| Title |
Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data |
| ID_Doc |
44708 |
| Authors |
Ebrahimpour, Z; Wan, WG; García, JLV; Cervantes, O; Hou, L |
| Title |
Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data |
| Year |
2020 |
| Published |
Isprs International Journal Of Geo-Information, 9, 2 |
| DOI |
10.3390/ijgi9020125 |
| Abstract |
Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen's behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people's lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens. |
| Author Keywords |
human mobility; location-based social network; geographic mobility patterns |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000522449700065 |
| WoS Category |
Computer Science, Information Systems; Geography, Physical; Remote Sensing |
| Research Area |
Computer Science; Physical Geography; Remote Sensing |
| PDF |
https://www.mdpi.com/2220-9964/9/2/125/pdf?version=1582951871
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