{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:10:02Z","timestamp":1760242202668,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,12]],"date-time":"2017-01-12T00:00:00Z","timestamp":1484179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/ijgi6010015","type":"journal-article","created":{"date-parts":[[2017,1,12]],"date-time":"2017-01-12T10:02:08Z","timestamp":1484215328000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5511-9533","authenticated-orcid":false,"given":"Jean","family":"Mazimpaka","sequence":"first","affiliation":[{"name":"Geoinformatics Group, University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabine","family":"Timpf","sequence":"additional","affiliation":[{"name":"Geoinformatics Group, University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1080\/13658816.2015.1132424","article-title":"Analysis of movement data","volume":"30","author":"Dodge","year":"2016","journal-title":"Int. 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