The Bartlett Centre for Advanced Spatial Analysis


Alumni Spotlight: Oliver Lock

31 May 2018

Oliver studied with us on our MRes Advanced Spatial Analysis & Visualisation (now MRes Spatial Data Science & Visualisation) in 2013-14.

Oliver Lock

On his experience at CASA, Oliver reflects:

 “CASA has always been at the leading edge of research for the science of cities and were one of the first groups in the world to introduce dedicated postgraduate coursework and research programmes in this field. CASA’s unique interdisciplinary blend of both research and teaching staff, positioned in one of the most (open) data rich cities in the world, makes it a great hub for both developing knowledge and creating innovation in this space.”

Since completing his Masters, Oliver has gone on to work on consulting in the transport and planning industry back home in Australia at global engineering/design firm Arup. This work ranges from performing data analytics using Opal (Sydney’s version of the Oyster card), large-scale transport modelling projects, transport accessibility studies, web mapping projects to experiments with machine learning and road crash statistics.

“Many of the skills I learnt while undertaking my postgraduate research and coursework, such as Python (pandas), GIS, R, Processing, Unity I have been able to apply directly to work in urban and transport planning. Being able to come up with data-driven and visual analytics solutions to support evidence-based planning is a valuable skill which was supported by teachings and learnings on the course.”

Oliver is now a Scientia PhD student at the University of New South Wales (UNSW) exploring the use of up-and-coming technologies (such as augmented reality) in improving the dialogue between citizens, stakeholders and governments when making transport planning decisions.

“We are reaching a point where there is not only a plethora of data available to all, but a plethora of tools and a whole lot of conflicting facts. The communication, use and dissemination of our work should be equally as important as the data and algorithms behind it.”