Geographical Analysis

A Data Science Framework for Movement

Early View

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.

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Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.