Measuring spatio-temporal autocorrelation in time series data of collective human mobility

Abstract

Massive spatio-temporal big data about human mobility have become increasingly available. Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment. Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time. The most widely used spatial autocorrelation measurements, such as Moran’s I and local indicators of spatial association (LISA), only apply to static data, so are powerless to spatio-temporal big data about human mobility. Thus, we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data. Then the method was applied to taxi ride data in Beijing, China to reveal the spatial pattern of collective human mobility. The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road, weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road, and almost no spatio-temporal autocorrelation between the roads. Local spatial patterns of taxi travel were also recognized. This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.

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