{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T05:52:31Z","timestamp":1770616351925,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,18]],"date-time":"2017-03-18T00:00:00Z","timestamp":1489795200000},"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>Social media data provide a great opportunity to investigate event flow in cities. Despite the advantages of social media data in these investigations, the data heterogeneity and big data size pose challenges to researchers seeking to identify useful information about events from the raw data. In addition, few studies have used social media posts to capture how events develop in space and time. This paper demonstrates an efficient approach based on machine learning and geovisualization to identify events and trace the development of these events in real-time. We conducted an empirical study to delineate the temporal and spatial evolution of a natural event (heavy precipitation) and a social event (Pope Francis\u2019 visit to the US) in the New York City\u2014Washington, DC regions. By investigating multiple features of Twitter data (message, author, time, and geographic location information), this paper demonstrates how voluntary local knowledge from tweets can be used to depict city dynamics, discover spatiotemporal characteristics of events, and convey real-time information.<\/jats:p>","DOI":"10.3390\/ijgi6030088","type":"journal-article","created":{"date-parts":[[2017,3,20]],"date-time":"2017-03-20T11:39:09Z","timestamp":1490009949000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data"],"prefix":"10.3390","volume":"6","author":[{"given":"Xiaolu","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Geology and Geography, Georgia Southern University, P.O. Box 8149, Statesboro, GA 30460, USA"}]},{"given":"Chen","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Wyoming, 1000 E. University Ave., Laramie, WY 82071, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,18]]},"reference":[{"key":"ref_1","unstructured":"Statistics TU Twitter Usage Statistics n.d.. Available online: http:\/\/www.internetlivestats.com\/twitter-statistics."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hasan, S., Zhan, X., and Ukkusuri, S.V. (2013, January 11\u201314). Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, Chicago, IL, USA.","DOI":"10.1145\/2505821.2505823"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Walther, M., and Kaisser, M. (2013, January 24\u201327). Geo-spatial event detection in the twitter stream. 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