How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern

The context in which a moving object moves contributes to the movement pattern observed. Likewise, the movement pattern reflects the properties of the movement context. In particular, big events influence human mobility depending on the dynamics of the events. However, this influence has not been ex...

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Main Authors: Jean Damascène Mazimpaka, Sabine Timpf
Format: Article
Language:English
Published: MDPI AG 2017-01-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Online Access:http://www.mdpi.com/2220-9964/6/1/15
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spelling doaj-3f9946a298dc412a9c2f65dce99e9f7f2020-11-24T23:15:50ZengMDPI AGISPRS International Journal of Geo-Information2220-99642017-01-01611510.3390/ijgi6010015ijgi6010015How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility PatternJean Damascène Mazimpaka0Sabine Timpf1Geoinformatics Group, University of Augsburg, Alter Postweg 118, 86159 Augsburg, GermanyGeoinformatics Group, University of Augsburg, Alter Postweg 118, 86159 Augsburg, GermanyThe context in which a moving object moves contributes to the movement pattern observed. Likewise, the movement pattern reflects the properties of the movement context. In particular, big events influence human mobility depending on the dynamics of the events. However, this influence has not been explored to understand big events. In this paper, we propose a methodology for learning about big events from human mobility pattern. The methodology involves extracting and analysing the stopping, approaching, and moving-away interactions between public transportation vehicles and the geographic context. The analysis is carried out at two different temporal granularity levels to discover global and local patterns. The results of evaluating this methodology on bus trajectories demonstrate that it can discover occurrences of big events from mobility patterns, roughly estimate the event start and end time, and reveal the temporal patterns of arrival and departure of event attendees. This knowledge can be usefully applied in transportation and event planning and management.http://www.mdpi.com/2220-9964/6/1/15mobility datageographic contextbig eventsspatiotemporal analysis
collection DOAJ
language English
format Article
sources DOAJ
author Jean Damascène Mazimpaka
Sabine Timpf
spellingShingle Jean Damascène Mazimpaka
Sabine Timpf
How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern
ISPRS International Journal of Geo-Information
mobility data
geographic context
big events
spatiotemporal analysis
author_facet Jean Damascène Mazimpaka
Sabine Timpf
author_sort Jean Damascène Mazimpaka
title How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern
title_short How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern
title_full How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern
title_fullStr How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern
title_full_unstemmed How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern
title_sort how they move reveals what is happening: understanding the dynamics of big events from human mobility pattern
publisher MDPI AG
series ISPRS International Journal of Geo-Information
issn 2220-9964
publishDate 2017-01-01
description The context in which a moving object moves contributes to the movement pattern observed. Likewise, the movement pattern reflects the properties of the movement context. In particular, big events influence human mobility depending on the dynamics of the events. However, this influence has not been explored to understand big events. In this paper, we propose a methodology for learning about big events from human mobility pattern. The methodology involves extracting and analysing the stopping, approaching, and moving-away interactions between public transportation vehicles and the geographic context. The analysis is carried out at two different temporal granularity levels to discover global and local patterns. The results of evaluating this methodology on bus trajectories demonstrate that it can discover occurrences of big events from mobility patterns, roughly estimate the event start and end time, and reveal the temporal patterns of arrival and departure of event attendees. This knowledge can be usefully applied in transportation and event planning and management.
topic mobility data
geographic context
big events
spatiotemporal analysis
url http://www.mdpi.com/2220-9964/6/1/15
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