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.@en

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.

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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.@en

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.

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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.

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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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

As climate change is becoming a reality, there is an increasing demand to improve urban resilience. Planning Support Systems (PSS) enable climate-informed planning. However, previous research confirms difficulties in the uptake of PSS due to their resource-intensive nature and lack of awareness of their usefulness. This chapter aims to make a headway in understanding research priorities and gaps that need to be addressed for PSS to address climate resilience in the long run. To this end, we review the emerging body of knowledge in academia and practice, by conducting a text-mining analysis of academic (n = 36,405) and non-academic (practice) (n = 86) literature on urban planning and climate resilience. We extract trends in climate pressures, infrastructure drivers, and planning approaches. A key finding from the academic literature is that long-term planning continues to be limited to a few fixed scenarios and places a strong focus on single sector strategies. Practice documents continue to be designed to inform high-level policies, but not spatial plans that require integrated thinking. Our analysis concludes with a research agenda for improving PSS to (1) identify and integrate the full range of variables in the long-term; (2) support selection of appropriate planning responses across multiple infrastructure systems; and (3) improve flexibility in planning by a deeper understanding of temporal aspects such as planning timeframes.@en

The resilience of water distribution systems (WDSs) has gained increasing attention in recent years. Various performance loss and recovery behaviors have been observed for WDSs subject to disruptions. However, a model for their characterization, which could provide further insight for resilience assessment and enhancement, is still lacking. Here, the authors develop a recovery function to model WDS performance over time following a disruption. This function is useful to compare system responses under different disruption and recovery scenarios and supports the identification of areas for improvement within various aspects of the resilience of a WDS. The proposed model was applied to two benchmark networks. Different scenarios were analyzed in which one node at a time was disrupted and two recovery strategies were implemented. It was found that the developed model supports the implementation of tailored strategies to improve WDS resilience according to the location of the disruption, therefore enhancing the efficient allocation of resources.

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Water Distribution Systems (WDSs) are critical infrastructures for providing water to sustain life and human activities. Some recent trends, such as climate change, urbanization, and increasing system interdependence, have led to more frequent threats, with detrimental effects on WDSs. In recent years, resilience has been considered an effective approach to address those threats, for which it is difficult to estimate the likelihood and consequences (Henry and Ramirez-Marquez, 2012). In the literature, two approaches were used to assess WDS resilience. On the one hand, performance-based metrics were used to quantify the impacts of disruptions on the WDS. Specifically, recovery functions were developed to model the time-continuous system response following a disruption, during periods of loss and restoration of performance (Cassottana et al., 2019). On the other hand, indexes based on system attributes were developed to classify WDSs and identify structural vulnerabilities. For example, algebraic connectivity, clustering coefficient, and average path length were used as proxies for robustness, redundancy, and efficiency, respectively (Yazdani et al., 2011). However, those approaches were applied separately, and the relationship between the performance of WDSs and their attributes is still unknown. Hence, the question arises from the above analysis on how to identify the key structural factors determining the resilience of a WDS. The goal of this research is to understand how and to what extent different network topologies determine different performance losses and recovery behaviors given increasing magnitude of disruption, i.e., pipe breakdown. To this end, we consider different network topologies as case studies and vary their structural attributes, e.g., water source head and tank capacity. We then simulate disruption scenarios of increasing magnitude and model the resulting system performance by means of recovery functions for the assessment of resilience. The estimated parameters of these functions are useful for characterizing different system responses, including severe or limited performance losses and fast or slow recoveries. By systematically varying the network topologies and the structural attributes, the function parameters could be in turn associated with key structural factors. We find that, while increasing the WDS supply capacity results in limited performance loss in terms of satisfied demand for water, increasing the reserve capacity improves the robustness of the system by delaying the loss of performance. This analysis will inform the design of resilient water networks based on their topology and unique attributes.@en

Route restoration is considered to be a task of foremost priority in disaster relief. In this paper, we propose a robust optimization approach for post-disaster route restoration under uncertain restoration times. We present a novel decision rule based on restoration time ordering that yields optimal restoration sequencing and propose conditions for complexity reduction in the model and prove probability bounds on the satisfaction of these conditions. We implement our models in a realistic study of the 2015 Gorkha earthquake in Nepal.

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