Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science. (February 2022)

• Record Type: Journal Article
• Authors: Hinds, Joanne; Brown, Olivia; Smith, Laura G. E.; Piwek, Lukasz; Ellis, David A.; Joinson, Adam N.
• Is Part Of: Current directions in psychological science. Volume 31:Number 1(2022)
• Page Start: 88
• Page End: 95
• Publication Date: 2022-02
• Subjects: computational social science -- digital data -- human movement patterns -- mobility; Psychology -- Periodicals; 150
• DOI: 10.1177/09637214211042324
• Languages: English
• ISSNs: 0963-7214

Abstract

Understanding people's movement patterns has many important applications, from analyzing habits and social behaviors, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social-media interactions. Research has largely used data-driven modeling to detect patterns in people's movements, but such approaches are often devoid of psychological theory and fail to capitalize on what movement data can convey about associated thoughts, feelings, attitudes, and behavior. This article outlines trends in current research in this area and discusses how psychologists can better address theoretical and methodological challenges in future work while capitalizing on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve researchers' and policy makers' abilities to make predictions about individuals' or groups' movement patterns. At the same time, an interdisciplinary research agenda will provide greater capacity to advance psychological theory.