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

Yang, Chao; Xiao, Meng; Ding, Xuan; Tian, Wenyan; Zhai, Ya; Chen, Jie; Liu, Lei; Ye, Xinyue

doi:10.1080/14498596.2017.1421487

Cited by 28 publications

(19 citation statements)

References 31 publications

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“…They concluded that time and speed patterns are beneficial for the classification of trajectories. In a similar work, Chao et al [31] studied Sina Weibo data to analyse students' activities on campus. Using the Chinese University of Geosciences Wuhan (CUG Wuhan) community, as a case study, they found out the influence of distance on students' spatio-temporal mobility patterns.…”

Section: Related Workmentioning

confidence: 99%

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

Ebrahimpour

Wan

García

et al. 2020

IJGI

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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.

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Section: Related Workmentioning

confidence: 99%

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

Ebrahimpour

Wan

García

et al. 2020

IJGI

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“…As shown in (9), the estimation procedure requires repeated evaluation of the multiplication between a 1 × 1 matrix and a 1 dimensional vector; the computation complexity is of the order 2 1 . Although 1 can be taken as much smaller than the number of nodes in observations ( ), it still has to increase as increases.…”

Section: a Fast Algorithmmentioning

confidence: 99%

“…Flow data has been widely studied by different disciplines [1][2][3][4][5][6]. Especially in recent years, the development of internet makes an increasing amount of flow data sets publicly available, among them new types of flows are emerging and attracted more and more attentions from scholars [7,8].…”

Section: Introductionmentioning

confidence: 99%

Mining the Hidden Link Structure from Distribution Flows for a Spatial Social Network

et al. 2019

Self Cite

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This study aims at developing a non-(semi-)parametric method to extract the hidden network structure from the {0,1}-valued distribution flow data with missing observations on the links between nodes. Such an input data type widely exists in the studies of information propagation process, such as the rumor spreading through social media. In that case, a social network does exist as the media of the spreading process, but its link structure is completely unobservable; therefore, it is important to make inference of the structure (links) of the hidden network. Unlike the previous studies on this topic which only consider abstract networks, we believe that apart from the link structure, different social-economic features and different geographic locations of nodes can also play critical roles in shaping the spreading process, which has to be taken into account. To uncover the hidden link structure and its dependence on the external social-economic features of the node set, a multidimensional spatial social network model is constructed in this study with the spatial dimension large enough to account for all influential social-economic factors. Based on the spatial network, we propose a nonparametric mean-field equation to govern the rumor spreading process and apply the likelihood estimator to make inference of the unknown link structure from the observed rumor distribution flows. Our method turns out easily extendible to cover the class of block networks that are useful in most real applications. The method is tested through simulated data and demonstrated on a data set of rumor spreading on Twitter.

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“…While Gao and Liu [96] argued that temporal features and ranking of a user's geo-location history are considered to be irrelevant with the integration of human mobility in LBSN. Yang et al [97] explored check-in behavior and mobility patterns by analyzing the spatiotemporal distribution of geotagged social media data messages and activity patterns. Moreover, References [92,98,99] analyzed the large LBSN datasets to study the variation of urban spaces and observed the spatial characteristics of the social networks, which may arise in LBSN users.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

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

Cited by 28 publications

References 31 publications

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

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

Mining the Hidden Link Structure from Distribution Flows for a Spatial Social Network

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

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