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

By Yu Liu, Zhengwei Sui, Chaogui Kang, Yong Gao. PLoS ONE, 2013

380 citations recorded by the source corpus.

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.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/246d1ec848c0e6879e0cdf2993eeefe48ca48029

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistical and Nonlinear Physics

1. Li Gong
2. Xi Liu
3. Yu Liu
4. Yongxi Gong

In Transportation

1. Li Gong
2. Xi Liu
3. Yu Liu
4. Yongxi Gong

Citations in the corpus

• Revealing daily travel patterns and city structure with taxi trip data (2013, background): “Keywords: GPS-enabled taxi data, intra-urban human mobility, urban structure, land use condition, Shanghai”