The Future Ground: Urban Planning Under Climate Uncertainty

As the focal point of the climate crisis, urban regions face a critical mismatch between the century-long lifecycles of physical infrastructure and the short-term institutional planning timeframes that govern them. This research addresses the dual constraints of temporal myopia (short-sightedness) and sectoral fragmentation, addressing the lack of mechanisms to systematically embed climate uncertainty into spatial masterplans.

Positioned within the emerging paradigm of climate urbanism, this dissertation examines how urban planning can operationalize resilience thinking and apply design thinking to steer transformation under uncertainty. The research adopts a mixed-methods design grounded in Multi-Case Theory building (MCT), enabling a rigorous exchange between theory and practice. The approach is empirically refined through two contrasting global contexts: the Metropolitan Region of Amsterdam (MRA) and the Mumbai Metropolitan Region (MMR).

The research culminates in a new theories and conceptual framework that connects resilience and uncertainty approaches to physical planning interventions and an advanced understanding of the temporal dynamics of urban systems. Furthermore, it introduces a methodological innovation: a stepwise Design Thinking methodology for blending computational models and scenario narratives that embeds expert feedback into the modelling process to navigate and design for complex urban futures.

Humanity has always faced extreme events that pose significant challenges due to their unpredictability and substantial impact. While, in the past, these events were relatively rare, recently, the frequency, intensity, and geographic spread of extreme events have increased dramatically, driven by factors like climate change and globalisation. The European heatwaves of 2022, 2023, and 2024, for instance, demonstrated how climate-driven extremes can severely impact modern societies, with tens of thousands of heat-related deaths. Likewise, the COVID-19 pandemic revealed the vulnerabilities in global preparedness for health crises, causing severe economic impacts and widespread social disruption. Both phenomena underscore the urgent need for enhanced resilience and preparedness in the face of increasingly frequent and intense extreme events....

Fostering disaster resilience depends on effective coordination between diverse groups. At the same time, the volatile conditions of a disaster require different actors to self-organize in a decentralized way. While information sharing has been described as central to both coordination and self-organization, effective information management remains challenging during disasters, given shifting organizational patterns, evolving roles, changing information needs, and the high information load caused by volatility. This thesis addresses these challenges by analyzing coordinated self-organization through effective inter-group information exchange between communities and professional responders during disasters (1).

This thesis puts forward a way to systematically study disaster information sharing from an actor-centered perspective through a combination agent-based modeling and empirical case study research. It does so by developing a methodology to create Agent-Based Models (ABMs) for studying disaster information sharing through qualitative inquiry (2). This approach is chosen given the challenges of gathering quantitative data about disaster response (e.g., through surveys and disaster simulation exercises).

The methodology was applied to the case of Jakarta to develop an empirical descriptive ABM. Findings from case study research and simulations with this ABM show that, when actors are unaware of the information they need (i.e., their needs are latent), delivering information on time to them becomes particularly challenging. Further, the results show that communities tend to address a higher portion of their information needs compared to professional responders (3,2). Communities’ highly localized situational awareness needs to be combined with a more global perspective concerning a disaster and its development to foster effective coordination. To achieve this, a two-way communication between professional response organizations and communities is essential.

The empirical ABM was then extended and abstracted from the Jakarta case to develop a theoretical ABM to study the emergence of Informational Boundary Spanners (IBSs); i.e. of actors that facilitate inter-group information exchange. Findings from simulations with this ABM suggest that individually learning who provides high-quality information is a mechanism that fosters the emergence of IBSs (4). This collectively intelligent behavior is contingent on stable information sources and a high number of trusted inter-group connections among communities and professional response organizations, especially at high levels of volatility (4).

The methodology proposed in this thesis provided the means to systematically study actor-centered disaster information sharing. First, the methodology was found to be rigorous in translating qualitative data into ABMs, and to provide a way to balance cross-case comparability with the flexibility to capture the nuances of specific cases (1,2). Second, the methodology was found to be versatile in developing both empirical and theoretical ABMs to study actor centered disaster information sharing, including mechanisms leading to the emergence of intergroup information exchange during disasters (2,4).

Future research will focus on exploring synergies among coordinated self-organization, collective intelligence, and resilience beyond the context of disaster response, concentrating on how collective learning, sensing, and remembering can foster adaptive, transformative, and absorptive resilience capacities in both the short and long term.

Amidst ongoing climate change, communities worldwide face increasing frequency and severity of floods, which cause extensive destruction and pose significant health threats. Post-flood effects include drowning, injuries, and both communicable and non-communicable diseases, varying by context. This study addresses a critical knowledge gap by exploring the complex relationship between flood disasters, healthcare systems, and communicable diseases. It aims to understand how socio-economic, institutional, and geographical factors interact with health outcomes following floods.

The study highlights vulnerabilities in countries like Peru, Sri Lanka, and Pakistan, where past floods have significantly impacted communicable disease incidence. It evaluates healthcare system resilience by analyzing vulnerability and resilience factors that determine a country’s capacity to manage health impacts from floods. Vulnerability is influenced by socio-economic conditions, public health factors, and behaviors, while resilience is shaped by healthcare system quality, institutional effectiveness, and individual capacities.

Quantitative methods, including exploratory data analysis and causal-comparative design, form the core of this study. Natural flood events are used to investigate cause-and-effect relationships, supported by a conceptual framework developed through desk studies and concept mapping. Systematic data collection and analysis explore the relationship between flood events, healthcare systems, and communicable diseases. Principal component analysis identifies key indicators linking health characteristics with increased communicable disease incidences post-flood.

Synthesizing insights from data analysis, the study proposes an empirically validated framework to guide effective disaster response strategies. Understanding flood impacts and enhancing community resilience are crucial for developing targeted mitigation measures. By identifying vulnerabilities and resilience factors in healthcare and emphasizing individual empowerment, this research contributes to holistic disaster preparedness and response approaches.

Due to climate change, man-made conflicts, and rising inflation, a growing number of people around the world are struggling to have consistent access to safe and nutritious food. This phenomenon is known as food insecurity (FI). Therefore, we take in this thesis the first steps towards developing a monitoring process for assessing FI using Human-Centered AI (HCAI). We developed this process for, and in collaboration with, the Integrated Food Security Phase Classification (IPC). The IPC is an organization that helps countries classify levels of food insecurity in their regions to inform humanitarian decision-making. During our research process, we found that any form of HCAI for the IPC would need to be informed by input from their domain experts, and we concluded that we could not start implementing HCAI until we found a way to formalize their input in a way that was robust and suited their technical capabilities. To this end, we ran an experiment with 18 IPC experts in Malawi to see whether they could quantify their assumptions by setting thresholds for food security drivers. The results are encouraging but show that there is still much to be done to bridge the gap between domain knowledge and technical expertise. We also show in this thesis that there is a lack of real-life case studies on HCAI development and share therefore our lessons learned from our real-world HCAI case study on FI monitoring. In this way, we hope to promote the development of a practice-informed methodology for HCAI.

Responding rapidly to epidemic outbreaks presents significant challenges, due to resource, capacity and time limitations. Anticipatory Action (AA) is a newly emerging strategy in the field of humanitarian aid, designed to preemptively address potential crises. By taking impact-reducing actions before a disaster strikes, AA seeks to minimize human loss. However, AA frameworks currently use static prepared-in-advance plans. As a result, AA is not sufficiently able to deal with the uncertainty levels in the onset and spread of epidemics. Effective epidemic control requires plans that can adapt to a constantly changing environment and incoming information, such as the number and location of suspected cases, weather forecasts and population movement, while balancing flexibility with an effective management approach. We show how the (DAPP) framework for decisionmaking under deep uncertainty can be adapted to enhance the common anticipatory action approach with flexibility and effective management for epidemic control. More specifically, we show how DAPP allows to take into account newly available information and change the strategy to minimize human loss. We illustrate it with a case study of cholera in Cameroon, for which the French, Netherlands, and Cameroon Red Cross, supported by EHESP, are developing an early action protocol and a model that assesses the cost-effectiveness of actions for different risk levels and external shocks. Our results suggest that DAPP increase flexibility and coordination in anticipatory action for epidemics and helps optimizing early action strategies, which could have larger implications for global disease control.

Palladium is a critical raw material that is considered economically and strategically important by various national governments. The palladium supply chain is subject to several supply chain risks, including the geopolitical risk of potential Russian palladium export restrictions. Considering these disruption risks, it is essential to gain insight into the resilience of the palladium supply chain. Accordingly, this study investigates the temporal dynamics of resilience in the palladium supply chain using literature review, data analysis, and regression modelling. To that end, the diversity of supply, stockpiling, price, and substitution mechanisms from the qualitative resilience framework by Sprecher et al. (2015) are operationalised in terms of quantitative proxy variables. A PCA-weighted compound resilience index is constructed and quantitatively validated using the palladium market balance as a resilience performance indicator. It is found that the palladium supply chain exhibited an overall improvement of resilience, but still a structural lack of resilience during the years 2012-2021. This thesis contributes to the scientific study of how material criticality and resilience change over time. Moreover, this study informs policy-makers about potential risks for the palladium supply chain and makes policy recommendations to improve the resilience of the palladium supply chain.

Lead time reduction of military supply chain (MSC) processes is essential for well-functioning military organisations. MSCs are complex and uncertain systems, including interdependencies between actors and processes. Flexibility is needed for MSCs to deal with complexity and uncertainty. Furthermore, reducing lead times contributes to the flexibility of supply chains. Primary and secondary supply chain processes are highly interrelated. Therefore, reducing lead times in primary and secondary SC processes is relevant.

The current scientific literature is lacking related to lead time reduction in MSCs. Besides this, supply chain management must be improved to minimise lead times in MSCs. However, insufficient empirical research exists on information sharing within supply chain management literature. Information sharing is essential for effective MSCs. Thus, the relevance of information sharing for improving SCM, combined with the lacking scientific literature on lead time reduction in MSCs, indicates a new specific research area that contributes to reducing lead times in MSCs by focusing on information sharing.

This research focuses on formal information sharing, defined as the sharing of structured and traceable information in a formal setting. Inadequate formal information sharing results in the scattering of formal information throughout a multi-actor system referred to as 'information fragmentation’. Since information sharing is a crucial component of SCs, information fragmentation may influence the lead times of SCs. However, the issue of information fragmentation in terms of lead times in MSCs is not apparent, and neither is how to solve information fragmentation problems in MSCs.

This research contributes by developing and applying an information fragmentation perspective towards lead time reduction in MSCs. A case study at the Dutch Ministry of Defence (the invoice case) is used to analyse whether the perspective is valuable in practice. The invoice case is a financial supportive MSC process encountering problems related to lead times and formal information sharing. Additionally, two types of formal information are distinguished: process in practice and process design information.

Three main results are obtained in this research. Firstly, this research contributes with an information fragmentation perspective towards lead time reduction for MSCs. Secondly, identifying a significant problem in the invoice case shows the effect of information fragmentation on MSC lead times due to non-value-adding activities. Lastly, exploring two boundary objects - a track and trace system (integrating formal process in practice information) and an integrated process design overview (integrating formal process design information) is a first contribution to the practical support of boundary objects to reduce information fragmentation in MSCs.

In further research, the proposed boundary objects can be further developed. Moreover, the effect of these boundary objects on information fragmentation and reduction of lead times can be quantitatively analysed. Furthermore, the impact of informal information sharing on lead times can be incorporated.

The ongoing war between Russia and Ukraine has shown the importance of a well-organized military logistic system. The Dutch Ministry of Defence has concluded that their military logistic system is not used most efficiently. The need to build greater confidence in the ability of a defence organization as a whole to meet and sustain logistic support for future tasks has been acknowledged in the past. One such driver to build greater confidence is that of ‘trust’

Previous research on trust building between supply chain partners has been done manifold. However, the Dutch Ministry of Defence is a unique organization where the roles of distributor and client are within the same organization. Thus we are dealing with an intra-organizational supply chain, rather than a common inter-organizational one. Additionally, as a military organization the main priority of the supply chain is the responsiveness of the supply chain, rather than the efficiency. The uniqueness of the organization is the reason that additional research is needed on the role of trust within the supply chains with these special characteristics.

Multiple variables and their underlying relations, which create complexity and uncertainty, need to be addressed to implement policies that stimulate tryst within the organization. An System Dynamics (SD) Modelling approach will be used to access the dynamic complexity and uncertainty. The Exploratory Modelling Analysis (EMA) workbench enables the use and development of SD models, characterized by dynamic complexity, and deep uncertainty.
The results of the several analysis have given plentiful insights to consider regarding the future of trust dynamics between military supply chain partners. The initial values in a system such as this one have a huge impact on the progress of the model runs. Since the system consists of two major reinforcing feedback loops, Thus the system either strengthens or weakens itself, depending on the initial state of the model. Thus it is important to get the system to such a positive state, since it is likely to remain in a positive state.

As for the policy levers present in this system, the correction capacity lever seems the policy lever to have the most impact on the model outcomes. The correction capacity is the speed of which the incorrect orders get corrected. The results of the analyses conclude that regarding the state of trust in the model, it is crucial to keep this factor as efficient as possible. Since too low performance regarding correcting orders results in such delays that the trust level in the system completely disappears and does not come back within the time frame of the model run.

To summarize, I applied SD modelling and scenario discovery to explore the trust dynamics between a distributor and their client in a military intra-organizational supply chain context. Hereby I proved the importance of the initial state of the system, which due to its structure of reinforcing feedback loops is a big factor for its outcome. Additionally, it shows that the policy lever regarding performance by the distributor has the most impact on the trust level in the system. This insight tells us that performance is the biggest levers to adjust by policymakers to improve trust dynamics within their organization. I recommend policymakers to formulate strategies that give this insight to employees functioning as the distributor as well as the client. The interdependencies between one another should be emphasized. This should give the employees more incentive to improve their performance and thereby build trust between their collaboration partners.

Big data analytics (BDA) and supply chain resilience are both present and important topics, but research on the relation between the subjects is limited. This also holds for the translation of the subject for a specific industry. This research therefore addresses this relation and translates it to a comprehensive partial resilience assessment tool that provides a benchmark for the Fast Moving Consumer Goods (FMCG) industry. The tool is based on a deterministic model that incorporated 14 resilience enablers and their corresponding interdependence. Results show that current industry BDA based resilience levels are, compared to a theoretical optimum, on average 48% and have a better practice of 66%. It is recommended that the tool is further implemented within the industry to gain more reliable and substantiated results.

The demand for electric vehicles (EVs) and wind turbines have been rising significantly in the EU as the EU wants to become carbon neutral by 2050 and generate 40% of its energy through renewable sources in order to avoid climate change and its consequences. However, both these technologies use several critical raw materials essential for their functioning. Wind turbines generate electricity through generators, and EVs are driven by motors. An important component used in both these technologies is neodymium magnets. The magnets contain rare earth elements (mainly neodymium, dysprosium, and praseodymium) which are defined as critical raw materials by the European Commission. Against the massive usage of neodymium magnets in these green technologies, there is no collection plan that focuses on recovering and reusing the magnets from EVs and wind turbines. Currently, less than 1% of neodymium magnets are recycled in the EU due to no collection in place and a lack of large-scale recycling. End-of-life (EoL) EVs and wind turbines will be massive sources of scrap neodymium magnets in the EU. However, in order to reuse or recycle neodymium magnets, these magnets first need to be collected, which is seen as an opportunity. Only when a high collection rate is achieved, a significant amount of magnets can be recycled into magnets or their constituent rare earth elements (REEs) and thus contribute to more secure access to REEs in the EU. This master thesis uses a design approach to develop a set of collection strategies that can be implemented in the EU. To develop the strategy, requirements and functions are identified by the means of four analyses- neodymium magnet market analysis, reuse and recyclability of neodymium magnets, legal analysis, and stakeholder analysis. Based on the functions formulated, means are identified that can fulfil the functions. Finally, the chosen means are categorised in five themes: Legislation and regulation, Finance, Infrastructure development, Information exchange and Material flow. Furthermore, the different solutions are categorised into short-term (can be implemented 3-6 years), medium-term (can be implemented 6-9 years), and long-term plans (can be implemented 9-12 years).

Global climate change has results in a higher frequency of extreme disaster events and is therefore a serious challenge in disaster impact management. Disaster risk is composed of several components, such as vulnerability, susceptibility, exposure and the probability of occurence and intensity of a hazard. Vulnerability has become a topical issue due to the major role it plays in disaster risk reduction strategies. Hence, this research focuses on the development of a method to understand the dynamics of social vulnerability. The study area comprises Burkina Faso. Therefore the following research question was developed: 'how to calculate a social vulnerability index for Burkina Faso that characterizes the spatial and temporal dynamics of social vulnerability"?"

The results showed that despite drawbacks, principal component analysis provides good insight in de internal and externaly dynamics of social vulnerability. However, large differences are found in the ranking of the social vulnerability score of communes when other methods are used. Hence, it is deemed important to develop more research in the semantic meaning of social vulnerability an thus understand better which mathicmatical approach is the most suitable.

This research has found the highest social vulnerability in communes prone to conflict which are hosting many IDPs in the North, Centre-Nord, Sahel and East of the country. A statistically significant increase of social vulnerability was found from 2015 - 2017 in Boucle du Mouhoun, the Nord and the Centre-Nord.

This thesis examines the trade-off relation between flexibility- and redundancy-orientated strategies and their impact on improving supply chain performance under disruptive forces. It does so by evaluating the supply chain practices at the Ministry of Defence in the Netherlands. Seeking to improve performance in terms of cost efficiency, and continuity of supply flow while disruptions affect multiple regions of the supply chain.

The Dutch Ministry of Defence fulfils a crucial role in keeping the Dutch kingdom safe. From missions overseas to providing aid in the case of natural disasters, correct and efficient information-flow is critical for their operations. When a situation occurs, everything is dropped to make sure that they can handle it. The Royal Dutch Army, Navy, Airforce and Marechaussee make up the armed forces part of the military. Besides the branches of the armed forces, there are two support branches. Under one of them, Defensie Ondersteuningscommando, the Air Transport Unit of the Dutch military is located. This unit plans and advises on all forms of air transport for the Ministry of Defence, a unit where correct and timely information is flow critical.
This thesis attempts to answer the following research question: What are the effects of formal network structures within public organisations on their information-flow quality? Attempting to fill gaps in literature surrounding the effect of formal network structures on information-flow quality. The study defines information-flow which is used for the KPIs. The effects of formal network structures on information-flow quality are studied using an ABM model developed in this thesis. The model is tested on the Air Transport Unit network and a diverse set of randomly generated networks. In this case, key findings from the model results indicate that in the case hierarchy, a more hierarchical structure positively affects their data correctness. However, this more hierarchical structure negatively affects their information timeliness. Employee business does not seem to be affected by hierarchy. Other findings relating to the number of employees whose function it is to control data for mistakes, have significant effects on improving data correctness while at the same time, negatively affecting the timeliness of information. Indicating that the optimum, hierarchical, or less hierarchical organisation lies with the ambitions and goals of an organisation.

Communicative diseases have put a burden on societal well-being throughout history. The global advent of the COVID-19 virus made the world painstakingly aware of the impact a pandemic can have on all levels of society. To combat the spread and impact of a pathogen, policymakers turn to specialized tools to craft policy. One of these tools is mathematical modeling and simulation. By using the agent-based modeling paradigm, it is possible to evaluate the impact of policy measures on the viral spread and healthcare burden. An agent-based model always contains a set of agents. Agents are the digital representations of a studied entity, for example, a person, but agents can also be an organization or a vehicle. One of the characteristics of agent-based modeling is the reliance on data to create a valid agent population. However, data on the level of individual people, also known as microdata, can be difficult to acquire and work with due to GDPR regulations. Aggregated population data, also known as open data, may serve as a substitute for microdata. Both open and microdata may be used to synthesize a population of agents, this process is known as synthetic population generation. Although both of these forms of data can be used to synthesize an agent population, it is currently understudied how the results of the agent population synthesis process are affected by the type of used population data. This research project focuses on this gap in knowledge and systematically analyzes the differences between an open-data-based and a microdata-based agent population. The populations that are synthesized are part of the HERoS model. The HERoS model is an epidemiological agent-based model that maps the spread of COVID-19 in the city of The Hague. The agents in the HERoS model are digital individuals, each characterized by a set of attributes. These attributes define the behavior of an agent within the model. Examples of agent attributes in the HERoS model are age, household size, and social role. Two agent populations for the HERoS model are synthesized, one using a sample-free algorithm that uses open data, and one using an algorithm that converts microdata to an agent population. The differences between the agent populations are quantified by introducing two new metrics, the modified Freeman-Tukey statistic, and agent matching. It was found that the agent populations show similar characteristics on a city level, but show significant differences at the neighborhood level. Furthermore, it is shown that the quality of an agent population is both dependent on the number of attributes that are synthesized, as well as the type of attributes. Moreover, the relationship between the quality of an agent population and the outcome a model produces is also understudied. This relationship is investigated by using the open-data-based and microdata-based agent populations as input for the HERoS model. The model outcomes are then compared on a global and neighborhood level, using phase comparison and the quantification of the precision of the model. It is found that the effect of using open data instead of microdata is multifaceted. The model outcome curves show similar characteristics, albeit they differ in a numerical sense. Using open-data increases the number of hospitalizations, ICU occupants, and deaths in the HERoS model. Furthermore, the precision of the model decreases when open data is used. Finally, it observed that neighborhoods that significantly differ in input population also significantly differ in model outcomes, showing the importance of the quality of an agent population for smaller resolutions. Conclusively, this research project shows that open data can serve as a viable alternative to microdata when it comes to synthesizing agent populations for epidemiological agent-based models.

The cities of the Global South need to become more flood resilient in light of climate change and rapid urbanisation. In this thesis, a land-use modelling case study for the Mumbai Metropolitan Region is executed on the interaction of flood risk and land use, through which future flood resilience was assessed for several urban planning strategies. Concluded was that the urban environment will become less resilient in case no governmental intervention occurs, and urban planning can contribute to the resilience of the urban environment. Furthermore, the effectiveness of urban planning was largely dependent on the strictness with which these policies were implemented and regulated, but largely independent of the perceived climate future.

Climate change is an omnipresent issue in politics, industry, science, and society. Scientist knew of its existence for several decades, but only recently has the perception of it shifted from a solely environmental problem to a matter of international security. Climate change is thought to impact the likelihood of extreme weather events, intensity of droughts, rising sea levels and more. In this way climate change contributes to ongoing conflicts around the world by destroying resources or forcing people out of habitats, creating tensions among groups of people.

As the impacts of climate change are being felt more directly, calls for bolder climate action are getting louder. At the same time, there is also resistance by climate skeptics in industry, government, and among voters. Although most people appear aware and concerned with the development of climate change, the attitude towards climate action is extremely broad. Political rhetoric and disinformation in the media compound the disagreement on climate change, causing polarization. Although some polarization is beneficial for democratic deliberation and for mobilizing voters, if polarization divides the electorate into mutually distrustful groups, it can trap societies in a stifling gridlock or gradually erode democratic institutions. These pernicious effects are what necessitates closer study to monitor societal polarization, prevent conflict and improve climate action efforts.

Social media offers huge amounts of textual data to study discourse on climate change, which this study leverages to make first steps in understanding what topics make up the public discourse, how it
varies across linguistic regions (English, German, Dutch) and how it changes considering climate-related events, namely floods and a climate protest. Using both topic modelling and sentiment analysis
on Twitter data, it could be identified that activity spiked around major events: namely the 2019 Climate Strike and the 2021 Flood in Western Europe. However, reaction to events appears to be
mainly regional with reaction to floods seen only in the Dutch and German tweets. The English Twittersphere seemed to be mainly skewed towards events in America. Polarization appeared low as
most tweets were neutral. Examining only tweets with high sentiment, a different behavior was detected in terms of news coverage as there existed more high negative sentiment tweets, but almost
no high positive sentiment tweets. During the 2019 Global Climate Strike, extreme sentiment tweets went both positive and negative, possibly indicating more intense discourse and a more polarized
atmosphere.

Overall, results show that conflict-triggering climate events are very likely to be domestic events. Thus, from a security perspective, it suggests on the short-term to focus on the monitoring of domestic
actors. On the long-term however, policies need to be implemented which address underlying factors that make society susceptible to polarization, such as socioeconomic inequalities and exclusion,
disinformation in media or corruption. Lastly, from an academic perspective, more research on other dimensions of polarization needs to be conducted. Polarization is a complex phenomenon, whose full
implications cannot be captured by the sentiment metrics. Thus, research moving forward should strive towards a more refined multi-method approach to further investigate climate change polarization and its conflict potential. A better understanding could benefit practitioners in many areas such as security and social policy, for strategic planning regarding climate change.

Place-making initiatives have gained momentum in recent years to establish stronger urban communities. Through place-making, people attach meaning to spaces and they become places. While place-making initiatives have traditionally been designed and implemented from top-down, more and more scholars call for a participatory and bottom-up approach, for place-making to realise its full potential in creating strong neighbourhood communities. In this context, the thesis explored how the knowledge from Participatory Design and place-making may confluence to move from spaces to places in a more inclusive and community-driven way. The main research question was: How can Participatory Design facilitate place-making in urban settings across physical space, social connections, and institutional support? This thesis presents a framework for participatory place-making, build from place-making and Participatory Design literature and evaluated using six participatory place-making interventions that were designed, implemented, and evaluated in neighbourhoods in The Hague, Rotterdam, and Cork (Ireland). The framework contains five principles (emergent, empowering, inclusive, playful, reflective) that should guide the design of participatory place-making interventions and is complemented with five guidelines to design for participatory place-making.

Since the outbreak of COVID-19, far-reaching measures were introduced to isolate people and reduce the chance of transmission among people. In the Rohingya refugee camps in Bangladesh, the camps were locked down for months. As a result, economic dependencies between the refugees and host communities which were built in previous decades were cut-off for months. So far, no research has been published on how to release the burden of limited refugee-host interactions, which can be a vital source of livelihood generation. In this research, the implementation of different vaccine allocation strategies were analysed and compared, to analyse what prioritization strategy can best be introduced to most effectively revive these interactions and control the COVID-19 outbreak. Results show that either prioritizing elderly or high-risk transmission groups can be effective. Prioritizing elderly distinguishes itself as it most effectively reduces severely & critically symptomatic infections. Prioritizing transmission-groups shows maximize refugee-host interactions. When combining both strategies with a dynamic approach of opening/closing the camp from host communities, a more effective strategy is possible for protecting the vital economic functions of an open-system refugee camp. An implication of this research is reflected by the granularity of the model. As the model has a high resolution and is highly computational, insufficient model experiments could be conducted to cover a complete solution space of the model. However, as this research proposes unique study in the dynamics between vaccine allocation and open-system refugee camp, a foundation is grounded for further research.

In the past few years, the frequency of onset natural disaster has been rising, causing significant damage to communities and infrastructure around the world. When it happens, airports in the region affected have to rapidly adjust and evolve from serving regular passengers to becoming a humanitarian hub, that handles a massive surge in both passenger, but especially cargo handling. A number of regions are especially vulnerable and prone to experience such a devastating event, and while there are existing initiatives aiming to raise awareness and improve airports’ preparedness, authorities are often isolated in their efforts to become more resilient. This research is an exploration of how data science and novel machine learning algorithms could help in establishing a base for forming collaborations between airports that might face similar challenges when it comes to disaster preparedness efforts. The goal was to build a comprehensive data set describing airports from the perspective of their disaster preparedness and find similarities between them, based on their intrinsic sociotechnical features, so that perhaps an airport in Indonesia could be matched with its ’sibling’ airport in the Caribbeans. The research involved a number of programming operations starting with collecting data, through data processing up till applying the Self Organising Maps (SOM) algorithm and visualising results.