Exploring group-level human mobility from location-based social media check-in data

Liu, Zhongqiu; Yang, Chao

doi:10.1109/upinlbs.2018.8559796

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…This could be the movement of people between cities or the volume of trade between countries. Gao et al [37] proved that check-in data are a suitable and efficient model to predict human mobility using their gravity model, despite some other research works [29] where authors believe the gravity model [30] is not well-fitted to elucidate spatial interactions. In order to solve the challenge of low graph density, they used the particle swarm optimization (PSO) method to obtain the best fit.…”

Section: Related Workmentioning

confidence: 99%

“…They believe the results are applicable for decision-making policies. In another research, Liu et al [29] focused on examining the mobility differences between four different communities of Wuhan City, based on Sina Weibo data. They classified communities according to their check-in activities at specific areas and investigated their spatio-temporal behavior in six groups of categories.…”

Section: Related Workmentioning

confidence: 99%

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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%

Section: Related Workmentioning

confidence: 99%