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.
Highlights
A number of social media websites that support geo-tagged information submission and sharing have been recently introduced and achieved great commercial success
We investigate the implications of distance decay effect in regionalizing the study area based on spatial interactions between cities
We construct a spatially-embedded interaction network and introduce the gravity model to quantify the distance impact behind the network and to examine whether the distance decay can reproduce the observed displacement distribution, which is critical in human mobility studies
Summary
A number of social media websites that support geo-tagged information submission and sharing have been recently introduced and achieved great commercial success. Human mobility patterns have drawn much attention in the areas of physics [7], geography [8,9], and computer science [10], with the availability of multi-sourced trajectory data [11] These studies either do not distinguish motion patterns at different spatial scales or focus on intra-urban trip patterns. We use a social media check-in data set submitted by about half millions users to study the inter-urban trip patterns. We try to link patterns at the collective level of spatial interactions versus the individual level of human movements, and to make a comparison with intra-urban patterns revealed from mobile phone or taxi data sets. We investigate the implications of distance decay effect in regionalizing the study area based on spatial interactions between cities
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
