A Data Science Framework for Movement

Somayeh Dodge

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)92-112
Number of pages21
JournalGeographical Analysis
Volume53
Issue number1
DOIs
StatePublished - Jan 2021

Bibliographical note

Publisher Copyright:
© 2019 The Ohio State University

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Fingerprint Dive into the research topics of 'A Data Science Framework for Movement'. Together they form a unique fingerprint.

Cite this