in this research, I study the Nitrogen Crisis by comparing Twitter content to real-life events, answering the following research question: How does the intensity of emotions and discussed topics on social media relate to the characteristics of real-life demonstrations during a sustainability policy crisis? To characterise online events, sentiment analysis and topic modelling are applied to analyse tweets about the nitrogen crisis to paint a picture of what subjects were discussed online, and how people are feeling about them. To characterise offline events, a codebook is developed with categories that reveal information on the scale, intensity and extremism during real-life demonstrations. The most promising finding is that topic models with higher numbers of topics but lower coherence scores prove to be more interesting sources of information on underlying topics and trends on Twitter.

In today’s world, the transport system is essential in helping people reach the activities they want or need to attend. However, like in many other fields, inequality can exist in access to various opportunities based on personal characteristics. Little is known about how gender and intersectional factors impact people's perceived accessibility. This thesis aims to fill this knowledge gap using a mixed methods approach. First, desk research shows that perceived accessibility can be influenced by a large variety of factors, including, but not limited to, the primary mode used, perceived safety in transport and socio-demographic factors. Second, Structural Equation Modelling is used to investigate the strength of relationships between the various found factors and gender intersectional factors. This analysis shows that being a woman has a negative effect on perceived accessibility, which can for a part be explained by women feeling less safe in transport at night. Additionally, the analysis shows that for women having young children negatively impacts perceived accessibility, but not for men, while for men income positively relates to access to a car and in turn to perceived accessibility, an effect which is not found for women. Finally, the subsequent qualitative analysis, using workshops, shows that little attention has been paid in the past to (social) safety issues in transport and to differences in needs between different kinds of travellers. In the future, more cooperation between transport institutions and integrated intersectional policymaking in transport would be of significant value.

Cities around the world constitute complex systems, as sets of sub-components that are connected and interact with each other. Due to this inherent complexity, there are numerous emerging challenges that need to be tackled. In those complex environments, various-interest groups collaborate and compete to achieve their goals and interests, constituting a vital part of urban planning. The advancement of technology and ICT, and the abiding need to keep citizens engaged, marked a critical shift in the way that urban planning confronts the increasingly contemporary complex issues. This advancement has offered citizens a way to digitally participate in urban planning procedures, overcoming certain limitations, while posing, also, challenges related to the level of its effectiveness. While many methods and tools have been developed over the years, for enhancing citizen engagement and the effectiveness of public participation, there seems to be an evident gap in substantial and two-way collaboration among the (relevant) urban stakeholders. This thesis project aims to hark the perspectives and needs of the urban stakeholders, translating them into a conceptual design of a digital public participation platform. Next to that, except for the importance of the platform itself, as a tool, the focus was also given to participation, as a process, considering that such a platform could and should act as a promoter of effective public participation rather than a stand-alone solution. Consequently, the main research question of this study is: Which characteristics need to be included in designing a public participation platform so that it can enhance citizen engagement and facilitate more effective public participation in urban planning? For this research, a design science research approach was chosen. A theoretical background was developed on existing theories, methods and frameworks related to public participation and digital platforms. Data collection was conducted using multiple methods, including workshops, semi-structured interviews, and questionnaires, while data analysis was conducted inductively, to identify all the emerging patterns and their interrelations. From the data analysis, five core themes emerged: General findings; (De)Motivational factors for public participation; Characteristics; Technical requirements; Technological features. Retaining these themes, the analyzed results were then critically synthesized, both in qualitative and quantitative terms, to provide a holistic overview. Combing the obtained results, the main research outputs were developed, providing an overall answer to the main research question. First, a trifold validation of the results, related to the past, present, and future, was conducted, fulfilling the rigor and design cycle of the design science research, and attaining a continuous assessment. Building upon the evaluation results, for the platform as a tool, a conceptual design was developed, based on the different views of the urban stakeholders. For the process of public participation in general, a set of guidelines was developed, providing recommendations on how to enhance citizen engagement and the effectiveness of public participation, based on the different views of the urban stakeholders. This thesis concludes with some important final remarks, including limitations, recommendations for future research, research relevance, and the author’s final reflections on the research.

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.

Transportation systems are being put under increasing pressure in urban areas. These systems are concentrated, highly used and vulnerable to rainfall-induced disruptions. Improving the resilience of infrastructure systems to these disruptions has become a vital aspect of managing an urban area that is both economically viable as well as liveable. Studies show that disruptions disproportionally impact low-income and minority groups and with that lead to increased inequalities. Cities have increased their focus on resilience assessments but have stopped short of how different societal groups are affected by a disruption, and how interventions can impact societal groups differently. In this research the effect of different equity principles in the context of decision-making for resilient transport infrastructure is being addressed. Three different equity principles, namely Equal Sharing, Rawl’s Theory of Justice, and Utilitarianism, are being compared to understand how the benefits that arise from possible interventions are being distributed over society. To do so, a methodology has been developed and applied to the case study of The Hague. This research studied the accessibility in business-as-usual conditions, accessibility after a rainfall-induced disruption and accessibility for different societal groups after interventions are placed based on the three equity principles. The results show that i) depending on the underlying equity principle a different intervention location is prioritized. Simply changing the underlying assumptions on which the interventions are placed, changes the impact significantly for the most socially vulnerable group and the general population. To reduce inequalities in accessibility, it is important to increase awareness amongst policymakers and researchers that the most common (sometimes unconsciously) used equity principle underlying decision making is the Utilitarian principle. As is being illustrated in this research, there are other options that could greatly influence the preferred intervention and with that the impact to different societal groups.

Call Detail Record (CDR) data enables the analysis of human behaviour on a large scale and the information that it contains can be promising. Not only does it allow us to track the movements of many individuals throughout time, it uncovers patterns in a persons decision making process that potentially tell us a lot about the effects of different interventions. The opportunity of finding new information on human behaviour has been noticed in several research fields, but every researcher eventually finds the same blockade: privacy. The data represents a detailed track of individuals and therefore these individuals must give approval (almost certainly lowering the amount of data that can be collected), or the data must be aggregated to the point that user anonymity is guaranteed. As a consequence of aggregated data, potentially important information could be lost. Especially in the case that both the dimension of location and time are aggregated, as these two could be considered as the essence of the CDR data. There are however techniques that increase the aggregation level, by de-aggregating the data. Naive Bayes classification has shown to be a functioning method within Machine Learning to de-aggregate a dataset that has incorporated information on at least one of the two essential dimensions; location in this case. By using the same variables to describe the administrative areas within the country that were used to describe the rows within the data, Naive Bayes classification can find the area that is most likely to fit the row. Matching the variables of the areas to the variables within the displacement dataset represents the backbone of the process, as the de-aggregation is driven by the closeness of datapoints between the two datasets.

Migrants are moving in great numbers towards urban areas as a result of urbanization and drivers such as political-, economical- and climate crises. The influx of new people brings new challenges for cities and municipalities to provide for a suitable environment for new and existing citizens whilst preventing adverse effects to occur. Current literature describes the dynamics of the city using effects such as segregation, gentrification and urban decay. However, the changing character of urban areas is not linked with the decision-making behavior of citizens in current literature. Furthermore, the dynamics of urban areas are rarely observed from a meso-scale perspective. This research aims to describe the relation between migration patterns and changes to the fabric of the city. A conceptual model on the dynamics involved in observing decision-making of citizens and observing changes to the city fabric is made. Using real-world data of the city of The Hague in The Netherlands, a Data-Driven Agent-Based model is made in Netlogo. Data analysis on the outcomes of the model using Python provide a better understanding of the impact of migration on the changes to the city fabric. The model output describes the impact on the city caused by the influx of migrants. More migrants entering the city can drastically increase homelessness or the "pushing out" of citizen groups because of housing shortages. This is most prevalent for the poorest citizens, since social rent housing is first to become scarce when more people enter the city. An increase in income inequality or a decrease in average migrant income results in even more pronounced housing shortages. The model simulates 6 policy interventions for the municipality of The Hague. The model shows that all policies that increase housing options significantly increases well-being of citizens. However, the policies that try to improve safety and health do not show an increase in well-being of citizens. By means of historical validation the model is tested for validity. The model is able to describe much of the decision-making behavior of migrants and citizens. However, for certain ethnicities, the model is currently unable to describe the decision-making process in such a way that it can represent the observed real-world moving patterns. A framework has been presented that can be used to better explain the impact of migration on cities. Furthermore, the conceptual model can be applied to different aspects of city dynamics, for instance the transport or energy sector. The ABM shows that current housing options and expected influx of migrants become problematic within the coming 10 years. An indication of the usefulness of proposed policy interventions is presented. More research and surveys are needed to better understand these mechanics and their relation to changes to the fabric of the city. Additional research into the changes to the neighborhood amenities and businesses as a result of changes in citizen composition can aid policy making and understanding the developments of the ever-changing city landscape.

In the wake of tackling energy poverty by its roots and protecting vulnerable groups, the European Commission introduced the so-called Renovation Wave, aiming to renovate 35 million inefficient buildings by 2050. However, even though the Renovation Wave was aimed to tackle energy poverty, recent studies have shown that renovation policies have failed to address energy poverty as renovation and social policies lack intersectoral integration resulting in the exclusion of vulnerable groups and an increased risk of social inequity. Moreover, various scholars have tried to identify vulnerability indicators that give rise to energy poverty; however, these have only focused on identifying socio- economic and energy indicators. Consequently, scholars have called to include justice in renovation policies and explore how vulnerable groups can be identified. To address this knowledge gap, the following main research question was formulated: Using a Case-Study Mixed Methods (CS-MM) approach, how can justice be included in renovation policies considering the socio-spatial vulnerability to energy poverty? The results showed that by identifying vulnerable groups based on a locally developed index of the socio-spatial vulnerability to energy poverty and acknowledging their vulnerability, recognitional justice can be included in renovation policies. Besides, identifying tailored policy strategies in a multi-stakeholder environment that, on the one hand, focus on the distribution of renovation resources based on the needs and characteristics of the identified vulnerable groups and, on the other hand, on how the vulnerable groups can be included in the decision-making, distributional and procedural justice can be included in renovation policies. Thus, it can be concluded that by deploying the current CS-MM approach, justice can be included.  The outcomes of the present study are twofold. Firstly, from a scientific perspective, the present study contributes to the current body of scientific knowledge on: (1) how vulnerable groups in need of renovation can be identified, (2) how justice can be included in renovation policies to address energy poverty and (3) how a CS-MM approach can be deployed to research how justice can be included in renovation policies considering the socio-spatial vulnerability to energy poverty. Secondly, from a societal perspective, this study provides insights to improve policy strategies that enable local decision-makers to identify vulnerable groups and include justice in renovation, considering the socio-spatial vulnerability to energy poverty.

This research uses high resolution satellite images in combination with an unsupervised Convolutional Neural Network Autoencoder to identify features that can be used to cluster different built environment typologies. Previous remote sensing research uses ground truth data which for some areas is not available or needs manually labeled training data. This research attempts to circumvent the issue of information scarcity in order to create a methodology that can be applied on any city as long as satellite images are available. From the resulting clusters, clusters can be selected which represent areas with high levels of deprivation which in turn can help identifying the deprived areas.

Transport decision-making determines people’s level of accessibility and deeply influences an individual’s access to social and economic opportunities and the quality of life. Socially vulnerable populations are highly dependent on yet often more likely to have less access to transport services and experience lower accessibility. This creates and reinforces social and spatial inequalities by trapping people in disconnected neighborhoods and segregated areas that continue to be deprived of access to opportunities. This research aims to develop a Spatial Decision Support System (SDSS) to explore how re-purposing existing streets for walking and biking could influence the accessibility of vulnerable neighborhoods to support decision-making in enhancing equity of accessibility. In the SDSS, equity of accessibility is formulated as a generative design (GD) problem, named the Street Allocation Decision Problem (SADP), a single-objective optimization problem that searches for generated designs with the maximum weighted improvement in accessibility per unit of its cost. A GD model is built to solve SADP. Lastly, an operational framework is developed to guide prospective users in tuning and operating the SDSS for their specific context, problem, and objective. The SDSS is tested on a toy problem, a 0.09km2 area in The Hague, The Netherlands. The toy problem is small in scale, easy and fast to implement and useful for initial testing of the model. Preliminary results have demonstrated the feasibility of the model. This is the first and humble attempt at developing an SDSS for enhancing equity of accessibility with a GD model. However, there are shortcomings in methodology and result quality, which compromise the practicality of the model and the interpretability of results. Although, only a proof of concept at the moment, the SDSS is a valuable starting point due to its advantages, such as transparency, modularity, humane-ness, and flexibility. This SDSS is built to involve decision-makers in the design process, which could serve as a useful learning experience for questioning and understanding what is the problem, what is considered equitable, and what are possible solutions. The SDSS has the potential to facilitate learning during transport decision-making in experimental settings or as explorative aids in early design stages.

As the urban population grows worldwide and cities are becoming increasingly unequal and segregated, Urban Commons emerge as a potential driver of inclusion and resilience for city dwellers. According to Feinberg et al. (2021), Urban Commons can promote social, environmental, and economic resilience, and Park et al. (2020) suggest they can act as a haven for the excluded. Commons could, therefore, become a policy alternative to address the grand urban challenges of the 21st Century. However, it is still unclear whether the benefits of Urban Commons are ultimately accessible to the culturally and socioeconomically vulnerable. For instance, in a literature review study, Huron (2017) found significant uncertainties in how Urban Commons relate to feminism, race, gender, and ethnicity – factors that could constitute immaterial barriers of access, following Vrasti & Dayal’s (2017) terminology. In fact, while several studies have investigated the commons’ role in promoting urban justice, explorations of whether access to the commons itself is just have been neglected. This master’s thesis explores what constitutes material and immaterial barriers to accessing Urban Commons to understand whether these initiatives can work, policy-wise, as a driver of social inclusion. 29 Urban Commons in Amsterdam were selected as a case study. A quantitative geo-spatial analysis was conducted to evaluate the material accessibility to each initiative. In parallel, commoners were surveyed about their perception of immaterial barriers and benefits of commoning. Finally, a representative of the municipality of Amsterdam was interviewed to assess the potential challenges and benefits of policies involving Urban Commons. Concurring with Park et al. (2020), the case study findings suggest commons could indeed promote social inclusion. Moreover, it was also found that the excluded could face, in addition to the initially proposed material and immaterial walls, an ecosystem barrier to access commons. Yet this could be addressed from a policy standpoint with the development of a commons ecosystem that is capable of scaling the benefits of commoning across an entire city. The results have academic and societal relevance. This study provides a new theoretical framework to research the factors influencing access to commons and also contributes to literature with a unique city-wide case study. It can also function as a reference for policy-makers who wish to learn about the commons landscape in Amsterdam and who interested in understanding the factors that contribute to participation in a commons.

Accessibility is a fundamental concept concerning urban and transport planning as it is the elementary basis for socio-economic development in cities. It can be described as the potential to reach spatially distributed opportunities. Recent research has identified that excluding person-based features from this analysis might cause inaccurate measurement of accessibility. Age, gender, and income, for instance, are responsible for varying accessibility levels drastically. In addition, studies worldwide have shown that women face different challenges in reaching locations and spatially distributed opportunities. Despite the findings, this research identifies a gap in understanding how a person-based perspective, mainly gender, and other personal characteristics, affect accessibility levels, considering various travel purposes and transport modes. Thus, this research aims to answer the question, ”How do person-based characteristics, mainly gender, can impact accessibility levels?”. The primary objective is to explore how these characteristics influence accessibility metrics, identify the urban groups most affected by the absence of this perspective, and determine the key personal characteristics that significantly impact accessibility levels. To address these objectives, this study considers a combination of quantitative and case-study research approaches. It investigates the Metropolitan Region Rotterdam- The Hague in The Netherlands by applying surveys to obtain perceived accessibility data. In the same region, spatial analysis is conducted by mapping transport networks and points of interest. Then, this study compares spatially calculated accessibility with self-reported accessibility and the presence of mismatches. In addition, cluster analysis identifies the urban profiles most vulnerable to mismatches and their main characteristics. A Binary logistic regression is conducted to determine the variables’ importance in the mismatch occurrence. From the survey answers, it is identified that women have less access to cars than men. In addition, the comparison between perceived accessibility and spatial accessibility uncovers that women present the most critical mismatches to reach activities by car. In other words, several women perceive the car as an impractical option to access points of interest that are spatially considered reachable by car. It raises the hypothesis that the lack of car access highly impacts their accessibility perception. Furthermore, the clustering analysis reveals that foreign women exhibit a higher prevalence of car-related mismatches when compared to other urban groups. Moreover, this research identifies that fathers of young children also encounter greater disparities across all transportation modes. Additionally, the binary logistic regression underscores the importance of safety as a critical factor influencing women’s perception of walking as a viable mode of transportation. This safety importance is also identified from the survey answers. The findings raise new hypotheses that warrant investigation in transport engineering, urban planning, and social sciences. Consequently, this research can contribute to developing more inclusive transport policies and establishing a more equitable transport system.

Urban areas are the biggest consumers of electricity and energy consumption is only likely to increase with rapid urbanization. Out of the urban building stock residential buildings require continuous supply of energy for space heating and appliances. To answer to this demand in a sustainable way policy maker need to design energy efficiency strategies that must rely on accurate and traceable models. These models estimate energy demand based on a series of building features, out of which building age is of prime importance because it predicts the insulation properties of the building. To support the energy modelling process, we propose a method of automatically identifying building age from spatial data at a large scale. We identify features of buildings that are significant for age prediction and determine which set of features has best prediction power at national scale, in Germany. It is expected that the accuracy of classification will be strongly related to sampling design and data availability. The final results will be used to identify the impact of misclassification errors on estimating energy use in urban energy models, providing in this manner a measure of the reliability of such models.

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.

We find ourselves at a crossroads; without immediate and deep emissions reductions, limiting global warming to 1.5 degrees Celsius will be impossible. For the Netherlands to achieve its climate targets, Dutch municipalities have been delegated the crucial task of the heat transition. Prior research reveals that the Dutch energy transition and policy documents are often techno-economic centred and hardly stress citizens’ central role. However, it remains a thoroughly social affair as changes in energy technologies are accompanied by societal ramifications, and the exclusion of social aspects (public support and energy justice) has detrimental societal effects. This research aims to evaluate the impact of including social information in the information provided for the decision-making process on decisions made by local Dutch policymakers for the heat transition. Central to this thesis was the following main question: “What is the impact of including social information in the information provided for decision-making processes on the social responsibility of policymakers’ decisions for the heat transition?”. To assess this impact, interviews were conducted, and a 1x2 between-subjects experiment was set up where participants were randomly allocated to one of two conditions: group A: exposure to only technical and economic information, and group B: exposure to technical, economic and social information. With a Mann-Whitney U test, it was determined whether there were differences between the two groups. The results of this study indicated that the inclusion of social aspects leads to more socially responsible decisions in the heat transition for nine out of 20 neighbourhoods, meaning that neighbourhoods with specific social characteristics are prioritised more than after including this information. These neighbourhoods often had extreme values for the social indicators. For example, neighbourhoods with high public participation or support were prioritised more. Future research should study which social factors increase residents’ participation or public support so policymakers can meaningfully include such social indicators for heat transition decision-making. Another interesting finding is that many policymakers (and therefore municipalities) seemed to have diverging approaches to the heat transition. Future studies should research what causes these differences between municipalities by focusing on personal (of the policymaker) or organisational (of the municipality) characteristics.

Floods cause major problems around the world. Over 35 million people were affected by floods in 2018. They have a growing worldwide impact on life and property. Changes in climate conditions lead to unanticipated variations in glacial runoffs, snowmelt and precipitation, which all significantly changing river flows. An imbalance in river network equilibrium leads to flooding and often ends up causing tremendous damage to society and the environment. Regions that are perceived to be downstream from the source of flooding may end up taking the brunt of the river force due to flood cascades. Floods account for about a third of all natural catastrophes worldwide, they cause more than half of all fatalities and are responsible for a third of the overall economic loss. Modelling approaches are often used to determine flood consequences. Two types of flood models are commonly used: statistical models and flow simulation models. Statistical methods are easy to use but provide limited insight into flood problems. Flow simulation models’ results can be very accurate, especially for hydraulic simulation models. However, these models are expensive to use and develop, and they require a lot of data. These requirements make them unsuitable for application in developing countries and analysing large watersheds. Flood risk screening models try to solve these problems. They are suitable for use in data-sparse regions and are efficient in terms of omputational costs. However, there is a lack of knowledge between river structure and cascading flood effects, and there is a lack of models that are efficient, easy to understand, use topological data and have the purpose of risk screening. In this research, we show a flood model based on complex network theory to efficiently study the cascading effects of floods in riverine systems. Cascading effects are defined as floods that occur as a result of water waves through the system that originate from upstream sources. The developed model uses the hydrological Muskingum routing method. We found that it was possible, notwithstanding many assumptions and a lack of data, to reproduce system behaviour during an extreme flood event in the Ganges-Brahmaputra Basin. Satellite elevation data were used to construct the river network, and satellite precipitation data was used to feed the model. The model can indicate high risk reaches based on the simulated overflow, the flow exceeding a predefined capacity. No existing models are known that can do this, on a laptop, within seven min- utes per simulated day, with limited data for a watershed that exceeds the size of one million square kilometres. The network structure of the model makes it possible to achieve a better understanding between river typology and cascading flood effects. The model is not without its limitations. It cannot pinpoint when and where floods will occur, because it only calculates overflow. Moreover, flood failure mechanisms are not yet included in this model. Failure mechanisms will change model behaviour: when a flood occurs water temporarily leaves the system, which reduces downstream risk. Overflow cascades, therefore, would be shorter in reality than in this model. The model is a proof of concept that shows the potential of a network theory-based risk screening method in flood simulation context. Its properties make it suitable for analysing the effects of changing precipitation patterns, which, for example, could originate from climate change studies. Another use case is real-time forecasting of discharge levels if the mode is combined with real-time discharge levels and precipitations forecasts. The model can be used as an early warning system: alerting when and where high discharge levels are expected. We anticipate our model to be a starting point for policy screening and scenario analysis. Sugges- tions are made to include policy options within the model. Policy analysts can then use the model to compare different policy interventions for all kinds of (future) scenarios. The model should not be seen as a replacement of the advanced hydraulic simulation models, but as a complementary tool useful at an earlier moment in a design process with the purpose of screening options. Ultimately it can become a framework with the aim to support informed decision-making.

Throughout history, social movements have often been catalysts for radical societal change. In the past two decades, hashtag activism, the use of social media platforms for internet activism, has become a driving force behind the development of social movements across the world. From #MeToo to #IdleNoMore and most recently #JusticeForGeorgeFloyd, social media has been used strategically by activists to mobilize communities to come together and protest against different forms of injustice. In the field of social movements science, a large body of research has studied the role of hashtag activism for the formation of social movements, but less efforts have been allocated towards the study of the spatio-temporal relationship that exists between hashtag activism and political processes. In other words, in spite of the a-spatial nature of social media, can the study of hashtag activism help us understand human behaviours and societal processes that occur off-line, in the physical space? Such conundrum is the basis of the research in this master thesis, where a data-driven framework is implemented to investigate the spatio-temporal relationship between hashtag activism and two important political processes: physical protest activity and legislative action. Through a combination of time series analysis, regression, geo-spatial analysis, and machine learning, and the use of a large Twitter data-set of geo-located social media posts, such relationships are quantified, modelled and visualized for the 2020 #JusticeForGeorgeFloyd social movement. First, time series analysis is used to measure the temporal relationship between hashtag activism, physical protest, and legislative action. Second, geo-spatial analysis is used to compare the nature of this relationship across different spatial resolutions. Finally, regression analysis and deep learning are used to model, generalize and forecast such spatio-temporal relationships. The outcomes of this research are two-fold. First, in the case of #JusticeForGeorgeFloyd, it was found that at the national, state, and county scale, hashtag activism bears a very strong positive temporal relationship with physical protest activity. Through the statistical modelling of this relationship, it is possible to predict the intensity of national, state, and county level protest activity on a given day based on the intensity of state and county level hashtag activism the day before. At the county level, it was found that in some "outlier" counties the temporal relationship between hashtag activism and physical protest is characterized by a disproportional amount of physical protest compared to the intensity of hashtag activism, while in other counties this relationship is characterized by a disproportional amount of hashtag activism compared to the intensity of physical protests. In order to make sense of this tendency this research proposes the Mobilization Synergy Index (MSI), which makes it possible to statistically quantify this trade-off, and to visualize its spatial distribution in an intuitive way. Through this index, both local policy makers and activists could gain valuable insight into their communities and as a result adopt strategies that better reflect community needs and concerns. Second, the analysis of instances of hashtag activism in relation to legislative responses revealed that states in which people engaged more in hashtag activism on average during #JusticeForGeorgeFloyd also experienced more legislative responses related to policing. Additionally, results from temporally disaggregated national level and state level data suggests that the number of legislative responses on a given day bears a very similar positive relationship with each of hashtag activism activity and physical protest activity taken at a specific time lag prior to the day in which the legislative responses occurred. At the state level, it was found that while both hashtag activism and physical protest activity result in future legislative responses, this temporal relationship is highly variable by state. Such tendency may serve as an indicator of state-level political responsiveness during times of crisis. To the best of our knowledge, this is the first national level quantitative study aimed at measuring the temporal relationships between hashtag activism, physical protest activity, and legislative action, at various spatial resolutions. Therefore, the study provides several contributions to the field of social movements research and, more broadly, the computational social sciences.

In the past few decades, we have witnessed unprecedented impacts of climate change. The increase in Green house gas emissions due to human activities has disastrous implications for earth including an increase in global mean temperatures, rise in sea level and melting of polar ice caps. Climate change has impacted all forms of human life on earth and if unchecked, poses a threat to human existence. With more than 50% of global population currently living in the cities and the upward trend of people migrating to the cities expected to increase in the next few decades, cities are one of the major contributors to climate change. Nearly 80% of global energy and 75% of global resources are consumed in cities. Thus, there is an urgent need to tackle the environmental impacts of cities. In this research, we develop a methodology to quantify and analyze the environmental impacts of cities by considering the consumption of all resources occurring in a city. The methodology is applied to the city of the Hague in the Netherlands but can be replicated for other cities as well. The research is divided into the following components: Firstly, a small literature review is conducted to identify different elements in a urban system. The literature review also assesses different models used to quantify environmental impacts of a city . Out of the three models reviewed in the study, Life Cycle Assessment (LCA) is found to be the best fit for assessing environmental impacts of city. Knowledge gaps surrounding the applicability of LCA to a city are identified and based on the knowledge gap, research question is framed. Following the literature review, a top down approach is used to identify products or activities by residents of a city that have an environmental impact. Following that, a data disaggregation methodology is developed to downscale data related to resource consumption and activities from the national or European level to local level of neighbourhoods in a city. The disaggregated data is then quantified using LCA and analyzed for different geographical regions, different resource use categories and different demographic groups in a city. Finally, based on the environmental impacts, commonly implemented policies in cities around the world to reduce GHG emissions are modelled and analyzed for the case study : The Hague. This is followed by detailed discussion on results, limitations, conclusions and directions for further research. The main conclusions that can be drawn from the research are that resource use categories in which intervention by cities is possilble account for nearly 70% of Global warming potential (GWP): 45% mobility, 15% waste,10% energy. Larger households have a higher impact due to mobility whereas smaller households have higher impact due to energy. Environmental impacts are further analyzed for clusters of neighbourhoods based on their socioeconomic indicators. Finally, the policy interventions analyzed show a potential to reduce net GWP by 25% in the Hague

Electricity is essential in the modern world. Although India is near reaching 100% electrification, for many of its citizens, reliable access to electricity is still a major issue. It is not clear how, when, and where the reliability issues arise. Previous studies have not shed light on electricity outages at a granular level. Furthermore, identifying outages is difficult without proper metering. This research aims to acquire insight into how electrical outages vary during the nighttime in the Uttar Pradesh, one of the states experiencing the most outages in India. Remote sensing data in the form of nighttime radiance, wind, precipitation, temperature, air quality, land cover, and population are used to identify outages, as they have been identified in the literature to be influencing or correlated with outages. Using a Random Forest (RF) classification algorithm, outages are identified in India for the year 2018. The RF is trained on Electricity Supply Monitoring Initiative (ESMI) real-time electricity household senors. RF is cross-validated using multiple strategies to accurate measure the performance and to ensure no data leakage. Using the resampling technique Synthetic Minority Oversampling technique (SMOTE), the performance of the RF is increased for outage classification. RF is used to classify three classes: Never Access, Normal Access, and Outage. The distribution of these three classes is highly imbalanced, with the Outage class being in the minority. To further validate the methodology, the spatial and temporal sampling was done during cross-validation. Using this method, 91% of Never Access samples were categorized as such, with a precision of 84%. 69% of Normal Access samples were categorized as such, but with 84% precision. The performance of the outage classification is the worst. Nonetheless, 55% of the Outage samples were categorized as such, although with a precision of just 39%. A map is created for Uttar Pradesh showing the results of the rates for each of the classes in 2018 between 00:00 and 02:00 with a spatial granularity of 0.1° x 0.1°. The map indicate that the electricity usage during the nighttime in Uttar Pradesh is in large parts of the state non-existent. The map be used as a precursor for future field work and help policymaker and researchers identify reliability and fairness issues.

More and more people are living in cities. As these cities grow, the benefits of living in them are increasingly unequally distributed. One factor that can turn the tide of glooming urban inequality is equitable access to opportunities. Designing policies for equitable access requires taking into account several components that impact accessibility: (i) land-use, (ii) transportation, (iii) individual needs and opportunities, and (iv) temporal constraints. In this research, the interaction between a novel mode of public transport, microtransit, and the first three components of accessibility is investigated. The accessibility implications of microtransit in the case study area: the Southern suburbs of the Minneapolis-St. Paul metropolitan, are assessed. It is concluded that microtransit manages to reach vulnerable rider groups and interacts with the land-use environment by pairing residential areas with commercial centers. Microtransit also interacts with other modes of transit by adding both extra demand and lowering the demand for ride-hailing and personal car use predominantly. Microtransit rarely seems to replace public transit or walking alternatives. Overall, it is found that microtransit does significantly increase accessibility levels in the case study area and especially impacts groups with low car ownership. Lastly, the benefits of microtransit are more fairly distributed than those of traditional transit in the studied area. This research demonstrates the positive access equity impact of microtransit and suggests further research into choice-riders, the application of microtransit in urban (core) and rural environments, and the interplay between public transit and microtransit, as well as policy interventions for policy-makers and transit providers.