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

TLDR

The study revisits spatial interaction and distance decay in human mobility, noting that individual movements may not follow the same decay due to ecological fallacy. The authors extracted nationwide inter‑urban movements from a half‑million‑person check‑in dataset across 370 Chinese cities and built a weighted spatial network to analyze trip patterns. The gravity model shows a power‑law distance decay that reproduces the exponential trip displacement distribution, and community detection reveals province‑aligned spatial clusters explained by differing intra‑ and inter‑province decay parameters.

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 million individuals and 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 well reproduces the exponential trip displacement distribution. However, due to the ecological fallacy issue, the movement of an individual may not obey the same distance decay effect. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially connected and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips.