Movement is the driving force behind the form and function of many ecological and human systems. Identification and analysis of movement patterns that may relate to the behavior of individuals and their interactions is a fundamental first step in understanding these systems. With advances in IoT and the ubiquity of smart connected sensors to collect movement and contextual data, we now have access to a wealth of geo‐enriched high‐resolution tracking data. These data promise new forms of knowledge and insight into movement of humans, animals, and goods, and hence can increase our understanding of complex spatiotemporal processes such as disease outbreak, urban mobility, migration, and human‐species interaction. To take advantage of the evolution in our data, we need a revolution in how we visualize, model, and analyze movement as a multidimensional process that involves space, time, and context. This paper introduces a data science paradigm with the aim of advancing research on movement.

中文翻译：

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移动数据科学框架

运动是许多生态系统和人类系统的形式和功能的原动力。识别和分析可能与个人行为及其相互作用有关的运动模式是理解这些系统的基本第一步。随着物联网的发展和智能连接传感器的广泛使用，以收集运动和上下文数据，我们现在可以访问大量地理丰富的高分辨率跟踪数据。这些数据为人类，动物和商品的移动提供了新的知识形式和见解，因此可以加深我们对复杂的时空过程的理解，例如疾病暴发，城市流动性，迁徙和人类与物种之间的相互作用。要利用数据的演变，我们需要在可视化，建模，并将运动分析为涉及空间，时间和环境的多维过程。本文介绍了一种数据科学范式，旨在促进对运动的研究。