Urban (U) Session 1
Time and Date: 14:15 - 15:45 on 19th Sep 2016
Room: R - Raadzaal
Chair: Neil Huynh Hoai Nguyen
|390|| Spatio-temporal analysis of maritime shipping and its impact on international trade.
Abstract: Maritime shipping accounts for 90% of the volume of worldwide trade, making it the most important mode of cargo transportation. The global time-evolving network of cargo ship movements therefore gives an accurate representation of international trade and provides valuable insights on the development and evolution of economic relations. Here we conduct a novel longitudinal multi-layer network analysis on a unique dataset, comprising 30 years of worldwide shipping movements and making it the largest and most comprehensive of its kind. We use state-of-the-art temporal network analysis tools to study world trade from the perspectives of spatial, temporal and complex networks to uncover patterns of network growth, evolution and shifts with respect to different commodity classes. We uncover correlations between the shipping patterns of different commodities over time and investigate the extent to which these correlations can be explained by a number of different factors such as geospatial (e.g. preference to trade across shorter distances) or economic (e.g. countries’ comparative advantage policies, commodity availability and shipping tariffs) constraints. We then examine the effects of exogenous events such as the establishment or dissolution of trade agreements, embargoes and diplomatic relations has on changing the structure of the overall trade network. Moreover we investigate the structure of the overall network in order to detect possible structural weakness, conditional on the supremacy of some ports, and its resolutions.
|Claire Lagesse, Francesca Lipari, Leto Peel and César Ducruet|
|47|| Sustainable urban transport: complex systems evaluation
Abstract: Visions of a walkable city will meet the objectives of much environmental legislation by reducing pollution and carbon emissions associated with highly congested city transport routes. Other desirable outcomes will emerge such as improved health and fitness through increased walking and cycling, increased personal safety through reduced accidents in highly pedestrianised areas, greater community cohesion through increased face-to-face encounters and greater vibrancy and innovation through increased diversity of contacts. But municipal planning to progressively reduce vehicle numbers in the urban environment cannot be considered in insolation from the places people need to go for jobs, and socializing; and from the goods and waste that need transporting in the urban city. Curtailing personal mobility will need to be perceived as a new measure of wealth, substituting uncertainties about congested commute times, with certainties for journey times, safety, and healthier lifestyles. City transition must reflect spatial and temporal states, i.e. the densities of different places and attractivities at different times. The spatial and temporal connectedness between origins and destinations of journeys and the wider multi-modal connectedness of the greater city will need consideration as will places to store personal vehicles. This research takes a complex systems perspective on sustainable transport planning. It identifies existing methods of transport evaluation and adapts the best method to accommodate complexity to assess whether the city is on track to meet its walkable city targets. The research then identifies areas for interventions that would help to achieve targets or adapt them. The method aims to be transferrable to other functional challenges in the city, such as sustainable energy and sustainable food systems, which are interdependent with sustainable transport. The method also aims to accommodate scenarios of city change, such as aging population, weather extremes, and smart city technologies such as the internet of things and autonomous vehicles.
|Liz Varga, Peter Allen, Hendrik Reefke and Laszlo Torjai|
|571|| Complex Systems Analysis for the Quantitative Life Cycle Sustainability Assessment
Abstract: Practical application of Life Cycle Sustainability Assessment (LCSA) framework requires integration of various methods, tools, and disciplines. However, there is a lack of cohesion between environmental, social, and economics disciplines as well as methods and tools. To address these challenges, we present a complete discussion about the overarching role of systems thinking to bring tools, methods & disciplines together, and provide practical examples from the earlier studies that have employed various system-based methods. We discuss the importance of integrated system-based methods for advancement of LCSA framework in the following directions: (1) regional and global level LCSA models using multi region input-output analysis that is capable of quantitatively capturing macro-level social, environmental, and economic impacts, (2) dealing with uncertainties in LCSA results during multi-criteria decision-making process, and (3) integration of system dynamics modeling to reveal complex interconnections, dependencies, and causal relationships between sustainability indicators. We suggest that LCSA practitioners and researchers should adopt systems thinking, which is defined as the ability to see the parts of bigger mechanisms, recognizing patterns and interrelationships, and restructuring these interrelationships in more effective and efficient ways. Adoption of systems thinking can help with the dissemination of the LCSA framework, increase its applicability, and can bridge the environmental, economic, and social sciences. Developing a common system language and a shared understanding of the inherent interconnectedness and complexity of sustainable development can be very helpful for cohesion of different disciplines and adoption of systems thinking. Future direction for developing methods and tools should help the scientific community to move from approaches based on isolated disciplines towards inter/trans-disciplinarily and a holistic/systematic perspective in order to address emerging sustainability problems.
|Murat Kucukvar and Nuri Onat|
|430|| Planning with uncertainty. How the Complex Sciences inspire an adaptive approach to urban planning.
Abstract: The development trajectories of cities include a wide variety of uncertainties that challenge spatial planners and policy makers in guiding urban development towards socially desired outcomes (Albrechts, 2010; Woerkum et al., 2011). In response, ideas and concepts derived from complexity science are being explored in planning literature to develop enhanced ways of dealing with these uncertainties (Portugali, 2011; Batty, 2013). Taking a complexity science perspective, this paper presents a dynamic, time-sensitive understanding of spatial transformations that helps to clarify the interconnected and changeable nature of the underlying processes. The paper continuous by exploring an adaptive approach to planning that strengthens the capacity of urban areas to respond and incorporate to both the expected and unexpected changes these processes give rise to. The argument is made that adaptive planning first and foremost implies a focus on influencing and creating conditions, followed by attention to content and process. Keywords: Albrechts, L. (2010). More of the same is not enough!: How could strategic spatial planning be instrumental in dealing with the challenges ahead? Environment and planning B: Planning & design, 37: 1115- 1127. Batty, M. (2013). The new science of cities. Cambridge: Mit Press. Portugali, J. (2011). Complexity, Cognition and the City. Understanding Complex Systems. Berlin: Springer-Verlag. Van Woerkum, C., Aarts, N., & Van Herzele, A. (2011). Changed planning for planned and unplanned change. Planning Theory, 10(2): 144 - 160.
|457|| Observability transition in real networks
Abstract: We consider the observability model in networks with arbitrary topologies. We introduce a system of coupled nonlinear equations, valid under the locally tree-like ansatz, to describe the size of the largest observable cluster as a function of the fraction of directly observable nodes present in the network. We perform a systematic analysis on 95 real-world graphs and compare our theoretical predictions with numerical simulations of the observability model. Our method provides almost perfect predictions in the majority of the cases, even for networks with very large values of the clustering coefficient. Potential applications of our theory include the development of efficient and scalable algorithms for real-time surveillance of social networks, and monitoring of technological networks.
|Yang Yang and Filippo Radicchi|