Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data
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 plan...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/9/2/125 |
| _version_ | 1819076446675533824 |
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| author | Zeinab Ebrahimpour Wanggen Wan José Luis Velázquez García Ofelia Cervantes Li Hou |
| author_facet | Zeinab Ebrahimpour Wanggen Wan José Luis Velázquez García Ofelia Cervantes Li Hou |
| author_sort | Zeinab Ebrahimpour |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T18:41:26Z |
| format | Article |
| id | doaj.art-dc57f6fc36184364be8731b60d0a6bfb |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-21T18:41:26Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-dc57f6fc36184364be8731b60d0a6bfb2022-12-21T18:54:01ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-02-019212510.3390/ijgi9020125ijgi9020125Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media DataZeinab Ebrahimpour0Wanggen Wan1José Luis Velázquez García2Ofelia Cervantes3Li Hou4School of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaDepartment of Computer Science, Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla 72840, MexicoDepartment of Computing, Electronics, and Mechatronics, Universidad de las Américas Puebla, Puebla 72810, MexicoSchool of Information Engineering, Huangshan University, Huangshan 245041, ChinaSocial 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.https://www.mdpi.com/2220-9964/9/2/125human mobilitylocation-based social networkgeographic mobility patterns |
| spellingShingle | Zeinab Ebrahimpour Wanggen Wan José Luis Velázquez García Ofelia Cervantes Li Hou Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data ISPRS International Journal of Geo-Information human mobility location-based social network geographic mobility patterns |
| title | Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data |
| title_full | Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data |
| title_fullStr | Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data |
| title_full_unstemmed | Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data |
| title_short | Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data |
| title_sort | analyzing social geographic human mobility patterns using large scale social media data |
| topic | human mobility location-based social network geographic mobility patterns |
| url | https://www.mdpi.com/2220-9964/9/2/125 |
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