University of Leeds Featured PhD Programmes
University of Glasgow Featured PhD Programmes
University of Liverpool Featured PhD Programmes
Catalysis Hub Featured PhD Programmes
University of Sheffield Featured PhD Programmes

Quantification and Modelling of Human Mobility Patterns under Partial or Spurious Information. Computer Science PhD

  • Full or part time
  • Application Deadline
    Monday, May 13, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The University of Exeter EPSRC DTP (Engineering and Physical Sciences Research Council Doctoral Training Partnership) is offering up to 4 fully funded doctoral studentships for 2019/20 entry. Students will be given sector-leading training and development with outstanding facilities and resources. Studentships will be awarded to outstanding applicants, the distribution will be overseen by the University’s EPSRC Strategy Group in partnership with the Doctoral College.

Prof. Ronaldo Menezes, Department of Computer Science, College of Engineering, Mathematics and Physical Sciences
Dr. Fillipo Simini, Department of Engineering Mathematics, University of Bristol

Project description:
The scientific analysis of the regularities observed in individual and collective human movement trajectories is of fundamental relevance to a wide range of areas—urban planning, wire- less communication design, the prevention of epidemics, and natural security issues such as detection of clandestine activity, to name but a few. The ubiquity of mobile phones and location-based social media has enabled the capture of comprehensive time-resolved individual information, offering a unique opportunity to observe and predict human activity on an unprecedented scale. Yet, major gaps remain in our understanding of human dynamics. In particular, the limitations involved with the development of theories and frameworks with any predictive power that are applicable when presented with incomplete or partial information, i.e. sparse, missing, or corrupted data. Inspired by recent breakthroughs in our research, in this proposal we aim to fill this gap through a novel combination of machine learning techniques, statistical physics, and the analysis of dynamical social networks.
Our proposal is grounded on the idea of recency as a universal phenomenon in human activity. Indeed, cognitive psychology has demonstrated a measurable enhancement in the accuracy of human beings recollecting newer events as compared to older ones. In previous work, we demonstrated that this effect strongly manifests itself in human movement due to the existence of strong biases towards the return to recently-visited locations, independent of the frequency of visit to said location. It is also likely that there is a strong interconnection between human movement and social ties, whereby people within the same social network are known to visit similar or identical locations. While there is limited quantitative understanding about the connection between these two facets of human activity, progress can be made by measuring the effect of recency in social tie formation. That is, the probability of forming social ties in the future given recent encounters between individuals. A comparison of the recency effect in movement and tie formation may serve as a clue towards connecting the dynamics of the two activities. Consequently, the first part of the proposal will deal with measuring the recency effect in social tie formation (if any) and compare it to our discovered statistical properties of recency in mobility.

The influence of recency on human mobility and the potential effect it may have in social tie formation, leads naturally to the question of observation windows—the temporal range that may be considered “recent” and the optimal period of observation required to accurately capture the dynamics of the two human activities. In other words, what level of predictability can one achieve as function of size of the observation window of past activities? Thus, the second part of this project will determine: whether the limits of predictability of these phenomena can be improved by combining the two. That is, we will determine whether the knowledge of an individual’s mobility can be enhanced by awareness of the associated social network.

Funding Notes

For successful eligible applicants the studentship comprises:

An index-linked stipend for up to 3.5 years full time (currently £14,777 per annum for 2018/19), pro-rata for part-time students.
Payment of University tuition fees (UK/EU)
Research Training Support Grant (RTSG) of £5,000 over 3.5 years, or pro-rata for part-time students

Related Subjects

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully

FindAPhD. Copyright 2005-2019
All rights reserved.