Discovering spatiotemporal flow patterns: where the origin–destination map meets empirical orthogonal function decomposition

ABSTRACT

Flows are usually represented as vector lines from origins to destinations and can reflect the movements of individuals or groups in space and time. Revealing and analyzing the spatiotemporal flow patterns are conducive to understanding information underlying movements. This paper proposes a new method called the OD – EOF (Origin – Destination – Empirical Orthogonal Function) to discover important spatiotemporal flow patterns on the premise of maintaining the pairwise connections between origins and destinations. We first construct a spatiotemporal flow matrix that contains connection information between origins and destinations and temporal flow information by adding a temporal dimension to the OD map. Then, we decompose the spatiotemporal flow matrix into spatial modes and corresponding time coefficients by EOF decomposition. The decomposition results depict the prominent spatial distribution of and temporal variation in flows, with most of the spatiotemporal characteristics highly concentrated into the first few spatial modes. The method is evaluated by five synthetic datasets and a user study and subsequently applied to analyze the impact of the COVID-19 pandemic on the spatiotemporal patterns of human mobility in China during the Spring Festival travel rush in 2020 and 2021. The results show the prominent spatiotemporal patterns of human mobility during these periods under the influence of the COVID-19 pandemic outbreak and the normalization of pandemic prevention and control.

KEYWORDS:

• Flows
• flow visualization
• spatiotemporal patterns
• OD map
• EOF decomposition
• COVID-19

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in Baidu Migration Big Data at https://qianxi.baidu.com and Didi Chuxing GAIA Initiative at https://gaia.didichuxing.com.

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2023.2171490

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [41901314]; Open Research Fund Program of Key Laboratory of Digital Mapping and Land Information Application Engineering, MNR [ZRZYBWD201902].