Abstract
Space, time and the social realm are intrinsically linked. While an array of studies have tried to untangle these factors and their influence on human behaviour, hardly any have taken their effects into account at the same time. To disentangle these factors, we try to predict future encounters between students and assess how important social, spatial and temporal features are for prediction. We phrase our problem of predicting future encounters as a link-prediction problem and utilise set of Random Forest predictors for the prediction task. We use data collected by the Copenhagen network study; a study unique in scope and scale and tracks 847 students via mobile phones over the course of a whole academic year. We find that network and social features hold the highest discriminatory power for predicting future encounters.
| Original language | English |
|---|---|
| Number of pages | 18 |
| Journal | Environment and Planning B: Urban Analytics and City Science |
| Early online date | 4 Jun 2021 |
| DOIs | |
| Publication status | E-pub ahead of print - 4 Jun 2021 |
Bibliographical note
Funding Information:The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was in part supported by an Economic and Social Research Council PhD scholarship (ES/J50001X/1).
Publisher Copyright:
© The Author(s) 2021.
Keywords
- Geographic context
- link prediction
- social networks
ASJC Scopus subject areas
- Architecture
- Geography, Planning and Development
- Urban Studies
- Nature and Landscape Conservation
- Management, Monitoring, Policy and Law