Research Areas
The group is flexible and open to many research interests, but most work is devoted to the following lines
Structure and Dynamics of Urban Complex Systems
Urban areas are socio-economic systems that take up space in a particular geography. They are built upon tightly knitted layers of personal interactions and infrastructural networks (communication, transportation, services) offering, in turn, outstanding opportunities for living and working. However, their impact on the surrounding environment is extensive leaving behind a significant footprint –not only ecological, but also in terms of public health, social discrimination, etc. This view of cities creates interdisciplinary opportunities and challenges, bringing together data science, GIS, multiplex complex networks and social science, with consequences for policy decision makers, or landscape and urban planners.
With the increasing popularity of open data technologies and portals, a vast amount of information about these layers is now available to us. This information ranges from structural and dynamical information about the different cities’ infrastructures (collected by sensors deployed in a city) to social information about urban dwellers.
Sidewalk networks and pedestrian dynamics
Among all transportation modes, pedestrian mobility (and its associated infrastructure, sidewalks) has been the most neglected by large. This is in part due to a general lack of open data on this urban resource, but also, probably, because of the pressing challenges that stem from car-oriented cities (congestion, pollution). Following existing research, this proposal aims at a better understanding of pedestrian space both as a mobility and a social interaction asset.
Dynamics of
transportation systems
Data-driven research, exploiting complexity theory (complex networks, percolation, self-organized criticality, agent-based modelling), helps in understanding the internal city dynamics and achieving sustainability from a transportation perspective, improving efficiency and performance. This includes multimodal transportation systems: surface (private and public) connected to underground (public) transport, represented as multiplex complex networks.
Computational
urban ecology
City resilience comprises the assessment and understanding of the robustness, “stress limits”, and recovery capacity of the networks of infrastructures. These measures are strongly related to crisis response in case of emergencies (traffic re-routing, accident assistance).
Computational Social Science
After four decades of contributions from “sociophysics” and “econophysics”, it was clear, at the turn of the century, that huge challenges –and new opportunities– lied ahead: the digital communication technologies, and their associated data deluge, began to nurture those efforts with empirical significance. Only a decade later, the advent of the Web 2.0, the Internet of Things, and a general adoption of mobile technologies have convinced researchers that theories can be mapped to real scenarios and put into empirical test, closing in this way the experiment-theory cycle in the best scientific tradition.
Information
ecosystems
Detect, quantify and understand competitive/cooperative dynamics in temporally evolving, information-driven systems. Modeling and analysis of bipartite mutualistic networks: the user-information interplay.
Community detection and dynamic community structure
Modeling, tracking and forecasting dynamic groups in social media. Alternative approaches to the analysis of mesoscale structure in unipartite and bipartite networks.
Information diffusion in online social networks
Social contagion processes at individual and aggregate levels, temporal and geographical patterns of information diffusion, online socio-political mobilizations.
Foundations of Complex Systems
Often unnoticed, one of the main contributions of Complex Systems to the scientific community comes under the form of new descriptors, innovative algorithms or modelling developments: take for instance community detection techniques, which are nowadays standard tools in many areas. Along this line, a Complex Systems research group permanently works in the creation (or adaptation) of methods and software that yield better, deeper insight in the understanding of the problems it deals with.
Of particular interest, the group has focused on hybrid network structures (e.g., those that combine modularity with nestedness), and the dynamical implications of such patterns in ecological and sociotechnical systems.
Structure and Dynamics of Urban Complex Systems
Urban areas are socio-economic systems that take up space in a particular geography. They are built upon tightly knitted layers of personal interactions and infrastructural networks (communication, transportation, services) offering, in turn, outstanding opportunities for living and working. However, their impact on the surrounding environment is extensive leaving behind a significant footprint –not only ecological, but also in terms of public health, social discrimination, etc. This view of cities creates interdisciplinary opportunities and challenges, bringing together data science, GIS, multiplex complex networks and social science, with consequences for policy decision makers, or landscape and urban planners.
With the increasing popularity of open data technologies and portals, a vast amount of information about these layers is now available to us. This information ranges from structural and dynamical information about the different cities’ infrastructures (collected by sensors deployed in a city) to social information about urban dwellers.
Sidewalk networks and pedestrian dynamics
Among all transportation modes, pedestrian mobility (and its associated infrastructure, sidewalks) has been the most neglected by large. This is in part due to a general lack of open data on this urban resource, but also, probably, because of the pressing challenges that stem from car-oriented cities (congestion, pollution). Following existing research, this proposal aims at a better understanding of pedestrian space both as a mobility and a social interaction asset.
Dynamics of transportation systems
Data-driven research, exploiting complexity theory (complex networks, percolation, self-organized criticality, agent-based modelling), helps in understanding the internal city dynamics and achieving sustainability from a transportation perspective, improving efficiency and performance. This includes multimodal transportation systems: surface (private and public) connected to underground (public) transport, represented as multiplex complex networks.
Computational urban ecology
City resilience comprises the assessment and understanding of the robustness, “stress limits”, and recovery capacity of the networks of infrastructures. These measures are strongly related to crisis response in case of emergencies (traffic re-routing, accident assistance).
Computational Social Science
After four decades of contributions from “sociophysics” and “econophysics”, it was clear, at the turn of the century, that huge challenges –and new opportunities– lied ahead: the digital communication technologies, and their associated data deluge, began to nurture those efforts with empirical significance. Only a decade later, the advent of the Web 2.0, the Internet of Things, and a general adoption of mobile technologies have convinced researchers that theories can be mapped to real scenarios and put into empirical test, closing in this way the experiment-theory cycle in the best scientific tradition.
Information ecosystems
Detect, quantify and understand competitive/cooperative dynamics in temporally evolving, information-driven systems. Modeling and analysis of bipartite mutualistic networks: the user-information interplay.
Community detection and dynamic community structure
Modeling, tracking and forecasting dynamic groups in social media. Alternative approaches to the analysis of mesoscale structure in unipartite and bipartite networks.
Information diffusion in online social networks
Social contagion processes at individual and aggregate levels, temporal and geographical patterns of information diffusion, online socio-political mobilizations.
Foundations of Complex Systems
Often unnoticed, one of the main contributions of Complex Systems to the scientific community comes under the form of new descriptors, innovative algorithms or modelling developments: take for instance community detection techniques, which are nowadays standard tools in many areas. Along this line, a Complex Systems research group permanently works in the creation (or adaptation) of methods and software that yield better, deeper insight in the understanding of the problems it deals with.
Of particular interest, the group has focused on hybrid network structures (e.g., those that combine modularity with nestedness), and the dynamical implications of such patterns in ecological and sociotechnical systems.
