What do you consider to be your greatest achievement to date?
What I am most proud of in my academic career to date is setting up the Geographic Data Science Lab (GDSL) at the University of Liverpool and, more specifically, the opportunities that this has enabled for those embedded within it.
GDSL is almost 10 years old and grew from just me with three PhD students crammed into a tiny room (cupboard!) to a more spacious 40 seat open plan office. We have been fortunate to host large grants such as the ESRC Consumer Data Research Centre and the ESRC CDT Data Analytics and Society, which has generated a lot of activity. We were also supported by the University of Liverpool in realising this vision, enabling our core faculty staff to grow substantially.
I tried very much to set the lab up with a progressive culture: more akin to a start-up tech company (without the bad bits!) than a traditional academic lab. So it has a very flat structure where everyone can interact; lots of external engagement with different organisations and visitors; and really only two core rules. Firstly, that you do 'cool stuff' (that’s easy, geography is cool!) and the second that you are a good citizen of the lab. As a result, we have a lot of collective activities involving all career stages including grant reviews, panel interview preparation, journal and book clubs, student or staff talks alongside a range of external seminars.
GDSL has also generated great opportunities for others. Our outgoing PhDs and postdocs now have some quite senior roles in very interesting organisations: the OECD in Paris doing data science policy; a Deputy Programme Director at the Alan Turing Institute; lead to the Local Data Spaces project, supporting the analytical demands of local authorities during the pandemic. Others are at the UN or the World Bank.
Where did your interest in geographic data science come from?
Geography was always big in my house while growing up; my dad had done a joint geography/geology degree at Bristol and worked for the British Antarctic Survey. My childhood was peppered with stories and photos of his adventures. Computers came later though. I participated in a gap year scheme after my A Levels where I ended up living with a computer engineer who introduced me to hardware, networks and building computers; and an aspiring computer scientist, who showed me how to code in HTML (this was 1998, so early web!).
During my time at university, I worked over the summer holidays in a lab at the Open University called Knowledge Media Institute. This was where I learnt to code more extensively, but also got me thinking about careers in academia. Over the course of my degree, I learnt a lot about geographic information systems and Science, but there wasn’t really any coding within geography curriculums during the early 2000s in the UK, so much of this was very software focused. Although this was helpful, it always left me wanting to do things analytically and with data that the buttons of software wouldn’t allow, and that was really the starting point for my PhD, which became very data science focused; although that was before the term really existed.
Is there a particularly memorable project on which you have worked?
I had quite an unusual PhD as it was all completed part time and alongside other things, firstly integrated into a Knowledge Transfer Partnership with UCAS/UCL and then as a research assistant, also at UCL. I always had quite a lot of other activities alongside my research. One involved supporting my supervisor Paul Longley on various book projects, notably his textbook Geographic Information Systems and Science. Watching the process of how this was put together was extremely interesting, both at a conceptual level about how you write accessibly and concisely, but also in terms of the structure and how you organise pedagogic materials effectively and progressively.
Although I have been involved in writing a number of books, my most recent was a textbook on urban analytics and is one of my most memorable projects to date. Writing textbooks requires a very different set of skills and discipline than research papers or monographs. They require you to think more in abstract, but also offer more variety of content. In my book on urban analytics, I got to blend both content that you might find in a traditional GIS textbook with new material about how these technologies are being used within urban settings.
I was also able to interview a lot of people about their work in. One of the more memorable of these was with Charlie Catlett who is Director of the Urban Centre for Computation and Data at the University of Chicago and Argon National Labs. He has some interesting projects about how sensors can be used within urban environments but is also exceptionally busy. My slot for a lengthy chat was completed over a mobile phone as he drove solo across country in the US including gas station stops.
How do you think this field will evolve in the future?
The first trend is that geographic data science will continue to grow. Since we developed the first programme at Liverpool, a host of new programmes have launched. Many more will emerge over the next few years, also with greater emphasis within undergraduate curriculums. This is in part aligned to the growing number of jobs that require geographic data science; it is a great destination for graduates!
Second, the significant expansion in methods aligned to emerging and maturing technologies. For example, a plethora of new space technologies are generating huge volumes of remotely sensed imagery that when explored through a framework of geographic data science can tell you new things about places in which we live.
Finally, as geographic data science matures, there will be more integrated methods. Geographers will not only borrow data science methodologies but also contribute new frameworks where spatial relationships, scale and process are more closely integrated into the methods themselves.
What advice would you give to someone wanting to go into a career in this field?
I speak to a lot of recruiters and the critical things they highlight are the combination of strong technical skills (the ability to code with Python or R, for example) alongside the ability to tell stories with data and think about problems creatively and with context. There are a lot of free online resources out there for students and professionals, and many of the courses that we deliver are in the public domain. The advantage of learning these within geography or a more specialist geographic data science programme is that they are typically delivered from a perspective of trying to understand how the world looks or works.