Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data.
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.
Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, China.
Yu
Y
Liu
Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, China.
Zhengwei
Z
Sui
Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, China.
Chaogui
C
Kang
Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, China.
Yong
Y
Gao
D002681:China; D002947:Cities; D005843:Geography; D006801:Humans; D008962:Models, Theoretical; D009068:Movement; D012111:Residence Characteristics; D061108:Social Media; D062206:Spatial Analysis; D014195:Travel