The new Medical Device Regulation (MDR) addresses patient safety by mandating supporting clinical evidence for the sale and clinical use of medical devices. However, it places pressure on medical device manufacturers to supply regulators with robust clinical evidence to obtain regulatory approval of new devices they develop. Regulatory compliance is therefore more stringent, requiring more effort on part of both regulators and manufacturers. This thesis project explored the possibility of developing a new method to be used as a regulatory decision-making tool for the approval of newly developed knee implants. A Bayesian probabilistic method was developed and proposed. This method utilises existing arthroplasty registry and arthroplasty registry annual report data to determine if a newly released implant variant is “good”, where a “good” implant is one which meets standard regulatory thresholds in term of revision risk. This method was implemented on two datasets which were selected based on conditions of granularity, measures of implant performance, and accessibility for the thesis project. Some advantages and limitations of this method as a regulatory tool were proposed, the limitations being based on a clinical appraisal framework. Based on this it was concluded that the method indeed showed potential as such a regulatory tool, but needs further improvement due to considerations of reliability, precision, and the probabilistic nature of the results it yields. Both surgeons and patients can also potentially stand to benefit from this method. Aside from the regulatory context, also, the method has limitations in the fact that it does not account for other factors that may be responsible for complications related to knee arthroplasty surgery. Overall, however, this thesis presents a novel approach towards estimating the performance of knee implants from existing clinical data.

To advance the energy transition and prepare for the accompanying increase in electricity demand, the electrical grid of the Netherlands requires rigorous investments for expansion and maintenance. In order to solve and further prevent grid congestion, a lot of projects have to be executed and this demands collaboration of grid operators and local governments. In addition to the technical problems of the transition, there is also a growing part of the population that is suffering from energy poverty and energy injustice which prevents them from benefiting from new innovations, while wealthier households are financially profiting from these. The negative socioeconomic effects are not accounted for in the investment plans of grid operators, even though not addressing these problems will further increase inequality in the population.
In order to research how investments in the electricity grid are related to the socioeconomic status of neighbourhoods and how this affects equality in the province of South-Holland, this thesis is conducted. The study aims to answer the research question: "To what extent can spatial relations between electricity grid infrastructure and socioeconomic characteristics be identified and quantified in the province of South-Holland and what does this relation imply for inequality?"

To answer this question, first a literature study was conducted in order to identify socioeconomic variables related to energy poverty and energy justice to use in the research. Subsequently, data for these selected variables is collected on a neighbourhood level and the electricity infrastructure in the province of South-Holland is mapped. The research focuses on the low and middle voltage grid infrastructure as these grid levels directly influence households and their impact can be assessed on a neighbourhood level.
The collected data was then used in a correlation analysis to discover a possible relationship between the socioeconomic status of a neighbourhood and the amount of low and middle voltage stations located in it. As the electricity demand of wealthier households is higher than the demand of lower income households, it was expected that there would be a positive correlation between prosperity and electricity infrastructure. The found correlations, however, did not result in any definitive conclusions, except for the relation between the amount of inhabitants and the amount of electricity infrastructure, so another analysis was performed that focused more on the spatial correlations. In this hotspot analysis the variables with a strong relation from the correlation analysis were used in a clustering research. This resulted in indications being found that linked areas of socioeconomic prosperity to more investments in electricity infrastructure, as was assumed. This implicates that there is an unbalanced distribution of costs and benefits on the grid as everyone pays the same for the investments, but wealthier neighbourhoods profit more.

In order to learn how this relation came to be and how it affects equality, experts on the topic were interviewed. In total five interviews were conducted with people from main stakeholders in the system, namely: grid operators, municipalities and researchers on energy justice. The interviewees were asked about their thoughts on the results of the analyses, the developments of the electricity grid, its effect on equality and the role of the province of South-Holland in the system. From these interviews it was concluded that the indicated unbalanced distribution of electricity grid investments among different socioeconomic groups in the population is not necessarily a problem for equality on the short term, although it might provide difficulties in the future. The investments follow demand and thus it is a logical consequence that neighbourhoods with a higher electricity usage require more electricity infrastructure.
The problem in the system is that there are socioeconomic groups who have no access to the energy transition and can therefore not benefit from the developments, while others are enriched by it. This unequal balance of costs and benefits causes inequality to grow in the future, so governments will have to ensure lower income households have access to energy transition innovations in order to secure a balanced and sustainable future.

Climate change intensifies the frequency and severity of flooding in urban areas, such as Jakarta. While government-led adaptation measures are crucial, the limitation of planned adaptations in providing complete protection emphasises household-level strategies in mitigating risks. However, limited adaptive capacity and bounded rationality can lead to maladaptive behaviours. This study investigates the dynamics of household-level climate change adaptations in Jakarta, focusing on their impact on vulnerability and potential maladaptation. Employing a mixed-methods approach, the research analyses micro-level survey data and agent-based modelling to examine adaptation measures like elevation, dry-proofing, and wet-proofing. Findings reveal that socioeconomic factors, such as income and fatalistic attitudes, influence adaptation choices. Low-income households are particularly vulnerable to suboptimal strategies, like wet-proofing, which can exacerbate their risk. The study also highlights a discrepancy between these measures' perceived and actual efficacy, emphasising the need for targeted policies. Recommendations include promoting effective adaptation, providing targeted financial aid, and enhancing community and governmental support. By addressing these factors, policymakers can reduce the likelihood of maladaptation in urban Jakarta.

This thesis investigates the role of critical infrastructure (CI) in disaster management, focusing on Hatay in the 2023 Turkey - Syria Earthquake. Given the extensive destruction that impacted critical infrastructure systems, this study aims to investigate both the underlying factors that contributed to infrastructure failures and the effectiveness of the long-term recovery plans for the city. Through a case study approach, this thesis provides insights that not only aid in understanding the earthquake’s impact on Hatay's infrastructure but also offers broader implications for improving resilience in other disaster-prone regions.

The earthquake devastated critical infrastructure in Hatay, delaying rescue and emergency response efforts and exacerbating loss of life and property damage. This research seeks to uncover the root causes that contributed to these failures and evaluate post-disaster recovery plans. The study’s primary question centers on the planning and management shortcomings within critical infrastructure systems and explores how these might be rectified in future disaster responses. The objectives include identifying the key critical infrastructures, analyzing root causes of the key critical infrastructure's system failures, and evaluating the long-term recovery plans’ effectiveness in mitigating risks.

A mixed-methods approach was selected for this case study to provide a comprehensive understanding of the key factors at play. The study includes qualitative analysis based on semi-structured interviews enabling the identification of key critical infrastructures based on stakeholder experiences and perspectives. To investigate the root causes of system failures, Fault Tree Analysis (FTA) is employed, offering a systematic and logical approach to identify underlying issues and contributing factors. Additionally, the study evaluates the effectiveness of long-term recovery plans through a comparative analysis of retrospective and prospective risk assessments.

In the analysis of the interviews education sector, healthcare services and transportation systems emerged as the key critical infrastructure, with their failure significantly impeding immediate response efforts and their importance in recovery. The education sector, in particular, emerged as a crucial component for long-term recovery and the restoration of societal normalcy. Meanwhile, the healthcare system struggled to manage overwhelming patient surges, while transportation bottlenecks, worsened by damage to road networks and airports, severely delayed the delivery of essential services and resources.

The FTA revealed several critical failures in CI planning and management. Healthcare services experienced capacity overload and staff shortage resulting from resource allocation and distribution failures, lack of seismic retrofitting and neglected structural faults in hospital buildings and restricted access to hospitals originated from transportation system failure. Transportation systems suffered from non-functional airport and road network failure originating from poor site location, previous inaccurate site assessments, inadequate design for site conditions, lack of maintenance and lack of redundancy, leaving many areas inaccessible post-disaster. A recurring theme across CIs was a lack of region-specific resilience planning to account for Hatay’s unique geographic context.

While the recovery plans address several CI weaknesses identified in the root cause analysis, significant gaps remain, especially in aligning strategies with the identified pre-disaster vulnerabilities. Although the recovery efforts incorporate bed capacity improvements and increased redundancy, heightened risk concerning neglected structural faults inaccurate site assessment were observed.

The increased risk suggests that the recovery plans may be addressing symptoms rather than the root causes of vulnerabilities. This points to a need for recovery strategies that go beyond immediate repairs, focusing instead on addressing foundational risks to ensure lasting resilience and reduce the likelihood of repeated failures.

This case study highlights the devastating effects of infrastructure failures during disasters. By addressing the root causes of these failures and implementing more comprehensive, resilient recovery plans, Turkey can build a disaster management framework that is robust enough to withstand future natural disasters. Although disaster management is highly context-dependent and cannot be universally applied, this study is important at a broader level, highlighting the need to identify the root causes of system failures in order to effectively mitigate future disaster risks.

The Sahel region has become a focal point of concern due to the alarming escalation of terrorism, culminating in devastating attacks that have claimed numerous lives and disrupted regional stability. Mali, in particular, has experienced a significant surge in terrorist activity. This thesis presents a novel approach of using criticality measures based on network science as pay-offs for a game theoretical model in order to protect connectivity of road networks in conflict areas. Through two comprehensive literature reviews the network scan approach, based on shortest paths, population sizes, travel distances, and travel times and the two-player zero-sum game structure were selected as the basis for the methodology. In order to enhance understanding of dynamics on the road network, semi-structured expert interviews were conducted. The model is applied to the region of Segou in Mali and optimal strategies were compared to empirical terrorist attack data of the ACLED database. It is concluded that road network protection can be strengthened to minimize the impact of terrorist attacks through the allocation of protective resources. Firstly, experts highlight the positive impact of combining military patrols and convoys with explosive ordnance disposal on road safety. Secondly, results revealed the network scan approach's potential as a valuable tool for criticality assessment. Thirdly, contexts lacking empirical traffic data, directing network protection efforts towards roads identified as optimal strategies by the model becomes prudent. Lastly, the comparison of optimal strategies against emperical data provides the means enhance insurgent behaviour's understanding. While the developed model can be considered rudimentary, it is believed to have laid down essential foundational principles. Further research is recommended the extend the model further, including validate the model's effectiveness and determine its applicability to different types of attacks and different insurgency groups. This thesis hopes to have made a contribution to the academic fields encompassing counter-terrorism, network science, and game theory within conflict settings.

Nature-based solutions (NbS) are increasingly being used around the globe as a climate action tool. In the Global South, which faces significant climate risks, higher urbanisation rates emphasise the importance of climate justice. While social valuations and interactions with nature-based solutions have been well researched, the relationships between nature-based solutions and social vulnerability to climate change remain difficult to capture. Furthermore, there exist little support for decision-makers to integrate social vulnerability into NbS planning. In this thesis, a social vulnerability framework to determine ecosystem service demand is presented using principal component analysis (PCA), and used with an NbS matching model to determine the types and locations of NbS to be prioritised, applied to a case study of Cape Town, South Africa. The presented framework allows for leveraging openly available quantitative data sets and incorporating different risk factors, while depending on expert interviews to contextualise the model and emphasize its limitations. Three potential policy considerations are recommended for the city of Cape Town to facilitate climate just NbS implementation: cooperation with conservation, potential for climate adaptation and justice, and the importance of community involvement. Using this method of prioritisation and selection, decision-makers of the city of Cape Town can incorporate climate justice into its urban planning for NbS, while taking consideration of the framework limitations and restrictions.

Due to climate change, there is a push toward a reduction of greenhouse gasses. Through spatial planning, the province of South Holland plays a role in this process. However, this requires insights into relation between the energy system and the built environment that is currently lacking. This thesis shows how spatial clustering using the K-prototype algorithm can be used to find areas with similar characteristics. It also describes how this information can be used in spatial planning by decisionmakers like the province of South Holland.

Densification, the increase of built environment elements in urban areas, has been identified as a tool to mitigate social and environmental consequences of urban sprawl, i.e. the continuous extension of urban boundaries and dispersed construction of built-up areas.
At the same time, unique environmental and social challenges are requiring urban developers to adapt their approaches to changed long-term or short-term physical conditions while considering the social consequences.
Many cities around the globe, and amongst them Amsterdam, The Netherlands, are striving to align urban interventions in the field of densification and climate action. This led to the research question: Which social impacts arise from simultaneous urban densification and climate action in the form of climate change adaptation and the energy transition?
This study provides insight into the current knowledge in scientific literature and the perceptions of urban policymakers. A qualitative mixed methods approach was applied. The approach was constituted of a systematic literature review and semi-structured interviews. A social impact framework was combined with an understanding of the natural sciences, engineering and urban planning to assess the social impacts of urban densification in times of climate action.

I first collected the current literature at the interface of urban densification, the energy transition and climate change adaptation to compile the perspective of academic literature in a knowledge base.
Secondly, I conducted semi-structured interviews with urban policymakers of the Dutch capital of Amsterdam to gain insight into the perspective of policymakers. In the analysis of both my data sets, I discerned explicitly mentioned social impacts as well as social impacts which were not explicitly stated, but could be reasoned from the collected literature and the conducted interviews.
Finally, the comparison of both perspectives shows a strong prevalence of social impacts in the spheres of livability in the built environment, health and economic relations. How densification, the energy transition and climate change adaptation exert consequences on communities and institutional relations is much less observed at the current state. Lastly, impacts on cultural or gender relations are close to absent from the discussion - in academic literature and the perceptions of urban policy-makers in the case study alike.
While the results are largely connected to the conceptualisation and methods applied, implications can be drawn for future research and policy-making. It is essential to explore the origins of the low occurrence of cultural and gender impacts in the data set to evaluate whether these are due to knowledge constraints or actual low impact rates. But, given the understandings gained, I urge to observe these spheres more strongly, in particular in relation to indirect social impacts. Moreover, the case study shows that social impacts of densification and climate action transitions are highly context-driven. They are subject to questions of ownership, human behaviour, urban inequalities and social differentiation within the constraints of physical space and monetary as well as environmental budgets. Additionally, it is indispensable to further strengthen the interaction and communication between researchers, policy-makers and other stakeholders.

Urban areas are evolving in terms of their growing population and their developing systems and infrastructure. This evolution brings about concerns regarding social equity, which refers to the fairness of distribution of certain attributes to groups or individuals. Transport equity is one of the dimensions of this issue, and accessibility distribution is a commonly used indicator for its measurement. Such measurements are applied to urban transport systems to analyze the ease of accessing public services and areas of economic activity. With the development of complex network metrics and increased availability of historical data, temporal analysis of transport systems is gaining more attention. Complex network models based on graph-theoretical approaches provide a repeatable means for quantitative analysis of transport systems in terms of accessibility to opportunities. This research investigates the development of transport equity from the lens of spatial accessibility distribution over time and seeks to analyze its evolution using complex network theory. A random walk based accessibility methodology is developed for this purpose. This methodology is tested in a quantitative case-study, where the City of Helsinki is analyzed in terms of accessibility to education opportunities via walking as a travel mode. The results show that the developed methodology is useful for the identification of accessibility distribution, as well as equity evaluation. With the aid of topological network indicators, and spatial analysis of school locations, the effect of transport systems and land use can be distinguished. This method requires a complete data availability for each historical timestep, as well as adaptation of its parameters based on real-world travel behavior and infrastructural changes. Future research should investigate the multi-layer approach with the inclusion of other travel modes in the model. Furthermore, accessibility to other opportunities such as job locations can be tested with this methodology.

Cities and regions around the world are experiencing an increase in extreme weatherrelated disruptions as a result of climate change. These disruptions are testing the integrity of energy systems including electricity grids. An emerging concept to assess the vulnerability of systems to disruptions is the concept of resilience. Resilience is used to assess the entire cycle of a disruptive event or specific phases such as the before, during, and after the event. In this thesis, a set of criteria to assess the resilience of the electricity grid is defined. Then the electricity grid sectors of Harris County, Texas are assessed by applying the developed and operationalized resilience criteria. This is done to understand the current resilience conditions of electricity grid sectors. The criteria are applied for assessment purposes by using the MultiAttribute Value Theory (MAVT) method of MultiCriteria Decision Analysis (MCDA). The results of this method are displayed using a series of maps. Results from the electricity grid sectors’ assessment provide an understanding of the resilience of a city’s electricity grid sectors, highlighting which low scoring sectors need more attention. In turn, having an overview of the resilience of electricity grid sectors can be used to plan or improve infrastructure including the integration of local renewable energy generation.

Land-use change models are often used to explore future land-use. Currently, most land-use change models run a small number of predetermined scenarios. To better address the multidimensional nature of uncertainty about the future, previous studies have argued for covering a wider range of the uncertainty space than is possible with existing scenario approaches. One way to approach this, is by using exploratory modelling. Instead of running the model on a small number of pre-defined scenarios, one uses sampling techniques over a plausible range of the uncertainty space to generate large-scale simulation experiments. With respect to land-use change models, this results in a large number of future land-use maps. To better make sense of the resulting maps, the next step necessitates the identification of plausible distinctive land-use patterns through clustering algorithms. Previous studies regarding quantification of land-use maps (dis)similarity focus only on comparing maps on a one-to-one basis or in small numbers for validation and calibration purposes. In this study the use of different map of similarity metrics on the resulting clusters of land-use patterns is systematically investigated. Specifically, the implications of using various cell-by-cell similarity metrics and landscape structure similarity metrics to cluster the resulting land-use maps are tested. The Land Use Scanner model is used for this purpose. It was found that the choice of (dis)similarity metric plays a significant role in the formed clusters of maps. If exploratory modelling is to be applied to land-use change models, it is thus important that great care is taken in the selection of the proper clustering algorithm.

Urbanization makes cities more vulnerable in the face of climate change risks. Understanding urban growth under climate change can help planners optimize land allocation and development strategies that are resilient to the impacts of climate change. However, the complexity and uncertainty of climate change hinder the urban growth projections and make it hard to form local climate adaptation plans. Therefore, this research aims to develop a methodology to incorporate climate uncertainties into land-use models to explore plausible futures.

The literature review results indicate the lack of a methodology to quantitatively link climate change effects with land-use models and to systematically explore the full parameter space of the climate uncertainties. Hence, our main research question becomes “How can an integrated land-use modelling methodology be developed to help systematically explore the impacts of climate uncertainties on urban growth?” This research question is answered by integrating Metronamica, a cellular-automata-based land-use modelling framework with Exploratory Modelling. The Metropolitan Region of Amsterdam (MRA) is selected as the case city to demonstrate the proposed methodology, and the research scope focuses on flooding, a typical and important climate impact. Specifically, we include the flooding probability maps into the “suitability” section under Metronamica, based on the principle that the higher the flooding risk of an area, the lower the suitability value. These flooding suitability values are deemed as uncertain in our research and they are not given fixed values but certain uncertainty ranges.

The flooding factors and their defined uncertainty ranges are added to a model established for the case city. Then this model is connected with the Exploratory Modelling Analysis (EMA) workbench and generates 2000 experiments by the random samplings and combinations of the uncertainties. In the result analysis step, we use clustering algorithms to select 34 representative maps, followed by the comparisons between them and the base map where no flooding factor is included. The 34 representative maps show some land-use change characteristics because of the introduction of the flooding suitability variables and the uncertainties. These characteristics of the projected land-use outcomes are extracted and the reasons behind these observations are explored. Finally, the land-use changes from 2015 to 2050 with the flooding risk considerations are summarized. Based on the summarized changes, we formulate some policy implications for urban planning under climate uncertainties.

This research presents a complete workflow of dealing with climate uncertainties in land-use analysis, including the selection of methods, the model conceptualization to include climate variables, the model implementation to set up a Metronamica model for the case city, and the connection between the Metronamica and the Exploratory modelling techniques. In the last step, we carry out result analysis and interpretation, finding out the land-use changes caused by the inclusion of flooding into urban dynamics. This integrated framework could be further applied and improved in the intersections of climate change and urban development, to provide insights into climate adaptation and urban climate resilience.

The use of scenario planning has a long history in decision-making and public policy (Bryant and Lempert, 2010). Traditional scenario planning, as, for example, used by the Shell Scenarios group, provides tools to communicate and characterize uncertainty,allowing decision-makers to anticipate the future and create more robust strategies (Bradfield et al., 2005).

However, the classical qualitative approach, where scenario narratives are developed, has some severe limitations. While this approach provides results which are readily communicated to decision-makers, it often overlooks truly unexpected (but plausible) scenarios (Kwakkel and Cunningham, 2016). Besides, they are not readily implemented for problems where the structure is also disputed (Bryant and Lempert, 2010).

Scenario discovery, a quantitative, brute-force approach to scenario development,developed by Bryant et al. (2010) aims to address these limitations. This approach has been successfully applied to numerous grand challenges, among others climate change (Kwadijk et al., 2010), sustainable water management (Haasnoot et al., 2011), and global natural resource management (Kwakkel et al., 2013).

The original approach to quantitative scenario discovery relies on three consecutive steps: sampling, labeling and searching for subspaces leading to the regions of interest.The sample is typically collected using Latin Hypercube Sampling (Kwakkel, 2017). For uniform, independent sampling, the chance that a random sample falls within the region of interest decreases quadratically with increasing size of the bounds (Vrugt, 2016). Therefore, if the prior distribution cannot be estimated, or the problem at hand demands a wide range of possible values, and the problem has a high number of dimensions, these sampling techniques require an unreasonably high number of samples to adequately delineate the region(s) of interest.

Therefore, this thesis proposes using dependent sampling, which concentrates the sampling on the regions of interest, rather than attempting to represent the entire input space. After convergence, the resulting sample approximates the true posterior distribution. By performing a Kolmogorov-Smirnov test for uniformity, the uncertain factors leading to the behavior of interest can be derived. The relevant ranges of parameter values can be recognized and communicated to decision-makers.