CUSP London Seminar: Dani Arribas-Bel

This past Thursday we were really lucky to catch Dani Arribas-Bel, Senior Lecturer in Geographic Data Science at the University of Liverpool and major contributor to PySAL, on his way back home following two weeks’ teaching in the Caribbean. Dani kindly agreed to give a talk in two parts on “Infusing Urban and Regional analysis with Geographic Data Science” (‘GDS’) which we will summarise below… As one of the first CUSP London-branded seminars, it was great to see so many Urban Informatics staff and students there (and even a few from UCL’s CASA!)

Sexiest job of the 21st century…

Geography & Computers

The first half of Dani’s talk covered highlights from a recently-published paper in Geography Compass titled “Geography & Computers: Past, Present, and Future” (an author pre-print is available via KCL’s Institutional Repository); in it, Dani and KCL’s Jon Reades link shifts in computing power and access to shifts in the ways in which geographers use computers to ‘do’ geography.

The basic contention is that there have been three waves of change that they (we) summarise as: 1) a computer in every institution (50s–70s); 2) a computer in every office (80s–00s); 3) a computer in every thing (10s–). We don’t need to revisit the article in full here since highlights are available in a previous blog post, but Dani’s focus was on the links to ‘data science’ the ‘sexiest job of the 21st century‘.

This led through to a discussion of ‘data-driven methods’ which, to a geographer, can sound like putting the cart before the horse. However, it’s important to keep in mind that we, as researchers, have little to no control over how the kinds of data underpinning a (geographic) data science are created and therefore need to adapt our approach to the data, and not the other way around.

I particularly appreciated Dani’s observation on the importance of data processing/handling as part of this shift: sometimes dismissed as ‘mere cleaning’, this stage is critical to ensuring that the data is both well-understood (shows what we think it shows) and fit-for-purpose (does what we want it to do).

I’ve seen the term ‘feature engineering‘ pop up in my own news feeds with increasing regularity and that has a nice ring to it (it’s engineering, not cleaning!) but it doesn’t quite capture the full scope of what good data science really entails. And it also doesn’t take into account the ‘baking’ of geo-data that is really required to ensure methods and models are appropriate.

Dani wrapped up this section with a discussion of how GDS can serve as the interface between geographers and data scientists, supporting the co-production of systems (a.k.a. tools), methods (spatially aware ML), and epistemologies (ways of knowing that are appropriate to these types of data).

Applications of Geographic Data Science

The second half of Dani’s talk covered a work-in-progress using a large building data set from Spain to delineate urban and employment boundaries. This nicely illustrated one of the key concepts elaborated in the first half of the talk: the importance of data-driven methods in geographical data science.

The question Dani and his co-authors are exploring is how one can meaningfully delimit the spatial extent of urban areas and economic activity with the minimum number of prior assumptions about spatial configuration or ‘auxiliary geographies’; by this we mean using other steps or data, such as rasterisation or regional boundaries, to constrain the process to our preconceived notions of what the answer ‘should be’.

The issues with rasterisation and the MAUP are well-known, but what do you do when you have 15 million data points to cluster and can no longer load the data set into memory? This is what we mean by data-driven methods: Dani’s exiting addition (which prompted a good deal of questioning from the audience) is a way to make an existing algorithm work not only in a large data context but which also does so in a way that works around what I feel is an important conceptual flaw in the existing algorithm to give you insights into the robustness of your results!

Such a method is not without theory, nor without empirical input: Dani and his colleagues use research findings on commuting distances and employment to provide essential parameters. I’m not able to share additional details at this stage, but I’m really looking forward to seeing this algorithm ‘in the wild’ since it addresses a number of issues that I have with some work that I’m (slowly) undertaking…

King’s Water Events – Spring 2019 – Documentary Screening and Book Launch

Water at the Margins (2018) Documentary screening and discussion With director Maria Rusca (Uppsala University, Sweden) and story consultant Nathalie Richards (King’s College London, UK) ‘Water at the Margins (2018)’ Drawing on our experience in undertaking a videography project in Maputo, Mozambique, this seminar reflects … Continue reading

Fully Funded PhD Studentship Opportunity

We are inviting applications for a fully funded PhD in ‘Improving Efficiency and Equity of Ambulance Services through Advanced Demand Modelling‘. See full details below.

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Job posting – Full-funded PhD position (LISS-DTP 1+3/3+)

Overview of the position
We are looking for a PhD student who will join the Geocomputation Research Domain at the Geography Department of King’s College London. The appointed PhDstudent will work on the project titled “Improving Efficiency and Equity of Ambulance Services through Advanced Demand Modelling”, funded by the ESRC – LISS-DTP and supervised by Dr Chen Zhong and Prof. Judith Green. This PhD project is a collaborative project together with London Ambulance Service (LAS), where Dr Leanne Smith will supervise the project as an industrial advisor. We are seeking a candidate for 1+3/3+ term depending on the qualification of the candidate. For more information about the scholarship, please visit LISS-DTP (https://liss-dtp.ac.uk/case-studentships-student-applicants/). 

King’s Geocomputation research (https://kingsgeocomputation.org/) spans a range of contemporary social, environmental and geographical issues, collaborating intensively with experts in the field from universities abroad. PhD candidate selected for this project will also have the opportunity to closely work with the forecasting and planning team at LAS, and research domain/centres at KCL including CUSP, SUPHI.

Project non-technical summary
Demand for Ambulance Services in England has risen dramatically over recent years, with growing pressure anticipated for future years. The disparity between the increasing demand and limited ambulance resources makes the major challenge for maintaining a high-quality service. In 2017, NHS England undertook a significant national reform called the Ambulance Response Programme (ARP), designed to address efficiency and performance issues. It noted the over-use of immediate dispatch decisions and the insufficient allocation of resources to incidents. Key issues concerned: the quality of care; its cost-effectiveness, and the equality of provision across areas and population groups. In view of the growing pressures of NHS, and the necessity of ambulance services to understand the needs of the populations they serve, the proposed PhD project aims to develop an advanced demand prediction model for ambulance services taking LAS as a case study. The research is to find the best correlated socioeconomic, environmental, and spatiotemporal factors and to model these factors as predictors of ambulance demand. The final component of the PhD will develop the implications of the model as Demand Management innovations, for future testing.

Required qualifications
You must be eligible for ESRC funding and will be asked to pursue either the 1+3 (MSc+PhD) or +3 (PhD only) track.  The cost of the MSc is included in the ESRC award.
– MSc/BSc degree in Urban Informatics, Urban Analytics, Spatial Analysis, Transport planning, Geography, or related field 
– Excellent written and oral communication in English
– Knowledge of spatial data analysis techniques and GIS applications are an asset

Application instructions 
Applications for admission to the 2019/20 Masters programme close in March 2019. To ensure anyone on 1+3 track to be admitted to the relevant Master’s programme in time, we have set an application deadline of 23:59 on Thursday 21ST Feb 2019. All studentship applications must include:

1. A CV (no more than 2 pages) highlighting relevant study and work experience. 
2. A cover letter (no more than 2 pages).
3. 2 references, at least one of which will be academic.
4. copies of transcripts for all relevant degrees

We expect to conduct in-person interviews in late February or the first week of March. Please contact Dr Chen Zhong (chen.zhong@kcl.ac.uk) for any project related questions. You should email LISS DTP (liss-dtp@kcl.ac.uk) if you have any general questions regarding the application process, or core methods training requirements.

Academic Carbon Footprints

by Mary Langsdale

As part of our annual criteria, each member of the Sustainability Champions for the Department of Geography was asked by King’s Sustainability team to calculate our carbon footprint. I’m someone who cycles daily, doesn’t own a car, tries to eat a meat-free diet and lives in a shared house with electricity provided by a renewable energy provider. I have to admit, I was expecting my carbon footprint to be fairly small (and even to feel a bit smug). However, when I calculated it using the WWF carbon footprint calculator, I was shocked to discover that I was using 216% of my share (with 100% the average for each UK citizen to meet the UK’s 2020 emissions reduction targets). Of my share, 86% was from travel contributions, as shown in Figure 1. Continue reading

Understanding Gentrification through ML

Although it has taken rather a long time to see the light of day, our just-published paper is one of the reasons I love my job: drawing on a mix of data science and deep geographical knowledge, we look at the role that new Machine Learning (ML) techniques – normally seen as just a ‘black box’ for making predictions – can play in helping us to develop a deeper understanding of gentrification and neighbourhood change. For those of a ‘TL;DR’ nature (or without the privilege of an institutional subscription!), we wanted to share some of our key ideas in a more accessible format. Continue reading