The complexity of movement: Empirical data analysis and modelling of dynamical processes

The continuously growing amount of data, commonly referred as Big Data,
represents two challenges for the scientific community. First, the large size
datasets require the development of methods for a scalable analysis, like machine
learning or statistical measurements, increasing the capacity for the recognition
of the patterns that characterize the data. Secondly, in order to describe the
underlying mechanisms in those patterns and increase the forecast power, it
highlights the need for simple models that, with very few parameters, are able
to capture the observed dynamical processes.
In this thesis, from the analysis of empirical data to the modelling of processes
in mobility networks, we present five mobility works under the spirit of these
two challenges.
Traditionally, animal movement researchers made remarkable efforts for developing
their studies using small size datasets, as the data collection was expensive
in terms both of human resources and devices. However, the development of
new transmission devices, that are lighter and cheaper, is speeding up the data
collection processes, leading to larger datasets. Complexity science has developed
methods that are fast and computationally cheap for analysing human
movement, and we argue that these methods will help in the study of animal
movement. This aim is tackled in the second and the third chapters, where we
study first the case of southern elephant seals, revealing a universal behaviour
despite the observed idiosyncrasy among individuals, for finding later the relevant
drivers of marine megafauna movement, in a joint dataset including fifty
different species. These two studies were developed in the framework of the
Marine Megafauna Analytical Program (https://mmmap.wordpress.com), that
promotes cross-disciplinary collaborations to analyse the movement of marine
animals.
Mobility in the oceans is not only restricted to animals, with most of economic
traffic being transported in vessels, both for goods, raw materials and fuel.
Specifically, according to the 2017 Review of Maritime Transport, ships carry
more than 70% of global trade value. In fact, characterizing the paths followed
by vessels in the oceans is not only important for economical purposes, but also
for the protection of the species living in this environment. This motivates the
analysis of the spatial patterns described by vessel traffic in the ocean, in chapter 4.
The second part of the thesis focuses on dynamical processes and mobility models.
Nowadays, transport networks play a key role in disease spreading in a
global scale, allowing pathogens to travel, within their hosts, for thousands of
kilometres in a few hours. This potential risk needs the formulation of simple
models that reveal the basic mechanisms underlying the spreading in mobility
networks. In chapter 5 we show how, for short interaction ranges, and counterintuitively,
the mobility can have a detrimental effect on a contagion process, with
the disease affecting a higher fraction of population when the network is static
rather than in the mobile case. In chapter 6 we review recently introduced cooperative
disease spreading models, for later studying them in static and mobile
scenarios. While the static scenario allows us to match the short and long range
interaction limits, which were leading to different behaviours, in the mobile case
we show how, for short range interactions, the mobility-induced mixing can lead
to the observed effects in a system with long range interactions. Finally, in an
effort to bridge the gap between the two parts of the thesis, we propose a databased
model, in which we analyse the effects of cooperative disease spreading
dynamics in an empirical contact network, finding that the temporal correlations
and the specific activity pattern play a key role in our results.