Ciravegna, F. orcid.org/0000-0001-5817-4810, Gao, J., Ingram, C. et al. (3 more authors) (2018) Mapping mobility to support crisis management. In: ISCRAM 2018 Conference Proceedings. 15th Annual Conference for Information Systems for Crisis Response and Management, 20-23 May 2018, Rochester NY, USA. Rochester Institute of Technology , pp. 305-316. ISBN 978-0-692-12760-5
Abstract
In this paper we describe a method and an infrastructure for rapid mapping of mobility patterns, based on a combination of a mobile mobility tracker, a large-scale data collection infrastructure, and a data and visual analytics tool. The combination of the three enables mapping everyday mobility patterns for decision makers, e.g. city council, motorways authorities, etc. and can support emergency responders in improving their preparedness and the recovery in the aftermath of a crisis. The technology is currently employed over very large scale: (i) in England it is used by a public body to incentivise physical mobility (400,000 app downloads and hundreds of millions of data point since September 2017); (ii) in Sheffield UK, through the MoveMore initiative, tracking active mobility of users (5,000 downloads); and (iii) the European project SETA, to track multimodal mobility patterns in three cities (Birmingham, Santander and Turin).
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Rochester Institute of Technology. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 688082 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 May 2018 14:13 |
Last Modified: | 13 Feb 2019 16:28 |
Published Version: | http://idl.iscram.org/files/fabiociravegna/2018/15... |
Status: | Published |
Publisher: | Rochester Institute of Technology |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130788 |