to another. However, open challenges remain in (i) assessing the long-term implications of change for resilience and (ii) understanding how resilience propagates throughout urban systems over time. Despite the increasing reliance on data in smart cities, few studies empirically investigate long-term urban co-evolution using data-driven methods, leading to a gap in urban resilience assessments. This paper presents an approach that combines Getis-ord Gi* statistical and correlation analyses to investigate how cities recover from crises and adapt by analysing how the spatial patterns of urban characteristics and their relationships changed over time. We illustrate our approach through a study on Helsinki’s road infrastructure, socioeconomic system and built-up area from 1991 to 2016, a period marked by a major socioeconomic crisis. By analysing this case study, we provide insights into the co-evolution over more than two decades, thereby addressing the lack of longitudinal studies on urban resilience.

In a time defined by high urbanisation rates and looming or existing crises, it is critical to understand how cities can turn into places of resilience and strength, rather than become centres of vulnerabilities. Cities face several challenges today, starting from the unpredictability of climate change. The COVID-19 pandemic showed how the magnitude and duration of disruptions are difficult to predict, challenging traditional risk-based management approaches to cope with crises. In this respect, resilience science has been taken up as it highlights the intricate, complex, and interdependent nature of urban systems. While a strict universal definition of resilience is lacking, it generally refers to the capacity to anticipate, withstand, adapt to and recover from shocks and stresses, such as natural disasters, economic downturns, public health crises, and social turmoil.

Very often, novel crises and emergencies tend to highlight and reveal long-existing, underlying problems. To increase resilience in an all-encompassing way, cities should focus on the deep-seated structural issues that hinder their capacity to adapt and thrive, such as inequality. In many urban areas, socioeconomic disparities are ingrained, with marginalised communities suffering most from crises. This policy brief is aimed at urban/local policymakers and stresses the need to consider inclusiveness in urban resilience. It discusses two (of the many) urban challenges that are periodically highlighted and exacerbated by new crises, such as energy poverty and service accessibility. The brief also offers some practical suggestions to develop an inclusive approach to a wider array of challenges derived from the programme Inclusive Climate Action Rotterdam.

Increasing frequency of climate-related disruptions requires transformational responses over the lifecycles of interconnected urban systems with short- and long-term change dynamics. However, the aftermath of disruptions is often characterised by short-sighted decision-making, neglecting long-term urban shifts. In this study, we present a first attempt to develop the theoretical foundation for temporal dynamics for increasingly disrupted yet ”connecting and moving” cities that can be used in planning for urban resilience. Using the lens of climate urbanism, we conceptualise the interplay of temporal dynamics to empirically examine how planning practice perceives and addresses temporality in two regions - Amsterdam, the Netherlands, and Mumbai, India. Our findings reinforce that disruptions do not inform long-term planning. Endogenous and exogenous dynamics of change are not viewed together nor used to embed short-term planning goals within long-term resilience visions. To address the lack of systematic planning approaches that can leverage temporal dynamics, we propose two options for temporally flexible urban planning processes.

Climate change is one of the main drivers of uncertainty in urban planning, but only a few studies systematically address these uncertainties, especially in the long term. Urban resilience theory presents principles to manage uncertainty but largely focuses on individual urban systems rather than complex interdependent dynamics. Further, most planning and resilience theory originates from the Global North and is unsuitable for capturing the dynamics of the Global South. This study uses an exploratory multi-case analysis towards developing an enhanced understanding of urban planning for climate uncertainty. We argue that long-term urban planning for climate uncertainty can benefit from systematically integrating resilience principles. We use a two-step qualitative research approach: (1) To propose a conceptual framework connecting urban resilience principles, approaches to urban planning under uncertainty and planning responses in urban systems. (2) To use the conceptual framework to analyse climate-related planning responses in two contrasting case studies in the Global North (GN) and Global South (GS) (Amsterdam and Mumbai). We conclude with four propositions towards an enhanced understanding of urban planning for climate uncertainty by drawing upon the empirical insights from the two case studies.

Enhancing the resilience of critical infrastructure systems requires substantial investment and entails trade-offs between environmental and economic benefits. To this aim, we propose a methodological framework that combines resilience and economic analyses and assesses the economic viability of alternative resilience designs for a Water Distribution System (WDS) and its interdependent power and transportation systems. Flow-based network models simulate the interdependent infrastructure systems and Global Resilience Analysis (GRA) quantifies three resilience metrics under various disruption scenarios. The economic analysis monetizes the three metrics and compares two resilience strategies involving the installation of remotely controlled shutoff valves. Using the Micropolis synthetic interdependent water-transportation network as an example, we demonstrate how our framework can guide infrastructure stakeholders and utility operators in measuring the value of resilience investments. Overall, our approach highlights the importance of economic analysis in designing resilient infrastructure systems.

Rapid urbanization has posed challenges to accessibility to critical services that require in-depth analysis. Complex networks theory has been used to evaluate the evolution of network topologies or the overall accessibility of transportation systems. However, topological metrics to explain the temporal changes in accessibility levels do not fully capture the dynamics and implications of accessibility to specific critical services. In this study, we address this gap and investigate the opportunities of using a self-avoiding random walk (SARW) algorithm to evaluate and explain the evolution of spatial accessibility to education facilities. We used hotspot analysis to understand the temporal changes and investigated changes in hot and cold spots over time. Furthermore, we explored the relationship between the network indicators and the SARW-based accessibility metric. We illustrated this method in a case study from Helsinki, where large-scale open data spanning from 1991 to 2016 is available. Our findings indicate that the SARW-based metric delivers more detailed node-level results than the traditional isochrone-based metric. The latter generates accessibility zones where accessibility is assumed to be uniform, while the SARW metric captures the dynamic nature of educational facility accessibility more accurately. The developed methodology helps to identify the impacts on the historical development of accessibility and can be applied to investigate accessibility to other critical services.

Increasing urbanization, impervious space, and the impact of climate change are threatening the future of cities. Nature-based solutions, specifically urban green infrastructures, are seen as a sustainable strategy to increase resilience against extreme weather events, including the escalating occurrence of stormwater runoff flooding. Consequently, urban planners and decision-makers have pushed their efforts toward implementing green infrastructure solutions to reduce the impact of stormwater floods. Among others, green roofs help store water and decrease stormwater runoff impacts on a local scale. This research aims to investigate the effect of surface permeability and green roof implementation on reducing stormwater flooding and subsequently provide urban planners with evidence-based geospatial planning recommendations to improve urban resilience in Helsinki. First, we modeled the current impact of stormwater flooding using the Arc-Malstrom model in Helsinki. The model was used to identify districts under high stormwater flood risk. Then, we zoomed in to a focus area and tested a combination of scenarios representing four levels of green roof implementation, two levels of green roof infiltration rates under 40-, 60-, 80-, 100 mm precipitation events on the available rooftops. We utilized open geographic data and geospatial data science principles implemented in the GIS environment to conduct this study. Our results showed that low-level implementation of green roofs with low retention rates reduces the average flood depth by only 1 %. In contrast, the maximum green roof scenario decreased most of the average flood depth (13 %) and reduced the number of vulnerable sites. The proposed methodology can be used for other cities to develop evidence-based plans for green roof implementations.

The challenges for sustainable cities to protect the environment, ensure economic growth, and maintain social justice have been widely recognized. Along with the digitization, availability of large datasets, Machine Learning (ML) and Artificial Intelligence (AI) are promising to revolutionize the way we analyze and plan urban areas, opening new opportunities for the sustainable city agenda. Especially urban spatial planning problems can benefit from ML approaches, leading to an increasing number of ML publications across different domains. What is missing is an overview of the most prominent domains in spatial urban ML along with a mapping of specific applied approaches. This paper aims to address this gap and guide researchers in the field of urban science and spatial data analysis to the most used methods and unexplored research gaps. We present a scoping review of ML studies that used geospatial data to analyze urban areas. Our review focuses on revealing the most prominent topics, data sources, ML methods and approaches to parameter selection. Furthermore, we determine the most prominent patterns and challenges in the use of ML. Through our analysis, we identify knowledge gaps in ML methods for spatial data science and data specifications to guide future research.

Cities are complex socio-technical systems (STSs) under tremendous stress due to climate change. To incorporate resilience into urban plans and move towards evidence-based long-term decision-making, we must unravel complex land-use dynamics and the effect of climate uncertainties on cities. Currently, land-use dynamics are explored through Cellular Automata models to investigate the impacts of urban planning scenarios. What is, however, missing to support resilience decisions, is a systematic analysis of long-term climate uncertainties on land-use change. This study addresses this gap by analysing the effects of flood uncertainties on land-use patterns. While conventionally, urban planning decisions for climate uncertainty are based on a few scenarios, we use exploratory modeling to sample and combine uncertain climate variables to scenarios and understand the implications of the climate scenarios on land use via computational experiments. Specifically, we integrate flood probability maps into land-use maps to assess land suitability. Agglomerative clustering allows us to analyze the resulting land-use maps based on their similarity. Finally, we select representative maps from each cluster and compare them with the baseline map. We apply our integrated modeling approach in the Metropolitan Region of Amsterdam (MRA). Our results show spatially explicit alternatives for high-density residential development that is climate-resilient. The proposed framework can be applied to other cities to investigate the long-term impacts of climate uncertainties and adopt resilience-informed decision-making.

The Covid19 crisis has highlighted once more that socio-economic inequalities are a main driver of vulnerability. Especially in densely populated urban areas, however, these inequalities can drastically change even within neighbourhoods. To better prepare for urban crises, more granular techniques are needed to assess these vulnerabilities, and identify the main drivers that exacerbate inequality. Machine learning techniques enable us to extract this information from spatially geo-located datasets. In this paper, we present a prototypical study on how Principal Component Analysis (PCA) to analyse the distribution of labour and residential characteristics in the urban area of Helsinki, Finland. The main goals are twofold: 1) identify patterns of socio-economic activities, and 2) study spatial inequalities. Our analyses use a grid of 250x250 meters that covers the whole city of Helsinki, thereby providing a higher granularity than the neighbourhood-scale. The study yields four main findings. First, the descriptive statistical analysis detects inequalities in the labour and residential distributions. Second, relationships between the socio-economic variables exist in the geographic space. Third, the first two Principal Components (PCs) can extract most of the information about the socio-economic dataset. Fourth, the spatial analyses of the PCs identify differences between the Eastern and Western areas of Helsinki, which persist since the 1990s. Future studies will include further datasets related to the distribution of urban services and socio-technical indicators.