Dr Jon Reades

Brief Bio

I completed by undergraduate studies in Comparative Literature (on Foucault and McLuhan) in 1997 and was fortunate to graduate into the first dot.com boom. Working for a New York-based start-up, I learned to code (Perl, later Java) and ultimately moved into database mining and marketing for telcos – while working on behavioural data linked to WAP usage, I become interested in the impact that mobile telecommunications was having on office location and cities.

This led me to UCL’s Bartlett School of Planning and an MPhil/PhD under Prof. Sir Peter Hall and Mike Batty. I then worked for Mike as a post-doc at CASA on transport-oriented research involving Oyster Card data (including during the 2012 Olympics) before leaving to help set up the Geocomputation & Spatial Analysis theme at King’s College London with Dr. James Millington. I now focus much of my research on open and semi-open data and am increasingly interested in issues of replication and validation. Recently, I have become increasingly intrigued by the capacity of machine learning to extract patterns from masses of data.

Fun Fact

I may well be the only literature graduate with an Erdös number (4, thanks to this article).

Research Interests

My research draws on geographical theory and ‘quantitative social science’ methods to address contemporary challenges in urban and regional development. My experience in planning and geography, as well as data and programming, enables me to translate concepts and applications across disciplinary boundaries while paying attention to the details of data, method, and context.

Thematic Research Areas

  • Smart Cities & ‘Big Data’ – I work with event data from urban infrastructures to understand human behaviour, contextualising these insights as part of planning and governance.
  • Economic Geography – I focus on ‘knowledge economy’, and the interactions between individuals and organisations, to understand the developmental trajectories of cities.
  • Housing – I am interested in quantitative and computational approaches to understanding the relationship of housing to tenure and demography.

Methodological Research Areas

  • From Open Data to Open Teaching & Reproducible Research – I am interested in how open data and tools (both new and old) create opportunities for more robust, replicable and accessible teaching and research.
  • Emerging & Overlooked Quantiative Methods – I seek to understand the implications of machine learning and older analytical approaches for high-dimensional human and environmental geodata.

Links