From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data
Abstract This paper examines the population heterogeneity of travel behaviours from a combined perspective of individual actors and collective behaviours. We use a social media dataset of 652,945 geotagged tweets generated by 2,933 Swedish Twitter users covering an average time span of 3.6 years. No...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-11-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1140/epjds/s13688-019-0212-x |
| _version_ | 1818116860905783296 |
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| author | Yuan Liao Sonia Yeh Gustavo S. Jeuken |
| author_facet | Yuan Liao Sonia Yeh Gustavo S. Jeuken |
| author_sort | Yuan Liao |
| collection | DOAJ |
| description | Abstract This paper examines the population heterogeneity of travel behaviours from a combined perspective of individual actors and collective behaviours. We use a social media dataset of 652,945 geotagged tweets generated by 2,933 Swedish Twitter users covering an average time span of 3.6 years. No explicit geographical boundaries, such as national borders or administrative boundaries, are applied to the data. We use spatial features, such as geographical characteristics and network properties, and apply a clustering technique to reveal the heterogeneity of geotagged activity patterns. We find four distinct groups of travellers: local explorers (78.0%), local returners (14.4%), global explorers (7.3%), and global returners (0.3%). These groups exhibit distinct mobility characteristics, such as trip distance, diffusion process, percentage of domestic trips, visiting frequency of the most-visited locations, and total number of geotagged locations. Geotagged social media data are gradually being incorporated into travel behaviour studies as user-contributed data sources. While such data have many advantages, including easy access and the flexibility to capture movements across multiple scales (individual, city, country, and globe), more attention is still needed on data validation and identifying potential biases associated with these data. We validate against the data from a household travel survey and find that despite good agreement of trip distances (one-day and long-distance trips), we also find some differences in home location and the frequency of international trips, possibly due to population bias and behaviour distortion in Twitter data. Future work includes identifying and removing additional biases so that results from geotagged activity patterns may be generalised to human mobility patterns. This study explores the heterogeneity of behavioural groups and their spatial mobility including travel and day-to-day displacement. The findings of this paper could be relevant for disease prediction, transport modelling, and the broader social sciences. |
| first_indexed | 2024-12-11T04:29:14Z |
| format | Article |
| id | doaj.art-805941235c3e464285831dd49851c3d2 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-11T04:29:14Z |
| publishDate | 2019-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-805941235c3e464285831dd49851c3d22022-12-22T01:20:55ZengSpringerOpenEPJ Data Science2193-11272019-11-018112210.1140/epjds/s13688-019-0212-xFrom individual to collective behaviours: exploring population heterogeneity of human mobility based on social media dataYuan Liao0Sonia Yeh1Gustavo S. Jeuken2Department of Space, Earth and Environment, Division of Physical Resource Theory, Chalmers University of TechnologyDepartment of Space, Earth and Environment, Division of Physical Resource Theory, Chalmers University of TechnologySchool of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of TechnologyAbstract This paper examines the population heterogeneity of travel behaviours from a combined perspective of individual actors and collective behaviours. We use a social media dataset of 652,945 geotagged tweets generated by 2,933 Swedish Twitter users covering an average time span of 3.6 years. No explicit geographical boundaries, such as national borders or administrative boundaries, are applied to the data. We use spatial features, such as geographical characteristics and network properties, and apply a clustering technique to reveal the heterogeneity of geotagged activity patterns. We find four distinct groups of travellers: local explorers (78.0%), local returners (14.4%), global explorers (7.3%), and global returners (0.3%). These groups exhibit distinct mobility characteristics, such as trip distance, diffusion process, percentage of domestic trips, visiting frequency of the most-visited locations, and total number of geotagged locations. Geotagged social media data are gradually being incorporated into travel behaviour studies as user-contributed data sources. While such data have many advantages, including easy access and the flexibility to capture movements across multiple scales (individual, city, country, and globe), more attention is still needed on data validation and identifying potential biases associated with these data. We validate against the data from a household travel survey and find that despite good agreement of trip distances (one-day and long-distance trips), we also find some differences in home location and the frequency of international trips, possibly due to population bias and behaviour distortion in Twitter data. Future work includes identifying and removing additional biases so that results from geotagged activity patterns may be generalised to human mobility patterns. This study explores the heterogeneity of behavioural groups and their spatial mobility including travel and day-to-day displacement. The findings of this paper could be relevant for disease prediction, transport modelling, and the broader social sciences.http://link.springer.com/article/10.1140/epjds/s13688-019-0212-xGeotagged activity patternsIndividual mobilityData miningHierarchical clustering |
| spellingShingle | Yuan Liao Sonia Yeh Gustavo S. Jeuken From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data EPJ Data Science Geotagged activity patterns Individual mobility Data mining Hierarchical clustering |
| title | From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data |
| title_full | From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data |
| title_fullStr | From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data |
| title_full_unstemmed | From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data |
| title_short | From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data |
| title_sort | from individual to collective behaviours exploring population heterogeneity of human mobility based on social media data |
| topic | Geotagged activity patterns Individual mobility Data mining Hierarchical clustering |
| url | http://link.springer.com/article/10.1140/epjds/s13688-019-0212-x |
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