Open AccessThe role of space, time and sociability in predicting social encounters
Author(s) -
Christoph Stich,
Emmanouil Tranos,
Mirco Musolesi,
Sune Lehmann
Publication year - 2021
Publication title -
environment and planning. b, urban analytics and city science/environment and planning. b, urban analytics and city science
Language(s) - English
Resource type - Journals
eISSN - 2399-8091
pISSN - 2399-8083
DOI - 10.1177/23998083211016871
Subject(s) - predictive power , realm , set (abstract data type) , phrase , space (punctuation) , task (project management) , scope (computer science) , computer science , scale (ratio) , data science , psychology , cognitive psychology , artificial intelligence , geography , cartography , philosophy , management , archaeology , epistemology , economics , programming language , operating system
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.