One of the projects I have been involved with recently was a collaboration with the Agents, Interaction, and Complexity group at University of Southampton. The same group who we are also involved with in the Orchid project. This particular project was on Measuring and Predicting Departures from Routine in Human Mobility, building on the PhD work by James McInerney (now at Princeton) and his paper Breaking the Habit.
It is well known that humans generally follow very regular and predicable mobility patterns, both spatially and temporally. Lots of work has looked at exploiting and predicting those patterns but much less so on looking specifically at departures from those regular patterns. What can we learn from those departures from routine? How predictable are they?
The first part of this project was concluded successfully and I presented an overview of the work done at the recent PyData London conference. Slides and abstract are below. We have also submitted a paper about this to the upcoming KDD conference and hopefully that gets accepted.
The conference itself was really good. Lots of friendly, smart folk (with quite a few familiar faces) and a stellar venue on the 39th floor of Canada Square One in Canary Wharf. Met many interesting people, got grilled on the industry panel, and even managed to fit in a quick lightning talk on the soon to be announced UK Community for Research Software Engineers. Thanks Ian & co for doing such a great organising job!
Abstract: Understanding human mobility patterns is a significant research endeavor that has recently received considerable attention. Developing the science to describe and predict how people move from one place to another during their daily lives promises to address a wide range of societal challenges: from predicting the spread of infectious diseases, improving urban planning, to devising effective emergency response strategies. This presentation will discuss a Bayesian framework to analyse an individual’s mobility patterns and identify departures from routine. It is able to detect both spatial and temporal departures from routine based on heterogeneous sensor data (GPS, Cell Tower, social media, ..) and outperforms existing state-of-the-art predictors. Applications include mobile digital assistants (e.g., Google Now), mobile advertising (e.g., LivingSocial), and crowdsourcing physical tasks (e.g., TaskRabbit).