The timescape of the city: Example of spatial interactions based on big data

Abstract

The paper develops the concept of a timescape of a city based on empirical analysis of geospatial big data. We understand the timescape as the temporal shape of a city, which reflects temporal dynamics of a city structure and rhythms based on aggregated time-space behaviour of individuals. The paper uses the concepts of rhythmanalysis and spatial interactions as theoretical basis. We analyse two data types: mobile phone location data and taxi trajectory data, and the city of Prague is taken as the example. The data capture the population mobility peak before the Covid-19 pandemic has burst out. We examine both the temporal (weekly and daily rhythms) and spatial (intra urban and ‘extra’ urban flows) hierarchies of the city time-space. We have confirmed the existence of distinct differences between workdays and weekend days. It has also been shown that the mobility based on taxi trajectories has specific temporal distribution. Based on ‘extra’ urban flows, rhythms and thus on the present population of the city we have identified two hierarchically different time waves, long (weekly) and short (daily), when the latter has opposite amplitude and span during workdays and during weekend days.