Movement Research in the Era of Big Data, AI, and Open Science
Somayeh Dodge (University of California)
Robert Weibel (Universitat Zurich)
Christophe Claramunt (Naval Academy Research Institute Lanvéoc)
Gavin McArdle (University College Dublin)
Zhiyong Zhou (University of Wisconsin-Madison)
Yanan Xin (TU Delft - Civil Engineering & Geosciences)
Anita Graser (Austrian Institute of Technology)
Tumasch Reichenbacher (Universitat Zurich)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The increasing availability of movement data and rapid advancements in artificial intelligence have opened new frontiers in movement pattern analysis with implications for a wide range of domains such as urban planning, transportation management, public health, pandemic management, disaster response, and wildlife conservation. The sensitive nature and heterogeneity of novel movement data, however, create technical and methodological challenges around data governance and open movement analytics. This article explores these challenges and identifies key opportunities for the geography discipline to drive analytical advances in human mobility research and open science. In this vision, the geographic perspective is emphasized in future developments of artificial intelligence for movement research for its ability to enrich mobility analyses with spatial and temporal context. Strategies for effective governance of large-scale mobility data are discussed, with special attention to fostering open science and reproducible research practices to ensure transparency and transferability in this rapidly evolving field.
help_outline