Big Data-Driven Approach to Analyzing Spatio-Temporal Mobility Pattern

Abstract

It is imperative to understand human movement and behavior, from epidemic monitoring to complex communications. So far, most research and studies on investigating and interpreting human movements have traditionally depended on private and accumulated data such as mobile records. In this work, social network data is suggested as a proxy for human mobility, as it relies on a large amount of publicly accessible data. A mechanism for urban mobility mining and extraction scheme is proposed in this research to shed light on the importance and benefits of the publicly available social network data. Given the potential value of the Big Data obtained from social network platforms, we sought to demonstrate the process of analyzing and understanding human mobility patterns and activity behavior in urban areas through the social network data. Human mobility is far from spontaneous, follows well-defined statistical patterns. This research provides evidence of spatial and temporal regularity in human mobility patterns by examining daily individual trajectories of users covering an average time span of three years (2018 to 2020). Despite the diversity of individual movements history, we concluded that humans follow simple, reproducible patterns. Additionally, we studied and evaluated the effect of COVID-19 on human mobility and activity behavior in urban areas and established a strong association between human mobility and COVID-19 spread. Numerous years of mobility data analysis can reveal well-established trends, such as social or cultural activities, which serve as a baseline for detecting anomalies and changes in human mobility and activity behavior.

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