Katie is a mixed methods researcher working for the Personal Finance Research Centre (PFRC). She conducts policy-relevant research across a range of areas relating to personal finance. She specialises in quantitative survey design and analysis but also has experience conducting qualitative interviews and focus groups. She is particularly interested in research relating to poverty, inequality, financial well-being and living standards in the UK. She is part of the Geography Equality, Diversity and Inclusion Committee and is a staff Mental Health Champion.
Joe is a Lecturer in Historical Geography and Economic History. Joe is primarily interested in the process by which Britain industrialised and urbanised in the eighteenth and nineteenth century. Consequently, Joe has collaborated with the I-CeM project (https://icem.data-archive.ac.uk), most notably matching individuals’ birthplaces to a GIS. Joe publishes on the determinants of migration and urbanisation in England and Wales and is interested in how individuals’ perceptions of place influenced their migration decisions. As a driver of migration, Joe is also interested in wages and poverty, and is currently digitising all official wage data created between 1837 and 1914. Joe is the Principal Investigator of a £300,000 ESRC-funded project: Migration, Urbanization and Socio-Economic Change: England and Wales, 1851-1911. Joe is interested in hearing from prospective PhD students interested in the quantitative spatial analysis of historic data. @ManAmongstKings
I am a human geographer interested in topics at the intersection of political science, development studies, and political geography. The primary focus of my research is modelling the relationship between political institutions and organised violence in low-income countries with a specific emphasis upon variation within and between countries. I also work on topics in urban and regional geography including urban protest mobilisation, urban systems and boundary analysis, and poverty mapping. Methodologically, I am primarily interested in the application of multilevel modelling, spatial statistics, and causal inference to political and geographic questions.
I am an Associate Professor in Global Development at the University of Bristol and a Fellow at the Alan Turing Institute. My current research explores the causes and consequences of global urbanization, the political economy of urban governance, and sustainable city futures. I completed bachelors degrees in economics and literature at the University of California Santa Cruz, and MSc and PhD degrees in the Department of International Development at the London School of Economics.
Professor Richard Harris uses data, geocomputation and quantitative methods to study social geography: to understand the causes and consequences of socio-spatial patterns, showing where you are matters, why it matters, and to whom places lend socio-economic dis-/advantages. Early research looked at spatial statistics, GIS and geodemographics in marketing and urban geography – early applications of geographic data science and urban analytics. Recent work includes the geographies of Covid-19, measuring and mapping social and ethnic segregation, and geographies of education. Rich has a long-standing interest in promoting quantitative and statistical literacy: in 2014 he won The Royal Geographical Society (with IBG) Taylor & Francis Award for excellence in the promotion and practice of teaching quantitative methods. He was founding director of Bristol’s Q-Step Centre, supporting undergraduate quantitative social science. Rich is a fellow of the Academy of Social Sciences and, presently, The Alan Turing Institute.
Lenka is a Ph.D. candidate in Advanced Quantitative Methods. Her research project is focused on spatial interaction models, the role of space in them, and their application for real-world data. This project ties together concepts from geography and network science and helps our understanding of the methods and their evaluation. Pushing the boundaries of geographical research and the boundaries of our understanding is an essential part of this research.
David is a highly-experienced social scientist and lecturer in social research methods, specialising in quantitative methodology, research evaluation and analysis. He has extensive experience of analysing large-scale survey data, using a range of techniques including multilevel modelling and cluster analysis, and has particular expertise in evaluations, research design, and the design, implementation and analysis of quantitative surveys. David is proficient in effectively disseminating his research and analysis though a range of mediums. His main research interests include financial inclusion among vulnerable groups, gender equality in the Global South, and the links between mental health and gambling. He directs and teaches a number of courses in the Department, mainly within the postgraduate school and on the Advanced Quantitative Methods programme.
I am a research associate of Quantitative Human Geography. My research interests mainly focus on understanding people’s spatial behaviors in the context of urban environments. During my doctoral study, I employed Wi-Fi sensors for tracking large-scale (e.g., more than 3,000,000 individuals) tourist activities in a tourism-oriented urban community. Recently, I have been working on the spatial distribution of gambling opportunities and their geographical impacts on people’s gambling behaviors.
David focuses on social inequalities in the urban environment investigating how individuals sort into the places in which they live, how these processes influence the structure of the urban environment and the people-place relationships that result. Collaborating across Europe he seeks to better understand how regions, cities and neighbourhoods develop, and how these configurations affect social, economic and health outcomes for people, often at multiple spatial scales. David’s research uses longitudinal data (UK’s Census Longitudinal Studies, the UKHLS and Population Register Data in Sweden and The Netherlands) to move beyond the single-point-in-time relationships initially apparent and explore the longer run observing individual biographies and trajectories. Methodologically, this requires the use of large and complex models (less about ‘big data’ and more about ‘big models’!), frequently developing an explicitly multilevel approach incorporating multiple scales simultaneously and net of each other.
Caitlin is an Research Fellow and Proleptic Lecturer in Urban Analytics in the School of Geographical Science. As a quantitative human geographer, Cait’s research investigates the causes and consequences of different types of spatial inequality, with a particular interest in energy poverty, energy justice and climate-related inequalities. She takes a theory-led approach to spatial analyses, using quantitative, spatial datasets and methods to understand inequality across multiple scales. Cait is currently leading a UKRI Future Leaders Fellowship project mapping ambient vulnerabilities in UK cities. Twitter: @caithrobin
I am a Lecturer in Quantitative Human Geography in the School of Geographical Science. My research focuses on the emerging transformations in population, economy, and space in urban China. This includes modelling the evolving role of the household registration (hukou) system in shaping social disparities, visualizing the changing socio-spatial patterns across multiple scales, and exploring the spatiality of new urban economies by using novel sources of geodata. I strive to conduct research that is grounded in quantitative empirical evidence and informed by a reflexive theoretical framework. By adopting a comparative approach, I aim to use urban China to enrich traditional Western-centric perspectives in urban studies.
I am a Professor of Quantitative Human Geography and a Fellow at the Alan Turing Institute. My research focuses on the spatial dimensions of digital technologies and the digital economy. I have published on the geography of the internet infrastructure, the economic impacts that such digital infrastructure can generate on cities and regions and the position of cities within spatial, complex networks. I have a strong interest and expertise on the use of new sources of big data to understand the complexities of smart cities and urban systems. Using such data and relevant computational methods, I have explored the interplay between geography, spatial structure and social networks. For instance, how access to digital tools can intervene with some key dimensions of spatial structure including commuting and distance. Recently, I have been working on the evolution of online content and its interrelation with cities and spatial structure using web archives. etranos.info/ |@EmmanouilTranos.
Winnie’s research interests concern demographic processes within and across urban areas. She is particularly interested in applying an institutional approach to study rural-urban migration in China and its relationship with development. Her current research focuses on the impact of migration, including return migration, on (dis)empowerment of women in China. Additionally, she is interested in using mobile trajectory data to understand how urban mobility patterns vary by sociodemographic characteristics, with the aim of providing insight to address mobility inequality within cities. Finally, she is also interested in studying aging population and health in China.
I am a Senior Lecturer/Assistant Professor in Quantitative Human Geography at the University of Bristol’s Quantitative Spatial Science Lab, Fellow at the University of Chicago Center for Spatial Data Science, Editor for Environment and Planning B: Urban Analytics & City Science, and Fellow at the Alan Turing Institute. I work in spatial data science, building new methods and software to learn new things about social and natural processes. I’m broadly interested in figuring out ways to integrate spatial reasoning into data science techniques. I’ve worked on problems about redistricting, elections (psephology), segregation, inequality, and urban systems. Find me on twitter @levijohnwolf!
Ce is a Lecturer in Environmental Data Science in the School of Geographical Sciences, also a Fellow of UK Centre for Ecology and Hydrology. He has extensive expertise in Artificial Intelligence, Machine Learning and Deep Learning, and the application of these techniques in geospatial data science and remote sensing. His central methodological innovation is on geospatial information understanding (object, context, scene, and semantics), and deep learning-based feature representation across spatial scales and ontologies. These novel AI and Data Science techniques have been embedded into environmental and socio-ecological modelling and analytics, to tackle some of the greatest challenges in our society. Ce’s research is highly innovative and trans-disciplinary, with applications widely across the whole spectrum of human and physical geography, ecology and environmental science.
I am currently a Postdoctoral Research Associate at the School of Geographical Sciences at the University of Bristol and an Early-Career Fellow at Future Earth. My research focuses on the social and spatial dimensions of energy geography, with a particular interest in energy poverty, energy vulnerability and climate-related inequalities. I specialize in analysing quantitative surveys at household scale but also have experience investigating carbon emissions data of various sectors at a macro level, which helped me understand the complexities of energy related topics at multiple scales. I recently became a part of a UKRI Future Leaders Fellowship project led by Dr Caitlin Robinson: mapping ambient vulnerabilities in UK cities, working on how energy and climate factors are interconnected and influence vulnerabilities around households within cities. @LinZhang55
Rui is a Lecturer at the School of Geographical Sciences. He is a geographer by training with a background in data science (so you can call him a spatial data scientist). Broadly speaking, he studies how humans and machines organize spatial knowledge, as well as their interactions with the environment. More specifically, he combines theory-informed (e.g., geography and semantics) and data-driven (e.g., machine learning and spatial statistics) approaches to address geospatial challenges such as geospatial data interoperability, spatial predictions, and spatial reasoning. His work has been applied to a wide range of applications, including urban studies, global health, environmental modeling, as well as humanitarian relief. ruizhugeographer.com