Energy Modelling and Spatial Data Science Climate Change
The responsibility of decarbonising urban regions is increasingly framed as an individual burden. Even progressive cities around the world that assume the role of supporting the transformation of consumption patterns to more sustainable pathways, merely focus on piecemeal and reactive policymaking. This process of public administration usually entails offering subsidies in renovation of building stock and adoption of solar energy, tax deductions for reconsidering energy or transit use, or providing financial incentives for new market-based development initiatives? The burden of understanding, translating or even accessing such policy solutions is often disregarded. Because of multiple vulnerabilities, people who have lower access to education, live in fragmented and segregated social networks, and are not digitally oriented, are not able to effectively adopt such policy solutions in the long-term. The idea of individual responsibility does not intersect with how we build livelihoods in cities, where people seek social networks for work and innovation, and build collective living spaces. The role of citizens in their communities is never taken into account in developing policy solutions.
To facilitate decarbonisation that is just, effective and encompasses all needs, muncipalities must shift from offering solutions to brokering citizen communities that are capable of adopting solutions on larger scales. To understand the role of communities in decarbonisation, and illustrate this hypothesis, this project will develop a multi-modelling framework that can flexibly adapt to the level of communal engagment muncipalities have the capacity to foster. You will develop decarbonisation scenarios on a spectrum of engagment from individual to beehive, and measure realistic gains in decarbonisation for each scenario. Several models will be neccessary: for example, spatial modelling and clustering analysis to estimate social vulnerability, network analysis to scope engagment processes, machine learning for measuring or estimating energy usage and decarbonisation potential of various solutions, and dynamical systems modelling for measuring solar radiation or other technical phenomenon. As a particiopant in this project, you will be part of a large team, where others will contribute with several models or datasets and analysis. The role of a master thesis student will primarily be to conceptualise the multi-modelling framework, make datasets interoperable with multiple modelling techniques, and make the framework modular. You are flexible to choose one decarbonisation scenario, like adoption of renewable energy solutions (e.g., a combination of solar panels, heat pumps, district heating and renovation subsidies). Based on the averarching concept, you will then combine/develop models and translate them into outputs. The outputs should strongly be focused on visualisation to initiate a dialogue with muncipalities on their role as brokers of citizen engagment.
The outcomes will be situated in the broader discussions of spatial and social justice, such that communal engagement can be shaped in a meaningful way, where communities who are disadvantaged are not left behind in any decarbonisation process. We expect that municpailities can incorporate such a framework into their spatial data landscapes to model different kinds of scenarios (e.g., solar adoption, heat pumps, district heating, etc.). You will have access to several stakeholders but also several datasets and models. Data can come from the city of Glasgow (where a dashboard is also being developed professionally to start incorporating such ideas in the long-term), The Hague (extensive amount of climate related datasets are available) and Rotterdam (besides data, we also have extensive relationships with community organisations).
If you are interested in this topic in any capacity (MSc, internship (credits or paid)), please contact Dr. Trivik Verma [firstname.lastname@example.org] with the email subject [MSc – Climate Change Brokering] and a CV (max 2 pages). This project will provide a lot of exposure in theory development/conceptualisation, programming and model building, data analysis and visualisation.
Urban Inequalities Modelling and Spatial Data Science
Studies of inequalities usually focus on identifying specific relations among urban sub-systems Meerow et al., 2016 using case-studies that uncover associations and in some cases, causal links among urban processes that shape inequalities. For example, some frontiers that emerged in the recent decade expand the notion of inequalities in specific domains (e.g., energy Robinson, 2019), highlighting hidden vulnerabilities among various populations associated with gender, age, race or disability and providing specific guidance to policymakers in making the distributional outcomes of their practice fair and inclusive. Evidence from different disciplines put together highlights the interplay among different factors in society, showing how inequalities scale superlinearly with size of populations or urban spaces, the benefits of investments are distributed unequally across income groups Sarkar, 2018, and such conditions continue to amplify across generations depending on one’s social networks Tóth et al., 2021. Despite the broader insights generated through empirical place-based studies in individual disciplines, so far the field is lacking a systematic analysis of the interlinked, intersectional and systemic accumulations of inequalities spanning dimensions of well-being across various urban systems (e.g., energy, water, transport, labour, housing, etc.) and spatial scales (e.g., household, neighbourhood and functional areas of movement).
The data-driven analysis in this research will focus on the cumulative, spatial and differential impact of inequalities associated with a person across three scales of living and movement: at home, within a neighbourhood, and while moving through the city. Using developments at CUSP on the conceptualisation of inequalities, we will develop, for the first time, a new empirical base of evidence on accumulation of multidimensional inequalities. First, we will set up several realisable hypotheses drawing associations of multiscalar and multidimensional vulnerabilities prevalent among certain social groups, and how might those be related with age and gender, markers of health, or the ability of an individual to participate in institutional and legal processes in the city. For example, older populations living on pension in peripheral neighbourhoods may be facing several deprivations: poorly built living environments, energy or transport poverty, inadequate access to important services, and in extreme cases be additionally vulnerable to impacts of climate change such as heat stress, sagging and flooding. Of this population, women and other non-male genders particularly face the maximum impact of all climate related vulnerabilities Neumayer et al., 2007, and such injustices are compounded by their under-representation in the labour force, indicating informality embedded in culturally sustained work like unpaid care Sudarshan et al., 2009. To account for such intersectional burdens Crenshaw, 2017 in the analysis, we will design a cross-scale spatial data landscape using geographic space as an anchor for information gathering and processing Franklin, et al., 2022. We will curate and embed the case-study’s (The Hague) specific datasets into this landscape, and subsequently analyse the nonlinear interdependencies in inequalities among its dimensions and across spatial scales to discern the implications it has for people and places in a city. Such an analyses can be used to answer questions of the sort: how do inequalities in accessibility to mobility infrastructure disadvantage a person on the basis of their income, home-ownership, gender identity and disability, specifically under different configurations of amenity distribution in urban space?
By building this landscape, and analysing the linkages among the different dimensions of inequalities across scales, this RL will bring novel insights into accumulation of inequalities among communities. However, any spatially-explicit measurements focus on administrative or arbitrary spatial boundaries. To standardise measurements delineating urban space and enable a spatially reasonable comparison, we will explore the nature of inequalities in an experimentation process across 3 sizes of spatial units (e.g., administrative boundaries of local authorities can be replaced with hexagons, and functional areas can be identified either as cities’ administrative boundaries or clustered by daily movement of people in and out of commuter zones.
If you are interested in this topic, please contact Dr. Trivik Verma [email@example.com] with the email subject [MSc – accumulation of inequalities].
Feminist Modelling and Spatial Data Science Accessibility Perception
Accessibility is a key concept in urban planning, particularly important for transport development. There is a wide range of different metrics to evaluate accessibility (Páez et al., 2012). Accessibility metrics can be generally divided in two approaches: place-based and person-based metrics (Ryan and Pereira, 2021). The former only accounts for interaction between land use and transportation systems, while the latter considers how transport and location characteristics interact with personal characteristics. Person-based metrics can include, for example, activity schedules or constraints that may prevent certain segments of the population to access some types of opportunities, or at specific times (Hagerstrand, 1989; Chen and Kwan, 2012; Patterson and Farber, 2015; Mahmoudi et al., 2019). Person-based metrics are important to understand how personal characteristics such as age, gender, and physical capacity, influence the levels of accessibility a person has. Indeed, gender differences in mobility have been reported in the literature (Lo and Houston, 2018; Tiznado-Aitken et al., 2020). Moreover, in most studies, person-based features are specified in a normative way (Páez et al., 2012). For example, the analyst makes assumptions about how far people are willing to walk to reach grocery shops or recreational spaces. However, these assumptions may not reflect people’s perceptions of their accessibility. As result, normative approaches tend to overestimate accessibility levels and underestimate inequalities in accessibility across different social groups (Ryan and Pereira, 2021).
This project has a twofold objective. First, the project explores how normative assumptions about personal characteristics affect accessibility metrics. A sensitivity analysis will be performed to identify which person-based feature influence the accessibility metrics the most. Second, the project focuses particularly on features that may describe gender differences in mobility, such as daily schedule or preferred mode of transportation. Empirical existing research and surveys may provide information on how gender affects perceived accounts of accessibility. Insights from this project may help urban planners and policy makers to design cities that provide gender-equitable access. Furthermore, the most influential person-based features identified through the sensitivity analysis may be topic of future empirical research on perceived accessibility.
If you are interested in this topic, please contact Dr. Juliana Goncalves [firstname.lastname@example.org] with the email subject [Gender and accessibility: A sensitivity analysis].
Theory and Co-creation Inequalities Well-being Indicators
Inequalities are shaped by a combination of several structural social, economic, environmental, and political factors, and a fragmented approach across disciplines and development of policy acts as a barrier for effectively dealing with their consequences. Transformative developments in the field of studying inequalities have largely been through systematic literature reviews Nijman and Wei, 2020, development of box-and-arrow frameworks around concepts essential to studying inequalities (ex. accessibility Geurs and van Wee, 2004, transport poverty Lucas et al., 2016 or energy poverty Robinson, 2019), empirical approaches in developing indicators [Xu et al., 2020, Lowe et al., 2022], moving beyond an economic lens to understand the distribution of infrastructure capabilities Pereira et al., 2017, and incorporating notions of equity and justice in evaluating social exclusion van Wee and Geurs, 2011. While excellent in their work, none of these frameworks support the view of how inequalities are systemically linked. Having a systemic view of the multifaceted phenomenon of inequalities will situate case-studies under appropriate systemic relationships and identify linkages across dimensions and scales of inequalities.
Thus, this thesis you will tackle the question: what are the different dimensions of inequalities that people face in an urban environment and how are they related to each other? In this thesis, through the lens of urban systems-thinking Meerow et al., 2016, you will co-create Lang et al., 2012 an integrated framework built upon the theory of social impact assessment (SIA) Vanclay, 2002. The SIA framework, when adjusted to an urban issue, constitutes the different social aspects of impact that might be faced by an urban population. These include, but are not limited to, health and well-being, quality of built-environment, economic and material well-being, cultural aspects, community relations, institutional and legal aspects, and gender relations. The research will draw on other case and place based studies of indicators measuring various forms of inequalities and identify how they fall into or expand the categories of SIA. To acquire knowledge about other forms of inequalities that do not fit into theoretical or empirical evidence bases, you will co-create and extend this framework to incorporate knowledge from stakeholders associated with the problem of inequality: namely by hosting participatory workshops for 3 case-studies in The Hague, The Netherlands; Delhi, India; and Sao Paolo, Brazil. (these can also be 3 cities in The Netherlands for ease of managing stakeholder participation). The goal is to use the framework as a deliberative tool - a boundary object Star et al., 1989 that becomes a point of focus for exchange of multiple social realities and perspectives - for eliciting knowledge crucial to every actor Dany et al., 2016, simultaneously staying well-defined yet adaptive. Your focus will be on The Hague where we will facilitate stakeholder deliberation workshops with policymakers in different sectors of mobility, health, energy, environment etc., community-led NGOs supporting citizen groups in urban commons or communal efforts, infrastructure developers and operators of social housing, transit and energy, and citizen representative groups from a specific region that faces the highest forms of inequality in the city. Due to lack of structured datasets available for the other case studies, they will be used only in part of the thesis, brokered through local academic and policy networks, to understand how various actors view the definition of inequalities proposed, and what is the scope for the framework’s adaptive use in different policy contexts. This will be followed by building a bipartite ontological mapping of indicators of the various social and economic impacts onto the different dimensions of inequalities identified in co-creation process. To identify existing indicators and develop new ones necessary to represent the dimensions of inequalities, we will use a well-established approach in environmental change research that uses multiple indicators for aggregating the extent of social vulnerability and assessing the ranking and spatial distribution of such indicators Cutter et al., 2003. Following the co-creating phase, we will use desk research methods and interviews with the stakeholders, and examine the data at the neighbourhood level (together with anopther thesis student) to identify specific or proxy indicators measuring inequalities.
This thesis will lead to a). developing an overarching conceptual framework for studying inequalities by expanding the scope beyond economic or wealth implications, and casting it as a multidimensional problem instead of tackling it across disciplines and sectors individually; b). a comprehensive translation and mapping of socio-economic indicators for which datasets are maintained by municipalities onto the ontologies of multifaceted inequalities; and, c) methodological insights into transdisciplinary co-creation of frameworks and its use and validation in other contexts.
If you are interested in this topic, please contact Dr. Trivik Verma [email@example.com] with the email subject [MSc – systemic and interconnected nature of inequalities].
Nature-based Climate Change Tradeoff analysis
Nature-based solutions (NbS) are “actions to protect, sustainably manage, and restore natural and modified ecosystems that address societal challenges effectively and adaptively, simultaneously benefiting people and nature” IUCN. Examples include green roofs, pervious pavement, bio-swales, and rain gardens. Nature-based solutions not only address pressing urban challenges, such as water management and climate change, but also bring additional benefits to urban dwellers, as they help to restore local ecosystems, improve both air and water quality, enhance physical and mental health, and promote social and cultural well-being (Keeler et al., 2019). Because of the wide range of benefits associated with nature-based solutions, assessing their potential is a challenge.
One way to evaluate the impact of NbS from a holistic perspective is via the interdisciplinary concept of ecosystem services (Seppelt et al., 2011; Prudencio & Null, 2018). The ecosystem services concept emphasises the links between nature and human well-being, offering ways to understand where and when nature-based solutions deliver these benefits (Keeler et al., 2019). Furthermore, the ecosystem services concept can also account for disservices (for example, unintended negative impacts) and, thus, enables trade-off assessments. Having such a holistic perspective is critical for three reasons: (1) it provides ways to account for co-benefits when comparing NbS with conventional water infrastructure, (2) it identifies disservices which need to be addressed before NbS are implemented, and (3) it provides insights into potential trade-offs, which are critical for informed decision-making. The goal of this thesis is to bring a spatial dimension to the ecosystem services concept to explore questions like: How to quantify ecosystem services (and disservices) associated with nature-based solutions? How are ecosystem services spatially distributed (across a chosen city) and who benefits from them? How do ecosystem services conflict spatially with their disservices? How can trade-off assessments support decision-making when implementing nature-based solutions in cities?
This project will involve the following tasks: (1) Conducting a review on how to measure ecosystem services and disservices; (2) Developing a theoretical framework for characterising ecosystem services according to the literature review; (3) Defining an operationalisation of the measures and suitable methods to explore/test the conceptual relationships defined in the theoretical framework; and (4) Defining a case study and collecting data to test the framework and methodology.
If you are interested in this topic, please contact Dr. Juliana Goncalves [firstname.lastname@example.org] with the email subject [MSc – Nature-based solutions & ecosystem services].
Resilience Theory/Conceptualisation Review
Building urban resilience imposes difficult choices for allocating risks, benefits, and costs, bringing into the discussion the winners and losers of designing flexible, robust, and adaptable cities. Under the global pressure for increasing competitiveness, such allocation can be skewed towards high-value assets owned by the already better-off, leading to an even more unequal city. Therefore, the increasing interest in embedding the practice of urban resilience within justice and social conflicts is no surprise. This concern is elegantly presented by (Meerow et al. (2019)). Negotiating resilience in urban planning inevitably entails defining resilience for whom, what, when, where, and why: “Working through these questions could help to foreground debates about equity and justice in resilience policy-making”. In this regard, literature has extensively discussed justice approaches for integrating justice into urban resilience policy and planning (Fainstein, 2018; Doorn, 2019, Shi et al., 2016, Ranganathan and Bratman, 2021).
However, there is less attention on the organisational form by which resilience policy and planning hit the ground in concrete programs, partnerships, and projects. The inequality and justice problem can fire back at such more concrete decision-making levels. Resilience decisions are ultimately shaped within the boundaries of institutional constraints, grant funding, and financing reality that requires manageable and implementable projects (Fastiggi et al., 2020). Consequently, projects with higher chances of implementation must yield a more attractive return on financial investment, fit political priorities, or make the city more attractive to high-skilled inhabitants and investors. In this regard, even though resilience advocators see self-organisation as the answer to materialising resilience, their implications for justice are not fully understood.
On the one hand, self-organisation is a foundational characteristic of complex adaptive systems, grounding our current understanding of resilient systems (Martin-Breen and Anderies, 2011). On the other hand, self-organisation is at the core of ongoing economic transitions setting the context for urban development (Frantzeskaki, 2016). These transitions refer to post-industrialization dynamics as much as the emergence of new economies based on solidarity and social value. Furthermore, self-organisation also lies at the basis of contemporary forms of public governance in the Netherlands (van Buuren and Meulenbeld, 2016). Accordingly, the first resilience strategy published by Rotterdam municipality argues that “the way the Rotterdam society operates has many characteristics of resilience including self–organisation, strong networks and various coalitions of citizens and businesses, and a government offering customised services and a facilitating role” (Gemeente, 2016).
This project will involve the following tasks: (1) Conducting a review on how to measure self-organization and justice in the context of urban resilience and economic transitions; (2) Developing a theoretical framework for characterising self-organization and justice according to the literature review; (3) Defining an operationalisation of the measures and suitable methods to explore/test the conceptual relationships defined in the theoretical framework; and (4) Defining a case study and collecting data to test the framework and methodology.
If you are interested in this topic, please contact Dr. Camilo Benitez [email@example.com] and Dr. Juliana Goncalves [firstname.lastname@example.org] with the email subject [MSc - Resilience, justice, and self-organisation].
Walkability Space Syntax Citizen Perception Data Science
Ideas around the regenerative city have prompted us to rethink the way our cities are designed to promote urban development that is in harmony with the local conditions and the boundaries of the planet. Planning and designing for walking are crucial for promoting a healthy public life, creating sustainable neighbourhoods, stimulating social inclusion and the economy. Whilst several indices for walkability exist, (ie. the space syntax walkability index) they are often based on the assumption that all citizens experience walkability in the same way or may not be directly linked to specific policies or design strategies. Thus, we would like to build on existing knowledge by developing a walkability index for the Dutch context, that takes into account and could be adapted to the diverse needs of different kinds of citizens, such as women, ethnic minorities or the elderly and then directly linked to specific policy and design recommendations.
At the neighbourhood scale, factors such as density (Ewing and Cervero) accessibility to destinations (Nicoletti et al, 2022), distance to transit (Ewing and Cervero) block length (Singh, 2016) and socio-economic factors such as crime rates (Golan et al, 2019) are associated with flows of pedestrian activity. At the street level, factors such as human scale, transparency of facades and imageability (the extent to which a place is easily recognisable) are considered important (Ewing and Handy, 2009). Characteristics of the built environment can be measured and described both through the experience of citizens and geo-spatial analytics. Geo-spatial analysis tends to rely on data derived from point of interest, socio-economic, transportation variables and more recently, street view imagery (Li, et al., 2015). Whereas, more experiential measures are usually sourced from surveys and interviews with citizens (Lin & Moudon, 2010; Naik et al., 2014; Golan et al, 2019).
Addressing the main knowledge gap would encompass identifying which variables have the strongest relation with walkability in the Dutch context, taking into account the needs of diverse citizens. Combining data sets into GIS layers with community feedback, and making this data available to the urban designers and policy makers working in different areas could help them understand the main challenges and priorities of diverse citizens. Developing a walkability index, that could be applied across Dutch cities, therefore becomes crucial in identifying which areas may require specific kinds of interventions or be subject to certain vulnerabilities which could negatively impact walkability for different populations.
We are looking for two students to address the main knowledge gap. Collectively they would create a walkability index that could be adapted to cater for the needs of diverse populations for the purpose of conveying vulnerabilities and linking them to policy and design strategies.
Both Students would be required to:
Student 1 would focus specifically on:
Student 2 would focus specifically on:
Geospatial data for the Netherlands: PDOK
Census Data for the Netherlands: CBS
Rotterdam 3D Model: Rotterdam 3D
If you are interested in this topic, please contact Ruth Nelson [email@example.com] with the email subject [MSc - Walkability].