A novel metric to measure spatio-temporal proximity

Abstract

The present study aims to infer individuals’ social networks from their spatio-temporal behavior acquired via wearable sensors. Previously proposed static network metrics (e.g., centrality measures) cannot capture the complex temporal patterns in dynamic settings (e.g., children’s play in a schoolyard). Moreover, existing temporal metrics overlook the spatial context of interactions. This study aims first to introduce a novel metric on social networks in which both temporal and spatial aspects of the network are considered to unravel the spatio-temporal dynamics of human behavior. This metric can be used to understand how individuals utilize space to access their network, and how individuals are accessible by their network. We evaluate the proposed method on real data to show how the proposed metric impacts performance of a clustering task. Second, this metric is used to interpret interactions in a real-world dataset collected from children playing in a playground. Moreover, by considering spatial features, this metric provides unique knowledge of the spatio-temporal accessibility of individuals in a community, and more clearly captures pairwise accessibility compared with existing temporal metrics. Thus, it can facilitate domain scientists interested in understanding social behavior in the spatio-temporal context. Furthermore, We make our collected dataset publicly available for further research.