scholarly journals From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Yuan Liao ◽  
Sonia Yeh ◽  
Gustavo S. Jeuken
Keyword(s):  
Social Media ◽  
Multiple Scales ◽  
Human Mobility ◽  
Activity Patterns ◽  
Population Heterogeneity ◽  
Easy Access ◽  
Long Distance ◽  
Transport Modelling ◽  
Social Media Data ◽  
Media Data

AbstractThis 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.

scholarly journals Towards Detecting Social Events by Mining Geographical Patterns with VGI Data

2018 ◽  
Vol 7 (12) ◽  
pp. 481
Author(s):  
Zhewei Liu ◽  
Xiaolin Zhou ◽  
Wenzhong Shi ◽  
Anshu Zhang
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Human Mobility ◽  
Ground Truth ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Geographical Patterns ◽  
Outlier Test ◽  
Social Events ◽  
Media Data

Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts” of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.

scholarly journals Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Donal Bisanzio ◽  
Moritz U.G. Kraemer ◽  
Isaac I. Bogoch ◽  
Thomas Brewer ◽  
John S Brownstein ◽  
...  
Keyword(s):  
Social Media ◽  
Human Mobility ◽  
Mainland China ◽  
Vulnerability Index ◽  
Global Scale ◽  
Study Cohort ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Current Outbreak ◽  
Media Data

As of February 27, 2020, 82,294 confirmed cases of coronavirus disease (COVID-19) have been reported since December 2019, including 2,804 deaths, with cases reported throughout China, as well as in 45 international locations outside of mainland China. We predict the spatiotemporal spread of reported COVID- 19 cases at the global level during the first few weeks of the current outbreak by analyzing openly available geolocated Twitter social media data. Human mobility patterns were estimated by analyzing geolocated 2013–2015 Twitter data from users who had: i) tweeted at least twice on consecutive days from Wuhan, China, between November 1, 2013, and January 28, 2014, and November 1, 2014, and January 28, 2015; and ii) left Wuhan following their second tweet during the time period under investigation. Publicly available COVID-19 case data were used to investigate the correlation among cases reported during the current outbreak, locations visited by the study cohort of Twitter users, and airports with scheduled flights from Wuhan. Infectious Disease Vulnerability Index (IDVI) data were obtained to identify the capacity of countries receiving travellers from Wuhan to respond to COVID-19. Our study cohort comprised 161 users. Of these users, 133 (82.6%) posted tweets from 157 Chinese cities (1,344 tweets) during the 30 days after leaving Wuhan following their second tweet, with a median of 2 (IQR= 1–3) locations visited and a mean distance of 601 km (IQR= 295.2–834.7 km) traveled. Of our user cohort, 60 (37.2%) traveled abroad to 119 locations in 28 countries. Of the 82 COVID-19 cases reported outside China as of January 30, 2020, 54 cases had known geolocation coordinates and 74.1% (40 cases) were reported less than 15 km (median = 7.4 km, IQR= 2.9–285.5 km) from a location visited by at least one of our study cohort’s users. Countries visited by the cohort’s users and which have cases reported by January 30, 2020, had a median IDVI equal to 0.74. We show that social media data can be used to predict the spatiotemporal spread of infectious diseases such as COVID-19. Based on our analyses, we anticipate cases to be reported in Saudi Arabia and Indonesia; additionally, countries with a moderate to low IDVI (i.e. ≤0.7) such as Indonesia, Pakistan, and Turkey should be on high alert and develop COVID- 19 response plans as soon as permitting.

scholarly journals Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

2020 ◽  
Vol 9 (2) ◽  
pp. 125 ◽  
Author(s):  
Zeinab Ebrahimpour ◽  
Wanggen Wan ◽  
José Luis Velázquez García ◽  
Ofelia Cervantes ◽  
Li Hou
Keyword(s):  
Social Media ◽  
Large Scale ◽  
City Planning ◽  
Smart Cities ◽  
Human Mobility ◽  
Location Based Services ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Planning Decisions ◽  
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 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.

Exploring human mobility patterns using geo-tagged social media data at the group level

2018 ◽  
Vol 64 (2) ◽  
pp. 221-238 ◽  
Author(s):  
Chao Yang ◽  
Meng Xiao ◽  
Xuan Ding ◽  
Wenwen Tian ◽  
Yong Zhai ◽  
...  
Keyword(s):  
Social Media ◽  
Human Mobility ◽  
Group Level ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Media Data

scholarly journals Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data

2021 ◽  
Vol 10 (12) ◽  
pp. 834
Author(s):  
Feng Gao ◽  
Guanping Huang ◽  
Shaoying Li ◽  
Ziwei Huang ◽  
Lei Chai
Keyword(s):  
Social Media ◽  
Spatial Structure ◽  
Human Activities ◽  
Temporal Structure ◽  
Activity Patterns ◽  
Urban Spatial Structure ◽  
Social Media Data ◽  
Function Identification ◽  
Effective Principal ◽  
Media Data

Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, is essential but challenging. This study presented a novel approach that integrated the eigendecomposition method and k-means clustering for inferring building function types according to location-based social media data, Tencent User Density (TUD) data. The eigendecomposition approach was used to extract the effective principal components (PCs) to characterize the temporal patterns of human activities at building level. This was combined with k-means clustering for building function identification. The proposed method was applied to the study area of Tianhe district, Guangzhou, one of the largest cities in China. The building inference results were verified through the random sampling of AOI data and street views in Baidu Maps. The accuracy for all building clusters exceeded 83.00%. The results indicated that the eigendecomposition approach is effective for revealing the temporal structure inherent in human activities, and the proposed eigendecomposition-k-means clustering approach is reliable for building function identification based on social media data.

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