Emily Sheard

What course are you studying?

Having completed an MSc in GIS in 2015 I am now in the third year of my PhD studies. My thesis falls under the umbrella project ‘Urban Mobility and Movement Patterns’ and is titled ‘Developing a Dynamic Risk Model for the Prediction of Temporally Clustered Crime Series’.

Why did you decide to study a PhD/Masters at the University of Leeds?

I saw an advert for a fully-funded PhD ‘Big Data’ studentship based in the Consumer Data Research Centre (CDRC) at the University of Leeds and felt that the project presented a fantastic opportunity to combine different aspects of my academic and professional experience to date, including an undergraduate degree in geography and time spent working as an analyst in both the public and private sectors.

What has been the best aspect of studying on your course and at the University so far and why?

Definitely acquiring lots of new skills, particularly in relation to data processing and analysis – for example, I was introduced to programming during my MSc course and have since gone on to learn both R and Python coding languages in more detail. This has facilitated some novel analysis during my PhD research. I have also demonstrated GIS on a number of undergraduate and postgraduate courses in the School of Geography including ‘Quantitative & Spatial Methods’ and ‘Predictive Analytics’, as well as those run by the CDRC. This experience has aided my own learning and I would encourage any new students to get involved whenever possible. Further to demonstrating I will be leading my first course in October which I am really looking forward to!

Tell us about some of the exciting projects you have completed.

I have had the opportunity to visit some amazing places during my studies, including a week spent in Montpellier, France as a member of support staff on an undergraduate field trip. I have also presented my research at various events and conferences throughout the country, including: ECTQG 2017, York; EuroCrim 2017, Cardiff University; the CDRC Data Partner Forum 2016, Oxford University; and two seminars at the Leeds Institute for Data Analytics (LIDA). Through my involvement with the N8 Policing Research Partnership (N8 PRP), I have facilitated a workshop on data sharing, drafted content for a consultation report on data analytics, and acted as the lab assistant on a Machine Learning training day, all of which have been extremely useful from the point of view of being able to exchange ideas and knowledge with an external partner organisation.

What does Leeds as a city have to offer students?

I haven’t lived in the City myself, but I can say that it’s great for shopping and socialising, presents a wide range of business and career opportunities and has some wonderful attractions on its doorstep, including the Yorkshire Dales and North Yorkshire Moors.

What are your ambitions for the future?

Hopefully to continue working in the field of crime analysis, focusing primarily on how ‘new’ forms of data can be applied, in conjunction with open source software such as R, to both current and emerging crime problems. I am also passionate about ensuring that university research impacts ‘frontline’ policing.

What experiences at Leeds do you think will help you in your future career?

I have been given the opportunity to work at the forefront of data analysis during my time at Leeds and I feel that the skills I have gained will be transferable in a variety of contexts. The fostering of a multi-discipline environment within the School has enabled me to establish a large network of professional contacts, not least through my membership of the Centre for Spatial Analysis and Policy (CSAP). I have also been given the space to work on my own initiative, however, support has always been available when required, for example, through access to relevant training.

What would you say to students coming to do the same course?

I would say that I have found my time at Leeds to be academically challenging but extremely rewarding and to remember that – if it was easy, everyone would do it!