Abstract

Recent technological advances in movement data acquisition have enabled researchers in many disciplines to study movement at increasingly detailed spatial and temporal scales. Yet there is little overlap in the sharing of methods and models between disciplines, despite similar research objectives and data models. Attempts to bridge this gap are leading towards the establishment of an overarching interdisciplinary science, termed the Integrated Science of Movement. Here we present opportunities and challenges of this process and outline the crucial role that GIScience as a discipline with a focus on space, place, and time can play in the integrated science of movement.

Highlights

  • Movement is a basic property of human and animal behavior and is studied across many disciplines

  • Studies of human mobility are found across many disciplines: Geographic Information Science (GIScience), quantitative geography, transportation science, computer science, physics, and public health have all contributed to study of human mobility

  • We argue that GIScientists are in a unique position to support the development of the integrated science of movement through our linkages across disciplines

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Summary

Introduction

Movement is a basic property of human and animal behavior and is studied across many disciplines. DEMS AR, LONG, SIŁA-NOWICKA many problems (and solutions!) overlap This has led to calls to bridge this gap, both in terms of establishing a theoretical data science framework for movement [10] and in terms of converging theories and concepts from the animal and human domains into a new discipline—termed the integrated science of movement [21]. This will require efforts to move out of our respective disciplinary silos and build connections, a process that is already starting through interdisciplinary collaborations. We argue that GIScientists are in a unique position to support the development of the integrated science of movement through our linkages across disciplines

Big data opportunities: new sources and data fusion
Big data problems: geoprivacy
Big models: machine learning and artificial intelligences
Big problems require interdisciplinary collaborations
Places and pathways
Timing space and spacing time
Scales of movement
Conclusions
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