Inferring spatial interaction patterns from sequential snapshots of spatial distributions

ABSTRACT

Spatial interactions underlying consecutive sequential snapshots of spatial distributions, such as the migration flows underlying temporal population snapshots, can reflect the details of spatial evolution processes. In the era of big data, we have access to individual-level data, but the acquisition of high-quality spatial interaction data remains a challenging problem. Most research has been focused on distributions of movable objects or the modelling of spatial interaction patterns, with few attempts to identify hidden spatial interaction patterns from temporal transitions of spatial distributions. In this article, we introduced an approach to infer spatial interaction patterns from sequential snapshots of spatial population distributions by incorporating linear programming and the spatial constraints of human movement. Experiments using synthetic data were conducted using four simple scenarios to explore the characteristics of our method. The proposed method was used to extract interurban flows of migrants during the Chinese Spring Festival in 2016. Our research demonstrated the feasibility of using discrete multi-temporal snapshots of population distributions in space to infer spatial interaction patterns and offered a general analytical framework from snapshot data to spatial interaction patterns.

KEYWORDS:

• Spatial interaction
• spatial heterogeneity
• linear programming
• migration flow
• big geodata

Acknowledgements

The authors thank X. Chen, Y. Wang and D. Tong for their advice; L. Dong for providing the Baidu migration data and professor May Yuan, professor Shawn Laffan and the anonymous referees for their insightful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Although the daily active users and population are close in quantity, we are not saying that the data set is a near perfect sampling of China’s population or that everyone in China is using Tencent apps. However, we do treat the number of users as a proxy for population.

2. This data set is aggregated from individual locations collected by Baidu Mobile Apps, where a migration is recorded if there is a change in residential city for a person from 2015Q4 to 2016Q1.

Additional information

Funding

This research was supported by the National Key Research and Development Program of China [Grant Number: 2017YFB0503602] and the National Natural Science Foundation of China [Grant Number: 41625003].