CASA Seminar Series 2024-25: Term 1

The CASA seminar series explores topics like urbanism, data science, geography, planning, complexity, and network science. It is a chance to learn from experts in these fields and engage in discussions with fellow attendees. Each seminar will feature presentations and discussions, offering insights into how data science is impacting our understanding of cities, the ins and outs of urban planning, and the complexities of urban networks. 

Whether you're a professional in these fields, a student looking to expand your knowledge, or just someone curious about the dynamics of cities, the CASA Seminar Series is a great opportunity to gain insights and connect with others who share your interests.

This series runs weekly in a hybrid format with the opportunity to join in person at CASA's Living Laboratory in London or engage online, register for each seminar in the drop down below. The series is curated by Dr Fulvio Lopane and Dr Ollie Ballinger. 

2 October | 13:00 | Gennadii Donchyts

Unlocking the Power of Google Earth Engine: A Guide to Best Practices for Geospatial Analysis 

Abstract

This talk provides a walkthrough over some applications and best practices for utilising Google Earth Engine (GEE) in geospatial analysis. GEE offers a powerful cloud-based platform for processing vast geospatial datasets and conducting complex analyses, but effectively harnessing its capabilities requires understanding key strategies and techniques. We will cover topics such as: effective exploration of image collections, sampling and conditioning when working with noisy optical and radar satellite imagery at scale, how to use advanced AI models with GEE, and a few more to leverage GEE for advanced geospatial research. 

Biography

Gennadii Donchyts is a Cloud Geographer at Google. He helps public and private sector customers to use Earth Engine and Google Cloud platforms across multiple industries in the EMEA region and beyond. With expertise in remote sensing and software engineering, he previously led research at Deltares as a principal scientist, focusing on Earth Observation and Earth Engine for water-related applications. Gennadii holds a PhD in remote sensing from Delft University of Technology, where he studied surface water mapping using open satellite imagery. He is a passionate coder and advocate for Earth Engine, geospatial technology, and machine learning. 

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9 October | 13:00 | Binbin Lu

Geographically Weighted Models: Diversified Application and Development Actions 

Abstract

With the recent rise in studies focused on characterising spatial non-stationarity in processes that vary across space, Geographically Weighted (GW) models have emerged as the de facto statistical toolkit for addressing such challenges. This has attracted a diverse range of users and developers from across various scientific disciplines. In this talk, Dr Lu will introduce several newly developed GW models and demonstrate their applications through a series of case studies from different fields. To support the widespread adoption of GW models in research, he will also present the R package ‘GWmodel’ alongside a newly developed standalone software, ‘GWmodelS,’ which is built upon the GWmodel framework. The standalone software offers several key advantages, including a user-friendly graphical interface, enhanced operational efficiency, and improved accessibility. These features are designed to encourage usage across a broad spectrum of users, regardless of their technical expertise. 

Biography

Dr. Binbin Lu holds a Ph.D. in Geocomputation from the National University of Ireland, Maynooth, where he was affiliated with the National Centre for Geocomputation. He is currently an Associate Professor at the School of Remote Sensing and Information Engineering, Wuhan University, China. Dr. Lu's research expertise spans geocomputation, spatial statistics, geographically weighted (GW) modelling, open-source GIS, R programming, and the analysis of spatio-temporal big data. As the main developer and maintainer of the GWmodel R package, he has integrated a wide range of localised techniques, such as GW regression and GW principal component analysis, providing valuable tools for spatial data analysis. 

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23 October | 13:00 | Francisco Rowe 

Producing population-level estimates of internal displacement in Ukraine using GPS mobile phone data 

Abstract

Nearly 110 million people are forcibly displaced people worldwide. However, estimating the scale and patterns of internally displaced persons in real time, and developing appropriate policy responses, remain hindered by traditional data streams. They are infrequently updated, costly and slow. Mobile phone location data can overcome these limitations, but only represent a population segment. Drawing on an anonymised largescale, high-frequency dataset of locations from 25 million mobile devices, we propose an approach to leverage mobile phone data and produce population-level estimates of internal displacement. We use this approach to quantify the extent, pace and geographic patterns of internal displacement in Ukraine during the early stages of the Russian invasion in 2022. Our results produce reliable population-level estimates, enabling real-time monitoring of internal displacement at detailed spatio-temporal resolutions. Accurate estimations are crucial to support timely and effective humanitarian and disaster management responses, prioritising resources where they are most needed. 

Biography

Francisco Rowe is Professor in Population Data Science and Lead of the Geographic Data Science Lab at the University of Liverpool. His areas of expertise are: human mobility and migration, spatial inequalities, and geographic data science. Francisco is interested in the use of data from digital sources to understand population movements in real time at high spatial and temporal resolutions and how the resulting insights can be used in decision making processes. Francisco is part of the United Nations (UN) Committee of Experts on Big Data and Data Science for Official Statistics. He works closely with the UN International Organization for Migration, particularly the Global Data Analysis Centre (GMDAC) and the Displacement Tracking Matrix (DTM) unit, and UN the Economic Commission for Latin America (CEPAL). 

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30 October | 13:00 | Doug Leasure

Oxford NowPop: Population nowcasting in a digital world for targeted humanitarian action and sustainable development. 

Abstract

Population nowcasting is an emerging field that aims to estimate current population sizes and demographics for small areas. This is particularly impactful in otherwise data-scarce contexts such as crisis response or supporting official statistics in the absence of a census. In our digital world, there is an increasingly dizzying array of potential data sources for population nowcasting ranging from satellite remote sensing to digital traces generated from our internet and mobile phone activity. This seminar will present several papers highlighting innovative applications of population nowcasting from around the world including census support in Nigera, DRC, and Colombia as well as rapid-response humanitarian action in Ukraine and Gaza. Discussing future directions and opportunities for collaboration. 

Biography

Doug Leasure is a Senior Researcher and Data Scientist at the Leverhulme Centre for Demographic Science in the Department of Population Health at the University of Oxford. Doug's interdisciplinary background brings together concepts and methods from demography, ecology, remote sensing and GIS, underpinned by methods in Bayesian statistics and machine learning. He currently leads the Oxford NowPop Project which develops methods in Bayesian Statistics for population nowcasting to support targeted humanitarian action in Ukraine, Gaza, and beyond. Prior to joining the University of Oxford, Doug led the Spatial Statistical Population Modelling team in the WorldPop Research Group at the University of Southampton. After completing his PhD in Population Ecology at the University of Arkansas, he held a postdoctoral research position at the Odum School of Ecology working on a NASA ecological forecasting project. 

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27 November | 13:00 | Bikila Merga Leta

Spatial investigation of flood‑resilient neighbourhoods, the case of Adama City, Ethiopia 

Abstract

Adama is the second most populous city in Ethiopia and experiences frequent flash floods that have a detrimental impact on the community’s livelihood. To this effect, this study emphasizes the significance of conducting a comprehensive investigation to identify flood-resilient neighbourhoods in Adama City.

By considering the existing spatial pattern and morphology that are resilient to urban flooding, the city can enhance its flood management strategies in the future. The findings of this study demonstrate varying levels of spatial connectivity within Adama City. Detailed examination of two neighbourhoods identified through space syntax analysis with high and low spatial connectivity revealed important insights into flood resilience.

The high-connective neighbourhoods exhibited well interconnected street systems with manageable street spacing, facilitating efficient runoff flow and effective flood management during flooding events. These neighbourhoods also had shorter block sizes with frequent intersections, promoting better water drainage and reducing the risk of flooding during heavy rain events. The grid pattern observed in these areas allowed for efficient water runoff through multiple drainage paths, including the street surfaces. On the contrary, neighbourhoods with low spatial connectivity exacerbated urban flooding. The lack of connectivity and abundance of dead-end streets posed challenges for flood evacuation during emergencies. Irregular block arrangements disrupted the natural drainage system, aggravating the potential for urban flooding. These findings have implications for other flood-prone areas of neighbourhoods in Adama City and similar urban areas in the global south on how human settlements are arranged spatially to mitigate urban flood vulnerability. 

Biography

Bikila Merga Leta is an Assistant Professor in the Architecture Department at Addis Ababa Science and Technology University in Ethiopia. He is a practicing landscape architect and urban planner with a strong academic background. He completed his PhD in Urban and Regional Planning from the Technical University of Dortmund (Germany) and Addis Ababa University’s Ethiopian Institute of Architecture, Building Construction and City Development (AAU, EiABC). In addition to his doctorate, he holds an MSc in Landscape Architecture from the Free University of Brussels and Addis Ababa University, as well as a BSc in Urban Planning from Addis Ababa University. His research focuses on spatial analytics, urban development, urban flood resilience, urban sustainability, green infrastructure, and the application of GIS, remote sensing, and satellite imagery. 

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4 December | 13:00 | Bea Taylor

From Brains to Buildings

Abstract

In her talk, Bea Taylor will take us on a journey of how she came to the Centre of Advanced Spatial Analysis, UCL. The journey begins with her PhD work, developing and applying machine learning models for understanding disease progression in dementia. Whilst here she will *attempt* to elucidate the challenges of applying AI in the healthcare domain. The next step takes us to a policy placement, where she researched regional variation in dementia diagnosis. Arriving at CASA, Bea will discuss her initial plans for her postdoctoral work, where she will be exploring how London is evolving – in particular, changing patterns of residential developments across the city using the recently launched London Planning Datahub. Be warned – in the spirit of collective intelligence she is expecting some audience participation. 

Biography

Bea Taylor recently joined CASA, UCL as a research fellow, working on the Smart Cities project as part of the new AI for Collective Intelligence hub. She has just completed a PhD at the Centre for Medical Image Computing, in the Computer Science department here at UCL. She has an MMath in Mathematics from Warwick University.

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11 December | 13:00 | Laura Mazzarino | rearranged from 20 November 

Does education affect macroeconomic dynamics? An Agent-Based approach 

Abstract

A macroeconomic agent-based model is presented, featuring a community of profit-maximizing firms facing a heterogeneous labour force whose behavioural attitudes are influenced by workers' educational backgrounds. Two educational systems are analysed: a general system, which provides broad skills, and a "vocational" one, focused on specific job-related skills. Additionally, the model is extended to explore how the economy responds to technological shocks. 

Biography

Laura Mazzarino has a background in Economics and Finance and holds a PhD in Complex Systems from the University of Catania (Italy). Her research primarily focuses on the impact of economic agents' skills on outcomes in financial and labour markets. She uses network science, data-driven models, and simulated agent-based models to analyse complex market interactions at both microeconomic and macroeconomic levels.

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More Information:

• Study at The UCL Centre for Advanced Spatial Analysis 

Image: Data Output created by Dr Fulvio Lopane from The London Data Store