Achieving inclusion and fairness in policy interventions within cities has become a central objective for urban planners around the world. Modern cities have challenged planners to provide access to basic urban services to communities from all socio-economic groups. However, many urban communities are distinctly segregated in their access to amenities. Micro-transit services are slowly filling this void for serving communities that do not generally have good access in cities. In this project, Spare will provide high-resolution trip data (anonymised and suitably redacted for privacy purposes) from key transit agencies in a variety of cities and countries. Using this data, in conjunction with the built environment, socio-economic and points-of-interest data, the student will analyse the impact of micro-transit services on access to amenities and its variability by socio-economic stratification among communities.
In its provision of Software-as-a-Service (Saas) for the transportation industry, Spare helps operate dozens of on-demand transportation services worldwide. As a company with a philosophy of openness and transparency that is conscious of its impact on communities and the environment, Spare is keen to quantify how its micro-transit operations are changing the fabric of its partner cities. In this research, we want to determine the impact of introducing on-demand transportation on accessibility to goods and services (e.g. healthcare, education, food, shopping) (e.g. Mayaud et al., 2019), develop methods to better visualize and communicate the positive and negative impacts/tradeoffs of introducing on-demand transportation in certain regions (e.g. Nicoletti et al, 2021), and examine the equity impacts of on-demand transportation, by associating modal shifts and travel behaviour changes with socio-demographic variables (e.g. Marquet, 2020).
Geocoded socio-economic data is fundamental for urban planning. From research on human migration to equity-driven studies, spatial socio-economic data helps in understanding complex social patterns in cities. Most of the time, comprehensive socio-economic data at small spatial scales is provided by census programs such as the American Community Survey, the Canadian Census, or The Hague Cijfers. Census data, however, has strong limitations, three main ones being that (1) it is recorded every 5 or 10 years, (2) the process of gathering, processing, and making the data available is costly, labour-intensive and affects the digital privacy of citizens, and (3) census data that is detailed enough to be useful for planners/researchers is only available for a select group of developed countries (e.g. the United States, Canada, Australia, New Zealand). This makes it difficult for planners in many cities in the world to develop policies that appropriately reflect the needs and realities of the communities they serve. Not surprisingly, this has a disproportionate impact on communities that are already underrepresented, to begin with (i.e. indigenous communities, racial minorities, refugees, etc).
In this project, the student will develop a methodology for sensing socio-economic attributes within cities using sources of data that may have originally been developed for different purposes, e.g. satellite imagery. These sets could be used to train a “Socio-Demographic” model that could accurately classify city blocks into socio-economic categories (e.g. by income, education, rent, population density, building age, etc). The Global Human Settlement Layer data could be useful in understanding how other adjacent methodologies are developed to assess human footprint. The availability of granular census data in countries like the United States and Canada would allow training, calibration and testing the accuracy of the models used in the new methodology on a large number of cities. Such a model could offer valuable insights for planners in many cities without socio-economic census data being available.
By 2050, 80% of the world population will inhabit cities. To accommodate the associated growth and development, planners need to weigh several competing objectives that affect our social, economic and environmental well-being. But planning can be a slow and inefficient process where control is distributed among many fragmented departments within municipal and regional governments, project developers, investors, transport, energy and water operators. The process of coordinating the competing priorities and actors is often expensive and complex and the design options quickly become limited, without fully identifying the impact of those options on the communities.
In this project, the student will develop a generative design tool for supporting decision-makers in planning urban areas (neighbourhoods, cities and regions) that reflect the priorities and needs of communities. First, using machine learning and computational design the student will build a model of a test urban area that integrates core layers of an urban system: infrastructure (buildings, streets, amenities and open spaces) and networked material and energy flows (Meerow, 2015), to explore millions of design options for the new development. Then, using urban growth models (cellular automata, ABM or network affects), the student will assess the long-term social, economic and environmental consequences of decisions for different urban communities. As a result, the various computer-generated design options for development or transformation projects can help evaluate all the scenarios extensively. Although such intelligent designs clearly lay out the impact of development on communities and their quality of life (e.g. mobility, health and energy), they do not account for the contrasting durée in which impact is measured and consequences unfold for communities. For example, while a neighbourhood could benefit from new development by prioritising free-market housing and job opportunities, it could also lead to long-term changes in the composition of the communities of a neighbourhood by enabling gentrification. The student should place a special focus on embedding and measuring the dynamics of change, in addition to static measures of quality of life.
COVID-19 has proven to be one of the deadliest pandemics in history. Political interventions in most parts of the world have largely been driven by competing and ill-informed priorities of decision-makers. Millions of people have lost their jobs, children are out of school and safety nets for food and familial security, and overall abuse, crime and suffering have risen. In many countries, multiple communities of migrant labourers, minorities and children have disproportionately been affected by the pandemic. It is imperative to investigate where our policy measures are needed most, and how to implement them with equity in mind, to reduce the disproportionate impact on the life and work of countless families across the world through this and future health crises.
Through a case study in India, the primary goals of this project are to (1) identify the socio-economic composition of communities affected by COVID-19 across the country, (2) compare the characteristics of these communities based on their geography and socio-economic indicators, and (3) investigate the impacts of policy measures and how they intersect with the health outcomes of the pandemic for the communities in question. Open-source COVID-19 data that tracks the spread of the disease in India has been curated by the Development Data Lab. The student will obtain publicly available region-level data from the source above (demographics, health indicators, etc.) and combine it with COVID-19 case counts at the region level. They can use state-wide policy data (if available), to understand and correlate the trends in case numbers. Socio-economic data of India can be found from the same source here. The student is welcome to extend the methodology to other countries, to understand similarities and differences in social, economic and political settings that lead to various disproportionate outcomes for communities.
Buildings are responsible for a large share of energy use and greenhouse gas emissions. Most European residential buildings were built before performance standards were implemented and have, generally, a high energy use (Filippidou & Jimenez Navarro, 2019; Garbasevschi et al., 2021). The European Green Deal comes with the proposal to double the renovation rate: A Renovation Wave. With nearly 34 million Europeans unable to afford heating their houses, the Renovation Wave is also a public response to energy poverty. However, recent research has shown that renovation programmes have failed to address energy poverty (Seebauer et al., 2019) and called for an explicit consideration of equity in the European renovation policy (Lihtmaa et al., 2018). Moreover, the increased market value of renovated buildings raises the risk of social inequity and gentrification (Mangold et al., 2016).
This projects explores ways to identify energy vulnerable groups and include equity into renovation policy at the city level. That is, how should subsidies be distributed within the city in order to minimize energy poverty? One of the challenges is identifying which households are more vulnerable to energy poverty. For that, the project combines socio-economic data with data about building energy performance. Vulnerability frameworks such as proposed by Robinson (2019) may be a starting point. This project should provide a policy framework to support the equitable distribution of renovation resources in cities.
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.
The impact of human activities on Earth is so significant that a new geological epoch has been proposed - The Anthropocene. In this epoch, cities became the main habitat of human-kind. But how should we live in the Anthropocenic city? It has been argued that urban commons have the potential to stimulate people to re-imagine ways of living with each other (Simone, 2015). In particular, urban commons have been described as an inclusive driver: a “haven” where “the excluded are protected” (Park, Shin and Kim, 2020).
The concept of urban commons involves the collective creation, use, and management of urban resources by means of self-organisation. An urban commons might be a physical resource, such as a building, a park, or a means of transportation used by various individuals. But urban commons are more-than-property (Williams, 2017): they might also be an intangible or virtual aspect, such as the vibrant environment of a street (Harvey, 2012), the social network in a given neighbourhood (Williams, 2017), or the knowledge shared by a group of people (Hess and Ostrom, 2007).
Access is a recurring theme in the urban commons literature (Vrasti and Dayal, 2016; Williams, 2017; Feinberg ,Ghorbani and Herder, 2021). On the one hand, access may be understood as physical proximity or the capacity to reach an urban commons, i.e, a material barrier to the urban commons. On the other hand, access to the urban commons may be organised via written or unwritten rules – the immaterial walls. Vrasti and Dayal, 2016 explain the difference: “Whiteness, masculinity, and class privilege are good examples of immaterial walls with material effects”.
This project aims at a better spatial understanding of access to urban commons. Methodologically, the project has a twofold perspective. A quantitative perspective focuses on the socio-spatial distribution of urban commons in a city : Who has physical access to urban commons (the material walls)? A qualitative perspective brings an in-depth analysis of one (or more) urban commons initiatives in the city, revealing the immaterial walls inscribed in the written and unwritten rules. A comparative analysis between the two perspectives may reveal whether material and immaterial walls have a spatial relationship. The project may also help to understand whether urban commons are indeed a driver for inclusion and protection of vulnerable groups.
 Cities of interest are Amsterdam or The Hague. Other locations may be possible upon agreement between the student and the supervisors.
Achieving the energy transition requires the large-scale deployment of solar photovoltaic (PV) in buildings. Most studies that estimate the solar PV potential in a city are solely based on technical parameters, such as the local irradiance conditions and the roof area suitable for PV installation (e.g. Lukac et al., 2013; Freitas et al., 2018). Very few studies go beyond such technical calculations to analyse how the solar PV potential intersects in space with the socio-demographic characteristics of the population (Suomalainen et al., 2017; Schunder et al., 2020). A comprehensive analysis of the socio-spatial distribution of solar PV potential is key to ensure that the benefits from solar PV energy are equitably distributed across the population (Bouzarovski and Simcock, 2017; Forman, 2017; Sovacool and Dworkin, 2015).
The goal of this project is to improve the spatial understanding of solar PV potential in urban spaces by combining socio-demographic data with the conventional technical estimation. The project proposes the measure of access to solar energy (ASE) as the ratio between the technical solar PV potential and the number of households in a given unit of area (e.g. a building, a bloc, or a census tract). By comparing ASE results with socio-demographic data, the project uncovers who has ‘real’ access to solar energy. Furthermore, data about building energy performance may provide further insights useful to prioritise household with a higher energy demand: who has more need for solar energy.
By identifying who has access to solar energy and who has the most pressing need for it, the findings of this project support equitable urban energy policies, contributing to a just energy transition. In general, there are two ways citizens can benefit from solar PV and contribute to the energy transition: either they install PV panels in their own houses, or they may join a solar energy community. In this context, households with limited economic resources but high access to solar energy may be approached with policies that provides economic subsidies to install solar panels. In contrast, households with limited economic resources and low solar accessibility may be approached through energy community policies. In addition, energy and housing policies may be combined to target households under energy poverty conditions.