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.

了解人们的运动模式有许多重要的应用，从分析习惯和社会行为到预测疾病的传播。现在，有关这些运动及其位置的信息已深深嵌入通过智能手机、可穿戴传感器和社交媒体互动生成的数字数据中。研究主要使用数据驱动的模型来检测人们的运动模式，但这种方法通常缺乏心理学理论，也无法利用运动数据可以传达的有关思想、感受、态度和行为的信息。本文概述了该领域当前研究的趋势，并讨论了心理学家如何更好地应对未来工作中的理论和方法挑战，同时利用数字运动数据带来的机会。我们认为，结合心理学和数据科学的方法将提高研究人员和政策制定者预测个人或群体运动模式的能力。同时，跨学科研究议程将为推进心理学理论提供更大的能力。