Infrastructure, Planning and Environment

We use multifractal analysis to explore the process by which housing prices and income distributions interact in several cities. This technique allows the gathering of statistical information, represented by a curve called the spectrum, which takes into account the spatial pattern of the distributions at multiple scales. In turn, this information can be interpreted in terms of inequality and segregation [1]. We use agent-based modelling to test different housing development scenarios aiming at reducing economic segregation by positively influencing the spectra.

The understanding of network patterns in terms of their scale-invariant properties and common ontogenetic drivers is critically important for designing optimal transportation networks. Here, network features of bike sharing networks in selected U.S. cities are considered. Namely, the degree distribution, average path length and clustering coefficient are considered over time. We compare networks between cities as well as over time allowing us to build a generalized picture of the structure and evolution of bike sharing networks.

Facing a global challenge such as climate change brings out consequences on wealth inequalities due to diverse propensities or capacities for cooperation. Here, we focus on the effect of resource inequality on climate change mitigation, and how individuals with different resources act when cooperating in this crucial endeavour. We addressed these questions by performing a lab-in-the-field experiment with 320 participants, and based it on the collective-risk dilemma setup by Milinski et al.

Many networked systems are embedded into space [1]. One of their hallmarks is the existence of a cost associated to the edge’s length which, in turns, affects the topology of the connectivity pattern. Given a fixed amount of total resources (i.e. total link length Ltot), there are multiple ways of building a spatial network satisfying such constraint. Therefore, a criterion to compare the design of different spatial networks for a given node layout and a fixed link length is needed.

One of the most difficult issues in urban analytics is to portray local complexity and to visualize how that complexity changes over space. We present here an approach that captures ethnic or other composition changes across all scales within a metropolitan area. Underpinning this approach is an entirely novel multi-scalar and multi-focal method to describe a city, both qualitatively and quantitatively, from a myriad of viewpoints – thus revealing the complex intertwining of local neighbourhoods into the broader pattern of the full-scale city, in all its dazzling variety.

CoEGSS (Center of Excellence for Global System Science) [1] is a center of excellence of the European Union for enhancing the power of Global System Science simulations with the calculation power of High Performance Computing. Three pilots are studied: the diffusion of health habits, the attitude to green mobility and urbanization. In the case of social simulations, it is crucial to properly model the social ties among individuals. Data about the real population are aggregated, due to privacy constraints.

Global climate change is projected to have a variety of local to regional scale impacts on human societies and ecosystems. The severity of these impacts (risk magnitude) depends upon the extent to which humans at the global scale mitigate Green House Gas (GHG) emissions through switching their fossil fuel-intensive behaviors to green behaviors, as well as adapting to adverse impacts of climatic change at local scales.

Many motorways are running above their capacity and therefore suffer congested traffic. The traffic breakdown from free flow becomes increasingly likely around a critical flow, a critical number of vehicles per minute. Here we discuss congestion durations which are distributed with a power law over two decades, from minutes to hours [1] (see Figure 1 on the left). This finding suggests a robust mechanism behind it.

Forests are subjected to ecological and socio-economic forces of change, interacting in complex ways. To cope with these changes, forest management consists of integrating socio-cultural, ecological, and economic considerations in an analytic and systemic way. In this vein, social-ecological systems (SES) frameworks have been developed to help analyzing key factors that derive the dynamics of such complex adaptive systems [1]. However, a research gap exists with regard to understanding how forest management implemented impacts the overall forest system.