Modeling human and cities' behaviors: from communication synchronization to spatio-temporal networks

Recent years have seen a huge increase in the amount of data collected from multiple sources: mobile phones are ubiquitous, social networks are widely used, cities are more and more connected and the mobility of people and goods has risen to a global scale. The Big Data Era has opened the doors to new kinds of studies that were unthinkable with previous qualitative methods: human behavior can now be analyzed with a fine-grained resolution, patterns of mobility and behavior can be extracted from the incredible amount of data collected every day. Modern large cities are becoming more and more interconnected and this phenomenon leads to an increasing communication and activities’ synchronization. Due to the amount of data available or for anonymization reasons, it is often necessary to aggregate data spatially and temporally. A natural representation of clustered mobility data is the temporal network representation. In this thesis we focus on these two aspects of spatial distance in human mobility: (i) we study the synchronization of 76 Italian cities, using mobile phone data, showing that both distance between cities and city size determine the synchronization in communication rhythms. Moreover, we show that the effect of the distance in synchronization decreases when the size of the city increases; (ii) we investigate how clustering continuous spatio-temporal data affects spatio-temporal network measures for real-life and synthetic datasets and analyze how spatio-temporal networks’ measures vary at different aggregation levels.