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School of Geography

Exploring Crowd-Sourced Individual - Level Data for the Understanding of Social Phenomena and the Enhancement of Individual -Level Models

Supervisors: Dr Nick Malleson and Dr Alison Heppenstall

It is difficult to analyse individual peoples’ behaviour and attitudes using traditional large-scale data sources, such as population censuses and social surveys, because they tend to deal with aggregate groups rather than individuals. They are focused on the attributes and characteristics of the population, rather than attitudes and behaviours, and they offer a snapshot view rather than a dynamic and continuous perspective. However, new data sources (such as that available through public social networks) contain a wealth of information about peoples’ spatio-temporal behaviour at an individual-level and have the potential to revolutionise our understanding of social phenomenon. These sources are commonly referred to as crowd-sourced data or volunteered geographical information.

These new data are particularly relevant in an era where episodes of social unrest are common but we still have little understanding of the underlying social dynamics that lead to the observed occurrences of unrest. This project will analyse new, large-scale, disaggregate data sources to gain a greater understanding of individual peoples’ daily behaviours and explore how these dynamics can potentially lead to civil unrest and other social phenomena.


The objectives of the research are to:

  • Identify knowledge gaps in the existing quantitative research on civil unrest and that could be filled through the use of novel (crowd-sourced) data sources
  • Identify specific spatio- temporal patterns of behaviour, attitudes and interactions that could not be elucidated through the analysis of ‘traditional’ aggregate sources (e.g. population censuses)
  • Develop methods – both analytical and visual – to express alternative modes of behaviour and their relationship to different geographical settings and social contexts
  • With reference to alternative sources of information, such as measures of community deprivation and socioeconomic statistics, explore the relationships between individual behaviour, socio-demographic characteristics and occurrences of civil unrest

Applications are invited from candidates from either computational science or social science backgrounds; candidates with a good understanding of criminology or socioeconomics are welcome, and training in programming, GIS and agent -based modelling can be provided


For information on funding opportunities click here


For project related enquiries please contact the supervisors.
For application enquiries please contact Jacqui Manton