Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data
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 ad...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-03-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2220-9964/6/3/88 |
| _version_ | 1818020910975680512 |
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| author | Xiaolu Zhou Chen Xu |
| author_facet | Xiaolu Zhou Chen Xu |
| author_sort | Xiaolu Zhou |
| collection | DOAJ |
| description | 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’ visit to the US) in the New York City—Washington, 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. |
| first_indexed | 2024-04-14T08:11:50Z |
| format | Article |
| id | doaj.art-66c84ef42b8f489fbb4497f7d900e6f7 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-04-14T08:11:50Z |
| publishDate | 2017-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-66c84ef42b8f489fbb4497f7d900e6f72022-12-22T02:04:31ZengMDPI AGISPRS International Journal of Geo-Information2220-99642017-03-01638810.3390/ijgi6030088ijgi6030088Tracing the Spatial-Temporal Evolution of Events Based on Social Media DataXiaolu Zhou0Chen Xu1Department of Geology and Geography, Georgia Southern University, P.O. Box 8149, Statesboro, GA 30460, USADepartment of Geography, University of Wyoming, 1000 E. University Ave., Laramie, WY 82071, USASocial 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’ visit to the US) in the New York City—Washington, 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.http://www.mdpi.com/2220-9964/6/3/88social media datageographic information systemsspace-time eventspatial analysis |
| spellingShingle | Xiaolu Zhou Chen Xu Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data ISPRS International Journal of Geo-Information social media data geographic information systems space-time event spatial analysis |
| title | Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data |
| title_full | Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data |
| title_fullStr | Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data |
| title_full_unstemmed | Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data |
| title_short | Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data |
| title_sort | tracing the spatial temporal evolution of events based on social media data |
| topic | social media data geographic information systems space-time event spatial analysis |
| url | http://www.mdpi.com/2220-9964/6/3/88 |
| work_keys_str_mv | AT xiaoluzhou tracingthespatialtemporalevolutionofeventsbasedonsocialmediadata AT chenxu tracingthespatialtemporalevolutionofeventsbasedonsocialmediadata |