A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThis doctoral dissertation proposed several statistical approaches to analyse urban dynamics with
aiming to provide tools for decision making processes and urban studies. It assumed that human
activity and human mobility compose urban dynamics. Initially, it studied geolocated social media
data and considered them as a proxy for where and when people carry out what it is defined as the
human activity. It employed techniques associated with generalised linear models, functional data
analysis, hierarchical clustering, and epidemic data, to explain the spatio-temporal distribution
of the places where people interact with their social networks. Afterwards, to understand the
mobility in urban environments, data coming from an underground railway system were used.
The information was considered repeated daily measurements to capture the regularity of
human behaviour. By implementing methods from functional principal components data analysis
and hierarchical clustering, it was possible to describe the system and identify human mobility
patterns
