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Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data

Overview of attention for article published in PLOS ONE, January 2014
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Title
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data
Published in
PLOS ONE, January 2014
DOI 10.1371/journal.pone.0086026
Pubmed ID
Authors

Yu Liu, Zhengwei Sui, Chaogui Kang, Yong Gao

Abstract

The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 334 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 1%
China 2 <1%
United Kingdom 2 <1%
Australia 2 <1%
Colombia 1 <1%
Austria 1 <1%
Germany 1 <1%
Denmark 1 <1%
Italy 1 <1%
Other 2 <1%
Unknown 316 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 96 29%
Student > Master 51 15%
Researcher 31 9%
Student > Doctoral Student 20 6%
Student > Bachelor 18 5%
Other 65 19%
Unknown 53 16%
Readers by discipline Count As %
Computer Science 54 16%
Social Sciences 43 13%
Engineering 35 10%
Earth and Planetary Sciences 33 10%
Environmental Science 22 7%
Other 73 22%
Unknown 74 22%